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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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Deep Transfer learning Approach for Facial and Verbal Disease Detection

Deep Transfer Learning Approach for Facial and Verbal Diseas...

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Advancements in Computing (ICAC), 2019 international conference on

作者： Dilki Madara Manage A. M. Ishan Shavinda Alahakoon Kavindi Weerathunga Thenuka Weeratunga Dilani Lunugalage Hansi De Silva Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9781665408639

Millions of people have been subjected to different kind of acute diseases, some of them are eye diseases, facial skin diseases, tongue diseases and voice abnormalities. Most of eye diseases cause fully or partial blindness. Skin and tongue complications can be signs of cancers. Voice abnormalities can be cured at initial stages. Well-practiced medical practitioners have the ability of diagnose these diseases, but due to the pandemic situations and high consultation costs people do not tend to consult doctors. This research is predominantly focused on development of an application for automatic detection of eye, skin, tongue and verbal diseases using transfer learning (TL) based deep learning (DL) approach. Deep learning is a part of machine learning (ML) which has been used in most computer vision approaches. Transfer learning has been used to rebuild the existing convolutional neural network (CNN) models and used in disease detection. DenseNet121, MobileNetV2, RestNet152V2, models have been used to detect eye, skin and tongue diseases respectively and a new model has been used to detect voice abnormalities. CNN models are capable of automatically extracting features from the given images and voice data. All the trained models have been given accuracy rate of 80%-95%.

关键词： Deep learning Tongue Pandemics Computational modeling Transfer learning Medical services Feature extraction

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AutoML as a Catalyst for Predictive Maintenance Innovation: Strategies and Outcomes

AutoML as a Catalyst for Predictive Maintenance Innovation: ...

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Artificial Intelligence For Internet of Things (AIIoT), international conference on

作者： B Jayasurya M Suguna Pooja Saravanan M Revathi School of Computer Science Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

The intersection of predictive maintenance and Automated machine learning (AutoML) ushers in a transformative era for industrial optimization, wherein the foresight of equipment failures becomes both attainable and actionable. Conventional methodologies for predictive maintenance often necessitate profound technical knowledge and comprehensive data manipulation. This proposed work introduces an innovative framework based on AutoML, employing tools such as AutoKeras, Sweetviz, NumPy, pandas, Streamlit, and PyCaret, to democratize the crafting of predictive models. The outlined methodology automates the stages of data preprocessing, feature selection, and model optimization, markedly simplifying the process of predictive maintenance. The application of this framework illustrates a streamlined model development cycle, attaining significant achievements in the prediction of equipment failures, thereby augmenting operational efficiency, and diminishing downtimes. Through practical deployment, the proposed work demonstrates how this strategy not only alleviates the complexity inherent in predictive maintenance but also yields considerable enhancements in maintenance strategy and resource allocation. This exploration validates the potential of AutoML to revolutionize predictive maintenance, enhancing its efficiency and accessibility.

关键词： Analytical models machine learning algorithms machine learning Predictive models Data models Maintenance Naive Bayes methods

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Labour Exploitation Investigation using Satellite based Vessel Monitoring Systems

Labour Exploitation Investigation using Satellite based Vess...

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Computing, Analytics and Networks (Indo-Taiwan ICAN), Indo – Taiwan 2nd international conference on

作者： P. Karthikeyan Pao-Ann Hsiung Department of Computer Science and Information Engineering National Chung Cheng University Chiayi Taiwan

ISBN: (纸本)9781665499453

Taiwan is one of the most extensive distant water fishing (DWF) and has more than 1,000 ships producing 40 billion N.T. dollars. More than 90% of Taiwanese fishermen are recruited from Indonesia, the Philippines, and Vietnam. However, labour exploitation has become a severe problem in the Taiwan distant water fishing (DWF) industry. The Taiwan government recently amended the law and stipulated that multiple CCTV cameras be installed on DWF vessels to protect against labour abuse. Current technology only records videos captured by CCTV cameras. Thus, detecting labour abuse is nearly impossible. To resolve this problem, we have developed a deep neural network model to predict labour abuse in DWF. The developed model uses satellite-based vessel monitoring systems data for the predictions of labour abuse. The proposed deep neural network model gives better prediction than logistic regression, support vector machine, Random Forest and decision tree.

