Article
Biochemical Research Methods
Fengsheng Wang, Leyi Wei
Summary: In this study, we propose a novel multi-scale end-to-end deep learning model, MSTLoc, for identifying protein subcellular locations in the imbalanced multi-label immunohistochemistry (IHC) images dataset. We demonstrate that the proposed MSTLoc outperforms current state-of-the-art models in multi-label subcellular location prediction. Through feature visualization and interpretation analysis, we show that the multi-scale deep features learned from our model exhibit better ability in capturing discriminative patterns underlying protein subcellular locations, and the features from different scales are complementary for the improvement in performance. Case study results indicate that our MSTLoc can successfully identify some biomarkers from proteins that are closely involved in cancer development.
Article
Telecommunications
Ramesh Chinnaraj
Summary: This paper presents a novel framework for predicting customer churn by using a deep learning model and the Elephant herding optimization method, successfully identifying churn customers.
WIRELESS PERSONAL COMMUNICATIONS
(2023)
Article
Business
Dana AL-Najjar, Nadia Al-Rousan, Hazem AL-Najjar
Summary: This study aims to develop a credit card customer churn prediction model using a feature-selection method and five machine learning models. The results showed that the C5 tree machine learning model performed the best and merging multi-categorical variables improved the performance of the prediction models.
JOURNAL OF THEORETICAL AND APPLIED ELECTRONIC COMMERCE RESEARCH
(2022)
Article
Thermodynamics
Zicheng Fei, Fangfang Yang, Kwok-Leung Tsui, Lishuai Li, Zijun Zhang
Summary: This paper proposes a comprehensive machine learning framework for accurately predicting the lifetime of lithium-ion batteries in early cycles. The support vector machine model combined with wrapper feature selection statistically shows the best result for battery lifetime prediction. Overall, the proposed framework outperforms existing methods in improving the prediction performance for early-cycle battery lifetime.
Article
Computer Science, Artificial Intelligence
Xianhua Chen, Zhigang Tian, Meng Rao
Summary: A hybrid model with multiple sub-classifiers is proposed for intelligent fault diagnosis of gearboxes. Each sub-classifier is optimized to deal with different fault types, and the selected features are then combined to form the optimal features for all types of gearbox failures in the hybrid model. The proposed method, known as block feature selection (BFS), utilizes NSGA-II and a new sorting algorithm to optimize the feature selection process for each sub-classifier. The effectiveness of BFS is demonstrated through experiments on bearing and gear faults in a planetary gearbox rig, and its robustness is verified by incorporating white noise at different levels.
ADVANCED ENGINEERING INFORMATICS
(2023)
Article
Computer Science, Software Engineering
Kaifang Wan, Jianmei Wang, Bo Li, Daqing Chen, Linyu Tian
Summary: This paper proposes a feature selection method based on three-way decision to address the problem of target recognition with high-dimensional and few-shot data. By improving the existing algorithm to increase fault tolerance and reduce dimension, experimental results show that the proposed algorithm improves recognition accuracy and stability.
Article
Computer Science, Artificial Intelligence
Aditya Gupta, Amritpal Singh
Summary: With the increase in pandemics and stress levels, heart disease has become a leading cause of premature deaths worldwide. This research work proposes the use of the NSGA-II feature selection technique to improve prediction accuracy and achieve a high prediction accuracy of 97.32% for heart disease. The results demonstrate the utility of this approach over other variants.
Article
Engineering, Mechanical
Makram Soui, Nesrine Mansouri, Raed Alhamad, Marouane Kessentini, Khaled Ghedira
Summary: The COVID-19 pandemic has overwhelmed medical systems worldwide, making diagnosis and treatment difficult. To reduce infection risks, a prediction model is proposed to differentiate infected cases and aid in healthcare resource allocation. The study utilizes NSGA-II with AdaBoost classifier to build the model, demonstrating improved efficiency compared to existing methods.
