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User Centric Block-Level Attribute Based Encryption in Cloud Using Blockchains

computer Systems Science & engineering 2022年第8期42卷 605-618页

作者： S.Godfrey Winster A.Siva Kumar R.Ramesh Department of Computer Science and Engineering School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyChengalpattu603203India Department of Information Technology Sri Sai Ram Engineering CollegeChennai600044India Department of Electronics and Communication Engineering Saveetha Engineering CollegeChennai602105India

Cloud computing is a collection of distributed storage Network which can provide various services and store the data in the efficient *** advantages of cloud computing is its remote access where data can accessed in real time using Remote Method Innovation(RMI).The problem of data security in cloud environment is a major concern since the data can be accessed by any time by any *** to the lack of providing the efficient security the cloud computing they fail to achieve higher performance in providing the efficient *** improve the performance in data security,the block chains are used for securing the data in the cloud ***,the traditional block chain technique are not suitable to provide efficient security to the cloud data stored in the *** this paper,an efficient user centric block level Attribute Based Encryption(UCBL-ABE)scheme is presented to provide the efficient security of cloud data in cloud *** proposed approach performs data transaction by employing the block *** proposed system provides efficient privacy with access control to the user access according to the behavior of cloud user using Data Level Access Trust(DLAT).Based on DLAT,the user access has been restricted in the cloud *** proposed protocol is implemented in real time using Java programming language and uses IBM *** implementation results justifies that the proposed system can able to provide efficient security to the data present in and cloud and also enhances the cloud performance.

关键词： Cloud blockchain data security ABE block-level

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Improving insulation performance with woven flax/PP hybrid composite reinforced by synthetic alumina and aluminum trihydrate

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Interactions 2024年第1期245卷 1-16页

作者： Selvam, Prabu Saravanan, P. Palanisamy, Satishkumar Umate, Laxmikant Saminathan, Rajasekaran School of Computer Science and Engineering VIT University Tamilnadu Chennai India School of Computing SRM Institute of Science and Technology Tiruchirappalli Campus Tiruchirappalli India Department of Mechanical Engineering Rathinam Technical Campus Tamilnadu Coimbatore India Directorate of Research Jawaharlal Nehru Medical College Datta Meghe Institute of Medical Sciences Maharashtra Wardha India Department of Mechanical Engineering College of Engineering and Computer Science Jazan University Jazan Saudi Arabia

The addition of microparticles of aluminum trihydrate (ATH) and alumina (AL) to flax-based polypropylene (PP) composites enhances their mechanical strength and fire resistance. The composite materials were produced via the vacuum bagging technique. Various weight ratios of fillers (ATH & AL) to polypropylene were obtained through the process of ultrasonication (3, 6, 9, and 12 wt%). The mechanical properties of the hybrid composites were thoroughly evaluated, including their thermal conductivity, flammability, flexural and tensile strengths, and moisture absorption. Based on XRD analysis, it has been determined that the flax composite contained AL and ATH microparticles. The research indicates that the use of additional synthetic fillers in the flax-based composite reduces both the burning and moisture absorption rates, in comparison to typical flax/PP composites. The incorporation of a hybrid composite consisting of 9 wt % AL and 3 wt % ATH results in a 10.36% enhancement in tensile strength and a 3.21% improvement in flexural strength. The improved interfacial bonding strength between the fibers and resin can be attributed to the uniform dissemination of ATH/AL particles. The stress transfer rate will be enhanced as a result of the efficient cross-linking it produces. This was verified by the utilization of scanning electron microscopy (SEM). The AL/ATH hybrid composites possess a thermal conductivity of 0.0596 W/mk as an additional characteristic. The thermal insulation property of the composites remains unaffected by the presence of flame retardant AL and ATH particles, as their thermal conductivity is below 0.065 W/mk. These composites are suitable for applications in the automotive, aerospace, and construction sectors, where lightweight, durable, and fire-resistant materials are needed for interior panels, insulation materials, and structural components. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Tensile strength

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Performance Evaluation of Biometric Authentication Using Fragment Jaya Optimizer-Based Deep CNN with Multi-kernel SVM

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SN computer Science 2024年第4期5卷 337页

作者： Umasankari, N. Muthukumar, B. Shanmuganathan, C. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India School of Computing Department of CSE Sathyabama Institute of Science and Technology Chennai India Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India

