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Hybrid classification of XGBoost-based ADAM optimization for coronary artery disease diagnosis

Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 10035-10044页

作者： Nagamani, T. Logeswari, S. Department of Computer Science and Engineering Kongu Engineering College Tamilnadu Perundurai India Department of Information Technology Karpagam College of Engineering Tamilnadu Coimbatore India

A common cardiovascular illness with high fatality rates is coronary artery disease (CAD). Researchers have been exploring alternative methods to diagnose and assess the severity of CAD that are less invasive, cost-effective, and utilize noninvasive clinical data. Machine learning algorithms have shown promising and potential results. Accordingly, this study focuses on assisting medical practitioners with CAD detection by using a hybrid classification system combining XGBoost and Adam optimization. The primary approach incorporates One-Hot encoding to transform categorical attributes within the dataset, enhancing the precision of predictions. The secondary approach constitutes a hybrid classification model integrating XGBoost and employing Adam optimizations for CAD detections. The efficacy of the recommended method is assessed using the cleveland, Hungarian, and Statlog heart-disease data sets. The proposed system and the standard Grid and Random Search classifiers are compared. The experimental outcomes indicate that the suggested model achieves a notable prediction accuracy of 94.19%. This represents an improvement of 7 to 8% over the existing grid search algorithm and 2 to 3% improvement over the random search algorithm for the above all datasets. Hence, the proposed system can be a valuable tool for identifying CAD patients, offering enhanced prediction accuracy. © 2024 – IOS Press.

关键词： Diseases

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A Multi-Agent Proximal Policy Optimized Joint Mechanism in mmWave HetNets with CoMP Toward Energy Efficiency Maximization

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2024年第1期8卷 265-278页

作者： Lotfolahi, Amin Ferng, Huei-Wen National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei106319 Taiwan

A novel cluster-based traffic offloading and user association (UA) algorithm alongside a multi-agent deep reinforcement learning (DRL) based base station (BS) activation mechanism is proposed in this paper. Our design aims to maximize the energy efficiency (EE) of the heterogeneous network (HetNet) while maintaining high quality of service (QoS). By taking advantage of the dense deployment of BSs in a HetNet, a clustering algorithm is first proposed to facilitate traffic offloading among BSs. Then, a multi-agent proximal policy optimization (MAPPO) based DRL algorithm is employed to trigger the BS activation decision based on the current environmental condition. Finally, a UA algorithm is deployed to improve further the (normalized) data rate of all users, known as the (normalized) sum rate. Via simulation, we show that our proposed mechanism can remarkably enhance the EE and excel over the closely related mechanisms. It satisfies the required data rate, improves the sum rate, and exhibits excellent scalability when many BSs are deployed. © 2017 IEEE.

关键词： Quality of service

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Hyperspectral Target Detection-Based 2-D-3-D Parallel Convolutional Neural Networks for Hyperspectral Image Classification

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2024年 17卷 9451-9469页

作者： Chen, Shih-Yu Hsu, Kai-Hsun Kuo, Tzu-Hsien National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Yunlin64002 Taiwan

This article presents a novel hyperspectral target detection (HTD) based two-dimensional (2-D)-three-dimensional (3-D) parallel convolutional neural network (HTD 2D-3D-PCNN) model, which integrates the HTD technique to achieve outstanding performance in hyperspectral image classification. The proposed model effectively leverages both spectral and spatial information through a dual-branch architecture in hyperspectral imaging. In the first branch, HTD is utilized to enhance the spectral features of targets of interest, while suppressing background. The resulting enhanced image is then inputted to a 2D-CNN, augmented with an additional deconvolution layer to highlight spatial characteristics. Concurrently, the second branch employs dimensionality reduction via principal component analysis, and a 3D-CNN is employed to capture both spectral and spatial attributes. Subsequently, the feature maps from both convolutional neural networks (CNNs) are combined and processed through fully connected layers for classification. To validate the effectiveness of the proposed HTD-2D-3D-PCNN, extensive experiments are conducted on five widely used hyperspectral public datasets (Indian Pines, Pavia University, Salinas Scene, Kennedy Space Center, and Botswana) with a consistent training sample ratio of either 10% or 5%. The results show that HTD-2D-3D-PCNN can achieve overall accuracy values of 98.41%, 99.85%, 99.92%, 99.82%, and 98.82% for the respective datasets, surpassing the performance of recent methodologies. © 2008-2012 IEEE.

