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Dynamic Traffic Optimization System: Leveraging IoT and Fog Computing for Enhanced Urban Mobility with the RAO Algorithm 7th

Dynamic Traffic Optimization System: Leveraging IoT and Fog ...

引用

7th International Conference on Green Technology and Sustainable Development

作者： Thinh Vinh Le Duy Lap Le Huan Thien Tran HCMC Univ Educ & Technol Ho Chi Minh City Vietnam Saigon Univ Ho Chi Minh City Vietnam

ISBN: (纸本)9783031761966;9783031761973

This paper introduces a cutting-edge method for urban traffic management that harnesses the power of the RAO (named after Professor Ravipudi Venkata Rao) optimization algorithm, enhanced by the integration of the Internet of Things (IoT) and fog computing technologies. By collecting real-time traffic data from IoT devices, such as smart cameras and sensors, our approach provides a detailed and up-to-the-minute view of urban traffic conditions. This data is efficiently processed through fog computing infrastructure, which ensures minimal latency and supports immediate responses to fluctuating traffic patterns. The deployment of the RAO algorithm on this rich dataset stands out as a novel contribution, dynamically optimizing traffic flows and routes based on current conditions without the need for specific map data sources. Our research extends beyond traditional traffic management applications, offering significant benefits in emergencies by facilitating quicker medical and security responses and enhancing the management of military operations with the need for rapid and secure route planning. Through experimental simulations conducted in urban environments, our findings demonstrate substantial improvements in traffic management and congestion alleviation. The results indicate that our integrated strategy offers a viable and adaptable solution for both everyday traffic oversight and exceptional scenario planning. This study lays the groundwork for future advancements in smart city technologies, underscoring the role of sophisticated technological integration in fostering more sustainable, efficient, and secure urban landscapes.

关键词： Optimization RAO Dijkstra Traffic Fog computing IoT

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Connecting COVID-19 with Autoimmunity

Connecting COVID-19 with Autoimmunity

引用

Intelligent systems Conference (IntelliSys)

作者： Ghosh, Aritra Pavlovic, Mirjana D. Larrondo-Petrie, Maria M. Florida Atlant Univ Comp & Elect Engn & Comp Sci Boca Raton FL 33431 USA

ISBN: (纸本)9783031477171;9783031477188

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS COV 2) is the infectious virus that causes the 2019 Coronavirus Disease (COVID-19). It is well established that SARS-CoV-2 infection can cause several autoimmune and autoinflammatory disorders. Fewfindings on the connection between SARS-CoV2 and autoimmune illnesses have been published in the literature, albeit this number has been rising since 2020. SARS-CoV-2 infections and autoimmune disorders are linked in several ways. Both illnesses cause immune-mediated tissue damage, the immunological reaction is followed by an increase in inflammatory cytokine release, and both illnesses are treatable with immunomodulatory medications. Patients who suffer from certain autoimmune diseases, including systemic lupus erythematosus, rheumatoid arthritis, type 1 diabetes, cardiac sarcoidosis, idiopathic pulmonary fibrosis, autoimmune hepatitis, multiple sclerosis, and others, are more likely to develop SARS-CoV-2 infection, either because the autoimmune disease is active or because of the medications used to treat it. On the other hand, autoimmune disorders can also be brought on by SARS-CoV-2 infection. Autoimmune illnesses are chronic multifactorial disorders. The ability to identify patterns in patient data and use those patterns to forecast patient outcomes for better clinical treatment is made feasible by machine learning (ML), a subfield of artificial intelligence. Here we present a machine learning (ML) approach to run a case-control study of Autoimmunity for Candidates after COVID-19 Vaccination which can assist investigators to get an initial idea of whether COVID-19 vaccination can enhance the probability of autoimmunity occurrence. We might be able to comprehend the long-term impacts of COVID-19 vaccines better through case-control studies based on passive reporting. Passive reporting is important for giving early signals even though it is subject to bias and under- and over-reporting. By concentrating on autoimmune

