Introduction Oral Squamous Cell Carcinoma (OSCC) poses a significant challenge in oncology due to the absence of precise diagnostic tools, leading delays identifying condition. Current methods for OSCC have limitations accuracy and efficiency, highlighting need more reliable approaches. This study aims explore discriminative potential histopathological images oral epithelium OSCC. By utilizing database containing 1224 from 230 patients, captured at varying magnifications publicly available,...

According to the WHO (World Health Organization), lung cancer is leading cause of deaths globally. In future, more than 2.2 million people will be diagnosed with worldwide, making up 11.4% every primary cancer. Furthermore, expected biggest driver cancer-related mortality worldwide in 2020, an estimated 1.8 fatalities. Statistics on rates are not uniform among geographic areas, demographic subgroups, or age groups. The chance effective treatment outcome and likelihood patient survival can...

Abstract Brain tumor classification using MRI images is a crucial yet challenging task in medical imaging. Accurate diagnosis vital for effective treatment planning but often hindered by the complex nature of morphology and variations Traditional methodologies primarily rely on manual interpretation images, supplemented conventional machine learning techniques. These approaches lack robustness scalability needed precise automated classification. The major limitations include high degree...

The necessity of prompt and accurate brain tumor diagnosis is unquestionable for optimizing treatment strategies patient prognoses. Traditional reliance on Magnetic Resonance Imaging (MRI) analysis, contingent upon expert interpretation, grapples with challenges such as time-intensive processes susceptibility to human error.

Deep learning has attained state-of-the-art results in general image segmentation problems; however, it requires a substantial number of annotated images to achieve the desired outcomes. In medical field, availability is often limited. To address this challenge, few-shot techniques have been successfully adapted rapidly generalize new tasks with only few samples, leveraging prior knowledge. paper, we employ gradient-based method known as Model-Agnostic Meta-Learning (MAML) for segmentation....

This paper explores the application of artificial intelligence (AI) in forecasting Saudi Arabia’s non-oil export trajectories, aligning with Kingdom’s Vision 2030 objectives for economic diversification. A range machine learning models, including LSTM, Transformers, Ensemble Stacking, Random Forest, and XGBRegressor, were employed to analyse historical GDP data. Among these, Advanced Transformer model demonstrated superior predictive accuracy, achieving a MAPE 0.73%, underscoring its ability...

This paper investigates the application of artificial intelligence (AI) in forecasting Saudi Arabia’s non-oil export trajectories, contributing to Kingdom’s Vision 2030 objectives for economic diversification. A suite machine learning models, including LSTM, Transformer variants, Ensemble Stacking, XGBRegressor, and Random Forest, was applied historical GDP data. Among them, Advanced model, configured with an increased attention head size, achieved highest accuracy (MAPE: 0.73%), effectively...

Abstract Breast cancer has become the leading cause of mortality among women worldwide. The timely diagnosis such is always in demand researchers. This research pours light on improving design computer-aided detection (CAD) for earlier breast classification. Meanwhile, CAD tools using deep learning becoming popular and robust biomedical classification systems. However, gives inadequate performance when used multilabel problems, especially if dataset an uneven distribution output targets. And...

Autism spectrum disorder (ASD) poses a complex challenge to researchers and practitioners, with its multifaceted etiology varied manifestations. Timely intervention is critical in enhancing the developmental outcomes of individuals ASD. This paper underscores paramount significance early detection diagnosis as pivotal precursor effective intervention. To this end, integrating advanced technological tools, specifically eye-tracking technology deep learning algorithms, investigated for...

One of the most prevalent cancers is oral squamous cell carcinoma, and preventing mortality from this disease primarily depends on early detection. Clinicians will greatly benefit automated diagnostic techniques that analyze a patient's histopathology images to identify abnormal lesions. A deep learning framework was designed with an intermediate layer between feature extraction layers classification for classifying histopathological into two categories, namely, normal carcinoma. The...