关键词： Image classification

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Compliant Control of Lower Limb Rehabilitation Exoskeleton Robot Based on Flexible Transmission

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Journal of Bionic engineering 2023年第3期20卷 1021-1035页

作者： Keping Liu Li Li Wanting Li Jian Gu Zhongbo Sun Department of Control Science and Engineering Changchun University of TechnologyChangchun130012China Department of Electrical and Information Engineering Jilin Engineering Normal UniversityChangchun130052China

To ensure the safety, comfort, and effectiveness of lower limb rehabilitation exoskeleton robots in the rehabilitation training process, compliance is a prerequisite for human–machine interaction safety. First, under the premise of considering the mechanical structure of the lower limb rehabilitation exoskeleton robot (LLRER), when conducting the dynamic transmission of the exoskeleton knee joint, the soft axis is added to ensure that the rotation motion and torque are flexibly transmitted to any position to achieve flexible force transmission. Second, to realize the active compliance control of LLRER, the sliding mode impedance closed-loop controller is developed based on the kinematics and dynamics model of LLRER, and the stability of the designed control system is verified by Lyapunov method. Then the experiment is designed to track the collected bicycle rehabilitation motion data stably, and the algorithm and dynamic model are verified to satisfy the experimental requirements. Finally, aiming at the transmission efficiency and response performance of the soft shaft in the torque transmission process of the knee joint, the soft shaft transmission performance test is carried out to test the soft shaft transmission performance and realize the compliance of the LLRER, so as to ensure that the rehabilitation training can be carried out in a safe and comfortable interactive environment. Through the design of rehabilitation exercise training, it is verified that the LLRER of flexible transmission under sliding mode impedance control has good adaptability in the actual environment, and can achieve accurate and flexible control. During the experiment, the effectiveness of monitoring rehabilitation training is brought through the respiratory belt.

关键词： Lower limb rehabilitation exoskeleton Flexible control Impedance control Stability

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A Comprehensive Data Description for LoRaWAN Path Loss Measurements in an Indoor Office Setting: Effects of Environmental Factors

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IEEE Access 2025年 13卷 83148-83170页

作者： Obiri, Nahshon van Laerhoven, Kristof University of Siegen Department of Electrical Engineering and Computer Science Siegen57076 Germany

This paper presents a comprehensive dataset of LoRaWAN technology path loss measurements collected in an indoor office environment, focusing on quantifying the effects of environmental factors on signal propagation. Utilizing a network of six strategically placed LoRaWAN end devices (EDs) and a single indoor gateway (GW) at the University of Siegen’s Hölderlinstraße Campus in the City of Siegen, Germany, we systematically measured signal strength indicators such as the Received Signal Strength Indicator (RSSI) and the Signal-to-Noise Ratio (SNR) under various environmental conditions, including temperature, relative humidity, carbon dioxide (CO2) concentration, barometric pressure, and particulate matter levels (PM2.5). Our empirical analysis confirms that transient phenomena such as reflections, scattering, interference, occupancy patterns (induced by environmental parameter variations), and furniture rearrangements can alter signal attenuation by as much as 10.58 dB, highlighting the dynamic nature of indoor propagation. As an example of how this dataset can be utilized, we tested and evaluated a refined Log-Distance Path Loss and Shadowing Model that integrates both structural obstructions (Multiple Walls) and Environmental Parameters (LDPLSM-MW-EP). Compared to a baseline model that considers only Multiple Walls (LDPLSM-MW), the enhanced approach reduced the root mean square error (RMSE) from 10.58 dB to 8.04 dB and increased the coefficient of determination (R2) from 0.6917 to 0.8222. By capturing the extra effects of environmental conditions and occupancy dynamics, this improved model provides valuable insights for optimizing power usage and prolonging device battery life, enhancing network reliability in indoor Internet of Things (IoT) deployments, among other applications. This dataset offers a solid foundation for future research and development in indoor wireless communication. © 2013 IEEE.

