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3rd International Conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Jayakarthik, R. Srinivasan, Aravindan Goswami, Sohan Shivaranjini Mahaveerakannan, R. Vels Institute of Science Technology and Advanced Studies School of Computing Sciences Dept of Comp Science India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering AP Vaddeswaram India Technique Polytechnic Institute West Bengal 712102 India Viswam Degree College Department of Computer Science Madanapalle India Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

Elderly individuals worldwide have a significant risk of injury or death due to falls. An aged person's mobility, independence, and quality of life can be significantly impacted if he or she falls and does not receive timely assistance. This paper proposes the creation of a low-power network, smart devices, and cloud computing-based fall detection system for senior individuals in indoor spaces as part of this study. 10 volunteers volunteered to have their fall and non-fall movement data collected using a wireless telemetry surface electromyography capture system. Generative Adversarial Network (GAN) models use the effective signal segments as input. Notifications are automatically sent to caregivers of the elderly if a fall is detected, and an alert is triggered. As the final step, the system offers cloud-based services. There is a service that allows healthcare professionals to obtain the fall data for further examination from a medical standpoint. In experiments, fall detection has been found to be highly accurate, precise, and recallable. © 2022 IEEE.

关键词： Big data

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Leveraging Deep Learning Models for Machine-Generated Text Detection Using Transformer-Based Models

Leveraging Deep Learning Models for Machine-Generated Text D...

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Innovative computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： P. Suthanthiradevi G Revathy K. Rejini Muthu Lakshmi.V Department of Data Science and Business Systems School of Computing SRM institute of Science and Technology Kattankulathur Chennai Department of Computer science and engineering Vels Institute of science Technology and Advanced studies Chennai Department of Computer Science Engineering Amrita college of Engineering and Technology Nagercoil Department of Computer Science and Engineering St. Joseph's College of Engineering OMR Chennai

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

GPT is a large language model (LLM) derived from natural language processing that can generate a human-like text using machine learning. However, these models raise questions about authenticity and reliability of material, particularly in fields such as journalism, social media, and academia, despite their usefulness for automating text-based tasks. Detecting machine-generated text is thus an important difficulty in ensuring content integrity. This study investigates the use of huge language models as a technique for recognizing machine-generated material. The author proposes a comprehensive detection model by evaluating the language patterns, syntactic structures, and stylistic traits that separate AI-generated literature from human writing. In addition, this research investigate the possibilities of fine-tuning models designed expressly for text identification tasks and evaluate their performance using LLM - Detect AI Generated Text datasets. In digital ecosystems, LLMs are effective at detecting AI-generated text, providing a novel approach for content moderation, academic integrity checks, and synthetic media detection. An increasingly AI-powered future will require a model that can discriminate between human and machine-generated writing in real-time. According to experimental findings, the CNN architecture's design combined with the use of DistilBERT embeddings allows for the effective and efficient classification of AI generated text data, achieving an exceptional 98% accuracy rate.

关键词： Deep learning Deepfakes Accuracy Social networking (online) Biological system modeling Large language models Text detection Writing Transformers Natural language processing

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Identification of Diabetic Retinopathy by Transfer Learning Based Retinal Images 9

Identification of Diabetic Retinopathy by Transfer Learning ...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Sagili, Sreenivas Reddy Goswami, Chandrashekhar Bharathi, V.C. Ananthi, S. Rani, K. Sathya, R. Rochester Institute Of Technology Department of computer science Lomb Memorial Drive RochesterNY14623 United States Mit School of Computing Mit Art Design and Technology University Department of Computer Science & Engineering Maharashtra Pune India School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India Advanced Research Centre for Future Networks and Digital Twin Sri Eshwar College of Engineering Tamil Nadu Coimbatore India M. Kumarasamy College of Engineering Department of Information Technology Tamilnadu Karur India Kongunadu College of Engineering and Technology Department of It Tamil Nadu Trichy India