关键词： COVID-19 SARS COV2 Coronavirus Antibodies Antigenicity RNA Vaccines Autoimmunity Molecular mimicry

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Automating Dose Prediction in Radiation Treatment Planning Using Self-attention-Based Dense Generative Adversarial Network 1

引用

4th Congress on Intelligent systems (CIS)

作者： Aparna, V. Hridika, K. V. Nair, Pooja S. Chandran, Lekshmy P. Nazeer, K. A. Abdul Natl Inst Technol Calicut Kerala India

ISBN: (数字)9789819990375

ISBN: (纸本)9789819990368;9789819990375

Radiation treatment being a crucial step in cancer treatment, accurate radiation treatment planning is extremely important to reduce the effect of radiation on Organs-At-Risk (OAR). In order to determine the radiation treatment dose distributions received by the tumour and surrounding organs, this paper proposes a self-attention-based dense generative adversarial network (GAN). This is accomplished by adding a self-attention module to GAN which uses dense U-Net as generator. Using the DVH and dose scores, the performance is evaluated and compared to a few state-of-the-art models like simple GAN, self-attention-based GAN and dense GAN. The dataset was acquired from the OpenKBP 2020 AAPM Grand Challenge run by CodaLab. The DVH score and dose score of the self-attention-based dense GAN are 2.126 and 3.522, respectively. The proposed model performed better than other state-of-the-art models considered.

关键词： Organs-At-Risk (OAR) U-Net Dose volume histogram (DVH) Convolutional neural network (CNN) Generative adversarial network (GAN)

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Development and Pilot Testing of a Web-Based E-Learning Tool for Imparting Livelihood Skills of Intellectually Disabled Person 8th

Development and Pilot Testing of a Web-Based E-Learning Tool...

引用

8th Smart Trends in Computing and Communications (SmartCom)

作者： Poonkuzhali, S. Vijay, Priya Shanthi, D. Sorna Archana, J. Sangeetha Rajalakshmi Engn Coll Ctr Excellence Assist Technol Dept CSE Chennai India

ISBN: (纸本)9789819713196;9789819713202

Imparting livelihood skills to people with intellectual disabilities is seeking more attention so that the person with such disabilities can survive more independently with at most societal protection. There is a lack of exclusive assistive technology with 3D animation to train the person with such disabilities. Moreover, the families with persons affected by mild and moderate intellectual disabilities are mostly economically weak or illiterate and they do not have awareness on available technologies to educate their kids to learn livelihood skills so that they can lead an improved quality of life. The special schools across districts follow different sets of livelihood skills to train their kids, but mostly in all schools, there is no systematic or organized way of providing such training. The proposed system for imparting livelihood skills to persons with mild and moderate intellectual disability (ID) seeks to bridge this gap by offering a web-based e-learning tool that includes a collection of videos, 3D animations, and augmented reality with a standard structure for curriculum.

关键词： 3D animations Assistive technology Assessment Intellectually disabled Livelihood skills

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Effect of Infill Struktureon Mechanical Parameters of FDM 3D Printed PLA and PLA plus CF Materials: Characterization and Comparative Analysis 10th

Effect of Infill Struktureon Mechanical Parameters of FDM 3D...

引用

International Conference on New Technologies, Development and Application - Advanced Production Processes and Intelligent systems

作者： Hozdic, Emine Hozdic, Elvis Kranj Sch Ctr Mech Engn Kidriceva Cesta 55 Kranj 4000 Slovenia Univ Novo Mesto Fac Mech Engn Na Loko 2 Novo Mesto 8000 Slovenia

ISBN: (纸本)9783031662676;9783031662683

This scientific article investigates the impact of different infill structures on the mechanical parameters of Fused Deposition Modeling (FDM) 3D printed materials, specifically polylactic acid (PLA) and polylactic acid reinforced with carbon fibers (PLA+CF). The study involves a comprehensive characterization and comparative analysis of tensile strength, nominal strain at break, and other key mechanical properties. Through a systematic approach, the research aims to provide insights into optimizing the infill structure for enhanced performance and usability of 3D printed products. The comparative analysis includes a detailed examination of PLA and PLA+CF materials, highlighting their distinct mechanical behaviours under varying infill structures. The findings contribute to a deeper understanding of the interplay between infill design and mechanical properties, facilitating advancements in the field of additive manufacturing.

