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Skin Lesion Classification via ensemble method on deep learning

Multimedia Tools and Applications 2024年 1-19页

作者： Amiri, Sekineh Asadi Nasrolahzadeh, Mahda Mohammadpoory, Zeynab Kordkheili, Amir Hossein Zare Department of Computer Engineering University of Mazandaran Babolsar Iran Department of Electrical Engineering University of Torbat Heydarieh Torbat Heydarieh Iran Department of Electrical Engineering Shahrood University of Technology Shahrood Iran

Deep neural networks have shown promising results in the classification of skin lesion images, particularly when they focus on the most significant regions of an image. However, the identification of melanoma continues to pose a significant challenge, primarily because of the substantial variability both within and between classes in images of moles. A new framework is proposed in this paper to develop an innovative method for effective melanoma diagnosis, presenting a new approach for Skin Lesion Classification (SLC) that combines a modified Inception ResNet v2 and Efficient Net-B4 in an ensemble. In the modified Inception ResNet v2, we incorporate a Soft-Attention (SA) mechanism. The goal is to enhance the network’s performance by using the SA mechanism to highlight informative features and suppress those that induce noise. Given the diversity of skin lesions in terms of type, texture, color, shape, and distribution, our proposed method extracts crucial features from the images by employing both the SA and merging the features of two layers of Inception ResNet ***, we have modified EfficientNet-B4 by adding two dense layers to improve its classification capabilities. After classifying the images with these two models, we employ soft voting for the final classification. We evaluated the performance of our proposed method on the ISIC-2017 and ISIC-2018 datasets, considering metrics such as Accuracy, Precision, Recall, and F-measures. Experimental results and a comparison with existing techniques demonstrate that our method achieves higher accuracy than current state-of-the-art methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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IRS-enabled NOMA communication systems:A network architecture primer with future trends and challenges

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Digital Communications and Networks 2024年第5期10卷 1503-1528页

作者： Haleema Sadia Ahmad Kamal Hassan Ziaul Haq Abbas Ghulam Abbas Muhammad Waqas Zhu Han Faculty of Electrical Engineering and Telecommunications and Networking(TeleCoN)Research Center Ghulam Ishaq Khan Institute of Engineering and Technology(GIKI)Topi 23640Pakistan Faculty of Computer Science and Engineering GIKITopi 23640Pakistan Department of Computer Engineering College of Information TechnologyUniversity of Bahrain32028Bahrain School of Engineering Edith Cowan UniversityPerth WA6027Australia Department of Electrical and Computer Engineering University of HoustonHoustonTX 77004USA Department of Computer Science and Engineering Kyung Hee UniversitySeoul 446-701South Korea

Non-Orthogonal Multiple Access(NOMA)has already proven to be an effective multiple access scheme for5th Generation(5G)wireless *** provides improved performance in terms of system throughput,spectral efficiency,fairness,and energy efficiency(EE).However,in conventional NOMA networks,performance degradation still exists because of the stochastic behavior of wireless *** combat this challenge,the concept of Intelligent Reflecting Surface(IRS)has risen to prominence as a low-cost intelligent solution for Beyond 5G(B5G)*** this paper,a modeling primer based on the integration of these two cutting-edge technologies,i.e.,IRS and NOMA,for B5G wireless networks is *** in-depth comparative analysis of IRS-assisted Power Domain(PD)-NOMA networks is provided through 3-fold ***,a primer is presented on the system architecture of IRS-enabled multiple-configuration PD-NOMA systems,and parallels are drawn with conventional network configurations,i.e.,conventional NOMA,Orthogonal Multiple Access(OMA),and IRS-assisted OMA *** by this,a comparative analysis of these network configurations is showcased in terms of significant performance metrics,namely,individual users'achievable rate,sum rate,ergodic rate,EE,and outage ***,for multi-antenna IRS-enabled NOMA networks,we exploit the active Beamforming(BF)technique by employing a greedy algorithm using a state-of-the-art branch-reduceand-bound(BRB)*** optimality of the BRB algorithm is presented by comparing it with benchmark BF techniques,i.e.,minimum-mean-square-error,zero-forcing-BF,and ***,we present an outlook on future envisioned NOMA networks,aided by IRSs,i.e.,with a variety of potential applications for 6G wireless *** work presents a generic performance assessment toolkit for wireless networks,focusing on IRS-assisted NOMA *** comparative analysis provides a solid foundation for the dev

