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Sustainable Expert systems (ICSES), International Conference on

作者： V. Sathya Shuchi Bhadula R. Srinivasan Bhargav H K Bhavani Sankar Panda Amit Barve Department of Computer Science Periyar Maniammai Institute of Science and Technology (Deemed to be University) Vallam Thanjavur Tamil Nadu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and Engineering Veltech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Avadi Chennai Tiruvallur Tamil Nadu India Department of Computer Science and Engineering Industry Institute Interaction Cell (IIIC) and Associate Professor Shridevi Institute of Engineering and Technology Tumakuru Karnataka India Department of Computer Science and Engineering Raghu Engineering College Visakhapatnam Andhra Pradesh India Department of Computer Science and Engineering Faculty of Engineering and Technology Parul Institute of Technology Parul University Limda Gujarat India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Disasters can be mitigated by an early warning signal and proper communication within the hazardous environment using the MANET technology. However, the exact prediction of disaster situation is needed for the timely disaster management. Hence in addition to MANET technology, deep learning algorithms and Inter of Things (IoT) can also be integrated into disaster detection and communication systems. The presented research developed a novel parallely distributed slimmable neural network with orchard algorithm for the accurate flood disaster prediction. The model processed the dataset containing various measurements such as temperature, humidity, precipitation, air, soil moisture and rainfall level that are collected by the IoT sensor deployed in the various location of the hazardous environment. The gathered data are initially pre-processed using the correlation coefficient min-max normalization approach. Further, the relevant characteristics of the flood disaster from the data are extracted through spike driven transformer process. These refined and dimensionality reduced data are then entered into the proposed framework for the disaster prediction. Here the integrated orchard algorithm increased the disaster monitoring accuracy on the basis of better optimized parameters. The model resulted 95% accuracy, 93% precision, 94% recall and 96% f-score. Therefore, the presented method is an effective prediction mechanism for the disaster management in IoT MANET.

关键词： Temperature sensors Temperature measurement Accuracy Disasters Neural networks Disaster management Prediction algorithms Transformers Mobile ad hoc networks Load modeling

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Development of Autonomous Robots for Industrial and Domestic Applications

Development of Autonomous Robots for Industrial and Domestic...

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International Conference on Inventive Research in Computing Applications (ICIRCA)

作者： P. Ramadevi VijayaBhaskar Sadu Veeresh Fuskele Siva Nagaiah Bolleddu T. Ch. Anil Kumar M. Kalyan Chakravarthi Department of Computer Science Engineering (Cyber Security) Institute of Aeronautical Engineering Hyderabad Telangana India Department of Mechanical Engineering Jawaharlal Nehru Technological University Kakinada Andhra Pradesh India Department of Mechanical Engineering BabulalTarabai Institute of Research and Technology Sironja Madhya Pradesh India Associate Professor of English and Coordinator KL-CDOE Koneru Lakshmaiah Education Foundation - Deemed to be University Guntur Andhra Pradesh India Department of Mechanical Engineering Vignan's Foundation for Science Technology and Research Guntur Andhra Pradesh India School of Electronics Engineering VIT-AP University Amaravathi Andhra Pradesh India

This research study investigates the potential design of autonomous robots for developing the domestic and industrial applications. The system underwent rigorous testing in both commercial and residential settings, demonstrating exceptional performance in navigation and achieving near-perfect scores in manipulation. Autonomous robots possess the capacity to enhance productivity, reduce expenses, and enhance safety in various scenarios, as emphasized in the literature. The report also outlines potential avenues for future research, which encompass the development of enhanced sensors and perceptual systems, collaborative systems, and advanced decision-making algorithms. In general, this study constitutes a crucial contribution to the expanding corpus of information pertaining to self-governing robots and will facilitate their prospective conception, advancement, and integration.