关键词： FDM PLA PLA plus CF infill structure mechanical parameters

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Perceptions and Experiences of Severe Content in Content Moderation Teams: A Qualitative Study 9th

Perceptions and Experiences of Severe Content in Content Mod...

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9th International Congress on Information and Communication Technology (ICICT)

作者： Strongylou, Dimitra Eleftheria Savio, Marlyn Thomas Steiger, Miriah Bharucha, Timir Manuel, Wilfredo R. Torralba, III Huang, Xieyining Guevara, Rachel Lutz TaskUs Inc 1650 Independence Dr New Braunfels TX 78132 USA

ISBN: (纸本)9789819745807;9789819745814

Existing Trust & Safety policies predominantly focus on protecting content moderators' (CoMos) safety against severe content violations. Nevertheless, in reality, CoMos might encounter content across a broad severity spectrum while perceptions of content severity among CoMos are yet to be fully understood in relation to their well-being and job accuracy. The current study employs a sequential study design to qualitatively examine the views of 34 CoMos in the USA and the Philippines and of 166 professionals (i.e., mental health and learning experience staff, team leads) supporting CoMos on content severity and its perceived impact on CoMos well-being and job accuracy. Of note, consistent with ethical guidelines and wellness best practices, all CoMos received tiered wellness support services from TaskUs Inc. Collected data were thematically analyzed and two overarching themes emerged. Within the first theme, namely, 'perceived content categories', both CoMos and professionals, after considering the potential distress caused by various contents, broadly distinguished between 'moderate' and 'graphic' content, while they further attributed their own definitions to each of the two categories. Under theme two-'content impact on perceived stress- most study participants discussed how reviewing moderate content might have a more detrimental impact upon CoMos stress and job accuracy compared to reviewing graphic content in the lack of wellness support and care. This study is the first of its kind to give voice to both CoMos and support staff to discuss in depth their views on content moderation while also making participant-led recommendations for work policy and future research.

关键词： Content moderation Content severity Thematic analysis Content categories Trust and safety policies

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Incorporating Machine Learning Methods for Predictive Maintenance and Fuzzy Inventory Optimization

Incorporating Machine Learning Methods for Predictive Mainte...

引用

International Conference on Intelligent and Fuzzy systems (INFUS)

作者： Shobana, S. Wavare, Mahesh Sahebrao Kalaiarasi, K. Bhaskar, T. Anand, M. Clement Joe Sindhuja, N. Sri GVG Visalakshi Coll Women Dept Comp Sci Udumalpet 642128 Tamil Nadu India Rajarshi Shahu Mahavidyalaya Latur Autonomous Dept Math Chandranagar 413512 Latur India Bharathidasan Univ Cauvery Coll Women Autonomous Pg & Res Dept Math Tiruchirappalli 620018 Tamil Nadu India KSR Coll Engn Dept Math Tiruchengodu 637215 Tamil Nadu India Mt Carmel Coll Autonomous Dept Math Bengaluru 560052 Karnataka India

ISBN: (纸本)9783031671944;9783031671951

The symbiotic relationship between predictive maintenance and inventory optimization is the focus of this paper, emphasizing the integration of machine learning techniques into both domains. By examining historical data, real-time sensor data, and advanced analytics, predictive maintenance predicts equipment failures, enabling proactive measures to minimize downtime and optimize resource allocation. Similarly, inventory optimization employs machine learning algorithms to analyze demand trends, anticipate market fluctuations, and adjust inventory levels in real time, ensuring customer satisfaction while minimizing expenses. Real-world examples from the manufacturing, retail, and transportation sectors demonstrate the effectiveness of these approaches. Through the combination of predictive maintenance and inventory optimization with machine learning, organizations can achieve notable enhancements in operational efficiency, cost savings, and customer contentment, thereby gaining a competitive advantage in the ever-evolving business environment. Valuable insights, address challenges, and recommendations for researchers, practitioners, and decision-makers are provided to harness the transformative potential of machine learning in operations management.

