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2024 International Conference on computer, electrical and Communication engineering, ICCECE 2024

作者： Md. Muttakin, G.K. Yesmin, Farhana Alam, Syeda Sadia Ashraf, Md Sadi Akuthota, Vishwanath Quader, Montaser Abdul Rajshahi University of Engineering and Technology Department of Electrical and Computer Engineering India Metropolitan University Department of Computer Science and Engineering United Kingdom Brac University Department of Computer Science and Engineering Bangladesh Drpinnacle Hyderabad India Green University of Bangladesh Department of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350386479

Blood cancer cell diagnosis is crucial in medical diagnostics. It demands accurate classification of blood cell images. Proper classification of blood cancer cells is fundamental for accurately diagnosing the specific type and subtype of blood cancer as well as efficient treatment planning. It provides specific information, helping healthcare professionals predict the prognosis, survival rates, and the potential for disease recurrence. On this basis, deep learning models have demonstrated remarkable performance. In this paper, we have introduced a comprehensive research into blood cancer cell classification, employing a diverse dataset encompassing various blood cell types. We explore the effectiveness of VGG19 with batch normalization, Vision Transformer (ViT), Ensemble Adversarial Inception-ResNetV2, DenseNet201, and ResNeXt50 architectures in this challenging task. In order to enhance the model performance, we integrate essential data preprocessing techniques, including resizing, cropping, and normalization. Additionally, novel data augmentation strategies, such as random cropping, and flipping are introduced to augment the training dataset and improve model generalization. Remarkably, VGG19 with batch normalization has shown tremendous success by achieving 99.86% accuracy. Moreover, the DenseNet201 model has performed brilliantly by achieving an accuracy of 98.55%. The ResNeXt50 model shows excellent performance with an accuracy of 98.31%. The Vision Transformer (ViT) achieves a solid accuracy of 98.91%. Lastly, Ensemble Adversarial Inception-ResNet V2 also performed no-tably achieving 96.38%. In this context, VGG19 with batch normalization is able to show more excellent performance than other models by achieving the highest accuracy. © 2024 IEEE.

关键词： Cancer cells

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Game Theory Meets Data Augmentation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6080-6094页

作者： Kang, Yuhan Zare, Samira Lin, Alex Tong Han, Zhu Osher, Stanley Nguyen, Hien Van Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States University of California Department of Mathematics Los AngelesCA90095 United States University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

Data augmentation is a critical component in building modern deep-learning systems. In this article, we propose MFG Augment, a novel data augmentation method based on the mean-field game (MFG) theory that can synthesize a sequence of data between every two images or features. The central idea is to consider every image as a distribution over its pixel or feature space. Using MFG theory, we can generate a time-continuous "path"from one distribution to another so that the points along the "path"are augmented images or features. Empirically, the experiment results on MNIST, CIFAR-10, and ImageNet demonstrate that the proposed technology has better generalization ability and higher classification accuracy as compared to several benchmark methods. More importantly, our MFG Augment improves the test accuracy significantly when the dataset size is small. MFG Augment consistently shows better affinity and diversity scores, two important empirical metrics for evaluating the generalization of data augmentation techniques. © 2020 IEEE.

关键词： Game theory

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Review of dehazing techniques: challenges and future trends

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Multimedia Tools and Applications 2025年第3期84卷 1103-1131页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. EL-Rabaie, El-Sayed M. Department of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh11671 Saudi Arabia Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt

