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Search results for: deep learning

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for: deep learning</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">8379</span> Forecasting the Temperature at a Weather Station Using Deep Neural Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Debneil%20Saha%20Roy">Debneil Saha Roy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Weather forecasting is a complex topic and is well suited for analysis by deep learning approaches. With the wide availability of weather observation data nowadays, these approaches can be utilized to identify immediate comparisons between historical weather forecasts and current observations. This work explores the application of deep learning techniques to weather forecasting in order to accurately predict the weather over a given forecast hori­zon. Three deep neural networks are used in this study, namely, Multi-Layer Perceptron (MLP), Long Short Tunn Memory Network (LSTM) and a combination of Convolutional Neural Network (CNN) and LSTM. The predictive performance of these models is compared using two evaluation metrics. The results show that forecasting accuracy increases with an increase in the complexity of deep neural networks. <p class="card-text"><strong>Keywords:</strong> <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=long%20short%20term%20memory" title=" long short term memory"> long short term memory</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-layer%20perceptron" title=" multi-layer perceptron"> multi-layer perceptron</a> </p> <a href="https://publications.waset.org/abstracts/124787/forecasting-the-temperature-at-a-weather-station-using-deep-neural-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/124787.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">177</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">8378</span> Positive Bias and Length Bias in Deep Neural Networks for Premises Selection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jiaqi%20Huang">Jiaqi Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Yuheng%20Wang"> Yuheng Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Premises selection, the task of selecting a set of axioms for proving a given conjecture, is a major bottleneck in automated theorem proving. An array of deep-learning-based methods has been established for premises selection, but a perfect performance remains challenging. Our study examines the inaccuracy of deep neural networks in premises selection. Through training network models using encoded conjecture and axiom pairs from the Mizar Mathematical Library, two potential biases are found: the network models classify more premises as necessary than unnecessary, referred to as the ‘positive bias’, and the network models perform better in proving conjectures that paired with more axioms, referred to as ‘length bias’. The ‘positive bias’ and ‘length bias’ discovered could inform the limitation of existing deep neural networks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automated%20theorem%20proving" title="automated theorem proving">automated theorem proving</a>, <a href="https://publications.waset.org/abstracts/search?q=premises%20selection" title=" premises selection"> premises selection</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=interpreting%20deep%20learning" title=" interpreting deep learning"> interpreting deep learning</a> </p> <a href="https://publications.waset.org/abstracts/108017/positive-bias-and-length-bias-in-deep-neural-networks-for-premises-selection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/108017.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">183</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">8377</span> Market Index Trend Prediction using Deep Learning and Risk Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shervin%20Alaei">Shervin Alaei</a>, <a href="https://publications.waset.org/abstracts/search?q=Reza%20Moradi"> Reza Moradi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Trading in financial markets is subject to risks due to their high volatilities. Here, using an LSTM neural network, and by doing some risk-based feature engineering tasks, we developed a method that can accurately predict trends of the Tehran stock exchange market index from a few days ago. Our test results have shown that the proposed method with an average prediction accuracy of more than 94% is superior to the other common machine learning algorithms. To the best of our knowledge, this is the first work incorporating deep learning and risk factors to accurately predict market trends. <p class="card-text"><strong>Keywords:</strong> <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=LSTM" title=" LSTM"> LSTM</a>, <a href="https://publications.waset.org/abstracts/search?q=trend%20prediction" title=" trend prediction"> trend prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20management" title=" risk management"> risk management</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20networks" title=" artificial neural networks"> artificial neural networks</a> </p> <a href="https://publications.waset.org/abstracts/148318/market-index-trend-prediction-using-deep-learning-and-risk-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148318.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">156</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">8376</span> Use Cloud-Based Watson Deep Learning Platform to Train Models Faster and More Accurate </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Susan%20Diamond">Susan Diamond</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Machine Learning workloads have traditionally been run in high-performance computing (HPC) environments, where users log in to dedicated machines and utilize the attached GPUs to run training jobs on huge datasets. Training of large neural network models is very resource intensive, and even after exploiting parallelism and accelerators such as GPUs, a single training job can still take days. Consequently, the cost of hardware is a barrier to entry. Even when upfront cost is not a concern, the lead time to set up such an HPC environment takes months from acquiring hardware to set up the hardware with the right set of firmware, software installed and configured. Furthermore, scalability is hard to achieve in a rigid traditional lab environment. Therefore, it is slow to react to the dynamic change in the artificial intelligent industry. Watson Deep Learning as a service, a cloud-based deep learning platform that mitigates the long lead time and high upfront investment in hardware. It enables robust and scalable sharing of resources among the teams in an organization. It is designed for on-demand cloud environments. Providing a similar user experience in a multi-tenant cloud environment comes with its own unique challenges regarding fault tolerance, performance, and security. Watson Deep Learning as a service tackles these challenges and present a deep learning stack for the cloud environments in a secure, scalable and fault-tolerant manner. It supports a wide range of deep-learning frameworks such as Tensorflow, PyTorch, Caffe, Torch, Theano, and MXNet etc. These frameworks reduce the effort and skillset required to design, train, and use deep learning models. Deep Learning as a service is used at IBM by AI researchers in areas including machine translation, computer vision, and healthcare.  <p class="card-text"><strong>Keywords:</strong> <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=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=cognitive%20computing" title=" cognitive computing"> cognitive computing</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20training" title=" model training"> model training</a> </p> <a href="https://publications.waset.org/abstracts/84279/use-cloud-based-watson-deep-learning-platform-to-train-models-faster-and-more-accurate" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/84279.