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Unveiling Effective Heuristic Strategies: A Review of Cross-Domain Heuristic Search Challenge Algorithms

computer Modeling in engineering & Sciences 2025年第2期142卷 1233-1288页

作者： Mohamad Khairulamirin Md Razali MasriAyob Abdul Hadi Abd Rahman Razman Jarmin Chian Yong Liu Muhammad Maaya Azarinah Izaham Graham Kendall Faculty of Information Science and Technology Universiti Kebangsaan MalaysiaBangi43600SelangorMalaysia Center for Artificial Intelligence Technology Universiti Kebangsaan MalaysiaBangi43600SelangorMalaysia Faculty of Medicine Universiti Kebangsaan Malaysia Medical CentreCheras56000Kuala LumpurMalaysia School of Engineering and Computing MILA UniversityNilai71800Negeri SembilanMalaysia School of Computer Science University of Nottingham-Malaysia CampusSemenyih43500SelangorMalaysia

The Cross-domain Heuristic Search Challenge(CHeSC)is a competition focused on creating efficient search algorithms adaptable to diverse problem *** hyper-heuristics are a class of algorithms that dynamically choose heuristics during the search *** selection hyper-heuristics have different imple-mentation ***,comparisons between them are lacking in the literature,and previous works have not highlighted the beneficial and detrimental implementation methods of different *** question is how to effectively employ them to produce an efficient search ***,the algorithms that competed in the inaugural CHeSC have not been collectively *** work conducts a review analysis of the top twenty competitors from this competition to identify effective and ineffective strategies influencing algorithmic performance.A summary of the main characteristics and classification of the algorithms is *** analysis underlines efficient and inefficient methods in eight key components,including search points,search phases,heuristic selection,move acceptance,feedback,Tabu mechanism,restart mechanism,and low-level heuristic parameter *** review analyzes the components referencing the competition’s final leaderboard and discusses future research directions for these *** effective approaches,identified as having the highest quality index,are mixed search point,iterated search phases,relay hybridization selection,threshold acceptance,mixed learning,Tabu heuristics,stochastic restart,and dynamic *** are also compared with recent trends in *** work enhances the understanding of selection hyper-heuristics,offering valuable insights for researchers and practitioners aiming to develop effective search algorithms for diverse problem domains.

关键词： Hyper-heuristics search algorithms optimization heuristic selection move acceptance learning diversification parameter control

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VisTA: vision transformer-attention enhanced CNN ensemble for optimized classification of acute lymphoblastic leukemia benign and progressive malignant stages

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International Journal of information technology (Singapore) 2024年 1-18页

作者： Nunna, Hasmitha Krishna Altable, Ali Gundala, Pallavi Rangarajan, Prasanna Kumar Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Computer Engineering Prince Sattam bin Abdulaziz University Al-Kharj 11942 Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21589 Saudi Arabia

Acute lymphoblastic leukemia (ALL), a rare blood disorder marked by excessive immature lymphocyte proliferation in the bone marrow, primarily affects children but poses a more challenging prognosis in adults due to late detection. Pathologists employ bone marrow morphology and immunophenotyping to establish cell lineage based on antigens to diagnose acute lymphoblastic leukemia. These manual approaches are time-consuming and need specialized expertise. It is critical for the decision support system to train an accurate classification model to discriminate between mature and immature white blood cells along with the stratification of its phases which is a crucial component in diagnostic accuracy. Another major challenge in ALL classification arises from the morphological resemblance between leukemic B-lymphoblast cells and normal B-lymphoid progenitors. To improve the detection of leukemia, this work integrates an ensemble model of Vision transformer with attention-based pre-trained architectures that use high-resolution blood smear images, to accelerate the prompt detection of this insidious illness and its stages. The ensemble model, which combines ViT with attention-based CNNs, uses dual feature extraction methods from cell images to improve classification results. Our suggested model demonstrates promising performance with an accuracy of 99.96% on a test dataset with 1600 images. The suggested model provides a promising methodology that could be extremely useful in medical settings, allowing for the accurate classification and diagnosis of acute lymphoblastic leukemia. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Acute lymphoblastic leukemia classification Attention mechanism Benign Blood smears CLAHE Ensemble model Leukemia detection Malignant Vision transformers

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Enhancing single-lead electrocardiogram arrhythmia detection with empirical mode decomposition

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Neural Computing and Applications 2025年第17期37卷 11583-11605页