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Optimizing smart grid flexibility with a hybrid MINLP framework for renewable integration in urban energy systems

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Energy Reports 2025年 14卷 508-523页

作者： Sameer Algburi Omer Al-Dulaimi Hassan Falah Fakhruldeen Shakhlo Isametdinova I.B. Sapaev Saiful Islam Quadri Noorulhasan Naveed Ayodele Lasisi Israa alhani Qusay Hassan Doaa H. Khalaf Michael Ssebunya Feryal Ibrahim Jabbar College of Engineering Al-Kitab University Kirkuk 36015 Iraq Electrical Technical College Al-Farahidi University Baghdad Iraq Computer Techniques Engineering Department Faculty of Information Technology Imam Ja’afar Al-Sadiq University Baghdad 10011 Iraq Associate professor Kimyo International University in Tashkent Shota Rustaveli street 156 Тashkent 100121 Uzbekistan Head of the department Physics and Chemistry Tashkent Institute of Irrigation and Agricultural Mechanization Engineers” National Research University Tashkent Uzbekistan Civil Engineering Department College of Engineering King Khalid University Abha 61421 Saudi Arabia Department of Computer Science College of Computer Science King Khalid University Abha Saudi Arabia Mazaya University College Iraq College of Engineering University of Deyala Diyala Iraq Design Department Al-Turath University College Baghdad Iraq Medical Physics Department College of Sciences Al-Mustaqbal University Babil 51001 Iraq

Urban grids face the challenge of expanding renewable deployment while curbing emissions and minimizing the capital burden of network reinforcements, all of which depend on effective flexibility integration. A hybrid optimization framework is introduced, combining Mixed-Integer Nonlinear Programming with a reformulated MILP structure to jointly size photovoltaic systems, battery storage, staged network upgrades, and the dynamic participation of electric vehicles as both load and distributed storage. Thousands of EV constraints are consolidated through a polytope-based approach, and reinforcement costs are captured using a piece-wise linear model tailored to feeder capacity increments. Application to the Tuwaiq Smart City network, covering 3780 households, employs one-minute resolution data for 2024 to benchmark five operational schemes: No Flexibility, Demand Response, Smart Charging, Vehicle-to-Grid, and Integrated Decentralised Energy Management (IDEM). Compared with the baseline, IDEM achieves a 43.8 % reduction in annualised system cost, 46 % decrease in peak imports, and capacity cuts of 75 % and 82 % for PV and storage respectively, alongside a 65 % drop in grid integration expenses. A Monte Carlo test of 150 runs confirms cost stability within ±6 %, validating the robustness of layered flexibility under stochastic solar and mobility profiles. Solving across a full-year span is achieved within minutes on standard hardware, confirming the framework’s practical value for strategic energy planning

关键词： BESS Battery Energy Storage system DER Distributed Energy Resources DR Demand Response EV Electric Vehicle IDEM Integrated Decentralized Energy Management MILP Mixed-Integer Linear Programming NF No Flexibility PV Photovoltaic SC Smart Charging V2G Vehicle-to-Grid E_ i , e_ i+1 Interval of demand A _PV , A_B, Factors for PV system and BESS B _BESS Factor for BESS B _op Factor for operational costs B _PV Factor for PV system C Matrix of coefficients for linear inequalities C _active Available energy for use C _B BESS capacity C _cap Maximum capacity of the EV battery C _depart Required energy at time of departure C _EV Electric vehicle energy c _i , d_ i Coefficients in cost function C _init Initial stored energy c _PV , c_B Cost Coefficients for PV system and BESS C b0 Initial battery capacity at the start of the time period C b Current battery capacity C bmax Maximum capacity of the battery D Set of all time periods d Right-Hand side of linear inequalities D _peakt Peak demand D Set of all time periods considered E _eff Efficiency of power electronics E _S Energy Capacity of BESS Ein EVt Energy charged by the EV Eout BESSt Energy discharged by the BESS Eout EVt Energy discharged by the EV F _op Operational expenses FSPs Service providers g D_peak Cost function for peak demand g _sum Sum of costs not depending on sizing variables G gridt Power exchanged with the grid G pvt Power generated by the PV system I t Solar Irradiance K _derate Derate factor for PV system L t Load or the energy consumption l′ Unit price of PV equipment M Identity matrix MINLP Mixed-Integer Non-Linear Programming NOCT Modified nominal operating cell temperature Q _comb Combined polytope Q _gridt Grid Power Q _i Individual polytopes Q _max Maximum Grid Power Rt Charging/Discharging rate R_ min , R_ max Min/Max charging/discharging rates R c t , R dt Rates of charging and discharging Rct Rate of charging at time t R cdL Minimum charging rate R cdU Maximum charging/discharging rate R dt Rate of discharging at time t S _efft Efficiency ratio o

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Photovoltaic Powered Fuzzy Algorithm for IoT-Monitored Four Switch Induction Motor Drive

Photovoltaic Powered Fuzzy Algorithm for IoT-Monitored Four ...