关键词： Deep learning Industries Analytical models Satellites Computational modeling Neural networks Predictive models

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Research on Automatic Composition Based on Multiple machine learning Models 2021

Research on Automatic Composition Based on Multiple Machine ...

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2021 3rd international conference on Artificial Intelligence and Advanced Manufacture

作者： Xiaoran Yang Hefei University of Technology China

ISBN: (纸本)9781450385046

Music is everywhere, let the machine learn to compose music, so as to help people create music better, and in the problem of music generation, there are many aspects worth exploring. Using the genetic algorithm in machine learning to generate music in the given multiple tone sandhi of a melody, an interactive system is used in this process. This paper mainly introduces the research of automatic composition based on various machine learning models. In this paper, based on a variety of machine learning model of automatic arrangement research, using genetic algorithm for automatic arrangement design, reasonable analysis of the feasibility of machine learning and sequence learning model, using melody generation to test the arrangement. The system is divided into two levels: first, the interactive development standard is formed through the joint evaluation of short phrases by users; then, the appropriate short phrases are selected from the randomly generated music clips to form music. The experimental results show that the efficiency of automatic music arrangement is increased by 18% for the research of automatic music arrangement based on multiple machine learning models. The limitations of the research of automatic music arrangement based on multiple machine learning models are analyzed, discussed and summarized to provide a good method and way of indoor path planning for the application of genetic algorithm, so as to enrich the academic research results.

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A Method of Extracting Discipline Inspection Cases Based on Deep learning

A Method of Extracting Discipline Inspection Cases Based on ...

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Education, Knowledge and Information Management (ICEKIM), international conference on

作者： XiaoLing Liu JunJie Chen Jing Gao Hao Fan JianMin Dong College of Computer and Information Engineering Inner Mongolia Agricultural University Inner Mongolia Autonomous Region Key Laboratory of Large Data Research and Application of Agriculture and Animal Husbandry Inner Mongolia Discipline Inspection and Supervision Big Data Laboratory Hohhot China

ISBN: (纸本)9781665416672

Event extraction is one of the key tasks in information extraction tasks, and it has been widely used in various fields in recent years. In the field of disciplinary inspection and supervision, disciplinary inspection case text has the characteristics of multiple types, multiple professional terms, and strong correlation between event types and event arguments. In the face of huge data, relying on manual analysis by manpower has seriously affected the efficiency of disciplinary inspection. However, there is no corpus in the field of discipline inspection currently available. This article uses BIO annotation to construct a corpus of discipline inspection to lay the foundation for subsequent work. Proposed BERT-BiGRU-CRF event joint extraction model. Use the BERT model to train the discipline inspection corpus, and Combine BiGRU network and CRF network to realize event type recognition and argument extraction. The experimental results show that the model can effectively extract event information in the discipline inspection field. At the same time, in order to better facilitate the work of disciplinary inspection personnel, a disciplinary inspection and supervision event extraction system is constructed to systematically extract all event information contained in the case.

关键词： Deep learning Training Annotations Biological system modeling Semantics Inspection Regulation

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Requirements Prediction for Medical Products Based on Named Entity Recognition

Requirements Prediction for Medical Products Based on Named ...

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Electronic Information Engineering and computer Science (EIECS), 2021 international conference on

作者： Peiguang Ruan Ruanchong Liu Bo Guo Zhepeng Liu Chengying Zhang Alibaba Cloud Big Data Application College Zhuhai College of Science and Technology Zhuhai China The First Clinical Medicine School Guangdong Pharmaceutical University Guangzhou China School of Finance and Trade Zhuhai College of Science and Technology Zhuhai China College of Engineering Shantou University Shantou China School of Management Xihua University Chengdu China