The earlier research clearly indicated that the bimodal authentication system has more efficiency than unimodal and multimodal. This is due to the reason for the best intact biometric traits of fingerprint and retina. There is a chance to additionally improve further performance of the proposed biometric trait combination by additional or alternate algorithms or methodologies. Therefore, in this research work, the multi-kernel support vector machine (MK-SVM), a machine learning classification approach, is proposed and is used for the implementation. In addition, a hybrid algorithm of fragment Jaya optimizer-based deep convolutional neural network (FJO-DCNN) approach is also used to improve the performance value for bimodal biometric authentication and classification. The recognition systems analyze both biometrics independently, and their conclusions are combined to determine whether to give or refuse access to the user in the end. According to the findings of the implementation, this work demonstrates more dependability than the cascaded biometric system. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Bimodal biometric authentication Deep convolutional neural network Fragment Jaya optimizer convolutional neural network Multi-kernel support vector machine

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YOLOv8n-CGW: A novel approach to multi-oriented vehicle detection in intelligent transportation systems

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Multimedia Tools and Applications 2025年第7期84卷 3809-3840页

作者： Berwo, Michael Abebe Fang, Yong Sarwar, Nadeem Mahmood, Jabar Aljohani, Mansourah Elhosseini, Mostafa School of Information and Engineering Chang’an University Shaanxi Xi’an710064 China Department of Computer Science Bahria University Lahore Campus Punjab Lahore54600 Pakistan Department of Information Technology Faculty of Computing and Information Technology University of Sialkot Punjab Sialkot51040 Pakistan College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

In the context of Intelligent Transportation Systems (ITS), the role of vehicle detection and classification is indispensable for streamlining transportation management, refining traffic control, and conducting in-depth accident analyses. However, the intricate task of accurately detecting multi-oriented vehicles in diverse scenarios remains a challenge, even with the advancements in ITS. Factors such as vehicle morphology, design, road conditions, structural details, and climatic variables provide a significant part in the detection. To address this, our study introduces an advanced YOLOv8n model, specifically the YOLOv8n-CGW, which is built upon pre-trained deep learning frameworks. This model is meticulously tailored to enhance the detection capabilities for multi-oriented vehicles. Comprehensive evaluations on esteemed datasets like MtV, MERGED, Custom dataset, DAWN, WEDGE, and UA-DETRAC underscore the superiority of our approach over existing models. Notably, our YOLOv8n-CGW model achieves a remarkable mAP of 83.3% and boasts a swift inference time of 0.0019s, setting a new benchmark in the domain. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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Game Therapy for Specially-Abled Individuals with PPO Reinforcement Learning in VR-based Educational Games 5

Game Therapy for Specially-Abled Individuals with PPO Reinfo...

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5th International Conference on Data Intelligence and Cognitive informatics, ICDICI 2024

作者： Joypriyanka, M. Surendran, R. Sathish Kumar, P.J. Sivasangari, A. Saveetha Institute of Medical and Techinical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Sathyabama Institue of Science and Technology School of Computing Chennai India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

This research study presents a new application of reinforcement learning (RL), specifically Proximal Policy Optimization (PPO), in creating immersive virtual reality (VR) games aimed at aiding skill acquisition and emotional regulation for individuals with anger management difficulties. People with Down syndrome, Autism Spectrum Disorder, Cerebral Palsy (CP), Intellectual Disabilities (ID), and Stroke Survivors are mainly considered. The study examines how PPO-trained agents improve cognitive, social, and emotional regulation. Techniques include thorough hyperparameter optimization for the PPO algorithm with two sets. Iterative agent training in Unity and AWS Sagemaker uses cloud computing resources for parallelized training. Emotional regulation and positive behaviour change are assessed empirically and through user studies. Virtual reality (VR) cooperative RL games may offer comprehensive treatments for anger management, according to the results, which show promising gains in emotional regulation and teamwork. By enhancing interest and effectiveness in satisfying emotional regulation demands, the study demonstrates that AI-driven therapies and "gamified therapy,"which mixes game components into therapy, can revolutionize mental health. © 2024 IEEE.

关键词： Reinforcement learning

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Medical Insurance Premium Prediction Using Linear Support Vector Machine

Medical Insurance Premium Prediction Using Linear Support Ve...

