587 20 16481 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Performance Evaluation of Convolutional Neural Networks for Stellar Image Classification: A Comparative Study Subject The topic of the resource CNN; Galaxy; Parallel CNN; ResNet; Star; VGG16 Description An account of the resource This study analyzes three distinct convolutional neural network (CNN) models, ResNet, Parallel CNN, and VGG16, for object classification using the Star-Galaxy Classification dataset. The dataset comprises a vast collection of celestial object images, including galaxies, stars, and quasars. The effectiveness of each CNN model is evaluated based on accuracy, a commonly used performance metric. The results reveal that the Parallel CNN model achieved the highest accuracy of 90.08% in classifying celestial objects, followed by VGG16 with an accuracy of 86%, and ResNet with an accuracy of 83%. Specifically, the Parallel CNN model demonstrates superior performance in classifying galaxies and stars. These findings provide valuable insights into the strengths and weaknesses of each model for this specific classification task, guiding the development of more effective CNN models for similar applications in cosmology and other fields. This research contributes to the growing literature on CNN models' application in astronomy and underscores the importance of selecting appropriate models to achieve high accuracy in object classification tasks. The study's insights can be utilized to inform the development of more effective CNN models for similar tasks and facilitate advancements in astronomical research. 2023 IEEE. Creator An entity primarily responsible for making the resource Premanand N.; Tarun V.G.; Pawar S.; Jawakar D.N.; Utsav; Deepa S.; Jayapriya J.; Vinay M. Source A related resource from which the described resource is derived 2023 International Conference on Data Science and Network Security, ICDSNS 2023 Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICDSNS58469.2023.10244794" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICDSNS58469.2023.10244794</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173624526&doi=10.1109%2FICDSNS58469.2023.10244794&partnerID=40&md5=134a5ea65247673d52968a5a95799e46" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173624526&doi=10.1109%2fICDSNS58469.2023.10244794&partnerID=40&md5=134a5ea65247673d52968a5a95799e46</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835030159-5 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Premanand N., Christ University, Department of Computer Science, Bengaluru, India; Tarun V.G., Christ University, Department of Computer Science, Bengaluru, India; Pawar S., Christ University, Department of Computer Science, Bengaluru, India; Jawakar D.N., Christ University, Department of Computer Science, Bengaluru, India; Utsav, Christ University, Department of Computer Science, Bengaluru, India; Deepa S., Christ University, Department of Computer Science, Bengaluru, India; Jayapriya J., Christ University, Department of Computer Science, Bengaluru, India; Vinay M., Christ University, Department of Computer Science, Bengaluru, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Simhadri, Vineeth; Preetham Noel, P.; Jayapandian, Natarajan Title A name given to the resource Performance Evaluation of CPU and GPU Processors Using Advanced Data Analysis Techniques Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Lecture Notes in Electrical Engineering;Volume;1420 LNEE;pp.63-74 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-6406-1_6" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-6406-1_6</a> <a href="https://www.scopus.com/pages/publications/105019642095?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105019642095?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Simhadri V., Department of Computer Science and Engineering, Christ University, Bangalore, India; Preetham Noel P., Department of Computer Science and Engineering, Christ University, Bangalore, India; Jayapandian N., Department of Computer Science and Engineering, Christ University, Bangalore, India Description An account of the resource The modern industry is developing advanced CPU and GPU processors. The standard efficiency difference between the Intel Core i5 and 11th Gen with Nvidia GTX3050 processors is being discussed in this article. However, the reduction factor between these processors is determined to be as 2.5. Various data visualization techniques were applied to give a comprehensive analysis of the performance of CPU and GPU-based processors for execution of intensive tasks. The results were analyzed to understand the various performance parameters related to their functioning and efficiency. A model was proposed for enhancing the performance and throughput of the processor by easing the internal communication process between the CPU and the GPU by converting from electrical signals to light signals, though being faced with many challenges in the current time, holds a large scope of further research in the pursuit of higher computational efficiency. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Asymmetric multicore processors; Graphing processing unit; Heterogeneous computing systems; Processing units; Simultaneous multithreading Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 18761100; ISBN: 978-981966405-4; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Performance evaluation of diesel engine using genetic algorithm Subject The topic of the resource Diesel engine; Exhaust gas; Genetic algorithm; Performance evaluation Description An account of the resource ?Abstract: Engine analysis and optimization is not a new approach to the field of automobiles. It has always been a keen focus in the research of experts domestically as well as internationally, the control of Air-Fuel Ratio (AFR) in transient operating conditions of engine. For the last few decades, the industry and economic expansion of developed countries has showed a clean increase in the vehicle production as well as transport volume. Global warming, acid rain, greenhouse effect and air pollution problems related to emission of CO2, NOx, PM, CO and unburned HC, together with the consumption of fossil fuels, unite to create serious problems at a global level. Therefore it is a research study considering all these current issues and taking it to a new level of optimization for the output of a better efficiency, better economy and less pollution. Performance of Diesel Engine is evaluated by parameters like Power, Torque and Specific Fuel Consumption. 