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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Bisht, Girish Singh Ganesh, D. Kumar, Santosh Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Science and Information Technology Bangalore India Noida Institute of Engineering and Technology Department of Internet of Things Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350383522

Several intriguing research works on soft computing techniques have recently been presented and for the creation of a pulmonary TB diagnosis system, researchers continue to build an accurate and trustworthy intelligent decision-making approach. The current diagnostic testing system processes are cumbersome and time-consuming to analyse. Since increased sensitivity and specificity can be attained, the diagnosis of TB still has to be improved upon using new speedy and accurate diagnostic models and procedures, which will help with disease prevention and control. Therefore, this review paper's significance lies in its ability to discriminate between the many soft computing methodologies used to help the diagnosis, identification, prediction, and intelligent categorization of pulmonary TB illness. Researchers and medical professionals anticipate employing soft computing methods in the field. 'Artificial neural networks, genetic algorithms, support vector machines, fuzzy logic', and others are a few of these. The most recent diagnostic techniques involve artificial neural networks. Predictive inference accuracy and easy-to-optimize, adoptable non-linear modelling of large data sets are some additional advantages of artificial neural networks, showing that they could be an important decision support tool in a variety of industries, including medicine. © 2024 IEEE.

关键词： Artificial Neural Network Fuzzy and Fuzzy Logic Genetic Algorithm Pulmonary Tuberculosis Support Vector Machine

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Enhanced Ransomware Detection using a Hybrid Quantum-Classical Convolutional Neural Network with Boosted Sooty Tern Optimization 4

Enhanced Ransomware Detection using a Hybrid Quantum-Classic...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Chaithanya, B.N. Patil, Renuka R. Gitam School of Technology Gitam University Department of Computer Science and Engineering Nagadenahalli Bengaluru India

ISBN: (纸本)9798331523923

Ransomware attacks threaten organizations by encrypting files or locking systems and keeping them inaccessible unless a ransom is paid. Early detection of ransomware attacks helps organizations avoid financial losses, theft of data, and harm to their reputations. This early detection will also reduce the risks related to malicious activities. Detection by signature-based or heuristic approaches tends to be traditional and fails when evolvement in ransomware attacks is considered. Thus, a need for innovative techniques of deep learning and optimization algorithms can be observed. The method proposed in this study focuses on developing a novel multi-stage framework for detecting ransomware attacks. Firstly, correlation coefficient-based min-max normalization (CCMMN) for data preprocessing is used since it makes the data elements more uniform and makes the detection system more efficient. Feature selection is then carried out and optimized using the hybrid Osprey optimization algorithm with an enhancing sparrow search algorithm (OOA-ESSA) to arrive at the most relevant feature for classification. Last, this paper applies a hybrid quantum-classical convolutional neural network with boosted sooty tern optimization (HQCCNN-BSTO) in ransomware detection. The classic model is replaced by a quantum and hybrid classical neural network that may be complemented by an optimization algorithm to increase the speed and efficiency of the detection. The method that has been proposed here offers a flexible and efficient approach to the problems associated with the detection of ransomware threats in the practical environment. Thus, high effectiveness is proved by the 99% accuracy rate of the suggested method. This exceptional outcome proves the robustness of the approach and its ability to adapt to various patterns of dependence and data changes. © 2025 IEEE.

关键词： Convolutional neural networks

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Detection of Deceptive Hotel Reviews Through the Application of Machine Learning Techniques 2nd

Detection of Deceptive Hotel Reviews Through the Application...

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2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Hossain, Md Jakir Chowdhury, Md Tanvir Rahman, Habibur Hossain, Md Shohrab Shara, Shabrina Akter Choudhury, Tashfia Chowdhury, Maleha Israt Synthia, Tasnim Israk Talha, Abu Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Textile Engineering Ahsanullah University of Science and Technology Dhaka Bangladesh

