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Anmala</title> <meta name="description" content="Search results for: Jagadeesh Anmala"> <meta name="keywords" content="Jagadeesh Anmala"> <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 d-lg-none 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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="Jagadeesh Anmala"> <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> 17</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Jagadeesh Anmala</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">17</span> Application of Random Forest Model in The Prediction of River Water Quality</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Turuganti%20Venkateswarlu">Turuganti Venkateswarlu</a>, <a href="https://publications.waset.org/abstracts/search?q=Jagadeesh%20Anmala"> Jagadeesh Anmala</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Excessive runoffs from various non-point source land uses, and other point sources are rapidly contaminating the water quality of streams in the Upper Green River watershed, Kentucky, USA. It is essential to maintain the stream water quality as the river basin is one of the major freshwater sources in this province. It is also important to understand the water quality parameters (WQPs) quantitatively and qualitatively along with their important features as stream water is sensitive to climatic events and land-use practices. In this paper, a model was developed for predicting one of the significant WQPs, Fecal Coliform (FC) from precipitation, temperature, urban land use factor (ULUF), agricultural land use factor (ALUF), and forest land-use factor (FLUF) using Random Forest (RF) algorithm. The RF model, a novel ensemble learning algorithm, can even find out advanced feature importance characteristics from the given model inputs for different combinations. This model鈥檚 outcomes showed a good correlation between FC and climate events and land use factors (R2 = 0.94) and precipitation and temperature are the primary influencing factors for FC. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=water%20quality" title="water quality">water quality</a>, <a href="https://publications.waset.org/abstracts/search?q=land%20use%20factors" title=" land use factors"> land use factors</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=fecal%20coliform" title=" fecal coliform"> fecal coliform</a> </p> <a href="https://publications.waset.org/abstracts/140075/application-of-random-forest-model-in-the-prediction-of-river-water-quality" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/140075.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">197</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">16</span> Antioxidant Activity Studies of Novel Schiff and Mannich Bases</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=D.%20J.%20Madhu%20Kumar">D. J. Madhu Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Jagadeesh%20Prasad"> D. Jagadeesh Prasad</a>, <a href="https://publications.waset.org/abstracts/search?q=Sana%20Sheik"> Sana Sheik</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20P.%20Rejeesh"> E. P. Rejeesh </a> </p> <p class="card-text"><strong>Abstract:</strong></p> A series of Mannich bases derived from 1,2,4-triazole(3a-k and 4a-k) are synthesized by treating a Schiff base with various substituted primary/secondary amines and formaldehyde. The Schiff base is prepared by treating 3-methyl-4-amino-5-mercapto-1,2,4-triazole with 3,4-dimethoxybenzaldehyde in the presence of acid catalyst. The triazole is prepared by treating acetic acid with thiocarbohydrazide at reflux temperature. All the synthesized samples are characterised by FT-IR, 1H-NMR, and LC-MASS spectral studies and screened for their anti-oxidant activity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=mannich%20bases" title="mannich bases">mannich bases</a>, <a href="https://publications.waset.org/abstracts/search?q=anti-oxidant%20activity" title=" anti-oxidant activity"> anti-oxidant activity</a>, <a href="https://publications.waset.org/abstracts/search?q=schiff%20base" title=" schiff base"> schiff base</a>, <a href="https://publications.waset.org/abstracts/search?q=triazole" title=" triazole"> triazole</a> </p> <a href="https://publications.waset.org/abstracts/1711/antioxidant-activity-studies-of-novel-schiff-and-mannich-bases" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1711.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">516</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">15</span> Modern Machine Learning Conniptions for Automatic Speech Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Jagadeesh%20Kumar">S. Jagadeesh Kumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This expose presents a luculent of recent machine learning practices as employed in the modern and as pertinent to prospective automatic speech recognition schemes. The aspiration is to promote additional traverse ablution among the machine learning and automatic speech recognition factions that have transpired in the precedent. The manuscript is structured according to the chief machine learning archetypes that are furthermore trendy by now or have latency for building momentous hand-outs to automatic speech recognition expertise. The standards offered and convoluted in this article embraces adaptive and multi-task learning, active learning, Bayesian learning, discriminative learning, generative learning, supervised and unsupervised learning. These learning archetypes are aggravated and conferred in the perspective of automatic speech recognition tools and functions. This manuscript bequeaths and surveys topical advances of deep learning and learning with sparse depictions; further limelight is on their incessant significance