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11th International Conference on Soft Computing and Machine intelligence, ISCMI 2024

作者： Mulerikkal, Jaison Paul Janani, K. Sreelakshmi, P.S. James, Divya Lakshmi, K.S. Sajanraj, T.D. Binu, A. Department of Information Technology India Rajagiri School of Engineering & Technology Kochi India Department of Computer Science and Engineering India Department of Computer Science and Business Systems India Department of Artificial Intelligence and Data Science India Department of Computer Science Loughborough University Leicester United Kingdom

ISBN: (纸本)9798331518127

The goal of this research is to integrate an artificial intelligence framework for predicting Kathakali mudras, a crucial component of the traditional Indian dance style that is renowned for its complex hand and facial motions. Our goal is to protect and improve the accessibility of this traditional art form by utilising deep learning technology. To be more precise, we used the object identification model YOLOv8 together with convolutional neural networks (CNNs) like VGG 19, Inception V3, and ResNet 152 to predict and analyse Kathakali mudras. Furthermore, Few-shot learning (FSL) and support vector machines (SVMs) were used to offer a comparative viewpoint on the efficacy of machine learning method in this situation. The study entailed a thorough analysis of how well-performing different deep learning techniques performed. The predictive accuracy of VGG 19, Inception V3, and ResNet 152 for intricate mudras of Kathakali was subjected to a thorough evaluation process. In addition, an evaluation of YOLOv8's real-time object identification capabilities was conducted in order to investigate its potential in live performance circumstances. The results of the experiment showed that VGG 19 performed better than the other models and had the highest accuracy in mudra prediction. As a result, VGG 19 was chosen as the best algorithm for this particular application. The results of this study demonstrate how artificial intelligence (AI) technologies may support and preserve traditional artistic forms. This AI-integrated framework is a useful tool for scholars, educators, and dancers, promoting a deeper knowledge and enjoyment of this cultural legacy through its accurate analysis and prediction of Kathakali mudras. © 2024 IEEE.

关键词： Convolutional neural networks

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Combination of Local Outlier Factor and Winsorization for Clustering Outlier in Medical Records 11

Combination of Local Outlier Factor and Winsorization for Cl...

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11th International Conference on Information and Communication technology, ICoICT 2023

作者： Pinio Sinaga, Gohan Bonar Budhiarti Nababan, Erna Mawengkang, Herman Universitas Sumatera Utara Faculty of Computer Science and Information Technology Medan Indonesia Universitas Sumatera Utara Department of Data Science and Artificial Intelligence Medan Indonesia Medan Indonesia

ISBN: (纸本)9798350321982

In medical records, the doctors need to stay updated about the condition of their patients, but it would make a problem for the medical records. The medical records would be inconsistent due to changes in information regarding the result of examinations and leads to data that contains noises and outliers. But in medical records, the data sometimes can be mistaken as noise and it cannot be deleted as it is, because the medical records might contain one or more important information. In this research, the medical records are taken from one of the Regional Public Hospital in North Sumatera with limited work of ethics, and efforts will be made to solve the outlier problem in medical records by clustering the outliers into normal cluster by combining the Local Outlier Factor and Winsorization technique. The dataset consists 2 medical record data which contains data on outpatients and inpatients with various types of diagnoses in the last 3 years, and the variable that will be used are Gender, Date of Birth, and the Diagnosis, but it will be reduced into 2 variables, which is Diagnosis and Age (Years Old). In the end, the results are evaluated using a Confusion Matrix to see the percentage of performance for the proposed combination between the Local Outlier Factor method and the Winsorization technique, and the results are: Precision 96.8%, Recall 99.8%, Accuracy 96.9%, F1 Score 98.3%. The results have achieved the best percentage between the K-values. © 2023 IEEE.