ISBN: (纸本)9798350377972

The human eye is frequently considered to deliver a space into a person's strength because it is only with diabetic retinopathy (DR), a devastating eye illness caused by diabetes mellitus, that one may understand the subject's unprotected surface without invasive operations. There are certain disorders, notably vascular illness, that authority telltale indicators in the eye retina. Microaneurysms (MAs) are primary indicators of diabetic retinopathy (DR), making their detection crucial for an effective screening program that adheres to clinical procedures. Retinal examinations can reveal pathological deviations caused by limited ocular diseases such as arteriosclerosis, diabetes, cardiovascular disease hypertension and stroke. The proposed work introduces image processing algorithms, such as shady object identification, to predict the disorder or improve the contribution frame, thereby improving the perceptibility of vessels in color fundus frames. Following this enhancement, an automated classification algorithm, specifically a Convolutional Neural Network (CNN), is executed. The CNN architecture is designed to efficiently extract features from retinal frames, capturing complex patterns connected with diabetic retinopathy. The trained CNN is then assessed on a separate test condition, with its performance system of measurement-accuracy, specificity, recall, and Fl measure-being reported. © 2024 IEEE.

关键词： Convolutional neural networks

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Performance Optimization for Vehicular Cooperative Sensing: A Graph Attention Based Reinforcement Learning Approach

Performance Optimization for Vehicular Cooperative Sensing: ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Li, Xuefei Chen, Mingzhe Liu, Danpu Zhang, Zhilong Quek, Tony Q. S. Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL33146 United States Information Systems Technology and Design Pillar Singapore University of Technology and Design 487372 Singapore

ISBN: (纸本)9798350351255

In this paper, the problem of collaborative vehicle sensing is investigated. In the considered model, a set of cooperative vehicles provide sensing information to sensing request vehicles with limited sensing and communication resources. A base station (BS) determines the subset of sensing request vehicles that each cooperative vehicle will serve and the sub-regions that each cooperative vehicle will detect. We formulate an optimization problem aiming to maximize the number of successfully detected sub-regions of sensing request vehicles while satisfying the cooperative sensing energy requirement by jointly determining the cooperative vehicle association and the sensing sub-region selection. To solve this problem, we propose a graph attention based reinforcement learning (RL) algorithm that can generate the graph information vectors based on the correlation between each cooperative vehicle and each sensing request vehicle. Using the learned graph information, the joint cooperative vehicle association and sensing sub-region selection strategy will be determined. Simulation results show that the proposed scheme can improve the number of successfully detected sub-regions of sensing request vehicles by up to 12.5% compared to the conventional RL algorithm without using graph attention networks (GANs). © 2024 IEEE.

关键词： Reinforcement learning

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Artificial Intelligence based System to Prevent Animal Accidents in the Railway Tracks

Artificial Intelligence based System to Prevent Animal Accid...

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International Conference on Inventive Systems and Control (ICISC)

作者： Balaji Kannan R.Bagavathi Lakshmi M. Sakthivanitha R. Maruthi Department of Information Technology School of Computing Science Vel’s Institute of Science Technology and Advanced Studies(VISTAS) Chennai Tamil Nadu India Department of Computer Applications Hindustan Institute of Technology and Science Chennai Tamil Nadu India

ISBN: (数字)9798350386578

ISBN: (纸本)9798350386585

The wildlife population has been in danger due to frequent animal accidents on railway tracks. In India, animal train collisions cause an average of 20 elephant deaths every year all over the nation. At the same time railway departments are taking necessary steps to identify the cause and implement prevention methods. Despite this, lots of accidents are happening in the railway tracks near the forest areas. This problem has been addressed by proposing a well-defined technological mechanism to alert the animals to be away from the railway tracks to reduce the wildlife deaths. In this study, Machine Learning(ML) frameworks are employed to detect the animals that are coming near the railway tracks and the developed module will provide appropriate warning or signals to the animals as well as to the loco pilot.

关键词： Logistic regression Prevention and mitigation Wildlife Forestry Control systems Rail transportation Artificial intelligence

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A Heuristic Sparsification Method to Improve the Performance of Neural Networks on Autism Spectrum Disorder Identification Tasks 13

A Heuristic Sparsification Method to Improve the Performance...

