Big Data and Cognitive Computing最新文献_第5页

Data-Driven Short-Term Load Forecasting for Multiple Locations: An Integrated Approach 数据驱动的多地点短期负荷预测：综合方法

Big Data and Cognitive Computing Pub Date : 2024-01-26 DOI: 10.3390/bdcc8020012

Anik Baul, Gobinda Chandra Sarker, Prokash Sikder, Utpal Mozumder, A. Abdelgawad

{"title":"Data-Driven Short-Term Load Forecasting for Multiple Locations: An Integrated Approach","authors":"Anik Baul, Gobinda Chandra Sarker, Prokash Sikder, Utpal Mozumder, A. Abdelgawad","doi":"10.3390/bdcc8020012","DOIUrl":"https://doi.org/10.3390/bdcc8020012","url":null,"abstract":"Short-term load forecasting (STLF) plays a crucial role in the planning, management, and stability of a country’s power system operation. In this study, we have developed a novel approach that can simultaneously predict the load demand of different regions in Bangladesh. When making predictions for loads from multiple locations simultaneously, the overall accuracy of the forecast can be improved by incorporating features from the various areas while reducing the complexity of using multiple models. Accurate and timely load predictions for specific regions with distinct demographics and economic characteristics can assist transmission and distribution companies in properly allocating their resources. Bangladesh, being a relatively small country, is divided into nine distinct power zones for electricity transmission across the nation. In this study, we have proposed a hybrid model, combining the Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU), designed to forecast load demand seven days ahead for each of the nine power zones simultaneously. For our study, nine years of data from a historical electricity demand dataset (from January 2014 to April 2023) are collected from the Power Grid Company of Bangladesh (PGCB) website. Considering the nonstationary characteristics of the dataset, the Interquartile Range (IQR) method and load averaging are employed to deal effectively with the outliers. Then, for more granularity, this data set has been augmented with interpolation at every 1 h interval. The proposed CNN-GRU model, trained on this augmented and refined dataset, is evaluated against established algorithms in the literature, including Long Short-Term Memory Networks (LSTM), GRU, CNN-LSTM, CNN-GRU, and Transformer-based algorithms. Compared to other approaches, the proposed technique demonstrated superior forecasting accuracy in terms of mean absolute performance error (MAPE) and root mean squared error (RMSE). The dataset and the source code are openly accessible to motivate further research.","PeriodicalId":505155,"journal":{"name":"Big Data and Cognitive Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139595275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI-Based User Empowerment for Empirical Social Research 基于人工智能的用户赋权实证社会研究

Big Data and Cognitive Computing Pub Date : 2024-01-23 DOI: 10.3390/bdcc8020011

Thoralf Reis, Lukas Dumberger, Sebastian Bruchhaus, Thomas Krause, Verena Schreyer, M. X. Bornschlegl, Matthias L. Hemmje

{"title":"AI-Based User Empowerment for Empirical Social Research","authors":"Thoralf Reis, Lukas Dumberger, Sebastian Bruchhaus, Thomas Krause, Verena Schreyer, M. X. Bornschlegl, Matthias L. Hemmje","doi":"10.3390/bdcc8020011","DOIUrl":"https://doi.org/10.3390/bdcc8020011","url":null,"abstract":"Manual labeling and categorization are extremely time-consuming and, thus, costly. AI and ML-supported information systems can bridge this gap and support labor-intensive digital activities. Since it requires categorization, coding-based analysis, such as qualitative content analysis, reaches its limits with large amounts of data and could benefit from AI and ML-based support. Empirical social research, its application domain, benefits from Big Data’s ability to create more extensive human behavior and development models. A range of applications are available for statistical analysis to serve this purpose. This paper aims to implement an information system that supports researchers in empirical social research in performing AI-supported qualitative content analysis. AI2VIS4BigData is a reference model that standardizes use cases and artifacts for Big Data information systems that integrate AI and ML for user empowerment. Thus, this work’s concepts and implementations try to achieve an AI2VIS4BigData-compliant information system that supports social researchers in categorizing text data and creating insightful dashboards. Thereby, the text categorization is based on an existing ML component. Furthermore, it presents two evaluations that were conducted for these concepts and implementations: a qualitative cognitive walkthrough assessing the system’s usability and a quantitative user study with 18 participants revealed that though the users perceive AI support as more efficient, they need more time to reflect on the recommendations. The research revealed that AI support increased the correctness of the users’ categorizations but also slowed down their decision-making. The assumption that this is due to the UI design and additional information for processing requires follow-up research.","PeriodicalId":505155,"journal":{"name":"Big Data and Cognitive Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139602938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quality and Security of Critical Infrastructure Systems 关键基础设施系统的质量和安全

