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2024 IEEE Inter.a.ional Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Muruganandam, S. Gnanavel, S. Narayana, K.E. Jaeyalakshmi, M. Antony Kumar, K. Panimalar Engineering College Department of Computer Science and Business Systems Chennai India SRM Institute of Science and Technology Kattankulathur Campus Faculty of Engineering and Technology Department of Computing Technologies Chengalpattu India Rajalakshmi Engineering College Department of Information Technology Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331522988

In Mobile sensor Networks, Each and every wireless node dynamically changes its location due to its mobility nature. The routing infor.a.ion is maintained by the master node to deliver.a.data packet from source to destination. The attackers easily hack this wireless network. The perfor.a.ce of the complete networks is dropped by packets transmission attacks. Attacker node drops the packet. An enhanced protection technique in response to packets transmission misbehaviour.a.tacks is provided to incr.a.e the energy efficiency of mobile nodes. This paper proposed the Cryptographic formed Malicious Node Detection and Secure routing framework (CMNDSr). This model incorporates three various strategies specially algorithm for control of access, to protects initial illegal node in the network. The suspected identification and prevention techniques based on hop count value. Secure routing method with a private key generated at random like one time password (OTP). The proposed methods are power efficient, by operating a minimum number of nodes in challenging environment. The misbehaviour nodes are promptly identified and isolated using this proposed appr.a.h. The Cryptographic based Secure routing algorithm is applied to provide secure transmission in wireless networking environment. The outcomes of the simulation display how effectively the recommended appr.a.h can identify malicious nodes in a MANET. In comparison with previous methods, the energy consumption is 10% lower.a.d the suspicious node detection rate is incr.a.ed by 95%. © 2024 IEEE.

关键词： Encryption algorithms

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4th Inter.a.ional Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： roselinkiruba, r. Jude Moses Anto Devakanth, J. Sharmila, L. Vasumathy, M. Misba, M. Saranya Jothi, C. Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology Chennai Avadi India Madanapalle Institute of Technology and Science Department of Computer Applications Andra Pradesh India Department of Information Technology Kingston Engineering College Vellore India

ISBN: (纸本)9798331515720

Steganography is the method that convert data into bits to make it suitable for hiding. While embedding data the factors such as stego image quality and distortion which cannot be observed by the human eyes. The aim of this study is to incr.a.e the stego image with good quality. Initially, the image pixels are separated into different bit planes then it is compared with the secret data bit planes using Fruitfly Optimization Improved Hybridized Algorithm Pixel Similarity (FOIS-PS). For that similar pixel Fitness Function (FF) is applied based on the three impor.a.t criter.a.such as edge, entropy and correlation. Finally, the data is embedded in the LSB bits based on their Embedding Capacity (EC). Using this method, the size of the input is reduced. Then, the data is embedded based on difference of pixels within each group of similar pixels. Evaluation metrics shows the quality of the stego image is boosted and also reduces the processing time. © 2024 IEEE.

关键词： Image quality

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An Efficient Fog Computing Platform Through Genetic Algorithm-Based Scheduling 1st

An Efficient Fog Computing Platform Through Genetic Algorith...

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1st Inter.a.ional Conference on Machine Intelligence for research and Innovations, MAiTrI 2023

作者： Chauhan, Shivam Swain, Chinmaya Kumar Behera, Lalatendu Department of Computer Science and Engineering Dr. B. R. Ambedkar National Institute of Technology Jalandhar India Department of Computer Science and Engineering SRM University Andhra Pradesh Amaravati India

ISBN: (纸本)9789819981281

The Internet of Things (IoT) has led to the adoption of fog computing for data-intensive applications that require low-latency processing. Fog computing uses distributed nodes near the network edge to reduce delays and save bandwidth. To optimize resource usage and achieve goals in fog computing, task scheduling plays a crucial role. To tackle this challenge, we propose an appr.a.h based on genetic algorithms, inspired by genetics and natural selection. We evaluate GA algorithm based on their.a.ility to complete tasks and prioritize them effectively. The results show that the genetic algorithm performs exceptionally well, completing more tasks and handling priorities efficiently. Its adaptability to changing situations enables optimal resource usage and task completion. Our findings underscore the significance of genetic algorithms in fog computing systems. The insights gained from this study contribute to the advancement of fog computing systems, benefiting IoT applications and services. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Fog

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Transfer Function Based Feature Selection Using Harris Hawk Optimization for Biomedical Applications

Transfer Function Based Feature Selection Using Harris Hawk ...

