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identification</title> <meta name="description" content="Search results for: disease identification"> <meta name="keywords" content="disease identification"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="disease identification"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 6470</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: disease identification</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6470</span> Parkinson's Disease Gene Identification Using Physicochemical Properties of Amino Acids</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Priya%20Arora">Priya Arora</a>, <a href="https://publications.waset.org/abstracts/search?q=Ashutosh%20Mishra"> Ashutosh Mishra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Gene identification, towards the pursuit of mutated genes, leading to Parkinson’s disease, puts forward a challenge towards proactive cure of the disorder itself. Computational analysis is an effective technique for exploring genes in the form of protein sequences, as the theoretical and manual analysis is infeasible. The limitations and effectiveness of a particular computational method are entirely dependent on the previous data that is available for disease identification. The article presents a sequence-based classification method for the identification of genes responsible for Parkinson’s disease. During the initiation phase, the physicochemical properties of amino acids transform protein sequences into a feature vector. The second phase of the method employs Jaccard distances to select negative genes from the candidate population. The third phase involves artificial neural networks for making final predictions. The proposed approach is compared with the state of art methods on the basis of F-measure. The results confirm and estimate the efficiency of the method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=disease%20gene%20identification" title="disease gene identification">disease gene identification</a>, <a href="https://publications.waset.org/abstracts/search?q=Parkinson%E2%80%99s%20disease" title=" Parkinson’s disease"> Parkinson’s disease</a>, <a href="https://publications.waset.org/abstracts/search?q=physicochemical%20properties%20of%20amino%20acid" title=" physicochemical properties of amino acid"> physicochemical properties of amino acid</a>, <a href="https://publications.waset.org/abstracts/search?q=protein%20sequences" title=" protein sequences"> protein sequences</a> </p> <a href="https://publications.waset.org/abstracts/116365/parkinsons-disease-gene-identification-using-physicochemical-properties-of-amino-acids" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/116365.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">140</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6469</span> Investigating the Factors Affecting Generalization of Deep Learning Models for Plant Disease Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Praveen%20S.%20Muthukumarana">Praveen S. Muthukumarana</a>, <a href="https://publications.waset.org/abstracts/search?q=Achala%20C.%20Aponso"> Achala C. Aponso</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A large percentage of global crop harvest is lost due to crop diseases. Timely identification and treatment of crop diseases is difficult in many developing nations due to insufficient trained professionals in the field of agriculture. Many crop diseases can be accurately diagnosed by visual symptoms. In the past decade, deep learning has been successfully utilized in domains such as healthcare but adoption in agriculture for plant disease detection is rare. The literature shows that models trained with popular datasets such as PlantVillage does not generalize well on real world images. This paper attempts to find out how to make plant disease identification models that generalize well with real world images. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=agriculture" title="agriculture">agriculture</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title=" convolutional neural network"> convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=plant%20disease%20classification" title=" plant disease classification"> plant disease classification</a>, <a href="https://publications.waset.org/abstracts/search?q=plant%20disease%20detection" title=" plant disease detection"> plant disease detection</a>, <a href="https://publications.waset.org/abstracts/search?q=plant%20disease%20diagnosis" title=" plant disease diagnosis"> plant disease diagnosis</a> </p> <a href="https://publications.waset.org/abstracts/127286/investigating-the-factors-affecting-generalization-of-deep-learning-models-for-plant-disease-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/127286.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">145</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6468</span> An Image Processing Scheme for Skin Fungal Disease Identification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20A.%20M.%20A.%20S.%20S.%20Perera">A. A. M. A. S. S. Perera</a>, <a href="https://publications.waset.org/abstracts/search?q=L.%20A.%20Ranasinghe"> L. A. Ranasinghe</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20K.%20H.%20Nimeshika"> T. K. H. Nimeshika</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20M.%20Dhanushka%20Dissanayake"> D. M. Dhanushka Dissanayake</a>, <a href="https://publications.waset.org/abstracts/search?q=Namalie%20Walgampaya"> Namalie Walgampaya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, skin fungal diseases are mostly found in people of tropical countries like Sri Lanka. A skin fungal disease is a particular kind of illness caused by fungus. These diseases have various dangerous effects on the skin and keep on spreading over time. It becomes important to identify these diseases at their initial stage to control it from spreading. This paper presents an automated skin fungal disease identification system implemented to speed up the diagnosis process by identifying skin fungal infections in digital images. An image of the diseased skin lesion is acquired and a comprehensive computer vision and image processing scheme is used to process the image for the disease identification. This includes colour analysis using RGB and HSV colour models, texture classification using Grey Level Run Length Matrix, Grey Level Co-Occurrence Matrix and Local Binary Pattern, Object detection, Shape Identification and many more. This paper presents the approach and its outcome for identification of four most common skin fungal infections, namely, Tinea Corporis, Sporotrichosis, Malassezia and Onychomycosis. The main intention of this research is to provide an automated skin fungal disease identification system that increase the diagnostic quality, shorten the time-to-diagnosis and improve the efficiency of detection and successful treatment for skin fungal diseases. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Circularity%20Index" title="Circularity Index">Circularity Index</a>, <a href="https://publications.waset.org/abstracts/search?q=Grey%20Level%20Run%20Length%20Matrix" title=" Grey Level Run Length Matrix"> Grey Level Run Length Matrix</a>, <a href="https://publications.waset.org/abstracts/search?q=Grey%20Level%20Co-Occurrence%20Matrix" title=" Grey Level Co-Occurrence Matrix"> Grey Level Co-Occurrence Matrix</a>, <a href="https://publications.waset.org/abstracts/search?q=Local%20Binary%20Pattern" title=" Local Binary Pattern"> Local Binary Pattern</a>, <a href="https://publications.waset.org/abstracts/search?q=Object%20detection" title=" Object detection"> Object detection</a>, <a href="https://publications.waset.org/abstracts/search?q=Ring%20Detection" title=" Ring Detection"> Ring Detection</a>, <a href="https://publications.waset.org/abstracts/search?q=Shape%20Identification" title=" Shape Identification"> Shape Identification</a> </p> <a href="https://publications.waset.org/abstracts/82490/an-image-processing-scheme-for-skin-fungal-disease-identification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/82490.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">232</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6467</span> Morphological and Biological Identification of Fusarium Species Associated with Ear Rot Disease of Maize in Indonesia and Malaysia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Darnetty%20Baharuddin%20Salleh">Darnetty Baharuddin Salleh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Fusarium ear rot disease is one of the most important diseases of maize and not only causes significant losses but also produced harmful mycotoxins to animals and humans. A total of 141 strains of Fusarium species were isolated from maize plants showing typical ear rot symptoms in Indonesia, and Malaysia by using the semi-selective medium (peptone pentachloronitrobenzene agar, PPA). These strains were identified morphologically. For strains in Gibberella fujikuroi species complex (Gfsc), the identification was continued by using biological identification. Three species of Fusarium were morphologically identified as Fusarium in Gibberella species complex (105 strains, 74.5%), F. verticillioides (78 strains), F. proliferatum (24 strains) and F. subglutinans (3 strains) and five species from other section (36 strains, 25.5%), F. graminearum (14 strains), F. oxysporum (8 strains), F. solani ( 1 strain), and F. semitectum (13 strains). Out of 105 Fusarium species in Gfsc, 63 strains were identified as MAT-1, 25 strains as MAT-2 and 17 strains could not be identified and in crosses with nine standard testers, three mating populations of Fusarium were identified as MP-A, G. moniliformis (68 strains, 64.76%), MP-D, G. intermedia (21 strains, 20%) and MP-E, G. subglutinans (3 strains, 2.9%), and 13 strains (12.38%) could not be identified. All trains biologically identified as MP-A, MP-D, and MP-E, were identified morphologically as F. verticillioides, F. proliferatum, and F. subglutinans, respectively. Thus, the results of this study indicated that identification based on biological identification were consistent with those of morphological identification. This is the first report on the presence of MP-A, MP-D, and MP-E on ear rot-infected maize in Indonesia; MP-A and MP-E in Malaysia. