Recently, peoples’ awareness of online purchases has significantly risen. This has given rise to online retail platforms and the need for a better understanding of customer purchasing behaviour. Retail companies are pressed with the need to dea...
The landscape of cybercrime has undergone significant transformations over the past decade. Present-day threats include AI-generated attacks, deep fakes, 5G network vulnerabilities, cryptojacking, and supply chain attacks, among others. To remain res...
An accurate prediction of house prices is a fundamental requirement for various sectors, including real estate and mortgage lending. It is widely recognized that a property’s value is not solely determined by its physical attributes but is sign...
In recent decades, artificial intelligence has undergone transformative advancements, reshaping diverse sectors such as healthcare, transport, agriculture, energy, and the media. Despite the enthusiasm surrounding AI’s potential, concerns persi...
The Holy Bible is the most read book in the world, originally written in Aramaic, Hebrew, and Greek over a time span in the order of centuries by many people, and formed by a combination of various literary styles, such as stories, prophecies, poetry...
Website loading speed time matters when it comes to users’ engagement and conversion rate optimization. The websites of libraries, archives, and museums (LAMs) are not an exception to this assumption. In this research paper, we propose a method...
Researchers are starting to design AI-powered systems to automatically select and summarize the reports most relevant to each analyst, which raises the issue of bias in the information presented. This article focuses on the selection of relevant repo...
In this survey, the issues of urban routing are analyzed, and critical considerations for smart and cost-effective delivery services are highlighted. Smart cities require intelligent services and solutions to address their routing issues. This articl...
This study compares three machine learning and deep learning models—Support Vector Regression (SVR), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM)—for predicting market prices using the NGX All-Share Index dataset. Th...
Learning analytics (LA) has the potential to significantly improve teaching and learning, but there are still many areas for improvement in LA research and practice. The literature highlights limitations in every stage of the LA life cycle, including...
Macroeconomic factors have a critical impact on banking credit risk, which cannot be directly controlled by banks, and therefore, there is a need for an early credit risk warning system based on the macroeconomy. By comparing different predictive mod...
Introduction: Prostate cancer (PCa) is one of the deadliest and most common causes of malignancy and death in men worldwide, with a higher prevalence and mortality in developing countries specifically. Factors such as age, family history, race and ce...
Bankruptcy prediction is critical for financial risk management. This study demonstrates that machine learning models, particularly Random Forest, can substantially improve prediction accuracy compared to traditional approaches. Using data from 8262...
The dominance of social media has added to the channels of bullying for perpetrators. Unfortunately, cyberbullying (CB) is the most prevalent phenomenon in today’s cyber world, and is a severe threat to the mental and physical health of citizen...
This bibliometric review investigates the advancements in machine learning techniques for predictive maintenance, focusing on the use of Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) for fault detection in wheelset axle bearing...
We present a number of upper and lower bounds for the total variation distances between the most popular probability distributions. In particular, some estimates of the total variation distances in the cases of multivariate Gaussian distributions, Po...
Maximal Extractable Value (MEV) presents a significant challenge to the fairness and efficiency of decentralized finance (DeFi). This paper provides a game-theoretic analysis of the strategic interactions within the MEV supply chain, involving search...
Making predictions in the stock market is a challenging task. At the same time, several studies have focused on forecasting the future behavior of the market and classifying financial assets. A different approach is to classify correlated data to dis...
This paper addresses the practical problem of scheduling operating room (OR) elective surgeries to minimize the likelihood of surgical delays caused by the unavailability of capacity for patient recovery in a central post-anesthesia care unit (PACU)....
Stroke is a major cause of death worldwide, resulting from a blockage in the flow of blood to different parts of the brain. Many studies have proposed a stroke disease prediction model using medical features applied to deep learning (DL) algorithms t...
The exoskeleton technology has been rapidly advancing in the recent past due to its multitude of applications and diverse use cases in assisted living, military, healthcare, firefighting, and industry 4.0. The exoskeleton market is projected to incre...
We present a hierarchical reinforcement learning (RL) architecture that employs various low-level agents to act in the trading environment, i.e., the market. The highest-level agent selects from among a group of specialized agents, and then the selec...
Coral-reefs are a significant species in marine life, which are affected by multiple diseases due to the stress and variation in heat under the impact of the ocean. The autonomous monitoring and detection of coral health are crucial for researchers t...
The rapid proliferation of cyberthreats necessitates a robust understanding of their evolution and associated tactics, as found in this study. A longitudinal analysis of these threats was conducted, utilizing a six-year data set obtained from a decep...
