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Smart Electronics and Communication (ICOSEC), International Conference on

作者： C N Gireesh Babu Kaparapu Satish Babu Sajja Suneel K.Srinivasa rao divya Muralitharan r Aarthi Dept.of CSE/ISE BMS Institute of Technology and Management Bengaluru Karnataka Department of Electronics and Communication Engineering Geetanjali College of Engineering and Technology Hyderabad Telangana India Department of CSE(Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad Telangana India Department of AI&ML K.S.R.M. College of Engineering Kadapa Andhra Pradesh Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of ECE R.M.D Engineering College R. S. M Nagar Gummidipoondi

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

The Hybrid CNN-Transformer Model (HCNNT) is a novel model which exploits Convolutional Neural Networks (CNN) for spatial feature extraction and Transformers for capturing contextual and long range dependencies in order to carry complex image classification. In order to evaluate the algorithm proposed, a new model has been introduced and trained on a data set of gastrointestinal (GI) image classification, which is derived from a balanced data set with 226 images available on Kaggle. Although the cascaded CNN part is to learn elaborate visual characteristics, the improved understanding of complex patterns and relationships is achieved by the Transformer component to process these features. When both are integrated, we gain a powerful system that has high accuracy in distinguishing GI bleeding condition from normal condition. Extensive training and optimization of the hybrid model using binary cross-entropy loss and Adam optimizer has achieved state-of-the-art performance. Through the experiments evaluated on the HCNNT model, it is evident that an accuracy of 97.34% proves to be significantly beneficial, its practicality in terms of reliability for medical image classification that essentially improves the accuracy of diagnosis in gastrointestinal healthcare.

关键词： Training Visualization Accuracy Transformers Feature extraction Gastrointestinal tract robustness Convolutional neural networks Image classification Context modeling

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Hydrometeorological Monitoring Using Opportunistic Sensing Networks in the Amsterdam Metropolitan Area

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BULLETIN OF THE AMErICAN METEOrOLOGICAL SOCIETY 2020年第2期101卷 E167-E185页

作者： de Vos, L. W. droste, A. M. Zander, M. J. Overeem, A. Leijnse, H. Heusinkveld, B. G. Steeneveld, G. J. Uijlenhoet, r. Royal Netherlands Meteorol Inst KNMI R&D Observat & Data Technol De Bilt Netherlands Wageningen Univ & Res Hydrol & Quantitat Water Management Grp Wageningen Netherlands Wageningen Univ & Res Meteorol & Air Qual Sect Wageningen Netherlands Deltares Catchment & Urban Hydrol Dept Delft Netherlands

The ongoing urbanization and climate change urges further understanding and monitoring of weather in cities. Two case studies during a 17-day period over the Amsterdam metropolitan area, the Netherlands, are used to illustrate the potential and limitations of hydrometeorological monitoring using nontraditional and opportunistic sensors. We employ three types of opportunistic sensing networks to monitor six important environmental variables: 1) air temperature estimates from smartphone batteries and personal weather stations, 2) rainfall from commercial microwave links and personal weather stations, 3) solar radiation from smartphones, 4) wind speed from personal weather stations, 5) air pressure from smartphones and personal weather stations, and 6) humidity from personal weather stations. These observations are compared to dedicated, traditional observations where possible, although such networks are typically sparse in urban areas. First, we show that the passage of a front can be successfully monitored using data from several types of nontraditional sensors in a complementary fashion. Also, we demonstrate the added value of opportunistic measurements in quantifying the urban heat island (UHI) effect during a hot episode. The UHI can be clearly determined from personal weather stations, though UHI values tend to be high compared to records from a traditional network. Overall, this study illustrates the enormous potential for hydrometeorological monitoring in urban areas using nontraditional and opportunistic sensing networks.

关键词： Smartphones

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Exploring decentralized Identity Verification Systems Using Blockchain Technology: Opportunities and Challenges

Exploring Decentralized Identity Verification Systems Using ...

