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2nd International Conference on electronics and Renewable Systems, ICEARS 2023

作者： Mohan, Ravi Chalasani, Srinivas Suma Christal Mary, S. Chauhan, Amit Parte, Smita Athanere Anusuya, S. Shri Ram Institute of Technology Ece Department Madya Pradesh Jabalpur India Eluru Vatluru Department of Computer Science and Engineering Andhra Pradesh India Panimalar Engineering College Department of Information Technology Tamilnadu Chennai India Deemed to Be University Christ Department of Life Sciences Karnataka Bengaluru India Mits Gwalior Department of Computer Science and Engineering Madhya Pradesh India K.Ramakrishnan College of Engineering Department of Electronics and Communication Engineering Tamilnadu India

ISBN: (纸本)9798350346640

In the field of accident avoidance systems, figuring out how to keep drivers from getting sleepy is a major challenge. The only way to prevent dozing off behind the wheel is to have a system in place that can accurately detect when a driver's attention has drifted and then alert and revive them. This paper presents a method for detection that makes use of image processing software to examine video camera stills of the driver's face. Driver inattention is measured by how much the eyes are open or closed. This paper introduces Regularized Extreme Learning Machine, a novel approach based on the structural risk reduction principle and weighted least squares, which is applied following preprocessing, binarization, and noise removal. Generalization performance was significantly improved in most cases using the proposed algorithm without requiring additional training time. This approach outperforms both the CNN and ELM models, with an accuracy of around 99% being achieved. © 2023 IEEE.

关键词： Video cameras

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Seagull Optimization Algorithm for Underwater Sensor Network Localization

Seagull Optimization Algorithm for Underwater Sensor Network...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Alsalami, Zaid Ramya, G. Barolia, Pushpa Arya Yadav, Sachin Satyanarayana, A.N. Pattathu Rani, L. The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Ies College of Technology Department of Electronics & Communication Engineering M.P. Bhopal India Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India Sreenidhi Institute of Science and Technology Ece Department Hyderabad India Kcg College of Technology Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350390759

This paper presents a new technique to a position sensor in UWSNs with the recognition of the Seagull Optimization Algorithm. UWSNs are particularly useful for applications in environmental monitoring and military monitoring, but accurate sensor localization is difficult due to the signal loss in underwater environments and in the presence of external factors. Based on the Motivation Model, the SOA, motivated by the seagulls' distributed foraging behavior, is a possible solution to the dilemma by striking a midway between exploration and exploitation. The synthesis of exact methodology is outlined here along with the creation of synthetic dataset and a proof of concept MATLAB code for the evaluation of the proposed SOA-based localization algorithm. A performance evaluation, which involved the use of computational simulations on a wide variety of antennas, clearly shows that the SOA achieves a higher localization accuracy in much shorter time, while consuming less power compared to other methods such as PSO, GA, or hybrid methods. The stability is one more strongest side of proposed algorithm which has been confirmed by robustness analysis, so it can be effectively used in dynamic underwater environment. The current research is not only useful in showing how the SOA can be used for the UWSN localization in specific domains, but also valuable to the development of the new UWSN localization method that is efficient and accurate, for the further expansion of the UWSN in the vital marine sphere. © 2024 IEEE.

关键词： MATLAB

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An Internet of Things (IoT) Assisted Forest Fire Detection Mechanism Using Hybrid Classification and Learning Model

An Internet of Things (IoT) Assisted Forest Fire Detection M...

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

作者： Balachandran, G. Govindasamy, Suresh Sankar, R. Vidhyasree, V. Sunheriya, Neeraj Attar, H. Jeppiaar Engineering College Department of Ece Chennai India Sona College of Technology Department of Electrical and Electronics Engineering Salem India S.A. Engineering College Department of Master of Computer Applications Chennai India Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University Department of Energy Engineer zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

For all the primary and secondary advantages they provide, forests are an indispensable part of humanity's natural resource arsenal. Forest fires and other natural catastrophes significantly affect both the rate of global warming and the viability of Earth's biota. Consequently, studying how to automatically detect forest fires is crucial for reducing the frequency and severity of such catastrophes. Determinants of fire prevention and suppression strategies can benefit from early detection as well. In this study, we use computer vision algorithms based on artificial intelligence to identify fire and smoke in photographs. This paper presents a new hybrid methodology called Enhanced Learning with Fire Classification Model (ELFCM). It uses learning in conjunction with classification norms to accurately report fire and smoke details. To test how well the proposed scheme works, it is cross-validated with the conventional Random Forest Classifier (RFC). According to the findings of the experiments, some of the unreported or incorrectly detected fires that ELFCM reported may be confirmed by using IoT devices. The fire department receives this recorded report for their records. In the end, we tested the fire classification findings by comparing our method's performance to that of other previously announced fire detection methods that employed commonly used performance matrices. © 2024 IEEE.

