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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Nisha Chandran, S. Garg, Ashish Namdev, Dheeraj Kukreja, Vinay Mehta, Shiva School of Computing Graphic Era Hill University Uttarakhand Dehradun India Graphic Era Deemed to be University Computer Science and Engineering Uttarakhand Dehradun India IIMT College of Engineering Artificial Intelligence & Data Science Uttar Pradesh Greater Noida201310 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350383652

A hybrid approach integrating both CNN and RF is investigated in this research study of the Survey regarding the classification of maize leaf diseases with the help of CNN and RF. Our research carefully contrasts five created disease severities as a reported diagnostic tool for agriculturalists. The high accuracy of the model was extensively validated by applying various performance metrics with excellent precision of the different classes ranging from 95.90% to 98.47% for recall ranged between 95.01% to 99.21%, and F1- scores ranged between 96.55% to 98.4. The model achieved a general accuracy of 97.42851 %. We employed various averaging approaches to generalize the performance results across varying illness levels. A macro average yielded an accuracy of 97.37%, recall of 97.48%, and F1-score of 97.42%, implying similarity of performance across classes. Considering class imbalance, the weighted average was accurate at 97.44%, recalled at 97.43%, and the F1-score was similar at 97.43%. The micro average, as the aggregation of results across classes, was remarkably stable at 97.43 %, representing the performance of the model well and especially favoring frequent courses. Early identification and classification of diseases affecting maize leaves significantly contributed to precision agriculture, and this paper presents a reliable and efficient method for a timely diagnosis. It shows the stable performance of the CNN-RF model because many statistical measures are indicated. This implies that it possesses reasonable practicality in agricultural applications that highly support pest control and increase crop yields. © 2024 IEEE.

关键词： Agriculture Convolutional neural networks (CNNs) Image classification Maize Leaf Diseases Random Forest

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Lightweight Authenticated Key Agreement Protocol for Smart Power Grid Systems Using PUF

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2024年 5卷 3568-3580页

作者： Zahoor, Amina Mahmood, Khalid Saleem, Muhammad Asad Badar, Hafiz Muhammad Sanaullah Le, Tuan-Vinh Das, Ashok Kumar Department of Computer Science Sahiwal57000 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Emerson University Multan Department of Emerging Computing Technologies Multan61000 Pakistan Fu Jen Catholic University Bachelor's Program of Artificial Intelligence and Information Security New Taipei City24206 Taiwan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India

The Smart Power Grid (SPG) is pivotal in orchestrating and managing demand response in contemporary smart cities, leveraging the prowess of Information and Communication Technologies (ICTs). Within the immersive SPG environment, the ubiquitous deployment of smart meters stands as a testament to their paramount importance in the realm of vigilant monitoring and oversight. These smart meters are installed on high-tension electricity lines and transmit information about electricity outages and other issues to the utility center. To access services from utility centers, smart meters need to communicate securely over a public channel, even though the network itself is insecure. However, potential attacks from adversaries ( Ad) can exploit this communication. Therefore, protecting this communication is of utmost importance. Several privacy-preserving authentication protocols designed for SPG have been introduced in the literature. Nevertheless, a significant number of these protocols exhibit vulnerabilities to diverse security attacks. This article introduces a lightweight and anonymous authentication protocol specifically designed to address these concerns in the SPG environment. Our protocol ensures both security and efficiency, surpassing other comparable protocols in terms of its lightweight nature. By employing both formal and informal analysis, we showcase the robustness of our protocol against significant attacks while remaining lightweight. The proposed protocol provides added security features and incurs 23.8879% lower computation cost than related protocols. Consequently, our protocol is highly suitable for implementation in the SPG system. © 2020 IEEE.

关键词： Hash functions

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Advanced Deep Learning Model for Enhanced Kidney Disease Detection 5

Advanced Deep Learning Model for Enhanced Kidney Disease Det...

