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Diversified deep hierarchical kernel ensemble regression

Multimedia Tools and Applications 2025年第15期84卷 15553-15573页

作者： Liu, Zhifeng Xu, Zhengqin Ebhohimhen Abhadiomhen, Stanley Qian, Xiaoqin Shen, Xiang-Jun School of Computer Science and Communication Engineering JiangSu University Zhenjiang212013 China Department of Computer Science University of Nigeria Nsukka410105 Nigeria Department of Ultrasound Affiliated People’s Hospital of Jiangsu University Zhenjiang212002 China

Deep ensemble learning models that combine multiple independent deep learning models with multi-layer processing architectures have proven to be effective techniques for improving the accuracy and robustness of deep learning models. In this paper, we propose a diversified kernel ensemble regression method, which is developed from the well-known kernel ridge regression methods. Motivated by multi-view data modeling ideas, we treat each individual kernel as one view of original data in kernel representation space. Therefore, we develop a deep kernel ensemble ridge regression method in Neural Tangent Kernel (NTK) to address the problem faced by traditional kernel ridge regression methods in finding appropriate types of kernels and their parameters. Specifically, as multiple deep kernel regressors share common information from the multi-view kernel representations, our proposed method is built through a hierarchical modeling method, where deep kernel regressors share a common parameter and also have model-specific parameters in individual regressors, which is further helpful in improving our model’s performance. Furthermore, to achieve better diversified deep kernel representations in our proposed method, the Hilbert-Schmidt Independence Criterion (HSIC) is used to regularize our proposed model. In this way, we can find more diversified kernel representations among multiple kernel ensemble regressors to achieve better generalization performance. Experiments on several classification and regression datasets, such as MNIST, TinyImageNet-200, ORL, Credit and YearPredictionMSD demonstrate that our proposed method can achieve best regression performance than other state-of-the-art methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Classification (of information)

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A novel enhanced lemurs optimization-based hybrid attention network for classifying arrthymia from ECG signal using optimal weighted feature

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Australian Journal of Electrical and Electronics engineering 2024年第3期21卷 311-335页

作者： Wajgi, Rakhi Brammya, G. Ramesh, Gopisetty Deotare, Vilas Vasudeo Govindasamy, Chinnaraj Preetha, N.S. Ninu Department of Computer Technology Yeshwantrao Chavan College of Engineering Nagpur India Department of Electronics and Communication Engineering Resbee Info Technologies Private Limited Chennai India Department of EIE CVR College of Engineering Mangalpalli India Professor in E&TC Savitribai Phule Pune University Pune India Department of Computer Science and Engineering Saveetha School of Engineering Kuthambakkam India

An arrhythmia is just a disease associated with irregular heart rhythm. Any kind of arrhythmia can be determined with an ECG test. However, this requires long and excellent-quality of ECG samples. This makes an affordable, reliable, and automated system for recognizing and diagnosing arrhythmias. To meet the aforementioned criteria, a new technique with the aid of an attention-aided deep learning approach is proposed to classify arrhythmia in this paper. "Higher-order statistical (HOS)" features are extracted from the original EEG signal and the deep features are obtained from the analyzed QRS complex signals and RR interval by means of a "One-Dimensional Convolution Neural Network (1DCNN)". The Risk Rate Enhanced Lemurs Optimization Algorithm (RRE-LOA) is utilized to modify the weight, and the extracted features are selected optimally before carrying out the feature selection followed by feature concatenation with weight fusion in order to generate the Weighted Fused Features (WFF). A range of indicators is used to compare the performance of the executed classification model with the traditional models. The results demonstrate that the proposed methodology for classifying arrhythmias offers an enhanced performance while classifying the arrhythmia thus aiding in faster treatment of the arrhythmia disease. ©, Engineers Australia.

