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Progressive Transfer Learning-based Deep Q Network for DDOS Defence in WSN

computer Systems Science & engineering 2023年第3期44卷 2379-2394页

作者： S.Rameshkumar R.Ganesan A.Merline Department of Electronics and Communication Engineering Sethu Institute of TechnologyVirudhunagarTamilnaduIndia Department of Electrical and Electronics Engineering E.G.S.Pillay Engineering collegeNagapattinamTamilnaduIndia

In The Wireless Multimedia Sensor Network(WNSMs)have achieved popularity among diverse communities as a result of technological breakthroughs in sensor and current *** utilising portable technologies,it achieves solid and significant results in wireless communication,media transfer,and digital *** nodes have been used in agriculture and industry to detect characteristics such as temperature,moisture content,and other environmental conditions in recent *** have also made apps easier to use by giving devices self-governing access to send and process data connected with appro-priate audio and video *** video sensor network studies focus on lowering power consumption and increasing transmission capacity,but the main demand is data *** of the obstacles in the sensor nodes,WMSN is subjected to a variety of attacks,including Denial of Service(DoS)*** Convolutional Neural Network is designed with the stateaction relationship mapping which is used to identify the DDOS Attackers present in the Wireless Sensor Networks for Smart *** Proposed work it performs the data collection about the traffic conditions and identifies the deviation between the network conditions such as packet loss due to network congestion and the presence of attackers in the *** reduces the attacker detection delay and improves the detection *** order to protect the network against DoS assaults,an improved machine learning technique must be *** efficient Deep Neural Network approach is provided for detecting DoS in *** required parameters are selected using an adaptive particle swarm optimization *** ratio of packet transmission,energy consumption,latency,network length,and throughput will be used to evaluate the approach’s efficiency.

关键词： DOS attack wireless sensor networks for smart agriculture deep neural network machine learning technique

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A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1429-1442页

作者： Bharati, Subrato Mondal, M. Rubaiyat Hossain Podder, Prajoy Bangladesh University of Engineering and Technology Institute of Information and Communication Technology Dhaka1205 Bangladesh Concordia University Department of Electrical and Computer Engineering MontrealQCH3G 1M8 Canada

Artificial intelligence (AI) models are increasingly finding applications in the field of medicine. Concerns have been raised about the explainability of the decisions that are made by these AI models. In this article, we give a systematic analysis of explainable artificial intelligence (XAI), with a primary focus on models that are currently being used in the field of healthcare. The literature search is conducted following the preferred reporting items for systematic reviews and meta-analyses standards for relevant work published from 1 January 2012 to 2 February 2022. The review analyzes the prevailing trends in XAI and lays out the major directions in which research is headed. We investigate the why, how, and when of the uses of these XAI models and their implications. We present a comprehensive examination of XAI methodologies as well as an explanation of how a trustworthy AI can be derived from describing AI models for healthcare fields. The discussion of this work will contribute to the formalization of the XAI field. © 2020 IEEE.

关键词： Medical imaging

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Automated face recognition using deep learning technique and center symmetric multivariant local binary pattern

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Neural Computing and Applications 2025年第1期37卷 263-281页

作者： Sekhar, J.C. Josephson, P. Joel Chinnasamy, A. Maheswari, M. Sankar, S. Kalangi, Ruth Ramya Department of Computer Science and Engineering NRI Institute of Technology Andhra Pradesh Guntur India Department of Electronics and Communication Engineering Malla Reddy Engineering College Telangana Hyderabad India Department of Data Science and Business Systems School of Computing SRMIST Kattankulathur Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vijayawada India

