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

共 811 条记录，以下是161-170 订阅

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Analysis Techniques for Pharmaceutical Drugs Biomedicine Big Data Analytics and machine learning 7

Analysis Techniques for Pharmaceutical Drugs Biomedicine Big...

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

作者： Padmaavathy, P.A. Celina, A. Devi, R. Prameela Arathi, B.N. Sureshkumar, G. Ramachandran, G. Faculty of Arts Science Commerce and Management Department of Management Karpagam Academy of Higher Education Coimbatore India College of Management SRMIST Kattankulathur India CVR College of Engineering Department of ECE Telangana Hyderabad India PES University Dept of Computer Science and Engineering Karnataka Bengaluru India ECE Vinayaka Mission's Kirupananda Variyar Engineering College Salem India

ISBN: (纸本)9798350397253

the phases of drug discovery and development, starting with drug design and moving through crucial clinical trials to clinical practice, are being revolutionized by machine learning techniques. A more sophisticated kind of learning is machine learning (ML), which may be used to identify patterns in the solutions to solve current problems and learn to solve unsolved problems in future. Following the pre-processing of the necessary data, the objective is to learn from the data and identify patterns. the results can then be obtained by applying the patterns found to a different dataset. In general, when using larger, more agile data samples on high-dimensional datasets, ML algorithms can make predictions that are more accurate. Along with the rapid development of bioinformatics, there has been a significant global evolution in drug discovery and design technologies over the past ten years. Various artificial intelligence and machine learning (ML) methodologies have been used in drug discovery and design, leading to changes in all of their stages, including the time-consuming drug-target interaction (DTI) prediction and medicines and compounds discrimination. © 2023 IEEE.

关键词： Big data

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machine learning for Translating Pseudocode to Python: A Comprehensive Review 7

Machine Learning for Translating Pseudocode to Python: A Com...

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

作者： Tiwari, Satya Prakash Prasad, Shivam thushara, M.G. Amrita Vishwa Vidyapeetham Amritapuri Amrita School of Computing Department of Computer Science and Applications Kollam India

ISBN: (纸本)9798350397253

Translating pseudocode to a programming language can be difficult and time-consuming, especially for individuals unfamiliar with the languages involved. However, recent advances in Natural language processing (NLP) have made it possible to create a model that can automatically convert pseudocode written in natural language to code in a specific programming language. this research study proposes a solution to this problem by using an NLP transformer model to translate pseudocode into Python code automatically. the model will be trained on pseudocode examples and corresponding Python code translations. this study leverages a large dataset of pseudocode examples and their corresponding Python code translations to achieve this. By training the model on this dataset, it will be able to learn the patterns and structures present in the pseudocode and match them with their corresponding Python syntax. this will allow the model to translate pseudocode accurately into Python code, even for complex algorithms and structures. © 2023 IEEE.

关键词： Large dataset

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Multi-Modal Misinformation Detection: An Exhaustive Review 7

Multi-Modal Misinformation Detection: An Exhaustive Review

引用

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

作者： Barve, Yashoda Saini, Jatinderkumar R. Rathod, Rutuja Gaikwad, Hema Suryadatta College of Management Information Research & Technology Pune India Pune India

ISBN: (纸本)9798350304268

Social media and the web have become the source of misinformation dissemination today. the users generate a variety of misinformation in different categories and forms. the researchers have taken an interest in detecting and classifying false information, mainly in textual data, while multi-modal misinformation detection has started gaining attraction recently. Although some survey papers discuss false information detection techniques, the authors didn't find a survey paper focusing only on multi-modal misinformation detection. therefore, in this study, authors have reviewed the literature related to multi-modal misinformation detection from 2010 to 2023 to identify the techniques utilized by the researchers, understand the available datasets, and identify the research gaps in this area. this will assist the researchers in understanding the methods used to detect multi-modal misinformation, work on gaps identified and contribute mainly to this area. © 2023 IEEE.

关键词： Deep learning machine learning Misinformation Detection Multi-modal

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Constructive Analysis on Prediction and Detection of Diabetic Retinopathy (DR) using machine learning Algorithms - A Generic Survey 7

Constructive Analysis on Prediction and Detection of Diabeti...

