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检索条件"机构=Mobile Application & Technology Program Computer Science Department School of Computer Science"

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Legal Protection of Copyright on Copyrighted Content Downloaded Through the Internet 2020

Legal Protection of Copyright on Copyrighted Content Downloa...

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5th International Conference on Intelligent Information technology, ICIIT 2020

作者： Indrawan, Arvin Stevens, Garry Brianto, Gratianus Martin Gaol, Ford Lumban Oktavia, Tanty Mobile Application and Technology-School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Program-Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Program-School of Information Systems Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781450376594

The development of the internet made new problems within the scope of copyright. The internet is used to download copyrighted content and use it without permission from the creators. The legal protection of copyright the creators of content downloaded through the internet done in a way that the administration, through the instrument of criminal law and civil lawsuit. Legal efforts to protect the copyright of songwriters downloaded via the internet are carried out in several ways, namely the Ministry of Information and technology to play an active role in blocking sites that provide free song download facilities and prepare human resources in the field of investigation such as from the National Police The Republic of Indonesia as well as Civil Servants and other Investigators such as prosecutors and judges who have the ability and knowledge in the field of IPR as well as the role of the Copyright Council to provide knowledge of the importance of IPR. © 2020 ACM.

关键词： Copyrights

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Accessible Authentication Using Keyword Search through Homomorphic Encryption 1

Accessible Authentication Using Keyword Search through Homom...

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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Vekariya, Vipul Manjunath, R. Gill, Jasmeen Gopianand, M. Kumar, Avneesh Jha, Amaresh Parul University Department of CSE PIET Gujarat Vadodara India Department of Data Science & Mathamatics Bangalore India RIMT University Department of CSE Godindgarh Mandi India PSNA College of Engineering and Technology Department of Computer Applications Dindigul India Galgotias University Department of Computer Application Uttar Pradesh Greater Noida India School of Modern Media UPES Dehradun India

ISBN: (纸本)9781665456876

Data storage presents substantial issues due to concerns about data security and privacy. In this study, we provide a search-based, interoperable, verified public key cryptography system. The searchability is significantly improved by our recommended method, which allows the cloud servers to create invert crypto indexes while needing a query trap and leveraging asymmetric cryptography developed by van Vries, Miller, Showing different, and Vaikuntanathan. Contrarily, the recommended method offers a brand-new permitted dataset built on the inversion decryption scheme and shows how it may be used to evaluate the thoroughness and correctness of serps. Additionally, using the provided method, many users may do encrypted searches on cryptography. Finally, it is shown that the offered technique is secure utilizing the approximate-GCD problem. The experiment's results showed that the proposed technique had less computational complexity than the methods currently in use. © 2022 IEEE.

关键词： Public key cryptography

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Assessing the Robustness of Machine Learning Algorithms against Emerging Privacy and Security Threats in Medical applications

Assessing the Robustness of Machine Learning Algorithms agai...

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International Conference on Computing and Networking technology (ICCNT)

作者： Srikrishna Baskar Rao Sarbojeet Goswami Manish Kumar Goyal Department of Aerospace Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Department of optometry ARKA JAIN University Jamshedpur Jharkhand India Department of Computer Science & Application Vivekananda Global University Jaipur India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Using gadgets to realize algorithms in medical packages can provide new insights and talents, including new approaches to diagnose illnesses or reveal treatments. Rising privacy and security threats pose potential dangers to those structures that can restrict their efficacy and average performance. To make sure tool mastering algorithms are resilient to those threats, it is critical to assess their robustness. This summary offers an assessment of techniques to evaluate the robustness of tool-getting-to-know algorithms against emerging privacy and protection threats in medical programs. First, we check the kinds of threats and discuss how they’ll affect the general performance of machine studying algorithms. Secondly, we compare a selection of strategies for measuring the robustness of gadgets and gaining knowledge of algorithms, which include static, dynamic, and adverse check strategies. Ultimately, we speak about techniques for mitigating threats in scientific packages, which provides for incorporating relaxed layout thoughts in tool studying structures and utilizing smoke assessments and strain tests. Through this assessment, we aim to provide more facts on the techniques for assessing the robustness of gadget-studying algorithms against rising privacy and security threats in scientific programs.