关键词： Intelligent reflecting surface Non-orthogonal multiple access 6G Beamforming Sum rate Energy efficiency

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A Data-driven Variable Reduction Approach for Transmission-constrained Unit Commitment of Large-scale Systems

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Journal of Modern Power Systems and Clean Energy 2023年第1期11卷 254-266页

作者： Yuzhou Zhou Qiaozhu Zhai Lei Wu Moammad Shahidehpour Systems Engineering Institute MOEKLINNS LabXi’an Jiaotong UniversityXi’an 710049China Electrical and Computer Engineering Department Stevens Institute of TechnologyHobokenU.S. Electrical and Computer Engineering Department Illinois Institute of TechnologyChicagoU.S. IEEE

This paper presents a data-driven variable reduction approach to accelerate the computation of large-scale transmission-constrained unit commitment(TCUC).Lagrangian relaxation(LR)and mixed-integer linear programming(MILP)are popular approaches to solving ***,with many binary unit commitment variables,LR suffers from slow convergence and MILP presents heavy computation *** proposed data-driven variable reduction approach consists of offline and online calculations to accelerate computational performance of the MILP-based large-scale TCUC problems.A database including multiple nodal net load intervals and the corresponding TCUC solutions is first built offline via the data-driven and all-scenario-feasible(ASF)approaches,which is then leveraged to efficiently solve new TCUC instances ***/off statuses of considerable units can be fixed in the online calculation according to the database,which would reduce the computation burden while guaranteeing good solution quality for new TCUC instances.A feasibility proposition is proposed to promptly check the feasibility of the new TCUC instances with fixed binary variables,which can be used to dynamically tune parameters of binary variable fixing strategies and guarantee the existence of feasible UC solutions even when system structure *** tests illustrate the efficiency of the proposed approach.

关键词： Unit commitment accelerated algorithm data driven variable reduction

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Road Surface Analysis through Machine Learning Techniques

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IEIE Transactions on Smart Processing and Computing 2024年第4期13卷 344-353页

作者： Singh, Prabhat Sharma, Shilpi Kamal, Ahmed E. Kumar, Sunil Department of Computer Science and Engineering Amity School of Engineering & Technology Uttar Pradesh Noida India Department of Electrical and Computer Engineering Iowa State University Ames United States Computer Science and Engineering Amity School of Engineering & Technology Uttar Pradesh Noida India

Roads are an important part of transporting goods and products from one place to another. In developing countries, the main challenge is to maintain road conditions regularly. Roads can deteriorate from time to time. Monitoring the conditions of the roads, which may degrade with time, is very difficult, resulting in a delay in transportation and damage to the vehicles moving on the roads. Poor road conditions cause road accidents. A model is being proposed to monitor the conditions of the road surface by smartphone sensors. Accelerometer, gyroscope, and GPS sensors are deployed in the mobile phones, which will help to collect data on the road conditions. After collecting the data about the road conditions, various machine learning approaches, such as supervised, multi-layered, and multiclass, are applied to data filtration. Road conditions are divided into three categories to achieve this methodology: potholes, deep traverse cracks, and smooth roads. This categorization helped in analyzing the road surface condition through smartphone sensors over all three axes instead of taking it over a single axis. Neural networks helped analyze data or road conditions more accurately than Decision Tree and SVM. Copyrights © 2024 The Institute of Electronics and Information Engineers.

关键词： Decision trees

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Theoretical connection from the dielectric constant of films to the capacitance of capacitors under high temperature

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High Voltage 2023年第4期8卷 707-716页

作者： Yongxin Zhang Qikun Feng Shaolong Zhong Jiayao Pei Fangyi Chen Lei Huang Zhe Yang Zhimin Dang Department of Electrical Engineering Tsinghua UniversityBeijingChina Department of Electrical and Computer Engineering Carnegie Mellon UniversityPittsburghPennsylvaniaUSA Department of Energy Technology Aalborg UniversityAalborgDenmark

In the process of coping with energy and environmental protection issues,technologies such as energy materials,energy devices,and energy systems have made great *** excellent performance,film capacitors play an increasingly important role in energyrelated *** the increase of application scenarios and the continuous development of film material technology,it is urgent to establish a better theoretical connection from films to ***,the main components of the capacitor including the film and the positional relationships among them are ***,from the two perspectives of indirect calculation according to the volume and the direct calculation according to the winding process,the equation between the dielectric constant of films and the corresponding capacitance of capacitors is ***,the measurement data and error analysis results of the built test platform prove the accuracy and great potential of the proposed calculation *** addition,error sources,including film thickness uniformity,are ***,the challenges faced by the proposed calculation methods and the paths that can be referenced for future research are summarised and discussed.