引用

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

作者： Keerthana, Chukka Tejasree, Peram Venkata Subba Rao, Mudivarthi Sai Pavan Kumar, R.S. Yalla, Prasanth Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India

ISBN: (纸本)9781665464086

Diabetic retinopathy is an eye disorder that affects people with diabetes. When high blood sugar levels harm the retina's blood vessels, the blood vessels may enlarge and leak or they may block the flow of blood. All these alterations may cause visual loss. In the current era, machine learning is very useful and plays a key role in medical applications. In this research, investigate the effectiveness and capability of different types of machine learning (ML) algorithms based diabetic retinopathy detection systems. the authors tested large quantities of retinal fundus images & gray scale pictures from Kaggle, Messidor, IEMRC and multiple available datasets. the application of an ensemble of machine learning classification algorithms to characteristics taken from the results of various retinal image processing algorithms is used to determine the presence of disease. the proposed goal is to anticipate the DR before it results in the worst scenario and prevent the patient from losing their visual acuity. the proposed method uses both supervised algorithms like Support Vector machine (SVM), ResNet and DensNet, Naive Bayes, K-Nearest Neighbors, and Neural Networks (NN), as well as unsupervised algorithms like k-means clustering, hierarchical clustering, and Markov chains to classify a variety of characteristics in DR. the best overall result is displayed by ResNet and DensNet with the accuracy of 96.22%. © 2023 IEEE.

关键词： Blood vessels

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Computational Techniques for Predicting Response to therapy for Cancer 7

Computational Techniques for Predicting Response to Therapy ...

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

作者： Narendran, Aanchal Nishanth, M.S. Surendra, Anish Churamani, Nityam Srinivasa, Gowri Pes University Department of Cse KA Bengaluru India

ISBN: (纸本)9798350304268

Personalizing cancer therapies is a key goal in oncology research. this study proposes a novel approach to tackling the problem of predicting drug response in the form of IC50 values from gene expression data of cancer cell lines. Working on publicly available data, we applied various dimensionality reduction techniques are used to address the problem of high dimensionality, including biological data reduction methods using datasets such as LINCS L1000 and COSMIC CGC and statistical data reduction methods such as LIME and SHAP. We ran multiple versions of the reduced data on machine learning models (Elastic Net and XGBoost). Finally, we evaluated the utility of our models based on clinical research in the field of oncology by performing post-hoc analysis on key genes indicated by statistical modelling. Our results show that machine learning models can accurately predict drug response and provide insights to the underlying biological mechanisms driving drug response even after a reduction of 99% of genes. this study has important implications for personalized cancer therapy. It provides a framework for predicting drug response based on gene expression data, even when working on a reduced gene set. © 2023 IEEE.

关键词： cancer therapy drug response feature selection genomics machine learning

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Optimised Home Electricity Management using machine learning 7

Optimised Home Electricity Management using Machine Learning

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

作者： Arunkumar, M. Devadharshini, S. Dept of Information Technology IFET College of Engineering Villupuram India

ISBN: (纸本)9781665464086

the interactive home automation system that is based on the Internet of things (IoT) is a cutting-edge technical breakthrough that allows remote control and monitoring of home appliances in addition to the actual purchase of items. the Internet of Objects (IoT) aims to bring everything in the world together under a unified infrastructure, which will give us the ability to manage the things that are around us and provide us with continual information on the state of those things. the fundamental objective of the study is to provide a recommendation for an effective Internet of things implementation that may be put to use to carry out remote monitoring and control of household appliances. the user interface for this system is implemented in a manner that makes advantage of portable devices. In addition, it provides control of home appliances using mobile devices by employing Wi-Fi as the communication protocol and the Raspberry Pi Uno as the controller. the home user will be responsible for following the system as it travels over the Internet via the web-based user interface. For the purpose of anticipating people's usage of power and so enabling them to become less reliant on it, machine learning is being utilised here. © 2023 IEEE.

关键词： Python

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Federated learning with Class Balanced Loss Optimized by Implicit Stochastic Gradient Descent 7th

Federated Learning with Class Balanced Loss Optimized by Imp...

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7th international conference on soft computing in Data Science (SCDS)

作者： Zhou, Jincheng Zheng, Maoxing Qiannan Normal Univ Nationalities Sch Comp & Informat Duyun 558000 Peoples R China Baoji Univ Arts & Sci Sch Comp Sci Baoji 721007 Peoples R China Key Lab Complex Syst & Intelligent Optimizat Guiz Duyun 558000 Peoples R China

ISBN: (纸本)9789819904044;9789819904051

Federated learning is a paradigm for distributed machine learning in which a central server interacts with a large number of remote devices to create the optimal global model. System and data heterogeneity are now the two largest impediments to federated learning. this work suggests a federated learning strategy based on stochastic gradient descent optimization as a solution to the problem of heterogeneity-induced slow convergence, or even non-convergence, of the global model. this work estimates the average global gradient using locally uploaded model parameters without computing the first derivative or updating global model parameters through gradient descent. Allowing the global model to be used with fewer communication rounds. Obtain faster and more reliable convergence results. In experiments simulating varying degrees of heterogeneous settings, the strategy proposed in this work delivered faster and more stable convergence than FedProx and FedAvg. this work offers a strategy that decreases the number of communication cycles on highly heterogeneous synthetic datasets by around 50% compared to FedProx, therefore considerably enhancing the stability and durability of federated learning.

