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SSRN 2023年

作者： Deivakani, M. Jothi, J. Nirmal Srividhya, G. Farhaoui, Yousef Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Kothandaraman Nagar Tamil Nadu 624622 India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Tamil Nadu Chennai600062 India Department of Electronics and Communication Engineering Panimalar engineering college Poonamallee Tamil Nadu Chennai600123 India Department of Computer Science Faculty of sciences and Technic Moulay Ismail University B.P 509 Boutalamine Errachidia Morocco

To overcome this issue, use Low-Density Parity-Check (LDPC) architecture-based encryption and decryption, as well as analyses the bus links using Error Correcting Codes (ECC) for data transmission signals. In this method, the butterfly Density Accumulator (bDA) algorithm is based on Minimum Semi-Parallel (MSP) structure for high data throughput with an address pattern from the IP (Internet protocol) and supplies the output from the data transmission. A fault secure detector is available that can detect any pair of problems in the received code word and defects in the detector circuit. This fault secure detector can validate the encoder and corrector performance. This data is dependent on the numeric per row, memory access times, and time required for pattern detection. The output improves the efficiency of one recurring operation that is performed throughout each encoding: checking the received code block for correctness before deciding whether to continue or stop the decoding process. The total performance evaluation of LPDC shows that each approach reduces area by 26% on average, but the productivity of the delay decreases by 16%. © 2023, The Authors. All rights reserved.

关键词： Signal encoding

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blind Source Separation: A Performance Review Approach

Blind Source Separation: A Performance Review Approach

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International Conference on Signal Processing and Information Security (ICSPIS)

作者： Luay Yassin Taha Esam Abdel-Raheem Div. of Business Eng. and Inf. Sciences and Tech. Altoona Pennsylvania 16601 United States Department of Electrical and Computer Engineering University of Windsor Windsor ON N9B 3P4 Canada

ISBN: (纸本)9781665492669

blind source separation (bSS) has been used extensively for the last three decades to extract different sources such as speech, biomedical, and image sources. The quality of extraction is governed by the extraction metrics of the bSS algorithms. The aim of this work is to review the extraction metrics for 27 different algorithms used in some previous works. Results show that the best algorithms in speech extraction is obtained when using independent low-rank matrix analysis (ILRMA) and canonical correlation (CC) based bSS. For image source separation, ZEODS algorithm has shown the highest extraction evaluation score. For fetal electrocardiogram (FEEG) source separation, the null space idempotent transformation matrix (NSITM) and the generalized functional link artificial neural network (GFLANN) show the highest evaluation scores. For electroencephalogram(EEG) signals de-noising and source extraction, the short and long term prediction blind source extraction (SLTP-bSE), b-spline mutual information independent component analysis (bMICA), and FASTICA, show the highest evaluation scores. The impact of evaluating the extraction algorithms result in optimum source separation.

关键词： Measurement Signal processing algorithms Null space Prediction algorithms blind source separation Data mining Signal denoising

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Smartcharge: Reducing Power Usage in Intelligent Homes

Smartcharge: Reducing Power Usage in Intelligent Homes

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Communication, Computing and Energy Efficient technologies (I3CEET), International Conference on

作者： A. Sonya Neha Chhabra Abhishek Kumar Gupta P. Lakshmi Harika b. Aishwarya R. Karthika Department of Information Technology B. S. Abdur Rahman Crescent Institute of Science and Technology Chennai Department of Information Technology Jagannath International Management School New Delhi India Department of Information Technology Jagannath International Management School New Delhi Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Bowrampet Hyderabad Telangana Department of Computer Science and Engineering(Cyber Security) Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi India Department of EEE Theni Kammavar Sangam College of Technology Theni India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

Smart homes oriented towards reminders and monitoring for healthcare must be reasoned, definitely centered on matured home or disability in a house. Accurately predicting the movements of people would be essential to offer such level support for residents in this situation. Our study helps to cater this requirement by proposing an improved forecasting model which is both accurate and has faster learning. For doing that, we here develop the recurrent version of an existing model; i.e., Extreme Learning Machine [11] to keep using temporal dependency of inputs into network in different time steps. This model is different since it can be constantly adapted to the needs of people in a smart home environment, especially those who anticipate needing medical support. So, finally this Recurrent Extreme Learning Machine model validates that for smart home technology evolutions as the time progresses continuously evolved intelligent are required to solve when it comes health care critical challenges. This type of learning model is not only apt in accuracy and speed which should be enough during the learning phase though; but it also allows machine to learn more about human behavior making them generally intelligent because now you have a pattern how humans behave. Advantages Of Sexual Health These are certainly rewarding Smart Home Systems..

