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

作者： Vats, Satvik Shastri, Suryakant Mehta, Shiva Computer Science and Engineering Graphic Era Hill University Dehradun248002 India Graphic Era Deemed to be University Dehradun248002 India Iimt College of Engineering Artificial intelligence and Data Science Department Greater Noida India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350370249

Herein proposes a new classification technique of SMS spam within a federated learning system, based on which the process occurs on the user's device. The objective is to realize the perfect balance of the two;on the one hand, it should be proactive with spam detection, and on the other hand, it should have the highest respect for privacy protection. Our research demonstrates that this method has significantly boosted classification accuracy without privacy issues by equating an appropriate evaluation technique for extensive testing and assessment. The statistical analysis shows that our federated learning algorithm significantly reduces loss;according to the statistical analysis results, this value increases from 0.267 to 0.389 from the first to the tenth epoch. Also, the system reveals that this mechanics remains excellent and keeps showing values of more than 90%, and the third epoch shows nothing but a high accuracy value of 98.3%. In effect, we analyze the cornerstone of our knowledge based on the method of the critical priorities potentially tightened privacy protection for federated learning. The federated learning solution offers a transparent approach that enables users to access essential services directly on their devices, and then the trained machine learning models are merged securely. This study underscores the competency to differentiate spam from anything useful in electronic communication and considers it a significant issue as regards privacy protection. The outcome gives a sense of hope in the idea of federated learning to have specific SMS spam. It creates a good gap between effectiveness and privacy protection, which is especially important considering that mobile communication covers a vast territory today. © 2024 IEEE.

关键词： Federated Learning Machine Learning Mobile Communication On-Device Learning Privacy Preservation SMS Spam Detection

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Machine Learning Approach for Detecting Liver Tumours in CT images using the Gray Level Co-Occurrence Metrix 1

Machine Learning Approach for Detecting Liver Tumours in CT ...

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1st IEEE International Conference on Applied intelligence and Sustainable Computing, ICAISC 2023

作者： Aruna, S. Saranya, A. Guru Pandi, D. Kavya, S.P. Pareek, Piyush Kumar Computational Intelligence SRMIST Chennai India SRMIST Computational Intelligence Department Chennai India Panimalar Engineering College Department of ECE Chennai India KPR Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Arasur India Nitte Meenakshi Institute of Technology Department of AI and ML Bengaluru India

ISBN: (纸本)9798350323795

Over 2.4 % of deaths in India each year are caused by liver diseases. Due to its mild early signs, liver disease is also challenging to diagnose. Frequently, the signs only become obvious when it is too late. Even more challenging than segmenting the liver is segmenting the tumour from the liver. Imaging procedures like computed tomography, magnetic resonance imaging, and ultrasound are utilised to separate the liver and liver tumour once a sample of liver tissue has been removed. This research suggests a machine learning method from CT images-based automatic assistance system for stage categorization. Then, the features are extracted from the CT images using the Gray-Level Co-Occurrence Matrix (GLCM) method. Finally, it is suggested that the computed tomography (CT) pictures of livers containing tumours be categorised using a Random Forest technique. Using the described method, liver tumour images are classified as benign or malignant. The real value accuracy for liver segmentation was found to be approximately 99.55%, 97.85%, and 98.16% based on the experimental investigation. Using the described method, liver tumour images are classified as benign or malignant. These modifications improve the system's ability to recognise the tumour from the CT pictures. © 2023 IEEE.

关键词： Machine learning

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Efficient Plant Disease Detection using K-Means clustering and DenseNet-based Classification 3

Efficient Plant Disease Detection using K-Means clustering a...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Yashodha, G. Rajakumar, B. Krishnamoorthy, M. Surendran, R. Kg College of Arts and Science Department of Computer Technology Coimbatore641035 India J.N.N. Institute of Engineering Department of Artificial Intelligence and Data Science Chennai601102 India Panimalar Engineering College Department of Computer Science and Engineering Chennai600 123 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

Early detection is vital in crop health, yet improvement in productivity faces time-consuming and inefficient challenges due to traditional manual techniques of plant disease detection. Thus, we present a deep learning-based novel framework with a method called k-means clustering with DenseNet Classification denoted by Kmc-Densenet. The approach streamlines this plant disease detection process involving the overall pipeline of extensive preprocessing, segmentation, and classification. In the preprocessing stage, we employ contrast enhancement techniques to improve the visibility of disease symptoms, ensuring clearer and more consistent leaf images for further analysis. For the segmentation task, we leverage a k-means clustering which effectively isolates regions of interest, distinguishing between healthy and diseased areas of the plant leaf. In the classification stage, we use a DenseNet variant of CNN, known for its feature reuse capabilities, to classify the plant diseases with high accuracy. The DenseNet model helps overcome the challenges posed by class imbalance, enhancing the overall detection performance. Finally the parameters of the Densenet model is fine-tuned using the Lyrebird Optimization Algorithm. The proposed Kmc-Densenet method is evaluated on the publicly available Plant village dataset, demonstrating superior performance in both segmentation and classification tasks. This framework offers a more efficient and reliable solution for early plant disease detection in precision agriculture, with potential applications in improving crop yield and quality. © 2025 IEEE.

