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Scientific African 2025年

作者： Abdelkrim Hammimou Hassan Ezzahori Abdelghani Boudaoud Mounaim Aqil Engineering and Applied Physics Team Superior School of Technology Sultan Moulay Slimane University Beni Mellal Morocco

Due to their similarity, skin lesions are challenging for early and precise diagnosis, and segmenting them manually is a laborious and time-consuming task. Therefore, several methods, including traditional and artificial intelligence methods, have been proposed for segmenting the skin lesions automatically. The majority of recent reviews, however, have overlooked the traditional methods and instead only briefly discussed their advantages and disadvantages. Consequently, this review aims to provide a thorough overview of this field and offer insights about the role and performance of the traditional methods in the era of artificial intelligence. We collected 128 peer-reviewed and high-quality publications using inclusion and exclusion criteria. These publications have been selected from four well-known databases, IEEE Xplore, ScienceDirect, Springer, and MDPI, and classified systematically into three categories: traditional, artificial intelligence, and hybrid methods. The main contributions of this paper are: (1) Exploring the most used traditional methods and performance enhancement techniques. (2) Providing the State of the art of the convolutional neural network and deep learning methods. (3) Highlighting the role of traditional methods in hybrid frameworks. (4) Providing an extensive performance analysis and discussing the computational cost and real-world applicability challenges associated with segmentation methods. The results highlight the superiority of deep learning methods over traditional methods, while also recognizing the relevance of traditional methods like Active contours and clustering as trusted tools for skin lesion segmentation. These methods benefit from improvements such as ensembling techniques and optimization algorithms. Additionally, 9% of reviewed papers were motivated by the combination of benefits from traditional methods and deep learning methods to develop more robust models. In conclusion, significant challenges remain in developing

关键词： Skin lesion segmentation Automatic Segmentation Traditional methods Convolutional neural network Deep Learning Ensembling methods optimization algorithms Hybrid frameworks ABCDE Asymmetry, Border, Color, Diameter, and Evolution method AC Active contour ACC Accuracy ACO Ant colony optimization algorithm AI Artificial intelligence AK Actinic keratosis BCC Basal cell carcinoma BUZOA Buzzards optimization algorithm CAD Computer aided diagnosis CNN Convolutional neural network COVIDOA Coronavirus optimization algorithm CPU Central Processing Unit CST Color space transformation DC Dice coefficient DF Dermatofibroma FCM Fuzzy c-means FLOP Floating-point operation FN False negative FP False positive FPS Frames per second FRCN Full-resolution convolution network GA Genetic algorithm GAN Generative adversarial networks GPU Graphical processing unit IoU Intersection over union IPO interior-point optimization JI Jaccard index KM K-means MAC Multiply-accumulate operation Mel Melanoma Nev Nevus PM Performance metric PSO Particle swarm optimization ROI Region of interest SCC Squamous cell carcinoma SENS Sensitivity SFS Stochastic fractal search SGO Social group optimization SIFT Scale-invariant feature transform SK Seborrheic keratosis SLC Skin lesion classification SLS Skin lesion segmentation SLIC Simple linear iterative clustering SPEC Specificity SSL Self supervised learning Swin-transformer Shifted window-based vision transformers TN True negative TP True positive WOA whale optimization algorithm

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Short-Term Traffic Flow Prediction Based On IWOA-WNN

Short-Term Traffic Flow Prediction Based On IWOA-WNN

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第33届中国控制与决策会议

作者： Qin Yu Yuepeng Chen Qingyong Zhang Li Li Wenqing Ma Wuhan University of Technology School of Automation

Aiming at the nonlinear and random characteristics of short-term traffic flow,an improved whale optimization algorithm(IWOA) is proposed to replace the gradient descent method to optimize the wavelet neural network(WNN) for short-term traffic flow ***,in view of the slow convergence speed and low convergence accuracy of the traditional whale optimization algorithm(WOA),a nonlinear convergence factor a is introduced to balance the global search and local search ability of the *** the same time,Kent chaotic mapping is used to increase population diversity and enhance the ability of jumping out to fall into local ***,aiming at the problem that the gradient descent method in the wavelet neural network is sensitive to the initial values of the weights and wavelet factors,and is easy to fall into local minimum values,the network weights of the wavelet neural network are optimized by improving the whale optimization ***,the wavelet threshold denoising algorithm is used to process the noise in the raw traffic flow sequence data,and the IWOA-WNN is used to test the short-term traffic flow data set after *** results show that the mean square error(MSE),mean absolute error(MAE) and mean absolute percentage error(MAPE) of the model are 18.03,2.82 and 13.13 %,*** results show that the improved algorithm has higher accuracy than the raw algorithm,and the model can effectively improve the prediction accuracy of short-term traffic flow.

