Article
Computer Science, Information Systems
Guoqiu Wen, Xianxian Li, Yonghua Zhu, Linjun Chen, Qimin Luo, Malong Tan
Summary: This paper proposes a one-step spectral rotation clustering method OSRCIH, which integrates self-paced learning and spectral rotation clustering in a unified framework to simultaneously consider sample selection and dimensionality reduction. Experimental analysis shows that OSRCIH can effectively recognize important samples and features in imbalanced high-dimensional data, improving clustering performance.
INFORMATION PROCESSING & MANAGEMENT
(2021)
Article
Computer Science, Information Systems
Myat Cho Mon Oo, Thandar Thein
Summary: In this paper, an efficient predictive analytics system for high dimensional big data is proposed by enhancing scalable random forest algorithm on the Apache Spark platform.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Chemistry, Multidisciplinary
Umar Subhan Malhi, Junfeng Zhou, Cairong Yan, Abdur Rasool, Shahbaz Siddeeq, Ming Du
Summary: This paper proposes a fashion image clustering method based on deep clustering, which uses convolutional neural networks to generate high-dimensional feature vectors and then reduces dimensions through auto-encoders before performing clustering. By jointly learning and optimizing the dimensionality reduction process and the clustering task, the proposed method achieves state-of-the-art performance.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Software Engineering
Jan-Tobias Sohns, Michaela Schmitt, Fabian Jirasek, Hans Hasse, Heike Leitte
Summary: This paper discusses the importance of embeddings of high-dimensional data and the difficulty in explaining them. By introducing Non-Linear Embeddings Surveyor (NoLiES) and a new augmentation strategy called rangesets, users are able to quickly observe the structure and detect outliers.
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
(2022)
Article
Mathematics, Applied
Erez Peterfreund, Matan Gavish
Summary: This paper investigates the performance of MDS in high dimensions and measurement noise, introducing MDS+ as an improved variant that offers better embedding quality and calculates the optimal embedding dimension. MDS+ is proven to be the unique, asymptotically optimal shrinkage function compared to traditional MDS.
APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS
(2021)
Article
Computer Science, Artificial Intelligence
Caio Flexa, Walisson Gomes, Igor Moreira, Ronnie Alves, Claudomiro Sales
Summary: Dimensionality Reduction (DR) is important in understanding high-dimensional data, and the Polygonal Coordinate System (PCS) presented in this work offers an efficient geometric approach for this purpose. The study also introduces a new version of the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm using a PCS-based deterministic strategy, showcasing the efficiency of PCS in data embedding compared to other DR algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Chemistry, Multidisciplinary
Hyeongmin Cho, Sangkyun Lee
Summary: This paper proposes two data quality measures that can compute class separability and in-class variability for a given dataset, focusing on large-scale high-dimensional data such as images and videos. The measures are efficient and compatible with classical measures on small-scale data, offering statistical benefits on large-scale datasets.
APPLIED SCIENCES-BASEL
(2021)
Article
Construction & Building Technology
Waqas Khan, Shalika Walker, Wim Zeiler
Summary: Worldwide cities are becoming more sustainable and are being monitored using data collection techniques. A framework is proposed to identify key features of high consumption and generation areas based on building characteristics by applying dimensionality reduction techniques to city-scale data. The evaluation results showed that UMAP algorithm quickly approaches a threshold and t-SNE algorithm is more sensitive to the perplexity parameter. The proposed framework can assist grid operators and energy planners in extracting information from energy consumption data at the neighbourhood level.
SUSTAINABLE CITIES AND SOCIETY
(2023)
Article
Mathematical & Computational Biology
Yasser Aleman-Gomez, Ana Arribas-Gil, Manuel Desco, Antonio Elias, Juan Romo
Summary: Functional magnetic resonance imaging (fMRI) is a non-invasive technique that studies brain activity by measuring blood flow changes. We propose a novel visualization technique for high-dimensional functional brain imaging data, aiding in the identification of neuroscientific patterns.
