2024 December - 60 articles
Among gynecological pathologies, cervical cancer has always represented a health problem with great social impact. The giant strides made as a result of both the screening programs perfected and implemented over the years and the use of new and accur...
Background: In the last decade, numerous methods have been proposed to define and detect outliers, particularly in complex environments like networks, where anomalies significantly deviate from normal patterns. Although defining a clear standard is c...
Soybean is a critical agricultural commodity, serving as a vital source of protein and vegetable oil, and contributing significantly to the economies of producing nations. However, soybean yields are frequently compromised by disease and pest infesta...
(1) Background: Existing Vehicle travel time prediction applications face challenges in modeling complex road network and handling irregular spatiotemporal traffic state propagation. (2) Methods: To address these issues, we propose a Graph Attention-...
The estimation of energy expenditure (EE) is often an integral part of algorithms for wearable electronics. In field practice, procedures based on an indirect estimation of EE from the heart rate (using empirically or statistically based relationship...
Background: Three-dimensional skeleton-based human motion prediction is an essential and challenging task for human–machine interactions, aiming to forecast future poses given a history of previous motions. However, existing methods often fail...
Machine learning (ML) is increasingly used to support or automate decision processes in critical decision systems such as self driving cars or systems for medical diagnosis. These systems require decisions in which human lives are at stake and the de...
In the digital era, social media platforms have seen a substantial increase in the volume of online comments. While these platforms provide users with a space to express their opinions, they also serve as fertile ground for the proliferation of hate...
Cryptocurrency is recognized as a leading digital currency by its peer-to-peer transfer capabilities and secure features. Accurately forecasting cryptocurrency price trends holds substantial significance for investors and traders, as they inform crit...
There are multiple uses for single-channel images, such as infrared imagery, depth maps, and others. To automatically classify objects in such images, an algorithm suited for single-channel image processing is required. This study explores the applic...
Large language models (LLMs) have transformed Natural Language Processing (NLP) by enabling robust text generation and understanding. However, their deployment in sensitive domains like healthcare, finance, and legal services raises critical concerns...
Curriculum learning is a method of prioritizing learning data to improve learning performance. In this paper, we propose a new algorithm that determines how to select learning data and when to start and stop curriculum learning by considering learnin...
The prioritization of bug reports based on severity is a crucial aspect of bug triaging, enabling a focus on more critical issues. Traditional methods for assessing bug severity range from manual inspection to the application of machine and deep lear...
Substance use disorders affect 17.3% of Americans. Digital health solutions that use machine learning to detect substance use from wearable biosignal data can eventually pave the way for real-time digital interventions. However, difficulties in addre...
This article proposes a framework for integrating ethical reasoning into AI systems through Continuous Logic Programming (CLP), emphasizing the improvement of transparency and accountability in automated decision-making. The study highlights requirem...
We report on the effectiveness of using generative neural networks in an antenna design. We considered the modeling of microstrip antennas as they have significant advantages, such as a low profile, lightness, and ease of manufacture, which make them...
Background: Large language models (LLMs) like GPT-3.5-Turbo and GPT-4 show potential to transform medical diagnostics through their linguistic and analytical capabilities. This study evaluates their diagnostic proficiency using English and German med...
This work proposes the use of a micro genetic algorithm to optimize the architecture of fully connected layers in convolutional neural networks, with the aim of reducing model complexity without sacrificing performance. Our approach applies the parad...
ChatGPT, developed by OpenAI, is a large language model (LLM) that leverages artificial intelligence (AI) and deep learning (DL) to generate human-like responses. This paper provides a broad, systematic review of ChatGPT’s applications in healt...
Background—Diabetes is a rapidly spreading chronic disease that poses a significant risk to individual health as the population grows. This increase is largely attributed to busy lifestyles, unhealthy eating habits, and a lack of awareness abou...
Background/Objectives: This paper presents a Residual Neural Network (ResNet) based framework tailored for structured traffic accident data, aiming to improve accident severity prediction. The proposed model leverages residual learning to effectively...
Introduction—Decision making (DM) is a fundamental responsibility for managers, with significant implications for organizational performance and strategic direction. The increasing complexity of modern business environments, along with the reco...
You Only Look Once (YOLO) and its variants have emerged as the most popular real-time object detection algorithms. They have been widely used in real-time smart transportation applications due to their low-latency detection and high accuracy. However...
Intelligent Speech Technology (IST) is revolutionizing healthcare by enhancing transcription accuracy, disease diagnosis, and medical equipment control in smart hospital environments. This study introduces an innovative approach employing federated l...
Accurately forecasting power consumption is crucial important for efficient energy management. Machine learning (ML) models are often employed for this purpose. However, tuning their hyperparameters is a complex and time-consuming task. The article p...
Machine learning (ML) techniques have permeated various domains, offering intelligent solutions to complex problems. ML has been increasingly explored for applications in active power filters (APFs) due to its potential to enhance harmonic compensati...
