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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Ghosh, Joita Mishra, Khushi Sindhuja, M. Nagadevi, S. Anand, M. School of Computing College of Engineering and Technology SRM Institute of Science and Technology Dept. of Computing Technologies Tamil Nadu Kattankulathur India

ISBN: (纸本)9798331530013

Cancer that affects the lining of the mouth, lips, or upper throat is called oral cancer. Other names for it include tongue cancer, mouth cancer, and oral cavity cancer. There is an urgent need for early cancer detection due to the rise in cancer cases in all age groups. Alcohol consumption and tobacco usage are risk factors. The risk of oral cancer is fifteen times higher for people who use both alcohol and tobacco than for people who do not. This paper presents a deep learning-based technique that uses the ResNet50 convolution neural network model to classify the images of malignant and non-malignant oral cell images. To enhance the pretrained ResNet50 model's capacity to differentiate malignant and benign samples, it was trained using a dataset of histopathological oral tissue pictures. A special virtual voice assistant has also been integrated along with the model to make it more assistive for all kinds of users. In order to track the advancement of these methods, this paper also analyses how combining NLP with point-of-care tools makes use of a variety of algorithms and sets. © 2024 IEEE.

关键词： Lung cancer

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Massively parallel algorithms for fully dynamic all-pairs shortest paths

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Frontiers of Computer Science 2024年第4期18卷 201-203页

作者： Chilei WANG Qiang-Sheng HUA Hai JIN Chaodong ZHENG National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China

1 Introduction In recent years,the Massively Parallel Computation(MPC)model has gained significant ***,most of distributed and parallel graph algorithms in the MPC model are designed for static graphs[1].In fact,the graphs in the real world are constantly *** size of the real-time changes in these graphs is smaller and more *** graph algorithms[2,3]can deal with graph changes more efficiently[4]than the corresponding static graph ***,most studies on dynamic graph algorithms are limited to the single machine ***,a few parallel dynamic graph algorithms(such as the graph connectivity)in the MPC model[5]have been proposed and shown superiority over their parallel static counterparts.

关键词： dynamic shortest gained

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E-Waste Alchemy: A Unified Solution for Recycling and Sustainability 5

E-Waste Alchemy: A Unified Solution for Recycling and Sustai...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Sharma, Vansh Sameer, Md Sindhuja, M. Nagadevi, S. Anand, M. School of Computing College of Engineering and Technology SRM Institute of Science and Technology Dept. of Computing Technologies Tamil Nadu Kattankulathur India

ISBN: (纸本)9798331530013

Through user-centric solutions and advanced features, E-Waste Alchemy is an innovative platform that aims to transform the administration of electronic waste (e-waste). The Recycle Centre on the platform is comprehensive, allowing users to specify the brand and model of laptops, smartphones, and a variety of accessories in order to schedule and select the retrieval. E-Waste Alchemy further improves user convenience by enabling real-time tracking of nearby recyclers based on the user's current location. Furthermore, the platform employs predictive analytics to determine the market values of the components contained within each device, including plastic, aluminum, and nickel. The responsible disposal of e-waste is promoted by this predictive feature, which also contributes to environmental sustainability by optimizing recycling processes. E-Waste Alchemy is a forward-thinking and adaptable solution that facilitates the administration of e-waste and promotes a circular economy. © 2024 IEEE.

关键词： Circular economy

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Dynamic Charging and Path Planning for UAV-Powered Rechargeable WSNs Using Multi-Agent Deep Reinforcement Learning

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IEEE Transactions on Automation Science and engineering 2025年 22卷 15610-15626页

作者： Betalo, Mesfin Leranso Leng, Supeng Seid, Abegaz Mohammed Abishu, Hayla Nahom Erbad, Aiman Bai, Xiaoshan Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Hamad Bin Khalifa University Division of Information and Computing Technology College of Science and Engineering Doha Qatar Shenzhen University College of Mechatronics and Control Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

Unmanned Aerial Vehicle (UAV)-powered 5G/6G networks integrated with rechargeable wireless sensor networks (RWSNs) offer promising solutions for extending system lifetime, collecting data, and providing computing services and power to sensor nodes (SNs). UAVs offer significant advantages, including exceptional mobility, cost-effective deployment, and the ability to be easily reprogrammed for a wide range of missions. However, the limited onboard power capacity of UAVs, coupled with the lack of dynamic and intelligent charging station (CS) management and inefficient path planning, can lead to SN failure in dynamic mobile environments. To address these challenges, we propose an energy-efficient laser-charged UAV (LCU)-enabled RWSN environment, wherein UAVs, powered by laser beams from ground-based stations, provide services, collect data, and transfer energy to SNs. We formulate a joint optimization problem involving power allocation, dynamic charging strategy (DCS), and path planning to minimize task completion time and sensor node death time. Given the NP-hard nature of the problem, we employ a stochastic game model based on a Markov decision process (MDP) for its solution. To solve this problem, we propose a deep reinforcement learning (DRL) based algorithm that enables real-time charging scheduling decisions while optimizing network performance. We introduce a multi-agent double deep Q-network (MA-DDQN) model to determine the optimal trajectories for all UAVs in large and complex environments. Simulation results demonstrate that the MA-DDQN approach outperforms state-of-the-art techniques, showing significant improvements in terms of average delay, energy consumption, and task completion time. © 2004-2012 IEEE.

