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Emerging technology (INCET), International Conference for

作者： Giriprasad. S T. Karpagam V. Nalini Nilamadhab Mishra N. Mohankumar N. Malathi Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science R.M.K College of Engineering and Technology Chennai Tamil Nadu India Department of Mathematics Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering College Chennai Tamil Nadu India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh India Department of Electronics and Communication Engineering B.V. Raju Institute of Technology Telangana India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

The cutting-edge telemedicine program presents a game-changing alternative for patients seeking ENT (ear, nose, and throat) consultations from a distant location. Utilizing the capabilities of Raspberry Pi devices, powerful artificial intelligence, and cloud computing, the system allows otoscopic tests to be performed without interruption while securely transmitting data to the cloud. On a web-based platform, interactive patient-professional consultations are made possible via real-time communication, which is made possible by WebRTC. Cloud services are very important because of their ability to provide scalable storage as well as AI-driven diagnostics for accurate evaluations. The system complies with the requirements for healthcare compliance, emphasizes security and privacy, and features encryption from beginning to finish. This integrated approach promises scalability, reliability, and advanced diagnostic capabilities, and it reshapes the landscape of remote ENT care with accessible and technologically advanced healthcare solutions. With a user-friendly interface for patients and a cloud-hosted application for healthcare professionals, this integrated approach provides patients with advanced healthcare solutions.

关键词： Cloud computing Privacy Telemedicine Scalability Nose Medical services Ear

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5G Tiny-ML AI-based IoT eNose system for Hazardous Odour Detection and Classification

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IEEE Sensors Journal 2025年

作者： Fayos-Jordan, Rafael Alselek, Mohammad Khadmaoui-bichouna, Mohamed Segura-Garcia, Jaume Alcaraz-Calero, Jose M. University of the West of Scotland School of Computing Engineering and Physics Scotland PA1 2BE United Kingdom Tech Lead London United Kingdom ETSE Universitat de Valencia Valencia46100 Spain Aston University School of Computer Science and Digital Technologies BirminghamB4 7ET United Kingdom

More than 2 million people have died in the world in 2019 exposed to hazard substances. In this context, it is of paramount importance to deliver effective systems to help minimizing such number of dead. The usage of advanced technologies, such as artificial intelligence (AI) and the Internet of Things (IoT), plays a critical role in the detection of hazardous substances within Industry 4.0. The combination of these technologies enhance the efficiency and accuracy of monitoring harmful materials, improving safety standards and operational processes in industrial environments. AI can be used to analyse vast amounts of data for identifying patterns and predicting potential hazards, while IoT connects various devices and sensors to ensure real-time tracking and prompt responses to risks. This technological synergy is essential for modern industries aiming to create safer and more automated systems. In this work, we propose a 5G AI-IoT e-Nose system for the real-time detection and classification of 5 hazardous odours. The proposed AI model is very lightweight and it is affordable for our IoT micro controller unit. The system has been validated in laboratory conditions, but has the advantage and potential impact to be effective in any scenario. © 2001-2012 IEEE.

关键词： Industrial poisons

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Design and Optimization of Heterogeneous Coded Distributed Computing with Nonuniform File Popularity

arXiv

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arXiv 2023年

作者： Deng, Yong Dong, Min Department of Software Engineering Lakehead University Thunder BayONP7B 5E1 Canada Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto TorontoONM5S 3G4 Canada Department of Electrical Computer and Software Engineering Ontario Tech University OshawaONL1G 0C5 Canada

