Convolutional neural networks (CNNs) are one of the main types of neural networks used for image recognition and classification. CNNs have several uses, some of which are object recognition, image processing, computer vision, and face recognition. In...
This research paper examines Sports Analytics, focusing on injury patterns in the National Basketball Association (NBA) and their impact on players’ performance. It employs a unique dataset to identify common NBA injuries, determine the most af...
The escalating complexity of cyber threats, coupled with the rapid evolution of digital landscapes, poses significant challenges to traditional cybersecurity mechanisms. This review explores the transformative role of LLMs in addressing critical chal...
In the rapidly evolving domain of cryptocurrency trading, accurate market data analysis is crucial for informed decision making. Candlestick patterns, a cornerstone of technical analysis, serve as visual representations of market sentiment and potent...
Time-series analysis is a widely used method for studying past data to make future predictions. This paper focuses on utilizing time-series analysis techniques to forecast the resource needs of logistics delivery companies, enabling them to meet thei...
This paper presents an analysis of stock price forecasting in the financial market, with an emphasis on approaches based on time series models and deep learning techniques. Fundamental concepts of technical analysis are explored, such as exponential...
The forecasting of presidential election results (PERs) is a very complex problem due to the diversity of electoral factors and the uncertainty involved. The use of a hybrid approach composed of techniques such as machine learning (ML) and Simulation...
This study introduces a data-driven and machine-learning approach to design a personalized tourist recommendation system for Nepal. It examines key tourist attributes, such as demographics, behaviors, preferences, and satisfaction, to develop four su...
Three-dimensional porous scaffolds are substitutes for traditional bone grafts in bone tissue engineering (BTE) applications to restore and treat bone injuries and defects. The use of computational modelling is gaining momentum to predict the paramet...
Vaccine hesitancy is a significant public health concern, with numerous studies demonstrating its negative impact on immunization rates. One factor that can influence vaccine hesitancy is media coverage of vaccination. The media is a significant sour...
Depression is one of the most common mental health disorders in the world, affecting millions of people. Early detection of depression is crucial for effective medical intervention. Multimodal networks can greatly assist in the detection of depressio...
In this work, we present a new way of studying the mathematical structure of the genetic code. This study relies on the use of mathematical computations involving five Fibonacci-like sequences; a few of their “seeds” or “initial con...
The demand for online education is gradually growing. Most universities and other institutions are faced with the fact that it is almost impossible to track how honestly test takers take exams remotely. In online formats, there are many simple opport...
In order to achieve a specific result, a firm’s problem-solving activities can be thought of as a process that combines physical and cognitive actions. Its internal organization determines how information inputs are distributed among different...
This paper introduces a simulation model designed as a decision-making tool to assess and analyze various crowd management strategies with a focus on enhancing sustainability in airport operations. This model specifically addresses the challenges and...
Machine learning techniques play an increasingly prominent role in medical diagnosis. With the use of these techniques, patients’ data can be analyzed to find patterns or facts that are difficult to explain, making diagnoses more reliable and c...
This work aims to create a web-based real-time monitoring system for electrical energy consumption inside a specific residence. This electrical energy is generated from a micro-CPV system lying on the roof of this residence. The micro-CPV is composed...
Many industrial processes, several natural processes involving non-living matter, and all the processes occurring within living organisms take place in solution. This means that the molecules playing active roles in the processes are present within a...
Advances in artificial intelligence (AI) are transforming assisted reproductive technologies by significantly enhancing fertility diagnostics. This review focuses on integrating AI with Computer-Aided Sperm Analysis (CASA) systems to improve assessme...
New analytical solutions of the heat conduction equation obtained by utilizing a self-similar Ansatz are presented in cylindrical and spherical coordinates. Then, these solutions are reproduced with high accuracy using recent explicit and uncondition...
The paper focuses on predicting the next purchase day (NPD) for customers in e-commerce, a task with applications in marketing, inventory management, and customer retention. A novel transformer-based model for NPD prediction is introduced and compare...
This study investigates the transformative potential of blockchain technology in financial accounting by examining its applications, challenges, and implications. The study begins with a review of blockchain’s origins and its ability to address...
In recent times, there has been an increased demand for electric vehicles. In this context, the energy management of the electric motor, which are an important constituent of electric vehicles, plays a pivotal role. A lot of research has been conduct...
The work presented in this paper makes multiple scientific contributions with a specific focus on the analysis of misinformation about COVID-19 on YouTube. First, the results of topic modeling performed on the video descriptions of YouTube videos con...
This paper examines the performance of two popular GPU programming platforms, Numba and CuPy, for Monte Carlo radiation transport calculations. We conducted tests involving random number generation and one-dimensional Monte Carlo radiation transport...
