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Fortifying Healthcare Data Security in the Cloud:A Comprehensive Examination of the EPM-KEA Encryption Protocol

computers, Materials & Continua 2024年第5期79卷 3397-3416页

作者： Umi Salma Basha Shashi Kant Gupta Wedad Alawad SeongKi Kim Salil Bharany Computer Science and Information Technology Jazan UniversityJazanSaudi Arabia CSE Eudoxia Research UniversityNew CastleUSA Department of Information Technology College of Computer ScienceQassim UniversityBuraydahSaudi Arabia National Center of Excellence in Software Sangmyung UniversitySeoulKorea Department of Computer Science and Engineering Lovely Professional UniversityPhagwaraPunjabIndia

A new era of data access and management has begun with the use of cloud computing in the healthcare *** the efficiency and scalability that the cloud provides, the security of private patient data is still a majorconcern. Encryption, network security, and adherence to data protection laws are key to ensuring the confidentialityand integrity of healthcare data in the cloud. The computational overhead of encryption technologies could leadto delays in data access and processing rates. To address these challenges, we introduced the Enhanced ParallelMulti-Key Encryption Algorithm (EPM-KEA), aiming to bolster healthcare data security and facilitate the securestorage of critical patient records in the cloud. The data was gathered from two categories Authorization forHospital Admission (AIH) and Authorization for High Complexity *** use Z-score normalization forpreprocessing. The primary goal of implementing encryption techniques is to secure and store massive amountsof data on the cloud. It is feasible that cloud storage alternatives for protecting healthcare data will become morewidely available if security issues can be successfully fixed. As a result of our analysis using specific parametersincluding Execution time (42%), Encryption time (45%), Decryption time (40%), Security level (97%), and Energyconsumption (53%), the system demonstrated favorable performance when compared to the traditional *** suggests that by addressing these security concerns, there is the potential for broader accessibility to cloudstorage solutions for safeguarding healthcare data.

关键词： Cloud computing healthcare data security enhanced parallel multi-key encryption algorithm(EPM-KEA)

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Blockchain-Enabled Mitigation Strategies for Distributed Denial of Service Attacks in IoT Sensor Networks:An Experimental Approach

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computers, Materials & Continua 2024年第12期81卷 3679-3705页

作者： Kithmini Godewatte Arachchige Mohsin Murtaza Chi-Tsun Cheng Bader M.Albahlal Cheng-Chi Lee Department of Engineering Technologies School of ScienceComputing and Engineering TechnologiesSwinburne UniversityMelbourne3122Australia School of Engineering Science Technology Engineering and Mathematics CollegeRMIT UniversityMelbourneVIC 3001Australia Department of Information Technology College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Department of Library and Information Science Fu Jen Catholic UniversityNew Taipei City24205TaiwanChina Department of Computer Science and Information Engineering Asia UniversityTaichung City41354TaiwanChina

information security has emerged as a crucial consideration over the past decade due to escalating cyber security threats,with Internet of Things(IoT)security gaining particular attention due to its role in data communication across various ***,IoT devices,typically low-powered,are susceptible to cyber ***,blockchain has emerged as a robust solution to secure these devices due to its decentralised ***,the fusion of blockchain and IoT technologies is challenging due to performance bottlenecks,network scalability limitations,and blockchain-specific security ***,on the other hand,is a recently emerged information security solution that has great potential to secure low-powered IoT *** study aims to identify blockchain-specific vulnerabilities through changes in network behaviour,addressing a significant research gap and aiming to mitigate future cybersecurity *** blockchain and IoT technologies presents challenges,including performance bottlenecks,network scalability issues,and unique security *** paper analyses potential security weaknesses in blockchain and their impact on network *** developed a real IoT test system utilising three prevalent blockchain applications to conduct *** results indicate that Distributed Denial of Service(DDoS)attacks on low-powered,blockchain-enabled IoT sensor networks cause measurable anomalies in network and device performance,specifically:(1)an average increase in CPU core usage to 34.32%,(2)a reduction in hash rates by up to 66%,(3)an increase in batch timeout by up to 14.28%,and(4)an increase in block latency by up to 11.1%.These findings suggest potential strategies to counter future DDoS attacks on IoT networks.