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fusarium" title="Fusarium">Fusarium</a>, <a href="https://publications.waset.org/abstracts/search?q=MAT-1" title=" MAT-1"> MAT-1</a>, <a href="https://publications.waset.org/abstracts/search?q=MAT-2" title=" MAT-2"> MAT-2</a>, <a href="https://publications.waset.org/abstracts/search?q=MP-A" title=" MP-A"> MP-A</a>, <a href="https://publications.waset.org/abstracts/search?q=MP-D" title=" MP-D"> MP-D</a>, <a href="https://publications.waset.org/abstracts/search?q=MP-E" title=" MP-E"> MP-E</a> </p> <a href="https://publications.waset.org/abstracts/37088/morphological-and-biological-identification-of-fusarium-species-associated-with-ear-rot-disease-of-maize-in-indonesia-and-malaysia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37088.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">310</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6466</span> Comparison of Deep Convolutional Neural Networks Models for Plant Disease Identification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Megha%20Gupta">Megha Gupta</a>, <a href="https://publications.waset.org/abstracts/search?q=Nupur%20Prakash"> Nupur Prakash</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Identification of plant diseases has been performed using machine learning and deep learning models on the datasets containing images of healthy and diseased plant leaves. The current study carries out an evaluation of some of the deep learning models based on convolutional neural network (CNN) architectures for identification of plant diseases. For this purpose, the publicly available New Plant Diseases Dataset, an augmented version of PlantVillage dataset, available on Kaggle platform, containing 87,900 images has been used. The dataset contained images of 26 diseases of 14 different plants and images of 12 healthy plants. The CNN models selected for the study presented in this paper are AlexNet, ZFNet, VGGNet (four models), GoogLeNet, and ResNet (three models). The selected models are trained using PyTorch, an open-source machine learning library, on Google Colaboratory. A comparative study has been carried out to analyze the high degree of accuracy achieved using these models. The highest test accuracy and F1-score of 99.59% and 0.996, respectively, were achieved by using GoogLeNet with Mini-batch momentum based gradient descent learning algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=comparative%20analysis" title="comparative analysis">comparative analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20networks" title=" convolutional neural networks"> convolutional neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=plant%20disease%20identification" title=" plant disease identification"> plant disease identification</a> </p> <a href="https://publications.waset.org/abstracts/138543/comparison-of-deep-convolutional-neural-networks-models-for-plant-disease-identification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/138543.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">199</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6465</span> Features Reduction Using Bat Algorithm for Identification and Recognition of Parkinson Disease </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20Shrivastava">P. Shrivastava</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Shukla"> A. Shukla</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Verma"> K. Verma</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Rungta"> S. Rungta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Parkinson's disease is a chronic neurological disorder that directly affects human gait. It leads to slowness of movement, causes muscle rigidity and tremors. Gait serve as a primary outcome measure for studies aiming at early recognition of disease. Using gait techniques, this paper implements efficient binary bat algorithm for an early detection of Parkinson's disease by selecting optimal features required for classification of affected patients from others. The data of 166 people, both fit and affected is collected and optimal feature selection is done using PSO and Bat algorithm. The reduced dataset is then classified using neural network. The experiments indicate that binary bat algorithm outperforms traditional PSO and genetic algorithm and gives a fairly good recognition rate even with the reduced dataset. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=parkinson" title="parkinson">parkinson</a>, <a href="https://publications.waset.org/abstracts/search?q=gait" title=" gait"> gait</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=bat%20algorithm" title=" bat algorithm"> bat algorithm</a> </p> <a href="https://publications.waset.org/abstracts/31393/features-reduction-using-bat-algorithm-for-identification-and-recognition-of-parkinson-disease" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31393.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">545</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6464</span> Generating Synthetic Chest X-ray Images for Improved COVID-19 Detection Using Generative Adversarial Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muneeb%20Ullah">Muneeb Ullah</a>, <a href="https://publications.waset.org/abstracts/search?q=Daishihan"> Daishihan</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiadong%20Young"> Xiadong Young</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Deep learning plays a crucial role in identifying COVID-19 and preventing its spread. To improve the accuracy of COVID-19 diagnoses, it is important to have access to a sufficient number of training images of CXRs (chest X-rays) depicting the disease. However, there is currently a shortage of such images. To address this issue, this paper introduces COVID-19 GAN, a model that uses generative adversarial networks (GANs) to generate realistic CXR images of COVID-19, which can be used to train identification models. Initially, a generator model is created that uses digressive channels to generate images of CXR scans for COVID-19. To differentiate between real and fake disease images, an efficient discriminator is developed by combining the dense connectivity strategy and instance normalization. This approach makes use of their feature extraction capabilities on CXR hazy areas. Lastly, the deep regret gradient penalty technique is utilized to ensure stable training of the model. With the use of 4,062 grape leaf disease images, the Leaf GAN model successfully produces 8,124 COVID-19 CXR images. The COVID-19 GAN model produces COVID-19 CXR images that outperform DCGAN and WGAN in terms of the Fréchet inception distance. Experimental findings suggest that the COVID-19 GAN-generated CXR images possess noticeable haziness, offering a promising approach to address the limited training data available for COVID-19 model training. When the dataset was expanded, CNN-based classification models outperformed other models, yielding higher accuracy rates than those of the initial dataset and other augmentation techniques. Among these models, ImagNet exhibited the best recognition accuracy of 99.70% on the testing set. These findings suggest that the proposed augmentation method is a solution to address overfitting issues in disease identification and can enhance identification accuracy effectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classification" title="classification">classification</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=medical%20images" title=" medical images"> medical images</a>, <a href="https://publications.waset.org/abstracts/search?q=CXR" title=" CXR"> CXR</a>, <a href="https://publications.waset.org/abstracts/search?q=GAN." title=" GAN."> GAN.</a> </p> <a href="https://publications.waset.org/abstracts/176073/generating-synthetic-chest-x-ray-images-for-improved-covid-19-detection-using-generative-adversarial-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/176073.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">96</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6463</span> Cardiovascular Disease Prediction Using Machine Learning Approaches</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20Halder">P. Halder</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Zaman"> A. Zaman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> It is estimated that heart disease accounts for one in ten deaths worldwide. United States deaths due to heart disease are among the leading causes of death according to the World Health Organization. Cardiovascular diseases (CVDs) account for one in four U.S. deaths, according to the Centers for Disease Control and Prevention (CDC). According to statistics, women are more likely than men to die from heart disease as a result of strokes. A 50% increase in men's mortality was reported by the World Health Organization in 2009. The consequences of cardiovascular disease are severe. The causes of heart disease include diabetes, high blood pressure, high cholesterol, abnormal pulse rates, etc. Machine learning (ML) can be used to make predictions and decisions in the healthcare industry. Thus, scientists have turned to modern technologies like Machine Learning and Data Mining to predict diseases. The disease prediction is based on four algorithms. Compared to other boosts, the Ada boost is much more accurate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=heart%20disease" title="heart disease">heart disease</a>, <a href="https://publications.waset.org/abstracts/search?q=cardiovascular%20disease" title=" cardiovascular disease"> cardiovascular disease</a>, <a href="https://publications.waset.org/abstracts/search?q=coronary%20artery%20disease" title=" coronary artery disease"> coronary artery disease</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest" title=" random forest"> random forest</a>, <a href="https://publications.waset.org/abstracts/search?q=AdaBoost" title=" AdaBoost"> AdaBoost</a>, <a href="https://publications.waset.org/abstracts/search?q=SVM" title=" SVM"> SVM</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree" title=" decision tree"> decision tree</a> </p> <a href="https://publications.waset.org/abstracts/155940/cardiovascular-disease-prediction-using-machine-learning-approaches" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155940.