Article
Computer Science, Artificial Intelligence
Kapil K. Wankhade, Kalpana C. Jondhale, Snehlata S. Dongre
Summary: In the era of data mining, a hybrid method combining different clustering methods is proposed for handling concept drifting data streams, showing good performance on both synthetic and real datasets. The method achieves high accuracy rates compared to state-of-the-art algorithms.
APPLIED SOFT COMPUTING
(2021)
Review
Computer Science, Information Systems
Mustafa Tareq, Elankovan A. Sundararajan, Aaron Harwood, Azuraliza Abu Bakar
Summary: Clustering data streams, especially evolving data streams, presents challenges for conventional density grid-based clustering algorithms. In this study, a systematic literature review was conducted to summarize existing grid-based clustering algorithms, their limitations, and the challenges they face in handling evolving data streams. The findings revealed a variety of active research studies on density grid-based clustering.
Article
Computer Science, Information Systems
Abraham Otero, Paulo Felix, David G. Marquez, Constantino A. Garcia, Gabriel Caffarena
Summary: The increasing number of sensors may lead to data transmission delays or malfunctions, requiring the development of algorithms capable of handling missing or delayed data. This study presents a dynamic ensemble clustering algorithm based on evidence accumulation that can process multiple data streams, providing a method for generating final results even in the case of incomplete data.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Alessio Bechini, Francesco Marcelloni, Alessandro Renda
Summary: This article presents a novel fuzzy clustering algorithm TSF-DBSCAN, which shows competitive performance in handling streaming data. The algorithm deals with outliers and evolution of data streams by introducing fuzziness and a fading model, while ensuring computational and memory efficiency.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Computer Science, Information Systems
Conor Fahy, Shengxiang Yang
Summary: The proposed MDSC algorithm addresses the challenges of change in dynamic stream mining by using multiple density clustering and outlier buffering. Experimental results demonstrate its superior performance on a variety of real and synthetic data streams, showing good scalability and noise robustness.
IEEE TRANSACTIONS ON BIG DATA
(2022)
Article
Engineering, Electrical & Electronic
Alexander Hoogsteyn, Marta Vanin, Arpan Koirala, Dirk Van Hertem
Summary: This study implemented several phase identification methods based on voltage and power measurements, and conducted a consistent comparison across different smart meter accuracy classes and penetration levels using publicly available data. The results show that voltage data from smart meters generally yield better results compared to power data, and the proposed novel ensemble method can improve the accuracy of phase identification obtained from voltage data alone.
ELECTRIC POWER SYSTEMS RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Anh Vu Luong, Tien Thanh Nguyen, Alan Wee-Chung Liew, Shilin Wang
Summary: Ensemble learning is widely used for data classification, with heterogeneous ensemble systems combining different learning models to achieve greater diversity and performance. This study introduces a novel HEES method for dynamically selecting base classifier subsets to predict data streams, which shows competitive performance in experiments.
PATTERN RECOGNITION
(2021)
Article
Physics, Multidisciplinary
Mingjing Du, Fuyu Wu
Summary: The study proposes a new grid-based clustering algorithm called GCBD, which effectively addresses some of the problems in traditional algorithms, such as the difficulty in parameter tuning and handling clusters with overlapping regions and varying densities.
Article
Construction & Building Technology
Elham Eskandarnia, Hesham M. Al-Ammal, Riadh Ksantini
Summary: This study proposes an unsupervised deep clustering framework for load profiling. The framework utilizes an autoencoder for data representation and combines dimensionality reduction with clustering, resulting in improved load profiling performance.
SUSTAINABLE CITIES AND SOCIETY
(2022)
Article
Computer Science, Artificial Intelligence
Hui Tian, Lulu Wang, Hong Shen, Alan Wee-Chung Liew
Summary: This article addresses the challenge of feature evolution in evolving data stream classification and presents efficient ensemble methods for both single-label and multi-label evolving data streams. For single-label classification, an improved unsupervised algorithm is used, along with the Ball-tree searching technique to reduce the time complexity of feature selection. For multi-label classification, an effective fixed-size ensemble classifier with a weight adaptation strategy is proposed to cope with arbitrary feature evolution. Experimental results show that the algorithms outperform existing methods in terms of classification accuracy and efficiency.
IEEE INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Elham S. Kashani, Saeed Bagheri Shouraki, Yaser Norouzi, Bernard De Baets
Summary: In this paper, a novel density-grid-based method for clustering k-dimensional data is proposed, which simultaneously leverages the advantages of fuzzy logic, density-based and grid-based clustering. The method divides the k-dimensional data space into cells and spreads data points within these cells to compute the clusters.