Big Data and Cognitive Computing Pub Date : 2024-01-22 DOI: 10.3390/bdcc8010010

I. Izonin, Tetiana Hovorushchenko, Shishir K. Shandilya

引用次数: 0

Deep Learning and YOLOv8 Utilized in an Accurate Face Mask Detection System 深度学习和 YOLOv8 在精确人脸面具检测系统中的应用

Big Data and Cognitive Computing Pub Date : 2024-01-16 DOI: 10.3390/bdcc8010009

Christine Dewi, Danny Manongga, Hendry, Evangs Mailoa, K. Hartomo

{"title":"Deep Learning and YOLOv8 Utilized in an Accurate Face Mask Detection System","authors":"Christine Dewi, Danny Manongga, Hendry, Evangs Mailoa, K. Hartomo","doi":"10.3390/bdcc8010009","DOIUrl":"https://doi.org/10.3390/bdcc8010009","url":null,"abstract":"Face mask detection is a technological application that employs computer vision methodologies to ascertain the presence or absence of a face mask on an individual depicted in an image or video. This technology gained significant attention and adoption during the COVID-19 pandemic, as wearing face masks became an important measure to prevent the spread of the virus. Face mask detection helps to enforce mask-wearing guidelines, which can significantly reduce the spread of respiratory illnesses, including COVID-19. Wearing masks in densely populated areas provides individuals with protection and hinders the spread of airborne particles that transmit viruses. The application of deep learning models in object recognition has shown significant progress, leading to promising outcomes in the identification and localization of objects within images. The primary aim of this study is to annotate and classify face mask entities depicted in authentic images. To mitigate the spread of COVID-19 within public settings, individuals can employ the use of face masks created from materials specifically designed for medical purposes. This study utilizes YOLOv8, a state-of-the-art object detection algorithm, to accurately detect and identify face masks. To analyze this study, we conducted an experiment in which we combined the Face Mask Dataset (FMD) and the Medical Mask Dataset (MMD) into a single dataset. The detection performance of an earlier research study using the FMD and MMD was improved by the suggested model to a “Good” level of 99.1%, up from 98.6%. Our study demonstrates that the model scheme we have provided is a reliable method for detecting faces that are obscured by medical masks. Additionally, after the completion of the study, a comparative analysis was conducted to examine the findings in conjunction with those of related research. The proposed detector demonstrated superior performance compared to previous research in terms of both accuracy and precision.","PeriodicalId":505155,"journal":{"name":"Big Data and Cognitive Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139527599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluating the Robustness of Deep Learning Models against Adversarial Attacks: An Analysis with FGSM, PGD and CW 评估深度学习模型应对对抗性攻击的鲁棒性：利用 FGSM、PGD 和 CW 进行分析

Big Data and Cognitive Computing Pub Date : 2024-01-16 DOI: 10.3390/bdcc8010008

W. Villegas-Ch., Ángel Jaramillo-Alcázar, Sergio Luján-Mora

{"title":"Evaluating the Robustness of Deep Learning Models against Adversarial Attacks: An Analysis with FGSM, PGD and CW","authors":"W. Villegas-Ch., Ángel Jaramillo-Alcázar, Sergio Luján-Mora","doi":"10.3390/bdcc8010008","DOIUrl":"https://doi.org/10.3390/bdcc8010008","url":null,"abstract":"This study evaluated the generation of adversarial examples and the subsequent robustness of an image classification model. The attacks were performed using the Fast Gradient Sign method, the Projected Gradient Descent method, and the Carlini and Wagner attack to perturb the original images and analyze their impact on the model’s classification accuracy. Additionally, image manipulation techniques were investigated as defensive measures against adversarial attacks. The results highlighted the model’s vulnerability to conflicting examples: the Fast Gradient Signed Method effectively altered the original classifications, while the Carlini and Wagner method proved less effective. Promising approaches such as noise reduction, image compression, and Gaussian blurring were presented as effective countermeasures. These findings underscore the importance of addressing the vulnerability of machine learning models and the need to develop robust defenses against adversarial examples. This article emphasizes the urgency of addressing the threat posed by harmful standards in machine learning models, highlighting the relevance of implementing effective countermeasures and image manipulation techniques to mitigate the effects of adversarial attacks. These efforts are crucial to safeguarding model integrity and trust in an environment marked by constantly evolving hostile threats. An average 25% decrease in accuracy was observed for the VGG16 model when exposed to the Fast Gradient Signed Method and Projected Gradient Descent attacks, and an even more significant 35% decrease with the Carlini and Wagner method.","PeriodicalId":505155,"journal":{"name":"Big Data and Cognitive Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139620212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Credit Card Fraud Detection: An Ensemble Machine Learning Approach 加强信用卡欺诈检测：一种集合机器学习方法

