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检索条件"任意字段=7th International Conference on Machine Learning and Soft Computing, ICMLSC 2023"

共 787 条记录，以下是71-80 订阅

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A Study of Challenges and Limitations to Applying machine learning to Highly Unstructured Data 7

A Study of Challenges and Limitations to Applying Machine Le...

引用

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

作者： Singh, Sahib Hooda, Susheela Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350304268

In recent years, there has been a lot of curiosity about the use of machine learning algorithms to analyze unstructured data, including social media posts and videos. the limitations and difficulties posed by these technologies must be overcome in order to guarantee accurate and trustworthy results, despite the promise they hold. this study examines the restrictions and difficulties associated with using machine learning algorithms on unstructured data. We identify the main difficulties in working with unstructured data, such as the lack of standardization and the difficulty in handling noise and bias, through a literature review and case studies. We then look at methods for overcoming these obstacles, such as feature engineering, pre-processing techniques, and ensemble methods. the results of this study have implications for both theoretical and applied work in machine learning, particularly in the fields of feature engineering, model selection, and data cleaning. this paper's goal is to provide a thorough understanding of the constraints and difficulties associated with using machine learning algorithms on unstructured data, as well as to contribute to the creation of best practices for handling these data type © 2023 IEEE.

关键词： Deep learning machine learning Natural Language Processing (NLP) Sentiment Analysis Social Media Posts Text classification Unstructured Data

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Prediction on Impact of Electronic Gadgets in Students Life using machine learning 7

Prediction on Impact of Electronic Gadgets in Students Life ...

引用

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

作者： Saraswathi, K. Devadharshini, B. Kavina, S. Srinidhi, S. Department of Computer Technology Kongu Engineering College Erode India

ISBN: (纸本)9781665464086

Nowadays electronic gadgets play an important role in students' life as a source of learning. the Dependency of services provided by electronic gadgets has reached a large scale. Electronic gadgets like smart phones have a major impact on people in their day-to-day life. Among all, students are the important one, as they rely on electronic gadgets for their academic activities. the major impact is that it can affect the students mental and physical health. Students are getting addicted to these electronic gadgets as it becomes inevitable. this study uses machine learning techniques to demonstrate how gadgets affect students' daily lives. To examine the addictiveness of gadgets among the students, a questionnaire has been circulated to get to know the student's necessity on electronics. the parameters include how many electronic devices they use and how long they use them for, whether the usage of electronic gadgets shows any improvement in their academic performance. machine learning employs the preprogrammed algorithms, to predict output values for the given input data. It is considered to be an aspect of artificial intelligence. machine learning algorithms are used in a variety of fields, such as computer vision, voice recognition, medicine etc. Where it is difficult or impractical to create conventional algorithms to perform the necessary tasks. the collected dataset is taken to analyse the performance of prediction for various machine learning algorithms like K-Nearest Neighbour, Random Forest, Decision Tree, Logistic regression, Support Vector machine. As a result of this study, accuracy of several performance measures were evaluated. In the future the performance of an algorithm can be improved by using optimization techniques. © 2023 IEEE.

关键词： Students

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Predicting the Success of Bank Telemarketing using Deep Convolutional Neural Network 7

Predicting the Success of Bank Telemarketing using Deep Conv...

引用

7th international conference of soft computing and Pattern Recognition (SoCPaR)

作者： Kim, Kee-Hoon Lee, Chang-Seok Jo, Sang-Muk Cho, Sung-Bae Yonsei Univ Dept Comp Sci Seoul South Korea

ISBN: (纸本)9781467393607

Recently, exploitations of the financial big data to solve the real world problems have been to the fore. Deep neural networks are one of the famous machine learning classifiers as their automatic feature extractions are useful, and even more, their performance is impressive in practical problems. Deep convolutional neural network, one of the promising deep neural networks, can handle the local relationship between their nodes which can make this model powerful in the area of image and speech recognition. In this paper, we propose the deep convolutional neural network architecture that predicts whether a given customer is proper for bank telemarketing or not. the number of layers, learning rate, initial value of nodes, and other parameters that should be set to construct deep convolutional neural network are analyzed and proposed. To validate the proposed model, we use the bank marketing data of 45,211 phone calls collected during 30 months, and attain 76.70% of accuracy which outperforms other conventional classifiers.

关键词： Deep learning Convolutional neural network Bank telemarketing Fintech Big data

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F1 Race Winner Predictor 7

F1 Race Winner Predictor

引用

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

作者： Shelke, Priya Mirajkar, Riddhi Pande, Anurag Kale, Srujan Paralikar, Yash Vishwakarma Institute of Information Technology Pune India Vishwakarma Institute of Information Technology Information Technology Pune India

ISBN: (纸本)9798350304268

With the help of machine learning, a winning Formula One (F1) race prediction model is what this project hopes to create. the model is trained using historical data from F1 races, such as lap times, sector times, qualification times, and information about the drivers and teams. To make the predictions we will be using Python and Support Vector machines (SVM). the outcome of this initiative could be quite interesting for F1 fans and could influence wagering and other related activity. With this project we aim to create a machine-learning model that can forecast an F1 race winner based on a variety of inputs or display the effectiveness of SVMs by comparing predicted values and actual values. © 2023 IEEE.

