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38th International Conference on computers and Their Applications, CATA 2023

作者： Murad, Saydul Akbar Rahimi, Nick Roy, Indranil Gupta, Bidyut School of Computing Sciences & Computer Engineering University of Southern Mississippi HattiesburgMS United States Department of Computer Science Southeast Missouri State University Cape GirardeauMO United States School of Computing Southern Illinois University CarbondaleIL United States

In recent years, there has been massive growth in the usage of IoT devices. Cloud computing architecture is unable to meet the requirements of bandwidth, real-time response, and latency. To overcome these limitations, fog computing architecture is introduced, which responds to requests from IoT devices and only, if necessary, forwards requests to the cloud. Nonetheless, there are still some requests that need to go to the cloud and get affected by the shortcomings of the cloud. In thi s work, we propose to add a peer-to-peer (P2P) structure to the fog layer. We have considered our recently reported 2-layer non-DHT-based architecture for P2P networks in which at each level of the hierarchy existing networks are all structured and each such network has a diameter of 1 overlay hop. Such low diameters have huge significance in our proposed P2P fog model and improve fog computing by presenting very efficient data lookup algorithms. In this model, fog nodes can work together to complete the client requests. Consequently, fog nodes are able to fulfill the client requests in the fog layer, which ultimately decreased overheads on the cloud. Additionally, to improve the security in communication in the architecture, we have utilized ciphertext policy attribute-based encryption (CP-ABE) and presented a new secure algorithm. © 2023, EasyChair. All rights reserved.

关键词： Fog

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Suggesting Exit Path in a Multi-Path in Case of an Emergency 18

Suggesting Exit Path in a Multi-Path in Case of an Emergency

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18th INDIAcom;11th International Conference on computing for Sustainable Global Development, INDIACom 2024

作者： Vignesh, M.S. Kumar, Nikhil Mukhopadhyay, Adwitiya Rani, Pooja Kumar, Manoj Joshi, Pallavi Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science Mysuru India Bml Munjal University School of Engineering and Technology Gurugram India Vspp Tech Llc TX United States University of Wollongong in Dubai School of Computer Science Feis Dubai United Arab Emirates Middle East University Meu Research Unit Amman Jordan

ISBN: (纸本)9789380544519

This study proposes an innovative approach to address the challenges of navigating multi-exit scenarios during emergencies by utilizing the VGG19 model and incorporating voice guidance. A crucial component of this research is the extensive CubiCasa5K dataset, comprising 5,000-floor plans with detailed annotations, including exit paths and structural information from diverse indoor environments. These floor plans serve as a valuable resource for generating synthetic images of exit paths within various buildings. The synthetic images undergo pre-processing and are inputted into the VGG19 model for feature extraction and exit path classification. To enhance user experience, a text-to-speech synthesis system is integrated, generating a tailored voice message offering clear and concise instructions based on the VGG19's classification of the exit path. The solution achieves an impressive accuracy of 94.96%, promising to significantly improve individual safety by providing accurate and timely guidance in complex emergency situations. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： accuracy CubiCasa5K Dataset deep learning floor plans VGG19 voice guidance

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An Empirical study and assessment of minority oversampling with Dynamic Ensemble Selection on COVID-19 utilizing Blood Sample

An Empirical study and assessment of minority oversampling w...

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2022 International Conference on engineering and MIS, ICEMIS 2022

作者： Srikanth, Panigrahi Behera, Chandan Kumar Gmr Institute of Technology Department of Computer Science and Engineering GMR NagarAndhra Pradesh Rajam532127 India Vit University School of Computing Science & Engineering Bhopal466114 India

ISBN: (数字)9781665454360

ISBN: (纸本)9781665454360

The COVID-19 virus disease outbreak that erupted in China at the end of 2019 has had a tremendous and disastrous impact on the rest of the world. It has struck the globe to its core, and the destruction has substantially increased the diagnostic burden. In the pandemic zone, clinicians will be able to cut down on their workload and get the right diagnosis of the new disease great to the use of machine learning. A blood test has emerged as a critical tool for identifying false-positive or false-negative real-time rRT-PCR diagnostics. Notably, this is mostly because it is such a cost-effective and convenient method of detecting probable COVID-19 patients. Among the numerous hard consequences associated with COVID-19 illness has been established as one of the most prevalent among COVID-19 patients. The impetus for this research is the scarcity of post-COVID-19 dataset. Following pre-processing to manage address missing values, oversampling with SMOTE ENN is used to generate several instances and model training is carried out on these data sets. However, it has been demonstrated that normatively dynamic ensemble selection outperforms static selection and dynamic selection. The DI+SMOTEENN+DESKNU exceed existing benchmark Classification algorithms and obtain the best accuracy of 99.6%, according the results. © 2022 IEEE.