关键词： Privacy Machine learning algorithms Heuristic algorithms Medical services Robustness Security Protection Medical diagnostic imaging Monitoring Strain

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Correlation Between 3D Magnetic Resonance Imaging and Clinical Outcomes in Traumatic Brain Injury

Correlation Between 3D Magnetic Resonance Imaging and Clinic...

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International Conference on Computing and Networking technology (ICCNT)

作者： H. Srikantha Ginu Anie Joseph Surendra Yadav Department of Civil Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Department of Nursing ARKA JAIN University Jamshedpur Jharkhand India Department of Computer Science & Application Vivekananda Global University Jaipur India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This article identifies a correlation between the 3D Magnetic Resonance Imaging (MR) of patients with worrying brain harm (TBI) and the patient’s medical results. The research team compares 3-D MR imaging outcomes with the patient’s observe-up go-to facts, including neurologic assessments, Glasgow Coma Scale rating, and other parameters. The effects confirmed that patients who had a better degree of injury discovered from three-D MR scans had worse scientific effects in comparison to sufferers who had milder harm. This correlation became great for acute and subacute TBI patients. The authors endorse that 3-D MR scans can be used to predict the clinical results of TBI patients and can have the potential to inform treatment selections.

关键词： Correlation Three-dimensional displays Sensitivity Magnetic resonance imaging Brain injuries Biomedical imaging

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Exploring Consumer Behavior in E-Commerce with AI Personalization and Market Trends Analysis Models

Exploring Consumer Behavior in E-Commerce with AI Personaliz...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Gaurav Kumar G. Devayani C Saraswathi R. Melba Kani Sandeep Dongre M. S. Mohamed Mallick School of Computer Application Lovely Professional University Phagwara India Department of Computer Science and Business Systems M.Kumarasamy College of Engineering Karur India School of Management Studies Bannari Amman Institute of Technology Erode India Master of Business Administration Mar Ephraem college of Engineering and Technology Kanyakumari India Symbiosis Institute of Business Management (SIBM) Constituent of Symbiosis International (Deemed University) Nagpur India Department of Computer Science Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology (Deemed to be University) Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Online retailers must monitor and respond to changing consumer trends to stay competitive. AI-driven customization's revolutionary impact on market trends is the focus of this study on online buying and customer behavior. As AI advances, e-commerce platforms will be able to customize user experiences. Preprocessing, feature extraction, and model training comprise the AI-driven personalization process. Customer data from online marketplaces is collected first. It manages missing values, balance the data, encode categorical variables, scale the features, and minimize the dimensions as part of our thorough data preprocessing to ensure fair and applicable results. To trained the algorithms with CNN and Convolutional LSTM Based on key criteria, the AI model beat LSTM and CNN. It provides personalized insights and recommendations with an average accuracy of 92.03%. Personalized experiences and product suggestions enabled by AI-powered personalization have transformed e-commerce. It's important to use smart algorithms to engage customers and examine large databases. This proposed approach to shows that AI can transform online shopping and improve customer experiences.

关键词： Consumer behavior Accuracy Data preprocessing Transforms Market research Electronic commerce Convolutional neural networks Integrated circuit modeling Artificial intelligence Long short term memory

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Enabling Clinical Decision-Making in Smart Healthcare through AI/ML Algorithms

Enabling Clinical Decision-Making in Smart Healthcare throug...

