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International Conference on Innovation and Technological Advances for Sustainability, ITAS 2023

作者： Badawi, Ahmed Ali, Hassan Ismail, Nasser A. Zyoud, Alhareth Yusoff, Siti Hajar College of Engineering Technology University of Doha for Science and Technology Doha Qatar Department of Electrical and Computer Engineering Birzeit University Birzeit Ramallah Palestine Department of Electrical and Computer Engineering International Islamic University Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9781032803722

In this paper, a novel algorithm had been proposed to improve the efficiency performance of the wind energy conversion system (WECS) to reach the maximum possible captured power under fast varying wind speed. The permanent magnet synchronous generator (PMSG) had been selected because of its reliability and efficient performance, especially in low-speed conditions environment. The novel algorithm applied the power prediction mode to make optimization for the reached decision to the maximum peak point. The proposed algorithm shows better efficiency performance for different wind speed conditions. The novel algorithm accelerates convergence to detect the maximum peak point without sacrificing efficiency. The novel algorithm’s simplicity reduces the overall time required by 33% when compared to the three-mode algorithm. © 2025 The Author(s).

关键词： Windmill

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Improving Skin Cancer Diagnosis: A Comprehensive Analysis of Transfer Learning and Ensemble Models 26

Improving Skin Cancer Diagnosis: A Comprehensive Analysis of...

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26th International Conference on computer and Information technology, ICCIT 2023

作者： Rahman, Saifur Siddique, Md. Abu Ismail Ashik, S. M. Abdullah Rajshahi University of Engineering & Technology Computer Science & Engineering Rajshahi Bangladesh Rajshahi University of Engineering & Technology Electrical & Computer Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350359015

Skin cancer has become a major global health issue, necessitating improved diagnostic methods for better clinical outcomes. This study thoroughly evaluates deep learning methods for classifying skin cancer using dermoscopic images. We compare the performance of popular pre-trained models, including VGG16, ResNet50, Xception, and MobileNet, against a natively trained SqueezeNet model. The results show that VGG16 and ResNet50 models have achieved state-of-the-art accuracy of 94%, with SqueezeNet following closely at 93%. This validates the effectiveness of transfer learning. We suggest using ensemble models that combine pre-trained networks (VGG16, MobileNet, ResNet, and Xception) to achieve a maximum accuracy of 97% using model averaging. Beyond accuracy, our ensemble models excel across Precision, Recall, and F1-score metrics, emphasizing their potential for real-world usage. Additionally, we present ROC curve analysis and confusion matrices to provide clinical insights. This study highlights the capabilities of transfer learning for skin cancer classification while introducing novel ensemble techniques that can profoundly impact computer-aided diagnosis through improved accuracy and robustness. © 2023 IEEE.

关键词： and computer Vision CNN MobileNet ResNet50 SqueezeNet Transfer Learning VGG16 Xception

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A Host-based Intrusion Detection: Using Signature-based and AI-driven Anomaly Detection for Enhanced Cybersecurity∗ 4

A Host-based Intrusion Detection: Using Signature-based and ...

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4th International Conference on Digital Futures and Transformative Technologies, ICoDT2 2024

作者： Rehman, Fazalur Mushtaq, Farhan Zaman, Hafsah National University of Sciences and Technology School of Electrical Engineering and Computer Science Islamabad Pakistan Department of Electrical Engineering Taxila Pakistan

ISBN: (纸本)9798350391862

This research introduces a Hybrid Intrusion Detection System (HIDS) that merges signature-based detection, with AI-powered anomaly detection to enhance the accuracy and effectiveness of identifying cyber threats. The proposed HIDS demonstrates an ability to detect uncommon and sophisticated cyber threats with an accuracy rate of 90.37%. By combining Gradient Boosting and K-Nearest Neighbors (KNN) algorithms the system improves detection precision, speeds up response times, and expands coverage across network traffic. This comprehensive approach overcomes the limitations of traditional methods by enabling threat responses while reducing false positive rates. The study highlights the potential of integrating signature-based and AI-driven techniques to strengthen cybersecurity defences which emphasizes the benefits of this approach. When the system detects a potential threat, alerts are sent to the Security Operations Center (SOC) or Network Operations Center (NOC), with details such as nature of the threat, and the affected system. This study establishes a foundation for real-time cyber threat detection and intelligence sharing in cloud environments, with future Hybrid IDS versions operating across multiple hosts and supported by a web service for cross-platform compatibility and centralized alert system. © 2024 IEEE.