关键词： COVID-19

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MCHeRA: An Efficient Multi Cloud Heuristic Resource Allocation using Grey Wolf Optimization 5

MCHeRA: An Efficient Multi Cloud Heuristic Resource Allocati...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Gandhimathi, P. Kiruthiga, S. BIndu, K.V. Shanthi, H.J. Velmurugan, V. Sangeethaa, S.N. Srm Madurai College for Engineering and Technology Pottapalayam Sivagangai District630612 India School of Computing Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science &engineering Chennai India Puduvoyal India Department: Computer Applications Hindustan Institute of Technology and Science Deemed to be University Padur India Vel Tech Rangarajan Dr Sagunlatha R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi Chennai India Bannari Amman Institute of Technology India

ISBN: (纸本)9798350389609

Cloud computing is a cutting-edge technology that has gained immense popularity in the present era. The burgeoning demand for services has necessitated the development of a framework for efficiently allocating resources to incoming requests. This approach not only enhances the network's efficiency but also reduces costs significantly. The convergence of cloud and edge computing has emerged as a burgeoning field of study in the computing industry over the past few years. The exponential rise in the number of customers and requests for cloud data centers (CDCs) has highlighted the critical need for robust servers and energy-efficient mechanisms. As CDCs expand to meet growing demands, they face significant challenges related to energy consumption, environmental impact, and operational costs. Employing suitable algorithms for resource allocation in CDCs is crucial in order to minimize energy consumption. The objective of this research endeavor was to devise an ingenious approach for dynamically allocating resources in cloud networks by leveraging the power of the Gray Wolf Optimization algorithm. The proposed work performs Multi-Cloud Heuristic Resource Allocation (MCHeRA) using Grey Wolf Optimization (GWO) algorithm and produces 61% of computational resource utilization, 79% of CPU utilization, 64% memory utilization, 74% bandwidth utilization. The algorithm that has been put forth is meticulously assessed and contrasted with its counterparts. The outcome of the simulation manifests the remarkable efficacy of the novel MCHeRA in comparison to alternative algorithms. © 2024 IEEE.

关键词： Cloud platforms

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Empowering Diagnosis: Cutting-Edge Segmentation and Classification in Lung Cancer Analysis

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computers, Materials & Continua 2024年第6期79卷 4963-4977页

作者： Iftikhar Naseer Tehreem Masood Sheeraz Akram Zulfiqar Ali Awais Ahmad Shafiq Ur Rehman Arfan Jaffar Faculty of Computer Sciences&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Information System Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia School of Computer Science and Electronic Engineering(CSEE) University of EssexWivenhoe ParkColchesterCO43SQUK

Lung cancer is a leading cause of global mortality *** detection of pulmonary tumors can significantly enhance the survival rate of ***,various computer-Aided Diagnostic(CAD)methods have been developed to enhance the detection of pulmonary nodules with high ***,the existing method-ologies cannot obtain a high level of specificity and *** present study introduces a novel model for Lung Cancer Segmentation and Classification(LCSC),which incorporates two improved architectures,namely the improved U-Net architecture and the improved AlexNet *** LCSC model comprises two distinct *** first stage involves the utilization of an improved U-Net architecture to segment candidate nodules extracted from the lung ***,an improved AlexNet architecture is employed to classify lung *** the first stage,the proposed model demonstrates a dice accuracy of 0.855,a precision of 0.933,and a recall of 0.789 for the segmentation of candidate *** suggested improved AlexNet architecture attains 97.06%accuracy,a true positive rate of 96.36%,a true negative rate of 97.77%,a positive predictive value of 97.74%,and a negative predictive value of 96.41%for classifying pulmonary cancer as either benign or *** proposed LCSC model is tested and evaluated employing the publically available dataset furnished by the Lung Image Database Consortium and Image Database Resource Initiative(LIDC-IDRI).This proposed technique exhibits remarkable performance compared to the existing methods by using various evaluation parameters.

关键词： Lung cancer segmentation AlexNet U-Net 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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FedEdge: Accelerating Edge-Assisted Federated Learning 23

FedEdge: Accelerating Edge-Assisted Federated Learning

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32nd ACM World Wide Web Conference, WWW 2023

作者： Wang, Kaibin He, Qiang Chen, Feifei Jin, Hai Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Federated learning (FL) has been widely acknowledged as a promising solution to training machine learning (ML) model training with privacy preservation. To reduce the traffic overheads incurred by FL systems, edge servers have been included between clients and the parameter server to aggregate clients' local models. Recent studies on this edge-assisted hierarchical FL scheme have focused on ensuring or accelerating model convergence by coping with various factors, e.g., uncertain network conditions, unreliable clients, heterogeneous compute resources, etc. This paper presents our three new discoveries of the edge-assisted hierarchical FL scheme: 1) it wastes significant time during its two-phase training rounds;2) it does not recognize or utilize model diversity when producing a global model;and 3) it is vulnerable to model poisoning attacks. To overcome these drawbacks, we propose FedEdge, a novel edge-assisted hierarchical FL scheme that accelerates model training with asynchronous local federated training and adaptive model aggregation. Extensive experiments are conducted on two widely-used public datasets. The results demonstrate that, compared with state-of-the-art FL schemes, FedEdge accelerates model convergence by 1.14 × -3.20 ×, and improves model accuracy by 2.14% - 6.63%. © 2023 ACM.

