AI, Volume 6, Issue 2

Introduction: The Earth’s growing population is increasing resource consumption, heavily pressuring agriculture, which, currently, uses 70% of the world’s freshwater from rivers and lakes, which, themselves, comprise only 1% of the Earth&...

University students often face challenges in managing academic demands and difficulties like time management, task prioritization, and effective study strategies. This scoping review investigates the application of Deep Learning (DL) and Reinforcemen...

Background: Pressure injuries (PIs) are increasing worldwide, and there has been no significant improvement in preventing them. Traditional assessment tools are widely used to identify a patient at risk of developing a PI. This study aims to construc...

Background: Conventional medical image retrieval methods treat images and text as independent embeddings, limiting their ability to fully utilize the complementary information from both modalities. This separation often results in suboptimal retrieva...

Background: Machine learning methods for clinical assistance require a large number of annotations from trained experts to achieve optimal performance. Previous work in natural language processing has shown that it is possible to automatically extrac...

Purpose: The impact of hybrid quantum-classical neural network (NN) architectures with multiple backbones and quantum transformation as a data augmentation (DA) technique on image classification tasks was investigated using the CIFAR-10 and MedMNIST...

Background/Objectives: Artificial intelligence (AI) is transforming higher education (HE), reshaping teaching, learning, and feedback processes. Feedback generated by large language models (LLMs) has shown potential for enhancing student learning out...

The integration of renewable energy sources and electric vehicles has become a focal point for industries and academia due to its profound economic, environmental, and technological implications. These developments require the development of a robust...

Thisstudy introduces a new model for detecting insults in Roman Urdu, filling an important gap in natural language processing (NLP) for low-resource languages. The transliterated nature of Roman Urdu also poses specific challenges from a computationa...

Effectively training deep learning models relies heavily on large datasets, as insufficient instances can hinder model generalization. A simple yet effective way to address this is by applying modern deep learning augmentation methods, as they synthe...

The extraction of Adverse Drug Reactions from biomedical text is a critical task in the field of healthcare and pharmacovigilance. It serves as a cornerstone for improving patient safety by enabling the early identification and mitigation of potentia...

Large language models (LLMs) and generative artificial intelligence (AI) have demonstrated notable capabilities, achieving human-level performance in intelligent tasks like medical exams. Despite the introduction of extensive LLM evaluations and benc...

Industry 4.0 is an aggregate of recent technologies including artificial intelligence, big data, edge computing, and the Internet of Things (IoT) to enhance efficiency and real-time decision-making. Industry 4.0 data analytics demands a privacy-focus...

Retinal diseases account for a large fraction of global blinding disorders, requiring sophisticated diagnostic tools for early management. In this study, the author proposes a hybrid deep learning framework in the form of AdaptiveSwin-CNN that combin...

In environments like the RoboCup Middle Size League (MSL), precise and rapid localisation of robots is crucial for effective autonomous interaction. This study addresses the limitations of conventional localisation approaches—often based on sin...

Integrating Internet of Things (IoT) devices and machine learning (ML) techniques holds immense potential for transforming beekeeping practices. This review paper offers a critical analysis of state-of-the-art IoT-enabled precision beekeeping systems...

Predicting crop performance is key to decision making for farmers and business owners. Tacna is the main olive-producing region in Perú, with an annual yield of 6.4 t/ha, mainly of the Sevillana variety. Recently, olive production levels have...

This study explores the extraction of remote Photoplethysmography (rPPG) signals from images using various neural network architectures, addressing the challenge of accurate signal estimation in biomedical contexts. The objective is to evaluate the e...

The combinatorial fusion cascade provides a surprisingly simple and complete explanation for the origin of the genetic code based on competing protocodes. Although its molecular basis is only beginning to be uncovered, it represents a natural pattern...

This study advances AI-powered news delivery by introducing an innovative chatbot capable of providing personalized news summaries and real-time event analysis. This approach addressed a critical gap identified through a comprehensive review of 52 AI...

Accuratesnake species identification is essential for effective snakebite management, particularly in regions like Morocco, where approximately 400 snakebite incidents are reported annually, with a case fatality rate of 7.2%. Identifying venomous sna...

The .NET framework is widely used for software development, making it a target for a significant number of malware attacks by developing malicious executables. Previous studies on malware detection often relied on developing generic detection methods...

This study introduces a novel evaluation framework for predicting web page performance, utilizing state-of-the-art machine learning algorithms to enhance the accuracy and efficiency of web quality assessment. We systematically identify and analyze 59...