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2nd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Soorji Martina, K. Shobana, M. Sneha, S. Shaini, Vellanki Sreemathy, J. Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350327533

Rice is cultivated almost allover the world. In spite of the usage of several pesticides, a variety of diseases and pests reduce rice yield. Pesticide use reduces crop quality while increasing crop production. Thus, one of the key project in the field of computer science and agriculture department is the identification of diseases from captures of rice. This research study uses CNN to describe and classify various rice plant diseases. Processing techniques for images such as changing the size., rescaling, restoring equilibrium, and transforming into colors are carried out for recovering vital disease-related features. The approaches are highly helpful for farmers to quickly take the necessary corrective actions to protect their crops from diseases. In order to examine the current situation regarding the agricultural decision support system, this study has explored a number of research articles on the identification of rice plant diseases. This research study analyzes the datasets to obtain the required deep learning framework. The proposed model differentiates healthy leaves and disease-affected leaves. © 2024 IEEE.

关键词： CNN Deep Learning Dense Net Algorithm Image Processing

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Windmill Pitch Prediction And Error Correction With Adaptive Control And Current Generation Analysis Using data Analysis

Windmill Pitch Prediction And Error Correction With Adaptive...

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2023 International Conference on Energy, Materials and Communication engineering, ICEMCE 2023

作者： Minu, M.S. Rohit Suryaa, S. Jovan Titus, J. Sharan, S. Srm Institute of Science and Technology Dept. of Computer Science Engineering Chennai India Srm Institute of Science and Technology Dept. Computer Science Engineering Specialization with Big Data Analytics Chennai India

ISBN: (纸本)9798350393378

The growing prominence of wind energy as an eco-friendly power alternative amplifies the need for optimal turbine performance. Central to this is the ability to accurately predict and modulate the wind turbine pitch. Our research focuses on integrating adaptive control strategies with advanced power generation analytics to enhance pitch prediction precision. We have processed and analyzed vast amounts of wind data using state-of-the-art data analytics to discern patterns in wind velocity changes accurately. The continuous stream of real-time data is the backbone of our adaptive controls, ensuring perpetual pitch adjustments that optimize energy capture. In tandem with this, we present a thorough analysis linking momentary wind shifts to power production, ensuring consistent and efficient power output. The culmination of these methodologies offers a breakthrough solution in wind energy optimization. Preliminary results highlight the paramount importance of real-time data analytics in revolutionizing green energy advancements. © 2023 IEEE.

关键词： Wind power

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Performance Analysis of Vulnerability Detection Tools and Techniques 6

Performance Analysis of Vulnerability Detection Tools and Te...

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6th Parul International Conference on engineering and Technology, PICET 2024

作者： Pareek, Kailash Kumar Ameta, Gaurav Kumar Department of Computer Science and Engineering Parul University Gujarat Vadodara India Department of Computer Science and Engineering Parul Institute of Technology Parul University Gujarat Vadodara India

ISBN: (纸本)9798350369748

In today’s world security is a major concern. To strengthen security, organizations are investing huge amounts of money for incorporating different tools and techniques. Penetration testing is one of the security measures used to test the security of the system. Penetration testing is a procedure to identify vulnerabilities in the system by using different testing tools like BrupSuite, DirBuster, SQLMap, cURL, etc. This paper identifies the best penetration testing tool based on the study of different parameters, like multi-threading at over 6000 requests/sec, cross-platform supportiveness, ability to work over both HTTP and HTTPS, capability to support all databases, automatic recognition of password hash formats, ability to support multiple URLs, to support in crawling website, and to support the parallel transmission. Performance parameter values can be adjusted and tuned while the program is in running condition. After going through a study of all these parameters, it was observed that the DirBuster tool supports seven out of eleven parameters for penetration testing. DirBuster also supports both the Command line interface (CLI) and Graphical user interface (GUI). It is also seamlessly integrated into both Linux and Windows Operating Systems. As per our study, the DirBuster performed well as compared to the other tools of the same domain like BrupSuite, DirBuster, SQLMap, and cURL. © 2024 IEEE.