关键词： film dielectric capacitance

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Selection of Wind Turbine Systems for the Sultanate of Oman

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computer Systems Science & engineering 2023年第4期45卷 343-359页

作者： M.A.A.Younis Anas Quteishat Department of Electrical and Computer Engineering Sohar UniversitySultanate of Oman Electrical Engineering Department Faculty of Engineering TechnologyAl-Balqa Applied UniversityAl-SaltJordan

The Sultanate of Oman has been dealing with a severe renewable energy issue for the past few decades,and the government has struggled to find a *** addition,Oman’s strategy for converting power generation to sources of renewable energy includes a goal of 60 percent of national energy demands being met by renewables by 2040,including solar and wind ***,the use of small-scale energy from wind devices has been on the rise in recent *** upward trend is attributed to advancements in wind turbine technology,which have lowered the cost of energy from *** calculate the internal and external factors that affect the small-scale energy of wind technologies,the study used a fuzzy analytical hierarchy process technique for order of preference by similarity to an ideal *** a result,in the decision model,four criteria,seventeen sub-criteria,and three resources of renewable energy were calculated as options from the viewpoint of the Sultanate of *** research is based on an examination of statistics on energy produced by wind turbines at various locations in the Sultanate of ***,six distinct miniature wind turbines were investigated for four different *** outcomes of this study indicate that the tiny wind turbine has a lot of potential in the Sultanate of Oman for applications such as homes,schools,college campuses,irrigation,greenhouses,communities,and small *** government should also use renewable energy resources to help with the renewable energy issue and make sure that the country has enough renewable energy for its long-term growth.

关键词： Multi criteria decision making model fuzzy theory fuzzy sets renewable energy wind turbine supplier selection

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Full-model-free Adaptive Graph Deep Deterministic Policy Gradient Model for Multi-terminal Soft Open Point Voltage Control in Distribution Systems

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Journal of Modern Power Systems and Clean Energy 2024年第6期12卷 1893-1904页

作者： Huayi Wu Zhao Xu Minghao Wang Youwei Jia Department of Electrical and Electronic Engineering The Hong Kong Polytechnic UniversityHong KongChina Shenzhen Research Institute The Hong Kong Polytechnic UniversityShenzhenChina Department of Electrical and Computer Engineering University of MacaoMacaoChina Department of Electrical and Electronic Engineering Southern University of Science and TechnologyShenzhenChina

High penetration of renewable energy sources(RESs)induces sharply-fluctuating feeder power,leading to volt-age deviation in active distribution *** prevent voltage violations,multi-terminal soft open points(M-sOPs)have been integrated into the distribution systems to enhance voltage con-trol ***,the M-SOP voltage control recalculated in real time cannot adapt to the rapid fluctuations of photovol-taic(PV)power,fundamentally limiting the voltage controllabili-ty of *** address this issue,a full-model-free adaptive graph deep deterministic policy gradient(FAG-DDPG)model is proposed for M-SOP voltage ***,the attention-based adaptive graph convolutional network(AGCN)is lever-aged to extract the complex correlation features of nodal infor-mation to improve the policy learning ***,the AGCN-based surrogate model is trained to replace the power flow cal-culation to achieve model-free ***,the deep deterministic policy gradient(DDPG)algorithm allows FAG-DDPG model to learn an optimal control strategy of M-SOP by continuous interactions with the AGCN-based surrogate *** tests have been performed on modified IEEE 33-node,123-node,and a real 76-node distribution systems,which demonstrate the effectiveness and generalization ability of the proposed FAG-DDPGmodel.