STATISTICS IN MEDICINE
(2022)
Article
Computer Science, Software Engineering
Zonglin Tian, Xiaorui Zhai, Daan van Driel, Gijs van Steenpaal, Mateus Espadoto, Alexandru Telea
Summary: Multidimensional projections are effective for visualizing high-dimensional datasets to find structures, and explanatory mechanisms can enhance insights, while image-based approach stands out for large MP scatterplots.
COMPUTERS & GRAPHICS-UK
(2021)
Article
Biotechnology & Applied Microbiology
Alexandra A. Portnova-Fahreeva, Fabio Rizzoglio, Maura Casadio, Ferdinando A. Mussa-Ivaldi, Eric Rombokas
Summary: Dimensionality reduction techniques are useful for simplifying complex hand kinematics. Training practices that make the relationship between low-dimensional controls and high-dimensional systems more explicit can aid learning.
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
(2023)
Article
Computer Science, Hardware & Architecture
Guoqiu Wen, Yonghua Zhu, Linjun Chen, Mengmeng Zhan, Yangcai Xie
Summary: The proposed method considers both local and global structures for nonlinear clustering, achieving competitive clustering performance on real data sets compared to state-of-the-art methods.
Article
Neurosciences
Shengchao Zhang, Sarah E. Goodale, Benjamin P. Gold, Victoria L. Morgan, Dario J. Englot, Catie Chang
Summary: Patterns in fMRI data can reflect dynamic changes in the brain and are related to individual and group differences in behavior, cognition, and clinical traits. Detecting vigilance states in fMRI data without external measurements is challenging. This study shows that vigilance levels can be detected in the low-dimensional structure of fMRI data, even within individual time frames.
Article
Computer Science, Artificial Intelligence
Chun-Na Li, Yuan-Hai Shao, Wei-Jie Chen, Zhen Wang, Nai-Yang Deng
Summary: This paper proposes a generalized Lp-norm 2DLDA framework named G2DLDA, which can achieve robustness by selecting proper p value and improve generalization performance and avoid singularity by introducing a regularization term.
Article
Computer Science, Artificial Intelligence
Xiang Wang, Junxing Zhu, Zichen Xu, Kaijun Ren, Xinwang Liu, Fengyun Wang
Summary: In this paper, a local nonlinear dimensionality reduction method named Vec2vec is proposed, which utilizes a neural network with one hidden layer to reduce computational complexity. Experimental results demonstrate that Vec2vec outperforms other dimensionality reduction methods in data classification and clustering tasks, and it also requires less computational time in high-dimensional data. Additionally, a lightweight method called Approximate Vec2vec (AVec2vec) is introduced, which achieves competitive performance with UMAP and other local dimensionality reduction methods.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Information Systems
Haseeb Tariq, Muhammad Kashif Hanif, Muhammad Umer Sarwar, Sabeen Bari, Muhammad Shahzad Sarfraz, Rozita Jamili Oskouei
Summary: This study utilizes time series to predict crime rates in order to find practical crime prevention solutions. Machine learning plays a crucial role in understanding and analyzing future trends in violations. Different time-series forecasting models are used to predict crimes.
SECURITY AND COMMUNICATION NETWORKS
(2021)
Article
Mathematics, Interdisciplinary Applications
Muhammad Kashif Hanif, Naba Ashraf, Muhammad Umer Sarwar, Deleli Mesay Adinew, Reehan Yaqoob
Summary: Autism spectrum disorder is a neurological disorder that typically begins in early childhood and has complex causes. Early detection of autism spectrum disorder is beneficial for children's mental health. This study applied various machine and deep learning algorithms to classify the severity of autism spectrum disorder and utilized optimization techniques to improve performance. The deep neural network outperformed other approaches.
Article
Mathematics, Interdisciplinary Applications
Maria Bibi, Muhammad Kashif Hanif, Muhammad Umer Sarwar, Muhammad Irfan Khan, Shouket Zaman Khan, Casper Shikali Shivachi, Asad Anees
Summary: Multiple prediction models were developed to monitor the population dynamics of Asian citrus psyllid in citrus-growing regions of Pakistan, using regression algorithms of machine learning. A deep neural network-based prediction model resulted in the least root mean squared error values when predicting egg, nymph, and adult populations of the pest.
