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2024 IEEE Global communications Conference, GLOBECOM 2024

作者： Sarkar, Kounteya Misra, Sudip Sonu, Mayank Iit Department of Computer Science and Engineering Kharagpur India Iit Department of Electronics and Electrical Communication Engineering Kharagpur India

ISBN: (纸本)9798350351255

This work proposes routeNow, a routing algorithm designed for wireless Software Defined Internet of Things by dynamic path relaxation based on real-time heuristic that always gives the best least hop path. This is aimed towards providing an alternative backup network for a smart hospital that functions independently on the failure of the primary hospital network. Given a pair of source and destination nodes, a central controller with a global network view first calculates a minimum hop path greedily by minimizing the remaining distance to destination. Hence, the path so found is relaxed dynamically by substituting certain links with other links keeping the hop count constant if a new path is found with a lesser latency than the previous one. For this, the controller maintains a matrix containing real-time delay between neighboring IoT nodes given the current network state and whose values are periodically refreshed using a heuristic function based on actual delay measurements. This enables the controller to dynamically modify routing paths. Simulation analysis of routeNow shows that it formulates routing paths with significant less time even with large networks and by always maintaining a route with least hops, on average a least latency path is also ensured. © 2024 IEEE.

关键词： Internet of things

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Gaze-Assisted Autism Spectrum Disorder Identification: A Fusion of Machine Learning and Deep Learning Approaches for Preemptive Identification 5

Gaze-Assisted Autism Spectrum Disorder Identification: A Fus...

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5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Simon, Judy Kapileswar, N. Datchinamoorthi, M. Muthukumar, S. Keerthana Devi, G. SRM Institute of Science and Technology Dept of Electronics and Communication Engineering Chennai India SRM Institute of Science and Technology Dept of Electronics and Computer Engineering Chennai India

ISBN: (纸本)9798350395235

This research proposes an all-encompassing method for diagnosing Autism Spectrum Disorder (ASD) by leveraging eye-tracking data and advanced machine learning techniques. In this study, gaze-tracking data was collected from individuals diagnosed with Autism Spectrum Disorder and those not exhibiting ASD characteristics as they participated in various tasks and stimuli. Principal Component Analysis was employed to streamline the eye-tracking data, retaining crucial information while enhancing computational efficiency. These refined features were then integrated into Convolutional Neural Networks as the primary modelling approach. CNNs excel in recognizing relevant spatial and temporal patterns within the visual data, leading to intricate feature extraction and classification. Additionally, we explored the efficacy of transfer learning by enhancing pre-trained CNN models with our eye-tracking dataset. This approach enables us to leverage knowledge from large-scale visual tasks, potentially enhancing the model's performance in ASD classification. The research findings highlight the success of this multifaceted approach in accurately categorizing individuals with ASD. The synergy of ML, CNN, and transfer learning models significantly enhances the accuracy of ASD diagnosis, offering a promising avenue for early identification and intervention. Consequently, this research has the potential to aid in the creation of accurate noninvasive instruments for diagnosing ASD, thereby accelerating timely assistance and treatment for individuals impacted by the condition. © 2024 IEEE.

关键词： Image classification

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Metaheuristics Based Node Localization Approach for Real-Time Clustered Wireless Networks

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computer Systems science & engineering 2023年第1期44卷 1-17页

作者： R.Bhaskaran P.S.Sujith Kumar G.Shanthi L.Raja Gyanendra Prasad Joshi Woong Cho Department of Information Technology PSNA College of Engineering and TechnologyDindigul624622India Department of Computer Science and Engineering MEA Engineering CollegeMallapuram679325KeralaIndia Department of Electronics and Communication Engineering Rajalakshmi Institute of TechnologyKuthambakkamChennai600124India Department of Electronics and Communication Engineering Sri Eshwar College of EngineeringCoimbatore641202India Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea

In recent times,real time wireless networks have found their applicability in several practical applications such as smart city,healthcare,surveillance,environmental monitoring,*** the same time,proper localization of nodes in real time wireless networks helps to improve the overall functioning of *** study presents an Improved Metaheuristics based Energy Efficient Clustering with Node Localization(IM-EECNL)approach for real-time wireless *** proposed IM-EECNL technique involves two major processes namely node localization and ***,Chaotic Water Strider Algorithm based Node Localization(CWSANL)technique to determine the unknown position of the ***,an Oppositional Archimedes Optimization Algorithm based Clustering(OAOAC)technique is applied to accomplish energy efficiency in the ***,the OAOAC technique derives afitness function comprising residual energy,distance to cluster heads(CHs),distance to base station(BS),and *** performance validation of the IM-EECNL technique is carried out under several aspects such as localization and energy efficiency.A wide ranging comparative outcomes analysis highlighted the improved performance of the IM-EECNL approach on the recent approaches with the maximum packet delivery ratio(PDR)of 0.985.

关键词： Wireless networks real time applications clustering node localization energy efficiency metaheuristics

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Optimized Deep Learning Classification Model for Intelligent Edge devices

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Journal of engineering science and Technology Review 2024年第3期17卷 88-94页

作者： Naveen, Soumyalatha Kounte, Manjunath R Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal India School of Electronics and Communication Engineering REVA University Karnataka Bangalore560064 India

Deep learning models enable state-of-the-art accuracy in computer vision applications. However, the deeper, computationally expensive, and densely connected architecture of deep neural networks (DNN) have limitations for deploying the model on resource-constraint embedded IoT devices. We propose an efficient neural network compression framework that performs filter pruning, fine-tuning and 8-bit quantization to reduce computational complexity, inference time, and memory footprint. Furthermore, reducing the bit widths of activation and weights helps design a compact deployment model on resource-limited IoT devices such as smartphones. The proposed system is evaluated extensively on the CIFAR-10 dataset for Resnet34 and VGG16 models. In addition, we examine the efficacy of a larger model. The result shows that pruning followed by quantization compresses the neural network and compared to the baseline model, achieved an accuracy of 78.01% for Resnet34 and 82.34% for Vgg16 after pruning and quantization which is © 2024 School of science, DUTH. All rights reserved.

关键词： Object recognition

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A novel approach to detect, analyze and block adversarial web pages

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International Journal of Information Technology (Singapore) 2024年第6期16卷 3617-3622页

作者： Kumar, M. Ranjith Naik, Darshana A. Kapila, Neha Mohan, Chinnem Rama Prasad, Ch Raghava Shelke, Chetan Rao, C. V. Guru School of Computer Science and Engineering Vellore Institute of Technology Department of Computer Science and Engineering M S Ramaiah Institute of Technology Department of Electronics and Electrical Engineering Lovely Professional University Department of Computer Science and Engineering Narayana Engineering College Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Alliance College of Engineering and Design Alliance University School of CS & amp AI SR Universtity

The phenomenon of distraction is very common, and its adverse effects are seen among people. The major cause underlying this issue is the ease with which adversarial web sites and web pages can be accessed. It is of utmost importance to locate, evaluate, and actively block such web pages in an effort to comprehensively and globally solve this societal issue. Thus, the given paper proposes an extension or plug-in to detect, analyze, and block websites smartly. The proposed approach takes keywords entered by the user as input into consideration, which eventually leads to the generation of web pages and web links. The filtering of web links is done by the proposed extension, followed by the extraction of features, the utilization of support vector machines (SVM) for binary classification, and the summarization of textual data using natural language processing (NLP). Lastly, the precise results corresponding to relevant web links are presented to the users, which will increase their productivity, thereby reducing their distraction levels under all working circumstances. The performance of the proposed approach is validated against existing recent studies based on evaluation metrics such as adversarial website detection time (ms) and accuracy (%). © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Adversarial web pages Distraction Natural language processing (NLP) Support vector machine (SVM) Text summarization

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5G Advanced: A Step Towards 6G

5G Advanced: A Step Towards 6G

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2024 IEEE International Conference on communication, Computing and Signal Processing, IICCCS 2024

作者： Routray, Sudhir K. Singh, Monika Samal, Laxmipriya Samal, Debashisa Sarangi, Susanta K. Cmr University Department of Computer Science and Engineering Bangalore India Cmr Institute of Technology Department of Electronics and Communication Engineering Bangalore India SoA University Department of Electronics and Communication Engineering Bhubaneswar India

