Article
Computer Science, Information Systems
S. Pradeepa, N. Sasikaladevi, K. R. Manjula
Summary: Feature-based online comments from users on e-commerce platforms play a crucial role in influencing purchasing decisions, highlighting the need for aspect-based opinion mining frameworks. The large datasets are valuable for studying user preferences and behavior, but e-commerce service providers face challenges in evaluating the vast amount of data to derive client feedback. Proposed methodologies in extracting aspect-sentiment from online product reviews are more efficient in terms of accuracy and time complexity compared to alternative approaches.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Shuangjie Li, Kaixiang Zhang, Yali Li, Shuqin Wang, Shaoqiang Zhang
Summary: Feature selection is crucial in many fields, especially in machine learning. The proposed method OFS-Gapknn effectively addresses the challenges of online streaming features by defining a new neighborhood rough set relation and analyzing relevance and redundancy features. Experimental results demonstrate the dominance and significance of this method.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Information Systems
Zhihong Wang, Hongmei Chen, Xiaoling Yang, Jihong Wan, Tianrui Li, Chuan Luo
Summary: Dimensionality reduction is an important step in many learning methods to achieve optimal performance using discriminative features. This study proposes a fuzzy rough dimensionality reduction method that combines feature selection and feature extraction, and compares its performance with other algorithms, showing higher classification performance.
INFORMATION SCIENCES
(2023)
Article
Chemistry, Physical
Pierre-Paul De Breuck, Geoffroy Hautier, Gian-Marco Rignanese
Summary: The MODNet framework, utilizing a feedforward neural network and selecting physically meaningful features, outperforms current graph-network models on small datasets by accurately predicting material properties and enabling the prediction of multiple properties at once.
NPJ COMPUTATIONAL MATERIALS
(2021)
Article
Computer Science, Information Systems
Jihong Wan, Hongmei Chen, Tianrui Li, Xiaoling Yang, Binbin Sang
Summary: Feature selection is a crucial data preprocessing approach in data mining, and the interaction between features and their dynamic changes should be taken into consideration to prevent the loss of useful information.
INFORMATION SCIENCES
(2021)
Article
Water Resources
Chaode Yan, Ziwei Li, Muhammad Waseem Boota, Muhammad Zohaib, Xiao Liu, Chunlong Shi, Jikun Xu
Summary: This study focuses on the discrimination of river patterns in the Yellow River using Rough Set theory. A hierarchical structure integrating the boundary and the interior was proposed to describe the morphological feature of river patterns. The main feature factors were selected using Rough Set theory, and river pattern discriminant rules were generated based on the reduced feature subsets. The results demonstrate good performance in expressing the morphological features of different river patterns.
JOURNAL OF HYDROLOGY-REGIONAL STUDIES
(2023)
Article
Computer Science, Artificial Intelligence
Shuyin Xia, Cheng Wang, Guoyin Wang, Xinbo Gao, Weiping Ding, Jianhang Yu, Yujia Zhai, Zizhong Chen
Summary: This article introduces a granular-ball rough set (GBRS) model based on granular-ball computing, which can process continuous data and use equivalence classes for knowledge representation. Experimental results demonstrate that GBRS outperforms traditional rough set models in terms of learning accuracy and feature selection.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Hua Mao, Shengyu Wang, Chang Liu, Gang Wang
Summary: Attribute reduction is a critical aspect of rough set theory in data analysis. Existing methods mainly focus on theories, leading to complexities in searching for attribute reducts. This paper proposes a visual method that overcomes this limitation and demonstrates its effectiveness in a conventional information system.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Pankhuri Jain, Anoop Tiwari, Tanmoy Som
Summary: This paper introduces a technique for missing value imputation and feature selection using fuzzy rough set-based approaches. The experimental results demonstrate its high applicability and robustness, as well as its ability to significantly reduce data dimensionality while maintaining high performance.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Jihong Wan, Hongmei Chen, Tianrui Li, Binbin Sang, Zhong Yuan
Summary: This study proposes a method for feature selection in data with uncertainty, fuzziness, and noise. A robust fuzzy rough set model is constructed to enhance the robustness and antinoise ability. Uncertainty measures are defined to analyze the interactivity and redundancy of features. Experimental results demonstrate the significance of the proposed method.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Swarnajyoti Patra, Barnali Barman
Summary: A novel feature selection technique based on rough set theory is proposed in this work to reduce the dimensionality of hyperspectral images. The technique defines a new criterion by combining relevance and significance measures, and adopts a first order incremental search to select the most informative bands, showing better results compared to existing techniques. The proposed dependency measure definition is completely parameter free and computationally very cheap.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Keyu Liu, Tianrui Li, Xibei Yang, Xin Yang, Dun Liu
Summary: This paper introduces a novel ensemble feature selection method, which selects features with local significance through cross-class sample granulation and ensemble feature selection strategies.
