Review
Management
Andrew Greasley, John Steven Edwards
Summary: This article discusses the combination of discrete-event simulation technology and big data analytics methods, proposing a framework for DES development methodology.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2021)
Article
Chemistry, Analytical
Jenniffer S. Guerrero-Prado, Wilfredo Alfonso-Morales, Eduardo F. Caicedo-Bravo
Summary: The paper introduces a Data Analytics/Big Data framework applied to AMI data in Smart Cities, encompassing architectural view, methodological view, and human expertise as a binding element. This framework aims to support optimal decision-making and transform knowledge into wisdom efficiently.
Article
Urban Studies
Jens Kandt, Michael Batty
Summary: The analysis of big data is expected to define a new era in urban research, planning, and policy-making, promising smoother decision-making processes as part of a more evidence-based and smarter urbanism. However, there are also risks and challenges associated with over-reliance on data. Currently, there is limited research on how big data can realistically contribute to addressing urban policy problems.
Review
Multidisciplinary Sciences
Caihua Liu, Guochao Peng, Yongxin Kong, Shuyang Li, Si Chen
Summary: This study systematically reviewed the impact of data quality on big data analytics in smart factories and identified key research themes. The findings can help practitioners better address data quality issues to support the application of digital symmetry in smart factories.
Article
Computer Science, Hardware & Architecture
Nasir Ali Khan, Abid Khan, Mansoor Ahmad, Munam Ali Shah, Gwanggil Jeon
Summary: Future networking technologies like 5G and 6G will offer significant performance improvements and opportunities. Existing URL filtering techniques have limitations, but we have developed a real-time, fault-tolerant, and scalable machine learning model to classify URL traffic efficiently.
COMPUTERS & ELECTRICAL ENGINEERING
(2021)
Article
Chemistry, Analytical
Ebtesam Alomari, Iyad Katib, Aiiad Albeshri, Tan Yigitcanlar, Rashid Mehmood
Summary: This paper focuses on detecting road traffic-related events using big data and distributed machine learning, particularly in Arabic language Twitter data. The study demonstrates the effectiveness of using support vector machines, naive Bayes, and logistic regression-based classifiers to detect and validate real events in Saudi Arabia, showcasing the potential of Twitter media in detecting important events without prior knowledge.
Article
Social Sciences, Interdisciplinary
Md Altab Hossin, Jie Du, Lei Mu, Isaac Owusu Asante
Summary: This study explores the potential of big data analytics in public policy systems and identifies common big data sources and techniques that can be used at various stages of the policy process. It argues that big data analytics has the potential to enhance policy formulation, execution, and evaluation, and can transform traditional governance systems into digital and smart governance.
Article
Computer Science, Information Systems
Mohammed Hasan Ali, Mustafa Musa Jaber, Sura Khalil Abd, Ahmed Alkhayyat, Mustafa Fahem Albaghdadi
Summary: Big data and cloud computing are becoming increasingly critical in transportation systems. Through predictive analytics, transportation companies can identify and predict potential traffic problems and offer appropriate responses. Research shows that an intelligent transportation system built with big data analytics and cloud computing technologies has high accuracy in traffic forecasting.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Lu Zheng, Cong Wang, Xue Chen, Yihang Song, Zihan Meng, Ru Zhang
Summary: The development of machine learning has promoted the construction of smart education platforms. In this paper, we explore how to utilize evolutionary algorithms and machine learning algorithms to build a smart education big data platform to promote the intelligent development of higher education and better assist the establishment of the smart education system.
APPLIED SOFT COMPUTING
(2023)
Review
Computer Science, Interdisciplinary Applications
Fangyu Li, Yuanjun Laili, Xuqiang Chen, Yihuai Lou, Chen Wang, Hongyan Yang, Xuejin Gao, Honggui Han
Summary: The construction industry is undergoing an intelligent revolution enabled by technologies like IoT, cloud computing, and robotics. Utilizing diverse big data from multiple sources can enhance efficiency, reduce waste and expenses, improve planning and decision-making processes, lower errors, and enhance safety at construction sites. This article provides a comprehensive review of the advantages and current state of big data in the construction industry, addressing unresolved difficulties and offering thoughts on its potential future.
JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION
(2023)
Article
Green & Sustainable Science & Technology
Christopher James Pettit, Simone Zarpelon Leao, Oliver Lock, Matthew Ng, Jonathan Reades
Summary: This paper examines the application of big data in urban planning, using the case of transportation planning in Sydney as an example. By analyzing smart card data, the paper explores the mobility patterns in the city through the lens of the 30 min city concept.
Article
Agronomy
Chouaib El Hachimi, Salwa Belaqziz, Said Khabba, Badreddine Sebbar, Driss Dhiba, Abdelghani Chehbouni
Summary: Smart management of weather data is crucial for sustainable and precise agriculture. This paper presents a smart weather data management system that utilizes advanced statistical methods, machine learning, and deep learning models to provide various services including weather time series forecasts, visualization and analysis of meteorological data, and machine learning estimation of reference evapotranspiration (ET0). The system represents an incremental step towards implementing smart and sustainable agriculture in Morocco.
