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2024 International Conference on Advances in Computing Research on Science engineering and Technology, ACROSET 2024

作者： Nallasivan, G. Thatikonda, Ramya Geetha, M. Bhuvanesh, A. Ramraj, S. Sreethu, S. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India University of the Cumberlands Department of Information Technology Williamsburg United States Sri Eshwar College of Engineering Department of Electrical and Electronics Engineering Coimbatore India Psn College of Engineering and Technology Department of Electrical and Electronics Engineering Tirunelveli India

ISBN: (纸本)9798350388800

The use of OpenCV and its capabilities for person tracking and detection is suggested by this project. The technique works well for following people in a variety of situations, such as busy places, people who are obscured, and people who are moving. The robustness and precision of the estimated model are illustrated through the use of visuals to visualize tracking data. The suggested approach surpasses existing sophisticated algorithms in terms of Intersection over Union (IoU) and mean Average Precision (mAP), achieving excellent accuracy. Potential applications for the proposed method include robots, human-computer interaction, and surveillance. The limits of the suggested approach are also covered, along with suggestions for further research and development. © 2024 IEEE.

关键词： Adversarial machine learning

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Emerging Technology for Smart Farming in The Agriculture domain: Application and Future Perspective

Emerging Technology for Smart Farming in The Agriculture Dom...

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2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Konduru, Preethi Tripathi, Mukesh Kuamr Khairnar, dipali Patil, Priyadarshini Patil, Madhavi Prashant, Ghongade Vasavi College of Engineering Department of It Hyderabad India Vardhaman College of Engineering Department of Cse Hyderabad India D y Patil University Department of Computer Engineering Ambi Pune India Gndec Department of Eee Karnataka Bidar India

ISBN: (纸本)9798350344387

Food is necessary for basic needs, farming is regarded as a global source of commerce. Horticulture is a significant source of employment in non-industrialized countries like India. The activities involved in farming are broadly categorized into three key areas: reaping and post-collection. AI is the present innovation helping agriculture people cultivate bits of knowledge about the pre-harvesting and post-harvesting approaches. This paper presents a broad study of most recent AI & Ml application in agribusiness to mitigate the issues in pre collecting, reaping, and post gathering. © 2023 IEEE.

关键词： Harvesting

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Forest Fire detection using CNN-RF and CNN-XGBOOST Machine learning Algorithms 3

Forest Fire Detection using CNN-RF and CNN-XGBOOST Machine L...

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

作者： Umamaheswari, R. Shanmuga Priya, S. Ganesan, R. Uma, S. Saveetha Engineering College Department of Computer Science and Engineering Chennai India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr Sagunthala R and D Institute of Science and Technology Chennai India

ISBN: (纸本)9781665462167

detection of forest fire should be quick and accurate as forests are the important sources to lead a vital life on earth. detection of fire can be extremely difficult using existing methods of smoke sensors installed and they are slow and cost inefficient, so in order to avoid large scale fires, detection from visual scenes is required. In this work detection of fire in an image is done by extracting features using deep learning algorithm and with those features as input to machine learning algorithm, a model is build with the help of different machine learning algorithms like Random Forest, Support Vector Machine, XGBoost and K-Means Clustering. Using these algorithms the data sets are classified into fire and non fire images to build the model and the test data of the data set is provided as input for getting the validation accuracy of the model. Then comparison is done among machine learning algorithms to find which algorithm provides more accuracy. To test the accuracy of the fire presence classification evaluation metrics are used in the model and find that accuracy of CNN-RF and CNN-XGBOOST are 98.53% which is greater than accuracy of CNN-SVM 97.06%. © 2023 IEEE.