关键词： Short-Term Traffic Flow Prediction whale optimization algorithm Wavelet Neural Network Kent Chaos Mapping Wavelet Threshold Denoising

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Research on Bearing Fault Diagnosis Based on Vibration Signals and Deep Learning Models

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Electronics 2025年第10期14卷 2090-2090页

作者： Bin Yuan Lingkai Lu Suifan Chen College of Mechanical and Energy Engineering Zhejiang University of Science and Technology Hangzhou 310023 China

To overcome the limitations of characteristic parameter identification and inadequate fault recognition rates in bearings, a bearing fault diagnosis method combining the improved whale optimization algorithm (IWOA), variational mode decomposition (VMD), and kernel extreme learning machine (KELM) is proposed. Firstly, to improve the convergence behavior and global search capability of the WOA, we introduced adaptive weight, a variable spiral shape parameter, and a Cauchy neighborhood perturbation strategy to improve the performance of the original algorithm. Secondly, to enhance the effectiveness of feature extraction, the IWOA was used to optimize the number of modal components and penalty coefficients in the VMD algorithm; then, we could obtain the optimal modal components and construct feature vectors based on the optimal modal components. Next, we used the IWOA to optimize the two key parameters, the regularization coefficient C and kernel parameter γ of KELM, and the feature vector was used as the input of KELM to achieve fault diagnosis. Finally, data collected from different experimental platforms were used for experimental analysis. The results indicate that the IWOA-VMD-KELM bearing fault diagnosis model significantly improved its accuracy compared to other models, achieving accuracies of 98.8% and 98.4% on the CWRU dataset and Southeast University dataset, respectively.

关键词： bearing fault diagnosis whale optimization algorithm variational mode decomposition kernel extreme learning machine

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IoT Resource Allocation and optimization Based on Heuristic algorithm

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SENSORS 2020年第2期20卷 539-539页

作者： Sangaiah, Arun Kumar Hosseinabadi, Ali Asghar Rahmani Shareh, Morteza Babazadeh Bozorgi Rad, Seyed Yaser Zolfagharian, Atekeh Chilamkurti, Naveen VIT Sch Comp Sci & Engn Vellore 632014 Tamil Nadu India Islamic Azad Univ Behshahr Branch Dept Comp Engn Behshahr 51148515 Iran Islamic Azad Univ Babol Branch Dept Comp Engn Babol Sar *** Iran La Trobe Univ Dept Comp Sci & Comp Engn Melbourne Vic 3086 Australia

The Internet of Things (IoT) is a distributed system that connects everything via internet. IoT infrastructure contains multiple resources and gateways. In such a system, the problem of optimizing IoT resource allocation and scheduling (IRAS) is vital, because resource allocation (RA) and scheduling deals with the mapping between recourses and gateways and is also responsible for optimally allocating resources to available gateways. In the IoT environment, a gateway may face hundreds of resources to connect. Therefore, manual resource allocation and scheduling is not possible. In this paper, the whale optimization algorithm (WOA) is used to solve the RA problem in IoT with the aim of optimal RA and reducing the total communication cost between resources and gateways. The proposed algorithm has been compared to the other existing algorithms. Results indicate the proper performance of the proposed algorithm. Based on various benchmarks, the proposed method, in terms of "total communication cost", is better than other ones.

关键词： internet of things resource allocation problem whale optimization algorithm communication cost

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Research on Short-Term Electric Load Forecasting Using IWOA CNN-BiLSTM-TPA Model

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印刷与数字媒体技术研究 2025年第1期 179-187页

作者： MEI Tong-da SI Zhan-jun ZHANG Ying-xue College of Artificial Intelligence Tianjin University of Science and TechnologyTianjin 300457China

Load forecasting is of great significance to the development of new power *** the advancement of smart grids,the integration and distribution of distributed renewable energy sources and power electronics devices have made power load data increasingly complex and *** places higher demands on the prediction and analysis of power *** order to improve the prediction accuracy of short-term power load,a CNN-BiLSTMTPA short-term power prediction model based on the Improved whale optimization algorithm(IWOA)with mixed strategies was ***,the model combined the Convolutional Neural Network(CNN)with the Bidirectional Long Short-Term Memory Network(BiLSTM)to fully extract the spatio-temporal characteristics of the load data ***,the Temporal Pattern Attention(TPA)mechanism was introduced into the CNN-BiLSTM model to automatically assign corresponding weights to the hidden states of the *** allowed the model to differentiate the importance of load sequences at different time *** the same time,in order to solve the problem of the difficulties of selecting the parameters of the temporal model,and the poor global search ability of the whale algorithm,which is easy to fall into the local optimization,the whale algorithm(IWOA)was optimized by using the hybrid strategy of Tent chaos mapping and Levy flight strategy,so as to better search the parameters of the *** this experiment,the real load data of a region in Zhejiang was taken as an example to analyze,and the prediction accuracy(R2)of the proposed method reached 98.83%.Compared with the prediction models such as BP,WOA-CNN-BiLSTM,SSA-CNN-BiLSTM,CNN-BiGRU-Attention,etc.,the experimental results showed that the model proposed in this study has a higher prediction accuracy.

