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All Journal Vokasi UNESA Bulletin of Engineering, Technology and Applied Science
Ore-Ofe, Ajayi
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Computer Science & IT Engineering Mechanical Engineering Transportation

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Development of a Head Gesture-Controlled Robot Using an Accelerometer Sensor Ore-Ofe, Ajayi; Umar, Abubakar; Ibrahim, Ibrahim; Abiola, Ajikanle Abdulbasit; Olugbenga, Lawal Abdulwahab
Vokasi Unesa Bulletin of Engineering, Technology and Applied Science Vol. 1 No. 2 (2024)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v1i2.35114

Abstract

In this research, a head gesture-controlled robot was designed and developed to assist individuals with disabilities in performing tasks by translating head movements into robot commands. Using an accelerometer sensor embedded in a headgear device, the system interprets specific gestures—such as forward nods for forward movement, backward nods for reversing, and lateral tilts for turning left or right—into corresponding robotic actions. The design involved constructing a mechanical framework for the robot, assembling the headgear, and integrating both with Arduino-based programming to ensure accurate and responsive movements. Testing was conducted in a controlled setting, where the robot consistently followed head gestures with a high degree of accuracy, showing rapid response times to user inputs. Quantitative results demonstrated the system’s reliability, with over 95% accuracy in gesture recognition and minimal latency. This innovative system underscores the potential of head gesture-controlled robotics in assistive technology, offering an affordable, user-friendly solution to enhance mobility and autonomy for individuals with limited physical capabilities
Early Heart Disease Prediction Using Data Mining Techniques Sylvester Aondonenge, Dugguh; Ore-Ofe, Ajayi; Hassan Taiwo , Kamorudeen; Umar, Abubakar; Abdulrazaq Imam , Isa; Daniel Emmanuel , Dako; Ibrahim , Ibrahim
Vokasi Unesa Bulletin of Engineering, Technology and Applied Science Vol. 2 No. 2 (2025)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v2i2.36735

Abstract

This study develops a predictive model for early heart disease detection using data mining techniques to enhance timely and accurate diagnosis. Heart disease prediction is complex due to the need to analyze various risk factors, such as age, cholesterol, and blood pressure. The model integrates multiple machines learning algorithms, including Random Forest, Support Vector Machine, and a hybrid ensemble approach, aiming to achieve higher prediction accuracy and reliability. The methodology follows five phases which include data collection, data pre-processing, feature extraction, model construction, and model evaluation. Data was gathered from publicly available health repositories, preprocessed to remove missing values and irrelevant information, and subjected to feature extraction techniques to identify influential predictors. The data was split into an 80:20 ratio for model training and testing to assess model performance across various classification algorithms. The hybrid model achieved an accuracy of 97.56%, precision of 98.04%, and recall of 97.09%, surpassing the individual algorithms tested. These findings indicate that the hybrid approach effectively supports early intervention for heart disease, particularly in healthcare settings with limited diagnostic resources. The study demonstrates that advanced data mining techniques offer a viable solution for improving patient outcomes through early detection of heart disease.
Design Of an Enterprise Network Terminal Security Solution Idris Abubakar, Muhammad; Ore-Ofe, Ajayi; Umar, Abubakar; Ibrahim, Ibrahim; Olugbenga, Lawal Abdulwahab; Abdulbasit Abiola, Ajikanle
Vokasi Unesa Bulletin of Engineering, Technology and Applied Science Vol. 2 No. 3 (2025)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v2i3.39105

Abstract

This paper develops a secured enterprise network terminal security solution that seeks to safeguard the confidentiality, integrity, and availability of critical data and network resources, the paper presents a logical approach to designing an enterprise network security solution with a primary focus on optimizing and enhancing the performance of the network terminals (and datacenter critical end devices) security solution. The traditional network infrastructure has predominantly centered the security measured on core network components such as Firewalls, Intrusion Detection Systems/Intrusion Prevention Systems (IDS/SPS) but there are encountered security incidences, this is due to the exponential growth of the Internet of Things (IoT) devices, Bring Your Device (BYOD), and remote workforce trends, the network terminals have become the key points through which users access and utilize network resources for malicious attack and in most cases critical end devices such as servers/storage are the end target. This paper presents a comprehensive framework that places considerable emphasis on improving the terminal security performance by utilizing the existing encryption techniques (VPN) to provide double-step tunnels (VPN). However, in the event of an inevitable attack, the paper also presents a framework of how data center core end components, such as server and storage can be protected from the attack. The paper starts by studying the terminal ecosystem, the current terminal security solution, and the latest terminal security solution and designing the solution deemed fit to secure the terminal network.  
Co-Authors Abdulbasit Abiola, Ajikanle Abdulrazaq Imam , Isa Abiola, Ajikanle Abdulbasit Daniel Emmanuel , Dako Hassan Taiwo , Kamorudeen Ibrahim , Ibrahim Ibrahim Ibrahim Idris Abubakar, Muhammad Olugbenga, Lawal Abdulwahab Sylvester Aondonenge, Dugguh Umar, Abubakar