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Comprehensive overview of Alzheimer's disease utilizing Machine Learning approaches

Multimedia Tools and Applications 2024年第37期83卷 85277-85329页

作者： Kumar, Rahul Azad, Chandrashekhar Department of Computer Science and Engineering National Institute of Technology Jamshedpur India

Alzheimer's disease is a common and complex brain disorder that primarily affects the elderly. Because it is progressing and has few effective therapies, it requires a thorough understanding of the condition;our study offers a comprehensive analysis of AD with a dual approach that combines both bibliometric and experimental analyses. The bibliometric analysis applies statistical and mathematical techniques to figure out the states of AD research, including publishing trends, prestigious journals, and collaborative networks. Concurrently, the experimental examination explores current advancements, focusing on Machine Learning, Deep Learning, and Metaheuristic approaches, tackling complex issues resulting from varied datasets. The experimental work is fascinating because it uses twenty classifiers and two datasets, initially without feature selection and then with seven feature selection techniques. This thorough investigation focuses on developments in disease processes, therapeutic approaches, and diagnostic tool development. This research offers a multidimensional overview of Alzheimer's disease by combining bibliometric and experimental methods, addressing problems and highlighting the shortcomings of earlier studies. By helping academics, policymakers, and healthcare professionals navigate the complexities of AD, this novel methodology advances a more thorough understanding of the Alzheimer's disease domain. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Speech processing

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L3Buddy: a location-aware academic content-recommendation system through machine learning based cache techniques

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Multimedia Tools and Applications 2025年第5期84卷 2633-2649页

作者： Guddadmani, Ashwini Chougale, Supriya Gokanvi, Megha Tapale, Manisha Pattar, Santosh Department of Computer Science and Engineering KLE Technological University Belagavi Campus India

At present, recommendation systems have become pivotal in personalized education learning management systems, where there is a growing need for location-based suggestions. Our problem addresses the inefficiency of current systems in integrating real-time location data, leading to latency and irrelevant content delivery. We propose L3Buddy, a context-aware system leveraging machine learning for location-specific video recommendations. This approach enhances accuracy and reduces latency, improving the user experience by delivering timely, relevant content. Our results demonstrate significant improvements in both recommendation precision and system responsiveness. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Adversarial machine learning

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Energy-efficient resource allocation in blockchain-based Cybertwin-driven 6G

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 103-114页

作者： Salim, Mikail Mohammed Pan, Yi Park, Jong Hyuk Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul Korea Republic of Department of Computer Science Georgia State University Atlanta United States

Inefficient task scheduling schemes compromise network performance and increase latency for delay intolerant tasks. Cybertwin based 6G services support data logging of operational queries for appropriate resource allocation in Multi-Access Edge and Cloud Computing. In this paper, a Cybertwin assisted task scheduling scheme is proposed for energy-efficient job allocation to Edge-Cloud based on delay tolerance. Cybertwins using the Logger Function and the Digital Asset Function exchange Digital Assets with cloud operators using Smart Contracts to ensure maximum computational resources. The novelty of this study resides in using Communication Assistant Cybertwins to build profiles for all network entities and assign tasks based on their delay tolerance and the available resources of each Edge Datacenter and Cloud service provider. Compared with the Full Offloading scheme, experimental results demonstrate the higher energy efficiency of the task scheduling scheme. Offloading tasks to multiple data centers and smart contract-based selection of cloud operators result in reduced computation time. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： Smart contract

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Blockchain enabled MediVault for healthcare system

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Multimedia Tools and Applications 2025年第15期84卷 14805-14825页

作者： Chaurasia, Brijesh Kumar Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur India

The management of healthcare data has significantly benefited from the use of cloud-assisted MediVault for healthcare systems, which can offer patients efficient and convenient digital storage services for storing their medical records. Nevertheless, there are security risks associated with the current digital healthcare data, as malicious parties may work with cloud storage service providers to alter patient records or to directly disclose health record content to other adversaries for monetary advantage. In this paper, a blockchain-enabled MediVault for healthcare systems is proposed not only to provide safe storage of healthcare data in digital form but also secure access for authenticated entities such as patients, doctors, and pharmacists. In this MediVault, we introduced NFT generation and storage over the cloud using Interplanetary File System (IPFS) and FireBase as per user adaptability, Ehtereum Blockchain for immutability, and encryption and decryption using asymmetric keys for confidentiality. The empirical results and the performance evaluation demonstrate that the proposed MediVault is secure, simple, and efficient with a limited computation overhead. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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BEFF-SIGS: blockchain-enhanced fog framework- securing IoT data integrity and green sustainability through scalable authentication-authorization

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International Journal of computers and Applications 2025年第3期47卷 293-311页

作者： Khan, Neelam Saleem Mir, Roohie Naaz Chishti, Mohammad Ahsan Department of Computer Science & Engineering NIT Srinagar J&K India

