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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Padmakala, S. Khond, Smita Al-Farouni, Mohammed Punitha, S. Kaliappan, S. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Malla Reddy Engineering College for Women Department of Cse Hyderabad India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Deemed to Be University Vinayaka Mission's Kirupananda Variyar Engineering College Vinayaka Mission's Research Foundation Department of Mathematics Salem India Lovely Professional University Department of Research and Development Phagwara India

ISBN: (纸本)9798350375442

Edge computing (EC) nodes undertake an increasing number of tasks with rise of business density. However, unreasonable edge computing resource allocation suffers from high energy consumption and resource waste. This paper, Opposition-Based Learning with Harris Hawk Algorithm (OHHO) using task scheduling in EC, distributed involves complex and dynamic environment with changing tasks and resources availability. The HHO has mechanisms to maintain exploration and exploitation, reduce high dimensionality in search space where algorithm avoids to fall in local optima. OBL helps in this dynamic context by ensuring suboptimal solutions are quickly discarded and replaced with their opposites, which allows algorithm to adapt better to recent situation. This is adaptability is crucial in edge computing, where tasks and resources are constantly fluctuating. The resource allocation involved Dynamic Priority Task Scheduling Algorithm (DPTSA) helps in balancing system load by distributing tasks based on their priority and availability of resources. The proposed OHHO algorithm achieves high performance such energy 0.1mJ, Makespan of 1s and Delay of 45ms respectively when compared to existing techniques Deep Q Network (DQN) and Particle Swarm Optimization (PSO) - Grey Wolf Optimizer (GWO) approach. © 2024 IEEE.

关键词： Scheduling algorithms

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Quantum Computing for Learning Analytics: An Overview of Challenges and Integration Strategies 2

Quantum Computing for Learning Analytics: An Overview of Cha...

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2nd Workshop on Quantum Programming for Software engineering, QP4SE 2023, co-located with ESEC/FSE 2023

作者： Pagano, Alessandro Angelelli, Mario Calvano, Miriana Curci, Antonio Piccinno, Antonio Department of Economics University of Bari Aldo Moro Bari Italy Department of Innovation Engineering Campi - Centre of Applied Mathematics and Physics for Industry University of Salento Lecce Italy Department of Computer Science University of Bari Aldo Moro Bari Italy

ISBN: (纸本)9798400703768

Quantum computing has emerged as a promising technology with the potential to revolutionize various fields, including learning analytics. This research paper explores the applications of quantum computing in learning analytics and discusses the suitability of quantum techniques for addressing the challenges posed by large-scale educational datasets. It also investigates the integration of quantum computing with existing learning analytics pipelines, highlighting compatibility issues, data representation and transformation challenges, algorithmic complexity, and evaluation considerations. By understanding the potential benefits, limitations, and integration strategies, researchers can pave the way for the development of innovative tools and approaches to analyze educational data and provide personalized learning experiences. © 2023 ACM.

关键词： Integration

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Intelligent recognition system for viewpoint variations on gait and speech using CNN-CapsNet

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International Journal of Intelligent Computing and Cybernetics 2022年第3期15卷 363-382页

作者： G.Merlin Linda N.V.S.Sree Rathna Lakshmi N.Senthil Murugan Rajendra Prasad Mahapatra V.Muthukumaran M.Sivaram Department of Computer Science and Engineering SRM Institute of Science and Technology–Vadapalani CampusChennaiIndia Department of Electronics and Communication Engineering Agni College of TechnologyChennaiIndia School of Computer Science and Engineering VIT-AP UniversityAmaravatiIndia Department of Computer Science and Engineering SRM Institute of Science and TechnologyGhaziabadIndia Department of Mathematics School of Applied SciencesREVA UniversityBangaloreIndia Faculty of Information Technology Ton Duc Thang UniversityHo Chi Minh CityVietnam

Purpose-The paper aims to introduce an intelligent recognition system for viewpoint variations of gait and *** proposes a convolutional neural network-based capsule network(CNN-CapsNet)model and outlining the performance of the system in recognition of gait and speech *** proposed intelligent system mainly focuses on relative spatial hierarchies between gait features in the entities of the image due to translational invariances in sub-sampling and speech ***/methodology/approach-This proposed work CNN-CapsNet is mainly used for automatic learning of feature representations based on CNNand used capsule vectors as neurons to encode all the spatial information of an image by adapting equal variances to change in *** proposed study will resolve the discrepancies caused by cofactors and gait recognition between opinions based on a model of ***-This research work provides recognition of signal,biometric-based gait recognition and sound/speech *** evaluations are conducted on three aspects of scenarios,namely fixed-view,cross-view and multi-view *** main parameters for recognition of gait are speed,change in clothes,subjects walking with carrying object and intensity of *** limitations/implications-The proposed CNN-CapsNet has some limitations when considering for detecting the walking targets from surveillance videos considering multimodal fusion approaches using hardware sensor *** can also act as a pre-requisite tool to analyze,identify,detect and verify the malware *** implications-This research work includes for detecting the walking targets from surveillance videos considering multimodal fusion approaches using hardware sensor *** can also act as a pre-requisite tool to analyze,identify,detect and verify the malware ***/value-This proposed research work proves to be performing better for the recognition of gait and speech when

关键词： Intelligent system Gait recognition Convolutional neural network Deep learning Capsule network Viewpoint variations

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SEES-QL: An Improved Scalable and Energy-Efficient Scheme for WSNs based on Lightweight Q-learning

SEES-QL: An Improved Scalable and Energy-Efficient Scheme fo...

