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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Kovarasan, Rajesh Kambattan Shibu, N.V. Sairam, A. Manivannan, K.K. Ishwarya, M.V. GaneshBabu, T.R. Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Mechatronics Engineering KCG College of Technology Tamil Nadu Chennai India Artificial Intelligence and Data science Department Agni College of Technology Tamil Nadu Chennai India Tamil Nadu Namakkal India

ISBN: (纸本)9798350374933

Quick and precise source identification is crucial for developing successful remediation solutions to address soil contamination in industrial locations, which presents serious dangers to both the environment and human health. To locate the origins of soil contamination, this research presents a new method that combines neural network models with data collected from the Internet of Things (IoT). To monitor soil type, pollution levels, and weather conditions in real-time, set up IoT devices. After that, advanced neural network algorithms are used to process and analyze the data to find connections and patterns that might indicate the origins of the pollution. Our findings show that this method is useful for identifying where soil pollution inside industrial zones is coming from. Achieve high accuracy in source identification by integrating IoT technology and machine learning approaches. This allows us to address environmental harm and protect public health via focused remediation activities. Policymakers, environmental scientists, and stakeholders engaged in sustainable development and pollution control may benefit greatly which highlight the potential of IoT data and neural networks in challenges difficult environmental concerns. © 2024 IEEE.

关键词： Environmental monitoring

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A Systematic Method of Stroke Prediction Model based on Big Data and Machine Learning 2

A Systematic Method of Stroke Prediction Model based on Big ...

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2nd International Conference on Smart Technologies, Communication and Robotics, STCR 2022

作者： Viswapriya, S.E. Rajeswari, D. SRM Institute of Science and Technology School of Computing College of Engineering and Technology Department of Data Science and Business System Tamilnadu India

ISBN: (纸本)9781665460477

There is an enormous increase in number of diseases worldwide. The non-communicable diseases such as cardio vascular disease will leads to death. The second major reason of death in people worldwide occurs due to stroke. It affects any portion of brain due to interruption or reduction of Blood supply. The brain damage can be reduced if required actions taken earlier. So there is necessary requirement to build stroke predictive models. The combined techniques of Machine Learning (ML) and Deep Learning (DL) techniques play the vital role in Disease Prediction. There are many researches has been done for stroke prediction using various ML Algorithms. In order to improve accuracy, the proposed model will work on the hybrid ANNRF (Artificial Neural Network-Random Forest). The proposed method can be reached 94% in classification accuracy. © 2022 IEEE.

关键词： Forecasting

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A Deep Learning-based Innovative Approach for Enhancing the Precision of Tool Wear Prediction 8

A Deep Learning-based Innovative Approach for Enhancing the ...

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Vinod, D. Syed, Karimulla Sampath Kumar, S. Babu, B. Sankara Pandey, Shatrudhan Loganathan, Raja Velur Srm Institute of Science and Technology School of Computing College of Engineering and Technology Department of Computing Technologies SRM Nagar Kattankulathur Chennai India Koneru Lakshmaiah Education Foundation 522501 India Sri Eshwar College of Engineering Computer Science and Engineering Coimbatore India Department of Cse Telangana Bachupally India Birla Institute of Technology Department of Production and Industrial Engineering Mesra Ranchi835215 India Loyola Institute of Technology Department of Mechanical Engineering Chennai India

