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
Jinhui Liu, Bo Tang, Guishan Dong, Yong Yu
Summary: With the rapid development of artificial intelligence, the Internet of Things, and next-generation mobile communication, the Internet of Medical Things (IoMT) has attracted attention as a typical application due to its convenience and practicality. To address privacy preservation issues in accessing patients' records and sensitive information in the IoMT, a secure privacy-preserving medical record search scheme based on ELGamal blind signatures is proposed. An improved scheme based on the elliptical curve discrete logarithm problem is also introduced. The proposed scheme provides enhanced privacy protection, data security, and improved performance.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jingmin An, Guanyu Li, Wei Jiang
Summary: Predicting a user's next actions based on previously visited points of interest is important, but preserving privacy is a critical challenge. To address this challenge, we propose a decentralized user preference learning method that models user demand and implements privacy protection to achieve accurate next point of interest recommendations.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Redhwan Algabri, Hyunsoo Shin, Sungon Lee
Summary: This study presents a novel framework for estimating head pose in real-time without the need for landmark localization. The proposed method utilizes deep neural networks and multi-loss approach to predict head poses by analyzing RGB-D data.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Saravana Perumaal Subramanian, Selva Kumar Chandrasekar
Summary: Robotic Mobile Fulfillment Systems (RMFS) can greatly benefit large e-commerce warehouse operations. This paper proposes a Simultaneous Allocation and Sequencing of Orders Reinforcement Learning (SASORL) algorithm to minimize the distance traveled by mobile robots. The SASORL algorithm optimizes order allocation and sequencing concurrently, reducing mobile robot travel distance significantly. It outperforms soft computing techniques in terms of distance reduction, standard deviation, total distance, and computation time.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Elif Haktanir, Cengiz Kahraman
Summary: This paper proposes a novel intuitionistic Z-AHP and Z-TOPSIS methodology for evaluating and ranking alternatives, considering uncertainties and complexities in decision-making. Empirical analysis demonstrates that the proposed method effectively addresses decision environment issues and provides valuable insights for decision support systems.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Masoud Ahmadipour, Zaipatimah Ali, Muhammad Murtadha Othman, Rui Bo, Mohammad Sadegh Javadi, Hussein Mohammed Ridha, Moath Alrifaey
Summary: The optimal power flow (OPF) is a crucial tool in power system operation and control that aims to obtain the most economical combination of power plants to meet operational, economic, and environmental constraints. This study proposes an enhanced democratic political algorithm (DPA) to solve multi-objective OPF problems. The proposed method is tested on different power system cases and compared with other popular multi-objective evolutionary algorithms, showing its effectiveness in handling different scales and non-convex optimization problems.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Kai Sun, Jifan Yu, Juanzi Li, Lei Hou
Summary: Taxonomy is a knowledge graph used in semantic entailment and natural language processing tasks. Taxonomy expansion involves adding new concepts to enrich an existing taxonomy. Our method, TaxoSeq, converts taxonomy expansion into a sequence to sequence setting, effectively utilizing structural features and handling various expansion cases. It outperforms other methods on SemEval's benchmark datasets.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Li Sun, Zhenghua Cai, Kaibo Liang, Yuzhi Wang, Wang Zeng, Xueqian Yan
Summary: This paper presents the development of an intelligent system for the identification and detection of high-density small target pests using deep learning. The proposed system addresses limitations observed in previous detection systems and manual sorting, offering a potential solution to challenges in agricultural settings.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Wei Zhou, Yiheng Zhao, Yi Xiao, Xuanlin Min, Jun Yi
Summary: This paper proposes a novel hierarchical local-global framework based on the Transformer network for point cloud classification, named TNPC. The framework reduces computation and memory consumption by implementing downsampling operation and utilizing parallel branches. Experimental results show that the proposed method achieves state-of-the-art performance in terms of classification accuracy and efficiency.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Lianpeng Qiu, Cuipeng Qiu, Chengyun Song
Summary: In this paper, a method called ESDTW is proposed for fast and accurate alignment of time series. It introduces local extrema to represent the original time series and aligns the descriptors of extrema shape using DTW. Experimental results show that ESDTW achieves more accurate warping paths compared to other methods, and when combined with the nearest neighbor classifier, it achieves higher classification accuracy.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Raghav Agrawal, Harshit Mishra, Ilanthenral Kandasamy, Shrishail Ravi Terni, W. B. Vasantha
Summary: The enhanced answer evaluation system is an automated tool that utilizes Natural Language Processing (NLP) and deep learning techniques to evaluate the accuracy of subjective answers. It leverages various criteria such as keywords, similarity, and named entity recognition to provide precise evaluation scores. The system demonstrates remarkable performance in evaluating long answers and sets a new standard in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shanquan Gao, Yihui Wang, Huaxiao Liu
Summary: The study proposes a function layout analysis method called UiAnalyzer to help app developers evaluate the conformity of their UI's function layout with design conventions. The method compares the analyzed UI with similar UIs, generates semantic wireframes, extracts visual features, and determines the abnormality of the UI's function layout.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zihang Zhang, Qianrui Yu, Haichuan Yang, Jiayi Li, Jiujun Cheng, Shangce Gao
Summary: Renewable energy sources, particularly wave energy, have significant potential and minimal ecological impact. Optimizing the layout of wave energy converters is a complex challenge that requires substantial computing power. This study proposes a chaos-based differential evolutionary algorithm to optimize the energy output of oscillating buoy-type wave energy generators. Experimental results show significant improvements over other intelligent algorithms in various wave scenarios.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jianghui Cai, Jinqian Yang, Jie Wen, Haochen Zhao, Zhihua Cui
Summary: This study proposes a many-objective hybrid tensor decomposition model and a many-objective optimization algorithm based on game theory for skin cancer prediction. Experimental findings demonstrate significant performance improvements compared to existing models.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
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
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
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
Pablo Lara-Navarra, Enrique A. Sanchez-Perez, Antonia Ferrer-Sapena, Angels Fito-Bertran
Summary: This article presents a tool that identifies and categorizes unique elements in the university system to develop disruptive educational proposals. By collecting and analyzing data from innovative schools, a model is developed based on 16 variables. The data is processed using statistical techniques and AI, classifying the schools into four prototypical models. The findings provide a useful tool for analyzing school models and innovation levels and guiding upgrade strategies.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Lennart Johannes Gruber, Jan Egger, Andrea Boensch, Joep Kraeima, Max Ulbrich, Vincent van den Bosch, Ila Motmaen, Caroline Wilpert, Mark Ooms, Peter Isfort, Frank Hoezle, Behrus Puladi
Summary: This study compares the clinical implications of manual segmentation and AI-based segmentation methods for the mandible. The results show that manual segmentation is more accurate and precise than AI-based segmentation, but it is also more time-consuming.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