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Self Sustainable Artificial Intelligence systems (ICSSAS), International Conference on

作者： S. Jayaprakash T. Kanthimathi N. Rathika P. Sathyanathan C. Srinivasan Associate Professor School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Department of Mechanical Engineering Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India Department of Electrical and Electronics Engineering Mahendra Institute of Technology Tiruchengode Tamil Nadu India Department of Electrical and Electronics Engineering VelTech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

There has been a lot of focus on the potential benefits to the industry of combining Renewable Energy Sources (RES) with Internet of Things (IoT) technology. The conventional motor-driven control system often uses traditional power sources and has poor continuous tracking capabilities, which limits its energy economy and control accuracy. This research introduces a unique method for enhanced energy efficiency and control by integrating a fuzzy algorithm, a photovoltaic (PV) power source, IoT-monitored four-switch induction motor (IM) drive. A fuzzy algorithm is used to improve the motor drive system. According to input factors like torque, load conditions and speed, the fuzzy logic controller (FLC) modifies the motor driving settings. Because of the adaptive properties of the fuzzy algorithm, the IM may be controlled effectively, improving system performance. Additionally, the IoT monitoring feature allows for remote control and real-time data collecting, improving system performance and issue detection. MATLAB/Simulink is employed to demonstrate the obtained results of the developed system. The results of this research may be used in a variety of fields, including robots, electric cars, and industrial automation powered by renewable energy.

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Enhancing detection rates in intrusion detection systems using fuzzy integration and computational intelligence

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computers & Security 2025年 157卷

作者： Hannah Jessie Rani R Amit Barve Ashwini Malviya Vivek Ranjan Rubal Jeet Nilesh Bhosle Assistant Professor Department of Electrical and Electronics Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Associate Professor Department of Computer science and Engineering Faculty of Engineering and Technology Parul institute of Engineering and Technology Parul University Vadodara Gujarat India Associate Professor Department of uGDX ATLAS SkillTech University Mumbai Maharashtra India Assistant Professor Department of Computer Science Noida Institute of Engineering & Technology Greater Noida Uttar Pradesh India Department of Computer Science and Engineering Chandigarh Engineering College Chandigarh Group of Colleges-Jhanjeri Mohali 140307 Punjab India Department of Computing Science and Artificial Intelligence NIMS Institute of Engineering & Technology NIMS University Rajasthan Jaipur India

Intrusion Detection systems (IDS) show a major part in computer cyber defense by detecting and reacting to unauthorized activities. These systems monitor network and system activity, evaluating developments to identify possible security breaches. Enhancing Detection Rates in IDS includes optimizing algorithms, employing Machine Learning (ML) approaches, and employing intrusion detection to enhance the system's functionality to find novel vulnerabilities immediately. Continuous improvement in detection capabilities is essential for adapting to evolving challenges from cyberspace and maintaining resilience of the online infrastructure. To enhance the detection rates, data preprocessing like min-max normalization, followed by t-distributed Stochastic Neighbor Embedding (t-SNE) feature extraction technique to capture most discriminative attributes for attack classifications. The established Genetic Fuzzy systems (GFS) throughout paired learning framework for detecting input attack. The model enhances accuracy for unusual attack occurrences by better distinguishing between normal activity and distinct attack categories. To proposed Generative Adversarial Network (GAN) as a classifier for enhancing detection rates. This research explores the performance of the proposed GFS-GAN model on two prominent intrusion detection datasets are the TII-SSRC-23 for dataset 1 and NSL-KDD for dataset 2. The suggested GFS-GAN model demonstrated exceptional performance on the TII-SSRC-23 dataset, achieving 99.23 % accuracy. The GFS-GAN model also performed well on the NSL-KDD dataset, with an accuracy of 99.13 %, The findings illustrate GANs' capabilities to progress the efficacy and durability of IDS, resulting in effective protection against complicated cyber-attacks.