关键词： Green computing

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Securing Next-Generation Quantum IoT Applications using Quantum Key Distribution

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IEEE Internet of Things Magazine 2025年第1期8卷 50-56页

作者： Bera, Basudeb Das, Ashok Kumar Sikdar, Biplab The Department of Electrical and Computer Engineering National University of Singapore Singapore The International Institute of Information Technology Hyderabad India

The next generation of Quantum Internet of Things (QIoT) has the potential to revolutionize various sectors, including smart homes, healthcare, and smart cities, by enabling more sophisticated and interconnected systems. These applications incorporate advanced features, such as autonomous decision-making based on Quantum Artificial Intelligence and Machine Learning (QAI/ML) and context-aware functionality. The security of the data in these applications relies on traditional cryptographic techniques, which, however, face a growing threat due to the significant advancements in quantum computing, especially with the Shor’s algorithm. This algorithm poses a substantial risk of breaking conventional cryptographic methods within a feasible timeframe. To address these emerging security challenges in the quantum realm, we propose a Quantum Key Distribution (QKD) based on the BB84 protocol. This approach aims to provide robust authentication using certificates through a classical channel and secure quantum key exchange via a quantum channel in a public environment. The proposed QKD scheme is implemented using a client-server model in Python and Qiskit, demonstrating its practicality in real-world applications. The obtained results showcase the successful establishment of a secure session key between IoT smart (sensor) devices and gateway nodes, effectively mitigating potential threats such as eavesdropping. © 2018 IEEE.

关键词： Smart homes

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Centralized RANSAC-Based Point Cloud Registration With Fast Convergence and High Accuracy

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2024年 17卷 5431-5442页

作者： Chung, Kuo-Liang Chang, Wei-Tai National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei106335 Taiwan

For point cloud registration, the purpose of this article is to propose a novel centralized random sample consensus (RANSAC) (C-RANSAC) registration with fast convergence and high accuracy. In our algorithm, the novel contributions are, first, the proposal of a scale histogram-based outlier removal to delete outliers from the initial line vector set L for constructing a reduced line vector set Lred;second, the handshake cooperation between the host RANSAC (H-RANSAC) only working on L and the local RANSAC (LCL-RANSAC) only working on Lred;third, in each handshake process, after receiving the global registration solution and the global iteration number xH from H-RANSAC, LCLRANSAC uses the received global solution as the initial solution of the modified TEASER++ (M-TEASER++) method to calculate its first local registration solution. If the first local registration solution satisfies the global iteration number inheritance condition, LCL-RANSAC directly sends the accumulated iteration number, xH + 1, and the first local solution back to H-RANSAC;otherwise, LCL-RANSAC iteratively refines its local solution using the M-TEASER++ method, and then sends the resultant local solution and the required local iteration number xLCL to H-RANSAC for updating the global solution, the global iteration number to xH := xH + xLCL, and the global confidence level. Due to |Lred| |L| and employing the global iteration number inheritance condition test into our algorithm, we have conducted extensive experiments on testing point cloud pairs to show the registration accuracy and execution time merits of our algorithm relative to the state-of-theart methods. © 2008-2012 IEEE.

关键词： Iterative methods

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REAL-TIME GLUCOSE MONITORING SYSTEM AND DIETARY RECOMMENDATIONS FOR DIABETES MELLITUS USING FOG COMPUTING

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第5期84卷 55-67页

作者： Sathish, N. Elangovan, D. Nagalakshmi, R. Suresh, G. Chitra, Devi D. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Saveetha Engineering College Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Artificial Intelligence and Data Science Kings Engineering College Sriperumbudur India Department of Computer Science And Engineering S.A. Engineering College Chennai India