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2023 Winter Summit on Smart computing and Networks, WiSSCoN 2023

作者： Linija, K.P. Geetha, S. Mavaluru, Dinesh Ravali, Sahithya Mary, S Angel Latha Sri Sairam College of Engineering Department of Aiml Bangalore India Sri Sairam College of Engineering Department of Electronics and Communication Engineering Bangalore India College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Srm College of Pharmacy Department of Pharmacy Practice Chennai India Sri Eshwar College of Engineering Department of Computer Science and Business Systems Coimbatore India

ISBN: (纸本)9798350334050

In healthcare, data mining algorithms are utilized to make people's lives better by predicting diseases and trying to diagnose them faster than most doctors. When the middleman is eliminated, as it is with computerized medical insurance, there is an unbroken line of communication between the insurance company and the insured. Predicting the premium of medical insurance is a major source of worry. The only way for a health insurance provider to turn a profit is to receive more in premium payments than it pays out in claims. Linear Support Vector Machines (LSVM)are developed for Premium Prediction in the Well-beingAssurance Sector. Eleven different criteria were used to train and evaluate LSVM. Experimental results showed a 99% accuracy, and the researchers analysed the effectiveness of the algorithm using success metrics. © 2023 IEEE.

关键词： Health insurance

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Enhancing Organizational Performance: Synergy of Cyber-Physical Systems, Cloud Services, and Crowdsensing

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International Journal of Crowd Science 2025年第1期9卷 44-55页

作者： Al-Qerem, Ahmad Ali, Ali Mohd Nabot, Ahmad Jebreen, Issam Alauthman, Mohammad Alangari, Someah Aburub, Faisal Aldweesh, Amjad Computer Science Department Faculty of Information Technology Zarqa University Zarqa13110 Jordan Communications and Computer Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman19328 Jordan Department of Information Security University of Petra Amman11196 Jordan Department of Computer Science College of Science and Humanities Dawadmi Shaqra University Shaqra11911 Saudi Arabia Department of Business Intelligence and Data Analytics University of Petra Amman11196 Jordan College of Computing and Information Technology Shaqra University Shaqra11911 Saudi Arabia

In the contemporary business landscape, software has evolved into a strategic asset crucial for organizations seeking sustainable competitive advantage. The imperative of ensuring software quality becomes evident as low-quality software systems pose formidable challenges to organizational performance. This study delves into the profound impact of three key dimensions of information system quality on organizational performance—information quality (IQ), quality of service (QoS), and software quality (SQ). Anchored in the DeLone and McLean information system (IS) success model, a quantitative questionnaire was administered to 360 industry experts and academics. Rigorous data analysis, employing exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM), revealed significant positive effects of all three quality dimensions on organizational performance. Among these dimensions, software quality emerged as the most influential, showcasing substantial total effects, closely followed by information and service qualities. The study underscores the tangible value derived from strategic investments in enhancing software, information, and service quality. Elevating these facets manifests as a catalyst for improved organizational performance, empowering decision-makers with accurate and timely information while enhancing user satisfaction with the system. This research contributes significantly to the IS success literature by empirically validating the synergistic relationship between information quality, service quality, software quality, and organizational outcomes. The systematic analysis offered in this study goes beyond theoretical validation, providing actionable insights for managers. The findings guide the prioritization of quality initiatives and resource allocation, enabling organizations to maximize competitive advantage. As a future research direction, investigating moderator influences and exploring alternate qualit

关键词： Data quality

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Optimized deep transfer learning techniques for spine fracture detection using CT scan images

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Multimedia Tools and Applications 2025年 1-34页

作者： Kanna, G. Prabu Kumar, Jagadeesh Parthasarathi, P. Bhardwaj, Priya Kumar, Yogesh VIT Bhopal University Bhopal India Department of Computer Science and Engineering Kathir College of Engineering Coimbatore India Department of CS Bannari Amman Institute of Technology Sathyamangalam India School of Computing DIT University Dehradun India Department of CSE School of Technology Pandit Deendayal Energy University Gujarat Gandhinagar India