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">209</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">8375</span> Foot Recognition Using Deep Learning for Knee Rehabilitation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rakkrit%20Duangsoithong">Rakkrit Duangsoithong</a>, <a href="https://publications.waset.org/abstracts/search?q=Jermphiphut%20Jaruenpunyasak"> Jermphiphut Jaruenpunyasak</a>, <a href="https://publications.waset.org/abstracts/search?q=Alba%20Garcia"> Alba Garcia</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The use of foot recognition can be applied in many medical fields such as the gait pattern analysis and the knee exercises of patients in rehabilitation. Generally, a camera-based foot recognition system is intended to capture a patient image in a controlled room and background to recognize the foot in the limited views. However, this system can be inconvenient to monitor the knee exercises at home. In order to overcome these problems, this paper proposes to use the deep learning method using Convolutional Neural Networks (CNNs) for foot recognition. The results are compared with the traditional classification method using LBP and HOG features with kNN and SVM classifiers. According to the results, deep learning method provides better accuracy but with higher complexity to recognize the foot images from online databases than the traditional classification method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=foot%20recognition" title="foot recognition">foot recognition</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=knee%20rehabilitation" title=" knee rehabilitation"> knee rehabilitation</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network" title=" convolutional neural network"> convolutional neural network</a> </p> <a href="https://publications.waset.org/abstracts/105495/foot-recognition-using-deep-learning-for-knee-rehabilitation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/105495.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">161</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">8374</span> Prediction of PM₂.₅ Concentration in Ulaanbaatar with Deep Learning Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Suriya">Suriya</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Rapid socio-economic development and urbanization have led to an increasingly serious air pollution problem in Ulaanbaatar (UB), the capital of Mongolia. PM₂.₅ pollution has become the most pressing aspect of UB air pollution. Therefore, monitoring and predicting PM₂.₅ concentration in UB is of great significance for the health of the local people and environmental management. As of yet, very few studies have used models to predict PM₂.₅ concentrations in UB. Using data from 0:00 on June 1, 2018, to 23:00 on April 30, 2020, we proposed two deep learning models based on Bayesian-optimized LSTM (Bayes-LSTM) and CNN-LSTM. We utilized hourly observed data, including Himawari8 (H8) aerosol optical depth (AOD), meteorology, and PM₂.₅ concentration, as input for the prediction of PM₂.₅ concentrations. The correlation strengths between meteorology, AOD, and PM₂.₅ were analyzed using the gray correlation analysis method; the comparison of the performance improvement of the model by using the AOD input value was tested, and the performance of these models was evaluated using mean absolute error (MAE) and root mean square error (RMSE). The prediction accuracies of Bayes-LSTM and CNN-LSTM deep learning models were both improved when AOD was included as an input parameter. Improvement of the prediction accuracy of the CNN-LSTM model was particularly enhanced in the non-heating season; in the heating season, the prediction accuracy of the Bayes-LSTM model slightly improved, while the prediction accuracy of the CNN-LSTM model slightly decreased. We propose two novel deep learning models for PM₂.₅ concentration prediction in UB, Bayes-LSTM, and CNN-LSTM deep learning models. Pioneering the use of AOD data from H8 and demonstrating the inclusion of AOD input data improves the performance of our two proposed deep learning models. <p class="card-text"><strong>Keywords:</strong> <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=AOD" title=" AOD"> AOD</a>, <a href="https://publications.waset.org/abstracts/search?q=PM2.5" title=" PM2.5"> PM2.5</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction" title=" prediction"> prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=Ulaanbaatar" title=" Ulaanbaatar"> Ulaanbaatar</a> </p> <a href="https://publications.waset.org/abstracts/185289/prediction-of-pm25-concentration-in-ulaanbaatar-with-deep-learning-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185289.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">48</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">8373</span> Deep Learning and Accurate Performance Measure Processes for Cyber Attack Detection among Web Logs</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Noureddine%20Mohtaram">Noureddine Mohtaram</a>, <a href="https://publications.waset.org/abstracts/search?q=Jeremy%20Patrix"> Jeremy Patrix</a>, <a href="https://publications.waset.org/abstracts/search?q=Jerome%20Verny"> Jerome Verny</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As an enormous number of online services have been developed into web applications, security problems based on web applications are becoming more serious now. Most intrusion detection systems rely on each request to find the cyber-attack rather than on user behavior, and these systems can only protect web applications against known vulnerabilities rather than certain zero-day attacks. In order to detect new attacks, we analyze the HTTP protocols of web servers to divide them into two categories: normal attacks and malicious attacks. On the other hand, the quality of the results obtained by deep learning (DL) in various areas of big data has given an important motivation to apply it to cybersecurity. Deep learning for attack detection in cybersecurity has the potential to be a robust tool from small transformations to new attacks due to its capability to extract more high-level features. This research aims to take a new approach, deep learning to cybersecurity, to classify these two categories to eliminate attacks and protect web servers of the defense sector which encounters different web traffic compared to other sectors (such as e-commerce, web app, etc.). The result shows that by using a machine learning method, a higher accuracy rate, and a lower false alarm detection rate can be achieved. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=anomaly%20detection" title="anomaly detection">anomaly detection</a>, <a href="https://publications.waset.org/abstracts/search?q=HTTP%20protocol" title=" HTTP protocol"> HTTP protocol</a>, <a href="https://publications.waset.org/abstracts/search?q=logs" title=" logs"> logs</a>, <a href="https://publications.waset.org/abstracts/search?q=cyber%20attack" title=" cyber attack"> cyber attack</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a> </p> <a href="https://publications.waset.org/abstracts/136582/deep-learning-and-accurate-performance-measure-processes-for-cyber-attack-detection-among-web-logs" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/136582.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">210</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">8372</span> Enabling Non-invasive Diagnosis of Thyroid Nodules with High Specificity and Sensitivity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sai%20Maniveer%20Adapa">Sai Maniveer Adapa</a>, <a href="https://publications.waset.org/abstracts/search?q=Sai%20Guptha%20Perla"> Sai Guptha Perla</a>, <a href="https://publications.waset.org/abstracts/search?q=Adithya%20Reddy%20P."> Adithya Reddy P.