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共 564 条记录，以下是151-160 订阅

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Optimizing Speed and Accuracy Trade-off in machine learning Models via Stochastic Gradient Descent Approximation

Optimizing Speed and Accuracy Trade-off in Machine Learning ...

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international conference on soft computing and machine Intelligence (ISCMI)

作者： Jasper Kyle Catapang Department of English Language and Linguistics University of Birmingham United Kingdom

Stochastic gradient descent (SGD) is a widely used optimization algorithm for training machine learning models. However, due to its slow convergence and high variance, SGD can be difficult to use in practice. In this paper, the author proposes the use of the 4th order Runge-Kutta-Nyström (RKN) method to approximate the gradient function in SGD and replace the Newton boosting and SGD found in XGBoost and multilayer perceptrons (MLPs), respectively. the new variants are called ASTRA-Boost and ASTRA perceptron, where ASTRA stands for “Accuracy-Speed Trade-off Reduction via Approximation”. Specifically, the ASTRA models, through the 4th order Runge-Kutta-Nyström, converge faster than MLP with SGD and they also produce lower variance outputs, all without compromising model accuracy and overall performance.

关键词： Training machine learning algorithms Stochastic processes Multilayer perceptrons Boosting Approximation algorithms Optimization

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Using Reinforcement learning Algorithms to Explore COVID-19 Spread in South Africa 4

Using Reinforcement Learning Algorithms to Explore COVID-19 ...

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4th international conference on Artificial Intelligence, Big Data, computing and Data Communication Systems, icABCD 2021

作者： Hanie, Ruan Le Van Rensburg, Jt Janse School of Computer Science and Information Systems North-West University Vanderbijlpark South Africa

ISBN: (纸本)9781728185927

Many learning opportunities for machine learning (ML) exist within the context of how viruses and their spread can be combatted. If the agents can be trained to demonstrate optimal behaviour in a pandemic, their actions can possibly be replicated to improve spread in a real-life scenario. the aim of this research is to train reinforcement learning (RL) agents to survive in a rule-based AI environment that simulates the spread of COVID-19 in South Africa. the RL agents used in the training environment were created using Unity's ML agent SDK. the ML agent SDK supports the usage of two RL-specific algorithms, Proximal Policy Optimization (PPO), and soft Actor-Critic (SAC). this study contributes to the AI space by providing insight into how a virus and its interaction with a population can be modelled using Unity and machine learning. the agents were able to combat COVID-19 effectively and did so by self-Training how to maintain social distance and have regular check-ups at the hospital. It was also observed that susceptible agents pay frequent visits to the hospital without ever being rewarded for doing so. the code will be open-sourced to the Unity machine learning agent's SDK community and discords. © 2021 IEEE.

关键词： COVID-19

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NAMAQ - Arabic Handwriting Recognition Using Deep learning, AI, and ML with Sentiment Analysis

NAMAQ - Arabic Handwriting Recognition Using Deep Learning, ...

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computing and Information Technology (ICCIT-1441), 2020 international conference on

作者： Wareef Yousuf Aljomaee Saeed M Alshahrani Nayyar Ahmed Khan Department of Computer Science College of Computing and Information Technology Shaqra University Shaqra Saudi Arabia

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

the Arabic language is one of the delicate languages in terms of usage. Many characters in the language make it difficult for management to identify the script quickly. the holy book of the Muslims, the Quran, is written in Arabic. Several valuable resources from ancient and present times have helpful information on Arabic. the handwritten scripts vary from person to person, and it is not simple to understand and realize what is mentioned inside the scripts. In this study, the main emphasis is on recognizing Arabic handwritten words. the convolution neural network (CNN) is used, and feature extraction is done with the help of CNN for the Arabic text images uploaded into the system. With the help of machine learning classifiers, these handwritten scripts are recognized for similarity from the training data set. A recognition rate of up to 96.4% for 29 classes is achieved. the proposed model is tested using large datasets of characters and formations in the Arabic language. the identified features are further passed on to the machine learning classification model. Various researchers have worked on several models that yield variable rates of recognition. the highest rate is received at 96.3%. the next phase of the model proposed in this study focuses on the sentiment analysis of the scripts or texts classified in the recognition phase. the author's sentiment is identified for classifying the results.