关键词： Training Adaptation models Accuracy Extreme learning machines Smart homes Medical services Predictive models Telecommunication computing Forecasting Monitoring

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Correction: Materials laboratories of the future for alloys, amorphous, and composite materials (MRS bulletin, (2025), 50, 2, (190-207), 10.1557/s43577-024-00846-y)

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MRS bulletin 2025年 1-2页

作者： banerjee, Sarbajit Meng, Y. Shirley Minor, Andrew M. Zhang, Minghao Zaluzec, Nestor J. Chan, Maria K.Y. Seidler, Gerald McComb, David W. Agar, Joshua Mukherjee, Partha P. Melot, brent Chapman, Karena Guiton, beth S. Klie, Robert F. McCue, Ian D. Voyles, Paul M. Robertson, Ian Li, Ling Chi, Miaofang Destino, Joel F. Devaraj, Arun Marquis, Emmanuelle A. Segre, Carlo U. Liu, Huinan H. Yang, Judith C. Momeni, Kasra Misra, Amit Abdolrahim, Niaz Medvedeva, Julia E. Cai, Wenjun Sehirlioglu, Alp Dizbay-Onat, Melike Mehta, Apurva Graham-brady, Lori Maruyama, benji Rajan, Krishna Warner, Jamie H. Taheri, Mitra L. Kalinin, Sergei V. Reeja-Jayan, b. Schwarz, Udo D. Simon, Sindee L. brown, Craig M. Department of Chemistry and Department of Materials Science & Engineering Texas A & M University College Station United States Pritzker School of Molecular Engineering The University of Chicago Chicago United States Department of Materials Science & Engineering University of California Berkeley Berkeley United States National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory Berkeley United States Center for Nanoscale Materials Argonne National Laboratory Lemont United States Department of Physics University of Washington Seattle United States Department of Materials Science & Engineering The Ohio State University Columbus United States Department of Mechanical Engineering and Mechanics Drexel University Philadelphia United States School of Mechanical Engineering Purdue University West Lafayette United States Department of Chemistry University of Southern California Los Angeles United States Department of Chemistry Stony Brook University The State University of New York Stony Brook United States Department of Chemistry University of Kentucky Lexington United States Department of Physics University of Illinois at Chicago Chicago United States Department of Materials Science & Engineering Northwestern University Evanston Bangladesh Department of Materials Science & Engineering University of Wisconsin–Madison Madison United States Department of Materials Science and Engineering University of Pennsylvania Philadelphia United States Department of Mechanical Engineering and Materials Science Duke University Durham United States Department of Chemistry Creighton University Omaha United States Physical and Computational Sciences Directorate Pacific Northwest National Laboratory Richland United States Department of Materials Science & Engineering University of Michigan–Ann Arbor Ann Arbor United States Department of Physics Illinois Institute of Technology Chicago United States Department of Bioengineering and the Mater

This article was updated to correct Minghao Zhang’s affiliation from "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA" to "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA;Argonne National Laboratory, Lemont, USA." Argonne National Laboratory was left off the original publication. © The Author(s) 2025.

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Comparison of tree based classifications and neural network based classification 21

Comparison of tree based classifications and neural network ...

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21st International Arab Conference on Information technology, ACIT 2020

作者： Sarada, b. Dandu, Madhavi Tarun, Saikonda Krishna School of CIT Reva University Bangalore India ME Electrical Computer Engg Queens University Engineering Department Kingston Canada B. Tech in Instrumentation Engg Manipal Institute of Technology Engineering Department Manipal India

ISBN: (纸本)9781728188553

In this paper, we empirically analyze and compare the performance of neural network-based classification (MLP) and decision tree based classifications (CART and Random Tree) on data sets with banking and medical purpose information. We included structural parameters for distinguishing the classification methods. We also introduced up sampling and down sampling along with feature selection and found a more detailed analysis of the Precision, Recall, F1 score, Area under the Curve, Test and Train accuracies which are necessary in order to judge the performance of the learning and classification method of all the models. However, we identified some limitations involved with these sampling techniques during the research such as loss of vital data and overfitting outcomes. © 2020 IEEE.