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Global Conference for Advancement in Technology (GCAT)

作者： V. Saravanan Sumalatha A desidi Narsimha reddy B. Shamreen Ahamed Udayakumar K Arthi Priyadharshini M Department of Nano Electronics Materials and Sensors Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India Department of Computer Science Kristu Jayanti College Autonomous Bengaluru Data Consultant (Data Governance Data Analytics: Enterprise Performance Management AI&ML) Soniks Consulting LLC Texas USA Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Jeppiaar Nagar Chennai Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology India Department of CSE Dhanalakshmi College of Engineering Chennai

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

The rapid advancement of digital technologies has underscored the critical necessity for robust and reliable identity verification systems in today's interconnected world. Traditional centralized identity management systems encounter myriad challenges, from vulnerabilities to data breaches and privacy concerns, to inefficiencies inherent in verification processes. This paper delves into the transformative potential of decentralized identity verification systems using blockchain technology as a robust solution to these pressing issues. By harnessing blockchain's immutable, transparent, and secure characteristics, decentralized identity systems not only promise enhanced privacy, security, and user control over personal data but also foster a paradigm shift towards more efficient and user-centric identity management *** study rigorously examines the foundational principles of blockchain technology and its strategic application in decentralized identity verification. It highlights key opportunities such as strengthened security measures through cryptographic protocols, enhanced user autonomy in managing and sharing personal information, and streamlined verification processes that reduce operational complexities and costs. Additionally, the paper critically evaluates significant challenges that must be addressed, including scalability concerns as blockchain networks grow, complexities in navigating regulatory landscapes designed for centralized systems, and overcoming barriers to widespread user adoption. Through an exhaustive analysis of existing models, real-world case studies, and emerging trends, this paper aims to provide comprehensive insights into the feasibility and future prospects of decentralized identity verification systems. By contributing to the ongoing discourse on innovative identity management solutions in the digital age, this research seeks to pave the way for more secure, efficient, and equitable digital ecosystems.

关键词： data privacy Navigation Scalability Pressing Market research Blockchains Complexity theory Security reliability Identity management systems

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A Hybrid Blockchain Framework for Secure, Scalable, and Energy-Efficient digital Identity management with Token-based Selective disclosure

A Hybrid Blockchain Framework for Secure, Scalable, and Ener...

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Electronics and renewable Systems (ICEArS), International Conference on

作者： Velmurugan M Shinzeer C K G. Maya r. ramya r Kanimozhi Syed Ismail Abdul Lathif Department Of Computer Science & Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science And Technology Chennai Department of School of Computer Applications Lovely Professional University Phagwara Punjab Faculty of Management SRM Institute of Science and Technology Kattankulathur Department of Electronics and Communication Engineering K.Ramakrishnan College of Engineering Samayapuram Computer Science and Engineering Muthayammal Engineering College Rasipuram Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

data breaches, identity theft, and the lack of user control within traditional digital identity management systems, it begs a more secure and decentralized alternative. In this study we propose a blockchain based digital identity management framework that implements privacy, security and transparency by employing distributed ledger technology. To provide tamper proof identity storage and automated verification, the proposed model uses cryptographic hashing and smart contracts. The use of a novel token-based access mechanism enables users to safely share their identity data with third parties, while keeping sensitive information out of sight. Security metrics are enhanced by 30% for data integrity with 45% reduction in unauthorized access attempts all through simulations. The framework also achieves improved efficiency reducing identity verification time by 40 percent compared to traditional systems. It offers a promising approach to address important vulnerabilities in centralized identity management in line with the development of marketable and user-friendly digital identity solutions.

关键词： resistance Privacy Fault tolerance Smart contracts Fault tolerant systems Throughput Energy efficiency Blockchains Identity management systems Interoperability

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Predictors of new bone erosion in rheumatoid arthritis patients receiving conventional synthetic disease-modifying antirheumatic drugs: Analysis of data from the drIVE and dESIrABLE studies

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MOdErN rHEUMATOLOGY 2021年第1期31卷 34-41页

作者： Takeuchi, Tsutomu Soen, Satoshi Ishiguro, Naoki Yamanaka, Hisashi Tanaka, Sakae Kobayashi, Makiko Okubo, Naoki Nitta, Takaya Tanaka, Yoshiya Keio Univ Div Rheumatol Dept Internal Med Sch Med Tokyo Japan Kindai Univ Dept Orthopaed Surg & Rheumatol Nara Hosp Nara Japan Nagoya Univ Dept Orthopaed Rheumatol Grad Sch Med Nagoya Aichi Japan Sanno Med Ctr Tokyo Japan Univ Tokyo Hosp Orthopaed Surg & Spinal Surg Tokyo Japan Daiichi Sankyo Co Ltd Med Affairs Div Med Sci Dept Tokyo Japan Daiichi Sankyo Co Ltd Biostat & Data Management Dept R&D Div Tokyo Japan Daiichi Sankyo Co Ltd Clin Dev Dept R&D Div Tokyo Japan Univ Occupat & Environm Hlth Sch Med Grad Sch Med Sci Dept Internal Med 1 Kitakyushu Fukuoka Japan