关键词： Deep learning Handwriting recognition Sentiment analysis Text recognition Convolution Text categorization Neural networks Training data Feature extraction Information technology

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A Review on Cosmetic Product Recommendation Using Deep learning

A Review on Cosmetic Product Recommendation Using Deep Learn...

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soft computing for Security Applications (ICSCSA), international conference on

作者： Ruchika Chouhan Snehlata Barde Dept. Of Computer Science Engineering Sage University Indore India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

Cosmetic products play an important role in every one’s life. Cosmetic products also called as makeup are substance that is applied on the body especially on face. the modern lifestyle can be demanding, leaving little time for self-care. However, it’s crucial to prioritize self-care, including skincare, despite hectic schedules. Indeed, relying solely on best-seller products or in-store recommendations may not always be effective in determining compatibility between a skincare product and an individual’s skin condition. this is because everyone’s skin tone and type of the skin is unique, with varying sensitivities, concerns, and needs. Many people buy their goods online today, using various e-commerce sites. So, to choose the best skincare product during online purchasing is difficult, since it depends entirely on skin type and usage. the main aim of this paper is to survey and explores the existing recommendation systems designed to assist users in selecting cosmetics personalized to their unique skin type, by using advanced algorithms and machine learning. this study has reviewed various methods like face recognition, cosmetic product recognition, tag recommendation, facial skin image classification using convolution neural network, a new examples-rules guided deep neural network approach. through a comprehensive review of literature and existing research, this paper examines the methodologies, algorithms, and techniques employed in cosmetic recommendation systems. Key factors such as user preferences, skin type, skin concerns, and product attributes are analysed to understand how recommendation systems use this information to generate personalized recommendations.

关键词： Deep learning machine learning algorithms Convolution Reviews Face recognition Artificial neural networks Skin Classification algorithms Usability Recommender systems

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Transforming IoT with Federated Multimodal learning: A Privacy-Centric Approach

Transforming IoT with Federated Multimodal Learning: A Priva...

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Intelligent Technologies (CONIT), international conference on

作者： Ankita Suryavanshi Shiva Mehta Mukesh Kumar Siddhant thapliyal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

Suppliers face significant challenges when discussing cloud-based health apps, such as implementing highly secure procedures and rigorous privacy safeguards. this applies to the fact that the acceptance of these applications is limited by the need for such quality of service, which is achieved mainly by reducing latency. this work is devoted to tackling these hurdles through the cutting-edge application of edge computing and model-federated learning aspects. Federated learning, a complex and distributed paradigm for machine learning helps perform computations near the data source and overcomes the constraint problem of a distant data source. Integrating the newest edge computing, our system includes an intelligent instrument that is applied to extract clinical data using Clustered Federated learning (CFL). the value of this framework is evaluated using the performance metrics of two benchmark datasets, one of which represents different kinds of problems that occur in the day-to-day life of people, which concludes the effectiveness and significance of implementing machine learning at the edge in the health care sector the presented results above strongly suggest the fact that CFL promotes this model results in improvement. the performance indicators of the CFL model showed an appraisal boost with the increments in X-ray and Ultrasound datasets by 16% and 11%, respectively, when compared to those trained conventionally. Hence, edge AI through CFL can achieve the same tasks, and the possible scenarios can also be remarkably superior compared to a centralized data system.

关键词： COVID-19 Training Ultrasonic imaging Federated learning soft sensors Medical services Quality of service

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Colon Cancer Prediction Using machine learning in Jordanian Patients: Preliminary Results

Colon Cancer Prediction Using Machine Learning in Jordanian ...

