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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Ratheesh, R. Vijayalakshmi, S. Arunsundar, B. Swarna, M. Saranya, G. Agni College of Technology Department of Ece Chennai India R.M.K. Engineering College Department of Ece Chennai India School of Computing College of Engineering And Technology Srm Institute of Science And Technology Department of Data Science And Business Systems Kattankulathur India Dr. M.G.R. Educational And Research Institute University Department of Ece Chennai India Sri Krishna College of Engineering And Technology Department of Electronics And Communication Engineering Coimbatore Kuniyamuthur India

ISBN: (纸本)9798350365092

In the realm of modern transportation systems, Vehicular Ad Hoc Networks (VANETs) play a pivotal role in enabling efficient communication among vehicles and infrastructure elements for enhanced road safety and traffic management. However, the inherent vulnerabilities in VANETs, such as privacy breaches and security threats, necessitate robust frameworks to mitigate risks and ensure secure data transmission. This paper presents a comprehensive architecture for Privacy-Enriched data Communication over VANETs using Software-Defined Network (SDN) assistance. The proposed framework integrates key components including vehicles, Roadside Units (RSUs), and SDN controllers to facilitate secure and privacy-aware communication. Vehicles act as data sources, employing pseudonyms and encryption mechanisms to protect user privacy. RSUs serve as access points, collecting and relaying data while enforcing access control policies. SDN controllers provide centralized management, dynamically allocating resources and enforcing privacy policies through encryption, pseudonymization, and access control mechanisms. Communication protocols such as IEEE 802.11p and LTE enable vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, while the OpenFlow protocol facilitates communication between SDN controllers and network devices. Additionally, trust management mechanisms evaluate the trustworthiness of network entities based on behavioral analysis, reliability metrics, and security considerations, ensuring the integrity and resilience of the network. By amalgamating SDN-based management with privacy enhancement mechanisms and trust management protocols, the proposed framework offers a robust solution for addressing privacy and security challenges in VANETs, thereby fostering safer and more secure transportation ecosystems. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Enabling Protection for Critical Infrastructure through Security and Privacy in the Industrial Internet of Things 9

Enabling Protection for Critical Infrastructure through Secu...

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9th International Conference on Communication and Electronics systems, ICCES 2024

作者： Yasotha, B. Vijayanandh, T. Thirumoorthy, P. Priya, M.Sangeetha Sasidaran, S. Prakalya, S.B. Srmist Department of Data Science and Business Systems Kattankulathur India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Avadi Tamil Nadu Chennai India Dhanalakshmi College of Engineering Department of Electrical and Electronics Engineering Chennai India Chennai India Panimalar Engineering College Department of Information Technology Chennai600123 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350377972

Industrial Internet of Things can improve critical infrastructure in energy, transportation, and manufacturing. However, IIoT device and system integration opened security weaknesses that bad actors may exploit, causing huge disruptions and financial losses. IoT network security and privacy must be prioritized to protect key infrastructure. IIoT security issues and solutions are examined in this research. First, we take a look at the state of security and the unique risks that industrial IoT devices are up against, including hacking, illegal access, and cyber-physical assaults. Then, to lessen the impact of these dangers, we investigate cutting-edge security solutions, such as authentication, encryption, and access control systems. Building a robust security architecture specifically for IIoT applications is one of the main goals of the project. To improve safety and anonymity, this system uses cutting-edge innovation like blockchain, AI, and edge computing. We also talk about how privacy-preserving methods like differential privacy and anonymization are important for keeping sensitive information safe while allowing data sharing for analysis and operations. We assess the efficacy of the suggested security protocols and procedures using a battery of case studies and simulations. Reducing the attack surface and improving the resilience of IIoT systems can be achieved by a multi-layered security approach that includes effective privacy protections. We conclude by outlining our research's larger implications, which include the importance of standardization, regulatory compliance, and stakeholder collaboration in ensuring the safe and privacy-protecting deployment of IIoT technologies in vital infrastructure. Protecting vital infrastructure from cyber threats is our ultimate goal, and our work helps build IIoT systems that are more secure and dependable. © 2024 IEEE.