关键词： Diagnosis

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A genetic engineering algorithm for the generalized quadratic assignment problem

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Neural Computing and Applications 2025年 1-27页

作者： Sohrabi, Majid Fathollahi-Fard, Amir M. Gromov, Vasilii A. Dulebenets, Maxim A. School of Data Analysis and Artificial Intelligence National Research University Higher School of Economics 11 Pokrovsky Boulevard Moscow109028 Russia Laboratory for Models and Methods of Computational Pragmatics National Research University Higher School of Economics Moscow Russia Department of Engineering Science Faculty of Innovation Engineering Macau University of Science and Technology Taipa China College of Engineering 2035 E Paul Dirac Dr. Sliger Building Suite 275 TallahasseeFL32310 United States

The generalized quadratic assignment problem (GQAP) poses a significant challenge in optimization, known for its NP-hard complexity and wide-ranging applications in supply chain and manufacturing contexts. While various metaheuristic algorithms have addressed the GQAP, this paper introduces a novel modification of the genetic algorithm (GA), namely the genetic engineering algorithm (GEA). A traditional GA initializes the population of solutions randomly and refines these solutions iteratively through crossover and mutation operators. However, its reliance on randomness often leads to premature convergence, susceptibility to local optima, and suboptimal solutions at termination of the algorithmic run. To address these drawbacks, our research presents a modified GA framework inspired by genetic engineering principles. This framework introduces novel search mechanisms mimicking gene mining processes, targeting population enhancement through insertion, purification, and genetic material exchange from elite solutions. The GEA adds three search scenarios to the traditional GA framework to exhibit desired characteristics from chromosomes, enhancing solution quality and convergence speed. This GEA demonstrates a superior performance across various search scenarios against state-of-the-art algorithms and exact solutions for the benchmark instances of the GQAP. Additionally, this study conducts comprehensive sensitivity analyses on the search scenarios of the GEA. By employing gene mining strategies, the proposed GEA offers promising solutions for effectively addressing the NP-hard optimization challenges of the GQAP on large-scale datasets. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Sensitivity analysis

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An Efficient Algorithm for Software Reliability Prediction via Harris Hawks Optimization 2nd

An Efficient Algorithm for Software Reliability Prediction v...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Kong, Li Sheng Jasser, Muhammed Basheer Issa, Bayan Ajibade, Samuel-Soma M. Majeed, Anwar P. P. Abdul Luo, Yang Department of Smart Computing and Cyber Resilience School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Department of Data Science and Artificial Intelligence School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Faculty of Informatics Engineering University of Aleppo Aleppo Syria School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

Software reliability has become an increasingly important characteristic as software systems continue to be integrated into all parts of our lives. While the occurrence of failure after a period of time is within the nature of all software systems, the ability to estimate and predict failure is greatly beneficial to software development and maintenance. Hence, Software Reliability Growth Models (SRGMs) were developed to fulfill this requirement. Conventional parameter estimation methods of SRGM functions were complex and left more to be desired. Consequently, metaheuristics such as Swarm intelligence algorithms were introduced to estimate and optimize the parameters of SRGM functions. Likewise, Swarm intelligence algorithms were found to be exceptionally effective in estimating and optimizing the parameters of SRGM functions. This paper presents a novel approach to parameter estimation on three different SRGM functions through Harris’ Hawks Optimization (HHO). The proposed HHO design is compared against numerous different variants of Swarm intelligence algorithms and is shown to be significantly more efficient than the majority of the compared designs. Moreover, as the proposed design is a base variant of Swarm intelligence algorithms, various avenues of future improvements have also been presented to improve its overall prediction accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Harris’ Hawks Optimization Parameter Estimation Software Reliability Growth Models Software Reliability Prediction Swarm intelligence

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LQTTrack:Multi-Object Tracking by Focusing on Low-Quality Targets Association

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Computers, Materials & Continua 2024年第10期81卷 1449-1470页

作者： Suya Li Ying Cao Hengyi Ren Dongsheng Zhu Xin Xie Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information EngineeringHenan UniversityKaifeng475001China College of Information Science and Technology&Artificial Intelligence Nanjing Forestry UniversityNanjing210037China School of Computer Nanjing University of Posts and TelecommunicationsNanjing210023China

Multi-object tracking(MOT)has seen rapid improvements in recent ***,frequent occlusion remains a significant challenge in MOT,as it can cause targets to become smaller or disappear entirely,resulting in lowquality targets,leading to trajectory interruptions and reduced tracking *** from some existing methods,which discarded the low-quality targets or ignored low-quality target ***,with a lowquality association strategy(LQA),is proposed to pay more attention to low-quality *** the association scheme of LQTTrack,firstly,multi-scale feature fusion of FPN(MSFF-FPN)is utilized to enrich the feature information and assist in subsequent data ***,the normalized Wasserstein distance(NWD)is integrated to replace the original Inter over Union(IoU),thus overcoming the limitations of the traditional IoUbased methods that are sensitive to low-quality targets with small sizes and enhancing the robustness of low-quality target ***,the third association stage is proposed to improve the matching between the current frame’s low-quality targets and previously interrupted trajectories from earlier frames to reduce the problem of track fragmentation or error tracking,thereby increasing the association success rate and improving overall multi-object tracking *** experimental results demonstrate the competitive performance of LQTTrack on benchmark datasets(MOT17,MOT20,and DanceTrack).