Article
Computer Science, Information Systems
Muhammad Kashif Hanif, Karl-Heinz Zimmermann, Asad Anees
Summary: Bipartite graphs are commonly used in biological and physical sciences, and finding shortest paths in these graphs can be efficiently solved using dynamic programming algorithms. This study introduces parallel versions of Floyd-Warshall and Torgasin-Zimmermann algorithms, implemented on graphics processing unit using tropical matrix product. The performance of these algorithms is compared under different scenarios and parameters.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Shaeela Ayesha, Muhammad Kashif Hanif, Ramzan Talib
Summary: Predictive analytics is crucial in health informatics, with multi-source, multi-modal data improving disease prediction, diagnosis, and medication processes. Dimensionality reduction techniques and multi-modal data fusion enhance analysis performance, although handling diverse data poses challenges.
Article
Mathematics, Interdisciplinary Applications
Muhammad Irfan Khan, Muhammad Kashif Hanif, Ramzan Talib
Summary: This study proposes a cross-domain qualitative feature-based approach to match caricature with a mugshot. It uses Haar-like features and point distribution measure to locate exaggerated facial features and calculates the difference vector based on the ratios between different facial features. The implementation based on convolutional neural network achieves better performance.
Article
Computer Science, Hardware & Architecture
Zubair Nabi, Ramzan Talib, Muhammad Kashif Hanif, Muhammad Awais
Summary: Digitalization has changed the way of information processing and new techniques of legal data processing are evolving. This research paper presents a three-tier contextual text mining framework through ontologies for judicial corpora, and the experimental results and evaluations show significant improvements.
COMPUTER SYSTEMS SCIENCE AND ENGINEERING
(2022)
Article
Computer Science, Information Systems
Sami Ullah, Muhammad Ramzan Talib, Toqir A. Rana, Muhammad Kashif Hanif, Muhammad Awais
Summary: This paper proposes a model that utilizes deep learning and machine learning approaches for the classification of users' emotions from Urdu conversational text. The experimental evaluation shows encouraging results with 67% accuracy for Urdu dialogue datasets.
CMC-COMPUTERS MATERIALS & CONTINUA
(2022)
Article
Computer Science, Information Systems
Muhammad Zubair, Muhammad Kashif Hanif, Eatedal Alabdulkreem, Yazeed Ghadi, Muhammad Irfan Khan, Muhammad Umer Sarwar, Ayesha Hanif
Summary: The secondary structure of a protein is crucial for understanding its tertiary structure. In this study, deep learning models were proposed to predict the protein secondary structure by processing amino acid sequences, achieving high accuracy rates.
CMC-COMPUTERS MATERIALS & CONTINUA
(2022)
Proceedings Paper
Computer Science, Information Systems
Kainat Rizwan, Sehar Babar, Sania Nayab, Muhammad Kashif Hanif
Summary: Cybersecurity is crucial in the digital market as users often face harassment in online chats, and it is important for organizations to tackle this issue effectively.
2021 INTERNATIONAL CONFERENCE OF MODERN TRENDS IN INFORMATION AND COMMUNICATION TECHNOLOGY INDUSTRY (MTICTI 2021)
(2021)
Article
Computer Science, Information Systems
Muhammad Bilal Sarwar, Muhammad Kashif Hanif, Ramzan Talib, Muhammad Younas, Muhammad Umer Sarwar
Summary: This paper proposes a method for detecting and categorizing darknet traffic using deep learning, achieving significant results in darknet traffic identification through steps such as data preprocessing, feature selection, and machine learning algorithms.
