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COMSYS Hackathon-1 2023: Igniting machine learning Marvels 4th

COMSYS Hackathon-1 2023: Igniting Machine Learning Marvels

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4th international conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Paul, Aryan Mittal, Om Ghosh, Sounak Dasgupta, Sushmit Bhattacharjee, Debotosh Sarkar, Ram Department of Computer Science and Engineering Jadavpur University Kolkata700032 India

ISBN: (纸本)9789819726103

the COMSYS Hackathon-1 2023 was a dynamic competition integrated into the COMSYS-2023 conference, aiming to push the boundaries of expertise in machine learning and data science. the hackathon comprised two challenging rounds, each emphasizing different facets of these rapidly evolving fields. In the initial round, participants were encouraged to showcase their skills in two distinct tracks: text classification and regression analysis. In the text classification track, participants were tasked with demonstrating their natural language processing (NLP) prowess by developing models capable of accurately categorizing tweets into predefined categories. Meanwhile, the regression analysis track challenged participants to test their regression analysis skills by predicting the transfer market value of football players. the second round elevated the competition with an on-site presentation challenge, focusing on time series analysis. Top performers from Round 1 navigated the complexities of time-dependent data, unveiling patterns that informed future predictions of CO2 levels. the report provides comprehensive details about the competition, including information about the dataset, the evaluation measures employed, and brief descriptions of each submitted method. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Regression analysis

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An Analysis of Bangla Tweets on Social Media Platform for Polarity Detection Using machine learning Algorithms 4

An Analysis of Bangla Tweets on Social Media Platform for Po...

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4th international conference on Big Data Analytics and Practices, IBDAP 2023

作者： Rahman, Habibur Kazi, Md. Munam Islam, Md. Samiul Maoya, Jannatul Arif, Md. Monjurul Tagwar, Mahir Hasan, Mahamudul Ali, Md Sawkat Jabid, Taskeed Islam, Maheen East West University Department Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350300192

Nowadays, social media is a platform that is gaining popularity. Public and private thinking on a wide range of issues is continually expressed and transmitted via social media. So, the sentiment analysis of those tweets is a topic of the study. Bangla sentiment analysis is one of those research studies. In this regard, this paper proposes a supervised machine learning method for sentiment polarity detection in Bangla social media tweets, whether they are depressive or non-depressive. For analysis, we first preprocessed the data by removing stopwords (punctuation and less important words) from the corpus. For assigning the importance of words, we used TF-IDF feature extraction with NGrams (Unigram, Bigram, and Trigram) concepts. Eight machine learning algorithms, including logistic regression, decision tree, random forest, K-nearest neighbor, multinomial naive bayes, linear SVM, radial basis function SVM, and stochastic gradient descent, are used to detect polarity in social media tweets. the confusion matrix calculates accuracy, precision, recall, and f1score. Using Unigram TF-IDF, the highest score achieved by Random Forest (RF) was 99.65%;using Bigram TF-IDF, the highest score achieved by Logistic Regression (LR) was 98.72%;and using Trigram TF-IDF, the highest score achieved by Radial Basis Function SVM (RBFSVM) was 99.19%. this study might make a small addition to Bangla textual analysis's model in natural language processing (NLP). © 2023 IEEE.

关键词： Sentiment analysis

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Research on real-time optimization technology of intelligent manufacturing based on learning algorithm 4

Research on real-time optimization technology of intelligent...

引用

4th international conference on machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Bai, Guihai Jiangsu Automation Research Institute Lianyungang222000 China

In the manufacturing industry, production efficiency is the core competitiveness of enterprises, but also an important goal of enterprise production planning management, and in recent years, the production efficiency of China's manufacturing industry does not meet the requirements, and there are still many problems to be solved. In the face of this situation, the relevant fields have explored intelligent manufacturing and proposed intelligent manufacturing technology, which mainly integrates information technology with traditional manufacturing system, can realize the important measures of intelligence, automation, digitalization and information in the manufacturing process, and can help solve the current key problems. therefore, this paper takes ship manufacturing as the background. the real-time optimization technology of intelligent ship manufacturing based on learning algorithm is studied. this technology can effectively improve ship production efficiency and reduce production cost. © 2024 the Authors.

