Chest radiography needs timely diseases diagnosis and reporting of potential findings in the images, as it is an important diagnostic imaging test in medical practice. A crucial step in radiology workflow is the fast, automated, and reliable detectio...
Active learning is a label-efficient machine learning method that actively selects the most valuable unlabeled samples to annotate. Active learning focuses on achieving the best possible performance while using as few, high-quality sample annotations...
Background: Deep-learning models for Alzheimer’s disease (AD) diagnosis frequently report revolutionary accuracies exceeding 95% yet consistently fail in clinical translation. This scoping review investigates whether methodological flaws, parti...
The growing adoption of deep learning (DL) in early-stage cancer diagnosis has demonstrated remarkable performance across multiple imaging tasks. Yet, the lack of transparency in these models (“black-box” problem) limits their adoption in...
Three-dimensional (3D) reconstruction from images has significantly advanced due to recent developments in deep learning, yet methodological variations and diverse application contexts pose ongoing challenges. This systematic review examines the stat...
The ever-growing interest in and requirement for green energy have led to an increased focus on research related to forecasting solar irradiance recently. This study aims to develop forecast models based on deep learning (DL) methodologies and multip...
The purpose of this research was to provide a “systematic literature review” of knee bone reports that are obtained by MRI, CT scans, and X-rays by using deep learning and machine learning techniques by comparing different approaches&mdas...
Pose estimation (PE) is a cutting-edge technology in computer vision, essential for AI-driven sport analysis, advancing technological applications, enhancing security, and improving the quality of life. Deep learning has markedly advanced accuracy an...
The monitoring of water resources using Autonomous Surface Vehicles with water-quality sensors has been a recent approach due to the advances in unmanned transportation technology. The Ypacaraí Lake, the biggest water resource in Paraguay, suffers fr...
Traffic crashes are a leading cause of death and injury worldwide, with far-reaching societal and economic consequences. To effectively address this global health crisis, researchers and practitioners rely on the analysis of crash data to identify ri...
Classical machine learning techniques have dominated Music Emotion Recognition. However, improvements have slowed down due to the complex and time-consuming task of handcrafting new emotionally relevant audio features. Deep learning methods have rece...
Deep learning (DL), often called artificial intelligence (AI), has been increasingly used in Pathology thanks to the use of scanners to digitize slides which allow us to visualize them on monitors and process them with AI algorithms. Many articles ha...
Deep vision multimodal learning aims at combining deep visual representation learning with other modalities, such as text, sound, and data collected from other sensors. With the fast development of deep learning, vision multimodal learning has gained...
Fault diagnosis in manufacturing systems represents one of the most critical challenges dealing with condition-based monitoring in the recent era of smart manufacturing. In the current Industry 4.0 framework, maintenance strategies based on tradition...
This paper provides a critical narrative review of the literature on climate-related credit-risk and its implications for the banking sector. Banks’ exposure to physical and transition climate risks can significantly affect credit quality, lend...
Brain tumors are a deadly disease with a high mortality rate. Early diagnosis of brain tumors improves treatment, which results in a better survival rate for patients. Artificial intelligence (AI) has recently emerged as an assistive technology for t...
The rapid expansion of utility-scale and commercial photovoltaic (PV) plants demands high-efficiency predictive and proactive maintenance schemes to safeguard operational availability and minimize degradation losses. Photovoltaic generators are subje...
The discovery and development of new drugs are extremely long and costly processes. Recent progress in artificial intelligence has made a positive impact on the drug development pipeline. Numerous challenges have been addressed with the growing explo...
A novel wearable solution using soft robotic sensors (SRS) has been investigated to model foot-ankle kinematics during gait cycles. The capacitance of SRS related to foot-ankle basic movements was quantified during the gait movements of 20 participan...
Semantic representation of unstructured text is crucial in modern artificial intelligence and information retrieval applications. The semantic information extraction process from an unstructured text fragment to a corresponding representation from a...
Genetic regulation of organisms involves complicated RNA–RNA interactions (RRIs) among messenger RNA (mRNA), microRNA (miRNA), and long non-coding RNA (lncRNA). Detecting RRIs is beneficial for discovering biological mechanisms as well as desig...
Due to the vast amount of available tracking sensors in recent years, high-frequency and high-volume streams of data are generated every day. The maritime domain is no different as all larger vessels are obliged to be equipped with a vessel tracking...
Scientific and technological advances in the field of rotatory electrical machinery are leading to an increased efficiency in those processes and systems in which they are involved. In addition, the consideration of advanced materials, such as hybrid...
Efficiently obtaining leaf nitrogen content (LNC) in rice to monitor the nutritional health status is crucial in achieving precision fertilization on demand. Unmanned aerial vehicle (UAV)-based hyperspectral technology is an important tool for determ...
Exchange rate forecasting has been an important topic for investors, researchers, and analysts. In this study, financial sentiment analysis (FSA) and time series analysis (TSA) are proposed to form a predicting model for US Dollar/Turkish Lira exchan...