关键词： Premixed flames

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An Automatic Detection of Citrus Fruits and Leaves Diseases Using Enhanced Convolutional Neural Network

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Remote Sensing in Earth Systems Sciences 2023年第3-4期6卷 123-134页

作者： Shastri, Rajveer Chaturvedi, Abhay Mouleswararao, B. Varalakshmi, S. Prasad, G.N.R. Ram, Mylavarapu Kalyan Department of E & T C Engineering Vidya Pratishthan’s Kamalnayan Bajaj Institute of Engineering & Technology Baramati 413133 India Department of Electronics and Communication Engineering GLA University Uttar Pradesh Mathura 281406 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur India Department of ECE Bharath Institute of Higher Education and Research Tamil Nadu Chennai India Department of MCA Chaitanya Bharathi Institute of Technology (A) Gandipet Hyderabad 500075 India Department of Computer Science & Engineering Aditya Engineering College Surampalem India

Accurate and timely detection of diseases present in citrus crops is a crucial task for effective crop management and the prevention of yield loss. Traditional methods of disease detection, such as visual inspection, can be time-consuming and prone to human error. In this paper, we propose a novel approach for automatic and accurate disease detection using convolutional neural networks (CNNs). By investigating the numerous images of infected citrus fruits and leaves, the proposed CNN model has attained prominent recognition and classification accuracy results. The proposed Enhanced-CNN (E-CNN) model is trained using diverse collection of three benchmark datasets such as A Citrus Fruits and Leaves Dataset, Citrus Pest and Disease dataset and Citrus Leaf dataset. Due to careful investigation on layer details and image pre-processing techniques, the proposed E-CNN model secures remarkable performance in citrus fruit and leaf disease detection and type classification. The proposed model achieves significant improvement in disease detection and classification performance by securing the average f1 score 92.06, precision score 95.14, recall score 96.67, recognition accuracy 98% and classification accuracy 99%. These results are comparatively higher than earlier approaches and show more than 6% improvements in disease detection and classification performance. We believe that this unique approach has the potential to significantly improve disease management practices in the citrus industry, helping to improve crop yield and reduce the spread of diseases. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Automatic detection Citrus fruits Diseases Enhanced convolutional neural network Leaves

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Implementation of an Effective Methodology to Avoid DDoS Attacks using Cybersecurity Norms 2

Implementation of an Effective Methodology to Avoid DDoS Att...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and electronics, ICIITCEE 2024

作者： Balamurugan, R. Anni Princy, B. Kanchana, D. Murugesan, M. Josphin Selsia, A. Dinesh, M. Ajk College of Arts and Science Department of Artificial Intelligence and Machine Learning Tamil Nadu Coimbatore India Panimalar Engineering College Department of Computer and Communication Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computer Applications Tamil Nadu Chennai India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Cse Tamil Nadu Chennai India Shri Venkateshwara Padmavathy Engineering College Department of Ece Prince Tamil Nadu Chennai India Simats Saveetha School of Engineering Department of Vlsi Microelectronics Tamil Nadu Chennai India

ISBN: (纸本)9798350306415

Today's world wouldn't function without the Internet. Because the internet is a universal resource for information gathering, its accessibility is crucial. This research focuses on the Distributed Denial of Service (DDoS) attack because of the severe impact it has on network availability. DDoS attacks work by flooding the target with a large amount of unauthorized data, thereby occupying its available bandwidth as well as preventing genuine traffic from passing throughout. Modern cyber attacks are highly complex and difficult to detect in a decentralized network. In a distributed context, Denial of Service (DoS) and Distributed Denial of Service (DDoS) assaults induce resource unavailability. This has spurred the scientific community to develop viable ways in dispersed situations as a means of minimizing such attacks. This research focuses on the Distributed Denial of Service assault due to its devastating effects on network accessibility as a result of its flood of illegal data that usurps the victim's bandwidth and stifles the flow of lawful traffic. This paper introduces a new method for detecting DDoS attacks, the Novel Attack Detection Protocol (NADP), and validates its effectiveness by comparing it to a more traditional routing protocol, Dynamic Source Routing (DSR). We have also detailed the various methods used in distributed denial of service attacks on Internet service providers. The goal of this project is to identify potential solutions to this problem, including methods for preventing and mitigating these attacks. © 2024 IEEE.

