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A novel feature selection approach with integrated feature sensitivity and feature correlation for improved prediction of heart disease

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12005-12019页

作者： Saranya, G. Pravin, A. Department of Computer Science and Engineering School of Computing Sathyabama Institute of Science and Technology Chennai600119 India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai603203 India

This paper presents a random forest-feature sensitivity and feature correlation (RF-FSFC) technique for enhanced heart disease prediction. The proposed methodology is implemented using the Cleveland heart disease dataset which comprises a total of 120 heart disease patient records. data imputation was utilized for missing values, and min–max normalization was utilized for data transformation. We attempted to construct an RF-based classifier for coronary heart disease in this paper by combining feature sensitivity and correlation analysis. The sensitivity-based feature selection process ranks features according to their value in assessing CHD risk, and the feature correlation analysis phase analyses if there are any correlations between features. The heart disease prediction accuracy of 81.16% was obtained using the proposed RF-FSFCA technique by omitting five features (sex, hemoglobin, TD, CRF, and cirrhosis). When compared to the Naïve Bayes, decision tree, regression analysis, and support vector machine models, the proposed model offered a higher accuracy of 86.141% without omitting any features. It also offered sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) scores of 87.321%, 87.364%, 91.23, and 91.02 respectively. Experiment findings demonstrated that the proposed RF-FSFC approach significantly improves prediction accuracy as compared to other approaches that do not use the integrated Feature selection method. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Sensitivity analysis

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Comparing Local Binary Pattern and Gray Level Co-occurrence Matrix for Feature Extraction in Diabetic Retinopathy Classification 8

Comparing Local Binary Pattern and Gray Level Co-occurrence ...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Makmur, Nathanael Matthew Kwan, Felicia Rana, Astrid Dewi Kurniadi, Felix Indra Computer Science Department Department School of Computer Science Bina Nusantara University Jakarta11530 Indonesia

Diabetic retinopathy, a condition characterized by retinal damage and vision loss, is a prevalent complication of diabetes arising from elevated blood sugar levels. With a growing number of individuals affected, efficient and accurate diagnosis is crucial. This study aims to implement and compare the Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) feature extraction techniques, which have demonstrated success in prior research. The comparison will provide a comprehensive understanding of the image features, extract relevant data, and improve the performance of the image analysis pipeline for diabetic retinopathy classification. The result showed that from three scenarios the best accuracy provided by Support Vector Machine with the accuracy score between 73% until 74%, however, other algorithm have little difference which the result on 73%. © 2023 The Authors. Published by Elsevier B.V.

关键词： computer Vision Diabetic Retinophaty GLCM LBP

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Hybrid Water Quality Prediction with Multimodal Low-rank Fusion and Localized Attention

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IEEE Internet of Things Journal 2025年第12期12卷 21158-21169页

作者： Bi, Jing Li, Yibo Yuan, Haitao Wang, Mengyuan Wang, Ziqi Zhang, Jia Zhou, MengChu Beijing University of Technology College of Computer Science Beijing100124 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Beihang University School of Energy and Power Engineering Beijing100191 China Southern Methodist University Department of Computer Science DallasTX75206 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

Water quality prediction methods forecast the short-or long-term trends of its changes, providing proactive advice for preventing and controlling water pollution. Existing water quality prediction methods typically fail to capture water quality's nonlinear characteristics accurately and only consider historical time series data. However, meteorology and other factors also significantly impact water quality indicators. Therefore, considering only historical data of water quality time series is not feasible. To solve this problem, this work proposes a hybrid water quality prediction model called CMLIP, which integrates ConvNeXt V2, Multimodal bottleneck transformer, Low-rank multimodal fusion, ITransformer, and PatchTST. CMLIP inputs water quality time series and meteorological remotely sensed rainfall images into a multimodal fusion module before prediction. Specifically, CMLIP integrates the model of ConvNeXt V2 to extract image features. Its multimodal fusion module combines a multimodal bottleneck transformer and the low-rank multimodal fusion to fuse the time series and images. Furthermore, CMLIP combines iTransformer and PatchTST to form an improved prediction module that realizes the prediction of fused features. Experimental results with real-life water quality time series and remotely sensed rainfall images demonstrate that CMLIP when fusing meteorological data, achieves an average improvement of 17% in water quality forecasting accuracy compared to forecasts using only water quality time series. Moreover, CMLIP outperforms other state-of-the-art algorithms in both data fusion and prediction, with an average enhancement of 6% in fusion effectiveness and an average improvement of 22% in prediction accuracy. © 2025 IEEE.