关键词： data collection F1 race Formula One machine learning neural network prediction

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international Joint conference SOCO’14-CISIS’14-ICEUTE’14: Bilbao, Spain, June 25th-27th, 2014, Proceedings 1

引用

丛书名： Advances in Intelligent Systems and computing

2014年

作者： José Gaviria de la Puerta, Iván García Ferreira, Pablo Garcia Bringas, Fanny Klett, Ajith Abraham, André C.P.L.F. de Carvalho, Álvaro Herrero, Bruno Baruque, Héctor Quintián, Emilio Corchado (eds.)

ISBN: (数字)9783319079950

ISBN: (纸本)9783319079943;9783319079950

this volume of Advances in Intelligent and soft computing contains accepted papers presented at SOCO 2014, CISIS 2014 and ICEUTE 2014, all conferences held in the beautiful and historic city of Bilbao (Spain), in June 2014. soft computing represents a collection or set of computational techniques in machine learning, computer science and some engineering disciplines, which investigate, simulate, and analyze very complex issues and *** a through peer-review process, the 9th SOCO 2014 international Program Committee selected 31 papers which are published in these conference proceedings. In this relevant edition a special emphasis was put on the organization of special sessions. One special session was organized related to relevant topics as: soft computing Methods in Manufacturing and Management *** aim of the 7th CISIS 2014 conference is to offer a meeting opportunity for academic and industry-related researchers belonging to the various, vast communities of Computational Intelligence, Information Security, and Data Mining. the need for intelligent, flexible behaviour by large, complex systems, especially in mission-critical domains, is intended to be the catalyst and the aggregation stimulus for the overall *** a through peer-review process, the CISIS 2014 international Program Committee selected 23 papers and the 5th ICEUTE 2014 international Program Committee selected 2 papers which are published in these conference proceedings as well.

关键词： soft computing Artificial intelligence

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Artificial Intelligence, machine learning, and Natural Language Processing Capabilities in Modern Business Intelligence 7

Artificial Intelligence, Machine Learning, and Natural Langu...

引用

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

作者： Ukhalkar, Prakash Bhate, Manasi Hingane, Suraj Pimpri Chinchwad College of Engineering Mca Department Pune India MES's Institute of Management and Career Courses Mca Department Pune India Abhinav Education Society's Institute of Management and Research Mca Department Pune India

ISBN: (纸本)9798350304268

Operational business intelligence capabilities in modern business applications demand the simultaneous processing of analytical queries and business transactions using the same data. Mixed workloads become the result of this. the business sector is being disrupted by artificial intelligence, machine learning, and natural language processing. the voice-based search, high-tech and telecommunications, automotive and assembly, and financial services industries are just a few of the sectors setting the pace for adopting these particular technologies. this study demonstrates that real-time analysis, machine learning, and natural language processing are essential for current business intelligence systems to make well-informed decisions and provide company value across various industries. © 2023 IEEE.

关键词： Artificial Intelligence BI Tools Business Intelligence machine learning Natural Language Processing

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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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A Survey on Drug Suggestion Mechanisms using machine learning Algorithm 7

A Survey on Drug Suggestion Mechanisms using Machine Learnin...

引用

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

作者： Aravind, S. Sivakumar, V. Vellore India Vellore India

ISBN: (纸本)9798350397253

this research study intends to bring convenience to any user to find the appropriate medicine that suits them. the application finds the brand and name of medicine that has the least side-effects and lack harmful chemicals. the primary objective of this research study is to design a prototype for a computer software that would make it simpler and more straightforward for users to evaluate the potential utility of a new pharmaceutical product in an unbiased manner. © 2023 IEEE.

关键词： User interfaces

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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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A Hybrid Flood Forecasting Approach Using Hidden Markov Model and machine learning Techniques 7

A Hybrid Flood Forecasting Approach Using Hidden Markov Mode...

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

作者： Sri Sakthi, L. Chitra, P. Harini Sree, M.S. Subbulakshmi, P. Umayal, C. Thiagarajar College of Engineering Dept. of Computer Science and Business Systems Madurai India

ISBN: (纸本)9798350304268

Floods are one of the most common and adverse disasters that the world faces. In India, floods are seemed as the disaster that happens often and pose extreme risk to lives and belongings. Forecasting floods can greatly reduce the risk and effect of the forthcoming flood. A large quantity of parameters is answerable for flood occurrence. the amount of rainfall and temperature are the most vital parameters for inflicting floods. Hence rainfall and temperature are used as determinants inside the look at. machine learning model such as logistic regression, Naive bayes, k-Nearest neighbours, support vector machines (SVM) and random forests generally generate a model or computation method to label the results. Further, in latest days, hybridization techniques are expected to yield better results, wherein multiple models are combined to provide a new model. the proposed work is to develop a hybrid model called HMM-ML which includes Hidden Markov model with the above cited ML models. as a result, HMM acts as a first-class-tuner to stimulate the model. the developed model is validated against the region Kozhikode located in the State of Kerala, India which gets the maximum amount of rainfall during the period between June - August. © 2023 IEEE.

关键词： Flood prediction Hidden Markov Model(HMM) Hybridization machine learning (ML) Rainfall Temperature

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