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6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Darwich, Mahmoud Khalil, Kasem Bayoumi, Magdy University of Mount Union Department of Mathematics and Computer Science AllianceOH44601 United States University of Mississippi Department of Electrical and Computer Engineering MS38677 United States University of Louisiana Department of Electrical and Computer Engineering LafayetteLA70504 United States

ISBN: (纸本)9798350344349

The demand for video streaming services over Internet of Things (IoT) networks has surged, yet maintaining a high Quality of Experience (QoE) remains challenging due to network heterogeneity and resource constraints. This paper presents an innovative deep learning-based approach to optimize QoE in IoT-based video streaming. Leveraging convolutional neural networks (CNN) and long short-Term memory (LSTM) networks, the proposed framework dynamically adapts video streaming parameters in real-Time, such as bit rate, frame rate, and resolution, based on the existing network conditions and device capabilities. Through extensive simulations and real-world deployments, our approach exhibited a 24.5% reduction in re-buffering ratio, a 12.5% increase in average bitrate, and a 15% improvement in video quality measured by SSIM, compared to conventional methods. Furthermore, it reduced network bandwidth consumption by up to 150% without compromising on video quality. These results underscore the effectiveness of applying deep learning algorithms to optimize video streaming in complex IoT environments. © 2024 IEEE.

关键词： Video streaming

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A Review of Lightweight Security and Privacy for Resource-Constrained IoT Devices

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computers, Materials & Continua 2024年第1期78卷 31-63页

作者： Sunil Kumar Dilip Kumar Ramraj Dangi Gaurav Choudhary Nicola Dragoni Ilsun You Department of Computer Science and Engineering National Institute of Technology JamshedpurJamshedpurIndia School of Computing Science and Engineering VIT University BhopalBhopal466114India Center for Industrial Software The Maersk Mc-Kinney Moller InstituteUniversity of Southern DenmarkSonderborgOdenseDenmark Department of Applied Mathematics and Computer Science Technical University of DenmarkLyngby2800Denmark Department of Information Security Cryptologyand MathematicsKookmin UniversitySeoulSouth Korea

The widespread and growing interest in the Internet of Things(IoT)may be attributed to its usefulness in many different *** settings are probed for data,which is then transferred via linked *** are several hurdles to overcome when putting IoT into practice,from managing server infrastructure to coordinating the use of tiny *** it comes to deploying IoT,everyone agrees that security is the biggest *** is due to the fact that a large number of IoT devices exist in the physicalworld and thatmany of themhave constrained resources such as electricity,memory,processing power,and square *** research intends to analyse resource-constrained IoT devices,including RFID tags,sensors,and smart cards,and the issues involved with protecting them in such restricted *** lightweight cryptography,the information sent between these gadgets may be *** order to provide a holistic picture,this research evaluates and contrasts well-known algorithms based on their implementation cost,hardware/software efficiency,and attack resistance *** also emphasised how essential lightweight encryption is for striking a good cost-to-performance-to-security ratio.

关键词： IoT a sensor device lightweight cryptography block cipher smart card security and privacy

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Some Results on Ricci-Yamabe Soliton and Its Cosmological Models

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IAENG International Journal of applied mathematics 2025年第4期55卷 979-983页

作者： Pandey, Pankaj Yadav, Santosh Kumar Sharma, Kamakshi Chaturvedi, Braj Bhushan Department of Mathematics School of Chemical Engineering and Physical Sciences Lovely Professional University Punjab Phagwara144411 India Department of Mathematics School of Computer Science & Artificial Intelligence SR University Telangana Warangal506371 India Department of Pure & Applied Mathematics Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur495009 India

This article aims to investigate the characteristics of Ricci-Yamabe Soliton (briefly: (RYS)n). We study the cosmological models on (RYS)4 under Lorentzian para Sasakian (LPS)4 spacetime. Parallel Ricci tensor, Poisson structure and gradient property also reveal on (RYS)n. Example of (RYS)4 constructed to verify the results. © (2025), (International Association of Engineers). All rights reserved.

