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检索条件"任意字段=5th Asia Conference on Machine Learning and Computing, ACMLC 2022"

共 595 条记录，以下是171-180 订阅

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Predicting Ratings of Trending Youtube Videos using machine learning 5

Predicting Ratings of Trending Youtube Videos using Machine ...

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5th International conference on Contemporary computing and Informatics, IC3I 2022

作者： Gupta, Geetika Tiwari, Sakshi Sharma, Nonita Singh, Harjit Indira Gandhi Delhi Technical University for Women Electronics & Communication Dept Delhi India Indira Gandhi Delhi Technical University for Women Information Technology Dept Delhi India Lovely Professional University Department of Computer Science Punjab Phagwara India

ISBN: (纸本)9798350398267

YouTube is a global platform on which its users can upload their videos and have interactive review of other users through built-in features such as like, comment, rate, share, upload content, and save. A few videos which have tremendous number of views and likes over an explicit span of time become overly favored and such videos are known as 'Trending YouTube Videos'. this research suggests predicting whether ratings for a Trending YouTube video are disabled. the sensitivity of a video's content can be determined by its YouTube ratings. It is implied that a video has restricted or sensitive content and is not intended for children if there is no rating, i.e., ratings disabled. Research is based on data from 999 YouTube videos that were trending within a specific time period. Statistics consists of the number of views, likes, dislikes and comment counts, the research was performed using the following models of machine learning namely Random Forest, Decision Tree, Support Vector machine, Logistic Regression, and K-Nearest Neighbor. © 2022 IEEE.

关键词： Python

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IICETA 2022 - 5th International conference on Engineering Technology and its Applications

IICETA 2022 - 5th International Conference on Engineering Te...

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5th International conference on Engineering Technology and its Applications, IICETA 2022

ISBN: (纸本)9781665472159

the proceedings contain 104 papers. the topics discussed include: enhancing recommendation system using adapted personalized PageRank algorithm;diagnosing bacteria samples using data mining: review study;modeling the performance of wireless body sensor networks;spectrum sensing enhancement based on segmentation of the confused regions;thermal performance of inline and staggered bank of tubes with laminar cross flow;oil spill detection based on machine learning and deep learning: a review;on estimation of hazard, survival and density functions for Weibull Pareto distribution by ranking algorithm;cataract disease detection used deep convolution neural network;design and development of a bracelet to monitor vital signs of COVID-19 patients;design methodology of diagonally reinforced concrete coupling beams;and fire spread monitoring system of a forest based on interpolation and cloud computing.

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A machine-learning Ensemble Method for Temporal-aware Sales Forecasting 5

A Machine-Learning Ensemble Method for Temporal-aware Sales ...

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5th International conference on Data Science and Information Technology, DSIT 2022

作者： Aun, Yichiet Khaw, Yen-Min Jasmina Gan, Ming Lee Chern, Debra Lee Hooi Universiti Tunku Abdul Rahman Faculty of Information Communication and Technology Malaysia

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

machine-learning-driven sales forecasting is gaining momentum in inventory management based on customer shopping behaviors. However, it is difficult to build a one-size-fits-all sales forecasting model for general purpose use due to the volatility of product turnaround and complex consumer behaviors. Traditionally, single forecasting models such as ARIMA modeling are used to predict sales. However, existing models have little real- world applicability given the apparent forecast disparity between experimental and real-world predictions due to model overfitting. this project proposes an ensemble machine-learning method using weighted XGBoost, ARIMA, and Holt-Winters for sales forecasting of a grocery chain to address model overfitting issues. the intuition of the work is to combine several ML models to reduce variance (bagging), bias (boosting), and improve predictions (stacking). the contribution of this work includes: (1) implementation of exploratory modeling for in-depth exploratory data analysis (EDA) in grocery datasets and (2) feature engineering for the weighted integration of designed forecasting models using a lag and sliding window techniques. the preliminary result of the research places the model developed at the 70th percentile of the Corporacion Favorita Sales Forecasting Competition in Kaggle. © 2022 IEEE.

关键词： Big data

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A Comparative Analysis of Credit Card Detection Models 8th

A Comparative Analysis of Credit Card Detection Models

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8th International conference on the Applications of Science and Mathematics, SCIEMAthIC 2022

作者： Kim, Kimberly Chan Li Mustapha, Aida Palaniappan, Vaashini Mun, Woon Kah Kasinathan, Vinothini Faculty of Applied Sciences and Technology Universiti Tun Hussein Onn Malaysia KM1 Jalan Pagoh Pagoh Johor 84600 Malaysia School of Computing and Technology Asia Pacific University of Technology and Innovation Taman Teknologi Malaysia Jalan Teknologi 5 Kuala Lumpur57000 Malaysia

ISBN: (纸本)9789819928491

Data mining and machine learning are gaining popularity for fraud detection due to their effective results to cater the exponentially growing card transactions that comes with the fast-growing frauds. the aim of this paper is to explore which of the many techniques are capable to detect fraudulent transactions the best. Methods such as logistic regression, decision tree, support vector machine (SVM), Naive Bayes, and random forest are evaluated on their performance based on factors such as accuracy, precision, recall, and F1-score. the results showed that the random forest performed the best among the five methods investigated for credit card fraud detection. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023

关键词： Decision trees

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Soil Classification Using machine learning, Deep learning, and Computer Vision: A Review 5th

Soil Classification Using Machine Learning, Deep Learning, a...