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Thyroid nodules can often be diagnosed with ultrasound imaging, although differentiating between benign and malignant nodules can be challenging for medical professionals. This work suggests a novel approach to increase the precision of thyroid nodule identification by combining machine learning and deep learning. The new approach first extracts information from the ultrasound pictures using a deep learning method known as a convolutional autoencoder. A support vector machine, a type of machine learning model, is then trained using these features. With an accuracy of 92.52%, the support vector machine can differentiate between benign and malignant nodules. This innovative technique may decrease the need for pointless biopsies and increase the accuracy of thyroid nodule detection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=thyroid%20tumor%20diagnosis" title="thyroid tumor diagnosis">thyroid tumor diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=ultrasound%20images" title=" ultrasound images"> ultrasound images</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=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20auto-encoder" title=" convolutional auto-encoder"> convolutional auto-encoder</a>, <a href="https://publications.waset.org/abstracts/search?q=support%20vector%20machine" title=" support vector machine"> support vector machine</a> </p> <a href="https://publications.waset.org/abstracts/182971/enabling-non-invasive-diagnosis-of-thyroid-nodules-with-high-specificity-and-sensitivity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/182971.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">58</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">8371</span> A Case Study of Deep Learning for Disease Detection in Crops</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Felipe%20A.%20Guth">Felipe A. Guth</a>, <a href="https://publications.waset.org/abstracts/search?q=Shane%20Ward"> Shane Ward</a>, <a href="https://publications.waset.org/abstracts/search?q=Kevin%20McDonnell"> Kevin McDonnell</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the precision agriculture area, one of the main tasks is the automated detection of diseases in crops. Machine Learning algorithms have been studied in recent decades for such tasks in view of their potential for improving economic outcomes that automated disease detection may attain over crop fields. The latest generation of deep learning convolution neural networks has presented significant results in the area of image classification. In this way, this work has tested the implementation of an architecture of deep learning convolution neural network for the detection of diseases in different types of crops. A data augmentation strategy was used to meet the requirements of the algorithm implemented with a deep learning framework. Two test scenarios were deployed. The first scenario implemented a neural network under images extracted from a controlled environment while the second one took images both from the field and the controlled environment. The results evaluated the generalisation capacity of the neural networks in relation to the two types of images presented. Results yielded a general classification accuracy of 59% in scenario 1 and 96% in scenario 2. <p class="card-text"><strong>Keywords:</strong> <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=disease%20detection" title=" disease detection"> disease detection</a>, <a href="https://publications.waset.org/abstracts/search?q=precision%20agriculture" title=" precision agriculture"> precision agriculture</a> </p> <a href="https://publications.waset.org/abstracts/95339/a-case-study-of-deep-learning-for-disease-detection-in-crops" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/95339.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">259</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">8370</span> Gaits Stability Analysis for a Pneumatic Quadruped Robot Using Reinforcement Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Soofiyan%20Atar">Soofiyan Atar</a>, <a href="https://publications.waset.org/abstracts/search?q=Adil%20Shaikh"> Adil Shaikh</a>, <a href="https://publications.waset.org/abstracts/search?q=Sahil%20Rajpurkar"> Sahil Rajpurkar</a>, <a href="https://publications.waset.org/abstracts/search?q=Pragnesh%20Bhalala"> Pragnesh Bhalala</a>, <a href="https://publications.waset.org/abstracts/search?q=Aniket%20Desai"> Aniket Desai</a>, <a href="https://publications.waset.org/abstracts/search?q=Irfan%20Siddavatam"> Irfan Siddavatam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Deep reinforcement learning (deep RL) algorithms leverage the symbolic power of complex controllers by automating it by mapping sensory inputs to low-level actions. Deep RL eliminates the complex robot dynamics with minimal engineering. Deep RL provides high-risk involvement by directly implementing it in real-world scenarios and also high sensitivity towards hyperparameters. Tuning of hyperparameters on a pneumatic quadruped robot becomes very expensive through trial-and-error learning. This paper presents an automated learning control for a pneumatic quadruped robot using sample efficient deep Q learning, enabling minimal tuning and very few trials to learn the neural network. Long training hours may degrade the pneumatic cylinder due to jerk actions originated through stochastic weights. We applied this method to the pneumatic quadruped robot, which resulted in a hopping gait. In our process, we eliminated the use of a simulator and acquired a stable gait. This approach evolves so that the resultant gait matures more sturdy towards any stochastic changes in the environment. We further show that our algorithm performed very well as compared to programmed gait using robot dynamics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=model-based%20reinforcement%20learning" title="model-based reinforcement learning">model-based reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=gait%20stability" title=" gait stability"> gait stability</a>, <a href="https://publications.waset.org/abstracts/search?q=supervised%20learning" title=" supervised learning"> supervised learning</a>, <a href="https://publications.waset.org/abstracts/search?q=pneumatic%20quadruped" title=" pneumatic quadruped"> pneumatic quadruped</a> </p> <a href="https://publications.waset.org/abstracts/140524/gaits-stability-analysis-for-a-pneumatic-quadruped-robot-using-reinforcement-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/140524.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">316</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">8369</span> Robot Movement Using the Trust Region Policy Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Romisaa%20Ali">Romisaa Ali</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Policy Gradient approach is one of the deep reinforcement learning families that combines deep neural networks (DNN) with reinforcement learning RL to discover the optimum of the control problem through experience gained from the interaction between the robot and its surroundings. In contrast to earlier policy gradient algorithms, which were unable to handle these two types of error because of over-or under-estimation introduced by the deep neural network model, this article will discuss the state-of-the-art SOTA policy gradient technique, trust region policy optimization (TRPO), by applying this method in various environments compared to another policy gradient method, the Proximal Policy Optimization (PPO), to explain their robust optimization, using this SOTA to gather experience data during various training phases after observing the impact of hyper-parameters on neural network performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20neural%20networks" title="deep neural networks">deep neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20reinforcement%20learning" title=" deep reinforcement learning"> deep reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=proximal%20policy%20optimization" title=" proximal policy optimization"> proximal policy optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=state-of-the-art" title=" state-of-the-art"> state-of-the-art</a>, <a href="https://publications.waset.org/abstracts/search?q=trust%20region%20policy%20optimization" title=" trust region policy optimization"> trust region policy optimization</a> </p> <a href="https://publications.waset.org/abstracts/158075/robot-movement-using-the-trust-region-policy-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158075.