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2024 Inter.a.ional Conference on Smart Electronics and Communication Systems, ISENSE 2024

作者： Jayachitra, S. Prasanth, A. Prasannakiruba, G.S. Abbineni, Srichandana PSNA College of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Bannari Amman Institute of Technology Department of Computer Technology Sathyamangalam India Hyderabad India

ISBN: (纸本)9798331528126

Feature Selection plays key role to truncate the high dimensionality in the datasets through eradicating the unwanted and noisy attributes to improve the perfor.a.ce of classification. The Meta-heuristic technique is one of the most optimistic search techniques to travel the feature space to locate finest feature subset. The Harris Hawk Optimization (HHO) excited from hunting appr.a.h of hawk in nature. The cost effectiveness and simplicity formulates the algorithm prominent and superior in solving optimization problems. The binary version of the optimization is accountable for delineating continuous searching space to discrete feature space. Here, 6 transfer functions with S-Shaped and V-Shaped families were interposed and analyzed. Twelve benchmark datasets from UCI repository has been introduced to examine the transfer functions and choose the best feature per.a.ning to mitigate local minima and high speed of convergence. To analyze the efficacy of transfer function, the proposed method was compared over exiting algorithms and the proposed results attain higher classification accuracy in many of the datasets. © 2024 IEEE.

关键词： Heuristic methods

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Cultivating Cluster Enactment in Wireless Sensor Networks Through Hybrid Metaheuristics Intended for Energy Optimization

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SN computer Science 2025年第3期6卷 1-12页

作者： Kushwaha, Ajay Goswami, Anjali Sharada, K. Shar.a. Manmohan Berar, Gagandeep Edara, Deepak Chowdary Department of Computer Science and Engineering Rungta College of Engineering and Technology R1 CG Bhilai India Department of Mathematics and Statistics College of Science and Theoretical Studies Saudi Electronic University Riyadh 13323 Saudi Arabia Department of Computer Science and Engineering GITAM (Deemed to be University) AP Visakhapatnam India Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur India Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Department of CSE Vignan’s Foundation for Science Technology and Research Vadlamudi AP Guntur India

Though traditional search methods shine when faced with local goals and a plethora of decision factors, they could struggle when faced with engineering design limitations. On the other hand, metaheuristic appr.a.hes promise to find global optima in r.a.-world optimization problems, even in nonlinear.a.d multimodal settings. In order to improve cluster head selection in Wireless Sensor Networks (WSNs), this research presents a new metaheuristic method that combines the principles of Cuckoo Search method (CSA) and Selection by Optimization (SBO). When choosing a Chief Cluster Head (CCH), the CSA algorithm takes hop count into account, wher.a. SBO finds the best cluster heads by considering communication range. By comparing the suggested appr.a.h to both stand-alone SBO and CSA methods, we can see how well it performs in terms of throughput, latency, energy usage, delivery ratio, and packet overhead. The combined SBO-CSA method improves cluster perfor.a.ce, simplifies cluster head selection in WSNs, and incr.a.es network efficiency overall, according to the results. To prove its efficacy in enhancing network perfor.a.ce, the suggested solution outperforms standalone SBO and CSA appr.a.hes across a range of perfor.a.ce parameters, such as throughput, latency, energy consumption, and packet overhead. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： CCH CH CSA Metaheuristic methods SBO

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An Explainable Artificial Intelligence model in the assessment of Brain MrI Lesions in Multiple Sclerosis using Amplitude Modulation - Frequency Modulation multi-scale feature sets 24

An Explainable Artificial Intelligence model in the assessme...

