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Design and Implementation of English Listening teaching System Based on Virtual Environment Technology 3rd

Design and Implementation of English Listening Teaching Syst...

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3rd EAI international conference on Application of Big Data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Xue, Zhou Yu, Yawei Yinchuan University of Science and Technology Yinchuan750021 China Yunnan Normal University Yunnan Kunming650504 China

ISBN: (纸本)9783031631290

English listening teaching is an important part of English teaching. English listening should be the first skill for English learners to master. But for Chinese students, English listening has become the most difficult barrier on their way to English learning. The design and implementation of English listening teaching system based on virtual environment technology is a unique product to help students improve their listening skills. It provides them with the best learning experience. The main purpose of this product is to improve students’ listening skills. Students will be able to use this device for at least 30 min a day for 3 months or more to learn from it. The equipment can help students improve their pronunciation, fluency and understanding through the effective use of pictures, videos, animation and other audio-visual tools. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Students

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A Review of Various Strategies used for Adaptation in Adaptive Elearning Systems 14

A Review of Various Strategies used for Adaptation in Adapti...

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14th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2023

作者： Vinchurkar, Rupali A. Bamnote, Gajendra R. Prof. Ram Meghe Institute of Technology and Research Badnera Amravati India

Adaptive elearning systems, Intelligent tutoring have been an interesting area of research and experimentation since Artificial intelligence has taken a strong foothold, but the relevance of adaptive elearning systems could not have been realised more, than after the Covid 19 pandemic. The pandemic forced us into finding and implementing better ways of teaching learning. Ways which wouldn’t be affected by such calamities. Adaptive elearning has been experimented with since long but the complexity of the models has dissuaded many from actually implementing such systems. This paper aims to review the various strategies in literature that have proposed conceptual frameworks or implemented and tested actual models for Adaptive elearning systems. It also aims to identify the technical complexities involved in making a learning system adaptive and tries to propose simpler conceptual solutions. © Grenze Scientific Society, 2023.

关键词： learning systems

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Machine learning Models for Improved Cell Screening 20th

Machine Learning Models for Improved Cell Screening

引用

20th international conference on Intelligent computing (ICIC)

作者： Liu, Jia-Song Yi, Zhi-Heng Huang, Bo Wu, Fan Zhang, Zu-Ping Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China

ISBN: (纸本)9789819756889;9789819756896

In the cell engineering pharmaceutical industry, the quality of the cell screening process directly affects the quality of monoclonal cell lines, which in turn impacts the cost and efficiency of the entire production process. Current screening methods largely rely on time-consuming and costly manual operations, not only increasing the risk of subjective bias but also reducing the efficiency and quality of screening. To address this challenge, this paper proposes a method based on binary classification prediction, introducing two machine learning models: the Stacked Machine learning Model (SMLM) and the Simple Linear Model (SLM). SMLM makes initial predictions using four different base learning models, which are then integrated through a shallow artificial neural network to produce a final prediction. In contrast, SLM employs a deeper artificial neural network for direct prediction. We analyzed these two models from the perspectives of generalizability and interpretability and conducted a quantitative evaluation using accuracy as the primary metric. The results show that while SMLM offers stronger interpretability, SLM performs better on multiple evaluation metrics and has a simpler structure. This research not only provides a new direction for the automation of cell screening but also opens up new pathways for the development of the cell engineering pharmaceutical industry.

关键词： Cell Screening Model Stacking Artificial Neural Network Machine learning

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Hand Gesture Vision: Integrating Computer Vision and Machine learning for Enhanced Communication 1

Hand Gesture Vision: Integrating Computer Vision and Machine...

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1st IEEE international conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Kukreja, Sonia Singh, Ankit Chauhan, Vaishnavi Galgotias University School of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350356816

This research introduces a different real-time static sign language recognition system we named as Gesture Vision, which aims to combine computer vision and effective machine learning to address the communication difficulties experienced by individuals with hearing impairments. Leveraging MediaPipe and OpenCV libraries, the system captures and analyzes live hand gestures, employing a Random Forest Classifier for precise recognition. The paper meticulously explores the nuances of dataset collection, the intricacies of feature extraction, and the intricacies of model training, providing a comprehensive understanding of the system's inner workings. The study places a strong emphasis on performance evaluation, employing robust metrics to validate the system's accuracy and effectiveness indicating a promising level of proficiency in recognizing diverse static sign language gestures in real-time scenarios. © 2024 IEEE.

关键词： Adversarial machine learning

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An Enhanced Ensemble Framework for Disaster Tweet Categorization 1

An Enhanced Ensemble Framework for Disaster Tweet Categoriza...

