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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

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application of Single Minute Exchange of Die to Reduce Changeover Time in a Winding machine of a Capacitor Line 3rd

Application of Single Minute Exchange of Die to Reduce Chang...

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3rd Biennial international conference on Future learning Aspects of Mechanical Engineering, FLAME 2022

作者： Shilpa, M. Shivakumar, M.R. Hamritha, S. Phanden, Rakesh Kumar Gupta, Amrit Pushpak, Abhishek Department of Industrial Engineering and Management Ramaiah Institute of Technology Bangalore India Department of Mechanical Engineering Amity School of Engineering & Technology Amity University Uttar Pradesh Noida India Pushpak Products India Pvt. Ltd Bangalore India

ISBN: (纸本)9789819913275

Setup time or changeover time in a machine is the time required to change the setup required to run the next batch of production. The changeover time can range anywhere between few minutes to few hours and due to this changeover, the total time required for production reduces in a shift. This necessitates that the setup or changeover time has to be significantly reduced. Single Method Exchange of Die (SMED) concept is widely used in such situations where drastic reduction in changeover time is expected. This work proposes the application of SMED to reduce the changeover time in a winding machine of the capacitor line. Two machines—Low Voltage and High Voltage machines are considered for the study. The setup activities that take place over these machines are observed and classified as internal and external activities depending on whether the activity can be carried out even during the operation of the machine, or it could be separately carried out and made ready for immediate changeover. Presently, the changeover time for Low Voltage machines is 24.82 min and that of High Voltage machine is 12.67 min. With the application of SMED, the changeover time is reduced to 17.49 min on Low Voltage machine and 8.35 min on the high voltage machine. This paper also indicates the reduction in downtime costs on these machines that could be realized with the application of SMED concept. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Winding

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A Survey of Vehicle Trajectory Prediction Based on Deep learning Models 3rd

A Survey of Vehicle Trajectory Prediction Based on Deep Lear...

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3rd international conference on Sustainable Expert Systems, ICSES 2022

作者： Manish Dohare, Upasana Kumar, Sushil School of Computer & Systems Sciences Jawaharlal Nehru University New Delhi India School of Computing Science & Engineering Galgotias University Uttar Pradesh Greater Noida India

ISBN: (纸本)9789811978739

In the field of smart urban traffic networks, Vehicle Trajectory Prediction or VTP is a crucial research field in the area of driving assistance and autonomous vehicles. A vehicle can be autonomous or manual;trajectory prediction involves future location and turns (Left or Right). Unlike the pedestrian trajectory prediction problem, Vehicle Trajectory Prediction is more complex. The trajectory prediction problem involves the human decision-making process and many factors affecting the decision for predicting the trajectory of the vehicle. Therefore, the nature of the problem is non-linear and comes under classification or regression problems. Recent developments in the field of artificial intelligence models of machine learning and deep learning, have made researchers provide promising solutions to the problem in different traffic situations. In this paper, firstly, we are providing the taxonomy of popular existing machine learning and deep learning models that have been used to solve the problem of vehicle trajectory prediction so far. Further, a discussion of some existing deep learning models follows. Secondly, we are listing some public datasets for the study of vehicle trajectory prediction and different performance metrics used to measure the performance of the models by researchers. Finally, a discussion on the limitations of the deep learning models is presented. After reading this paper, one can start their initial research in the area of vehicle trajectory prediction. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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The Most Efficient and Accurate Face Mask Detection in Crowded Area using machine learning Algorithm 3

The Most Efficient and Accurate Face Mask Detection in Crowd...

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Josphineleela, R. Vekariya, Vipul Bharathidasan, B. Alfurhood, Badria Sulaiman Kumar, V. Pradeep Tripathi, Vikas Panimalar Engineering College India Parul Institute of Engineering and Technology Parul University Department of CSE Gujarat Vadodara India Sree Rama Engineering College ECE Tirupati India Princess Nourah Bint Abdulrahman University College of Computer Information Sciences Department of Computer Sciences Saudi Arabia B v Raju Institute of Technology CSE Telangana Narsapur India Graphic Era Deemed to Be University Department of CSE Uttarakhand Dehradun India

