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2023 Power Electronics and Power System Conference, PEPSC 2023

作者： Ayenew, Melak Lei, Hang Li, Xiaoyu Kekeba, Kulla Abeje, Eyerusalem Tegene, Abebe Alemu, Habtamu School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Addis Ababa Science and Technology University Center of Big Data Analytics and Hpc Addis Ababa Ethiopia Kotebe Education University Collage of Natural Department of Mathematic Science Shared Campus Addis Ababa Ethiopia

ISBN: (纸本)9798350341294

Public blockchain networks have been used for peer-to-peer energy trading;however, most of these networks are inefficient. The power-intensive consensus algorithm, Proof-of-Work (PoW), is one of the reasons for power inefficiency. This article proposes a lightweight consensus algorithm for demand response and energy trading models to improve efficiency in the smart grid. A cooperative approach encompassing all energy entities is also necessary for demand response management strategies to be more efficient. In the proposed model, we redesign the Proof-of-Fit (PoF) consensus algorithm for a smaller number of message exchanges and computations. We also propose a service-based use of grid infrastructure for energy trading and exchange by all energy trading entities in a cooperative approach while free energy price negotiation is exercised. The simulation results show that the redesigned consensus algorithm, Energy-Proof-Fit, reduces the average number of message broadcasts required to complete a consensus by 35 percent. Furthermore, the peak-to-average ratio of peak hour demand is reduced in the proposed model. © 2023 IEEE.

关键词： Blockchain

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Leveraging Machine Learning to Assess Factors Influencing Undernourishment in Nigeria 1st

Leveraging Machine Learning to Assess Factors Influencing Un...

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1st International Conference on Intelligent Systems, Blockchain, and Communication Technologies, ISBCom 2024

作者： Mobark, Nada Elfiky, Noha Ahmedin, Ahmed Ibrahim, Ameera Abdel-Aziz, M. Hamad, Safwat Faculty of Computers and Information South Valley University Qena83523 Egypt Business Analytics and Data Science Department School of Economics and Business Administration Saint Mary’s College of California MoragaCA94575 United States Department of Artificial Intelligence Engineering Faculty of Computer Science and Engineering King Salman International University South Sinai46511 Egypt

ISBN: (纸本)9783031823763

Notwithstanding economic progress, Nigeria continues to countenance significant food security issues. This study investigates the root causes of this problem and attempts to develop predictive models to address hunger in Nigeria. The research covering data from 2000 to 2022, sourced primarily from the World Bank and other institutions. The trend analysis, along with the linear model, indicates that investment in farming is crucial for reducing undernourishment. Additionally, there is a strong relationship between agricultural expenditure, exports, access to electricity, and unemployment in relation to undernourishment. In this paper, a linear model, correlation analysis, ARIMA, and trend analysis are utilized to demonstrate how undernourishment, representing hunger and food insecurity, is influenced by other factors. As a result, it is recommended to maintain the unemployment rate between 3.5% and 3.7%, while ensuring that exceeds 5% of government spending is recommended. In other words, to combat hunger in Nigeria, it is important to focus on investing in agriculture, creating jobs, and improving infrastructure. This aligns with our findings and with another research around the world. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： ARIMA Nigeria predictive model Undernourishment

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Feature Subset Selection for Detecting Fatigue in Runners Using Time Series Sensor data 3rd

Feature Subset Selection for Detecting Fatigue in Runners Us...

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3rd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2022

作者： Kathirgamanathan, Bahavathy Buckley, Cillian Caulfield, Brian Cunningham, Pádraig School of Computer Science University College Dublin Dublin Ireland School of Public Health Physiotherapy and Sports Science University College Dublin Dublin Ireland Insight Centre for Data Analytics University College Dublin Dublin Ireland

ISBN: (纸本)9783031090363

Time Series data collected from wearable sensors such as Inertial Measurement Units (IMU) are becoming popular for use in classification tasks in the exercise domain. The data from these IMU sensors tend to have multiple channels of data as well as the potential to augment new time series based features. However, this data also tends to have high correlations between the channels which means that often only a small subset of features are required for classification. A challenge in working with human movement data is that there tends to be inter-subject variabilities which makes it challenging to build a generalised model that works across subjects. In this work, the feasibility of generating generalisable feature subsets to predict fatigue in runners using a correlation based feature subset selection approach was investigated. It is shown that personalised classification systems where the feature selection is also tuned to the individual provides the best overall performance. © 2022, Springer Nature Switzerland AG.

