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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Mamatha Bai, B.G. Divya, N. Sree Hamedshnain, Ammar Kumar, S. Senthil Vasumathi, T. Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India Mahatma Gandhi Institute of Technology Department of Information Technology Hyderabad India The Islamic University Department of Computers Techniques Engineering College of Technical Engineering Najaf Iraq New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India Gojan School of Business and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331529246

In recent years, rapid development in the domain of Predictive Maintenance have resulted in improved equipment lifetime span, decrease maintenance costs and reduced the unplanned downtime. Predictive maintenance is an approach that addresses any equipment or component failures in the systems proactively. However, integration of this approach in automated systems has several challenges such as ensuring the accuracy of fault predictions, management of large-scale data and maintenance of system reliability under various operational conditions. To overcome these challenges Improved Support Vector Machine (ISVM) is proposed. Initially, the data is collected from the Predictive Maintenance Dataset (PMD) and the preprocessing is performed through Inter Quartile Range (IQR) which enhances the quality of data and modeling efforts. Further relevant features are extracted using Recursive Feature Elimination (RFE). Finally, the prediction is performed using ISVM which achieved better outcomes compared to existing a One-Class Support Vector Machine (SVM) in terms of accuracy as well as recall by achieving 90.58%, 80.79% respectively. © 2025 IEEE.

关键词： Predictive maintenance

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Reflect-and-Amplify: a Novel SWIPT Technique through Hybrid Active/Passive RIS

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IEEE Internet of Things Journal 2025年第12期12卷 22073-22089页

作者： Ata, Yalcin Zedini, Emna Vegni, Anna Maria Alouini, Mohamed-Slim Ostim Technical University Ostim Department of Electrical and Electronics Engineering Yenimahalle Ankara06374 Turkey University of Michigan-Flint College of Innovation and Technology FlintMI United States Roma Tre University Department of Industrial Electronics and Mechanical Engineering Rome Italy Computer Electrical and Mathematical Science and Engineering Division Thuwal23955-6900 Saudi Arabia

This paper presents a novel technique for the simultaneous wireless information and power transfer (SWIPT) toward low-power devices, by means of Reconfigurable Intelligent Surfaces (RISs). Our approach is based on a Reflect-and-Amplify protocol meaning that, based on the RIS beam-steering functionality and assuming different reflection coefficients for the RIS elements, it allows to reflect and amplify the impinging wireless signal from the transmitter toward the receiver, which will be able to both collect energy and download data. Specifically, by considering a hybrid RIS comprised of both passive and active elements, the proposed SWIPT approach is defined Active/Passive Switching (APS). The reflection of the wireless beam toward the receiver is accomplished by means of passive RIS elements, while an amplification of the wireless signal is provided through active RIS elements. Such amplification allows the receiver to convert the incoming wave power into a DC voltage, but also causes an increase of power consumption due to active elements. To assess the performance of the proposed SWIPT approach, we derive the closed-form expression of the joint data transmission and energy harvesting outage probability (OP) in case of both a single and multiple users. We compare the proposed technique to other SWIPT approaches from the literature, such as the power splitting and the time switching, under power fairness conditions. Our results validate the benefits of using hybrid RISs for SWIPT systems, as compared to other existing approaches showing reduced performance, also in case of asymptotic analysis for high power and high number of RIS elements. © 2014 IEEE.

关键词： Inductive power transmission

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Machine learning based cardiovascular disease prediction

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Materials Today: Proceedings 2022年 64卷 459-463页

作者： Chinnasamy, P. Arun Kumar, S. Navya, V. Lakshmi Priya, K. Sruthi Boddu, Siva Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of Computer Science and Engineering Bethesda Institute of Technology and Science Gwalior India Department of Electronics and Communication Engineering East Point College of Engineering & Technology Bangalore India Department of Electronics and Communication Engineering PERI Institute of Technology Chennai India UG Student Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

In past few years, one of the really tough situations in medicine is already making predictions cardiac dysfunction. Almost any moment, roughly one patient dies through myocardial infarction with in present era. Due to the difficulty of forecasting cardiac sickness, it is vital to systematize the procedure in place to minimize the risk associated with and to educate the patient well enough in preparation. The risk of cardiovascular disease in the World is tremendous. The accurate and thorough assessment of a physician's cardiovascular risk is critical for reducing the incidence and severity attack and stroke and improving cardiovascular protection. In order to solve this issue, we are proposing method to discover these heart abnormalities as soon as reasonably practicable to avert fatal consequences. The next step is to determine whether or not the user is at risk of developing cardiovascular disease. It also answers difficult problems concerning heart detection of diseases, allowing clinicians to make better clinical decisions. Medical specialists have accumulated a large number of medical data that can be analysed and important fact retrieved. Machine learning approaches can improve in the prognosis and early identification of heart illness after hypothesis testing. © 2022

关键词： Machine learning

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Exploring the Impact of Node Velocity on Communication Overhead and Energy Consumption in WSNs Using Fuzzy Clustering

Exploring the Impact of Node Velocity on Communication Overh...