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5th International conference on Recent Innovations in computing, ICRIC 2022

作者： Kumar, Ashish Kaur, Jagdeep Dr B R Ambedkar National Institute of Technology Jalandhar Grand Trunk Road Barnala-Amritsar Bypass Rd Punjab Jalandhar144011 India

ISBN: (纸本)9789811998751

the goal of this work is to examine the primary soil variables that impact crop development, such as organic matter, vital plant nutrients, and micronutrients, and to determine the appropriate connection proportion among those qualities using ML and DL models. Agriculture relies heavily on the soil. Different kinds of dirt can be found in various locations. Each type of soil can have unique characteristics to develop crops. To cultivate crops in diverse soil types, we need to know which kind of soil is best. Accurate soil moisture and temperature forecast are beneficial to agriculture planting factors. According to the review articles, machine learning approaches, deep learning prediction models, and computer vision are helpful in this instance. the external temperature and humidity and soil moisture and temperature are used to train and test to anticipate soil moisture and temperature. A deep learning model was developed based on the extended short-term memory network (LSTM). machine learning models such as k-nearest neighbor, SVM, and RF methods are utilized for soil classification. Several contributions of computer vision models in fields like planting, harvesting, enhanced weather analysis, weeding, and plant health detection and monitoring have been shown in the application of computer vision in soil categorization. this paper presents a review of soil classification and various challenges associated with it using machine learning, deep learning, and computer vision. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computer vision

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Fake News Detection of South African COVID-19 Related Tweets using machine learning 5

Fake News Detection of South African COVID-19 Related Tweets...

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5th International conference on Artificial Intelligence, Big Data, computing and Data Communication Systems (ICABCD)

作者： Khan, Yaseen thakur, Surendra Durban Univ Technol Dept Informat Technol Durban South Africa

ISBN: (数字)9781665484220

ISBN: (纸本)9781665484220

Social Media has grown in popularity in recent years' comprising of billions of users who in turn exchange and communicate content at a volume and rate impractical to examine manually. Fake News are now being used on these platforms to manipulate and affect societies across the world as was the case in the 2016 United States of America (USA) elections and of recent during the 2019 coronavirus disease (COVID-19) pandemic. South Africa is not immune to the spread of Fake News, particularly, through Social Media platforms such as Twitter, Facebook and TikTok. It is, therefore, important to detect the presence of Fake News computationally in order assist the mitigation of its spread and prevent perceivable negative effects. this study addresses the issue by developing a machine learning (ML) model to analyze large amounts of data associated with Social Media. Curated annotated datasets from CONSTRAINT AAAI 2021;COVID-19 Rumour, FNIR and Zenodo's COVID-19 datasets;Googk and Polifact Fact Checked websites;were utilized to develop the ML model Specifically, the model was trained on 36254 data points and applied on a South African related COVID-19 Twitter dataset collected for cursory analysis. In total, 27 ML models were experimented with and the collected South African COVID-19 related Twitter dataset comprised of 976087 tweets from 8 November 2020 until 19 July 2021. the results detected 329107 tweets as being 'Fake' based on the LightGBM Classifier which was chosen as the most feasible model in terms of speed and a balanced accuracy score of 0.82. the proposed model is unique as it is trained on a larger combined dataset and supplements existing efforts to combat misinformation, disinformation and malinformation spread on Twitter.

关键词： machine learning fake news covid-19 NLP

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An Advanced Cloud-Based Plant Health Detection System Based on Deep learning 5

An Advanced Cloud-Based Plant Health Detection System Based ...

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5th International conference on Contemporary computing and Informatics, IC3I 2022

作者： Abbas, Syed Hauider Vashisht, Swati Bhardwaj, Garima Rawat, Ruchira Shrivastava, Anurag Rani, Kavita Integral University Department of Computer Science & Engineering Uttar Pradesh Lucknow India Amity University Uttar Pradesh Greater Noida India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India Lloyd Institute of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350398267

Agricultural diseases can hinder plant growth, which ultimately causes crop death. Farmers traditionally identify diseases by speaking with regional experts and conducting additional experiments. Farmers may find it challenging to comprehend the current situation given the prevalence of new diseases. India suffers 15% of the world's annual agricultural losses, which is significant. To stop further crop damage, automated disease detection using machine learning and the cloud is crucial. the existing machine learning models in this proposal lacked sophistication, reliability, portability, and ease of use in some respects, making it cumbersome for others to actually examine the results in real time. In this proposal, by using Amazon's cloud service AWS (Amazon web Services) to implement the proposal and host the website via the cloud, a farmer can take a picture of a leaf and send it to the users of the website. It can apply it to interface uploads to learn how it works. Using MobileNet, a convolutional neural network (CNN), and machine learning libraries such as TensorFlow and OpenCV to detect plant diseases with an average accuracy of 98.78% using a data set of approximately 2000 plant leaf images identified in real time. © 2022 IEEE.