2018, Blue Eyes Intelligence Engineering and Sciences Publication. All rights reserved. Creator An entity primarily responsible for making the resource Lukose A.; Krishna A.V.N. Source A related resource from which the described resource is derived International Journal of Engineering and Advanced Technology, Vol-8, No. 2, pp. 155-158. Publisher An entity responsible for making the resource available Blue Eyes Intelligence Engineering and Sciences Publication Date A point or period of time associated with an event in the lifecycle of the resource 2018-01-01 Identifier An unambiguous reference to the resource within a given context <a href="" target="_blank" rel="noreferrer noopener"></a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062004855&partnerID=40&md5=2d8f2800710b43b6ed6a1f9daf1c8c4f" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062004855&partnerID=40&md5=2d8f2800710b43b6ed6a1f9daf1c8c4f</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 22498958 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Lukose A., Department of Information Technology, Faculty of Engineering, Christ University (Deemed), Bengaluru, Karnataka, India; Krishna A.V.N., Department of Computer Science and Engineering, Faculty of Engineering, Christ University (Deemed), Bengaluru, Karnataka, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Keshavamurthy, R.; Naveena, B.E.; Gowda, P. N. Vikram; Shrivathsa, T.V. Title A name given to the resource Performance Evaluation of Friction Stir Spot Welding of Al 5754 and Al 6111 using Machine Learning Approaches Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Journal of Materials Engineering and Performance;Volume;35;Issue;12;pp.11533-11551 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s11665-025-11766-6" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s11665-025-11766-6</a> <a href="https://www.scopus.com/pages/publications/105013271257?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105013271257?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Keshavamurthy R., Department of Mechanical and Automobile Engineering, CHRIST University, Karnataka, Bengaluru, 560074, India; Naveena B.E., Department of Automobile Engineering, Dayananda Sagar College of Engineering, Bangalore, 560078, India; Gowda P.N.V., Department of Automobile Engineering, Dayananda Sagar College of Engineering, Bangalore, 560078, India; Shrivathsa T.V., Department of Artificial Intelligence and Machine Learning, Shri Madhwa Vadiraja Institute of Technology and Management, Karnataka, Bantakal, 574115, India Description An account of the resource This study evaluates advanced machine learning (ML) and deep learning (DL) models for predicting the tensile shear and bending strength of friction stir spot welding joints involving Al 5754 and Al 6111 alloys. ML techniques include Linear Regression, Decision Tree, Random Forest (RF), K-Nearest Neighbors, Support Vector Regression, and XGBoost, while DL models comprise Recurrent Neural Network (RNN) and Backpropagation Neural Network (BPNN). The models were assessed for discrepancies between experimental and predicted results, with the best-performing model identified using R-squared (R2), Root-Mean-Square Error, Mean Square Error, and Mean Absolute Error. The data preprocessing phase included feature scaling and an 85:15 train-test split. Key input process parameters included spindle speed, dwell time, plunge depth, and tool pin profile. The results demonstrate that XGBoost yielded the highest predictive accuracy, achieving an R2 score of 99.99% for both tensile shear and bending strength, while RF offered a strong balance between accuracy and robustness. Other ML models struggled with the datasets complexity, resulting in lower performance. Among DL approaches, the BPNN outperformed the RNN, achieving approximately 99.8% accuracy by effectively capturing complex data patterns. ASM International 2025. Subject The topic of the resource aluminum alloys; deep learning; friction stir spot welding; machine learning; neural network; predictive modeling; regression Publisher An entity responsible for making the resource available Springer Relation A related resource ISSN: 10599495; CODEN: JMEPE Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Babu, K.; Mohammed, Mustfa; Fallah, Mohammed; Vichitra, R.; Ahila, R.; Radhamani, R. Title A name given to the resource Performance Evaluation of Hybrid Lifi-Wifi Internet Systems Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Journal of Internet Services and Information Security;Volume;15;Issue;4;pp.633-646 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.58346/JISIS.2025.I4.046" target="_blank" rel="noreferrer noopener">https://doi.org/10.58346/JISIS.2025.I4.046</a> <a href="https://www.scopus.com/pages/publications/105025118538?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105025118538?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Babu K., Department of Computational Intelligence, SRM Institute of Science & Technology, Tamil Nadu, India; Mohammed M., College of Engineering Technique, Al-Farahidi University, Baghdad, Iraq; Fallah M., Department of Computers Techniques Engineering, College of Technical Engineering, The Islamic University, Najaf, Iraq, Department of Computers Techniques Engineering, College of Technical Engineering, The Islamic University of Al Diwaniyah, Al Diwaniyah, Iraq; Vichitra R., School of Business and Management, Christ (Deemed to be University), Bangalore, India; Ahila R., Department of CSE, New Prince Shri Bhavani College of Engineering and Technology, Tamil Nadu, Chennai, India; Radhamani R., Department of Electrical and Electronics Engineering, K.S. Rangasamy College of Technology, Tiruchengode, India Description An account of the resource The rapid development of wireless technology made Light Fidelity, or LiFi, leave the laboratory and move onto the list of the next big thing, alongside everyday WiFi. WiFi also provides freedom of movement, but once everyone in a large area is streaming video, the signal becomes like a traffic jam. LiFi is going another way: data is sent via the rays of LED lights, meaning it can push files at light speed. The only problem is that the connection will not last forever, as soon as you leave the arc of the lamp or walk too far. In this paper, we explore what would happen when you combine the wide coverage of WiFi with the high speed of Li-Fi to form a hybrid system that avoids the weaknesses of both. Various test-based classrooms, halls, and open laboratories measured the speed of bit movement, the consistency of the beams, and the responsiveness of the games, and the mixed environment performed better in nearly all tests than the technology alone. It even figured out which to use as people entered and left the building, alternating between LiFi and WiFi, increasing uptime and doubling peak throughput. Hospitals, campuses, airports, and factories in need of constant, high-speed connections now have a better roadmap for using this blend so machines keep running smoothly and users remain engaged. The paper itself contains the unraveling of the hardware, the explanation of the test setup, and the weighing of the numbers, accompanied by some thought on what this combination can offer the future of wireless worlds. The findings justify integrating LiFi with WiFi as an innovative, scalable solution to ensure it keeps up with the massive data volumes generated by modern phones, tablets, and smart devices. 