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3rd International Conference on Emergent Converging Technologies and Biomedical Systems, ETBS 2023

作者： Sharma, Annu Devasenapathy, Deepa Raja, M. Shadrach, Finney Daniel Shirgire, Anil Arun, R. Yau, Thomas Moh Shan Department of Computer Science and Applications Rajarajeshwari College of Engineering Karnataka Bengaluru560074 India Instructor-II/Computing and Software Engineering U.A. Whitaker College of Engineering Florida Gulf Coast University Fort Myers United States Department of Computer Science and Engineering CMR Institute of Technology Karnataka Bengaluru560037 India Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Tamil Nadu Coimbatore641407 India Department of Civil Engineering Dr. D Y Patil Institute of Technology Maharashtra Pimpri Pune411018 India Department of Computer Science and Engineering Hindusthan Institute of Technology Coimbatore India Director of Centre of University Courses and Innovative Learning University of Technology Sarawak Jalan Universiti Sibu Sarawak96000 Malaysia

ISBN: (纸本)9789819986453

Video surveillance is mostly used for monitoring distant videos and the cloud memory in the system provides more secure to the data. But, in the existing methodologies poor intrusion detection accuracy as well as poor performance rate is considered as the main demerits. So, to overcome the above-mentioned demerits, a novel video surveillance-based Intrusion Detection System (IDS) (VS-IDS) in the edge cloud environment via deep learning method has been developed in this research. Different algorithms are used for object detection and intrusion detection. Subsequently, the proposed model can be implemented and verified in NVIDIA Jetson TX2 platform. The proposed model attains better rate of performance than the existing models. The Performance of the proposed model can be validated with accuracy, precision, recall, MSE and F-measure. Higher performance rate indicates that the system can detect intrusion more accurately than the existing mechanisms. 99% of detection accuracy can be attained through the proposed model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Intrusion detection

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A multimodal approach for enhanced disease management in cauliflower crops:integration of spectral sensors, machine learning models and targeted spraying technology

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Frontiers of Agricultural science and engineering 2025年第2期12卷 261-273页

作者： Rohit ANAND Roaf Ahmad PARRAY Indra MANI Tapan Kumar KHURA Harilal KUSHWAHA Brij Bihari SHARMA Susheel SARKAR Samarth GODARA Shideh MOJERLOU Hasan MIRZAKHANINAFCHI Division of Agricultural Engineering ICAR-IARI Vasantrao Naik Marathwada Krishi Vidyapeeth Central Arid Zone Research Institute Division of Vegetable Sciences ICAR-IARI Division of Design of Experiments ICAR-IASRI Division of Computer Applications ICAR-IASRI Department of Agroecology-Entomology and Plant Pathology Aarhus University Electrical Engineering and Computer Science Daktronics Engineering Hall South Dakota State University

This research explored a novel multimodal approach for disease management in cauliflower crops. With the rising challenges in sustainable agriculture, the research focused on a patch spraying method to control disease and reduce crop losses and environmental impact. For non-destructive disease assessment, a spectral sensor was used to collect spectral information from diseased and healthy cauliflower parts. The spectral data sets were analyzed using decision tree and support vector machine（SVM） algorithms to identify the most accurate model for distinguishing diseased and healthy plants. The chosen model was integrated with a low-volume sprayer(50-150 L·ha-1),equipped with an electronic control unit for targeted spraying based on sensor-detected regions. The decision tree model achieved 89.9% testing accuracy, while the SVM model achieved 96.7% accuracy using hyperparameters: cost of 10.0 and tolerance of 0.001. The research successfully demonstrated the integration of spectral sensors, machine learning, and targeted spraying technology for precise input ***, the optimized sprayer achieved a 72.5% reduction in chemical usage and a significant time-saving of 21.0% compared to a standard sprayer for black rot-infested crops. These findings highlight the potential efficiency and resource conservation benefits of innovative sprayer technology in precision agriculture and disease management.

