Article
Computer Science, Artificial Intelligence
Mohammed Kayed, Sara Dakrory, A. A. Ali
Summary: The study highlights the lack of postal addresses for user's POIs in LBS applications, with some missed addresses potentially retrievable from the Web. No prior survey has compared previous Web postal address extraction approaches, and the issue remains unaddressed in many countries.
ARTIFICIAL INTELLIGENCE REVIEW
(2022)
Article
Computer Science, Artificial Intelligence
Xiuliang Gong, Bo Cheng, Xiaomei Hu, Wen Bo
Summary: Manual term extraction is similar to literal meaning: A translator browses text, classifies words, and prepares for translation. Terminology, as a centralized carrier of expertise, creation, popularization, and disappearance, dynamically reflects the development and evolution of an industry.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Junfeng Jing, Tian Gao, Weichuan Zhang, Yongsheng Gao, Changming Sun
Summary: In this paper, a comprehensive review is conducted on the application of image feature information extraction techniques in interest point detection. A taxonomy is proposed to systematically introduce the existing methods and different types of image feature information extraction techniques are discussed. The unresolved issues and undiscussed methods are identified, popular datasets and evaluation standards are provided, and the performances of fifteen state-of-the-art approaches are evaluated and discussed. Future research directions on image feature information extraction techniques for interest point detection are also elaborated.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Biochemical Research Methods
Sare Amerifar, Mahammad Norouzi, Mahmoud Ghandi
Summary: Advances in sequencing technologies have led to the extraction of a vast amount of biological data, creating opportunities for machine learning methods to grow. A holistic tool proposed in this paper extracts features from biological sequences, supporting 30 additional features compared to existing tools. Our tool, based on R language, outperforms iLearnPlus in conversion time, with speedups ranging from 2.8X for small nucleotides to 23.9X for amino acids.
BRIEFINGS IN BIOINFORMATICS
(2022)
Review
Computer Science, Artificial Intelligence
Sarah Osama, Hassan Shaban, Abdelmgeid A. Ali
Summary: This review explores the applications of machine learning-based data reduction and classification algorithms in microarray gene expression data. It summarizes various data preprocessing methods, reviews different feature selection algorithms, and discusses feature extraction and hybrid methods. It also examines widely used machine learning algorithms for tumor and nontumor classification. Finally, the challenges and unanswered questions in accurate cancer classification and detection are highlighted.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Review
Construction & Building Technology
Zeyu Wang, Lisha Xia, Hongping Yuan, Ravi S. Srinivasan, Xiangnan Song
Summary: This study analyzes the current research status and future directions in feature engineering for building energy prediction. The concepts and methods of feature engineering are discussed, and 172 relevant articles are comprehensively reviewed to summarize the research status and characteristics. Critical issues in feature engineering for data-driven building energy prediction are also discussed, and promising research directions are identified. The results provide a better understanding of the state of the art and future research trends in feature engineering for data-driven building energy prediction.
JOURNAL OF BUILDING ENGINEERING
(2022)
Article
Computer Science, Interdisciplinary Applications
Eriyeti Murena, Khumbulani Mpofu, Alfred T. Ncube, Olasumbo Makinde, John A. Trimble, Xi Vincent Wang
Summary: Sheet metal bending manufacturing companies require changeable and adaptable process planning systems to meet the demands of globalisation and rapidly changing market needs. This paper proposes a web-based feature extraction and recognition system that automates the planning of bending machine processes and successfully extracts bending features in CAD files.
INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING
(2021)
Review
Computer Science, Artificial Intelligence
Monika Sethi, Munish Kumar, M. K. Jindal
Summary: This paper focuses on gender prediction using handwriting in non-Indic and Indic scripts and provides various feature extraction methods, datasets, and tools for classification based on traditional and machine learning techniques.
Article
Computer Science, Artificial Intelligence
Xiaojun Chang, Pengzhen Ren, Pengfei Xu, Zhihui Li, Xiaojiang Chen, Alex Hauptmann
Summary: Scene graph is a structured representation of a scene, expressing objects, attributes, and relationships. With the development of computer vision, people aim for a higher level of understanding and reasoning about visual scenes. Scene graphs have attracted researchers' attention as a powerful tool for scene understanding.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Engineering, Mechanical
Runqiong Wang, Qinghua Song, Yezhen Peng, Jing Qin, Zhanqiang Liu, Zhaojun Liu
Summary: Cutting tool condition monitoring (TCM) techniques are important for optimizing production cost and machining quality in smart manufacturing. This paper proposes an autonomous TCM model that selects and fuses local-temporal features automatically, without relying on manual operation and domain knowledge. The model improves feature fusion performance by 9.48% on average compared to conventional methods and can be adapted to different cutting conditions with minimal computational resources.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Review
Computer Science, Artificial Intelligence
Xiaoting Wu, Xiaoyi Feng, Xiaochun Cao, Xin Xu, Dewen Hu, Miguel Bordallo Lopez, Li Liu
Summary: This paper provides a comprehensive review of the problem of Facial Kinship Verification (FKV), covering various aspects such as problem definition, challenges, applications, benchmark datasets, taxonomy of methods, and state-of-the-art performance. The paper also identifies gaps in current research and suggests potential future research directions.
INTERNATIONAL JOURNAL OF COMPUTER VISION
(2022)
Article
Automation & Control Systems
Yingshang Ge, Jianhua Zhang, Guohao Song, Kangyi Zhu
Summary: This paper proposes a tool wear predictive model based on the stacked multilayer denoising autoencoders (SMDAE) technique, particle swarm optimization with an adaptive learning strategy (PSO-ALS), and least squares support vector machine (LSSVM) to achieve real-time and precise monitoring of tool wear in the milling process. Cutting force and vibration information are used as monitoring signals. The model employs unique feature extraction and fusion methods, including multi-domain features extraction, PCA-based dimension reduction, and SMDAE-based dimension increment. Experimental results demonstrate the superior predictive performance of the proposed model compared to PSO-LSSVM, and the effectiveness of the SMDAE technique in improving prediction accuracy.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2023)
Article
Optics
Min Wang, Zhihao Zhuang, Kang Wang, Shudao Zhou, Zhanhua Liu
Summary: The proposed method for ground-based visible image classification based on TCNN combines the abilities of DL and TL. By using a sample database containing all ten cloud types and expanding it four-fold through enhancement processing, the method achieved a recognition accuracy of 92.3% for all ten ground-based cloud types after layer-by-layer fine-tuning to determine the optimal method.
Review
Computer Science, Artificial Intelligence
Ganesh Chandrasekaran, Tu N. Nguyen, D. Jude Hemanth
Summary: Sentiment analysis is crucial for identifying and classifying opinions on products or services, with traditional text-based methods no longer meeting the needs of analyzing multimodal data effectively.
WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY
(2021)
Article
Engineering, Electrical & Electronic
Renwang Li, Xiaolei Ye, Fangqing Yang, Ke-Lin Du
Summary: In order to improve the accuracy of tool wear prediction, a new attention-based composite neural network model called ConvLSTM-Att (1DCNN-LSTM-Attention) is proposed. The model uses a one-dimensional convolutional neural network (1D-CNN) to extract local multidimensional feature vectors and a long short-term memory (LSTM) network to learn the temporal relationship between the feature vectors. An attention mechanism is applied to enhance the extraction of key information from the tool-wearing temporal features. The proposed model outperforms other state-of-the-art models in terms of prediction accuracy while maintaining similar computational complexity.
