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3rd international conference on Advancements in Smart computing and Information Security, ASCIS 2024

作者： Gunje, Shamali Waghmare, Kalyani Sonawane, Sheetal Department of Computer Engineering PICT Pune India

ISBN: (纸本)9783031862892

Death is a tragedy regardless of its form, time, or manner in which it occurs. Death can be natural, unnatural through deliberate action of other, self-inflicted, or it can be due to the environmental factors. The primary aim of this paper lies in the comprehensive analysis and synthesis of existing literature concerning unnatural mortality. The literature is surveyed from various digital repositories for analyzing the trends and patterns of unnatural deaths. The study of literature identified the common causes of deaths. This paper offers a comprehension of the existing literature by combining and summarizing the patterns, trends, and contributing elements of unnatural fatalities. Additionally, a machine learning classification model is built to categorize news articles from the dataset into five different categories based on death causes. We conducted a comparative analysis using machine learning classifiers on 20,000 news articles from the Indian Express dataset. The Decision Tree model emerged as the most effective, with an accuracy of 90%. This comparison analysis demonstrates the various strengths and shortcomings of different classifiers in categorizing death causes. Our findings shed light on mortality patterns and show how machine learning can automate the classification of death-related data, allowing for early interventions and informed policy decisions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Taxonomies

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Artificial Intelligence in Public Health: Climate Change-Induced Vector-Borne Diseases in India 15th

Artificial Intelligence in Public Health: Climate Change-Ind...

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15th international conference on Soft computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Dash, Yajnaseni Priyanshu Sarangi, Sudhir C. Abraham, Ajith Choudhary, Samarjeet Santosh School of Artificial Intelligence Bennett University UP Greater Noida India School of Computer Science Engineering and Technology Bennett University UP Greater Noida India Department of Pharmacology AIIMS New Delhi India School of Artificial Intelligence Sai University Chennai India

ISBN: (纸本)9783031810794

Climate change has a significant impact on the health of the world’s population, particularly because vectors that spread diseases breed more due to its effects. Some of the diseases transmitted by vectors include Dengue fever, Malaria, as well as Chikungunya. This study examines how climate change is related to vector-borne diseases (VBDs) in India through artificial intelligence and data visualization methods. The present work has used extensive data on disease incidence and climate to show that there is a strong relationship between seasonal climatic changes such as monsoons and upsurge in VBD cases. The unpredictability of monsoon rain complicates forecasting of these outbreaks. With models from Artificial Intelligence (AI), we could recognize patterns and give some predictions illustrating how much this technology can help monitor public health issues. The necessity for strategies that adapt to VBD management like controlling mosquito populations, creating awareness on public health matters, and improving infrastructure is emphasized by our discoveries. It has been delineated in this study how climate change-induced health hazards can be countered by using combined approaches of climatology together with a knowledge base from epidemiology besides AI. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： artificial intelligence (AI) Chikungunya Climate change climatology Dengue epidemiology machine learning (ML) Malaria predictive analytics vector-borne diseases (VBDs)

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Analysis on Stock Market Stream Data Using Kafka, AWS and PowerBI 3rd

Analysis on Stock Market Stream Data Using Kafka, AWS and Po...

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3rd international conference on Machine Intelligence and Smart Systems, MISS 2023

作者： Baseer, K.K. Siva Siddartha Reddy, B. Vishnuvardhan, D. Chandravathi, K. Abhishek, L. Karthikaa, M. Tirupati India Department of Information Technology Sree Vidyanikethan Engineering College Tirupati India

ISBN: (纸本)9783031691140

Real-time data analysis is becoming increasingly important, as it allows businesses to act quickly and with greater decision accuracy. Traditionally, data analysis occurs after data has been collected and saved, but real-time data analysis provides an opportunity for businesses to extract valuable insights from data as it’s generated. A streaming platform like Kafka is necessary to handle the constant intake of data in a systematic and efficient way, and to build data pipelines that reliably integrate and transfer information between platforms. Cloud computing services like AWS S3, AWS Crawler, AWS Glue Data Catalog, and AWS Athena can be used in conjunction with Kafka to process and analyze the data. This framework is useful for processing real-time data in various sectors, such as the Stock market, e-commerce applications, gaming, and more, and extracting useful features and hidden patterns. Machine learning models can also be trained on the data for better analysis and *** enhance this framework, it’s important to integrate various cloud computing services with Kafka more seamlessly to better handle large-scale data. Additionally, real-time data visualization tools could help generate insights and provide interactive feedback on data streams. The incorporation of predictive analytics capabilities and intelligent, data-driven decision-making processes could help businesses make more informed decisions. By integrating real-time data analysis with intelligent automation tools, human error can be reduced, and overall efficiency and effectiveness in the decision-making process can be improved. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Marketplaces

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Scrumming with Educators Cross-Departmental Collaboration for a Summer Software engineering Capstone 3

Scrumming with Educators Cross-Departmental Collaboration fo...

