Mesopotamian Journal of Big Data最新文献

{"title":"Portable Smart Emergency System Using Internet of Things (IOT)","authors":"Batool Jamal, Muneera Alsaedi, Parag Parandkar","doi":"10.58496/mjbd/2023/011","DOIUrl":"https://doi.org/10.58496/mjbd/2023/011","url":null,"abstract":"The portable smart emergency system is a pre-test tool, implemented using a set of modern devices and technologies to monitor the patient's health. This tool has capability to send reports of the patient to the doctor treating the patient as well as to the relatives, close friends of the patient in real time. Health parameters of the patient viz. heart rate, blood oxygen and temperature are monitored using electronic devices viz. WEMOS D1, MAX30100, DS18B20, SIM808 on the LCD screen and stored using the MySQL database. PHP script is used to connect MySQ database for easy tracking and analysis of medical data. Doctors are facilitated to monitor the health update in real time, at the same time, communicate the same to the patient and their relatives, close friends through a dynamic web site constituted of HTML, CSS and JavaScript for the purpose of easy tracking and analysis of the medical data. To aid further, as a part of value addition, an Android based mobile app is also developed by using App Inventor to further facilitate patients, family members & close friends to monitor sensor data, receive messages and access medical history details, all in real time. Terminal cases, where the health update received from the sensor shows alarmingly high or low readings, then web enabled computing system, also sends a high alert message by playing a warning sound to the doctor, at the same time, also communicates patient’s location to him via text message to enable immediate help. By using Wi-Fi technology and the SIM808 module, the patient's location can be monitored in emergency situations and a text message containing the patient's geographical location can be sent to the treating doctor. This application also includes an option to enter the patient's medical history information using a PHP script into the database.","PeriodicalId":325612,"journal":{"name":"Mesopotamian Journal of Big Data","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133544444","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}

{"title":"The Data-Driven Future of Healthcare: A Review","authors":"Muhammad Miftahul Amri, Saad Abbas Abed","doi":"10.58496/mjbd/2023/010","DOIUrl":"https://doi.org/10.58496/mjbd/2023/010","url":null,"abstract":"The future of disease detection, treatment, and prevention may very well lie in data-driven healthcare. Here, we take stock of where things stand and highlight certain emerging issues and long-standing difficulties. We looked at all the research that has been published on the topic of data-driven healthcare decision-making. Our research shows that the use of data in healthcare has already improved patient care and results. However, there are substantial obstacles that must be overcome, such as the need to more effectively integrate data from multiple sources, as well as issues with data quality, privacy, and security. Furthermore, healthcare data use raises significant ethical concerns. We examine what these results mean for the future of data-driven healthcare and offer suggestions on where future research should focus. We conclude that data-driven healthcare has the potential to transform healthcare delivery and enhance patient outcomes, but that the inherent difficulties and dangers of this approach must be carefully considered.","PeriodicalId":325612,"journal":{"name":"Mesopotamian Journal of Big Data","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117102896","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}

{"title":"Active learning with Bayesian CNN using the BALD method for Hyperspectral Image Classification","authors":"Mahmood Siddeeq Qadir, G. Bilgin","doi":"10.58496/mjbd/2023/008","DOIUrl":"https://doi.org/10.58496/mjbd/2023/008","url":null,"abstract":"Deep learning DL techniques have recently been used to examine the classification of remote sensing data like hyperspectral images HSI. However, DL models are difficult to obtain since they rely largely on a large number of labeled training data. Therefore, a current challenge in the field of HSI classification is how to effectively incorporate DL models in constrained labeled data. The Bayesian Convolutional Neural Networks BCNN method is robust against overfitting on small datasets. One of the key methods for automating data selection is active learning AL, which has gained popularity in recent decades. By choosing the most informative samples, AL aims to reduce the costly data labeling procedure and build a robust training set that is resource-efficient. In this work, we aim to improve the performance of BCNN using AL method to build a competitive classifier considering the Bayesian Active Learning Disagreement BALD acquisition function (Dropout Bayesian Active Learning by Disagreement), which incorporates model uncertainty information. In a previous work, BCNN was built and applied on Pavia datasets giving 99.7% classification accuracy. For comparison traditional BCNN with BALD, The techniques were applied on the Indian Pines dataset. The average accuracy of the classification had increased from 90% to 98% using BALD method.","PeriodicalId":325612,"journal":{"name":"Mesopotamian Journal of Big Data","volume":"246 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122920435","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}