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Dynamic fingerprint construction and tree generation approach for biometric template protection

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AIP Conference Proceedings 2023年第1期2917卷

作者： Ashoka Rajan Swaminathan Annadurai Abishi Chowdhury Ernest Johnson Assistant Professor School of Computer Science and Engineering Vellore Institute of Technology Chennai India Associate Professor Paul J. Hill School of Business Faculty of Business Administration University of Regina Regina Saskatchewan Canada

This paper proposes a novel security method for protecting biometric fingerprint templates and storing them safely by creating a combined fingerprint template from different fingerprints, thereby creating a new virtual identity during enrolment. However, the second fingerprint image combined would be an image that was dynamically created by merging parts of several different images based on a merging algorithm used. We further extract the minutiae features from the two fingerprints. A combined fingerprint template is produced using the extracted information. Finally, the biometric values from the template are stored in a user-defined tree created. A new virtual identity is thus created to protect biometric templates from hackers and crackers. Even though the hacker has access to the database, they will not be able to match the biometric template with a person’s identity. Thus, when the database is stolen, the method avoids compromising complete minutiae features belonging to a single fingerprint. The proposed method achieves 0.3% FRR and 0.1% FAR. Hence, it balances both the recognition and security of the system.

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Blockchain Confidence Protection and Cloud Chain Management Support

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Journal of Physics: Conference Series 2021年第4期1964卷

作者： M Nagalakshmi K Saravanan Mohammad Jabirullah T Rajesh Kumar Associate Professor Department of Computer Science and Engineering Marri Laxman Reddy Institute of Technology Dundigal Telangana India Associate Professor Department of Information Technology R.M.D Engineering College Kavaraipettai Tamil Nadu India Associate Professor Department of Electronics and Communication Engineering CMR Engineering College Hyderabad Telangana India Associate Professor Department of Computer Science and Engineering Koneru Lakshmaiah Deemed to be University Guntur Andhra Pradesh India

Verification and completeness are main challenges for today's ever more diverse supply chains. Even with the ability to counter blockchain technologies by offering a trail of manipulation-resistant audit It does not address the confidence issue associated with the source chain activities also information related to a produce life cycle Information itself. Reputation mechanisms are a promising solution for this faith problem. Yet existing structures of credibility are Not ideal for supply chain applications based on blockchain as centred on restricted findings, lack of granularity and their overhead was not discussed and automation. We recommend the system as a three-layer faith in this job. Management platform is using a blockchain consortium tracking relationships between actors in the supply chain and Assign trust and prestige dynamically dependent on these interactions. Its novelty is based on a Model for credibility assessing product quality and the trust of individuals based on many observations funding for credibility qualities in supply chain incidents separating the member in the supply chain from the goods, enables brand credibility to be reserved for smart contracts for straightforward use by the same participant, Effective, secure, and automatic credibility scoring measurement, and the latency and throughput minimum overhead as compared to a straightforward supply chain model based on blockchain.

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Hydroponics Cultivation Using Real Time Iot Measurement system

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Journal of Physics: Conference Series 2020年第1期1712卷

作者： Piyush Mishra Laishram Jimmy Gabriel A. Ogunmola Tran Vang Phu A. Jayanthiladevi T. P. Latchoumi Department of Computer Engineering and Applications GLA University Mathura Uttar Pradesh India Department of Computer Science and Engineering Manipur Technical University Govt. Polytechnic Campus Imphal Manipur India Faculty of Business Sharda University Uzbekistan School of Law Can Tho University Professor-Computer Science & Information Science Srinivas University Karnataka India Department of Computer Science and Engineering Vignan's University Vadlamudi Guntur Andhra Pradesh

The concept hydroponic cultivation is performed in greenhouses or in various plat factories. This sort of cultivation is generally evaluated with pH and electrical conductivity. It may not provide complete information regarding any imbalance that is encountered in cultivation process. This causes poor yield or wastage of resources Thus, to overcome these limitation IoT measurement system has to be implanted in this cultivation process, where sensors and actuators may measure the corresponding reading when need and given to man power. Therefore, imbalance in attaining nutrients is eliminated by constant monitoring of resources towards plant cultivation. This facilitates farmers to handle the nutrients issues that are encountered in cultivation. The performance measurement of the system developed was computed with feasibility of IoT system for automatic measurements. The outcomes of the systems are computed and validated for further processing. Some specific measures are considered where there is no specific relationship among standardized analysis. The sensitive responses have to be examined and analyzed.

