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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

2023 7th International Conference On Computing, Communicatio...

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

ISBN: (纸本)9798350304268

the proceedings contain 326 papers. the topics discussed include: kidney localization and stone segmentation from a CT scan image;a research survey on traffic management, accident detection and emergency notification systems to improve society health;YOLO on GPU implementation for a vision-based fruit select and place system with a 5-DOF robotic arm;machine learning approach in melanoma cancer stage detection;unleashing the potential of convolutional neural networks for automated depression detection using audio modality;artificial intelligence, machine learning, and natural language processing capabilities in modern business intelligence;performance evaluation of various risk importance measures for large scale grid-connected solar photovoltaic systems;and automated generation of OpenSCENARIO scenarios for evaluating advanced driver assistance systems.

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Systematic Review of Deep learning Models for Dental Images 7

Systematic Review of Deep Learning Models for Dental Images

引用

7th international conference on computing Methodologies and Communication, ICCMC 2023

作者： Usha Kiruthika, S. Priya, J. Kanaga Suba Raja, S. Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli India Easwari Engineering College Chennai India

ISBN: (纸本)9781665464086

Dental practitioners use digital radiographs in conjunction with clinical diagnosis to examine dental disorders of patients. Automation in dental imaging in an effort to lighten the workload of dentists is a rising subject of study within the ever-expanding healthcare sector. Recognition of similar patterns coupled with deep learning tasks is a major boon to the dental industry in the automatic diagnosis of dental diseases by examining complex anatomy. Rather than traditional image processing and machine learning, the development of deep learning algorithms has made autonomous feature extraction possible without the assistance of a human. the current study briefly discusses the robust deep learning architectures adopted for the automation of dental imaging tasks like object detection, segmentation, and classification tasks. Along with offering fresh viewpoints on this fascinating new development in the discipline, we also point out some shortcomings such as blurry edges, overfitting and within-label variation with the need for potential improvement in the discipline of dental radiology adopting deep learning techniques to focus on content-rich information in the dental radiographs. © 2023 IEEE.

关键词： Object detection

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7th international conference on soft computing: theories and Applications, SoCTA 2023

7th International Conference on Soft Computing: Theories and...

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7th international conference on soft computing: theories and Applications, SoCTA 2023

ISBN: (纸本)9789819720880

the proceedings contain 36 papers. the special focus in this conference is on soft computing: theories and Applications. the topics include: Detection of Duplicate Question Pairs by Applying Proposed BoW, TF-IDF, and USE Approach;the Hand Glove Enabling Voice and Text Communication;advancing Rheumatoid Arthritis Care: Exploring Technological Breakthroughs and Future Directions;twitter Trolling Detection Using machine learning;HMHDTML: Human Mental Health Detection Using Text and machine learning Model;analyzing the Growth Profile of Brain Tumor with Caputo Fractional Operator via Sumudu Transform;multi-labelled Topic Classification of Research Articles Using machine learning;security in Mobile Ad Hoc Networks: Impact of Attacks and Countermeasure Approaches;optimization of Vibrational Frequencies for Orthotropic Parallelogram Plates With Circular Variations in Tapering at Simply Supported Boundary;drowsiness Detection Using Adaboost Method and Haar Cascade Classifier to Improve Safety of Drivers;early Detection of Colorectal Cancer from Polyps Images Using Deep learning;motion Control of Underactuated Cart-Double-Pendulum System Via Fractional-Order Sliding Mode Controller;a Comparative Study of Pedestrian Detection Techniques Over the Last Decade;approximation Properties of Modified-Bernstein Operators Having Szász Weight Functions;fourier-Laguerre Expansion of Signals by Composite Summable Technique;a Multiple Linear Regression Model to Estimate Global, Direct and Diffuse Irradiance in Gurugram, India, Using Python;supervised machine learning Approaches for Customer Reviews Sentiment Analysis;stock Price Prediction on Indian Share Market Using machine learning;transparent Price Forecasting for Basic Food Commodities in a Developing Economy;Parallel Deep Convolution Neural Network (P-DCNN) Prediction of Paddy Crop Disease;Convolutional-LSTM Network for Emotion Recognition Using EEG Data in Valence-Arousal Dimension;Analysis of Multiply-Accumulate (MAC) Unit Using C

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Sparse-learning-based High-order Dynamic Functional Connectivity Networks for Brain Disease Classification 23

Sparse-learning-based High-order Dynamic Functional Connecti...

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7th international conference on machine learning and soft computing, icmlsc 2023

作者： Wang, Jianhui Jie, Biao Zhang, Xingyu Li, Wen Wu, Zhaoxiang Yang, Yang School of Computer and Information Anhui Normal University Anhui Wuhu China

ISBN: (纸本)9781450398633

Dynamic functional connectivity network (DFCN) derived from resting-state functional magnetic resonance imaging (rs-fMRI), which characterizes the dynamic interaction between brain regions, has been applied to classification of brain diseases. However, existing studies usually focus on dynamic changes of low-order (i.e., pairwise) correlation of brain regions, thus neglecting their high-order dynamic information that could be important for brain disease diagnosis. therefore, in this paper, we first propose a novel sparse learning based high-order DFCNs construction method, and then build a novel learning framework to extract high-level and high-order temporal features from the constructed high-order DFCNs for brain disease classification. the experimental results on 174 subjects from from the Alzheimer's Disease Neuroimaging Initiative (ADNI) demonstrate the effectiveness of our proposed method in comparison with state-of-the-art methods. © 2023 ACM.

