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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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Finding of Maturity Level of Crops for Harvesting using IOT and ML Based Structured Framework 4

Finding of Maturity Level of Crops for Harvesting using IOT ...

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4th IEEE International Conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Swaranambigai, R. Mythili, R. Aeron, Anurag Thilakavathy, P. Alassedi, Zainab Al-Azzawi, Waleed Jaishriram Engineering College Department of Ece Tiruppur India Srm Institute of Science and Technology Department of Information Technology Chennai Ramapuram India Miet Meerut Department of Cse Meerut India Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India National University of Science and Technology Collage of Nursing Dhi Qar64001 Iraq Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350360165

IoT designs make it possible to collect data for vast and remote agricultural regions, which may then be used to use machine learning algorithms to anticipate crops. In order to establish the suggested crop, recommendations are based on factors including temperature, humidity, rainfall, pH, N, P, and K. The dataset includes 2200 occurrences and 8 characteristics. For different combinations of these attributes, about 22 alternative crops are suggested. Using specific machine learning algorithms in WEKA, supervised learning techniques are used to create an ideal model. The rules-based classifier JRip, the decision table classifier, and the multilayer perceptron are the machine learning methods selected for classification. Establishing a model that can forecast high-yield crops and facilitate precision agriculture is the main goal of this case study. IoT technologies and crucial agricultural metrics are included in the suggested system modeling. A weighted average receiver operator characteristic of one and an accuracy of 98.2273% are shown by the performance evaluation of the chosen classifiers. 8.05 seconds is the maximum amount of time needed to construct the model. © 2024 IEEE.

关键词： Adversarial machine learning

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IoT-Enabled Automated Smart Irrigation System Incorporating Sensor Parameters 5th

IoT-Enabled Automated Smart Irrigation System Incorporating ...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Singh, Jagendra Neeraj Srinivas Reddy, P. Hasan, Dler Salih Alsahlanee, Abbas Thajeel Rhaif Shaik, Nazeer Alliance School of Advanced Computing Alliance University Bangalore India School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Mechanical Engineering CVR College of Engineering Hyderabad India Department of Computer Science and Information Technology University of Salahaddin-Erbil Erbil Iraq Development and Continuous Education Center University of Thi-Qar Thi-Qar Iraq Department of Computer Science and Engineering Srinivasa Ramanujan Institute of Technology—Autonomous Anantapur India

ISBN: (纸本)9789819763177

This article presents the experimental findings of a smart irrigation system evaluated in two separate agricultural settings—small-scale farming and large-scale agriculture. The system combines sensors, microcontrollers, and actuators to precisely monitor and regulate the irrigation process in order to optimum water use and crop output. Water savings range from 20 to 45% when using smart irrigation technology in small-scale agriculture. By only turning on the irrigation pump when necessary, the technology optimizes water use and fosters sustainable farming methods. Furthermore, by sustaining perfect growing conditions, the approach boosts crop output, with yield benefits ranging from 10 to 22%. These improvements assist small-scale farmers in becoming more profitable and raising their level of living. In large-scale agriculture, smart irrigation systems allow excellent water management and resource optimization across huge agricultural fields. The technology consistently saves a large quantity of water, often between 25 and 50%, addressing water scarcity issues. Additionally, this method has led to a 12–25% increase in aggregate crop yields, thereby raising farm income and increasing agricultural firm profitability. Experiments shows that this smart irrigation system could bring cost-effective, sustainable techniques to extensive and intensive agriculture alike. Intelligence-guided decision-making technology is applied here, and cutting-edge technologies are brought to bear on the water use problem. The system conserves precious resources and increases agricultural output at the same time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Smart agriculture

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A Novel Deep Belief Network with Butterfly Optimization Algorithm for the Classification of Paddy Leaf Disease Detection

A Novel Deep Belief Network with Butterfly Optimization Algo...

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Artificial Intelligence and Smart Systems (ICAIS), International Conference on

作者： U. Lathamaheswari J. Jebathangam Department of Computer Science Vels Institute of Science Technology and Advanced Studies Chennai India

The widespread presence of a wide variety of diseases during paddy farming is one of the most significant elements that annually contributes to enormous economic losses. These losses occur as a direct result of the widespread prevalence of these diseases. In this paper, a deep learning algorithm using Deep Belief Network (DBN) and a meta-heuristic optimization using Butterfly optimization algorithm (BOA) is used to classify the images to detect the diseases in a Plant Leaf. The steps of classification involve three different process that includes pre-processing, feature extraction and classification. The simulation is conducted in python to test the efficacy of the classifier. The result of simulation shows that the proposed method has obtained higher classification rate than the existing machine learning classifiers. .

