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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Avv, Sudhakar Mohan, M. Maharajan, K. Habelalmateen, Mohammed I. Naga Saranya, N. SR University Department of Electrical and Electronics Engineering Telangana India Nitte Meenakshi Institute of Technology Department of ISE Bengaluru India School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankoil Bangalore India Universiti Tun Hussein Onn Malaysia Parit Raja Faculty of Electrical and Electronic Engineering Johor Batu Pahat Malaysia College of Technical Engineering the Islamic University Department of Computer Technical Engineering Najaf Iraq Saveetha College of Liberal Arts and Science SIMATS Department of Computer Applications Chennai India

ISBN: (纸本)9798350317558

Mobile networks are flexible enough to support a range of resource allocation models and service-based choices in computing domains, which affects both virtual reality and the Industrial Internet of Things (IIOT). Virtual resource management is made easier by the Mobile Edge Computing (MEC) paradigm, which also makes edge connectivity between data terminals and execution in the core network under heavy load possible. Meeting customer requirements efficiently is achieved through thoughtful planning, aided by cognitive agents. User data, incorporating behavioral patterns, is amalgamated to cater to IIOT service types. Neural caching for memory during task execution is made easier by the use of swarm intelligence and reinforcement learning techniques. Prediction strategies optimize business operations and caching to reduce execution delays. Predictive models evaluate performance by taking into account variables like nearby user equipment and mobile edge computing resources. This method's efficacy is demonstrated by a cognitive agent model that manages resource distribution and creates networks of communication to improve service quality. For accurate resource distribution among end users, reinforcement learning techniques - more especially, Multi-Objective Particle Swarm Optimization (MOPSO) algorithms - are used. This includes building cost mapping tables and optimizing allocation in MEC. The proposed method outperforms existing algorithms like Task-Offloading and Resource Allocation Strategy and achieves a better throughput value of 785.32. © 2024 IEEE.

关键词： Communication Industrial Internet of Things Mobile Edge Computing Optimization Quality of Service Reinforcement Learning

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Mitigation for cloud computing security risks and governance

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International Journal of Cloud Computing 2022年第5-6期11卷 560-567页

作者： Jabez, J. Narmadha, R. Porkodi, S. Devi, L. School of Computing Sathyabama Institute of Science and Technology Chennai India School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Chennai India College of Engineering and Computer Science Lebanese French University Erbil Iraq PGCS Department Muthayammal College of Arts and Science Rasipuram India

The recent prosperous of cloud computing has become the biggest revenue for originalities with the issues of intricate servers that need regular maintenance, security, power and cooling systems throughout. Hence, it is extremely significant for any cloud service earner is to offer dense security models and policies to all users involves. Although creating best suited security strategies might necessitate enormous amount of reserve and endurance, it will bounce the casual for end user to relay on a computerised security system throughout. The cloud environs supplementary strongly secure, qualifying security hazards, appropriate controls must be imposed. Major contest is to consider cloud security and governance explanation together. In this segment, the emphasis will be in the contests involved in preserving a dependable security policy computerisation in at the application layer, network layer and host level of cloud computing. Moreover, securities related to models will be discussed to recruit for those security applications which are more composite and active. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Security systems

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Signet Ring Cell Detection from Histological Images Using Deep Learning

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computers, Materials & Continua 2022年第9期72卷 5985-5997页

作者： Muhammad Faheem Saleem Syed Muhammad Adnan Shah Tahira Nazir Awais Mehmood Marriam Nawaz Muhammad Attique Khan Seifedine Kadry Arnab Majumdar Orawit Thinnukool Department of Computer Science UETTaxila47050Pakistan Department of Computer Science HITEC University TaxilaTaxilaPakistan Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Department of Civil Engineering Imperial College LondonLondon SW72AZUK College of Arts Mediaand TechnologyChiang Mai UniversityChiang Mai 50200Thailand