The phenomenon of atmospheric haze arises due to the scattering of light by minute particles suspended in the atmosphere. This optical effect gives rise to visual degradation in images and videos. The degradation is primarily influenced by two key factors: atmospheric attenuation and scattered light. Scattered light causes an image to be veiled in a whitish veil, while attenuation diminishes the image inherent contrast. Efforts to enhance image and video quality necessitate the development of dehazing techniques capable of mitigating the adverse impact of haze. This scholarly endeavor presents a comprehensive survey of recent advancements in the domain of dehazing techniques, encompassing both conventional methodologies and those founded on machine learning principles. Traditional dehazing techniques leverage a haze model to deduce a dehazed rendition of an image or frame. In contrast, learning-based techniques employ sophisticated mechanisms such as Convolutional Neural Networks (CNNs) and different deep Generative Adversarial Networks (GANs) to create models that can discern dehazed representations by learning intricate parameters like transmission maps, atmospheric light conditions, or their combined effects. Furthermore, some learning-based approaches facilitate the direct generation of dehazed outputs from hazy inputs by assimilating the non-linear mapping between the two. This review study delves into a comprehensive examination of datasets utilized within learning-based dehazing methodologies, elucidating their characteristics and relevance. Furthermore, a systematic exposition of the merits and demerits inherent in distinct dehazing techniques is presented. The discourse culminates in the synthesis of the primary quandaries and challenges confronted by prevailing dehazing techniques. The assessment of dehazed image and frame quality is facilitated through the application of rigorous evaluation metrics, a discussion of which is incorporated. To provide empiri

关键词： Generative adversarial networks

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Capacitor voltage unbalance minimization for three-phase five-level diode-clamped inverter using hexagonal hysteresis space vector modulation

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第2期7卷 661-671页

作者： Ramkumar, R. Singh, Manmohan Ramkumar, R. Usha, S. Thentral, T. M. Thamizh Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Chengalpattu India Department of Computer Engineering and Application GLA University Mathura India Department of Electrical and Electronics Engineering School of Engineering and Technology Dhanalakshmi Srinivasan University Trichy India Department of Renewable Energy University of Maroua National Advanced School of Engineering of Maroua Maroua Cameroon

This article suggests a method for diminishing the voltage unbalance in a three-phase five-level diode-clamped inverter (DCI) through the use of hexagonal hysteresis space vector modulation (HHSVM). Capacitor voltage balancing leads to enhanced system efficiency, reduced stress on components, enhanced performance, abridged electromagnetic interference, and reduced total harmonic distortion. The proposed modulation technique and its implementation are thoroughly examined in this study, along with modeling and experiment data that show how efficient the method is at lowering the capacitor voltage unbalance in the proposed five-level DCI. Capacitor voltage unbalance is reduced with the use of this HHSVM approach to 0.95%, which is a superior reduction compared to traditional PWM methods. The paper also discusses the advantages of the proposed method over other existing methods, making it a promising solution for practical applications in power electronics systems.

关键词： Diode-clamped inverter (DCI) Capacitor voltage balancing Hexagonal hysteresis space vector modulation (HHSVM) Total harmonic distortion

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Dark Forest Algorithm:A Novel Metaheuristic Algorithm for Global Optimization Problems

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computers, materials & Continua 2023年第5期75卷 2775-2803页

作者： Dongyang Li Shiyu Du Yiming Zhang Meiting Zhao Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo315000China Engineering Laboratory of Advanced Energy Materials Ningbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingbo315000China School of Material and Chemical Engineering Ningbo UniversityNingbo315000China

Metaheuristic algorithms,as effective methods for solving optimization problems,have recently attracted considerable attention in science and engineering *** are popular and have broad applications owing to their high efficiency and low *** algorithms are generally based on the behaviors observed in nature,physical sciences,or *** study proposes a novel metaheuristic algorithm called dark forest algorithm(DFA),which can yield improved optimization results for global optimization *** DFA,the population is divided into four groups:highest civilization,advanced civilization,normal civilization,and low *** civilization has a unique way of *** verify DFA’s capability,the performance of DFA on 35 well-known benchmark functions is compared with that of six other metaheuristic algorithms,including artificial bee colony algorithm,firefly algorithm,grey wolf optimizer,harmony search algorithm,grasshopper optimization algorithm,and whale optimization *** results show that DFA provides solutions with improved efficiency for problems with low dimensions and outperforms most other algorithms when solving high dimensional *** applied to five engineering projects to demonstrate its *** results show that the performance of DFA is competitive to that of current well-known metaheuristic ***,potential upgrading routes for DFA are proposed as possible future developments.

关键词： Metaheuristic algorithm global optimization

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Analyzing energy efficiency of flying objects in Wireless Sensor and Mobile Ad Hoc Networks 5

Analyzing energy efficiency of flying objects in Wireless Se...