关键词： Deep learning Machine learning algorithms Metaheuristics Real-time systems Classification algorithms Internet of Things Task analysis

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A Survey of advanced Learning Techniques Used for Initial Detection of vital Human organs Disorders through Iris

A Survey of Advanced Learning Techniques Used for Initial De...

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International Conference on Smart Structures and Systems (ICSSS)

作者： M Sandhiya A S Aneetha Department of Computer Science and Engineering VELS Institute of Science Technology & Advanced Studies (VISTAS)

An approach of early detection that makes use of an optimization technique that was brought about by learning techniques. Artificial intelligence technology breakthroughs that can scan a patient’s body and diagnose the conditions they have would be expected by certain doctors. To gain a deeper understanding of the human body, iridology is an applied health practice. Images of iris can be acquired and interpreted using a variety of innovative techniques. Information on the human body and organ health is contained in Iris. Iridology is an academic discipline that examines the strengths and limitations of iris tissue to gain insight into the health of the body’s various organs. Iridology has grown more well-liked and trustworthy as a result of the quick growth of picture processing. Many iridology-based methods have been created recently that use certain iris features to detect the human disorders. The information contained herein displays every iris recognition method that has been studied along with the precision of each region. Future iridology research’ design and methodological decisions can be influenced by the studies outcomes.

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Retraction Notice: A Survey of advanced Learning Techniques Used for Initial Detection of vital Human organs Disorders through Iris

Retraction Notice: A Survey of Advanced Learning Techniques ...

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International Conference on Smart Structures and Systems (ICSSS)

作者： M Sandhiya A S Aneetha Department of Computer Science and Engineering VELS Institute of Science Technology & Advanced Studies (VISTAS)

ISBN: (数字)9798350384208

ISBN: (纸本)9798350384215

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A unique ADAGRAD optimized DCNN with RESNET-18 Architecture for Indoor Agriculture-Based Crop Yield

A unique ADAGRAD optimized DCNN with RESNET-18 Architecture ...

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computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： D. Radha S. Prasanna Vels Institute of Science Technology and Advanced Studies (VISTAS) Pallavaram Tamil Nadu India Department of Computer Applications Vels Institute of Science Technology and Advanced Studies (VISTAS) Tamil Nadu India

A new kind of cityscape has evolved in the so-called “megacities” in the last few decades. By 2030, experts predict that over five billion people will call cities home. The World Health Organization estimates that by 2050, there will be 10 billion people on Earth, with 6.5 billion of them residing in metropolitan areas. To fulfill this demand, food production must grow by 70%. In order to facilitate the transition to more sustainable and efficient food supply solutions and the transportation of clean, fresh vegetables, several initiatives have been launched in megacities to bolster the new ecosystem services. An unusual agricultural technique that may address food insecurity, indoor agriculture (IA) consumes less area, provides better yields, and is resource-efficient. Crop monitoring, biotic and abiotic stress detection, irrigation, microclimate prediction, energy efficient controls, and crop growth prediction are just a few of the many uses of deep learning (DL) that have recently been brought to IA. But there hasn’t been a comprehensive evaluation of DL’s capabilities to address a wide range of IA issues. The setting, such as the circumstances within a greenhouse, is crucial in agriculture. A number of recent studies have shown that farmers might benefit from data, reminders, and warnings provided by sensors that track productivity or by cameras that capture information on agriculture. Using an improved RESNET-18-based DCNN model with an Adagrad optimizer, we provide a new approach to indoor tomato yield prediction in this research. The suggested model outperforms the state-of-the-art models and demonstrates a considerable improvement in prediction accuracy.