ISBN: (纸本)9798350396638

Throughout the cutting operation, among the most crucial steps is that has a direct impact on machining accuracy and product quality is tool wear. If you can accurately forecast how quickly tools will wear out, you can make the necessary adjustments early on, minimizing downtime and maximizing product quality. The evaluation of cutting tool wears during the production stage is crucial. The main objective of this study is to track tool degradation over time to guarantee regular tool changes and prevent workpiece waste and potential machine damage from excessive tool wear or unexpected tool breakage. For this reason, it is necessary to develop a system that is both intelligent and capable of providing accurate solutions to these issues. The criteria of intelligent production are great, though, and traditional methods simply cannot match them. Because of this, a deep learning-based innovative approach is presented to advance the precision of tool wear prediction. Parallel convolutional neural networks were constructed and used to achieve multi-scale feature fusion. To improve the model's efficiency, the channel attention mechanism was integrated with the residual connection to take into account the relative importance of each feature map. To prove that the created approach is superior, several experiments were carried out to anticipate tool wear, and the findings are more reliable and accurate than those obtained using existing methods. To guarantee the excellence of the engine cylinder and the steadiness of the machining procedure, a tool wear monitoring system was created. Using the PHM 2010 milling cutter wear dataset, tests were performed to validate the model effect. Based on the experimental data, this model has an average RMSE of 2.67 and an average MAE of 2.5. The trial outcomes proved the effectiveness of the suggested approach in evaluating the tool's wear. © 2023 IEEE.

关键词： Cutting tools

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Intelligent Edge-Based System for Driver Drowsiness Monitoring in Mobile Crowd-sourcing Environments 2

Intelligent Edge-Based System for Driver Drowsiness Monitori...

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2nd International Conference on Recent Trends in Microelectronics, Automation, computing, and Communications Systems, ICMACC 2024

作者： Neels Ponkumar, D. David Ramesh, S. Ahamed, A. Niyas Baskar, S. Ashok, Chitrada Charan, M. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India School of Computing College of Engineering and Technology SRMIST Department of Computing Technologies Kattankulathur Campus Tamil Nadu Chengalpattu India Thamirabharani Engineering College Department of Electronics and Communication Engineering Tamil Nadu Tirunelveli India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Civil Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350366570

Using CNN and GAN improves road safety, says study. Modern machine learning techniques for mobile crowdsourcing may identify driver fatigue early. Our convolutional neural network (CNN) model detects drivers' fatigue using facial expressions, head movements, and other visual clues. Mobile devices and high-resolution cameras make the system straightforward to install in automobiles. GAN (Generative Advisal Network) handles unequal and limited training data. Creating realistic, synthetic weary driver photos improves the dataset and helps CNN learn from different driving scenarios and face features. Our edge-based computing distinguishes it. In mobile crowdsourcing with unpredictable internet connections, processing data locally on users' smartphones minimizes latency and bandwidth. The edge-based solution processes sensitive data locally on the device rather than sending it to servers, increasing user privacy. Extensive testing showed our system diagnosed driver weariness better than others. GAN-generated data increased model performance in under-represented training datasets. The study shows how to merge CNN and GAN in edge computing with 98.4% accuracy and driver fatigue monitoring. We found important implications for mobile crowdsourcing and intelligent transportation system development. © 2024 IEEE.

关键词： Convolutional neural networks

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LSTM Based Monophonic Piano Melody Synthesis 7

LSTM Based Monophonic Piano Melody Synthesis

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7th IEEE Conference on Information and Communication technology, CICT 2023

作者： Annamalai, R. Sudharson, S. Pratap, Turimerla Kaushik, H. Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Computer Science and Engineering Vellore Institute of Technology Chennai India Electronics and Communication Engineering Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

ISBN: (纸本)9798350305173

Music composition has benefited from advancements in artificial intelligence (AI). This study explores AI-driven melody generation using machine learning techniques, specifically the Long Short-Term Memory (LSTM) network. The research follows three stages: Data pre-processing, model selection and training, and model evaluation. During pre-processing, songs with non-acceptable durations are filtered out, and the songs are transposed for compatibility and analysis. Symbolic representations, such as Musical Instrument Digital Interface (MIDI) notation, help identify patterns. Melodies are generated by feeding a seed sequence into the LSTM network, selecting the highest probability note iteratively. Decoding the representations produces a list of note and chord objects, forming coherent sheet music. Results demonstrate the potential of LSTM networks in producing diverse and high-quality music. Model evaluation includes loss function analysis and comparing the generated melodies with the original dataset. Future research should enhance model performance and address limitations. This study contributes to the understanding of AI in music composition, highlighting challenges, opportunities, and future potential. © 2023 IEEE.