关键词： Electrocardiograms

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Revolutionizing age and gender recognition: an enhanced CNN architecture

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2024年第4期8卷 27-45页

作者： Tumpal, Rashna Sharmin Khaliluzzaman, Md. Hoque, M.D. Jiabul Tasnim, Roshni Department of Computer Science and Engineering International Islamic University Chittagong Chattogram 4318 Bangladesh Department of Computer and Communication Engineering International Islamic University Chittagong Chattogram 4318 Bangladesh

The recognition of age and gender in images has had a significant impact on computer vision, particularly with the increasing application of digital platforms. Deep Convolutional Neural Networks (DCNNs) show promising performance. However, they demand substantial computational resources, limiting their deployment in real systems, especially those with constraints on resources or cost. This study performs a sensitivity analysis in order to show how some changes in the architecture of the network can influence the tradeoff between accuracy and performance. For that, in this work, we have investigated various CNN architectures and introduced an effective convolutional neural network (CNN) model to precisely predict gender and age attributes using the Adience dataset. Amidst unfiltered and diverse image sources from various devices, our model exhibits an impressive 92.24% accuracy across eight distinct age groups and two gender categories. The model’s strength lies in its adeptness at handling intricate image data, allowing comprehensive adjustments to age and gender parameters. By employing advanced deep learning techniques and comparing with MiniVGGNet, our model showcases exceptional performance. © (2024), (Society of Sytematic Innovation). All rights reserved.

关键词： Adience dataset Age and Gender recognition computer Vision Deep Convolutional Neural Network (DCNN) MiniVGGNet

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Towards Seamless Glucose Monitoring: A Smart Non-Invasive System for Diabetes Patients 2

Towards Seamless Glucose Monitoring: A Smart Non-Invasive Sy...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Pawar, Adesh Kelzarkar, Sanket G. Choudhary, Vishal Kumar Daburkar, Ashwini Kumar, Praveen Hore, Rutuja Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha India Faculty of Engineering and Technology Department of Biomedical Engineering Maharashtra Wardha India

ISBN: (纸本)9798331528713

Blood Glucose Monitoring levels are essential to the treatment of diabetes and must be done continuously. Patients often don't comply with conventional glucose monitoring techniques since they cause discomfort and are invasive. An Arduino microcontroller and infrared (I.R.) sensor are used in this work to provide a robust, non-invasive glucose monitoring system. I.R. light is transmitted through the skin to measure the quantity of light reflected, and this relationship exists between the amount of light and the amount of glucose in the interstitial fluid. In real-time, the Arduino analyses blood glucose levels by analyzing IR Spectroscopy and using machine learning techniques. The device features wireless connectivity, enabling it to send glucose readings to a mobile application for continuous monitoring, historical data analysis, and customized alerts. Initial trials indicate that this non-invasive system provides accurate glucose measurements comparable to traditional methods, significantly improving user comfort and compliance. This innovative approach promises a pain-free, user-friendly, and cost-effective solution for diabetes management, paving the way for more widespread adoption of continuous glucose monitoring. © 2024 IEEE.

关键词： Glucose sensors

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A Variational Inference-Based LSTM-enhanced Deep Neural Model for Sequential Recommendations

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IEEE Access 2025年 13卷 95945-95962页

作者： Ngaffo, Armielle Noulapeu Kamgang, Ines Raissa Djouela Maka, Ebenezer Maka Malong, Yannick University of Douala Laboratory of Computer Science Data Science and Artificial Intelligence National Higher Polytechnic School of Douala Douala8580 Cameroon Department of computer engineering University of Buea Faculty of Engineering and Technology Cameroon

Long Short-Term Memory (LSTM) networks are particularly useful in recommender systems since user preferences change over time. Unlike traditional recommender models which assume static user-item interactions, LSTM models capture sequential dependencies and temporal dynamics, making them ideal for personalized, session-based, and sequential recommendation tasks. However, traditional LSTM-based recommender systems do not model uncertainty since they perform deterministic predictions. Moreover, those recommender models exhibit poor generalization on small datasets, and poor-handle cold-start and data sparsity problems. To overcome those limitations, this paper presents an LSTM-based recommender model that leverages an improved deep matrix factorization to accurately address the data sparsity problem. In addition, an enhanced variational inference coupled to the Evidence Lower Bound Optimization is applied on LSTM unit layers to perform uncertainty-aware predictions. Thereafter, latent vectors obtained from LSTM networks are effectively involved in the sequential recommendation process. Experiments have been conducted on several real-world datasets to evaluate our proposal and the results show the proposed model presents significant performances compared to state-of-the-art models. © 2013 IEEE.