关键词： Network intrusion

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Interference Suppression and Jitter Elimination Ability-Based Adaption Tracking Guidance for Robotic Fishes

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 126-137页

作者： Dongfang Li Jie Huang Rob Law Xin Xu Limin Zhu Edmond Q.Wu IEEE the School of Electrical Engineering and Automation Fuzhou University the Key Laboratory of System Control and Information Processing Ministry of Education the School of Electrical Engineering and Automation Fuzhou University 5G+Industrial Internet Institute Fuzhou University the University of Macau the College of Mechatronics and Automation National University of Defense Technology the School of Mechanical Engineering Shanghai Jiao Tong University the Department of Computer Science and Engineering Shanghai Jiao Tong University

This work presents an adaptive tracking guidance method for robotic fishes. The scheme enables robots to suppress external interference and eliminate motion jitter. An adaptive integral surge line-of-sight guidance rule is designed to eliminate dynamics interference and sideslip issues. Limited-time yaw and surge speed observers are reported to fit disturbance variables in the model. The approximation values can compensate for the system's control input and improve the robots' tracking ***, this work develops a terminal sliding mode controller and third-order differential processor to determine the rotational torque and reduce the robots' run jitter. Then, Lyapunov's theory proves the uniform ultimate boundedness of the proposed method. Simulation and physical experiments confirm that the technology improves the tracking error convergence speed and stability of robotic fishes.

关键词： Adaptive integral surge line-of-sight approximation robotic fish speed observer

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Deep learning and vision transformers-based framework for breast cancer and subtype identification

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Neural Computing and Applications 2025年第16期37卷 9311-9330页

作者： Jahan, Ishrat Chowdhury, Muhammad E. H. Vranic, Semir Al Saady, Rafif Mahmood Kabir, Saidul Pranto, Zahid Hasan Mim, Sabiha Jahan Nobi, Sadia Farhana Department of Computer Science and Engineering Shahjalal University of Science and Technology Sylhet Bangladesh Department of Electrical Engineering Qatar University 2713 Doha Qatar College of Medicine QU Health Qatar University Doha Qatar Department of Electrical and Electronic Engineering University of Dhaka Dhaka1000 Bangladesh

Breast cancer, marked by uncontrolled cell growth in breast tissue, is the most common cancer among women and a second-leading cause of cancer-related deaths. Among its types, ductal and lobular carcinomas are the most prevalent, with invasive ductal carcinoma accounting for about 70–80% of cases and invasive lobular carcinoma for about 10–15%. Accurate identification is crucial for effective treatment but can be time-consuming and prone to interobserver variability. AI can rapidly analyze pathological images, providing precise, cost-effective identification, thus reducing the pathologists’ workload. This study utilizes a deep learning framework for advanced, automatic breast cancer detection and subtype identification. The framework comprises three key components: detecting cancerous patches, identifying cancer subtypes (ductal and lobular carcinoma), and predicting patient-level outcomes from whole slide images (WSI). The validation process includes visualization using Score-CAM to highlight cancer-affected areas prominently. Datasets include 111 WSIs (85 malignant from the Warwick HER2 dataset and 26 benign from pathologists). For subtype detection, there are 57 ductal and 8 lobular carcinoma cases. A total of 28,428 annotated patches were reviewed by two expert pathologists. Four pre-trained models—DenseNet-201, MobileNetV2, an ensemble of these two, and a Vision Transformer-based model—were fine-tuned and tested on the patches. Patient-level results were predicted using a majority voting technique based on the percentage of each patch type in the WSI. The Vision Transformer-based model outperformed other models in patch classification, achieving an accuracy of 96.74% for cancerous patch detection and 89.78% for cancer subtype classification. For WSI-based cancer classification, the majority voting method attained an F1-score of 99.06 and 96.13% for WSI-based cancer subtype classification. The proposed deep learning-based framework for advanced breast cancer det