关键词： Music

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Multi-Agent RL for Sleep Mode and Antenna Configuration with User Offloading Under Dynamic Traffic in Massive MIMO Networks

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IEEE Transactions on Vehicular technology 2025年第6期74卷 9734-9749页

作者： Zhang, Shuai Cai, Tianzhang Demir, Ozlem Tugfe Cavdar, Cicek KTH Royal Institute of Technology Stockholm164 40 Sweden Nanjing University of Aeronautics and Astronautics College of Astronautics Nanjing211106 China University of Cambridge Institute of Computing for Climate Science CambridgeCB3 0WA United Kingdom TOBB University of Economics and Technology Ankara Turkey KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm164 40 Sweden

In this paper, we focus on minimizing the total energy consumption of multi-cell massive multiple-input multiple-output (MIMO) networks while simultaneously guaranteeing user quality of service (QoS). This is achieved by optimizing the multi-level advanced sleep modes (ASM), antenna switching, and user association of the base stations (BSs). Due to the interdependence of user association and inter-cell interference in the network, collaborative efforts among individual BSs become imperative. The problem is modeled as a decentralized partially observable Markov decision process (DEC-POMDP) and a multi-agent proximal policy optimization (MAPPO) algorithm is proposed to obtain a collaborative BS control policy. Simulation results demonstrate that the obtained policy can significantly improve network energy efficiency, adaptively switch the BSs into different depths of sleep, reduce inter-cell interference, and maintain good QoS compared to the two benchmark algorithms. The results also validate that enabling user offloading among BSs can improve both user QoS and system performance. The superiority of MAPPO is further affirmed by comparing it with the single-agent deep Q network (DQN) algorithm. © 1967-2012 IEEE.

关键词： Energy utilization

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Predictive Modeling of Cardiovascular Disease Using ML Algorithms

Predictive Modeling of Cardiovascular Disease Using ML Algor...

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2023 International Conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Vijayalakshmi, M. Cherukuvada, Srikanth Chinnappa, A. Kavitha, G. Soujanya, A. Nareshkumar, R. Care College of Engineering Artificial Intelligence and Data Science Trichy India Srm Institute of Science and Technology School of Computing Department of Networking and Communications Chennai Kattankulathur India Srm Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai Kattankulathur India Department of Mathematics Namakkal India Ibrahimpatnam India

ISBN: (纸本)9798350348910

One of the most significant contributors to morbidity in the global population is the ability to forecast a heart attack. Clinical data analysis is considered one of the most significant aspects for making a forecast, and that aspect is cardiovascular illness, a highly important disease. This can be a very useful tool. Using ML to its full potential, this investigation aims to achieve improved accuracy in diagnosing cardiac disease. In this research, we provide a machine knowledge heart attack diagnosis (MLHAP) technique that involves the study of different risk indicators, and prediction for heart attacks is carried out via ML methodologies, including Support Vector Machines, Logistic Regression, Naive Bayes, Knn, and XGBoost. In this method, the ML-HAP method predicts heart attacks. This approach aims to reduce the mortality rate from heart attacks. The forecast has been optimized as much as possible based on many parameters, which has been the primary focus. KNN, with its parameters optimized, made the most accurate prediction of any of the algorithms. The accuracy obtained using KNN Optimization is 85.24, while the accuracy obtained using Logistic Regression is 78.68. The prediction made by machine learning models for recognizing the signs of a heart attack is extremely effective, particularly when using boosting algorithms. © 2023 IEEE.

关键词： Support vector machines

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Design and Development of an IoT-Based Plant Growth Monitoring System Using Esp-32 Cam

Design and Development of an IoT-Based Plant Growth Monitori...