{"title":"Analysing the Connection Between AI and Industry 4.0 from a Cybersecurity Perspective: Defending the Smart Revolution","authors":"I. Bala, Maad M. Mijwil, Guma Ali, Emre Sadıkoğlu","doi":"10.58496/mjbd/2023/009","DOIUrl":"https://doi.org/10.58496/mjbd/2023/009","url":null,"abstract":"In recent years, the significance and efficiency of business performance have become dependent heavily on digitization, as jobs in companies are seeking to be transformed into digital jobs based on smart systems and applications of the fourth industrial revolution. Cybersecurity systems must interact and continuously cooperate with authorized users through the Internet of Things and benefit from corporate services that allow users to interact in a secure environment free from electronic attacks. Artificial intelligence methods contribute to the design of the Fourth Industrial Revolution principles, including interoperability, information transparency, technical assistance, and decentralized decisions. Through this design, security gaps may be generated that attackers can exploit in order to be able to enter systems, control them, or manipulate them. In this paper, the role of automated systems for digital operations in the fourth industrial revolution era will be examined from the perspective of artificial intelligence and cybersecurity, as well as the most significant practices of artificial intelligence methods. This paper concluded that artificial intelligence methods play a significant role in defending and protecting cybersecurity and the Internet of Things, preventing electronic attacks, and protecting users' privacy.","PeriodicalId":325612,"journal":{"name":"Mesopotamian Journal of Big Data","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128306964","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}

{"title":"Effectual Text Classification in Data Mining: A Practical Approach","authors":"I. Salem, Alaa Wagih Abdulqader, Atheel Sabih Shaker","doi":"10.58496/mjbd/2023/007","DOIUrl":"https://doi.org/10.58496/mjbd/2023/007","url":null,"abstract":"Text classification is the process of setting records into classes that have already been set up based on what they say. It automatically puts texts in natural languages into categories that have already been set up. Text classification is the most crucial part of text retrieval systems, which find texts based on what the user requests, and text understanding systems, which change the text in some way, like by making summaries, answering questions, or pulling out data. Existing algorithms that use supervised learning to classify text automatically need enough examples to learn well. The algorithms for data mining are used to classify texts, as well as a review of the work that has been done on classifying texts. Design/Methodology/Approach: Data mining algorithms that are used to classify texts were talked about, and studies that looked at how these algorithms were used to classify texts were looked at, with a focus on comparative studies. Findings: No classifier can always do the best job because different datasets and situations lead to different classification accuracy. Implications for Real Life: When using data mining algorithms to classify text documents, it's important to keep in mind that the conditions of the data will affect how well the documents are classified. For this reason, the data should be well organized.","PeriodicalId":325612,"journal":{"name":"Mesopotamian Journal of Big Data","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129617784","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}

{"title":"Large Scale Data Using K-Means","authors":"Omaima Essaad Belhaj, Raheela zaib, Ourlis Ourabah","doi":"10.58496/mjbd/2023/006","DOIUrl":"https://doi.org/10.58496/mjbd/2023/006","url":null,"abstract":"Regular data base questioning tactics are insufficient to extract meaningful data due to the exponential expansion of high layered datasets; therefore, analysts nowadays are forced to build new processes to satisfy the increased needs. Because of the development in the number of data protests as well as the expansion in the number of elements/ascribes, such vast articulation data leads to numerous new computational triggers. To increase the effectiveness and accuracy of mining activities on highly layered data, the data should be preprocessed using a successful dimensionality decrease technique. So we have collected ideas of different researchers. In several fields, cluster analysis has recently gained popularity as a method for data analysis. A popular parceling-based clustering method called K-means searches for a certain number of clusters that may be found by their centroids. However, the results are quite dependent on the original cluster focus sites. Once more, the number of distance calculations significantly grows as the complexity of the data increases. This is because building a high-precision model frequently necessitates a sizable and dispersed preparatory set. A large preparation set could also need a significant amount of preparation time. There is a trade-off between speed and accuracy when creating orders, especially for large data sets. Vector data are frequently clustered, packed, and summed using the k-means approach. We provide No Concurrent Specific Clumped K-means, a rapid and memory-effective GPU-based approach for cautious k-means (ASB K-means). In contrast to previous GPU-based k-means methods, which require stacking the entire dataset onto the GPU for clustering, our methodology may be tailored to consume far less GPU RAM than the size of the complete dataset. As a result, we may cluster datasets that are bigger than the available RAM. In order to effectively handle large datasets, the method employs a clustered architecture and applies the triangle disparity in each k-means focus to eliminate a data point on the off chance that its enrollment task, or the cluster it is a member of, remains unchanged. As a result, fewer data guides have to be sent between the Slam of the computer processor and the global memory of the GPU.","PeriodicalId":325612,"journal":{"name":"Mesopotamian Journal of Big Data","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127975963","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}