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">153</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6462</span> Suppression Subtractive Hybridization Technique for Identification of the Differentially Expressed Genes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tuhina-khatun">Tuhina-khatun</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Hanafi%20Musa"> Mohamed Hanafi Musa</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohd%20Rafii%20Yosup"> Mohd Rafii Yosup</a>, <a href="https://publications.waset.org/abstracts/search?q=Wong%20Mui%20Yun"> Wong Mui Yun</a>, <a href="https://publications.waset.org/abstracts/search?q=Aktar-uz-Zaman"> Aktar-uz-Zaman</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahbod%20Sahebi"> Mahbod Sahebi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Suppression subtractive hybridization (SSH) method is valuable tool for identifying differentially regulated genes in disease specific or tissue specific genes important for cellular growth and differentiation. It is a widely used method for separating DNA molecules that distinguish two closely related DNA samples. SSH is one of the most powerful and popular methods for generating subtracted cDNA or genomic DNA libraries. It is based primarily on a suppression polymerase chain reaction (PCR) technique and combines normalization and subtraction in a solitary procedure. The normalization step equalizes the abundance of DNA fragments within the target population, and the subtraction step excludes sequences that are common to the populations being compared. This dramatically increases the probability of obtaining low-abundance differentially expressed cDNAs or genomic DNA fragments and simplifies analysis of the subtracted library. SSH technique is applicable to many comparative and functional genetic studies for the identification of disease, developmental, tissue specific, or other differentially expressed genes, as well as for the recovery of genomic DNA fragments distinguishing the samples under comparison. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=suppression%20subtractive%20hybridization" title="suppression subtractive hybridization">suppression subtractive hybridization</a>, <a href="https://publications.waset.org/abstracts/search?q=differentially%20expressed%20genes" title=" differentially expressed genes"> differentially expressed genes</a>, <a href="https://publications.waset.org/abstracts/search?q=disease%20specific%20genes" title=" disease specific genes"> disease specific genes</a>, <a href="https://publications.waset.org/abstracts/search?q=tissue%20specific%20genes" title=" tissue specific genes"> tissue specific genes</a> </p> <a href="https://publications.waset.org/abstracts/36148/suppression-subtractive-hybridization-technique-for-identification-of-the-differentially-expressed-genes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/36148.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">433</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6461</span> Phenotypical and Genotypical Assessment Techniques for Identification of Some Contagious Mastitis Pathogens</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ayman%20El%20Behiry">Ayman El Behiry</a>, <a href="https://publications.waset.org/abstracts/search?q=Rasha%20Nabil%20Zahran"> Rasha Nabil Zahran</a>, <a href="https://publications.waset.org/abstracts/search?q=Reda%20Tarabees"> Reda Tarabees</a>, <a href="https://publications.waset.org/abstracts/search?q=Eman%20Marzouk"> Eman Marzouk</a>, <a href="https://publications.waset.org/abstracts/search?q=Musaad%20Al-Dubaib"> Musaad Al-Dubaib</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mastitis is one of the most economic disease affecting dairy cows worldwide. Its classic diagnosis using bacterial culture and biochemical findings is a difficult and prolonged method. In this research, using of matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) permitted identification of different microorganisms with high accuracy and rapidity (only 24 hours for microbial growth and analysis). During the application of MALDI-TOF MS, one hundred twenty strains of Staphylococcus and Streptococcus species isolated from milk of cows affected by clinical and subclinical mastitis were identified, and the results were compared with those obtained by traditional methods as API and VITEK 2 Systems. 37 of totality 39 strains (~95%) of Staphylococcus aureus (S. aureus) were exactly detected by MALDI TOF MS and then confirmed by a nuc-based PCR technique, whereas accurate identification was observed in 100% (50 isolates) of the coagulase negative staphylococci (CNS) and Streptococcus agalactiae (31 isolates). In brief, our results demonstrated that MALDI-TOF MS is a fast and truthful technique which has the capability to replace conventional identification of several bacterial strains usually isolated in clinical laboratories of microbiology. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=identification" title="identification">identification</a>, <a href="https://publications.waset.org/abstracts/search?q=mastitis%20pathogens" title=" mastitis pathogens"> mastitis pathogens</a>, <a href="https://publications.waset.org/abstracts/search?q=mass%20spectral" title=" mass spectral"> mass spectral</a>, <a href="https://publications.waset.org/abstracts/search?q=phenotypical" title=" phenotypical"> phenotypical</a> </p> <a href="https://publications.waset.org/abstracts/8669/phenotypical-and-genotypical-assessment-techniques-for-identification-of-some-contagious-mastitis-pathogens" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8669.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">333</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6460</span> Alzheimer’s Disease Measured in Work Organizations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Katherine%20Denise%20Queri">Katherine Denise Queri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The effects of sick workers have an impact in administration of labor. This study aims to provide knowledge on the disease that is Alzheimer’s while presenting an answer to the research question of when and how is the disease considered as a disaster inside the workplace. The study has the following as its research objectives: 1. Define Alzheimer’s disease, 2. Evaluate the effects and consequences of an employee suffering from Alzheimer’s disease, 3. Determine the concept of organizational effectiveness in the area of Human Resources, and 4. Identify common figures associated with Alzheimer’s disease. The researcher gathered important data from books, video presentations, and interviews of workers suffering from Alzheimer’s disease and from the internet. After using all the relevant data collection instruments mentioned, the following data emerged: 1. Alzheimer’s disease has certain consequences inside the workplace, 2. The occurrence of Alzheimer’s Disease in an employee’s life greatly affects the company where the worker is employed, and 3. The concept of workplace efficiency suggests that an employer must prepare for such disasters that Alzheimer’s disease may bring to the company where one is employed. Alzheimer’s disease can present disaster in any workplace. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=administration" title="administration">administration</a>, <a href="https://publications.waset.org/abstracts/search?q=Alzheimer%27s%20disease" title=" Alzheimer's disease"> Alzheimer's disease</a>, <a href="https://publications.waset.org/abstracts/search?q=conflict" title=" conflict"> conflict</a>, <a href="https://publications.waset.org/abstracts/search?q=disaster" title=" disaster"> disaster</a>, <a href="https://publications.waset.org/abstracts/search?q=employment" title=" employment"> employment</a> </p> <a href="https://publications.waset.org/abstracts/33630/alzheimers-disease-measured-in-work-organizations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33630.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">445</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6459</span> A Survey of Skin Cancer Detection and Classification from Skin Lesion Images Using Deep Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Joseph%20George">Joseph George</a>, <a href="https://publications.waset.org/abstracts/search?q=Anne%20Kotteswara%20Roa"> Anne Kotteswara Roa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Skin disease is one of the most common and popular kinds of health issues faced by people nowadays. Skin cancer (SC) is one among them, and its detection relies on the skin biopsy outputs and the expertise of the doctors, but it consumes more time and some inaccurate results. At the early stage, skin cancer detection is a challenging task, and it easily spreads to the whole body and leads to an increase in the mortality rate. Skin cancer is curable when it is detected at an early stage. In order to classify correct and accurate skin cancer, the critical task is skin cancer identification and classification, and it is more based on the cancer disease features such as shape, size, color, symmetry and etc. More similar characteristics are present in many skin diseases; hence it makes it a challenging issue to select important features from a skin cancer dataset images. Hence, the skin cancer diagnostic accuracy is improved by requiring an automated skin cancer detection and classification framework; thereby, the human expert’s scarcity is handled. Recently, the deep learning techniques like Convolutional neural network (CNN), Deep belief neural network (DBN), Artificial neural network (ANN), Recurrent neural network (RNN), and Long and short term memory (LSTM) have been widely used for the identification and classification of skin cancers. This survey reviews different DL techniques for skin cancer identification and classification. The performance metrics such as precision, recall, accuracy, sensitivity, specificity, and F-measures are used to evaluate the effectiveness of SC identification using DL techniques. By using these DL techniques, the classification accuracy increases along with the mitigation of computational complexities and time consumption. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=skin%20cancer" title="skin cancer">skin cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=performance%20measures" title=" performance measures"> performance measures</a>, <a href="https://publications.waset.org/abstracts/search?q=accuracy" title=" accuracy"> accuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=datasets" title=" datasets"> datasets</a> </p> <a href="https://publications.waset.org/abstracts/151256/a-survey-of-skin-cancer-detection-and-classification-from-skin-lesion-images-using-deep-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151256.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">129</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6458</span> Disability, Stigma and In-Group Identification: An Exploration across Different Disability Subgroups</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sharmila%20Rathee">Sharmila Rathee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Individuals with disability/ies often face negative attitudes, discrimination, exclusion, and inequality of treatment due to stigmatization and stigmatized treatment. While a significant number of studies in field of stigma suggest that group-identification has positive consequences for stigmatized individuals, ironically very miniscule empirical work in sight has attempted to investigate in-group identification as a coping measure against stigma, humiliation and related experiences among disability group. In view of death of empirical research on in-group identification among disability group, through present work, an attempt has been made to examine the experiences of stigma, humiliation, and in-group identification among disability group. Results of the study suggest that use of in-group identification as a coping strategy is not uniform across members of disability group and degree of in-group identification differs across different sub-groups of disability groups. Further, in-group identification among members of disability group depends on variables like degree and impact of disability, factors like onset of disability, nature, and visibility of disability, educational experiences and resources available to deal with disabling conditions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=disability" title="disability">disability</a>, <a href="https://publications.waset.org/abstracts/search?q=stigma" title=" stigma"> stigma</a>, <a href="https://publications.waset.org/abstracts/search?q=in-group%20identification" title=" in-group identification"> in-group identification</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20identity" title=" social identity"> social identity</a> </p> <a href="https://publications.waset.org/abstracts/48888/disability-stigma-and-in-group-identification-an-exploration-across-different-disability-subgroups" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/48888.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">324</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6457</span> Forensic Challenges in Source Device Identification for Digital Videos</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mustapha%20Aminu%20Bagiwa">Mustapha Aminu Bagiwa</a>, <a href="https://publications.waset.org/abstracts/search?q=Ainuddin%20Wahid%20Abdul%20Wahab"> Ainuddin Wahid Abdul Wahab</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohd%20Yamani%20Idna%20Idris"> Mohd Yamani Idna Idris</a>, <a href="https://publications.waset.org/abstracts/search?q=Suleman%20Khan"> Suleman Khan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Video source device identification has become a problem of concern in numerous domains especially in multimedia security and digital investigation. This is because videos are now used as evidence in legal proceedings. Source device identification aim at identifying the source of digital devices using the content they produced. However, due to affordable processing tools and the influx in digital content generating devices, source device identification is still a major problem within the digital forensic community. In this paper, we discuss source device identification for digital videos by identifying techniques that were proposed in the literature for model or specific device identification. This is aimed at identifying salient open challenges for future research. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=video%20forgery" title="video forgery">video forgery</a>, <a href="https://publications.waset.org/abstracts/search?q=source%20camcorder" title=" source camcorder"> source camcorder</a>, <a href="https://publications.waset.org/abstracts/search?q=device%20identification" title=" device identification"> device identification</a>, <a href="https://publications.waset.org/abstracts/search?q=forgery%20detection" title=" forgery detection "> forgery detection </a> </p> <a href="https://publications.waset.org/abstracts/21641/forensic-challenges-in-source-device-identification-for-digital-videos" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21641.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">631</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6456</span> Identification of Dynamic Friction Model for High-Precision Motion Control</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Martin%20Goubej">Martin Goubej</a>, <a href="https://publications.waset.org/abstracts/search?q=Tomas%20Popule"> Tomas Popule</a>, <a href="https://publications.waset.org/abstracts/search?q=Alois%20Krejci"> Alois Krejci</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper deals with experimental identification of mechanical systems with nonlinear friction characteristics. Dynamic LuGre friction model is adopted and a systematic approach to parameter identification of both linear and nonlinear subsystems is given. The identification procedure consists of three subsequent experiments which deal with the individual parts of plant dynamics. The proposed method is experimentally verified on an industrial-grade robotic manipulator. Model fidelity is compared with the results achieved with a static friction model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mechanical%20friction" title="mechanical friction">mechanical friction</a>, <a href="https://publications.waset.org/abstracts/search?q=LuGre%20model" title=" LuGre model"> LuGre model</a>, <a href="https://publications.waset.org/abstracts/search?q=friction%20identification" title=" friction identification"> friction identification</a>, <a href="https://publications.waset.org/abstracts/search?q=motion%20control" title=" motion control"> motion control</a> </p> <a href="https://publications.waset.org/abstracts/51897/identification-of-dynamic-friction-model-for-high-precision-motion-control" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51897.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">413</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6455</span> Parkinson’s Disease Detection Analysis through Machine Learning Approaches</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhtasim%20Shafi%20Kader">Muhtasim Shafi Kader</a>, <a href="https://publications.waset.org/abstracts/search?q=Fizar%20Ahmed"> Fizar Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=Annesha%20Acharjee"> Annesha Acharjee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Machine learning and data mining are crucial in health care, as well as medical information and detection. Machine learning approaches are now being utilized to improve awareness of a variety of critical health issues, including diabetes detection, neuron cell tumor diagnosis, COVID 19 identification, and so on. Parkinson’s disease is basically a disease for our senior citizens in Bangladesh. Parkinson's Disease indications often seem progressive and get worst with time. People got affected trouble walking and communicating with the condition advances. Patients can also have psychological and social vagaries, nap problems, hopelessness, reminiscence loss, and weariness. Parkinson's disease can happen in both men and women. Though men are affected by the illness at a proportion that is around partial of them are women. In this research, we have to get out the accurate ML algorithm to find out the disease with a predictable dataset and the model of the following machine learning classifiers. Therefore, nine ML classifiers are secondhand to portion study to use machine learning approaches like as follows, Naive Bayes, Adaptive Boosting, Bagging Classifier, Decision Tree Classifier, Random Forest classifier, XBG Classifier, K Nearest Neighbor Classifier, Support Vector Machine Classifier, and Gradient Boosting Classifier are used. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=naive%20bayes" title="naive bayes">naive bayes</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20boosting" title=" adaptive boosting"> adaptive boosting</a>, <a href="https://publications.waset.org/abstracts/search?q=bagging%20classifier" title=" bagging classifier"> bagging classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20tree%20classifier" title=" decision tree classifier"> decision tree classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest%20classifier" title=" random forest classifier"> random forest classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=XBG%20classifier" title=" XBG classifier"> XBG classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=k%20nearest%20neighbor%20classifier" title=" k nearest neighbor classifier"> k nearest neighbor classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20classifier" title=" support vector classifier"> support vector classifier</a>, <a href="https://publications.waset.org/abstracts/search?q=gradient%20boosting%20classifier" title=" gradient boosting classifier"> gradient boosting classifier</a> </p> <a href="https://publications.waset.org/abstracts/148163/parkinsons-disease-detection-analysis-through-machine-learning-approaches" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148163.