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International Conference on Computing and Networking technology (ICCNT)

作者： Vikas Kumar Kharbas Y R Meena Praveen Kumar Thakur Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Civil Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Department of Law ARKA JAIN University Jamshedpur Jharkhand India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Using AI/ML algorithms in clever healthcare has emerged as a promising improvement to facilitate green and effective medical choice-making. This novel era uses gadgets mastering and predictive analytics to extract deep insights from numerous assets of healthcare statistics, together with clinical statistics, development, imaging, and laboratory results. through leveraging huge datasets and effective algorithms, AI/ML-primarily based structures are capable of extra as it should be and suddenly become aware of correlations between elements that contribute to the development of diverse sicknesses. it can permit the implementation of more evidence-based interventions and customized treatments tailored to an individual-affected character’s health profile. moreover, AI/ML algorithms can also robotize the scientific workflow, contemplating the automated detection and prognosis of sure illnesses and medical conditions. As a result, AI/ML structures can seriously lessen healthcare prices and enhance patient consequences. in the end, using AI/ML algorithms in smart healthcare can empower clinical vendors to make more informed decisions approximately the analysis and treatment of their patients.

关键词： Accuracy Medical services Prediction algorithms Approximation algorithms Natural language processing Prognostics and health management Robots Recommender systems Cancer Biomedical imaging

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Exploring Spectrum Sharing Algorithms for 6G Cellular Communication Networks

Exploring Spectrum Sharing Algorithms for 6G Cellular Commun...

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International Conference on Computing and Networking technology (ICCNT)

作者： Ashutosh Pattanaik Shweta Zahid Ahmed Department of Mechanical Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India Department of Pharmacy ARKA JAIN University Jamshedpur Jharkhand India Department of Computer Science & Application Vivekananda Global University Jaipur India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

6G cell verbal exchange networks might be the successor to 5G networks, and their success relies heavily upon optimization measures that are enabled through the exploration of spectrum-sharing algorithms (SSAs). SSAs are hard and fast techniques implemented to maximize machine assets, performance metrics, and operational performance. That is performed by way of lowering interference, permitting spectrum contextualization, and improving the sharing of underutilized and congested spectrum. For 6G verbal exchange networks, a heightened attention on advanced database storage, distributed computing, and interference avoidance techniques must be taken a good way to ensure the maximum green use of be had spectrum. This paper explores a spread of SSAs for 6G mobile communication networks, consisting of dynamic spectrum get admission to, non-orthogonal more than one get right of entry to, and direct tool-enabled opportunistic spectrum usage. We evaluate current research that has been implemented to the improvement of SSAs and offer insight into future demanding situations and programs inside the subject. Furthermore, we talk about the significance of massive records and systems getting to know in the creation of SSA algorithms, providing capability enhancements to the ever-changing 6G communique networks. Standard, the usage of SSAs in 6G verbal exchange networks can make certain the most effective and green usage of be had spectrum, at the same time as additionally imparting extraordinary stages of aid flexibility

关键词： 6G mobile communication Wireless communication Measurement Distributed databases Interference Oral communication Security Wireless fidelity Standards Optimization

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Improving Cancer Prediction in Recommender Systems through Gradient Boosting

Improving Cancer Prediction in Recommender Systems through G...

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International Conference on Computing and Networking technology (ICCNT)

作者： Pompi Das Sengupta Vikas Kumar Kharbas H Adarsha Department of Management ARKA JAIN University Jamshedpur Jharkhand India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Mechanical Engineering School of Engineering and Technology JAIN (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The analysis of various styles of most cancers is frequently reliant on accurate prediction models. Recommender systems are powerful equipment that allows the prediction of cancer development and recurrence based totally on information accumulated from affected person information. But, standard feed-forward neural networks (FFNNs) utilized in current cancer prediction fashions are limited via their potential to capture subtle nonlinear relationships among affected person variables. Gradient boosting is a device gaining knowledge of set of rules that makes use of an ensemble of weak selection bushes to produce an effective predictive version. This have a look at examines the capability for boosting algorithms to enhance the predictive ability of the FFNN fashions currently used in recommender structures for predicting most cancers progression and recurrence. The outcomes of our test reveal that boosting algorithms are able to boom model accuracy over FFNNs whilst additionally lowering training time. Moreover, nonlinear relationships between affected person variables are captured extra successfully by means of boosting than with FFNNs. those findings keep crucial implications for the development of powerful predictive models for cancer progression and recurrence.