2025, Innovative Information Science and Technology Research Group. All rights reserved. Subject The topic of the resource Data Transmission; Hybrid Wireless Networks; LiFi; Network Latency; Performance Evaluation; Signal Stability; WiFi Publisher An entity responsible for making the resource available Innovative Information Science and Technology Research Group Relation A related resource ISSN: 21822069; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Swain, Kaliprasanna; Tak, Tan Kuan; Upreti, Kamal; Kshirsagar, Pravin R.; Krishnan, Sivaneasan Bala; Poonia, Ramesh Chandra; Mohapatra, Sumant Kumar; Nayak, Sumya Ranjan Title A name given to the resource PERFORMANCE EVALUATION OF IPE AND IE-AFFECTED PATIENTS USING A MODIFIED PSO AND ANFIS Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Journal of Mechanics of Continua and Mathematical Sciences;Volume;20;Issue;6;pp.65-89 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.26782/jmcms.2025.06.00005" target="_blank" rel="noreferrer noopener">https://doi.org/10.26782/jmcms.2025.06.00005</a> <a href="https://www.scopus.com/pages/publications/105008696027?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105008696027?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Swain K., Department of ETC, Trident Academy of Technology, Bhubaneswar, India; Tak T.K., Singapore Institute of Technology, Singapore; Upreti K., CHRIST University, Delhi NCR, Ghaziabad, India; Kshirsagar P.R., J. D. College of Engineering & Management, Maharashtra, Nagpur, India; Krishnan S.B., Singapore Institute of Technology, Singapore; Poonia R.C., CHRIST University, Delhi NCR, Ghaziabad, India; Mohapatra S.K., Department of ETC, TAT, Odisha, Bhubaneswar, 24, India; Nayak S.R., School of Computer Engineering, KIIT University, Bhubaneswar, India Description An account of the resource Epilepsy, a complex neurological disorder, is particularly challenging to diagnose and manage when driven by genetic factors. This study focuses on the analysis of Idiopathic Partial Epilepsy (IPE) and Idiopathic Epilepsy (IE) in both children and women, using a novel approach combining Modified Particle Swarm Optimization (MPSO) with a 9-rule Adaptive Neuro-Fuzzy Inference System (ANFIS). Four feature extraction techniquesDiscrete Wavelet Transform (DWT), Shearlet Transform (SLT), Contourlet Transform (CLT), and Stockwell Transform (SWT)are employed to process electroencephalogram (EEG) signals. The performance of the proposed MPSO-ANFIS model is evaluated and compared with existing methods. Results indicate that the SWT-ANFIS-MPSO method achieves superior classification accuracy for both IE and IPE patients, highlighting its potential to improve epilepsy diagnosis and treatment strategies. 2025, Institute of Mechanics of Continua and Mathematical Sciences. All rights reserved. Subject The topic of the resource ANFIS; Idiopathic Epilepsy (IE); Idiopathic Partial Epilepsy (IPE); Modified Particle Swarm Optimization (MPSO) Publisher An entity responsible for making the resource available Institute of Mechanics of Continua and Mathematical Sciences Relation A related resource ISSN: 9738975; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Anand, T.R.; Priya, Mohana T; Senthilkumar, Poorana S; Vijayalakshmi, P.S.; Thirunavukkarasu, V.; Rajesh Kannae, R. Title A name given to the resource Performance Evaluation of Machine Learning Models for Detecting Vulnerabilities in Internet of Things Network Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived International Journal of Drug Delivery Technology;Volume;16;Issue;2;pp.627-638 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.25258/ijddt.16.627-638" target="_blank" rel="noreferrer noopener">https://doi.org/10.25258/ijddt.16.627-638</a> <a href="https://www.scopus.com/pages/publications/105034584009?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105034584009?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Anand T.R., Department of Computer Technology, Dr. N. G. P. Arts and Science College, Tamil Nadu, Coimbatore, India; Priya M.T., Department of Computer Science, CHRIST University, Karnataka, Bangalore, India; Senthilkumar P.S., Department of Computer Applications, Dr. N. G. P. Arts and Science College, Tamil Nadu, Coimbatore, India; Vijayalakshmi P.S., Department of Computer Applications, Dr. N. G. P. Arts and Science College, Tamil Nadu, Coimbatore, India; Thirunavukkarasu V., Department of Computer Science, CHRIST University, Karnataka, Bangalore, India; Rajesh Kannae R., Department of Computer Science, CHRIST University, Karnataka, Bangalore, India Description An account of the resource Security threats and attacks are a growing concern in the field of Internet of Things (IoT) infrastructure. Internet-based automated network application models are used across various domains; commensurately, different security vulnerabilities and anomaly attacks are also increased at the same level. These attacks could cause failures in IoT infrastructure and network systems. In the modern world, Machine Learning (ML) models support various predictive analyses, providing more accurate results for future forecasting in various fields. In this article, we compare existing classical Machine Learning (ML) algorithms supported by Artificial Intelligence (AI) to evaluate and predict the performance and accuracy of different vulnerabilities in IoT infrastructure. We considered and compared the results of Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN) using publicly available datasets. Through this evaluation, we obtained an accuracy of 99.4% from DT, RF, and ANN. Additionally, RF demonstrated a highest accuracy of F1 is 0.994 and lowest STD variance is 0.014 than compared models in the selected dataset. 2026, Dr. Yashwant Research Labs Pvt. Ltd. All rights reserved. Subject The topic of the resource Attacks and detection; Dataset; Internet of Things; Machine Learning; Vulnerabilities Publisher An entity responsible for making the resource available Dr. Yashwant Research Labs Pvt. Ltd. Relation A related resource ISSN: 9754415; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Performance evaluation of machinelearning techniques indiabetes prediction Subject The topic of the resource Artificial neural network; Logistic regression; Neural network with 10-fold; Random forest; Support vector machine Description An account of the resource Diabetes diagnosis is very important at preliminary stage rather than treatment. In todays world devices like sensors are used for detection of diabetes. Accurate classification techniques are required for automatic identification of diabetes disease. In regards to research diabetes prediction with minimal number of attributes (test parameters) is to be identified earlier research states about feature reduction but with less predictive accuracy. In this regards, this work exploits machine learning techniques(methodology) such as Logistic Regression (LR), Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF) and