{"title":"ChatGPT and Big Data: Enhancing Text-to-Speech Conversion","authors":"Hatim Abdelhak Dida, D. Chakravarthy, F. Rabbi","doi":"10.58496/mjbd/2023/005","DOIUrl":"https://doi.org/10.58496/mjbd/2023/005","url":null,"abstract":"Text-to-speech (TTS) conversion is a crucial technology for various applications, including accessibility, education, and entertainment. With the rapid growth of big data, TTS conversion systems face new challenges in terms of data size and diversity. In this paper, we propose to use the state-of-the-art language model ChatGPT to enhance TTS conversion for big data. We first introduce the background of TTS conversion and big data, and then review the existing TTS conversion systems and their limitations. Next, we describe the architecture and training of ChatGPT, and how it can be applied to TTS conversion. Finally, we evaluate the performance of the ChatGPT-based TTS conversion system on a large-scale real-world big data dataset, and compare it with the existing TTS systems. Our experimental results demonstrate that ChatGPT can significantly improve the quality and efficiency of TTS conversion for big data.","PeriodicalId":325612,"journal":{"name":"Mesopotamian Journal of Big Data","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128073091","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}

{"title":"Transfer Learning: A New Promising Techniques","authors":"Ahmed Ali, M. Yaseen, Mohammad Aljanabi, Saad Abbas Abed, C. Gpt","doi":"10.58496/mjbd/2023/004","DOIUrl":"https://doi.org/10.58496/mjbd/2023/004","url":null,"abstract":"Transfer Learning[1] is a machine learning technique that involves utilizing knowledge learned from one task to improve performance on another related task. This approach has been widely adopted in various fields such as computer vision, natural language processing, and speech recognition. The goal of this paper is to provide an overview of transfer learning and its recent developments. Transfer learning is particularly useful in situations where there is limited labeled data available for the target task. In these cases, the model can leverage knowledge learned from a related task with a larger amount of labeled data. This allows the model to overcome the problem of overfitting and improve performance on the target task.","PeriodicalId":325612,"journal":{"name":"Mesopotamian Journal of Big Data","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122398650","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}

{"title":"Mobile Big Data Analytics Using Deep Learning and Apache Spark","authors":"Muhammad Azeem, Bassam M Abualsoud, Dimuthu Priyadarshana","doi":"10.58496/mjbd/2023/003","DOIUrl":"https://doi.org/10.58496/mjbd/2023/003","url":null,"abstract":"The new mobile big data is the result of the proliferation of mobile devices such as PDAs and Internet of Things (IoT) gadgets. Collecting MBDs is not economically viable unless appropriate analytical and learning approaches are applied to extract key facts and hidden designs from the data. In the current study we have used published data of different researchers from 2015 to 2021. This white paper validates flexible learning structures via Apache Spark and provides an introduction to deep learning in MBD analysis and a simple training exercise. In particular, guided iterations are used to perform certain deep learning tasks. We have reduced the number of many Spark employees. With the prevalence of big data, there have been some recent advances in this area. Each Spark worker trains a fractional deep model on some common MBD and averages the range of all Midway models to build an expert deep model. For example, systems such as Apache Hadoop and Apache Spark have grown in popularity in recent years and are fairly well known, especially in the commercial world. It is becoming increasingly clear that effective big data analytics are essential to address issues related to artificial intelligence. As such, MLlib, a multi-computational library, has been implemented in his Spark system. The library supports a wide variety of AI computations, but the Spark setup can be effectively used to do very slow and computationally intensive approaches like deep learning.","PeriodicalId":325612,"journal":{"name":"Mesopotamian Journal of Big Data","volume":"372 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113993911","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}

{"title":"Parallel Machine Learning Algorithm","authors":"S. Salman, Saad Ahmed Dheyab, Q. M. Salih, Waleed A. Hammood","doi":"10.58496/mjbd/2023/002","DOIUrl":"https://doi.org/10.58496/mjbd/2023/002","url":null,"abstract":"Parallel machine learning algorithms are a class of algorithms that can be run on multiple processors or computers in parallel in order to speed up the training process. These algorithms are becoming increasingly important as the volume and complexity of data continue to grow, and as organizations seek to extract valuable insights from data in a timely and cost-effective manner. In this review, we provide an overview of the various approaches that have been proposed for parallelizing machine learning algorithms, including data parallelism, model parallelism, and hybrid approaches. We also discuss the challenges and opportunities of parallel machine learning, including issues related to data partitioning, communication, and scalability. We evaluate the performance of different approaches on a range of machine learning tasks and datasets, and discuss the limitations and trade-offs of different approaches. Finally, we provide insights on the future direction of research in this area and identify areas where further work is needed. Overall, this review provides a comprehensive overview of the field of parallel machine learning and highlights the importance of this area for organizations seeking to extract insights from large datasets.","PeriodicalId":325612,"journal":{"name":"Mesopotamian Journal of Big Data","volume":"402 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134105827","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}