作者： Issa, Mohamed F. Yousry, Ahmed Tuboly, Gergely Wang, Zeyu Zoltan, Juhasz Selim, Mazen M. AbuEl-Atta, Ahmed H. Department of Scientific Computing Faculty of Computers and Artificial Intelligence Benha University Benha13511 Egypt Department of Computer Science Faculty of Computers and Artificial Intelligence Benha University Benha13511 Egypt Department of Electrical Engineering and Information Systems University of Pannonia Veszprem8200 Hungary Department of Mechatronics Delta University for Science and Technology Gamasa35712 Egypt

Cardiac arrhythmias pose a significant challenge to health care, requiring accurate and reliable detection methods to enable early diagnosis and treatment. However, traditional ECG beat classification methods often lack robustness and fail to generalize effectively to diverse clinical scenarios, particularly when using single-lead-I recordings, which are less studied compared to lead II. To address this gap, this paper proposes a novel method that combines deep learning and empirical mode decomposition (EMD) for arrhythmia detection using single-lead-I ECG recordings obtained with electrodes on each hand. The hybrid model comprises 149 layers of one-dimensional convolution, max pooling, and residual blocks. By leveraging EMD, it classifies ECG beats into normal beats, premature ventricular contractions, and atrial premature beats with high sensitivity (99.86%, 99.72%, and 99.84%, respectively) using the first four intrinsic mode functions (IMFs). The proposed method is applicable in clinical and out-of-hospital monitoring scenarios, and an accompanying web application provides real-time analysis and diagnostic results. This approach addresses a critical gap in lead-I ECG analysis, enhancing diagnostic precision and expanding the scope of ECG applications in health care. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Empirical mode decomposition

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Exams Item Analysis, Online Examination, and Education Evaluation System Via Internet 20

Exams Item Analysis, Online Examination, and Education Evalu...

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20th International Conference on Electrical engineering/Electronics, computer, Telecommunications and information technology, ECTI-CON 2023

作者： Chaidee, Kamonlak Boontasri, Kanchana Chaidee, Ekkachai Punthura, Kittichai Kumtip, Narong Pankham, Yotsanan Rajamangala University of Technology Lanna Chiangrai Faculty of Business Administration and Liberal Arts Department of Computer Information System Chiang Rai Thailand Rajamangala University of Technology Lanna Chiangrai Faculty of Science and Agricultural Technology Department of Information Technology Chiang Rai Thailand Rajamangala University of Technology Lanna Chiangrai Faculty of Electrical Engineering Department of Electrical Engineering Chiang Rai Thailand

ISBN: (纸本)9798350310467

The objective of this study is to develop an exams item bank management system on the internet. The exam item analysis system is used to determine the reliability of the exams using K-R 20 and K-R 21 formulas. The difficulty and power of discrimination values are determined using a choice analysis and percentages of high and lower groups technique. The create exams set system can be performed randomly or identified through the difficulty level. The examination can be performed online and the exam results can be immediately announced. The grading system is used to support the decision-making to evaluate learning achievement. The system's accuracy was tested and found to be accurate compared with the hand calculation results. The system was tested with a sample group and satisfaction was assessed through questionnaires. The satisfaction of teachers and students was highest, while that of administrators was high. The developed exams item bank management system operates as a web application with a security system for supporting in learning activity. © 2023 IEEE.

关键词： Decision making

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A Combined Approach of Principal Component Analysis and Support Vector Machine for Early Development Phase Modeling of Ohrid Trout(Salmo Letnica)

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computer Modeling in engineering & Sciences 2021年第3期126卷 991-1009页

作者： Sunil Kr.Jha Ivan Uzunov Xiaorui Zhang School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Faculty of Computer Science and Engineering University of Information Science and TechnologyOhrid6000Republic of Macedonia

Ohrid trout(Salamo letnica)is an endemic species of fish found in Lake Ohrid in the Former Yugoslav Republic of Macedonia(FYROM).The growth of Ohrid trout was examined in a controlled environment for a certain period,thereafter released into the lake to grow their natural *** external features of the fish were measured regularly during the cultivation period in the laboratory to monitor their *** data mining methods-based computational model can be used for fast,accurate,reliable,automatic,and improved growth monitoring procedures and classification of Ohrid *** this motivation,a combined approach of principal component analysis(PCA)and support vectormachine(SVM)has been implemented for the visual discrimination and quantitative classification of Ohrid trout of the experimental and natural breeding and their growth *** PCA results in better discrimination of breeding categories of Ohrid trout at different development phases while the maximum classification accuracy of 98.33% was achieved using the combination of PCA and *** classification performance of the combination of PCA and SVM has been compared to combinations of PCA and other classification methods(multilayer perceptron,naive Bayes,randomcommittee,decision stump,random forest,and random tree).Besides,the classification accuracy of multilayer perceptron using the original features has been studied.