关键词： Blockchain cyber intrusions DDoS IoT low powered sensors vulnerabilities wireless network

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Automated Detection of Heart Arrhythmia Signals by Using a Convolutional Takagi–Sugeno–Kang-type Fuzzy Neural Network

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Sensors and Materials 2024年第2-3期36卷 639-653页

作者： Lin, Cheng-Jian Cheng, Han Chang, Chun-Lung Department of Computer Science & Information Engineering National Chin-Yi University of Technology Taichung411 Taiwan Department of Artificial Intelligence & Computer Engineering National Chin-Yi University of Technology Taichung411 Taiwan

In clinical practice, electrocardiography is used to diagnose cardiac abnormalities. Because of the extended time required to monitor electrocardiographic signals, the necessity of interpretation by physicians, and the vulnerability of electrocardiographic signals to noise interference, electrocardiography is laborious and places a heavy burden on healthcare providers. Therefore, in this paper, a convolutional Takagi–Sugeno–Kang (TSK)-type fuzzy neural network (CTFNN) is proposed to address the challenges of arrhythmia signal classification. The proposed CTFNN is divided into three parts, namely, a convolutional layer, a feature fusion layer, and a TSK fuzzy neural network. The TSK fuzzy neural network is used to replace the fully connected neural network, thereby reducing the number of parameters and enabling the model to mimic the human brain when classifying signals. In addition, because the parameters of the CTFNN are difficult to determine, the uniform experimental design method, which requires only a small number of experiments, is used to determine the optimal parameter combination. The proposed model was tested using the Massachusetts Institute of technology-Beth Israel Hospital (MIT-BIH) arrhythmia database, which contains 1000 records belonging to 17 categories. Each record has a duration of 10 s and contains 3600 sampling points. According to our experimental results, the accuracy, recall, precision, and F1-score of the CTFNN for long-term signals were 97.33, 97.96, 96.00, and 96.97%, respectively. In addition, the number of parameters for the proposed model was only 558,728, which was less than that for LeNet (i.e., 1734501). © 2024 M Y U Scientific Publishing Division. All rights reserved.

关键词： Electrocardiograms

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Securing Autonomous UAV Cluster With Blockchain-Based Threshold Key Management System Utilizing Crypto-Asset and Multisignature

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5765-5778页

作者： Kharjana, Mebanjop Sahana, Subhas Chandra Saha, Goutam North-Eastern Hill University Department of Information Technology Shillong793022 India National Institute of Technology Department of Computer Science and Engineering Durgapur713209 India

Unmanned aerial vehicles deployed in remote locations rely on self-governed key management for their protection. However, conventional key management depends on a centralized ground-based station or single vehicle. Such a system is vulnerable to compromised certificate authority problems and single-points-of-failure. This paper proposed to resolve these vulnerabilities using a blockchain-based threshold key management system. The proposed system utilized blockchain’s concepts of crypto-asset and multisignature. Keys are defined as crypto-assets to improve their management in the blockchain network. Multisignature facilitates collaboration during key management based on a threshold value. The threshold value is also configurable to meet systems’ security and performance requirements. The proposed system secured the process of re-enforcement, sub-clustering, re-merging, and inter-cluster migration. Security analysis revealed that the proposed system complied with most key management security guidelines. The custom signature module used to authenticate intra-cluster communication was also verified as safe. Threats to the cluster were identified, assessed for risk, and mitigated accordingly. Performance analysis found that both AODV and DSDV routing protocols offer consistent performance but DSDV prevailed during the worst-case network scenario. The paper finally identified research gaps, including the requirement for an optimized mechanism for collecting consent signatures. © 2002-2012 IEEE.

关键词： Blockchains Autonomous Aerial Vehicles Security Authentication Drones Public Key Mobile Computing Smart Contracts Collaboration Global Positioning System Autonomous UAV Cluster Threshold Key Management System Blockchain Crypto Asset Multisignature Unmanned Aerial Vehicles Autonomous Unmanned Aerial Vehicles Key Management System Unmanned Aerial Vehicles Cluster Worst Case Scenario Performance Requirements Security Analysis Security Requirements Certificate Authority Energy Consumption Application Programming Interface Secret Key Directed Acyclic Graph Public Key Denial Of Service Elliptic Curve Best Case Scenario Secret Sharing Smart Contracts Public Key Infrastructure Key Operations Unmanned Aerial Vehicles Networks Distributed Ledger technology Signature Scheme Digital Signature Blockchain Platform Random Oracle Model Bitcoin