This paper introduces a comprehensive framework designed to address security concerns in the Internet of Things (IoT) environment while contributing to the global Green Environment Initiative. The proposed mechanism utilizes a secure and scalable Authentication-Authorization system, employing JSON Web Token (JWT) and Casbin to ensure that only authorized entities access critical data. Data processing is strategically offloaded to the fog layer to enhance scalability and reduce latency, optimizing IoT data handling. The framework actively supports environmental sustainability by promoting eco-friendly practices within industrial setups. Industries are incentivized to embrace environmentally responsible approaches through tax incentives, fostering a greener future. Our model is a robust mechanism to guarantee the consistency and authenticity of the data stored in the cloud environment, offering assurance against potential tampering attempts by securing data integrity. This holistic approach demonstrates how governments can leverage cutting-edge technology to incentivize industries and promote a sustainable and eco-friendly future. Utilizing blockchain technology ensures unparalleled transparency, robust security, and tamper-resistant record-keeping. We evaluate the impact on network consumption, latency, and energy consumption for analyzing the performance of the proposed system. The proposed framework offers better latency, network efficiency, and distributed computational power as major benefits over depending exclusively on cloud resources in IoT deployments. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Blockchain

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Undergraduate Research Experience Impact on Retention in an Electrical engineering and computer science (EECS) Program

Undergraduate Research Experience Impact on Retention in an ...

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2023 Congress in computer science, computer engineering, and Applied Computing, CSCE 2023

作者： Salmani, Hassan Mosleh, Mohsen Washington, Gloria Department of Electrical Engineering and Computer Science WashingtonDC United States Department of Mechanical Engineering WashingtonDC United States

ISBN: (纸本)9798350327595

Research has demonstrated the positive influence of Undergraduate Research Experience (URE) programs in science, Technology, engineering, and Mathematics (STEM) on students' educational journey and their development as scientists, ultimately aiding them in making informed career choices. However, traditionally, URE programs have primarily targeted junior and senior students who already possess disciplinary knowledge and exhibit a strong inclination to persist in STEM fields. This study aims to examine the effects of involving freshmen in the Industry-Research Inclusion in STEM Education (I-RISE) program, specifically in the disciplines of electrical engineering (EE) and computer science (CS), on student retention. The I-RISE program integrated research opportunities for undergraduate students with mentorship activities, facilitating the acquisition of relevant skills in applied computing and engineering techniques, research methodologies, and the attainment of internships. Analyzing the retention rates of three distinct cohorts of I-RISE participants over a span of three years revealed significantly higher retention rates compared to students who did not partake in the I-RISE program. © 2023 IEEE.

关键词： Mentoring Retention Undergraduate Research Experience

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Electrocardiogram-based heart disease prediction using hybrid deep feature engineering with sequential deep classifier

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Multimedia Tools and Applications 2025年第10期84卷 7443-7475页

作者： Golande, Avinash L. Pavankumar, T. Department of Computer Science & Engineering KLEF A. P. Vijayawada India

Several approaches can detect early heart problems. Electrocardiograms (ECGs) are better and more affordable for early heart disease prediction. ECG data can better predict heart diseases and abnormalities. Standard methods are slow and limited to a narrow ECG signal pattern range. Newer deep learning-based algorithms require tedious and unimportant human feature extraction from raw ECG data. This study presents a new electrocardiogram-based cardiac disease detection system using an ensemble method for feature extraction and deep learning for classification. The approach includes pre-processing of input ECG signals, hybrid feature engineering, and disease categorization. Pre-processing the input signal preserved its integrity by reducing the artifact and noise using a robust filtering technique. ECG feature extraction should use generic automation and beat-specific features. We designed the hybrid feature engineering approach using a Convolutional Neural Network (CNN) and Stationary Wavelet Transform (SWT). The SWT method employs dynamic thresholding to segment ECG data into individual heartbeats, from which the beat-specific feature vector is derived. To automate the process of classifying cardiac diseases, a powerful 3-layer CNN has been trained to do so and now extracts those attributes automatically. Manifold learning of feature selection is then used to normalize the combined features from the two methods. Long Term Short Memory (LSTM), a deep learning classifier, has been given the ensemble feature vector. For LSTM, the sequential characteristics vector transmits information to the LSTM layer and then calculates the probabilities for each class to provide precise disease classification in the classification phase. The suggested model surpasses cutting-edge deep learning-driven techniques when used with a publicly available multi-disease ECG dataset. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Electrocardiograms

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Pneumonia Net: Pneumonia Detection and Categorization in Chest X-ray Images

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Recent Advances in computer science and Communications 2024年第3期17卷 20-31页