关键词： Fast convergence Optimization algorithm System heterogeneity Data heterogeneity Implicit stochastic gradient descent Global model Central server Distributed machine learning Federated learning

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Solving Arithmetic English Word Problems Using Intermediate Representation 7

Solving Arithmetic English Word Problems Using Intermediate ...

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

作者： Kotwal, Harshal Patnaik, Girish Kumar SSBT's College of Engineering & Technology Department of Computer Engineering Jalgaon India SSBT's College of Engineering & Technology Jalgaon India

ISBN: (纸本)9798350304268

Developing a system to tackle math word problem solving is recognized as a complex undertaking that necessitates machine common sense, including logical inference. Despite the ongoing endeavors devoted to this field, developing a solver system that can effectively handle diverse datasets continues to pose a significant challenge in the present times. this project pioneers the evaluation of the Encoder-Decoder approach as a means of constructing an English language math question-answering system. Unlike methods requiring human feature engineering, this approach has displayed promising potential as a solver for the Chinese language. In our research, we customized the model for the English language, performed assessments using a subset of the MathQA English datasets, and carried out a sequence of experiments with the goal of improving the model's precision and scope. © 2023 IEEE.

关键词： Artificial Intelligence machine learning Math Word Problems Natural Language Processing Recurrent Neural networks

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Bullyproof Sentinel: Fortifying Online Spaces Against Bullying using machine learning 7

Bullyproof Sentinel: Fortifying Online Spaces Against Bullyi...

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7th international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Dilshani, M.M.T. Vinoshan, T. Wijesinghe, W.A.S.D. Jathushan, V. Senarathne, Amila Gunasinghe, Amali Sri Lanka Institute of Information Technology Faculty of Computing Cyber Security Specialization Malabe Sri Lanka Sliit Cisco Academy Sri Lanka Institute of Information Technology Industry Engagement Unit Department of Computer Systems Engineering Manager Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350341485

In the current social media landscape, cyberbullying and online harassment remain pressing issues. therefore, we planned a cyberbullying detection system for social media platforms. It can protect online communities from toxic behaviors and make a global impact by fostering a safer and more constructive online environment. We are mainly focused on developing an improved technique to detect bot profiles on social media, developing a mechanism to detect abusive content and sarcasm, developing a mechanism to detect defamatory comments on social media, and developing an effective method for detecting offensive and abusive content in image-format reviews on social media using machine learning algorithms. © 2023 IEEE.

关键词： machine learning

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A Study on Self-Configuring Intrusion Detection Model based on Hybridized Deep learning Models 7

A Study on Self-Configuring Intrusion Detection Model based ...

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

作者： Bajpai, Sanchika Abhay Patankar, Archana B. Computer Department Thadomal Sahani Engineering College Bandra West Maharashtra Mumbai400050 India

ISBN: (纸本)9781665464086

In order to achieve the necessary security guarantee, Intrusion Detection Systems (IDSs) play a vital role in all networks and information systems worldwide. One of the best ways to prevent malicious assaults is to use an IDS. In order to keep up with attacker's constant evolution of attack strategies and discovery of new attack vectors, IDS must likewise advance by implementing more advanced detection techniques. New research in the deep learning sector, including intrusion detection, has been made possible by the enormous rise in data as well as the considerable advancements in computer hardware technology. machine learning (ML) techniques that are adapted on data learning representationsinclude deep learning as a sub-field. this study analyses 25 research papers that discuss about machine learning techniques and intrusion detection models by highlighting numerous drawbacks and the advantages of the current methodologies, which provides the interpretation of the suitable method for most of the IDS systems. this case shows different ways to explore the journals, publication years, metrics, achievements of numerical evaluation techniques, and a number of variables support other factors. On the other hand, a technique analysis that considers the benefits and drawbacks of the methods is also presented. the relative assessment of the methodologies provides a thorough clarification of the proposed motivation. © 2023 IEEE.

关键词： Intrusion detection

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