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">129</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6454</span> Multi-Labeled Aromatic Medicinal Plant Image Classification Using Deep Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tsega%20Asresa">Tsega Asresa</a>, <a href="https://publications.waset.org/abstracts/search?q=Getahun%20Tigistu"> Getahun Tigistu</a>, <a href="https://publications.waset.org/abstracts/search?q=Melaku%20Bayih"> Melaku Bayih</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Computer vision is a subfield of artificial intelligence that allows computers and systems to extract meaning from digital images and video. It is used in a wide range of fields of study, including self-driving cars, video surveillance, medical diagnosis, manufacturing, law, agriculture, quality control, health care, facial recognition, and military applications. Aromatic medicinal plants are botanical raw materials used in cosmetics, medicines, health foods, essential oils, decoration, cleaning, and other natural health products for therapeutic and Aromatic culinary purposes. These plants and their products not only serve as a valuable source of income for farmers and entrepreneurs but also going to export for valuable foreign currency exchange. In Ethiopia, there is a lack of technologies for the classification and identification of Aromatic medicinal plant parts and disease type cured by aromatic medicinal plants. Farmers, industry personnel, academicians, and pharmacists find it difficult to identify plant parts and disease types cured by plants before ingredient extraction in the laboratory. Manual plant identification is a time-consuming, labor-intensive, and lengthy process. To alleviate these challenges, few studies have been conducted in the area to address these issues. One way to overcome these problems is to develop a deep learning model for efficient identification of Aromatic medicinal plant parts with their corresponding disease type. The objective of the proposed study is to identify the aromatic medicinal plant parts and their disease type classification using computer vision technology. Therefore, this research initiated a model for the classification of aromatic medicinal plant parts and their disease type by exploring computer vision technology. Morphological characteristics are still the most important tools for the identification of plants. Leaves are the most widely used parts of plants besides roots, flowers, fruits, and latex. For this study, the researcher used RGB leaf images with a size of 128x128 x3. In this study, the researchers trained five cutting-edge models: convolutional neural network, Inception V3, Residual Neural Network, Mobile Network, and Visual Geometry Group. Those models were chosen after a comprehensive review of the best-performing models. The 80/20 percentage split is used to evaluate the model, and classification metrics are used to compare models. The pre-trained Inception V3 model outperforms well, with training and validation accuracy of 99.8% and 98.7%, respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aromatic%20medicinal%20plant" title="aromatic medicinal plant">aromatic medicinal plant</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title=" convolutional neural network"> convolutional neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=plant%20classification" title=" plant classification"> plant classification</a>, <a href="https://publications.waset.org/abstracts/search?q=residual%20neural%20network" title=" residual neural network"> residual neural network</a> </p> <a href="https://publications.waset.org/abstracts/175749/multi-labeled-aromatic-medicinal-plant-image-classification-using-deep-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/175749.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">186</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6453</span> Identification of Nonlinear Systems Structured by Hammerstein-Wiener Model </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Brouri">A. Brouri</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20Giri"> F. Giri</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Mkhida"> A. Mkhida</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Elkarkri"> A. Elkarkri</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20L.%20Chhibat"> M. L. Chhibat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Standard Hammerstein-Wiener models consist of a linear subsystem sandwiched by two memoryless nonlinearities. Presently, the linear subsystem is allowed to be parametric or not, continuous- or discrete-time. The input and output nonlinearities are polynomial and may be noninvertible. A two-stage identification method is developed such the parameters of all nonlinear elements are estimated first using the Kozen-Landau polynomial decomposition algorithm. The obtained estimates are then based upon in the identification of the linear subsystem, making use of suitable pre-ad post-compensators. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20system%20identification" title="nonlinear system identification">nonlinear system identification</a>, <a href="https://publications.waset.org/abstracts/search?q=Hammerstein-Wiener%20systems" title=" Hammerstein-Wiener systems"> Hammerstein-Wiener systems</a>, <a href="https://publications.waset.org/abstracts/search?q=frequency%20identification" title=" frequency identification"> frequency identification</a>, <a href="https://publications.waset.org/abstracts/search?q=polynomial%20decomposition" title=" polynomial decomposition"> polynomial decomposition</a> </p> <a href="https://publications.waset.org/abstracts/7969/identification-of-nonlinear-systems-structured-by-hammerstein-wiener-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7969.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">511</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6452</span> Nontuberculous Mycobacterium Infection – Still An Important Disease Among People With Late HIV Diagnosis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jakub%20M%C5%82o%C5%BAniak">Jakub Młoźniak</a>, <a href="https://publications.waset.org/abstracts/search?q=Adam%20Szyma%C5%84ski"> Adam Szymański</a>, <a href="https://publications.waset.org/abstracts/search?q=Gabriela%20Stondzik"> Gabriela Stondzik</a>, <a href="https://publications.waset.org/abstracts/search?q=Dagny%20Krankowska"> Dagny Krankowska</a>, <a href="https://publications.waset.org/abstracts/search?q=Tomasz%20Miku%C5%82a"> Tomasz Mikuła</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nontuberculous mycobacteria (NTM) are bacterial species that cause diversely manifesting diseases mainly in immunocompromised patients. In people with HIV, NTM infection is an AIDS-defining disease and usually appears when the lymphocyte T CD4 count is below 50 cells/μl. The usage of antiretroviral therapy has decreased the prevalence of NTM among people with HIV, but the disease can still be observed especially among patients with late HIV diagnosis. Common presence in environment, human colonization, clinical similarity with tuberculosis and slow growth on culture makes NTM especially hard to diagnose. The study aimed to analyze the epidemiology and clinical course of NTM among patients with HIV. This study included patients with NTM and HIV admitted to our department between 2017 and 2023. Medical records of patients were analyzed and data on age, sex, median time from HIV diagnosis to identification of NTM infection, median CD4 count at NTM diagnosis, methods of determining NTM infection, type of species of mycobacteria identified, clinical symptoms and treatment course were gathered. Twenty-four patients (20 men, 4 women) with identified NTM were included in this study. Among them, 20 were HIV late presenters. The patients' median age was 40. The main symptoms which patients presented were fever, weight loss and cough. Pulmonary disease confirmed with positive cultures from sputum/bronchoalveolar lavage was present in 18 patients. M. avium was the most common species identified. M. marinum caused disseminated skin lesions in 1 patient. Out of all, 5 people were not treated for NTM caused by lack of symptoms and suspicion of colonization with mycobacterium. Concomitant tuberculosis was present in 6 patients. The median diagnostic time from HIV to NTM infections was 3.5 months. The median CD4 count at NTM identification was 69.5 cells/μl. Median NTM treatment time was 16 months but 7 patients haven’t finished their treatment yet. The most commonly used medications were ethambutol and clarithromycin. Among analyzed patients, 4 of them have died. NTM infections are still an important disease among patients who are HIV late presenters. This disease should be taken into consideration during the differential diagnosis of fever, weight loss and cough in people with HIV with lymphocyte T CD4 count <100 cells/μl. Presence of tuberculosis does not exclude nontuberculous mycobacterium coinfection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mycobacteriosis" title="mycobacteriosis">mycobacteriosis</a>, <a href="https://publications.waset.org/abstracts/search?q=HIV" title=" HIV"> HIV</a>, <a href="https://publications.waset.org/abstracts/search?q=late%20presenter" title=" late presenter"> late presenter</a>, <a href="https://publications.waset.org/abstracts/search?q=epidemiology" title=" epidemiology"> epidemiology</a> </p> <a href="https://publications.waset.org/abstracts/185865/nontuberculous-mycobacterium-infection-still-an-important-disease-among-people-with-late-hiv-diagnosis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185865.