ISBN: (纸本)9798350399264

Since 2019, the COVID-19 epidemic has been rapidly spreading throughout the world. The complexity and volubility of human existence have increased as a result of the epidemic. As a consequence of this infection, many individuals have perished. One of the elements promoting the spread of the Covid-19 virus is the absence of antiviral medications. This illness is spreading swiftly and readily because of a number of universal human flaws, including the way sick people breathe, cough, and sneeze. The primary symptom is the typical flu. Therefore, the greatest defence against this sickness in the current situation is a face mask that totally covers the mouth and the nose. It still causes damage to several people and organisations in 2022. While the world heals from the virus and prepares to resume regular operations, everyone feels tension, particularly those who will continue to communicate in person. According to studies, having insurance not only provides a means of confidence but also greatly reduces the probability of an irresistible expert action. The solution to solving this issue and minimising this downside lies in this effort and innovation. It provides a broad, a system built around deep learning for spotting situations were face masks were not worn as planned. We are shielded as from symptoms of many ailments by the mask. Given the epidemic concern, gloves now are widely accepted to be necessary. The Covid virus spreads to new persons because there is a dearth of understanding about face masks as a result of the quick decline in Covid cases. Despite the fact that fewer occurrences occurred as a result of the efforts of many, people began to disregard the face masks. To combat the Corona disease, various simple tools will be required. One of the most basic is the face mask. Although nobody was required to cover their face at first, experts and medical professionals now recommend that everyone do *** may now be used by the general public for a number of pur

关键词： Viruses

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Examining Social Media Posts for Identification of Anxiety and Depression Utilizing machine learning Techniques 3

Examining Social Media Posts for Identification of Anxiety a...

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3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Satpute, Babasaheb S. Rahane, Wasudeo P. Bharati, Rajesh Dept. of Computer Science and Engg. Bangalore India STES's Nbn Sinhgad School of Engg. Dept. of Information Technology Pune India Dr. D.Y. Patil Institute of Technology Dept. of Computer Engg. Pune India

ISBN: (纸本)9798350342338

The prevalence of mental diseases like depression and anxiety among students has become a growing concern in recent years. With the widespread use of social media platforms, individuals often express their emotions, thoughts, and experiences through posts and interactions. This paper investigates the feasibility of classifying students' anxiety and depression based on the content of their social media posts. Recently, some research communities in various domains have shown enthusiasm for investigating the potential of leveraging social media platforms for extracting valuable insights for decision-making and creative knowledge. Social media has been utilized to aid software development activities, analyze cryptocurrency trends, identify network communities, and make recommendations, among others. Apart from other fields within the eHealth domain, the application of social media platforms and the sophisticated analysis of extensive datasets to anticipate mental health disorders like depression, schizophrenia, eating disorders, anxiety, and addictive tendencies has garnered substantial attention and interest. However, one of the challenges lies in either having enough historical data or regular monitoring of a patient's activities to identify a person's mental health condition using traditional methods. To tackle this concern, we introduce an approach aimed at categorizing individuals with past mental health conditions through the utilization of information sourced from a widely-used social media platform such as Facebook. © 2023 IEEE.

关键词： artificial intelligence depression and anxiety machine learning mental health Social media

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Perceiving Correlation Among Spatiotemporal Gait Parameters and Verifying Its Relation Using machine learning Classification Technique Pilot Study for Indian Population 3rd

Perceiving Correlation Among Spatiotemporal Gait Parameters ...

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3rd international conference on Recent Trends in machine learning, IoT, Smart Cities and applications, ICMISC 2022

作者： Sathe, Neha Hiwale, Anil Ranade, Archana MIT World Peace University Pune India Deenanath Mangeshkar Hospital and Research Centre Pune India

ISBN: (纸本)9789811960871

World is surrounded with key buzz emerging from technological use by common man. Technology lends a hand to achieve the fitness by providing assistance. Walking is predominantly and usually preferred exercise by mass. Walking pattern analysis or gait analysis expands the probable zone for researcher to contribute in gaining and maintaining fitness for common people. There are number of parameters influencing walking pattern of the person. The motive of this study is to verify the relation among various spatiotemporal parameters used in gait analysis. Paper tests and comments on correlation among selected spatiotemporal parameters on the basis of analysis performed on 50 healthy participants from Pune, India. Contributing participants are in the age group of 20–75 years combining male and female members. GAITRite Walkway is used to perform recording of the gait pattern. Pearson’s correlation coefficient is used for the testing the correlation within two selected parameters. The correlation coefficient varies within range of −1 to +1, indicating positive, negative, or no correlation. Correlation results are verified through application of them in classification of subjects in two age groups. Support vector machine, logistic regression, and K-nearest neighbor machine learning approaches are tested. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Gait analysis

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A machine-learning Based Approach for Detecting Phishing URLs 3

A Machine-Learning Based Approach for Detecting Phishing URL...