关键词： Deep neural networks

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Assessing the Effectiveness of Machine Learning Algorithms in Vehicle Insurance Fraud Prediction: A Comparative Approach 3

Assessing the Effectiveness of Machine Learning Algorithms i...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Satone, Aditya P. Daronde, Subodh Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Biomedical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331509675

Due to the wealth of information accessible on many insurance issues, including travel and auto insurance, individuals are becoming more and more impacted by these kinds of insurance in today's society. But this tendency also makes room for dishonest practices. Insurance fraud is harmful and unlawful, regardless of who commits it - the vendor or the customer. Fraud can come from both the customer and the vendor. The consumer may commit fraud by filing exaggerated claims or using out-of-date policies, while the vendor may market insurance from phoney firms or neglect to submit premiums. This paper presents a comparative analysis of various classification algorithms, including Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Linear Regression (LR), and Polynomial Regression, for detecting insurance fraud. The performance of these algorithms is evaluated using key metrics such as Precision, Recall, and F1-Score. The analysis reveals that the bestperforming model is the Random Forest, with a Mean Squared Error (MSE) of 6.50, outperforming the other techniques. © 2025 IEEE.

关键词： Random forests

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Towards a blockchain-SDN-based secure architecture for cloud computing in smart industrial IoT

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Digital communications and Networks 2023年第2期9卷 411-421页

作者： Anichur Rahman Md Jahidul Islam Shahab S.Band Ghulam Muhammad Kamrul Hasan Prayag Tiwari Department of Computer Science and Engineering National Institute of Textile Engineering and Research(NITER)Constituent Institute of the University of DhakaSavarDhaka1350Bangladesh Department of Computer Science and Engineering Green University of BangladeshDhakaBangladesh Future Technology Research Center College of FutureNational Yunlin University of Science and TechnologyDouliouTaiwanChina Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia Department of Information and Communication Engineering Hankuk University of Foreign StudiesSouth Korea Department of Computer Science Aalto UniversityFinland

Some of the significant new technologies researched in recent studies include BlockChain(BC),Software Defined Networking(SDN),and Smart Industrial Internet of Things(IIoT).All three technologies provide data integrity,confidentiality,and integrity in their respective use cases(especially in industrial fields).Additionally,cloud computing has been in use for several years *** information is exchanged with cloud infrastructure to provide clients with access to distant resources,such as computing and storage activities in the *** are also significant security risks,concerns,and difficulties associated with cloud *** address these challenges,we propose merging BC and SDN into a cloud computing platform for the *** paper introduces“DistB-SDCloud”,an architecture for enhanced cloud security for smart IIoT *** proposed architecture uses a distributed BC method to provide security,secrecy,privacy,and integrity while remaining flexible and *** in the industrial sector benefit from the dispersed or decentralized,and efficient environment of ***,we described an SDN method to improve the durability,stability,and load balancing of cloud *** efficacy of our SDN and BC-based implementation was experimentally tested by using various parameters including throughput,packet analysis,response time,bandwidth,and latency analysis,as well as the monitoring of several attacks on the system itself.

关键词： Smart IIoT Blockchain SDN IoT Security Privacy OpenFlow SDN-Controller Data security Cloud computing Cloud management

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Uncovering Causal Relationships for Debiased Repost Prediction Using Deep Generative Models

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computers, Materials & Continua 2024年第12期81卷 4551-4573页

作者： Wu-Jiu Sun Xiao Fan Liu School of Computer Science and Engineering Southeast UniversityNanjing211189China Department of Media and Communication City University of Hong KongHong Kong SAR999077China