关键词： Rain

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Impact of mastery learning instructional scheme on post-basic school students’ engagement levels in mathematics in Osogbo, Nigeria

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Discover Education 2025年第1期4卷 1-18页

作者： Akinpelu, Gabriel Akinyemi Akinpelu, Samson Adebisi Folorunsho, Medinat Salman Ameen, Khadijat Saka Adebayo, Segun Rapheal Daramola, Comfort Yetunde Department of Science Education University of Ilorin Ilorin Nigeria School of Mathematics Statistics and Computer Science University of Kwazulu-Natal Durban South Africa Department of Mathematical Science Osun State University Osogbo Nigeria Department of Computer Science Federal University Oye-Ekiti Oye-Ekiti Nigeria

The extent of engagement between students and their teachers, peers, academic and extracurricular activities goes a long way in creating a sense of belonging for students and effectual improvement in their performance in the subject matter. This study investigated the effect of mastery learning instructional scheme (MLIS) on post-basic school students’ engagement levels in Mathematics in Osogbo, Nigeria. The purpose of this quasi-experimental study was to determine the effect of MLIS on student affective, cognitive and behavioural engagement in Mathematics based on students’ gender and score levels. Two co-educational government senior secondary schools were purposively selected as the study’s population, out of which a total of 191 Post-basic school II students from two intact classes were engaged as samples from the two schools. A 31-item Student Engagement Questionnaire (SEQ) was adapted as a research instrument. SEQ was subjected to a reliability test using Cronbach Alpha and the value of 0.73 was obtained. Descriptive statistics and ANCOVA were used to examine data gathered in accordance with the hypotheses. Outcomes of the study among others revealed significant improvement in students’ cognitive, behavioural and affective engagement for MLIS class (t ₍₁₈₉₎ = 2.68, p < 0.05). Gender had a statistically significant influence on affective (F _{(1, 94)} = 2.04, p < 0.05) and behavioural (F _{(1, 94)} = 0.77, p < 0.05) engagement. It was concluded that MLIS improved students, cognitive, affective and behavioural engagement in Mathematics. Thus, the study recommended that mathematics teachers should consider using MLIS in teaching Mathematics while taking into consideration students’ sitting arrangements with respect to their gender and score levels. © The Author(s) 2025.

关键词： Affective engagement Behavioural engagement Cognitive engagement Mastery learning instructional strategy Mathematics

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Integrating Brainwave Analysis and Facial Detection for Enhanced Security in Transactional Systems 8

Integrating Brainwave Analysis and Facial Detection for Enha...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Talasila, Smeeth Vadlamudi, Gnana Abhinay Vadlamudi, Anvitha School of Computer Science and Engineering Plano West Senior High School TX United States Department of Computer Science and Engineering New York United States School of Computer Science and Engineering Vit Ap University Amaravati India

ISBN: (纸本)9798331505462

This paper proposes an innovative user authentication system tailored for high-value asset transactions, leveraging advancements in brainwave analysis and emotional state detection. Traditional authentication methods are increasingly vulnerable to sophisticated attacks, necessitating novel approaches to bolster security in transactional systems. Our research explores the untapped potential of Electroencephalography (EEG) data to establish a unique user identifier based on distinctive brainwave patterns, including alpha, beta, gamma, delta, and theta waves. We identify specific patterns associated with the user's relaxed state through rigorous analysis, presenting a robust and bypass-resistant authentication factor. Furthermore, our system integrates facial expression recognition technology to detect negative emotional states, such as stress or panic, during transactional interactions. By combining brainwave analysis with facial recognition, our multi-layered approach enhances security by considering both physiological and emotional indicators. If the brainwave analysis identifies a relaxed state while facial recognition detects negative emotions, the system identifies potential coercion attempts and restricts transaction completion, thus safeguarding against fraudulent activities. © 2024 IEEE.

关键词： Face recognition

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3D Based CNN for Improved Segmentation from the Hyperspectral Images 1

3D Based CNN for Improved Segmentation from the Hyperspectra...

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1st International Conference on computer science and Emerging Technologies, CSET 2023

作者： Narmadha, T. Arockia Raj, A. Saravanan, P. Kumar, A. Suresh Sekaran, Ramesh Gowri Shankar, J. School of Computer Science and Engineering Jain Deemed to Be University Bengaluru India Madanapalle Institute of Technology and Science Department of Computer Science and Engineering - Data Science Madanapalle India School of Computing Science and Engineering Galgotias University Delhi NCR India

ISBN: (纸本)9798350341737

In this paper, we develop a 3D based CNN for Improved Segmentation of Paddy Fields from the HSI. Within the scope of this research, we will investigate a unique deep learning model, specifically 3D-CNN. In order to evaluate the validity of the model, it is applied to three separate datasets, each of which is then subjected to one of four distinct dimensionality reduction strategies. The purpose of this is to determine which of these four models is the most effective at reducing the number of dimensions. According to the findings, the 3D-CNN model is superior to both the AlexNet and CNN models in terms of the accuracy of its segmentations. In addition to this, the findings demonstrate that the 3D-CNN models perform better than the other approaches in terms of segmentation accuracy with a smaller number of datasets. © 2023 IEEE.

