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Soft switching high step-up three-level boost DC-DC converter with Quasi Z-source for Photovoltaic application Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-03-06 Ehsan Mohammadi, Mahdi Rezvanyvardom, Amin Mirzaei
A three-level Boost DC-DC converter is proposed in this paper. Quasi Z-source converter acts as an auxiliary circuit to create soft switching condition for all active elements. Consequently. The converter efficiency is very high. Using a three-level structure in the proposed converter causes the maximum voltage stress across the semiconductor devices is limited to half of the output voltage. The converter
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Towards safer online communities: Deep learning and explainable AI for hate speech detection and classification Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-03-06 Hareem Kibriya, Ayesha Siddiqa, Wazir Zada Khan, Muhammad Khurram Khan
The internet and social media facilitate widespread idea sharing but also contribute to cyber-crimes and harmful behaviors, notably the dissemination of abusive and hateful speech, which poses a significant threat to societal cohesion. Hence, prompt and accurate detection of such harmful content is crucial. To address this issue, our study introduces a fully automated end-to-end model for hate speech
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Multi-class center dynamic contrastive learning for unsupervised domain adaptation person re-identification Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-03-05 Qing Tian, Xiaoxin Du
Unsupervised domain adaptation person re-identification (UDA Re-ID) aims to leverage the pedestrian knowledge learned from labeled source domain to assist in learning the pedestrian knowledge in the unlabeled target domain. Most of existing investigations typically utilize single-class center clustering algorithms to group unlabeled target domain instances. Unfortunately, single-class center clustering
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Multi-scale surface defect detection method for bottled products based on variable receptive fields and Gather–Distribute feature fusion mechanism Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-03-05 Deping Chen, Jian Zhang, Zeyu Jiao, Huan Lei, Jingqi Ma, Liangsheng Wu, Zhenyu Zhong
The inspection of visual quality represents a crucial step in the production process of bottled products. Numerous machine vision methodologies have demonstrated proficient identification of significant defects on bottle surfaces in well-controlled imaging environments. However, the actual production scenario introduces a myriad of surface defect types on bottled products, exhibiting diverse shapes
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TBFF-DAC: Two-branch feature fusion based on deformable attention and convolution for object detection Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-03-04 Chuanxi Liu, Zhiwei Meng
Designing an architecture based on feature fusion between global information and local information is an important topic for object detection. The current problem is the trade-off between high prediction precision and low computing cost. In general, the prediction precision is improved while increasing the computational cost; examples include the YOLO series. To solve this problem, we propose the two-branch
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Pairing-free certificateless public key encryption with equality test for Internet of Vehicles Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-03-02 Rashad Elhabob, Mazin Taha, Hu Xiong, Muhammad Khurram Khan, Saru Kumari, Pradeep Chaudhary
In the domain of the Internet of Vehicles (IoV), the integration of intelligent devices for traffic data collection is pivotal, with data storage occurring in cloud-assisted IoV systems. Despite its importance, this raises trust concerns regarding cloud servers, necessitating the use of encryption. However, encrypted data search remains a complex challenge. To address this, alongside issues of certificate
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An approach based on deep learning methods to detect the condition of solar panels in solar power plants Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-03-02 Tolga Özer, Ömer Türkmen
Solar panels are increasingly popular due to global energy shortages and rising costs. However, managing large or elevated panel systems requires regular oversight, leading to potential time and cost challenges. This study was focused on developing an AI-based drone for panel detection to address these issues and facilitate the control process. A low-cost system for AI-based identification of dusty
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The control of superheater steam temperature in power plants using model predictive controller Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-03-02 S. Prasanth, S. Narayanan, N. Sivakumaran, H. Pratheesh
The temperature of the steam produced by the superheater has been regarded as one of the most crucial parameters for steam power plant regulation. It must be precisely regulated within a restricted temperature range; overheating would result in the degradation of the substance. The recommended hybrid technique employed the Model Predictive Controller and Improved Fruit Fly Optimization Algorithm to
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Reliability enhancement of hybrid microgrid protection against communication data loss and converter faults using cubic-spline interpolation, Savitzky Golay filtering and GRU network Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-03-02 Awagan Goyal Rameshrao, Ebha Koley, Subhojit Ghosh
A primary challenge towards adoption of hybrid AC-DC microgrid pertains to the complexity of the protection scheme. Among other challenges, the high resemblance of voltage and current dynamics for converter faults and line faults, hinders use of threshold-based protection schemes. Further the high dependence of the protection mechanism on the communication network results in reduced reliability during
