Anietie Ekong

• Akwa Ibom State University

Cerebrovascular disease commonly known as stroke is the third leading cause of disability and mortality in the world. In recent years, technological advancements have transformed the way information is acquired and how problems are solved in diverse fields of human endeavors, including the medical and healthcare sectors. Machine Learning (ML) and d...

The global crisis caused by the coronavirus outbreak and other diseases has significantly changed daily life, work, and education, forcing individuals and organizations to adapt to evolving virtual environments. These challenges have led to conditions induced by an inability to process information effectively with computer technologies. This study...

Quantum computing holds transformative potential, but its adoption is hindered by the complexity of generating efficient, hardware-specific code. This work presents a modular, extensible compiler framework that bridges high-level quantum languages with diverse hardware architectures. The framework consists of three modules: a front-end for parsing...

A pipeline burst or rupture causing a leak may significantly impact the environment and the reputation of the company operating the pipeline. In recent years, oil and gas pipelines have been expected to be equipped with leak detection systems for monitoring operations and detecting leaks. Although the leak detection methods used today may not pr...

This research delves into the crucial area of hazard detection and risk severity assessment within the construction industry, using machine learning techniques. The dataset utilized is from the Chinese Construction Company (CCECC), Uyo, Nigeria. Comprising over 100,000 instances, it captures various hazard categories prevalent in construction sites...

This research investigates intelligent-based methodologies for navigating the dual imperatives of innovation and standard adoption within system developments aimed at sustainable technological progress. Employing a mixed-methods approach that includes literature reviews, case studies, surveys, interviews, workshops, and focus groups, the study exam...

Article Info This paper investigated the advent of robotics and artificial intelligence in libraries: librarians' perception, readiness, and attitude in some selected Universities in Nigeria's south-south and southeast geopolitical regions. This was done through a proportionate sampling technique of twenty (20) selected University libraries. A 27 i...

Leveraging Bayesian Network modelling, the study aimed to enhance prediction accuracy by capturing complex dependencies in network traffic patterns. Comprehensive data was collected from diverse network environments, including real-time logs and synthetic data. Through rigorous pre-processing, which addressed missing values and standardized format...

The growing threat of abuse from obscene jokes and other types of objectifying content especially among women has caused harassment and created a hostile environment for some users of social media space. To reduce the rate of hostility, filtering, therefore, becomes necessary for checking uncontrolled posting of contents of obscene jokes. The prima...

This study focuses on developing an efficient urban waste management system using deep learning algorithms and Internet of Things (IoT) technology. The goal is to improve waste management in Ikot Ekpene municipality by enabling quick disposal responses to prevent environmental pollution. The researchers employed an Object-Oriented Analysis and Desi...

This study investigates the performance of senior secondary school students in mathematics at Uyo High School over the past decade (2013-2022) using Support Vector Machine (SVM) analysis. The research aims to identify patterns and trends in student performance, exploring the impact of demographic factors such as gender, age, ethnicity, and socio...

This study proposed a machine learning approach to predict the scalability of broadband networks, which is crucial for ensuring fast and reliable internet connectivity. Scalability measures a network's ability to handle increasing users, devices, and data traffic without compromising performance. The researchers leveraged the strengths of Random Fo...

This study addresses the growing concern of technostress, a condition caused by the overwhelming use of digital technologies, exacerbated by the COVID-19 pandemic. The researchers developed a predictive model using machine learning algorithms, Random Forest (RF) and Support Vector Machine (SVM), to assess and manage technostress levels. The model c...

In the digital era, the proliferation of digital content has intensified concerns over intellectual property rights infringement, highlighting the need for robust copyright protection solutions. This paper presents a software solution designed to address these challenges by combining advanced algorithms with intuitive user interfaces for effective...

This study addresses the growing concern of technostress, a condition caused by the overwhelming use of digital technologies, exacerbated by the COVID-19 pandemic. The researchers developed a predictive model using machine learning algorithms, Random Forest (RF) and Support Vector Machine (SVM), to assess and manage technostress levels. The model c...

The bladder, a crucial organ for urine storage, can be considered as an Overactive Bladder (OAB), a dysfunction causing an urgent and sudden urge to urinate. OAB significantly impacts various aspects of daily life, affecting interpersonal interactions and routine activities such as work, travel, exercise, sleep, and leisure. The updated Global Cons...

Breast cancer is a type of cancer that forms in the cells of the breasts. It can occur in both men and women, but it is far more common in women with an increase likelihood of development caused by some risk factors like age, family history, gene mutations, hormonal factors, and exposure to estrogen. There are several essential methods for early di...