ISBN: (纸本)9798350390759

This article explores the features and proximity of 5G advanced (well known as 5.5G) to 6G cellular communication system, emphasizing its speed, connectivity, and intelligence. Expected benefits of 5.5G include an enhanced user experience, industrial revolution, and support for the Internet of Things (IoT). The motivation behind 5.5G arises from addressing 5G limitations, accommodating IoT growth, enabling real-time applications, providing personalized experiences, and driving economic growth. Anticipated applications span from enhanced mobile broadband to ultra-reliable low latency communication and IoT-based massive machine-type communication, extending to personalized experiences, next-gen gaming, connected healthcare, and advanced education. Despite being in development, 5.5G holds immense significance for bridging the physical and digital world, fostering innovation, and unlocking the potential of the digital age. © 2024 IEEE.

关键词： 5G mobile communication systems

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The Human Eye Pupil Detection System Using BAT Optimized Deep Learning Architecture

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computer Systems science & engineering 2023年第7期46卷 125-135页

作者： S.Navaneethan P.Siva Satya Sreedhar S.Padmakala C.Senthilkumar Department of Electronics and Communication Engineering Saveetha Engineering CollegeChennai602105India Department of Information Technology Seshadri Rao Gudlavalleru Engineering CollegeKrishna521356India Department of Computer Science Engineering Saveetha School of EngineeringSIMATSChennai602105India Department of Electronics and Communication Engineering Saveetha School of EngineeringSIMATSChennai602105India

The pupil recognition method is helpful in many real-time systems,including ophthalmology testing devices,wheelchair assistance,and so *** pupil detection system is a very difficult process in a wide range of datasets due to problems caused by varying pupil size,occlusion of eyelids,and *** Convolutional Neural Networks(DCNN)are being used in pupil recognition systems and have shown promising results in terms of *** improve accuracy and cope with larger datasets,this research work proposes BOC(BAT Optimized CNN)-IrisNet,which consists of optimizing input weights and hidden layers of DCNN using the evolutionary BAT algorithm to efficiently find the human eye pupil *** proposed method is based on very deep architecture and many tricks from recently developed popular *** results show that the BOC-IrisNet proposal can efficiently model iris microstructures and provides a stable discriminating iris representation that is lightweight,easy to implement,and of cutting-edge ***,the region-based black box method for determining pupil center coordinates was *** proposed architecture was tested using various IRIS databases,including the CASIA(Chinese academy of the scientific research institute of automation)Iris V4 dataset,which has 99.5%sensitivity and 99.75%accuracy,and the IIT(Indian Institute of Technology)Delhi dataset,which has 99.35%specificity and MMU(Multimedia University)99.45%accuracy,which is higher than the existing architectures.

关键词： BAT algorithm IRIS datasets DCNN pupil detection black box method

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MASSFormer: Mobility-Aware Spectrum Sensing using Transformer-Driven Tiered Structure

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IEEE communications Letters 2025年第6期29卷 1220-1224页

作者： Janu, Dimpal Mushtaq, Faisel Mandia, Sandeep Singh, Kuldeep Kumar, Sandeep Malaviya National Institute of Technology Electronics and Communication Engineering department Jaipur302017 India SRM University AP Electronics and Communication Engineering department Amaravati522502 India Malaviya National Institute of Technology Department of Computer Science and Engineering Jaipur302017 India Thapar Institute of Engineering and Technology Patiala147004 India Bharat Electronics Ltd Central Research Lab Ghaziabad201010 India

In this paper, we develop a novel mobility-aware transformer-driven tiered structure (MASSFormer) based co-operative spectrum sensing method that effectively models the spatio-temporal dynamics of user movements. Unlike existing methods, our method considers a dynamic scenario involving mobile primary users (PUs) and secondary users (SUs) and addresses the complexities introduced by user mobility. The transformer architecture utilizes an attention mechanism, allowing the proposed method to model the temporal dynamics of user mobility by effectively capturing long-range dependencies. The proposed method first computes tokens from the sequence of covariance matrices (CMs) for each SU. It processes them in parallel using the SU-transformer to learn the spatio-temporal features at SU-level. Subsequently, the collaborative transformer learns the group-level PU state from all SU-level feature representations. The main goal of predicting the PU states at each SU-level and group-level is to improve detection performance even more. The proposed method is tested under imperfect reporting channel scenarios to show robustness. The efficacy of our method is validated with simulation results that demonstrate its higher performance compared to existing methods in terms of detection probability Pd, sensing error, and classification accuracy (CA). © 1997-2012 IEEE.