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An approach based on hexagram model for quantifying security risks with Performance Key Indicators (PKI) Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-03-01 Mayukha S, R Vadivel
As organizations grapple with an ever-evolving threat landscape, the need for effective security risk quantification methodologies becomes paramount. This research paper introduces and explores a novel approach to security risk quantification through the application of a hexagram model. Drawing inspiration from the I Ching, an ancient Chinese divination text, this hexagram model encompasses six key
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Industrial kitchen appliance consumption forecasting: Hour-ahead and day-ahead perspectives with post-processing improvements Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-03-01 Vasco Andrade, Hugo Morais, Lucas Pereira
Forecasting techniques have gained considerable prominence within the electric energy sector. Many studies have been documented in the literature, addressing various facets of the energy grid, ranging from power generation to end-user consumption. However, it is noteworthy that the prediction of individual appliance demand has remained relatively unexplored despite its increasing significance, particularly
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Optimal allocation of distribution generation sources with sustainable energy management in radial distribution networks using metaheuristic algorithm Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-02-29 Amit Chakraborty, Saheli Ray
The optimal allocation and distribution of power from various distribution sources, supported by effective energy management, pose significant research challenges in distribution networks. This article aims to minimize the daily operational cost of distributed generators, reduce average daily active power loss, and improve the average daily voltage profile. Focusing on 33-bus and 69-bus systems, the
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Hyperspectral Image Restoration via Tensor Multimode Low-Rank Prior and Spatial-Spectral Smoothness Regularization Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-02-28 Heng Jiang, Chen Xu, Lilin Liu
Hyperspectral images (HSI) can be naturally viewed as a third-order tensor with strong correlations between various dimensions. Thus, tensor-based methods are better than vector-based or matrix-based methods on exploiting the underlying structural information of the HSIs. However, existing HSI compressed sensing reconstruction (HSI-CSR) methods cann't sufficiently utilize this internal underlying structure
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Analysis of influence of sensor placement for source localization based on TDOA algorithm Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-02-28 Narathep Phruksahiran
Locating the source of a radio wave signal is an essential research topic in various applications, such as tracking systems, navigation systems, and systems related to safety and security. This research has studied and analyzed the effect of sensor device positioning on the efficiency in determining the source’s location based on Time Difference of Arrival (TDOA) technology. We focused on the system’s
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Enhancing urban economic efficiency through smart city development: A focus on sustainable transportation Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-02-27 Haojie Jiang, Manjiang Xing
An urban energy execution for smart transportation, evaluation, and impacting element model has been additionally built-in smart cities by the improved stochastic sustainable investigation technique (ISSIT), and board information from commonplace capitals has been considered for instance to do an exact examination. The urban energy execution appraisal and impacting variable model could diminish the
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Enhancing road safety through advanced predictive analytics in V2X communication networks Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-02-24 Fuad A.M. Al-Yarimi
"SafeRouteX," which stands for "Enhancing Road Safety through Advanced Predictive Analytics in V2X Communication Networks," is a novel approach to Vehicle-to-Everything (V2X) networks. Despite advancements in vehicle and road safety technologies, traffic accidents continue to be a significant problem. To increase road safety, SafeRouteX uses cutting-edge machine learning to forecast traffic accidents
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A systematic review of cybersecurity assessment methods for HTTPS Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-02-24 Abdelhadi Zineddine, Oumaima Chakir, Yassine Sadqi, Yassine Maleh, Gurjot Singh Gaba, Andrei Gurtov, Kapal Dev
Cybersecurity assessments are critical for ensuring that security measures in organizational infrastructures, systems, and applications meet necessary requirements. Given the significant HTTPS vulnerabilities exposed in recent years, assessing HTTPS deployments is increasingly important. However, there has been no systematic literature review (SLR) comparing different cybersecurity assessment methods
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Session-based recommendations with sequential context using attention-driven LSTM Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-02-23 Chhotelal Kumar, Mukesh Kumar
A Session-based recommender system (SBRS) captures the dynamic behavior of a user to provide recommendations for the next item in the current session. On providing the user’s past interactions of ongoing sessions, the SBRS predicts the next item that a user is likely to interact with. Sessions can vary in duration, from minutes to hours. Many recommender systems prioritize longer sessions, but most
