Article
Computer Science, Information Systems
Wan-Lei Zhao, Hui Wang, Peng-Cheng Lin, Chong-Wah Ngo
Summary: This paper addresses the issue of merging k-nearest neighbor (k-NN) graphs in two different scenarios. A symmetric merge algorithm is proposed to combine two approximate k-NN graphs, facilitating large-scale processing. A joint merge algorithm is also proposed to expand an existing k-NN graph with a raw dataset, enabling the incremental construction of a hierarchical approximate k-NN graph.
IEEE TRANSACTIONS ON BIG DATA
(2022)
Article
Computer Science, Interdisciplinary Applications
Sherif Ahmed Abu El-Magd, Sk Ajim Ali, Quoc Bao Pham
Summary: This study implemented different machine learning algorithms to predict landslide events in the Jabal Farasan area of northwest Jeddah, Saudi Arabia, identifying factors such as mining activities and high slope angles as contributors to landslide susceptibility. The model accuracy for predicting landslide occurrence locations using remote sensing data ranged between 86% and 89%. The generated landslide susceptibility map aims to assist in hazard management and control for natural disasters in the region.
EARTH SCIENCE INFORMATICS
(2021)
Article
Computer Science, Hardware & Architecture
Martin Aumueller, Sariel Har-Peled, Sepideh Mahabadi, Rasmus Pagh, Francesco Silvestri
Summary: This paper studies the r-NN problem in similarity search in the context of individual fairness and equal opportunities. The authors propose efficient data structures for the fair NN problem and highlight the inherent unfairness of existing NN data structures through experimental evaluation.
COMMUNICATIONS OF THE ACM
(2022)
Article
Engineering, Biomedical
Chayashree Patgiri, Amrita Ganguly
Summary: Detection of anomalous cells in blood diseases is crucial, and automatic recognition with robust segmentation and classification methods can improve efficiency. A novel hybrid segmentation method using features extracted from cells for training and testing classifiers shows potential for high performance in classifying normal and sickle cells.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2021)
Article
Automation & Control Systems
Hongjiao Guan, Long Zhao, Xiangjun Dong, Chuan Chen
Summary: Imbalanced data classification is a challenging problem in many applications. We propose an extended natural neighbor (ENaN) concept without parameter k to improve the quality of generated examples by accurately reflecting the local distribution. ENaN-based SMOTE (ENaNSMOTE) can improve the sample distribution obtained by SMOTE and NaNSMOTE.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Jianping Gou, Liyuan Sun, Lan Du, Hongxing Ma, Taisong Xiong, Weihua Ou, Yongzhao Zhan
Summary: This article proposes a novel representation coefficient-based k-nearest centroid neighbor method (RCKNCN) aiming to improve the classification performance and reduce the sensitivity to the neighborhood size k. The method captures both the proximity and geometry of k-nearest neighbors and learns to differentiate the contribution of each neighbor to the classification of a testing sample. A weighted majority voting algorithm is also proposed under the RCKNCN framework.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Wan-Lei Zhao, Hui Wang, Chong-Wah Ngo
Summary: This paper presents a simple yet effective solution for approximate k-nearest neighbor search and graph construction. The solution integrates graph construction and search tasks, and supports dynamic updates on the built graph.
IEEE TRANSACTIONS ON MULTIMEDIA
(2022)
Article
Automation & Control Systems
Amit Kumar Gangwar, Om Prakash Mahela, Bhuvnesh Rathore, Baseem Khan, Hassan Haes Alhelou, Pierluigi Siano
Summary: This article introduces an algorithm for protecting transmission lines, which detects and locates faults using k-means clustering and weighted k-nearest neighbor (k-NN) regression. The algorithm synchronizes and samples three-phase current signals, computes cumulative differential sum (CDS), and uses various case studies to validate its robustness.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Benqiang Wang, Shunxiang Zhang
Summary: The study proposes a new locally adaptive k-nearest centroid neighbour classification method based on average distance, which improves classification performance by finding nearest centroid neighbours to determine k neighbours and deriving discrimination classes with different k values based on the number and distribution of neighbours, resulting in better performance compared to other state-of-the-art KNN algorithms.
CONNECTION SCIENCE
(2022)
Article
Computer Science, Artificial Intelligence
Simone Disabato, Manuel Roveri
Summary: Tiny machine learning (TML) is a new research area focused on designing machine and deep learning techniques for embedded systems and IoT devices. This article introduces a TML for concept drift (TML-CD) solution, which utilizes deep learning feature extractors and a k-nearest neighbors (k-NNs) classifier to adapt to changes in the data-generating process. Experimental results demonstrate the effectiveness of the proposed solution.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Information Systems
Yibang Ruan, Yanshan Xiao, Zhifeng Hao, Bo Liu
Summary: The paper introduces a nearest-neighbor search model for distance metric learning (NNS-DML), which constructs metric optimization constraints by searching different optimal nearest-neighbor numbers for each training instance. This model reduces the influence of irrelevant features on similar and dissimilar instance pairs and develops a k-free nearest-neighbor model for classification problems. Extensive experiments show that NNS-DML outperforms state-of-the-art distance metric learning methods.
INFORMATION SCIENCES
(2021)
Article
Biochemistry & Molecular Biology
Hardeep Sandhu, Rajaram Naresh Kumar, Prabha Garg
Summary: The study aims to distinguish between AChE inhibitors and non-inhibitors using machine learning models, with key features identified through descriptor analysis. The fingerprint model based on the random forest algorithm performed the best, achieving an accuracy of 85.38% on the test set.
MOLECULAR DIVERSITY
(2022)
Article
Computer Science, Artificial Intelligence
Shinan Lang, Fangyi Chen, Yiheng Cai
Summary: This paper proposes a highly transparent material classification method based on the imaging model of a time-of-flight (ToF) camera, using features such as refractive index, reflectivity, and transmissivity. The experimental results show that the classification accuracy of this method reaches 94.1% in transparent material classification.
MACHINE VISION AND APPLICATIONS
(2023)
Article
Automation & Control Systems
Atsutake Kosuge, Keisuke Yamamoto, Yukinori Akamine, Takashi Oshima
Summary: This article presents an FPGA-based ICP accelerator which accelerates the object-pose estimation for picking robots through algorithm-level and hardware-level techniques, achieving significant improvement in picking throughput.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2021)
Article
Engineering, Electrical & Electronic
Xianhao Fan, Jiefeng Liu, Benghui Lai, Yiyi Zhang, Chaohai Zhang
Summary: A model for moisture estimation in transformer oil-paper insulation is proposed using frequency-domain spectroscopy (FDS) and intelligent algorithm, with accuracy and applicability discussed in laboratory and field conditions.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
