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 

This paper presents a comprehensive approach to face detection utilizing the YOLOv8 model, specifically trained on a diverse dataset consisting of images from four individuals. The trained model is seamlessly integrated into an AI module from Huada, a leading AI company, equipped with a camera and LED indicators, enabling real-time face recognition and classification of known and unknown individuals. The model’s performance is evaluated across various metrics, demonstrating its high accuracy, ro...

Artificial Intelligence  Applications of Communication Systems  Simulation/Analytical Evaluation of Communication Systems  Machine Learning  Information and Communication: Security, Privacy, and Trust 

Cardiovascular Diseases (CVDs) remain a leading cause of death in the United States. These diseases, including coronary heart disease, heart attack, and stroke, pose significant health risks. Accurate prediction of CVD probability can aid in prevention and management. To address this challenge, we analyzed data from the Behavioral Risk Factor Surveillance System (BRFSS) spanning 1995-2017. We developed innovative methods to handle missing data and normalize values. Deep learning models were empl...

Information retrieval  Machine Learning  Bioinformatics 

This has led to the rise of a paradigm shift in machine learning called federated learning (FL) that allows for decentralized model training over distributed data sources. With FL, devices, servers, or edges train the model together without sharing their privacy-sensitive data, effectively addressing the arising data privacy regulation, data residency, and data silos types of issues, among many others. The FL ecosystem has also been through a series of significant developments, leading to the em...

Information Management  Machine Learning 

Heart disease is one of the most important problems the world faces. It is an ongo-ing problem and it is leading to the cause of death globally. To solve this issue, predicting early heart disease is important. This research focuses on supervised machine learning techniques as a potential tool for heart disease prediction. This study has done a comprehensive review of 30 articles published between 1997 to 2023 about machine learning techniques to predict heart disease. The common problem is auth...

Machine Learning 

With the rapid increase in population, the rate of diseases like cancer is also increasing. Lung cancer is a leading cause of cancer-related deaths with a minimum survival rate; there is a need to find better, faster, and more accurate methods for early diagnosis of this disease. Although previous research in lung cancer has presented numerous prediction schemes, the feature selection utilized in the schemes and learning process has failed to enhance the accurate performance of lung cancer diagn...

Machine Learning