Spine fractures pose an important health concern that requires a quick diagnosis to protect people from any long-term complications. This paper aims to propose an automatic prediction system that uses deep transfer learning techniques for spine fractures and minimizes the time required for classification compared to traditional techniques. The CT scan images of the cervical spine have been collected in the form of JPEG format and subsequently segmented using several phases such as mask exploring, detecting, zooming, and cropping the affected area at 6 different angles followed by the computation of contour features to analyze their geometric as well as intensity-based parameters. Ten advanced deep learning models along with ADAM optimizer are trained where InceptionResNetV2 obtained the highest accuracy of 99.70% while the lowest loss, as well as root mean error square score, has been obtained by ResNet50V2 with 0.0493 and 0.2220.36 respectively. In the case of precision, recall, and F1 score, DenseNet169 computed the highest precision and recall values of 99.99% and 98.495% respectively while Xception did well in terms of F1 score with 98.32%. These models perform better because InceptionResNetV2’s architecture is well-suited for capturing intricate spatial hierarchies, DenseNet169 benefits from dense connectivity for feature reuse, and ResNet50V2’s optimized residual blocks contribute to minimizing error rates. Apart from this, less diversity and class imbalance are also seen within the dataset which affects model performance. These limitations highlight the need for future work to expand the dataset and ensure a balanced distribution of classes to enhance the robustness and clinical relevance of the proposed AI-based system. However, despite these challenges, the results underscore the transformative potential of deep learning in improving clinical diagnosis of spine fractures. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part

关键词： computerized tomography

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Android Malware Detection and Classification using Stacked Machine Learning 4

Android Malware Detection and Classification using Stacked M...

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4th International Conference on Distributed Sensing and Intelligent Systems, ICDSIS 2023

作者： Nawshin, Faria Gad, Radwa Unal, Devrim Suganthan, Ponnuthurai Nagaratnam KINDI Center for Computing Research College of Engineering Qatar University Doha Qatar Department of Computer Science & Engineering College of Engineering Qatar University Doha Qatar

ISBN: (纸本)9781839539954

The widespread use of Android smartphones in daily life can be attributed to the extensive prevalence stemming from the Android OS and the availability of open-source applications. People have become accustomed to performing various tasks, such as online banking transactions, scheduling appointments, attending virtual meetings and classes, and online shopping, on their mobile devices instead of computers. However, the significant use of mobile phones also presents opportunities for attackers to access sensitive and confidential information without the user’s knowledge. Intruders often use malicious applications found on the Google Play Store and other third-party app markets. The increased volume of Android malware in recent years has severely threatened human privacy and security. In this paper, we present a novel solution that utilizes a stacking ensemble machine learning approach to detect whether an application is malware or benign, which achieved 98% accuracy and F1-Score, and to identify the malware category with 96% accuracy and F1-Score. We conducted the experiment on the CCCS-CIC-AndMal-2020 dataset, which comprises 27,125 samples. Static features such as permissions, meta-data, and intents were analyzed, and the performance of standard machine learning algorithms was compared to our proposed model. Our results demonstrate that the stacked ensemble model shows promising results for malware detection (binary classification) and malware type classification (multi-class classification). © 2024 The Author(s).

关键词： Android malware

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A Novel Q-Learning Optimization Approach for Flight Path Prediction in Asian Cities 3

A Novel Q-Learning Optimization Approach for Flight Path Pre...

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3rd IEEE Asian Conference on Innovation in Technology, ASIANCON 2023

作者： Reddy, Keshavagari Smithin Natarajan, B. Arthi, A. Tamilselvi, M. Sridevi, R. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India Karpagam College of Engineering Department of Computer Science and Technology Coimbatore India Roever Engineering College Department of Computer Science and Engineering Perambalur India Samayapuram Department of Computer Science and Engineering Tamil Nadu Trichy621112 India

ISBN: (纸本)9798350302288

The domains of logistics and transportation have long been interested in the optimization of flight paths between cities. This research aims to use Skyscanner data to estimate the optimal flight path between 42 Asian destination cities using Reinforcement Learning (RL) techniques, notably Q-learning. RL is a great strategy for addressing the Travelling Salesman Problem (TSP) connected to aircraft route optimization because of the distinctive reward structure it provides. The main objective of the proposed research is to create a model that learns to suggest aircraft routes based on factors such as cost, time, and number of intermediate points that maximize benefits. The proposed research work incorporates a novel Q-learning approach for training an RL agent to predict optimal flight paths. The proposed research work showcases the power-fullness of Q-Learning based RL agents in suggesting optimal flight routes and lays the groundwork for future developments in this research domain. The proposed algorithm provides useful information for tourists and business people looking for accurate and affordable flight path forecasts in the Asian region. The various performance metrics such as Reward Accumulation(RA), Episode Length(EL), and Exploration and Exploitation evaluates the proposed model performance and yields an optimal solution. © 2023 IEEE.

关键词： Reinforcement learning

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