Neural Network (NN) with 10-fold Cross Validation (CV) for classification and prediction of diabetes with Feature Selection Methods (FSMs) using R platform. Above all models enable us to investigate the relationship between a categorical outcome and a set of explanatory variables. The experiment was conducted on PIMA Indian diabetes dataset selected from UCI machine learning repository. From the experimental results it is identified that for full set of diabetes dataset attributes, Classification Accuracy (CA) achieved was 84.25%whereas with reduced set attributes an accuracy of 85.24% is achieved using NN with 10-fold CV technique compared to others which will help in medical application to predict diabetes with minimal features. BEIESP. Creator An entity primarily responsible for making the resource Raghavendra S.; Santosh Kumar J.; Raghavendra B.K. Source A related resource from which the described resource is derived International Journal of Engineering and Advanced Technology, Vol-8, No. 3, pp. 363-369. Publisher An entity responsible for making the resource available Blue Eyes Intelligence Engineering and Sciences Publication Date A point or period of time associated with an event in the lifecycle of the resource 2019-01-01 Identifier An unambiguous reference to the resource within a given context <a href="" target="_blank" rel="noreferrer noopener"></a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062716931&partnerID=40&md5=8894b68faf7c616fa11fe23d6a26271a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062716931&partnerID=40&md5=8894b68faf7c616fa11fe23d6a26271a</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 22498958 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Raghavendra S., Department of Computer Science and Engineering at Christ Deemed To Be University, Bangalore, India; Santosh Kumar J., Department of Computer Science and Engineering at K.S.School of Engineering and Management, Bangalore, India; Raghavendra B.K., VTU Belgaum, Bangalore, Karnataka, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Performance evaluation of Map-reduce jar pig hive and spark with machine learning using big data Subject The topic of the resource Cloudxlab; Flink; Hadoop; Hbase; HDFS; Hive; Map-reduce; Pig; Spark Description An account of the resource Big data is the biggest challenges as we need huge processing power system and good algorithms to make a decision. We need Hadoop environment with pig hive, machine learning and hadoopecosystem components. The data comes from industries. Many devices around us and sensor, and from social media sites. According to McKinsey There will be a shortage of 15000000 big data professionals by the end of 2020. There are lots of technologies to solve the problem of big data Storage and processing. Such technologies are Apache Hadoop, Apache Spark, Apache Kafka, and many more. Here we analyse the processing speed for the 4GB data on cloudx lab with Hadoop mapreduce with varing mappers and reducers and with pig script and Hive querries and spark environment along with machine learning technology and from the results we can say that machine learning with Hadoop will enhance the processing performance along with with spark, and also we can say that spark is better than Hadoop mapreduce pig and hive, spark with hive and machine learning will be the best performance enhanced compared with pig and hive, Hadoop mapreduce jar. Copyright 2020 Institute of Advanced Engineering and Science. All rights reserved. Creator An entity primarily responsible for making the resource Santosh Kumar J.; Raghavendra B.K.; Raghavendra S.; Meenakshi Source A related resource from which the described resource is derived International Journal of Electrical and Computer Engineering, Vol-10, No. 4, pp. 3811-3818. Publisher An entity responsible for making the resource available Institute of Advanced Engineering and Science Date A point or period of time associated with an event in the lifecycle of the resource 2020-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.11591/ijece.v10i4.pp3811-3818" target="_blank" rel="noreferrer noopener">https://doi.org/10.11591/ijece.v10i4.pp3811-3818</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079907640&doi=10.11591%2Fijece.v10i4.pp3811-3818&partnerID=40&md5=4663b5cad9d9091a213fc4454f707c89" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079907640&doi=10.11591%2fijece.v10i4.pp3811-3818&partnerID=40&md5=4663b5cad9d9091a213fc4454f707c89</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Relation A related resource ISSN: 20888708 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Santosh Kumar J., Department of Computer Science and Engineering, KSSEM, Affiliated to VTU Belagavi, Bangalore, India; Raghavendra B.K., Department of Computer Science and Engineering, BGSIT (ACU) Deemed to be University, India; Raghavendra S., Department of Computer Science and Engineering, Christ Deemed to be University, India; Meenakshi, Department of Computer Science and Engineering, Jain Deemed to be University, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Sushma Chowdary, P.; Bolisetty, V.Vijayasri; Yedukondalu, U.; Sailaja, Bokkisam Venkata Sai Title A name given to the resource Performance evaluation of multi band disk-shaped terahertz MIMO antenna with hexagon slots on ground for future 6?G and terahertz communication system Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Optik;Volume;347;Issue;;Article No.;172671; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.ijleo.2026.172671" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.ijleo.2026.172671</a> <a href="https://www.scopus.com/pages/publications/105027551196?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105027551196?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sushma Chowdary P., Department of Electronics and Instrumentation Engineering, Siddhartha Academy of higher education, Deemed to be University, Vijayawada ?7, Andhra Pradesh, India; Bolisetty V.V., Department of Electronics and Communication Engineering, Aditya University, Andhra Pradesh, Surampalem, 533437, India; Yedukondalu U., Department of ECE, MVR college of Engineering and Technology, Andhra Pradesh, Paritiala, 521180, India; Sailaja B.V.S., Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, 522502, India Description An account of the resource In this paper, a four-port wideband MIMO antenna is developed for future 6 G wireless communication systems. The proposed disk-shaped antenna consists of four identical disk-shaped elements arranged uniformly around a circular structure. This uniform arrangement helps maintain geometric balance and minimizes mutual coupling. All the disk patterns are arranged equidistantly around the centrally etched flower-like structure, which ensures the symmetrical geometry of the proposed antenna. These slots are helpful in generating a super-wide bandwidth ranging from 1.81 THz to 4.1513 THz. To further enhance the efficiency of the antenna, hexagonal slots are