关键词： Low-quality targets association strategy feature fusion multi-object tracking tracking-by-detection

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Centroid-based clustering validity: method and application to quantification of optimal cluster-data space

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Soft Computing 2024年第19期28卷 10853-10872页

作者： Nguyen, Sy Dzung Laboratory for Computational Mechatronics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh Viet Nam Faculty of Mechanical - Electrical and Computer Engineering School of Technology Van Lang University Ho Chi Minh Viet Nam

Evaluation of clustering validity to set up an optimal cluster-data space (CDS) is a vital task in many fields related to data mining. Almost existing clustering validity indexes (CVIs) lack stability due to being too sensitive to noise, especially impulse noise. Here, we (1) propose a new CVI named DzI (Dzung Index) or fRisk2 using analysis of fuzzy-set-based accumulated risk degree (FARD), and (2) present a new algorithm named fRisk2-bA for determining the optimal number of data clusters. It is a method of evaluation of the centroid-based fuzzy clustering validity. In essence, the fRisk2 still focuses on enhancing the data compression in each cluster and expanding the separation between cluster centroids. However, these features are exploited indirectly through FARD. As a result, the proposed method not only can avoid the difficulties of the traditional ones relying on the compression and separation properties directly but also can distill better local and global attributes in the data distribution to estimate the CDS more fully. Along with the proved theory basis, surveys, including the ones based on noisy datasets from measurements, showed the compared advantages of fRisk2 as follows. (1) The accuracy, stability, and convergence of the fRisk2 are outstanding. (2) Its total calculating cost is lower than the other surveyed CVIs. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Fuzzy clustering

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Psychological Counseling with Integration of Knowledge Graph and Multi-Agent Collaboration

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Journal of Shanghai Jiaotong University (science) 2025年 1-13页

作者： Liu, Xi Zheng, Xiaohu Cui, Weiwei College of Information Science and Technology Donghua University Shanghai201620 China Institute of artificial Intelligence Donghua University Shanghai201620 China Shanghai Engineering Center of Industrial Big Data and Intelligent System Shanghai201620 China Management College Wuzhou University Guangxi Wuzhou543003 China

Applying large language models (LLMs) to psychological counseling can reduce costs and lower barriers to access. However, issues inherent to LLMs, such as output hallucinations, knowledge lags, and response biases, limit the quality of counseling services. To address these challenges, we propose a psychological counseling method that integrates a knowledge graph (KG) and multi-agent collaboration (MAC). First, we design an ontology model and use an improved convolution residual network (CRN) to extract relational triples for constructing a psychological medicine KG. Second, we perform low-rank adaptation (LoRA) on Qwen1.5-7B-chat using the Text2Cypher training set. Finally, we design MAC prompts based on the characteristics of the retrieval-based question-answering (QA) process to facilitate the collaborative implementation of psychological medicine knowledge QA and updates. The experiments showed that the improved CRN achieved an extraction accuracy of 80%. The mean squared error of the general-domain capability of the LLM before and after LoRA was only 0.738, with a Text2Cypher score of 99.8 after LoRA. The results generated after prompting received a 70.44% approval rate compared to other methods. Thus, the proposed method can effectively enhance the intelligence, accuracy, and controllability of psychological counseling. © Shanghai Jiao Tong University 2024.