关键词： Salamo letnica growth phase modeling PCA SVM

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Optimistic Entanglement Purification in Quantum Networks 4

Optimistic Entanglement Purification in Quantum Networks

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4th IEEE International Conference on Quantum Computing and engineering, QCE 2023

作者： Mobayenjarihani, Mohammad Vardoyan, Gayane Towsley, Don Manning College of Information and Computer Sciences University of Massachusetts Amherst United States Delft University of Technology QuTech Faculty of Electrical Engineering Mathematics and Computer Science United States

ISBN: (纸本)9798350343236

Noise and photon loss encountered on quantum channels pose a major challenge for reliable entanglement generation in quantum networks. In near-term networks, heralding is required to inform endpoints of successfully generated entanglement. If after heralding, entanglement fidelity is too low, entanglement purification may be utilized to probabilistically increase fidelity. Traditionally, purification protocols proceed as follows: generate heralded EPR pairs, execute a series of quantum operations on two or more pairs between two nodes, and classically communicate results to check for success. Purification may require several rounds while qubits are stored in memories, vulnerable to decoherence. In this work, we explore notions of optimistic purification, wherein classical communication required for heralding and purification is delayed, possibly to the end of the process. Optimism reduces the overall time EPR pairs are stored in memory, increasing fidelity while possibly decreasing EPR pair rate due to decreased heralding and purification failure. We apply optimism to the entanglement pumping scheme, ground- and satellite-based EPR generation sources, and current state-of-the-art purification circuits that include several measurement and purification checkpoints. We evaluate performance in view of a number of parameters, including link length, EPR source rate and fidelity;and memory coherence time. We show that while our optimistic protocol increases fidelity, the traditional approach may even decrease fidelity for longer distances. We study the trade-off between rate and fidelity under entanglement-based QKD, and find that optimistic schemes can yield higher rates compared to non-optimistic counterparts, with most advantages seen in scenarios with low initial fidelity and short coherence times. © 2023 IEEE.

关键词： Purification

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Robust Multiresolution and Multistain Background Segmentation in Whole Slide Images 23rd

Robust Multiresolution and Multistain Background Segmentatio...

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Proceedings of the 23rd Polish Conference on Biocybernetics and Biomedical engineering, PCBEE 2023

作者： Jurgas, Artur Wodzinski, Marek Atzori, Manfredo Müller, Henning Information Systems Institute Sierre Switzerland Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Science and Technology Krakow Poland Department of Neuroscience University of Padova Padova Italy Medical Faculty University of Geneva Geneva Switzerland

ISBN: (纸本)9783031384295

Background segmentation is an important step in analysis of histopathological images. It allows one to remove irrelevant regions and focus on the tissue of interest. However, background segmentation is challenging due to the variability of stain colors and intensity levels across different images, modalities, and magnification levels. In this paper, we present a learning-based model for histopathology background segmentation based on convolutional neural networks. We compare two multiresolution approaches to deal with the variability of magnification in histopathology images: (i) model that uses upscaling of smaller patches of the image, and (ii) model simultaneously trained on multiple resolution levels. Our model is characterized by solid performance both in multiresolution and multistain dyes (H &E and IHC), achieving good performance on publicly available dataset. The quantitative scores are, in terms of the Dice score, close to 94.71. The qualitative analysis presents strong performance on previously unseen cases from different distributions and various dyes. We freely release the model, weights, and ground-truth annotations to promote the open science and reproducible research. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Pathology

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Investigating Privacy and Security Concerns in a Running eHealth information System 3

Investigating Privacy and Security Concerns in a Running eHe...