关键词： Predictive maintenance inventory optimization machine learning operations management equipment reliability real-time analytics cost reduction efficiency enhancement

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Impact of Learning Data Statistics on the Performance of a Recommendation System Based on MovieLens Data 1

引用

19th International Conference on Dependability of Computer systems (DepCoS-RELCOMEX)

作者： Kuzelewska, Urszula Falkowski, Michal Bialystok Tech Univ Wiejska 45a PL-15351 Bialystok Poland

ISBN: (数字)9783031618574

ISBN: (纸本)9783031618567;9783031618574

Recommendation systems are an efficient way to improve user satisfaction on the internet and personalise web services. They use various algorithms to analyse large amounts of data and offer customers the most useful products. The accuracy of the offered propositions is the most important feature of all recommendation systems, which is closely related to the principle of the methods. However, the input data also influences their performance. The objective of this study is to determine whether there is a correlation between data statistics, such as density, shape and skewness, and the performance of the algorithm as measured by RMSE. Additionally, we examined the coverage of both items and users with respect to the input data. The verification was performed using classical item and user-based methods with cosine similarity.

关键词： Recommender systems Evaluation of recommender systems Data statistics

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A Novel Approach for Agricultural Crop Classification with Incremental Learning 5th

A Novel Approach for Agricultural Crop Classification with I...

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5th International Conference on Communication and Intelligent systems (ICCIS)

作者： Saini, Jatinderkumar R. Vaidya, Shraddha Dhulekar, Isha Symbiosis Int Deemed Univ Symbiosis Inst Comp Studies & Res Pune Maharashtra India Heavy Handa Ind Co Ltd Business Dev Off Engn Dept Handa Japan

ISBN: (纸本)9789819720811;9789819720828

Agriculture is a vital element that contributes significantly to feeding the world's growing population. Agricultural crop classification based on various parameters can assist farmers in forecasting which crop to yield in a given season. The proposed machine learning models in the literature make the classification with static data and do not handle changes in data occurring at a later time. To overcome this limitation, the authors in this research have developed an innovative approach with an incremental learning-based model which has the ability to remember the previously learned knowledge while handling newly arriving data. Authors have considered more than dozens of crop categories for the classification of crops with five machine learning classifiers and four ensemble learning classifiers to develop models with incremental learning. In addition, to prove the robustness of the proposed model, the authors have compared the newly developed models' performance with state-of-the-art methods. The proposed approach showed the best performance with the Logistic Regression classifier.

关键词： Agriculture Crop Incremental learning Classification Machine learning

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Unsolved Problems in the Field of Procedural Shaders and Procedural Terrain 8th

Unsolved Problems in the Field of Procedural Shaders and Pro...

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8th Smart Trends in Computing and Communications (SmartCom)

作者： Choure, Malhar Jain, Harsh Surve, Chinmay Munot, Mousami, V Jaiswal, Rupesh SCTRs Pune Inst Comp Technol Pune Maharashtra India

ISBN: (纸本)9789819713226;9789819713233

In lieu of the increasing amount of work being done in the field of computer graphics, game development and procedural generation, this paper aims to provide a bird sight view of the evolution in the field of shader research. There are several topics that demand further research and analysis such as performant replacements for water ecosystems by computing noise, advanced techniques that are supported by the increase in hardware capabilities and the lack of a structured methodology to create procedurally generated textures. This paper will catalogue and review the problems that require further research throughout the various research papers written, as well as discuss the open challenges that remain in the field.

关键词： Shader Procedurally generated

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