关键词： Edge computing

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Automated Detection of Hate Speech in Hindi using Supervised Learning Models

Automated Detection of Hate Speech in Hindi using Supervised...

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Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： Nikhil Rajak Ajay Kumar Phulre Subhash Chandra Patel Narottam Das Patel Ganesh Khekare School of Computing Science Engineering and Artificial Intelligence VIT Bhopal University Sehore India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

This study addresses the pressing issue of hate speech on online platforms. Traditional text-based analysis is no longer adequate for identifying hate speech due to the growth of internet platform data. The project aims to create scalable automated methods for web platforms to detect hate speech in real-time. To find the best algorithms for recognizing hate speech, the study compares many machine learning models using a large dataset in Hindi from Twitter. Where the study matters is in the creation of advanced content filtering systems that offer a more secure and friendly online environment. Support Vector Machine (SVM) has the best performance, with outstanding precision (0.9468) and F1-score (0.944), showcasing the top-performing ones and talking about the difficulties and ways ahead in this field. The study recommends improving Hindi hate speech detection systems. With problems such as imbalanced data sets and the dynamic nature of online language, language research using hybrid models, constant model adjustments, and language trend monitoring are all necessary.

关键词： Support vector machines Social networking (online) Computational modeling Hate speech Supervised learning Speech recognition Pressing Data models Real-time systems Monitoring

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Exploring Advanced Techniques for Enhancing Data Privacy and Security in Digital Systems

Exploring Advanced Techniques for Enhancing Data Privacy and...

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Advances in computing, Communication and Networking (ICAC2N), International Conference on

作者： Rimjhim Kumari Srinivasan Sriramulu Department of Computer Science and Engineering Galgotias University Greater Noida India School of Computing Science and Engineering Galgotias University Greater Noida India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

As digital interactions proliferate, the imperative to fortify data privacy and security becomes paramount. This paper explores advanced techniques—encryption algorithms, biometric authentication, machine learning for anomaly detection, and blockchain technology—as integral components of a comprehensive strategy for safeguarding digital systems. The research reveals that a holistic approach, synthesizing these techniques, offers robust defense against evolving cyber threats. Findings underscore the significance of user-centric security measures, continuous adaptation to emerging threats, and the ethical considerations inherent in technological advancements. The contribution of this research lies in providing practical insights for practitioners and researchers, shaping a nuanced understanding of the dynamic landscape of data privacy and security. Through actionable recommendations, the paper empowers stakeholders to navigate the intricate challenges and opportunities presented by the digital age, ensuring the resilience and trustworthiness of digital systems. The proposed paper talked about how encryption algorithms, biometric authentication, machine learning for anomaly detection and blockchain work. The paper equips stakeholders with tangible recommendations to skillfully navigate the complex landscape of the digital era, promoting the durability and dependability of digital systems.

关键词： Data privacy Ethics Machine learning algorithms Digital systems Machine learning Biometric authentication Encryption Blockchains Security Anomaly detection

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Maverick: Personalized Edge-Assisted Federated Learning with Contrastive Training 25

Maverick: Personalized Edge-Assisted Federated Learning with...

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34th ACM Web Conference, WWW 2025

作者： Wang, Kaibin He, Qiang Dong, Zeqian Chen, Rui He, Chuan Chua, Caslon Chen, Feifei Yang, Yun Swinburne University of Technology Melbourne Australia Huazhong University of Science and Technology Wuhan China Deakin University Melbourne Australia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9798400712746

In an edge-assisted federated learning (FL) system, edge servers aggregate the local models from the clients within their coverage areas to produce intermediate models for the production of the global model. This significantly reduces the communication overhead incurred during the FL process. To accelerate model convergence, FedEdge, the state-of-the-art edge-assisted FL system, trains clients’ models in local federations when they wait for the global model in each training round. However, our investigation reveals that it drives the global model towards clients with excessive local training, causing model drifts that undermine model performance for other clients. To tackle this problem, this paper presents Maverick, a new edge-assisted FL system that mitigates model drifts by training personalized local models for clients through contrastive local training. It introduces a model-contrastive loss to facilitate personalized local federated training by driving clients’ local models away from the global model and close to their corresponding intermediate models. In addition, Maverick includes anomalous models in contrastive local training as negative samples to accelerate the convergence of clients’ local models. Extensive experiments are conducted on three widely-used models trained on three datasets to comprehensively evaluate the performance of Maverick. Compared to state-of-the-art edge-assisted FL systems, Maverick accelerates model convergence by up to 16.2x and improves model accuracy by up to 12.7%. © 2025 Copyright held by the owner/author(s).

关键词： Contrastive Learning

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