关键词： Smart manufacturing

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Prediction of Dementia Using Deep learning 1

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4th international conference on computer and Communication Technologies, IC3T 2022

作者： Baliyan, Tushar Singh, Tarun Pandey, Vedant Kartheek, G.C.R. Department of Computer Science and Engineering CMR Institute of Technology Bengaluru India

ISBN: (数字)9789811985638

ISBN: (纸本)9789811985621

Artificial intelligence and its sub-field machine learning are continuously evolving and being applied in medicine and healthcare amongst other important fields. machine learning and deep learning are frequently used to aid dementia prediction and diagnosis. Deep learning models are better than other machine learning models for dementia detection and prediction, but they are more computationally very expensive. the objective of the work is to build a deep learning model to predict dementia. this model is designed to predict dementia from brain MRI images and is based on the concepts of deep learning and convolutional neural network (CNN). the developed model is able to identify demented and non-demented MRI images with an accuracy of 99.35%, better than existing models. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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8th international Symposium on Algorithmic Aspects of Cloud Computing, ALGOCLOUD 2023

8th International Symposium on Algorithmic Aspects of Cloud ...

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8th international Symposium on Algorithmic Aspects of Cloud Computing, ALGOCLOUD 2023

ISBN: (纸本)9783031493607

the proceedings contain 13 papers. the special focus in this conference is on Algorithmic Aspects of Cloud Computing. the topics include: Olive Leaf Infection Detection Using the Cloud-Edge Continuum;intent-Based Allocation of Cloud Computing Resources Using Q-learning;a Double-Decision Reinforcement learning Based Algorithm for Online Scheduling in Edge and Fog Computing;real-Time Leakage Zone Detection in Water Distribution Networks: A machine learning-Based Stream Processing Algorithm;multi-agent Reinforcement learning-Based Energy Orchestrator for Cyber-Physical Systems;clustering-Based Numerosity Reduction for Cloud Workload Forecasting;planning Workflow Executions over the Edge-to-Cloud Continuum;decentralized Algorithms for Efficient Energy Management over Cloud-Edge Infrastructures;i-Deliver P&D Engine: A Decentralized Middleware for a Delivery-as-a-Service System;preface;application of Federated learning Techniques for Arrhythmia Classification Using 12-Lead ECG Signals.

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Sankalpa - Interactive Digital learning Platform to Enrich Students with Special Needs 5

Sankalpa - Interactive Digital Learning Platform to Enrich S...

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5th international conference on Advancements in Computing, ICAC 2023

作者： Basnayaka, A.I. Epitakaduwa, E.K.G.D. Jayawardena, C.P.U. Zoysa, Ashan Dharmakeerthi, Uditha Jayawardena, Sanath Sri Lanka Institute of Information Technology Dept of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept of Computer Science and Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept of Computer Science and Network Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

the term special needs mean the special educational needs that the students who suffer from learning disabilities must have due to several reasons. learning impairment is a condition of the neurological system that impairs one's capacity to efficiently acquire and process knowledge. Failures in diagnosing the people with learning disabilities have been repeatedly highlighted and remain unpopular among people in the developing countries like Sir Lanka. Dyscalculia (difficulties in math), dysgraphia (difficulties in writing) and dyslexia (difficulties in reading) can be considered as some of the major learning disability issues. the 'mobile application based' solution 'Sankalpa Math Master' has been created to offer a game based all-in-one solution including screening, and intervention to identify one of the above-mentioned learning disabilities called 'Dyscalculia'. Dyscalculia is generally distinguished by challenges in learning math, including organizing problems, multi-step calculations, transposing numbers, distinguishing directions, and understanding mathematical signs, leading to confusion with basic operations and facts. the provided solution has been accurate with the use of machine learning and deep learning techniques. In the process of screening with the use of Support Vector machine (SVM) machine learning algorithm 99.77% of accuracy level has been obtained. the skill level prediction process has acquired an accuracy level of 97% with the use of Random Forest machine learning algorithm. Sri Lankan traditional education system will be vastly benefited by the high level of accuracy in screening, skill predicting, and other game and voice chat-based activities. the students with special needs will gain more effective and personalized learning experience which will improve their learning abilities and help to close the technology gap in education. © 2023 IEEE.

关键词： Forecasting

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proceedings - 2023 international conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2023

Proceedings - 2023 International Conference on Cyber-Enabled...

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15th international conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2023

ISBN: (纸本)9798350308693

the proceedings contain 57 papers. the topics discussed include: on the basin of attraction and capacity of restricted Hopfield network as an auto-associative memory;a type of recharging scheduling strategy based on adjustable request threshold in WRSNs;classification and identification of phishing websites based on machine learning;patch-based multi-level attention mechanism for few-shot multi-label medical image classification;exploring event-based dynamic topic modeling;improvement of passkey entry protocol for secure simple pairing;application of Voformer-EC clustering algorithm to stock multivariate time series data;and multimodal deep learning for enhanced arrhythmia detection using ECG time series and image data.

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WONGA: the Future of Personal Finance Management - A machine learning-Driven Approach for Predictive Analysis and Efficient Expense Tracking 4

WONGA: The Future of Personal Finance Management - A Machine...