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An attribute-encryption-based cross-chain model in urban internet of vehicles Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-02-21 Mao Chen, Yuan Jiang, Ju Huang, Wei Ou, Wenbao Han, Qionglu Zhang
With the maturity of Internet of Vehicles (IoV) technology and blockchain technology, numerous application scenarios have emerged, including urban Internet of Vehicles, Highway Internet of Vehicles, and Specific Area Internet of Vehicles. However, the urban Internet of Vehicles systems face data security and operational mode discretization challenges. To address these issues, we propose a data security
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An efficient scheme for secret image sharing through wavelet decomposed audio signal Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-02-21 Krishnendu Maity, Susanta Mukhopadhyay
The well-known Thien and Lin (TL-SIS) provides reduced-size shadows that are cost-efficient for storage and transmission in secret image sharing. However, it suffers from drawbacks, like lossy reconstruction, the requirement of image encryption before sharing, and lack of randomness in the shares. Therefore, the authors attempt to design a lossless, efficient, low-cost image-sharing scheme that can
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A trust-centric approach to intrusion detection in edge networks for medical internet of thing Ecosystems Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-02-21 G. Nagarajan, Martin Margala, Siva Shankar S, Prasun Chakrabarti, RI Minu
The Internet of Medical Things (IoMT) promises transformative benefits for patient care and remote monitoring but raises significant security and privacy concerns. Ensuring the trustworthiness and security of IoMT systems at the edge is a critical challenge. This research paper presents a novel framework to address the security and trust issues specific to IoMT environments. The primary objective of
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A power prediction approach for a solar-powered aerial vehicle enhanced by stacked machine learning technique Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-02-19 Neha Sehrawat, Sahil Vashisht, Amritpal Singh, Gaurav Dhiman, Wattana Viriyasitavat, Norah Saleh Alghamdi
This study aims to enhance the solar energy harvesting capabilities of Unmanned Aerial Vehicles (UAVs), with a focus on integrating solar power to improve overall energy harvesting systems. The proposed method combines two independent renewable systems to extract electricity from the environment. UAV wings equipped with solar panels capture solar energy, employing optimal power point tracking for increased
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NSNP-DFER: A Nonlinear Spiking Neural P Network for Dynamic Facial Expression Recognition Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-02-17 Zheng Han, Xia Meichen, Peng Hong, Liu Zhicai, Guo Jun
Dynamic Facial Expression Recognition (DFER) is considered a more challenging task in computer vision due to its closer to the emotional demands of the real world. The Spiking Neural P (SNP) system is a biomimetic model that imitates the functioning of the human brain and aligns with human perception, providing better interpretability for biological features. Therefore, based on these biologic characteristics
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Deep learning model for short-term photovoltaic power forecasting based on variational mode decomposition and similar day clustering Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-02-15 Meng Li, Wei Wang, Yan He, Qinghai Wang
Short-term photovoltaic power forecasting (PVPF) is crucial in the scheduling and functioning of contemporary electrical systems. A short-term PVPF model based on correlation analysis, similar day clustering, mode decomposition and hybrid deep learning is proposed to address the volatility and stochasticity of PV output. To construct a similar day clustering model, the Kendall rank correlation coefficient
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LPCHISEL: Automatic power intent generation for a chisel-based ASIC design Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-02-15 Fahad Bin Muslim, Kashif Inayat, Safiullah Khan
Chisel-based design description brings the objected-oriented as well as functional programming aspects inherent to software design into the digital design realm. The associated productivity benefits can especially be utilized to write deep learning (DL) accelerator generators using Chisel to cater to the diverse and swiftly evolving user requirements. Additionally such application-specific integrated
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Efficient prediction of coronary artery disease using machine learning algorithms with feature selection techniques Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-02-15 Md. Mehedi Hassan, Sadika Zaman, Md. Mushfiqur Rahman, Anupam Kumar Bairagi, Walid El-Shafai, Rajkumar Singh Rathore, Deepak Gupta
In recent years, there has been a notable surge in the prevalence of cardiovascular diseases (CVD), presenting a significant global public health challenge and a leading cause of mortality worldwide. Among the myriad complications stemming from CVD, heart failure stands out as a critical concern. Addressing heart failure through surgical means poses considerable challenges. The primary objective of
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Simplifying complex digital sequential circuit by an innovative mixed-signal circuit alternative Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-02-15 Shuza Binzaid, Abhitej Divi, Md. Rokonuzzaman
This research paper presents an innovative circuit design of a Next Clock Auto-Generator (NCA). A sequential analog circuit designed to produce a digital pulse for triggering digital components using the innovative Auto-Sensing Mechanism (ASM). The NCA is notable for its minimalistic design, employing only a basic set of resistors and a capacitor. Furthermore, it offers seamless integration with analog
