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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Yadav, Rakesh Kumar Sudhamsu, Gadug Goyal, Manish Kumar Yuvaraj, S. Deshpande, Leena A. Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350370249

The application of synthetic Intelligence (AI) to records science has spread out a massive array of new possibilities. Via the combination of advanced evaluation strategies, effective understanding-pushed system gaining knowledge of algorithms, and modern computing architectures, AI is allowing researchers to quick and as it should be discover insights from huge and complicated datasets. Furthermore, AI tools are offering corporations with the ability to make higher and more knowledgeable choices from the evaluation of hundreds of thousands of statistics points. The skills of information technology powered by means of AI are revolutionizing industries which includes healthcare, finance, and manufacturing-producing savings, uncovering efficiencies, and identifying new opportunities. As AI-driven records technology maintains to strengthen, organizations that capitalize on its ability stand to gain precious insights and unlock new degrees of overall performance. © 2024 IEEE.

关键词： Smart manufacturing

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Mortality Risk Prediction in Patients After Cardiac Surgery Using Machine Learning

Mortality Risk Prediction in Patients After Cardiac Surgery ...

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2024 International Conference on computer, Electronics, Electrical Engineering and their applications, IC2E3 2024

作者： Khan, Shahnawaz Thakur, Gopal Kumar Kulkarni, Shridhar Ayyalasomayajula, Madan Mohan Tito Reddy, Pundru Chandra Shaker Master of Computer Application Bundelkhand University Utter Pradesh Jhansi India Harrisburg University of Science and Technology Department of Data Sciences Pennsylvania United States School of Business and Technology Aspen University Phoenix Arizona United States School of Computer Science and Artificial Intelligence SR University Warangal Telangana India

ISBN: (纸本)9798350388534

Norwood surgery restores systemic flow in neonates with solitary ventricle inherited heart abnormalities, but it is complicated and deadly. We created machinelearning(ML) strategies to anticipate cardiac surgery mortality using preoperative and intraoperative risk indicators. Mortality after surgery was the main consequence. Sensitivity, specificity, accuracy, and AUC were used to estimate model performance. The visualization algorithm used Shapley's additive explanations. Random-forest, neural-network, SVM, and gradient-boosting had AUC-values of 0.87, 0.79, 0.81, and 0.82 for anticipating cardiac surgery operative mortality, while EuroSCORE I and II, STS, and LR had 0.70, 0.73, 0.71, and 0.74. Shapley's additive explanations study of RF identified the top-20 predictors and individual-level explanations. ML strategies based on clinical data may anticpate heart surgery postoperative mortality better than clinical grading methods. Explanatory models can account for affecting factors to provide tailored risk profiles. These machine learning-driven models need more prospective multicenter studies to prove their therapeutic efficacy. © 2024 IEEE.

关键词： Cardiovascular surgery

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Improving the Quality of P4AI mobile Apps through User Interface Results and User Experience Surveys 11

Improving the Quality of P4AI Mobile Apps through User Inter...

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11th International Conference on Cyber and IT Service Management, CITSM 2023

作者： Indrianti, Yasinta Sasmoko Manalu, Sonya Rapinta Kurniawan, Lukas Agung Podomoro University Research Interest Group in Education Technology Entrepreneurship Department Jakarta Indonesia Bina Nusantara University Faculty of Humanities Research Interest Group in Education Technology Primary Teacher Education Department Jakarta Indonesia Bina Nusantara University Mobile Application & Technology School of Computer Science Research Interest Group in Education Technology Jakarta Indonesia Bina Nusantara University Computer Science School of Computer Science Research Interest Group in Education Technology Jakarta Indonesia

ISBN: (纸本)9798350305968

The Profiling For Aptitude Inventory (P4AI) application is an aptitude test developed to determine the capacity of students' interests and talents. The aptitude profile will help students in setting goals and chosen academic fields so that they can support and predict student success in the future. Profiling For Aptitude Inventory (P4AI) application was created using Flutter framework as the frontend and Flask framework as the backend. This research aims to analyze the results of the user interface and user experience to improve application quality. Based on the analysis results of the UI/UX evaluation survey of the P4AI application, it can be concluded that users tend to agree on the attractiveness of the appearance of the P4AI application. However, there are several indicators and items that need to be improved and developed to increase user satisfaction with the appearance and flow of the application. Therefore, changes have been made to the application to improve the quality of application use. © 2023 IEEE.