关键词： Fading channels

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Load Balancing and Energy Efficient Routing in Software-Defined Networking 1st

Load Balancing and Energy Efficient Routing in Software-Defi...

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1st International Conference on Advanced Computing, Machine Learning, Robotics and Internet Technologies, AMRIT 2023

作者： Ramkumar, M.P. Rani, J. Lece Elizabeth Jeyarohini, R. Selvan, G. S. R. Emil Konar, Rahul Shiva Department of Computer Science and Engineering Thiagarajar College of Engineering Madurai India Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Dindigul India

ISBN: (纸本)9783031472237

Software-defined networking (SDN), a newly developed architecture that alters the route (paths) of traffic packets, is a flexible and logically centralized control plane that provides a reduction in the network’s energy usage. SDN is designed in such a way that the switches and links can handle a large amount of traffic. Their energy use is not proportional to traffic. Due to the increase in network communication, SDN devices demand much more load and energy. The SDN controller has to be modeled efficiently. The system model of the controller is proposed which efficiently distributes the load. This model helps in traffic routing with minimal energy consumption for networking devices. In addition to the above, it balances the load based on the heterogeneous traffic distribution. The proposed method optimizes the energy consumed by the networking devices by means of using the few active switches, minimizing the number of device state transitions, and handling the volume of traffic. Load balancing is performed for multiple controllers. The switch migration technique is used for migrating the traffic from one controller to the other. Energy-efficient routing is done by the proposed algorithm with reduced device state transition. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Software defined networking

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Anomaly Detection in Social Media Texts Using Optimal Convolutional Neural Network

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Intelligent Automation & Soft Computing 2023年第4期36卷 1027-1042页

作者： Swarna Sudha Muppudathi Valarmathi Krishnasamy Department of Computer Science and Engineering Ramco Institute of TechnologyRajapalayam626117India Department of Electronics and Communication Engineering P.S.R.Engineering CollegeSivakasi626140India

Social Networking Sites(SNSs)are nowadays utilized by the whole world to share ideas,images,and valuable contents by means of a post to reach a group of *** use of SNS often inﬂicts the physical and the mental health of the ***,researchers often focus on identifying the illegal beha-viors in the SNS to reduce its negative inﬂ*** state-of-art Natural Language processing techniques for anomaly detection have utilized a wide anno-tated corpus to identify the anomalies and they are often time-consuming as well as certainly do not guarantee maximum *** overcome these issues,the proposed methodology utilizes a Modiﬁed Convolutional Neural Network(MCNN)using stochastic pooling and a Leaky Rectiﬁed Linear Unit(LReLU).Here,each word in the social media text is analyzed based on its *** stochastic pooling accurately detects the anomalous social media posts and reduces the chance of overﬁ*** LReLU overcomes the high computational cost and gradient vanishing problem associated with other activation *** also doesn’t stop the learning process when the values are *** MCNN computes a speciﬁed score value using a novel integrated anomaly detection *** on the score value,the anomalies are identiﬁed.A Teaching Learn-ing based Optimization(TLBO)algorithm has been used to optimize the feature extraction phase of the modiﬁed CNN and fast convergence is *** this way,the performance of the model is enhanced in terms of classiﬁcation *** efﬁciency of the proposed technique is compared with the state-of-art techni-ques in terms of accuracy,sensitivity,speciﬁcity,recall,and *** proposed MCNN-TLBO technique has provided an overall architecture of 97.85%,95.45%,and 97.55%for the three social media datasets namely Facebook,Twitter,and Reddit respectively.

关键词： Anomaly detection convolutional neural network social networking sites stochastic pooling teacher learner-based optimization

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