Signet Ring Cell(SRC)Carcinoma is among the dangerous types of cancers,and has a major contribution towards the death ratio caused by cancerous *** and diagnosis of SRC carcinoma at earlier stages is a challenging,laborious,and costly *** detection of SRCs in a patient’s body through medical imaging by incorporating computing technologies is a hot topic of *** the presented framework,we propose a novel approach that performs the identification and segmentation of SRCs in the histological images by using a deep learning(DL)technique named Mask Region-based Convolutional Neural Network(Mask-RCNN).In the first step,the input image is fed to Resnet-101 for feature *** extracted feature maps are conveyed to Region Proposal Network(RPN)for the generation of the region of interest(RoI)proposals as well as they are directly conveyed to ***,RoIAlign combines the feature maps with RoI proposals and generates segmentation masks by using a fully connected(FC)network and performs classification along with Bounding Box(bb)generation by using FC *** annotations are developed from ground truth(GT)images to perform experimentation on our developed *** introduced approach achieves accurate SRC detection with the precision and recall values of 0.901 and 0.897 respectively which can be utilized in clinical *** aim to release the employed database soon to assist the improvement in the SRC recognition research area.

关键词： Mask RCNN deep learning SRC segmentation

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Cluster Head Selection for Single and Multiple data Sinks in Heterogeneous WSN using Wild Horse Optimization 2

Cluster Head Selection for Single and Multiple data Sinks in...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Habelalmateen, Mohammed I. Kumar, Gotte Ranjith Nayana, B.P. Venkatramulu, S. Saranya, N. Naga Universiti Tun Hussein Onn Malaysia Electrical and Electronic Engineering Johor Malaysia College of Technical Engineering the Islamic University Department of Computer Technical Engineering Najaf Iraq SR University School of Computer Science and Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Department of ISE Bengaluru India Kakatiya Institute of Technology and Science CSE Dept Telangana Warangal India Saveetha College of Liberal Arts and Science SIMATS Department of Computer Applications Chennai India

ISBN: (纸本)9798350317558

Enhancing a network lifetime by balancing energy as well as minimizing delay is a significant aim of Wireless Sensor Network (WSN). Besides, number of clustering approaches are developed to this process through selecting the optimum Cluster Heads (CHs) in heterogeneous WSN, but it has a significant challenge. In this research, the Wild Horse Optimization (WHO)-based Optimized Clustering approach for single and multiple data sinks is proposed for optimizing CH selection through combining the parameters of sink distance, residual energy in its formulated fitness function. The proposed method achieves better results and it is evaluated by various performance metrices like residual energy, network lifetime, throughput, alive nodes, FND as well as LND and it achieves the values of about each as 61.37, 98.36, 7.8, 96.39, 6129 and 8347 respectively when compared to the existing methods such as Energy-Efficient CH selection utilizing Improved version of Grey Wolf Optimization-based (EECHIGWO) and Enhanced Pelican Optimization Algorithm for the CH Selection (EPOA-CHS). © 2024 IEEE.

关键词： Cluster Head selection Multiple data sinks Network lifetime Wild Horse Optimization and Wireless Sensor Network

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DanmuA11y: Making Time-Synced On-Screen Video Comments (Danmu) Accessible to Blind and Low Vision Users via Multi-Viewer Audio Discussions 25

DanmuA11y: Making Time-Synced On-Screen Video Comments (Danm...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Xu, Shuchang Jin, Xiaofu Qu, Huamin Yan, Yukang Hong Kong University of Science and Technology Hong Kong IIP(Computational Media and Arts) The Hong Kong University of Science and Technology Hong Kong Hong Kong Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Department of Computer Science University of Rochester Rochester NY United States

ISBN: (纸本)9798400713941

By overlaying time-synced user comments on videos, Danmu creates a co-watching experience for online viewers. However, its visual-centric design poses significant challenges for blind and low vision (BLV) viewers. Our formative study identified three primary challenges that hinder BLV viewers' engagement with Danmu: the lack of visual context, the speech interference between comments and videos, and the disorganization of comments. To address these challenges, we present DanmuA11y, a system that makes Danmu accessible by transforming it into multi-viewer audio discussions. DanmuA11y incorporates three core features: (1) Augmenting Danmu with visual context, (2) Seamlessly integrating Danmu into videos, and (3) Presenting Danmu via multi-viewer discussions. Evaluation with twelve BLV viewers demonstrated that DanmuA11y significantly improved Danmu comprehension, provided smooth viewing experiences, and fostered social connections among viewers. We further highlight implications for enhancing commentary accessibility in video-based social media and live-streaming platforms. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Blind Bullet Comment Danmaku Danmu Low Vision Social media Video Visual Impairment

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Anomaly Detection in Conventional Meters and Electricity Consumption Using Optimized Extreme Gradient Boosting 3

Anomaly Detection in Conventional Meters and Electricity Con...