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Forecasting Directional Movement of Stock Prices using Deep Learning

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Annals of Data science 2023年第5期10卷 1361-1378页

作者： Chandola, Deeksha Mehta, Akshit Singh, Shikha Tikkiwal, Vinay Anand Agrawal, Himanshu Department of Electrical Engineering and Computer Science York University Toronto Canada Hebert Werthim College of Engineering University of Florida Gainesville Gainesville United States Department of Electronics and Communication Engineering Jaypee Institute of Information Technology Noida India Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Stock market’s volatile and complex nature makes it difficult to predict the market situation. Deep Learning is capable of simulating and analyzing complex patterns in unstructured data. Deep learning models have applications in image recognition, speech recognition, natural language processing (NLP), and many more. Its application in stock market prediction is gaining attention because of its capacity to handle large datasets and data mapping with accurate prediction. However, most methods ignore the impact of mass media on the company’s stock and investors’ behaviours. This work proposes a hybrid deep learning model combining Word2Vec and long short-term memory (LSTM) algorithms. The main objective is to design an intelligent tool to forecast the directional movement of stock market prices based on financial time series and news headlines as inputs. The binary predicted output obtained using the proposed model would aid investors in making better decisions. The effectiveness of the proposed model is assessed in terms of accuracy of the prediction of directional movement of stock prices of five companies from different sectors of operation. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Large dataset

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Interpreting Randomly Wired Graph Models for Chinese NER

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computer Modeling in engineering & sciences 2023年第1期134卷 747-761页

作者： Jie Chen Jiabao Xu Xuefeng Xi Zhiming Cui Victor S.Sheng The School of Electronic and Information Engineering SuzhouUniversity of Science and TechnologySuzhouChina The School of Computer Science Texas Tech UniversityTexasUSA

Interpreting deep neural networks is of great importance to understand and verify deep models for natural language processing(NLP)***,most existing approaches only focus on improving the performance of models but ignore their *** this work,we propose a Randomly Wired Graph Neural Network(RWGNN)by using graph to model the structure of Neural Network,which could solve two major problems(word-boundary ambiguity and polysemy)of ***,we develop a pipeline to explain the RWGNNby using Saliency Map and Adversarial *** results demonstrate that our approach can identify meaningful and reasonable interpretations for hidden states of RWGNN.

关键词： Named entity recognition graph neural network saliency map random graph network interpretation

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Fuzzy validation of Taguchi-based convolutional fuzzy neural classifier for lung cancer imaging

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Multimedia Tools and Applications 2024年第41期83卷 89415-89437页

作者： Chang, Tsang-Chuan Lin, Cheng-Jian Yang, Tang-Yun Department of Intelligent Production Engineering National Taichung University of Science and Technology Taichung404336 Taiwan Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung411030 Taiwan

While deep learning technology is widely used in the field of image classification and recognition, parameter setting for convolutional neural networks is complex, and a high number of parameters make the technology difficult to apply in practice. Therefore, this study proposes a Taguchi-based convolutional fuzzy neural classifier (T-CFNC) to classify computed tomography (CT) images. Two layers of convolution and pooling are used to extract features of the input images, and a fuzzy neural network is used to replace fully-connected neural networks to reduce the number of model parameters. To reduce cost and time, the Taguchi experimental design method determines the optimal combination of model parameters with the minimal number of experiments. The SPIE-AAPM lung CT challenge dataset was used to validate the proposed T-CFNC model. Experimental results indicate accuracy of 99.95%, a true positive rate of 99.97%, and a true negative rate of 99.94%. While the confusion matrix is commonly applied to evaluate model performance, its accuracy varies with the quality of model training, and it is easily affected by extreme values. Single and average values also incur the possibility of misjudgment. We therefore further propose fuzzy validation for performance evaluation. Results confirm the superiority of the proposed T-CFNC model in terms of lung cancer image classification over the traditional CFNC model. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Lung cancer

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Advanced deep learning models for automatic detection of driver’s facial expressions, movements, and alertness in varied lighting conditions: a comparative analysis

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Multimedia Tools and Applications 2025年第6期84卷 2947-2983页

