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Multi-trial Vector-based Whale Optimization Algorithm

Journal of Bionic engineering 2024年第3期21卷 1465-1495页

作者： Mohammad H.Nadimi-Shahraki Hajar Farhanginasab Shokooh Taghian Ali Safaa Sadiq Seyedali Mirjalili Faculty of Computer Engineering Najafabad BranchIslamic Azad UniversityNajafabad***Iran Big Data Research Center Najafabad BranchIslamic Azad UniversityNajafabad***Iran Department of Computer Science Nottingham Trent UniversityNottinghamNG118NSUK Centre for Artificial Intelligence Research and Optimisation Torrens UniversityBrisbane4006Australia

The Whale Optimization Algorithm(WOA)is a swarm intelligence metaheuristic inspired by the bubble-net hunting tactic of humpback *** spite of its popularity due to simplicity,ease of implementation,and a limited number of parameters,WOA’s search strategy can adversely affect the convergence and equilibrium between exploration and exploitation in complex *** address this limitation,we propose a new algorithm called Multi-trial Vector-based Whale Optimization Algorithm(MTV-WOA)that incorporates a Balancing Strategy-based Trial-vector Producer(BS_TVP),a Local Strategy-based Trial-vector Producer(LS_TVP),and a Global Strategy-based Trial-vector Producer(GS_TVP)to address real-world optimization problems of varied degrees of ***-WOA has the potential to enhance exploitation and exploration,reduce the probability of being stranded in local optima,and preserve the equilibrium between exploration and *** the purpose of evaluating the proposed algorithm's performance,it is compared to eight metaheuristic algorithms utilizing CEC 2018 test ***,MTV-WOA is compared with well-stablished,recent,and WOA variant *** experimental results demonstrate that MTV-WOA surpasses comparative algorithms in terms of the accuracy of the solutions and convergence ***,we conducted the Friedman test to assess the gained results statistically and observed that MTV-WOA significantly outperforms comparative ***,we solved five engineering design problems to demonstrate the practicality of *** results indicate that the proposed MTV-WOA can efficiently address the complexities of engineering challenges and provide superior solutions that are superior to those of other algorithms.

关键词： Swarm intelligence algorithms Metaheuristic algorithms Optimization engineering design problems Whale optimization algorithm

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MLPFormer: MLP-integrated transformer for colorectal histopathology whole slide image segmentation

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Neural Computing and Applications 2025年第6期37卷 4651-4661页

作者： Wang, Yuxuan Li, Dan Li, Xuechen Guo, Yan Zuo, Yanfei Shen, Linlin Department of Computer Science Wenzhou-Kean University Wenzhou China School of Electronics and Information Engineering Wuyi University Jiangmen China Department of Histo Pathology Shanghai Histo Pathology Diagnostic Center Shanghai China College of Computer Sicence and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

Colorectal intraepithelial neoplasia is a precancerous lesion of colorectal cancer, which is mainly diagnosed using pathological images. According to the characteristics of lesions, precancerous lesions can be classified into four different grades, i.e., background, normal, low and high level. However, due to the continuity of lesion development, tissue in different stages has high similarity. Recently, visual transformers have achieved impressive results in different visual tasks, due to their capacity of capturing long-range dependencies. However, due to computational cost, transformer cannot well extract detailed feature, which is important for lesion grading. To accurately segment pathological tissues with high similarity, in this work, we embed the multi-head MLP mixer module in the transformer network to extract cell and edge information. Furthermore, we propose a hierarchical MLP decoder to facilitate multi-scale feature fusion. The proposed transformer, namely MLPFormer, achieves remarkable results against competitive baselines on the Histo-CRC Biopsy dataset. The experimental results demonstrate that MLPFormer significantly outperforms the competitive baselines, i.e., a 3% dice improvement is achieved over the SegFormer series. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image segmentation

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CQFFA:A Chaotic Quasi-oppositional Farmland Fertility Algorithm for Solving engineering Optimization Problems

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Journal of Bionic engineering 2023年第1期20卷 158-183页

作者： Farhad Soleimanian Gharehchopogh Mohammad H.Nadimi-Shahraki Saeid Barshandeh Benyamin Abdollahzadeh Hoda Zamani Department of Computer Engineering Urmia BranchIslamic Azad UniversityUrmia969Iran Faculty of Computer Engineering Najafabad BranchIslamic Azad UniversityNajafabad517Iran Big Data Research Center Najafabad BranchIslamic Azad UniversityNajafabad517Iran Afagh Higher Education Institute Urmia969Iran