etched on the ground plane. The hexagonally etched slots on the ground reduce signal reflection losses. The overall dimensions of the four-port MIMO antenna are 800 800 50 m , and it is designed on a silicon substrate with a relative permittivity of 11.9. The proposed antenna achieves a super-wide bandwidth ranging from 0.926 THz to 5.5411 THz and a peak gain of 7 dB. MIMO performance parameters such as diversity gain, Total Active Reflection Coefficient (TARC), Envelope Correlation Coefficient (ECC), and Channel Capacity Loss (CCL) are evaluated, and all lie within acceptable ranges. The disk-shaped antenna demonstrates super-wideband characteristics, high resolution, and a low reflection coefficient. The disk-shaped antenna operates at 1.8175 THz, 2.5911 THz, 3.286 THz, and 4.1513 THz, with reflection coefficients of ?28.02 dB, ?35.723 dB, ?37.11 dB, and ?32.35 dB, respectively. Considering to its compact size, wide bandwidth, and stable radiation characteristics, the proposed disk-shaped antenna is well suited for high-speed THz communication and beyond-6G wireless applications. Copyright 2026. Published by Elsevier GmbH. Subject The topic of the resource High-efficiency Four-port MIMO Super wide band disk-shaped-patch-terahertz antenna (DSPT)antenna; THz frequency band Silicon-integrated antenna High-speed data Publisher An entity responsible for making the resource available Elsevier GmbH Relation A related resource ISSN: 304026; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Performance Evaluation of OTFS Under Different Channel Conditions for LEO Satellite Downlink Subject The topic of the resource Detection; Doppler; LMMSE; Low Earth Orbit; MRC; OTFS; Satellite Description An account of the resource Orthogonal Time Frequency Space (OTFS) modulation scheme is being actively pursued as a viable alternative to Orthogonal Frequency Division Multiplexing (OFDM) modulation scheme in future wireless standards due to the inherent ability of OTFS to mitigate the Doppler effects in high mobility scenarios. The inclusion of Non Terrestrial Network (NTN) in Release 17 of 3GPP (3rd generation partnership project) New Radio (NR) standard signifies the vital role of Satellite Communications to achieve coverage extension, capability augmentation and seamless global connectivity. In this context, it becomes important to study the suitability of OTFS modulation scheme with respect to satellite channel scenarios. In this paper, we consider the downlink channel scenarios defined by 3GPP NR NTN for Low Earth Orbit (LEO) satellites at sub-6 GHz and millimetre wave frequencies for evaluating the performance of OTFS modulation schemes. Simulation results using LMMSE (Linear Minimum Mean Square Error) and MRC (Maximum Ratio Combining) detection algorithms confirm that OTFS modulation is highly robust against Doppler effects and performs consistently across all channel conditions. From simulation results, it is observed that the performance of iterative MRC detection is better than LMMSE for 16QAM and 64QAM modulation schemes by achieving respective gains of around 5 dB and 10 dB for corresponding Bit Error Rate (BER) values of 0.01 and 0.1. 2023 IEEE. Creator An entity primarily responsible for making the resource Devarajalu S.K.; Jose D. Source A related resource from which the described resource is derived Proceedings - 10th International Conference on Wireless Networks and Mobile Communications, WINCOM 2023 Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/WINCOM59760.2023.10322957" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/WINCOM59760.2023.10322957</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179501311&doi=10.1109%2FWINCOM59760.2023.10322957&partnerID=40&md5=a107eb6519860d6b2c112654474c751f" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179501311&doi=10.1109%2fWINCOM59760.2023.10322957&partnerID=40&md5=a107eb6519860d6b2c112654474c751f</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835032967-4 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Devarajalu S.K., Christ University, Department of Ece, Bengaluru, India; Jose D., Christ University, Department of Ece, Bengaluru, India https://archives.christuniversity.in/files/original/a041e5ea4332d2f525d1f5acc44315a7.jpg aef8f902d41826f50c5dcaac97c0760b Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Performance evaluation of parallel genetic algorithm for brain MRI segmentation in hadoop and spark / Subject The topic of the resource Brain MRI Segmentation Description An account of the resource Indian Journal of Science and Technology, Vol.8, Issue 48, pp.1-7, ISSN: 0974-6846 (Print), 0974-5645 (Online). Creator An entity primarily responsible for making the resource Augustine, D. Peter., Raj, Pethuru. Source A related resource from which the described resource is derived <a href="https://indjst.org/articles/performance-evaluation-of-parallel-genetic-algorithm-for-brain-mri-segmentation-in-hadoop-and-spark" target="_blank" title="Performance evaluation of parallel genetic algorithm for brain MRI segmentation in hadoop and spark" rel="noreferrer noopener">https://indjst.org/articles/performance-evaluation-of-parallel-genetic-algorithm-for-brain-mri-segmentation-in-hadoop-and-spark</a> Publisher An entity responsible for making the resource available Indian Society for Educations and Environment Date A point or period of time associated with an event in the lifecycle of the resource 2016-02-13 Language A language of the resource English Type The nature or genre of the resource Journal Article Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Performance Evaluation of Predicting IoT Malicious Nodes Using Machine Learning Classification Algorithms Subject The topic of the resource Classification Algorithms; Internet of Things (IoT); Machine Learning; Malicious Nodes; Network Security Description An account of the resource The prediction of malicious nodes in Internet of Things (IoT) networks is crucial for enhancing network security. Malicious nodes can significantly impact network performance across various scenarios. Machine learning (ML) classification algorithms provide binary outcomes ("yes" or "no") to accurately identify these nodes. This study implements various classifier algorithms to address the problem of malicious node classification, using the SensorNetGuard dataset. The dataset, comprising 10,000 records with 21 features, was preprocessed and used to train multiple ML models, including Logistic Regression, Decision Tree, Naive Bayes, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). Performance evaluation of these models followed the ML workflow, utilizing Python libraries such as scikit-learn, Seaborn, Matplotlib, and Pandas. The results indicated that the Naive Bayes classifier outperformed others with an accuracy of 98.1%. This paper demonstrates the effectiveness of ML classifiers in detecting malicious nodes in IoT networks, providing a robust predictive model for real-time application. The SensorNetGuard dataset is available on the IEEE data port and Kaggle platform. 