Article
Engineering, Multidisciplinary
Zicheng Zhang
Summary: This paper proposes a hybrid system for speech emotion recognition, which adopts a two-stage design concept and utilizes random forest algorithm and logistic regression algorithm for feature importance evaluation. Experimental results show that the proposed method achieves satisfactory sentiment classification performance.
ALEXANDRIA ENGINEERING JOURNAL
(2021)
Article
Computer Science, Artificial Intelligence
Qiangda Yang, Yichuan Fu, Jie Zhang
Summary: This article presents a hybrid model for predicting the temperature of molten steel in a ladle furnace, utilizing a neural network-based empirical part trained with a modified cuckoo search algorithm. The proposed Information Interaction-enhanced Cuckoo Search algorithm enhances search capability by improving information exchange between individuals. Results show that the hybrid prediction model is effective with relatively high accuracy when applied to actual production data.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Behrouz Samieiyan, Poorya MohammadiNasab, Mostafa Abbas Mollaei, Fahimeh Hajizadeh, Mohammadreza Kangavari
Summary: Feature selection techniques are crucial for simplifying problems, improving performance, and optimizing computational efficiency while ensuring interpretability. This study presents a novel feature selection algorithm based on the crow search algorithm, which optimizes the balance between global and local search processes and achieves significant feature reduction.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Lalit Maurya, Viney Lohchab, Prasant Kumar Mahapatra, Janos Abonyi
Summary: Many vision-based systems suffer from poor levels of contrast and brightness due to inadequate and improper illumination during image acquisition. By using nature-inspired optimization, a balance between contrast and brightness can be achieved in image enhancement, improving image quality.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Metallurgy & Metallurgical Engineering
Yeongwon Song, Hyukjun Ha, Wonkoo Lee, Kwon-Yeong Lee, Junghyun Kim
Summary: This study presents the development of a decision support system that focuses on predicting the endpoint temperature of molten steel in order to manage the process of an electric arc furnace more systematically. The system leverages a data-driven approach with modules for data preprocessing, feature selection, regression modeling, and sensitivity analysis. A validation study using real-world operational data from Hyundai Steel in South Korea demonstrates the applicability of the system, with predicted endpoint temperatures within 5% errors of the actual temperatures. The study also identifies CaO, power, and melting score as the most significant factors impacting the endpoint temperature.
STEEL RESEARCH INTERNATIONAL
(2023)
Article
Computer Science, Information Systems
Tapas Bhadra, Sanghamitra Bandyopadhyay
Summary: The paper proposes a novel supervised feature selection approach based on dense subgraph discovery. The algorithm proceeds in two phases to select features with maximal average class relevance, minimal average pairwise redundancy, and good discriminating power. Experimental results show the proposed approach is competitive with conventional and state-of-the-art algorithms in supervised feature selection.
INFORMATION SCIENCES
(2021)
Article
Construction & Building Technology
Min-Yuan Cheng, Minh-Tu Cao, Aris Yan Jaya Mendrofa
Summary: Productivity is crucial for managing construction operations effectively. The study introduces an AI-based inference model, SOS-LSSVM-FS, which achieves the highest accuracy of productivity prediction. With the support of SOS, the model can run without human intervention.
JOURNAL OF BUILDING ENGINEERING
(2021)
Article
Automation & Control Systems
Shuai Tan, Aimin Wang, Hongbo Shi, Lei Guo
Summary: This study proposes a novel framework for incipient bearing fault diagnosis using MMI, VMD, and CS algorithm, achieving more effective fault characteristic extraction through optimizing VMD parameters and feature extraction.
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
(2022)
Article
Computer Science, Information Systems
Lijun Sun, Yan Xin, Tianfei Chen, Binbin Feng
Summary: A feature selection method based on clustering hybrid binary cuckoo search is proposed to improve the accuracy in rolling bearing fault diagnosis. The method utilizes Hilbert-Huang transform to extract fault features, applies a clustering hybrid initialization technique for feature selection, and introduces a mutation strategy based on Levy flight. The experimental results demonstrate the effectiveness of the proposed method.
Article
Engineering, Electrical & Electronic
Ashish Kumar Bhandari, Kankanala Srinivas, Anil Kumar
Summary: This paper proposes a novel optimized histogram modification framework using a cuckoo search algorithm for brightness preserved contrast enhancement. The method aims to find a fair tradeoff between contrast improvement and mean-brightness preservation by generating a new target histogram using the CS algorithm with tuning parameters. The technique can control enhancement levels and retain enhanced image features by selecting appropriate values, and the performance is validated against state-of-the-art techniques.
DIGITAL SIGNAL PROCESSING
(2021)
Article
Green & Sustainable Science & Technology
Saida Makhloufi, Smail Khennas, Sami Bouchaib, Amar Hadj Arab
Summary: This paper presents the most credible options to increase the share of renewable energy resource in the Algerian electricity power system by 2050, and uses the screening curve method and EnergyPlan tool for evaluation and estimation, while adopting the multi-objective cuckoo search algorithm to formulate the energy transition strategy.
Article
Computer Science, Information Systems
Avinash Chandra Pandey, Ankur Kulhari, Himanshu Mittal, Ashish Kumar Tripathi, Raju Pal
Summary: Sentiment analysis is a contextual text mining technique to determine people's sentiment towards emotional issues discussed on social media. This study proposes an improved exponential cuckoo search-based clustering method to find optimal cluster centers from sentiment datasets and determine the sentiment polarity of emotive contents.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Green & Sustainable Science & Technology
Yingjie Xu, Ning Chen, Xi Shen, Liangfeng Xu, Zhongyu Pan, Fan Pan
Summary: The fan fault diagnosis method using the CS algorithm optimized ELM model can learn signal features more effectively, improve fan performance, and bring benefits of energy, economy, and safety.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
(2021)
Article
Geosciences, Multidisciplinary
Xuan-Nam Bui, Hoang Nguyen, Quang-Hieu Tran, Dinh-An Nguyen, Hoang-Bac Bui
Summary: The study aimed to predict the intensity of ground vibration induced by mine blasting operations to reduce severe damages to the surroundings. A novel CSO-ANN model was proposed and validated based on 118 blasting events, showing that the CSO algorithm significantly improved the performance of the ANN model.
NATURAL RESOURCES RESEARCH
(2021)
Article
Computer Science, Information Systems
Maohua Xiao, Yabing Liao, Petr Bartos, Martin Filip, Guosheng Geng, Ziwei Jiang
Summary: A new fault diagnosis method based on back propagation neural network optimized by cuckoo search algorithm is proposed in this paper. By using wavelet packet decomposition for feature extraction of vibration signals, the method achieved high diagnostic accuracy rate in initial experiments.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
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)