Forecasting sales trends is a valuable activity for companies of all types and sizes, as it enables more efficient decision making to avoid unnecessary expenses from excess inventory or, conversely, losses due to insufficient inventory to meet demand...
The advancement of machine learning in industrial applications has necessitated the development of tailored solutions to address specific challenges, particularly in multi-class classification tasks. This study delves into the customization of loss f...
Day-ahead electricity price forecasting (DAEPF) is vital for participants in energy markets, particularly in regions with high integration of renewable energy sources (RESs), where price volatility poses significant challenges. The accurate forecasti...
We derive the joint probability-generating function of the number of customers served and the number of customers lost in busy periods of
Time series models are considered among the most intricate models in machine learning. Due to sharp temporal variations, time series models normally fall short in predicting the peaks or local minima accurately. To overcome this challenge, we propose...
Raman spectroscopy is a powerful diagnostic tool in biomedical science, whereby different disease groups can be classified based on subtle differences in the cell or tissue spectra. A key component in the classification of Raman spectra is the applic...
The Remedial Action Scheme (RAS) is designed to take corrective actions after detecting predetermined conditions to maintain system transient stability in large interconnected power grids. However, since RAS is usually designed based on a few selecte...
The generation of synthetic medical data has become a focal point for researchers, driven by the increasing demand for privacy-preserving solutions. While existing generative methods heavily rely on real datasets for training, access to such data is...
Physics-informed machine learning is emerging through vast methodologies and in various applications. This paper discovers physics-based custom loss functions as an implementable solution to additive manufacturing (AM). Specifically, laser metal depo...
Particulate matter has become one of the major issues in environmental sustainability, and its accurate measurement has grown in importance recently. Low-cost sensors (LCS) have been widely used to measure particulate concentration, but concerns abou...
Clouds are one of the major limitations to crop monitoring using optical satellite images. Despite all efforts to provide decision-makers with high-quality agricultural statistics, there is still a lack of techniques to optimally process satellite im...
Smart greenhouses rely on precise environmental control to optimize crop yields and resource efficiency. In this study, we propose a novel hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architecture to predict fan actuato...
In the era of rapid technological advancement and ever-increasing data availability, the field of risk modeling faces both unprecedented challenges and opportunities. Traditional risk modeling approaches, while robust, often struggle to capture the c...
In recent years, large language models (LLMs) have shown an impressive ability in translating text to SQL queries. However, in real-world applications, standard loss functions frequently fail to capture the complexity of queries adequately. Therefore...
This paper presents an efficient strategy for transformer planning to reduce the system losses by means of transformer rearrangement. The customer connected to the distribution transformer are first investigated by the field survey, and the loads of...
This paper investigates an
We study a phenomenon causing server time loss in ticket queues with balking and calling time. A customer who balks from the queue after printing a ticket leaves a virtual entity in the queue that requires server time to be cleared. The longer the qu...
In recent years, the topic of contactless biometric identification has gained considerable traction due to the COVID-19 pandemic. One of the most well-known identification technologies is iris recognition. Determining the classification threshold for...
28 September 2022
Object detection is a predominant challenge in computer vision and image processing to detect instances of objects of various classes within an image or video. Recently, a new domain of vehicular platforms, e-scooters, has been widely used across dom...
Flash flood events are some of the most life-threatening natural disasters, so it is important to predict extreme rainfall events effectively. This study introduces an LSTM model that utilizes a customized loss function to effectively predict extreme...
Vision-based inspection systems are essential for quality control in manufacturing industries, and advances in artificial intelligence (AI) have significantly enhanced their accuracy. However, the high-precision requirements of products such as conta...
Identifying road surface cracks by semantic segmentation is a difficult problem. This is because segmentation typically detects objects by area, whereas cracks are string-like. Conventional loss functions such as Binary Cross-Entropy (BCE), Dice, and...
This article focuses on both classical and Bayesian inference of traffic intensity in a single-server Markovian queueing model considering balking. To reflect real-world situations, the article introduces the concept of balking, where customers opt n...
Queueing models with disasters can be used to evaluate the impact of a breakdown or a system reset in a service facility. In this paper, we consider a discrete-time single-server queueing system with general independent arrivals and general independe...
The use of priorities allows us to improve the quality of service of inhomogeneous customers in telecommunication networks, inventory and health-care systems. An important modern direction of research is to analyze systems in which priority of a cust...
Faced with capacity-demand mismatch and customer churn during order surges in instant delivery, selective vehicle routing provides a promising alternative by actively rejecting low-value orders instead of passively accepting all requests. This study...
