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Aug 11, 2026Open Access
This study investigates the application of Artificial Neural Networks (ANN) for predicting welding-induced transverse distortion in Tungsten Inert Gas (TIG) welding of 10 mm thick ASTM A36 mild steel plates. Experimental data from a Face-Centered Central Composite Design (FCCD) were used to train, validate, and test a feedforward ANN model with four input parameters (current, voltage, gas flow rate, and welding speed) to produce a single response output of transverse distortion. The ANN model de...
Feb 03, 2026Open Access
This paper presents a novel framework for optimizing Carbon Release (CR) through an AI-driven approach to Fossil Fuel Intake (FFI) management. We propose a new training methodology for AI models to enhance their effectiveness in reducing FFI by learning from historical energy consumption trends and emissions data. By attributing specific quantities to key optimization components such as combustion efficiency, renewable integration, and AI impact, we derive a detailed matrix that quantifies their...
Oct 28, 2025Open Access
Understanding why a machine learning model makes a certain prediction is just as critical as how accurately it predicts, especially when it comes to diagnosing and treating cardiovascular disease. In this study, we applied explainable artificial intelligence (XAI) techniques to improve both the predictive power and interpretability of heart disease detection models. A dataset of 1,025 patient records was thoroughly preprocessed, including the handling of missing values, the ...
Oct 10, 2025Open Access
Artificial intelligence (AI) technologies are progressing rapidly, presenting opportunities and intricate ethical and legal issues. This evaluation delineates modern methodologies and classifications of AI governance to facilitate its secure and beneficial implementation. Ethical considerations must be integrated into AI frameworks emphasizing transparency, accountability, and fairness. The paper also addresses the imperative of financing AI safety research to mitigate dangers, especially those ...
Aug 25, 2025Open Access
This study presents a comprehensive and interpretable machine learning pipeline for predicting treatment resistance in psychiatric disorders using synthetically generated, multimodal data. The simulated dataset integrates gene expression profiles and clinically relevant features, such as age of onset, BMI, comorbidities, treatment response scores, and disease progression markers, thereby mimicking real-world pharmacogenomic and clinical complexities. The primary objective is to assess the feasib...
Aug 22, 2025Open Access
Major Depressive Disorder (MDD) remains a complex and debilitating psychiatric condition characterized by heterogeneous symptom profiles and substantial variability in treatment response. Despite extensive clinical research, the process of selecting effective pharmacological interventions for individual patients continues to rely heavily on trial-and-error, leading to prolonged suffering and increased healthcare costs. Neuroimaging-based predictive models have shown potential to support precisio...
Aug 21, 2025Open Access
Bipolar disorder is a complex psychiatric condition characterized by alternating mood episodes, ranging from depression to mania. Accurate and timely detection of a patient’s current mood state is critical for optimizing treatment strategies and preventing relapse. However, traditional clinical assessments are often subjective and prone to variability. This study proposes a data-driven, machine learning-based framework to classify mood states using synthetic but clinically informed patient data....
Aug 18, 2025Open Access
Bipolar disorder is a multifaceted psychiatric illness characterized by unpredictable mood episodes and highly variable treatment responses across individuals. Predicting response to specific pharmacological treatments remains a key challenge in personalized psychiatry. This study aims to develop predictive models for treatment response subtypes—non-responders, lithium responders, and anticonvulsant responders—using a diverse array of biomarkers, including genetic variants, serum levels, neuroim...
Jun 16, 2025Open Access
This study presents a comparative analysis of machine learning models for threat detection in Internet of Things (IoT) devices using the CICIoT2023 dataset. We evaluate Logistic Regression, K-Nearest Neighbors, and Random Forest algorithms across three classification granularities: binary (benign vs. attack), multi-class (8 categories), and fine-grained (34 subtypes). Our methodology incorporates comprehensive preprocessing including feature engineering, variance thresholding, correlation filter...
Jun 13, 2025Open Access
Background: Treatment-resistant depression (TRD) poses significant clinical challenges, with many patients inadequately responding to augmentation strategies like aripiprazole. Pramipexole, a dopamine agonist, has emerged as a promising alternative, though predictors of response remain unclear. This study applies machine learning (ML) to identify predictors and subgroups influencing pramipexole augmentation (PA) effectiveness in TRD, especially among patients previously failing ...
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