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MIWET: Medical image watermarking using encryption and fusion technique Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-02-14 Ashima Anand, Jatin Bedi, Imad Rida
Digital augmentation of contemporary healthcare has directed the expansion of ICT-based systems. At this stage, a variety of telemedical support has been implemented to ensure the provision of medical utilities. Data Privacy, reliability, and credibility are the three security prerequisites that demand constant monitoring and corrective measures. Watermarking and cryptographic techniques offer a potentially
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Towards explainable artificial intelligence in deep vision-based odometry Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-02-14 Alireza Ghasemieh, Rasha Kashef
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Electric vehicle based smart cloud model cyber security analysis using fuzzy machine learning with blockchain technique Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-02-13 Pengfei Yang
Electric vehicles' growing need on in-car and inter-car connection can cause major issues for the infrastructure. By providing a secure and trustworthy intelligent framework, this article will mainly tackle the problem of cyberattacks in electric cars and help to keep them safe from hackers. This paper introduces a novel approach to cyber security analysis that makes use of blockchain technology for
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Coordination of modular nano grid energy management using multi-agent AI architecture Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-02-13 P.N. Renjith, Badria Sulaiman Alfurhood, K.V. Prashanth, Varsha Santosh Patil, Neetu Sharma, Abhay Chaturvedi
Many innovative creations of businesses and academics are a result of the ever-increasing intricacy of the contemporary, renewable electricity, and fuel networks. To handle the complicated aspects of networks, the right architectural and testing techniques, methodologies, theories, and accompanying technologies are required. Intermittent sustainable energy resources like wind and solar present some
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A hybrid evolutionary weighted ensemble of deep transfer learning models for retinal vessel segmentation and diabetic retinopathy detection Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-02-13 Richa Vij, Sakshi Arora
Segmentation of retinal blood vessels in fundus images is critical for early detection and treatment of diabetic retinopathy(DR). Due to the complex distribution of blood vessels, variations in noise, illumination, and vessel orientation in fundus images, the segmentation process becomes extremely challenging and time-consuming. In recent years, deep learning(DL)-based methods have been recognized
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Abnormal behavior identification of enterprise cloud platform financial system based on artificial neural network Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-02-10 Yingli Wang
This paper presents an effective and accurate method to analyze the network traffic risk of enterprise public cloud financial system by using deep learning algorithm. To build the analytical model, the collected data is preprocessed to extract relevant features that contribute to risk analysis. The pre-processed data is used to train the deep learning model. The model is designed to learn patterns
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An integrated intrusion detection framework based on subspace clustering and ensemble learning Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-02-10 Jingyi Zhu, Xiufeng Liu
In the rapidly evolving landscape of the Internet of Things (IoT), ensuring robust intrusion detection is paramount for device and data security. This paper proposes a novel method for intrusion detection in IoT networks that leverages a unique blend of subspace clustering and ensemble learning. Our framework integrates three innovative strategies: Clustering Results as Features (CRF), Two-Level Decision
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ConvLSTM-based real-time power flow estimation of smart grid with high penetration of uncertain PV considering measurement noise Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-02-09 Fanta Senesoulin, Komsan Hongesombut, Issarachai Ngamroo, Sanchai Dechanupaprittha
A modern smart grid tends to have increasingly various uncertain renewable generations. Due to different geographical areas and network topology constraints, operations of a smart grid become complicated and challenging. Moreover, using existing methods, power flow estimation in real-time could be time-consuming and computationally expensive. This paper proposes an efficient deep learning approach
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Sustainable energy management in electric vehicle secure monitoring and blockchain machine learning model Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-02-06 Weijia Jin, Chenhui Li, Min Yi Zheng
Electric vehicles (EVs) are seen as one of the most promising methods to combat climate change, primarily because they lessen reliance on fossil fuels and the pollutants that result from fuel combustion. This study suggests a unique approach to managing the energy consumption of electric vehicles while analysing security utilizing blockchain machine learning (ML) algorithms. In this case, an adaptive
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A novel approximate cache block compressor for error-resilient image data Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-02-06 Payman Loloeyan, Hooman Nikmehr, Mehran Rezaei