关键词： Web services

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A Novel Deep learning based Black Fungus Detection using the Bagging Ensemble with K-Nearest Neighbor 5

A Novel Deep Learning based Black Fungus Detection using the...

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5th International conference on Contemporary computing and Informatics, IC3I 2022

作者： Charan, Panthangi Venkata Sai Ramkumar, G. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350398267

machine learning is widely employed, and broadly speaking, scientists consider applying it everywhere. Around the same period, we can see that India has been devastated by the second corona wave. In a single day, more than 4 lakh instances arrive. Meanwhile, reports of the arrival of a new, fatal fungus called Mucormycosis emerged (Black fungus). Additionally, this fungus expanded quickly throughout numerous states, leading some of them to designate this illness an epidemic. People with weak immunity functions, including those who have had the corona virus and some of whom are still recovering, are more likely to get a black fungus infection since their bodies can't successfully fight it off. Bagging Ensemble with K-Nearest Neighbor is a modified machine learning approach that will be developed in this study (BKNN). the traditional methods, including K-Nearest Neighbor ensemble with bagging classification, are the basis for the suggested methodology. After the image processing techniques, including pre-processing and segmentation, were reviewed, the accuracy score for this classifier was 96.4 percent, which would have been the highest of all the findings. this paper described how machine learning was beneficial during the Corona era, much as it would be beneficial during epidemics like mucormycosis. the last section of this essay presents accurate, graphical evidence for all items addressed, along with explicit specifications. © 2022 IEEE.

关键词： Viruses

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Hypothyroidism Prediction and Detection Using machine learning 5

Hypothyroidism Prediction and Detection Using Machine Learni...

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5th International conference on Engineering Technology and its Applications, IICETA 2022

作者： Almahshi, Hebatullah Mohammad Almasri, Esraa Abdallah Alquran, Hiam Mustafa, Wan Azani Alkhayyat, Ahmed Jordan University of Science and Technology Department of Biomedical Engineering Irbid21163 Jordan Yarmouk University Biomedical Systems and Medical Informatics Engineering Irbid21163 Jordan Faculty of Electrical Engineering Technology Pauh Putra Campus Perlis Arau02600 Malaysia Advanced Computing Centre of Excellence Pauh Putra Campus Perlis Arau02600 Malaysia The Islamic University Faculty of Engineering Najaf Iraq

ISBN: (纸本)9781665472159

the world is facing an increase in the incidence of thyroid diseases, including in Jordan as well, especially hypothyroidism. this condition can be diagnosed through blood samples to know the percentage of hormones (TSH, T3, and T4) and take the appropriate decision after that. the major goal of this study is to make diagnosis easier and reduce the risk of misdiagnosis, which happens all too often. Methods: the use of machine learning in the prediction of disease is a crucial step in the process of data analysis. this paper presents an analysis and classification model that takes into account the various factors involved in the prediction of disease. We applied a number of machine learning algorithms like support vector machine (SVM), Nave Bayes, decision trees, and ensemble to get the best prediction. the decision tree algorithm showed the highest accuracy and lowest cost with a percentage of 97.6 percent and 90, respectively. the results appeared in the form of three classes (compensated hypothyroidism, primary hypothyroidism, and negative). Conclusion: the best results were obtained at the milestone point in computer-aided diagnosis of hypothyroidism. Future work is considered to increase the number of cases that can be detected and diagnosed using machine learning. © 2022 IEEE.

关键词： Decision trees

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machine learning Model Development Using Computational Neurology 1

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5th International conference on Soft computing and Signal Processing, ICSCSP 2022

作者： Kanrar, Soumen Department of Computer Science and Engineering Amity University Jharkhand Ranchi801001 India Department of Computer Science Vidyasagar University West Bengal Midnapore India

ISBN: (数字)9789811986697

ISBN: (纸本)9789811986680

the ever-enhancement of techniques and technology helps computer scientists to collect voluminous data with specified resolution. the bottleneck in understanding how computational neurology works is consequently shifting away from the amount and type of voluminous data. the general query raised by everyone mined is, what do we do with that voluminous data? there has been a growing interest in leveraging the vast volume of data. Computational neurology gains more insight into the voluminous data with the help of various levels of analysis, measurement techniques, and experimental paradigms. this paper presents the algorithmic development, implementation, and validation of a machine learning model based on the spike response model (SRM-0) of neuronal dynamics. We have considered 'spike, time-dependent plasticity’ (STDP) as our strategy for reinforcement learning in this work. the performance of the model is exhibited through the pattern recognition task of recognizing handwritten digits from the MNIST dataset. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Character recognition

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