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1st IEEE international conference on Electronics, computing, Communication and Control Technology, ICECCC 2024

作者： Shanthi, N. Suganthe, R.C. Aravindhraj, N. Ganasri, S. Anish, R.M. Agalya, E.P. Kongu Engineering College Department of Computer Science and Engineering Perundurai Tamil Nadu Erode India

ISBN: (纸本)9798350371802

In the contemporary landscape, Twitter has evolved into an omnipresent platform, serving diverse functions ranging from expressing movie reviews to swiftly disseminating breaking news, notably in the crucial domain of disaster reporting. The ubiquity of smartphones empowers individuals to share immediate and firsthand disaster observations, while institutions like Disaster Relief Organizations and News Agencies strategically deploy Twitter for systematic and automated monitoring. This proactive strategy enables rapid global disaster identification and response, facilitating well-informed decision-making. Twitter stands as an indispensable tool in the lives of the general public, acting as a primary medium for both information dissemination and disaster reporting. The inherent challenge lies in distinguishing between informative and non-informative disaster announcements amid the vast array of tweets. This challenge is compounded by the sheer volume of information and the need for swift and accurate identification. To confront this challenge, sophisticated methodologies are employed, leveraging advanced technologies such as machine learning, deep learning, and ensemble approaches that integrate both modalities. Tweets are categorized into informative or non-informative classes using machine learning models. Additionally, deep learning models like BERT are employed for their ability to capture intricate contextual nuances in language. Leveraging ensemble learning techniques, which amalgamate the capabilities of various models, significantly improves the effectiveness of disaster monitoring on the Twitter platform. Ensemble learning, through methods like stacking and voting, helps in optimizing performance by considering diverse perspectives and mitigating the limitations of individual models. This comprehensive approach not only enhances public safety but also fortifies global disaster response capabilities, elevating the precision and efficiency of tweet classificati

关键词： learning systems

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ML Based Valve Regulation for Optimal Water Relies Based on Root Zone Soil Moisture

ML Based Valve Regulation for Optimal Water Relies Based on ...

引用

2024 international conference on Advances in Data engineering and Intelligent computing Systems, ADICS 2024

作者： Manikandababu, C.S. Rajesh, R. Miruthyunjsanjay, S. Rohith, P. Sri Ramakrishna Engineering College Department Of Ece Tamil Nadu Coimbatore India

ISBN: (纸本)9798350364828

This study investigates the viability of using Machine learning (ML) to improve valve management and water delivery, focusing on soil hydration in the root zones. The System ensures that the appropriate amount of water is supplied to the crops by continuously adjusting the settings of the irrigation valves in response to measurements from sensors that monitor soil moisture levels. Moreover, aside from further developing water system effectiveness and moderating water assets, this approach can expand horticultural yields by up to 20% and decrease water wastage by up to 30%. Ecological insurance is additionally supported by the innovation's ability to diminish water contamination and the requirement for water removal from regular sources. ML-based valve guideline addresses a planned mechanical progression in accuracy horticulture that can improve water the executives rehearse and advance farming maintainability. © 2024 IEEE.

关键词： Soil moisture

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Markov Model Based Risk Prediction of Patients with Kidney Diseases

Markov Model Based Risk Prediction of Patients with Kidney D...

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2024 OPJU international Technology conference on Smart computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Chaki, Shambhab Rakshit, Pratyusha Chowdhury, Archana Jadavpur University Department of Electronics and Telecommunication Engineering India Christian College of Engineering and Technology Department of Computer Science and Engineering Bhilai India

ISBN: (纸本)9798350373783

The paper proposes a novel machine learning approach for early prediction of risk of a patient suffering from severe kidney-related diseases (KD). The training phase consists of two steps. First, the records of the already existing patients with KD are divided into groups based on the similarity of the sequences of their clinical activities, including diagnoses, lab tests and medications. Next, a Markov chain model is employed to mathematically represent the clinical activity-sequences of patients in each group. The prediction phase also consists of two steps. First, the support of each group to the clinical-activities of the unknown patient P is evaluated. P is then assigned with the group with the highest support. Next, the Markov chain model of the winning group is used to predict the possible finite sequence of the clinical activities of P in future. Comparative study shows better performance of the proposed algorithm over other state-of-the-art algorithms. © 2024 IEEE.

关键词： Electronic health record

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A UAV Edge computing Task Offloading Method Based on Reinforcement learning and the Underwater Lobster Optimization Algorithm 9

A UAV Edge Computing Task Offloading Method Based on Reinfor...

