Article
Construction & Building Technology
Lei Pan, Yuanfeng Wang, Kai Li, Xiaohui Guo
Summary: This paper investigates the prediction of green concrete compressive strength using a hybrid artificial neural network with genetic algorithm. By constructing new parameters, using them as input variables, and performing feature selection, the performance of the prediction model is improved. The results demonstrate that the hybrid model with genetic algorithm and artificial neural network has the best prediction performance for green concrete compressive strength.
STRUCTURAL CONCRETE
(2023)
Article
Automation & Control Systems
Mahdieh Khorashadizade, Soodeh Hosseini
Summary: The most challenging issue in dealing with big datasets is their high dimensions. Feature selection is a technique that reduces the dimensionality of datasets by removing irrelevant and useless features, thus enhancing algorithm efficiency. This paper introduces a novel feature selection procedure called BMTLBO, which combines a binary-modified teaching learning-based optimization algorithm with pool-based diversity enhancement.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2023)
Article
Computer Science, Information Systems
Farrukh Hasan Syed, Muhammad Atif Tahir, Jaroslav Frnda, Muhammad Rafi, Muhammad Shahid Anwar, Jan Nedoma
Summary: Multi Target Regression (MTR) is a machine learning method for predicting multiple real-valued outputs simultaneously. This research proposes multiple feature subset alternatives for MTR using genetic algorithm and compares their performance with MTR algorithms. Experimental results indicate that optimal and structured feature selection can significantly improve performance and yield comparatively simple MTR models.
Article
Energy & Fuels
Hao Zhang, Hui Liu, Guoqing Ma, Yang Zhang, Jinxia Yao, Chao Gu
Summary: A back-propagation neural network (BPNN) model is used to evaluate the wildfire risk distribution by selecting the optimal feature subset from 14 types of wildfire-related features. The model is trained with five feature subsets and optimized using genetic algorithm (GA). The prediction results from the optimal model are used to draw a wildfire risk distribution map, showing that 90.26% of new fire incidents occur in medium-, high-, and very-high-risk zones, indicating the practical applicability of the proposed BPNN model.
FRONTIERS IN ENERGY RESEARCH
(2023)
Article
Engineering, Civil
Amir Molajou, Vahid Nourani, Abbas Afshar, Mina Khosravi, Adam Brysiewicz
Summary: Rainfall-runoff modeling at different time scales is a significant issue in hydro-environmental planning. In this study, a hybrid GA-EANN model was proposed and showed better performance compared to sole ANN and EANN models. The hybrid model achieved up to 19% and 35% improvement in testing suitability criteria for the Aji Chai and Murrumbidgee catchments, respectively.
WATER RESOURCES MANAGEMENT
(2021)
Article
Mathematics
Chun-Yao Lee, Meng-Syun Wen, Guang-Lin Zhuo, Truong-An Le
Summary: This paper introduces a fault-detection system for faulty induction motors utilizing MRA, CFFS, and ANN. By comparing and optimizing feature extraction and selection methods, the system achieves high accuracy while reducing operational costs.
Article
Computer Science, Artificial Intelligence
Mohammed Ghaith Altarabichi, Slawomir Nowaczyk, Sepideh Pashami, Peyman Sheikholharam Mashhadi
Summary: This paper proposes a two-stage surrogate-assisted evolutionary approach to address computational issues in using Genetic Algorithm for feature selection in large datasets. A lightweight qualitative meta-model is constructed based on the active selection of data instances, and this meta-model is then used for feature selection. Experimental results demonstrate that this method converges faster to higher accuracy feature subset solutions.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Ensar Arif Sagbas, Serdar Korukoglu, Serkan Balli
Summary: Stress is a mood of pressure and tension that a person experiences. Real-time stress detection is important in medical systems, but acquiring physiological data is challenging. This study developed a stress detection system using behavioral data from smartphone typing behaviors. Features were extracted from sensor data and reduced using techniques like filter-based methods and genetic algorithms. The kNN method achieved the best classification accuracy of 89.61% and F-Measure of 0.9052. A mobile service and relaxation application were also developed for stress detection and reduction.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Engineering, Chemical
Hasan Sildir, Erdal Aydin
Summary: This study introduces a piecewise-linear approximation method for handling non-convex activation and objective functions in artificial neural networks, achieving optimal, global, and simultaneous training and feature selection in regression problems, with efficient approximations and significant improvement in test accuracy.
CHEMICAL ENGINEERING SCIENCE
(2022)
Article
Computer Science, Artificial Intelligence
Motahare Akhavan, Seyed Mohammad Hossein Hasheminejad
Summary: A new two-phase gene selection method for microarray data is proposed in this study. This method reduces the number of genes significantly and improves the classification accuracy through anomaly detection and guided genetic algorithm.