Nowadays, the advancements of wearable consumer devices have become a predominant role in healthcare gadgets. There is always demand to obtain robust recognition heterogeneous human activities complicated IoT environments. The knowledge attained using these models will be then combined with applications. In this way, paper proposed novel deep learning framework recognize multimodal sensor data. composed four phases: employing dataset and processing, implementation model, performance...

Our research addresses the critical need for accurate segmentation in medical healthcare applications, particularly lung nodule detection using Computed Tomography (CT). investigation focuses on determining particle composition of nodules, a vital aspect diagnosis and treatment planning.

ABSTRACT Drinking water purity analysis is an essential framework that demands several real-world parameters to ensure the quality of water. So far, sensor-based in specific environments done concerning certain including PH level, hardness, TDS, etc. The outcome such methods analyzes whether environment provides potable or not. Potable denotes purified free from all contaminations. This gives absolute solution whereas demand for drinking a growing problem where multiple-level estimations are...

<title>Abstract</title> Skin cancer remains a formidable global health challenge, necessitating precise and timely diagnostic methodologies. This study focuses on advancing the field through development evaluation of deep learning algorithms tailored for skin detection using 3D Total Body Photography (3D-TBP). Leveraging ISIC 2024 dataset, comprising diverse array high-resolution lesion images, our approach integrates rigorous data preprocessing, sophisticated model architecture design,...

<title>Abstract</title> The digitalization of higher education has introduced a wealth data, presenting significant opportunities for utilizing Big Data and data science analytics to streamline processes improve institutional efficiency. This paper delves into the practical applications across various domains education, including student enrolments management, academic advising, learning analytics, effectiveness. Through implementation predictive personalized interventions, data-informed...

<title>Abstract</title> Autism spectrum disorder (ASD) is a common neurological illness marked by difficulties in social communication and the presence of repetitive behaviors. Timely precise identification crucial but continues to be substantial clinical obstacle ASD. This study investigates an artificial intelligence approach using deep learning models trained on publicly available eye-tracking datasets differentiate between autistic typically developing children. The focused prospective...

<title>Abstract</title> In modern law enforcement, the integration of data science and analytics has become instrumental in enhancing decision-making processes proactively addressing crime patterns. This paper investigates potential these technologies within initiatives like Smart Policing Station, emphasizing their transformative role enforcement agencies. A key contribution is introduction Crime Prediction Recognition (CPR) algorithm, a novel approach designed to excel analysis tasks...

Creative augmentation methods in medical imaging, particularly diagnosing Alzheimer’s disease, is a breakthrough approach the current field. condition that causes gradual deterioration of cognitive abilities, presents considerable difficulties accurately and interpreting brain early stages. Neural-enhance Style Transfer (NST), once recognized creative field for its capacity to combine styles many images, currently being adapted improve clarity comprehensibility scans used diagnose disease....

<title>Abstract</title> Cloud computing has become a cornerstone of modern IT infrastructure, and effective resource management is essential for maximizing performance minimizing costs. This paper explores the application machine learning algorithms to optimize cloud management. We utilize datasets that capture key metrics such as CPU usage, memory consumption, network traffic. Our methodology involves preprocessing analysing these develop predictive optimization models aimed at improving...

Early Lung Cancer (LC) detection is essential for reducing the global mortality rate. The limitations of traditional diagnostic techniques cause challenges in identifying LC using medical imaging data. In this study, we aim to develop a robust model. Positron Emission Tomography / Computed (PET CT) images are utilized comprehend metabolic and anatomical data, leading optimal diagnosis. order extract multiple features, enhance MobileNet V3 LeViT models. weighted sum feature fusion technique...

This paper introduces a modified genetic learning PSO (MGLPSO) algorithm for task matching problem in grid systems. MGLPSO incorporates operators to create candidate solutions (exemplars) guide the particles search space. Results show that is more efficient and effective handling large-scale instances. Compared with Genetic Learning Particle Swarm Optimization (GLPSO), minimizes makespan by 52% 43%, respectively. Further, requires few iterations obtain high quality solutions. also reveal can...