Objective: To investigate new bone erosion and cartilage destruction predictors in rheumatoid arthritis (rA) patients treated with conventional synthetic disease-modifying antirheumatic drugs (csdMArds). Methods: Placebo-treated patient data from two 12-month, randomized, double-blind, phase 2 (drIVE) and 3 (dESIrABLE) trials that evaluated denosumab efficacy in csdMArd-treated rA patients were used. Change from baseline in erosion score (ES) of >= 1.0 at 12 months was considered new bone erosion;predictors were identified using a multivariate model. results: Among 306 patients, mean +/- standard deviation disease activity score 28-C-reactive protein (CrP) at baseline was 3.58 +/- 1.03. New bone erosion was observed in 90 patients (29.4%). Univariate analysis identified female sex, anti-cyclic citrullinated peptide (CCP) antibody positivity, rheumatoid factor (rF) positivity, tender joint count >= 6, CrP >= 0.3 mg/dL, erythrocyte sedimentation rate (ESr) >= 28 mm/h, and baseline ES >= 3 as significant predictors for new bone erosion. In multivariate analysis, predictors were anti-CCP antibody positivity, CrP >= 0.3 mg/dL, and baseline ES >= 3;rF and ESr were excluded as they strongly correlated with anti-CCP antibody and CrP, respectively. Conclusion: In rA patients treated with csdMArds, new bone erosion predictors were seropositivity, elevated inflammatory markers, and baseline ES >= 3.

关键词： Anti-cyclic citrullinated peptide bone erosion predictors rheumatoid arthritis tender joint count

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Machine Learning Approaches for Advanced Threat detection in Cyber Security

Machine Learning Approaches for Advanced Threat Detection in...

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Global Conference for Advancement in Technology (GCAT)

作者： V. Saravanan Talluri Upender E. d. Kanmani ruby P. deepalakshmi desidi Narsimha reddy Ananthi S N Department of Nano Electronics Materials and Sensors Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India Department of CSE CMR College of Engineering and Technology. Medchal Hyderabad Telangana Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science Technology School of Computing Director - Accreditation and Ranking Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu India Data Consultant (Data Governance Data Analytics: Enterprise Performance Management AI&ML) Soniks Consulting LLC Texas USA Department of Computer Science and Engineering S.A. Engineering College Chennai

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

The dynamic nature of cyber threats demands that threat detection mechanisms be continuously improved in order to protect digital assets and infrastructures. In cybersecurity, this study investigates the use of machine learning (ML) algorithms for enhanced threat detection. This work intends to improve threat detection systems' accuracy, speed, and adaptability by utilizing ML algorithms' capabilities. The abstract begins by outlining the significance of machine learning in transforming traditional cybersecurity measures. It highlights the limitations of conventional signature-based methods, which often fail to detect novel and sophisticated threats. In contrast, ML-based approaches offer dynamic and proactive defenses capable of identifying previously unseen attack vectors. Furthermore, the paper delves into the practical implementation of ML models in real-world cybersecurity environments. It covers data collection and preprocessing techniques critical for training accurate and reliable models, as well as the challenges associated with maintaining and updating these models to adapt to evolving threat landscapes. The importance of feature selection and engineering in improving model performance is also emphasized. To validate the effectiveness of ML approaches, the study presents case studies and experimental results from various cybersecurity applications. The abstract concludes by addressing the future prospects and potential advancements in machine learning for cybersecurity. It discusses the role of emerging technologies, such as deep learning and federated learning, in enhancing threat detection capabilities. Additionally, it considers the ethical and privacy concerns associated with deploying ML models in cybersecurity and the need for robust governance frameworks. In summary, this study presents a thorough analysis of machine learning techniques for enhanced threat detection in cybersecurity, including useful applications, advantages, and difficulties.