in the evolution of automatic speech recognition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automatic%20speech%20recognition" title="automatic speech recognition">automatic speech recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning%20methods" title=" deep learning methods"> deep learning methods</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning%20archetypes" title=" machine learning archetypes"> machine learning archetypes</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian%20learning" title=" Bayesian learning"> Bayesian learning</a>, <a href="https://publications.waset.org/abstracts/search?q=supervised%20and%20unsupervised%20learning" title=" supervised and unsupervised learning"> supervised and unsupervised learning</a> </p> <a href="https://publications.waset.org/abstracts/71467/modern-machine-learning-conniptions-for-automatic-speech-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71467.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">448</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">14</span> An Erudite Technique for Face Detection and Recognition Using Curvature Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Jagadeesh%20Kumar">S. Jagadeesh Kumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Face detection and recognition is an authoritative technology for image database management, video surveillance, and human computer interface (HCI). Face recognition is a rapidly nascent method, which has been extensively discarded in forensics such as felonious identification, tenable entree, and custodial security. This paper recommends an erudite technique using curvature analysis (CA) that has less false positives incidence, operative in different light environments and confiscates the artifacts that are introduced during image acquisition by ring correction in polar coordinate (RCP) method. This technique affronts mean and median filtering technique to remove the artifacts but it works in polar coordinate during image acquisition. Investigational fallouts for face detection and recognition confirms decent recitation even in diagonal orientation and stance variation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=curvature%20analysis" title="curvature analysis">curvature analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=ring%20correction%20in%20polar%20coordinate%20method" title=" ring correction in polar coordinate method"> ring correction in polar coordinate method</a>, <a href="https://publications.waset.org/abstracts/search?q=face%20detection" title=" face detection"> face detection</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=human%20computer%20interaction" title=" human computer interaction"> human computer interaction</a> </p> <a href="https://publications.waset.org/abstracts/70748/an-erudite-technique-for-face-detection-and-recognition-using-curvature-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70748.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">287</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">13</span> Reminiscence Therapy for Alzheimer鈥檚 Disease Restrained on Logistic Regression Based Linear Bootstrap Aggregating</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20S.%20Jagadeesh%20Kumar">P. S. Jagadeesh Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Mingmin%20Pan"> Mingmin Pan</a>, <a href="https://publications.waset.org/abstracts/search?q=Xianpei%20Li"> Xianpei Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Yanmin%20Yuan"> Yanmin Yuan</a>, <a href="https://publications.waset.org/abstracts/search?q=Tracy%20Lin%20Huan"> Tracy Lin Huan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Researchers are doing enchanting research into the inherited features of Alzheimer鈥檚 disease and probable consistent therapies. In Alzheimer鈥檚, memories are extinct in reverse order; memories formed lately are more transitory than those from formerly. Reminiscence therapy includes the conversation of past actions, trials and knowledges with another individual or set of people, frequently with the help of perceptible reminders such as photos, household and other acquainted matters from the past, music and collection of tapes. In this manuscript, the competence of reminiscence therapy for Alzheimer鈥檚 disease is measured using logistic regression based linear bootstrap aggregating. Logistic regression is used to envisage the experiential features of the patient鈥檚 memory through various therapies. Linear bootstrap aggregating shows better stability and accuracy of reminiscence therapy used in statistical classification and regression of memories related to validation therapy, supportive psychotherapy, sensory integration and simulated presence therapy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alzheimer%E2%80%99s%20disease" title="Alzheimer鈥檚 disease">Alzheimer鈥檚 disease</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20bootstrap%20aggregating" title=" linear bootstrap aggregating"> linear bootstrap aggregating</a>, <a href="https://publications.waset.org/abstracts/search?q=logistic%20regression" title=" logistic regression"> logistic regression</a>, <a href="https://publications.waset.org/abstracts/search?q=reminiscence%20therapy" title=" reminiscence therapy"> reminiscence therapy</a> </p> <a href="https://publications.waset.org/abstracts/79402/reminiscence-therapy-for-alzheimers-disease-restrained-on-logistic-regression-based-linear-bootstrap-aggregating" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79402.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">309</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">12</span> Neural Network Based Decision Trees Using Machine Learning for Alzheimer's Diagnosis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20S.