关键词： Diseases

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Geyser Inspired Algorithm:A New Geological-inspired Meta-heuristic for Real-parameter and Constrained engineering Optimization

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Journal of Bionic engineering 2024年第1期21卷 374-408页

作者： Mojtaba Ghasemi Mohsen Zare Amir Zahedi Mohammad-Amin Akbari Seyedali Mirjalili Laith Abualigah Department of Electronics and Electrical Engineering Shiraz University of TechnologyShirazIran Department of Electrical Engineering Faculty of EngineeringJahrom UniversityJahromFrasIran Department of Electrical and Computer Engineering Tarbiat Modares UniversityTehranIran Department of Electrical and Computer Engineering University ofCyprusNicosiaCyprus Centre for Artificial Intelligence Research and Optimisation Torrens University AustraliaBrisbaneQLD 4006Australia University Research and Innovation Center Obuda University1034 BudapestHungary Department of Electrical and Computer Engineering Lebanese American UniversityByblos 13-5053Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman 19328Jordan MEU Research Unit Middle East UniversityAmman 11831Jordan Applied Science Research Center Applied Science Private UniversityAmman 11931Jordan

Over the past years,many efforts have been accomplished to achieve fast and accurate meta-heuristic algorithms to optimize a variety of real-world *** study presents a new optimization method based on an unusual geological phenomenon in nature,named Geyser inspired Algorithm(GEA).The mathematical modeling of this geological phenomenon is carried out to have a better understanding of the optimization *** efficiency and accuracy of GEA are verified using statistical examination and convergence rate comparison on numerous CEC 2005,CEC 2014,CEC 2017,and real-parameter benchmark ***,GEA has been applied to several real-parameter engineering optimization problems to evaluate its *** addition,to demonstrate the applicability and robustness of GEA,a comprehensive investigation is performed for a fair comparison with other standard optimization *** results demonstrate that GEA is noticeably prosperous in reaching the optimal solutions with a high convergence rate in comparison with other well-known nature-inspired algorithms,including ABC,BBO,PSO,and *** that the source code of the GEA is publicly available at https://***/projects/gea.

关键词： Nature-inspired algorithms Real-world and engineering optimization Mathematical modeling Geyser algorithm(GEA)

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A Design of Orthogonal Switchable Feed Networks Integrated Microstrip Antenna for Six Polarization States 3

A Design of Orthogonal Switchable Feed Networks Integrated M...

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3rd International Conference on electrical, computer and Communication engineering, ECCE 2023

作者： Rahman, Muhammad Asad Rahman, Md. Naimur Hossain, Md. Azad Chittagong University of Engineering and Technology Faculty of Electrical and Computer Engineering Bangladesh

ISBN: (纸本)9798350345360

In this paper, an aperture-coupled microstrip antenna with polarization reconfigurability in the sub-6 GHz range is proposed. The proposed antenna can excite horizontal and vertical linear polarization (LP), +45°and -45°LP, right-hand and left-hand circular polarization (CP). To attain polarization reconfigurability, a square shaped patch antenna is excited by either single or dual-feeding mode with two orthogonal aperture-coupled switchable feed networks where each network has either a quadrature length shifter or a 180° phase shifter. Single port is used to excite horizontal and vertical LP. On the other side, switchable dual port excitation can produce +45°LP, -45°LP, RHCP, and LHCP. Basically, 180° phase shifter helps to achieve ±45° LP and quadrature length shifter is used to excite dual-CP sense. For wireless applications, the suggested antenna is more appealing due to its low cross-polarization level and good isolation in each polarization state. The proposed antenna has 10-dB impedance bandwidth (BW) of 1.17% for LP, 0.72% for LHCP, and 0.60% for RHCP. Moreover, 3-dB axial ratio (AR) BW is more than 2.50% for both CP senses. Better isolation characteristic ( © 2023 IEEE.