关键词： Solitons

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Oppositional Harris Hawks Optimization with Deep Learning-Based Image Captioning

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computer Systems Science & engineering 2023年第1期44卷 579-593页

作者： V.R.Kavitha K.Nimala A.Beno K.C.Ramya Seifedine Kadry Byeong-Gwon Kang Yunyoung Nam Department of Computer Science and Engineering Prathyusha Engineering CollegeThiruvallur602025India Department of Networking and Communications SRM Institute of Science and TechnologyChennaiIndia Department of Electronics and Communication Engineering Dr.Sivanthi Aditanar College of EngineeringTiruchendur628215India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Department of Information and Communication Engineering Soonchunhyang UniversityAsanKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Image Captioning is an emergent topic of research in the domain of artificial intelligence(AI).It utilizes an integration of computer Vision(CV)and Natural Language Processing(NLP)for generating the image *** use in several application areas namely recommendation in editing applications,utilization in virtual assistance,*** development of NLP and deep learning(DL)modelsfind useful to derive a bridge among the visual details and textual *** this view,this paper introduces an Oppositional Harris Hawks Optimization with Deep Learning based Image Captioning(OHHO-DLIC)*** OHHO-DLIC technique involves the design of distinct levels of ***,the feature extraction of the images is carried out by the use of EfficientNet ***,the image captioning is performed by bidirectional long short term memory(BiLSTM)model,comprising encoder as well as *** last,the oppositional Harris Hawks optimization(OHHO)based hyperparameter tuning process is performed for effectively adjusting the hyperparameter of the EfficientNet and BiLSTM *** experimental analysis of the OHHO-DLIC technique is carried out on the Flickr 8k Dataset and a comprehensive comparative analysis highlighted the better performance over the recent approaches.

关键词： Image captioning natural language processing artificial intelligence machine learning deep learning

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Contrast Normalization Strategies in Brain Tumor Imaging:From Preprocessing to Classification

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computer Modeling in engineering & Sciences 2024年第8期140卷 1539-1562页

作者： Samar M.Alqhtani Toufique A.Soomro Faisal Bin Ubaid Ahmed Ali Muhammad Irfan Abdullah A.Asiri Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Electronic Engineering The University of LarkanoLarkanaSindh75660Pakistan Computer Science Department Sukkur IBA UniversitySukkurSindh65200Pakistan Eletrical Engineering Department Sukkur IBA UniversitySukkurSindh65200Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia

Cancer-related to the nervous system and brain tumors is a leading cause of mortality in various *** resonance imaging(MRI)and computed tomography(CT)are utilized to capture brain *** plays a crucial role in the diagnosis of brain tumors and the examination of other brain ***,manual assessment of MRI images by radiologists or experts is performed to identify brain tumors and abnormalities in the early stages for timely ***,early diagnosis of brain tumors is intricate,necessitating the use of computerized *** research introduces an innovative approach for the automated segmentation of brain tumors and a framework for classifying different regions of brain *** proposed methods consist of a pipeline with several stages:preprocessing of brain images with noise removal based on Wiener Filtering,enhancing the brain using Principal Component Analysis(PCA)to obtain well-enhanced images,and then segmenting the region of interest using the Fuzzy C-Means(FCM)clustering technique in the third *** final step involves classification using the Support Vector Machine(SVM)*** classifier is applied to various types of brain tumors,such as meningioma and pituitary tumors,utilizing the Contrast-Enhanced Magnetic Resonance Imaging(CE-MRI)*** proposed method demonstrates significantly improved contrast and validates the effectiveness of the classification framework,achieving an average sensitivity of 0.974,specificity of 0.976,accuracy of 0.979,and a Dice Score(DSC)of ***,this method exhibits a shorter processing time of 0.44 s compared to existing *** performance of this method emphasizes its significance when compared to state-of-the-art methods in terms of sensitivity,specificity,accuracy,and *** enhance the method further in the future,it is feasible to standardize the approach by incorporating a set of classifiers to increase the robustness of the brain classi

