Article
Environmental Sciences
Kouao Laurent Kouadio, Loukou Nicolas Kouame, Coulibaly Drissa, Binbin Mi, Kouamelan Serge Kouamelan, Serge Pacome Deguine Gnoleba, Hongyu Zhang, Jianghai Xia
Summary: This study applied support vector machines (SVMs) to predict flow rates in groundwater exploration, aiming to minimize unsuccessful drillings. The SVM models achieved prediction accuracies of 77% and 83% on multiclass and binary datasets, respectively. The use of optimal polynomial and radial basis function kernels resulted in higher accuracies of 81.61% and 87.36%. Learning curves showed that larger training data could improve prediction performance on the multiclass dataset, but not necessarily on the binary dataset.
WATER RESOURCES RESEARCH
(2022)
Article
Engineering, Biomedical
Asta Kizyte, Yuchen Lei, Ruoli Wang
Summary: This study investigates the impact of different EMG input features on the machine learning algorithm-based prediction of ankle joint torque. The results show that high-density EMG datasets performed better in isometric conditions compared to bipolar EMG datasets, while for dynamic tasks, a combination of bipolar EMG or reduced dimensionality high-density EMG with kinematic features achieved the highest torque prediction accuracy.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2023)
Article
Genetics & Heredity
Dashuai Fan, Yuangen Yao, Ming Yi
Summary: MicroRNAs are short non-coding ribonucleic acid molecules that regulate gene expression, and the computational identification of plant miRNAs is important for understanding biological functions. The plantMirP2 tool has been developed with promising performance metrics for predicting plant miRNAs, outperforming other existing plant pre-miRNA tools. This tool is available as a webserver and stand-alone version for easy accessibility.
Article
Geosciences, Multidisciplinary
Abdul-Lateef Balogun, Fatemeh Rezaie, Quoc Bao Pham, Ljubomir Gigovic, Sinisa Drobnjak, Yusuf A. Aina, Mahdi Panahi, Shamsudeen Temitope Yekeen, Saro Lee
Summary: In this study, multiple hybrid machine learning models were developed to enhance spatial prediction of landslide susceptibility models by addressing parameter optimization limitations. Gray wolf optimization, bat algorithm, and cuckoo optimization algorithm were applied to fine-tune parameters of the support vector regression model. The hybrid models showed improved predictive accuracy and outperformed the traditional model, highlighting the potential of metaheuristic algorithms in enhancing model performance.
GEOSCIENCE FRONTIERS
(2021)
Article
Multidisciplinary Sciences
Lin Li, Yuwei Ke, Tie Zhang, Jun Zhao, Zequan Huang
Summary: This paper proposes a human pre-defecation prediction method based on multi-domain features and improved support vector machine (SVM), achieving a high accuracy rate in experimental analysis and potentially solving the problem of difficult defecation in bedridden elderly individuals.
Article
Chemistry, Physical
Ahmadreza Roosta, Reza Haghbakhsh, Ana Rita C. Duarte, Sona Raeissi
Summary: In this study, hybrid machine learning models were developed to predict the viscosity of DESs using the group contribution concept and artificial neural network and support vector machine algorithms. The models can accurately determine the viscosity of DESs.
JOURNAL OF MOLECULAR LIQUIDS
(2023)
Article
Environmental Sciences
Deepak Gupta, Narayanan Natarajan, Mohanadhas Berlin
Summary: Wind energy is a potential renewable energy source globally. Accurate prediction of wind speed is crucial for estimating wind power accurately. Hybrid machine learning models were used in this study for short-term wind speed prediction, with LDMR model outperforming others in prediction accuracy and ELM model being computationally faster.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2022)
Article
Computer Science, Information Systems
Weichen Wu, Yitian Xu, Xinying Pang
Summary: A novel two-stage hybrid screening rule based on variational inequality and duality gap is proposed in this paper, which can accelerate the solving process of support vector machines by deleting more redundant samples while maintaining accuracy. This method also embeds shrinking technique into the fast iterative algorithm, further speeding up the solving process.