作者： Srivastava, Somya Verma, Seema Das, Nripendra Narayan Sharma, Shraddha Dubey, Gaurav Department of Computer Science ABES Engineering College Ghaziabad India Department of Computer Science and Engineering Guru Gobind Singh Indraprastha University Delhi India Department of Information Technology Manipal University Jaipur Rajasthan Jaipur India Department of Computer Science Pandit Deendayal Energy University Gujarat India Department of Computer Science KIET Group of Institutions Ghaziabad India

Background: Pneumonia is one of the leading causes of death and disability due to respiratory infections. The key to successful treatment of pneumonia is in its early diagnosis and correct classification. PneumoniaNet is a unique deep-learning model based on CNN for identifying pneumonia on chest X-rays. Objective: A deep learning model that combines convolutional, pooling, and fully connected layers is presented in this study. Methods: In order to learn how to identify cases of pneumonia and healthy controls on chest X-ray pictures, PneumoniaNet was trained on a large labeled library of such images. A robust data augmentation technique was adopted to enhance the model generalization and training set diversity. Standard measures like as accuracy, precision, recall, and F1-score were applied to PneumoniaNet's performance evaluation. Results: The suggested model performed effectively in detecting pneumonia cases with an accuracy of 93.88%. Conclusion: The model was evaluated against the current state-of-art methods and showed that PneumoniaNet outperformed the other models. © 2024 Bentham science Publishers.

关键词： Deep learning

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DLP4DA-RPL: A Distributed Lightweight Protocol for Detection and Avoidance of Discarded DIO and DAO Attacks on RPL Routing Protocol in IoT

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IEEE Sensors Journal 2025年第12期25卷 22880-22894页

作者： Deepavathi, P. Mala, C. Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli 620015 India

The Internet of Things (IoT) occupies the entire world in its hands. IoT devices have a resource-constrained nature known as Low Power and Lossy Networks (LLN). The Routing Protocol for Low Power and Lossy Networks (RPL) is provided by the Internet engineering Task Force (IETF) group to secure the IoT networks. The control messages Destination Oriented Directed Acyclic Graph (DODAG) Information Object (DIO) and DODAG Advertisement Object (DAO) play a crucial part in RPL. Attackers focused on these control messages to degrade the performance of IoT networks and slowly bring them to a halt. To overcome these problems, this paper proposes a DLP4DA-RPL protocol to detect and avoid Discarded DIO (DDIO) and Discarded DAO (DDAO) control message attacks. The Contiki operating system simulates this proposed protocol using the Cooja simulator and this proposed protocol is implemented in our college environment. It is inferred from the simulation and real-time results that the proposed DLP4DA-RPL protocol outperforms the existing RPL protocols, such as RPL with Attacks, RPL without Attacks, SecTrust-RPL, and DDoS-RPL concerning End-to-end Delay, Energy Consumption, Packet Delivery Ratio, Throughput and Network Performance. © 2025 IEEE.

关键词： Routing protocols

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Single-And Dual-Beam Pattern Reconfigurable Patch Antenna With Dual Switchable CP for Internet of Vehicles

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IEEE Internet of Things Journal 2025年第12期12卷 21904-21914页

作者： Wang, Shi-Tong Zhu, Lei University of Macau Faculty of Science and Technology Department of Electrical and Computer Engineering China

In this article, a novel method is proposed to facilitate the design of compact, low-profile, pattern reconfigurable antennas with fixed or switchable circular polarization (CP) for Internet of Vehicles (IoV) applications. The proposed method eliminates the need for a complex beamforming or phase-tuning network by innovatively exploiting four resonant modes of a square patch antenna. Specifically, the TM01 and TM10 modes are utilized to accomplish the single-beam CP pattern, while, the dualbeam CP pattern is achieved by combining the TM11 and TM02 modes. An electric current model is developed to investigate the far-field radiation, revealing that the radiation pattern of TM11 mode can be effectively reshaped from quad-beam to dual-beam by manipulating the surface current distribution. The complete design method is verified by an antenna prototype working at 3.5 GHz, which consists of a radiative patch, a pair of open-ended stubs, a pair of slots, and four equivalent switchable pins. Different from the conventional pin-loading method, the proposed equivalent switchable pins attain excellent CP performance during switching by compensating for the detrimental electric coupling. The prototype is fabricated and measured. Its radiation can be switched between single-beam (7.8 dBic) and dual-beam CP pattern (3.5 dBic), exhibiting an overlapped CP coverage from -123° to 73°. Furthermore, another prototype with the extended switchable CP capability is realized. Without increasing the number of p-i-n diodes, this prototype achieves four operating states with independent pattern (single-and dual-beam) and polarization (right-and left-handed CP) reconfiguration. The merits of compact size, low profile, and flexible reconfigurability make the proposed method and design cases excellent candidates for IoV applications and intelligent transportation systems (ITSs). © 2014 IEEE.

关键词： Circular polarization

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