关键词： Data mining

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Android malware detection amid COVID-19 9

Android malware detection amid COVID-19

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9th International Conference on System Modeling and Advancement in Research Trends, SMART 2020

作者： Srivastava, Rohit Mishra, R.P. Kumar, Vivek Shukla, Himanshu Kumar Goyal, Neha Singh, Chandrabhan Computer Science and Engineering F.O.E.T. University Lucknow India Electronics and Communication Dr R.M.L. Awadh University Ayodhya India Computer Science and Engineering Teerthanker Mahaveer University Moradabad India Computer Science and Engineering K.M. C. Language University Lucknow India Computer Science and Engineering B.N. College of Engg. and Tech. Lucknow India

ISBN: (纸本)9781728189086

Mobile is a rapidly growing web environment that attracts malware developers around the world. Smart phones, especially android phones are widely used and are the most popular new target for malware attacks. Most common type of malware found to attack android users was an unauthorized app repackaged as a normal app through a third party, unofficial app store. New apps found in the app store are hard to identify as malicious. Our work develops a malware detector and analyzer. This paper also links insights about malware attacks in COVID-19 on mobile devices. To meet the objectives, a model is implemented that extracts the inherent features of android application file and analyzes them for quick and accurate analysis. The model classifies the apps more accurately as benign or malicious. © 2020 IEEE.

关键词： COVID-19

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A study on Cloud enabled Applications and their Security Issues

A study on Cloud enabled Applications and their Security Iss...

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International Conference on Computing and Networking technology (ICCNT)

作者： Vidhi Mody Vrushti Mody Soham Parekh B.Tech. Computer Engineering Mukesh Patel School of Technology Management and Engineering NMIMS B.E. Computer Engineering Dwarkadas Jivanlal Sanghvi College of Engineering

ISBN: (数字)9781728168517

ISBN: (纸本)9781728168524

Cloud computing was first established to connect people and data at anytime from anywhere in the 1960s. Today cloud computing is one of the most widely used technologies. Its applications include Multi-cloud, healthcare, testing, finance, data storage and handling big data to increase efficiency and how it differs from traditional systems. We have stated how cloud computing can be used in various domains and have compared its efficiency to traditional systems in terms of data recovery, access control, management, security and cost. However, like any other technology, this one too might have flaws which can be exploited. This paper also lists the security challenges that will have to be kept in mind while implementing these systems.

关键词： Cloud computing Security Medical services Real-time systems Maintenance engineering Data privacy Computational modeling

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Next-Gen Human-computer Interaction: A Hybrid LSTM-CNN Model for Superior Adaptive User Experience

Next-Gen Human-Computer Interaction: A Hybrid LSTM-CNN Model...

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Electrical, Electronics, Information and Communication technologies (ICEEICT), International Conference on

作者： Ravindra Changala J. Manoranjini M. Misba Shital Kakad S Sai Deepthi Ponni Valavan M Department of Computer Science and Engineering Guru Nanak Institutions Hyderabad India Department Of AI&ML Rajalakshmi Engineering College Chennai India Department CSE (AI&ML) Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of AI & DS Vishwakarma Institute of Technology Pune Maharashtra Department IT B V Raju Institute of Technology (Autonomous) Narsapur Hyderabad India Department of AI & DS K. Ramakrishnan College of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369090

In the rapidly evolving landscape of human-computer interaction (HCI), the demand for personalized and adaptive user experiences has grown exponentially. To meet this demand, Research propose a groundbreaking approach leveraging the fusion of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) architectures. This hybrid LSTM-CNN model is designed to enhance adaptability, responsiveness, and user engagement across various interactive platforms. Traditional HCI models often struggle to effectively capture the dynamic nature of user behavior and preferences. by integrating LSTM and CNN, This model achieves a synergistic blend of temporal and spatial feature extraction capabilities. The LSTM component excels in capturing sequential dependencies and long-term patterns, enabling the system to learn from past interactions and anticipate future user actions. Meanwhile, the CNN component efficiently processes spatial information, extracting meaningful features from multimedia inputs such as images, videos, and text. One of the key strengths of proposed model is its adaptability to diverse user contexts and preferences. Through continuous learning and adaptation, it dynamically adjusts its interface, content, and interaction patterns to match the evolving needs and preferences of individual users. Moreover, the hybrid architecture enables real-time processing of multimodal inputs, facilitating seamless interaction across a wide range of devices and platforms. In experimental evaluations, Hybrid LSTM-CNN model demonstrated superior performance compared to baseline methods in terms of user satisfaction, engagement, and task completion rates. Furthermore, it exhibited robustness and scalability, making it suitable for deployment in real-world applications across domains such as e-commerce, entertainment, education, and healthcare. In summary, The proposed approach represents a significant advancement in HCI research, paving the way for next-generation interactiv

