Article
Computer Science, Artificial Intelligence
Huwei Liu, Junhui Zhao, Li Zhou, Jianglong Yang, Kaibo Liang
Summary: Amidst the development of the service economy and information technology, this research aims to establish a key index system for evaluating the quality of express services in the context of intelligent logistics. By proposing a quantitative and qualitative evaluation system and using analysis methods such as machine learning and big data technology, the model can effectively assess service quality and provide valuable feedback for improvement.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
C. Sugapriya, P. Saranyaa, D. Nagarajan, Dragan Pamucar
Summary: This paper examines the impact of manufacturing and remanufacturing processes on the supply chain, using the triangular intuitionistic fuzzy method to reduce overall inventory costs. The existence and uniqueness of the optimal solutions as well as the solution processes and sensitivity analysis are discussed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shahab Hosseini, Rashed Pourmirzaee
Summary: This study combines Monte-Carlo simulations and artificial neural networks to develop a probability-based deep neural network model for predicting dust pollution caused by mining bench blasting. The results show that this new predictive model greatly improves the accuracy of dust pollution prediction. Sensitivity analysis reveals that wind speed is the most influential factor, and wind analysis is performed to identify affected areas.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Aidin Shaghaghi, Rahim Zahedi, Mahsa Ghorbani, Zohreh Ranjbar, Sina Salek Arzhangi, Mansour Keshavarzzadeh, Hashem Alipour
Summary: This paper proposes a new method based on the particle community optimization algorithm for estimating the state of electricity distribution networks. By considering the uncertainty of loads and using virtual measurements, the estimation accuracy and efficiency are improved.
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
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
Tri Dung Duong, Qian Li, Guandong Xu
Summary: Counterfactual fairness addresses discrimination between model predictions in the actual and counterfactual worlds. This research proposes a minimax game-theoretic model that achieves counterfactual fairness without strict assumptions on structural causal models.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Ozlem Uzun Araz, Emine Kemiklioglu, Berfin Gurboga
Summary: This study evaluates the application of an ANFIS model in detecting toxic gas vapor. Experimental data using lyotropic cholesteric crystal as a sensor were used to establish the model, and ANFIS and GP were used for model partitioning and prediction. The results show that the ANFIS-GP5 model has high accuracy in predicting the response to toxic gas vapor.
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
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)
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)
Article
Computer Science, Artificial Intelligence
Zhen Zhang, Liang Wang, Songlin Liu, Yunfei Yin
Summary: The confined spaces of extrawide immersed tunnels pose challenges to firefighting strategies. This study proposes a deep-learning-based fire location detection model and creates a proprietary database for fire location detection tasks in extrawide immersed tunnels. The proposed model achieves exceptional performance in fire source location detection tasks.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Miro Schleicher, Petra Brueggemann, Benjamin Boecking, Uli Niemann, Birgit Mazurek, Myra Spiliopoulou
Summary: This study investigates the use of a minimal set of questionnaires to predict treatment outcomes for patients with chronic tinnitus, with the goal of reducing patient burden. The results indicate that only two questionnaires are needed to predict the target outcomes.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jing Zheng, Ying-Ming Wang, Jian-Qing Gao, Kai Zhang
Summary: This study proposes a novel heterogeneous multi-attribute case retrieval method and applies it to COVID-19 emergency decision-making. The method addresses the heterogeneity of case information and the subjective preference issue in multi-attribute decision making by introducing a new case similarity measurement and a comprehensive utility scoring method.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Camilo Jara Do Nascimento, Marcos E. Orchard, Christ Devia
Summary: This study presents an artificial neural architecture that predicts human ocular scanpaths during free viewing of different types of images. By comparing different metrics, the analysis aims to measure spatial and temporal errors in scanpath patterns. The results show significant differences in prediction when people view images with high visual content compared to low visual content. The study provides insights for improving gaze-controlled interfaces, virtual reality, and understanding human visual exploration.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Boban Djordjevic, Evelin Krmac, Chen -Yu Lin, Oskar Froidh, Behzad Kordnejad
Summary: Railway level crossings are weak spots in terms of safety due to the intersection of road and rail transport. The behavior of users as well as failures in the operation of level crossing equipment can affect safety levels and waiting times for road users. The introduction of digital twin systems can monitor and optimize level crossing operations in real time, reducing unnecessary waiting times for road users.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Santanu Roy, Saikat Raj, Tamal Chakraborty, Anirban Chakrabarty, Agostino Cortesi, Soumya Sen
Summary: This research introduces a novel approach to enhance the performance of distributed data warehouses by distributing data across multiple data centers for parallel processing and focusing on data availability locally to improve query processing speed. Empirical tests prove the method's effectiveness and superiority.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Junwei Dong, Xiaobo Li, Yuxin Zhao, Jingchao Ji, Shaolang Li, Hui Chen
Summary: This paper presents an improved Binary Dandelion Algorithm using Sine Cosine operator and Restart strategy (SCRBDA) for feature selection. The algorithm enhances its development and exploration ability by using sine cosine operator in the radius formula of core dandelions. It also incorporates a restart strategy to overcome local optima and utilizes mutual information to guide the generation of dandelions. Experimental results demonstrate that SCRBDA outperforms other classical feature selection algorithms in terms of feature reduction ability and overall performance.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