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3rd IEEE International Conference on Distributed Computing and electrical Circuits and Electronics, ICDCECE 2024

作者： Alzubaidi, Laith H. Sathyavani, B. Mamatha Bai, B.G. Dutta, Papiya Saranya, N. Naga College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Sr University Department Of Electrical And Electronics Engineering Warangal India Nitte Meenakshi Institute Of Technology Department Of Cse Karnataka Bengaluru India Bharat Institute Of Engineering And Technology Department Of Electronics And Communication Engineering Hyderabad India Saveetha College Of Liberal Arts & Science Simats Department Of Computer Applications Chennai India

ISBN: (纸本)9798350318609

With the progress of innovative information technologies, smart energy meters have designed a substantial quantity of real-time electricity consumption. Energy consumption is based on the various factors such as humidity, building area, temperature, habitation and so on and these factors are cooperatively describing the energy meter background. The smart meter supports to capable to identify data abnormalities for efficient monitoring as well as investigation. The anomaly detection supports for better decision making for minimizing the energy consumption. In this research, Machine Learning (ML) approach of Optimized Extreme Gradient Boosting (OXGBoosting) for the detection and classification of anomaly for energy consumption in conventional meters. This research collects the large-scale anomaly dataset for estimating the effectiveness. The proposed method is estimated by various performance metrices and it achieves an accuracy of 0.95, precision of 0.92, recall of 0.92, F1-score of 0.91 and AUC of 0.95 respectively when compared to the previous studies such as Extreme Gradient Boosting (XGB), Two-Class Boosted Decision Tree and NGBoost. © 2024 IEEE.

关键词： Smart meters

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Real-Time Audio Noise Reduction and Speech Enhancement Using LadderNet With Hybrid Spectrogram Time-Domain Audio Separation Network 4

Real-Time Audio Noise Reduction and Speech Enhancement Using...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Siddiqua, Ayesha Basha, C.H. Hussaian Abbas, Haider Mohammed Sheela, P. Merlin Indhumathi, V. Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Sr University Department of Electrical & Electronics Engineering School of Engineering Warangal India The Islamic University Department of Computers Techniques Engineering College of Technical Engineering Najaf Iraq Department of English New Prince Shri Bhavani College of Engineering and Technology Chennai India College of Arts and Science Department of Computer Applications Sona Salem India

ISBN: (纸本)9798350352931

In recent years, the rapid growth of modern technology has improved people lives, but it also comes with a downside: increased exposure to noise from complex industrial infrastructure and machinery. To overcome this issue, the existing method namely a single-channel Deep Neural Network (DNN) model have developed for speech signal processing, but it led to limitations in online processing. Therefore, this research proposes a LadderNet with Hybrid Spectrogram Time-domain Audio Separation Network (HSTasNet) model for reducing the noise and enhancing speech enhancement in real-time audio processing. This procedure begins with the collection of data from real-time audio signal and then preprocessed with spectral gating filter to reduce noise from signals. After that, t-distributed Stochastic Neighbor Embedding (t-SNE) technique to extract the optimal features from preprocessed signals. Finally, LadderNet and HS-TasNet techniques are employed for noise reduction and speech enhancement. As per the results, the proposed LadderNet-HSTasNet model offered outstanding results in terms of Perceptual Evaluation of Speech Quality (PESQ) of 3.847, Short-Time Objective Intelligibility (STOI) of 1.874, and Scale-Invariant Signal-to-Distortion Ratio (SI-SDR) of 16.792 when compared with existing Dual-Path Recurrent Neural Network (DPRNN) model. © 2024 IEEE.

