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A Parkinson's Auxiliary Diagnosis algorithm Based on a Hyperparameter Optimization Method of Deep Learning

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024年第4期21卷 912-923页

作者： Wang, Xingbo Li, Shujuan Pun, Chi-Man Guo, Yijing Xu, Feng Gao, Hao Lu, Huimin Nanjing Univ Posts & Commun Coll Automat Nanjing 210049 Peoples R China Nanjing Univ Posts & Commun Coll Artificial Intelligence Nanjing 210049 Peoples R China Hangzhou Zhuoxi Inst Brain & Intelligence Hangzhou 311100 Peoples R China Univ Macau Dept Comp & Informat Sci Macau 999078 Peoples R China Southeast Univ Dept Neurol Zhongda Hosp Nanjing 210009 Peoples R China Tsinghua Univ Sch Software Beijing 100084 Peoples R China Kyushu Inst Technol Dept Mech & Control Engn Kitakyushu 8048550 Japan

Parkinson's disease is a common mental disease in the world, especially in the middle-aged and elderly groups. Today, clinical diagnosis is the main diagnostic method of Parkinson's disease, but the diagnosis results are not ideal, especially in the early stage of the disease. In this paper, a Parkinson's auxiliary diagnosis algorithm based on a hyperparameter optimization method of deep learning is proposed for the Parkinson's diagnosis. The diagnosis system uses ResNet50 to achieve feature extraction and Parkinson's classification, mainly including speech signal processing part, algorithm improvement part based on artificial bee colony algorithm (ABC) and optimizing the hyperparameters of ResNet50 part. The improved algorithm is called Gbest Dimension artificial bee colony algorithm (GDABC), proposing "Range pruning strategy" which aims at narrowing the scope of search and "Dimension adjustment strategy" which is to adjust gbest dimension by dimension. The accuracy of the diagnosis system in the verification set of Mobile Device Voice Recordings at King's College London (MDVR-CKL) dataset can reach more than 96%. Compared with current Parkinson's sound diagnosis methods and other optimization algorithms, our auxiliary diagnosis system shows better classification performance on the dataset within limited time and resources.

关键词： Feature extraction Deep learning Residual neural networks Neural networks Signal processing algorithms Diseases Data models artificial bee colony algorithm deep learning hyperparameter optimization parkinson's auxiliary speech diagnosis

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A swarm intelligence approach to minimum weight independent dominating set problem

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COMPUTERS & ELECTRICAL ENGINEERING 2025年 123卷

作者： Rasheed, Mohd Danish Singh, Alok Mallipeddi, Rammohan Univ Hyderabad Sch Comp & Informat Sci Hyderabad 500 046 Telangana India Kyungpook Natl Univ Dept Artificial Intelligence Daegu 41566 South Korea

The minimum weight independent dominating set (MWIDS) problem is a challenging problem in graph theory with diverse practical applications. In this paper, we propose a novel approach to tackle the MWIDS problem efficiently using the artificial bee colony (ABC) algorithm, a metaheuristic optimization technique inspired by the foraging behavior of honeybees. Our approach integrates six heuristics tailored as per the characteristics of the MWIDS problem within the ABC algorithm to generate high-quality solutions by effectively exploring the solution space. We have conducted extensive experiments on standard benchmark instances to evaluate the effectiveness of our proposed approach. The experimental results demonstrate the competitiveness of our approach in comparison to the state-of-the-art for finding high quality solutions highlighting its potential for practical applications in real-world scenarios.

关键词： Independent dominating set Heuristic artificial bee colony algorithm Swarm intelligence

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A reinforcement learning-based approach for solving multi-agent job shop scheduling problem

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2025年第10期63卷 3512-3537页

作者： Dong, Zhuoran Ren, Tao Qi, Fang Weng, Jiacheng Bai, Danyu Yang, Jie Wu, Chin-Chia Northeastern Univ Software Coll Shenyang 110819 Peoples R China Dalian Maritime Univ Sch Maritime Econ & Management Dalian 116026 Peoples R China Feng Chia Univ Dept Stat Taichung Taiwan