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2024 International Conference on Control and Automation, Electronics, Robotics, Internet of Things, and Artificial Intelligence, CERIA 2024

作者： Basuel, Rex Fajardo, Arnel Perez, Novie Dave Graduate School Technological Institute of the Philippines - Quezon City Quezon City Philippines College of Computing Science Information and Communication Technology Isabela State University Cauayan City Philippines College of Engineering and Information Technology Caraga State University Cabadbaran City Cabadbaran City Philippines

ISBN: (纸本)9798331511166

This paper presents a study that combines the advances of technology specifically in the field of Internet-of-Things (IoT) and Image Processing. Plant growth monitoring is an essential task especially when growing a high value crop like okra. In this study, an IoT-enabled device like the ESP32-Cam was used to capture and record the growth development of a certain plant. ESP32-Cam is a microcontroller integrated with a camera and has the capability of doing IoT. The system was integrated with other modules such as Nitrogen-Phosphorus-Potassium (NPK), soil moisture sensor and soil temperature sensor. The NPK sensor was used to measure the amount of nutrient in the soil, while the soil moisture and temperature sensor detects the moisture content and temperature of the soil respectively. It is also integrated with a DC pump that will be activated to water the plants once the soil moisture drops to its threshold value. Upon evaluation of the developed prototype, it is evident that it functions based on its intended design. © 2024 IEEE.

关键词： Soil moisture

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Residual VGGNet for Unveiling Optimal Process Parameters of Oil Rig Welding Through Mathematical Modeling and Optimization 15

Residual VGGNet for Unveiling Optimal Process Parameters of ...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Dhas, John T. Mesia Saikia, Utpal Arun, K.K. Josphine, C.Vimala Kingslin, M.Theodore Padmavathi, N. School of Computing Department of Computer Science and Engineering Tamilnadu Chennai India Silapathar College Department of Mathematics Dhemaji Assam Silapathar India Kumaraguru College of Technology Department of Mechanical Engineering Tamilnadu Coimbatore India Artificial Intelligence and Data Science R.M.K Engineering College Kavaraipettai India R.M.K College of Engineering & Technology Department of Electronics & Communication Engineering Tamilnadu Puduvoyal India R.M.D. Engineering College Department of Electronics and Instrumentation Engineering Tamilnadu India

ISBN: (纸本)9798350370249

The optimization of welding techniques for oil rig components is crucial to upholding the structural integrity and safety of offshore drilling activities. In this study, we utilize a residual VGGNet architecture, which is a modified version of the well-known VGGNet, for the purpose of extracting crucial information from images of welding processes. The methodology employed in this study involves the collection of a complete dataset comprising welding process images obtained from diverse oil rig welding activities. The provided images are accompanied by annotations that provide the associated welding parameters, including current, voltage, and welding speed. The residual VGGNet is subsequently trained to acquire knowledge about the complex associations between picture features and welding parameters. In order to develop a reliable mathematical model, regression techniques are utilized to precisely establish the relationship between the extracted features and welding parameters. The performance of the model is evaluated using cross-validation and compared to traditional regression models to emphasize its greater ability to capture intricate patterns within the data. © 2024 IEEE.

关键词： mathematical modeling oil rig welding optimization Residual VGGNet welding parameters

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AsT: An Asymmetric-Sensitive Transformer for Osteonecrosis of the Femoral Head Detection 37

AsT: An Asymmetric-Sensitive Transformer for Osteonecrosis o...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Chen, Haoyang Liu, Shuai Lu, Feng Li, Wei Sheng, Bin Li, Mi Jin, Hai Zomaya, Albert Y. National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Centre for Distributed and High Performance Computing School of Computer Science The University of Sydney Australia Department of Computer Science and Engineering Shanghai Jiao Tong University China Tongji Hospital Tongji Medical College Huazhong University of Science and Technology China

ISBN: (纸本)9781577358800

Early diagnosis of osteonecrosis of the femoral head (ONFH) can inhibit the progression and improve femoral head preservation. The radiograph difference between early ONFH and healthy ones is not apparent to the naked eye. It is also hard to produce a large dataset to train the classification model. In this paper, we propose Asymmetric-Sensitive Transformer (AsT) to capture the uneven development of the bilateral femoral head to enable robust ONFH detection. Our ONFH detection is realized using the self-attention mechanism to femoral head regions while conferring sensitivity to the uneven development by the attention-shared transformer. The real-world experiment studies show that AsT achieves the best performance of AUC 0.9313 in the early diagnosis of ONFH and can find out misdiagnosis cases firmly. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Large dataset

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