关键词： Stochastic models

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Spontaneous Infection and Periodic Evolving of Domain in a Diffusive SIS Epidemic Model

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Acta Mathematicae Applicatae Sinica 2024年第1期40卷 164-191页

作者： Qiang Wen Guo-qiang Ren Bin Liu School of Mathematics and Statistics Huazhong University of Science and TechnologyWuhan430074China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and TechnologyWuhan430074China

In this paper, we consider a susceptible-infective-susceptible(SIS) reaction-diffusion epidemic model with spontaneous infection and logistic source in a periodically evolving domain. Using the iterative technique,the uniform boundedness of solution is established. In addition, the spatial-temporal risk index R0(ρ) depending on the domain evolution rate ρ(t) as well as its analytical properties are discussed. The monotonicity of R0(ρ)with respect to the diffusion coefficients of the infected dI, the spontaneous infection rate η(ρ(t)y) and interval length L is investigated under appropriate conditions. Further, the existence and asymptotic behavior of periodic endemic equilibria are explored by upper and lower solution method. Finally, some numerical simulations are presented to illustrate our analytical results. Our results provide valuable information for disease control and prevention.

关键词： diffusive SIS model spontaneous infection periodically evolving domain periodic endemic equilibrium

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Forecasting Pedestrian Traffic in Melbourne Central Business District: Integrating Sensor Data with COVID-19 Impacts to Inform City Planning 13

Forecasting Pedestrian Traffic in Melbourne Central Business...

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13th International Conference on Control, Automation and Information Sciences, ICCAIS 2024

作者： Anh Dang, Nhat Vu, Tung Luong, Stanley Ngoc Dinh, Tan Seyedmahmoudian, Mehdi Chandran, Jaideep Stojcevski, Alex Department of Computing Technologies School of Science Computing and Engineering Technologies Swinburne University of Technology VIC Australia Siemens-Swinburne Energy Transition Hub School of Science Computing and Engineering Technologies Swinburne University of Technology VIC Australia School of Science Engineering and Technology Rmit University Viet Nam Curtin Singapore

ISBN: (纸本)9798331542047

Accurate forecasting of pedestrian counts in the Central Business District of Melbourne during extreme scenarios such as the COVID-19 pandemic is crucial for optimizing resource allocation and ensuring public safety. Current models lack the incorporation of major disruptions, leading to a critical gap in urban traffic forecasting. This study addresses this gap by developing an adaptive forecasting model, namely hyperparam-eter finetuning-convolutional neural network-multivariate-long short-term memory. Using hourly pedestrian counts (2020-2022) and COVID-19 case data, historical trends are integrated with pandemic factors to capture long-term patterns and sudden disruptions. Based on three evaluation metrics (NRMSE, MAPE, and R2), the results demonstrate the accuracy of the model in predicting pedestrian traffic during disruptive events. This research advances context-aware pedestrian traffic predictive modelling, enabling informed decision-making in urban environments. © 2024 IEEE.

关键词： Prediction models

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SecuDroid : Android Malware Detection using ML classifier on Static Features 2

SecuDroid : Android Malware Detection using ML classifier on...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Vanusha, D. Singh, Shashank Jha, Abhijeet Kumar Delsi Robinsha, S. School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Kattankulathur603203 India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350365269