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3rd international conference on Intelligent Data Science Technologies and applications, IDSTA 2022

作者： Atari, Mahmoud Al-Mousa, Amjed Princess Sumayya University for Technology Computer Engineering Department Amman Jordan

ISBN: (数字)9781665499606

ISBN: (纸本)9781665499606

This research's focus is to utilize different machine learning classification models to predict whether a given URL is a legitimate or a phishing URL. A legitimate URL directs users to a benign authentic webpage and typically serves the user's request. In contrast, a phishing URL directs users to a fraudulent website, usually impersonating another entity, luring visitors to believe otherwise, and eventually allowing the attacker to perform limitless post-exploitation attacks. Given the little-to-no internet safety awareness of average individuals, this paper aims to take an adaptive approach to detect phishing URLs on the client-side, which can significantly protect users from falling victims to cyber-attacks such as stealing important personal credentials. The proposed approach is to build a machine-learning powered tool that can help individuals stay safe and assist security researchers in identifying patterns and relations that correlate to these attacks, which will help maintain high-security standards for everyday internet users. Finally, the proposed model yielded a 97% detection accuracy using the XGBoost classifier and the random forest classifier. © 2022 IEEE.

关键词： Websites

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An Approach to the Presumptive Detection of Road Incidents in Cuenca, Ecuador Using the Data from the Social Media Twitter and Spanish Natural Language Processing 3rd

An Approach to the Presumptive Detection of Road Incident...

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3rd international conference on Smart Technologies, Systems and applications, SmartTech-IC 2022

作者： Loja-Morocho, Pablo E. Reyes-Duchitanga, Robbyn T. León-Paredes, Gabriel A. Grupo de Investigación en Cloud Computing Smart Cities & High Performance Computing Universidad Politécnica Salesiana Cuenca Ecuador

ISBN: (纸本)9783031322129

The road situation in Ecuador, specifically in the city of Cuenca and its surroundings, can vary according to various aspects related mainly to the weather, or traffic incidents caused due to reckless actions by drivers. Therefore, many times these problems can cause some drivers to be forced to delay their trip or look for alternative routes to reach their destination, others can plan their departure according to the related news they find when browsing social media, which can be somewhat time-consuming and even unlikely to find. For this reason, in this paper, a mobile application is presented whose primary function is to collect publications from social media Twitter from different Twitter accounts that are related to road incidents and to classify them using Natural Language Processing (NLP) and machine learning (ML) models such as BERT. Then, we present an approximate location of the presumptive incident on an interactive map which allows each of these tweets to be displayed with a personalized icon that can indicate different types of road incidents and, at the same time, the details of the event that occurred. Two main experiments were carried out to find (a) the model accuracy, where a value of 80% was obtained as a result, which is considered a positive result for road incident classification, and (b) the execution of acceptance tests, where a questionnaire based on the ISO 9126 standard was presented to a group of bikers belonging to the city of Cuenca, obtaining as a result that for most users the application is new (85.8%), efficient (85.7%) and easy to use (64.3%). This research makes it possible to present news of road incidents in a centralized manner, which makes it easier for drivers to stay informed and thus avoid annoying interruptions in their circulation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： machine learning

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SuperView-1 satellite image-based winter wheat spatial distribution information refined extraction using the fusion of machine learning and deep learning 3

SuperView-1 satellite image-based winter wheat spatial distr...