Microblogging platforms like X(formerly Twitter)and Sina Weibo have become key channels for spreading information *** predicting information spread,such as users’reposting activities,is essential for applications including content recommendation and analyzing public *** advanced models rely on deep representation learning to extract features from various inputs,such as users’social connections and repost history,to forecast reposting ***,these models frequently ignore intrinsic confounding factors,which may cause the models to capture spurious relationships,ultimately impacting prediction *** address this limitation,we propose a novel Debiased Reposting Prediction model(DRP).Our model mitigates the influence of confounding variables by incorporating intervention operations from causal inference,enabling it to learn the causal associations between features and user reposting ***,we introduce a memory network within DRP to enhance the model’s perception of confounder *** network aggregates and learns confounding information dispersed across different training data batches by optimizing the reconstruction ***,recognizing the challenge of acquiring prior knowledge of causal graphs,which is crucial for causal inference,we develop a causal discovery module within DRP(CD-DRP).This module allows the model to autonomously uncover the causal graph of feature variables by analyzing microblogging *** results on multiple real-world datasets demonstrate that our proposed method effectively uncovers causal relationships between variables,exhibits strong time efficiency,and outperforms state-of-the-art models in prediction performance(improved by 2.54%)and overfitting reduction(by 7.44%).

关键词： Repost prediction causal inference causal discovery memory network

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A novel approach to detect, analyze and block adversarial web pages

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International Journal of Information Technology (Singapore) 2024年第6期16卷 3617-3622页

作者： Kumar, M. Ranjith Naik, Darshana A. Kapila, Neha Mohan, Chinnem Rama Prasad, Ch Raghava Shelke, Chetan Rao, C. V. Guru School of Computer Science and Engineering Vellore Institute of Technology Department of Computer Science and Engineering M S Ramaiah Institute of Technology Department of Electronics and Electrical Engineering Lovely Professional University Department of Computer Science and Engineering Narayana Engineering College Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Alliance College of Engineering and Design Alliance University School of CS & amp AI SR Universtity

The phenomenon of distraction is very common, and its adverse effects are seen among people. The major cause underlying this issue is the ease with which adversarial web sites and web pages can be accessed. It is of utmost importance to locate, evaluate, and actively block such web pages in an effort to comprehensively and globally solve this societal issue. Thus, the given paper proposes an extension or plug-in to detect, analyze, and block websites smartly. The proposed approach takes keywords entered by the user as input into consideration, which eventually leads to the generation of web pages and web links. The filtering of web links is done by the proposed extension, followed by the extraction of features, the utilization of support vector machines (SVM) for binary classification, and the summarization of textual data using natural language processing (NLP). Lastly, the precise results corresponding to relevant web links are presented to the users, which will increase their productivity, thereby reducing their distraction levels under all working circumstances. The performance of the proposed approach is validated against existing recent studies based on evaluation metrics such as adversarial website detection time (ms) and accuracy (%). © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Adversarial web pages Distraction Natural language processing (NLP) Support vector machine (SVM) Text summarization

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An intrusion detection system based on hybrid machine learning classifier

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Journal of Intelligent and Fuzzy Systems 2023年第3期44卷 4245-4255页

作者： Reji, M. Joseph, Christeena Nancy, P. Lourdes Mary, A. Department of Electronics and Communication Engineering Rohini College of Engineering and Technology Tamilnadu Kanyakumari India Department of Electronics and Communication Engineering Srm Institute of Science and Technology Tamilnadu Chennai India Department of Computing Technologies School of Computing Srm Institute of Science and Technology Kattankulathur Campus Chennai India Department of Computer Science and Engineering Rohini College of Engineering and Technology Tamilnadu Kanyakumari India

Intrusion detection systems (IDS) can be used to detect irregularities in network traffic to improve network security and protect data and systems. From 2.4 times in 2018 to three times in 2023, the number of devices linked to IP networks is predicted to outnumber the total population of the world. In 2020, approximately 1.5 billion cyber-attacks on Internet of Things (IoT) devices have been reported. Classification of these attacks in the IoT network is the major objective of this research. This research proposes a hybrid machine learning model using Seagull Optimization Algorithm (SOA) and Extreme Learning Machine (ELM) classifier to classify and detect attacks in IoT networks. The CIC-IDS-2018 dataset is used in this work to evaluate the proposed model. The SOA is implemented for feature selection from the dataset, and the ELM is used to classify attacks from the selected features. The dataset has 80 features, in the proposed model used only 22 features with higher scores than the original dataset. The dataset is divided into 80% for training and 20% for testing. The proposed SOA-ELM model obtained 94.22% accuracy, 92.95% precision, 93.45% detection rate, and 91.26% f1-score. © 2022 - IOS Press. All rights reserved.

关键词： Internet of things

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