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5th International Conference on data intelligence and Cognitive Informatics, ICDICI 2024

作者： Davamani, K. Anita Dhanya, D. Pushparani, S. Vidhya, R. Mohan, Geetha Jani Anbarasi, L. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Chennai India Mar Ephraem College of Engineering and Technology Department of Artificial Intelligence and Data Science Elavuvilai India Meenakshi College of Engineering Department of Cse Chennai India Agni College of Technology Department of Information Technology Chennai India Institute of Applied Technology Abu Dhabi United Arab Emirates Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350389609

Automated Chronic Kidney Disease (CKD) is a swiftly growing non-communicable condition that contributes to a substantial number of deaths and illnesses worldwide. Affecting over 10% of the global population, CKD results in millions of fatalities each year. In this study, an advanced deep learning model for kidney disease detection is proposed that combines convolutional and involution layers to improve classification accuracy. The model processes input images by first applying a 2D convolution with 32 filters of size 3x3, followed by ReLU activation to extract key features. Max-pooling layers are then used to reduce spatial dimensions while retaining crucial information. The convolutional layers are sequentially stacked with larger filter sizes, resulting in progressively richer feature representations. To enhance feature extraction, the model integrates custom involution layers, which extend traditional convolution operations and are particularly effective for processing large-scale image data. These involution layers are applied after the convolutional steps, utilizing 128 and 256 filters of size 3 × 3, followed by max-pooling to further refine the extracted features. A comparative analysis was carried out between the proposed custom Convolutional Neural Network (CNN) model and previous research works, utilizing metrics such as Recall, Precision, F1-Score, Accuracy, Loss, Area Under the Curve (AUC) and Receiver Operating Characteristic (ROC) curves. The results indicate that the proposed model achieves superior classification prediction performance, with an accuracy of 99% and reduced computational complexity. Additionally, it demonstrates a enhanced F1-Score of 100%, 98%, 98% and 100% for Cyst, Normal, Stone and Tumor respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Pheromone Matrix Eigenvalue-based Convergence Assessment Indicator for Ant Colony Optimization 11

Pheromone Matrix Eigenvalue-based Convergence Assessment Ind...

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11th International Conference on Machine intelligence Theory and Applications, MiTA 2024

作者： Li, Xu Shi, Lin Li, Jian-Yu Xu, Jing Zhan, Zhi-Hui South China University of Technology School of Computer Science and Engineering Guangzhou510006 China Guangdong University of Finance and Economics Research Center of Intelligent Computing & Big Data Technology Guangdong Guangzhou510320 China Nankai University College of Artificial Intelligence Tianjin300350 China

ISBN: (纸本)9798350385670

As a crucial member of the evolutionary computation (EC), ant colony optimization (ACO) converges to the optimal result through a continuous process of iterative optimization. However, determining the convergence status of an ant colony remains a challenging task without well-established methods. To this end, this paper proposes a global evaluation indicator - average absolute eigenvalue of pheromone matrix (AAEPM) by constructing a pheromone matrix and analyzing its eigenvalue variations. This indicator aims to assess the convergence status of the ant colony during the evolutionary process. Experimental results show that the AAEPM is correlated with the convergence status of ACO and is not significantly influenced by algorithm parameters and problem scale. © 2024 IEEE.

关键词： Anthropogenic

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artificial intelligence Techniques Based Energy Prediction Analysis and Load Forecasting Using a Dynamic Website 1

Artificial Intelligence Techniques Based Energy Prediction A...

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1st International Conference on Intelligent Technologies for Sustainable Electric and Communications Systems, iTech SECOM 2023

作者： Ramesh, R. Dharani, S. Asuvanti, M.A. Gowrijayasree, Gowrijayasree Indhu Rekha, U. Bannari Amman Institute of Technology Department of Electronics and Communication Engineering India Dr. Mahalingam College of Engineering and Technology Department of Electronics and Communication Engineering India Sengunthar Engineering College Department of Electronics and Communication Engineering India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science India

ISBN: (纸本)9798350371185

Electricity plays a crucial part in human living. The use of electricity has become a necessity. Though energy is a renewable resource it is being wasted often due to energy leakage, short circuit, and load balancing. Numerous actions and choices, including the use of load prediction affect countless decisions and actions, including transportation, unit involvement, fuel reservation, and out-of-service network study. Energy consumption data of a building is generated via an energy meter;it is then trained to fewer models for prediction and forecasting. Energy usage forecasting using LSTM with a higher accuracy of 87% than other Autoregressive models and with a Mean Absolute Error of 0.03 is suitable for implementation. Then the model is built into a Web App that enables users to keep track of the energy usage of their appliances. In the next step a threshold value is set, and whenever the appliances reach the maximum threshold value it is notified to the user or consumer which helps in reducing unwanted energy wastage in any particular area of use. © 2023 IEEE.