关键词： Time series analysis

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A Methodology for Stock Movement Prediction Using Sentiment Analysis on Twitter and StockTwits data 6

A Methodology for Stock Movement Prediction Using Sentiment ...

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6th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference, SEEDA-CECNSM 2021

作者： Nousi, Christina Tjortjis, Christos The Data Mining and Analytics Research Group School of Science and Technology International Hellenic University Thessaloniki Greece

ISBN: (纸本)9781665427425

Application of Machine Learning (ML) and sentiment analysis on data from microblogging services has become a common approach for stock market prediction. In this paper, we propose a methodology using sentiment analysis on Twitter and StockTwits data for Stock movement prediction. The methodology was evaluated by analyzing stock movement and sentiment data. We present a case study focusing on Microsoft stock. We collected tweets from Twitter and StockTwits along with financial data extracted from Finance Yahoo. Sentiment analysis was applied on tweets, and two ML models namely SVM and Logistic Regression were implemented. Best results were achieved when using tweets from Twitter with VADER and SVM. Top F-score was 76.3% and top Area Under Curve (AUC) was 67%. SVM also achieves the greatest accuracy equal to 65.8%, when using StockTwits with TextBlob on this imbalanced data set. © 2021 IEEE

关键词： Sentiment analysis

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Moral Storytelling Model Using Artificial Intelligence-Driven Image-to-Text Synthesis 2

Moral Storytelling Model Using Artificial Intelligence-Drive...

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2nd IEEE International Conference on data science and Network Security, ICDSNS 2024

作者： Rao, Abhishek S. Rakesh Kumar, B. Ranganatha, K. Poojary, Ramaprasad Karthik Pai, B.H. Lisa Bojamma, M.S. Jaleesh, Jeeva Nmam Institute of Technology Department of Information Science and Engineering Nitte India St. Aloysius Deemed to Be University Department of M.Sc Big Data Analytics Aimit Mangaluru India Canara Engineering College Department of Information Science & Engineering Benjanapadavu India School of Engineering & It Manipal Academy of Higher Education Dubai United Arab Emirates

ISBN: (纸本)9798350373110

Artificial intelligence in storytelling often struggles with conveying complex moral narratives due to its reliance on short and simplistic captions. This study introduces a novel approach to overcome this challenge by developing a model designed to craft more extensive and profound moral stories. Utilizing unsupervised deep learning techniques, the proposed model autonomously learns from a diverse corpus of short stories and images to generate extended narratives without requiring explicit human guidance. The study highlights three primary contributions: the creation of an autonomous model for generating moral stories with minimal human intervention, the development of a substantial reservoir of moral narratives for comprehensive training, and the empirical validation of the model's superior ability to produce innovative and captivating stories compared to existing methodologies. The proposed model exhibits remarkable accuracy, achieving 91.0% on the COCO dataset and 88% on the Flickr30K dataset, outperforming models such as Encoder-Decoder, Informative Cross-modal, LSTM, and AESOP. Additionally, the model's performance was evaluated using the BLEU and CIDEr scores, yielding 0.81 and 0.75, respectively. This model not only enhances the depth and quality of AI-generated moral stories but also has potential applications in education, entertainment, and virtual assistants, providing richer and more engaging content for users. © 2024 IEEE.

关键词： Deep learning

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Analysis and Performance Evaluation of Credit Card Fraud by Multi-model ML

Analysis and Performance Evaluation of Credit Card Fraud by ...