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

作者： Sharma, Dilip Kumar Yadav, Aaditya Patidar, Mukesh Patil, Pooja Sanjay Patidar, Praveen Kumar Mandloi, Devendra Singh Sheikh, Rashid Rajpoot, Shivshankar Ujjain Engineering College Department of Electronics and Communication Engineering Ujjain India Piet Parul University Department of Computer Science and Engineering Vadodara India Credit Acceptance Financial Institution in Southfield Michigan United States Parul Institute of Technology Parul University Department of Computer Science & Engineering Vadodara India Indore Institute of Science and Technology Department of Electronics and Communication Engineering Indore India Acropolis Institute of Technology and Research Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

Clustering, an unsupervised machine learning technique with numerous practical applications, has received a great deal of research attention. Along with deterministic and probabilistic approaches, fuzzy C-means clustering (FCM) is commonly employed. Over time, improvements to the FCM approach have been implemented to improve clustering efficiency. In this paper, we perform the results of communication overhead, energy consumption, and link distance travel in WSN with different velocities for the clustering-based protocol. In this performance, we are considering the number of nodes in the networks as 100, 175, and 200, having considered the start node as five using fuzzy clustering methodology in the unsupervised ML technique. In the communication overhead result analysis, we observed that the communication overhead increases as the nodes velocity increases, and another case of energy consumption also increases with the rising trend as node velocity increases. The results presented in this paper have been simulated using the Matlab R2020a tool. © 2024 IEEE.

关键词： Fuzzy clustering

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Multi-Class Skin Cancer Detection Using Fusion of Textural Features Based CAD Tool

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computers, Materials & Continua 2024年第12期81卷 4217-4263页

作者： Khushmeen Kaur Brar Bhawna Goyal Ayush Dogra Sampangi Rama Reddy Ahmed Alkhayyat Rajesh Singh Manob Jyoti Saikia Department of Electronics and Communication Engineering Chandigarh UniversityMohali140413India Chitkara University Institute of Engineering and Technology Chitkara UniversityChandigarh140401India Department of Physics&Electronics School of SciencesJAIN(Deemed to be University)Bangalore560001India College of Technical Engineering The Islamic UniversityNajaf54001Iraq Department of Electronics and Communication Engineering Uttaranchal Institute of TechnologyUttaranchal UniversityDehradun248007India Electrical and Computer Engineering Department University of MemphisMemphisTN 38152USA Biomedical Sensors&Systems Lab University of MemphisMemphisTN 38152USA

Skin cancer has been recognized as one of the most lethal and complex types of cancer for over a *** diagnosis of skin cancer is of paramount importance,yet the process is intricate and *** analysis and modeling of human skin pose significant difficulties due to its asymmetrical nature,the visibility of dense hair,and the presence of various substitute *** texture of the epidermis is notably different from that of normal skin,and these differences are often evident in cases of unhealthy *** a consequence,the development of an effective method for monitoring skin cancer has seen little ***,the task of diagnosing skin cancer from dermoscopic images is particularly *** is crucial to diagnose skin cancer at an early stage,despite the high cost associated with the procedure,as it is an expensive ***,the advancement of diagnostic techniques for skin cancer has been *** address this issue,there is a need for a more accurate and efficient method for identifying and categorizing skin cancer *** involves the evaluation of specific characteristics to distinguish between benign and malignant skin cancer *** present and evaluate several techniques for segmentation,categorized into three main types:thresholding,edge-based,and *** techniques are applied to a dataset of 200 benign and melanoma lesions from the Hospital Pedro Hispano(PH2)*** evaluation is based on twelve distinct metrics,which are designed to measure various types of errors with particular clinical ***,we assess the effectiveness of these techniques independently for three different types of lesions:melanocytic nevi,atypical nevi,and *** first technique is capable of classifying lesions into two categories:atypical nevi and melanoma,achieving the highest accuracy score of 90.00%with the Otsu(3-level)*** second technique also classifies lesions int