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2024 Advances in Science and engineering Technology International Conferences, ASET 2024

作者： Abdul-Qawy, Antar S. H. Adnan, Ali Idarous Mohammed, Mohammed Sultan Khatri, Narendra Shikhli, Amir Jarndal, Anwar Abdulrahman Al-Sumait University Department of Mathematics and Computer Science Zanzibar Tanzania United Republic of Hodeidah University Department of Computer Engineering Al Hudaydah Yemen The State University of Zanzibar Department of Computer Science and Information Technology Zanzibar Tanzania United Republic of Universiti Teknologi Malaysia Department of Electronic and Computer Engineering Johor Bahru Malaysia Manipal Institute of Technology Manipal Academy of Higher Education Department of Mechatronics Manipal India University of Sharjah Sustainable Engineering Asset Management Research Group Sharjah United Arab Emirates University of Sharjah Department of Electrical Engineering Sharjah United Arab Emirates

ISBN: (纸本)9798350344134

The energy efficiency of battery-operated sensing devices in IoT is a critical research area that needs further exploration. This paper employs lightweight reinforcement learning to improve energy savings in large-scale heterogeneous WSNs. We introduce SEES-QL (a Scalable and Energy-Efficient Scheme based on Q-Learning), an enhanced version of the zonal SEES protocol, that addresses the issue of frequent data transmission by dynamically adjusting nodes' transmission cycles without the need for a predefined model. In SEES-QL, on/off periods of radio transceivers are regulated based on transmission history and reading importance of each node independently, positively affecting total energy consumption, traffic load, and overall system lifetime. Performance evaluation demonstrates that SEES-QL achieves significant advancements in energy savings and transmission count reduction, leading to a remarkable 41% increase in the overall system lifetime compared to the traditional SEES protocol. © 2024 IEEE.

关键词： Radio transceivers

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Hyperstability of cubic functional equation in banach space

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Annali dell'Universita di Ferrara 2023年第2期69卷 317-328页

作者： Aribou, Youssef Dimou, Hajira Rossafi, Mohamed Laboratory Mathematical and Computer Engineering (IMI) Laayoune Higher School of Technology Ibn Zohr University Laayoune Morocco Department of Mathematics Faculty of Sciences Applied Ibn Zohr University Agadir Morocco LaSMA Laboratory Department of Mathematics Faculty of Sciences Dhar El Mahraz University Sidi Mohamed Ben Abdellah Fes Morocco

In this paper, we prove some hyperstability results of the following cubic functional equation f(2x+y)+f(2x-y)=2f(x+y)+2f(x-y)+12f(x)on a restricted domain. © 2022, The Author(s) under exclusive license to Univer... 详细信息

关键词： Banach space Cubic functional equation Hyperstability

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Joint IoT Device Selection and Health-Aware Beamforming Design for MIMO-WPT

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IEEE Transactions on Mobile Computing 2024年第9期23卷 8822-8838页

作者： Wang, Chih-Hang Shi, Yishuo Yang, De-Nian Chen, Wei-Yu Chen, Wen-Tsuen Academia Sinica Institute of Information Science Taipei115201 Taiwan Wenzhou University Department of Statistics and Information Science College of Mathematics and Physics Wenzhou325035 China Academia Sinica Institute of Information Science The Research Center for Information Technology Innovation Taipei115201 Taiwan University of Southern California Department of Electrical and Computer Engineering Los AngelesCA90007 United States National Tsing Hua University Department of Computer Science Hsinchu300044 Taiwan

Wireless power transfer (WPT) has emerged to enhance the robustness of the energy harvesting Internet of Things (EH-IoT), whereas beamforming has been leveraged to significantly boost the efficiency of far-field WPT. Nevertheless, potential negative impacts due to high electromagnetic fields (EMF) exposure for radiation-susceptible users have not been thoroughly considered in the design of WPT for EH-IoT with IoT application-level requirements (e.g., coverage). In this article, we explore the health-aware beamforming and IoT selection problem under the EH and human safety constraints. First, we formulate a new optimization problem Health-Aware Beamforming and IoT Selection (HABIS) and prove the NP-hardness. Second, we design an approximation algorithm, named Minimum Radiation Exposure and Maximum IoT Coverage (MREMIC), to exploit the EH-health dependency (EHHD) graph for properly addressing the trade-off between EH efficiency and potential EMF radiation exposure to human bodies. We also discover the optimal health-aware beamforming to minimize the total radiation energy absorption of humans. Simulation results show that MREMIC can effectively charge IoT devices and significantly outperforms existing EH approaches by more than 200% regarding human safety. © 2002-2012 IEEE.