This paper studies MapReduce-based heterogeneous coded distributed computing (CDC) where, besides different computing capabilities at workers, input files to be accessed by computing jobs have nonuniform popularity. We propose a file placement strategy that can handle an arbitrary number of input files. Furthermore, we design a nested coded shuffling strategy that can efficiently manage the nonuniformity of file popularity to maximize the coded multicasting opportunity. We then formulate the joint optimization of the proposed file placement and nested shuffling design variables to optimize the proposed CDC scheme. To reduce the high computational complexity in solving the resulting mixed-integer linear programming (MILP) problem, we propose a simple two-file-group-based file placement approach to obtain an approximate solution. Numerical results show that the optimized CDC scheme outperforms other alternatives. Also, the proposed two-file-group-based approach achieves nearly the same performance as the conventional branch-and-cut method in solving the MILP problem but with substantially lower computational complexity that is scalable over the number of files and workers. For computing jobs with aggregate target functions that commonly appear in machine learning applications, we propose a heterogeneous compressed CDC (C-CDC) scheme to further improve the shuffling efficiency. The C-CDC scheme uses a local data aggregation technique to compress the data to be shuffled for the shuffling load reduction. We again optimize the proposed C-CDC scheme and explore the two-file-group-based low-complexity approach for an approximate solution. Numerical results show the proposed C-CDC scheme provides a considerable shuffling load reduction over the CDC scheme, and also, the two-file-group-based file placement approach maintains good performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Mixed-integer linear programming

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Smart Water Management in Agriculture: Enhancing Crop Productivity with Restricted boltzmann Machine

Smart Water Management in Agriculture: Enhancing Crop Produc...

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Software, Systems and Information technology (SSITCON), International Conference on

作者： K. b. Gore N. Chidambararaj L. Kartheesan Manasi Vyankatesh Ghamande Ghanasham C. Sarode Gourav Kalra Department of Mechanical Engineering SNJB’s LS K. B. Jain College of Engineering Nashik India Department of Electrical and Electronics Engineering St. Joseph’s College of Engineering Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Engineering Science and Humanities Vishwakarma Institute of Technology Pune India Department of Civil Engineering Dr. D.Y. Patil Institute of Technology Pune India Department of Mechanical Engineering Maharishi Markandeshwar (Deemed to be University) Ambala India

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

Effective water management in agriculture is crucial for sustainable food production in the face of water scarcity and climate change. The focus of this literature is on new methods and technology that aim to maximize water usage in agricultural activities and improve irrigation efficiency. Efficient water management practices are critical for increasing agricultural yields while lowering water wastage. by implementing water-saving measures and utilizing efficient irrigation systems, farmers can enhance yields while reducing pressure on scarce freshwater resources. Preprocessing, feature selection, and model training are all part of the suggested method. During the preprocessing phase, an Adaptive Inverse Normalization Layer is employed. The RFE methodology relies on a ranking algorithm that sorts the dataset from best to worst according to the attributes that are considered most important. Throughout training, the proposed approach utilized the RbM model without interruption. With a 93.21% average accuracy, this cutting-edge approach surpasses both DbN and KNN.

关键词： Training Productivity Adaptation models Irrigation Accuracy Nearest neighbor methods Software systems Socioeconomics Water resources Crop yield

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Study on Multi-Tone Piano Transcription Algorithm based on bPNN

Study on Multi-Tone Piano Transcription Algorithm Based on B...

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International Conference on Smart technologies For Smart Nation (SmarttechCon)

作者： M. Sahaya Sheela Vijayakumari G Ahmed H. R. Abbas I. Parvin begum Anil Pratap Singh R J Anandhi Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (Deemed to be University) Chennai Tamil Nadu India New Prince Shri Bhavani College Of Engineering And Technology Tamilnadu India College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Applications B.S.Abdur Rahman Crescent Institute of Science and Technology Vandalur Tamil Nadu Department of Computer Science & Engineering IES University Bhopal MP India Department of Information Science & Engineering New Horizon College of Engineering Bengaluru

bPNN ensemble can significantly improve the generalization ability of knowledge system by training multiple bPNNs and synthesizing their conclusions. It not only helps scientists to study machinery knowledge and neural computing in depth, but also helps ordinary engineers and technicians to use bPNN technology to solve real-world problems. Ensemble knowledge has become one of the hot spots in the territory of machinery knowledge in recent years, and selective integration method has become an important direction of ensemble knowledge because of its advantages in adaptability, generalization and combination. In this paper, the transcription of multi-tone piano is studied based on bPNN. The corresponding study methods are used in the research. Through the establishment of data graph and algorithm formula, the corresponding study is carried out. From the research, it can be found that the piano transcription efficiency based on bPNN is very high, up to about 90.43%. In the future, people may pay more Focus to the piano study of bPNN.