关键词： K-means clustering

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AI based Analysis of Hyperspectral Images with Spectacular Efficacy in Video Analytics

AI based Analysis of Hyperspectral Images with Spectacular E...

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2024 International Conference on Intelligent and Innovative Practices in engineering and Management, IIPEM 2024

作者： Jadhav, Samarth Yogesh More, Rutuja Rajatam Tingare, Bhagyashree Ashok Lakshmi, G. Prasanna Kolse, Shivam Darwante, N.K. Sanjivani College of Engineering Department of Information Technology MH Kopargaon India D Y Patil College of Engineering Akurdi Department of Artificial Intelligence and Data Science Pune India Sandip University School of Computer Science and Engineering Maharashtra Nashik India Sanjivani College of Engineering Department of Electronics and Computer Engineering MH Kopargaon India

ISBN: (纸本)9798350390049

Hyperspectral imaging and artificial intelligence (AI) have transformed imaging and data processing through their ability to capture and analyze detailed spectral information. This paper explores the integration of hyperspectral imaging with AI, focusing on its impact on video analytics. Hyperspectral imaging provides comprehensive, multi-dimensional data by capturing a wide range of spectral wavelengths, enabling precise material identification and environmental monitoring beyond traditional RGB imaging. The synergy between hyperspectral imaging and AI enhances real-time analysis and decision-making by leveraging deep learning algorithms for pattern recognition and anomaly detection. This study introduces a novel AI-based hyperspectral image analysis approach for video analytics, utilizing convolutional neural networks (CNNs) and hybrid CNN-attention models to improve object recognition and classification. The methodology is validated through experiments that measure classification accuracy, processing speed, and resilience to varying conditions. Results demonstrate significant improvements in accuracy and efficiency over traditional methods, highlighting the potential for advanced applications in surveillance, environmental monitoring, and industrial quality control. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhancing Student Performance Prediction with Greylag Goose Optimization Algorithm

Enhancing Student Performance Prediction with Greylag Goose ...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Rizk, Faris H. Mohamed, Mahmoud Elshabrawy Sameh, Basant Zaki, Ahmed Mohamed Eid, Marwa M. El-Kenawy, El-Sayed M. Computer Science and Intelligent Systems Research Center BlacksburgVA24060 United States Delta Higher Institute of Engineering and Technology Department of Communications and Electronics Mansoura35111 Egypt Delta University for Science and Technology Faculty of Artificial Intelligence Mansoura35111 Egypt

ISBN: (纸本)9798350351408

The current paper addresses the central role of hyperparameter optimization in improving the predictive power of the MLP Regressor for forecasting student performance in Portuguese secondary schools. The uniqueness of this research lies in its exploration of metaheuristic optimization algorithms, specifically highlighting GGO (Greylag Goose Optimization) for enhancement. The study utilized a dataset crucial for understanding and predicting student performance, with a special focus on its distinctive features. By comprehensively tuning the MLP Regressor, the paper demonstrates remarkable improvements in various performance measures, as evident in the enclosed tables. Specifically, the MSE values calculated for the MLP Regressor both before and after GGO optimization are compared. Without optimization, the MLP Regressor had an MSE of 0.0103. After GGO optimization, the MSE significantly improved to 0.0060, indicating enhanced accuracy with GGO in the model. These findings emphasize that hyperparameter optimization, particularly the GGO technique, is crucial for refining the MLP Regressor in predicting student performance. The paper not only delves into the technical aspects but also concludes by highlighting the broader consequences of these outcomes. The potential educational applications are substantial, as improved models can provide more accurate predictions, empowering educators and policymakers to make informed decisions in education. This paper establishes a foundation for future research directions, contributing to the pool of ideas for educational predictive modeling. © 2024 IEEE.