关键词： Industrial internet of things (IIoT)

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NuclSeg: nuclei segmentation using semi-supervised stain deconvolution 5

NuclSeg: nuclei segmentation using semi-supervised stain dec...

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5th ACM International Conference on Multimedia in Asia, MMAsia 2023

作者： Wang, Haixin Yang, Jian Katayama, Ryohei Matsusaki, Michiya Miyao, Tomoyuki Zhou, Jinjia Graduate School of Science and Engineering Hosei University Tokyo Japan Cancer Chemotherapy Center Japanese Foundation for Cancer Research Tokyo Japan Department of Applied Chemistry Osaka University Osaka Japan Data Science Center Nara Institute of Science and Technology Nara Japan

ISBN: (纸本)9798400702051

Recently, deep learning-inferred stain deconvolution/separation-based nuclei segmentation works demonstrated significant results by translating low-cost and prevalent immunohistochemical (IHC) slides to more expensive-yet-informative multiplex immunofluorescence (mpIF) images. However, when the input stain style is changed to Hematoxylin and Eosin stain (H&E), which is one of the principal tissue stains used in histology, the stain deconvolution/separation based works can not achieve satisfactory performance because the features of the input stain image are greatly changed. To solve this problem, we integrate stain transfer (H&E->IHC) before stain deconvolution (IHC->mpIF) to revise the image style. Moreover, a new semi-supervised learning strategy collaborating supervised and unsupervised learning processes are employed to diversify training data content for stain ***, in the supervised learning process, generative adversarial network (GAN) based image-to-image mapping (stain deconvolution) and inverse mapping models are trained on the dataset of co-registered IHC staining and mpIF staining of the same slides to respectively convert IHC to mpIF (I2m), and mpIF to IHC (m2I). After that, in the unsupervised learning process, high-quality IHC images from m2I model are selected according to the Discriminator score on another unpaired dataset, and then, IHC images are paired with input mpIF as the unsupervised training data for further improving the I2m model. This semi-supervised scheme balances two supervised and unsupervised errors while optimizing to limit the effect of imperfect pseudo inputs but still enhance stain deconvolution. Furthermore, image enhancement is applied after the stain deconvolution model to obtain high-quality segmentation masks. We thoroughly evaluate our method on publicly available benchmark datasets. The results show the proposed model obtains significant improvement compared to the SOTAs. © 2023 Copyright held by the owner

关键词： Deep learning

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Smart Signaling: A Smart Internet of Things Assisted Traffic Light Controlling and Monitoring System using Intelligent Sensors 9

Smart Signaling: A Smart Internet of Things Assisted Traffic...

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Saranya, G. Ratheesh, R. Vijayalakshmi, S. Arunsundar, B. Swarna, M. Sri Krishna College of Engineering And Technology Department of Electronics And Communication Engineering Coimbatore Kuniyamuthur India Agni College of Technology Department of Ece Chennai India R.M.K. Engineering College Department of Ece Chennai India School of Computing College of Engineering And Technology Srm Institute of Science And Technology Department of Data Science And Business Systems Kattankulathur India Dr. M.G.R. Educational And Research Institute University Department of Ece Chennai India