作者： Das, Shiplu Pratihar, Sanjoy Pradhan, Buddhadeb Computer Science and Engineering Indian Institute of Information Technology West Bengal Kalyani India University of Engineering and Management Kolkata West Bengal New Town India

Emotion recognition is vital in understanding human behavior and improving the driving experience. We present a systematic analysis of automatic detection of driver’s facial expressions and movements while crying, feeling sad, experiencing anger, drowsiness, fear and surprise to reduce road accident using a deep learning method in varied lighting conditions. The proposed approach aims to accurately detect and analyze driver emotions in real time, it would help in advancing the growth of smart driver assistance systems. To conduct our analysis, we create a comprehensive dataset comprising annotated facial images of drivers exhibiting crying, sadness, anger, drowsiness, fear and surprise. The dataset is carefully designed to encompass diverse lighting conditions, facial orientations, and driver demographics, ensuring the generalizability and robustness of the proposed method. The CNN-BiLSTM model is compared against the CNN, LSTM, CNN-LSTM, BiLSTM, and CNN-LSTM models in the research to perform precise localization of facial expressions and movements. The trial findings show how well our technique accurately identifies and evaluates a driver’s facial expressions. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Advanced driver assistance systems

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Enhancing the efficiency of lung cancer screening: predictive models utilizing deep learning from CT scans

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Neural Computing and Applications 2025年第17期37卷 11459-11477页

作者： Tawfeek, Medhat A. Alrashdi, Ibrahim Alruwaili, Madallah Shaban, Warda M. Talaat, Fatma M. Department of Computer Science Faculty of Computers and Information Menoufia University Shebin Elkom32511 Egypt Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Communication and Electronics Engineering Nile Higher Institute for Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt

Lung cancer is the most lethal form of cancer. This paper introduces a novel framework to discern and classify pulmonary disorders such as pneumonia, tuberculosis, and lung cancer by analyzing conventional X-ray and CT scan images called lung cancer risk prediction (LCRP) model. LCRP has four modules, namely data collection and preprocessing, data augmentation module, image segmentation module, and prediction module. Actually, LCRP employs three deep learning models;sequential model, functional model, and transfer model on publicly available training datasets. Convolutional neural networks (CNNs) have emerged as a highly effective field in machine learning, particularly for image datasets in the field of biomedical applications. The primary goal is to validate these models by comparing their performance with other models in order to determine their effectiveness in addressing challenging datasets. Our research has revealed a noteworthy enhancement in the efficiency of binary and multi-class classification using mask R-CNN image segmentation. During the model training process, a combination of Adam and stochastic gradient descent dual optimizers has been used to improve performance. LCRP have outperformed current pre-trained models by minimizing training parameters, computational costs, and overhead. It introduces 98.5% accuracy, 88.7% specificity, 89% sensitivity, 89.2% precision, and 89.09% F-measure. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Lung cancer

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information Extraction Based on Multi-turn Question Answering for Analyzing Korean Research Trends

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computers, Materials & Continua 2023年第2期74卷 2967-2980页

作者： Seongung Jo Heung-Seon Oh Sanghun Im Gibaeg Kim Seonho Kim School of Computer Science and Engineering KOREATECHCheonan31253Korea Korea Institute of Science and Technology Information KISTIDaejeon34141Korea

Analyzing Research and Development(R&D)trends is important because it can influence future decisions regarding R&D *** typical trend analysis,topic or technology taxonomies are employed to compute the popularities of the topics or codes over *** it is simple and effective,the taxonomies are difficult to manage because new technologies are introduced ***,recent studies exploit deep learning to extract pre-defined targets such as problems and *** on the recent advances in question answering(QA)using deep learning,we adopt a multi-turn QA model to extract problems and solutions from Korean R&D *** the previous research,we use the reports directly and analyze the difficulties in handling them using QA style on information Extraction(IE)for sentence-level benchmark *** investigating the characteristics of Korean R&D,we propose a model to deal with multiple and repeated appearances of targets in the ***,we propose a model that includes an algorithm with two novel modules and a prompt.A newly proposed methodology focuses on reformulating a question without a static template or pre-defined *** show the effectiveness of the proposed model using a Korean R&D report dataset that we constructed and presented an in-depth analysis of the benefits of the multi-turn QA model.

关键词： Natural language processing information extraction question answering multi-turn Korean research trends

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