(1) Background: The mutual relationship between evolution and learning is a controversial argument among the artificial intelligence and neuro-evolution communities. After more than three decades, there is still no common agreement on the matter. (2)...
The widespread use of machine and deep learning algorithms for anomaly detection has created a critical need for robust explanations that can identify the features contributing to anomalies. However, effective evaluation methodologies for anomaly exp...
As machine learning (ML) transforms industries, the need for efficient model development tools using high-performance computing (HPC) and ensuring interpretability is crucial. This paper presents SIBILA, an AutoML approach designed for HPC environmen...
Background: This article introduces SC-Phi2, a fine-tuned StarCraft II small language model. Small language models, like Phi2, Gemma, and DistilBERT, are streamlined versions of large language models (LLMs) with fewer parameters that require less com...
The irruption of GenAI such as ChatGPT has changed the educational landscape. Therefore, methodological guidelines and more empirical experiences are needed to better understand these tools and know how to use them to their fullest potential. This co...
(1) Background: Detecting people and technical objects in various situations, such as natural disasters and warfare, is critical to search and rescue operations and the safety of civilians. A fast and accurate detection of people and equipment can si...
Background: The Internet of Things (IoT) has improved many aspects that have impacted the industry and the people’s daily lives. To begin with, the IoT allows communication to be made across a wide range of devices, from household appliances to...
Background: Advances in medical image classification have recently benefited from general augmentation techniques. However, these methods often fall short in performance and interpretability. Objective: This paper applies the Unified Model Agnostic C...
App reviews provide crucial feedback for software maintenance and evolution, but manually extracting useful reviews from vast volumes is time-consuming and challenging. This study investigates the effectiveness of six Naïve Bayes variants for au...
The artificial bee colony (ABC) algorithm is a famous swarm intelligence method utilized across various disciplines due to its robustness. However, it exhibits limitations in exploration mechanisms, particularly in high-dimensional or complex landsca...
This study analyzes the nonlinear optical properties exhibited by graphene, focusing on the nonlinear absorption coefficient and the nonlinear refractive index. The evaluation was conducted using the Z-scan technique with a 532 nm wavelength laser at...
Background: Dynamic multiobjective optimization problems (DMOPs) involve multiple conflicting and time-varying objectives, and dynamic multiobjective algorithms (DMOAs) aim to find Pareto optima that are closer to the real one in the new environment...
Human action recognition has become crucial in computer vision, with growing applications in surveillance, human–computer interaction, and healthcare. Traditional approaches often use broad feature representations, which may miss subtle variati...
The global community is awaiting the advent of a self-driving vehicle that is safe, reliable, and capable of navigating a diverse range of road conditions and terrains. This requires a lot of research, study, and optimization. Thus, this work focused...
Recent research has drawn attention to the ambiguity surrounding the definition and learnability of Out-of-Distribution recognition. Although the original problem remains unsolved, the term “Out-of-Model Scope” detection offers a clearer...
Objective: This paper describes the development of hybrid artificial intelligence strategies for drone navigation. Methods: The navigation module combines a deep learning model with a rule-based engine depending on the agent state. The deep learning...
A machine learning method was applied to solve an inverse airfoil design problem. A conditional VAE-WGAN-gp model, which couples the conditional variational autoencoder (VAE) and Wasserstein generative adversarial network with gradient penalty (WGAN-...
Machine learning (ML) has transformed the financial industry by enabling advanced applications such as credit scoring, fraud detection, and market forecasting. At the core of this transformation is deep learning (DL), a subset of ML that is robust in...
Autosomal dominant polycystic kidney disease (ADPKD) is the predominant hereditary factor leading to end-stage renal disease (ESRD) worldwide, affecting individuals across all races with a prevalence of 1 in 400 to 1 in 1000. The disease presents sig...
Innovative wood inspection technology is crucial in various industries, especially for determining wood quality by counting rings in each stave, a key factor in wine barrel production. (1) Background: Traditionally, human inspectors visually evaluate...
Various Artificial Intelligence (AI) techniques in water resource management highlight the current methodologies’ strengths and limitations in forecasting, optimization, and control. We identify a gap in integrating these diverse approaches for...
This paper presents a new approach in the field of probability-informed machine learning (ML). It implies improving the results of ML algorithms and neural networks (NNs) by using probability models as a source of additional features in situations wh...
Background: Nonsurgical treatment of uncomplicated appendicitis is a reasonable option in many cases despite the sparsity of robust, easy access, externally validated, and multimodally informed clinical decision support systems (CDSSs). Developed by...
Recent significant advances in the healthcare industry due to artificial intelligence (AI) and machine learning (ML) have been shown to revolutionize healthcare delivery by improving efficiency, accuracy, and patient outcomes. However, these technolo...
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