关键词： Disease management site-specific sprayer spectral sensor machine learning models cauliflower crop black-rot disease

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IoT-Enabled Asset Management and Tracking in Organizations 2

IoT-Enabled Asset Management and Tracking in Organizations

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Kumar, Brajesh Anjali, Y. Mudholkar, Megha Mudholkar, Pankaj Bharati, P. Vijaya Abdullayeva, Nilufar Dr. Apj Abdul Kalam Women's Institute of Technology Department of Computer Science & Engineering Bihar Darbhanga India Mlr Institute of Technology Department of Cybersecurity Hyderabad India Marwadi University Department of Computer Engineering Gujarat Rajkot360003 India Marwadi University Faculty of Computer Applications Gujarat Rajkot360003 India Vignans Institute of Engineering for Women Department of Cse Andhra Pradesh Visakhapatnam530049 India Tashkent State University of Economics The Department of Budget Accounting and Treasury Tashkent Uzbekistan

ISBN: (纸本)9798350364699

The manner in which assets are monitored, controlled, and utilized has been substantially altered by organizations that have implemented IoT-enabled asset management and monitoring technologies. Businesses can achieve improved operational efficiency and cost savings by perpetually monitoring the location, activities, and performance of their assets through the use of IoT technologies. Minimized periods of inactivity and enhanced asset lifecycle management are among the three benefits of this predictive maintenance approach. When it comes to resource allocation and strategic decision-making, the data insights provided by IoT-enabled devices are of paramount importance. Companies that implement these technologies should anticipate an increase in productivity, a decrease in losses, and an improvement in their ability to comply with regulatory standards. © 2024 IEEE.

关键词： Predictive maintenance

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User Behaviour based Insider Threat Detection using a Hybrid Learning Approach

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Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4573-4593页

作者： Singh, Malvika Mehtre, B.M. Sangeetha, S. Govindaraju, Venu Centre of Excellence in Cyber Security Institute for Development and Research in Banking Technology Established by Reserve bank of India Telangana Hyderabad500057 India Department of Computer Applications National Institute of Technology Trichy Tamil Nadu Tiruchirappalli620015 India Center for Unified Biometrics and Sensors Department of Computer Science and Engineering State University of New York at Buffalo New York BuffaloNY14260 United States

Insider threats constitute a major cause of security breaches in organizations. They are the employees/users of an organization, causing harm by performing any malicious activity. Most of the existing methods to detect insider threats are based on machine and deep learning and have the following limitations: they use predefined rules or stored signatures and fail to detect new or unknown threats;they require explicit feature engineering, which results in more false positives;they require a large amount of training data, and are computationally expensive. In this paper, an improved user behavior-based insider threat detection method is proposed using a hybrid learning approach that overcomes the above limitations. It uses bi-directional long-short-term memory for feature extraction, a feed-forward artificial neural network (using distance measurements) for feature selection, and a support vector machine for classification-normal user or malicious user. The genetic algorithm’s fast global search strategy is used for the support vector machine’s initial kernel selection. Finally, alerts are generated for each user based on their combined anomaly score. The proposed method is tested using the CMU-CERT r4.2 insider threat dataset, and its performance is evaluated using the following parameters: accuracy, precision, recall, f-measure, and area under curve-receiver operating characteristic curve. The results show a significant improvement over the existing methods. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Support vector machines

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Optimal Confidential Mechanisms in Smart City Healthcare

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computers, Materials & Continua 2022年第3期70卷 4883-4896页

作者： R.Gopi P.Muthusamy P.Suresh C.G.Gabriel Santhosh Kumar Irina V.Pustokhina Denis A.Pustokhin K.Shankar Department of Computing Science and Engineering Dhanalakshmi Srinivasan Engineering CollegePerambalur621212India School of Computing Science and Engineering Galgotias UniversityNCRDelhi203201India Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Electrical and Electronics Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia Department of Computer Applications Alagappa UniversityKaraikudi630001India

Smart City Healthcare(SHC2)system is applied in monitoring the patient at home while it is also expected to react to their needs in a timely *** system also concedes the freedom of a *** is a part of this system and it helps in providing care to the *** healthcare devices are trustworthy since it almost certainly recognizes the potential intensifications at very early stage and alerts the patients and medical experts to such an extent that they are provided with immediate *** methodologies exhibit few shortcomings in terms of computational complexity,cost and data ***,the current research article examines SHC2 security through LightWeight Cipher(LWC)with Optimal S-Box model in PRESENT *** procedure aims at changing the sub bytes in which a single function is connected with several bytes’information to upgrade the security level through Swam *** key contribution of this research article is the development of a secure healthcare model for smart city using SHC2 security via LWC and Optimal S-Box *** study used a nonlinear layer and single 4-bit S box for round configuration after verifying SHC2 information,constrained by Mutual Authentication(MA).The security challenges,in healthcare information systems,emphasize the need for a methodology that immovably concretes the *** methodology should act practically,be an effective healthcare framework that depends on solidarity and adapts to the developing *** service providers integrated the IoT applications and medical services to offer individuals,a seamless technology-supported healthcare *** proposed SHC^(2) was implemented to demonstrate its security levels in terms of time and access *** model was tested under different parameters such as encryption time,decryption time,access time and response time inminimum ***,the level of the model and throughput were analyzed by maximum value i.e.,50Mbp