NONLINEAR DYNAMICS
(2021)
Review
Biochemical Research Methods
Alhassan Alkuhlani, Walaa Gad, Mohamed Roushdy, Abdel-Badeeh M. Salem
Summary: This paper discusses the importance of computational intelligence techniques for glycosylation site prediction and their applications. Various studies have analyzed the performance of intelligent techniques in different aspects, highlighting the challenges and difficulties faced by software developers and knowledge engineers in this field.
CURRENT BIOINFORMATICS
(2021)
Article
Chemistry, Analytical
Hoa Thi Pham, Joseph Awange, Michael Kuhn
Summary: Machine learning has played a crucial role in crop yield forecasting, but identifying critical features from datasets remains challenging. This study proposes a framework that compares feature selection, feature extraction, and their combination to enhance model performance, finding that the combined approach performs the best. The results emphasize the significant role of feature selection, feature extraction, and their combination with various machine learning algorithms in improving the accuracy of crop yield predictions.
Article
Green & Sustainable Science & Technology
Hongbin Dai, Guangqiu Huang, Huibin Zeng, Fan Yang
Summary: This study optimized the prediction model for atmospheric pollutant PM2.5 concentration using XGBoost and MSCNN, extracting spatio-temporal feature relationships and optimizing parameters with genetic algorithm. The experimental results demonstrate that the model offers higher accuracy and generalization ability in predicting PM2.5 concentration.
Article
Microbiology
Hasan Zulfiqar, Zahoor Ahmed, Bakanina Kissanga Grace-Mercure, Farwa Hassan, Zhao-Yue Zhang, Fen Liu
Summary: This study developed a machine learning-based model to predict promotors in Agrobacterium tumefaciens strain C58. Promotor sequences were encoded using three different types of feature descriptors and optimized using correlation and mRMR algorithm. The optimized features were then inputted into a random forest classifier to discriminate promotor sequences from non-promotor sequences. The model achieved an overall accuracy of 0.837 in the 10-fold cross-validation. This model will be helpful for the study of promotors in A. tumefaciens C58 strain.
FRONTIERS IN MICROBIOLOGY
(2023)
Article
Chemistry, Multidisciplinary
Fatima Enehezei Usman-Hamza, Abdullateef Oluwagbemiga Balogun, Luiz Fernando Capretz, Hammed Adeleye Mojeed, Saipunidzam Mahamad, Shakirat Aderonke Salihu, Abimbola Ganiyat Akintola, Shuib Basri, Ramoni Tirimisiyu Amosa, Nasiru Kehinde Salahdeen
Summary: Customer churn is a critical issue in the telecommunications industry. Researchers have developed intelligent decision forest models to predict churn, which outperformed existing methods. These models efficiently distinguish churn customers from non-churn ones.
APPLIED SCIENCES-BASEL
(2022)
Review
Computer Science, Information Systems
Leonardo Canete-Sifuentes, Raul Monroy, Miguel Angel Medina-Perez
Summary: Decision trees are popular due to their interpretability, Multivariate Decision Trees are used to improve classification performance, but there is a lack of adequate statistical comparison for understanding the capabilities of existing algorithms.
Article
Computer Science, Artificial Intelligence
Yu Zhang, Shangce Gao, Pengxing Cai, Zhenyu Lei, Yirui Wang
Summary: The discovery of protein tertiary structure is crucial for genetic engineering, medicinal design, and other biological applications. Protein structural class plays a vital role in protein folding and function analysis. Existing methods for confirming protein folding cannot handle the increasing number of protein sequences. In this paper, a novel super-large-scale feature based on secondary structure, evolutionary information, chemical properties, and global descriptors is constructed to predict protein class.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Information Systems
Vuong M. Ngo, Sven Helmer, Nhien-An Le-Khac, M-Tahar Kechadi
Summary: The humanities are undergoing significant changes due to digital transformation, but there is still a lack of adequate search functionality in digitized archives, especially for textile collections. A new approach is introduced for recognizing similar weaving patterns in textile archives by representing textile structures using hypergraphs and clustering multisets of k-neighbourhoods. This approach demonstrates efficiency and quality in querying and clustering large datasets containing textile samples, aiming to establish a solid baseline for modeling complex weaving patterns.
