Data Storage Systems Commons^™

Paradigm Review Of Data Localization In India And Its Implications For China, Ying Fan

Bulletin of Chinese Academy of Sciences (Chinese Version)

Data localization is a focal point of global data governance and its impact on global data governance is no longer confined to a single country. Over the years, India has followed a unique policy framework in terms of cross-border data flows and data localization, and its insistence on data sovereignty reflects its position in the international arena. This study uses the Indian data localization paradigm as a research base to discuss the common phenomenon of disconnect between policy motivations and practical effects of data localization, and as an entry point to introduce the latest Indian research findings in this area. …

Research On Multi-Source Heterogeneous Big Data Fusion Based On Wsr, Aihua Li, Weijia Xu, Yong Shi

Bulletin of Chinese Academy of Sciences (Chinese Version)

In the era of multi-source heterogeneous big data, big data presents new features such as cross, diversity and variability. The applications of big data in a wider range of fields have new requirements for data fusion. Under this background, the connotation of data fusion is enriched and expanded. The generalized data fusion includes the fusion of data resources, the fusion of model methods, and the fusion of decision-makers' knowledge and experience. This study analyzes the characteristics of multi-source heterogeneous data fusion at three different fusion levels: data level, information level and decision level, and discusses challenges for data fusion in …

Performance Modeling Of Inline Compression With Software Caching For Reducing The Memory Footprint In Pysdc, Sansriti Ranjan

All Theses

Modern HPC applications compute and analyze massive amounts of data. The data volume is growing faster than memory capabilities and storage improvements leading to performance bottlenecks. An example of this is pySDC, a framework for solving collocation problems iteratively using parallel-in-time methods. These methods require storing and exchanging 3D volume data for each parallel point in time. If a simulation consists of M parallel-in-time stages, where the full spatial problem has to be stored for the next iteration, the memory demand for a single state variable is M ×Nx ×Ny ×Nz per time-step. For an application simulation with many state …

Transaction Management Sysyem For A Publisher, Hassain Shareef Mohammed Jr

Electronic Theses, Projects, and Dissertations

Managing the day-to-day operations of a publishing house isn't easy. For example, it's hard to keep track of stocks, manage vendor orders, and maintain transaction records. Our project titled Transaction Management System aims to solve these problems by offering a single tool that helps standardize and digitize publishing operations. It assists in automating and improving the efficiency of the various processes involved in a publishing house. The Transaction Management System is a full-stack web application that can be accessed through an internet browser. The user experience and interface are simple and easy to use so that users can find information …

Deep Learning For Multi-Structured Javanese Gamelan Note Generator, Arik Kurniawati, Eko Mulyanto Yuniarno, Yoyon Kusnendar Suprapto

Knowledge Engineering and Data Science

Javanese gamelan, a traditional Indonesian musical style, has several song structures called gendhing. Gendhing (songs) are written in conventional notation and require gamelan musicians to recognize patterns in the structure of each song. Usually, previous research on gendhing focuses on artistic and ethnomusicological perspectives, but this study is to explore the correlation between gendhing as traditional music in Indonesia and deep learning technology that replaces the task of gamelan composers. This research proposes CNN-LSTM to generate notation of ricikan struktural instruments as an accompaniment to Javanese gamelan music compositions based on balungan notation, rhythm, song structure, and gatra …

Finchain: Adaptation Of Blockchain Technology In Finance And Business - An Ethical Analysis Of Applications, Challenges, Issues And Solutions, Naresh Kshetri, Keith Miller, Gaurango Banerjee, Bikesh Raj Upreti

International Journal of Emerging and Disruptive Innovation in Education : VISIONARIUM

Blockchain Technology is a distributed database technology that has emerged as a ground-breaking technology with several possible solutions to critical applications, say from supply chain management, agribusiness, marketing to healthcare industry including internet of medical things. Although it started as a digital coin (popularly known as bitcoin), it is slowly influencing business, marketing policy and society. We have presented an in-depth study and ethical analysis of how blockchain is applied over the economic and financial sector including banks, credit unions and other retail giants. During our research, we have also investigated how blockchain technology can affect financial institutions around the …

An Enhanced Adaptive Learning System Based On Microservice Architecture, Abdelsalam Helmy Ibrahim, Mohamed Eliemy, Aliaa Abdelhalim Youssif

Future Computing and Informatics Journal

This study aims to enhance Adaptive Learning Systems (ALS) in Petroleum Sector in Egypt by using the Microservice Architecture and measure the impact of enhancing ALS by participating ALS users through a statistical study and questionnaire directed to them if they accept to apply the Cloud Computing Service “Microservices” to enhance the ALS performance, quality and cost value or not. The study also aims to confirm that there is a statistically significant relationship between ALS and Cloud Computing Service “Microservices” and prove the impact of enhancing the ALS by using Microservices in the cloud in Adaptive Learning in the Egyptian …