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">169</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">8368</span> Applications of AI, Machine Learning, and Deep Learning in Cyber Security </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hailyie%20Tekleselase">Hailyie Tekleselase</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Deep learning is increasingly used as a building block of security systems. However, neural networks are hard to interpret and typically solid to the practitioner. This paper presents a detail survey of computing methods in cyber security, and analyzes the prospects of enhancing the cyber security capabilities by suggests that of accelerating the intelligence of the security systems. There are many AI-based applications used in industrial scenarios such as Internet of Things (IoT), smart grids, and edge computing. Machine learning technologies require a training process which introduces the protection problems in the training data and algorithms. We present machine learning techniques currently applied to the detection of intrusion, malware, and spam. Our conclusions are based on an extensive review of the literature as well as on experiments performed on real enterprise systems and network traffic. We conclude that problems can be solved successfully only when methods of artificial intelligence are being used besides human experts or operators. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title="artificial intelligence">artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</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=cyber%20security" title=" cyber security"> cyber security</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20data" title=" big data"> big data</a> </p> <a href="https://publications.waset.org/abstracts/124424/applications-of-ai-machine-learning-and-deep-learning-in-cyber-security" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/124424.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">126</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">8367</span> Multi-Classification Deep Learning Model for Diagnosing Different Chest Diseases</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bandhan%20Dey">Bandhan Dey</a>, <a href="https://publications.waset.org/abstracts/search?q=Muhsina%20Bintoon%20Yiasha"> Muhsina Bintoon Yiasha</a>, <a href="https://publications.waset.org/abstracts/search?q=Gulam%20Sulaman%20Choudhury"> Gulam Sulaman Choudhury</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Chest disease is one of the most problematic ailments in our regular life. There are many known chest diseases out there. Diagnosing them correctly plays a vital role in the process of treatment. There are many methods available explicitly developed for different chest diseases. But the most common approach for diagnosing these diseases is through X-ray. In this paper, we proposed a multi-classification deep learning model for diagnosing COVID-19, lung cancer, pneumonia, tuberculosis, and atelectasis from chest X-rays. In the present work, we used the transfer learning method for better accuracy and fast training phase. The performance of three architectures is considered: InceptionV3, VGG-16, and VGG-19. We evaluated these deep learning architectures using public digital chest x-ray datasets with six classes (i.e., COVID-19, lung cancer, pneumonia, tuberculosis, atelectasis, and normal). The experiments are conducted on six-classification, and we found that VGG16 outperforms other proposed models with an accuracy of 95%. <p class="card-text"><strong>Keywords:</strong> <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=image%20classification" title=" image classification"> image classification</a>, <a href="https://publications.waset.org/abstracts/search?q=X-ray%20images" title=" X-ray images"> X-ray images</a>, <a href="https://publications.waset.org/abstracts/search?q=Tensorflow" title=" Tensorflow"> Tensorflow</a>, <a href="https://publications.waset.org/abstracts/search?q=Keras" title=" Keras"> Keras</a>, <a href="https://publications.waset.org/abstracts/search?q=chest%20diseases" title=" chest diseases"> chest diseases</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=multi-classification" title=" multi-classification"> multi-classification</a> </p> <a href="https://publications.waset.org/abstracts/158065/multi-classification-deep-learning-model-for-diagnosing-different-chest-diseases" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158065.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">92</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">8366</span> Optimizing Machine Learning Through Python Based Image Processing Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Srinidhi.%20A">Srinidhi. A</a>, <a href="https://publications.waset.org/abstracts/search?q=Naveed%20Ahmed"> Naveed Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=Twinkle%20Hareendran"> Twinkle Hareendran</a>, <a href="https://publications.waset.org/abstracts/search?q=Vriksha%20Prakash"> Vriksha Prakash</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work reviews some of the advanced image processing techniques for deep learning applications. Object detection by template matching, image denoising, edge detection, and super-resolution modelling are but a few of the tasks. The paper looks in into great detail, given that such tasks are crucial preprocessing steps that increase the quality and usability of image datasets in subsequent deep learning tasks. We review some of the methods for the assessment of image quality, more specifically sharpness, which is crucial to ensure a robust performance of models. Further, we will discuss the development of deep learning models specific to facial emotion detection, age classification, and gender classification, which essentially includes the preprocessing techniques interrelated with model performance. Conclusions from this study pinpoint the best practices in the preparation of image datasets, targeting the best trade-off between computational efficiency and retaining important image features critical for effective training of deep learning models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title="image processing">image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning%20applications" title=" machine learning applications"> machine learning applications</a>, <a href="https://publications.waset.org/abstracts/search?q=template%20matching" title=" template matching"> template matching</a>, <a href="https://publications.waset.org/abstracts/search?q=emotion%20detection" title=" emotion detection"> emotion detection</a> </p> <a href="https://publications.waset.org/abstracts/193107/optimizing-machine-learning-through-python-based-image-processing-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/193107.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">13</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">8365</span> A Comparison of Methods for Neural Network Aggregation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=John%20Pomerat">John Pomerat</a>, <a href="https://publications.waset.org/abstracts/search?q=Aviv%20Segev"> Aviv Segev</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, deep learning has had many theoretical breakthroughs. For deep learning to be successful in the industry, however, there need to be practical algorithms capable of handling many real-world hiccups preventing the immediate application of a learning algorithm. Although AI promises to revolutionize the healthcare industry, getting access to patient data in order to train learning algorithms has not been easy. One proposed solution to this is data- sharing. In this paper, we propose an alternative protocol, based on multi-party computation, to train deep learning models while maintaining both the privacy and security of training data. We examine three methods of training neural networks in this way: Transfer learning, average ensemble learning, and series network learning. We compare these methods to the equivalent model obtained through data-sharing across two different experiments. Additionally, we address the security concerns of this protocol. While the motivating example is healthcare, our findings regarding multi-party computation of neural network training are purely theoretical and have use-cases outside the domain of healthcare. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neural%20network%20aggregation" title="neural network aggregation">neural network aggregation</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-party%20computation" title=" multi-party computation"> multi-party computation</a>, <a href="https://publications.waset.org/abstracts/search?q=transfer%20learning" title=" transfer learning"> transfer learning</a>, <a href="https://publications.waset.org/abstracts/search?q=average%20ensemble%20learning" title=" average ensemble learning"> average ensemble learning</a> </p> <a href="https://publications.waset.org/abstracts/128037/a-comparison-of-methods-for-neural-network-aggregation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/128037.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">162</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">8364</span> Utilizing Federated Learning for Accurate Prediction of COVID-19 from CT Scan Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jinil%20Patel">Jinil Patel</a>, <a href="https://publications.waset.org/abstracts/search?q=Sarthak%20Patel"> Sarthak Patel</a>, <a href="https://publications.waset.org/abstracts/search?q=Sarthak%20Thakkar"> Sarthak Thakkar</a>, <a href="https://publications.waset.org/abstracts/search?q=Deepti%20Saraswat"> Deepti Saraswat</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, the COVID-19 outbreak has spread across the world, leading the World Health Organization to classify it as a global pandemic. To save the patient’s life, the COVID-19 symptoms have to be identified. But using an AI (Artificial Intelligence) model to identify COVID-19 symptoms within the allotted time was challenging. The RT-PCR test was found to be inadequate in determining the COVID status of a patient. To determine if the patient has COVID-19 or not, a Computed Tomography Scan (CT scan) of patient is a better alternative. It will be challenging to compile and store all the data from various hospitals on the server, though. Federated learning, therefore, aids in resolving this problem. Certain deep learning models help to classify Covid-19. This paper will have detailed work of certain deep learning models like VGG19, ResNet50, MobileNEtv2, and Deep Learning Aggregation (DLA) along with maintaining privacy with encryption. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=federated%20learning" title="federated learning">federated learning</a>, <a href="https://publications.waset.org/abstracts/search?q=COVID-19" title=" COVID-19"> COVID-19</a>, <a href="https://publications.waset.org/abstracts/search?q=CT-scan" title=" CT-scan"> CT-scan</a>, <a href="https://publications.waset.org/abstracts/search?q=homomorphic%20encryption" title=" homomorphic encryption"> homomorphic encryption</a>, <a href="https://publications.waset.org/abstracts/search?q=ResNet50" title=" ResNet50"> ResNet50</a>, <a href="https://publications.waset.org/abstracts/search?q=VGG-19" title=" VGG-19"> VGG-19</a>, <a href="https://publications.waset.org/abstracts/search?q=MobileNetv2" title=" MobileNetv2"> MobileNetv2</a>, <a href="https://publications.waset.org/abstracts/search?q=DLA" title=" DLA"> DLA</a> </p> <a href="https://publications.waset.org/abstracts/163979/utilizing-federated-learning-for-accurate-prediction-of-covid-19-from-ct-scan-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163979.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">73</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">8363</span> Augmented Reality Sandbox and Constructivist Approach for Geoscience Teaching and Learning </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Muhammad%20Nawaz">Muhammad Nawaz</a>, <a href="https://publications.waset.org/abstracts/search?q=Sandeep%20N.%20Kundu"> Sandeep N. Kundu</a>, <a href="https://publications.waset.org/abstracts/search?q=Farha%20Sattar"> Farha Sattar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Augmented reality sandbox adds new dimensions to education and learning process. It can be a core component of geoscience teaching and learning to understand the geographic contexts and landform processes. Augmented reality sandbox is a useful tool not only to create an interactive learning environment through spatial visualization but also it can provide an active learning experience to students and enhances the cognition process of learning. Augmented reality sandbox can be used as an interactive learning tool to teach geomorphic and landform processes. This article explains the augmented reality sandbox and the constructivism approach for geoscience teaching and learning, and endeavours to explore the ways to teach the geographic processes using the three-dimensional digital environment for the deep learning of the geoscience concepts interactively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=augmented%20reality%20sandbox" title="augmented reality sandbox">augmented reality sandbox</a>, <a href="https://publications.waset.org/abstracts/search?q=constructivism" title=" constructivism"> constructivism</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=geoscience" title=" geoscience"> geoscience</a> </p> <a href="https://publications.waset.org/abstracts/69803/augmented-reality-sandbox-and-constructivist-approach-for-geoscience-teaching-and-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/69803.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">402</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">8362</span> A Deep Learning Approach for Optimum Shape Design</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cahit%20Perkg%C3%B6z">Cahit Perkgöz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Artificial intelligence has brought new approaches to solving problems in almost every research field in recent years. One of these topics is shape design and optimization, which has the possibility of applications in many fields, such as nanotechnology and electronics. A properly constructed cost function can eliminate the need for labeled data required in deep learning and create desired shapes. In this work, the network parameters are optimized differentially, which differs from traditional approaches. The methods are tested for physics-related structures and successful results are obtained. This work is supported by Eskişehir Technical University scientific research project (Project No: 20ADP090) <p class="card-text"><strong>Keywords:</strong> <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=shape%20design" title=" shape design"> shape design</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title=" artificial intelligence"> artificial intelligence</a> </p> <a href="https://publications.waset.org/abstracts/143575/a-deep-learning-approach-for-optimum-shape-design" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143575.