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3rd international conference on Remote Sensing, Surveying, and Mapping, RSSM 2024

作者： Zhen, Haocheng Li, Ting Ji, Min He, Yawen College of Geodesy and Geomatics Shandong University of Science and Technology Qingdao0532-86057276 China College of Oceanography and Space Informatics China University of Petroleum Qingdao0532-86980518 China

ISBN: (数字)9781510680081

ISBN: (纸本)9781510680074

Wheat is one of the most important cereal crops in the world. Efficiently and accurately extracting spatial distribution information of winter wheat from high-resolution remote sensing images has become crucial research and application fields in agricultural monitoring. However, the complex texture features and spatial heterogeneity in high-resolution imagery often require deep learning models to be trained with a substantial number of samples to achieve the desired outcomes. In response to the challenges posed by insufficient sample volumes, which impair the accuracy of recognition in the field of extracting spatial distribution information of winter wheat, this paper introduces a new methodology that synergizes deep learning neural networks with machine learning technologies to address these issues. The new process first applied a Grey-Level Co-occurrence Matrix (GLCM) to enhance the image texture. It then utilized a random forest (RF) model to generate a confidence map of winter wheat distribution. Next, the method recommended a supervised correction to this map. It integrated the corrected map into an improved Segformer model as a priori knowledge to increase the weight of the high-confidence regions to achieve high extraction accuracy and robustness. This study selected Shouguang City, Shandong Province, as the case study and the SuperView-1 satellite data as the input image. This study compared the proposed GLCM-RF-Segformer model with ResUnet, DeepLabV3Plus, Hrnet, FCN8S, and RF to validate the experiment's effectiveness. The experimental results have shown that the new model has achieved average metrics of 91.387% for Precision, 94.467% for Recall, 94.180% for Overall Accuracy (OA), 87.053% for mean Intersection over Union (mIoU), 85.087% for Kappa coefficient, and 92.630% for F1-Score. These accuracy statistics surpass those of the five other comparative models, highlighting the model's high accuracy and robustness in extracting the spatial distribution of

关键词： Remote sensing

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An Enhanced Approach to Predict Re-occurrences of Breast Cancer Using machine learning 1

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3rd international conference on Communication, Computing and Electronics Systems, ICCCES 2021

作者： Sheshikala, M. Ramesh, Dadi Mohmmad, Sallauddin Pasha, Syed Nawaz Head of School of Computer Science and Artificial Intelligence SR University Telangana Warangal India School of Computer Science and Artificial Intelligence SR University Telangana Warangal India

ISBN: (数字)9789811688621

ISBN: (纸本)9789811688614

The most affected cancer among women is breast cancer (BC). After skin cancer, the most affected cancer in the world is BC. The term re-occurrence is used for BC, which affects after successful treatment. The prediction of re-occurrence BC can reduce the chances of death. In this paper, we proposed a machine learning approach and neural network model to calculate recurrence chances of BC. We used the Wisconsin dataset of the UCI machine learning repository in our approach, implemented an artificial neural network (ANN), and compared the results with the support vector machine. Our model outperformed when compared to other models with an accuracy of 95% with support vector machine. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Transformer-Based Multilingual G2P Converter for E-learning System 3rd

Transformer-Based Multilingual G2P Converter for E-Learning ...

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3rd international conference on Artificial Intelligence in HCI (AI-HCI) Held as Part of the 24th international conference on Human-computer Interaction (HCII)

作者： Liu, Jueting Ren, Chang Luan, Yaoxuan Li, Sicheng Xie, Tianshi Seals, Cheryl Atkins, Marisha Speights Auburn Univ Auburn AL 36849 USA Northwestern Univ Evanston IL 60208 USA

ISBN: (纸本)9783031056437;9783031056420

Phonetic transcription is an approach to represent speech sounds to specific symbols. The most common alphabet we used is the international Phonetic Alphabet (IPA), and the characters in the IPA are phonetic symbols. To support the phonetic transcription process in the phonetic exams of our linguistic E-learning system, we designed a machine translation tool that aims to translate English words to their phonetic formats. This progress can also be expressed as grapheme to phoneme (G2P). The Transformer model has been utilized to develop this G2P module. Also, to improve the functionality of the E-learning system, we trained multiple language models and generated a multilingual G2P translator. Moreover, we evaluated our G2P system by word error rate (WER) and phoneme error rate (PER) with edit distance.

关键词： E-learning international phonetic alphabet Grapheme-to-phoneme Transformer

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