关键词： Smart city healthcare security block cipher LWC

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Machine learning-based predictive analytics and big data in the automotive sector 3

Machine learning-based predictive analytics and big data in ...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Lourens, Melanie Sharma, Seema Pulugu, Revathy Gehlot, Anita Manoharan, Geetha Kapila, Dhiraj Durban University of Technology Faculty of Management Sciences South Africa Mriirs Department of Computer Applications Haryana Faridabad India Narsimha Reddy Engineering College Department of Computer Science and Engineering Telangana State Hyderabad500100 India Uttaranchal University Uttaranchal Institute of Technology India Sr University School of Business Telangana India Lovely Professional University Department of Computer Science & Engineering Punjab Phagwara India

ISBN: (纸本)9798350399264

Data science and machine learning's inherent capacities to automatically learn and optimize processes and products are making them more indispensable to the automotive industry of the future. This essay will define data science and machine learning and draw parallels between the two fields. A definition of automatic optimization is provided, and its value as a tool when paired with data analytics is shown. It illustrates how these technologies are currently being applied in the industry and presents examples using the important subprocesses in the automotive value chain. The sector is only beginning to explore the many of applications for these innovations, therefore we utilize futuristic use cases to illustrate their transformative potential. The article concludes by demonstrating how these technological advancements may increase productivity in the automotive industry and fortify the sector's customer focus across the board, from product and creation process to customers and their interaction with the product. © 2023 IEEE.

关键词： Automotive industry

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A Systematic Literature Review of Decentralized applications in Web3: Identifying Challenges and Opportunities for Blockchain Developers

A Systematic Literature Review of Decentralized Applications...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Hossain Faruk, Md Jobair Raya, Pratusha Siam, Md Kamrul Cheng, Jerry Q. Shahriar, Hossain Cuzzocrea, Alfredo Bringas, Pablo Garcia New York Institute of Technology Department of Computer Science United States Kennesaw State University Department of Software Engineering United States University of West Florida Center for Cybersecurity United States University of Deusto D4K Group Bilbao Spain University of Calabria IDEA Lab Rende Italy University of Paris City Department of Computer Science Paris France

ISBN: (纸本)9798350362480

The Internet has opened the floor to stakeholders by redefining the way of organizing, communicating, and collaborating that was initiated by the Web's development. The advancement of the World Wide Web is an outright phenomenon and significant and we witnessed the evolution of the Web. As decentralized technologies continue to gain traction, Web3, or the decentralized internet, has emerged as a promising approach to enable a more secure, transparent, and privacy-preserved digital landscape. In this paper, we thoroughly conduct a systematic study to explore the challenges and opportunities encountered by blockchain developers in the context of decentralized applications (dApps) in Web3. We analyze a set of peer-reviewed research articles, whitepapers, and technical reports and present an in-depth understanding of the current state of Web3 development and its implications. Our finding indicates the opportunities that Web3 can facilitate, such as expanded use cases, enhanced security and privacy, decentralized infrastructure, and the potential for enabling inclusive development resources for blockchain developers. Additionally, we highlight various challenges that blockchain developers deal with including scalability, security, privacy, interoperability, and the need for standardized tools and frameworks along with various challenges in the software development lifecycle (SDLC). While there are significant challenges to overcome, the potential benefits of Web3 are substantial and could lead to a more inclusive, secure, and transparent digital ecosystem. Furthermore, we emphasize the importance of continued research, collaboration, and innovation among stakeholders to address the identified challenges and capitalize on Web3's opportunities. © 2024 IEEE.