This research article develops a dynamic framework for the Walrasian pure exchange economy and thus extends the static Walrasian general equilibrium theory into a dynamic one with price adjustments. An evolution equation for the price vector is deriv...
Many scientific fields show great interest in the extraction and processing of spatiotemporal data, such as medicine with an emphasis on epidemiology and neurology, geology, social sciences, meteorology, and a great interest is also observed in the s...
This paper presents the object detection algorithms GRASS GIS applied for Landsat 8-9 OLI/TIRS data. The study area includes the Sudd wetlands located in South Sudan. This study describes a programming method for the automated processing of satellite...
Sentiment analysis is a technique used to understand the public’s opinion towards an event, product, or organization. For example, sentiment analysis can be used to understand positive or negative opinions or attitudes towards electric vehicle...
Archives comprise information that individuals and organizations use in their activities. Archival theory is the intellectual framework for organizing, managing, preserving and access to archives both while they serve the needs of those who produce t...
Breast cancer is the most prevalent type of disease among women. It has become one of the foremost causes of death among women globally. Early detection plays a significant role in administering personalized treatment and improving patient outcomes....
As the levels of automation and reliance on modern artificial intelligence (AI) approaches increase across multiple industries, the importance of the human-centered perspective becomes more evident. Various actors in such industrial applications, inc...
1. Purpose: One of the consequences of the COVID-19 pandemic period was the migration of educational centers from face-to-face learning to e-learning. Most centers adapted their educational services and technological resources so that the students co...
In the competitive field of business intelligence, optimizing talent recruitment through data-driven methodologies is crucial for better decision-making. This study compares the effectiveness of various machine learning models to improve recruitment...
Developing analytics capability has become one of the main priorities in organizations today. Despite the increasing use of analytics, the necessary conditions to obtain the expected benefits from such investment still need to be examined. Relying on...
Recent advances in artificial intelligence (AI) for quantitative trading have led to its general superhuman performance among notable trading performance results. However, if we use AI without proper supervision, it can lead to wrong choices and huge...
Cell phone technology has advanced rapidly with the start of 5G being rolled out across the networks. To keep up with this demand, cell tower companies have responded by erecting numerous towers. Engineers and researchers analyze the network topograp...
Traffic prediction, as a core task to alleviate urban congestion and optimize the transport system, has limitations in the integration of multimodal data, making it difficult to comprehensively capture the complex spatio-temporal characteristics of t...
In this work, we evaluated the efficacy of Google’s Pathways Language Model (GooglePaLM) in analyzing sentiments expressed in product reviews. Although conventional Natural Language Processing (NLP) techniques such as the rule-based Valence Awa...
This work proposes an effective solution to the Vehicle Routing Problem, taking into account all phases of the delivery process. When compared to real-world data, the findings are encouraging and demonstrate the value of Machine Learning algorithms i...
There are several Knowledge Graphs expressed in RDF (Resource Description Framework) that aggregate/integrate data from various sources for providing unified access services and enabling insightful analytics. We observe this trend in almost every dom...
We propose a universal readability index,
Retail banking is undergoing considerable product competitiveness and disruptions. New product development is necessary to tackle such challenges and reinvigorate product lines. This study presents an instrumental real-life banking case study, where...
Global optimization is a widely used technique that finds application in many sciences such as physics, economics, medicine, etc., and with many extensions, for example, in the area of machine learning. However, in many cases, global minimization tec...
Domestication is a translation theory in which the source text (to be translated) is matched to the foreign reader by erasing its original linguistic and cultural difference. This match aims at making the target text (translated text) more fluent. On...
Artificial intelligence adoption in financial services presents uncertain implications for competitive dynamics, particularly for smaller institutions. The literature on AI in finance is growing, but there remains a notable absence regarding the impa...
Interactive visual tools and related visualization technologies, built to support explorative data analysis, ultimately lead to sense making and knowledge discovery from large volumes of raw data. These processes namely rely on human visual perceptio...
The theme of this paper is that the SP Theory of Intelligence (SPTI), and its realisation in the SP Computer Model, is a promising foundation for the development of artificial intelligence at the level of people or higher, also known as ‘artifi...
A web-based Java programming learning assistant system (JPLAS) has been developed for novice students to study Java programming by themselves while enhancing code reading and code writing skills. One type of the implemented exercise problem is code w...
This study investigates the relationship between capital expenditure (CAPEX) and long-term corporate profitability in South Korea’s electronics industry. Using panel data from 126 listed electronics firms covering 2005–2019, the research...
Food commodities and energy bills have experienced rapid undulating movements and hikes globally in recent times. This spurred this study to examine the possibility that the shocks that arise from fluctuations of one market spill over to the other an...