关键词： HTTP

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Cyber-Attacks and Anomaly Detection in Networking Based on Deep Learning—A Survey 1

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2nd International Conference on Computational Intelligence in Machine Learning, ICCIML 2022

作者： Swathi, K. Narsimha, G. Department of Computer Science and Engineering CVR College of Engineering Jawaharlal Nehru Technological University Hyderabad India Department of Computer Science and Engineering Jawaharlal Nehru Technological University Hyderabad India

ISBN: (数字)9789819979547

ISBN: (纸本)9789819979530

The rapid development in Internet has brought great convenience in our daily life, as well as cyber criminals focus shifted from real to virtual life as well. These security problems threaten user’s privacy and also increasing prominently because hackers launch attacks by injecting malicious software in to the network and disturbs normal flow of network activities. Traditional Artificial Intelligence specifically Machine Learning Algorithms is no longer effective in detecting new attack patterns and unknown abnormal behavior in the network traffic. The anomaly detection process helps in providing the report of malicious activities and gives better results. To detect abnormal traffic in network, we need a strong robust deep learning abnormal traffic detection framework. In this paper, different machine learning algorithms were studied in detecting the anomalies and cyber-attacks in network and to identify the problem in existing solutions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Anomaly detection

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A Survey on Privacy-Preserving Communication Frameworks in Machine Learning for Cybersecurity Threat Detection 4

A Survey on Privacy-Preserving Communication Frameworks in M...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Suneetha, Banothu Kesavan, R. Saveetha School of Engineering Simats Department of Computer Science & Engineering Chennai India

ISBN: (纸本)9798331529635

This survey covers the new trend of privacy-preserving communication frameworks in machine learning in the FL, DP and SMPC for cyber threat detection domains. The specific research performed between the year of 2020 and 2024 has been surveyed and included in this paper. The main contribution is monitoring cybersecurity threats using privacy-preserving frameworks in distributed systems such as IoT and industrial networks. 40 research papers have been reviewed and categorized to several related themes such as FL implementations, privacy-preserving methods, and cybersecurity threat detection. The relevance of privacy-preserving frameworks for the real-world cybersecurity scenario is illustrated with dataset CISA 2022 Vulnerability dataset. The study of privacy-preserving communication shows that FL can be combined with DP and SMPC to achieve data privacy preservation while deduction accuracy of detecting cyber threats can be well-maintained. Moreover, the research gaps and challenges of scalability, communication overhead have been addressed and identified to facilitate the future research and development. © 2024 IEEE.

关键词： Federated learning

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Heart Stroke Risk Prediction Using Machine Learning and Deep Learning Algorithm 6th

Heart Stroke Risk Prediction Using Machine Learning and Deep...

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6th International Conference on Communications and Cyber-Physical engineering, ICCCE 2023

作者： Prajwala Priyanka, M. Kumar, Sheo Lakshmi Bhargav, A. Department of Computer Science Engineering CMR Engineering College Hyderabad India Department of Computer Science Engineering Nalla Narasimha Reddy Education Society’s Group of Institutions Hyderabad India

ISBN: (纸本)9789819971367

Heart attack is a catch-all term for a variety of conditions affecting the heart. Analysis of large amounts of data and comparisons between them are essential for the prediction, prevention, and management of cardiovascular illnesses including heart attacks. This paper aims to develop an Intelligent System using various ML techniques such as Naive Bayes, KNN, Random Forest and Decision Tree. Users of this web-based software will answer a prepared series of questions. data analytics is crucial for organizing, analyzing, and controlling large data sets. It has shown beneficial in the diagnosis, prevention, and treatment of cardiovascular disease. With this in mind, we want to apply SVM- and GA-based data analytics to the challenge of identifying cardiac problems. A comparison of these algorithms’ prediction models indicates which one is the most developed. It may aid physicians in making more educated diagnoses of heart illness than would have been possible with older, more traditional methods of decision assistance. By providing efficient treatments, it contributes to cost-effective healthcare. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Heart

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Bangladeshi Crop Disease Detection Using Convolutional Neural Network 6

Bangladeshi Crop Disease Detection Using Convolutional Neura...

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6th International Conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Ibna Mizan, Md. Arafat Ahmed, Mohiuddin Ali, Md. Hasan Rajshahi University of Engineering and Technology Department of Computer Science and Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350385779

Self-sufficiency in food production is a vital survival tool for a country like Bangladesh in a deregulated world. However, crop diseases pose a significant obstacle to achieving self-sufficiency in food production in this small, agriculture dependent economy. The primary crops in Bangladesh include corn, rice, potato, and wheat. Every year, approximately 10% to 15 % of crops are lost due to insect infestations. Additionally, the lack of accurate diagnostic tools results in the unnecessary and excessive use of pesticides, which not only increases the cost of production but also contaminates the food supply. But nowadays, accurate and fast diagnosis is possible with the help of deep learning like Convolutional Neural Network (CNN). The primary goal of this paper is to accurately identify diseases in the main crops of Bangladesh, including corn, rice, potato, and wheat. This is achieved by utilizing EfficientNet-B3 as the backbone network. The model achieved an accuracy of 99% for corn, 92% for rice, and 100% for both potato and wheat, surpassing the performance of previous models. © 2024 IEEE.

