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...

Machine Learning 

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 ...

Artificial Intelligence  Machine Learning 

Agriculture is critical for economic stability and food security, particularly in regions like Owerri North, Nigeria, where inconsistent rainfall, waterlogging, and soil erosion threaten productivity. This study leverages Geographic Information Systems (GIS) and Remote Sensing (RS) to identify suitable dam construction sites to improve agricultural productivity. Key thematic layers such as precipitation, stream density, geomorphology, geology, land use/land cover (LULC), and elevation were analy...

Information Management  Technology  Agricultural Engineering  Machine Learning 

Migraines are a prevalent and debilitating neurological disorder, affecting millions worldwide. Characterized by symptoms such as nausea, photophobia, phonophobia, and visual disturbances, diagnosing and classifying migraines remains a challenge due to their heterogeneous nature. This study leverages machine learning techniques to analyze a dataset comprising 400 patient records, identifying key factors that contribute to migraine classification. Using statistical analysis, correlation matrices,...

Artificial Intelligence  Psychiatry & Psychology  Machine Learning 

Introduction: Systemic Lupus Erythematosus (SLE) is a chronic autoimmune disease that affects multiple organs and significantly impacts quality of life, particularly in women, with a global incidence of 5.14 cases per 100,000 person-years. Many SLE patients experience psychiatric complications, such as anxiety, with 55.4% affected in Morocco. To improve patient management and early detection of anxiety, a study proposes developing a machine learning algorithm to analyze patient data and identify...

Psychiatry & Psychology  Global Health  Machine Learning 

Clustering is an unsupervised machine learning technique used to organize unlabeled data into groups based on similarity. This paper applies the K-means and Fuzzy C-means clustering algorithms to a vehicle crash dataset in order to explore various patterns in the data. K-means assigns data points to clusters based on the similarity between the data point and the cluster centroids, which results in partitioning the data into distinct clusters. On the other hand, fuzzy C-means clustering allows da...

Information Management  Applications of Communication Systems  Numerical Methods  Technology  Machine Learning 

Face recognition is rapidly becoming one of the most popular biometric authentication methods. Most face recognition systems are focused on extracting features and enhancing their verification and identification capabilities. The detection of security vulnerabilities of different types of attacks has been given attention only in recent years. These attacks can include, but are not limited to: Obfuscation Spoofing and morphing; for example, a hacker can masquerade as a target to gain access to th...

Artificial Intelligence  Computer Vision  Image Processing  Machine Learning 

This study presents a comprehensive comparative analysis of machine learning models for real-time detection of Mirai botnet attacks in IoT networks. With the proliferation of IoT devices expected to reach 75 billion by 2025, the need for robust security solutions is critical, especially given the estimated $100 billion in annual global damages from IoT security breaches. We evaluated four machine learning models—Logistic Regression, Random Forest, Gradient Boosting, and Support Vector Machine—us...

Machine Learning 

With the exponential growth of the global population, particularly in underdeveloped regions, traffic congestion has become a pressing issue, exacerbated by limited resources and infrastructure. Conventional solutions like constructing new roads face feasibility challenges in third-world countries. In this context, we propose an innovative approach leveraging IoT, blockchain technology, Artificial Intelligence (AI), and sensor technologies for automatic traffic management. The proposed system ai...

Artificial Intelligence  Computer and Network Security  Machine Learning 

In an era marked by rapid technological advancement, the fusion of Artificial Intelligence (AI), Machine Learning (ML), and Distributed Ledger Technology (DLT), commonly referred to as blockchain, represents a pioneering frontier in healthcare and psychology. This paper explores the transformative potential of integrating these technologies to reimagine traditional practices and unlock novel approaches to patient care, diagnostics, therapy, and mental health management. Specifically, it investig...

Artificial Intelligence  Information Management  Technology  Machine Learning