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Deep Transfer Learning Techniques in Intrusion Detection System-Internet of vehicles: A State-of-the-Art Review

Computers, Materials & Continua 2024年第8期80卷 2785-2813页

作者： Wufei Wu Javad Hassannataj Joloudari Senthil Kumar Jagatheesaperumal kandala n.v.p.srajesh Silvia Gaftandzhieva Sadiq Hussain Rahimullah Rabih najibullah Haqjoo Mobeen nazar Hamed vahdat-nejad Rositsa Doneva School of Information Engineering Nanchang UniversityNanchang330031China Department of Computer Engineering Technical and Vocational University(TVU)Tehran***Iran Department of Computer Engineering University of BirjandBirjand***Iran Department of Computer Engineering Babol BranchIslamic Azad UniversityBabol***Iran Department of Electronics&Communication Engineering Mepco Schlenk Engineering CollegeSivakasi626005India School of Electronics Engineering VIT-AP UniversityNear Vijayawada522237India Faculty of Mathematics and Informatics University of Plovdiv Paisii HilendarskiPlovdiv4000Bulgaria Examination Branch Dibrugarh UniversityDibrugarh786004India Department of Computer Engineering Faculty of EngineeringUniversity of BirjandBirjand***Iran Faculty of Electrical and Computer Engineering University of BirjandBirjand***Iran Malaysian Institute of Information Technology Universiti Kuala LumpurKuala Lumpur50250Malaysia Faculty of physics and Technology University of Plovdiv Paisii HilendarskiPlovdiv4000Bulgaria

The high performance of IoT technology in transportation networks has led to the increasing adoption of Internet of vehicles(Iov)*** functional advantages of Iov include online communication services,accident prevention,cost reduction,and enhanced traffic *** these benefits,Iov technology is susceptible to cyber-attacks,which can exploit vulnerabilities in the vehicle network,leading to perturbations,disturbances,non-recognition of traffic signs,accidents,and vehicle *** paper reviews the state-of-the-art achievements and developments in applying Deep Transfer Learning(DTL)models for Intrusion Detection Systems in the Internet of vehicles(IDS-Iov)based on anomaly ***-Iov leverages anomaly detection through machine learning and DTL techniques to mitigate the risks posed by *** systems can autonomously create specific models based on network data to differentiate between regular traffic and *** these techniques,transfer learning models are particularly promising due to their efficacy with tagged data,reduced training time,lower memory usage,and decreased computational *** evaluate DTL models against criteria including the ability to transfer knowledge,detection rate,accurate analysis of complex data,and *** review highlights the significant progress made in the field,showcasing how DTL models enhance the performance and reliability of IDS-Iov *** examining recent advancements,we provide insights into how DTL can effectively address cyber-attack challenges in Iov environments,ensuring safer and more efficient transportation networks.

关键词： Cyber-attacks internet of things internet of vehicles intrusion detection system

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Capacitance Sensor for nondestructive Determination of Total Oil Content in peanut Kernels

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Journal of Sensor Technology 2013年第3期3卷 42-46页

作者： Chari v. kandala Rao C. n. Rachaputi Daniel O’Connor Agricultural Research Service United States Department of Agriculture Dawson USA Center for plant Science University of Queensland Kingaroy Australia

In this work, attempts were made to estimate the total oil content (TOC) in single peanut kernels, using the CI meter (Chari’s Impedance meter, described below). Mature peanut kernels of selected varieties with a range of oil contents from 47% to 61% were placed one at a time, between the parallel-plate electrodes of the CI meter, and the impedance (Z) and phase angle (q) of the system were measured, and capacitance, C was computed at 1, 5 and 9 MHz. After the measurements, the TOC of each kernel was determined by Soxhlet method. Using the known TOC values, and the corresponding C, Z and q values, initially on a calibration group of kernels, calibration equations were developed. Using the model coefficients from the calibration, the TOCs of kernel samples of 31 diverse peanut genotypes grown in different environments in Australia were determined. The method predicted the TOC values of peanut kernels of 31 peanut genotypes, within 2% of the Soxhlet values, with an R2 of 0.87 (p 0.001).

关键词： Impedance Analyzer parallel-plate Electrodes Capacitance phase Angle Total Oil Content (TOC)

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peanuts and Their nutritional Aspects—A Review

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Food and nutrition Sciences 2012年第12期3卷 1644-1650页

作者： v. S. Settaluri C. v. K. kandala n. puppala J. Sundaram national peanut Research Laboratory United States Department of Agriculture Agricultural Research Service Dawson USA new Mexico State University Las Cruces USA United States Department of Agriculture Agricultural Research Service Athens USA.

peanut is a legume crop that belongs to the family of Fabaceae, genus Arachis, and botanically named as Arachis hypogaea. peanuts are consumed in many forms such as boiled peanuts, peanut oil, peanut butter, roasted peanuts, and added peanut meal in snack food, energy bars and candies. peanuts are considered as a vital source of nutrients. nutrition plays an important role in growth and energy gain of living organisms. peanuts are rich in calories and contain many nutrients, minerals, antioxidants, and vitamins that are essential for optimum health. All these biomolecules are essential for pumping vital nutrients into the human body for sustaining normal health. This paper presents an overview of the peanut composition in terms of the constituent biomolecules, and their biological functions. This paper also discusses about the relationship between consumption of peanuts and their effect on human metabolism and physiology. It highlights the usefulness of considering peanuts as an essential component in human diet considering its nutritional values.

关键词： peanuts nutrition Biomolecules vitamins Minerals plant protein

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