关键词： whale optimization algorithm Convolutional Neural Network Long Short-Term Memory Temporal Pattern Attention Power load forecasting

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Exploring DC microgrid: Advanced applications and their control strategies

Energy 360

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Energy 360 2025年 3卷

作者： C. Elwely Salem A. Et-taleby Y. Chaibi N. Ayadi B. Elkari M. Benslimane Z. Chalh Sidi Mohamed Ben Abdellah University Fez Morocco ESME Research Lab Paris France IDEA School Euromed University Fez Morocco

The increasing reliance on microgrids (MG) as a power delivery system underscores the critical importance of advanced control strategies and application-specific solutions. With a focus on their technological advantages, possible uses and control mechanisms, this review evaluates the emerging role of DC microgrids as a viable substitute for conventional AC systems. Examining recent advancements, the paper explores key applications, including electric vehicle (EV) charging, smart buildings, hydrogen production, data centers and residential energy systems, highlighting the versatility of DC microgrids in optimizing renewable energy integration and enhancing energy efficiency. The review further categorizes and analyzes different control methods, including centralized, decentralized and distributed control, detailing their key features, limitations and mathematical representations. The findings emphasize that DC microgrids offer improved energy efficiency, reduced conversion losses and enhanced power reliability. Additionally, advanced control strategies play a crucial role in ensuring optimal energy management, system stability and operational resilience. By providing a critical analysis of these aspects, this review serves as a guide for future research and innovation in DC microgrid control and application optimization, contributing to the development of more sustainable and efficient energy systems.

关键词： AC, Alternative current AEMS, Automated energy management system BEMSs, Building energy management systems BESS, Battery energy storage system CAC, Centralized automated control CEMPC, Centralized economic model predictive control COS, Cooperative Online Scheduling CRAC, Computer room air conditioning CSWG, Climate Sustainability Work Group CVC, Clusters of vehicle control DC, Direct current DEMPC, Distributed economic model predictive control DERs, Distributed energy resources DGs, Distributed generators DMM, Direction method of multipliers DMS, Distributed management system DRC, Demand response control DTLR, Dynamic thermal line rating EMS, Energy management system ESS, Energy storage system EVCSs, Electric Vehicle Charging Stations EVs, Electric Vehicles FLC, Fuzzy logic control GA, Genetic algorithm GTBA, Game-theory based approach HDR, Holistic Demand Response HEMS, Hybrid energy management system HPWOA, Hybrid particle whale optimization algorithm HRSO, Hybrid Robust-Stochastic optimization HV, High voltage HVAC, Heat, ventilation and air conditioning IRENA, International Renewable Energy Agency IT, Information technology LC, Local control LCOE, Levelized Cost of Energy LV, Low voltage LVAC, Low voltage alternative current LVDC, Low voltage direct current MCO, Multiple criteria optimization MG, Microgrid MINLP, Mixed-integer non-linear programming MPPT, Maximum power point tracking MREAI, Microgrid renewable energy availability index MREEI, Microgrid renewable energy energy index MRI, Microgrid resiliency index MV, Medium voltage NPC, Net present cost NZB, Net zero building NZEB, Net-zero energy buildings OCO, Online Convex optimization P2P, Peer to peer PED, Peak energy demands PEVs, Plug-in electric vehicles PFF, Power fluctuation factor PHEVs, Plug-in hybrid electric vehicles PMS, Power management system PSO, Particle Swarm optimization PV, Photovoltaic RESs, Renewable energy sources RMOO, Robust Multi-Objective optimization SIU, Smart interlinking unit SLA, Service legal agreement SMC, Sliding mode cont

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A multi-compartment capacitated arc routing problem with intermediate facilities for solid waste collection using hybrid adaptive large neighborhood search and whale algorithm

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WASTE MANAGEMENT & RESEARCH 2019年第1期37卷 38-47页

作者： Mofid-Nakhaee, Elham Barzinpour, Farnaz Iran Univ Sci & Technol Sch Ind Engn Tehran 16844 Iran