%20Jagadeesh%20Kumar">P. S. Jagadeesh Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Tracy%20Lin%20Huan"> Tracy Lin Huan</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Meenakshi%20Sundaram"> S. Meenakshi Sundaram</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Alzheimer鈥檚 disease is one of the prevalent kind of ailment, expected for impudent reconciliation or an effectual therapy is to be accredited hitherto. Probable detonation of patients in the upcoming years, and consequently an enormous deal of apprehension in early discovery of the disorder, this will conceivably chaperon to enhanced healing outcomes. Complex impetuosity of the brain is an observant symbolic of the disease and a unique recognition of genetic sign of the disease. Machine learning alongside deep learning and decision tree reinforces the aptitude to absorb characteristics from multi-dimensional data鈥檚 and thus simplifies automatic classification of Alzheimer鈥檚 disease. Susceptible testing was prophesied and realized in training the prospect of Alzheimer鈥檚 disease classification built on machine learning advances. It was shrewd that the decision trees trained with deep neural network fashioned the excellent results parallel to related pattern classification. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alzheimer%27s%20diagnosis" title="Alzheimer's diagnosis">Alzheimer's diagnosis</a>, <a href="https://publications.waset.org/abstracts/search?q=decision%20trees" title=" decision trees"> decision trees</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20neural%20network" title=" deep neural network"> deep neural network</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=pattern%20classification" title=" pattern classification"> pattern classification</a> </p> <a href="https://publications.waset.org/abstracts/77725/neural-network-based-decision-trees-using-machine-learning-for-alzheimers-diagnosis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77725.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">11</span> Virtual Reality Based 3D Video Games and Speech-Lip Synchronization Superseding Algebraic Code Excited Linear Prediction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20S.%20Jagadeesh%20Kumar">P. S. Jagadeesh Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Meenakshi%20Sundaram"> S. Meenakshi Sundaram</a>, <a href="https://publications.waset.org/abstracts/search?q=Wenli%20Hu"> Wenli Hu</a>, <a href="https://publications.waset.org/abstracts/search?q=Yang%20Yung"> Yang Yung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In 3D video games, the dominance of production is unceasingly growing with a protruding level of affordability in terms of budget. Afterward, the automation of speech-lip synchronization technique is customarily onerous and has advanced a critical research subject in virtual reality based 3D video games. This paper presents one of these automatic tools, precisely riveted on the synchronization of the speech and the lip movement of the game characters. A robust and precise speech recognition segment that systematized with Algebraic Code Excited Linear Prediction method is developed which unconventionally delivers lip sync results. The Algebraic Code Excited Linear Prediction algorithm is constructed on that used in code-excited linear prediction, but Algebraic Code Excited Linear Prediction codebooks have an explicit algebraic structure levied upon them. This affords a quicker substitute to the software enactments of lip sync algorithms and thus advances the superiority of service factors abridged production cost. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=algebraic%20code%20excited%20linear%20prediction" title="algebraic code excited linear prediction">algebraic code excited linear prediction</a>, <a href="https://publications.waset.org/abstracts/search?q=speech-lip%20synchronization" title=" speech-lip synchronization"> speech-lip synchronization</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20games" title=" video games"> video games</a>, <a href="https://publications.waset.org/abstracts/search?q=virtual%20reality" title=" virtual reality"> virtual reality</a> </p> <a href="https://publications.waset.org/abstracts/78585/virtual-reality-based-3d-video-games-and-speech-lip-synchronization-superseding-algebraic-code-excited-linear-prediction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78585.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">474</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">10</span> Color Fusion of Remote Sensing Images for Imparting Fluvial Geomorphological Features of River Yamuna and Ganga over Doon Valley </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20S.%20Jagadeesh%20Kumar">P. S. Jagadeesh Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Tracy%20Lin%20Huan"> Tracy Lin Huan</a>, <a href="https://publications.waset.org/abstracts/search?q=Rebecca%20K.