关键词： Brain tumor magnetic resonance imaging principal component analysis fuzzy c-clustering support vector machine

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Suppressing electromagnetic local density of states via slow light in lossy quasi-one-dimensional gratings

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Physical Review A 2024年第4期109卷 L041501-L041501页

作者： Benjamin Strekha Pengning Chao Rodrick Kuate Defo Sean Molesky Alejandro W. Rodriguez Department of Electrical and Computer Engineering Princeton University Princeton New Jersey 08544 USA Department of Mathematics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Engineering Physics Polytechnique Montréal Montréal Québec H3T 1J4 Canada

We propose a spectral-averaging procedure that enables the computation of bandwidth-integrated local density of states (LDOS) from a single scattering calculation, and exploit it to investigate the minimum extinction achievable from dipolar sources over nonzero bandwidths in structured media. Structure-agnostic extinction bounds are derived, providing analytical insights into scaling laws and fundamental design tradeoffs with implications to bandwidth and material selection. We find that perfect LDOS suppression over a nonzero bandwidth Δω is impossible. Inspired by limits which predict nontrivial Δω scaling in systems with material dissipation, we show that the pseudogap edge states of quasi-one-dimensional bullseye gratings can—by simultaneously minimizing material absorption and radiation—yield arbitrarily close to perfect LDOS suppression in the limit of vanishing bandwidth.

关键词： Light propagation, transmission & absorption Local density of states Photonics Spontaneous emission Bragg structures Optical fibers

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REALM: Automating Real Estate Appraisal with Machine Learning Models

REALM: Automating Real Estate Appraisal with Machine Learnin...

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2023 IEEE World AI IoT Congress, AIIoT 2023

作者： Chiasson, Erin Kaniecki, Marco Koechling, Johannes Uppal, Neelam Hammad, Issam Dalhousie University Department of Electrical and Computer Engineering Halifax Canada Dalhousie University Department of Engineering Mathematics and Internetworking Halifax Canada

ISBN: (纸本)9798350337617

This paper presents REALM, an open-source tool for utilizing machine learning in real estate appraisal. The tool is built with the Django web framework, and it aims to provide researchers with a database-agnostic solution to utilize various machine-learning models in the appraisal. The platform can generate a PDF appraisal report containing the predicted price of the input property and details about the appraisal, including five comparable properties which are automatically extracted from a large database based on relevance. The tool uses the Random Forest (RF) model by default with the ability to swap models if required. Part of the research also focused on evaluating various machine learning models for price prediction using the Ames housing dataset. This involved comparing the accuracy and R2 scores of Linear Regression, Neural Network, and RF models. It was found that the RF model performed price prediction with the greatest R2 value, therefore it was selected as the default model for the platform. The R2 scores for the price prediction model were consistently above 0.9, indicating good accuracy. Finally, methods for optimization of the model including hyperparameter tuning and the addition of bagging were tested and discussed. © 2023 IEEE.

关键词： Machine learning

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Distributed optimization with Markovian switching targets and stochastic observation noises with applications to DC microgrids

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Science China(Information Sciences) 2022年第12期65卷 232-247页

作者： Siyu XIE Leyi WANG Masoud H.NAZARI George YIN Gun LI School of Aeronautics and Astronautics University of Electronic Science and Technology of China Department of Electrical and Computer Engineering Wayne State University Department of Mathematics University of Connecticut

A distributed optimization problem with Markovian switching targets and stochastic observation noises is considered in this paper. In order to solve target following and renewable following for microgrid(MG) optimal power balancing, and to attenuate observation noises simultaneously, distributed optimization algorithms are developed. The interaction between observation noises and Markovian switching targets may introduce a fundamental tradeoff in reducing the optimization errors and choosing the step size. Furthermore, under infrequent Markovian switching assumptions, the mean-square optimization error bounds, the switching ordinary differential equation(ODE) limit, and the asymptotic distributions of the optimization errors are established rigorously and comprehensively. A simulation example on a DC MG is presented to show the main results of the paper.