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5th IEEE International Conference on Recent Trends in computer science and Technology, ICRTCST 2024

作者： Siddiqui, Shams Tabrez Ahmad, Adeel Ahmad, Jorair Haseebuddin, Mohammad Siddiqui, Alighazi Salim, Abu Jazan University College of Engineering and Computer Science Department of Computer Science Jazan Saudi Arabia Jazan University College of Engineering and Computer Science Department of Electrical and Electronics Engineering Jazan Saudi Arabia

ISBN: (纸本)9798350351378

The convergence of flying objects, Mobile Ad Hoc Networks (MANETs), and Wireless Sensor Networks (WSNs) has been made feasible by the widespread proliferation of wireless communication technology. This study delves into the historical development and significant achievements of WSNs and MANETs, with a focus on the incorporation of drones and UAVs to enhance network capabilities. Our main goals involve conducting thorough assessments of communication protocols, routing algorithms, and network architectures in the presence of flying objects. Our particular emphasis is on maximizing energy efficiency. Examining the dynamics of flying objects in WSNs and MANETs, this analysis delves into the various challenges and opportunities that arise. A special emphasis is placed on the management of mobility, the optimization of energy usage, and the effectiveness of routing protocols. The study explores existing protocols such as LEACH, AODV and OLSR, highlighting their benefits and challenges in flying objects. A comparative analysis evaluates adapted protocols based on metrics such as energy efficiency, latency, and reliability. © 2024 IEEE.

关键词： Wireless sensor networks

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Tunable multicolor luminescence in vanadates from yttrium to indium with enhanced luminous efficiency and stability for its application in WLEDs and indoor photovoltaics

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Nano Research 2023年第8期16卷 11486-11494页

作者： Yaoyang Zhang Jing Mao Peifen Zhu Guofeng Wang Key Laboratory of Functional Inorganic Material Chemistry Ministry of EducationSchool of Chemistry and Materials ScienceHeilongjiang UniversityHarbin 150080China Department of Electrical Engineering and Computer Science University of MissouriColumbiaMissouri 65211USA

The preparation of high-efficiency phosphor is the key to the construction of white light-emitting diode(WLED)devices and their application in indoor *** with YVO_(4),InVO_(4)is not suitable as the host material of lanthanide ions because of its strong ***,the work focused on combining the broadband emission from InVO_(4)and the red luminescence from YVO_(4):Eu^(3+)to obtain enhanced and stable multicolor *** band structure,density of state,and optical properties were studied by density functional *** spectral configuration of YVO_(4):In^(3+)/Eu^(3+)with(112)surface appears to be broadening and redshifts with increasing layer *** the In^(3+)concentration is 3.5 mol%,the YVO_(4):30%Eu^(3+)/In^(3+)emits the strongest *** Judd-Ofelt parameterΩ2 of YVO_(4):In^(3+)/Eu^(3+)increases with increaing In^(3+)concentration,indicating that the symmetry *** adjusting In^(3+)/Eu^(3+)contents,the YVO_(4):In^(3+)/Eu^(3+)not only can emit white light with a color rendering index of 95,but also can be used as high-efficiency red phosphor to build WLED devices with blue emitting N/Tb codoped carbon quantum dots(CQDs-N:Tb^(3+))and green emitting MOF:Tb^(3+)(MOF=metal organic framework),for which the color rendering index can also reach 95 and the color temperature is 5549 *** manufactured WLED devices were further used to excite the silicon solar cell and make it show good photoelectric characteristics.

关键词： luminescence enhancement multicolor stability density functional theory(DFT)calculation white light-emitting diodes(WLEDs) indoor photovoltaics

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Securing Cloud-Encrypted Data:Detecting Ransomware-as-a-Service(RaaS)Attacks through Deep Learning Ensemble

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computers, materials & Continua 2024年第4期79卷 857-873页

作者： Amardeep Singh Hamad Ali Abosaq Saad Arif Zohaib Mushtaq Muhammad Irfan Ghulam Abbas Arshad Ali Alanoud Al Mazroa School of Computer Information Sciences University of CumberlandsWilliamsburgKentucky40769USA Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Electrical Engineering The University of LahoreLahore54000Pakistan Faculty of Computer and Information Systems Islamic University of MadinahMadinah42351Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware *** study provides a new approach for RaaS attack detection which uses an ensemble of deep learning *** this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is *** the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are ***,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested *** proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%*** empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual *** expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.