ISBN: (纸本)9798350365092

In contemporary urban environments, efficient traffic management stands as a paramount challenge, necessitating innovative solutions to mitigate congestion, enhance safety, and reduce environmental impact. This paper proposes a Smart Internet of Things (IoT)-assisted Traffic Light Controlling and Monitoring System designed to revolutionize traffic management through real-time data acquisition, analysis, and dynamic control mechanisms. The system integrates edge devices equipped with a variety of sensors, cloud-based infrastructure, and intelligent algorithms to optimize traffic flow at intersections. Leveraging technologies such as AWS IoT, Lambda, and QuickSight, alongside communication protocols like MQTT and HTTP, the system enables seamless device management, secure data transmission, and scalable real-time analytics. Hardware components including microcontrollers, sensors, and actuators facilitate precise data collection, allowing for accurate traffic monitoring and adaptive signal control. Through comprehensive traffic flow analysis, congestion reduction strategies, environmental impact assessments, safety enhancements, and system performance optimizations, the proposed system aims to revolutionize urban traffic management, leading to smoother traffic flow, reduced congestion, improved safety, and enhanced environmental sustainability. © 2024 IEEE.

关键词： Internet of things

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The Two Fundamental Shapes of Sleep Heart Rate Dynamics and Their Connection to Mental Health in College Students

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Digital Biomarkers 2024年第1期8卷 120-131页

作者： Fudolig, Mikaela Irene Bloomfield, Laura S.P. Price, Matthew Bird, Yoshi M. Hidalgo, Johanna E. Kim, Julia N. Llorin, Jordan Lovato, Juniper McGinnis, Ellen W. McGinnis, Ryan S. Ricketts, Taylor Stanton, Kathryn Dodds, Peter Sheridan Danforth, Christopher M. Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science University of Vermont BurlingtonVT United States Department of Mathematics and Statistics University of Vermont BurlingtonVT United States Computational Story Lab MassMutual Center of Excellence for Complex Systems and Data Science University of Vermont BurlingtonVT United States Gund Institute for Environment University of Vermont BurlingtonVT United States Department of Psychological Science University of Vermont BurlingtonVT United States Project Lemurs University of Vermont BurlingtonVT United States Department of Electrical and Biomedical Engineering University of Vermont BurlingtonVT United States Department of Computer Science University of Vermont BurlingtonVT United States Wake Forest University School of Medicine Winston-SalemNC United States Rubenstein School of Environment and Natural Resources University of Vermont BurlingtonVT United States Santa Fe Institute Santa FeNM United States

Introduction: Wearable devices are rapidly improving our ability to observe health-related processes for extended durations in an unintrusive manner. In this study, we use wearable devices to understand how the shape of the heart rate curve during sleep relates to mental health. Methods: As part of the Lived Experiences Measured Using Rings Study (LEMURS), we collected heart rate measurements using the Oura ring (Gen3) for over 25,000 sleep periods and self-reported mental health indicators from roughly 600 first-year university students in the USA during the fall semester of 2022. Using clustering techniques, we find that the sleeping heart rate curves can be broadly separated into two categories that are mainly differentiated by how far along the sleep period the lowest heart rate is reached. Results: Sleep periods characterized by reaching the lowest heart rate later during sleep are also associated with shorter deep and REM sleep and longer light sleep, but not a difference in total sleep duration. Aggregating sleep periods at the individual level, we find that consistently reaching the lowest heart rate later during sleep is a significant predictor of (1) self-reported impairment due to anxiety or depression, (2) a prior mental health diagnosis, and (3) firsthand experience in traumatic events. This association is more pronounced among females. Conclusion: Our results show that the shape of the sleeping heart rate curve, which is only weakly correlated with descriptive statistics such as the average or the minimum heart rate, is a viable but mostly overlooked metric that can help quantify the relationship between sleep and mental health. © 2024 The Author(s). Published by S. Karger AG, Basel.

关键词： Wearable technology

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Calligraphy Alphabet Perception Using Artificial Intelligence

Calligraphy Alphabet Perception Using Artificial Intelligenc...