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An ML-based approach to transform stimuli into characteristic oscillatory activity of the beta band

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AIP Conference Proceedings 2023年第1期2946卷

作者： Sunny bhatt Aditi Jindal Department of Computer Engineering (B.E. Computer Engineering) Gujarat Technological University Ahmedabad-382424 Gujarat India Department of Computer Science and Engineering (B. Tech. Computer Science) Gurugram University Gurugram-122003 Haryana India

brain oscillations are essential to math and spatial skills. Cognition, attention, memory processing and language are influenced by these oscillations. Neurological explanations proposed to account for the fluctuations in brain activity help us understand how important the relation of rhythmicity is within a neural network. Desynchronization refers to the sharp and irregular changes of the oscillations in brain waves called rapid-eye movement sleep spindle activity during sleep. It is the disruption of interactions between resting oscillations of brain regions typically producing rhythmic neuronal firing that occur simultaneously in different frequency bands like alpha or beta. The beta band is associated with attention and self-focused activities like perceiving, thinking, judging, and problem solving. In this paper, the authors develop a neural model of nonlinear oscillators that transforms stimuli into characteristic oscillatory activity of the beta band.

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A Novel Machine Learning technique for Diabetic Prediction in IoT-based Healthcare Monitoring System

A Novel Machine Learning Technique for Diabetic Prediction i...

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： S Aravinth Vijay Jesuraj R. Ganesh Vinda Manjramkar M. Sridharan Vivek Ravishankar Dubey M. R. Arun Department of Pharmacy Practice Nirmala College of Health Science Poolani-Puzhpagiri Rd Meloor Kerala India Department of Computer Science and Engineering (AI &ML) Institute of Aeronautical Engineering Hyderabad Telangana India B.N.Bandodkar College of Science(Autonomous) Thane west Maharashtra India Department of Science and Humanities NPR College of Engineering and Technology Natham Dindigul Dist Tamil Nadu India Department of Computer Science and Engineering Bharat Institute of Engg & Tech (BIET) Hyderabad Telangana India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai Tamil Nadu India

The successful development of a diagnosis system to identify diabetes in the Internet of Things (IoT) e-healthcare scenario has gained considerable attention to implement accurate diabetes diagnosis. IoT is playing an increasingly important role in healthcare environment by providing a structure for evaluating medical information to detect diseases via data mining techniques. The existing diagnostic methods have some challenges, such as lengthy calculation times and inaccurate predictions. To evade the limitations, this article suggested an IOT-based diagnosis system that uses Machine Learning (ML) techniques. Through the dataset from UCI Repository with medical sensors, a novel systematic technique is utilized to treat diabetic disease, and relevant medical data is produced to precisely predict those who would be seriously impacted by the condition. For predicting the illness and its severity, a brand-new classification technique called Tuna Swarm optimization-Aided Neural Classifier (TSO-NN) is suggested (T. The experimentation is conducted in MATLAb and the performance is evaluated using accuracy, precision, and F1-score. besides, the efficiency is tested and confirmed by comparing over SOTA methods.

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DeepMind Trainer: Harnessing Cognitive Neural Networks on Raspberry Pi for Advanced Cognitive Enhancement Games

DeepMind Trainer: Harnessing Cognitive Neural Networks on Ra...