关键词： Soft open point graph attention graph convolutional network reinforcement learning voltage control distribution system deep deterministic policy gradient

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A Novel Proof-of-Concept AI-Driven Approach for Advanced Electromagnetic Imaging

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Progress in Electromagnetics Research C 2025年 151卷 25-31页

作者： Ghaffarpour, Ali Vasei, Tahereh Ganesh, Mahindra Amineh, Reza K. Ravan, Maryam Department of Electrical and Computer Engineering New York Institute of Technology New YorkNY10023 United States

This paper introduces an artificial intelligence (AI) methodology designed to enhance the output of two-dimensional (2D) electromagnetic imaging systems, specifically tailored for the imaging of conductive objects utilizing inductive sensors. The core of our imaging system comprises a commercial data acquisition board, alongside custom-made multilayer planar coils developed by conventional printed circuit board technology. By leveraging recent advances in AI and machine learning, our approach significantly improves the resolution and clarity of electromagnetic images. The paper uses a multi-layer perceptron (MLP) classifier to process the raw electromagnetic data captured by the imaging system. These algorithms are trained to recognize patterns and anomalies in electromagnetic field data, which are often indicative of conductive objects. The enhanced imaging capability is demonstrated through a series of experiments that compare the AI-enhanced outputs with the ground truth. © 2025, Electromagnetics Academy. All rights reserved.

关键词： Image enhancement

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QoS-Aware Application Assignment and Resource Utilisation Maximization Using AHP in Edge Computing

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IEEE Internet of Things Journal 2025年第11期12卷 17717-17728页

作者： Koganti, Yasasvitha Sridhar, Vidhyuth Yadav, Ram Narayan Pratap, Ajay Iitram Department of Electrical and Computer Science Engineering Ahmedabad380026 India Indiana University Department of Computer Science and Engineering Bloomington United States Banaras Hindu University Indian Institute of Technology Department of Computer Science and Engineering Varanasi India

Edge computing has emerged as a promising technology to satisfy the demand for data computational resources in Internet of Things (IoT) networks. With edge computing, processing of the massive data-intensive tasks can be done in the proximity of IoT users. Thus, the required constraints related to tasks such as latency requirements, and the Quality Of Service (QoS) can be guaranteed. However, the question that how to determine the task offloading strategy under various constraints of resources, distance, and cost is still an open issue. In this paper, we study the task offloading problem from a matching perspective and propose an Edge-User Assignment Algorithm (EUAA) that aims to maximize the resource utilization of edge servers and maximize the number of assigned IoT users. The main concern, in any matching algorithm, is how to generate the preference order of either side. To generate preference orders for edge servers, we use the concept of the Analytical Hierarchy Process (AHP). We have considered the following criteria: distance from users to the server, latency, available resources, and pricing. This generates the priority of the users for matching to edge servers. From IoT users' perspective, we use cost, and QoS parameters to improve their satisfaction. We have used state-of-the-art schemes to compare the performance of the proposed algorithm under different simulation scenarios. We compare the performance using the number of assigned users, servers' profit, number of satisfied users, edge server resources used, and execution time. The simulation results confirm that significant profit and satisfied user can be achieved by the proposed algorithm. © 2014 IEEE.

关键词： Computation offloading

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Securing Next-Generation Quantum IoT Applications using Quantum Key Distribution

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IEEE Internet of Things Magazine 2025年第1期8卷 50-56页

作者： Bera, Basudeb Das, Ashok Kumar Sikdar, Biplab The Department of Electrical and Computer Engineering National University of Singapore Singapore The International Institute of Information Technology Hyderabad India

The next generation of Quantum Internet of Things (QIoT) has the potential to revolutionize various sectors, including smart homes, healthcare, and smart cities, by enabling more sophisticated and interconnected systems. These applications incorporate advanced features, such as autonomous decision-making based on Quantum Artificial Intelligence and Machine Learning (QAI/ML) and context-aware functionality. The security of the data in these applications relies on traditional cryptographic techniques, which, however, face a growing threat due to the significant advancements in quantum computing, especially with the Shor’s algorithm. This algorithm poses a substantial risk of breaking conventional cryptographic methods within a feasible timeframe. To address these emerging security challenges in the quantum realm, we propose a Quantum Key Distribution (QKD) based on the BB84 protocol. This approach aims to provide robust authentication using certificates through a classical channel and secure quantum key exchange via a quantum channel in a public environment. The proposed QKD scheme is implemented using a client-server model in Python and Qiskit, demonstrating its practicality in real-world applications. The obtained results showcase the successful establishment of a secure session key between IoT smart (sensor) devices and gateway nodes, effectively mitigating potential threats such as eavesdropping. © 2018 IEEE.

关键词： Smart homes

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