关键词： Melanoma computer-aided diagnosis segmentation PH2 ISIC(International Skin Imaging Collaboration) dermoscopy non-melanoma

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Advancing Medical Imaging: A Focus on Efficient Net for Brain Tumor Classification 2

Advancing Medical Imaging: A Focus on Efficient Net for Brai...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Shashidhar, R. Manasa, R. Megha, K.M. Priyanga, P. Manjunath, A.S. Roopa, M. Jss Science and Technology University Department of Electronics and Communication Engineering Mysuru India Dayananda Sagar College of Engineering Department of Electronics and Communication Engineering Bengaluru India Bengaluru India Jss Science and Technology University Department of Computer Applications Mysuru India

ISBN: (纸本)9798350372892

In the field of medical diagnosis, the increasing occurrence of brain tumors calls for creative approaches to detect and intervene at early stages. This proposed work focus on the Efficient Net (EffNet) architecture to address the challenges associated with brain tumour recognition and sorting from Magnetic Resonance Imaging (MRI) scans. The main objectives are outlined to develop a robust model capable of discriminating between various tumour types and normal brain tissues. Four distinct classes - glioma tumour, pituitary tumour, meningioma tumour, and no tumour compose the dataset, providing diversity for training and evaluation. Using projected method got the accuracy of 98.08% accuracy. Our proposed method detects the all the four classes effectively. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Enhancement in Quality of Routing Service Using Metaheuristic PSO Algorithm in VANET Networks

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Soft Computing 2023年第5期27卷 2739-2750页

作者： Javadpour, Amir Rezaei, Samira Sangaiah, Arun Kumar Slowik, Adam Mahmoodi Khaniabadi, Shadi School of Computer Science and Technology Guangzhou University Guangzhou510006 China Bernoulli Institute for Mathematics and Computer Science University of Groningen Groningen Netherlands School of Computing Science and Engineering VIT University Vellore India Department of Industrial Engineering and Management National Yunlin University of Science and Technology Douliu Taiwan Department of Electronics and Computer Science Koszalin University of Technology Koszalin Poland Department of Computer Science and Engineering USM Engineering Campus Pulau Pinang George Town14300 Malaysia

Vehicular ad hoc Networks (VANETs) can be designed in a way to organize road protection with no specific need for any fixed infrastructure. Accordingly, the movement of all vehicles can be planned according to perceived information, and Quality of Services Routing (QoSR) algorithms can be pressured according to its available options, paths, and links, and according to the criteria and reliability of the QoSR. Ensure that QoSR is aware of the environment of the network of vehicles, including location of vehicles, direction, and speed. This study is to reduce the effects of unpredictable problems on the best pathway to replace the broken path/link. A QoSR with Particle Swarm Optimization (QoSR-PSO) is used in this article for improving QoSs in vehicular ad hoc networks. By modeling the behavior of a group of particles, particle swarm optimization algorithms find the best possible solution to the problem. In order to perform simulation experiments, NS2 simulator and VanetMobisim have been used. The comparison results with benchmark studies show the improvement in packet delivery rate (PDR), delay, Packet Drop, and overload. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Vehicular ad hoc networks

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Power and Area Optimized Memristor-Based Majority Identifier for Digital Applications 3

Power and Area Optimized Memristor-Based Majority Identifier...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Raj, P. Michael Preetam Vittapu, Sravan K. Chinnasamy, P. Gupta, Manu Samatha, K. Sasivijaya, K. Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India Nalla Narasimha Reddy Education Society's Group of Institutions Department of Electronics and Communication Engineering Hyderabad India Kalasalingam Academy of Research and Education Dept. of Computer Science and Engineering Tamil Nadu India Sreenidhi Institute of Science and Technology Department of Electronics and Computer Engineering Hyderabad India

ISBN: (纸本)9798331518592

In earlier developments, CMOS technology played a great role in realizing majority identifiers. There are huge drawbacks that affect the circuit's performance. To overcome the drawbacks of CMOS-based majority Identifiers, novel memristor-based majority and minority Identifiers have been proposed in this work. The features offered by memristor have motivated us to use the component and achieve greatly improved results compared to CMOS-based majority identifier circuits. After observing the behavior of the basic memristor-based majority identifier circuit, we have decided to design and implement a memristor-based majority and minority identifier circuit which can be used in many automation systems to increase quality and productivity. © 2024 IEEE.