关键词： Attacks Communication Cybersecurity DDoS Attack DoS DSR Dynamic Source Routing NADP Novel Attack Detection Protocol Security

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Desertification Detection in Makkah Region based on Aerial Images Classification

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computer Systems Science & engineering 2022年第2期40卷 607-618页

作者： Yahia Said Mohammad Barr Taoufik Saidani Mohamed Atri Electrical Engineering Department College of EngineeringNorthern Border UniversityArarSaudi Arabia Laboratory of Electronics and Microelectronics(LR99ES30) Faculty of Sciences of MonastirUniversity of MonastirTunisia Department of Computer Sciences Faculty of Computing&Information TechnologyNorthern Border UniversityRafhaSaudi Arabia College of Computer Sciences King Khalid UniversityAbhaSaudi Arabia

Desertification has become a global threat and caused a crisis,especially in Middle Eastern countries,such as Saudi *** is one of the most important cities in Saudi Arabia that needs to be protected from *** vegetation area in Makkah has been damaged because of desertification through wind,floods,overgrazing,and global climate *** damage caused by desertification can be recovered provided urgent action is taken to prevent further degradation of the vegetation *** this paper,we propose an automatic desertification detection system based on Deep Learning *** images are classified using Convolutional Neural Networks(CNN)to detect land state variation in *** have been widely used for computer vision applications,such as image classification,image segmentation,and quality *** proposed CNN model was trained and evaluated on the Arial Image Dataset(AID).Compared to state-of-the-art methods,the proposed model has better performance while being suitable for embedded *** has achieved high efficiency with 96.47% *** light of the current research,we assert the appropriateness of the proposed CNN model in detecting desertification from aerial images.

关键词： Desertification detection deep learning convolutional neural networks(CNN) aerial images classification Makkah region

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Enhancing IoT Network Capabilities through Edge Computing Strategies for Real-Time Data Analytics and Efficient Information Processing 10

Enhancing IoT Network Capabilities through Edge Computing St...

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

作者： Tamilselvan, S. Babu, M. Rajasekar, P. Kalaiselvi, K. Shanmugam, Raju Kadumbadi, Vijayakumar Simats Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Rajalakshmi Engineering Çollege Thandalam Department of Information Technology Chennai India Sri Krishna College of Technology Department of ECE Coimbatore Tamil Nadu Kovaipudur India Hindusthan College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Karnavati University Unitedworld School of Computational Intelligence Gujarat India Adhiparasakthi Engineering College Department of Technology Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

The IoT devices proliferation speed has engendered a necessity for data processing and analytics capabilities as advanced as IoT networks are to take full advantage of the IoT networks capacity. The paradigm of edge computing has been developed as a very useful tool to meet this challenge by providing fast data analysis, storing data and quick response to problems at the point of data generation from network end points. In this study, we present a practical approach to iot-networks sure competence through the edge computing strategies implementation. The process starts with requirement analysis, continues with edge node selection, preprocessing of data, distributed data analytics, dynamic resource allocation, security and privacy measures, fault tolerance and lastly, it is integrated with the cloud services. Moreover, it provides performance evaluation and eventually, commissioned for the production. The specific way of systematic optimization is in place that will allow the productivity to be enhanced, the response time to be minimized, the security and privacy strengthened and the connectivity with the cloud to be scalable and that which can ensure reliable IoT deployments. In the light of our study, we argue edge node selection criteria, data preprocessing efficiency, dynamic resource allocation performance, and the effectiveness of security and privacy measures in computing environments should be the focus of the research. The results thus validate the usefulness of the suggested approach in lifting up the capacity of the IoT networks via edge computing approaches, which in turn will be tremendously instrumental in opening breeding ground for innovation of IoT synchronized applications and services. © 2024 IEEE.

关键词： Edge computing

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Human Faces Detection and Tracking for Crowd Management in Hajj and Umrah

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computers, Materials & Continua 2022年第6期71卷 6275-6291页

作者： Riad Alharbey Ameen Banjar Yahia Said Mohamed Atri Abdulrahman Alshdadi Mohamed Abid Department of Information Systems and Technology College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Electrical Engineering Department College of EngineeringNorthern Border UniversityArarSaudi Arabia Laboratory of Electronics and Microelectronics(LR99ES30) Faculty of Sciences of MonastirUniversity of MonastirTunisia College of Computer Sciences King Khalid UniversityAbhaSaudi Arabia CES Laboratory ENISUniversity of SfaxTunisia