Article
Computer Science, Information Systems
Fakeeha Fatima, Ramzan Talib, Muhammad Kashif Hanif, Muhammad Awais
Article
Computer Science, Information Systems
Muhammad Umer Sarwar, Muhammad Kashif Hanif, Ramzan Talib, Muhammad Haris Aziz
Article
Computer Science, Information Systems
Muhammad Arslan Amin, Muhammad Kashif Hanif, Muhammad Umer Sarwar, Mohsin Abbas, Muhammad Haroon Jilani, Usman Nasir, Muhammad Bilal Sarwar, Hafiz Muhammad Talha
INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY
(2019)
Article
Computer Science, Artificial Intelligence
C. Lopez-Molina, S. Iglesias-Rey, B. De Baets
Summary: Quantitative image comparison is a critical topic in image processing literature, with diverse applications. Existing measures of comparison often overlook the context in which the comparison takes place. This paper presents a context-aware comparison method for binary images, tested on the BSDS500 benchmark.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Lorenz Linhardt, Klaus-Robert Mueller, Gregoire Montavon
Summary: This paper investigates the issue of mismatches between the decision strategy of the explainable model and the user's domain knowledge, and proposes a new method EGEM to mitigate hidden flaws in the model. Experimental results demonstrate that the approach can significantly reduce reliance on Clever Hans strategies and improve the accuracy of the model on new data.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Zhimin Shao, Weibei Dou, Yu Pan
Summary: This paper proposes a novel algorithm, Dual-level Deep Evidential Fusion (DDEF), to integrate multimodal information at both the BBA level and multimodal level, aiming to enhance accuracy, robustness, and reliability. The DDEF approach utilizes the Dirichlet framework and BBA methods for effective uncertainty estimation and employs the Dempster-Shafer Theory for dual-level fusion. The experimental results show that the proposed DDEF outperforms existing methods in synthetic digit classification and real-world medical prognosis after BCI treatment.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Abhishek K. Ghosh, Danilo S. Catelli, Samuel Wilson, Niamh C. Nowlan, Ravi Vaidyanathan
Summary: The inability of current FM monitoring methods to be used outside clinical environments has made it challenging to understand the nature and evolution of FM. This investigation introduces a novel wearable FM monitor with a heterogeneous sensor suite and a data fusion architecture to efficiently capture and separate FM from interfering artifacts. The performance of the device and architecture were validated through at-home use, demonstrating high accuracy in detecting and recognizing FM events. This research is a major milestone in the development of low-cost wearable FM monitors for pervasive monitoring of FM in unsupervised environments.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Jianlei Kong, Xiaomeng Fan, Min Zuo, Muhammet Deveci, Xuebo Jin, Kaiyang Zhong
Summary: In this study, we propose an intelligent traffic flow prediction framework based on the adaptive dual-graphic transformer with a cross-fusion strategy, aiming to uncover latent graphic feature representations that transcend temporal and spatial limitations. By establishing a traffic spatiotemporal prediction model using a cross-fusion attention mechanism, our proposed model achieves superior prediction performance on practical urban traffic flow datasets, particularly for long-term predictions.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Huilai Zhi, Jinhai Li
Summary: This article addresses the issue that conflict analysis based on single-valued information systems is no longer valid. It proposes a conflict analysis method based on component similarity, which uses three-way n-valued concept lattices to handle set-valued formal contexts and realizes fast conflict analysis from an information fusion viewpoint. Experimental results verify the effectiveness of this method in reducing time consumption.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Huchang Liao, Jiaxin Qi, Jiawei Zhang, Chonghui Zhang, Fan Liu, Weiping Ding
Summary: In this paper, a hospital selection approach based on a fuzzy multi-criterion decision-making method is proposed. This approach considers sentiment evaluation values of unstructured data from online reviews and structured data of public indexes simultaneously. The methodology involves collecting and processing online reviews, classifying topics and sentiments, quantifying sentiment analysis results using fuzzy numbers, and obtaining final preference scores of hospitals based on patients' preferences. A case study and robustness analysis are conducted to validate the effectiveness of the method.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Faramarz Farhangian, Rafael M. O. Cruz, George D. C. Cavalcanti