Article
Computer Science, Information Systems
Reguieg Hicham, Benallal Mohamed Anis
Summary: Process mining is a business process management technique used to extract value from process execution logs. This study proposes a distributed implementation based on the Spark framework for efficient scalable process discovery in big data scenarios. Experimental results demonstrate that the proposed approach achieves significant speed-up and scalability when dealing with large datasets and varying cluster sizes.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Shih Yu Chang, Hsiao-Chun Wu
Summary: This paper proposes a new divide-and-iterate framework for efficient processing of big-data matrices and solving large linear systems of equations using factored matrices. The convergence of the new iterative algorithms is rigorously proved, and the time and memory complexities are studied to demonstrate the resource efficiency of the proposed algorithms. Numerical experiments are conducted to illustrate the effectiveness of the new framework.
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2022)
Article
Business
Hyoung-Yong Choi, Junyoung Park
Summary: This study empirically investigates the impact of using big data analytics (BDA) in corporate social responsibility (CSR) activities on CSR performance. The study finds that the positive interaction effect between BDA-enabled CSR and big data analytics capability (BDAC) is pronounced in the categories of environmental impact, employee relations, product safety, and corporate governance. The study contributes to the literature on BDA and CSR by demonstrating how BDA-enabled CSR and BDAC influence CSR performance.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2022)
Article
Computer Science, Artificial Intelligence
Manas Ranjan Pradhan, Karamath Ateeq, Beenu Mago
Summary: The study introduces a Disease Prediction and Symptom Recognition Model using IoT technology, incorporating data mining analysis and AI feedback. Through comprehensive genome interaction analysis, conditions of patients can be successfully predicted and warning emails with medical advice are sent to them, resulting in an improved prediction rate and accuracy.
INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS
(2021)
Article
Computer Science, Information Systems
Manas Ranjan Pradhan, Beenu Mago, Karamath Ateeq
Summary: This article proposes a fault-tolerant data processing method for handling uneven sensor data, and uses a support vector machine classifier to provide reliable recommendations. The method can be applied in IoT wearable technology, and its performance is verified using multiple metrics.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Mathematical & Computational Biology
Jesus Cuauhtemoc Tellez Gaytan, Karamath Ateeq, Aqila Rafiuddin, Haitham M. Alzoubi, Taher M. Ghazal, Tariq Ahamed Ahanger, Sunita Chaudhary, G. K. Viju
Summary: Capital structure is a crucial element in corporate finance that impacts the growth and operations of a company. The choice between debt and equity financing is vital for a company's sustainable growth in a financially constrained environment. Accurate estimation of the cost of capital is of great importance. This study examined the capital structure of the top ten stocks in the MSCI Emerging Index, using various forecasting methods. The findings suggest that artificial neural networks have the potential to replace traditional models in forecasting nonstationary data.
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
(2022)
Retraction
Computer Science, Artificial Intelligence
Karamath Ateeq, Beenu Mago, Manas Ranjan Pradhan
Article
Chemistry, Multidisciplinary
Ziyad R. Alashhab, Mohammed Anbar, Shaza Dawood Ahmed Rihan, Basim Ahmad Alabsi, Karamath Ateeq
Summary: This research proposes a publicly available benchmark dataset based on an actual cloud computing environment for evaluating and improving the detection system of distributed denial-of-service attacks. The dataset has the advantages of trustworthiness and validity, enabling reliable evaluations and comparisons. It includes both internal and external HTTP-GET flood DDoS attacks, aiming to enhance the security of cloud computing environments.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Artificial Intelligence
Arodh Lal Karn, Karamath Ateeq, Sudhakar Sengan, Indra Gandhi, Logesh Ravi, Dilip Kumar Sharma, V Subramaniyaswamy
Summary: Deep Learning in finance plays a crucial role in transaction processing, risk assessment, and behavior prediction. This article proposes a new composite structured Deep Sequential Learning model for complex data flows, which has proven to be highly efficient in cases like Fraud Detection Systems. Additionally, a trained NB classifier utilizing optimized transaction eigenvectors outperforms standard approaches in identifying transaction fraud.
MALAYSIAN JOURNAL OF COMPUTER SCIENCE
(2022)
Proceedings Paper
Business
Nidhi Oswal, Karamath Ateeq, Saju Mathew
Summary: The automation of industry and business practices, known as Industry 4.0, has led to the production of smart machines and integration of information systems with data analytics. This has reshaped job descriptions and recruitment processes for employees.AI, as a recruitment tool, is significantly impacting HRM strategies and has led to machines taking over cognitive human abilities in providing recruitment solutions.
PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON FINANCE, ECONOMICS, MANAGEMENT AND IT BUSINESS (FEMIB)
(2021)
Article
Computer Science, Information Systems
Shahan Yamin Siddiqui, Amir Haider, Taher M. Ghazal, Muhammad Adnan Khan, Iftikhar Naseer, Sagheer Abbas, Muhibur Rahman, Junaid Ahmad Khan, Munir Ahmad, Mohammad Kamrul Hasan, Afifi A. Mohammed, Karamath Ateeq
Summary: Breast cancer is a deadly disease that can be detected early to increase treatment opportunities and survival rates. Deep learning plays a crucial role in extracting features from medical image datasets for accurate diagnosis. It effectively assists existing methods in examining and diagnosing breast cancer.
