Article
Energy & Fuels
Xin Hu, Keyi Li, Jingfu Li, Taotao Zhong, Weinong Wu, Xia Zhang, Wenjiang Feng
Summary: This study proposes a load forecasting model based on data mining and long short-term memory network, which can predict future electricity consumption more accurately and explore the correlation between factors of various industries and electricity consumption.
Article
Computer Science, Artificial Intelligence
Yongbin Zhu, Wenshan Li, Tao Li
Summary: This paper proposes a hybrid feature selection method based on artificial immune algorithm optimization (HFSIA) to solve the feature reduction problem of high-dimensional data. Experimental comparisons show that HFSIA has comparable computational cost to classical feature selection methods known for their speed, while achieving higher average classification accuracy and feature reduction rate on benchmark datasets.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jing Wang, Yuanzi Zhang, Minglin Hong, Haiyang He, Shiguo Huang
Summary: This paper proposes a self-adaptive level-based learning artificial bee colony (SLLABC) algorithm for high-dimensional feature selection problem. The algorithm introduces novel mechanisms to accelerate convergence, balance exploration and exploitation abilities, and reduce the number of selected features. Experimental results show that the proposed SLLABC algorithm achieves competitive performance in terms of classification accuracy and feature subset size.
Article
Mathematics, Applied
Na Chen, Deliang Zhu
Summary: To solve noisy linear systems, a new greedy randomized extended Kaczmarz algorithm is proposed with an effective greedy criterion for row selection and a randomized orthogonal projection to reduce the influence of noise. It is proven that the solution of the algorithm converges to the least squares solution of the linear system. Theoretical analysis shows that the convergence rate of the greedy randomized extended Kaczmarz algorithm is much faster than the randomized extended Kaczmarz method, and numerical results demonstrate its superiority. Moreover, the proposed algorithm is more efficient for noisy linear systems compared to the greedy randomized Kaczmarz algorithm.
JOURNAL OF APPLIED ANALYSIS AND COMPUTATION
(2023)
Article
Automation & Control Systems
Emrehan Kutlug Sahin, Selcuk Demir
Summary: Automated machine learning (AutoML) is a method that aims to automate the end-to-end process of employing repetitive machine learning tasks for real-world problems. It has become a potential approach for developing complex machine learning models without human experience and support. While existing AutoML techniques have shown promising results, more research is needed to further develop this field.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Biochemical Research Methods
Can Chen, Scott T. Weiss, Yang-Yu Liu
Summary: Feature selection is an important dimension reduction technique for model construction, but it often fails under high-dimensional and low-sample size setting. In this study, we propose GRACES, a deep learning-based method, to select important features for HDLSS data. GRACES effectively reduces overfitting by exploiting latent relations between samples and achieves superior performance compared to other feature selection methods on synthetic and real-world datasets.
Article
Computer Science, Artificial Intelligence
Bahaeddin Turkoglu, Sait Ali Uymaz, Ersin Kaya
Summary: In this study, binary versions of the Artificial Algae Algorithm (AAA) were presented and used to determine the ideal attribute subset for classification processes. Experimental results and statistical tests confirmed the superior performance of the AAA algorithm in increasing classification accuracy compared to other state-of-the-art binary algorithms.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Boyang Xu, Ali Asghar Heidari, Zhennao Cai, Huiling Chen
Summary: This study proposes a variant of the colony predation algorithm (CPA) called Covariance Gaussian cuckoo Colony Predation Algorithm (CGCPA), which employs a designed gaussian cuckoo variable dimensional strategy to enhance population diversity and global search ability, and a covariance matrix adaptation evolution strategy to enhance convergence speed and capture the global optimal solution. Experimental results show that CGCPA outperforms state-of-the-art algorithms in terms of convergence speed and accuracy.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Management
Temitayo Ajayi, Taewoo Lee, Andrew J. Schaefer
Summary: Selecting appropriate clinical objectives is a challenging task in radiation therapy treatment plan optimization. This study infers a sparse set of objectives for prostate cancer treatment planning based on historical treatment data using inverse optimization. The proposed methods, including greedy heuristics and regularized problems, can find objectives that are near optimal and accurately represent latent clinical preferences, as evidenced by the results of curve analysis.
OPERATIONS RESEARCH
(2022)
Article
Automation & Control Systems
Federico Zocco, Marco Maggipinto, Gian Antonio Susto, Sean McLoone
Summary: This paper proposes a 'lazy' implementation of Forward Selection Component Analysis (L-FSCA) which is faster than FSCA while having comparable performance. Experimental results show that L-FSCA reduces computation time by 22% to 94% while yielding almost identical performance to FSCA.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Liang Zhao, Yuyang Gao, Jieping Ye, Feng Chen, Yanfang Ye, Chang-Tien Lu, Naren Ramakrishnan
Summary: The traditional forecasting methods struggle to comprehensively cover multiple societal aspects, while multi-source event forecasting may face challenges such as geographical hierarchies, missing values, and model updating. This article proposes a new feature learning model to address these issues.
ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
(2022)
Article
Computer Science, Artificial Intelligence
Elnaz Pashaei, Elham Pashaei
Summary: Microarray analysis of gene expression is helpful for disease and cancer diagnosis and prognosis. This paper proposes a new gene selection strategy based on the binary COOT optimization algorithm, and compares it to other techniques. The experimental results show that the BCOOT-CSA approach outperforms other methods in terms of prediction accuracy and selected gene number.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Agriculture, Multidisciplinary
Kittakorn Sriwanna
Summary: This paper proposes a system for predicting rice blast disease using weather data and employs an ensemble method to rank weather features. Experimental results demonstrate that the top ten features outperform others in rice blast disease prediction. Average visibility, amount of rainfall, hours of sun, maximum wind speed, and days of rain are identified as the five most effective weather features.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2022)
Article
Physics, Multidisciplinary
Isaac Xoese Ocloo, Hanfeng Chen
Summary: This paper studies the use of the LASSO method with extended Bayesian information criteria (EBIC) for feature selection in high-dimensional models. The energy distance correlation is proposed as a measure of dependence between two variables instead of the ordinary correlation coefficient. The new method is shown to be more powerful than Luo and Chen's method for feature selection, as demonstrated by simulation studies and a real-life example. It is also proven that the new algorithm is selection-consistent.
Article
Physics, Multidisciplinary
Takuya Wada, Hideki Takayasu, Misako Takayasu
Summary: We propose a new method for extracting multiple areas in a high-dimensional big data space with highly concentrated data points that satisfy specific conditions. Firstly, we use the Bayesian method to extract one-dimensional areas where the data satisfying specific conditions are mostly gathered. Secondly, we construct higher-dimensional areas with higher densities of focused data points compared to the combination of one-dimensional results, and validate the results with data validation. Lastly, we apply this method to identify the significant factors shared among successful firms with top 1% sales growth rates using 156-dimensional corporate financial report data for 12 years, encompassing approximately 320,000 firms. Additionally, we categorize high-growth firms into 15 groups based on different sets of factors.
Article
Computer Science, Artificial Intelligence
Jin Zhang, Zekang Bian, Shitong Wang
Summary: This study proposes a novel style linear k-nearest neighbor method to extract stylistic features using matrix expressions and improve the generalizability of the predictor through style membership vectors.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qifeng Wan, Xuanhua Xu, Jing Han
Summary: In this study, we propose an innovative approach for dimensionality reduction in large-scale group decision-making scenarios that targets linguistic preferences. The method combines TF-IDF feature similarity and information loss entropy to address challenges in decision-making with a large number of decision makers.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Hegui Zhu, Yuchen Ren, Chong Liu, Xiaoyan Sui, Libo Zhang
Summary: This paper proposes an adversarial attack method based on frequency information, which optimizes the imperceptibility and transferability of adversarial examples in white-box and black-box scenarios respectively. Experimental results validate the superiority of the proposed method and its application in real-world online model evaluation reveals their vulnerability.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jing Tang, Xinwang Liu, Weizhong Wang
Summary: This paper proposes a hybrid generalized TODIM approach in the Fine-Kinney framework to evaluate occupational health and safety hazards. The approach integrates CRP, dynamic SIN, and PLTSs to handle opinion interactions and incomplete opinions among decision makers. The efficiency and rationality of the proposed approach are demonstrated through a numerical example, comparison, and sensitivity studies.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Shigen Shen, Chenpeng Cai, Zhenwei Li, Yizhou Shen, Guowen Wu, Shui Yu
Summary: To address the damage caused by zero-day attacks on SIoT systems, researchers propose a heuristic learning intrusion detection system named DQN-HIDS. By integrating Deep Q-Networks (DQN) into the system, DQN-HIDS gradually improves its ability to identify malicious traffic and reduces resource workloads. Experiments demonstrate the superior performance of DQN-HIDS in terms of workload, delayed sample queue, rewards, and classifier accuracy.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu
Summary: In this paper, we propose a Chinese text classification algorithm based on deep active learning for the power system, which addresses the challenge of specialized text classification. By applying a hierarchical confidence strategy, our model achieves higher classification accuracy with fewer labeled training data.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Kaan Deveci, Onder Guler
Summary: This study proves the lack of robustness in nonlinear IF distance functions for ranking intuitionistic fuzzy sets (IFS) and proposes an alternative ranking method based on hypervolume metric. Additionally, the suggested method is extended as a new multi-criteria decision making method called HEART, which is applied to evaluate Turkey's energy alternatives.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Fu-Wing Yu, Wai-Tung Ho, Chak-Fung Jeff Wong