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computing and Information Technology (ICCIT-1441), 2020 international conference on

作者： Ahmad S. Tarawneh Dept. Data Science and Artificial Intelligence Mutah University Karak Jordan

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

Colon cancer remains a significant cause of cancer-related mortality globally, with early detection pivotal in enhancing patient survival rates. this study applies machine learning (ML) techniques to predict colon cancer using a dataset sourced from several hospitals across Jordan, featuring demographic data and complete blood count (CBC) test results. the methodology encompasses two primary phases: data preprocessing, which includes structuring the data, addressing missing values, and standardizing feature scales; and predictive analysis using various advanced ML models. these models were meticulously trained and validated within this region-specific context, employing a rigorous cross-validation strategy. the results, assessed through metrics such as precision, recall, and ROC curves, indicate that certain models, like the XGBoost classifier, achieved high performance, exemplified by an 80% precision and sensitivity rates, whereas others fell short in predictive effectiveness. Future efforts will focus on optimizing model parameters and further tuning to enhance the diagnostic capabilities of ML in the early detection of colon cancer.

关键词： Training Hospitals Computational modeling Colorectal cancer Predictive models Data models Predictive analytics Tuning Blood Testing

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A Systematic Review of Data Augmentation Techniques in Natural Language Processing for Sentiment Analysis using machine learning Techniques

A Systematic Review of Data Augmentation Techniques in Natur...

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soft computing for Security Applications (ICSCSA), international conference on

作者： Uday Dhanaji Patil Pradeep Sudhakaran Department of Computer Science & Engineering SRM Institute of Science and Technology Kattankulathur Tamil Nadu India KIT’s College of Engineering (Autonomous) Kolhapur Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Tamil Nadu India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

Due to the enormous growth of mobile technology, Social media has created a crucial platform to express the feelings and opinions of people. Based on these opinion considerations, it is very beneficial for making strategic decisions in organizations, businesses, trading, political entities, and so on. To achieve effective improvement, sentiment analysis is taken into account, and the public opinion polarity for further reference. In this perspective, the main contribution of the study is to explore the benefits and limitations of various learning techniques under the evaluation of sentiment analysis. Generally, Sentiment analysis is evaluated by Natural language processing tasks with various learning techniques, to achieve effective generalization ability and efficiency, data augmentation techniques are integrated, which reduces the overfitting problem effectively. To explore the limitations of the traditional methods, 25 literary papers were taken into account to analyze various approaches, and methods under the utilization of distinct datasets. Together with this, the achieved evaluation metrics along with the achievements are also discussed briefly in the study. Additionally, the study provides the shortcomings and future scope of the conventional methods with a detailed description. the main objective of the survey includes knowledgeable categorization, which aims to explore a new model with the illustration of recent trends and achieve effective data augmentation in future analysis.

关键词： Surveys Sentiment analysis Analytical models Social networking (online) Organizations Data augmentation Real-time systems Security Overfitting Systematic literature review

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Diabetic Retinopathy Detection using Deep learning 2020

Diabetic Retinopathy Detection using Deep Learning

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4th international conference on machine learning and soft computing (icmlsc)

作者： Nguyen, Quang H. Muthuraman, Ramasamy Singh, Laxman Sen, Gopa Anh Cuong Tran Nguyen, Binh P. Chua, Matthew Hanoi Univ Sci & Technol Hanoi Vietnam Natl Univ Singapore Inst Syst Sci Singapore Singapore Victoria Univ Wellington Sch Math & Stat Wellington New Zealand

ISBN: (纸本)9781450376310

Diabetic Retinopathy (DR) is an eye disease associated with chronic diabetes. DR is the leading cause of blindness among working aged adults around the world and estimated it may affect more than 93 million people. Progression to vision impairment can be slowed or controlled if DR is detected in time, however this can be difficult as the disease often shows few symptoms until it is too late to provide effective treatment. Currently, detecting DR is a time-consuming and manual process, which requires an ophthalmologist or trained clinician to examine and evaluate digital color fundus photographs of the retina, to identify DR by the presence of lesions associated with the vascular abnormalities caused by the disease. the automated method of DR screening will speed up the detection and decision-making process, which will help to control or manage DR progression. this paper presents an automated classification system, in which it analyzes fundus images with varying illumination and fields of view and generates a severity grade for diabetic retinopathy (DR) using machine learning models such as CNN, VGG-16 and VGG-19. this system achieves 80% sensitivity, 82% accuracy, 82% specificity, and 0.904 AUC for classifying images into 5 categories ranging from 0 to 4, where 0 is no DR and 4 is proliferative DR.