关键词： Adaptation models Machine learning algorithms Accuracy Heuristic algorithms Machine learning data collection Threat assessment data models Vectors Computer security

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Trajectories of care and outcomes of Veterans receiving home-based primary care

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JOUrNAL OF THE AMErICAN GErIATrICS SOCIETY 2024年第1期72卷 80-90页

作者： Edwards, Samuel T. O'Neill, Allison Niederhausen, Meike Salvi, Apoorva Laliberte, Avery Saha, Somnath Hynes, denise M. Pizer, Steven Kinosian, Bruce VA Portland Hlth Care Syst Sect Gen Internal Med Portland OR USA Oregon Hlth & Sci Univ Div Gen Internal Med & Geriatr Portland OR USA VA Portland Hlth Care Syst Ctr Improve Vet Involvement Care Portland OR USA Oregon Hlth & Sci Univ Oregon Hlth & Sci Univ Portland State Univ Sch Pu Portland OR USA Johns Hopkins Univ Div Gen Internal Med Baltimore MD USA Oregon State Univ Coll Hlth Sch Publ Hlth & Nutr Hlth Management & Policy Program Corvallis OR USA Oregon Hlth & Sci Univ Sch Nursing Portland OR USA Boston Univ Sch Publ Hlth Hlth Law Policy & Management Boston MA USA Partnered Evidence Based Policy Resource Ctr Dept Vet Affairs Boston MA USA Univ Penn Perelman Sch Med Div Geriatr Philadelphia PA USA Cpl Michael J Crescenz VA Med Ctr Geriatr & Extended Care Data Anal Ctr Philadelphia PA USA VA Portland Hlth Care Syst 3710 SW US Vet Hosp RdMailcode R&D 199 Portland OR 97239 USA

BackgroundVeterans Affairs (VA) home-based primary care (HBPC) provides comprehensive longitudinal care to patients with complex, chronic disabling disease. While enrollment is associated with lower hospitalization rates and costs, detailed trajectories have not been well *** performed a longitudinal descriptive study of patients newly enrolled in VA HBPC in fiscal year (FY) 2015. We extracted demographics, comorbidities, functional status, and social supports from VA and Medicare data and examined patterns of care and clinical outcomes, including hospital, nursing home (NH), hospice use and mortality from FY2015-2017. We present results using descriptive statistics, alluvial plots, and heat *** identified 10,571 HBPC enrollees in FY2015;mean age was 77.7. HBPC patients commonly had chronic medical conditions with high self-management burden (e.g., diabetes 48.2%) and disabling conditions such as dementia (39.3%). Over half had & GE;2 deficits in activities of daily living, 46% had caregivers with functional limitations or no caregiver, and 25% resided in a socially deprived area. Patients experienced variable care trajectories. Mean time enrolled in HBPC was 331 days, 8.3% of patients were discharged after 3 months, and 22.8% stayed enrolled for over 2 years. Institutional health care use declined in the 6 months after initial enrollment: (e.g., hospital: 41%-25%, NH: 34%-11%). At 2 years, 36% of patients had died;among decedents, 58% received hospice and 72% died in a non-institutional setting. In the last 180 days of life, 84% of time was spent at home, and once enrolled in hospice, 97% of time was spent outside of institutional *** patients experience highly variable care trajectories but on average have reductions in acute care use and spend a majority of time in non-institutional settings. These data allow for a nuanced understanding of HBPC, providing a platform for monitoring, evaluating, and improving program func

关键词： home care services home-based primary care hospice care primary health care Veterans

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Performance Analysis for Cloud-Based Olap over Big data

Performance Analysis for Cloud-Based Olap over Big Data

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2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022

作者： Jagan, S. Mishra, Pavan Turai, Anjaneya Krishna Joan, Nithesh derin Anuradha, Nandula Gangodkar, durgaprasad Perwej, Yusuf Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Ambalika Institute of Management & Technology Department of Computer Science and Engineering Uttar Pradesh Lucknow India Symbiosis Skills and Professional University Department of Data Science Maharashtra Mumbai India St.Joseph's Institute of Technology Department of Data Science Tamil Nadu Chennai India KoneruLakshmaiah Education Foundation Department of Computer Science and Engineering Telangana Hyderabad India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Department of Computer Science & Engineering Uttar Pradesh Lucknow India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

OLAP-based middleware for parallel OLAP query processing is presented in this study, which builds on previous research results to provide two new areas of authority application built on top of OLAP. This multi-faceted investigation of several business viewpoints is referred to as "online analytical processing"(OLAP).The fundamental purpose of OLAP systems is to allow users to conduct manual interpretation and analysis of corporate summary and extensive statistics. It's critical to realise that OLAP require users to know what data they are looking for OLAP merely analyses past facts, whereas "data-mining techniques"allow organisations to make predictions about future events. Clustering, identifying, and predictive analysis approaches are the three types of data-mining approaches. This research has considered secondary data collection method to gather informative and relevant "cloud based OLAP over big data"related information from different secondary sources such as journals, articles. © 2022 IEEE.