关键词： User interfaces

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Graph Regularized Sparse Non-Negative Matrix Factorization for Clustering

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IEEE Transactions on Computational Social Systems 2023年第3期10卷 910-921页

作者： Deng, Ping Li, Tianrui Wang, Hongjun Wang, Dexian Horng, Shi-Jinn Liu, Rui Southwest Jiaotong University National Engineering Laboratory of Integrated Transportation Big Data Application Technology School of Computing and Artificial Intelligence Chengdu611756 China National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei106 Taiwan North Sichuan Medical College School of Preclinical Medicine Department of Computer and Mathematics Nanchong637100 China

The graph regularized nonnegative matrix factorization (GNMF) algorithms have received a lot of attention in the field of machine learning and data mining, as well as the square loss method is commonly used to measure the quality of reconstructed data. However, noise is introduced when data reconstruction is performed;and the square loss method is sensitive to noise, which leads to degradation in the performance of data analysis tasks. To solve this problem, a novel graph regularized sparse NMF (GSNMF) is proposed in this article. To obtain a cleaner data matrix to approximate the high-dimensional matrix, the l1-norm to the low-dimensional matrix is added to achieve the adjustment of data eigenvalues in the matrix and sparsity constraint. In addition, the corresponding inference and alternating iterative update algorithm to solve the optimization problem are given. Then, an extension of GSNMF, namely, graph regularized sparse nonnegative matrix trifactorization (GSNMTF), is proposed, and the detailed inference procedure is also shown. Finally, the experimental results on eight different datasets demonstrate that the proposed model has a good performance. © 2014 IEEE.

关键词： Matrix algebra

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Malaria and Pneumonia Disease Prediction Using Deep Learning 2

Malaria and Pneumonia Disease Prediction Using Deep Learning

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rai, Vijendra Singhal, Manoj Dwivedy, Bhoopendra Jain, Pallavi Ritika Abes Institute of Technology Department of Information Technology Ghaziabad India Gl Bajaj Institute of Technology and Management Department of Information Technology Greater Noida India School of Computer Science and Engineering Galgotia's University Greater Noida India School of Computer Application and Technology Galgotia's University Greater Noida India

ISBN: (纸本)9798350371055

Early identification, intervention, and efficient treatment planning all rely heavily on disease prediction. This study examines the methods for disease prediction based on deep learning, concentrating on pneumonia and malaria in particular. Pneumonia and malaria are two common, sometimes fatal infections, especially in underdeveloped areas. Traditional diagnostic techniques for these disorders can be time-consuming and frequently need for specialized knowledge. Therefore, it is necessary to investigate automated and reliable illness prediction methods. This study's goal is to assess how well deep learning algorithms can foresee malaria and pneumonia from medical imaging data. The study makes use of a broad dataset that includes chest X-ray pictures for the identification of pneumonia and blood smear images with malaria infection. To improve the dataset's quality and variety, preprocessing procedures including picture normalization and augmentation are used. The results of this research demonstrate the potential of deep learning algorithms for malaria and pneumonia prediction, which advances the field of medical diagnostics. These automated prediction algorithms could help medical personnel diagnose patients quickly, allowing for quick treatment to start and better patient outcomes. © 2024 IEEE.

关键词： Backpropagation CNN Deep Learning Inspection V3 Model Jupiter Python Transfer Learning

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A New Tree Structure for Local Diagnosis

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Journal of Information science and Engineering 2024年第2期40卷 231-244页

作者： Chen, Meirun Li, Xiao-Yan Lin, Cheng-Kuan Pai, Kung-Jui School of Mathematics and Statistics Xiamen University of Technology Xiamen361024 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Department of Computer Science National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Undergraduate Degree Program of Systems Engineering and Technology National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Computer Science and Information Engineering Chung Cheng Institute of Technology National Defense University Taoyuan30010 Taiwan Department of Industrial Engineering and Management Ming Chi University of Technology New Taipei City243303 Taiwan

Diagnosability is an important parameter to measure the fault tolerance of a multiprocessor system. If we only care about the state of a node, instead of doing the global diagnosis, Hsu and Tan proposed the idea of local diagnosis. Chiang and Tan provided an extended star structure to diagnose a node under comparison model. In this work, we evaluate the local diagnosability better by proposing a tree structure around this node. We provide the corresponding algorithm to diagnose the node. Simulation results are presented for different failure probability of a node in the tree and different percentage of faulty nodes in the tree, showing the performance of our algorithm. © 2024 Institute of Information science. All rights reserved.

关键词： comparison model diagnosis algorithm fault diagnosis local diagnosability tree

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Web Data Mining with Organized Contents Using Naive Bayes Algorithm 2

Web Data Mining with Organized Contents Using Naive Bayes Al...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Ravinder, Bathini Seeni, Senthil Kumar Prabhu, V.S. Asha, P. Maniraj, S.P. Srinivasan, C. Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India Cognizant Technology Solutions Buffalo Grove Department of Mobile Application Development Illinois United States R.M.D. Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