KNOWLEDGE-BASED SYSTEMS
(2023)
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, Interdisciplinary Applications
Alen Costa Vieira, Gabriel Garcia, Rosa E. C. Pabon, Luciano P. Cota, Paulo de Souza, Jo Ueyama, Gustavo Pessin
Summary: This study addresses the problem of feature selection in order to improve a flood forecasting model. Through a case study using 18 different time series of thirty-five years of hydrological data, the proposed model predicts the level of the Xingu River in the Amazon rainforest in Brazil. By employing a Genetic Algorithm for feature selection and a Linear Regression model for forecasting, the final model achieves a high accuracy in predicting the river level, with a coefficient of determination equal to 0.988.
EARTH SCIENCE INFORMATICS
(2021)
Article
Endocrinology & Metabolism
Danhui Wang, Peyton Greenwood, Matthias S. Klein
Summary: This study aims to develop a model-agnostic, simple, and interpretable feature impact score. Feature Impact Assessment (FIA) is calculated by varying feature combinations and observing changes in prediction outcomes. FIA outperforms LIME and SHAP in selecting biologically meaningful features and is applicable to different ANN architectures.
Article
Computer Science, Artificial Intelligence
Ahmet Cevahir Cinar
Summary: Feature selection is a binary optimization problem aiming to maximize accuracy with fewer features. Metaheuristic algorithms are commonly used for feature selection. This work analyzes eleven existing and six novel fitness functions on various datasets using a new binary threshold Levy flight distribution (BTLFD) algorithm. The experimental results show that three rarely used fitness functions produce more accurate solutions.
Article
Computer Science, Artificial Intelligence
Emrah Hancer
Summary: Feature selection aims to select a feature subset that contributes the most to the performance of a further process. This paper introduces an improved cost-sensitive subset selection method that minimizes both the classification error rate and the feature cost. The proposed method outperforms other multi-objective optimization algorithms according to benchmark tests.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Hao Yang, Min Wang, Zhengfei Yu, Hang Zhang, Jinshen Jiang, Yun Zhou
Summary: In this paper, a novel method called CSTTA is proposed for test time adaptation (TTA), which utilizes confidence-based optimization and sample reweighting to better utilize sample information. Extensive experiments demonstrate the effectiveness of the proposed method.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Jin Liu, Ju-Sheng Mi, Dong-Yun Niu
Summary: This article focuses on a novel method for generating a canonical basis for decision implications based on object-induced operators (OE operators). The logic of decision implication based on OE operators is described, and a method for obtaining the canonical basis for decision implications is given. The completeness, nonredundancy, and optimality of the canonical basis are proven. Additionally, a method for generating true premises based on OE operators is proposed.
KNOWLEDGE-BASED SYSTEMS
(2024)
Review
Computer Science, Artificial Intelligence
Kun Bu, Yuanchao Liu, Xiaolong Ju
Summary: This paper discusses the importance of sentiment analysis and pre-trained models in natural language processing, and explores the application of prompt learning. The research shows that prompt learning is more suitable for sentiment analysis tasks and can achieve good performance.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xiangjun Cai, Dagang Li
Summary: This paper presents a new decomposition mechanism based on learned decomposition mapping. By using a neural network to learn the relationship between original time series and decomposed results, the repetitive computation overhead during rolling decomposition is relieved. Additionally, extended mapping and partial decomposition methods are proposed to alleviate boundary effects on prediction performance. Comparative studies demonstrate that the proposed method outperforms existing RDEMs in terms of operation speed and prediction accuracy.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xu Wu, Yang Liu, Jie Tian, Yuanpeng Li
Summary: This paper proposes a blockchain-based privacy-preserving trust management architecture, which adopts federated learning to train task-specific trust models and utilizes differential privacy to protect device privacy. In addition, a game theory-based incentive mechanism and a parallel consensus protocol are proposed to improve the accuracy of trust computing and the efficiency of consensus.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zaiyang Yu, Prayag Tiwari, Luyang Hou, Lusi Li, Weijun Li, Limin Jiang, Xin Ning
Summary: This study introduces a 3D view-based approach that effectively handles occlusions and leverages the geometric information of 3D objects. The proposed method achieves state-of-the-art results on occluded ReID tasks and exhibits competitive performance on holistic ReID tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Yongliang Shi, Runyi Yang, Zirui Wu, Pengfei Li, Caiyun Liu, Hao Zhao, Guyue Zhou