Summary: This research aims to enhance the energy management in commercial building air-conditioning systems, specifically focusing on chillers. Ridge regression is found to outperform lasso and elastic net regression when optimized with the appropriate hyperparameter, making it the most suitable method for modeling the system coefficient of performance (SCOP). The key variables that strongly influence SCOP include part load ratios, the operating numbers of chillers and pumps, and the temperatures of chilled water and condenser water. Additionally, July is identified as the month with the highest potential for performance improvement. This study introduces a novel approach that balances feature selection, model accuracy, and optimal tuning of hyperparameters, highlighting the significance of a generic and simplified chiller system model in evaluating energy management opportunities for sustainable operation. The findings from this research can guide future efforts towards more energy-efficient and sustainable operations in commercial buildings.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Xiaoyan Chen, Yilin Sun, Qiuju Zhang, Xuesong Dai, Shen Tian, Yongxin Guo
Summary: In this study, a method for dynamically non-destructive grasping of thin-skinned fruits is proposed. It utilizes a multi-modal depth fusion convolutional neural network for image processing and segmentation, and combines the evaluation mechanism of optimal grasping stability and the forward-looking non-destructive grasp control algorithm. The proposed method greatly improves the comprehensive performance of grasping delicate fruits using flexible hands.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Yuxuan Yang, Siyuan Zhou, He Weng, Dongjing Wang, Xin Zhang, Dongjin Yu, Shuiguang Deng
Summary: The study proposes a novel model, POIGDE, which addresses the challenges of data sparsity and elusive motives by solving graph differential equations to capture continuous variation of users' interests. The model learns interest transference dynamics using a time-serial graph and an interval-aware attention mechanism, and applies Siamese learning to directly learn from label representations for predicting future POI visits. The model outperforms state-of-the-art models on real-world datasets, showing potential in the POI recommendation domain.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
S. Karthika, P. Rathika
Summary: The widespread development of monitoring devices in the power system has generated a large amount of power consumption data. Storing and transmitting this data has become a significant challenge. This paper proposes an adaptive data compression algorithm based on the discrete wavelet transform (DWT) for power system applications. It utilizes multi-objective particle swarm optimization (MO-PSO) to select the optimal threshold. The algorithm has been tested and outperforms other existing algorithms.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jiaqi Guo, Haiyan Wu, Xiaolei Chen, Weiguo Lin
Summary: In this study, an adaptive SV-Borderline SMOTE-SVM algorithm is proposed to address the challenge of imbalanced data classification. The algorithm maps the data into kernel space using SVM and identifies support vectors, then generates new samples based on the neighbors of these support vectors. Extensive experiments show that this method is more effective than other approaches in imbalanced data classification.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qiumei Zheng, Linkang Xu, Fenghua Wang, Yongqi Xu, Chao Lin, Guoqiang Zhang
Summary: This paper proposes a new semantic segmentation network model called HilbertSCNet, which combines the Hilbert curve traversal and the dual pathway idea to design a new spatial computation module to address the problem of loss of information for small targets in high-resolution images. The experiments show that the proposed network performs well in the segmentation of small targets in high-resolution maps such as drone aerial photography.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Mojtaba Ashour, Amir Mahdiyar
Summary: Analytic Hierarchy Process (AHP) is a widely applied technique in multi-criteria decision-making problems, but the sheer number of AHP methods presents challenges for scholars and practitioners in selecting the most suitable method. This paper reviews articles published between 2010 and 2023 proposing hybrid, improved, or modified AHP methods, classifies them based on their contributions, and provides a comprehensive summary table and roadmap to guide the method selection process.
APPLIED SOFT COMPUTING
(2024)
Review
Computer Science, Artificial Intelligence
Gerardo Humberto Valencia-Rivera, Maria Torcoroma Benavides-Robles, Alonso Vela Morales, Ivan Amaya, Jorge M. Cruz-Duarte, Jose Carlos Ortiz-Bayliss, Juan Gabriel Avina-Cervantes
Summary: Electric power system applications are complex optimization problems. Most literature reviews focus on studying electrical paradigms using different optimization techniques, but there is a lack of review on Metaheuristics (MHs) in these applications. Our work provides an overview of the paradigms underlying such applications and analyzes the most commonly used MHs and their search operators. We also discover a strong synergy between the Renewable Energies paradigm and other paradigms, and a significant interest in Load-Forecasting optimization problems. Based on our findings, we provide helpful recommendations for current challenges and potential research paths to support further development in this field.
APPLIED SOFT COMPUTING
(2024)