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Advancement in Computation & computer technologies (InCACCT), International Conference on

作者： P. Abirami D. Antony Joseph Rajan T. Vivekanandan V. Saillaja N. Mohankumar M. Rajmohan Department of Artificial Intelligence and Data Science Velammal Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Mathematics Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering College Chennai Tamil Nadu India Faculty of Management Studies SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Electronics and Communication Engineering B.V. Raju Institute of Technology Telangana India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798350371314

ISBN: (纸本)9798350371321

The presentation summarizes a groundbreaking cognitive training program designed specifically for those who suffer from cognitive impairments or neurodevelopmental abnormalities. The work creates a dynamic platform for immersive and adaptable cognitive training games by combining cutting-edge Convolutional Neural Networks (CNNs) with Raspberry Pi technology. Memory, focus, and ability to think creatively are all qualities directly targeted by these games. The CNN model, trained on a broad dataset, interprets real-time user inputs, delivering quick feedback and altering game complexity depending on an individual’s performance. The incorporation of Raspberry Pi guarantees a solution that is both affordable and easily accessible. The capacity of the system to redefine cognitive rehabilitation by providing a solution that is individualizable, interactive, and broadly deployable is the potential source of the system’s influence. The goal of the work is to make a substantial contribution to the field, which will eventually be to the advantage of users, caregivers, and healthcare professionals and ultimately improve the cognitive well-being of those coping with cognitive issues.

关键词： Training Psychology Medical treatment Games Software Real-time systems Complexity theory

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Effectiveness of Machine Learning Algorithms in Forecasting AQI

Effectiveness of Machine Learning Algorithms in Forecasting ...

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technological Advancements and Innovations (ICTAI), International Conference on

作者： Sudhanshu Pal Debanshi Pramanik Ena Jain B. Tech Computer Science & Engineering DIT University Dehradun India School of Computing DIT University Dehradun India

ISBN: (纸本)9781665420884

Air pollution has become major cause of global concern in recent times. Global warming, climate changes and many major hazardous diseases are all reasons of air pollution. Rapid industrialization, urbanization, deforestation have all accelerated the air pollution level. Air is getting polluted due to release of poisonous gases by industrialization, vehicular emissions, unplanned construction activities. Everyone is at an alarming risk whether they are children or elder. Among metro cities of the world Delhi is known as one polluted metro cities in the world and we have used AQI data of Delhi city as our testbed. This work proposes a model in which Machine Learning algorithms are applied to measure the future AQI data from the current and past available AQI data. We have used various algorithms like Linear Regression, Lasso Regression, Random Forest and Decision Tree to predict the future AQI with past AQI and found that Decision Tree algorithm gives best prediction with limited number of data entries and Random Forest gives best result with larger dataset.

关键词： technological innovation Machine learning algorithms Urban areas Prediction algorithms Air pollution Pollution measurement Planning

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Advancing HRM with AI Powered Recruitment Performance Management and Employee Engagement Using One Class SVMs

Advancing HRM with AI Powered Recruitment Performance Manage...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Rafiya banu Ruchi Tripathi Saumya Sharma Loganathan Chenniappan K. Chandrasekaran Umanesan R Department of Commerce B. S. Abdur Rahman Crescent Institute of Science and Technology Chennai India Faculty of Management Studies SRM-IST Ghaziabad India Department of Management & Commerce The Northcap University Gurugram India Business Administration IBRI Sultanate of Oman Department of Mechanical Engineering K.S.R College of Engineering Tiruchengode India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

AI is making labour-intensive HRM operations like hiring, performance review, and employee engagement more efficient and effective. The incorporation of AI technology in HRM could change several sectors. Through efficient applicant sourcing, screening, and selection, AI-driven solutions simplify hiring processes and ensure fair and accurate picks. Performance management using AI-powered solutions provides data-driven feedback and customized development plans to align individual progress with organizational goals. Predictive analytics improve employee engagement by personalizing experiences and meeting needs. The method focuses on preprocessing and model training. Data are cleansed, ordinal and categorical variables encoded, scaling characteristics and engineering features applied, and dimensional homogeneity assured before processing. For model training, the simple and robust one-class SVM technique is used. One-class SVM model outperformed others with 95.28%. This method worked best compared to GAN and GRU models. AI could improve HRM by improving decision-making and offering data-driven solutions. The findings show that the proposed model outperforms current options, providing the framework for HRM AI applications.

关键词： Support vector machines Training Productivity Accuracy Uncertainty Reviews Artificial intelligence Integrated circuit modeling Predictive analytics Recruitment

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