关键词： Diabetic retinopathy classification image processing deep learning segmentation severity grade

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Deciphering Brain Tumor Diagnosis: A CNN-Based Deep learning Approach

Deciphering Brain Tumor Diagnosis: A CNN-Based Deep Learning...

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

作者： Pradeep Kumar Tiwari Prashant Johri School of Computing Science and Engineering Galgotias University Greater Noida India

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

A brain tumor is defined as the rapid growth of abnormal cells in the brain. Magnetic resonance imaging (MRI) is the best way to diagnose these tumors. Using MRI images, doctors can carefully identify and define specific areas of soft tissue growth to determine the presence of brain tumors. Today, brain cancer diagnosis uses machine learning and deep learning algorithms along with advances in artificial intelligence. Using these algorithms has many benefits, such as increasing the brain’s focus, reducing errors, and providing more accurate results. these benefits increase the efficiency of the decision-making process and make it easier for the patient to choose the most appropriate treatment options. this article introduces the use of convolutional neural networks (CNN) to detect brain tumors and evaluate their consequences. the main goal is to use neural networks, especially deep learning techniques, to identify brain tumors. From the results obtained in the training and testing phase, the pre-trained model achieved an accuracy of 0.9664 and a loss of 0.0934. Obtained are indicators for the classification of glioma, meningioma, non-neoplastic and pituitary tumors. Precision, recall, and F1 scores for glioma were 0.9781, 0.9477, and 0.9627, respectively. In the absence of tumor, precision, recall, and F1 scores were 0.9790, 0.9943, and 0.9866, respectively. the precision, sensitivity and harmonic mean values of the pituitary gland are 0.9589, 0.9951 and 0.9767, respectively. After analyzing the dataset, it is clear that convolutional neural networks (CNN) are the most reliable method for predicting the presence of brain tumors.s

关键词： Deep learning Training machine learning algorithms Accuracy Sensitivity Magnetic resonance imaging Reliability engineering

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Use of machine learning to Forecast Solar and Wind Energy Production for NEOM City

Use of Machine Learning to Forecast Solar and Wind Energy Pr...

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computing and Information Technology (ICCIT-1441), 2020 international conference on

作者： Muhammad Ayaz Artificial Intelligence and Sensing Technologies (AIST) Research Cente University of Tabuk Tabuk Saudi Arabia

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

NEOM, one of prominent future project of the Saudi Arabia, set to be the World's largest carbon-free system and globally leading hub to the clean energy, aiming to be powered entirely by renewable energy sources primarily solar and wind. Considering its geographic position which offers abundant solar irradiance and strong coastal winds, accurate forecasting of energy production is critical to ensure consistent energy supply, also for the effective integration of renewable resources into NEOM's smart energy infrastructure. Artificial intelligence (AI) is not only supporting renewable energy generation but also improving its reliability, efficiency, and scalability in order to make solar and wind energy a viable mainstream source. this study presents a comprehensive AI based approach to forecast solar and wind energy production in NEOM, utilizing advanced machine learning models and statistical methods, such as time series analysis, artificial neural networks (ANN), and hybrid forecasting models. By integrating historical weather data, satellite observations, and real-time environmental monitoring, precise predictions has been developed for solar irradiance and wind speed, both are critical for optimizing the performance of photovoltaic systems and wind turbines. the results demonstrate that provided methods are capable to enhance forecasting precision, and support to handle the dynamic energy needs of a city designed for renewable self-sufficiency.

关键词： Renewable energy sources Accuracy Wind energy Biological system modeling Urban areas Production machine learning Predictive models Forecasting Wind forecasting

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