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9th IEEE international conference on Progress in Informatics and computing, PIC 2024

作者： Jiang, Keqin Jiang, Mingyan Yuan, Dongfeng Zhao, Ze School of Information Science and Engineering Shandong University Qingdao China Qilu Institute of Technology Jinan China

ISBN: (纸本)9798350363203

With the rapid development of UAV edge computing, the randomness of task generation and the unpredictability of UAV mobility have made related problems increasingly complex. This not only poses a highly intricate integer optimization problem but also requires swift and effective decision-making based on real-time monitoring. Traditional offline algorithms face numerous challenges in addressing such issues and often struggle to meet the demands of dynamic environments. In response, this paper proposes an innovative heuristic algorithm-the V nderwater Lobster Optimization Algorithm-combined with reinforcement learning techniques to dynamically learn the optimal data transmission path. Through this approach, we can flexibly adjust the optimization algorithm's update strategy, effectively achieving dynamic management goals and improving the system's overall performance and response speed. The proposed algorithm successfully addresses the task offloading problem in UAV edge computing. Experimental results show that the algorithm significantly reduces system response time and improves task completion rates, fully demonstrating its potential and advantages in the field of AV edge computing. © 2024 IEEE.

关键词： Mobile edge computing

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Arrhythmia Detection and Classification with Hybrid Deep learning with Pity Beetle Optimization Model 2

Arrhythmia Detection and Classification with Hybrid Deep Lea...

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2nd IEEE international conference on Advances in Information Technology, ICAIT 2024

作者： Vinod, D. Karthikayan, P.N. Ojashwini, R.N. Srimathi, S. Saranya, R. Vigneshwaran, P. Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur TN Chennai India Department of Information Technology St Joseph' s Institute of Technology OMR Chennai600119 India Department of CSE RajaRajeswari College of Engineering Ramohalli Cross Kumbalgodu Mysore Road Karnataka Bengaluru560 074 India Department of Bio Technology Saveetha School of Engineering SIMATS Tamilnadu Chennai India Department of CSE RMK College of Engineering and Technology Chennai India Department of EEE K.Ramakrishnan College of Engineering Trichy India

ISBN: (纸本)9798350383867

Arrhythmia classification is a crucial field within the domain of medical diagnostics, utilizing advanced Machine learning (ML) and Deep learning (DL) methods to accurately recognize and categorize different cardiac arrhythmias, and abnormalities in the heart' s rhythm. This technology performs a significant part in cardiac healthcare, as arrhythmias may result in serious health problems, comprising stroke and heart attacks. By analyzing electrocardiogram (ECG) data, arrhythmia classification allows accurate diagnosis and earlier detection of irregular heart rhythms, facilitating timely involvement and tailored treatment approaches. It permits medical specialists to make informed decisions, leading to enhanced patient outcomes and improved cardiac medical management. In this study, we propose an Arrhythmia Detection and Classification with Hybrid Deep learning with the Pity Beetle Optimization Algorithm (ADC-HDLPBO) method. The ADC-HDLPBO technique is targeted at advancing the domain of arrhythmia analysis in cardiac healthcare. The proposed ADC-HDLPBO system encompasses classification through a combination of Convolutional Long Short-Term Memory networks (CLSTM) and Autoencoders (AE), developed to take temporal dependencies and recreate important features. For precision and performance optimization, the model incorporates PBA for hyperparameter tuning, adapting to the unique dataset features. The ADC-HDLPBO algorithm marks a major leap forward in arrhythmia detection and classification, providing a robust and accurate solution for the earliest diagnosis of cardiac abnormalities. © 2024 IEEE.

关键词： Diseases

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Enhancing Disease Prediction through Ensemble learning Techniques 18

Enhancing Disease Prediction through Ensemble Learning Techn...

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18th INDIAcom;11th international conference on computing for Sustainable Global Development, INDIACom 2024

作者： Wani, Suhail Rashid Attri, Shree Harsh Setia, Sonia Sharda University School of Engineering and Technology Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9789380544519

The study demonstrates how technology has had a dramatic influence on healthcare, allowing for the analysis of large clinical datasets using machine learning for early illness identification. Chronic disorders such as cardiovascular disease (CVD), Chronic renal illness, and Chronic Mellitus Diabetes offer significant global health issues, using existing diagnostic procedures that are time-consuming and error-prone. The report emphasizes the rising burden of chronic illnesses, estimating that 73% of deaths will be related to these ailments by 2025. Recognizing the links between illnesses such as CKD and CVD is critical for optimal care, with a particular emphasis on the increased prevalence and health hazards associated with Chronic Diabetes Mellitus (CDM). The fundamental goal of the research is to create an effective prediction model for early illness diagnosis by employing an adaptable machine learning model that can adapt to a variety of datasets. The review of the literature looks at several machine learning algorithms that have been utilized in prior research, emphasizing their potential for automating illness identification. The methods section describes the procedures required in collecting data, preparing it, and modeling it using ensemble learning. Metrics of performance evaluation confirm the Extra tree classifier's advantage and it performs the best among all the four ensemble techniques and improves the accuracy of predicting diseases. Finally, the above investigation highlights the promise of computational learning in solving the complex issues faced by chronic illnesses, emphasizing the crucial need for accurate early disease prediction in the context of a growing global health crisis. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Cat Boost Chronic Diabetes Mellitus Chronic Disease Computational learning Ensemble Model Feature Selection Heart Disease Random Forest XG Boost

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