Wafer processing is the most expensive, time-consuming, and complex stage in semiconductor manufacturing. It varies significantly based on orders of customers (agents). Optimising the wafer processing flow in a multi-agent scenario can meet customised requirements, speed up delivery, and reduce costs. This work models wafer processing as a multi-agent job shop scheduling problem (MAJSP) with release dates. The objective is to minimise the total weighted makespan of agents. To address both dynamic and static scheduling scenarios in the MAJSP context, two deep reinforcement learning-based (DRL) methods are proposed. In a dynamic scheduling scenario, the statuses of orders and production resources can change at any moment. A DRL method called Graph Transformer Network (GTN) is proposed to rapidly generate high-quality solutions. In a static scheduling scenario, the production plan can be formulated based on predetermined demand and resource conditions. A novel hybrid method (GTN-DABC) that combines GTN with the discrete artificial bee colony algorithm (DABC) is proposed to provide high-quality production plans for manufacturers within an acceptable computation time. Experimental results demonstrate that the proposed GTN outperforms existing heuristics, and the well-designed GTN-DABC is more competitive than other meta-heuristics.

关键词： Multi-agent job shop scheduling release dates deep reinforcement learning artificial bee colony algorithm transformer

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Forecasting Stock Market Volatility Using Housing Market Indicators: A Reinforcement Learning-Based Feature Selection Approach

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IEEE ACCESS 2025年 13卷 52621-52643页

作者： Zareeihemat, Pourya Mohamadi, Samira Valipour, Jamal Moravvej, Seyed Vahid Islamic Azad Univ Fac Management Dept Business Management Kermanshah Branch Kermanshah Iran Mehr Alborz Univ Fac Management Dept Business Management Tehran Branch Tehran Iran Islamic Azad Univ Dept Management Financial Management Tehran Iran Isfahan Univ Technol Dept Elect & Comp Engn Esfahan Iran

This study tackles the complex challenge of accurately predicting stock market volatility through indicators from the housing market. We propose a sophisticated Early Warning System (EWS) designed to forecast stock market instability by leveraging the predictive power of housing market bubbles. Current EWS methods often face significant hurdles, including model generalization, feature selection, and hyperparameter optimization challenges. To directly address these issues, our innovative approach utilizes a spatial attention-based Transductive Long Short-Term Memory (TLSTM) model combined with a Reinforcement Learning (RL) strategy, which is further enhanced by a novel scope loss function for refined feature selection and an artificial bee colony (ABC) algorithm for hyperparameter optimization. The TLSTM model surpasses traditional LSTM models by effectively capturing subtle temporal shifts and prioritizing data points proximate to the test sample, thereby enhancing model generalization. The RL component actively refines feature selection through continuous data interaction, ensuring the model captures the most significant features and effectively mitigates the risk of overfitting. The introduction of the scope loss function strategically manages the trade-off between exploiting known data and exploring new patterns, thereby maintaining a healthy balance between accuracy and generalizability. Additionally, the customized ABC algorithm specifically optimizes hyperparameters to increase the adaptability and performance of the model under varying market conditions. We validated our EWS using data from the Korean market, achieving an impressive accuracy of 90.427%. This validation demonstrates the robust capability of the system to forecast market dynamics. Our study significantly contributes to financial analytics by providing deeper insights into the interactions between housing and stock markets, particularly during periods of market bubbles. This research not only en

关键词： Real estate industry Feature extraction Stock markets Predictive models Economics Data models artificial bee colony algorithm Accuracy Reinforcement learning Long short term memory Early warning system housing market bubble transductive learning reinforcement learning feature selection artificial bee colony hyperparameter optimization

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Prediction of Angiotensin-Converting-Enzyme (ACE) Inhibitors as Antihypertensive Agents Using artificial Neural Network Optimized by artificial bee colony

Prediction of Angiotensin-Converting-Enzyme (ACE) Inhibitors...

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Computer Sciences, Engineering, and Technology Innovation (ICoCSETI), International Conference on

作者： Atilla Fejril Isman Kurniawan School of Computing Telkom University Bandung Indonesia

ISBN: (数字)9798331508616

ISBN: (纸本)9798331508623

This study uses a dataset from the ChEMBL database to predict the activity of Angiotensin-Converting Enzyme (ACE) inhibitors as antihypertensive medicines by use of the artificial bee colony (ABC) algorithm and artificial Neural Network (ANN). Although they are time-consuming, expensive, and prone to uncertainty, traditional methods include wet-lab testing are often utilized for ACE inhibitor identification. This study intends to increase predictive performance by including systematic optimization through the ABC algorithm with ANN. Architectural and hyperparameters of ANN models were optimized using the ABC algorithm. With an R 2 of 0.683 on the test set, the model with 10 population size showed the greatest performance among the five models evaluated, therefore proving its efficacy in reflecting the general data pattern. These findings show the possibilities of integrating the ABC algorithm with ANN for precise prediction of ACE inhibitor activity, therefore offering a faster and less expensive substitute for conventional approaches. Larger and more varied datasets are required for more thorough investigation to confirm the generalizability of this method for several drug discovery uses.