Farmland Fertility Algorithm(FFA)is a recent nature-inspired metaheuristic algorithm for solving optimization ***,FFA has some drawbacks:slow convergence and imbalance of diversification(exploration)and intensification(exploitation).An adaptive mechanism in every algorithm can achieve a proper balance between exploration and *** literature shows that chaotic maps are incorporated into metaheuristic algorithms to eliminate these ***,in this paper,twelve chaotic maps have been embedded into FFA to find the best numbers of prospectors to increase the exploitation of the best promising ***,the Quasi-Oppositional-Based Learning(QOBL)mechanism enhances the exploration speed and convergence rate;we name a CQFFA *** improvements have been made in line with the weaknesses of the FFA algorithm because the FFA algorithm has fallen into the optimal local trap in solving some complex problems or does not have sufficient ability in the intensification *** results obtained show that the proposed CQFFA model has been significantly *** is applied to twenty-three widely-used test functions and compared with similar state-of-the-art algorithms statistically and ***,the CQFFA algorithm has evaluated six real-world engineering *** experimental results showed that the CQFFA algorithm outperforms other competitor algorithms.

关键词： Nature-inspired algorithm Farmland fertility algorithm Chaotic maps Quasi engineering optimization problems

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CCTG-NET: Contextualized Convolutional Transformer-GRU Network for speech emotion recognition

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International Journal of Speech Technology 2023年第4期26卷 1099-1116页

作者： Tellai, Mohammed Mao, Qirong Department of Computer Science and Communication Engineering Jiangsu University Jiangsu Province Zhenjiang212013 China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications Jiangsu Province Zhenjiang212013 China

Speech is a crucial aspect of human-to-human interactions and plays a fundamental role in the advancement of human–computer interaction (HCI) systems. Developing an accurate speech emotion recognition (SER) system for human conversations poses a critical yet challenging task. Existing state-of-the-art (SOTA) research in SER primarily focuses on modeling vocal information within individual conversational speech utterances, overlooking the significance of incorporating transactional information from the interaction context. In this paper, we present a novel Contextualized Convolutional Transformer-GRU Network (CCTG-Net) for recognizing speech emotions using Mel-spectrogram features, effectively integrating contextual information for emotion recognition. Our experiments are conducted on the widely-used emotional benchmark dataset, IEMOCAP. Compared to SOTA methods in four-class emotion recognition, our proposed model achieves a weighted accuracy of 88.4% and an unweighted accuracy (UA) of 89.1%. This marks a substantial 3.0% enhancement in UA while maintaining an optimal balance between performance and complexity. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Emotion Recognition

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Permutation Invariant Agent-Specific Centralized Critic in Multi-Agent Reinforcement Learning 6

Permutation Invariant Agent-Specific Centralized Critic in M...

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6th International Conference on Information Technology, InCIT 2022

作者： Noppakun, Patsornchai Akkarajitsakul, Khajonpong King Mongkut's University of Technology Thonburi Computer Engineering Department Bangkok Thailand King Mongkut's University of Technology Thonburi Big Data Experience Center Computer Engineering Department Bangkok Thailand

ISBN: (纸本)9781665489126

We proposed a permutation invariant agent-specific centralized critic using graph convolutional networks in multiagent reinforcement learning. We consider an environment with partial observability where a joint observation of homogeneous agents is used as a state information in centralized training. A joint observation of homogeneous agents is permutation invariant, meaning that different permutations must be treated as the same. However, a traditional deep network like multilayer perceptron (MLP) outputs different values to different permutations, despite being the same data. A centralized critic using MLPs to represent joint observation of homogeneous agents suffers from data inefficiency because it only learns a single permutation instead of all permutations. Previous work has addressed this problem using graph convolutional networks (GCN) for 'agent-agnostic'' centralized critics. Our work extends the use of GCNs to an 'agent-specific'' centralized critic such as the critic used in Counterfactual Multi-Agent Policy Gradients (COMA) algorithm. We introduce three GCN variants of agentspecific critic architectures. Our experimental results on the multi-agent particle environment with COMA algorithm show that all GCN critics outperform the MLP baseline critics. Finally, we concluded that as the number of agents increases, the critic that takes advantage of agent homogeneity by separating global and local feature representation is the most scalable in terms of time complexity. © 2022 IEEE.