关键词： Polarization

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Hybrid Water Quality Prediction with Multimodal Low-rank Fusion and Localized Attention

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IEEE Internet of Things Journal 2025年第12期12卷 21158-21169页

作者： Bi, Jing Li, Yibo Yuan, Haitao Wang, Mengyuan Wang, Ziqi Zhang, Jia Zhou, MengChu Beijing University of Technology College of Computer Science Beijing100124 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Beihang University School of Energy and Power Engineering Beijing100191 China Southern Methodist University Department of Computer Science DallasTX75206 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

Water quality prediction methods forecast the short-or long-term trends of its changes, providing proactive advice for preventing and controlling water pollution. Existing water quality prediction methods typically fail to capture water quality's nonlinear characteristics accurately and only consider historical time series data. However, meteorology and other factors also significantly impact water quality indicators. Therefore, considering only historical data of water quality time series is not feasible. To solve this problem, this work proposes a hybrid water quality prediction model called CMLIP, which integrates ConvNeXt V2, Multimodal bottleneck transformer, Low-rank multimodal fusion, ITransformer, and PatchTST. CMLIP inputs water quality time series and meteorological remotely sensed rainfall images into a multimodal fusion module before prediction. Specifically, CMLIP integrates the model of ConvNeXt V2 to extract image features. Its multimodal fusion module combines a multimodal bottleneck transformer and the low-rank multimodal fusion to fuse the time series and images. Furthermore, CMLIP combines iTransformer and PatchTST to form an improved prediction module that realizes the prediction of fused features. Experimental results with real-life water quality time series and remotely sensed rainfall images demonstrate that CMLIP when fusing meteorological data, achieves an average improvement of 17% in water quality forecasting accuracy compared to forecasts using only water quality time series. Moreover, CMLIP outperforms other state-of-the-art algorithms in both data fusion and prediction, with an average enhancement of 6% in fusion effectiveness and an average improvement of 22% in prediction accuracy. © 2025 IEEE.

关键词： Rain

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Energy-Efficient Secure Federated Learning for UAV Swarms 7

Energy-Efficient Secure Federated Learning for UAV Swarms

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7th International Conference on Energy Conservation and Efficiency, ICECE 2024

作者： Afzal, Muhammad Usman Abdellatif, Alaa Awad Information Technology University Electrical Engineering Department Qatar University Computer Science and Engineering Department Qatar

ISBN: (纸本)9798350370898

Unmanned Aerial Vehicles (UAVs) have been recently leveraged in massive amount of Internet of Things (IoT) applications. However, given the stringent limitations of UAVs, investigating their performance in terms of their energy efficiency, tasks accuracy, and security has gained much interest recently. In this paper, we study the deployment of Federated Learning (FL) within the UAV swarms, with the aim of avoiding data exchange between different swarm's members. Specifically, we propose a secure FL framework for UAV swarms, such that the energy efficiency is maximized, while enhancing the learning performance. The suggested approach takes into account the issue of data imbalance in FL in addition to the strict energy and security limits placed on the *** findings show how effective our framework is in terms of security, energy efficiency, and classification accuracy when compared to baseline systems that don't take security or data imbalance into account. © 2024 IEEE.

关键词： Energy efficiency

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Data-driven electrical resistance tomography for robotic large-area tactile sensing

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Science China(Information Sciences) 2024年第9期67卷 336-337页

作者： Wendong ZHENG Huaping LIU Xiaofeng LIU Fuchun SUN School of Electrical Engineering and Automation Tianjin University of Technology Department of Computer Science and Technology Tsinghua University Key Laboratory of Maritime Intelligent Cyberspace Technology (Hohai University) Ministry of Education

Tactile sensing plays a crucial role in enabling robots to safely interact with objects in dynamic environments [1].Given that potential physical contact can occur at any location during robot interaction, there is a ... 详细信息

关键词： Robot applications

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