Big Data and Cognitive Computing Pub Date : 2024-01-03 DOI: 10.3390/bdcc8010006

Abdul Rehman Khalid, Nsikak Owoh, O. Uthmani, Moses Ashawa, Jude Osamor, John Adejoh

{"title":"Enhancing Credit Card Fraud Detection: An Ensemble Machine Learning Approach","authors":"Abdul Rehman Khalid, Nsikak Owoh, O. Uthmani, Moses Ashawa, Jude Osamor, John Adejoh","doi":"10.3390/bdcc8010006","DOIUrl":"https://doi.org/10.3390/bdcc8010006","url":null,"abstract":"In the era of digital advancements, the escalation of credit card fraud necessitates the development of robust and efficient fraud detection systems. This paper delves into the application of machine learning models, specifically focusing on ensemble methods, to enhance credit card fraud detection. Through an extensive review of existing literature, we identified limitations in current fraud detection technologies, including issues like data imbalance, concept drift, false positives/negatives, limited generalisability, and challenges in real-time processing. To address some of these shortcomings, we propose a novel ensemble model that integrates a Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), Bagging, and Boosting classifiers. This ensemble model tackles the dataset imbalance problem associated with most credit card datasets by implementing under-sampling and the Synthetic Over-sampling Technique (SMOTE) on some machine learning algorithms. The evaluation of the model utilises a dataset comprising transaction records from European credit card holders, providing a realistic scenario for assessment. The methodology of the proposed model encompasses data pre-processing, feature engineering, model selection, and evaluation, with Google Colab computational capabilities facilitating efficient model training and testing. Comparative analysis between the proposed ensemble model, traditional machine learning methods, and individual classifiers reveals the superior performance of the ensemble in mitigating challenges associated with credit card fraud detection. Across accuracy, precision, recall, and F1-score metrics, the ensemble outperforms existing models. This paper underscores the efficacy of ensemble methods as a valuable tool in the battle against fraudulent transactions. The findings presented lay the groundwork for future advancements in the development of more resilient and adaptive fraud detection systems, which will become crucial as credit card fraud techniques continue to evolve.","PeriodicalId":505155,"journal":{"name":"Big Data and Cognitive Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139451555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unveiling Sentiments: A Comprehensive Analysis of Arabic Hajj-Related Tweets from 2017–2022 Utilizing Advanced AI Models 揭示情感：利用先进的人工智能模型全面分析 2017-2022 年阿拉伯语朝觐相关推文

Big Data and Cognitive Computing Pub Date : 2024-01-02 DOI: 10.3390/bdcc8010005

Hanan M. Alghamdi

{"title":"Unveiling Sentiments: A Comprehensive Analysis of Arabic Hajj-Related Tweets from 2017–2022 Utilizing Advanced AI Models","authors":"Hanan M. Alghamdi","doi":"10.3390/bdcc8010005","DOIUrl":"https://doi.org/10.3390/bdcc8010005","url":null,"abstract":"Sentiment analysis plays a crucial role in understanding public opinion and social media trends. It involves analyzing the emotional tone and polarity of a given text. When applied to Arabic text, this task becomes particularly challenging due to the language’s complex morphology, right-to-left script, and intricate nuances in expressing emotions. Social media has emerged as a powerful platform for individuals to express their sentiments, especially regarding religious and cultural events. Consequently, studying sentiment analysis in the context of Hajj has become a captivating subject. This research paper presents a comprehensive sentiment analysis of tweets discussing the annual Hajj pilgrimage over a six-year period. By employing a combination of machine learning and deep learning models, this study successfully conducted sentiment analysis on a sizable dataset consisting of Arabic tweets. The process involves pre-processing, feature extraction, and sentiment classification. The objective was to uncover the prevailing sentiments associated with Hajj over different years, before, during, and after each Hajj event. Importantly, the results presented in this study highlight that BERT, an advanced transformer-based model, outperformed other models in accurately classifying sentiment. This underscores its effectiveness in capturing the complexities inherent in Arabic text.","PeriodicalId":505155,"journal":{"name":"Big Data and Cognitive Computing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139452708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0