关键词： Technological innovation Uncertainty Systematics Inhibitors Computational modeling artificial neural networks artificial bee colony algorithm Data models Tuning Testing

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artificial bee colony Optimization Techniques’ Utilization for Intrusion Detection Systems’ Analysis

Artificial Bee Colony Optimization Techniques’ Utilization ...

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AI in Cybersecurity (ICAIC), International Conference on

作者： Emre Tokgoz Computer Security Department State University of New York Farmingdale New York USA

ISBN: (数字)9798331518882

ISBN: (纸本)9798331518899

A variety of nonlinear optimization techniques have been used for measuring effectiveness of Intrusion Detection Systems (IDS). The variability of these optimization techniques resulted in several different approaches for different systems that depended on the IDS selection. There is a tremendous increase in the number of nature-inspired optimization techniques introduced in recent years and the associated applications in IDS appear recently after their introductions. artificial bee colony (ABC) optimization is one of the optimization techniques that attracted many researchers, and a comprehensive review of ABC is needed. While there are few other reviews of ABC algorithms in the literature, the combination of ABC with other optimization techniques and improvement opportunities with these optimization techniques are not covered in these articles. Therefore, in this work, an extensive review of the ABC optimization techniques introduced for IDS analysis as well as the most recent techniques that are applied to IDS in combination with ABC will be covered. New research areas to be explored are recommended as a part of the conclusions and future works. This work demonstrates a clear overview of the IDS and nonlinear optimization applications along with the optimization applications and directions followed by the research community.

关键词： Reviews Intrusion detection Programming artificial bee colony algorithm Computer security artificial intelligence Optimization

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Performance-based optimization of 2D reinforced concrete wall-frames using pushover analysis and ABC optimization algorithm

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EARTHQUAKES AND STRUCTURES 2024年第4期27卷 285-302页

作者： Faghirnejad, Saba Kontoni, Denise- Penelope N. Ghasemi, Mohammad Reza Univ Kansas Dept Civil Environm & Architectural Engn Lawrence KS USA Univ Peloponnese Sch Engn Dept Civil Engn GR-26334 Patras Greece Hellen Open Univ Sch Sci & Technol GR-26335 Patras Greece Univ Sistan & Baluchestan Dept Civil Engn Zahedan Iran

Conducting nonlinear pushover analysis typically demands intricate and resource-intensive computational efforts, involving a highly iterative process necessary for meeting both design-defined and requirements of codes in performance-based design. This study presents a computer-based technique for reinforced concrete (RC) buildings, incorporating optimization numerical approaches, optimality criteria and pushover analysis to automatically enhance seismic design performance. The optimal design of concrete beams, columns and shear walls in concrete frames is presented using the artificial bee colony optimization algorithm. The methodology is applied to three frames: a 4-story, an 8-story and a 12-story. These structures are designed to minimize overall weight while satisfying the levels of performance including Life Safety (LS), Collapse Prevention (CP), and Immediate Occupancy (IO). The process involves three main steps: first, optimization codes are implemented in MATLAB software, and the OpenSees software is used for nonlinear static analysis. By solving the optimization problem, several top designs are obtained for each frame and shear wall. Pushover analysis is conducted considering the constraints on relative displacement and plastic hinge rotation based on the nonlinear provisions of the FEMA356 nonlinear provisions to achieve each level of performance. Subsequently, convergence, pushover, and drift history curves are plotted for each frame, and leading to the selection of the best design. The results demonstrate that the algorithm effectively achieves optimal designs with reduced weight, meeting the desired performance criteria.

关键词： artificial bee colony algorithm nonlinear pushover analysis performance-based optimization reinforced concrete buildings seismic design

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Investigating the Implementation of the artificial bee colony Using the ArcGIS Environment: Dubai Drone Box Platform Case Study

Investigating the Implementation of the Artificial Bee Colon...