INFORMATION SCIENCES
(2021)
Article
Chemistry, Multidisciplinary
Yang Liu, Shuaiwen Huang, Di Wang, Guoli Zhu, Dailin Zhang
Summary: This paper proposes a new predictive model for determining the need to replace disc cutters in tunnel boring machines (TBM) based on operational parameters and geological conditions. The model achieves high accuracy and F-1 scores in predicting cutter replacement using specific parameters and established features.
APPLIED SCIENCES-BASEL
(2022)
Article
Thermodynamics
Lei Xu, Lei Hou, Zhenyu Zhu, Yu Li, Jiaquan Liu, Ting Lei, Xingguang Wu
Summary: A hybrid prediction method combining genetic algorithm and support vector machine is proposed for mid-term electrical energy consumption forecasting for crude oil pipelines, showing significant improvement in predictive accuracy. Forecasting mid-term electricity consumption can help make important decisions and enhance prediction accuracy.
Article
Engineering, Environmental
Yue Dong, Jun Niu, Qi Liu, Bellie Sivakumar, Taisheng Du
Summary: A hybrid model using support vector machine and genetic programming was proposed for wind speed prediction, with error compensation for residuals. Experimental results showed the efficiency and accuracy of the method in predicting wind speed.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2021)
Article
Computer Science, Artificial Intelligence
Jianjun Miao
Summary: This study improves the neural network algorithm, combines support vector machines to build a student grade prediction model, and reduces data dimensionality using PCA. The study also removes redundant information and compresses multiple features into typical ones.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2021)
Article
Engineering, Electrical & Electronic
Babu Munirathinam, Vijay Vasanth Aroulanandam, Prabakeran Saravanan
Summary: Vector-borne diseases are infections transmitted by infected species or arthropoda, resulting in significant impact on human health. This article proposes a novel approach combining SSC algorithm and hybrid SVM-RF approach to accurately detect vector-borne diseases and improve public health outcomes.
SIGNAL IMAGE AND VIDEO PROCESSING
(2023)
Article
Computer Science, Information Systems
Priyanka Nehra, A. Nagaraju
Summary: This paper proposes a Support Vector Regression-based methodology to predict a host's future utilization using multiple resource's utilization history. Compared to existing approaches, the proposed method performs better in terms of root mean square error, mean absolute percentage error, mean square error, mean absolute error, and R2.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Talal H. Noor, Ayman Noor, Mahmoud Elmezain
Summary: The total number of plant species worldwide is increasing annually, and these species vary from region to region. Computer vision techniques combined with support vector machine can effectively classify and predict the poisonous status of plant species. However, the lack of comprehensive datasets, including plant images, scientific names, descriptions, poisonous status, and local names, poses a challenge in predicting poisonous plant species. This paper proposes a hybrid model using transformers models and support vector machine for plant species classification and poisonous status prediction. Experiments conducted on the Arabian Peninsula plant species dataset show promising results with high accuracy, precision, and F1-Score.
Article
Physics, Multidisciplinary
Xiaoyue Feng, Yanchun Liang, Xiaohu Shi, Dong Xu, Xu Wang, Renchu Guan
Article
Computer Science, Artificial Intelligence
Xueying Bai, Peilin Yang, Xiaohu Shi
Article
Engineering, Manufacturing
D. Y. Ma, C. H. He, S. Q. Wang, X. M. Han, X. H. Shi
ADVANCES IN PRODUCTION ENGINEERING & MANAGEMENT
(2018)
Article
Computer Science, Artificial Intelligence
Yun Qian, Yanchun Liang, Mu Li, Guoxiang Feng, Xiaohu Shi
Article
Multidisciplinary Sciences
Renchu Guan, Chen Yang, Maurizio Marchese, Yanchun Liang, Xiaohu Shi
Article
Computer Science, Artificial Intelligence
Guiping Xu, Quanlong Cui, Xiaohu Shi, Hongwei Ge, Zhi-Hui Zhan, Heow Pueh Lee, Yanchun Liang, Ran Tai, Chunguo Wu
SWARM AND EVOLUTIONARY COMPUTATION
(2019)
Article
Physics, Multidisciplinary
Rui Gao, Shoufeng Li, Xiaohu Shi, Yanchun Liang, Dong Xu
Summary: This paper introduces an overlapping community detection algorithm based on membership degree propagation, which achieves both partition result and overlapping node identification simultaneously, reducing computational time significantly. The algorithm performs well in experiments, improving the accuracy and speed of overlapping node prediction while alleviating the computational complexity of community structure detection in general.