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International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE)

作者： Md Aminul Islam A. T. M. Asif Imran Md Habibur Rahman Md Amran Hossen Pabel Bhupesh Kumar Mishra Kashinath Basu Data Science School of Engineering and Technology Cheltenham University of Gloucestershire UK Masters in ICT Bangladesh University of Professionals(BUP) Dhaka Department of Mathematics University of Chittagong Chittagong Bangladesh Master of Science in Business Analytics Wright State University Data Science AI & Modelling Centre (DAIM) University of Hull Data Science School of Engineering Computing and Mathematics Oxford Brookes University UK

ISBN: (数字)9798350388282

ISBN: (纸本)9798350388299

Using credit cards is a convenient and efficient payment mechanism. Credit card fraud significantly impacts financial loss, mental health, and the reputation of financial institutions. This study would incorporate an analysis of the preceding statement about eliminating several obstacles associated with the availability of public data, the presence of unbalanced data, the dynamic nature of fraud tendencies, and the prevalence of false alarms. The authors discuss various machine-learning techniques used to detect credit card fraud. Extreme Learning, Decision Trees, Random Forests, Support Vector Machines, Logistic Regression, and XG Boost are among these techniques on the European card benchmark dataset. A comparative evaluation of the effectiveness of machine learning has also been conducted, and precision is increased by incorporating multiple layers. The study’s findings indicate significant improvements in several crucial metrics, including accuracy, f1-score, precision, and AUC curves. XGBoost model achieves optimized values of $99.9 \%$ accuracy. The proposed model outperforms contemporary machine learning approaches in credit card fraud detection. It has been observed that the combination of data balancing techniques significantly reduces the occurrence of false negatives. This study has substantial potential for credit card fraud detection applications in the real world, providing an effective and efficient method for addressing this ongoing critical issue.

关键词： Support vector machines Performance evaluation Logistic regression Accuracy Benchmark testing Credit cards Fraud

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Trend Analysis and Intervention Effect Starting Point Detection of COVID-19 Epidemics Using Recalibrated Time Series Models—Worldwide,2020

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China CDC weekly 2021年第20期3卷 417-422页

作者： Shu Li Chen Chen Shuyin Cao Kejia Hu Hao Lei Xiaolin Xu Qinchuan Wang Changzheng Yuan Sicong Wang Sisi Wang Junlin Jia Yuanqing Ye Xifeng Wu Center of Clinical Big Data and Analytics Second Affiliated Hospital and Department of Big Data Health Science School of Public HealthZhejiang University School of MedicineHangzhouZhejiangChina National Institute for Data Science in Health and Medicine HangzhouZhejiang UniversityZhejiangChina Department of Surgical Oncology Affiliated Sir Run Run Shaw HospitalZhejiang University School of MedicineHangzhouZhejiangChina.

Objective:This study aimed to identify a model for short-term coronavirus disease 2019(COVID-19)trend prediction and intervention ***:We compared the autoregressive integrated moving average(ARIMA)model and Holt exponential smoothing(Holt)model on predicting the number of cumulative COVID-19 cases in *** on the mean absolute percentage error(MAPE)value,the optimal model was selected and further tested using data from the United States,Italy and Republic of *** intervention effect starting time points and abnormal trend changes were detected by observing the pattern of differences between the predicted and real ***:The recalibrated ARIMA model with a 5-day prediction time span has the best model performance with MAPEs ranged between 2%and 5%.The intervention effects started to show on February 7 in the mainland of China,March 5 in Republic of Korea and April 27 in Italy,but have not been detected in the US as of May *** abnormal trends were detected in Korea and Italy,but the overall epidemic trends were stable since the effect starting ***:The recalibrated ARIMA model can detect the intervention effects starting points and abnormal trend changes;thus to provide valuable information support for epidemic trend analysis and intervention evaluation.