This systematic review gave special attention to diabetes and the advancements in food and nutrition needed to prevent or manage diabetes in all its forms. There are two main forms of diabetes mellitus: Type 1 (T1D) and Type 2 (T2D). The loss of beta cells brought on by an autoimmune reaction causes the pancreas to be unable to produce enough insulin, which leads to Type 1 diabetes. A deficiency of insulin could arise as the illness worsens. The most frequent cause is a combination of excessive body weight and inadequate exercise. Currently, 90–95% of patients have Type 2 diabetes mellitus (T2DM). Age-specific normal blood sugar ranges exist and knowing them is crucial for determining a target blood sugar level that is both healthy and appropriate. The main takeaway is that diet regimens ought to be tailored to the patient’s requirements and should account for their capacity for change. Carbohydrates are the nutrients that have the biggest impact on the rise in blood glucose. In this paper, we have proposed a novel method for glucose monitoring and diet recommendations for diabetic patients using fog computing. A paradigm known as fog computing makes use of the benefits that come from using both cloud and edge devices to support modern computing systems by offering high-quality services, cutting latency, facilitating mobility, supporting multi-tenancy, and many other features. © 2025 by Begell House, Inc.

关键词： Fog computing

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Educational Data Analysis using Generative AI

Educational Data Analysis using Generative AI

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2024 Joint of International Conference on Learning Analytics and Knowledge Workshops, LAK-WS 2024

作者： Berr, Abdul Leelaluk, Sukrit Tang, Cheng Chen, Li Okubo, Fumiya Shimada, Atsushi Department of Electrical Engineering and Computer Science Kyushu University Japan Graduate School of Information Science and Electrical Engineering Kyushu University Japan

With the advent of generative artificial intelligence (AI), the scope of data analysis, prediction of performances, real-time feedback, etc. in learning analytics has widened. The purpose of this study is to explore the possibility of using generative AI to analyze educational data. Moreover, the performances of two large language models (LLMs): GPT-4 and text-davinci-003, are compared with respect to different types of analyses. Additionally, a framework, LangChain, is integrated with the LLM in order to achieve deeper insights into the analysis, which can be beneficial for beginner data scientists. LangChain has a component called an agent, which can help study the analysis being performed step-by-step. Furthermore, the impact of the OpenLA library, which pre-processes the data by calculating the number of reading seconds of students, counting the number of operations performed by students, and making page-wise behavior of each student, is also studied. Besides, factors with the most significant impact on students’ performances were also discovered in this analysis. The results show that GPT-4, when using the data pre-processed by OpenLA, provides the best analysis in terms of both, the accuracy of the final answer, and the step-by-step insights provided by LangChain’s agent. Also, we learn the significance of reading time and interactions used (Add marker, bookmark, memo) by students in predicting grades. © 2024 CEUR-WS. All rights reserved.

关键词： Students

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On the bitmap compression for joint coding and data hiding of AMBTC compressed images

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Multimedia Tools and Applications 2024年第35期83卷 82131-82148页

作者： Hong, Wien Su, Guan-Zhong Chen, Tung-Shou Chen, Jeanne Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung City Taiwan

The compressed code of Absolute Moment Block Truncation Coding (AMBTC) consists of quantized values (QVs) and bitmaps. The QVs exhibit greater predictability, and the bitmaps themselves carry more randomness. While existing joint coding and data hiding based methods have focused on compressing the QVs, none of them have addressed the coding of bitmaps. Furthermore, evidence also reveals that the sub-divided bitmaps exhibit a highly uneven pattern distribution. Therefore, we propose an efficient method to compress the bitmaps by representing sub-divided bitmaps as decimal digits. This exploits the varying frequency of certain digits, allowing Huffman encoding for shorter codewords to represent frequently occurring digits. Moreover, we have observed that less frequent digits, which require longer codewords, tend to appear among blocks with smaller difference of QVs. As a solution, we employ an adaptive approach to directly record the original bitmap when the difference is below a specific threshold. Otherwise, we employ Huffman encoding to reduce the code length. The experimental results demonstrate the effectiveness of our approach in reducing the storage space required for Lena’s bitmaps by 7.36%. Moreover, the reduction in bitrate is more pronounced when the test image exhibits a smooth texture. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Textures

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