Summary: The proliferation of social networks has posed challenges in combating fake news, but automatic fake news detection using artificial intelligence has become more feasible. This paper revisits the definitions and perspectives of fake news and proposes an updated taxonomy, based on multiple criteria, for the field. The study finds that optimal feature extraction techniques vary depending on the dataset, and context-dependent models based on transformer models consistently exhibit superior performance.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Mariana A. Souza, Robert Sabourin, George D. C. Cavalcanti, Rafael M. O. Cruz
Summary: In this study, a dynamic selection technique is proposed to handle sparse and overlapped data. The technique leverages the relationships between instances and classifiers to learn a dynamic classifier combination rule. Experimental results show that the proposed method outperforms static selection and other dynamic selection techniques.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Bin Yu, Ruihui Xu, Mingjie Cai, Weiping Ding
Summary: This paper introduces a clustering method based on non-Euclidean metric and multi-granularity staged clustering to address the challenges posed by complex spatial structure data to traditional clustering methods. The method improves the similarity measure and employs an attenuation-diffusion pattern for local to global clustering, achieving good clustering results.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Jian Zhu, Pengbo Hu, Bingqian Li, Yi Zhou
Summary: The acquisition of multi-view hash representation for heterogeneous data is highly important for multimedia retrieval. Current approaches suffer from limited retrieval precision due to insufficient integration of multi-view features and failure to effectively utilize metric information from diverse samples. In this paper, we propose an innovative method called Fast Metric Multi-View Hashing (FMMVH), which demonstrates the superiority of gate-based fusion over traditional methods. We also introduce a novel deep metric loss function to leverage metric information from dissimilar samples. By optimizing and employing model compression techniques, our FMMVH method significantly outperforms existing state-of-the-art methods on benchmark datasets, with up to 7.47% improvement in mean Average Precision (mAP).
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Fayaz Ali Dharejo, Iyyakutti Iyappan Ganapathi, Muhammad Zawish, Basit Alawode, Moath Alathbah, Naoufel Werghi, Sajid Javed
Summary: The resource-limited nature of underwater vision equipment affects underwater robotics and ocean engineering tasks. Super-resolution methods, particularly using Vision Transformers (ViTs), have emerged to enhance low-resolution underwater images. However, ViTs face challenges in handling severe degradation in underwater imaging. In contrast, Multi-scale ViTs (MViTs) overcome these challenges by preserving long-range dependencies through evolving channel capacity. This study proposes a novel algorithm, SwinWave-SR, for efficient and accurate multi-scale super-resolution for underwater images.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Weiwei Jiang, Haoyu Han, Yang Zhang, Jianbin Mu
Summary: This study incorporates federated learning and split learning paradigms with satellite-terrestrial integrated networks and introduces a split-then-federated learning framework and federated split learning with long short-term memory to handle sequential data in STINs. The proposed solution is demonstrated to be effective through a case study of electricity theft detection based on a real-world dataset.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Najah Abuali, Mohammad Bilal Khan, Farman Ullah, Mohammad Hayajneh, Hikmat Ullah, Shahid Mumtaz
Summary: The demand for innovative solutions in biomedical systems for precise diagnosis and management of critical diseases is increasing. A promising technology, non-invasive and intelligent Internet of Medical Things (IoMT) system, emerges to assess patients with reduced health risks. This research introduces a comprehensive framework for early diagnosis of respiratory abnormalities through RF sensing and SDR technology. The results highlight the superior performance of deep learning frameworks in classifying respiratory anomalies.
INFORMATION FUSION
(2024)
Article
Computer Science, Artificial Intelligence
Shichen Huang, Weina Fu, Zhaoyue Zhang, Shuai Liu
Summary: In the era of adversarial machine learning (AML), developing robust and generalized algorithms has become a key research topic. This study proposes a global similarity matching module and a global-local cognition fusion training mechanism based on relationship adversarial sample generation to improve image-text matching algorithm. Experimental results show significant improvements in accuracy and robustness, performing well in facing security challenges and promoting the fusion of visual and linguistic modalities.
INFORMATION FUSION
(2024)