关键词： Cloud encryption RaaS ensemble threat detection deep learning cybersecurity

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Design and Analysis of a UWB Slotted Vivaldi Antenna for Microwave Imaging Applications

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IEIE Transactions on Smart Processing and Computing 2023年第4期12卷 350-357页

作者： Paul, Liton Chandra Majumder, Akash Rani, Tithi Hossain, Md. Najmul Rahim, Md. Abdur Shin, Jungpil Yun, Keun Soo Department of Electrical Electronic and Communication Engineering Faculty of Engineering and Technology Pabna University of Science and Technology Pabna6600 Bangladesh Department of Electronics & Telecommunication Engineering Faculty of Electrical & Computer Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Department of Computer Science and Engineering Faculty of Engineering and Technology Pabna University of Science and Technology Pabna6600 Bangladesh School of Computer Science and Engineering The University of Aizu / Fukushima Aizuwakamatsu965-8580 Japan School of Computer and Information Technology Ulsan College / Dong-gu Ulsan44610 Korea Republic of

An ultra-wideband (UWB) slotted compact Vivaldi antenna with a microstrip line feed was evaluated for microwave imaging (MI) applications. The recommended FR4 substrate-based Vivaldi antenna is 50×50×1.5 mm3 in size. The proposed compact Vivaldi antenna showed good radiation characteristics and spanned an ultra-wide bandwidth of 10 GHz, ranging from 3.6 to 13.6 GHz, using a microstrip line feed with a radial stub, tapered slots, and resonant cavities. After analyzing the simulation findings, the proposed compact slotted UWB Vivaldi antenna was compared with the basic Vivaldi antenna. The maximum gain and efficiency were 7.35 dB and 82%, respectively. The antenna was simulated in the time domain using the computer simulation technology (CST) microwave studio. The frequency domain solver was also used for cross-validation, and the results were equally good. In conclusion, the proposed compact Vivaldi antenna is a good option for microwave imaging applications. © 2023 The Institute of Electronics and Information Engineers.

关键词： Cavity resonators

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Enhancing Time Series Prediction in Industrial Processes: A Self-Attention BiLSTM Approach 7

Enhancing Time Series Prediction in Industrial Processes: A ...

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7th International Conference on Robotics, Control and Automation engineering, RCAE 2024

作者： Wen, Sicheng Xu, Jiajun University of Engineering Science Department of Electronic and Electrical Engineering Shanghai Shanghai China University of Shanghai for Science and Technology Department of Optical-Electrical and Computer Engineering Shanghai China

ISBN: (纸本)9798350355642

The development of data-driven soft sensors for modeling complex data, particularly in scenarios characterized by strong nonlinearity, high dimensionality, cross-correlation and autocorrelation, remains a significant challenge in process data analysis. Bidirectional Long Short-Term Memory (BiLSTM) networks are highly effective in capturing both forward and backward dependencies within sequences, thereby enhancing the extraction of temporal features from time series data. However, BiLSTM networks may struggle to capture global dependencies, especially in long sequences. To address this limitation, the self-attention mechanism, which excels in modeling global relationships across all positions in the input sequence, can be integrated with BiLSTM by applying weighted attention to the two different directions of BiLSTM, thereby enhancing its performance. This study proposes a novel method called SA-BiLSTM, which combines the strengths of the BiLSTM network and the self- attention mechanism. The self-attention layer captures global dependencies, while the BiLSTM layer extracts comprehensive temporal features in both directions, which are then passed to a fully connected layer for final prediction. Extensive comparisons with traditional methods such as BiLSTM, LSTM with added self-attention mechanism, LSTM, RNN, and SVM validate the superior performance of the proposed SA-BiLSTM method in handling complex time series data. © 2024 IEEE.

关键词： Spatio-temporal data

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