APPLIED INTELLIGENCE
(2023)
Article
Spectroscopy
Shaohui Yu, Jing Liu
Summary: This paper proposes an ensemble calibration model FDA-EM-PLS (functional data analysis-ensemble learning-partial least squares) for near-infrared spectroscopy, based on the functional data analysis method. By dividing the near-infrared spectroscopy into intervals and conducting functional data analysis, clustering, and Monte Carlo sampling, this model achieves accurate detection of corn and soil data.
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
(2022)
Article
Engineering, Chemical
Shuo Hu, Yonglin Pang, Yong He, Yuan Yang, Henian Zhang, Linmeng Zhang, Beiyi Zheng, Caiyun Hu, Qing Wang
Summary: With the continuous enrichment of big data technology application scenarios, the clustering analysis of a data stream has become a research hotspot. However, the existing data stream clustering algorithms usually have some defects, such as inability to cluster arbitrary shapes, difficulty determining some important parameters, and static clustering. In this study, a novel algorithm called MDDSDB-GC is proposed, which effectively overcomes these conventional defects and achieves better overall performance in clustering analysis of data streams.
Article
Chemistry, Analytical
Gheorghe Grigoras, Maria Simona Raboaca, Catalin Dumitrescu, Daniela Lucia Manea, Traian Candin Mihaltan, Violeta-Carolina Niculescu, Bogdan Constantin Neagu
Summary: The article discusses the promotion and implementation of the concept of smart grids, which includes smart metering, smart homes, and electric cars. It also explores the development of high-performance electrical equipment, telecommunications technologies, and computing infrastructure based on AI algorithms for the creation of smart cities. The article presents contributions in consumer classification and load profiling modeling, as well as the efficiency of clustering techniques in load analysis and simulation of medium-voltage/low-voltage distribution transformers to electricity meters.
Article
Chemistry, Multidisciplinary
Redhwan Al-amri, Raja Kumar Murugesan, Mubarak Almutairi, Kashif Munir, Gamal Alkawsi, Yahia Baashar
Summary: As applications generate massive amounts of data streams, analyzing and clustering this data has become a critical field of research. A new online clustering algorithm has been developed using a tempo-spatial hyper cube (TSHC) to handle evolving data streams.
APPLIED SCIENCES-BASEL
(2022)
Meeting Abstract
Oncology
Shifu Chen, Hongyue Qu, Bo Yang, Tanxiao Huang, Xiaoni Zhang, Yuanyuan Liu, Yanging Zhou, Qin You, Kamen Ivanov, Jia Gu
JOURNAL OF CLINICAL ONCOLOGY
(2019)
Correction
Computer Science, Interdisciplinary Applications
Zhanyong Mei, Kamen Ivanov, Guoru Zhao, Huihui Li, Lei Wang
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
(2020)
Article
Chemistry, Analytical
Yan Yan, Kamen Ivanov, Olatunji Mumini Omisore, Tobore Igbe, Qiuhua Liu, Zedong Nie, Lei Wang
Article
Computer Science, Artificial Intelligence
Imran Khan, Zongwei Luo, Joshua Zhexue Huang, Waseem Shahzad
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2020)
Article
Computer Science, Artificial Intelligence
Imran Khan, Zongwei Luo, Abdul Khalique Shaikh, Rachid Hedjam
Summary: In this paper, a new ensemble clustering method is proposed, which incorporates two new steps in the standard fuzzy k-means algorithm to determine the optimal number of input clusterings and the optimal number of clusters in each clustering. Experiments show that the proposed algorithm outperformed well-known clustering algorithms in real cancer gene expression profiles.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Engineering, Multidisciplinary
Zhanyong Mei, Kamen Ivanov, Guoru Zhao, Yuanyuan Wu, Mingzhe Liu, Lei Wang
Review
Information Science & Library Science
Abdul Khalique Shaikh, Nisar Ahmad, Imran Khan, Saqib Ali
Summary: This study used a bibliometric approach to conduct a systematic review of literature on e-participation and e-government, identifying leading sources of knowledge and exploring conceptual structures to show trends. The study outlines the current state of knowledge in this field, with key findings on influential journals, authors, and articles.
INTERNATIONAL JOURNAL OF ELECTRONIC GOVERNMENT RESEARCH
(2021)
Article
Multidisciplinary Sciences
Abdul Khalique Shaikh, Amril Nazir, Imran Khan, Abdul Salam Shah
Summary: Smart grids and smart homes are receiving attention in smart cities. Machine learning methods, such as neural networks, have been successful in energy consumption prediction, but face challenges due to uncertain data and algorithm limitations. Existing research has focused on small datasets and single user profiles, making it difficult to apply models to large datasets with different customer profiles. Proposed model enhances the N-BEATS method with a large dataset and improves prediction accuracy for multiple customers' energy consumption. Incorporating covariates into the model improves accuracy by learning past and future energy consumption patterns.
