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Efficient Task Allocation with Mentorship Mapping in Crowd Sourcing Platforms 7th

Efficient Task Allocation with Mentorship Mapping in Crowd S...

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7th IFIP TC 12 international conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Gelam, Sindhu Sahithi Manikanta thota, Sai Naga Voleti, Sai Anushree Nair, Lekshmi S. Teja Tummala, Durga Manikanta Naga Surya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri Clappana India

ISBN: (纸本)9783031699856

Crowdsourcing platforms have emerged as a powerful tool for harnessing the collective intelligence and expertise of a diverse pool of workers. However, efficiently allocating tasks to the most suitable workers remains a significant challenge due to the heterogeneity of workers, the complexity of tasks, and the dynamic nature of the crowdsourcing environment. this study addresses the task allocation problem on crowdsourcing platforms by proposing a novel mentorship mapping approach. the proposed approach categorizes workers into experienced and inexperienced workers based on their experience, successful completion percentage, and approved on-time rate. Mentorship mapping leverages the concept of mentorship, where experienced workers guide and assist less experienced workers, to enhance task completion rates and overall productivity. the proposed approach involves identifying and pairing experienced workers with less experienced workers based on their skill sets, experience levels, and task requirements. this mentorship-driven task allocation strategy aims to optimize worker-task matching, leading to efficient workforce utilization and improved task completion outcomes. the benefits of mentorship mapping include faster task completion times, reduced errors, and enhanced worker satisfaction, contributing to a more efficient and productive crowdsourcing ecosystem. © IFIP international Federation for Information Processing 2024.

关键词： Mapping

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advances in Home Care and Real-Time Vital Signs Monitoring 25th

Advances in Home Care and Real-Time Vital Signs Monitoring

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25th international conference on Intelligent Data Engineering and Automated Learning

作者： Lucca, Giancarlo Dalmazo, Bruno L. Bertaco, Debora Feijo, Jeferson P. de Topin, Luiz Oscar Homann de Oliveira, Icius M. Ribeiro, Luciano M. Univ Catol Pelotas Ctr Ciencias Sociais & Tecnol Mestrado Engenharia Eletron Compu Pelotas RS Brazil Univ Fed Rio Grande ITec FURG Sistemas Robot Automacao Rio Grande Brazil Univ Fed Rio Grande Ctr Ciencias Computac Programa Pos Grad Comput Rio Grande Brazil

ISBN: (纸本)9783031777370;9783031777387

With the advancement of technology, one of the areas that has shown significant progress is healthcare. In this sense, home care has gained considerable prominence nowadays. One of the key issues is the real-time monitoring of vital signs for patients outside the hospital environment. Taking this matter into account, this study provides a general literature review to enhance our understanding of the field. It has been demonstrated that this area exhibits a high publication rate and impact, with intriguing and significant research contributions.

关键词： Healthcare real-time monitoring vital signs

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Logical Path and Implementation Effect of Ideological and Ethical Curriculum with Different Courses with the Same Value Mode 7

Logical Path and Implementation Effect of Ideological and Et...

引用

7th international conference of Artificial Intelligence, Medical Engineering, Education, AIMEE 2023

作者： Lv, Kaibo Yue, Wei Yang, Wenting Chen, Zhuo School of Environmental and Biological Engineering Wuhan Technology and Business University Wuhan430065 China

ISBN: (纸本)9781643684901

there are both differences and commonalities between the construction logic of ideological and ethical courses in colleges and universities and the mode of 'different courses with the same education' in implementing the reform. 'Different courses, Same education' is a lesson preparation activity for the same course and subject through the 'four in one' links of expert guidance, teaching demonstration, free seminar and expert Q&A. the four Characteristics of Marxist Community thought, the implementation of the fundamental task of moral education and the superiority of other models of lesson preparation are the theoretical logic, practical logic and comparative logic of this model. Respect each other's similarities and differences, seek common ground while reserving differences, pay attention to the unification of value orientation, the unification of methodology education, the unification of independence and openness between courses, and effectively form a collaborative education mechanism. © 2024 the Authors.

关键词： computer circuits

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Supply Chain Management Using Optimization and Machine Learning Techniques 7th

Supply Chain Management Using Optimization and Machine Learn...