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international conference on learning and teaching in computing and engineering

作者： Yuen, Timothy T. Univ Texas San Antonio Dept Interdisciplinary Learning & Teaching San Antonio TX 78249 USA

ISBN: (纸本)9781479999675

This paper describes the design of a 10-week summer software engineering capstone course that provides a service learning opportunity through cross-departmental collaboration. The projects for this course centered on developing software products to support education faculty in their work in the greater university area. This paper will also discuss how Scrum was implemented over a 10-week summer semester, the outcomes and challenges of cross-departmental collaboration, the types of projects created for education faculty, and the advantages of connecting software engineering capstone courses with education faculty.

关键词： software engineering education interdisciplinary collaboration educational technology service learning

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Facebook as a Tool: Exploring the use of Facebook in teaching and learning

Facebook as a Tool: Exploring the use of Facebook in Teachin...

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international conference on teaching and learning in computing and engineering (LaTiCE)

作者： Said, Mohd Nihra Haruzuan Mohamad Tahir, Lokman Mohd Ali, Mohd Fadzli Univ Teknol Malaysia Fac Educ Skudai Malaysia

ISBN: (纸本)9781479935918

In this paper, we explore the use of Facebook as an alternative to learning management system for tertiary teaching and learning purposes in a Malaysian university context. Three embedded approaches for Facebook were implemented, namely for mixing information and learning resources, mixing expertise and mixing context of learning. Each of embedded approaches was analyzed through two ways: within and cross-case analysis. The results from cross-case analysis indicate that Facebook has the potential of mixing affordances of technical, social and pedagogical for teaching and learning, and thus can deliver positive tool for teaching and learning but can also be a negative tool if it is not appropriately utilize.

关键词： Facebook Social Networking Site Tertiary teaching and learning

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Evaluating Vaccine Effectiveness During the COVID-19 Pandemic: Insights from Statistical and Machine learning Methods 10th

Evaluating Vaccine Effectiveness During the COVID-19 Pandemi...

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10th international conference on Health Informatics and Medical Systems, HIMS 2024 and 10th international conference on Biomedical engineering, BIOENG 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied computing, CSCE 2024

作者： Etli, Dominic Purdue University Global West Lafayette United States

ISBN: (纸本)9783031859076

Background: The SARS-CoV-2 pandemic has had a profound impact on global labor markets, resulting in increased work absences and economic disruptions. This study aims to analyze the effectiveness of SARS-CoV-2 vaccination in reducing work absences, with a particular focus on the role of vaccination status. Methods: De-identified data spanning from February 2021 to February 2023, was analyzed using various statistical methods. The dataset included 2,107 participants who reported a confirmed or suspected SARS-CoV-2 infection during the study period. The primary outcome variable was the total days lost due to confirmed or suspected SARS-CoV-2 infection, and the main exposure variable was SARS-CoV-2 vaccination status (vaccinated or unvaccinated). Descriptive statistics, independent samples t-tests, ANOVA probing, multiple regression analysis, and a Random Forest model were employed to identify significant predictors of total days missed and assess the relative importance of these predictors. Results: The study found a counterintuitive result with vaccinated individuals having a higher attack rate (24.30%) compared to unvaccinated individuals (17.21%) and an absolute risk reduction (-0.0709). This was an unexpected finding and warrants further investigation (p-value © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Decision making

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Deep learning and IoT Based Robotics to Monitor the Traffic 2nd

Deep Learning and IoT Based Robotics to Monitor the Traffic

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2nd international conference on Cyber Warfare, Security and Space computing, SpacSec 2024

作者： Priya, V. Vishwa Pande, Soumitra S. Ilyas, Md Jayasudha, R. Ramesh, Janjhyam Venkata Naga Suganthi, D. Department of Computer Science Vels Institute of Science Technology and Advanced Studies Chennai India Ericsson Global India Private Limited Bangalore India Prestige Institute of Engineering Management and Research Indore India Dr. N. G. P. Institute of Technology Coimbatore India Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur Vaddeswaram India Saveetha College of Liberal Arts and Sciences SIMATS Thandalam Chennai India

ISBN: (纸本)9783031734939

In densely populated areas, managing traffic laws has become more difficult due to the rising number of cars on the road. One of the most critical components of effective traffic management and improved mobility is real-time traffic monitoring systems. Consequently, precise and dependable real-time traffic information has always been essential for cars and drivers. Many other approaches have been suggested as potential answers to the issues plaguing traffic conditions recently. A different approach is VCC or vehicle cloud computing. In addition, an advanced model called an Iot-aided robotic (IoRT), which uses cameras and Internet of Things (IoT) detector nodes to gather actual traffic information, has been created. Two deep learning methods, one based on improved LeNet-5 for real-time signs for traffic identification and the other on the Inception-V3 model for detecting and identifying traffic lights, are the critical contributions provided by this research effort. Additionally, to decrease road accidents, the ideal distance between barriers and ultrasonic sensors was determined by analyzing the timing and speed of ultrasonic waves. Data acquired by cameras and sensors is analyzed using a variety of image processing algorithms before being uploaded to the cloud and made accessible to commuters and drivers via a mobile app. According to the test findings, the suggested models are far more accurate. The expanded GTSRB (EGTSRB) dataset achieved 98.89% accuracy while the German Traffic Sign Recognition Benchmark (GTSRB) dataset achieved 98.23% accuracy using the modified LeNet-5. The second model achieved a precision of 99.7 percent using data from the Laboratory for the Intelligent and Safe Automobiles (LISA). The results of this study beat previous research on comparable traffic monitoring systems by 2.13% for traffic light recognition and detection and by 4.89% for traffic sign identification. © The Author(s), under exclusive license to Springer Nature Switzerland AG