INFORMATION RETRIEVAL JOURNAL
(2021)
Article
Agriculture, Multidisciplinary
Vuong M. Ngo, M-Tahar Kechadi
Summary: Agriculture generates large volumes of data that require effective analysis to improve crop production, reduce costs, and protect the environment. This paper presents an electronic farming record model and analysis techniques to optimize crop production and environmental protection.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2021)
Article
Chemistry, Analytical
Arsalan Shahid, Thien-An Ngoc Nguyen, M-Tahar Kechadi
Summary: Obesity, especially childhood obesity, is a major public health issue globally. The Big Data against Childhood Obesity (BigO) project aims to collect data from children to create obesity prevalence models and provide real-time monitoring. Data security and privacy protection are crucial in ensuring the success of the project.
Article
Agriculture, Multidisciplinary
Quoc Hung Ngo, Tahar Kechadi, Nhien-An Le-Khac
Summary: This research introduces a novel ontology-based knowledge map model for storing and managing data mining results in crop farming to facilitate the process of knowledge discovery. The proposed model is dynamic and allows easy access, updates, and exploitation of knowledge, while the system architecture includes knowledge modelling, extraction, assessment, publishing, and exploitation.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2022)
Review
Nutrition & Dietetics
Adele R. Tufford, Christos Diou, Desiree A. Lucassen, Ioannis Ioakimidis, Grace O'Malley, Leonidas Alagialoglou, Evangelia Charmandari, Gerardine Doyle, Konstantinos Filis, Penio Kassari, Tahar Kechadi, Vassilis Kilintzis, Esther Kok, Irini Lekka, Nicos Maglaveras, Ioannis Pagkalos, Vasileios Papapanagiotou, Ioannis Sarafis, Arsalan Shahid, Pieter van't Veer, Anastasios Delopoulos, Monica Mars
Summary: This article explores the roles of big data in obesity research, comparing traditional epidemiologic methods with emerging big data approaches. It also discusses three broad research domains: eating behavior, social food environments, and the built environment. The recent European Union project BigO used big data and mobile health tools to study childhood obesity, highlighting some limitations of big data.
CURRENT DEVELOPMENTS IN NUTRITION
(2022)
Article
Computer Science, Information Systems
Sahraoui Dhelim, Nyothiri Aung, Mohand Tahar Kechadi, Huansheng Ning, Liming Chen, Abderrahmane Lakas
Summary: Trust Management System (TMS) is crucial in IoT networks to ensure network security, data integrity, and promote legitimate devices while punishing malicious activities. Trust scores assigned by TMSs reflect devices' reputations, which help predict future behaviors and assess reliability in IoT networks. This article proposes Trust2Vec, a TMS for large-scale IoT systems that leverages a random-walk network exploration algorithm and network embeddings community detection algorithm to manage trust relationships and mitigate large-scale trust attacks by malicious devices.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Theory & Methods
Nabila Chergui, Mohand Tahar Kechadi
Summary: Recent advances in Information and Communication Technologies have led to the emergence of Digital Agriculture, which utilizes data mining techniques to improve crop yield management and monitoring. This paper provides a systematic review of the application of data mining techniques in digital agriculture, highlighting the impact of big data on the agriculture sector.