Article
Mathematical & Computational Biology
Tianhao Zhang, Jiawei Gu, Zeyu Wang, Chunguo Wu, Yanchun Liang, Xiaohu Shi
Summary: Protein subcellular localization prediction plays an essential role in understanding protein function and mechanism. GraphLoc, a deep learning model proposed in this paper, utilizes protein structure information to predict protein localization at the subcellular level, outperforming other methods on benchmark datasets.
INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES
(2022)
Article
Physics, Multidisciplinary
Mu Qiao, Yanchun Liang, Adriano Tavares, Xiaohu Shi
Summary: This paper proposes a method for modeling chaotic time series based on multi-layer perceptron networks and develops a comprehensive framework for analyzing and predicting chaotic time series. The effectiveness of the proposed method is verified through the application to artificial and real-world chaotic time series, showing that it can obtain the best model and perform well in multi-step prediction tasks.
Article
Engineering, Multidisciplinary
Huiyan Sun, Yanchun Liang, Yan Wang, Liang Chen, Wei Du, Yuexu Jiang, Xiaohu Shi
TEHNICKI VJESNIK-TECHNICAL GAZETTE
(2019)
Article
Automation & Control Systems
Y. F. Sun, M. L. Zhang, S. Chen, X. H. Shi
INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL
(2018)
Proceedings Paper
Computer Science, Theory & Methods
Guiping Xu, Gaoyang Li, Jingqing Jiang, Yuqing Lin, Yanchun Liang, Heow Pueh Lee, Xiaohu Shi, Chunguo Wu
2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND ENGINEERING (CSE) AND IEEE/IFIP INTERNATIONAL CONFERENCE ON EMBEDDED AND UBIQUITOUS COMPUTING (EUC), VOL 1
(2017)
Article
Biochemical Research Methods
Ying Li, Xiaohu Shi, Yanchun Liang, Juan Xie, Yu Zhang, Qin Ma
BMC BIOINFORMATICS
(2017)
Article
Computer Science, Software Engineering
Deyin Ma, Xuming Han, Yanchun Liang, Xiaohu Shi
INTERNATIONAL JOURNAL OF GRID AND DISTRIBUTED COMPUTING
(2016)
Proceedings Paper
Computer Science, Artificial Intelligence
Huaiqing Ren, Yanchun Liang, Xiaohu Shi
PROGRESS IN MECHATRONICS AND INFORMATION TECHNOLOGY, PTS 1 AND 2
(2014)
Article
Computer Science, Artificial Intelligence
Qianghua Liu, Yu Tian, Tianshu Zhou, Kewei Lyu, Ran Xin, Yong Shang, Ying Liu, Jingjing Ren, Jingsong Li
Summary: This study proposes a few-shot disease diagnosis decision making model based on a model-agnostic meta-learning algorithm (FSDD-MAML). It significantly improves the diagnostic process in primary health care and helps general practitioners diagnose few-shot diseases more accurately.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2024)
Article
Computer Science, Artificial Intelligence
Balazs Borsos, Corinne G. Allaart, Aart van Halteren
Summary: The study demonstrates the feasibility of predicting functional outcomes for ischemic stroke patients and the usability of multimodal deep learning architectures for this purpose.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2024)
Article
Computer Science, Artificial Intelligence
Abdelmoniem Helmy, Radwa Nassar, Nagy Ramdan
Summary: This study utilizes machine learning models to detect depression symptoms in Arabic and English texts, and provides manually and automatically annotated tweet corpora. The study also develops an application that can detect tweets with depression symptoms and predict depression trends.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2024)