关键词： Magnetic resonance imaging

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GUI based Heart using Disease Classification using machine learning 7

GUI based Heart using Disease Classification using Machine L...

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7th international conference on computing Methodologies and Communication, ICCMC 2023

作者： Revathy, G. Priya, P. Muruga Senthilnathan, K. Mythili, P. Haridharani, S.V. School of Computing SASTRA Deemed University Thanjavur India Department of CSE Surya Engineering College Erode India Department of EEE Surya Engineering College Erode India

ISBN: (纸本)9781665464086

Heart disease is a serious health problem that has afflicted a lot of people all over the world. In our work, we have proposed a GUI-based machine learning-basedapproach that is efficient and accurate for identifying heart illness. An correct diagnosis and the right treatment can save several lives. this testing method not only costs a lot of money, but the results also fail to correctly identify HD patients. © 2023 IEEE.

关键词： Intelligent systems

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Analyzing Data Scientist Salaries Dataset through machine learning Algorithms Using Tool 'Orange' 7

Analyzing Data Scientist Salaries Dataset Through Machine Le...

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2023 7th international conference On computing, Communication, Control And Automation, ICCUBEA 2023

作者： Mittal, Shikha Monga, Chetna Bansal, Ankit Singla, Neeraj Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350304268

the buzz surrounding artificial intelligence has made machine learning a hot topic right now. Although there are many tools for visualising data, scripting languages are primarily used for model training. the Orange Data Mining tool gives you a lot of ways to change how data is preprocessed, how it is displayed, how models are trained, and how models are tested. In order to establish which strategy has the best Classification Accuracy and Precision, the proposed research uses machine learning techniques to predict the size of an organization based on a variety of parameters, including employee experience, income, job type, employee type, etc. the effectiveness of various machine learning techniques, including Naive Bayes, random forests, support vector machines, neural network, logistic regression, was evaluated. Classification Accuracy evaluations are performed by cross validation. For this paper, we consulted the Kaggle dataset. © 2023 IEEE.

关键词： classification confusion matrix cross validation employee dataset machine learning orange tool for data mining

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7th international conference on soft computing: theories and Applications, SoCTA 2023

7th International Conference on Soft Computing: Theories and...

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7th international conference on soft computing: theories and Applications, SoCTA 2023

ISBN: (纸本)9789819720309

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Predictive Analysis for Retail Shops using machine learning for Maximizing Revenue 7

Predictive Analysis for Retail Shops using Machine Learning ...

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

作者： Malik, Aman Dargar, Gauri Sharma, Arti Pandey, Priyambada KIET Group of Institutions Computer Science Ghaziabad India

ISBN: (纸本)9798350397253

the issue of difficulties in controlling the inventory of small retailers with limited space and limited capital to buy just a few merchandises has just been led due to the continuous increase in the variety of products. In order to successfully manage the inventory of small stores, this article intends to investigate several machine learning algorithms for forecasting future product demand. the quality of the forecasts that are offered has a significant impact on the effectiveness of procedures not only in retail locations but also throughout the supply chain they are a part of Nowadays, there is a large amount of information that can be cleansed and used for forecasting by utilizing the right machine learning algorithms. this prediction system will help small retail shop owners in maximizing their profit margins. Along with sales forecasting, this study intends to perform customer segmentation to make effective decisions in order to generate a good amount of revenue. © 2023 IEEE.

关键词： machine learning

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Efficient Fetal Health Monitoring and Classification with machine learning 7

Efficient Fetal Health Monitoring and Classification with Ma...

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

作者： Singh, Kabir Shyry, Prayla Franklin, Ramya G Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397253

the goal of the research on fetal health categorization using machine learning is to create a model that can precisely predict the condition of a fetus during pregnancy. this is crucial because prompt action following early identification of fetal health issues might enhance the pregnancy's outcome. this research study suggests classifying fetal health status using machine learning approaches based on ultrasound images and other clinical parameters. To train and test our model, we will gather a sizable dataset of ultrasound images and clinical characteristics from pregnant women. To categorize the state of fetal health, the proposed model will employ machine learning algorithms and image processing techniques. Our approach should categorize fetal health status with a high degree of accuracy, and it will help obstetricians and gynecologists provide better treatment for pregnant patients. © 2023 IEEE.

关键词： Genetic programming

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machine learning based Ideal Job Role Fit and Career Recommendation System 7

Machine Learning based Ideal Job Role Fit and Career Recomme...

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7th international conference on computing Methodologies and Communication, ICCMC 2023

作者： Sunil Kumar Aithal, S. Santhosh, S. Ashwin Shenoy, M. Sandeep Kumar, S. Nitte India Nitte India Nitte India

ISBN: (纸本)9781665464086

With the advancement of technologies and in order to pace with the digital era, robust automated job recommendation systems need to be implemented which overcome the limitations of traditional systems and manual approaches. Identifying field specific jobs with personalized systems by incorporating awareness and information rich which can address many domains. For fresh graduates with a lack of experience and lack of awareness who need to look for a huge number of companies, the proposed system helps them to easily search for their respective job efficiently. In order to save time while searching and applying for jobs and also to avoid misleading job recommendations, it is highly valuable to recommend the most suitable education qualification specific to all the job finders. In the proposed methodology the requirements of the fresh graduates will be considered such as demographic information of the company, organization type, and job preferences using machine learning techniques. the candidate's background information, interests based skills and abilities and profile information will be considered by the automated recommendation system. © 2023 IEEE.

关键词： Recommender systems

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