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2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Sivakumar, Soubraylu Vinay, B. Sai Arulmurugan, A. Prakash, U.M. Kumar, T. Rajesh Sridevi, S. Srm Institute of Science and Technology Computer Technologies School of Computing Chennai Kattankulathur India Saveetha Institute of Medical and Technical Sciences Computer Science and Engineering Saveetha School of Engineering Chennai India Vels Institute of Science Technology and Advanced Studies Computer Science and Engineering Chennai India

ISBN: (纸本)9798350344387

In the rapidly advancing realm of healthcare, ensuring the well-being of critical care patients stands as a paramount mission. Various cutting-edge sensors are available in the market, proficient in monitoring vital health. A substantial number of healthcare facilities still rely on manual processes, a practice that is not only repetitive and labor-intensive but also fraught with the potential for human errors. This pressing need for immediate, accurate responses in the context of critical care has driven the development of a comprehensive solution. Our proposed system brings together security, data normalization, and efficient data exchange, underpinned by a unified approach. Security, a cornerstone of healthcare, finds its realization in the implementation of JSON Web Tokens (JWT). This technology ensures the privacy and integrity of communication between healthcare entities. Crucial patient data, including personal particulars, lifeline parameters, and historical records, is securely housed in a scalable and non-redundant Postgres SQL database. In emergencies, time is of the essence. To expedite responses, an innovative tagged QR code technique is introduced. The unified approach doesn't simply prioritize security;it underscores the significance of data standardization and efficient data exchange. A robust security framework has been meticulously designed to protect sensitive patient information, minimize the risk of data breaches, and prevent unauthorized access. Furthermore, data normalization ensures that patient data is consistently structured and formatted, facilitating compatibility with diverse healthcare platforms and devices. The normalization additionally enhances interoperability, enabling seamless information exchange between different systems and healthcare providers. In summary, our unified approach, marked by state-of-the-art security, data normalization, and efficient data tagging, sets a new standard for patient care in critical settings, ensurin

关键词： Critical Care Emergency Health Care JSON Web Token Patient monitoring Postgres SQL QR Code

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Resolving Attached USBs: Analysis of Windows 11 Artifacts 2

Resolving Attached USBs: Analysis of Windows 11 Artifacts

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2nd International Conference for Advancement in Technology, ICONAT 2023

作者： Budhrani, Akash Singh, Upasna Singh, Bhupendra Defence Institute of Advanced Technology School of Computer Engineering and Mathematical Science Pune India Indian Institute of Information Technology Computer Science and Engineering Department Pune India

ISBN: (纸本)9781665475174

The Windows Operating System is known for its convenience which tends to breed more and more user information in form of Artifacts. Artifacts are important repository of potential evidence while conducting any computer investigation. This paper thus provides an analysis of artifacts that will bring out the inside of USB devices attached or mounted on a particular system. Moreover, this paper will parse the data out into usable information and helps to interpret the findings of forensic examiner. In this work, Image file of the suspected system has been exported from the system for further analysis to bring out relevant information. © 2023 IEEE.

关键词： Windows operating system

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Enhanced Cancer Subclassification Using Multi-Omics Clustering and Quantum Cat Swarm Optimization

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer science and Mathematics 2024年第3期5卷 552-582页

作者： Ali, Ali Mahmoud Mohammed, Mazin Abed Informatics Institute for Postgraduate Studies Iraqi Commission for Computers & Informatics Baghdad Iraq Department of Artificial Intelligence College of Computer Science and Information Technology University of Anbar Anbar31001 Iraq

Integrating multiple omics data can significantly improve the accuracy of cancer subclassification, a challenging task due to the high dimensionality and limited sample sizes. The integration of these data sets can enhance model performance. This study addresses these challenges by employing Quantum Cat Swarm Optimization (QCSO) for feature selection, along with K-means clustering and Support Vector Machine (SVM) for classification. Using QCSO, the most significant features were identified, resulting in an increase in accuracy from 81% to 100%. Performance was evaluated using accuracy, F1-score, precision, recall, ROC, and the silhouette metric, all of which confirmed the effectiveness of the feature selection approach. Additionally, this method enhances the classification of samples while making the models more interpretable, providing better insights into the molecular mechanisms of cancer. This work contributes to advancing knowledge in the field of cancer research and biology in general. © 2024 College of Education, Al-Iraqia University. All rights reserved.