关键词： Crops

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Generalizing Vehicle Manoeuvre Prediction Across Diverse datasets 2

Generalizing Vehicle Manoeuvre Prediction Across Diverse Dat...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Venkatarao, Marella Reddy, Dodda Venkata Rao, S. Siva Nageswara Ul Hassan Khan, Ameen Avinash, Golla Koushik, Pendela Chenchu Narasaraopeta Engineering College Department of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9798331534950

Due to the complex nature of automotive components and sensor data, predictive maintenance is essential to ensure the reliability and safety of the vehicle. This work introduces a new predictive algorithm for automotive engine health, designed as it will provide higher accuracy and faster decisions in detecting potential engine failures, linear Through discriminant analysis, Gaussian naive edges, support vector machines, decision trees, random forests, gradient enhancement, and AdaBoost, the program displays patterns and abnormalities that may indicate impending engine *** data set undergoes extensive preprocessing steps such as standardization, handling missing values, and feature engineering to improve model performance. The evaluation criteria used include accuracy, precision, and confusion matrix, with special attention to prevent overfitting through regularization and the early stop method. In the developed model, the group method, especially stacked model 1, obtains impressive results with a model accuracy of 0.99. This high accuracy highlights the effectiveness of the ensemble approach in managing forecasts. The model's ability to deliver real-time analysis and early warning can help significantly reduce maintenance costs, prevent failures, and enhance vehicle safety, resulting in improved vehicle engine health during the maintenance process. © 2024 IEEE.

关键词： Support vector machines

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A proactive load balanced handoff scheme for vehicular ad-hoc networks

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International Journal of Vehicle Information and Communication Systems 2023年第4期8卷 345-362页

作者： Madli, Rajeshwari Varaprasad, G. Department of Computer Science and Engineering B.M.S. College of Engineering Bengaluru Karnataka Bangalore India

The embracement of the wireless technology by the automobile industry has led to novel research interests in the field of Vehicular Ad-hoc Networks (VANETs). In addition to ad-hoc mode, a VANET supports infrastructure mode of communication, which provides access to internet services such as Hyper Text Transfer Protocol (HTTP), Voice over Internet Protocol (VoIP) and File Transfer Protocol (FTP), through access points known as Road Side Units (RSU). Vehicles can benefit from these services when associated with an RSU. However, because of uneven distribution and high mobility of vehicles, some RSUs are overloaded, resulting in network congestion and affecting the packet delivery ratio and throughput. This paper proposes a proactive load-balanced handoff scheme, in which vehicles evade overloaded RSUs and choose an RSU based on signal strength, network load and service reliability. Simulation results indicate that vehicles using the load-balanced handoff method, experience a higher packet delivery ratio and throughput. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Vehicular ad hoc networks

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An Internet of Things Based Mobile Application for Remote Monitoring and Management of Aquariums 1

An Internet of Things Based Mobile Application for Remote Mo...

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1st International Conference on Trends in engineering Systems and Technologies, ICTEST 2024

作者： Jacob, Pramod Mathew Moni, Jeni Joy, Alen Gopan, Gayathri Abraham, Alwin Arjun, O. Providence College of Engineering Department of Computer Science and Engineering Kerala Chengannur India

ISBN: (纸本)9798350307795

Aquatic lives are very sensitive in nature and is difficult to manage them in an aquarium. A slight change in the water temperature, dissolved oxygen content or feeding scheme may result in the death of those fishes. So, intense care and monitoring procedures are required for managing aquatic lives in an aquarium. As, Internet of Things (IoT) is integrated in every discipline of life, we propose an IoT based mobile application system for the smart monitoring and management of an aquarium. Temperature sensors and ultrasonic sensors are deployed in the aquarium to sense water temperature and water level respectively. These values will be sent to the Android app with the help of NodeMCU. If any of these values exceeds the threshold automated water management is done with the help of mini water pump. We also deployed a module to add fungal medicines to the aquarium water tank in a periodical manner which may enhance the growth of fishes in a healthier manner. Thus, our model can monitor and manage aquariums remotely over internet in an efficient and economical manner © 2024 IEEE.

关键词： Internet of things

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