关键词： Fires

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RC structural system control subjected to earthquakes and TMd

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Structural engineering and Mechanics 2024年第2期89卷 213-223页

作者： Shao, Jenchung Noor, M. Nasir Ken, P. Chang, Chuho Wang, R. College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Taiwan College of Maritime National Kaohsiung University of Science and Technology Taiwan Information and Research Center of China 1/9 Road: 2 Block: D Section: 15 Dhaka1216 Bangladesh

This paper proposes a composite design of fuzzy adaptive control scheme based on TMd RC structural system and the gain of two-dimensional fuzzy control is controlled by parameters. Monitoring and learning in lMI then produces performance indicators with a weighting matrix as a function of cost. It allows to control the trade-off between the two efficiencies by adjusting the appropriate weighting matrix. The two-dimensional Boost control model is equivalent to the lMI-constrained multi-objective optimization problem under dual performance criteria. By using the proposed intelligent control model, the fuzzy nonlinear criterion is satisfied. Therefore, the data connection can be further extended. Evaluation of controller performance the proposed controller is compared with other control techniques. This ensures good performance of the control routines used for position and trajectory control in the presence of model uncertainties and external influences. Quantitative verification of the effectiveness of monitoring and control. The purpose of this article is to ensure access to adequate, safe and affordable housing and basic services. Therefore, it is assumed that this goal will be achieved in the near future through the continuous development of artificial intelligence and control theory. Copyright © 2024 Techno-Press, ltd.

关键词： Fuzzy control

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Enhancing electric vehicle range through real-time failure prediction and optimization: Introduction to dHBA-FPM model with an artificial intelligence approach

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ICT Express 2025年第3期11卷 547-558页

作者： Ekici, Yunus Emre Karadağ, Teoman Akdağ, Ozan Aydin, Ahmet Arif Tekin, Hüseyin Ozan Department of Electric Vehicle Technologies OIZ Vocational College Inonu University Malatya Turkey Department of Electric Electronic Engineering Inonu University Malatya Turkey TEMSA R&D Center Adana Turkey Department of Computer Engineering Malatya Turgut Özal University Malatya 44210 Turkey Department of Computer Engineering Inonu University Malatya 44000 Turkey Department of Medical Diagnostic Imaging University of Sharjah United Arab Emirates Department of Computer Engineering Istinye University Istanbul 34396 Turkey

Electrical and mechanical failures in electric vehicles (EVs) during passenger operation cause significant operational losses and elevated energy consumption, amplifying range anxiety. To address this issue, we utilized 250,000 rows of real-time data from electric trolleybuses operating in Türkiye to develop a robust artificial intelligence (AI)-based optimization model for failure mitigation. Initially, Tri layered Neural Network (TNN) was employed to create a predictive function for electrical and mechanical failures, followed by comparative analyses across six optimization algorithms widely adopted in failure prediction studies. Among these, the developed Honey Badger Algorithm with AI Approach (dHBA) emerged as the most effective, achieving a predictive accuracy improvement of 15 % over the standard Honey Badger Algorithm (HBA). The dHBA incorporates a dynamic Fitness-distance Balance (dFdB) mechanism and a novel spiral motion feature to enhance search precision, leading to the dHBA-FPM (developed-Honey Badger Algorithm - Failure Prediction Model). The final dHBA-FPM model was applied to the 10 highest-density bus routes in Türkiye to predict and optimize failures. Results indicate that applying the dHBA-FPM model across these routes yielded a 3.96 % average range increase in EVs, extending the total range by approximately 79,200 km annually. It can be concluded that the model could prevent the release of 238.7 tons/year of CO₂, NO, and NO₂ emissions through its potential to improve both the operational efficiency and sustainability of EVs in public transit networks. © 2025 The Author(s)

关键词： Electric vehicles Mechanical and electrical failure Optimization Range Anxiety

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Swarm Intelligence for Multi-Robot Coordination in Industrial Environments 9

Swarm Intelligence for Multi-Robot Coordination in Industria...

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9th International Conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Prakash, Kode Jaya Muthukumaran, M. Sandhiya, R. Rajpradeesh, T. deshmukh, Mrunalini A. Thanikodi, Sathish Vnr Vignana Jyothi Institute of Engineering & Technology Department of Mechanical Engineering Telangana Hyderabad500090 India Karpagam College of Engineering Department of Electrical And Electronics Engineering Tamil Nadu Coimbatore641032 India Rmk College of Engineering And Technology Department of Computer Science Engineering Tamil Nadu Puduvoyal601206 India Kalasalingam Academy of Research And Education Department of Mechanical Engineering Tamil Nadu Srivilliputhur626126 India Shree L. R. Tiwari College of Engineering Department of Civil Engineering Maharashtra Thane401107 India Saveetha School of Engineering Simats Department of Mechanical Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350365092