关键词： Support vector machines

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Ultra-low-defect homoepitaxial micro-LEDs with enhanced efficiency and monochromaticity for high-PPI AR/MR displays

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PhotoniX 2024年第1期5卷 335-356页

作者： Yibo Liu Guobin Wang Feng Feng Mengyuan Zhanghu Zhengnan Yuan Zichun Li Ke Xu Hoi Sing Kwok Zhaojun Liu Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyHong Kong SARChina Department of Electrical and Electronic Engineering Southern University of Science and TechnologyShenzhenChina Suzhou Institute of Nano-Tech and Nano-Bionics Chinese Academy of SciencesJiangsu Institute of Advanced SemiconductorsSuzhouChina

The issue of brightness in strong ambient light conditions is one of the critical obstacles restricting the application of augmented reality(AR)and mixed reality(MR).Gallium nitride(GaN)-based micro-LEDs,renowned for their exceptional brightness and stability,are considered the foremost contenders for AR ***,conventional heteroepitaxial growth micro-LED devices confront formidable challenges,including substantial wavelength shifts and efficiency *** this paper,we firstly demonstrated the high-quality homoepitaxial GaN-on-GaN micro-LEDs microdisplay,and thoroughly analyzed the possible benefits for free-standing GaN substrate from the material-level characterization to device optoelectronic properties and microdisplay application compared with sapphire *** GaN-on-GaN structure exhibits a superior crystal quality with ultra-low threading dislocation densities(TDDs)of~105 cm^(-2),which is three orders of magnitude lower than that of *** an in-depth size-dependent optoelectronic analysis of blue/green emission GaN-on-GaN/Sapphire micro-LEDs from 100×100 shrink to 3×3μm^2),real that a lower forward voltage and series resistance,a consistent emission wavelength(1.21 nm for blue and 4.79 nm for green@500 A/cm2),coupled with a notable reduction in efficiency droop ratios(15.6%for blue and 28.5%for green@500 A/cm^(2))and expanded color gamut(103.57%over Rec.2020)within GaN-on-GaN 10μm *** but not least,the GaN-on-GaN micro-display with 3000 pixels per inch(PPI)showcased enhanced display uniformity and higher luminance in comparison to its GaN-on-Sapphire counterpart,demonstrating significant potentials for high-brightness AR/MR applications under strong ambient light.

关键词： optoelectronic ambient epitaxial

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Analysing various algorithms applied to Alzheimer's disease dataset to help the Future Generation 2

Analysing various algorithms applied to Alzheimer's disease ...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Athinarayanan, S. Navaz, K. Tamilselvi, P. Shajeena, J. Vishwa Priya, V. Joel, M. Robinson School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Cse Chennai Tamil Nadu Avadi600062 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai600062 India Vels Institute of Science Technology and Advanced Studies Department of Computer Science Chennai600117 India Vels Institute of Science Technology and Studies Department of Computer Science Chennai India Kings Engineering College Department of Information Technology Chennai India

ISBN: (纸本)9798350306415

A sort of brain illness that affects the brain is Alzheimer's. Nowadays, older adults are more likely to have these ailments. People who have this illness have very poor memories and decreased memory. Additionally, their brain isn't working correctly. As a result, their regular practice is progressively beginning to decline. Alzheimer's is a kind of dementia for which there is no definitive treatment. This essay will help us learn more about Alzheimer's illness. We shall learn more about its causes, symptoms, and remedies. Additionally, we investigate several illness prediction methods employing Graph Neural Networks denoted as GNNs. Decision Tree, Random Forest algorithms, and more. However, the Random forest classifier gave good results with 86% accuracy and good recall values. © 2024 IEEE.