SCIENTIFIC REPORTS
(2022)
Article
Chemistry, Physical
Teodor I. Milenov, Dimitar A. Dimov, Ivalina A. Avramova, Stefan K. Kolev, Dimitar V. Trifonov, Georgi V. Avdeev, Daniela B. Karashanova, Biliana C. Georgieva, Kamen V. Ivanov, Evgenia P. Valcheva
Summary: This study focuses on the evaluation of the influence of chemical treatment on the structure and morphology of carbon phases. The chemical interactions of two types of graphite and carbon black with acetone, toluene, and phenol were studied. Experimental and theoretical methods were used to determine the chemical and phase composition, as well as the morphology and structure. The findings suggest that the treatment of graphite powders and carbon black with acetone, toluene, or phenol can be an effective preliminary stage for their modification and conversion into graphene-like phases.
JOURNAL OF CHEMICAL PHYSICS
(2023)
Article
Engineering, Electrical & Electronic
Yan Yan, Liang Ma, Yu-Shi Liu, Kamen Ivanov, Jia-Hong Wang, Jing Xiong, Ang Li, Yini He, Lei Wang
Summary: This article proposes a method for recognizing mental workload through topological investigation of brain functional connectivity network. Graph-filtration-based features are extracted using the persistent homology technique to reveal brain state variations. The experimental results demonstrate excellent distinguishing ability of the proposed method in brain state recognition. This work is the first investigation of EEG-based mental workload evaluation using persistent homology analysis.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Computer Science, Information Systems
Kamen Ivanov, Zhanyong Mei, Martin Penev, Ludwig Lubich, Omisore Olatunji Mumini, Sau Nguyen Van, Yan Yan, Lei Wang
Article
Computer Science, Information Systems
Huihui Li, Wenjing Du, Kai Fan, Junsong Ma, Kamen Ivanov, Lei Wang
Proceedings Paper
Computer Science, Artificial Intelligence
Zhanyong Mei, Kamen Ivanov, Ludwig Lubich, Lei Wang
INTELLIGENT ROBOTICS AND APPLICATIONS, ICIRA 2019, PT III
(2019)
Proceedings Paper
Engineering, Biomedical
Huihui Li, Wenjing Du, Kamen Ivanov, Yuchao Yang, Yang Zhan, Lei Wang
2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
(2019)
Proceedings Paper
Engineering, Biomedical
Olatunji Mumini Omisore, Shipeng Han, Tao Zhou, Yousef Al-Handarish, Wenjing Du, Kamen Ivanov, Lei Wang
2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
(2019)
Article
Computer Science, Artificial Intelligence
Rui Lv, Dingheng Wang, Jiangbin Zheng, Zhao-Xu Yang
Summary: In this paper, the authors investigate tensor decomposition for neural network compression. They analyze the convergence and precision of tensor mapping theory, validate the rationality of tensor mapping and its superiority over traditional tensor approximation based on the Lottery Ticket Hypothesis. They propose an efficient method called 3D-KCPNet to compress 3D convolutional neural networks using the Kronecker canonical polyadic (KCP) tensor decomposition. Experimental results show that 3D-KCPNet achieves higher accuracy compared to the original baseline model and the corresponding tensor approximation model.
Article
Computer Science, Artificial Intelligence
Xiangkun He, Zhongxu Hu, Haohan Yang, Chen Lv
Summary: In this paper, a novel constrained multi-objective reinforcement learning algorithm is proposed for personalized end-to-end robotic control with continuous actions. The approach trains a single model using constraint design and a comprehensive index to achieve optimal policies based on user-specified preferences.
Article
Computer Science, Artificial Intelligence
Zhijian Zhuo, Bilian Chen, Shenbao Yu, Langcai Cao
Summary: In this paper, a novel method called Expansion with Contraction Method for Overlapping Community Detection (ECOCD) is proposed, which utilizes non-negative matrix factorization to obtain disjoint communities and applies expansion and contraction processes to adjust the degree of overlap. ECOCD is applicable to various networks with different properties and achieves high-quality overlapping community detection.
Article
Computer Science, Artificial Intelligence
Yizhe Zhu, Chunhui Zhang, Jialin Gao, Xin Sun, Zihan Rui, Xi Zhou
Summary: In this work, the authors propose a Contrastive Spatio-Temporal Distilling (CSTD) approach to improve the detection of high-compressed deepfake videos. The approach leverages spatial-frequency cues and temporal-contrastive alignment to fully exploit spatiotemporal inconsistency information.