关键词： Traffic signs

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LEBFL: Lightweight authentication and efficient consensus for blockchained federated learning in vehicle–road cooperation systems with AIoT

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Computer Communications 2025年 239卷

作者： Liu, Peng He, Qian Li, Sen Chen, Yiting Liu, Anfeng The State and Local Joint Engineering Research Center for Satellite Navigation and Location Service Guilin University of Electronic Technology Guangxi Guilin541004 China Guangxi Key Laboratory of Digital Infrastructure Guangxi Zhuang Autonomous Region Information Center Guagnxi Nanning530022 China School of Electronic Information Central South University Hunan Changsha410073 China

Artificial Intelligence Internet of Things (AIoT) technology is gradually overcoming challenges related to traffic data transmission and processing in vehicle-road cooperative systems. However, the dynamism and openness of the vehicle-road cooperative networks make it susceptible to potential attacks, where attackers might intercept or tamper with transmitted local model parameters, thereby compromising the integrity of the models and leaking user privacy. Although existing solutions such as differential privacy and encryption can address these issues, they may reduce data availability or increase computational complexity. To tackle these challenges, we propose a lightweight authentication and efficient consensus for blockchained federated learning in vehicle–road cooperation systems(LEBFL), which provides lightweight privacy-enhanced authentication and efficient consensus while ensuring the privacy of local models and datasets. Specifically, we first introduce a blockchain-based federated learning architecture that enhances privacy and efficient consensus, utilizing the consortium blockchain to replace the centralized server. Subsequently, we design a lightweight anonymous authentication and key agreement protocol using efficient cryptographic primitives to establish secure session keys for the transmission of local models. Furthermore, we propose a utility-based Raft consensus algorithm, which selects the optimal fog server as the leader node using a resource matrix and weight vector, and enhances the performance of the blockchain network by leveraging the idle computing resources of fog servers. Security analysis and experimental results confirm that the proposed scheme shows superior performance without sacrificing security. © 2025 Elsevier B.V.

关键词： Federated learning

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Student learning Processes for Mathematical Modelling - An engineering Lecturer's Viewpoint 6

Student Learning Processes for Mathematical Modelling - An E...

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6th international conference on learning and teaching in computing and engineering (LaTiCE)

作者： Spooner, Kerri Auckland Univ Technol Sch Engn Dept Math Sci Auckland New Zealand

ISBN: (纸本)9781538678978

This paper presents the preliminary findings of a case study addressing the question "How does an engineering mathematical modelling lecturer think students learn?" A case study was carried out involving an engineering mathematical modelling lecturer. The lecturer was interviewed and observed to identify clues about beliefs in learning processes for tertiary students. learning theories and the preliminary results of the study are presented and discussed.

关键词： learning theories learning orientations learning processes mathematical modelling

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Extracting the Pneumonia Signature: A Deep learning Framework for Definitive Diagnosis 3rd

Extracting the Pneumonia Signature: A Deep Learning Framewor...

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3rd international conference on Advancements in Smart computing and Information Security, ASCIS 2024

作者： Vyas, Malay Mehta, Apurva. A. Dharmsinh Desai University Computer Engineering Nadiad387001 India

ISBN: (纸本)9783031862953

Pneumonia is a significant worldwide health and wellness concern that creates considerable health issues plus fatality, highlighting the importance of promptly and accurately detecting and treating it. Despite the improvements in imaging innovation, the hand-operated evaluation of chest radiographs by radiologists remains the fundamental technique for spotting pneumonia, bringing about hold-ups in medical diagnosis together with therapy. This research suggests a pneumonia discovery technique that utilizes deep learning strategies to automate the procedure. By harnessing an extensive data source of classified chest radiographs the recommended design intends to precisely determine locations influenced by pneumonia, making it possible for better and more efficient medical diagnosis and therapy. The design uses a custom convolutional neural network (CNN) that undergoes training on various pneumonia- positive and pneumonia-negative instances from multiple healthcare organizations. Before educating the design, various pre-processing actions were taken for the chest radiographs to boost integrity and efficiency. This research study adds to the growth of an automated, coupled with a trusted, pneumonia discovery system, which has the potential to enhance individual results and boost healthcare effectiveness. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Diagnosis

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