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7th IFIP TC 12 international conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Pandey, Honey Neelima, N. Nagaraja, K.V. Department of Computer Science Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Mathematics Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9783031699856

this study focuses on developing a predictive model for late deliveries in the supply chain, using the Data Co Supply Chain dataset. It involves data cleaning, visualization, and training various machine learning algorithms, selecting the most effective model based on accuracy and recall values. the research explores challenges like transportation delays, production issues, natural disasters, and supplier disruptions. Businesses are adopting proactive supply chain and risk management strategies to enhance resilience. the paper discusses how machine learning techniques can optimize supply chain management, offering insights into mitigating risks associated with late deliveries. By identifying challenges in advance, businesses can improve operational efficiency, minimize financial setbacks, and maintain customer trust. In a dynamic business environment, this research provides valuable solutions for supply chain professionals, integrating big data and machine learning to forecast and address late deliveries. It not only aids in predicting delays but also contributes to proactive decision-making, improving overall supply chain performance amidst uncertainties. © IFIP international Federation for Information Processing 2024

关键词： Adversarial machine learning

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Synthesis of Elementary Cellular Automata for Targeted Cache Applications 1

引用

Proceedings of the 7th international conference on advances in Computing and Data sciences, ICACDS 2023

作者： Sarkar, Sutapa Saha, Mousumi The Neotia University West Bengal Sarisha India National Institute of Technology Durgapur West Bengal Durgapur India

ISBN: (数字)9783031379406

ISBN: (纸本)9783031379390

Stephen Wolfram’s Cellular Automata (CA) is a powerful universal computing tool. It can provide design solution for various cache applications and also other discrete physical systems with the exploitation of single length cycle attractor(s). Analysis and synthesis are needed to employ a CA in various application domain. that requires various characterization tools for one-dimensional 3-neighbourhood null boundary and periodic boundary CA like De Bruijn graph, State transition diagram, Reachability tree etc. this research paper targets to focus on analysis and synthesis of single single-length cycle attractor CA (SACA), two single-length cycle attractor CA (TACA) and multiple single-length cycle attractor CA (MACA). the novelty of the paper is to provide formal analysis of the properties of SACA, TACA and MACA into a objective-specific manner, using state transition diagram (STD). A subset of ECA rules are provided here for the design space exploration of on-chip cache applications. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cellular automata

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Particle swarm optimizer based epithelial layer segmentation in CIElab color space 7

Particle swarm optimizer based epithelial layer segmentation...

引用

7th IEEE international conference on Recent advances and Innovations in Engineering, ICRAIE 2022

作者： Ray, Swarnajit Dhal, Krishna Gopal Kumar Naskar, Prabir Maulana Abul Kalam Azad University of Technology Dept. of Computer Science and Engineering West Bengal Nadia700064 India West Bengal Paschim Medinipur721101 India Government College of Engineering & Textile Technology Dept. of Computer Science and Engineering West Bengal Serampore712201 India

ISBN: (纸本)9781665489102

the study of epithelial tissue is critical for the pathologist to determine its grade and the presence of various diseases. Proper segmentation of the epithelial layer plays a significant role in different disease detection like Oral Sub-mucous Fibrosis (OSF), multi-class grading of Oral Squamous Cell Carcinoma (OSCC), and Epithelial Biopsy Image Classifier, etc. In this study, our main aim is automatic segmentation of epithelial layer from pathology images with is very helpful for computer-Aided Diagnosis (CAD) based system. Here author used particle swarm optimization (PSO) and k-means clustering (KM) methods in CIElab color space for oral epithelial layer segmentation and also used different color spaces and its component for comparative study. Experimental results showed that the CIElab color space gave the best result among all other color spaces. the segmented images are compared with ground truth images and PSO with CIElab color space outperformed all other methods with an accuracy of 98.43%. © 2022 IEEE.

关键词： Image segmentation

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Evaluating the Performance of Turkish Automatic Speech Recognition Using the Generative AI-Based Whisper Model 9

Evaluating the Performance of Turkish Automatic Speech Recog...

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9th international conference on computer science and Engineering, UBMK 2024

作者： Gokcimen, Tunahan Das, Bihter Das, Resul Firat University Technology Faculty Department of Software Engineering Elazig Turkey

ISBN: (纸本)9798350365887

Automatic speech recognition (ASR) for the Turkish language faces significant challenges due to its agglutinative structure and diverse phonetic variations. In this study, we evaluate the performance of OpenAI's Whisper models of varying sizes-tiny, base, small, and medium-on Turkish ASR tasks. the experiments were designed to measure each model's training loss, validation loss, word error rate (WER), and training time. Our findings indicate that larger models, specifically the whisper-medium, achieve superior performance with the lowest validation loss and WER, albeit at the cost of longer training times. the results demonstrate a substantial improvement in Turkish ASR capabilities compared to existing models, filling a significant gap in the literature. this study not only advances the state of ASR for the Turkish language but also provides valuable insights into the trade-offs between model complexity and performance, guiding future research and applications in the field. © 2024 IEEE.

关键词： Speech recognition

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Self-annotated Labelling and Training Data for Traffic Video Object Detection Using Machine Learning Techniques 7th

Self-annotated Labelling and Training Data for Traffic Video...