关键词： Energy harvesting

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Classification of Real and Deepfakes Visual Samples with Pre-trained Deep Learning Models 7th

Classification of Real and Deepfakes Visual Samples with Pre...

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Proceedings of the 7th International Conference on Advances in Computing and Data Sciences, ICACDS 2023

作者： Nawaz, Marriam Javed, Ali Nazir, Tahira Khan, Muhammad Attique Rajinikanth, Venkatesan Kadry, Seifedine Department of Software Engineering UET Taxila Taxila47050 Pakistan Department of Computing Riphah International University Islamabad Pakistan Department of Computer Science HITEC University Taxila Pakistan Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering SIMATS Chennai602105 India Department of Applied Data Science Noroff University College Kristiansand4612 Norway Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

ISBN: (纸本)9783031379390

Serious security and privacy problems have arisen as a result of significant advancements in the creation of deepfakes. Attackers can easily replace a person’s face with the target person’s face in an image using sophisticated Deep learning (DL) algorithms to spoof their identity. Deepfakes detection algorithms have been proposed in response to the growing concerns about the potential harm caused by deepfakes. However, a reliable deepfakes detector that can keep up with contemporary deepfakes creation techniques is required. In this work, we have proposed an end-to-end methodology for detecting manipulated visual content. We used multiple CNN models i.e., ResNet18, ResNet50, DenseNet65, DenseNet77, and DenseNet100 along with the SVM classifier to compute effective cues from the input facial faces to distinguish between actual and altered content. We have also applied the concept of transfer learning to solve the issue of model over-fitting and improve generalizability against different manipulation algorithms. A comparison study is carried out to evaluate the performance of several feature extractors. Through thorough experiments performed using the Deepfakes Detection Challenge dataset, our results demonstrated that DenseNet100 surpasses the other CNN models by better recognizing deepfakes. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Learning algorithms

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Security framework for IoT and deep belief network-based healthcare system using blockchain technology

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International Journal of Electronic Business 2022年第3期17卷 226-243页

作者： Nagarajan, Senthil Murugan Anandhan, Prathik Muthukumaran, V. Uma, K. Kumaran, U. School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Mathematics School of Applied Sciences REVA University Bengaluru India School of Advance Sciences Vellore Institute of Technology Vellore India Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

Nowadays, the computerisation and information systems need very fast, easier and secure data analysis models. So, blockchain technology and deep learning concepts play important roles for data security and analysis in field of healthcare applications. Blockchain technology has optimal reliability in various sectors like healthcare, security, information management, smart home, etc. In this paper, blockchain technology is used for the healthcare system to provide a secure and reliable data transmission. Furthermore, IoT-based wearable devices are used to collect the data from the patients. Moreover, deep belief network (DBN) is proposed to classify the obtained data in order to predict the type of disease or problems of the patients. The experimental results shows that the proposed framework and classification model abruptly outperforms when compared with other existing techniques. The efficiency of the proposed framework is analysed based on the performance metrics like throughput and latency. The obtained loss ratio for the proposed model seems very less of 0.0126 when compared with other existing techniques like MLP, SVM, ANN, and CNN. The accuracy obtained for the proposed DBN classifier is above 95% for the increase in the number of patients. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Internet of things

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Detecting Confounders in Multivariate Time Series using Strength of Causation

Detecting Confounders in Multivariate Time Series using Stre...

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European Signal Processing Conference (EUSIPCO)

作者： Yuhao Liu Chen Cui Daniel Waxman Kurt Butler Petar M. Djurić Department of Applied Mathematics and Statistics Department of Electrical and Computer Engineering Stony Brook University NY

One of the most important problems in science is understanding causation. This is particularly challenging when one has access to observational data only and is further compounded in the presence of latent confounders. In this paper, we propose a method for detecting confounders in multivariate time series using a recently introduced concept referred to as differential causal effect (DCE). The solution is based on feature-based Gaussian processes that are used for estimating both, the DCE of the observed time series and the latent confounders. We demonstrate the performance of the proposed method with several examples. They show that the proposed approach can detect confounders and can accurately estimate causal strengths.

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A generalization of dowson’s result

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UPB Scientific Bulletin, Series A: applied mathematics and Physics 2021年第1期83卷 225-232页

作者： Zamfir, Mariana Şerbănescu, Cristina Department of Mathematics and Computer Science Technical University of Civil Engineering of Bucharest Romania Faculty of Applied Sciences University Politehnica of Bucharest Romania

In this paper we obtain S-spectral operators which appear as restrictions and quotients of spectral operators. We generalize the result obtained by H.R. Dowson about the fact that the restriction and quotient of a spectral operator at a closed subspace of a complex Banach space are, in certain conditions, spectral operators. In this article we found a condition in which the restriction and quotient are even S-spectral operators thorough the separation and extraction of the restriction and quotient operators’ spectral part. Finally, the results are extended from one operator to an operator system. © 2021, Politechnica University of Bucharest. All rights reserved.

关键词： Banach spaces

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