关键词： Reinforcement learning

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A structural variation genotyping algorithm enhanced by CNV quantitative transfer

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Frontiers of computer Science 2022年第6期16卷 159-165页

作者： Tian ZHENG Xinyang QIAN Jiayin WANG Department of Computer Science and Technology School of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an710049China Institute of Data Science and Information Quality Shaanxi Engineering Research Center of Medical and Health Big DataXi’an Jiaotong UniversityXi’an710049China

Genotyping of structural variations considering copy number variations(CNVs)is an infancy and challenging ***,a prevalent form of critical genetic variations that cause abnormal copy numbers of large genomic regions in cells,often affect transcription and contribute to a variety of *** characteristics of CNVs often lead to the ambiguity and confusion of existing genotyping features and algorithms,which may cause heterozygous variations to be erroneously genotyped as homozygous variations and seriously affect the accuracy of downstream *** the allelic copy number increases,the error rate of genotyping increases *** instances with different copy numbers play an auxiliary role in the genotyping classification problem,but some will seriously interfere with the accuracy of the *** by these,we propose a transfer learning-based method to genotype structural variations accurately considering *** method first divides the instances with different allelic copy numbers and trains the basic machine learning framework with different genotype *** maximizes the weights of the instances that contribute to classification and minimizes the weights of the instances that hinder correct *** adjusting the weights of the instances with different allelic copy numbers,the contribution of all the instances to genotyping can be maximized,and the genotyping errors of heterozygote variations caused by CNVs can be *** applied the proposed method to both the simulated and real datasets,and compared it to some popular algorithms including GATK,Facets and *** experimental results demonstrate that the proposed method outperforms the others in terms of accuracy,stability and *** source codes have been uploaded at github/TrinaZ/CNVtransfer for academic use only.

关键词： genotyping copy number variations transfer earning

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Probabilistic time series forecasting with deep non-linear state space models

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CAAI Transactions on Intelligence Technology 2023年第1期8卷 3-13页

作者： Heming Du Shouguo Du Wen Li School of Computer Science College of Engineering and Computer ScienceAustralian National UniversityCanberraAustralian Capital TerritoryAustralia Shanghai Municipal Big Data Center ShanghaiChina Department of Economic Statistics School of Statistics and InformationShanghai University of International Business and EconomicsShanghaiChina

Probabilistic time series forecasting aims at estimating future probabilistic distributions based on given time series *** is a widespread challenge in various tasks,such as risk management and decision *** investigate temporal patterns in time series data and predict subsequent probabilities,the state space model(SSM)provides a general *** of SSM achieve considerable success in many fields,such as engineering and ***,since underlying processes in real-world scenarios are usually unknown and complicated,actual time series observations are always irregular and ***,it is very difficult to determinate an SSM for classical statistical *** this paper,a general time series forecasting framework,called Deep Nonlinear State Space Model(DNLSSM),is proposed to predict the probabilistic distribution based on estimated underlying unknown processes from historical time series *** fuse deep neural networks and statistical methods to iteratively estimate states and network parameters and thus exploit intricate temporal patterns of time series *** particular,the unscented Kalman filter(UKF)is adopted to calculate marginal likelihoods and update distributions recursively for non-linear *** that,a non-linear Joseph form covariance update is developed to ensure that calculated covariance matrices in UKF updates are symmetric and positive ***,the authors enhance the tolerance of UKF to round-off errors and manage to combine UKF and deep neural *** this manner,the DNLSSM effectively models non-linear correlations between observed time series data and underlying dynamic *** in both synthetic and real-world datasets demonstrate that the DNLSSM consistently improves the accuracy of probability forecasts compared to the baseline methods.

关键词： Artificial Intelligence machine learning time series

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Automated Testing for Service-Oriented Architecture: Leveraging Large Language Models for Enhanced Service Composition

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IEEE Access 2025年 13卷 89627-89640页

作者： Altin, Mahsun Mutlu, Behcet Kilinc, Deniz Cakir, Altan Istanbul Technical University Department of Computer Engineering Istanbul34467 Turkey Wingie Enuygun Group Istanbul34750 Turkey University of Bakirçay Department of Computer Engineering Izmir35665 Turkey Istanbul Technical University Artificial Intelligence Data Science Research and Application Center Istanbul34467 Turkey ITU Ari Teknokent Parton Big Data Analytics and Consulting Istanbul34467 Turkey Adin.Ai WilmingtonDE19806 United States