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7th IFIP TC 12 international conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Rahul Chiranjeevi, V. Swamy, Maanesh M. Krishna Prasath, M.K. Kumar, P. Department of Computer Science and Engineering Rajalakshmi Engineering College Thandalam Chennai India

ISBN: (纸本)9783031699818

Classical machine learning algorithms are susceptible to objective elements like video quality and the weather, which results in inferior detection results in an erroneous identification of Unmanned Aerial Vehicle (UAV) action photos. Deep learning is suggested for identifying cars in UAV video. thanks to the technology offered by road management systems, real-time visual information is now accessible in thousands of locations on road networks. the first step in identifying or preventing accidents is locating the vehicles on the path of travel. Convolutional neural networks have made substantial advancements in the field of object detection. Enhancing conventional computer vision techniques. However, there are drawbacks because the pre-trained models that are currently available only provide a low detection rate, especially for small objects. the major drawback is that, in order to retrain the automobile maker, they must manually identify the vehicles that appear in the photographs captured by each IP camera on the road infrastructure. this task will be impossible to complete even if we install hundreds of cameras throughout the extensive road network. the technique utilized in this study uses CCTV footage with a comparable range of images with the aim of identifying cars in an image. the spatial brightness of the original sample is first translated using the HSV (Hue, Saturation, Value) method to maximize sample diversity and adaptability to different lighting conditions. the performance of the basic SSD is enhanced by incorporating focus loss because of feature extraction. Following a trained neural network model’s examination of the UAV footage, the effectiveness of drone identification is assessed. the results show that the proposed method have a car detection rate of 96.49%. © IFIP international Federation for Information Processing 2024.

关键词： Deep learning

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Rumor detection and combat through Machine Learning 7

Rumor detection and combat through Machine Learning

引用

7th international conference on Image Information Processing, ICIIP 2023

作者： Agarwal, Ruchi Pradhan, Rahul Gla University Department of Computer Science And Engineering U.P. Mathura India

ISBN: (纸本)9798350371406

this research paper gives a complete overview of the current state of studies on fake news detection, together with the strategies and techniques which have been advanced, the effects performed to this point, and the challenges that continue to be. We survey the present literature on fake information detection, masking quite a number methods, together with truth-checking, natural language processing, machine mastering, and crowd-sourcing. We also speak the strengths and limitations of these techniques, as well as the possibilities for destiny research on this area. Our overview exhibits that at the same time as a few strategies for fake news detection have proven promise, there may be nonetheless a lot work to be done to develop effective and reliable techniques for detecting fake news at scale. One of the main demanding situations dealing with researchers on this field is the evolving nature of fake news, which could exchange swiftly in reaction to new trends and advances in era. © 2023 IEEE.

关键词： Machine learning

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Revolutionizing Healthcare by Accessing the Opportunities for Virtual and Augmented Reality 7

Revolutionizing Healthcare by Accessing the Opportunities fo...

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7th international conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Mehta, Kapil Aayushi Singh, Chamkaur Chugh, Himani Kumar, Manibhushan Chandigarh Group of Colleges Department of Computer Science and Engineering Mohali Punjab Landran India Chandigarh Group of Colleges Department of Computer Applications Mohali Punjab Landran India

ISBN: (纸本)9798350397253

In recent years, the use of new technologies in the healthcare industry has substantially increased. In particular, this research study explores the potential benefits of virtual reality (VR) and augmented reality (AR). With the use of these technologies, difficult judgments can be made by using the information and specialized expertise. Given that maintaining the health is among the most fundamental human aspirations, the healthcare industry affects every one of us. this paper discusses how advances in artificial intelligence, ARvR and blockchain technology will spur innovation and transform the healthcare sector. this research study witnesses the unparalleled growth in tailored, intelligent healthcare that is accessible everywhere at a reasonable cost when these technologies merge. the fusion of mobile healthcare and support, AR/VR for medical training, and customized pharmaceuticals based on health data from sensors implanted in the human body can give therapies that are specifically tailored to a person's genetic makeup. the acceptance and integration of VR in the healthcare industry are also discussed in this paper. It examines the development, the benefits, and the drawbacks, and offers viewpoints on the potential expansion and effects of these technologies in the future. the use of AR and VR in healthcare services may lead to problems with huge data availability and trustworthy sharing. Some systems that use software-type algorithms to simulate human cognitive processes in healthcare decision-making handle the privacy of a patient's data. the dentist and the support staff oversee managing data, which carries a risk of data privacy and security violations. this study examines how the use of VR and AR is allowing decentralized care, personalized treatment, and a new generation of intelligent detection and prevention. © 2023 IEEE.

关键词： Blockchain

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