During virus outbreaks in the recent past, web behavior mining, modeling, and analysis have served as means to examine, explore, interpret, assess, and forecast the worldwide perception, readiness, reactions, and response linked to these virus outbre...
The use of robot manipulators in engineering applications and scientific research has significantly increased in recent years. This can be attributed to the rise of technologies such as autonomous robotics and physics-based simulation, along with the...
This study focuses on testing the efficiency of alternative bankruptcy prediction models (Altman, Ohlson, Zmijewski) and on assessing the possible reasons that led to the confirmation or not of the prevailing model. Data from financial statements of...
This article was written to investigate the research on the scientific directions for flying cars at the preliminary design stage to provide a rationale for the choice of scientific research in the area of flying cars. At present, the population of t...
Predicting corporate bankruptcy is a key task in financial risk management, and selecting a machine learning model with superior generalization performance is crucial for prediction accuracy. This study evaluates the effectiveness of k-fold cross-val...
Biological networks such as protein interaction networks, gene regulation networks, and metabolic pathways are examples of complex networks that are large graphs with small-world and scale-free properties. An analysis of these networks has a profound...
The construction of a mathematical model of a complicated system is often associated with the evaluation of inputs’ (arguments, factors) influence on the output (response), the identification of important relationships between the variables use...
Brain tumor diagnosis traditionally relies on the manual examination of magnetic resonance images (MRIs), a process that is prone to human error and is also time consuming. Recent advancements leverage machine learning models to categorize tumors, su...
Artificial Intelligence and Deepfake Technologies have brought a new dimension to the generation of fake data, making it easier and faster than ever before—this fake data could include text, images, sounds, videos, etc. This has brought new cha...
Cardiac arrhythmias, characterized by deviations from the normal rhythmic contractions of the heart, pose a formidable diagnostic challenge. Early and accurate detection remains an integral component of effective diagnosis, informing critical decisio...
This paper addresses the global surge in heart disease prevalence and its impact on public health, stressing the need for accurate predictive models. The timely identification of individuals at risk of developing cardiovascular ailments is paramount...
Road accidents are on the rise worldwide, causing 1.35 million deaths per year, thus encouraging the search for solutions. The promising proposal of autonomous vehicles stands out in this regard, although fully automated driving is still far from bei...
Solving the university timetabling problem is crucial as it ensures efficient use of resources, minimises scheduling conflicts, and enhances overall productivity. This paper presents a comprehensive review of university timetabling problems using int...
Early detection and timely breast cancer treatment improve survival rates and patients’ quality of life. Hence, many computer-assisted techniques based on artificial intelligence are being introduced into the traditional diagnostic workflow. Th...
In the context of the increasing spread of misinformation via social network services, in this study, we addressed the critical challenge of detecting and explaining the spread of fake news. Early detection methods focused on content analysis, wherea...
This study explores the accuracy and efficiency of multiple sequence alignment (MSA) programs, focusing on Clustal
This study examined the effects of minimal, maximal and conventional running footwear on tibial strains and stress fracture probability using finite element and probabilistic analyses. The current investigation examined fifteen males running in three...
The task of time series forecasting is to estimate future values based on available observational data. Prediction Intervals methods are aimed at finding not the next point, but the interval that the future value or several values on the forecast hor...
The significance of robot manipulators in engineering applications and scientific research has increased substantially in recent years. The utilization of robot manipulators to save labor and increase production accuracy is becoming a common practice...
Traditional mean-variance (MV) models, considered effective in stable conditions, often prove inadequate in uncertain market scenarios. Therefore, there is a need for more robust and better portfolio optimization methods to handle the fluctuations an...
Attaining sustainable development goals is a complex process that involves a range of economic, social, and environmental factors. It requires investments in infrastructure, technology, and human capital. In this case, green finance is conducive to c...
The past few decades have witnessed remarkable progress in the application of artificial intelligence (AI) and machine learning (ML) in medicine, notably in medical imaging. The application of ML to dental and oral imaging has also been developed, po...
In the contemporary era, dizziness is a prevalent ailment among patients. It can be caused by either vestibular neuritis or a stroke. Given the lack of diagnostic utility of instrumental methods in acute isolated vertigo, the differentiation of vesti...
Today, medical image-based diagnosis has advanced significantly in the world. The number of studies being conducted in this field is enormous, and they are producing findings with a significant impact on humanity. The number of databases created in t...
Multilabel data share important features, including label imbalance, which has a significant influence on the performance of classifiers. Because of this problem, a widely used multilabel classification algorithm, the multilabel k-nearest neighbor (M...
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