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International Conference on science Technology engineering and Management (ICONSTEM)

作者： Venkatesh S D. Jeevitha J. Anitha Gnanaselvi Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Department of Computer Science and Engineering Jeppiaar Engineering College Chennai

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

The human brain has a simple time analyzing and processing images. The brain is able to rapidly deconstruct and distinguish an image's various components when the eye perceives it. With the Convolutional Neural Network (CNN) as its foundation, this research suggests deep learning conceptual models. When the algorithms are compared, it becomes clear that CNN-based classification of handwritten alphabets performs better than other algorithms in terms of accuracy. The Manual Net, Alex Net, and LeNet Architectures are among the CNN algorithms employed in this research. The convulational layer, max pooling, flattening, feature assortment, rectifier lined unit, and completely linked softmaxx layers are each components of the aforementioned designs. The proposed network is tested using an image dataset comprising 530 training photos and 2756 testing images. The top precision and cost-efficient model will be used in the Django context to build a handler line for supplying the appeal to be recognized and obtaining the productivity outcome of recognized appeal.

关键词： Training Productivity Accuracy Rectifiers User interfaces Mathematical models Classification algorithms

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Leveraging Machine Learning to Predict IFC Project Success and Failure 1

Leveraging Machine Learning to Predict IFC Project Success a...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Jayanthi, S. Ganesan, Jeya Begum, Ismatha Ramesh, E. Balaraman, Ravikumar Gunasree, Sunkam The ICFAI Foundation for Higher Education Faculty of Science and Technology Department of AI&DS Telangana India Sri Sairam Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Guru Nanak Institute of Technology Department of Information Technology Telangana Ibrahimpatnam India ICFAI Business School The ICFAI Foundation for Higher Education Department of Finance and Accounting Telangana India Teegala Krishna Reddy Engineering College Department of Information Technology Telangana India

ISBN: (纸本)9798331518943

This research expounds on the efficacy and impact of a machine learning-based approach in managing the investment services projects of the International Finance Corporation (IFC). It is achieved by focusing on the impact of financial interventions and project completion. We used a detailed dataset to assess trends in project status over time. The details about the growth of completed and active projects and the fluctuations in pending stages, like signing, disbursement, and approval are available in the dataset. We experimented with the ML models, including Random Forest, Gradient Boosting, Logistic Regression, SVC, and Decision Tree, to predict project outcomes. These models are evaluated with the metrics such as, accuracy, precision, recall, F1-score, and ROC AUC. After rigorous experiments, we observed that Gradient Boosting achieved the highest accuracy (90.76%) and Random Forest closely followed (90.43%). We identified significant trends that include a high success rate in project completion, an incremental growth in active projects, and a low occurrence of hold and pending approval statuses. We also performed statistical analysis and observed that loans dominate IFC's financial support and risk management and guarantees see less use. Additionally, correlations between total investments and other financial products highlight a diversified investment strategy. The findings emphasize opportunities for improving project approval efficiency, managing bottlenecks, and implementing data-driven decision-making to optimize investment strategies and enhance sustainable development outcomes. This research offers actionable insights for policymakers, demonstrating how ML can improve project management processes and maximize the impact of IFC investments in developing economies. © 2024 IEEE.

关键词： Decentralized finance

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Enhancing the Efficiency of the Grouping-Scoring-Modeling Framework with Statistical Pre-Scoring Component for Transcriptomic data Analysis

Enhancing the Efficiency of the Grouping-Scoring-Modeling Fr...

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18th International Joint Conference on Biomedical engineering systems and Technologies, BIOSTEC 2025

作者： Khokhar, Maham Bakir-Gungor, Burcu Yousef, Malik Department of Data Science Social Sciences Institute Abdullah Gul University Kayseri38080 Turkey Department of Computer Engineering Faculty of Engineering Abdullah Gul University Kayseri38080 Turkey Department of Information Systems Galilee Digital Health Research Center Zefat Academic College Zefat13206 Israel