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Optimal Deep Learning Driven Intrusion Detection in SDN-Enabled IoT Environment

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computers, Materials & Continua 2023年第3期74卷 6587-6604页

作者： Mohammed Maray Haya Mesfer Alshahrani Khalid A.Alissa Najm Alotaibi Abdulbaset Gaddah AliMeree Mahmoud Othman Manar Ahmed Hamza Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia SAUDI ARAMCO Cybersecurity Chair Networks and Communications DepartmentCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Prince Saud AlFaisal Institute for Diplomatic Studies RiyadhSaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Warwick UniversityCovetryUK Department of Computer Science Faculty of Computers and Information TechnologyFuture University in Egypt New Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

In recent years,wireless networks are widely used in different *** phenomenon has increased the number of Internet of Things(IoT)devices and their *** IoT has numerous advantages,the commonly-used IoT devices are exposed to cyber-attacks *** scenario necessitates real-time automated detection and the mitigation of different types of attacks in high-traffic *** Software-Defined Networking(SDN)technique and the Machine Learning(ML)-based intrusion detection technique are effective tools that can quickly respond to different types of attacks in the IoT *** Intrusion Detection System(IDS)models can be employed to secure the SDN-enabled IoT environment in this *** current study devises a Harmony Search algorithmbased Feature Selection with Optimal Convolutional Autoencoder(HSAFSOCAE)for intrusion detection in the SDN-enabled IoT *** presented HSAFS-OCAE method follows a three-stage process in which the Harmony Search Algorithm-based FS(HSAFS)technique is exploited at first for feature ***,the CAE method is leveraged to recognize and classify intrusions in the SDN-enabled IoT ***,the Artificial Fish SwarmAlgorithm(AFSA)is used to fine-tune the *** process improves the outcomes of the intrusion detection process executed by the CAE algorithm and shows the work’s *** proposed HSAFSOCAE technique was experimentally validated under different aspects,and the comparative analysis results established the supremacy of the proposed model.

关键词： Internet of things SDN controller feature selection hyperparameter tuning autoencoder

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Analysis of Various COVID-19 Prediction Techniques

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IEIE Transactions on Smart Processing and computing 2021年第4期10卷 323-329页

作者： Arora, Rakesh Kumar Gupta, Manoj Kumar Bhati, Bhoopesh Singh Department of Computer Science and Engineering Krishna Engineering College UP India Department of Computer Science and Engineering Trinity Institute of Innovations in Professional Studies UP India Department of Computer Science and Engineering Ambedkar Institute of Advanced Communication Technologies and Research Delhi India

There is no precise treatment available for COVID-19, but treatment of a number of signs/symptoms is possible. This treatment is provided in accordance with the clinical position of the patient. Furthermore, supplementary care is proven as efficient for infected people. COVID-19 prediction techniques follow various steps, which include dataset collection, pre-processing, feature extraction, and classification. Numerous COVID-19 prediction techniques have been proposed in the literature. In this paper, various techniques are systematically reviewed on the basis outcomes, advantages, and disadvantages. Copyrights © 2021 The institute of Electronics and Information Engineers

关键词： COVID-19

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advanced Optimizing Network Security in Manets: A Comprehensive Multilevel-Optimization and Monitoring Mechanism (Multi-Omm)

Advanced Optimizing Network Security in Manets: A Comprehens...

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

作者： A. Aravind A. Poongodi Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advance Studies Chennai Tamil Nadu India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

With the proliferation of the Internet of Things (IoT), mobile ad hoc networks (MANET) have become an essential component of contemporary wireless communications. These networks, which rely on multi-hop data transmission, offer significant flexibility and adaptability. However, their open and dynamic nature makes them vulnerable to various internal and external security threats. Among these, Black hole attacks by malicious nodes present serious challenges. Traditional security measures, such as firewalls and encryption, often fall short in addressing these threats. Therefore, there is an urgent need for advanced intrusion detection systems tailored for these environments. Additionally, node selfishness-where nodes conserve their resources at the expense of others-further disrupts network performance and violates protocols. To address these issues, we propose the Multilevel Optimization and Monitoring Mechanism (Multi-OMM), a novel algorithm designed for real-time detection and prevention of malicious activities. Multi-OMM employs a multi-phase approach to enhance network security through traffic pattern analysis, dynamic topology inspection, and targeted attack detection. By implementing and evaluating Multi-OMM through comprehensive experiments, we aim to demonstrate its effectiveness in identifying and mitigating both selfish and malicious behaviors, thereby improving overall network security and performance.

关键词： Wireless communication Protocols Network security Traffic control Throughput Topology Internet of Things Monitoring AODV Mobile ad hoc networks

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