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24th Inter.a.ional Conference on Digital Signal Processing, DSP 2023

作者： Nicolaou, Andr.a.Kakas, Antonis Pattichis, Constantinos S. Pattichis, Marios S. Fotso, Kevin Loizou, Christos P. Pantzaris, Marios Department of Computer Science University of Cyprus Nicosia Cyprus Department of Electrical Engineering and Computer Engineering University of New Mexico United States Department of Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus Cyprus Institute of Neurology and Genetics Nicosia Cyprus

ISBN: (纸本)9798350339598

The objective of this study was to implement an explainable artificial intelligence (AI) model with embedded rules to assess Multiple Sclerosis (MS) disease evolution based on brain Magnetic resonance Imaging (MrI) multi-scale lesion evaluation. Amplitude Modulation-Frequency Modulation (AM-FM) features were extracted from manually segmented brain MS lesions obtained using MrI and were labeled with the Expanded Disability Status Scale (EDSS). Machine learning models were used to classify the MS subjects with a benign course of the disease and subjects with advanced accumulating disability. rules were extracted from the selected model with high accuracy and then were modified to perform argumentation-based r.a.oning. It is demonstrated that the proposed explainable AI modeling can distinguish MS subjects and give meaningful infor.a.ion to track the progression of the disease. Future research will examine more subjects and add new feature sets and models. © 2023 IEEE.

关键词： Frequency modulation

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Pc Automation Using Hand Gestures

Pc Automation Using Hand Gestures

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2023 Inter.a.ional Conference on computer Communication and informatics, ICCCI 2023

作者： Purushotham, P. Bennet, Ben Sujin Vijayakarthik, P. Kiran, Ajmeera ramakrishna Pravallika, M. Shivani, Y. Chetan, Ch. MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India University of Technology and Applied Sciences Engineering Department Oman R.L.Jalappa Institute of Technology Dodabalapur Dept of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350348217

The human computer interaction platform can be implemented in a variety of different ways because to the fact that webcams and other equipment, such as sensors, are both relatively inexpensive and r.a.ily available on the market. The use of gestures as a mode of communication between humans and machines is widely regarded as the most effective appr.a.h. The employment of hand gestures as a means of communication between humans and machines or computers is particularly helpful in achieving higher levels of infor.a.ion conveyance. The use of hand gestures as a form of nonver.a. communication is something that can be put to use in a variety of contexts. Applications that make use of hand gestures have acquired a variety of alter.a.ive methodologies, including those that are backed by sensor technology and computer vision, according to research and survey reports. research is expanding into new territory, particularly in the ar.a. of pattern recognition and gesture recognition. Gestures of the hand can add a new layer of complexity to the operation of contactless computers. As a result, in this project, we produce software that shows a prototype of a system that is able to automatically recognize gestures and executes pc commands in response to cer.a.n motions. This software is shown as part of this project. Python is the language that we are utilizing to construct our solution, and we are taking advantage of integrated modules such as cv2 (OpenCV, or open source computer vision, is a collection of programming functions that is primarily geared toward r.a.-time computer vision.) & Mediapipe (detects hand and fingers data points.) © 2023 IEEE.

关键词： Human computer interaction

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A nonlinear recurrent encoders for early detection of strep thr.a. infection to prevent acute rheumatic fever

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Jour.a. of Ambient Intelligence and Humanized Computing 2024年第4期15卷 2201-2213页

作者： Kumar, K. Antony Belinda, M. J. Carmel Mary Kumar, V. Dhilip rajan, Jerlin Francy Arif, Muhammad Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of Medical Surgical Nursing Dr. Jeyashekaran Medical Trust Nagercoil India Superior University Lahore Pakistan

rheumatic Fever or.a.ute rheumatic Fever (rF) and rheumatic Heart Disease (rHD) are commonly suffered by people of all ages around the world. rF usually begins with strep thr.a. infections, skin problems, joint pain etc. Clinical tests and medical informatics are useful to save patients from Streptococcus Group-A bacter.a.using medical tr.a.ments and detection mechanism. Clinical data based statistical analysis appr.a.hes assist medical diagnosis model in detecting diseases based on standard decision. Standard decision making frameworks are effective when using these appr.a.hes. Nevertheless, existing mechanisms cannot detect or predict the incidence of strep thr.a. infection at an earlier stage based on unclear datasets or complex datasets. The recent computational healthcare informatics systems use Machine Learning (ML) and Deep Learning (DL) techniques, but they are not sufficient in terms of their field integration. The proposed work implements a nonlinear recurrent auto encoder using complex data evaluation procedures to accurately detect and predict the strep thr.a. infections accurately. In this regard, the proposed model builds on the technical features of Denoised Var.a.ional Stacked Auto Encoders (DVSE), nonlinear regression computations and Long Shot Term Memory (LSTM) to perform recurrent data analysis practices. Compared with the existing techniques such as Blood Serum Content Analysis model (BSCA), Smartphone based Strep Thr.a. Detection (SSTD) appr.a.h and Logistic regression Based rF Detection appr.a.h (LrrF), the multi-level medical data evaluation (thr.a. images and blood samples) provides 10–15% more accuracy for strep thr.a. detection. As shown in the implementation section, the proposed nonlinear recurrent system has the potential to detect strep thr.a. infections early. © The Author(s), under exclusive licence to Springer-Ver.a. GmbH Ger.a.y, part of Springer Nature 2024.