2024, Prof.Dr. ?skender AKKURT. All rights reserved. Creator An entity primarily responsible for making the resource Poorana S.S.; Wilfred B.N.R.; Rajesh K.R.; Karthik S. Source A related resource from which the described resource is derived International Journal of Computational and Experimental Science and Engineering, Vol-10, No. 3, pp. 341-349. Publisher An entity responsible for making the resource available Prof.Dr. ?skender AKKURT Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.22399/ijcesen.395" target="_blank" rel="noreferrer noopener">https://doi.org/10.22399/ijcesen.395</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201698353&doi=10.22399%2Fijcesen.395&partnerID=40&md5=5c1a7dfb66ae39d12ab225d6adf38ab9" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201698353&doi=10.22399%2fijcesen.395&partnerID=40&md5=5c1a7dfb66ae39d12ab225d6adf38ab9</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 21499144 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Poorana S.S., Dr. N.G.P. Arts and Science College, Department of Computer Applications, Tamil Nadu, Coimbatore, 641048, India; Wilfred B.N.R., University of Tech. and Applied Sciences, College of Computing and Information Sciences, Ibri, Oman; Rajesh K.R., Christ University, Department of Computer Science, Karnataka, 560029, India; Karthik S., Kristu Jayanti College, Department of Computer Science (PG), Karnataka, 560077, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Performance evaluation of random forest with feature selection methods in prediction of diabetes Subject The topic of the resource Classification accuracy; Data mining; Feature selection method; Percentage split; Random forest Description An account of the resource Data mining is nothing but the process of viewing data in different angle and compiling it into appropriate information. Recent improvements in the area of data mining and machine learning have empowered the research in biomedical field to improve the condition of general health care. Since the wrong classification may lead to poor prediction, there is a need to perform the better classification which further improves the prediction rate of the medical datasets. When medical data mining is applied on the medical datasets the important and difficult challenges are the classification and prediction. In this proposed work we evaluate the PIMA Indian Diabtes data set of UCI repository using machine learning algorithm like Random Forest along with feature selection methods such as forward selection and backward elimination based on entropy evaluation method using percentage split as test option. The experiment was conducted using R studio platform and we achieved classification accuracy of 84.1%. From results we can say that Random Forest predicts diabetes better than other techniques with less number of attributes so that one can avoid least important test for identifying diabetes. Copyright 2020 Institute of Advanced Engineering and Science. All rights reserved. Creator An entity primarily responsible for making the resource Raghavendra S.; Santosh Kumar J. Source A related resource from which the described resource is derived International Journal of Electrical and Computer Engineering, Vol-10, No. 1, pp. 353-359. Publisher An entity responsible for making the resource available Institute of Advanced Engineering and Science Date A point or period of time associated with an event in the lifecycle of the resource 2020-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.11591/ijece.v10i1.pp353-359" target="_blank" rel="noreferrer noopener">https://doi.org/10.11591/ijece.v10i1.pp353-359</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073363164&doi=10.11591%2Fijece.v10i1.pp353-359&partnerID=40&md5=bbbe979964eaf91d3b7e7eded475f799" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073363164&doi=10.11591%2fijece.v10i1.pp353-359&partnerID=40&md5=bbbe979964eaf91d3b7e7eded475f799</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Relation A related resource ISSN: 20888708 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Raghavendra S., Department of Computer Science and Engineering, CHRIST Deemed To Be University, Kanmanike, Kumbalgodu, Mysore Road, Bangalore, 560074, India; Santosh Kumar J., Department of Computer Science and Engineering, KSSEM, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Performance Evaluation of Refractory Bodies Fabricated from Composite Oxide Powders Beneficiated from Black Al-dross Subject The topic of the resource Aluminum oxide; Black aluminum dross; Industrial waste utilization; Magnesium aluminate; Oxide composites; Refractory Description An account of the resource Aluminum Oxide (Corundum, ?-Al2O3) and Magnesium-Aluminum Oxides (Spinel, MgAl2O4) are highly desired refractory materials due to their ability to withstand high-temperature service conditions without corroding and cracking. They are present in composite form in black Aluminum Dross (Al-dross), a hazardous industrial waste. 1 Kg batch of this composite powder was beneficiated from Al-dross to 98+% purity after removing the hazardous Aluminum Nitride (AlN) by aqueous treatment of Al-dross in an environment-friendly manner. The treated slurry was oven dried, ball milled to fine powder, hydraulically pressed, and sintered at 1500 C/6 h into solid cylinders (50 mm diameter 20 mm height). The structural phase analysis of the sintered product (refractory blocks) revealed a highly crystalline XRD pattern with peaks pertaining to only ?-Al2O3 and MgAl2O4. The blocks with Rockwell Hardness values of 4850 HRC, were subjected to thermal shock cycling by following the guidelines of IS 1528 (heat quench between 1000 C and air at ambient) which successfully withstood > 100 shock cycles without failure. SEM was employed to study the fracture surface in an as-sintered state and after thermal shock cycling, to reveal a fine-grained microstructure with clear grain boundaries in the as-sintered state to a glassy matrix with fine cracks at the end of the thermal shock cycle test. The potential for utilization of Al-dross for refractory applications was thus established. 