关键词： Alzheimer dementia GNN Graph Neural Network Prediction

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Text Clustering on CCSI System using Canopy and K-Means Algorithm 4

Text Clustering on CCSI System using Canopy and K-Means Algo...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Akila, D. Raja, S.R. Jeyalaksshmi, J. Revathi, M. Ashfaq, Farzeen Khan, A.A. Saveetha College of Liberal Arts & Sciences SIMATS Deemed University Department of Computer Applications Chennai India Centre for Open and Digital Education Hindustan Institute of Tech. & Science Chennai India Vels Institute of Science Technology and Advanced Studies Department of IT Chennai India Meas Institute of IT Department of Computer Science Chennai India School of Computer Science Taylor's University Subang Jaya Malaysia Negara Brunei Darussalam FIT Brunei

ISBN: (纸本)9798350353266

Text mining programs often offer numerous options for processing textual data. The inclusion of extensive meta-data-such as links to other websites, titles, authors, and publication dates-can enrich text analysis and aid in text grouping. However, this side information, or 'metadata,' can also introduce noise if not handled correctly. When used indiscriminately for clustering, this noise can degrade the quality of the resulting clusters. To address this challenge, we propose a methodology that leverages a Feature Selection procedure to identify and utilize metadata that enhances clustering effectiveness. The methodology, known as Co-Clustering with Side Information (C-CSI), is designed to maximize the benefits of relevant metadata while mitigating the effects of irrelevant or noisy data. C-CSI is implemented using datamining technologies, specifically focusing on Co-Clustering, which involves clustering both rows and columns of a matrix. In our study, we employed the canopy and k-means algorithms to perform the clustering tasks. This approach ensures that the metadata used in clustering is both relevant and beneficial, improving the overall quality of the clustering results © 2024 IEEE.

关键词： Data assimilation

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A model of service-oriented architecture of e-certification system to support boat registration and site visit inspection to support maritime safety and crew health inspection

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Marine Systems and Ocean Technology 2023年第1-2期18卷 45-63页

作者： Saputra, Diaz Gaol, Ford Lumban Abdurachman, Edi Sensuse, Dana Indra Matsuo, Tokuro Department of Computer Science BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Computer Science Binus Graduate Program - Master in Information System Bina Nusantara University Jakarta11480 Indonesia Department of Research in Management Bina Nusantara University Jakarta Indonesia Faculty of Computer Science University of Indonesia Jakarta Indonesia Advanced Institute of Industrial Technology Tokyo Japan City University of Macau China Asia University Taichung Taiwan

This article describes the architecture of software services that provide registration of small boat data to build maritime safety. The proposed system architecture which so-called SIMKAPEL aims to support small boat registration for maritime safety. It is developed from a conventional service-oriented architecture (SOA) with an open standard for data interoperability between elements. The goal of this research is to implement and evaluate the proposed architecture to support small boat registration and e-certification. Several parts of the architecture have been implemented as a platform that provides core services and data sources to be used by Ministry of Transportation, Directorate General of Sea Transportation, and other government agencies. Finally, the proposed SIMKAPEL platform with the SOA architecture is evaluated for its benefits to users through the expert opinion survey to measure its efficiency. The analysis results showed that SOA-based SIMKAPEL is feasible to use as a boat e-certification system. Furthermore, experiment results provide three parameters (e.g., maritime safety, namely, cargo/passengers, boat safety facilities, and maritime safety systems) with efficiency values of 42%, 46%, and 42%, respectively. The parameter with the highest efficiency shows a big effect on the level of safety of the boat and passengers. Thus, the parameters that need to be prioritized and need boat owners’ attention are cargo/passenger, boat safety equipment, and maritime safety system. © 2023, The Author(s).