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3rd IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2023

作者： Denkovski, Viktor Stojmenovska, Irena Gavrilov, Goce Radevski, Vladimir Trajkovik, Vladimir University American College Skopje School of Computer Science and Information Technology Skopje Macedonia University 'Ss. Cyril and Methodus' Faculty of Computer Science and Engineering Skopje Macedonia

ISBN: (纸本)9798350333732

The healthcare authorities should be seriously dedicated in maintaining a high level of data security and data privacy, due to the highly sensitive nature of patient data The study investigates the results explored via interview and survey examination on the privacy and security concerns related to medical providers' use of eHealth information systems in Macedonia. The purpose is to determine whether medical professionals are familiar with the security measures taken by healthcare authorities and to evaluate their opinions regarding the demand for system upgrades. About a dozen medical providers underwent in-depth email interviews to discover the privacy and security concerns in using the eHealth systems. This was a cornerstone to conduct an online questionnaire in which 370 medical providers including professionals from primary and secondary healthcare responded. The results underline how crucial it is to implement strong security controls and deal with system usability issues to guarantee the confidentiality and integrity of sensitive patient data. The study's conclusions highlight the importance of thorough privacy and security protocols in the context of eHealth technologies. © 2023 IEEE.

关键词： eHealth

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Beyond Traditional Motion Planning: A Proximal Policy Optimization Reinforcement Learning Approach for Robotics 5th

Beyond Traditional Motion Planning: A Proximal Policy Optimi...

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5th Joint International Conference on AI, Big Data and Blockchain, ABB 2024

作者： Rjoub, Gaith Drawel, Nagat Dssouli, Rachida Bentahar, Jamal Kassaymeh, Sofian Alweshah, Mohammed Faculty of Information Technology Aqaba University of Technology Aqaba Jordan Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Department of Computer Science 6G Research Center Khalifa University Abu Dhabi United Arab Emirates Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Salt Jordan

ISBN: (纸本)9783031731501

In today’s technologically-driven era, robotic systems have been integrated into various aspects of our daily lives, handling tasks from household chores to complex industrial applications. With these robotic systems becoming increasingly commonplace in homes, offices, and public spaces, the demand for effective and adaptable motion planning becomes paramount. Traditional motion planning methods, although having made noteworthy progress, still have difficulties with the challenges posed by dynamic environments characterized by unpredictable elements and rapid changes. Recognizing this gap, this paper introduces a novel reinforcement learning-based path planning algorithm specifically designed for dynamic scenarios. Through the integration of Proximal Policy Optimization (PPO) reinforcement algorithm, our approach empowers robots to actively chart their paths, adapting in real-time to unforeseen obstacles and shifts in the environment. To validate our methodology, we undertook extensive experiments comparing our approach to established benchmark methods, notably SARSA, A∗, and Regular Reinforcement Learning (RL) (i.e., non PPO-based). The results not only highlight the effectiveness of our proposed solution but also emphasize its potential to reshape how robotic systems navigate complex, ever-evolving environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Robot programming

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A high available and strong generalization capability data-driven-based wireless fingerprint positioning model 2

A high available and strong generalization capability data-d...

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2nd IEEE International Conference on Electrical engineering, Big Data and Algorithms, EEBDA 2023

作者： Wu, Xiaochong Hu, Minxun Dong, Ganrong Harbin Institute of Technology School of Information Science and Engineering Shandong Weihai China North East University of Qinhuangdao Faculty of Computer and Communication Engineering Hebei China Jiaxing University School of Information Science and Engineering Jiaxing China

ISBN: (纸本)9781665462532

The demand for indoor location-based services is far greater than supply, and many novel indoor localization technologies are emerging. Wi-Fi fingerprint-based method has drawn much attention by virtue of its user-friendliness and no extra device deployment. However, the traditional methods based on signal similarity or received-signal-strength-based (RSS-based) machine learning face their bottlenecks, such as the performance bottleneck or the generalization capability. Thus, a binary-classification model based on sample differences is proposed, which divides the original fingerprint data into positive and negative samples. Then, new relative features are proposed to replace the traditional media-access-control(MAC)-RSS features. Finally, the classification models are trained using decision tree (DT), support vector machine (SVM), and random forest (RF) algorithms, and several sets of controlled experiments are performed to evaluate the proposed method. It is noticeable that the proposed method has better performance in floor detection accuracy and point matching accuracy, and the mean error can reach 2.9m (DT), 3.09m (SVM), and 2.96 m(RF). Besides, the proposed method shows a very excellent model generalization capability, and the model trained by one building's data can still work well in another building. Those advantages have enormous potential in many large indoor localization scenes, like mall, theme park, airports, museums and exhibition halls. © 2023 IEEE.

关键词： Decision trees

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