关键词： Image enhancement

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Label Confidence Weighted Learning for Target-level Sentence Simplification

Label Confidence Weighted Learning for Target-level Sentence...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Qiu, Xinying Zhang, Jingshen Department of Computer Science School of Information Science and Technology Guangdong University of Foreign Studies China

ISBN: (纸本)9798891761643

Multi-level sentence simplification generates simplified sentences with varying language proficiency levels. We propose Label Confidence Weighted Learning (LCWL), a novel approach that incorporates a label confidence weighting scheme in the training loss of the encoder-decoder model, setting it apart from existing confidence-weighting methods primarily designed for classification. Experimentation on English grade-level simplification dataset shows that LCWL outperforms state-of-the-art unsupervised baselines. Fine-tuning the LCWL model on in-domain data and combining with Symmetric Cross Entropy (SCE) consistently delivers better simplifications compared to strong supervised methods. Our results highlight the effectiveness of label confidence weighting techniques for text simplification tasks with encoder-decoder architectures. © 2024 Association for Computational Linguistics.

关键词： Decoding

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TeleForensic: Delving into Digital Evidence with CDR Forensics 3

TeleForensic: Delving into Digital Evidence with CDR Forensi...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Srish, N. Sridharan, S. Udhaya Kumar, S. Amrita School of Computing Computer Science Department Chennai India

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354348

Call data Records (CDR) are important evidence in cases involving cybercrime and crimes such as murder and robbery. Nonetheless, analyzing CDR is arduous work because the amount of data is enormous, and if a person does not know where to look, they would overlook valuable information. In today's world, officials do their best to solve crimes, but as the phrase goes, every piece of evidence counts, and CDR is no exception. This research investigates the many processes and techniques used in the analysis of CDR data, particularly in the context of telecommunications, including data analytics, pattern matching, geographical mapping, and visualization methods. Furthermore, the paper covers problems and prospects in CDR forensic analysis, emphasizing the importance of improved tools and approaches to deal with growing communication technologies and increasing cyber threats. © 2024 IEEE.

关键词： Crime

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TES-CVIDS: A Transmission Efficient Sub-Map Based Collaborative Dense VI-SLAM Framework

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-14页

作者： Zhang, Tianjun Zhang, Lin Zhang, Fengyi Zhao, Shengjie Zhou, Yicong School of Software Engineering Tongji University Shanghai China Department of Computer and Information Science University of Macau Macau China

In recent years, how to achieve stable localization and construct high-quality dense maps in large-scale scenes has become a research highlight. In large-scale scenes, for the consideration of the mapping accuracy and efficiency, multi-agent systems rather than single-agent ones are usually employed. Currently, as far as we know, collaborative VI-SLAM (Visual Inertial Simultaneous Localization And Mapping) systems applicable to multi-agent systems are still sporadic, and systems those can achieve a good balance among the localization accuracy, the mapping density, and the transmission efficiency are temporarily lacking. In this paper, we propose a novel centralized collaborative VI-SLAM framework, namely TES-CVIDS (Transmission Efficient Sub-map based Collaborative Visual-Inertial Dense SLAM). In TES-CVIDS, instead of the original RGBD images, the compact sub-maps are transmitted, effectively reducing the transmission data redundancy. After that, the server completes key-frame processing, hierarchical pose-graph optimization, and global dense map construction in three separate threads. Besides, thanks to our depth search mechanism, the geometry information of all key-frames can be recovered on the server-end. Thus, sub-maps can be regenerated after the global pose-graph optimization to maintain the consistency between the localization and the mapping. Both the qualitative and the quantitative experimental results corroborate the superior performance of our TES-CVIDS. To make our results reproducible, the source code has been released at https://***/TES-CVIDS-MainPage/. IEEE

关键词： Mapping

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Off-Fusion: Robust LiDAR Fusion for Complex Off-Road Scenes

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-12页

作者： Ahat, Arzigul Firkat, Eksan Yuan, Xiaming Zhu, Jihong Hamdulla, Askar School of Computer Science and Technology Xinjiang University Urumqi China Department of Precision Instrument Tsinghua University Beijing China

In off-road scenes, the fusion of dual-LiDAR data is crucial for ensuring the accuracy of environmental perception in autonomous vehicles. The terrain in off-road scenes is complex and filled with unstructured information. This leads to significant noise and a lack of distinct structural features in the point cloud data, making traditional point cloud registration methods difficult to apply. To address these issues posed by complex off-road scenes, we propose a novel point cloud fusion framework named Off-Fusion. We first filter and segment the ground in the input point cloud data, focusing on preserving the core features of the ground while effectively removing noise caused by the terrain. Next, we propose a robust and efficient feature point extraction method based on voxel division and curvature weighting, ensuring extracting meaningful and representative feature points from complex off-road scenes. Based on this, we use feature matching to calculate rough relative transformation pairs, providing a high-quality starting position for the Iterative Closest Point (ICP) algorithm, effectively avoiding local optima. Finally, by combining the kd-tree accelerated ICP algorithm, we achieve precise point cloud registration, successfully calculating the optimal rotation and translation matrix between the two LiDARs. The experimental results show that our method significantly improves the quality and speed of data fusion. Compared to some of the most advanced methods, it performs better in off-road scenes, achieving the best results. IEEE

关键词： data mining

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