The Android operating system's widespread use has unfortunately made it a target for malware attacks, underlining the necessity for robust detection strategies. Existing literature has explored the potential of machine learning in combating Android malware, often employing a mix of static and dynamic analyses. However, a major portion of these malwares are zero-day vulnerabilities. In this context, our study makes a significant contribution by focusing on using static features extracted from Android application package (APK) files to differentiate between benign and malicious applications. Specifically, all training and testing in this study were performed on the Drebin-215 dataset, a comprehensive collection known for its relevance in Android security research. Improving malware detection using static features allows for detection of malwares before they are run on devices. We analyzed five ML classification algorithms, beginning with a Decision Tree, followed by SVM (Support Vector Machine) with an RBF kernel, then moving to Logistic Regression and K-Nearest Neighbor, and finally, we introduced our proposed model, the SecuDroid Neural Network - employing a methodology that employs precise hyper-parameter tuning, and evaluation through stratified 4-fold cross-validation. The evaluation on an unseen test dataset, based on accuracy, F1-score, and AUC metrics, revealed the SecuDroid Neural Network surpassed its counterparts by reaching an accuracy of 0.993, an F1-score of 0.991, and an AUC of 0.999. Our findings not only offer a comparative analysis of different machine learning models for malware detection but also introduce an efficient approach, thereby enriching the arsenal against Android malware threats, with all empirical validations grounded in the Drebin-215 dataset. © 2024 IEEE.

关键词： Android malware

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A Hybrid Approach of Black Widow Optimization with Firefly (FF) Algorithm for Efficient Routing in Wireless Sensor Network 2

A Hybrid Approach of Black Widow Optimization with Firefly (...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Sindhuja, M. Srm Institute of Science and Technology Department of Computing Technologies School of Computing College of Engineering and Technology Kattankulathur Tamil Nadu Chennai India

ISBN: (纸本)9798350368413

The sensor nodes that make up a Wireless Sensor Network (WSN) can be located anywhere and are linked wirelessly so that they can all keep tabs on the same set of physical characteristics in the area of interest. The nodes in a WSN run on internal batteries, thus when they run out of energy after repeated uses, the network has a shorter lifespan. Clustering and routing methods are frequently utilized in WSN to improve energy efficiency and network lifetime. For WSN's cluster-based routing, this work introduces the innovative black widow optimization with firefly algorithm (FFA). The suggested BWO-FF algorithm comprises BWO based clustering procedure to elect an optimal set of cluster heads (CHs). The BWO algorithm creates a fitness function utilizing input parameters. In addition, route selection for inter-cluster communication requires an FF-based routing method. This routing method aims to enhance network throughput and lifetime by calculating the ideal route from each node to the sink in an effort to ensure uniform energy consumption across the network. MATLAB simulations have been used to analyze the proposed approach from several perspectives. The BWO-FF algorithm's superiority to all other techniques studied is guaranteed by a selection of comparison evaluations. © 2024 IEEE.

关键词： Sensor nodes

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Graph-Theoretic Approaches in Semantic Web Service Orchestration and Discovery: A Comprehensive Literature Survey 5

Graph-Theoretic Approaches in Semantic Web Service Orchestra...

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

作者： Vignesh, T.V. Vijaya, K. Minu, R.I. Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies School of Computing Tamil Nadu Kattankulathur603203 India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

Semantic Web Services (SWS) have become a powerful means for automating the discovery, composition, and orchestration of web services by providing them with semantic descriptions. This paper provides a comprehensive survey of graph-theoretic approaches in the orchestration and discovery of SWS, systematically categorizing and analyzing various methodologies. We examine how graph-based models contribute to enhancing the efficiency, accuracy, scalability, performance, reliability, and predictability of SWS processes. Furthermore, the paper discusses research gaps, challenges, and potential future directions, offering insights into emerging solutions and state-of-the-art techniques in the field. The survey also integrates recent advancements such as hybrid frameworks combining knowledge graphs and semantic web services, service clustering using graph embedding, and enhanced recommendation models. © 2024 IEEE.

关键词： Knowledge graph

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Explaining in Local Differential Privacy Through the Lens of Quantitative Information Flow 37

Explaining in Local Differential Privacy Through the Lens of...

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37th IEEE Computer Security Foundations Symposium, CSF 2024

作者： Fernandes, Natasha McIver, Annabelle Sadeghi, Parastoo School of Computing Macquarie University Sydney Australia School of Engineering and Technology Unsw Canberra Australia

ISBN: (纸本)9798350362039

The study of leakage measures for privacy has been a subject of intensive research and is an important aspect of understanding how privacy leaks occur in computer systems. Differential privacy has been a focal point in the privacy community for some years and yet its leakage characteristics are not completely understood. In this paper we bring together two areas of research -information theory and the g-leakage framework of quantitative information flow (QIF)- to give an operational interpretation for the epsilon parameter of local differential privacy. We find that epsilon emerges as a capacity measure in both frameworks;via (log)-lift, a popular measure in information theory;and via max-case g-leakage, which we introduce to describe the leakage of any system to Bayesian adversaries modelled using 'worst-case' assumptions under the QIF framework. Our characterisation resolves an important question of interpretability of epsilon and consolidates a number of disparate results covering the literature of both information theory and Quantitative information flow. © 2024 IEEE.

关键词： Information leakage

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