Hajj and Umrah are two main religious duties for *** help faithfuls to perform their religious duties comfortably in overcrowded areas,a crowd management system is a must to control the entering and exiting for each *** the number of people is very high,an intelligent crowd management system can be developed to reduce human effort and accelerate the management *** this work,we propose a crowd management process based on detecting,tracking,and counting human faces using Artificial Intelligence *** detection and counting will be performed to calculate the number of existing visitors and face detection and tracking will be used to identify all the humans for security *** proposed crowd management system is composed form three main parts which are:(1)detecting human faces,(2)assigning each detected face with a numerical identifier,(3)storing the identity of each face in a database for further identification and *** main contribution of this work focuses on the detection and tracking model which is based on an improved object detection *** improved Yolo v4 was used for face detection and *** has been very effective in detecting small objects in highresolution *** novelty contained in thismethod was the integration of the adaptive attention mechanism to improve the performance of the model for the desired *** wise attention mechanism was applied to the output layers while both channel wise and spatial attention was integrated in the building *** main idea from the adaptive attention mechanisms is to make themodel focus more on the target and ignore false positive *** demonstrated the efficiency of the proposed method through expensive experimentation on a publicly available *** wider faces dataset was used for the train and the evaluation of the proposed detection and tracking *** proposed model has achieved good results with 91.2%of mAP and a processing speed

关键词： Crowdmanagement Hajj and Umrah face detection object tracking convolutional neural networks(CNN) adaptive attention mechanisms

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Innovations in Image Forensics: Designing a Smart Learning Based Methodology for Forgery Detection over Digital Images 1

Innovations in Image Forensics: Designing a Smart Learning B...

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1st International Conference on Pioneering Developments in computer Science and Digital Technologies, IC2SDT 2024

作者： Ramkumar, G. Pithamber, K. Srilakshmi, C.H. Rathika, S. Anni Princy, B. Kandaswamy, Vijay Anand Saveetha University Department of ECE Tamil Nadu Chennai India MLR Institute of Technology Department of Electronics and Communication Engineering Hyderabad Dundigal India R.M.D. Engineering College Department of Computer Science and Business Systems Tamil Nadu Kavaraipettai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Master of Business Administration Tamil Nadu Chennai India Panimalar Engineering College Department of Artificial Intelligence and Machine Learning Tamil Nadu Chennai India R.M.K. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Kavaraipettai India

ISBN: (纸本)9798350365016

Detecting forgery in digital images is crucial for several reasons. Firstly, with the widespread use and accessibility of image editing tools, individuals can easily manipulate digital images, raising concerns about the authenticity and integrity of visual information. This study introduces a novel approach, termed SFVGG, for detecting forgery in digital images by leveraging a smart learning methodology. The SFVGG method integrates the robust features of SIFT, the clustering capabilities of Fuzzy C-Mean clustering, and the deep learning process of VGG19. Through a synergistic combination of these techniques, the proposed forgery detection system achieves a comprehensive and effective solution. The methodology was implemented using Python, showcasing its practical applicability. Remarkably, the SFVGG framework demonstrates a high accuracy of 94.5%, underscoring its effectiveness in identifying forged content within digital images. This research represents a significant advancement in digital image forgery detection, offering both a sophisticated methodology and practical implementation. © 2024 IEEE.

关键词： Contrastive Learning

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Heart Disease Prediction and Classification using Machine Learning and Transfer Learning Model 1

Heart Disease Prediction and Classification using Machine Le...

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1st IEEE International Conference on Automation, Computing and Renewable Systems, ICACRS 2022

作者： Sivaprasad, R. Hema, M. Ganar, Bharati N. Sunil, D.M. Mehta, Vaishali Fahlevi, Mochammad Department of Electrical and Electronics Engineering Sri Sairam Engineering College India Department of ECE JNTU-GV College of Engineering India Department of Anesthesia ESIC Medical College India Department of ECE REVA University India Department of Computer Science and Engineering Panipat Institute of Engineering and Technology India Department of Management BINUS Online Learning Bina Nusantara University Indonesia

ISBN: (纸本)9781665460842

Heart disease is a dangerous condition that can lead to a fatal condition due to cardiac arrest. Recent studies have revealedvarious facts for analyzing cardiac data by sensing, monitoring, and learning data in IoT to predict early diagnosis and treatment. Through machine learning based feature analysis, accurate disease detection has been implemented. However, the dominant methods do not accurately predict the result since the incorrect features contain non-relatedsupport values to select the features to perform training validation and produce prediction inaccuracy. To overcome this limitation, a Machine Learning and Transfer Learning Model (TLM) is proposed to perform heart disease prediction. Initially, pre-processing has been carried out to reduce dimension, and the scaling factor was also used to calculate the margin rate. To increasing the prediction accuracy Disease Prone Impact Rate (DPIR) intends to find the support values. To select the labeled features, Relative Feature Margin Selection (RFMS) is used to select and train the model by Multilayer perception neural network (MLPNN). This classifier selects the margin weights to predict the heart disease risk level based on the class. This predicts higher impact of cardiac deficiency rate by attaining the relevant features based deep feature data learning model, which produce higher precision rate to increase the prediction accuracy than other methods. © 2022 IEEE

关键词： Forecasting

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