The operation of mass intelligence in order to improve multi-robot collaboration within artificial environments. With the ever-increasing demand for robotization in the field of diligence, the efficient collaboration of several robots is becoming increasingly necessary for optimising operations such as manufacturing, examination, and shipping. The collaborative gesture of social nonentity colonies served as the inspiration for swarm intelligence, which provides a decentralised and tone-organizing solution to the coordination of a platoon of robots. The technique that has been offered investigates mass algorithms, such as Ant Colony Optimisation and flyspeck mass Optimisation, with the purpose of facilitating efficient work allocation, path planning, and resource optimisation among the robots. The purpose of this is to evaluate the performance of mass intelligence in terms of refining collaboration, reducing the amount of time required to complete tasks, and improving the overall effectiveness of multi-robot systems in complex artificial settings. This evaluation is carried out through extensive simulations and experiments conducted in the actual world. The findings bring significant perceptivity to the field of robotics and robotization, emphasising the possibility of mass intelligence for addressing the issues of scalability and rigidity in artificial settings. © 2024 IEEE.

关键词： Swarm intelligence

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decision Systems for Adaptive Cybersecurity Incident Response 12th

Decision Systems for Adaptive Cybersecurity Incident Respons...

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12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Goyal, dinesh Gandhi, Yatin dongre, deepika Shailesh Bhagat, Gorakh P. Pawar, Rohit Department of Computer Engineering Poornima Institute of Engineering and Technology Rajasthan Jaipur India Competent Softwares Maharashtra Pune India Artificial Intelligence and Data Science VIIT Pune India Electronics and Communication Engineering Arya College of Engineering Jaipur India Department of Mechanical Engineering Dr. D. Y. Patil Institute of Technology Pimpri Maharashtra Pune India Shri Ramdeobaba College of Engineering and Management Maharashtra Nagpur India

ISBN: (纸本)9789819601462

Traditional methods of responding to cybersecurity incidents often use set playbooks or routines that don’t change, which might not work well with the constantly changing nature of modern cyber dangers. The idea behind this paper is to create and use decision systems that are specifically designed for adapting how to handle hacking incidents. Advanced artificial intelligence (AI) methods, such as machine learning and natural language processing, are used by these decision systems to analyze security events in real time and help people make quick, well-informed decisions. These systems improve the speed and efficiency of hacking reaction by constantly learning from past events and responding to new threats. Automatic danger recognition, risk assessment, and reaction planning are important parts of the suggested decision systems. Automated systems that look for threats use AI algorithms to look for signs of bad behavior in network traffic, system logs, and other relevant data sources. The risk assessment tools look at the dangers and how bad they could be, considering things like how sensitive the data is, how important the system is, and the rules that need to be followed. decision systems actively plan responses based on danger recognition and risk assessment results. These responses may include separating affected systems, blocking suspicious IP addresses, or sending events to human experts for further study. Over time, these systems get better at making decisions by using adaptive learning methods and constant feedback loops. This makes it easier for them to spot and stop new cyber threats. The creating and using decision systems for adaptive cybersecurity incident reaction is a big step toward making organizations’ defenses more resilient and effective in the face of changing cyber dangers. By using AI and machine learning, these systems stop threats before they happen, giving cybersecurity teams the tools they need to stay ahead of attackers in the ongoing battl

关键词： Cyber attacks

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Challenges in Cyber Physical Social Systems and Internet of Things 5

Challenges in Cyber Physical Social Systems and Internet of ...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Revathi, S. Raja, J. Mohanraj, M. Malathi, K. Mallala, Balasubbareddy Vidhya, R.G. B.S. Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India Sri Eshwar college of engineering Department of Computer Science and Business Systems Coimbatore India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Chennai India Chaitanya Bharathi Institute of Technology Department of Electrical and Electronics Engineering Telangana Hyderabad India Hkbk College of Engineering Department of Ece Bangalore India

ISBN: (纸本)9798331504403

This research analyses the complex dynamics of Cyber-Physical-Social Systems (CPSS), encompassing cyber-physical systems, cybersecurity, the Internet of Things (IoT), and social media. By exploring the interactions among these elements, this study analyses the vulnerabilities, risks, and potential benefits within the CPSS framework. This research study analyses the evolving landscape of cybersecurity by considering various aspects such as data integrity, privacy in the IoT era, and the influence of social media on user behaviour and influence dynamics. The study also introduces innovative strategies to enhance CPSS resilience, including methods to mitigate cyber threats and enhance overall system integrity. Additionally, we investigate how social media shapes CPSS behaviours, offering valuable insights into influence dynamics. This research advances the comprehensive understanding of CPSS to support future studies and advancements in the protection and improvement of interconnected systems. © 2024 IEEE.