关键词： starting absolute smoothing

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A Machine Learning Approach to Model Counting

A Machine Learning Approach to Model Counting

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Marco Dalla Andrea Visentin Barry O'Sullivan SFI Centre for Research Training in AI School of Computer Science & IT University College Cork Ireland Insight Centre for Data Analytics School of Computer Science & IT University College Cork Ireland

ISBN: (数字)9798331527235

ISBN: (纸本)9798331527242

Model counting (#SAT) is the problem of computing the number of satisfying assignments for a given Boolean formula. It has a significant theoretical and practical interest. Tackling it can be challenging since the number of potential solution grows exponentially with the number of variables. Due to the inherent complexity of the problem, approaches to approximate model counting have been developed as a practical alternative. These methods extract the number of solutions within user-specified tolerance and confidence levels and in a fraction of the time required by exact model counters. However, even these methods require extensive computations, restricting their applicability to relatively small instances. In this paper, we propose a new approximate machine learning model counter that overcome this limitation. Predicting the number of solutions can be seen as a regression problem. We deploy an array of machine learning techniques trained to infer the approximate number of solutions based on statistical features extracted from a SAT propositional formula. Extensive numerical experiments performed on synthetic crafted and benchmark datasets show that learning approaches can provide a good approximation of the number of solutions with a much lower computational time and resource cost than the state-of-the-art approximate and exact model counters, making it possible to approximate the model count of instances previously out of reach. We then investigated the structural factors that lead to a high model count using AI explainability approaches.

关键词： Deep learning Measurement Accuracy Machine learning algorithms Computational modeling Estimation Learning (artificial intelligence) Predictive models Feature extraction Numerical models

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Designing an Energy Tracker and Smart Home Solutions Using Design Thinking Methodology 10

Designing an Energy Tracker and Smart Home Solutions Using D...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Hiererra, Siti Elda Chandra, Victor Ivana, Jane Wibowo, Jessica Lynn Asadi, Faisal School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Business Analytics Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia

ISBN: (纸本)9798350339543

As energy consumption rises, finding ways to reduce energy usage in homes is crucial. This paper presents a case study using design thinking to create a daily energy tracker that helps users reduce energy consumption. We conduct user research, define the problem, brainstorm solutions (mobile app, smart thermostat, smart plug, physical tracker), and test prototypes. A mobile app that tracks energy usage in real-time and offers personalized tips emerges as the most promising solution. We develop and launch the app, partnering with an energy company. This case study showcases the effectiveness of design thinking in creating a user-centered solution for reducing energy consumption. © 2023 IEEE.

关键词： Energy utilization

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Machine Learning based IoT-BotNet Attack Detection Using Real-time Hetergenous data

Machine Learning based IoT-BotNet Attack Detection Using Rea...

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2022 IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022

作者： Ahmed, Aqeel Tjortjis, Christos Internationall Hellenic University 14th km Data Mining and Analytics Research Group School of Science and Technology 14th km Thessaloniki570 01 Greece

ISBN: (纸本)9781665470872

Security is a major concern for Internet of Things (IoT) devices. Due to their vulnerabilities, these devices are an easy target for unauthorized access. Traditional security mechanisms are not easily deployed on these tiny devices. Therefore, machine learning-based algorithms are proving to be key enablers in IoT attack detection. In this paper, we critically investigate the efficacy of classification algorithms;Logistic Regression (LR), Gaussian Naive Bayes (GNB), Decision Trees (DT), Random Forest (RF), K-Nearest Neighbors KNN), and Extreme Gradient Boosting (XGB) for malicious attack detection in an IoT network flow traffic. A real-time heterogeneous dataset is used in this study. The data is preprocessed before experimentation. In addition, a feature selection method is implemented to identify the most important features. Furthermore, we evaluate the classifiers using accuracy, precision, recall, and Fl-score. The results on binary classification are comparable with the state-of-the-art in this domain. The tree-based classifiers outperform LR and GNB with 99% accuracy and a 0.99 Fl-score. It is observed that the LR and GNB are not the best choices for multiclass classification. However, DT, KNN, RF, and XGB provide an accuracy of 9S% and an Fl-score of 0. 9S. We also analyze the impact of reducing the number of features on accuracy and training time. © 2022 IEEE.

关键词： Internet of things

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