%20Rossi"> Rebecca K. Rossi</a>, <a href="https://publications.waset.org/abstracts/search?q=Yanmin%20Yuan"> Yanmin Yuan</a>, <a href="https://publications.waset.org/abstracts/search?q=Xianpei%20Li"> Xianpei Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The fiscal growth of any country hinges on the prudent administration of water resources. The river Yamuna and Ganga are measured as the life line of India as it affords the needs for life to endure. Earth observation over remote sensing images permits the precise description and identification of ingredients on the superficial from space and airborne platforms. Multiple and heterogeneous image sources are accessible for the same geographical section; multispectral, hyperspectral, radar, multitemporal, and multiangular images. In this paper, a taxonomical learning of the fluvial geomorphological features of river Yamuna and Ganga over doon valley using color fusion of multispectral remote sensing images was performed. Experimental results exhibited that the segmentation based colorization technique stranded on pattern recognition, and color mapping fashioned more colorful and truthful colorized images for geomorphological feature extraction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=color%20fusion" title="color fusion">color fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=geomorphology" title=" geomorphology"> geomorphology</a>, <a href="https://publications.waset.org/abstracts/search?q=fluvial%20processes" title=" fluvial processes"> fluvial processes</a>, <a href="https://publications.waset.org/abstracts/search?q=multispectral%20images" title=" multispectral images"> multispectral images</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20recognition" title=" pattern recognition"> pattern recognition</a> </p> <a href="https://publications.waset.org/abstracts/87961/color-fusion-of-remote-sensing-images-for-imparting-fluvial-geomorphological-features-of-river-yamuna-and-ganga-over-doon-valley" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/87961.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">306</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">9</span> Ophthalmic Hashing Based Supervision of Glaucoma and Corneal Disorders Imposed on Deep Graphical Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20S.%20Jagadeesh%20Kumar">P. S. Jagadeesh Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Yang%20Yung"> Yang Yung</a>, <a href="https://publications.waset.org/abstracts/search?q=Mingmin%20Pan"> Mingmin Pan</a>, <a href="https://publications.waset.org/abstracts/search?q=Xianpei%20Li"> Xianpei Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Wenli%20Hu"> Wenli Hu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Glaucoma is impelled by optic nerve mutilation habitually represented as cupping and visual field injury frequently with an arcuate pattern of mid-peripheral loss, subordinate to retinal ganglion cell damage and death. Glaucoma is the second foremost cause of blindness and the chief cause of permanent blindness worldwide. Consequently, all-embracing study into the analysis and empathy of glaucoma is happening to escort deep learning based neural network intrusions to deliberate this substantial optic neuropathy. This paper advances an ophthalmic hashing based supervision of glaucoma and corneal disorders preeminent on deep graphical model. Ophthalmic hashing is a newly proposed method extending the efficacy of visual hash-coding to predict glaucoma corneal disorder matching, which is the faster than the existing methods. Deep graphical model is proficient of learning interior explications of corneal disorders in satisfactory time to solve hard combinatoric incongruities using deep Boltzmann machines. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=corneal%20disorders" title="corneal disorders">corneal disorders</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20Boltzmann%20machines" title=" deep Boltzmann machines"> deep Boltzmann machines</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20graphical%20model" title=" deep graphical model"> deep graphical model</a>, <a href="https://publications.waset.org/abstracts/search?q=glaucoma" title=" glaucoma"> glaucoma</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20networks" title=" neural networks"> neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=ophthalmic%20hashing" title=" ophthalmic hashing"> ophthalmic hashing</a> </p> <a href="https://publications.waset.org/abstracts/78678/ophthalmic-hashing-based-supervision-of-glaucoma-and-corneal-disorders-imposed-on-deep-graphical-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78678.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">250</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">8</span> Support Vector Machine Based Retinal Therapeutic for Glaucoma Using Machine Learning Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20S.%20Jagadeesh%20Kumar">P. S. Jagadeesh Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Mingmin%20Pan"> Mingmin Pan</a>, <a href="https://publications.waset.org/abstracts/search?q=Yang%20Yung"> Yang Yung</a>, <a href="https://publications.waset.org/abstracts/search?q=Tracy%20Lin%20Huan"> Tracy Lin Huan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Glaucoma is a group of visual maladies represented by the scheduled optic nerve neuropathy; means to the increasing dwindling in vision ground, resulting in loss of sight. In this paper, a novel support vector machine based retinal therapeutic for glaucoma using machine learning algorithm is conservative. The algorithm has fitting pragmatism; subsequently sustained on correlation clustering mode, it visualizes perfect computations in the multi-dimensional space. Support vector clustering turns out to be comparable to the scale-space advance that investigates the cluster organization by means of a kernel density estimation of the likelihood distribution, where cluster midpoints are idiosyncratic by the neighborhood maxima of the concreteness. The predicted planning has 91% attainment rate on data set deterrent on a consolidation of 500 realistic images of resolute and glaucoma retina; therefore, the computational benefit of depending on the cluster overlapping system pedestal on machine learning algorithm has complete performance in glaucoma therapeutic. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=machine%20learning%20algorithm" title="machine learning algorithm">machine learning algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=correlation%20clustering%20mode" title=" correlation clustering mode"> correlation clustering mode</a>, <a href="https://publications.waset.org/abstracts/search?q=cluster%20overlapping%20system" title=" cluster overlapping system"> cluster overlapping system</a>, <a href="https://publications.waset.org/abstracts/search?q=glaucoma" title=" glaucoma"> glaucoma</a>, <a href="https://publications.waset.org/abstracts/search?q=kernel%20density%20estimation" title=" kernel density estimation"> kernel density estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=retinal%20therapeutic" title=" retinal therapeutic"> retinal therapeutic</a> </p> <a href="https://publications.waset.org/abstracts/80153/support-vector-machine-based-retinal-therapeutic-for-glaucoma-using-machine-learning-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/80153.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">254</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">7</span> Tensor Deep Stacking Neural Networks and Bilinear Mapping Based Speech Emotion Classification Using Facial Electromyography</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20S.%20Jagadeesh%20Kumar">P. S. Jagadeesh Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Yang%20Yung"> Yang Yung</a>, <a href="https://publications.waset.org/abstracts/search?q=Wenli%20Hu"> Wenli Hu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Speech emotion classification is a dominant research field in finding a sturdy and profligate classifier appropriate for different real-life applications. This effort accentuates on classifying different emotions from speech signal quarried from the features related to pitch, formants, energy contours, jitter, shimmer, spectral, perceptual and temporal features. Tensor deep stacking neural networks were supported to examine the factors that influence the classification success rate. Facial electromyography signals were composed of several forms of focuses in a controlled atmosphere by means of audio-visual stimuli. Proficient facial electromyography signals were pre-processed using moving average filter, and a set of arithmetical features were excavated. Extracted features were mapped into consistent emotions using bilinear mapping. With facial electromyography signals, a database comprising diverse emotions will be exposed with a suitable fine-tuning of features and training data. A success rate of 92% can be attained deprived of increasing the system connivance and the computation time for sorting diverse emotional states. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=speech%20emotion%20classification" title="speech emotion classification">speech emotion classification</a>, <a href="https://publications.waset.org/abstracts/search?q=tensor%20deep%20stacking%20neural%20networks" title=" tensor deep stacking neural networks"> tensor deep stacking neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=facial%20electromyography" title=" facial electromyography"> facial electromyography</a>, <a href="https://publications.waset.org/abstracts/search?q=bilinear%20mapping" title=" bilinear mapping"> bilinear mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=audio-visual%20stimuli" title=" audio-visual stimuli"> audio-visual stimuli</a> </p> <a href="https://publications.waset.org/abstracts/78499/tensor-deep-stacking-neural-networks-and-bilinear-mapping-based-speech-emotion-classification-using-facial-electromyography" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78499.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">254</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">6</span> Climate Change Vulnerability and Agrarian Communities: Insights from the Composite Vulnerability Index of Indian States of Andhra Pradesh and Karnataka </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=G.%20Sridevi">G. Sridevi</a>, <a href="https://publications.waset.org/abstracts/search?q=Amalendu%20Jyotishi"> Amalendu Jyotishi</a>, <a href="https://publications.waset.org/abstracts/search?q=Sushanta%20Mahapatra"> Sushanta Mahapatra</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20Jagadeesh"> G. Jagadeesh</a>, <a href="https://publications.waset.org/abstracts/search?q=Satyasiba%20Bedamatta"> Satyasiba Bedamatta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Climate change is a main challenge for agriculture, food security and rural livelihoods for millions of people in India. Agriculture is the sector most vulnerable to climate change due to its high dependence on climate and weather conditions. Among India鈥檚 population of more than one billion people, about 68% are directly or indirectly involved in the agricultural sector. This sector is particularly vulnerable to present-day climate variability. In this contest this paper examines the Socio-economic and climate analytical study of the vulnerability index in Indian states of Andhra Pradesh and Karnataka. Using secondary data; it examines the vulnerability through five different sub-indicator of socio-demographic, agriculture, occupational, common property resource (CPR), and climate in respective states among different districts. Data used in this paper has taken from different sources, like census in India 2011, Directorate of Economics and Statistics of respective states governments. Rainfall data was collected from the India Meteorological Department (IMD). In order to capture the vulnerability from two different states the composite vulnerability index (CVI) was developed and used. This indicates the vulnerability situation of different districts under two states. The study finds that Adilabad district in Andhra Pradesh and Chamarajanagar in Karnataka had highest level of vulnerability while Hyderabad and Bangalore in respective states have least level of vulnerability. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=vulnerability" title="vulnerability">vulnerability</a>, <a href="https://publications.waset.org/abstracts/search?q=agriculture" title=" agriculture"> agriculture</a>, <a href="https://publications.waset.org/abstracts/search?q=climate%20change" title=" climate change"> climate change</a>, <a href="https://publications.waset.org/abstracts/search?q=global%20warming" title=" global warming"> global warming</a> </p> <a href="https://publications.waset.org/abstracts/23133/climate-change-vulnerability-and-agrarian-communities-insights-from-the-composite-vulnerability-index-of-indian-states-of-andhra-pradesh-and-karnataka" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23133.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">458</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">5</span> Enabling Cloud Adoption Based Secured Mobile Banking through Backend as a Service</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=P.%20S.%20Jagadeesh%20Kumar">P. S. Jagadeesh Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Meenakshi%20Sundaram"> S. Meenakshi Sundaram</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the increase of prevailing non-traditional rivalry, mobile banking experiences an ever changing commercial backdrop. Substantial customer demands have established to be more intricate as customers request more expediency and superintend over their banking services. To enterprise advance and modernization in mobile banking applications, it is gradually obligatory to deeply leapfrog the scuffle using business model transformation. The dramaturgical vicissitudes taking place in mobile banking entail advanced traditions to exploit security. By reforming and transforming older back office into integrated mobile banking applications, banks can engender a supple and nimble banking environment that can rapidly respond to new business requirements over cloud computing. Cloud computing is transfiguring ecosystems in numerous industries, and mobile banking is no exemption providing services innovation, greater flexibility to respond to improved security and enhanced business intelligence with less cost. Cloud technology offer secure deployment possibilities that can provision banks in developing new customer experiences, empower operative relationship and advance speed to efficient banking transaction. Cloud adoption is escalating quickly since it can be made secured for commercial mobile banking transaction through backend as a service in scrutinizing the security strategies of the cloud service provider along with the antiquity of transaction details and their security related practices. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=cloud%20adoption" title="cloud adoption">cloud adoption</a>, <a href="https://publications.waset.org/abstracts/search?q=backend%20as%20a%20service" title=" backend as a service"> backend as a service</a>, <a href="https://publications.waset.org/abstracts/search?q=business%20intelligence" title=" business intelligence"> business intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=secured%20mobile%20banking" title=" secured mobile banking"> secured mobile banking</a> </p> <a href="https://publications.waset.org/abstracts/78153/enabling-cloud-adoption-based-secured-mobile-banking-through-backend-as-a-service" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78153.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">254</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">4</span> Travel Delay and Modal Split Analysis: A Case Study</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.%20S.%20Sathish">H. S. Sathish</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20S.%20Jagadeesh"> H. S. Jagadeesh</a>, <a href="https://publications.waset.org/abstracts/search?q=Skanda%20Kumar"> Skanda Kumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Journey time and delay study is used to evaluate the quality of service, the travel time and study can also be used to evaluate the quality of traffic movement along the route and to determine the location types and extent of traffic delays. Components of delay are boarding and alighting, issue of tickets, other causes and distance between each stops. This study investigates the total journey time required to travel along the stretch and the influence the delays. The route starts from Kempegowda Bus Station to Yelahanka Satellite Station of Bangalore City. The length of the stretch is 16.5 km. Modal split analysis has been done for this stretch. This stretch has elevated highway connecting to Bangalore International Airport and the extension of metro transit stretch. From the regression analysis of total journey time it is affected by delay due to boarding and alighting moderately, Delay due to issue of tickets affects the journey time to a higher extent. Some of the delay factors affecting significantly the journey time are evident from F-test at 10 percent level of confidence. Along this stretch work trips are more prevalent as indicated by O-D study. Modal shift analysis indicates about 70 percent of commuters are ready to shift from current system to Metro Rail System. Metro Rail System carries maximum number of trips compared to private mode. Hence Metro is a highly viable choice of mode for Bangalore Metropolitan City. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=delay" title="delay">delay</a>, <a href="https://publications.waset.org/abstracts/search?q=journey%20time" title=" journey time"> journey time</a>, <a href="https://publications.waset.org/abstracts/search?q=modal%20choice" title=" modal choice"> modal choice</a>, <a href="https://publications.waset.org/abstracts/search?q=regression%20analysis" title=" regression analysis"> regression analysis</a> </p> <a href="https://publications.waset.org/abstracts/12472/travel-delay-and-modal-split-analysis-a-case-study" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12472.