关键词： Decentralized finance

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Dimensionality Reduction with DLMNN Technique for Handling Secure Medical Data in Healthcare-IoT Model 3

Dimensionality Reduction with DLMNN Technique for Handling S...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Lalithambigai, M. Kalpana, V. Sasi Kumar, A. Uthayakumar, J. Santhosh, J. Mahaveerakannan, R. Sri Krishna Adithya College of Arts and Science Department of Computer Applications Coimbatore641042 India Vel Tech University India Institute of Engineering and Technology Srinivas University Department of Cloud Technology and Data Science Surathkal Karnataka State Mukka574146 India Dhanalakshmi Srinivasan College of Engineering Department of Ai and Ds Coimbatore India Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9781665462167

Security and maintaining the integrity of sensitive patient information have emerged as major obstacles for healthcare service applications in the wake of IoT's meteoric rise in the healthcare industry. The diagnostic information included in medical photographs is of particular importance, and this research presents a hybrid security strategy to protect against such breaches. Chronic diseases such as Heart Disease (HD), cancer, and chronic respiratory sickness are the main causes of death worldwide. Those who have a heart attack suddenly have a very poor chance of surviving. As a result, this paper proposes a heart disease monitoring scheme, wherein a Deep Learning Modified Neural Network (DLMNN) centred on the Internet of Things is used to aid in the diagnosis and treatment of HD. There are three stages to implementing this strategy: First, there is encryption;second, a combination of information gain (IG) and principle component analysis (PCA) to reduce dimensionality;and third, a classification system. To begin, the patient is fitted with a wearable Internet of Things (IoT) sensor device, which simultaneously uploads its sensor data to a remote server. The sensor data is encrypted using a hybrid encryption scheme that combines the Advanced Encryption Standard (AES) and the Rivest, Shamir, and Adleman (RSA) algorithms. A DLMNN classifier is then used to classify the data after it has been decrypted. There are two types of classification outcomes. It provides information on the patient's heart and notifies the doctor in case of abnormalities. Estimated study results demonstrate that DLMNN is an improvement over previously used algorithms for HD diagnosis. The proposed AES-RSA used to support safe data transfer also yields the highest level of security, i.e. 94.32%, and does so in the quickest time for encryption and decryption. © 2023 IEEE.

关键词： Principal component analysis

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Guest Editorial: Special issue on computational methods and artificial intelligence applications in low-carbon energy systems

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IET Renewable Power Generation 2024年第3期18卷 303-305页

作者： Wang, Yishen Zhou, Fei Guerrero, Josep M. Baker, Kyri Chen, Yize Wang, Hao Xu, Bolun Xu, Qianwen Zhu, Hong Agwan, Utkarsha State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd. Beijing China Department of Energy Technology Aalborg University Aalborg Denmark Department of Civil Environmental and Architectural Engineering University of Colorado-Boulder BoulderCO United States Guangzhou China Department of Data Science and AI Monash University ClaytonVIC Australia Department of Earth and Environmental Engineering Columbia University New YorkNY United States Electric Power and Energy Systems Division KTH Royal Institute of Technology Stockholm Sweden State Grid Jiangsu Electric Power Co. Ltd. Nanjing China Department of Electrical Engineering and Computer Sciences University of California - Berkeley BerkeleyCA United States

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PERFORMANCE OF SATELLITE QUANTUM KEY DISTRIBUTION UNDER ATMOSPHERIC TURBULENCE-INDUCED PHASE FLUCTUATIONS 40

PERFORMANCE OF SATELLITE QUANTUM KEY DISTRIBUTION UNDER ATMO...

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40th International Communications Satellite Systems, ICSSC 2023

作者： Nguyen, Nam Nguyen, Thang V. Dang, Ngoc T. Mai, Vuong School of Electrical Engineering and Computer Science Oregon State University Corvallis United States Wireless Systems and Applications Lab. Posts and Telecommunications Institute of Technology Hanoi Viet Nam Bradford-Renduchintala Centre for Space AI University of Bradford Bradford United Kingdom

ISBN: (纸本)9781839539954

Satellite FSO/QKD systems employ free-space optical links between satellites and ground stations to achieve long-distance quantum key distribution. However, these systems face challenges from atmospheric turbulence. This paper introduces a theoretical framework to evaluate the influence of phase fluctuations caused by atmospheric turbulence on the system performance. Mathematical expressions for key performance metrics are derived and validated through Monte-Carlo simulations. Using results from the framework, we provide a recommended system setup to fulfil the QKD performance requirements under different atmospheric turbulence conditions. © The Institution of engineering & Technology 2023.

关键词： Atmospheric turbulence

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