This paper aims to eliminate the difficulties encountered in the diagnosis of faults in power generators, which have led to the adoption of various contemporary approaches to replace older and sometimes trial and error approaches adopted by some generator technicians. Most existing methods do not accurately diagnose these faults and these have led...

Pipeline leaks are a frequent occurrence in oil and gas infrastructure worldwide. Though leak detection systems are expected to be installed on all pipelines in the near future, relying on human efforts to physically monitor these pipelines is and will continue to be challenging. Though today's leak detection techniques are not able to completely s...

Pipeline leaks are a frequent occurrence in oil and gas infrastructure worldwide. Though leak detection systems are expected to be installed on all pipelines in the near future, relying on human efforts to physically monitor these pipelines is and will continue to be challenging. Though today's leak detection techniques are not able to completely s...

Diabetes mellitus is one of the metabolic disorders that results in blood glucose levels that are higher than normal. Insulin regulates the flow of sugar from the blood into our cells, where it can be stored or utilized as fuel. Diabetes makes the body unable to create enough insulin or to utilize it effectively. Attempts to classify patients with...

Diabetes mellitus is one of the metabolic disorders that results in blood glucose levels that are higher than normal. Insulin regulates the flow of sugar from the blood into our cells, where it can be stored or utilized as fuel. Diabetes makes the body unable to create enough insulin or to utilize it effectively. Attempts to classify patients with...

Zero-day malware is a type of malware that exploits system vulnerabilities before it is detected and sealed. This type of malware is a significant threat to enterprise cybersecurity and has tremendous impact on organizations' performance, as it can spread widely before organizations can clamp down on the threat. Unfortunately, exploit developers ca...

Phishing remains a major concern for security specialists over the years. Phishing attack are aimed at tricking people into giving out sensitive or confidential information using social engineering method. So far, machine learning (ML) algorithms like Artificial Neural Network (ANN), Decision Tree (DT), Support Vector Machines (SVM) Logistic Regres...

The effectiveness of three Machine Learning (ML) algorithms: Support Vector Machine (SVM), Random Forest (RF) and K-Nearest Neighbour (KNN) techniques for the early diagnosis of heart diseases were evaluated. Heart disease' dataset collected from kaggle.com data repository, which comprised of 303 data points with 13 features and a target variable w...

Fasting Blood Sugar (FBS) levels reveal important information regarding a person's blood sugar management. There is a strong relationship between a person’s FBS level and cardiovascular disease (CVD) because uncontrolled long-term high FBS level can lead to CVD. Devising a means of predicting Fasting blood Sugar level of a patient will go a lon...

Modeling wireless communication networks is essential for sound decision making regarding network planning, network optimization, resource allocation and scheduling. In mobile networks, most commonly used indicators to measuring handoff performance include Received Signal Strength (RSS), signal strength from the target base station, Handoff Latency...

Optimizing logistics allocation and utilization is essential for effective healthcare management. Apparently, less consideration is given to it in most hospitals in Nigeria where less resources are allocated to health sector in yearly budgetary. Hospital consists of several patient classes, each of which follows different treatment process flow pat...

The integration of chatbot engines and home automation systems is an area that is seeing a lot of development and resources dedicated to in recent years. Many devices nowadays are constantly connected to the Internet (IoT) and this is a huge opportunity to create a software application that can manage an area of our everyday life. This paper was ai...

This study covers an approach towards minimizing Disease diagnostic errors using weighted input variables and Fuzzy Logic rules with multiphase diagnostic engine. The weights were applied because different symptoms may have different degrees of importance in different diseases. This is to ensure that recommendations for disease confirmation based o...

Information security is the number one priority for any establishment concerned with its growth and privacy of its data. Attackers have devised a variety of strategies to get access to organizations' databases, both on cloud and offline systems. The incorporation of both cryptography and cloud folder model to secure cloud based storage presented in...

In many developing countries, several homes are yet to be fitted with the traditional electro-mechanical meter or newer smart meter for measuring electricity consumption. As such, distribution network operators resort to estimated billing. This work proposes a low cost and easy-to-install alternative capable of monitoring the duration of supply whi...

Any smart phone can now be turned into a sound capturing device because of its built-in microphone. The ubiquity of smart phones expands the number of people able to participate in meetings. This paper presents methods and results of a sound capturing system capable of functioning as an ad-hoc microphone that feeds into a public address system. The...

Several attempts had been made to analyze emotion words in the fields of linguistics, psychology and sociology; with the advent of computers, the analyses of these words have taken a different dimension. Unfortunately, limited attempts have so far been made to using Interval Type-2 Fuzzy Logic (IT2FL) to analyze these words in native languages. Thi...