Medical product demand forecasting is a necessary condition for building a medical intelligent system, and is an important means to alleviate the lack of medical resources, and the form of medical product demand is mainly unstructured text, including the number of medicines, medical record texts. These medical texts contain a large number of medical terms related to the patient health situation and medical terms are the most direct embodiment of medical knowledge. However, existing methods for requirements are concentrated on the neural network or machine learning algorithms, which ignores the tremendous information in the medical terms. In this work, we utilize the named entity recognition method to predict the demand of the medical products. Efficiently identifying these medical terms from medical texts is a key factor in ensuring the high performance of medical intelligence systems. Therefore, the goal of the medical named entity recognition task is to extract medically significant symptoms, medical history, diseases, etc. from medical texts and realize the downstream prediction task. From our extensive experiment simulation and comparison with existing prediction methods, we can conclude the proposed method can achieve the precise prediction accuracy with reasonable computation costs.

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Rabble Based Autonomous Assistance Using machine learning Algorithms

Rabble Based Autonomous Assistance Using Machine Learning Al...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Kamal Sutariya Mohan Vishal Gupta Bechoo Lal Sura Rahim Alatba G.V. Sriramakrishnan Vikas Tripathi Department of CSE Parul Institute of Engineering and Technology Parul University Vadodara Gujarat India College of Computing Science and Information Technology Teerthanker Mahaveer University Moradabad Uttar Pradesh India Department of CSE Koneru Lakshmaiah Education Foundation (KLEF) KL Deemed University Vijayawada Campus AP India Computer Science Department Al-Turath Universiy College Baghdad Iraq CSE Mohan Babu University Tirupati India Department of CSE Graphic Era Deemed To Be University Dehradun Uttarakhand India

In order to properly control rabbles, we shall investigate computer vision and mechanical aid technologies with lending devices here. machine intelligence, commonly referred to as AI, or algorithms, enables software to improve prediction performance while requiring a deliberate goal to do so. machine-learning algorithms anticipate new output using historical data as their input. Since information is so crucial, creating novel approaches for effectively administering the neighbourhood, pervasive data gathering systems of today is a crucial next stage for actors and thus is entirely independent. Therefore, deep learning and its autonomous rabbel control support network will be discussed here. With the availability of information expanding through time, education for application in numerous sectors has become more widespread. Many fields have benefited from the use of machine vision, encompassing research and academia database and process visualisation, biomedical imaging, and more. These methods have been applied to safety, computer components, internet websites, and mobile applications. Thus, we can draw an opinion regarding an autonomous assistance system for effective kernel maintenance that is based upon deep ***, COVID-19 is causing mayhem throughout the world. There have found over than 110 million cases, or the number grows daily. Due to the outbreak, or more 4 million people died. The three most affected nations are the Usa, India, and Brazil, and many agencies are working to develop a vaccine. The world is seeking get back to normal after a string of shutdowns while upholding safety precautions. As a consequence, it is necessary to keep an eye on anyone's hat and temperature within public spaces like malls, hospitals, offices, shrines, and the like. Costs increase as a consequence of the lengthy process and extensive contact with people. Consequently, a system is required that can detect respirators, gauge temperatures, and manage the crowd tha

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Fetal Health Classification using AI from Cardiotocography Features

Fetal Health Classification using AI from Cardiotocography F...

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international conference on Innovative Mechanisms for Industry applications (ICIMIA)

作者： G Jyostna Himanshu Rai Goyal Manzoore Elahi M. Soudagar Swapnil Parikh Harshal Patil Kamal Alaskar Department of CSE-DS Potti Sriramulu Chalavadi Mallikarjuna Rao College of Engineering & Technology Vijayawada Andhra Pradesh India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science and Engineering Parul Institute of Engineering and Technology Parul University Post Limda Waghodia Gujarat India Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India Department of Computer Application BharatiVidyapeeth (Deemed To Be University) Institute of Management Kolhapur Maharashtra India