关键词： Training Accuracy Predictive models Boosting Prediction algorithms Feature extraction Feedforward neural networks Recommender systems Standards Cancer

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DHAKR: Learning Deep Hierarchical Attention-Based Kernelized Representations for Graph Classification 39

DHAKR: Learning Deep Hierarchical Attention-Based Kernelized...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Qian, Feifei Bai, Lu Cui, Lixin Li, Ming Lyu, Ziyu Du, Hangyuan Hancock, Edwin School of Artificial Intelligence Engineering Research Center of Intelligent Technology and Educational Application Ministry of Education Beijing Normal University Beijing China School of Information Central University of Finance and Economics Beijing China Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua China Zhejiang Institute of Optoelectronics Jinhua China School of Cyber Science and Technology Sun Yat-Sen University Shenzhen China School of Computer and Information Technology Shanxi University Taiyuan China Department of Computer Science University of York York United Kingdom

ISBN: (纸本)157735897X

Graph-based representations are powerful tools for analyzing structured data. In this paper, we propose a novel model to learn Deep Hierarchical Attention-Based Kernelized Representations (DHAKR) for graph classification. To this end, we commence by learning an assignment matrix to hierarchically map the substructure invariants into a set of composite invariants, resulting in hierarchical kernelized representations for graphs. Moreover, we introduce the feature-channel attention mechanism to capture the interdependencies between different substructure invariants that will be converged into the composite invariants, addressing the shortcoming of discarding the importance of different substructures arising in most existing R-convolution graph kernels. We show that the proposed DHAKR model can adaptively compute the kernel-based similarity between graphs, identifying the common structural patterns over all graphs. Experiments demonstrate the effectiveness of the proposed DHAKR model. Copyright © 2025, Association for the Advancement of Artificia Intelligence (***). All rights reserved.

关键词： Deep learning

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Human-computer Interaction and Knowledge Transfer: Study of E-Learning Achievement

Human-Computer Interaction and Knowledge Transfer: Study of ...

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International Conference of computer and Informatics Engineering (IC2IE)

作者： Doni Purnama Alamsyah Norfaridatul Ahkmaliah Othman Johan Muliadi Kerta Doni Morika Yudi Ramdhani Creativepreneurship Department BINUS Business School Bina Nusantara University Jakarta Indonesia Department of Management Technology Universiti Teknikal Malaysia Melaka Melaka Malaysia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Design Interior Department School of Design Bina Nusantara University Jakarta Indonesia Information System Program Satu University Bandung Indonesia

ISBN: (数字)9798331505530

ISBN: (纸本)9798331505547

Human-computer interaction influences the effectiveness of e-learning knowledge transfer, so it is necessary to pay attention to user engagement and achievement. The aim of this research is to examine the factors that support e-learning knowledge transfer through achievement and user engagement (self-efficacy, self-confidence, self-esteem). The research method was carried out by surveying e-learning users, namely students, data from students was collected through quantitative questionnaires. The data is then processed using the SmartPLS tool through two tests, namely the PLS Algorithm and Boostrapping. The model was tested using inner and outer tests, and the data was analyzed based on the research hypothesis design. Student interactions with e-learning influence several behaviors that can support e-learning knowledge transfer performance. The research results prove that student e-learning achievement has a positive impact on e-learning knowledge transfer. Several supporting factors play a role in increasing e-learning achievement, namely self-efficacy, self-confidence and self-esteem of students. However, the supporting factors for students are only self-efficacy and self-confidence which are directly able to increase e-learning knowledge transfer. It is known that students who are less able to respect themselves in interacting with e-learning are unable to support knowledge transfer. The rules of human-computer interaction play a role in supporting e-learning knowledge transfer, studies on user behavior need to be considered, especially self-efficacy and self-confidence to support online learning academic performance.

关键词： Human computer interaction Analytical models Electronic learning Data models Informatics Knowledge transfer

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