关键词： Human computer interaction Adaptation models Scalability Streaming media Feature extraction User experience Robustness Convolutional neural networks Long short term memory Videos

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Research on varietal classification and germination evaluation system for rice seed using hand-held devices

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Acta Agriculturae Scandinavica, Section b — Soil & Plant Science 2021年第9期71卷 939-955页

作者： S. Durai C. Mahesh a Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Chennai India b Department of Information Technology Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Chennai India

AbSTRACTFormulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to *** Seed varietal classification and germination evaluation system is developed to identify the variety and evaluate the germination of rice seeds using Digital Image processing system. For economic and ease of usage, we have used mobile phones to take digital images. The objective of our research is, it will be easily used by farmers. Our research is done on four major rice varieties, which commonly cultivated by Tamilnadu farmers, namely (1) Andhra Ponni (2) Atchaya Ponni (3) KO50 and (4) IR 20 was collected from Tamilnadu Agricultural University Tiruchirappalli, Tamilnadu, India. We have extracted 24 features: 3 colour features, 13 morphological features and 8 textural features. Created data set tested with all possible classification algorithms, out of which Ensemble classification algorithm gives 91.6% accuracy for Variety Identification and SVM gives 63% of accuracy for germination prediction. According to the germination percentage, a support vector machine (SVM) was utilised to categorise the seeds specimens into 3 groups: healthy, old, and deceased. The categorisation prediction accuracy has always been significant. We have created the data set for successful identification of varieties and germination prediction for the above-mentioned varieties; it is publicly available for usage.

关键词： Rice seed varietal classification seed quality seed germination germination prediction

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AI in Social Media Marketing: Using Machine Learning to Analyze Trends and Predict Consumer Sentiment

AI in Social Media Marketing: Using Machine Learning to Anal...

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Pervasive Computational technologies (ICPCT), International Conference on

作者： bhavana Jamalpur Somanchi Hari Krishna Romica bhat Sneh binod Prasad Sourabh bhattacharya Dhiraj Kapila School of CS & AI SR University Warangal Telangana India Department of Business Management Vignana Bharathi Institute of Technology Telangana State India Journalism and Mass Communication Lovely Professional University B. Tech (CSE) SRM Institute of Science and Technology Chennai India Department of Management & Social Science Haldia Institute of Technology Haldia West Bengal Department of Computer Science & Engineering Lovely Professional University Phagwara Punjab India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Social media marketing (SMM) is prominent in the society at present. Individuals use social networking sites to buy different items. The rapid rise of social networking sites has rendered them essential tools for evaluating consumer sentiments and forecasting a wide range of societal and economic patterns. This study gathered statistics from a variety of social media channels. This study examined data to anticipate user activity on social networking sites. This study took into account user statistics from Twitter, LinkedIn, YouTube, Facebook, Instagram, and Pinterest, among other platforms. because social media networks provide a wide range of statistics at elevated speeds and volumes, predicted big data techniques were applied in this study. This investigation studied user conduct on social networking sites using specific metrics and variables. This study examined user perception and attitudes towards social networking platforms. This study preprocessed data to remove outliers, disturbances, errors, and duplicate records, resulting in high-quality results. As a result, in this paper, an integrated system combining sentiment evaluation and machine learning (ML) methods is constructed. This study utilized computational models and ML to anticipate user conduct on social networking platforms. The framework predicts user conduct on social networking platforms. Eighty percent of data has been employed for training, with twenty percent for testing. After testing many conventional and ensemble ML classifiers, decision trees outscored every other method.

关键词： Training Measurement Sentiment analysis Video on demand Social networking (online) Machine learning Market research Web sites Multimedia communication Testing

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