ISBN: (纸本)9789819735938

In the contemporary digital landscape, online reviews play a pivotal role in shaping tourists’ decisions, leveraging the pervasive influence of the internet. However, discerning the authenticity of reviews has become an intricate challenge, blurring the line between genuine and deceptive evaluations. This ambiguity provides fertile ground for individuals seeking to exploit the uncertainty, as witnessed in the proliferation of false reviews and the spread of unfavorable rumors through social media platforms. The consequences are profound, with travelers and hosts alike navigating a landscape where traditional verification systems prove insufficient, leaving consumers vulnerable to misinformation. To address this pressing issue, we propose a novel machine learning-based approach. Our strategy employs classifiers such as Random Forest, Support Vector Machine, and Decision Tree, each exhibiting robust validation accuracy values of 92.19%, 87.19%, and 86.56%, respectively. This innovative framework serves as a powerful tool to distinguish between authentic and fraudulent reviews, providing a more reliable foundation for decision-making in the travel sector. By leveraging the capabilities of machine learning, we aim to create a system that not only identifies deceptive practices but also contributes to restoring trust in online reviews. As the travel industry contends with the challenges posed by the digital era, this approach offers a promising avenue for mitigating the impact of false information and enhancing the overall integrity of the review ecosystem. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Privacy enhanced course recommendations through deep learning in Federated Learning environments

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International Journal of Information technology (Singapore) 2025年第1期17卷 629-635页

作者： Kolli, Chandra Sekhar Seelamanthula, Sreenivasu Reddy V, Venkata Krishna Babu, Padamata Ramesh Reddy, Mule Rama Krishna Gumpina, Babu Rao Department of Information Technology Shri Vishnu Engineering College for Women Andhra Pradesh Bhimavaram India Department of Information Technology Lakireddy Bali Reddy College of Engineering Andhra Pradesh Mylavaram India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Department of Computer Science and Engineering Narasaraopeta Engineering College Narasaraopeta India Department of Compute Science GITAM School of Science GITAM (Deemed to be University) Andhra Pradesh Visakhapatnam India

The increasing concerns around data security and privacy among users have significantly pushed the interest of the research community towards developing privacy-preserving recommendation systems. Amidst this backdrop, our study introduces a novel course recommendation methodology leveraging Federated Learning (FL) coupled with advanced Deep Learning techniques. This method executes the recommendation process across local nodes through several stages, including agglomerative matrix formulation, course clustering, bi-level matching, identification of learner-preferred courses, and ultimately, course recommendation. Notably, course clustering is achieved through Deep Fuzzy Clustering (DFC), while Deep Convolutional Neural Networks (DCNN) are employed for the recommendation phase. The efficacy of our DFC-DCNN-FL approach is rigorously evaluated based on several metrics: accuracy, False Positive Rate (FPR), loss function, Mean Square Error (MSE), Root MSE (RMSE), and Mean Average Precision (MAP). The results demonstrate remarkable performance with scores of 0.909, 0.116, 0.126, 0.291, 0.539, and 0.925, respectively. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Course recommendation Deep fuzzy clustering Deep learning Federated learning Privacy preservation

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Analysis and Impact of Electromagnetic Field Leakage in WPT on Central Nervous System: A Neural Network Approach 4

Analysis and Impact of Electromagnetic Field Leakage in WPT ...

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4th IEEE International Conference on Sustainable Energy and Future Electric Transportation, SEFET 2024

作者： Shukla, Garima Awasthi, Vanshaj Goswami, Prafull Singh, Sofia Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Maharashtra Mumbai India Indian Institute of Information and Technology Department of Computer Science and Engineering Sonipat India Amity School of Engineering and Technology Amity University Department of Artificial Intelligence Uttar Pradesh Noida India

ISBN: (纸本)9798350383997

The potential for wireless power transfer (WPT) technologies to transform the charging capabilities of a wide range of applications, from consumer electronics to medical devices has attracted a lot of attention. But uncertainties remain about how these technology's electromagnetic field (EMF) leakage affects human health, especially the central nervous system (CNS). To find a way to assess and forecast all of these consequences, this study explores at the possible effects of EMF leakage from WPT systems on the central nervous system (CNS) and suggests a distinctive approach that employs the applications for neural networks. Using real- world data and computational models, this study will produce an accurate model of EMF absorption and propagation in biological tissues, with a specific focus on the central nervous system. By focusing insights into potential risks to health and informing the emergence of requirements which assure the safe implementation of wireless power technologies, the research will enhance our understanding of the connections between EMF exposure and the central nervous system. The ultimate objective of this study is to encourage safer and more sustainable wireless charging technologies by enhancing the accuracy and efficiency of risk assessment in circumstances of EMF exposure from WPT systems through the implementation of Machine Learning (ML) strategies © 2024 IEEE.