Municipal solid waste collection is an increasingly difficult task and has the highest operation cost in the solid waste management process;thus, finding the optimal routes for the waste collection is a most tactically significant decision that should be focused on due to the population growth. This research investigated the multi-compartment capacitated arc routing problem with intermediate facilities (MCCARPIF) in the context of solid waste collection. This problem has been researched rarely in the past in the real world. The MCCARPIF develops the capacitated arc routing problem (CARP) by considering both the multi-compartment vehicles and intermediate facilities together. In case of waste separation, the fleet of vehicles should have multiple parts to avoid mixing waste together. In developing countries, the process of separating the wastes is not carried out comprehensively, so this subject is almost new and research about it can improve their waste collection process. Due to the complexity of this model, two algorithms are developed to solve it: an adaptive large neighborhood search algorithm (ALNS) and the hybrid ALNS with whale optimization algorithm. Results showed that hybrid ALNS with whale optimization algorithm got higher quality solutions in comparison to ALNS. A real case study in one of the districts of Tehran municipality has been considered and the results obtained show that the use of multi-compartment vehicles is more cost-effective than the use of single-compartment vehicles, reducing the total distance traveled.

关键词： Arc routing problem multi-compartment vehicle adaptive large neighborhood search whale optimization algorithm separation of waste municipal solid waste

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UNIQUE DRAGONFLY optimization algorithm FOR HARVESTING AND CLUSTERING THE KEY FEATURES 18

UNIQUE DRAGONFLY OPTIMIZATION ALGORITHM FOR HARVESTING AND C...

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18th IEEE International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC)

作者： Devarakonda, Nagaraju Anandarao, Sarvani Kamarajugadda, Raviteja Wang, Yingxu LBR Coll Engn Mylavaram Andhra Pradesh India Univ Calgary Calgary AB Canada

ISBN: (纸本)9781728114194

In many applications, the feature selection plays an important role, as best feature can bring out the accurate results. The features selected must represent the entire dataset. Here we have considered "Sequential Forward Selection" for feature extraction and used refined dragonfly algorithm to approach and to migrate from the best and worst features respectively. We improvised the conventional dragonfly algorithm by adding the convergence and fitness functions. To access the accuracy of the algorithm we introduced the fitness function. This paper has discussed about the general hunting behaviour of the dragonfly and dragonfly algorithm (DA) with convergence and fitness functions. A comparative study was shown for the best search agent position between modified DA and traditional DA, at the same time test function values of refined dragonfly algorithm (RDA) is compared with whale optimization algorithm (WOA) and Tornadogenesis optimization algorithm (TOA).We have evaluated refined DA on the 23 benchmark function corresponding values are shown in experiment.

关键词： Dragonfly algorithm sequential forward selection convergence Ilinction benchmark functions Tornadogenesis optimization algorithm whale optimization algorithm

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A WOA-SVDD based Axial Compressor Instability Detection

A WOA-SVDD based Axial Compressor Instability Detection

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第32届中国过程控制会议(CPCC2021)

作者： Xinyuan Dong Peng Lin Machine Learning and I-health International Cooperation Base of Zhejiang Province and with the Artificial Intelligence InstituteHangzhou Dianzi University

As one of the most critical module in aircraft engine,axial compressor instability limits the whole performance mostly,so the detection and control of it is particularly *** that the compressor instability is abnormal,an established one-class classifier called support vector data description(SVDD) is chosen for anomaly detection due to few observation data of compressor *** whale optimization(WOA) algorithm is taken to optimize the parameters of SVDD,and a WOASVDD model is proposed for the detection of axial compressor *** validity of the proposed WOA-SVDD model is proved by simulation on the data from a low speed axial compressor.

关键词： anomaly detection Support vector data description whale optimization algorithm Compressor instability

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Classifying Heart Disease in Medical Data Using Deep Learning Methods

Classifying Heart Disease in Medical Data Using Deep Learnin...

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作者： T.Velmurugan U.Latha PG and Research Department of Computer Science D.G.Vaishnav College

Recent days, heart ailments assume a fundamental role in the world. The physician gives different name for heart disease, for example, cardiovascular failure, heart failure and so on. Among the automated techniques to discover the coronary illness, this research work uses Named Entity Recognition(NER) algorithm to discover the equivalent words for the coronary illness content to mine the significance in clinical reports and different applications. The Heart sickness text information given by the physician is taken for the preprocessing and changes the text information to the ideal meaning, at that point the resultant text data taken as input for the prediction of heart disease. This experimental work utilizes the NER to discover the equivalent words of the coronary illness text data and currently uses the two strategies namely Optimal Deep Learning and whale optimization which are consolidated and proposed another strategy Optimal Deep Neural Network(ODNN) for predicting the illness. For the prediction, weights and ranges of the patient affected information by means of chosen attributes are picked for the experiment. The outcome is then characterized with the Deep Neural Network and Artificial Neural Network to discover the accuracy of the algorithms. The performance of the ODNN is assessed by means for classification methods, for example, precision, recall and f-measure values.

关键词： Named Entity Recognition algorithm Neural Network Methods whale optimization algorithm F-Measure Recall Precision

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