关键词： Optimization algorithms

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Machine Learning-Enhanced Self-Management for Energy-Effective and Secure Statistics Assortment in Unattended WSNs

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SN Computer science 2025年第2期6卷 1-10页

作者： Biswas, Preesat Mishra, Aishwarya Dixit, Rinku Sharma Dwivedi, Abhishek Choudhary, Shailee Lohmor Tiwari, Shweta Chakravarthi, M. Ayyappa Electronics & Telecommunications Government Engineering College C. G Jagdalpur India Department of Computer Science & Engineering IES College of Technology M.P Bhopal India Department of Artificial Intelligence and Machine Learning New Delhi Institute of Management Delhi India Department of Data Science and Engineering School of AIML IoT & IS CCE DS and Computer Applications Manipal University Jaipur Jaipur Rajasthan India Department of Computer Science and Engineering Gyan Ganga Institute of Technology and Sciences M.P Jabalpur India Department of CSE-Data Science KKR and KSR Institute of Technology and Sciences Guntur India

Unattended Wireless Sensor Networks (UWSNs) operate without human supervision and have limited resources. In a conventional WSN with a fixed sink for data collection, nodes one hop away from the sink consume more energy, causing a hotspot issue. This leads to network fragmentation and reduced sensing efficiency. To address this, a mobile sink is proposed. This study details the development and implementation of Secure MRL, focusing on legitimate message communication to the mobile sink via convex nodes. By using these convex nodes as data collection points, the mobile sink avoids traversing the entire network. We employ an enhanced system to generate keys and authenticate messages, assessing its resilience against various attacks, including sleep deprivation, snooze, network substitution, and modification. Findings indicate that Secure MRL maintains consistent performance across different node counts and significantly reduces energy consumption by 84%, improving the speed of attack detection. This improvement is achieved by monitoring the digital signature and residual energy of nodes. The proposed SVM-based network training establishes a residual energy cut-off, while convex nodes mitigate synchronization issues among other sensor nodes in Secure MRL. Additionally, convex nodes manage the availability checks for the mobile sink. The proposed approach demonstrates a 92% improvement in packet delivery ratio (PDR) and an 89% reduction in end-to-end delay, highlighting its efficacy in enhancing network performance. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Duty-cycled UWSN MRL Sec-MRL SEC-TMP SVM UWSN

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Internet of Things (IoT) Powered Child Rescue System from Open Borewells by Using WiFi Integrated ESP32 Controller and Smart Sensors Association

Internet of Things (IoT) Powered Child Rescue System from Op...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Vijayakumari, P. Mary, S. Jeya Divya, G. Vani, N. Narkhede, A. Sabri, M. Omar Sairam Engineering College Dept of Ece Chennai India Einstein College of Engineering Ece department Tirunelveli India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications TamilNadu Kattankulathur India R.M.K. College of Engineering and Technology Department of Artificial Intelligence and Data Science Puduvoyal Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University Faculty of Economic Department of Business Information Technology Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Currently, the negligence of individuals in society is the reason why children are falling into the Bore well. The current systems available to protect the child are both less effective and more expensive. Consequently, the society requires a novel approach that is both more effective and efficient. In the majority of cases that have been reported thus far, a parallel opening is excavated, and subsequently a horizontal path is established to access the infant. Not only is it a time-consuming procedure, but it also carries a variety of risks. The Borewells rescue system is capable of performing a variety of actions to rescue the child by moving within the same Borewells where the child has been confined. The purpose of this paper is to rescue children who have fallen into a bore well. It appears that abandoned bore wells are mortality traps for minors. The primary water framework requirements of cultivators in India are met by groundwater, as the country is primarily known for agriculture. The very significant requirement of bore wells is typically tunneled for the groundwater exploration requirement due to the increasing population, diminishing land assets, and the advancement of urbanization. In turn, these borewell have begun to claim the lives of numerous innocent individuals. The Dynamic Child Rescue Model (DCRM) is a novel rescue system that has been developed in this research. It is designed to easily protect children from the borewell and is cross-validated with the conventional approach, the Pipeline Navigation Model (PNM), to assess the efficacy of the proposed scheme. The system is supported by the Internet of Things (IoT). This work suggests a novel design that incorporates a sensor located at the top of the borewell opening to detect the presence of a child if they fall inside. © 2024 IEEE.

关键词： Abandoned wells

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Remote Sensing data-Based Satellite Image Analysis in Water Quality Detection for Public Health data Modelling

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 532-541页

作者： Balakrishnan, S. Preetam Raj, P Michael Somasekar, J. Kumar, Kambala Vijaya Amutha, S. Sangeetha, A. Department of Computer Science and Engineering Aarupadai Veedu Institute of Technology (AVIT) Vinayaka Mission’s Research Foundation (Deemed to be University) Chennai India Department of Computer Science and Engineering MLR Institute of Technology Telangana Dundigal Hyderabad 500043 India Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be-University) Karnataka Bengaluru 562112 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Tamilnadu Chennai India Department of Artificial Intelligence and Data Science Kongunadu College of Engineering and Technology TamilNadu Tholurpatti India