Researchers have recently created several deep learning strategies for various tasks, and facial recognition has made remarkable progress in employing these techniques. Face recognition is a noncontact, nonobligatory, acceptable, and harmonious biometric recognition method with a promising national and social security future. The purpose of this paper is to improve the existing face recognition algorithm, investigate extensive data-driven face recognition methods, and propose a unique automated face recognition methodology based on generative adversarial networks (GANs) and the center symmetric multivariable local binary pattern (CS-MLBP). To begin, this paper employs the center symmetric multivariant local binary pattern (CS-MLBP) algorithm to extract the texture features of the face, addressing the issue that C2DPCA (column-based two-dimensional principle component analysis) does an excellent job of removing the global characteristics of the face but struggles to process the local features of the face under large samples. The extracted texture features are combined with the international features retrieved using C2DPCA to generate a multifeatured face. The proposed method, GAN-CS-MLBP, syndicates the power of GAN with the robustness of CS-MLBP, resulting in an accurate and efficient face recognition system. Deep learning algorithms, mainly neural networks, automatically extract discriminative properties from facial images. The learned features capture low-level information and high-level meanings, permitting the model to distinguish among dissimilar persons more successfully. To assess the proposed technique’s GAN-CS-MLBP performance, extensive experiments are performed on benchmark face recognition datasets such as LFW, YTF, and CASIA-WebFace. Giving to the findings, our method exceeds state-of-the-art facial recognition systems in terms of recognition accuracy and resilience. The proposed automatic face recognition system GAN-CS-MLBP provides a solid basis for a

关键词： Principal component analysis

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Nonlinear coupling of closely spaced modes in atomically thin MoS2 nanoelectromechanical resonators

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Microsystems & Nanoengineering 2024年第6期10卷 581-591页

作者： S M Enamul Hoque Yousuf Steven W.Shaw Philip X.-L.Feng Department of Electrical and Computer Engineering University of FloridaGainesvilleFL 32611USA Department of Mechanical and Civil Engineering Florida Institute of TechnologyMelbourneFL 32901USA Department of Mechanical Engineering Department of Physics and AstronomyMichigan State UniversityEast LansingMI 48423USA

Nanoelectromechanical systems(NEMS)incorporating atomic or molecular layer van der Waals materials can support multimode resonances and exotic nonlinear *** we investigate nonlinear coupling of closely spaced modes in a bilayer(2L)molybdenum disulfide(MoS_(2))nanoelectromechanical *** model the response from a drumhead resonator using equations of two resonant modes with a dispersive coupling term to describe the vibration induced frequency shifts that result from the induced change in *** employ method of averaging to solve the equations of coupled modes and extract an expression for the nonlinear coupling coefficient(λ)in closed *** thermomechanical noise spectral measurements are used to calibrate the vibration amplitude of mode 2(a_(2))in the displacement *** drive mode 2 near its natural frequency and measure the shifted resonance frequency of mode 1(f_(1s))resulting from the dispersive *** model yieldsλ=0.027±0.005 pm^(-2)·μs^(-2) from thermomechanical noise measurement of mode *** model also captures an anomalous frequency shift of the undriven mode 1 due to nonlinear coupling to the driven mode 2 mediated by large dynamic *** study provides a direct means to quantifyingλby measuring the thermomechanical noise in NEMS and will be valuable for understanding nonlinear mode coupling in emerging resonant systems.

关键词： dynamics coupling nonlinear

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Embeddings Between State and Action Based Probabilistic Logics

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Formal Aspects of Computing 2025年第2期37卷 1-58页

作者： Das, Susmoy Sharma, Arpit Department of Electrical Engineering and Computer Science Indian Institute of Science Education and Research Bhopal Madhya Pradesh Bhopal India

This article defines embeddings between state-based and action-based probabilistic logics which can be used to support probabilistic model checking. First, we slightly modify the model embeddings proposed in the literature to allow invisible computation steps and the preservation of forward and backward bisimulation relations. Next, we propose the syntax and semantics of an action-based Probabilistic Computation Tree Logic (APCTL) and an action-based PCTL∗(APCTL∗) interpreted over action-labeled discrete-time Markov chains (ADTMCs). We show that both these logics are strictly more expressive than the probabilistic variant of Hennessy-Milner logic (prHML). We define an embedding aldl which can be used to construct APCTL∗formulae from PCTL∗formulae and an embedding sldl from APCTL∗formulae to PCTL∗formulae. Similarly, we define the embeddings and from PCTL to APCTL and APCTL to PCTL, respectively. We also define the reward-based variant of APCTL (APRCTL) interpreted over action-based Markov Reward Models (AMRM), and accordingly modify the logical embeddings and which allows us to take into account the notion of rewards. Additionally, we also show that the idea of rewards can be used to reason about the bounded until operator in PCTL and APCTL. Finally, we prove that our logical embeddings combined with the model embeddings enable one to minimize, analyze, and verify probabilistic models in one domain using state-of-the-art tools and techniques developed for the other domain. In order to validate the efficacy of our theoretical framework, we apply it to two case studies using the probabilistic symbolic model checker (PRISM). © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Probabilistic Markov chain equivalence bisimulation logic embeddings rewards model checking