2023 Creator An entity primarily responsible for making the resource Gomes S.A.; Ramaswamy P. Source A related resource from which the described resource is derived Materials Today: Proceedings Publisher An entity responsible for making the resource available Elsevier Ltd Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.matpr.2023.01.271" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.matpr.2023.01.271</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147585957&doi=10.1016%2Fj.matpr.2023.01.271&partnerID=40&md5=26835da80cac5ebc0350ac46da289f11" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147585957&doi=10.1016%2fj.matpr.2023.01.271&partnerID=40&md5=26835da80cac5ebc0350ac46da289f11</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 22147853 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Gomes S.A., Department of Mechanical and Automobile Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, 560074, India; Ramaswamy P., Department of Mechanical and Automobile Engineering, School of Engineering and Technology, CHRIST (Deemed to be University), Bangalore, 560074, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Performance evaluation of ternary blended geopolymer binders comprising of slag, fly ash and brick kiln rice husk ash Subject The topic of the resource Fly ash; Rice husk ash; Slag; Sustainability; Ternary geopolymer Description An account of the resource The use of industrial and agro-based precursor materials from local sources can achieve desirable properties for geopolymer binders, and thus realize the carbon-efficient sustainable materials in the construction industry. At the same time, the synergy between these precursors can be assessed using the multilevel material investigation, which has not been explored extensively. Moreover, there are limited studies on ternary geopolymer synthesized with rice husk ash from uncontrolled burning source such as brick kilns. Therefore, this study evaluates the performance of ternary blended geopolymer binders comprised of ground granulated blast furnace slag (GGBFS), fly ash (FLA), and brick kiln rice husk ask (BRHA), implementing the multilevel material approach. The experimental program includes assessment and comparative analyses of the properties of geopolymer binders such as setting time, flow, compressive strength, density, water absorption, and efflorescence. Additionally, X-ray diffraction (XRD) and scanning electron microscopy (SEM) analyses examine crystallographic structure and microscopic morphology of the composite binders. The initial setting time ranged from 21 min to 47 min for ternary mixes, in comparison to 21 min to 58 min for binary mixes. GGBFS significantly contribute in setting of binder due to hydration reaction and formation of C-S-H gel. The flow of ternary mixes exhibits standard deviation of 11.42 mm when compared to 20.96 mm of binary mixes. Lower dispersion in flow values suggests improved coaction between GGBFS, FLA, and BRHA. The compressive strength of ternary mixes improved when compared to the binary mixes. The optimum performance of 60 MPa was obtained for G60A40F95R5, which was 25% and 66.67% higher than binary mixes G60F40 and G60R40, respectively. Similarly, ternary mix G70A30F95R5 showed the least water absorption of 2.08% which was 53% and 58.4% lower than the binary mixes G70F30 and G70R30, respectively. The improvement in the properties of ternary mixes was confirmed from XRD analysis, which reveal coexistence of C-S-H along with crystalline SiO2 that positively improve the microstructure of the composite binder. Moreover, SEM analysis showed dense microstructure for ternary mixes when compared to binary mixes, which further validate the improvement in the strength of such binders. The sustainability analysis discloses the enhanced performance of ternary mixes, wherein, G60A40F95R5 showed 19.35% and 46.23% lower carbon dioxide parameter than binary mixes G60F40 and G60R40, respectively. All in all, the multilevel material investigation provides a great avenue to delve in to the best performing ternary mixes which will find desirable applications in construction industry. 2024 The Authors Creator An entity primarily responsible for making the resource Chelluri S.; Hossiney N. Source A related resource from which the described resource is derived Case Studies in Construction Materials, Vol-20 Publisher An entity responsible for making the resource available Elsevier Ltd Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.cscm.2024.e02918" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.cscm.2024.e02918</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183988775&doi=10.1016%2Fj.cscm.2024.e02918&partnerID=40&md5=37db366e37cbd9447b68180cee025b70" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183988775&doi=10.1016%2fj.cscm.2024.e02918&partnerID=40&md5=37db366e37cbd9447b68180cee025b70</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 22145095 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Chelluri S., School of Engineering and Technology, Department of Civil Engineering (CHRIST Deemed to be University), Bengaluru, India; Hossiney N., School of Engineering and Technology, Department of Civil Engineering (CHRIST Deemed to be University), Bengaluru, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Performance Evaluation of Time-based Recommendation System in Collaborative Filtering Technique Subject The topic of the resource Collaborative Filtering; Cosine Correlation Coefficient; Gower's Coefficient; Recommendation System; Similarity Measure; Time decay function Description An account of the resource The Collaborative Filtering (CF) technique is the most common neighbourhood-based recommendation strategy, that provides personalized recommendation to a user for the items using a similarity measure. Hence, the selection of the appropriate similarity measure becomes crucial in the CF based recommendation system. The traditional similarity measures merely focus only on the historical ratings provided by the users to compute the similarity, completely ignoring the fact that preferences change over a period of time. Considering this, the paper aims to develop an effective Recommendation System that uses temporal information to capture the changes in the preferences over a period of time. For this, the existing exponential and power time decay functions are integrated with Cosine, Pearson Correlation, and Gower's similarity measures to compute similarity. The similarity is computed at the similarity computation and prediction levels of recommendation processes. Experimental findings in terms of MAE and RMSE on the MovieLens-100k demonstrate that performance of Gower's coefficient is better when applied with the exponential function at the similarity computation level of the recommendation process. 2022 Elsevier B.V.. All rights reserved. Creator An entity primarily responsible for making the resource Jain G.; Mahara T.; Sharma S.C. Source A related resource from which the described resource is derived Procedia Computer Science, Vol-218, pp. 1834-1844. Publisher An entity responsible for making the resource available Elsevier B.V. Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.procs.2023.01.161" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.procs.2023.01.161</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85156146357&doi=10.1016%2Fj.procs.2023.01.161&partnerID=40&md5=dc3ca7394acef5132172db93668fc1af" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85156146357&doi=10.1016%2fj.procs.2023.01.161&partnerID=40&md5=dc3ca7394acef5132172db93668fc1af</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 18770509 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Jain G., Indian Institute of Technology Roorkee, Uttarakhand, Roorkee, India; Mahara T., Christ University, Bengaluru, India; Sharma S.C., Indian Institute of Technology Roorkee, Uttarakhand, Roorkee, India Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Kavitha, K.P.; Kirubanand, V.B. Title A name given to the resource Performance Evaluation of Transfer Learning VGG16 in Handwritten Text Using Word Beam Search and Language Model Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Journal of Internet Services and Information Security;Volume;15;Issue;2;pp.536-551 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.58346/JISIS.2025.I2.037" target="_blank" rel="noreferrer noopener">https://doi.org/10.58346/JISIS.2025.I2.037</a> <a href="https://www.scopus.com/pages/publications/105012537403?