关键词： Inductive power transmission

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A State-Aware Method for Flows With Fairness on NVMe SSDs With Load Balance

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IEEE Transactions on Cloud Computing 2023年第3期11卷 1-16页

作者： Wu C. Chen L. Hsu R. Dai J. Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei

Nowadays, solid-state drives (SSDs) have become the best choice of storage devices because of its brilliant advantages such as small size, low-power consumption, shake resistance, fast access and non-volatility, when compared with hard-disk drives (HDDs). More and more scenarios adopt a multi-SSD architecture to improve performance and expand storage capacity, such as cloud services, database centers, distributed systems and virtualized environments. When multiple users (flows) are competing for shared multiple SSDs concurrently, if the multi-SSD architecture lacks a fairness strategy among multiple users, a user that takes up more resources can affect other users. Meanwhile, if the multi-SSD architecture lacks a load-balance strategy among multiple shared SSDs, some specific SSDs may receive too many I/O requests to degrade the performance and shorten the lifespan. Therefore, we will propose a state-aware method to consider flows with fairness on NVMe SSDs with load balance. According to experimental results, we can show that the proposed method can improve the fairness by 1.15x

$\sim$

1.19x and the load balance by 1.18x

$\sim$

3.15x on average, when compared to other methods. IEEE

关键词： Ash Cloud computing computer architecture Fairness Hardware load balance NAND flash memory Nonvolatile memory Servers solid - state drives (SSDs) state awareness Time factors

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Secure Consumer-Centric Demand Response Management in Resilient Smart Grid as Industry 5.0 Application with Blockchain-Based Authentication

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 1370-1379页

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Shamshad, Salman Saleem, Muhammad Asad Omar, Marwan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan Illinois Institute of Technology Information Technology and Management ChicagoIL60616 United States

The development of the industrial Internet of Things and smart grid networks has emphasized the importance of secure smart grid communication for the future of electric power transmission. However, the current deployment of smart meters and wireless communication methods presents several security concerns that must be addressed to ensure sustainable and resilient smart grid networks for Industry 5.0, with a focus on consumer-centric technologies. In this context, we propose a privacy-focused authentication solution for secure consumer-centric demand response management in smart grid networks, incorporating consumer electronics. Our proposed protocol utilizes blockchain-based authentication to enhance resistance against potential attacks and ensure the integrity of demand response data. Additionally, we have incorporated a PUF to prevent physical attacks. We have conducted informal and formal security analyses to verify the effectiveness of our proposed protocol against various threats and to confirm the session key security. Our comparison study has demonstrated that our protocol provides superior security and added functionalities, with lower computation and communication costs compared to similar protocols. Furthermore, we have measured the computational time for implementing the blockchain component of our protocol while considering the impact of varying numbers of block additions and transactions per block. © 1975-2011 IEEE.

关键词： Authentication

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Research on Biomedical engineering 2025年第2期41卷 1-19页

作者： A, Padmashree Sankar, P. Alkhayyat, Ahmad Muniyandy, Elangovan Department of Computer Science and Business Systems Bannari Amman Institute of Technology Tamil Nadu Sathyamangalam India Department of Computer Science and Engineering Saveetha Engineering College Tamil Nadu Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Applied Science Research Center Applied Science Private University Amman Jordan