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A Privacy-preserving Energy Management System Based on Homomorphic Cryptosystem for IoT-enabled Active Distribution Network

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Journal of Modern Power Systems and Clean Energy 2024年第1期12卷 167-178页

作者： Qian Hu Siqi Bu Wencong Su Vladimir Terzija the Department of Physics Hong Kong Baptist UniversityHong KongChina the Department of Electrical and Electronic Engineering Shenzhen Research InstituteThe Hong Kong Polytechnic UniversityHong KongChina the Department of Electrical and Computer Engineering University of Michigan-DearbornDearbornMI 48128USA the School of Engineering Newcastle UniversityNewcastleUK

Active distribution network(ADN),as a typically cyber-physical system,develops with the evolution of Internet of Things(IoTs),which makes the network vulnerable to cybersecurity *** this paper,the eavesdropping attacks that lead to privacy breaches are addressed for the IoT-enabled ADN.A privacy-preserving energy management system(EMS)is proposed and empowered by secure data exchange protocols based on the homomorphic *** the information transmission among distributed generators and load customers in the EMS,private information including power usage and electricity bidding price can be effectively protected against eavesdropping *** correctness of the final solutions,e.g.,optimal market clearing price and unified power utilization ratio,can be deterministically *** simulation results demonstrate the effectiveness and the computational efficiency of the proposed homomorphically encrypted EMS.

关键词： Eavesdropping attack energy management system homomorphic cryptosystem Internet of Things(IOTs) active distribution network(ADN) privacy-preserving

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A 500 kHz Wide Output LLC-T Resonant Converter With Narrow Frequency Range, Reduced Circulating Energy, and Low Voltage Stress

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IEEE Transactions on Power Electronics 2025年第8期40卷 11749-11762页

作者： Hou, Zhengming Jiao, Dong Lai, Jih-Sheng Virginia Polytechnic Institute and State University Bradley Department of Electrical and Computer Engineering BlacksburgVA24061 United States

The resonant converters are promising topologies for the electric vehicle charging applications due to the soft switching feature. However, the conventional LLC resonant converter regulates the wide output voltage through a wide frequency range, which leads to low efficiency with large circulating energy and the difficulty of the resonant tank optimization. This article proposes a novel LLC-T resonant converter, which parallels an auxiliary transformer with the LC resonant tank for the wide output applications. The proposed LLC-T resonant converter narrows the frequency range with less circulating energy, lower voltage stress on the resonant capacitor, and smaller flux linkage in the resonant inductor compared to the conventional LLC resonant converter. Meanwhile, the zero-voltage switching turn-on is maintained throughout the wide output voltage range and load range. Furthermore, a precise time-domain based analysis and the design methodology are proposed. A 500-kHz, 1.35 kW, 400 V input 250 V-450 V output prototype is built to verify the concepts of the proposed topology and demonstrates a 97.82% peak efficiency. © 1986-2012 IEEE.