Data mining on the web has developed into a simple and crucial tool for finding relevant information. When it comes to file transfers, the World Wide Web is the user's first choice. Finding useful information and trends in the ever-expanding amount of data available online is becoming ever more challenging and time-consuming. When dealing with massive amounts of textual data often seen online, there are a number of benefits to using the Naive Bayes method for web data mining. Web data mining using the Naive Bayes algorithm seeks to mine massive amounts of textual material on the web for useful patterns, insights, and knowledge. Text classification and categorization tasks are the most common uses of the Naive Bayes method in online data mining. Expert and user-requested data may be difficult to mine from the sea of unorganized and contradictory material that is the World Wide Web. Relevant data (hyperlinks, contents, web use records) is extracted from the web using a variety of mining methods. Internet-centric data mining is a subfield of data science. Structure mining, content mining, and use mining are the three main categories of online data mining. Each of these categories employs a unique set of methods, instruments, strategies, and Naïve Bayes algorithm to mine the web's vast data stores for useful insights. The results show the density and velocity of web mining using Naïve Bayes algorithm. © 2024 IEEE.

关键词： Data mining

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Exploring the Power of Deep Learning in Anomaly Detection: A Comprehensive Review and Analysis 12th

Exploring the Power of Deep Learning in Anomaly Detection: A...

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12th International Conference on Soft Computing for Problem Solving, SocProS 2023

作者： Kumar, Gaurav Kumar, Ankit Pandy, Saroj Kumar Singh, Kamred Udham Singh, Teekam Kumar, Lalan Choudhury, Tanupriya Department of Computer Engineering and Applications GLA University UP Mathura India School of Computing Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Master of Computer Application GL Bajaj Institute of Technology and Management Greater Noida India Department of Computer Science and Engineering University of Petroleum and Energy Studies Dehradun India

ISBN: (纸本)9789819732913

This paper aims to provide a comprehensive overview of existing and state-of-the-art fraud and intrusion detection strategies, specifically focusing on incorporating neural networks. We begin by discussing the fundamental concept of data mining and its significance in detecting anomalies. By leveraging advanced machine learning and neural network techniques, we can effectively uncover subtle patterns and irregularities within complex datasets. We explore how the development of anomalies has enhanced our ability to identify and mitigate various threats, such as malware attacks and unlawful practices. Traditional detection strategies have proven effective, but as deep learning progresses, we uncover new opportunities and insights that can significantly enhance the accuracy and efficiency of anomaly detection systems. Throughout this study, we delve into the most cutting-edge approaches, ranging from broad neural networks to shallower architectures, and their applicability to fraud and intrusion detection. We analyze the strengths and limitations of these strategies, shedding light on their performance characteristics, scalability, and interpretability. By addressing these key issues, we aim to provide a comprehensive summary of the latest fraud and intrusion detection advancements driven by deep learning methodologies. This research contributes to the broader understanding of data mining's potential in uncovering anomalies and bolstering security measures across diverse domains. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Intrusion detection

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PROMO: PROactive MObility-support model for task scheduling in fog computing

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International Journal of computers and applications 2022年第11期44卷 1092-1101页

作者： Kaur, Navjeet Kumar, Ashok Kumar, Rajesh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Application Lovely Professional University Phagwara India Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala India

In fog computing, closer proximity of fog devices to the end-user provides several benefits, such as reduced End-to-End (E2E) delay and better Quality of Service (QoS). Although, fog computing gives promising benefits of uninterrupted services to mobile users. The critical challenge is to decide when and where to re-schedule tasks according to the user's mobility. This paper targets this issue and proposes a Proactive Mobility-support (PROMO) model for re-scheduling tasks to mitigate the issues that influence QoS in mobility. The PROMO model is implemented and validated on MobFogSim, which significantly improves delay with 37% and around 17% downtime during task re-scheduling. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Fog computing

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Investigating the Potential of Applying Permission Blockchain for IoT Networks 15

Investigating the Potential of Applying Permission Blockchai...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Manjunath, H.R. Moharana, Alok Kumar Infimum School of Engineering and Technology Department of Physics Karnataka Bangalore India Arka Jain University Department of Pharmacy Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350370249

This paper investigates the capability of applying permission block chain for IoT networks. Permission block chain is a dispensed ledger device that uses a consensus-based totally method to shop and manipulate records. Such a technique offers a comfy and dependable platform for trustless environments, permitting a comfy, open, and immutable statistics infrastructure. Moreover, permission block chain can allow authentication of statistics, creating a less complicated way for trusted entities to accept as true with facts from untrusted assets, along with IoT networks. Moreover, the paper explores the possible packages and demanding situations of permission block chain in deploying at ease, dependable, and electricity-efficient IoT networks. Demanding situations together with scalability, interoperability, strength performance, privateers, and safety need to be addressed when it comes to the development of permission block chain answers for IoT networks. In the end, tips and answers are mentioned, along with proposals for building a hybrid permission layer for IoT networks. © 2024 IEEE.

关键词： Authentication Complicated Infrastructure Permission Scalability Situations

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