关键词： Boat e-certification Boat online certification Cloud-based service Integrated system SOA architecture

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Catalyzing EEG signal analysis: unveiling the potential of machine learning-enabled smart K nearest neighbor outlier detection

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International Journal of Information Technology (Singapore) 2024年 1-12页

作者： Aymen, Abid Khediri, Salim El Thaljaoui, Adel Miladi, Moahmed Kachouri, Abdennaceur Computer & amp Embedded Systems Lab National Engineering School of Sfax University of Sfax Sfax Tunisia Department of Information Technology College of Computer Qassim University Buraydah Saudi Arabia Department of Computer Science Faculty of Science of Gafsa University of Gafsa Gafsa Tunisia Preparatory Institute for Engineering Studies of Gafsa Gafsa Tunisia Department of Computer Science and Information College of Science at Zulfi Majmaah University Al-Majmaah 11952 Saudi Arabia Higher Institute of Computer Science and Multimedia of Sfax University of Sfax Sfax Tunisia Advanced Fluid Dynamics Energetics and Environment Lab National Engineering School of Sfax University of Sfax Sfax Tunisia

Electroencephalogram (EEG) data are susceptible to artifacts, such as lapses in concentration or poor imagination, which can significantly impact the accuracy of disease diagnosis in e-health applications. To mitigate this issue, the use of machine learning (ML) and potentially artificial intelligence (AI) solutions to accurately identify outliers becomes crucial. Unlike many AI methods that incorporate unnecessary or redundant input variables, our study focuses on detecting anomalous values in EEG data through the K nearest neighbor (KNN) process and Euclidean distance metric. Our proposed unsupervised non-parametric algorithm, known as the smart KNN outlier detector (SKOD), eliminates the need for initial parameter configurations such as the number of neighbors (K), while achieving high performance. Evaluation of SKOD using real EEG data from 140 trials demonstrated sensitivity and specificity exceeding 60%, with nearly perfect accuracy in detecting outliers reaching close to 100%. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Data analysis EEG signal Healthcare Knowledge-based systems Machine learning Outlier detection

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Loan Prediction Using Machine Learning

Loan Prediction Using Machine Learning

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2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Sarisa, Hemanth Kumar Khurana, Varun Koti, Venkat Chandu Garg, Neha Manav Rachna International Institute of Research and Studies Department of Computer Science & Engineering India

ISBN: (纸本)9798350344387

Loan prediction is a significant problem in the banking industry. Using Technology, will change and make some improvements in Loan prediction by bank employees manually. By using "Machine learning "in loan prediction. The machine learning system will check the data for a loan of the applicant and the data will be checked so fast and without any human error. So, it will help to improve technology in Bank sectors. By this, processing the data will be checked quickly, making the data output in less time. It helps both the applicant and the bank *** learning algorithms can be used to analyze historical data and make accurate predictions about whether a loan application will be approved or not. In this study, we propose a machine learning-based loan prediction model that utilizes various features such as credit score, income, loan amount, and loan term. We explore different classification algorithms, including logistic regression, decision tree, and random forest, to build the loan prediction model. Our experimental results show that the random forest algorithm outperforms other models with an accuracy rate of 83.45%. The proposed model can help banks and financial institutions streamline their loan approval process, reduce manual effort, and minimize the risk of bad loans. © 2023 IEEE.

关键词： Data Pre-Processing Decision Tree K-Neighbours Classifier Machine Learning Python Random Forest Classifier

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Exploring the Intersection of IoT and Blockchain: An Analysis of Security and Privacy Risks

Exploring the Intersection of IoT and Blockchain: An Analysi...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Ashok, Harishma Ganesan, Swathi Somasiri, Nalinda PSG Institute of Advanced Studies Data Analytics Coimbatore India York St John University Department of Computer Science London United Kingdom

ISBN: (纸本)9798350348217

Internet of things (IoT) is one of the leading technologies of transforming the internet into the next level. Security of IoT is becoming a major challenge and its measures of stopping these insecurities have been developing. Improving IoT security is important since IoT provides a great environment for systems in the industrial world and other several applications. The challenges need to be addressed so that consumer's data can be safe. Measures available are to improve the protection of access by unauthorized persons, providing a safe way to transfer data, and ensuring synchronization of the system. Blockchain has also provided a safe way for IoT devices, each device is given the public key, IoT devices scramble and send the message dependent on the public key of the objective device so just the objective device can unscramble the message. © 2023 IEEE.

关键词： Internet of things

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