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International IEEE Conference on Intelligent Systems, IS

作者： Mariame Elsalhy Manar Abu Talib Sohail Abbas Qassim Nasir College of Computing and Informatics University of Sharjah Sharjah UAE

ISBN: (数字)9798350350982

ISBN: (纸本)9798350350999

Deploying drones in different real-life world applications can enhance user experience and maximize system benefits. The “Drone Box” project launched by Dubai aims to reduce the response time to traffic accidents and crimes; therefore, identifying optimal locations for these boxes is crucial. We propose a hybrid AI-GIS-based system to localize the drone boxes across Dubai strategically. We formulate the problem as a P-center Facility Location Problem (FLP) and construct a suitable objective function accordingly. Since the dataset needed was not provided, we designed a random dataset generator for testing purposes. We selected the Genetic algorithm (GA) for benchmarking and investigated the performance of artificial bee colony (ABC) algorithms due to their relevance to FLP. Geographic Information System (GIS) functions were employed to construct the random dataset generator and implement the ABC algorithm, utilizing the additional functionalities GIS offers for handling geographical data. After testing both algorithms, we deduced that the GA without GIS function integration consistently achieved a higher fitness value in a remarkable time, demonstrating its superiority over the ABC algorithm.

关键词： Software algorithms Urban areas Benchmark testing artificial bee colony algorithm Generators User experience Time factors Drones Geographic information systems Genetic algorithms

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Join query optimization in distributed database based on multi-source mating selection evolutionary algorithm

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第5期28卷 1-23页

作者： Du, Yan Ding, Zhiming Cai, Zhi Chi, Yuanying Beijing Univ Technol Beijing 100124 Peoples R China Chinese Acad Sci Inst Software Beijing 100190 Peoples R China

In a distributed database system, the data is distributed on multiple sites in the cluster. So for join queries involving large amount of data access and complex computation, how to efficiently use each site to complete data reading and computation is one of the key issues in query optimization. With the development of network communication technology, the cost of data transmission in network is no longer the only factor limiting the query efficiency, especially for distributed databases deployed in high-speed local area networks, the cost of CPU computation of local sites and the cost of data I/O also need to be considered. In this regard, a multi-source mating selection based differential evolutionary artificial bee colony algorithm is proposed in this paper to solve the distributed database query optimization problem under high-speed local area network deployment. In this algorithm, the population is first initialized using the good node set method so that the population can be more evenly distributed in the feasible domain, and then the genetic algorithm is combined with the artificial bee colony algorithm to improve the performance of the algorithm. At the same time, spectral clustering is introduced to mine the regular characteristics of the population, and a multi-source mating selection and recombination operator is designed to guide the algorithm search based on the obtained structured information of the population, which can accelerate the convergence of the algorithm by using the recombination of similar individuals while maintaining the diversity of the population by setting multiple sources of mating selection for each individual. Finally, simulation comparison experiments are conducted with other methods under different query sizes, and the results show that the proposed method is able to produce less costly query execution plans. And to a certain extent, it is able to reduce the query response time and improve the query efficiency.

关键词： Distributed database High-speed local area network artificial bee colony algorithm Good node set Spectral clustering Multi-source recombination operator

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artificial bee colony Optimization to Accelerate High-Speed Serial I/O Tx Equalization

Artificial Bee Colony Optimization to Accelerate High-Speed ...

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VLSI Test Symposium

作者： Cesar A. Sanchez-Martinez Paulo Lopez-Meyer Andres Viveros-Wacher I/O Pathfinding Laboratory Intel Corporation Guadalajara Jalisco Mexico Data Center and AI Intel Corporation Guadalajara Jalisco Mexico Client Computing Group Intel Corporation Guadalajara Jalisco Mexico

ISBN: (数字)9798331521448

ISBN: (纸本)9798331521455

A critical activity during electrical testing for High-Speed Serial l/O is the search for optimal Tx equalization settings that yield the cleanest eye diagram possible. Typical practices consist of performing an exhaustive grid search over two variables to define the best solution region, known as the pre-cursor and the post-cursor. This work introduces a methodology to obtain a precise approximation to the optimal equalization values based on the artificial bee colony (ABC) heuristic optimization algorithm. This algorithm explores the solution space with fewer combinations than the typically used procedure. In this work we demonstrate experimentally that the ABC algorithm converges to the solution region of the equalization settings in 36% of the time needed by a grid search. The algorithm was tested on PCIe Gen 5, but lower speed PCIe generations and other interfaces subject to Tx equalization could benefit from these optimization tools, e.g. CXL, UPI, DMI, etc.

关键词： Visualization Technological innovation Heuristic algorithms Very large scale integration artificial bee colony algorithm Approximation algorithms Space exploration Proposals Optimization Testing

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