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">152</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">8361</span> Count of Trees in East Africa with Deep Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nubwimana%20Rachel">Nubwimana Rachel</a>, <a href="https://publications.waset.org/abstracts/search?q=Mugabowindekwe%20Maurice"> Mugabowindekwe Maurice</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Trees play a crucial role in maintaining biodiversity and providing various ecological services. Traditional methods of counting trees are time-consuming, and there is a need for more efficient techniques. However, deep learning makes it feasible to identify the multi-scale elements hidden in aerial imagery. This research focuses on the application of deep learning techniques for tree detection and counting in both forest and non-forest areas through the exploration of the deep learning application for automated tree detection and counting using satellite imagery. The objective is to identify the most effective model for automated tree counting. We used different deep learning models such as YOLOV7, SSD, and UNET, along with Generative Adversarial Networks to generate synthetic samples for training and other augmentation techniques, including Random Resized Crop, AutoAugment, and Linear Contrast Enhancement. These models were trained and fine-tuned using satellite imagery to identify and count trees. The performance of the models was assessed through multiple trials; after training and fine-tuning the models, UNET demonstrated the best performance with a validation loss of 0.1211, validation accuracy of 0.9509, and validation precision of 0.9799. This research showcases the success of deep learning in accurate tree counting through remote sensing, particularly with the UNET model. It represents a significant contribution to the field by offering an efficient and precise alternative to conventional tree-counting methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=remote%20sensing" title="remote sensing">remote sensing</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=tree%20counting" title=" tree counting"> tree counting</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20segmentation" title=" image segmentation"> image segmentation</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=visualization" title=" visualization"> visualization</a> </p> <a href="https://publications.waset.org/abstracts/177935/count-of-trees-in-east-africa-with-deep-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/177935.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">71</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">8360</span> Deep Learning Approaches for Accurate Detection of Epileptic Seizures from Electroencephalogram Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ramzi%20Rihane">Ramzi Rihane</a>, <a href="https://publications.waset.org/abstracts/search?q=Yassine%20Benayed"> Yassine Benayed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Epilepsy is a chronic neurological disorder characterized by recurrent, unprovoked seizures resulting from abnormal electrical activity in the brain. Timely and accurate detection of these seizures is essential for improving patient care. In this study, we leverage the UK Bonn University open-source EEG dataset and employ advanced deep-learning techniques to automate the detection of epileptic seizures. By extracting key features from both time and frequency domains, as well as Spectrogram features, we enhance the performance of various deep learning models. Our investigation includes architectures such as Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), 1D Convolutional Neural Networks (1D-CNN), and hybrid CNN-LSTM and CNN-BiLSTM models. The models achieved impressive accuracies: LSTM (98.52%), Bi-LSTM (98.61%), CNN-LSTM (98.91%), CNN-BiLSTM (98.83%), and CNN (98.73%). Additionally, we utilized a data augmentation technique called SMOTE, which yielded the following results: CNN (97.36%), LSTM (97.01%), Bi-LSTM (97.23%), CNN-LSTM (97.45%), and CNN-BiLSTM (97.34%). These findings demonstrate the effectiveness of deep learning in capturing complex patterns in EEG signals, providing a reliable and scalable solution for real-time seizure detection in clinical environments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electroencephalogram" title="electroencephalogram">electroencephalogram</a>, <a href="https://publications.waset.org/abstracts/search?q=epileptic%20seizure" title=" epileptic seizure"> epileptic seizure</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=LSTM" title=" LSTM"> LSTM</a>, <a href="https://publications.waset.org/abstracts/search?q=CNN" title=" CNN"> CNN</a>, <a href="https://publications.waset.org/abstracts/search?q=BI-LSTM" title=" BI-LSTM"> BI-LSTM</a>, <a href="https://publications.waset.org/abstracts/search?q=seizure%20detection" title=" seizure detection"> seizure detection</a> </p> <a href="https://publications.waset.org/abstracts/193110/deep-learning-approaches-for-accurate-detection-of-epileptic-seizures-from-electroencephalogram-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/193110.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">12</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">8359</span> Deep Learning Based-Object-classes Semantic Classification of Arabic Texts</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Imen%20Elleuch">Imen Elleuch</a>, <a href="https://publications.waset.org/abstracts/search?q=Wael%20Ouarda"> Wael Ouarda</a>, <a href="https://publications.waset.org/abstracts/search?q=Gargouri%20Bilel"> Gargouri Bilel</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We proposes in this paper a Deep Learning based approach to classify text in order to enrich an Arabic ontology based on the objects classes of Gaston Gross. Those object classes are defined by taking into account the syntactic and semantic features of the treated language. Thus, our proposed approach is a hybrid one. In fact, it is based on the one hand on the object classes that represents a knowledge based-approach on classification of text and in the other hand it uses the deep learning approach that use the word embedding-based-approach to classify text. We have applied our proposed approach on a corpus constructed from an Arabic dictionary. The obtained semantic classification of text will enrich the Arabic objects classes ontology. In fact, new classes can be added to the ontology or an expansion of the features that characterizes each object class can be updated. The obtained results are compared to a similar work that treats the same object with a classical linguistic approach for the semantic classification of text. This comparison highlight our hybrid proposed approach that can be ameliorated by broaden the dataset used in the deep learning process. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep-learning%20approach" title="deep-learning approach">deep-learning approach</a>, <a href="https://publications.waset.org/abstracts/search?q=object-classes" title=" object-classes"> object-classes</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20classification" title=" semantic classification"> semantic classification</a>, <a href="https://publications.waset.org/abstracts/search?q=Arabic" title=" Arabic"> Arabic</a> </p> <a href="https://publications.waset.org/abstracts/176532/deep-learning-based-object-classes-semantic-classification-of-arabic-texts" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/176532.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">87</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">8358</span> Electroencephalogram Based Alzheimer Disease Classification using Machine and Deep Learning Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Carlos%20Roncero-Parra">Carlos Roncero-Parra</a>, <a href="https://publications.waset.org/abstracts/search?q=Alfonso%20Parre%C3%B1o-Torres"> Alfonso Parreño-Torres</a>, <a href="https://publications.waset.org/abstracts/search?q=Jorge%20Mateo%20Sotos"> Jorge Mateo Sotos</a>, <a href="https://publications.waset.org/abstracts/search?q=Alejandro%20L.