Atiku Baba Shidawa

Recent studies have identified coronary artery disease (CAD) as a leading cause of death globally. Early detection of CAD is crucial for reducing mortality rates. However, accurately predicting CAD poses challenges, particularly in treating patients effectively before a heart attack occurs due to the complexity of data and relationships in traditional methodologies. This research has successfully developed a machine learning model for CAD prediction by combining K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) Classifier techniques. The model, trained and tested on a dataset of 918 samples (508 with cardiac issues and 410 healthy cases), achieved an accuracy of 82% for KNN, 84.3% for SVM, and 88.7% for the hybrid model after rigorous training and testing.

Recent studies have identified coronary artery disease (CAD) as a leading cause of death globally. Early detection of CAD is crucial for reducing mortality rates. However, accurately predicting CAD poses challenges, particularly in treating patients effectively before a heart attack occurs due to the complexity of data and relationships in traditional methodologies. This research has successfully developed a machine learning model for CAD prediction by combining K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) Classifier techniques. The model, trained and tested on a dataset of 918 samples (508 with cardiac issues and 410 healthy cases), achieved an accuracy of 82% for KNN, 84.3% for SVM, and 88.7% for the hybrid model after rigorous training and testing.

Cyber-attacks have proven to be a force for hacking groups and state-sponsored organizations seeking to level the playing field with competitors. The hacker threat paired with the enormously hazardous and costly danger of fraud or intellectual property theft by insiders has created a volatile situation in private and public organizations. While a majority of internal breaches are due to employee negligence or human error, attacks by malicious insiders with access to sensitive company information have increased dramatically in recent years. Threats of financial loss, theft of sensitive information, and destruction to critical sectors have made cybersecurity a top security priority around the globe. Whereas the increase in frequency and complexity of attacks on the industry has increased the danger of being unprepared, it also has influenced the cost of preventing and recovering from cyber-attacks. To construct a machine learning bases instruction detection system is capable of detecting Cyber-attacks in the private and public sectors in Nigeria and the whole world. The results show that Random Forest and Random Tree algorithms outperform the other algorithms in their level of precision and F-measure as they are above 99% and 98% respectively, while the Random Forest outperforms the others by its detection rate. However, the Random Forest and Random Tree algorithms are more efficient in performing classification exercise on the Test datasets

The adverse impact caused by ransomware on computing systems poses a major threat to everyday users and society in general. With continuous growth in ransomware, and newer malicious families emerging every month, the need for strong defensive methods increases every day. While expert-based systems are developed over time, this rate of growth in ransomware creates a need for self-evolving methods of defence that can learn from available data and improve over time. Deep learning methods, in particular, can provide this ability to improve learning with the increasing availability of data. This research proposes an alternative ransomware detection technique using deep learning, specifically Recurrent-Convolutional Neural Network (R-CNN), which will speed up the training time and extract high level features to give more accurate classification. Consequently, our model has improved the classification accuracy of the existing systems to 98.00%. The comparisons with other state-of-the art peer approaches have proven that our empirical model is promising.

the rise in cyber attacks has overwhelmed the monetary resources and human ability to analyze and combat every new form of cyber threat in the cyber security industry. With the increasing digital presence, there is a large amount of personal and financial information that should be protected from cyber attacks. In fact, cyber attacks can ruin the reputation of an organization or letdown the organization completely. This research examines the use of AI in the enhancement of cyber security. Recent developments in artificial intelligence are transformational and have exceeded the level of human performance in tasks such as data analytics. The study adopted the thematic literature review method, and data were sourced from Google scholar, science direct, research gates, academia, and others. The investigation revealed that application of AI in controlling cyber attack has advantages and disadvantages; however, the advantages outweigh the disadvantages. This researcher discovers that with...