Maternal health issues during pregnancy are a major global concern because they result in fetal death, which is more frequent in rising and poor countries. Cardiotocography (CTG) is the major non-invasive and cost-effective method for monitoring fetal health. CTG delivers critical parameters such as Fetal Heart Rate (FHR) and Uterine Contraction (UC), with a total of 21 features for accurate monitoring. These characteristics assist obstetricians in categorizing fetal health as normal, suspect, or pathological. However, differences in CTG interpretation among obstetricians frequently result in incorrect procedures. This work presents a unique way to detect deficient fetal states using a hybrid machine learning (ML) model that combines Support Vector machine (SVM) and Random Forest (RF). The UCI repository dataset is pre-processed, which includes balancing, outlier reduction, and feature selection. The hybrid model’s performance is then compared to that of classic ML models such as SVM, K-Nearest Neighbour (KNN), RF, and Decision Tree (DT). The hybrid model outperforms the other models in simulation, attaining the greatest accuracy rate of 98.83%. This novel ML-based technique has the potential to improve fetal health detection while reducing the influence of human CTG interpretation discrepancies across healthcare providers.

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The application of DBSCAN Algorithm to Improve Variogram Estimation and Interpretation in Irregularly-Sampled Fields 3rd

The Application of DBSCAN Algorithm to Improve Variogram Est...

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3rd international conference on Applied Informatics, ICAI 2020

作者： Mosobalaje, O.O. Orodu, O.D. Ogbe, D. Department of Petroleum Engineering Covenant University Ota Nigeria African University of Science and Technology Abuja Nigeria

ISBN: (纸本)9783030617011

The empirical variogram is a measure of spatial data correlation in geostatistical modeling and simulations. Typically, the empirical variogram is estimated for some defined lag intervals by applying method of moments on an underlying variogram cloud. Depending on the distribution of pair-wise lag values, the variogram cloud of an irregularly-sampled field may exhibit clusteredness. Issues of noisy, uninterpretable and inconsistent empirical variogram plots are commonly encountered in cases of irregularly-sampled fields with clustered variogram clouds. An insightful diagnosis of these problems and a practical solution are the subject of this paper. This research establishes the fact that these problems are caused by the neglect of variogram cloud cluster configurations when defining lag intervals. It is here shown that such neglect hinders the optimal use of spatial correlation information present in variogram clouds. Specifically, four sub-optimal effects are articulated in this paper as the consequence of the neglect. Consequently, this research presents an efficient cluster-analysis – driven technique for variogram estimation in cases of irregularly-sampled fields with clustered variogram clouds. The cluster analysis required for this technique is implemented using an unsupervised machine learning algorithm known as Density-based Spatial Clustering of applications with Noise (DBSCAN). This technique has been applied to a real field to obtain a stable, interpretable and geologically consistent variogram plot. It has also been applied to a synthetic field and was found to give the lowest estimation error among other techniques. This technique would find usefulness in geo-modeling of natural resource deposits wherein irregular sampling is prevalent. © 2020, Springer Nature Switzerland AG.

关键词： Cluster analysis

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An FFM-based Model for Students' In-class learning Postures Estimation

An FFM-based Model for Students' In-class Learning Postures ...

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international conference on Information Systems and computer Aided Education (ICISCAE)

作者： Ziyun Zhao Shifeng Zhang Kangying Hu Zhan Chen Junqi Guo School of Artificial Intelligence Beijing Normal University Beijing China Hangzhou Hikvision Digital Technology Co. Ltd. Hangzhou China

ISBN: (数字)9781728183046

ISBN: (纸本)9781728194608

In the recent years, education quality monitoring has been attached great importance by Chinese government, and with the promulgation of the development plan for the new generation of artificial intelligence, the application of this technology in education quality monitoring fields has attracted wide attention of scholars. How to apply relevant algorithms of machine vision to classroom quality assessment is the research focus of smart education. In this paper, considering the timeliness of feedback, the stability of algorithms, the accuracy rate and speed in classroom quality assessment, we propose a model called Face Features Matching (FFM), based on face detection networks and feature points matching methods, which innovatively combines machine learning with parameter estimation and numerical methods. Our model is divided into two sub-modules: face feature extraction and optimal parameter estimation. It can be applied successfully to classroom scenarios and become an important part of automated classroom quality assessment.

关键词： Education Feature extraction Numerical models Quality assessment Mathematical model Monitoring Facial features

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