%20Borja"> Alejandro L. Borja</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this research, different methods based on machine/deep learning algorithms are presented for the classification and diagnosis of patients with mental disorders such as alzheimer. For this purpose, the signals obtained from 32 unipolar electrodes identified by non-invasive EEG were examined, and their basic properties were obtained. More specifically, different well-known machine learning based classifiers have been used, i.e., support vector machine (SVM), Bayesian linear discriminant analysis (BLDA), decision tree (DT), Gaussian Naïve Bayes (GNB), K-nearest neighbor (KNN) and Convolutional Neural Network (CNN). A total of 668 patients from five different hospitals have been studied in the period from 2011 to 2021. The best accuracy is obtained was around 93 % in both ADM and ADA classifications. It can be concluded that such a classification will enable the training of algorithms that can be used to identify and classify different mental disorders with high accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=alzheimer" title="alzheimer">alzheimer</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</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=EEG" title=" EEG"> EEG</a> </p> <a href="https://publications.waset.org/abstracts/165522/electroencephalogram-based-alzheimer-disease-classification-using-machine-and-deep-learning-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/165522.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">126</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">8357</span> Detecting Manipulated Media Using Deep Capsule Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Joseph%20Uzuazomaro%20Oju">Joseph Uzuazomaro Oju</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The ease at which manipulated media can be created, and the increasing difficulty in identifying fake media makes it a great threat. Most of the applications used for the creation of these high-quality fake videos and images are built with deep learning. Hence, the use of deep learning in creating a detection mechanism cannot be overemphasized. Any successful fake media that is being detected before it reached the populace will save people from the self-doubt of either a content is genuine or fake and will ensure the credibility of videos and images. The methodology introduced in this paper approaches the manipulated media detection challenge using a combo of VGG-19 and a deep capsule network. In the case of videos, they are converted into frames, which, in turn, are resized and cropped to the face region. These preprocessed images/videos are fed to the VGG-19 network to extract the latent features. The extracted latent features are inputted into a deep capsule network enhanced with a 3D -convolution dynamic routing agreement. The 3D –convolution dynamic routing agreement algorithm helps to reduce the linkages between capsules networks. Thereby limiting the poor learning shortcoming of multiple capsule network layers. The resultant output from the deep capsule network will indicate a media to be either genuine or fake. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20capsule%20network" title="deep capsule network">deep capsule network</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20routing" title=" dynamic routing"> dynamic routing</a>, <a href="https://publications.waset.org/abstracts/search?q=fake%20media%20detection" title=" fake media detection"> fake media detection</a>, <a href="https://publications.waset.org/abstracts/search?q=manipulated%20media" title=" manipulated media"> manipulated media</a> </p> <a href="https://publications.waset.org/abstracts/123371/detecting-manipulated-media-using-deep-capsule-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/123371.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">132</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">8356</span> Deep Reinforcement Learning Model for Autonomous Driving</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Boumaraf%20Malak">Boumaraf Malak</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The development of intelligent transportation systems (ITS) and artificial intelligence (AI) are spurring us to pave the way for the widespread adoption of autonomous vehicles (AVs). This is open again opportunities for smart roads, smart traffic safety, and mobility comfort. A highly intelligent decision-making system is essential for autonomous driving around dense, dynamic objects. It must be able to handle complex road geometry and topology, as well as complex multiagent interactions, and closely follow higher-level commands such as routing information. Autonomous vehicles have become a very hot research topic in recent years due to their significant ability to reduce traffic accidents and personal injuries. Using new artificial intelligence-based technologies handles important functions in scene understanding, motion planning, decision making, vehicle control, social behavior, and communication for AV. This paper focuses only on deep reinforcement learning-based methods; it does not include traditional (flat) planar techniques, which have been the subject of extensive research in the past because reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high dimensional environments. The DRL algorithm used so far found solutions to the four main problems of autonomous driving; in our paper, we highlight the challenges and point to possible future research directions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20reinforcement%20learning" title="deep reinforcement learning">deep reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=autonomous%20driving" title=" autonomous driving"> autonomous driving</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20deterministic%20policy%20gradient" title=" deep deterministic policy gradient"> deep deterministic policy gradient</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20Q-learning" title=" deep Q-learning"> deep Q-learning</a> </p> <a href="https://publications.waset.org/abstracts/166548/deep-reinforcement-learning-model-for-autonomous-driving" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/166548.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">85</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">8355</span> Combining Shallow and Deep Unsupervised Machine Learning Techniques to Detect Bad Actors in Complex Datasets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jun%20Ming%20Moey">Jun Ming Moey</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhiyaun%20Chen"> Zhiyaun Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=David%20Nicholson"> David Nicholson</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Bad actors are often hard to detect in data that imprints their behaviour patterns because they are comparatively rare events embedded in non-bad actor data. An unsupervised machine learning framework is applied here to detect bad actors in financial crime datasets that record millions of transactions undertaken by hundreds of actors (<0.01% bad). Specifically, the framework combines ‘shallow’ (PCA, Isolation Forest) and ‘deep’ (Autoencoder) methods to detect outlier patterns. Detection performance analysis for both the individual methods and their combination is reported. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=detection" title="detection">detection</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</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=unsupervised" title=" unsupervised"> unsupervised</a>, <a href="https://publications.waset.org/abstracts/search?q=outlier%20analysis" title=" outlier analysis"> outlier analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20science" title=" data science"> data science</a>, <a href="https://publications.waset.org/abstracts/search?q=fraud" title=" fraud"> fraud</a>, <a href="https://publications.waset.org/abstracts/search?q=financial%20crime" title=" financial crime"> financial crime</a> </p> <a href="https://publications.waset.org/abstracts/153061/combining-shallow-and-deep-unsupervised-machine-learning-techniques-to-detect-bad-actors-in-complex-datasets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153061.