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13th International Conference on Information science and Technology, ICIST 2023

作者： Zhang, Fangyu Wei, Yanjie Lin, Zhenli Pan, Yi Liu, Jin Centre for High Performance Computing Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Ophthalmology Shenzhen University General Hospital Shenzhen China Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Computer Science and Engineering Central South University Changsha China

ISBN: (纸本)9798350313925

Deep learning (DL) has been widely used in the detection of neurological diseases such as Autism spectrum disorder (ASD). Most studies in this field manually designed and adjusted the hyperparameters of DL models, but due to the huge search space, few studies have attempted to optimize the topology of neural networks which is another important factor related to the performance of DL models. This paper proposes a heuristic sparsification method named the Evolutionary Connection Sparsification Algorithm (ECSA) which is used to optimize the topology of fully connected neural networks (FC-NNs) and generate the corresponding sparsely connected neural networks (SC-NNs). The algorithm is based on the workflow of the genetic algorithm but with improved coding strategy, fitness evaluation approach, crossover and mutation operator, and including an effective convergence acceleration strategy. Experiments show that the SC-NNs generated by ECSA significantly outperform the original FC-NNs in terms of accuracy, F1-score, and AUC, and the model complexities are greatly reduced. © 2023 IEEE.

关键词： Topology

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Enhancing Stock Price Prediction Through Integration of Astrological and Astronomical Data using XGBoost

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Procedia computer science 2025年 258卷 3970-3979页

作者： Rekha R Nair Tina Babu Kishore S Vishal S Sheila Mahapatra Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Applications Nitte Deemed to be university Nitte Institute of Professional Education Mangalore Karnataka India Department of Computer Applications School of Engineering Dayananda Sagar University Bengaluru India Department of Electrical and Electronics Engineering Alliance University Bangalore India

Predicting stock prices continues to be a significant challenge in the financial industry, prompting researchers to investigate various methods to improve accuracy. Hence proposed a novel approach by incorporating astrological and astronomical data into XGBoost models for stock price classification and prediction, utilizing deep neural networks (DNN). The dataset is collected from NASA JPL Horizons and Jaganath Hora software. The raw data is preprocessed with feature engineering, data cleaning, []and normalization to obtain a better forecasting stock prices and classification results. The proposed data executed with machine learning models, such as logistic regression, decision trees, random forest, SVM, XGB Classifier, LSTM, RNN, and CNN other than XGB Regressor for the forcasting. The proposed research is evaluated for classification with accuracy, precision, recall, F1 score, and forecasting with RMSE, MAE, R 2 , AIC, and BIC. The XGBoost classifier achieved 99% accuracy in predicting stock market movements, surpassing other models in determining whether the next day’s stock price would be higher or lower than the previous day’s price, while the DNN performed the best among all algorithms for overall stock price prediction. This approach could influence investment strategies, potentially affecting wealth distribution and financial market stability.

关键词： Independent Component Analysis Principal Component Analysis Probable Neural Network Expectation Maximization algorithm Natural language processing

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A Robust Design of Real-Time Resilient Smile Recognition System using Hybrid Deep Learning Principles 10

A Robust Design of Real-Time Resilient Smile Recognition Sys...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Cherian, Aswathy K. Devipriya, S. Saoji, Bhushan P. Mallikeswari, B. Thiagarajan, R. Krishnamoorthy, R. Srm Institute of Science and Technology Department of Computing Technologies Chennai Kattankulathur India Jerusalem College of Engineering Department of Cse Chennai India Instrumentation Engineering Bharati Vidyapeeth College of Engineering Navimumbai India JusticeBasheer Ahmed Sayeed College For Women Dept of Computer Chennai India Prathyusha Engineering College Department of It Tamilnadu Chennai India Chennai Institute of Technology Centre For Advanced Wireless Integrated Technology Chennai India

ISBN: (纸本)9798350353068

We often convey our happiness via smiling or making a happy face. Simple, latent feelings like contentment and joy are brought to the surface in this way. In terms of social communication, this is the most difficult and potent task there is. One function of digital cameras is smile recognition, which prevents taking pictures of people unless they are smiling. There are two distinct stages to smile detection. After identifying a face in a video or picture, it watches for a grin. To be more precise, we may state that a motion detector takes a video or image and breaks it down into frames. It then examines the frameworks, such as the balanced level and flash, for the face area. The camera can tell whether someone is laughing or smiling because it looks for certain facial features - a jutted jaw, narrowed eyes, and an elevated cheekbone among others. The components of computer architecture and software advancements are advancing at a rapid pace, which is leading to a progressive increase in customer needs for electric products. A growing number of people are interested in human computer interaction systems that differ from the conventional user interface, which includes the keyboard and mouse. This article depicts the realtime implementation of deep learning algorithms in computer settings with limited resources. In this study, we provide HLSR, a new kind of deep learning model that combines the XGBoost and Convolutional Neural Network (CNN) algorithms for smile recognition. To get a clear picture of how well the suggested technique works, we cross-validate the HLSR model with the current CNN model. © 2024 IEEE.