Purpose: The primary objective of this research is to develop a comprehensive framework for the analysis of brain tumor images, addressing the complexities of detection, segmentation, and classification. Given the intricate nature of brain anatomy and tumor variability, the study aims to enhance the accuracy and efficiency of brain tumor diagnosis using advanced imaging and processing techniques. Methods: The framework utilizes Brain Tumor Segmentation (BraTS) 2019 and 2020 datasets, applying an AMW-PM filter to enhance image quality. Tumor segmentation employs a slimmable transformer with hybrid axial attention for precision. Classification uses similarity navigated graph neural networks (SNGNNs), optimized with the crayfish algorithm (CFO) to minimize loss and enhance performance. Results: The framework’s effectiveness was validated using key performance metrics, showing significant improvements in tumor detection and classification. The combination of advanced segmentation techniques and the SNGNN classifier, optimized by the CFO, led to a fivefold increase in diagnostic accuracy. These improvements demonstrated a substantial enhancement in the model's ability to correctly identify tumor types and provide reliable outputs for clinical use. Conclusion: The proposed framework delivers a robust solution for brain tumor analysis, integrating cutting-edge techniques for image processing, segmentation, and classification. The results not only highlight improvements in diagnostic accuracy but also contribute to better clinical decision-making in managing brain tumors. This framework sets the foundation for future advancements in brain tumor diagnostics. © The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering 2025.

关键词： Graph neural networks

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Advancements in Environmental Data Analysis for Climate-Resilient Agriculture Using Remote Sensing and Deep Learning

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 670-680页

作者： Nirmal, Khyati R. Reddy, Chilukala Mahender P, Jose Tulasi, Rajesh Maheswari, G. Uma Sapthami, I. SNJB’S Late Sau KBJ CoE Maharashtra Nashik India Department of Artificial Intelligence and Data Science GITAM Deemed to Be University Hyderabad India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Chennai Avadi India Dept. of Computer Science and Engineering Koneru Lakshmaiah Education Foundation A.P Vaddeswaram Guntur Dist. India Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Pudhuvoyal India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Weather significantly influences agricultural productivity. Plant biotic and abiotic stressors are primarily induced by climate change, resulting in a detrimental effect on worldwide agricultural productivity. These two components are closely interconnected. This paper presents an innovative approach in smart agriculture for climate change combining remote sensing and a deep learning algorithm. The input is obtained as a multispectral environmental image and then subjected to noise reduction and normalisation processing. The image has been retrieved using a primary convolutional component with a stacked encoder model. The retrieved features are identified using ResNet graph reinforcement neural networks. The resulting classification output displays environmental imagery depicting climate variations. The agriculture sector has been analysed based on the classified climate analysis. The experimental results have been conducted on diverse farm datasets related to climate change, evaluating the detection accuracy, recall, mean average precision, normalized correlation, and F-1 score. The proposed method achieved a detection accuracy of 98%, a normalised correlation of 95%, a mean average precision of 92%, a recall of 97%, and an F-MEASURE of 94%. Machine learning can assist in monitoring and predicting the impact of climate change on food security, as indicated by the findings. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Climate change Deep learning model Remote sensing Smart agriculture Stacked encoder model

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Rule-based Explaining Module: Enhancing the Interpretability of Recurrent Relational Network in Sudoku Solving

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Machine Graphics and Vision 2023年第3-4期32卷 125-145页

作者： Cheewaprakobkit, Pimpa Shih, Timothy K. Lau, Timothy Lin, Yu-Cheng Lin, Chih-Yang Department of Information Technology Asia-Pacific International University Saraburi Thailand Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan Department of Mechanical Engineering National Central University Taoyuan Taiwan

computer vision has gained significant attention in the field of information technology due to its widespread application that addresses real-world challenges, surpassing human intelligence in tasks such as image recognition, classification, natural language processing, and even game playing. Sudoku, a challenging puzzle that has captivated many people, exhibits a complexity that has attracted researchers to leverage deep learning techniques for its solution. However, the reliance on black-box neural networks has raised concerns about transparency and explainability. In response to this challenge, we present the Rule-based Explaining Module (REM), which is designed to provide explanations of the decision-making processes using Recurrent Relational Networks (RRN). Our proposed methodology is to bridge the gap between complex RRN models and human understanding by unveiling the specific rules applied by the model at each stage of the Sudoku puzzle solving process. Evaluating REM on the Minimum Sudoku dataset, we achieved an accuracy of over 98.00%. © 2023 Institute of Information technology, Warsaw University of Life sciences - SGGW. All rights reserved.

关键词： Image recognition

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