关键词： Diagnosis

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GuavaCareNet: Empowering Guava Farms with Federated Learning for r.a.-Time Disease Diagnosis 2

GuavaCareNet: Empowering Guava Farms with Federated Learning...

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2nd Inter.a.ional Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Karthikeyan, M. Saravana Kirubha, D. Krishnan, r. Santhana raj, J. relin Francis Soundiraraj, N. Murali, S. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Rajarajeswari College of Engineering Department of Computer Science and Engineering Bangalore India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India PSNA College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798331505745

This research presents a Federated Learning-based appr.a.h for the detection of guava leaf diseases, aimed at enhancing agricultural practices while ensuring data privacy. Guava farms across various regions provide images of guava leaves, both healthy and diseased, to train a deep learning model for disease classification. The EfficientNet-B3 model, optimized with Neural Architecture Search (NAS) and Transfer Learning, is used for.a.curate disease detection, including conditions like Anthracnose and Powdery Mildew. To ensure privacy, Federated Learning (FL) is employed, where the model is trained locally on edge devices such as smartphones and Jetson Nano devices situated at guava farms. These devices process the data locally and send model updates, rather than raw data, to a central server. This method enables the aggregation of knowledge from multiple farms without sharing sensitive infor.a.ion, thereby protecting farmers' data privacy. The use of Federated Learning allows the model to adapt to specific conditions at each farm, such as different diseases, leaf appearances, and lighting var.a.ions, improving its robustness and accuracy. Additionally, AWS IoT integration allows r.a.-time monitoring and alerts, further empowering farmers with timely disease detection. The decentralized nature of this appr.a.h not only enhances disease detection in guava crops but also fosters data privacy, ensuring farmers' infor.a.ion remains secure. © 2025 IEEE.

关键词： Sensitive data

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Trajectory-Driven Efficiency: An Energy-Efficient Opportunistic routing Protocol for Industr.a. IoT Wireless Sensor Networks 10

Trajectory-Driven Efficiency: An Energy-Efficient Opportunis...

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10th Inter.a.ional Conference on Communication and Signal Processing, ICCSP 2024

作者： rajulu, G. Govinda Murugesan, M. Mahaboog Basha, S.K. Kalaiselvan, S.A. Kumar, M. Lenin Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai Avadi India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Computer Science and Engineering Chennai Avadi India Tkr College of Engineering and Technology Department of Computer Science and Engineering Telangana India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Vignan Institute of Technology and Science Department of Electronics and Communications Engineering Telangana India

ISBN: (纸本)9798350353068

The rapid expansion of Wireless Sensor Networks (WSNs) in the r.a.m of Industr.a. Internet of Things (IIoT) demands sophisticated routing protocols to ensure energy efficiency and reliable communication. This paper introduces a novel Energy-Efficient Opportunistic routing Protocol Based on Trajectory Prediction (EOrB-TP) to address these challenges in industr.a. environments. The research identifies the need for dynamic adaptation to industr.a. dynamics and changing environmental conditions, prompting the development of trajectory prediction models using Hidden Markov Models (HMMs). EOrB-TP intelligently leverages these models for dynamic routing decisions, strategically optimizing data transmission paths to minimize energy consumption. The proposed system's efficiency is demonstrated through a comprehensive experimental study, showcasing superior perfor.a.ce in terms of network lifetime, energy consumption, end-to-end delay, and routing cost compared to existing protocols. This innovative protocol offers a promising solution for enhancing the sustainability and efficiency of Wireless Sensor Networks in Industr.a. IoT applications, paving the way for resilient and resource-conscious industr.a. monitoring systems. © 2024 IEEE.

关键词： routing protocols

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