关键词： Knowledge graph

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Synthetic Sentiment Cue Enhanced Graph Relation-Attention Network for Aspect-Level Sentiment Analysis

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IEEE Access 2025年 13卷 88121-88133页

作者： Tang, Hongwei Yan, Haining Song, Ran Northwest Institute for Non-Ferrous Metal Research Weiyang Xi'an710016 China Yunnan Key Laboratory of Artificial Intelligence Kunming650500 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650032 China

Aspect-level Sentiment Analysis (ASA) constitutes a critical task in natural language processing, aimed at detecting and classifying sentiment polarities associated with specific aspects in textual data. While conventional approaches often fall short in capturing fine-grained sentiment distinctions at the aspect level, neural network-based models, despite their advancements, frequently encounter challenges stemming from insufficient explicit emotional indicators such as aspect-sentiment correlations, contextual dependencies, and salient opinion expressions. To address these limitations, this paper presents a novel Synthetic Sentiment Cue Enhanced Graph Relation-Attention Network (SSC-GRAN), a hybrid framework that synergistically integrates large language models (LLMs) with graph neural networks (GNNs). Our method introduces three key innovations: 1) a synthetic data augmentation paradigm leveraging LLMs to generate semantically coherent sentiment cues, thereby enriching aspect-opinion interactions;2) a hierarchical graph architecture that models syntactic dependency structures and aspect-context relationships through relation-aware attention mechanisms;and 3) a contrastive learning objective that aligns representations from both authentic and synthetic data to enhance model robustness. Comprehensive experiments across benchmark datasets demonstrate statistically significant improvements in performance, with SSC-GRAN achieving state-of-the-art results. Ablation studies further validate the contributions of each component, while qualitative analyses reveal enhanced interpretability in aspect-sentiment reasoning. This work advances ASA research by bridging the gap between data scarcity and structural modeling, offering a robust framework for fine-grained sentiment understanding. © 2025 IEEE.

关键词： Emotion Recognition

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CrackLens: Automated Sidewalk Crack Detection and Segmentation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on artificial intelligence 2024年第11期5卷 5418-5430页

作者： Koh, Chan Young Ali, Mohamed Hendawi, Abdeltawab University of Rhode Island Department of Computer Science and Statistics KingstonRI02881 United States School of Engineering and Technology University of Washington TacomaWA98105 United States Cairo University Faculty of Computers and Artificial Intelligence Giza12613 Egypt

Automatic sidewalk crack detection is necessary for urban infrastructure maintenance to ensure pedestrian safety. Such a task becomes complex on overgrown sidewalks, where crack detection usually misjudges vegetation as cracks. A lack of automated crack detection targets overgrown sidewalk problems;most crack detection focuses on vehicular roadway cracks that are recognizable even at the aerial photography level. Hence, this article introduces CrackLens, an automated sidewalk crack detection framework capable of detecting cracks even on overgrown sidewalks. We include several contributions as follows. First, we designed an automatic data parser using a red, green, and blue (RGB)-depth fusion sidewalk dataset we collected. The RGB and depth information are combined to create depth-embedded matrices, which are used to prelabel and separate the collected dataset into two categories (with and without crack). Second, we created an automatic annotation process using image processing methods and tailored the tool only to annotate cracks on overgrown sidewalks. This process is followed by a binary classification for verification, allowing the tool to target overgrown problems on sidewalks. Lastly, we explored the robustness of our framework by experimenting with it using 8,000 real sidewalk images with some overgrown problems. The evaluation leveraged several transformer-based neural network models. Our framework achieves substantial crack detection and segmentation in overgrown sidewalks by addressing the challenges of limited data and subjective manual annotations. © 2024 IEEE.

关键词： Image segmentation

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Securing the Future after Pager Bombs:Lifecycle Protection of Smart Devices via Blockchain intelligence

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IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2355-2358页

作者： Fei Lin Qinghua Ni Jing Yang Juanjuan Li Nan Zheng Levente Kovács Radu Prodan Mariagrazia Dotoli Qing-Long Han Fei-Yue Wang the Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the School of Artificial Intelligence University of Chinese Academy of SciencesBei-jing 100049China IEEE Obuda University John von Neumann Faculty of InformaticsBecsi Str.96/bH-1034 BudapestHungary the Institute of Information Technology University of Klagenfurt9020 Klagenfurt am WortherseeAustria the Department of Electrical and Information Engineering Polytechnic of Bari70126 BariItaly the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190

THE Lebanese wireless device explosion incident has drawn widespread attention,involving devices such as pagers,walkie-talkies,and other common devices[1].This event has revealed and highlighted the security vulnerabilities in global supply chains from raw material manufacturing and distribution to the usage of devices and equipment,signaling the onset of a new wave of"supply chain warfare"[2].

关键词： Smart cycle chains

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