Human facial emotion detection is one of the challenging tasks in computer vision. Owing to high inter-class variance, it is hard for machine learning models to predict facial emotions accurately. Moreover, a person with several facial emotions incre...
Small and severely imbalanced optical coherence tomography (OCT) datasets pose a major challenge for deep learning algorithms while reflecting the reality of smaller ophthalmology centers, where rare retinal pathologies such as retinal artery occlusi...
The purpose of infrared and visible image fusion is to generate a composite image that can contain both the thermal radiation profile information of the infrared image and the texture details of the visible image. This kind of composite image can be...
This study investigates M/M/n:(m/FIFO) systems with a limited queue capacity (incorporating both “waiting and rejection”). This category of systems can be considered to be mixed-service systems. They operate as queuing systems for custome...
Monkeypox disease is caused by a virus that causes lesions on the skin and has been observed on the African continent in the past years. The fatal consequences caused by virus infections after the COVID pandemic have caused fear and panic among the p...
Background: Severe glenoid bone loss presents a major challenge in both primary and revision reverse shoulder arthroplasty (RSA). Standard implants often fail to achieve reliable fixation in these cases. Custom-made, 3D-printed glenoid components hav...
Robots play a pivotal role in the manufacturing industry. This has led to the development of computer vision. Since AlexNet won ILSVRC, convolutional neural networks (CNNs) have achieved state-of-the-art status in this area. In this work, a novel met...
With the development of techniques, such as the Internet of Things (IoT) and edge computing, home energy management systems (HEMS) have been widely implemented to improve the electric energy efficiency of customers. In order to automatically optimize...
Previous studies have primarily focused on predicting the remaining useful life (RUL) of tools as an independent process. However, the RUL of a tool is closely related to its wear stage. In light of this, a multi-task joint learning model based on a...
Deep learning has been employed to prognostic and health management of automotive and aerospace with promising results. Literature in this area has revealed that most contributions regarding deep learning is largely focused on the model’s archi...
In the dual-channel retail industry, excessive enthusiasm in offline retailers’ services often extends beyond the customer’s “interpersonal distance zone”, leading to psychological discomfort for customers and a subsequent los...
One of the strategic objectives of the European Union is a reduction in greenhouse gas emissions and improvement of energy efficiency by at least 32.5% in different areas of the economy by 2030. However, little is known about the impact of payment in...
Road safety depends heavily on the timely identification and repair of potholes; however, detecting potholes is challenging due to various lighting and weather conditions. This work presents an attention-enhanced object detection framework for aerial...
Road traffic crashes pose significant economic and public health burdens, necessitating an in-depth understanding of crash causation and its links to underlying factors. This study introduces a machine learning-based hurdle model framework tailored f...
Medical image segmentation is crucial for clinical applications, but challenges persist due to noise and variability. In particular, accurate glottis segmentation from high-speed videos is vital for voice research and diagnostics. Manual searching fo...
Background and Objectives: Treating segmental tibial and ankle bone loss after radical surgery for chronic osteomyelitis is one of the most challenging problems encountered by orthopaedic surgeons. Open tibia and ankle fractures occur with an inciden...
In order to meet customers’ demand for freshness and the time window of fresh product distribution, and achieve the sustainable development of the fresh product cold-chain logistics industry, green technology is used to solve the optimization p...
The use of computers to simulate facial aging or rejuvenation has long been a hot research topic in the field of computer vision, and this technology can be applied in many fields, such as customs security, public places, and business entertainment....
With the rapid development of the sharing economy, more and more platform operators apply the sharing concept in manufacturing, which increases the efficiency of assets utilization. Considering the apparel industry, clothing enterprises or manufactur...
Background and Objectives: Reconstruction of extensive acetabular bone defects in revision total hip arthroplasty (THA) remains technically demanding and is associated with a high risk of complications. Custom-made three-dimensional (3D)-printed impl...
The rise of social networks and the increasing amount of time people spend on them have created a perfect place for the dissemination of false narratives, propaganda, and manipulated content. In order to prevent the spread of disinformation, content...
Background/Objectives: Severe acetabular bone loss, particularly Paprosky type IIIA and IIIB defects and pelvic discontinuity, represents a major challenge in revision total hip arthroplasty (rTHA). Custom-made acetabular implants offer a patient-spe...
30 November 2024
This study presents a comparative analysis of YOLO detection models for the accurate identification of bean leaf diseases caused by Coleoptera pests in natural environments. By using a manually collected dataset of healthy and infected bean leaves in...
In recent years, the use of drones for surveillance tasks has been on the rise worldwide. However, in the context of anomaly detection, only normal events are available for the learning process. Therefore, the implementation of a generative learning...
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