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">94</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">8354</span> Face Tracking and Recognition Using Deep Learning Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Degale%20Desta">Degale Desta</a>, <a href="https://publications.waset.org/abstracts/search?q=Cheng%20Jian"> Cheng Jian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The most important factor in identifying a person is their face. Even identical twins have their own distinct faces. As a result, identification and face recognition are needed to tell one person from another. A face recognition system is a verification tool used to establish a person's identity using biometrics. Nowadays, face recognition is a common technique used in a variety of applications, including home security systems, criminal identification, and phone unlock systems. This system is more secure because it only requires a facial image instead of other dependencies like a key or card. Face detection and face identification are the two phases that typically make up a human recognition system.The idea behind designing and creating a face recognition system using deep learning with Azure ML Python's OpenCV is explained in this paper. Face recognition is a task that can be accomplished using deep learning, and given the accuracy of this method, it appears to be a suitable approach. To show how accurate the suggested face recognition system is, experimental results are given in 98.46% accuracy using Fast-RCNN Performance of algorithms under different training conditions. <p class="card-text"><strong>Keywords:</strong> <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=face%20recognition" title=" face recognition"> face recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=identification" title=" identification"> identification</a>, <a href="https://publications.waset.org/abstracts/search?q=fast-RCNN" title=" fast-RCNN"> fast-RCNN</a> </p> <a href="https://publications.waset.org/abstracts/163134/face-tracking-and-recognition-using-deep-learning-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163134.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">8353</span> Post-Processing Method for Performance Improvement of Aerial Image Parcel Segmentation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Donghee%20Noh">Donghee Noh</a>, <a href="https://publications.waset.org/abstracts/search?q=Seonhyeong%20Kim"> Seonhyeong Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Junhwan%20Choi"> Junhwan Choi</a>, <a href="https://publications.waset.org/abstracts/search?q=Heegon%20Kim"> Heegon Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Sooho%20Jung"> Sooho Jung</a>, <a href="https://publications.waset.org/abstracts/search?q=Keunho%20Park"> Keunho Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we describe an image post-processing method to enhance the performance of the parcel segmentation method using deep learning-based aerial images conducted in previous studies. The study results were evaluated using a confusion matrix, IoU, Precision, Recall, and F1-Score. In the case of the confusion matrix, it was observed that the false positive value, which is the result of misclassification, was greatly reduced as a result of image post-processing. The average IoU was 0.9688 in the image post-processing, which is higher than the deep learning result of 0.8362, and the F1-Score was also 0.9822 in the image post-processing, which was higher than the deep learning result of 0.8850. As a result of the experiment, it was found that the proposed technique positively complements the deep learning results in segmenting the parcel of interest. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=aerial%20image" title="aerial image">aerial image</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20process" title=" image process"> image process</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20vision" title=" machine vision"> machine vision</a>, <a href="https://publications.waset.org/abstracts/search?q=open%20field%20smart%20farm" title=" open field smart farm"> open field smart farm</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a> </p> <a href="https://publications.waset.org/abstracts/170203/post-processing-method-for-performance-improvement-of-aerial-image-parcel-segmentation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170203.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">80</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">8352</span> Cells Detection and Recognition in Bone Marrow Examination with Deep Learning Method</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shiyin%20He">Shiyin He</a>, <a href="https://publications.waset.org/abstracts/search?q=Zheng%20Huang"> Zheng Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, deep learning methods are applied in bio-medical field to detect and count different types of cells in an automatic way instead of manual work in medical practice, specifically in bone marrow examination. The process is mainly composed of two steps, detection and recognition. Mask-Region-Convolutional Neural Networks (Mask-RCNN) was used for detection and image segmentation to extract cells and then Convolutional Neural Networks (CNN), as well as Deep Residual Network (ResNet) was used to classify. Result of cell detection network shows high efficiency to meet application requirements. For the cell recognition network, two networks are compared and the final system is fully applicable. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cell%20detection" title="cell detection">cell detection</a>, <a href="https://publications.waset.org/abstracts/search?q=cell%20recognition" title=" cell recognition"> cell recognition</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=Mask-RCNN" title=" Mask-RCNN"> Mask-RCNN</a>, <a href="https://publications.waset.org/abstracts/search?q=ResNet" title=" ResNet"> ResNet</a> </p> <a href="https://publications.waset.org/abstracts/98649/cells-detection-and-recognition-in-bone-marrow-examination-with-deep-learning-method" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/98649.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">189</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">8351</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">198</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">8350</span> A Deep Learning-Based Pedestrian Trajectory Prediction Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Haozhe%20Xiang">Haozhe Xiang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the rise of the Internet of Things era, intelligent products are gradually integrating into people's lives. Pedestrian trajectory prediction has become a key issue, which is crucial for the motion path planning of intelligent agents such as autonomous vehicles, robots, and drones. In the current technological context, deep learning technology is becoming increasingly sophisticated and gradually replacing traditional models. The pedestrian trajectory prediction algorithm combining neural networks and attention mechanisms has significantly improved prediction accuracy. Based on in-depth research on deep learning and pedestrian trajectory prediction algorithms, this article focuses on physical environment modeling and learning of historical trajectory time dependence. At the same time, social interaction between pedestrians and scene interaction between pedestrians and the environment were handled. An improved pedestrian trajectory prediction algorithm is proposed by analyzing the existing model architecture. With the help of these improvements, acceptable predicted trajectories were successfully obtained. Experiments on public datasets have demonstrated the algorithm's effectiveness and achieved acceptable results. <p class="card-text"><strong>Keywords:</strong> <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=graph%20convolutional%20network" title=" graph convolutional network"> graph convolutional network</a>, <a href="https://publications.waset.org/abstracts/search?q=attention%20mechanism" title=" attention mechanism"> attention mechanism</a>, <a href="https://publications.waset.org/abstracts/search?q=LSTM" title=" LSTM"> LSTM</a> </p> <a href="https://publications.waset.org/abstracts/182188/a-deep-learning-based-pedestrian-trajectory-prediction-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/182188.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 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