关键词： Cyber-Physical Systems Cyber-Physical-Social Systems (CPSS) Cybersecurity Internet of Things (IoT) Social Media

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A Study on a decentralized Network Secured data Sharing using Blockchain 1

A Study on a Decentralized Network Secured Data Sharing usin...

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1st International Conference on Computational Science and Technology, ICCST 2022

作者： Swetha, S. Joeprathap, P.M. R.M.D Engineering College Department of Computer Science and Engineering Chennai601206 India

ISBN: (纸本)9781665476553

Sharing data is a crucial part of the research process that ensures the community as a whole benefit from everyone's efforts. The current data-sharing infrastructure requires the involvement of an impartial third party (TTP). Trust, transparency, security, and immutability are all lacking in TTP-based systems. This research intends to cut down on the amount of time it takes to effectively transmit a huge file by developing a unique idea of multihop concurrent communication across blockchain network peers to ease the sharing of massive data via threads in a multithreaded environment. If there is no network congestion, this three-tier scheme will guarantee the same throughput for every given node, regardless of how many others it is linked to. In this study, we explore how decentralization has the potential to aid in such situations. With decentralization, more people would be working to reroute data from a server node to a client node, and the blockchain would ensure the safety of this transaction. When using the suggested methodology, large files may be sent in a short amount of time. © 2022 IEEE.

关键词： Blockchain

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Robust Facial Biometric Authentication System Using Pupillary light Reflex for liveness detection of Facial Images

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computer Modeling in engineering & Sciences 2024年第4期139卷 725-739页

作者： Puja S.Prasad Adepu Sree lakshmi Sandeep Kautish Simar Preet Singh Rajesh Kumar Shrivastava Abdulaziz S.Almazyad Hossam M.Zawbaa Ali Wagdy Mohamed CSE Department Geethanjali College of EngineeringHyderabadTelangana501301India Department of Computer Science LBEF CampusKathmandu44600Nepal School of Computer Science Engineering and Technology(SCSET) Bennett UniversityGreater Noida201310India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia CeADAR Ireland’s Centre for AI Technological University DublinDublinD07 EWV4Ireland Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan

Pupil dynamics are the important characteristics of face spoofing *** face recognition system is one of the most used biometrics for authenticating individual *** main threats to the facial recognition system are different types of presentation attacks like print attacks,3d mask attacks,replay attacks,*** proposed model uses pupil characteristics for liveness detection during the authentication *** pupillary light reflex is an involuntary reaction controlling the pupil’s diameter at different light *** proposed framework consists of two-phase *** the first phase,the pupil’s diameter is calculated by applying stimulus(light)in one eye of the subject and calculating the constriction of the pupil size on both eyes in different video *** above measurement is converted into feature space using Kohn and Clynes model-defined *** Support Vector Machine is used to classify legitimate subjects when the diameter change is normal(or when the eye is alive)or illegitimate subjects when there is no change or abnormal oscillations of pupil behavior due to the presence of printed photograph,video,or 3d mask of the subject in front of the *** the second phase,we perform the facial recognition ***-invariant feature transform(SIFT)is used to find the features from the facial images,with each feature having a size of a 128-dimensional *** features are scale,rotation,and orientation invariant and are used for recognizing facial *** brute force matching algorithm is used for matching features of two different *** threshold value we considered is 0.08 for good *** analyze the performance of the framework,we tested our model in two Face antispoofing datasets named Replay attack datasets and CASIA-SURF datasets,which were used because they contain the videos of the subjects in each sample having three modalities(RGB,IR,depth).The CASIA-SURF datasets showed an 89.9%Equal Err

关键词： SIFT pupil CASIA-SURF pupillary light reflex replay attack dataset brute force

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