This article explores the application of Large Language Models (LLMs), including proprietary models such as OpenAI's ChatGPT 4o and ChatGPT 4o-mini, Anthropic's Claude 3.5 Sonnet and Claude 3.7 Sonnet, and Google's Gemini 1.5 Pro, Gemini 2.0 Flash, and Gemini 2.0 Flash-Lite, as well as open-source alternatives including Qwen2.5-14B-Instruct-1M, and commercially accessed models such as DeepSeek R1 and DeepSeek V3, which were tested via APIs despite having open-source variants, to automate validation and verification in Application Programming Interface (API) testing within a Service-Oriented Architecture (SOA). Our system compares internal responses from the Enuygun Web Server against third-party API outputs in both JSON and XML formats, validating critical parameters such as flight prices, baggage allowances, and seat availability. We generated 100 diverse test scenarios across varying complexities (1-4 flight results) by randomly altering request and response parameters. Experimental results show that Google Gemini 2.0 Flash achieved high accuracy (up to 99.98%) with the lowest completion time (85.34 seconds), while Qwen2.5-14B-Instruct-1M exhibited limited capability in processing complex formats. Models such as OpenAI's ChatGPT and Anthropic's Claude Sonnet models also demonstrated strong performance in single-flight validation scenarios, making them suitable for low-latency, high-precision tasks. Our findings indicate that some open-source models can offer promising cost-effective alternatives, though performance significantly varies. This integration of LLMs reduced manual workload, improved test scalability, and enabled real-time validation across large-scale datasets. As LLM technologies mature, we anticipate further advances in automation, accuracy, and efficiency in software validation systems. © 2013 IEEE.

关键词： Service oriented architecture (SOA)

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Earthquake Risk Assessment Approach Using Multiple Spatial Parameters for Shelter Demands

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computers, Materials & Continua 2022年第2期70卷 3763-3780页

作者： Wenquan Jin Naeem Iqbal Hee-Cheal Kang Dohyeun Kim Big Data Research Center Jeju National UniversityJeju63243Korea Department of Computer Engineering Jeju National UniversityJeju63243Korea Infomind Jeju63243Korea

The earthquake is considered one of the most devastating disasters in any area of the world due to its potentially destructive *** on the various earthquake-related parameters,the risk assessment is enabled in advance to prevent future earthquake *** this paper,for providing the shelter space demands to reduce the damage level and prevention costs,an earthquake risk assessment approach is proposed for deriving the risk index based on multiple spatial parameters in the gridded *** proposed assessment approach is comprised of pre-processing,methodologymodel,and data *** risk index model derives the earthquake risk index by multiple spatial parameters including indexes of earthquake,danger,shelter,and building for blocks in the quantitative gridded *** parameters are provided based onmathematicalmodels and combinedwith the risk index that presents the earthquake risk assessment result for each ***,the gridding approach is proposed to provide the elements of the risk assessment area that are used in the spatial *** gridded map is developed for the selected area to visualize risk index parameters associated with each risk *** on the derived result of the proposed earthquake risk indexmodel,emergency shelter requirements are provided according to the risk index for each location,which supports safety measures in advance to prevent future earthquake disasters.

关键词： Risk analysis risk index earthquake shelter demand data analysis

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Resource Optimization for Log Shipper and Preprocessing Pipeline in a Large-Scale Logging System 5

Resource Optimization for Log Shipper and Preprocessing Pipe...

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5th IEEE International Conference on Knowledge Innovation and Invention, ICKII 2022

作者： Lertwuthikarn, Thanarit Barroso, Vasco Chibante Akkarajitsakul, Khajonpong King Mongkut's University of Technology Thonburi Department of Computer Engineering Bangkok Thailand Geneva23 Switzerland King Mongkut's University of Technology Thonburi Big Data Experience Center Department of Computer Engineering Bangkok Thailand

ISBN: (数字)9781665479295

ISBN: (纸本)9781665479295

In resource management, resource optimization is a usual technique to proceed for most professional organizations in order to reduce expenses and to dispose unnecessary resource usages. The European Organization for Nuclear Research (CERN) intends to implement a logging system based on AI for A Large Ion Collider Experiment detector, or ALICE. This system has been being implemented by using the Elasticsearch, Kibana, Beats, and Logstash also called ELK Stack which gives us the capability for the logs aggregation from systems and applications. Log data are collected from involved servers at CERN called First Level Processors (FLPs) nodes by Beats. These nodes run a large number of services when tasks are executed and generate a large volume of log data. Filebeat is used as a log shipper to transfer the data to Logstash, a server-side preprocessing pipeline. When Filebeat and Logstash are working together, there are many configurable factors affecting their efficiency. We then apply a factorial experiment to identify the significant factors and their correlation. These parameters are also optimized to find the best possible values of their configurations. Then, the resource usage can be minimized while a suitable performance of the system is maintained. The results of this study show that we can increase the efficiency of the system thanks to the adjusted values of the parameters. This can be used as a guideline for tuning some configurable parameters to optimize resource usage when there is a large amount of log data to be handled. © 2022 IEEE.

关键词： Pipelines

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