Review
Computer Science, Artificial Intelligence
Laijin Meng, Xinghao Jiang, Tanfeng Sun
Summary: This paper provides a review of coverless steganographic algorithms, including the development process, known contributions, and general issues in image and video algorithms. It also discusses the security of coverless steganography from theoretical analysis to actual investigation for the first time.
Article
Computer Science, Artificial Intelligence
Yajie Bao, Tianwei Xing, Xun Chen
Summary: Visual question answering requires processing multi-modal information and effective reasoning. Neural-symbolic learning is a promising method, but current approaches lack uncertainty handling and can only provide a single answer. To address this, we propose a confidence based neural-symbolic approach that evaluates NN inferences and conducts reasoning based on confidence.
Article
Computer Science, Artificial Intelligence
Anh H. Vo, Bao T. Nguyen
Summary: Interior style classification is an interesting problem with potential applications in both commercial and academic domains. This project proposes a method named ISC-DeIT, which combines data-efficient image transformer architectures and knowledge distillation, to address the interior style classification problem. Experimental results demonstrate a significant improvement in predictive accuracy compared to other state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
Shashank Kotyan, Danilo Vasconcellos Vargas
Summary: This article introduces a novel augmentation technique called Dynamic Scanning Augmentation to improve the accuracy and robustness of Vision Transformer (ViT). The technique leverages dynamic input sequences to adaptively focus on different patches, resulting in significant changes in ViT's attention mechanism. Experimental results demonstrate that Dynamic Scanning Augmentation outperforms ViT in terms of both robustness to adversarial attacks and accuracy against natural images.
Article
Computer Science, Artificial Intelligence
Hiba Alqasir, Damien Muselet, Christophe Ducottet
Summary: The article proposes a solution to improve the learning process of a classification network by providing shape priors, reducing the need for annotated data. The solution is tested on cross-domain digit classification tasks and a video surveillance application.
Article
Computer Science, Artificial Intelligence
Dexiu Ma, Mei Liu, Mingsheng Shang
Summary: This paper proposes a method using neural dynamics solvers to solve infinity-norm optimization problems. Two improved solvers are constructed and their effectiveness and superiority are demonstrated through theoretical analysis and simulation experiments.
Article
Computer Science, Artificial Intelligence
Francesco Gregoretti, Giovanni Pezzulo, Domenico Maisto
Summary: Active Inference is a computational framework that uses probabilistic inference and variational free energy minimization to describe perception, planning, and action. cpp-AIF is a header-only C++ library that provides a powerful tool for implementing Active Inference for Partially Observable Markov Decision Processes through multi-core computing. It is cross-platform and improves performance, memory management, and usability compared to existing software.
Article
Computer Science, Artificial Intelligence
Zelin Ying, Dawei Cheng, Cen Chen, Xiang Li, Peng Zhu, Yifeng Luo, Yuqi Liang
Summary: This paper proposes a novel stock market trends prediction framework called SMART, which includes a self-supervised stock technical data sequence embedding model S3E. By training with multiple self-supervised auxiliary tasks, the model encodes stock technical data sequences into embeddings and uses the learned sequence embeddings for predicting stock market trends. Extensive experiments on China A-Shares market and NASDAQ market prove the high effectiveness of our model in stock market trends prediction, and its effectiveness is further validated in real-world applications in a leading financial service provider in China.
Article
Computer Science, Artificial Intelligence
Hao Li, Hao Jiang, Dongsheng Ye, Qiang Wang, Liang Du, Yuanyuan Zeng, Liu Yuan, Yingxue Wang, C. Chen
Summary: DHGAT1, a dynamic hyperbolic graph attention network, utilizes hyperbolic metric properties to embed dynamic graphs. It employs a spatiotemporal self-attention mechanism and weighted node representations, resulting in excellent performance in link prediction tasks.
Article
Computer Science, Artificial Intelligence
Jiehui Huang, Zhenchao Tang, Xuedong He, Jun Zhou, Defeng Zhou, Calvin Yu-Chian Chen
Summary: This study proposes a progressive learning multi-scale feature blending model for image deraining tasks. The model utilizes detail dilation and texture extraction to improve the restoration of rainy images. Experimental results show that the model achieves near state-of-the-art performance in rain removal tasks and exhibits better rain removal realism.
Article
Computer Science, Artificial Intelligence
Lizhi Liu, Zilin Gao, Yinhe Wang, Yongfu Li
Summary: This paper proposes a novel discrete-time interconnected model for depicting complex dynamical networks. The model consists of nodes and edges subsystems, which consider the dynamic characteristic of both nodes and edges. By designing control strategies and coupling modes, the stabilization and synchronization of the network are achieved. Simulation results demonstrate the effectiveness of the proposed methods.