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">496</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">3</span> The Role of Information Technology in Supply Chain Management</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=V.%20Jagadeesh">V. Jagadeesh</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Venkata%20Subbaiah"> K. Venkata Subbaiah</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20Govinda%20Rao"> P. Govinda Rao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper explaining about the significance of information technology tools and software packages in supply chain management (SCM) in order to manage the entire supply chain. Managing materials flow and financial flow and information flow effectively and efficiently with the aid of information technology tools and packages in order to deliver right quantity with right quality of goods at right time by using right methods and technology. Information technology plays a vital role in streamlining the sales forecasting and demand planning and Inventory control and transportation in supply networks and finally deals with production planning and scheduling. It achieves the objectives by streamlining the business process and integrates within the enterprise and its extended enterprise. SCM starts with customer and it involves sequence of activities from customer, retailer, distributor, manufacturer and supplier within the supply chain framework. It is the process of integrating demand planning and supply network planning and production planning and control. Forecasting indicates the direction for planning raw materials in order to meet the production planning requirements. Inventory control and transportation planning allocate the optimal or economic order quantity by utilizing shortest possible routes to deliver the goods to the customer. Production planning and control utilize the optimal resources mix in order to meet the capacity requirement planning. The above operations can be achieved by using appropriate information technology tools and software packages for the supply chain management. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=supply%20chain%20management" title="supply chain management">supply chain management</a>, <a href="https://publications.waset.org/abstracts/search?q=information%20technology" title=" information technology"> information technology</a>, <a href="https://publications.waset.org/abstracts/search?q=business%20process" title=" business process"> business process</a>, <a href="https://publications.waset.org/abstracts/search?q=extended%20enterprise" title=" extended enterprise"> extended enterprise</a> </p> <a href="https://publications.waset.org/abstracts/49770/the-role-of-information-technology-in-supply-chain-management" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49770.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">376</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">2</span> Multi-Residue Analysis (GC-ECD) of Some Organochlorine Pesticides in Commercial Broiler Meat Marketed in Shivamogga City, Karnataka State, India</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=L.%20V.%20Lokesha">L. V. Lokesha</a>, <a href="https://publications.waset.org/abstracts/search?q=Jagadeesh%20S.%20Sanganal"> Jagadeesh S. Sanganal</a>, <a href="https://publications.waset.org/abstracts/search?q=Yogesh%20S.%20Gowda"> Yogesh S. Gowda</a>, <a href="https://publications.waset.org/abstracts/search?q=Shekhar"> Shekhar</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20B.%20Shridhar"> N. B. Shridhar</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Prakash"> N. Prakash</a>, <a href="https://publications.waset.org/abstracts/search?q=Prashantkumar%20Waghe"> Prashantkumar Waghe</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20D.%20Narayanaswamy"> H. D. Narayanaswamy</a>, <a href="https://publications.waset.org/abstracts/search?q=Girish%20V.%20Kumar"> Girish V. Kumar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Organochlorine (OC) insecticides are among the most important organotoxins and make a large group of pesticides. Physicochemical properties of these toxins, especially their lipophilicity, facilitate the absorption and storage of these toxins in the meat thus possess public health threat to humans. The presence of these toxins in broiler meat can be a quantitative and qualitative index for the presence of these toxins in animal bodies, which is attributed to Waste water of irrigation after spraying the crops, contaminated animal feeds with pesticides, polluted air are the potential sources of residues in animal products. Fifty broiler meat samples were collected from different retail outlets of Bengaluru city, Karnataka state, in ice cold conditions and later stored under -20掳C until analysis. All the samples were subjected to Gas Chromatograph attached to Electron Capture Detector(GC-ECD, VARIAN make) screening and quantification of OC pesticides viz; Alachlor, Aldrin, Alpha-BHC, Beta-BHC, Dieldrin, Delta-BHC, o,p-DDE, p,p-DDE, o,p-DDD, p,p-DDD, o,p-DDT, p,p-DDT, Endosulfan-I, Endosulfan-II, Endosulfan Sulphate and Lindane(all the standards were procured from Merck). Extraction was undertaken by blending fifty grams (g) of meat sample with 50g Sodium Sulphate anahydrous, 120 ml of n-hexane, 120 ml acetone for 15 mins, extract is washed with distilled water and sample moisture is dried by sodium sulphate anahydrous, partitioning is done with 25 ml petroleum ether, 10 ml acetonitrile and 15 ml n-hexane shake vigorously for two minutes, sample clean up was done with florosil column. The reconstituted samples (using n-hexane) (Merck chem) were injected to Gas Chromatograph鈥揈lectron Capture Detector(GC-ECD). The present study reveals that, among the fifty chicken samples subjected for analysis, 60% (15/50), 32% (8/50), 28% (7/50), 20% (5/50) and 16% (4/50) of samples contaminated with DDTs, Delta-BHC, Dieldrin, Aldrin and Alachlor respectively. DDT metabolites, Delta-BHC were the most frequently detected OC pesticides. The detected levels of the pesticides were below the levels of MRL(according to Export Council of India notification for fresh poultry meat). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=accuracy" title="accuracy">accuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=gas%20chromatography" title=" gas chromatography"> gas chromatography</a>, <a href="https://publications.waset.org/abstracts/search?q=meat" title=" meat"> meat</a>, <a href="https://publications.waset.org/abstracts/search?q=pesticide" title=" pesticide"> pesticide</a>, <a href="https://publications.waset.org/abstracts/search?q=petroleum%20ether" title=" petroleum ether"> petroleum ether</a> </p> <a href="https://publications.waset.org/abstracts/64577/multi-residue-analysis-gc-ecd-of-some-organochlorine-pesticides-in-commercial-broiler-meat-marketed-in-shivamogga-city-karnataka-state-india" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/64577.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">327</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">1</span> Studies on Optimizing the Level of Liquid Biofertilizers in Peanut and Maize and Their Economic Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chandragouda%20R.%20Patil">Chandragouda R. Patil</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20S.%20Jagadeesh"> K. S. Jagadeesh</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20D.%20Kalolgi"> S. D. Kalolgi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Biofertilizers containing live microbial cells can mobilize one or more nutrients to plants when applied to either seed or rhizosphere. They form an integral part of nutrient management strategies for sustainable production of agricultural crops. Annually, about 22 tons of lignite-based biofertilizers are being produced and supplied to farmers at the Institute of Organic Farming, University of Agricultural Sciences, Dharwad, Karnataka state India. Although carrier based biofertilizers are common, they have shorter shelf life, poor quality, high contamination, unpredictable field performance and high cost of solid carriers. Hence, liquid formulations are being developed to increase their efficacy and broaden field applicability. An attempt was made to develop liquid formulation of strains of Rhizobium NC-92 (Groundnut), Azospirillum ACD15 both nitrogen-fixing biofertilizers and Pseudomonas striata an efficient P-solubilizing bacteria (PSB). Different concentration of amendments such as additives (glycerol and polyethylene glycol), adjuvants (carboxyl methyl cellulose), gum arabica (GA), surfactant (polysorbate) and trehalose specifically for Azospirillum were found essential. Combinations of formulations of Rhizobium and PSB for groundnut and Azospirillum and PSB for maize were evaluated under field conditions to determine the optimum level of inoculum required. Each biofertilizer strain was inoculated at the rate of 2, 4, 8 ml per kg of seeds and the efficacy of each formulation both individually and in combinations was evaluated against the lignite-based formulation at the rate of 20 g each per kg seeds and a un-inoculated set was included to compare the inoculation effect. The field experiment had 17 treatments in three replicates and the best level of inoculum was decided based on net returns and cost: benefit ratio. In peanut, the combination of 4 ml of Rhizobium and 2 ml of PSB resulted in the highest net returns and higher cost to benefit ratio of 1:2.98 followed by treatment with a combination of 2 ml per kg each of Rhizobium and PSB with a B;C ratio of 1:2.84. The benefits in terms of net returns were to the extent of 16 percent due to inoculation with lignite based formulations while it was up to 48 percent due to the best combination of liquid biofertilizers. In maize combination of liquid formulations consisting of 4 ml of Azospirillum and 2 ml of PSB resulted in the highest net returns; about 53 percent higher than the un-inoculated control and 20 percent higher than the treatment with lignite based formulation. In both the crops inoculation with lignite based formulations significantly increased the net returns over un-inoculated control while levels higher or lesser than 4 ml of Rhizobium and Azospirillum and higher or lesser than 2 ml of PSB were not economical and hence not optimal for these two crops. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rhizobium" title="Rhizobium">Rhizobium</a>, <a href="https://publications.waset.org/abstracts/search?q=Azospirillum" title=" Azospirillum"> Azospirillum</a>, <a href="https://publications.waset.org/abstracts/search?q=phosphate%20solubilizing%20bacteria" title=" phosphate solubilizing bacteria"> phosphate solubilizing bacteria</a>, <a href="https://publications.waset.org/abstracts/search?q=liquid%20formulation" title=" liquid formulation"> liquid formulation</a>, <a href="https://publications.waset.org/abstracts/search?q=benefit-cost%20ratio" title=" benefit-cost ratio"> benefit-cost ratio</a> </p> <a href="https://publications.waset.org/abstracts/81440/studies-on-optimizing-the-level-of-liquid-biofertilizers-in-peanut-and-maize-and-their-economic-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81440.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">493</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational 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