Article
Computer Science, Artificial Intelligence
Sarah Saad Eldin, Ammar Mohammed, Ahmed Sharaf Eldin, Hesham Hefny
Summary: With the increasing number of product reviews, customer sentiment analysis has become a cumbersome task. Feature-based opinion retrieval systems have emerged as an effective tool for analyzing and expressing customer sentiments towards services. The proposed enhanced retrieval approach in this study showed improved ranking results by extracting more features, both implicit and explicit, compared to other methods such as conditional random field and association rule mining.
JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Yinan Guo, Zirui Zhang, Fengzhen Tang
Summary: Feature selection is important in machine learning to reduce complexity and simplify interpretation. A novel non-linear method proposed in this paper uses kernelized multi-class support vector machines and fast recursive feature elimination to select features that work well for all classes, resulting in lower computational time complexity.
PATTERN RECOGNITION
(2021)
Review
Computer Science, Artificial Intelligence
Amit Purohit, Pushpinder Singh Patheja
Summary: Sentiment analysis is a technique in NLP that determines emotional tone in text. Existing methods for analyzing product reviews have limitations in accurately detecting product aspects. This study proposes a Detach Frequency Assort method that combines TF-ISF with POS tags and Feedback Neural Network for product aspect detection. The study also introduces Systemize Polarity Shift, a flow search based SVM technique, to classify sentiments in review comments, and Revival Extraction, a thematic analysis method, to identify specific products. The proposed framework shows optimized results with high accuracy, specificity, recall, sensitivity, F1-Score, and precision in sentiment analysis.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Manuela Gomez-Suta, Julian Echeverry-Correa, Jose A. Soto-Mejia
Summary: Stance detection plays a crucial role in recognizing fake information in social media. Most previous studies on stance detection focus on classification results without providing explanations. In this paper, a two-phase classification system is proposed for stance detection in tweets, utilizing topic modeling features and explaining stance labels through relevant terms within topics. The approach is flexible and adjusts to vocabulary leveraging topic information. The system's performance ranks second in the SemEval-2016 task 6 dataset, outperforming deep learning-based proposals. The results affirm that topic modeling features improve classification results and provide textual information about stance labels.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Tiankuo Li, Hongji Xu, Zhi Liu, Zheng Dong, Qiang Liu, Juan Li, Shidi Fan, Xiaojie Sun
Summary: With the rapid development of Internet technology and the explosive growth of digital text, opinion mining has become an important research hotspot in the field of natural language processing (NLP). This paper proposes a new deep learning framework for opinion mining, which is shown to outperform other algorithms in terms of performance.
Article
Computer Science, Information Systems
Marichi Gupta, Aditya Bansal, Bhav Jain, Jillian Rochelle, Atharv Oak, Mohammad S. Jalali
Summary: The study explores the controversial topic of weather's impact on COVID-19 transmission, analyzing Twitter users' evolving perceptions over time. Results show a lack of consensus among the public, with a shift towards more tweets claiming some effect of weather as the pandemic progressed. This research approach effectively measures population perceptions and identifies misconceptions that can inform public health communications.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2021)
Article
Computer Science, Hardware & Architecture
Yosef Masoudi-Sobhanzadeh, Shabnam Emami-Moghaddam
Summary: This study proposes a machine learning-based method to predict botnets, addressing the limitations of existing methods in real-time application, functionality, and consideration of attack types. The results show that the proposed method accurately classifies data streams into relevant groups and achieves a trade-off between feature selection and prediction model performance.
Article
Computer Science, Artificial Intelligence
Xin Yan, Hongmiao Zhu
Summary: This paper proposes a novel support vector machine model with feature mapping and kernel trick to handle datasets with different distributions. The model improves robustness by pre-selecting training points, and converts the problem into a convex quadratic programming problem solved efficiently by the sequential minimal optimization algorithm. Numerical tests demonstrate the superior performance of the proposed method compared to other classification methods.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Agriculture, Multidisciplinary
Hongfei Zhu, Lianhe Yang, Jianwu Fei, Longgang Zhao, Zhongzhi Han
Summary: This paper proposed an innovative carrot appearance detection method using a convolutional neural network and support vector machine, and found that the accuracy of deep features based on SVM was superior to transfer learning models, with the best model being ResNet101 + SVM achieving a recognition accuracy of 98.17%.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2021)
Review
Agriculture, Multidisciplinary
Zhi Hong Kok, Abdul Rashid Mohamed Shariff, Meftah Salem M. Alfatni, Siti Khairunniza-Bejo
Summary: The Support Vector Machine (SVM) shows excellent performance in precision agriculture (PA), with comparisons to other machine learning algorithms highlighting its strengths and weaknesses in model performance and characteristics.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2021)
Article
Computer Science, Artificial Intelligence
Reyhaneh Yaghobzadeh, Seyed Reza Kamel, Mojtaba Asgari
Summary: Various methods have been proposed to diagnose renal failure using data mining and artificial intelligence techniques. The present study aimed to increase the accuracy and efficiency of renal failure diagnosis by introducing a feature selection method based on the dragonfly algorithm and optimizing data classification using the optimal parameters of the support vector machine algorithm. The proposed method showed a significant improvement of 34.12% in accuracy compared to the latest available methods, with 3.37% and 9.17% higher accuracy ratings.
Review
Business
Cheng Yang, Lingang Wu, Kun Tan, Chunyang Yu, Yuliang Zhou, Ye Tao, Yu Song
Summary: This study introduces a new user research method that evaluates products and suggests improvement strategies by analyzing online reviews. The effectiveness of the method was validated in smartphone user reviews, accurately predicting the product's negative review rate and proposing improvement strategies.
JOURNAL OF THEORETICAL AND APPLIED ELECTRONIC COMMERCE RESEARCH
(2021)
Review
Engineering, Industrial
Atin Roy, Subrata Chakraborty
Summary: Support vector machine (SVM) is a powerful machine learning technique widely used in structural reliability analysis (SRA). This article provides a comprehensive review of various SVM approaches in SRA applications, including classification and regression algorithms. The article also discusses advanced variants of SVM and hyperparameter tuning algorithms. The review highlights the excellent capability of SVM in handling high-dimensional problems with relatively fewer training data in SRA applications.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Computer Science, Artificial Intelligence
Javier Alcaraz, Martine Labbe, Mercedes Landete
Summary: This paper introduces a Support Vector Machine with feature selection and proposes a bi-objective evolutionary algorithm to approximate the Pareto optimal frontier. Extensive computational experiments are conducted to compare the results obtained by different methods, and the properties of points in the Pareto frontier are discussed.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Rui Zhang, Jian Wang, Nan Jiang, Zichen Wang
Summary: This paper proposes a quantum support vector machine based on amplitude estimation (AE-QSVM) to improve machine learning. AE-QSVM eliminates the constraint of repetitive processes and saves quantum resources. The experimental results demonstrate that classification with a 95% probability of success only uses 12 qubits.
INFORMATION SCIENCES
(2023)
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)