关键词： Energy efficiency

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Enhancing Security and Privacy in Health Care Using Cyber-physical Systems Through Machine Learning 8th

Enhancing Security and Privacy in Health Care Using Cyber-ph...

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8th International Conference on Information and Communication technology for Intelligent Systems, ICTIS 2024

作者： Takale, Dattatray G. Gawali, Piyush P. Deshmukh, Gopal B. Mahalle, Parikshit N. Mehta, Pradnya S. Kashid, Shraddha S. Sule, Bipin Derle, Deepak R. Department of Computer Engineering Vishwakarma Institute of Information Technology SPPU Pune India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology SPPU Pune India Department of Computer Engineering MIT ADT University Pune India and Humanities Vishwakarma Institute of Technology Pune India Department of Computer Science and Application KTHM College Nashik India

ISBN: (纸本)9789819766772

The importance of securing cyber-physical systems (CPS) is paramount to safeguarding the interconnected networks and devices, especially in sectors such as blockchain, health care, and the "Internet of Things" (IoT). The importance of authentication for users and devices in CPS security is highlighted in this paper. As a result of the intricate interconnectivity of networks, the integration of cyber-physical systems (CPS) in health care has revolutionized the delivery of medical services in an era of rapidly advancing technology. There are, however, serious concerns surrounding the privacy and security of sensitive healthcare data as a result of this integration. In this paper, a revolutionary approach to address these concerns is presented by utilizing machine learning techniques to address these problems. To enhance security measures within healthcare CPSs and protect privacy, Random Forest, a powerful ensemble learning algorithm, is employed as an integral part of the security measures. The proposed system utilizes a combination of anomaly detection, adaptive learning, and multi-attribute feature selection in order to identify potential security breaches while simultaneously ensuring patient confidentiality. As an additional security precaution, encryption protocols are implemented to ensure that the transmission of data is secure, preventing unauthorized access to the data as well. "As a comprehensive evaluation of the system's performance, a number of evaluation metrics are used, including Accuracy, Precision, Recall, F1 Score, and AUC-ROC". Based on these results, it is evident that this approach is effective in providing robust security and privacy protections in healthcare CPS, thereby establishing a new paradigm for delivering safe and confidential health care in the digital age. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cybersecurity

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Machine Learning-Based Security for Cloud Computing Challenges and Implications 3

Machine Learning-Based Security for Cloud Computing Challeng...

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Dattangire, Rahul Burle, Rushikesh Biradar, Divya Dewangan, Leelkanth Data Engineering Independent Researcher HoustonTX77002 United States Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Maharashtra Wardha442001 India University of Texas at Arlington Computer Science ArlingtonTX76013 United States G H Raisoni College of Engineering Maharashtra Nagpur India

ISBN: (纸本)9798350364569

Cloud computing"is a computer model that provides end users with quantifiable, scalable, and on-demand services. These days, almost every organization uses computer technology extensively for infrastructure, cost-cutting, advanced platforms, data processing, data analytics, and other uses. End users can use any online application from anywhere to access the cloud service providers' (CSP) services. It is imperative to guarantee the security of cloud infrastructure, and a multitude of research employing various technologies is used to provide more accurate and potent defenses against cloud threats. Machine learning has shown to be a more useful technology in recent years for protecting cloud infrastructures. Machine learning algorithms are trained to generate models that mechanize the process of recognizing cloud threats more precisely than any other technology by using a range of actual datasets as training grounds. A couple of the most recent research that has worked on machine learning to defend against cloud attacks are examined in this paper. © 2024 IEEE.

关键词： Adversarial machine learning

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Machine Learning Models for Early Prediction of COVID-19 Infections Based on Clinical Signs

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SN Computer science 2024年第1期5卷 1-10页

作者： Ben Ammar, Boulbaba Salem, Ali Ben Said, Mouna Ben Aouicha, Mohamed Data Engineering and Semantics Research Unit Faculty of Science University of Sfax Sfax Tunisia Digital Research Center of Sfax Ministry of Higher Education and Scientific Research Sfax Tunisia Signals systeMs aRtificial intelligence and neTworkS Laboratory (SM@RTS) Digital Research Center of Sfax Sfax Tunisia