Summary: Neural implicit representations have gained attention due to their expressive, continuous, and compact properties. However, there is still a lack of research on city-scale continual implicit dense mapping based on sparse LiDAR input. In this study, a city-scale continual neural mapping system with a panoptic representation is developed, incorporating environment-level and instance-level modeling. A tailored three-layer sampling strategy and category-specific prior are proposed to address the challenges of representing geometric information in city-scale space and achieving high fidelity mapping of instances under incomplete observation.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ruihan Hu, Zhi-Ri Tang, Rui Yang, Zhongjie Wang
Summary: Mesh data is crucial for 3D computer vision applications worldwide, but traditional deep learning frameworks have struggled with handling meshes. This paper proposes MDSSN, a simple mesh computation framework that models triangle meshes and represents their shape using face-based and edge-based Riemannian graphs. The framework incorporates end-to-end operators inspired by traditional deep learning frameworks, and includes dedicated modules for addressing challenges in mesh classification and segmentation tasks. Experimental results demonstrate that MDSSN outperforms other state-of-the-art approaches.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Buliao Huang, Yunhui Zhu, Muhammad Usman, Huanhuan Chen
Summary: This paper proposes a novel semi-supervised conditional normalizing flow (SSCFlow) algorithm that combines unsupervised imputation and supervised classification. By estimating the conditional distribution of incomplete instances, SSCFlow facilitates imputation and classification simultaneously, addressing the issue of separated tasks ignoring data distribution and label information in traditional methods.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Deeksha Varshney, Asif Ekbal, Erik Cambria
Summary: This paper focuses on the neural-based interactive dialogue system that aims to engage and retain humans in long-lasting conversations. It proposes a new neural generative model that combines step-wise co-attention, self-attention-based transformer network, and an emotion classifier to control emotion and knowledge transfer during response generation. The results from quantitative, qualitative, and human evaluation show that the proposed models can generate natural and coherent sentences, capturing essential facts with significant improvement over emotional content.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Junchen Ye, Weimiao Li, Zhixin Zhang, Tongyu Zhu, Leilei Sun, Bowen Du
Summary: Modeling multivariate time series has long been a topic of interest for scholars in various fields. This paper introduces MvTS, an open library based on Pytorch, which provides a unified framework for implementing and evaluating these models. Extensive experiments on public datasets demonstrate the effectiveness and universality of the models reproduced by MvTS.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Reham R. Mostafa, Ahmed M. Khedr, Zaher Al Aghbari, Imad Afyouni, Ibrahim Kamel, Naveed Ahmed
Summary: Feature selection is crucial in classification procedures, but it faces challenges in high-dimensional datasets. To overcome these challenges, this study proposes an Adaptive Hybrid-Mutated Differential Evolution method that incorporates the mechanics of the Spider Wasp Optimization algorithm and the concept of Enhanced Solution Quality. Experimental results demonstrate the effectiveness of the method in terms of accuracy and convergence speed, and it outperforms contemporary cutting-edge algorithms.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ti Xiang, Pin Lv, Liguo Sun, Yipu Yang, Jiuwu Hao
Summary: This paper introduces a Track Classification Model (TCM) based on marine radar, which can effectively recognize and classify shipping tracks. By using a feature extraction network with multi-feature fusion and a dataset production method to address missing labels, the classification accuracy is improved, resulting in successful engineering application in real scenarios.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zhihao Zhang, Yuan Zuo, Chenghua Lin, Junjie Wu
Summary: This paper proposes a novel unsupervised context-aware quality phrase mining framework called LMPhrase, which is built upon large pre-trained language models. The framework mines quality phrases as silver labels using a parameter-free probing technique on the pre-trained language model BERT, and formalizes the phrase tagging task as a sequence generation problem by fine-tuning on the Sequence to-Sequence pre-trained language model BART. The results of extensive experiments show that LMPhrase consistently outperforms existing competitors in two different granularity phrase mining tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Kemal Buyukkaya, M. Ozan Karsavuran, Cevdet Aykanat
Summary: The study aims to investigate the hybrid parallelization of the Stochastic Gradient Descent (SGD) algorithm for solving the matrix completion problem on a high-performance computing platform. A hybrid parallel decentralized SGD framework with asynchronous inter-process communication and a novel flexible partitioning scheme is proposed to achieve scalability up to hundreds of processors. Experimental results on real-world benchmark datasets show that the proposed algorithm achieves 6x higher throughput on sparse datasets compared to the state-of-the-art, while achieving comparable throughput on relatively dense datasets.
KNOWLEDGE-BASED SYSTEMS
(2024)