关键词： CMOS integrated circuits

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Cognitive Digital Twin Systems for Predictive Security in AI-Enhanced IoT Environments 1

Cognitive Digital Twin Systems for Predictive Security in AI...

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1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Ravi Kumar, M. Jain, Upma Francis, Flory Senthil Kumar, S. Adhvaryu, Rachit Natrayan, L. Cambridge Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Ramaiah Institute of Technology Department of Electronics and Communication Engineering Bengaluru India R.M.K College of Engineering and Technology Department of Mechanical Engineering Thiruvallur India Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Vadodara India Simats Saveetha School of Engineering Department of Mechanical Engineering Chennai India

ISBN: (纸本)9798350352931

The Internet has grown in importance and impact over the years, causing people to become more reliant on it. The Internet has evolved into a major vector for cybercrime because to its ever-increasing user base. Over the last decade, the number of these computing systems - including desktops, laptops, smartphones, and the Internet of Things (IoT) - has skyrocketed. Among them, cell phones are practically integral to modern life. The popularity of web-based assaults has skyrocketed with the exponential growth in the number of individuals using the Internet. These web-based assaults are increasingly being combatted by security corporations. Unfortunately, new forms of these assaults are appearing all the time, making it hard for older security measures to stay up. Artificial intelligence (AI) is a source of optimism in the current cybersecurity landscape, offering a potential solution to the ever-changing digital dangers. The fast development of AI over the last decade has given rise to this optimism, because it is now impacting the expansion of every industry. With AI bringing so many advantages in every field, online security is one sector that just cannot afford to ignore it. This thesis represents progress in that direction. This thesis covers research that aims to use AI to tackle significant online security challenges. Security in Internet of Things (IoT) settings powered by artificial intelligence may be improved with the help of cognitive digital twin systems (CDTS). In order to provide proactive security measures, real-time monitoring, and predictive analysis, these systems use cutting-edge AI methods to digitally represent physical IoT devices. In order to anticipate and lessen the impact of security risks, this research introduces a new CDTS architecture that combines cognitive learning skills, anomaly detection, and machine learning models. The suggested approach reduced reaction time to security events by 87.5% and identified zero-day threats with a detect

关键词： Risk management

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Sustainable augmented framework using smart sensors with ATM inspired 5G technologies for enterprise networks

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International Journal of Information technology (Singapore) 2025年第3期17卷 1805-1812页

作者： Kagi, Shivakumar Venugopal, Anita Ramaiah, V. Subba Panda, Sunita Ambikapathy, A. MB, Abhishek Department of Computer Science and Engineering Sharnbasva University Karnataka Kalaburagi India IT Unit Dhofar University Salalah Oman Department of CSE Mahatma Gandhi Institute of Technology Telangana Hyderabad India Department of Electrical Electronics and Communication Engineering GITAM School of Technology Bengaluru Campus Karnataka Bengaluru India Department of Electronics and Communication Engineering ITS Engineering College Uttar Pradesh Greater Noida India Department of Electronics and Communication Engineering Dayananda Sagar College of Engineering Bengaluru India

The primary driving force behind the development of fifth-generation (5G) applications is the necessity of Concurrent Multipath Transfer (CMT). Nevertheless, CMT systems that rely on Transport Control Protocol/Internet Protocol (TCP/IP) will continue to be the key point in 5G as multipath transport protocols are not often utilized. In today’s worldwide networks, integrating TCP/IP with asynchronous transfer mode (ATM) is a difficult structural problem since the TCP/IP protocol has to accommodate the special features of ATM. An effective mechanism for the effective use of both approaches will be a detailed characterization of TCP/IP performance via ATM. ATM network performance may be negatively impacted by improper use of TCP error and flow control methods, which were created for best-effort networks. In order to facilitate effective communication, this article offers a comprehensive and standardized examination and analysis of TCP/IP over ATM in the context of the 5G network architecture. It also proposes a tuned energy efficient transmission control protocol (TEETCP) to enhance Quality of Service (QoS) in TCP/IP applications. The performance of the approach is validated against existing recent works based on evaluation metrics such as packet delivery ratio (PDR), average throughput (AP), end-to-end delay (e2e), utilization of energy and computation overhead. The results indicate the better performance of our proposed work in all aspects. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： 5G ATM network CMT Network performance TEETCP

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