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105012537403?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kavitha K.P., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India; Kirubanand V.B., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India Description An account of the resource This study evaluates the performance of transfer learning using the VGG16 model for handwritten text recognition, integrating Word Beam Search decoding and language modeling techniques. The VGG16 model, pre-trained on large-scale datasets, serves as a feature extractor for handwritten text images, capturing intricate patterns and structures inherent in handwriting. To convert these visual features into textual information, the system employs a Recurrent Neural Network (RNN) trained with the Connectionist Temporal Classification (CTC) loss function, producing a matrix of character probabilities for each time-step. The Word Beam Search algorithm is utilized for decoding these probabilities into coherent text, effectively constructing recognized text by referencing a predefined dictionary and addressing challenges such as arbitrary character strings and varying handwriting styles. The integration of language models incorporates context which further sharpens the output and improves precision and trustworthiness of recognition systems. Experimental results demonstrate that this combined approach significantly improves recognition performance, highlighting the efficacy of transfer learning and advanced decoding strategies in handwritten text recognition. This involves analyzing its effectiveness across various datasets. Transfer learning leverages pre-trained models, like VGG16, to address challenges such as limited labeled data and extensive training times. 2025, Innovative Information Science and Technology Research Group. All rights reserved. Subject The topic of the resource CNN; CTC; FSM; HTR; LM; NN; RNN; VGG Publisher An entity responsible for making the resource available Innovative Information Science and Technology Research Group Relation A related resource ISSN: 21822069; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Performance Improvement in E-Gun Deposited SiOx- Based RRAM Device by Switching Material Thickness Reduction Subject The topic of the resource E-gun deposition; NVM; RRAM; SiOx Description An account of the resource A performance improvement by reduction in switching material thickness in a e-gun deposited SiOx based resistive switching memory device was investigated. Reduction in thickness cause thinner filamentary path formation during ON-state by controlling the vacancydefects. Thinner filament cause lowering of operation current from 500 ?A to 100 ?A and also improves the reset current (from >400 ?A to <100 ?A). Switching material thickness reductionalso cause the forming free ability in the device. All these electrical parametric improvements enhance the device reliability performances. The device show >200 dc endurance, >3-hour dataretention and >1000 P/E endurance with 100 ns pulses. 2022 Institute of Physics Publishing. All rights reserved. Creator An entity primarily responsible for making the resource Roy S.; Maikap S. Source A related resource from which the described resource is derived Journal of Physics: Conference Series, Vol-2161, No. 1 Publisher An entity responsible for making the resource available IOP Publishing Ltd Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1088/1742-6596/2161/1/012040" target="_blank" rel="noreferrer noopener">https://doi.org/10.1088/1742-6596/2161/1/012040</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124706968&doi=10.1088%2F1742-6596%2F2161%2F1%2F012040&partnerID=40&md5=75c274bf9a6248b934ee492402e3770a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124706968&doi=10.1088%2f1742-6596%2f2161%2f1%2f012040&partnerID=40&md5=75c274bf9a6248b934ee492402e3770a</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 17426588 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Roy S., Electronics Engineering Department, School of Engineering and Technology, Kengeri Campus, CHRIST (Deemed to be) University, Mysore Road Kumbalgodu, Kanmanike, Karnataka, Bengaluru, 560074, India, School of Electronics Engineering, Chang Gung University, 259 Wen-Hwa 1st Road, Taoyuuan, Guishan, 333, Taiwan; Maikap S., School of Electronics Engineering, Chang Gung University, 259 Wen-Hwa 1st Road, Taoyuuan, Guishan, 333, Taiwan Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Performance improvement of triple band truncated spiked triangular patch antenna Subject The topic of the resource Inset feed; Return Loss; Triangular; Triple Band; Truncation Description An account of the resource In this paper, the design of a novel triple band triangular microstrip patch antenna with inset feed is proposed. The triangular patch is designed for a resonant frequency of 2 GHz. The inset feed is placed at a depth of 1/3rd of height from the bottom of the patch for improved return loss. The insertion of two slots and two tabs causes the antenna to resonate at multiple frequencies. The proposed antenna resonates at three frequencies: 1.939 GHz, 2.515 GHz and 3.212 GHz. The truncation of the edges of the patch and the tabs improves the gain and directivity of the antenna. 2016 IEEE. Creator An entity primarily responsible for making the resource Sacharias S.A.; Suganthi S. Source A related resource from which the described resource is derived 1st International Conference on Emerging Trends in Engineering, Technology and Science, ICETETS 2016 - Proceedings Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2016-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICETETS.2016.7603027" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICETETS.2016.7603027</a> <a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-84997327511&doi=10.1109%2FICETETS.2016.7603027&partnerID=40&md5=b4fc834fe41a59bd50b35e910e286286" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-84997327511&doi=10.1109%2fICETETS.2016.7603027&partnerID=40&md5=b4fc834fe41a59bd50b35e910e286286</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-146736725-7 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sacharias S.A., Communication Systems, Christ University, Bangalore, India; Suganthi S., Dept. of ECE, Christ University, Bangalore, India