Nowadays, the appearance of common symptoms, such as cough, fever, and loss of smell and taste, is the starting point of a battle against the coronavirus. The first standard method of COVID-19 infection assertion has become the RT-PCR test, which is however an uncomfortable solution for both patients and medical staff due to its high cost, timeliness, and false-negative result issue. This has raised the need for reliable automatic detection systems that aid in the early prediction of the COVID-19 infections with a lower cost. In this work, we aim at profiting from the Machine Learning (ML) advances to provide a reliable and low-cost COVID-19 prediction system. This system is based on the disease starting point, which is the patients’ clinical symptoms, that are still under-explored. We developed seven predictive models using traditional ML classification algorithms using a public dataset of obvious high-risk factors from patients’ clinical signs. The dataset has first undergone a pre-processing phase consisting of feature engineering and dataset resampling to deal with imbalanced dataset issue. Our best classification model is able to detect true positives and true negatives and weed out false positive and false negatives with an accuracy of 93%. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Clinical signs COVID-19 Early diagnosis Machine learning

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Deep Learning Based Classification and Prediction on Vegetable Crop Leaf 4

Deep Learning Based Classification and Prediction on Vegetab...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Pushpa, P. Priyanka, M. Somu, C.S. Venitha, E. Savija, J. Mohanaprakash, T.A. Chennai India Soet Cmr university Department of Cse Bangaluru India Sri Sairam Institute of Technology Department of Cse Chennai India Rajalakshmi Institute of Technology Department of Cse Chennai India Velammal Engineering College Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350352931

Globally speaking, India is a country that is growing quickly. Crop diseases pose a severe danger to food security, yet they are still hard to identify. Due to their reliance on a manually created feature extraction process, these systems' accuracy has reached its peak. This approach classification method needs to incorporate CNN to surpass grading accuracy for tomato leaf pestilence. To properly describe and categorize tomato infections, the Deep Learning algorithm is used. Using a sample of 3000 frames of tomato leaves with nine various pestilences and a better and healthier leaf, the full simulation was carried out using Google Colab. The targeted area of the input photographs is first segregated from the genuine snaps after preprocessing the input images. Second, the frames are further processed using various CNN model hyper-parameters. CNN also extracts additional qualities from frames, such as colours, borders, and textures. The results reveal that the predictions made by the demonstrated replica are 98.49% precise. © 2024 IEEE.

关键词： Fruits

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An Enhanced Smart Intelligent Detecting and Alerting System for Industrial Gas Leakage using IoT in Sensor Network 5

An Enhanced Smart Intelligent Detecting and Alerting System ...

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5th International Conference on Smart Systems and Inventive technology, ICSSIT 2023

作者： Senthil, G.A. Suganthi, P. Prabha, R. Madhumathi, M. Prabhu, S. Sridevi, S. Agni College of Technology Dept. of Information Technology Chennai India Sri Sai Ram Institute of Technology Dept. of Electronics and Communication Engineering Chennai India Karpaga Vinayaga College of Engineering and Technology Dept. of Artificial Intelligence and Data Science Chennai India Sri Ramachandra Engineering and Technology Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665474672

Leakage of any type of gas has emerged as a hazard in recent years, whether in a typical workplace, boiler, a commercial setting, a café, or a canteen, industries natural gas, coal, refinery products, crude oil, cement, electricity, steel, and fertilizers etc. In this article, I proposed a new invention of an IoT-based gas waste monitoring, leak detection, and alerting system. The research extends on the development of such an intelligent system that can intelligently preserve gas, ensure the safety precaution and pre vent accidents. The technique must be connected to the stove. The design incorporates ultrasonic sensors which identify bio gas leakage in circumstance with in square metre is usage area of industries or cooking place. The Proposed work smart intelligence system is cuts off the gas supply using an automated shutting off device. Whenever a gas leak is detected, consumers will be notified through SMS via GSM, location using GPRS, and will be able to resolve the situation as quickly as feasible. The flame sensor in the technology will supervise flame and fire. The buzzer starts to sound when a fire is detected. Aside from that, the system may save data on the cloud. With the help of this cloud storage solution, every user's daily gas use may be traced. Finally, this approach will aid in recognizing per user natural gas consumption. The system has been examined and is capable of monitoring gas loss, evacuation, and sending an SMS notification and location aid to the user. The precipitate demonstrated its usefulness in conserving a considerable amount of domestically squandered gas. © 2023 IEEE.

关键词： Liquefied petroleum gas

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