关键词： Big data

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reconstructing human activities via coupling mobile phone data with location-based social networks

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TrAVEL BEHAVIOUr ANd SOCIETY 2023年 33卷

作者： Huang, Le Xia, Fan Chen, Hui Hu, Bowen Zhou, Xiao Li, Chunxiao Jin, Yaohui Xu, Yanyan Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Data Driven Management Decis Making Lab Shanghai 200030 Peoples R China Shandong Univ Sch Informat Sci & Technol Qingdao 266237 Peoples R China China Unicom Digital Technol Co Ltd Data Intelligence Div Technol R&D Dept Beijing 100166 Peoples R China Shanghai Jiao Tong Univ Antai Coll Econ & Management Shanghai 200030 Peoples R China Renmin Univ China Gaoling Sch Artificial Intelligence Beijing 100872 Peoples R China

In the era of big data, the ubiquity of location-aware portable devices provides an unprecedented opportunity to understand inhabitants' behavior and their interactions with the built environments. Among the widely used data resources, mobile phone data is the one passively collected and has the largest coverage in the population. However, mobile operators cannot pinpoint one user within meters, leading to the difficulties in activity inference. To that end, we propose a data analysis framework to identify user's activity via coupling the mobile phone data with location-based social networks (LBSN) data. The two datasets are integrated into a Bayesian inference module, considering people's circadian rhythms in both time and space. Specifically, the framework considers the pattern of arrival time to each type of facility and the spatial distribution of facilities. The former can be observed from the LBSN data and the latter is provided by the points of interest (POIs) dataset. Taking Shanghai as an example, we reconstruct the activity chains of 1,000,000 active mobile phone users and analyze the temporal and spatial characteristics of each activity type. We assess the results with some official surveys and a real-world check-in dataset collected in Shanghai, indicating that the proposed method can capture and analyze human activities effectively. Next, we cluster users' inferred activity chains with a topic model to understand the behavior of different groups of users. This data analysis framework provides an example of reconstructing and understanding the activity of the population at an urban scale with big data fusion.

关键词： Human activity Activity inference Mobile phone data Location -based social networks Bayesian inference Topic model

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Leveraging Machine Learning for Predictive Maintenance in Supply Chain management Systems

Leveraging Machine Learning for Predictive Maintenance in Su...

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Science, Technology, Engineering and management (ICSTEM), International Conference on

作者： Bediga Sharan C. Vijai Kingshuk Srivastava Atul Singla Indira Bhati T. Ch. Anil Kumar School of CS &amp AI SR University Warangal Telangana India Department of Commerce and Business Administration Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Data Science UPES Dehradun Uttarakhand Lovely Professional University Phagwara India Department of Management MMIM Maharishi Markandeshwar (Deemed to beUniversity). Department of Mechanical Engineering Vignan's Foundation for Science Technology and Research Andhra Pradesh India

ISBN: (数字)9798350376913

ISBN: (纸本)9798350388473

The purpose of this exploratory research is to investigate the incorporation of machine literacy (ML) techniques for prediction conservation in supply chain management (SCM) systems. The successful operation of force chains is essential to the success of an organization in the modern business environment, which is always shifting and transitioning. When it comes to preemptively managing outfit failures, performing in expensive time-outs, and functional dislocations, conventional conservation techniques frequently fall short unexpectedly. To analyze literal data, detector inputs, and external variables, the utilization of machine learning algorithms makes it possible to anticipate conservation requirements. Through the utilization of prophetic analytics, supply chain management systems can identify hidden faults in the outfit, which enables visionary conservation actions. This study assesses various machine learning models and algorithms that have been adapted to forecast conservation circumstances in supply chain management (SCM) systems. Specific variables such as monitoring the status of the outfit and recognizing failure patterns are taken into consideration. In the end, the purpose of the investigation is to provide insight into the actual implementation of machine learning-driven prophetic conservation methods. This will enable organizations to improve their overall performance and optimize their force chain operations.

关键词： Supply chain management Machine learning algorithms Force Organizations Machine learning Predictive models Pattern recognition

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