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">42</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6451</span> E-Vet Smart Rapid System: Detection of Farm Disease Based on Expert System as Supporting to Epidemic Disesase Control</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Malik%20Abdul%20Jabbar%20Zen">Malik Abdul Jabbar Zen</a>, <a href="https://publications.waset.org/abstracts/search?q=Wiwik%20Misaco%20Yuniarti"> Wiwik Misaco Yuniarti</a>, <a href="https://publications.waset.org/abstracts/search?q=Azisya%20Amalia%20Karimasari"> Azisya Amalia Karimasari</a>, <a href="https://publications.waset.org/abstracts/search?q=Novita%20Priandini"> Novita Priandini</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Zoonos is as an infectiontransmitted froma nimals to human sand vice versa currently having increased in the last 20 years. The experts/scientists predict that zoonosis will be a threat to the community in the future since it leads on 70% emerging infectious diseases (EID) and the high mortality of 50%-90%. The zoonosis’ spread from animal to human is caused by contaminated food known as foodborne disease. One World One Health, as the conceptual prevention toward zoonosis, requires the crossed disciplines cooperation to accelerate and streamlinethe handling ofanimal-based disease. E-Vet Smart Rapid System is an integrated innovation in the veterinary expertise application is able to facilitate the prevention, treatment, and educationagainst pandemic diseases and zoonosis. This system is constructed by Decision Support System (DSS) method provides a database of knowledge that is expected to facilitate the identification of disease rapidly, precisely, and accurately as well as to identify the deduction. The testingis conducted through a black box test case and questionnaire (N=30) by validity and reliability approach. Based on the black box test case reveals that E-Vet Rapid System is able to deliver the results in accordance with system design, and questionnaire shows that this system is valid (r > 0.361) and has a reliability (α > 0.3610). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=diagnosis" title="diagnosis">diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=disease" title=" disease"> disease</a>, <a href="https://publications.waset.org/abstracts/search?q=expert%20systems" title=" expert systems"> expert systems</a>, <a href="https://publications.waset.org/abstracts/search?q=livestock" title=" livestock"> livestock</a>, <a href="https://publications.waset.org/abstracts/search?q=zoonosis" title=" zoonosis"> zoonosis</a> </p> <a href="https://publications.waset.org/abstracts/37126/e-vet-smart-rapid-system-detection-of-farm-disease-based-on-expert-system-as-supporting-to-epidemic-disesase-control" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37126.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">455</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6450</span> The Role of Critical Thinking in Disease Diagnosis: A Comprehensive Review</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Al-Mousawi">Mohammad Al-Mousawi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This academic article explores the indispensable role of critical thinking in the process of diagnosing diseases. Employing a multidisciplinary approach, we delve into the cognitive skills and analytical mindset that clinicians, researchers, and healthcare professionals must employ to navigate the complexities of disease identification. By examining the integration of critical thinking within the realms of medical education, diagnostic decision-making, and technological advancements, this article aims to underscore the significance of cultivating and applying critical thinking skills in the ever-evolving landscape of healthcare. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=critical%20thinking" title="critical thinking">critical thinking</a>, <a href="https://publications.waset.org/abstracts/search?q=medical%20education" title=" medical education"> medical education</a>, <a href="https://publications.waset.org/abstracts/search?q=diagnostic%20decision-making" title=" diagnostic decision-making"> diagnostic decision-making</a>, <a href="https://publications.waset.org/abstracts/search?q=fostering%20critical%20thinking" title=" fostering critical thinking"> fostering critical thinking</a> </p> <a href="https://publications.waset.org/abstracts/182359/the-role-of-critical-thinking-in-disease-diagnosis-a-comprehensive-review" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/182359.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">74</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6449</span> Varietal Screening of Watermelon against Powdery Mildew Disease and Its Management</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Asim%20Abbasi">Asim Abbasi</a>, <a href="https://publications.waset.org/abstracts/search?q=Amer%20Habib"> Amer Habib</a>, <a href="https://publications.waset.org/abstracts/search?q=Sajid%20Hussain"> Sajid Hussain</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Sufyan"> Muhammad Sufyan</a>, <a href="https://publications.waset.org/abstracts/search?q=Iqra"> Iqra</a>, <a href="https://publications.waset.org/abstracts/search?q=Hasnain%20Sajjad"> Hasnain Sajjad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Except for few scattered cases, powdery mildew disease was not a big problem for watermelon in the past but with the outbreaks of its pathotypes, races 1W and 2W, this disease becomes a serious issue all around the globe. The severe outbreak of this disease also increased the rate of fungicide application for its proper management. Twelve varieties of watermelon were screened in Research Area of Department of Plant pathology, University of Agriculture, Faisalabad to check the incidence of powdery mildew disease. Disease inoculum was prepared and applied with the help of foliar spray method. Fungicides and plants extracts were also applied after the disease incidence. Percentage leaf surface area diseased was assessed visually with a modified Horsfall-Barratt scale. The results of the experiment revealed that among all varieties, WT2257 and Zcugma F1 were highly resistant showing less than 5% disease incidence while Anar Kali and Sugar baby were highly susceptible with disease incidence of more than 65%. Among botanicals neem extract gave best results with disease incidence of less than 20%. Besides neem, all other botanicals also gave significant control of powdery mildew disease than the untreated check. In case of fungicides, Gemstar showed least disease incidence i.e. < 10%, however besides control maximum disease incidence was observed in Curzate (> 30%). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=botanicals" title="botanicals">botanicals</a>, <a href="https://publications.waset.org/abstracts/search?q=fungicides" title=" fungicides"> fungicides</a>, <a href="https://publications.waset.org/abstracts/search?q=pathotypes" title=" pathotypes"> pathotypes</a>, <a href="https://publications.waset.org/abstracts/search?q=powdery%20mildew" title=" powdery mildew"> powdery mildew</a> </p> <a href="https://publications.waset.org/abstracts/79893/varietal-screening-of-watermelon-against-powdery-mildew-disease-and-its-management" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79893.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">297</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6448</span> A Bayesian Hierarchical Poisson Model with an Underlying Cluster Structure for the Analysis of Measles in Colombia</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ana%20Corberan-Vallet">Ana Corberan-Vallet</a>, <a href="https://publications.waset.org/abstracts/search?q=Karen%20C.%20Florez"> Karen C. Florez</a>, <a href="https://publications.waset.org/abstracts/search?q=Ingrid%20C.%20Marino"> Ingrid C. Marino</a>, <a href="https://publications.waset.org/abstracts/search?q=Jose%20D.%20Bermudez"> Jose D. Bermudez</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In 2016, the Region of the Americas was declared free of measles, a viral disease that can cause severe health problems. However, since 2017, measles has reemerged in Venezuela and has subsequently reached neighboring countries. In 2018, twelve American countries reported confirmed cases of measles. Governmental and health authorities in Colombia, a country that shares the longest land boundary with Venezuela, are aware of the need for a strong response to restrict the expanse of the epidemic. In this work, we apply a Bayesian hierarchical Poisson model with an underlying cluster structure to describe disease incidence in Colombia. Concretely, the proposed methodology provides relative risk estimates at the department level and identifies clusters of disease, which facilitates the implementation of targeted public health interventions. Socio-demographic factors, such as the percentage of migrants, gross domestic product, and entry routes, are included in the model to better describe the incidence of disease. Since the model does not impose any spatial correlation at any level of the model hierarchy, it avoids the spatial confounding problem and provides a suitable framework to estimate the fixed-effect coefficients associated with spatially-structured covariates. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20analysis" title="Bayesian analysis">Bayesian analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=cluster%20identification" title=" cluster identification"> cluster identification</a>, <a href="https://publications.waset.org/abstracts/search?q=disease%20mapping" title=" disease mapping"> disease mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20estimation" title=" risk estimation"> risk estimation</a> </p> <a href="https://publications.waset.org/abstracts/115292/a-bayesian-hierarchical-poisson-model-with-an-underlying-cluster-structure-for-the-analysis-of-measles-in-colombia" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/115292.