引用

4th international conference for Emerging Technology, INCET 2023

作者： Uyanahewa, M.I.R. Jayawardana, G.V.H.D. Bandara, M.B.D.N. Hapugala, H.A.V.V. Attanayaka, Buddhima Nawinna, Dasuni Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798350335750

the financial literacy of Sri Lankans is relatively low, leading to difficulties in managing personal finances. this research presents a smart solution to simplify the complexities associated with money management and assist individuals in managing their finances more efficiently to achieve better financial health without requiring a comprehensive knowledge of money management from the user. the proposed system automates personal finance management with minimal user effort, reducing manual data entry by tracking cash flow by utilizing SMS messages and expense bills to extract bank transaction data and cash expenditures. Each extracted expense will automatically be categorized into the correct expense category. the system also generates a custom budget plan for each user based on spending patterns to help them stay on the budget throughout the month and avoid irrational overspending. Furthermore, the system provides a mechanism to predict future expenses associated with upcoming events based on calendar events, allowing users to devise the most efficient budget plan and avoid facing financially unprepared events in the upcoming month. All these smart solutions are bundled up in the "Wonga"mobile application to help users make better financial decisions to achieve personal financial success. © 2023 IEEE.

关键词： machine learning

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Predicting Retail Store Transaction Patterns: A Comparison of ARIMA and machine learning Models 1

引用

4th international conference on Optimization learning Algorithms and applications

作者： Vaz, Clara B. Sena, Ines Braga, Ana Cristina Novais, Paulo Fernandes, Florbela P. Lima, Jose Pereira, Ana I. Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Lab Assoc Sustentabilidade & Tecnol Regioes Monta Campus Santa Apolonia P-5300253 Braganca Portugal Univ Minho ALGORITMI Res Ctr LASI Campus Gualtar P-4710057 Braga Portugal

ISBN: (数字)9783031774263

ISBN: (纸本)9783031774256;9783031774263

Retail transactions represent sales of consumer goods, or final goods, by consumer companies. this sector faces security challenges due to the hustle and bustle of sales, affecting employees' workload. In this context, it is essential to estimate the number of customers who will appear in the store daily so that companies can dynamically adjust employee schedules, aligning workforce capacity with expected demand. this can be achieved by forecasting transactions using past observations and forecasting algorithms. this study aims to compare the ARIMA time series algorithm with several machine learning algorithms to predict the number of daily transactions in different store patterns, considering data variability. the study identifies four typical store patterns based on these criteria using daily transaction data between 2019 and 2023 from all retail stores of the leading company in Portugal. Due to data variability and the results obtained, the algorithm that presents the most minor errors in predicting daily transactions is selected for each store. this study's ultimate goal is to fill the gap in forecasting daily customer transactions and present a suitable forecasting model to mitigate risks associated with transactions in retail stores.

关键词： Forecasting Transactions Time series machine learning

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Diagnosis and Identification of Citrus Canker Growth Rate Using machine learning 4

Diagnosis and Identification of Citrus Canker Growth Rate Us...

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4th IEEE international conference on Pattern Recognition and machine learning, PRML 2023

作者： Zainab, Naureen Afzal, Hammad Arif, Fahim Ghafoor, Abdul Iltaf, Naima Zakria, Muhammad Hyder, Muhammad Javed Ahsan, Rafia National University of Sciences and Technology Department of Computer Software Engineering Islamabad44000 Pakistan Park Road Islamabad44000 Pakistan Department of Mechanical Engineering Islamabad44000 Pakistan

ISBN: (纸本)9798350324303

Citrus canker is a bacterial disease caused by Xanthomonas citri pv citri. Mainly the research in the field of Citrus canker has been done on fruits;early detection of disease on leaves can help in adopting preventive measures at early stage. the present study focuses on the detection of Citrus canker in leaves and classification of six different growth stages/levels of disease: water soaking, yellow initiation, chlorosis, and blister formation, canker infection (50%), canker infection (100%). the primary dataset was developed by taking images of citrus leaves infected by X. citri for the specified time. the proposed strategy is based on the detection of the lesion spot on citrus leaves and the classification of citrus disease stages/level was done to evaluate the growth rate of citrus canker through six distinct growth levels and evaluate affected area of leaf with each passing day. the entire processing includes Pre-processing, Segmentation, Features extraction and perform Classification using four different classifiers which include SVM, KNN, Naive Bayes, and Neural Network. the efficiency of different classifiers was 96.77% by SVM, followed by KNN 90%, NB 84% and NN 82% respectively. the lowest execution time was achieved by NB 0.011s, followed by KNN 0.015s, SVM 0.021s, and N.N. 0.216s. © 2023 IEEE.

关键词： Image classification

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