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Self-Awareness for Autonomous systems

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PROCEEDINGS OF THE IEEE 2020年第7期108卷 971-975页

作者： Dutt, Nikil Regazzoni, Carlo S. Rinner, Bernhard Yao, Xin Nikil Dutt (Fellow IEEE) received the Ph.D. degree from the University of Illinois at Urbana–Champaign Champaign IL USA in 1989.""He is currently a Distinguished Professor of computer science (CS) cognitive sciences and electrical engineering and computer sciences (EECS) with the University of California at Irvine Irvine CA USA. He is a coauthor of seven books. His research interests include embedded systems electronic design automation (EDA) computer architecture distributed systems healthcare Internet of Things (IoT) and brain-inspired architectures and computing.""Dr. Dutt is a Fellow of ACM. He was a recipient of the IFIP Silver Core Award. He has received numerous best paper awards. He serves as the Steering Committee Chair of the IEEE/ACM Embedded Systems Week (ESWEEK). He is also on the steering organizing and program committees of several premier EDA and embedded system design conferences and workshops. He has served on the Editorial Boards for the IEEE Transactions on Very Large Scale Integration (VLSI) Systems and the ACM Transactions on Embedded Computing Systems and also previously served as the Editor-in-Chief (EiC) for the ACM Transactions on Design Automation of Electronic Systems. He served on the Advisory Boards of the IEEE Embedded Systems Letters the ACM Special Interest Group on Embedded Systems the ACM Special Interest Group on Design Automationt and the ACM Transactions on Embedded Computing Systems. Carlo S. Regazzoni (Senior Member IEEE) received the M.S. and Ph.D. degrees in electronic and telecommunications engineering from the University of Genoa Genoa Italy in 1987 and 1992 respectively.""He is currently a Full Professor of cognitive telecommunications systems with the Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture (DITEN) University of Genoa and a Co-Ordinator of the Joint Doctorate on Interactive and Cognitive Environments (JDICE) international Ph.D. course started initially as EU Erasmus Mundus Project and

Autonomous systems are able to make decisions and potentially take actions without direct human intervention, which requires some knowledge about the system and its environment as well as goal-oriented reasoning. In computer systems, one can derive such behavior from the concept of a rational agent with autonomy (“control over its own actions”), reactivity (“react to events from the environment”), proactivity (“act on its own initiative”), and sociality (“interact with other agents”) as fundamental properties \n[1]\n. Autonomous systems will undoubtedly pervade into our everyday lives, and we will find them in a variety of domains and applications including robotics, transportation, health care, communications, and entertainment to name a few. \nThe articles in this month’s special issue cover concepts and fundamentals, architectures and techniques, and applications and case studies in the exciting area of self-awareness in autonomous systems.

关键词： Special issues and sections Autonomous systems Self-aware Robot sensing systems Computational modeling

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Automatic Characterization of Benign and Malignant Masses in Mammography

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Procedia computer science 2015年 46卷 1762-1769页

作者： K. Vaidehi T.S. Subashini Research Scholar Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University India Associate Professor Department of Computer Science and EngineeringFaculty of Engineering and Technology Annamalai University India

The paper aims to develop an automated breast mass characterization system for assisting the radiologist to analyze the digital mammograms. Mammographic Image Analysis Society (MIAS) database images are used in this study. Fuzzy C-means technique is used to segment the mass region from the input image. GLCM texture features namely contrast, correlation, energy and homogeneity are obtained from the region of interest. The texture features extracted from gray level co-occurrence matrix (GLCM) are computed at distance d=1 and θ=0 o , 45 o , 90 o , 135 o . These with three classifiers namely adaboost, back propagation neural network and sparse representation classifiers are used for characterizing the region containing either benign mass or malignant mass. The experimental results show the SRC classifier is more effective with an accuracy of 93.75% and with the Mathew's correlation coefficient (MCC) of 87.35%.

关键词： Benign and malignant mass classification Fuzzy C-Means clustering Sparse representation classifier Mathews correlation coefficient.

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