One of the most important aspects of environmental sustainability is water quality monitoring. The ecology is impacted by poor water quality in addition to aquatic life. With the volume of data being collected from satellite systems, the issue of effectively and automatically extracting water bodies is growing more pressing. One of the most important things to do for managing and safeguarding water resources is to conduct extensive monitoring of water quality parameters, or WQPs. It is still difficult to monitor optically inactive WQPs in inland waters, such as total nitrogen (TN), ammoniacal nitrogen (AN), and total phosphorus (TP). Water quality (WQ) classification and prediction can be greatly enhanced with the use of artificial intelligence (AI). This research proposes novel method in healthcare detection by water quality analysis based on classification utilising ML model. Input is collected as healthcare dataset based on water quality as well as processed for noise removal and normalisation. The processed data features are extracted and classified utilising reinforcement Gaussian stacked layer network with variational fuzzy Markov basis neural network. In order to suggest best course of action for the water bodies, it is imperative that any study look into the spatiotemporal changes of the dominant water quality parameters (WQPs). WQP concentrations have typically been determined by extensive fieldwork. These findings could help identify and regulate non-point source pollution, offer a large-scale spatial picture of the water quality, and pinpoint vulnerable locations and times of water pollution. Experimental analysis is carried out for various water sample dataset in terms of detection accuracy, mean precision, recall, F-1 score and AUC. The proposed technique obtained 97% of detection accuracy, 95% of recall, 91% of F-1 score, 96% of AUC, and 94% of MAP. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Classification Fuzzy Markov basis Healthcare detection Machine learning model Water quality analysis

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Generative AI in Agri: Sustainability in Smart Precision Farming Yield Prediction Mapping System Based on GIS Using Deep Learning and GPS 4

Generative AI in Agri: Sustainability in Smart Precision Far...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Senthil, G.A. Suganthi, S.U. Prinslin, L. Selvi, R. Prabha, R. Department of Information Technology Agni College of Technology Chennai India Department of Artificial Intelligence and Data Science Sri Sai Ram Institute of Technology Chennai India Department of Computer Science and Engineering Agni College of Technology Chennai India Assistant Professor Department of Computing Technologies SRM Institute of Science and Technology kattankulathur Chennai India Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India

Agriculture faces many challenges of precision farming, such as the need for sustainable practices, improving yields, ensuring high yields. In resolution to these challenges, the present research provides an AI-based system that enables the use of deep learning, Global Positioning System (GPS), and Geographic Information System (GIS) technologies to create a highly intelligent smart agricultural precision farming system. Its goal is to monitoring crop health and reduce disease risk, which will lead to improved resource utilization and environmentally sustainability techniques. The proposed framework addresses the urgent need for consistency in agricultural practices, especially as global agriculture deals with pressures from climate change, resource shortages, and increasing demand for food. Traditional agricultural methods for predicting and optimizing crop yields due to increasing factors affecting crop performance Not enough generative AI, especially the use of deep learning models, supports agricultural research in many cases, allowing patterns to be identified and future results to be predicted accurately. The integration of GPS and GIS allows for more accurate mapping, real-time analysis, and effective decision-making. Weather forecasting variability, resource constraints, and demand for more food are isolated from environmental influences using deep learning models, especially artificial Neural Networks (ANN). By using large data sets, including historical crop yield performance, soil properties, and weather conditions, the system provides highly accurate crop forecasts. Generative Adversarial Networks (GANs) and You Only Look Once (YOLO) hybrid model is playing a key role in generating crop yield and growth potential under different conditions, adjusting model accuracy over time, and this combination of ANN, GANs and YOLO optimization algorithms ensures that the system continuously enhances its predictive accuracy and overall effectiveness. The proposed gene

关键词： Generative adversarial networks

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Brain Tumour Detection from MRI Images Using Deep CNN 8

Brain Tumour Detection from MRI Images Using Deep CNN

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8th IEEE International Conference on science, technology, engineering and Mathematics, ICONSTEM 2023

作者： Josephin Shermila, P. Nishitha, J. Shoba, L.K. R.M.K. College of Engineering and Technology Department of Artificial Intelligence and Data Science Chennai India Arunachala College of Engineering for Women Department of Electronics and Communication Engineering Vellichanthai India Jeppiaar Engineering College Department of Information Technology Chennai India

ISBN: (纸本)9798350347791

The brain is one of the most important organs in humans. Abnormal growth in the brain occurs, which may be a tumor. In this world, humansin any age group suffer from brain tumors. Stress is also a cause of brain tumors. Early detection can cure completely, which became the main objective of the work. In this research work, brain tumor detection from its images is investigated. Experiments are done using deep learning, wherein a convolutional neural networkis used. The brain is one of the activeparts of the central nervous system. Detectinga tumor in the brain is very difficult as the size, shape, and location of the tumor differ for each individual. If the brain tumor is identified and detected primarily as early, the chance of patients'treatment is very high. Normally, diagnosis is madeusing magnetic resonanceimaging. Our methodologies predictive models obtainedvery promising visual and quantitative results at real-time speed. © 2023 IEEE.

关键词： Brain

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