关键词： Noise abatement

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Optimal Deep Canonically Correlated Autoencoder-Enabled Prediction Model for Customer Churn Prediction

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computers, Materials & Continua 2022年第11期73卷 3757-3769页

作者： Olfat M.Mirza GJose Moses R.Rajender E.Laxmi Lydia Seifedine Kadry Cheadchai Me-Ead Orawit Thinnukool Department of Computer Science College of Computers and Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science Engineering Malla Reddy Engineering College500100India Department of Computer Science Engineering Lendi Institute of Engineering&TechnologyDenkada535005India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeNorway Program in Statistics and Information Management Faculty of ScienceMaejo UniversityChiang Mai50290Thailand Department of Modern Management and Information Technology College of Arts Media and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Presently,customer retention is essential for reducing customer churn in telecommunication *** churn prediction(CCP)is important to predict the possibility of customer retention in the quality of *** risks of customer churn also get essential,the rise of machine learning(ML)models can be employed to investigate the characteristics of customer ***,deep learning(DL)models help in prediction of the customer behavior based characteristic *** the DL models necessitate hyperparameter modelling and effort,the process is difficult for research communities and business *** this view,this study designs an optimal deep canonically correlated autoencoder based prediction(ODCCAEP)model for competitive customer dependent application *** addition,the O-DCCAEP method purposes for determining the churning nature of the *** O-DCCAEP technique encompasses preprocessing,classification,and hyperparameter ***,the DCCAE model is employed to classify the churners or ***,the hyperparameter optimization of the DCCAE technique occurs utilizing the deer hunting optimization algorithm(DHOA).The experimental evaluation of the O-DCCAEP technique is carried out against an own dataset and the outcomes highlighted the betterment of the presented O-DCCAEP approach on existing approaches.

关键词： Churn prediction customer retention deep learning machine learning archimedes optimization algorithm

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Dual-Color Space Hierarchical Classification Network for Image-Sharing Chain Detection

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Wei, Jiaheng Yao, Heng Wang, Na Qin, Chuan University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Shanghai Institute of Publishing and Media Research Shanghai200093 China

Numerous controversial images spread across online social networks (OSNs) and identifying the sources of these images is crucial. Different OSNs manipulate uploaded images in various ways to reduce transmission and storage costs, leaving identifiable traces. Sharing chain detection aims to reconstruct the sharing history of images on OSNs based on these traces. This article proposes a new sharing chain detection method called the dual-color space hierarchical classification network. We first use the RGB and YCbCr color spaces to maximize perceiving traces within the image pixels, and we also employ an adaptive patch selection (APS) module to choose the most suitable patch for the network input. Furthermore, we innovatively use a hierarchical classification approach to address the sharing chain problem, treating it as a temporal process rather than a simple category. We use multiple interconnected classifiers to detect OSNs sequentially, with classifiers accepting features at different stages, from shallow to deep. The experimental results show that the proposed method can effectively detect the sharing chain of up to three different OSNs. Compared to the state-of-the-art method, the accuracy of detecting the last two OSNs has improved by 2.20% on R-SMUD and 2.09% on V-SMUD. © 1963-2012 IEEE.

关键词： Image classification

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Deploying Hybrid MAELM Approach for Human Emotion Detection Through Speech and Facial Expressions 3

Deploying Hybrid MAELM Approach for Human Emotion Detection ...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Senthil Kumar, K. Rukmani Devi, S. Ranjan, Nidhi Rath, Gitika Indira, G. Nishant, Neerav SRM Institute of Science and Technology School of Computing Department of Networking and Communications College of Engineering and Technology Chennai India SIMATS Deemed to Be University Saveetha College of Liberal Arts and Sciences Department of Computer Science Chennai India Computer Engineering Vasantdada Patil Pratishthan's College of Engineering and Visual Arts Mumbai India Marri Laxman Reddy Institute of Technology and Management Department of CSE Hyderabad India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai India Babu Banarasi das University School of Engineering Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798350343632

Emotion recognition is one of the most fascinating emerging fields of research. It's useful in a lot of different contexts. Some of the most exciting applications of this technology involve robots' ability to see and communicate with one another. Human emotions can be recognized through both visual and audible cues. Facial expressions are one of the most accurate indicators of a person's emotional state. Data preprocessing, feature extraction, and model training are the first three steps of the proposed methodology. Image resizing, a median filter, noise removal, and histogram equalization are all components of preprocessing. Gaussian mixture models and the gray level co-occurrence matrix are being used for feature extraction. After the extraction of features, models with those features are trained using MA-ELM. The proposed method outperforms PCA and ELM, two of the most popular alternatives. © 2023 IEEE.

关键词： Gaussian Mixture Models(GMM) Gray Level Co-Occurrence Matrix (GLCM) Principal Component Analysis (PCA)

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