Risk Assessment Approaches In Banking Sector –A Survey, Mona Sharaf, Shimaa Mohamed Ouf, Amira M. Idrees Ami

Future Computing and Informatics Journal

Prediction analysis is a method that makes predictions based on the data currently available. Bank loans come with a lot of risks to both the bank and the borrowers. One of the most exciting and important areas of research is data mining, which aims to extract information from vast amounts of accumulated data sets. The loan process is one of the key processes for the banking industry, and this paper examines various prior studies that used data mining techniques to extract all served entities and attributes necessary for analytical purposes, categorize these attributes, and forecast the future of their business …

News’ Credibility Detection On Social Media Using Machine Learning Algorithms, Farah Yasser, Sayed Abdelmawgoud, Amira M. Idrees Ami

Future Computing and Informatics Journal

Social media is essential in many aspects of our lives. Social media allows us to find news for free. anyone can access it easily at any time. However, social media may also facilitate the rapid spread of misleading news. As a result, there is a probability that low-quality news, including incorrect and fake information, will spread over social media. As well as detecting news credibility on social media becomes essential because fake news can affect society negatively, and the spread of false news has a considerable impact on personal reputation and public trust. In this research, we conducted a model …

Visual Question Answering: A Survey, Gehad Assem El-Naggar

Future Computing and Informatics Journal

Visual Question Answering (VQA) has been an emerging field in computer vision and natural language processing that aims to enable machines to understand the content of images and answer natural language questions about them. Recently, there has been increasing interest in integrating Semantic Web technologies into VQA systems to enhance their performance and scalability. In this context, knowledge graphs, which represent structured knowledge in the form of entities and their relationships, have shown great potential in providing rich semantic information for VQA. This paper provides an abstract overview of the state-of-the-art research on VQA using Semantic Web technologies, including knowledge …

Exploring The Impact Of Students Demographic Attributes On Performance Prediction Through Binary Classification In The Kdp Model, Issah Iddrisu, Peter Appiahene, Obed Appiah, Inusah Fuseini

Knowledge Engineering and Data Science

During the course of this research, binary classification and the Knowledge Discovery Process (KDP) were used. The experimental and analytical capabilities of Rapid Miner's 9.10.010 instructional environment are supported by five different classifiers. Included in the analysis were 2334 entries, 17 characteristics, and one class variable containing the students' average score for the semester. There were twenty experiments carried out. During the studies, 10-fold cross-validation and ratio split validation, together with bootstrap sampling, were used. It was determined whether or not to use the Random Forest (RF), Rule Induction (RI), Naive Bayes (NB), Logistic Regression (LR), or Deep Learning (DL) …

Maximum Marginal Relevance And Vector Space Model For Summarizing Students' Final Project Abstracts, Gunawan Gunawan, Fitria Fitria, Esther Irawati Setiawan, Kimiya Fujisawa

Knowledge Engineering and Data Science

Automatic summarization is reducing a text document with a computer program to create a summary that retains the essential parts of the original document. Automatic summarization is necessary to deal with information overload, and the amount of data is increasing. A summary is needed to get the contents of the article briefly. A summary is an effective way to present extended information in a concise form of the main contents of an article, and the aim is to tell the reader the essence of a central idea. The simple concept of a summary is to take an essential part of …

Inter-Frame Video Compression Based On Adaptive Fuzzy Inference System Compression Of Multiple Frame Characteristics, Arief Bramanto Wicaksono Putra, Rheo Malani, Bedi Suprapty, Achmad Fanany Onnilita Gaffar, Roman Voliansky

Knowledge Engineering and Data Science

Video compression is used for storage or bandwidth efficiency in clip video information. Video compression involves encoders and decoders. Video compression uses intra-frame, inter-frame, and block-based methods. Video compression compresses nearby frame pairs into one compressed frame using inter-frame compression. This study defines odd and even neighboring frame pairings. Motion estimation, compensation, and frame difference underpin video compression methods. In this study, adaptive FIS (Fuzzy Inference System) compresses and decompresses each odd-even frame pair. First, adaptive FIS trained on all feature pairings of each odd-even frame pair. Video compression-decompression uses the taught adaptive FIS as a codec. The features utilized …

Ant Colony Optimization For Resistor Color Code Detection, Slamet Wibawanto, Kartika Candra Kirana, Hani Ramadhan