关键词： constraint optimization noisy observation output variation Markovian switching target distributed algorithm DC microgrid

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Online Container Scheduling for Data-intensive Applications in Serverless Edge Computing 42

Online Container Scheduling for Data-intensive Applications ...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Shang, Xiaojun Mao, Yingling Liu, Yu Huang, Yaodong Liu, Zhenhua Yang, Yuanyuan Stony Brook University Department of Electrical and Computer Engineering United States Shenzhen University College of Computer Science and Software Engineering Shenzhen China Stony Brook University Department of Applied Mathematics and Statistics United States

ISBN: (纸本)9798350334142

Introducing the emerging serverless paradigm into edge computing could avoid over- and under-provisioning of limited edge resources and make complex edge resource management transparent to application developers, which largely facilitates the cost-effectiveness, portability, and short time-to-market of edge applications. However, the computation/data dispersion and device/network heterogeneity of edge environments prevent current serverless computing platforms from acclimating to the network edge. In this paper, we address such challenges by formulating a container placement and data flow routing problem, which fully considers the heterogeneity of edge networks and the overhead of operating serverless platforms on resource-limited edge servers. We design an online algorithm to solve the problem. We further show its local optimum for each arriving container and prove its theoretical guarantee to the optimal offline solution. We also conduct extensive simulations based on practical experiment results to show the advantages of the proposed algorithm over existing baselines. © 2023 IEEE.

关键词： Edge computing

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Tuning of renewable energy bids based on energy risk management: Enhanced microgrids with pareto-optimal profits for the utility and prosumers

Energy Conversion and Economics

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Energy Conversion and Economics 2022年第3期3卷 156-169页

作者： Mohan, Vivek Antonis, Anjula Mary Jisma, M. Krishnakumar, Nila Bu, Siqi Department of Electrical and Electronics Engineering National Institute of Technology Tiruchirappalli Tamil Nadu Tiruchirappalli India Department of Electrical and Computer Engineering University of Toronto Toronto Canada Department of Electrical Sustainable Energy Faculty of Electrical Engineering Applied Mathematics and Computer Science Delft University of Technology Delft Netherlands Department of Electrical Engineering The Hong Kong Polytechnic University Kowloon SAR Hong Kong

The increasing penetration of renewable energy sources (RES) and electric vehicles (EVs) demands the building of a microgrid energy portfolio that is cost-effective and robust against generation uncertainties (energy risk). Energy risk may trigger financial risk in the local energy market, depending on bid values, cost of generation and price of upstream grid power. In this study, a microgrid energy portfolio is built based on adjustments to both the financial and energy risks. These risks are managed in two ways: (1) by pre-tuning and prioritizing the bid prices for wind and solar energy sources based on their relative levels of energy risk as quantified through a conditional value-at-risk (CVaR) approach;and (2) by co-optimizing the conflicting profits of the utility and prosumers using non-dominated sorting particle swarm optimization (NSPSO) to obtain a risk-adjusted Pareto-optimal energy mix. Thus, the utility predicts the net power balancing cost from the scheduling time horizon, thereby moderating the adverse effect that the uncertainties in renewable energy could have on the collective welfare. The proposed method is tested on a grid-connected CIGRE low-voltage (LV) benchmark microgrid with solar and wind sources, microturbines, and EVs. The results demonstrate that the obtained portfolio is realistic, welfare-optimized and cost-efficient. © 2022 The Authors. Energy Conversion and Economics published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology and the State Grid Economic & Technological Research Institute Co., Ltd.

关键词： Profitability

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