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">151</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6447</span> Identification of Potential Small Molecule Regulators of PERK Kinase</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ireneusz%20Majsterek">Ireneusz Majsterek</a>, <a href="https://publications.waset.org/abstracts/search?q=Dariusz%20Pytel"> Dariusz Pytel</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20Alan%20Diehl"> J. Alan Diehl</a> </p> <p class="card-text"><strong>Abstract:</strong></p> PKR-like ER kinase (PERK) is serine/threonie endoplasmic reticulum (ER) transmembrane kinase activated during ER-stress. PERK can activate signaling pathways known as unfolded protein response (UPR). Attenuation of translation is mediated by PERK via phosphorylation of eukaryotic initiation factor 2α (eIF2α), which is necessary for translation initiation. PERK activation also directly contributes to activation of Nrf2 which regulates expression of anti-oxidant enzymes. An increased phosphorylation of eIF2α has been reported in Alzheimer disease (AD) patient hippocampus, indicating that PERK is activated in this disease. Recent data have revealed activation of PERK signaling in non-Hodgkins lymphomas. Results also revealed that loss of PERK limits mammary tumor cell growth in vitro and in vivo. Consistent with these observations, activation of UPR in vitro increases levels of the amyloid precursor protein (APP), the peptide from which beta-amyloid plaques (AB) fragments are derived. Finally, proteolytic processing of APP, including the cleavages that produce AB, largely occurs in the ER, and localization coincident with PERK activity. Thus, we expect that PERK-dependent signaling is critical for progression of many types of diseases (human cancer, neurodegenerative disease and other). Therefore, modulation of PERK activity may be a useful therapeutic target in the treatment of different diseases that fail to respond to traditional chemotherapeutic strategies, including Alzheimer’s disease. Our goal will be to developed therapeutic modalities targeting PERK activity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=PERK%20kinase" title="PERK kinase">PERK kinase</a>, <a href="https://publications.waset.org/abstracts/search?q=small%20molecule%20inhibitor" title=" small molecule inhibitor"> small molecule inhibitor</a>, <a href="https://publications.waset.org/abstracts/search?q=neurodegenerative%20disease" title=" neurodegenerative disease"> neurodegenerative disease</a>, <a href="https://publications.waset.org/abstracts/search?q=Alzheimer%E2%80%99s%20disease" title=" Alzheimer’s disease"> Alzheimer’s disease</a> </p> <a href="https://publications.waset.org/abstracts/18276/identification-of-potential-small-molecule-regulators-of-perk-kinase" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18276.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">482</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6446</span> Correlation between Peripheral Arterial Disease and Coronary Artery Disease in Bangladeshi Population: A Five Years Retrospective Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Syed%20Dawood%20M.%20Taimur">Syed Dawood M. Taimur</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: Peripheral arterial disease (PAD) is under diagnosed in primary care practices, yet the extent of unrecognized PAD in patients with coronary artery disease (CAD) is unknown. Objective: To assess the prevalence of previously unrecognized PAD in patients undergoing coronary angiogram and to determine the relationship between the presence of PAD and severity of CAD. Material & Methods: This five years retrospective study was conducted at an invasive lab of the department of Cardiology, Ibrahim Cardiac Hospital & Research Institute from January 2010 to December 2014. Total 77 patients were included in this study. Study variables were age, sex, risk factors like hypertension, diabetes mellitus, dyslipidaemia, smoking habit and positive family history for ischemic heart disease, coronary artery and peripheral artery profile. Results: Mean age was 56.83±13.64 years, Male mean age was 53.98±15.08 years and female mean age was 54.5±1.73years. Hypertension was detected in 55.8%, diabetes in 87%, dyslipidaemia in 81.8%, smoking habits in 79.2% and 58.4% had a positive family history. After catheterization 88.3% had peripheral arterial disease and 71.4% had coronary artery disease. Out of 77 patients, 52 had both coronary and peripheral arterial disease which was statistically significant (p < .014). Coronary angiogram revealed 28.6% (22) patients had triple vessel disease, 23.3% (18) had single vessel disease, 19.5% (15) had double vessel disease and 28.6% (22) were normal coronary arteries. The peripheral angiogram revealed 54.5% had superficial femoral artery disease, 26% had anterior tibial artery disease, 27.3% had posterior tibial artery disease, 20.8% had common iliac artery disease, 15.6% had common femoral artery disease and 2.6% had renal artery disease. Conclusion: There is a strong and definite correlation between coronary and peripheral arterial disease. We found that cardiovascular risk factors were in fact risk factors for both PAD and CAD. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=coronary%20artery%20disease%20%28CAD%29" title="coronary artery disease (CAD)">coronary artery disease (CAD)</a>, <a href="https://publications.waset.org/abstracts/search?q=peripheral%20artery%20disease%28PVD%29" title=" peripheral artery disease(PVD)"> peripheral artery disease(PVD)</a>, <a href="https://publications.waset.org/abstracts/search?q=risk" title=" risk"> risk</a>, <a href="https://publications.waset.org/abstracts/search?q=factors" title=" factors"> factors</a>, <a href="https://publications.waset.org/abstracts/search?q=correlation" title=" correlation"> correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=cathetarization" title=" cathetarization"> cathetarization</a> </p> <a href="https://publications.waset.org/abstracts/37628/correlation-between-peripheral-arterial-disease-and-coronary-artery-disease-in-bangladeshi-population-a-five-years-retrospective-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/37628.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">426</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6445</span> An Automated System for the Detection of Citrus Greening Disease Based on Visual Descriptors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sidra%20Naeem">Sidra Naeem</a>, <a href="https://publications.waset.org/abstracts/search?q=Ayesha%20Naeem"> Ayesha Naeem</a>, <a href="https://publications.waset.org/abstracts/search?q=Sahar%20Rahim"> Sahar Rahim</a>, <a href="https://publications.waset.org/abstracts/search?q=Nadia%20Nawaz%20Qadri"> Nadia Nawaz Qadri</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Citrus greening is a bacterial disease that causes considerable damage to citrus fruits worldwide. Efficient method for this disease detection must be carried out to minimize the production loss. This paper presents a pattern recognition system that comprises three stages for the detection of citrus greening from Orange leaves: segmentation, feature extraction and classification. Image segmentation is accomplished by adaptive thresholding. The feature extraction stage comprises of three visual descriptors i.e. shape, color and texture. From shape feature we have used asymmetry index, from color feature we have used histogram of Cb component from YCbCr domain and from texture feature we have used local binary pattern. Classification was done using support vector machines and k nearest neighbors. The best performances of the system is Accuracy = 88.02% and AUROC = 90.1% was achieved by automatic segmented images. Our experiments validate that: (1). Segmentation is an imperative preprocessing step for computer assisted diagnosis of citrus greening, and (2). The combination of shape, color and texture features form a complementary set towards the identification of citrus greening disease. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=citrus%20greening" title="citrus greening">citrus greening</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/98969/an-automated-system-for-the-detection-of-citrus-greening-disease-based-on-visual-descriptors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/98969.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">184</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6444</span> Molecular Identification and Genotyping of Human Brucella Strains Isolated in Kuwait</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abu%20Salim%20Mustafa">Abu Salim Mustafa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Brucellosis is a zoonotic disease endemic in Kuwait. Human brucellosis can be caused by several Brucella species with Brucella melitensis causing the most severe and Brucella abortus the least severe disease. Furthermore, relapses are common after successful chemotherapy of patients. The classical biochemical methods of culture and serology for identification of Brucellae provide information about the species and serotypes only. However, to differentiate between relapse and reinfection/epidemiological investigations, the identification of genotypes using molecular methods is essential. In this study, four molecular methods [16S rRNA gene sequencing, real-time PCR, enterobacterial repetitive intergenic consensus (ERIC)-PCR and multilocus variable-number tandem-repeat analysis (MLVA)-16] were evaluated for the identification and typing of 75 strains of Brucella isolated in Kuwait. The 16S rRNA gene sequencing suggested that all the strains were B. melitensis and real-time PCR confirmed their species identity as B. melitensis. The ERIC-PCR band profiles produced a dendrogram of 75 branches suggesting each strain to be of a unique type. The cluster classification, based on ~ 80% similarity, divided all the ERIC genotypes into two clusters, A and B. Cluster A consisted of 9 ERIC genotypes (A1-A9) corresponding to 9 individual strains. Cluster B comprised of 13 ERIC genotypes (B1-B13) with B5 forming the largest cluster of 51 strains. MLVA-16 identified all isolates as B. melitensis and divided them into 71 MLVA-types. The cluster analysis of MLVA-16-types suggested that most of the strains in Kuwait originated from the East Mediterranean Region, a few from the African group and one new genotype closely matched with the West Mediterranean region. In conclusion, this work demonstrates that B. melitensis, the most pathogenic species of Brucella, is prevalent in Kuwait. Furthermore, MLVA-16 is the best molecular method, which can identify the Brucella species and genotypes as well as determine their origin in the global context. Supported by Kuwait University Research Sector grants MI04/15 and SRUL02/13. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Brucella" title="Brucella">Brucella</a>, <a href="https://publications.waset.org/abstracts/search?q=ERIC-PCR" title=" ERIC-PCR"> ERIC-PCR</a>, <a href="https://publications.waset.org/abstracts/search?q=MLVA-16" title=" MLVA-16"> MLVA-16</a>, <a href="https://publications.waset.org/abstracts/search?q=RT-PCR" title=" RT-PCR"> RT-PCR</a>, <a href="https://publications.waset.org/abstracts/search?q=16S%20rRNA%20gene%20sequencing" title=" 16S rRNA gene sequencing"> 16S rRNA gene sequencing</a> </p> <a href="https://publications.waset.org/abstracts/56928/molecular-identification-and-genotyping-of-human-brucella-strains-isolated-in-kuwait" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56928.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">391</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6443</span> CMPD: Cancer Mutant Proteome Database</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Po-Jung%20Huang">Po-Jung Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Chi-Ching%20Lee"> Chi-Ching Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Bertrand%20Chin-Ming%20Tan"> Bertrand Chin-Ming Tan</a>, <a href="https://publications.waset.org/abstracts/search?q=Yuan-Ming%20Yeh"> Yuan-Ming Yeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Julie%20Lichieh%20Chu"> Julie Lichieh Chu</a>, <a href="https://publications.waset.org/abstracts/search?q=Tin-Wen%20Chen"> Tin-Wen Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Cheng-Yang%20Lee"> Cheng-Yang Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Ruei-Chi%20Gan"> Ruei-Chi Gan</a>, <a href="https://publications.waset.org/abstracts/search?q=Hsuan%20Liu"> Hsuan Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Petrus%20Tang"> Petrus Tang </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Whole-exome sequencing focuses on the protein coding regions of disease/cancer associated genes based on a priori knowledge is the most cost-effective method to study the association between genetic alterations and disease. Recent advances in high throughput sequencing technologies and proteomic techniques has provided an opportunity to integrate genomics and proteomics, allowing readily detectable mutated peptides corresponding to mutated genes. Since sequence database search is the most widely used method for protein identification using Mass spectrometry (MS)-based proteomics technology, a mutant proteome database is required to better approximate the real protein pool to improve disease-associated mutated protein identification. Large-scale whole exome/genome sequencing studies were launched by National Cancer Institute (NCI), Broad Institute, and The Cancer Genome Atlas (TCGA), which provide not only a comprehensive report on the analysis of coding variants in diverse samples cell lines but a invaluable resource for extensive research community. No existing database is available for the collection of mutant protein sequences related to the identified variants in these studies. CMPD is designed to address this issue, serving as a bridge between genomic data and proteomic studies and focusing on protein sequence-altering variations originated from both germline and cancer-associated somatic variations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=TCGA" title="TCGA">TCGA</a>, <a href="https://publications.waset.org/abstracts/search?q=cancer" title=" cancer"> cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=mutant" title=" mutant"> mutant</a>, <a href="https://publications.waset.org/abstracts/search?q=proteome" title=" proteome"> proteome</a> </p> <a href="https://publications.waset.org/abstracts/16077/cmpd-cancer-mutant-proteome-database" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16077.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">593</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6442</span> Molecular Interaction of Acetylcholinesterase with Flavonoids Involved in Neurodegenerative Diseases</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=W.%20Soufi">W. Soufi</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20Boukli%20Hacene"> F. Boukli Hacene</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Ghalem"> S. Ghalem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Alzheimer's disease (AD) is a neurodegenerative disease that leads to a progressive and permanent deterioration of nerve cells. This disease is progressively accompanied by an intellectual deterioration leading to psychological manifestations and behavioral disorders that lead to a loss of autonomy. It is the most frequent of degenerative dementia. Alzheimer's disease (AD), which affects a growing number of people, has become a major public health problem in a few years. In the context of the study of the mechanisms governing the evolution of AD disease, we have found that natural flavonoids are good acetylcholinesterase inhibitors that reduce the rate of ßA secretion in neurons. This work is to study the inhibition of acetylcholinesterase (AChE) which is an enzyme involved in Alzheimer's disease, by methods of molecular modeling. These results will probably help in the development of an effective therapeutic tool in the fight against the development of Alzheimer's disease. Our goal of the research is to study the inhibition of acetylcholinesterase (AChE) by molecular modeling methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alzheimer%27s%20disease" title="Alzheimer's disease">Alzheimer's disease</a>, <a href="https://publications.waset.org/abstracts/search?q=acetylcholinesterase" title=" acetylcholinesterase"> acetylcholinesterase</a>, <a href="https://publications.waset.org/abstracts/search?q=flavonoids" title=" flavonoids"> flavonoids</a>, <a href="https://publications.waset.org/abstracts/search?q=molecular%20modeling" title=" molecular modeling"> molecular modeling</a> </p> <a href="https://publications.waset.org/abstracts/156249/molecular-interaction-of-acetylcholinesterase-with-flavonoids-involved-in-neurodegenerative-diseases" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/156249.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">105</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">6441</span> Personalized Infectious Disease Risk Prediction System: A Knowledge Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Retno%20A.%20Vinarti">Retno A. Vinarti</a>, <a href="https://publications.waset.org/abstracts/search?q=Lucy%20M.%20Hederman"> Lucy M. Hederman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research describes a knowledge model for a system which give personalized alert to users about infectious disease risks in the context of weather, location and time. The knowledge model is based on established epidemiological concepts augmented by information gleaned from infection-related data repositories. The existing disease risk prediction research has more focuses on utilizing raw historical data and yield seasonal patterns of infectious disease risk emergence. This research incorporates both data and epidemiological concepts gathered from Atlas of Human Infectious Disease (AHID) and Centre of Disease Control (CDC) as basic reasoning of infectious disease risk prediction. Using CommonKADS methodology, the disease risk prediction task is an assignment synthetic task, starting from knowledge identification through specification, refinement to implementation. First, knowledge is gathered from AHID primarily from the epidemiology and risk group chapters for each infectious disease. The result of this stage is five major elements (Person, Infectious Disease, Weather, Location and Time) and their properties. At the knowledge specification stage, the initial tree model of each element and detailed relationships are produced. This research also includes a validation step as part of knowledge refinement: on the basis that the best model is formed using the most common features, Frequency-based Selection (FBS) is applied. The portion of the Infectious Disease risk model relating to Person comes out strongest, with Location next, and Weather weaker. For Person attribute, Age is the strongest, Activity and Habits are moderate, and Blood type is weakest. At the Location attribute, General category (e.g. continents, region, country, and island) results much stronger than Specific category (i.e. terrain feature). For Weather attribute, Less Precise category (i.e. season) comes out stronger than Precise category (i.e. exact temperature or humidity interval). However, given that some infectious diseases are significantly more serious than others, a frequency based metric may not be appropriate. Future work will incorporate epidemiological measurements of disease seriousness (e.g. odds ratio, hazard ratio and fatality rate) into the validation metrics. This research is limited to modelling existing knowledge about epidemiology and chain of infection concepts. Further step, verification in knowledge refinement stage, might cause some minor changes on the shape of tree. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=epidemiology" title="epidemiology">epidemiology</a>, <a href="https://publications.waset.org/abstracts/search?q=knowledge%20modelling" title=" knowledge modelling"> knowledge modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=infectious%20disease" title=" infectious disease"> infectious disease</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=risk" title=" risk"> risk</a> </p> <a href="https://publications.waset.org/abstracts/55891/personalized-infectious-disease-risk-prediction-system-a-knowledge-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/55891.pdf" target="_blank" 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