关键词： Resonant converters

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Forecasting Directional Movement of Stock Prices using Deep Learning

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Annals of Data Science 2023年第5期10卷 1361-1378页

作者： Chandola, Deeksha Mehta, Akshit Singh, Shikha Tikkiwal, Vinay Anand Agrawal, Himanshu Department of Electrical Engineering and Computer Science York University Toronto Canada Hebert Werthim College of Engineering University of Florida Gainesville Gainesville United States Department of Electronics and Communication Engineering Jaypee Institute of Information Technology Noida India Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Stock market’s volatile and complex nature makes it difficult to predict the market situation. Deep Learning is capable of simulating and analyzing complex patterns in unstructured data. Deep learning models have applications in image recognition, speech recognition, natural language processing (NLP), and many more. Its application in stock market prediction is gaining attention because of its capacity to handle large datasets and data mapping with accurate prediction. However, most methods ignore the impact of mass media on the company’s stock and investors’ behaviours. This work proposes a hybrid deep learning model combining Word2Vec and long short-term memory (LSTM) algorithms. The main objective is to design an intelligent tool to forecast the directional movement of stock market prices based on financial time series and news headlines as inputs. The binary predicted output obtained using the proposed model would aid investors in making better decisions. The effectiveness of the proposed model is assessed in terms of accuracy of the prediction of directional movement of stock prices of five companies from different sectors of operation. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Large dataset

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The Effect of Imperfect Channel-Sensing for Low Power Wide Area Networks with Listen-Before-Talk

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IEEE Internet of Things Journal 2025年第12期12卷 20512-20529页

作者： Hu, Yangqian Seo, Jun-Bae Jin, Hu Hanyang University Department of Electrical and Electronic Engineering Ansan15588 Korea Republic of Gyeongsang National University Department of Information and Communication Engineering Tongyeong53064 Korea Republic of Henan Normal University College of Computer and Information Engineering Xinxiang453007 China Hanyang University School of Electrical Engineering Ansan15588 Korea Republic of

This study investigates ALOHA with Listen-Before-Talk (LBT) to enhance the scalability of Low-Power Wide Area Networks (LPWANs), such as LoRa. The LBT allows devices to sense the channel prior to accessing so that it can mitigate interference by preventing devices from transmitting during ongoing transmissions. However, its effectiveness is compromised by inherent imperfections in channel sensing, such as false negatives and false positives. A false negative occurs when devices incorrectly find the channel idle while it is actually in use. Thus, this leads devices to unintended interferences with ongoing transmissions. A false positive arises when the channel is erroneously sensed as busy, despite the fact that it is free. This deprives devices of access opportunities. This work analyzes the impact of these imperfections of LBT on the performance of ALOHA in terms of throughput, access delay, and system stability. Additionally, we propose an online backoff control algorithm to optimize system performance under imperfect LBT. The results show that even when devices falsely identify the channel as idle or mistakenly detect it as busy nearly half the time, the throughput still outperforms that of ALOHA without LBT. The proposed backoff control algorithm is also shown to be essential to maximize the throughput in the presence of sensing errors. To demonstrate our analysis and algorithm, we incorporate LoRa's physical layer parameters into simulations and validate the results accordingly. © 2014 IEEE.

关键词： Low power electronics

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Joint Frame Drop and Object Detection Task Offloading for Mobile Devices via RL With Lyapunov Optimization

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6168-6182页

作者： Sohn, Vaughn Kim, Suhwan Lee, Hyang-Won Korea Advanced Institute of Science and Technology Department of Electrical Engineering Daejeon34141 Korea Republic of Konkuk University Department of Computer Science and Engineering Seoul05029 Korea Republic of

Object detection has become an increasingly important application for mobile devices. However, state-of-the-art object detection relies heavily on deep neural network, which is often burdensome to compute on mobile devices. To this end, we develop a layering framework for joint video frame drop and object detection task offloading. In the lower layer, by invoking Lyapunov optimization, we devise an algorithm for partitioning and offloading the computation tasks of deep neural networks. This algorithm also specifies the flow control for admitting the application traffic into the network. In the upper layer, we use the flow control as a form of guidance in the action space in order to develop a reinforcement learning (RL) algorithm that selectively drops video frames with object detection performance in consideration. By the nature of design, this Lyapunov-guided RL guarantees the network stability. We show through simulations that our Lyapunov-guided RL drops video frames with reasonable object detection performance and reduced latency while keeping the network stable. We also implemented our algorithm on the remote-controlled (RC) car equipped with microprocessor and GPU, and demonstrate the applicability of our algorithm to real-time object detection tasks from the video stream generated as the RC car moves. © 2002-2012 IEEE.

关键词： Reinforcement learning

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