PERFORMANCE OF CLASSIFICATION ALGORITHM IN THE PREDICTION OF HYPERTENSIVE HEART DISEASE

ABSTRACT Hypertensive heart disease poses a significant health challenge globally, contributing substantially to cardiovascular morbidity and mortality. This study focuses on assessing the performance of classification algorithms in predicting chronic heart disease, particularly hypertensive heart disease. The introduction highlights the rising prevalence of hypertension, especially in African regions, and its association with cardiovascular illnesses. The

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MULTI-STAGE PASSAGE RANKING AND ANSWER EXTRACTION MODEL FOR OPEN-DOMAIN COMPLEX FACTOID QUESTION ANSWERING SYSTEM

ABSTRACT Question Answering System (QAS) is a fast growing area of research and commercial interest. Despite the advancements in QAS, there remain gaps in handling complex factoid questions effectively. The key challenges include Low accuracy in Passage raking, scoring and aggregation prediction whereby some top rank passages does not contain the best answer and incapable

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MULTIFACTOR AUTHENTICATION PREVENT STORE-XSS SERVER MODEL FOR EFFECTIVE MITIGATION AGAINST CROSS-SITE SCRIPTING

ABSTRACT Web apps are essential for sectors such as education, banking, and social media; yet, their extensive utilization subject’s users to security vulnerabilities including code injection, Cross-Site Scripting (XSS), data breaches, and malware. Cross-Site Scripting (XSS), a form of injection attack, enables the insertion of malicious payloads into trusted websites, jeopardizing user data or accounts.

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LAPUC-DR: LLM-AUGMENTED PROMPT-BASED URGENCY CLASSIFICATION FOR DISASTER RESPONSE

ABSTRACT Appropriate urgency classification plays a very important role in successful disaster management. Social Media systems (Twitter, in particular) have emerged as an excellent source of real-time information; however, conventional systems lack contextual comprehension and require manual labeling, which is labor-intensive. This paper addresses the issues and proposes the LAPUC-DR framework, combining large language models

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HYBRID DETECTION FRAMEWORK FOR REAL-TIME NETWORK ANOMALIES USING THRESHOLD-BASED TRIGGERS AND TEMPORAL SLIDING WINDOW PROFILING

ABSTRACT The real-time identification of anomalous traffic in modern networks is still a big challenge for cyber security. We propose a hybrid anomaly detection model, which combines the threshold-based triggers with the sliding window temporal profiles, to improve the early detection of DoS attacks and network anomalies. Abnormal behaviors such as traffic volume surges, port

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GEOMETRIC FEATURE EXTRACTION FOR EAR IDENTIFICATION USING ASPECT RATIO PRINCIPLE

ABSTRACT In the realm of biometrics, ear recognition stands out as a unique approach for human authentication. Unlike other biometric methods such as face and fingerprint recognition, ear recognition offers several merits. The distinctive contour of each person’s ear serves as the primary reason for adopting this technique. We have proposed an ear identification system

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ENHANCING DIAGNOSTIC ACCURACY: ANFIS-PSO WITH EXPANDED RULE BASE FOR HEART DISEASE PREDICTION

ABSTRACT This study introduces a new method for diagnosing heart disease using adaptive neural-flow identification (ANFIS) technology, optimised by particle swarm optimization (PSO). The heart disease, the world’s leading killer, requires precise diagnosis. Traditional ANFIS models often suffer from limited rule bases and insufficient initialization. Here we propose an ANFIS-PSO with a 140 rule base,

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DEVELOPMENT OF AN ADAPTIVE ORGANISATIONAL CYBERSECURITY MATURITY (AOCM) MODEL

ABSTRACT The Right to be Forgotten (RTBF) is an important right of data subjects enshrined in data privacy laws. However, the integration of Artificial Intelligence (AI) into different applications raises concerns about enforcement. AI-integrated systems collect and retain personal data for continuous improvement. This study analyzed provisions in the GDPR and Nigeria Data Protection Act

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DETECTION AND CLASSIFICATION OF LUNG DISEASES USING DEEP LEARNING APPROACH

ABSTRACT: Lung diseases pose a significant global health challenge, often necessitating early detection for effective intervention. Medical imaging, particularly chest radiographs, plays a major role in the diagnosis and disease monitoring. This research explores the use of deep learning models, specifically VGG16, VGG19, MobileNetV2 and InceptionV3 in the computerized detection and classification of lung diseases

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