关键词： Face recognition

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Multi-omics graph convolutional networks for digestive system tumour classification and early-late stage diagnosis

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1572-1586页

作者： Lin Zhou Zhengzhi Zhu Hongbo Gao Chunyu Wang Muhammad Attique Khan Mati Ullah Siffat Ullah Khan School of Information Science and Technology University of Science and Technology of ChinaHefeiAnhuiChina Anhui Engineering Research Center on Information Fusion and Control of Intelligent Robot WuhuAnhuiChina Department of Breast Center West District of The Affiliated Hospital of University of Science and Technology of ChinaDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefeiAnhuiChina Institute of Advanced Technology University of Science and Technology of ChinaHefeiAnhuiChina School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore School of Biological and Environmental Engineering Chaohu UniversityChaohu Regional Collaborative Technology Service Center for Rural RevitalizationHefeiChina Department of Artificial Intelligence College of Computer Engineering and SciencePrince Mohammad Bin Fahd UniversityAl-KhobarSaudi Arabia School of Automation Northwestern Polytechnical UniversityXi'anShaanxiChina Institute of Engineering and Computing Science University of Science and Technology of BannuKPKBannuPakistan

The prevalence of digestive system tumours(DST)poses a significant challenge in the global crusade against *** neoplasms constitute 20%of all documented cancer diagnoses and contribute to 22.5%of cancer-related *** accurate diagnosis of DST is paramount for vigilant patient monitoring and the judicious selection of optimal *** this challenge,the authors introduce a novel methodology,denominated as the Multi-omics Graph Transformer Convolutional Network(MGTCN).This innovative approach aims to discern various DST tumour types and proficiently discern between early-late stage tumours,ensuring a high degree of *** MGTCN model incorporates the Graph Transformer Layer framework to meticulously transform the multi-omics adjacency matrix,thereby illuminating potential associations among diverse samples.A rigorous experimental evaluation was undertaken on the DST dataset from The Cancer Genome Atlas to scrutinise the efficacy of the MGTCN *** outcomes unequivocally underscore the efficiency and precision of MGTCN in diagnosing diverse DST tumour types and successfully discriminating between early-late stage DST *** source code for this groundbreaking study is readily accessible for download at https://***/bigone1/MGTCN.

关键词： digestive system tumors early-late stage multi-omics tumor types

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CNN-based Plant Leaf Disease Detection: A Key Solution for Enhancing Agricultural Productivity

CNN-based Plant Leaf Disease Detection: A Key Solution for E...

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Advancement in Technology (ICONAT), International Conference for

作者： Deepika Nayak Rekha R Nair TinaBabu S Kishore Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Applications Nitte Institute Of Professional Education Nitte(Deemed to be University) Mangaluru Karnataka India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Agriculture plays a pivotal role in our lives and holds significant importance in our economy. Proper management of agricultural practices is essential for maximizing profits in agricultural production. However, many farmers lack expertise in identifying and managing plant leaf diseases, leading to reduced crop yields. Since agricultural productivity directly impacts the profitability of farming, efficient disease detection and management are crucial. To address this issue, Convolutional Neural Networks (CNN) emerge as a viable solution for leaf disease detection and classification. The primary objective of this research is to develop a robust CNN-based system capable of detecting and classifying leaf diseases in various crops such as apple, grape, corn, potato, tomato, and more. In this research, the CNN algorithm was utilised, and the accuracy was $\mathbf{9 7. 5 8 \%}$. The system’s application will enable farmers to monitor large fields of crops, facilitating early detection and treatment of diseases. The significance of plant leaf disease detection spans across multiple sectors, including Biological Research and Agriculture institutes. Detecting diseases promptly can help implement timely medical treatments, thereby mitigating the negative impacts on crop health and productivity. Moreover, such a system can assist in monitoring crop health on a larger scale, benefiting the agricultural sector as a whole.

关键词： Productivity Accuracy Profitability Plants (biology) Crops Agriculture Real-time systems Convolutional neural networks Monitoring Diseases

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