The advent of high-throughput transcriptomic technologies has generated vast transcriptomic datasets, challenging current analytical methodologies with their sheer volume and complexity. The Grouping-Scoring Modeling (G-S-M) approach is one of the recent approaches that treat groups of genes (or clusters of genes) by embedding prior biological knowledge with machine learning in order to detect the most significant groups for classification tasks. The G-S-M might need to treat thousand ten thousand of groups (scoring those groups) which might affect the speed and performance of the algorithm. In response, this study introduces the Pre Scoring G-S-M model, an enhancement of the established Grouping-Scoring-Modeling (G-S-M) framework. This approach incorporates a Pre-Scoring component that leverages the Limma package for its empirical Bayes methods to optimize initial transcriptomic data evaluation through a percentage-based selection of statistically significant gene groups. Aimed at reducing computational demand and streamlining feature selection, the model also addresses data redundancy by eliminating duplicate gene-disease associations. Application to nine human gene expression datasets from the GEO database showed promising results. It demonstrated improvements in computational efficiency and analytical precision while reducing the number of features selected per dataset compared to the traditional G-S-M approach, without compromising accuracy. These initial findings highlight the Pre-Scoring G-S-M model's potential to enhance transcriptomic data analysis, indicating a promising direction for future bioinformatics research. © 2025 by SCITEPRESS– science and Technology Publications, Lda.

关键词： data accuracy

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Healthcare Chain Network Framework for Monitoring and Verifying Pharmaceutical Supply Chain 1

Healthcare Chain Network Framework for Monitoring and Verify...

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1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Mani, Vinodhini Prakash, M. Department of Computer Science Engineering SRM Institute of Science and Technology Kattankulathur Chennai603203 India Department of Computer Science Engineering Sathyabama Institute of Science and Technology Chennai600119 India SRM Institute of Science and Technology Data Science and Business Systems Kattankulathur603203 India

ISBN: (纸本)9781607685395

Prescription drug abuse and doctor shopping are major problems in the healthcare field that have led to a significant number of deaths as a consequence of drug misuse. Doctors prescribe too much fentanyl, demerol, and sleeping pills, along with anxiety medications that are legal and frequently abused. To combat prescription drug abuse, it is imperative to develop an efficient monitoring and identification system. Blockchain technology is a promising solution to tracking prescriptions because of its decentralization and auditability, which allow medical records to be more secure as well as more reliable. Blockchain technology can help ensure the authenticity, integrity, and transparency of drug distribution between pharmacies and doctors. In this paper, we aim to develop a system of managing the integrity of the drug supply chain by integrating Hyperledger Fabric blockchain technology with Interplanetary File System (IPFS). Because of its secure encryption framework and anti-fraud and anti-forgery measures, it has prevented prescription abuse. A smart health care ecosystem is created by using smart contracts to track drugs on the blockchain. In addition, by using this approach, we are able to recognize and track drugs that may be misused by individuals who shop for doctors and hope between pharmacies. The cryptographically safe and reliable framework created by Healthcare Chain can be used by doctors, pharmacists, and patients to improve how opioid prescriptions and medications are administered. © The Electrochemical Society

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Temporal-logic-based Exploration and Execution of Markov Decision Processes using Recursive Gaussian Processes

Temporal-logic-based Exploration and Execution of Markov Dec...

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IEEE International Conference on Automation science and engineering (CASE)

作者： Shumpei Tokuda Masaki Yamakita Hiroyuki Oyama Rin Takano Department of Systems and Control Engineering Tokyo Institute of Technology Meguro Japan Data Science Laboratories NEC Corporation Japan

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

When robots execute tasks in uncertain environments, they need to explore to obtain necessary information. In the non-stationary workspace, robots could change the thier behavior besed on informations about the workspace. We also need to pre-check whether robots can complete given tasks. We propose a model-based learning method for such non-stationary uncertain environments using linear temporal logic and recursive Gaussian processes. In the proposed method, we assume that the robot systems can be represented as Markov decision processes, and the robots can explore non-stationary workspaces and execute tasks based on task achievability. We demonstrated the efficiency of our method through computer simulations involving a simple robot pick-and-place task.

关键词： Learning systems Markov decision processes Computational modeling Design methodology Color Gaussian processes Logic Dynamical systems Robots Faces

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