Accurate forecasting remains a challenge, even with advanced techniques like deep learning (DL), ARIMA, and Holt–Winters (H&W), particularly for chaotic phenomena such as those observed in several areas, such as COVID-19, energy, and financ...
Despite recent major advances in autonomous driving, several challenges remain. Even with modern advanced sensors and processing systems, vehicles are still unable to detect all possible obstacles present in complex urban settings and under diverse e...
Short-Term Load Forecasting is critical for reliable power system operation, and the search for enhanced methodologies has been a constant field of investigation, particularly in an increasingly competitive environment where the market operator and i...
Recently, deep learning-based classification approaches have made great progress and now dominate a wide range of applications, thanks to their Herculean discriminative feature learning ability. Despite their success, for hyperspectral data analysis,...
The total metabolic tumor volume (TMTV) is a new prognostic factor in lymphomas that could benefit from automation with deep learning convolutional neural networks (CNN). Manual TMTV segmentations of 1218 baseline 18FDG-PET/CT have been used for trai...
An investment portfolio consists of a set of financial assets, such as stocks, fixed-income securities, mutual funds, and real estate, held to achieve diversification and to optimize returns. Accurate asset forecasting provides investors with valuabl...
In the automatic diagnosis of ocular toxoplasmosis (OT), Deep Learning (DL) has arisen as a powerful and promising approach for diagnosis. However, despite the good performance of the models, decision rules should be interpretable to elicit trust fro...
Autonomous vehicles make use of an Inertial Navigation System (INS) as part of vehicular sensor fusion in many situations including GPS-denied environments such as dense urban places, multi-level parking structures, and areas with thick tree-coverage...
The most effective and cutting-edge method for achieving a 0.004 mm precision on a typical material is to employ die-sinking electrical discharge machining (EDM). The material removal rate (MRR), tool wear rate (TWR), residual stresses, and crater de...
Traditional Chinese Medicine (TCM) diagnosis is characterized by substantial subjectivity and inter-practitioner variability. Although deep learning (DL) offers potential for standardization and intelligent diagnostic support, its methodological rigo...
In this paper, we have proposed a methodology for energy consumption prediction in residential buildings. The proposed method consists of four different layers, namely data acquisition, preprocessing, prediction, and performance evaluation. For exper...
Accurate solar resource forecasting is important because of the inherent variability associated with solar energy and its significant impact on the cost for energy producers. The traditional method applied in solar irradiance forecasting involves two...
Online condition-monitoring techniques that are used to reveal incipient faults before breakdowns occur are typically data-driven or model-based. We propose the use of a fundamental physics-based thermofluid model of a heat pump cycle combined with d...
Poultry production utilizes many available technologies in terms of farm-industry automation and sanitary control. However, there is a lack of robust techniques and affordable equipment for avian embryo detection and sexual segregation at the early s...
Non-Revenue Water (NRW) remains a major operational challenge for water utilities, particularly in small-sized distribution systems where limited monitoring and data availability constrain the development of efficient loss-management strategies. This...
As advancements in inertial measurement units (IMUs) for motion analysis progress, the inability to directly apply decades of research-based optical motion capture (OMC) methodologies presents a significant challenge. This study aims to bridge this g...
Legged robot locomotion remains a critical challenge in robotics, demanding control strategies that are not only dynamically stable and robust but also capable of adapting to complex and changing environments. deep reinforcement learning (DRL) has re...
This study combines Visible Light Communication (VLC) and Artificial Intelligence (AI) to enhance traffic signal control, reduce congestion, and improve safety, through real-time monitoring and dynamic traffic management. Leveraging VLC technology, t...
In this study, we evaluate predictive modelling techniques within project management, employing diverse architectures such as the LSTM, CNN, CNN-LSTM, GRU, MLP, and RNN models. The primary focus is on assessing the precision and consistency of predic...
This study investigated the impact of drying parameters on the quality of fingerroot (Boesenbergia rotunda) extract, focusing on phenolic compounds, flavonoids, and antioxidant activity. A Box–Behngen design was employed to evaluate the effects...
With the significant increase in cyber-attacks and attempts to gain unauthorised access to systems and information, Network Intrusion-Detection Systems (NIDSs) have become essential detection tools. Anomaly-based systems use machine learning techniqu...
Breast cancer is the most common cancer in women, a leading cause of morbidity and mortality, and a significant health issue worldwide. According to the World Health Organization’s cancer awareness recommendations, mammographic screening should...
The rapid and accurate estimation of aboveground forest phytomass remains a challenging research task. In general, methods for estimating phytomass fall mainly into the category of field measurements performed by ground-based methods, but approaches...
Breast cancer remains one of the primary causes of cancer-related deaths among women globally. Early detection via mammography is essential for improving prognosis and survival rates. However, mammogram diagnostic accuracy is severely hindered by den...
Background/Objectives: Deep learning segmentation of nasopharyngeal carcinoma is usually reported as one Dice similarity coefficient (DSC) against one reference mask, although multi-sequence MRI is often delineated separately on each sequence. We aim...
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