Knowledge Engineering and Data Science

In the early stages of learning resistors, introducing color-based values is needed. Moreover, some combinations require a resistor trip analysis to identify. Unfortunately, a resistor body color is considered a local solution, which often confuses resistor coloration. Ant Colony Optimization (ACO) is a heuristic algorithm that can recognize problems with traveling a group of ants. ACO is proposed to select commercial matrix values to be computed without preventing local solutions. In this study, each explores the matrix based on pheromones and heuristic information to generate local solutions. Global solutions are selected based on their high degree of similarity with other …

K-Means Clustering And Multilayer Perceptron For Categorizing Student Business Groups, Miftahul Walid, Norfiah Lailatin Nispi Sahbaniya, Hozairi Hozairi, Fajar Baskoro, Arya Yudhi Wijaya

Knowledge Engineering and Data Science

The research conducted in this study was driven by the East Java provincial government's requirement to assess the transaction levels of the Student Business Group (KUS) in the SMA Double Track program. These transaction levels are a basis for allocating supplementary financial aid to each business group. The system's primary objective is to assist the provincial government of East Java in making well-informed choices pertaining to the distribution of supplementary capital to the KUS. The classification technique employed in this study is the multilayer perceptron. However, the K-Means Clustering method is utilised to generate target data due to the limited …

Round-Robin Algorithm In Load Balancing For National Data Centers, I Kadek Wahyu Sudiatmika, Gede Indrawan, Sariyasa Sariyasa

Knowledge Engineering and Data Science

The Provincial Government of Bali assumes a crucial role in administering various public service applications to meet the requirements of its community, traditional villages, and regional apparatus. Nevertheless, the escalating magnitude of traffic and uneven distribution of requests have resulted in substantial server burdens, which may jeopardize the operation of applications and heighten the likelihood of downtime. Ensuring efficient load distribution is of utmost importance in tackling these difficulties, and the Round Robin algorithm is often utilized for this purpose. However, the current body of research has not extensively examined the distinct circumstances surrounding on-premise servers in the Bali Provincial …

Long-Term Traffic Prediction Based On Stacked Gcn Model, Atkia Akila Karim, Naushin Nower

Knowledge Engineering and Data Science

With the recent surge in road traffic within major cities, the need for both short and long-term traffic flow forecasting has become paramount for city authorities. Previous research efforts have predominantly focused on short-term traffic flow estimations for specific road segments and paths. However, applications of paramount importance, such as traffic management and schedule routing planning, demand a deep understanding of long-term traffic flow predictions. However, due to the intricate interplay of underlying factors, there exists a scarcity of studies dedicated to long-term traffic prediction. Previous research has also highlighted the challenge of lower accuracy in long-term predictions owing to …

Optimizing Random Forest Algorithm To Classify Player's Memorisation Via In-Game Data, Akmal Vrisna Alzuhdi, Harits Ar Rosyid, Mohammad Yasser Chuttur, Shah Nazir

Knowledge Engineering and Data Science

Assessment of a player's knowledge in game education has been around for some time. Traditional evaluation in and around a gaming session may disrupt the players' immersion. This research uses an optimized Random Forest to construct a non-invasive prediction of a game education player's Memorization via in-game data. Firstly, we obtained the dataset from a 3-month survey to record in-game data of 50 players who play 4-15 game stages of the Chem Fight (a test case game). Next, we generated three variants of datasets via the preprocessing stages: resampling method (SMOTE), normalization (min-max), and a combination of resampling and normalization. …

Characterization And Estimation Of Musculoskeletal Pain Using Machine Learning, Boluwatife Faremi

Master's Theses

Traditional scales utilized for recording pain are known to be highly subjective and biased due to inaccuracies in recollecting actual pain intensities. As a result, machine learning (ML) models that are trained using these scores as ground truth are reported to have low performance for objective pain classification because of the huge disparity between what was felt in moments of pain and the scores recorded afterward.

In the present study, two devices were designed for gathering real-time, continuous in-session subjective pain scores and the recording of the autonomic nervous system (ANS) altered endodermal (EDA) activity. 24 participants were recruited to …

Polyflowbuilder: An Intuitive Tool For Academic Planning At Cal Poly San Luis Obispo, Duncan Thomas Applegarth

Computer Engineering

PolyFlowBuilder is a web application that lets users create visually intuitive flowcharts to aid in academic planning at Cal Poly. These flowcharts can be customized in a variety of ways to accurately represent complex academic plans, such as double majors, minors, taking courses out- of-order, etc. The original version of PolyFlowBuilder, released Summer 2020, was not written for continued expansion and growth. Therefore, a complete rewrite was determined to be necessary to enable the project to grow in the future. This report details the process to completely rewrite the existing version of PolyFlowBuilder over the course of six months, using …