In this research, we introduce the Image Approximate Block Compressor (IABC), a fast (single cycle), simple and high-performance cache block compressor targeting domain-specific image data. Our work presents a high-quality cache block compression technique by applying approximation to image pixels used in selected error-resilient applications. IABC not only works seamlessly alongside mainstream block
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Public cloud networks oriented deep neural networks for effective intrusion detection in online music education Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-02-03 Jianan Zhang, J Dinesh Peter, Achyut Shankar, Wattana Viriyasitavat
The rapid growth of online music education has led to increased security risks from cyber intrusions. This paper proposes public cloud networks oriented deep neural networks for effective intrusion detection in online music education environments. Specifically, a novel intrusion detection framework is developed, comprising fuzzy logic based feature selection, chronological salp swarm algorithm optimized
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Energy management of a dual battery energy storage system for electric vehicular application Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-02-02 R.B. Selvakumar, C. Vivekanandan, Himanshu Sharma, Vipul Vekariya, Raj A. Varma, V. Mohanavel, Govindaraj Ramkumar, A.S.Mahesh Kumar, M. Abdullah-Al-Wadud
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Enhancing protection in AC microgrids: An adaptive approach with ANN and ANFIS models Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-02-01 Rani Kumari, Bhukya K. Naick
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Research on optimization algorithms for artificial intelligence network security management based on All IP Internet of Things fusion technology Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-02-01 Wang Fei
Due to the large scale of the Internet of Things, numerous devices, and the increasing threat of network attacks, traditional network security management methods are no longer able to meet the needs. This article aims to propose an artificial intelligence network security management optimization algorithm based on the full IP Internet of Things fusion technology, in order to improve the efficiency
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TransConvNet: Perform perceptually relevant driver’s visual attention predictions Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-02-01 Chuan Xu, Bo Jiang, Yan Su
Drivers adeptly allocate their attention to critical areas and targets in a dynamically evolving driving environment, thereby ensuring the utmost safety. However, prevailing research primarily focuses on static perspectives or relies solely on the feature extraction capabilities of the Convolutional Neural Network (CNN). CNN inherently possesses limitations in capturing long-range contextual information
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Enhanced implementation of a state machine-based decoder for optimal modulation of multilevel converters Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-01-31 Herbert O. Ramos, Hugo R. Torquato, Marcos A.S. Mendes, Frederico F.V. Matos, Clodualdo V. Sousa, Waner W.A.G. Silva, Victor F. Mendes
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CACRN-Net: A 3D log Mel spectrogram based channel attention convolutional recurrent neural network for few-shot speaker identification Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-01-31 Banala Saritha, Mohammad Azharuddin Laskar, Anish Monsley K, Rabul Hussain Laskar, Madhuchhanda Choudhury
Advancements in deep learning for speaker identification are constrained by the limited availability of data, especially in law enforcement applications. This has led to the emergence of few-shot speaker identification, a technique that classifies unseen test samples with the help of a few support samples. Despite several attempts to advance few-shot speaker identification, significant challenges persist
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Stroke classification based on deep reinforcement learning over stroke screening imbalanced data Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-01-29 Ting Zuo, Fenglian Li, Xueying Zhang, Fengyun Hu, Lixia Huang, Wenhui Jia
Stroke screening is a crucial measure for reducing stroke occurrence, disability, and mortality. However there are numerous risk factors and the limited number of high-risk stroke groups in all screening populations, the screening data is redundant and imbalanced. We propose an improved feature selection algorithm to identify stroke key risk factors and an oversampling MRF-SMOTE algorithm to balance
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Black hole attack detection using Dolphin Echo-location-based machine learning model in MANET environment Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-01-29 Ramesh Vatambeti, Srihari Varma Mantena, K.V.D. Kiran, Srinivasulu Chennupalli, M Venu Gopalachari
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DRL-based joint optimization for 3D-oriented multi-IRS communication systems Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-01-25 Muhammad Fawad Khan, Limei Peng, Pin-Han Ho
This paper investigates the achievable rates of multiple intelligent reflecting surface (IRS)-assisted multi-hop communications by exploring the impact of three-dimensional (3D) IRS orientation, represented by elevation and azimuth angles relative to the base station (BS). We first formulate the problem as the joint optimization of the deployment location, 3D orientation, phase shift of IRSs, and power
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FedTweet: Two-fold Knowledge Distillation for non-IID Federated Learning Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-01-24 Yanhan Wang, Wenting Wang, Xin Wang, Heng Zhang, Xiaoming Wu, Ming Yang