JOURNAL OF BIG DATA
(2022)
Article
Engineering, Civil
Nyothiri Aung, Sahraoui Dhelim, Liming Chen, Abderrahmane Lakas, Wenyin Zhang, Huansheng Ning, Souleyman Chaib, Mohand Tahar Kechadi
Summary: This paper proposes a social-aware vehicular edge computing architecture that utilizes vehicles as edge servers to deliver popular content to nearby users, addressing the challenges of content delivery in vehicular networks.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Nyothiri Aung, Tahar Kechadi, Tao Zhu, Saber Zerdoumi, Tahar Guerbouz, Sahraoui Dhelim
Summary: This paper reviews recent literature on the application of blockchain to Internet of Vehicles, especially intelligent transportation systems. It highlights the security and privacy management challenges faced by Internet of Vehicles and emphasizes the innovative solution provided by blockchain.
2022 IEEE 7TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION ENGINEERING, ICITE
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Quoc Hung Ngo, Tahar Kechadi, Nhien-An Le-Khac
Summary: Recent advances in machine learning have led to effective applications in Artificial Intelligence (AI), but there is a lack of human-understandable explanations for the results and decisions. Explainable Artificial Intelligence (XAI) aims to provide human-understandable explanations for decision-making and trained AI models. This paper introduces a framework, OAK4XAI, which utilizes a knowledge map model and ontology design to address this issue. The framework considers both data analysis and the semantic aspect of domain knowledge, providing consistent information and definitions.
ARTIFICIAL INTELLIGENCE XXXIX, AI 2022
(2022)
Proceedings Paper
Computer Science, Information Systems
Ranul Deelaka Thantilage, Nhien-An Le-Khac, M-Tahar Kechadi
Summary: This research aims to provide an efficient data warehousing solution with multiple privacy and security measures integrated by design. It explores data security from a holistic perspective and possible distributed analysis mechanisms while streamlining data sharing between healthcare centres to increase efficiency and better patient treatments.
FUTURE DATA AND SECURITY ENGINEERING. BIG DATA, SECURITY AND PRIVACY, SMART CITY AND INDUSTRY 4.0 APPLICATIONS, FDSE 2022
(2022)
Article
Health Care Sciences & Services
Arsalan Shahid, Mehran H. Bazargani, Paul Banahan, Brian Mac Namee, Tahar Kechadi, Ceara Treacy, Gilbert Regan, Peter MacMahon
Summary: Identification and re-identification pose major security and privacy threats to medical imaging data. This paper addresses the challenges in the de-identification process of DICOM medical data and proposes a two-stage method for de-identifying CT scan images. The first stage involves removing patients' PII at the hospital facility, while the second stage employs a DICOM tool for attribute-level investigation to ensure complete de-identification.
Article
Computer Science, Information Systems
Abdellah Akilal, M-Tahar Kechadi
Summary: Forensic-by-design is a new paradigm advocating for the integration of forensic requirements into system design and development stages. However, it may not be effective for some open boundaries systems and is not fully aligned with Systems and Software Engineering standards. A new framework emphasizing on Cloud computing systems is proposed to address the identified issues.
FORENSIC SCIENCE INTERNATIONAL-DIGITAL INVESTIGATION
(2022)
Article
Computer Science, Information Systems
Quoc Hung Ngo, Tahar Kechadi, Nhien-An Le-Khac
Summary: Agronomists in digital agriculture must make precise decisions based on knowledge and experience, including identifying agricultural entities in text data. This study proposes a new Agriculture Entity Recognition (AGER) approach, utilizing a two-stage process with deep learning to build an annotated corpus for agricultural entities and demonstrate the efficiency and robustness of the method.
Article
Computer Science, Artificial Intelligence
Dhai Eddine Salhi, Abdelkamel Tari, Mohand Tahar Kechadi
Summary: The study aims to protect email users by building an automatic checking and detecting system on servers to filter out bad emails from good ones. The authors use a new method of email clustering to extract bad and good emails based on calculating the importance of attributes.
INTERNATIONAL JOURNAL OF SOFTWARE SCIENCE AND COMPUTATIONAL INTELLIGENCE-IJSSCI
(2021)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