APPLIED SOFT COMPUTING
(2022)
Article
Operations Research & Management Science
Shivam Shreevastava, Priti Maratha, Tanmoy Som, Anoop Kumar Tiwari
Summary: This article proposes an innovative framework called intuitionistic fuzzy rough set (IFRS) to deal with uncertainty in judgement and identification. It integrates IF set and rough set with the concept of (alpha, beta)-indiscernibility to avoid noise. The framework is supported with proofs and a feature selection method is presented using this framework. A concrete illustration and comparative study with other methods are provided to demonstrate the effectiveness and superiority of the proposed technique.
Article
Computer Science, Artificial Intelligence
Pei Liang, Dingfei Lei, KwaiSang Chin, Junhua Hu
Summary: The current research on fuzzy rough sets for feature selection faces two major problems: the difficulty in evaluating the importance of feature subsets accurately in high-dimensional data space due to the use of multiple intersection operations of fuzzy relations, and the sensitivity to noisy information in the classical fuzzy rough sets model. To address these issues, this study proposes a radial basis function kernel-based similarity measure and introduces a relative classification uncertainty measure to improve the robustness of the fuzzy rough sets model.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Peng Zhou, Peipei Li, Shu Zhao, Yanping Zhang
Summary: Feature selection is a crucial dimensionality reduction technology, but the potential of early termination in online streaming feature selection for efficiency and satisfactory performance is a novel and exciting issue that has been investigated in this study.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
M. R. Gauthama Raman, Nivethitha Somu, Sahruday Jagarapu, Tina Manghnani, Thirumaran Selvam, Kannan Krithivasan, V. S. Shankar Sriram
ARTIFICIAL INTELLIGENCE REVIEW
(2020)
Article
Computer Science, Artificial Intelligence
Nivethitha Somu, Gauthama M. R. Raman, Akshya Kaveri, Akshay K. Rahul, Kannan Krithivasan, Shankar V. S. Sriram
APPLIED SOFT COMPUTING
(2020)
Article
Computer Science, Theory & Methods
Obulaporam Gireesha, Nivethitha Somu, Kannan Krithivasan, V. S. Shankar Sriram
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2020)
Article
Computer Science, Artificial Intelligence
Anila H. Glory, C. Vigneswaran, Shankar V. S. Sriram
KNOWLEDGE-BASED SYSTEMS
(2020)
Article
Computer Science, Information Systems
S. Kamakshi, V. S. Shankar Sriram
Article
Green & Sustainable Science & Technology
Nivethitha Somu, Gauthama M. R. Raman, Krithi Ramamritham
Summary: This study introduces a deep learning framework called kCNN-LSTM for accurate building energy consumption forecasts, utilizing k-means clustering, Convolutional Neural Networks (CNN), and Long Short Term Memory (LSTM) neural networks. The efficiency and applicability of kCNN-LSTM were demonstrated using real-time building energy consumption data, showing its suitability for energy consumption forecast problems through comparison with state-of-the-art models and observation of its ability to learn spatio-temporal dependencies in energy consumption data.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2021)
Article
Construction & Building Technology
Nivethitha Somu, Anirudh Sriram, Anupama Kowli, Krithi Ramamritham
Summary: This study addresses the issue of data inadequacy in thermal comfort modeling by utilizing transfer learning to achieve higher accuracy in target buildings, while facing challenges in adapting to different climate zones.
BUILDING AND ENVIRONMENT
(2021)
Article
Green & Sustainable Science & Technology
N. Venkata Subramanian, V. S. Shankar Sriram
Summary: With the increasing adoption of cloud computing, the number of cloud data centers is growing to meet customer demands and host various applications. Live Virtual Machine Migration faces challenges in maximizing resource efficiency, reducing energy consumption, and ensuring security. This study proposes a framework that uses an intelligent algorithm to select the most secure and optimal network path for resource utilization.
Proceedings Paper
Computer Science, Information Systems
Yogesh Kulkarni, Sayf Z. Hussain, Krithi Ramamritham, Nivethitha Somu
Summary: EnsembleNTLDetect is a robust and scalable electricity theft detection framework based on artificial intelligence techniques, which ensures the safety of smart energy systems by accurately analyzing consumers' electricity consumption patterns through data preprocessing and machine learning models.
21ST IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS ICDMW 2021
(2021)
Proceedings Paper
Computer Science, Information Systems
N. Neha, S. Priyanga, Suresh Seshan, R. Senthilnathan, V. S. Shankar Sriram
INVENTIVE COMMUNICATION AND COMPUTATIONAL TECHNOLOGIES, ICICCT 2019
(2020)
Proceedings Paper
Computer Science, Information Systems
P. S. Chaithanya, S. Priyanga, S. Pravinraj, V. S. Shankar Sriram
INVENTIVE COMMUNICATION AND COMPUTATIONAL TECHNOLOGIES, ICICCT 2019
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
H. Anila Glory, C. Vigneswaran, V. S. Shankar Sriram
FIRST INTERNATIONAL CONFERENCE ON SUSTAINABLE TECHNOLOGIES FOR COMPUTATIONAL INTELLIGENCE
(2020)