Federated Learning (FL) is a distributed learning approach that allows each client to retain its original data locally and share only the parameters of the local updates with the server. While FL can mitigate the problem of “data islands”, the training process involving non-independent and identically distributed (non-IID) data still faces the formidable challenge of model performance degradation due
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Complex chain integration and normalization model-based risk prediction in multiplex networked industrial chains Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-01-22 Fulin Chen, Kai Di, Yuanshuang Jiang, Pan Li, Yichuan Jiang
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Traffic accident severity prediction with ensemble learning methods Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-01-25 Süleyman Çeven, Ahmet Albayrak
In this study, decision tree-based models are proposed for classification of traffic accident severity. Traffic accident severity is classified into three categories. The data set used in the study belongs to the province of Kayseri, Turkey. The data consists of urban traffic accident reports (23074 accidents) between 2013 and 2021. There are 39 variables in the data set. As a result of data preprocessing
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Deep hierarchical reinforcement learning for collaborative object transportation by heterogeneous agents Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-01-23 Maram Hasan, Rajdeep Niyogi
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TPTrack: Strengthening tracking-by-detection methods from tracklet processing perspectives Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-01-23 Yuhang Li, Huihuang Zhao, Qingyun Liu, Xiaoman Liang, Xinwang Xiao
As a fundamental task in computer vision, multi-object tracking (MOT) has gained increasing attention due to its commercial and academic potential. However, accurately tracking multiple objects is highly challenging. The problems of object occlusion, deformation, and real-time requirements have long been obstacles to be tackled in the field of multi-object tracking. In this paper, we first propose
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Highway smart transport in vehicle network based traffic management and behavioral analysis by machine learning models Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-01-24 Xiong Xia, Shiqin Lei, Ya Chen, Shiyu Hua, HengLiang Gan
The intelligent transport system (ITS), which gets beyond the drawbacks of the conventional transport system, has become a crucial element and is frequently used in smart cities. This study suggests a revolutionary approach to managing traffic with intelligent highway vehicle behaviour analysis utilising machine learning techniques. Here, the multiagent reinforcement markov Bayesian Gaussian model
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UAV-to-UAV target re-searching using a Bayes-based spatial probability distribution algorithm Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-01-24 Rongqi Liu, Wenxi Zhang, Hongyu Wang, Jiaozhi Han
With the increasing amounts of UAVs usage, the supervision of unmanned aerial vehicles (UAV) has become particularly important, and the demand for detecting and following UAVs has grown rapidly. Compared with ground targets, UAVs are more difficult to track because of the high speed of the target and the interference caused by the shadow of either a target or a tracker. In addition, the problem of
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Introduction to the special section on electronic technology for intelligent world Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-01-18 Xiaohang Wang, Anu Gokhale, Paolo Terenziani, Letian Huang
Abstract not available
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Using chaos to encrypt images with reconstruction through deep learning model for smart healthcare Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-01-20 , N. Baranwal, K.N. Singh, A.K. Singh
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TAM-SenticNet: A Neuro-Symbolic AI approach for early depression detection via social media analysis Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-01-20 Rongyu Dou, Xin Kang
This paper introduces TAM-SenticNet, a Neuro-Symbolic AI framework uniquely designed for early depression detection through social media content analysis. Merging neural networks for feature extraction and sentiment analysis with advanced symbolic reasoning, TAM-SenticNet overcomes the limitations of traditional diagnostic tools, particularly in real-time responsiveness and interpretability. The symbolic
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Multimodal imputation-based stacked ensemble for prediction and classification of air quality index in Indian cities Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-01-20 Routhu Srinivasa Rao, Lakshmana Rao Kalabarige, Bhavya Alankar, Aditya Kumar Sahu
Nowadays, monitoring and predicting the air quality is very much needed to identify and control the adverse health effects due to the low air quality, especially in developing countries like India. Recently, it has been an interesting research topic to predict the air quality index (AQI) values and levels using machine learning algorithms. In this paper, we proposed a multimodal imputation based stacked
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TellMeTalk: Multimodal-driven talking face video generation Comput. Electr. Eng. (IF 4.3) Pub Date : 2024-01-20 Pengfei Li, Huihuang Zhao, Qingyun Liu, Peng Tang, Lin Zhang
In this paper, we present TellMeTalk, an innovative approach for generating expressive talking face videos based on multimodal inputs. Our approach demonstrates robustness across various identities, languages, expressions, and head movements. It overcomes four key limitations of existing talking face video generation methods: (1) reliance on single-modal learning from audio or text, lacking the complementary