Data mining approach to predicting the performance of first year student in a university using the admission requirements

The academic performance of a student in a university is determined by a number of factors, both academic and non-academic. Student that previously excelled at the secondary school level may lose focus due to peer pressure and social lifestyle while those who previously struggled due to family distractions may be able to focus away from home, and as a result excel at the university. University admission in Nigeria is typically based on cognitive entry characteristics of a student which is mostly academic, and may not necessarily translate to excellence once in the university. In this study, the relationship between the cognitive admission entry requirements and the academic performance of students in their first year, using their CGPA and class of degree was examined using six data mining algorithms in KNIME and Orange platforms. Maximum accuracies of 50.23% and 51.9% respectively were observed, and the results were verified using regression models, with R2 values of 0.207 and 0.232 recorded which indicate that students’ performance in their first year is not fully explained by cognitive entry requirements

Optimizing the stochastic deployment of small base stations in an interleave division multiple access-based heterogeneous cellular networks

The use of small base stations (SBSs) to improve the throughput of cellular networks gave rise to the advent of heterogeneous cellular networks (HCNs). Still, the interleave division multiple access (IDMA) performance in sleep mode active HCNs has not been studied in the existing literature. This research examines the 24-h throughput, spectral efficiency (SE), and energy efficiency (EE) of an IDMA-based HCN and compares the result with orthogonal frequency division multiple access (OFDMA). An energy-spectral-efficiency (ESE) model of a two-tier HCN was developed. A weighted sum modified particle swarm optimization (PSO) algorithm simultaneously maximized the SE and EE of the IDMA-based HCN. The result obtained showed that the IDMA performs at least 68% better than the OFDMA on the throughput metric. The result also showed that the particle swarm optimization algorithm produced the Pareto optimal front at moderate traffic levels for all varied network parameters of SINR threshold, SBS density, and sleep mode technique. The IDMA-based HCN can improve the throughput, SE, and EE via sleep mode techniques. Still, the combination of network parameters that simultaneously maximize the SE and EE is interference limited. In sleep mode, the performance of the HCN is better if the SBSs can adapt to spatial and temporal variations in network traffic.publishedVersio

A Bimodal Biometric Student Attendance System

A lot of attempts have been made to use biometrics in class attendance systems. Most of the implemented biometric attendance systems are unimodal. Unimodal biometric systems may be spoofed easily, leading to a reduction in recognition accuracy. This paper explores the use of bimodal biometrics to improve the recognition accuracy of automated student attendance systems. The system uses the face and fingerprint to take students’ attendance. The students’ faces were captured using webcam and preprocessed by converting the color images to grey scale images. The grey scale images were then normalized to reduce noise. Principal Component Analysis (PCA) algorithm was used for facial feature extraction while Support Vector Machine (SVM) was used for classification. Fingerprints were captured using a fingerprint reader. A thinning algorithm digitized and extracted the minutiae from the scanned fingerprints. The logical technique (OR) was used to fuse the two biometric data at the decision level. The fingerprint templates and facial images of each user were stored along with their particulars in a database. The implemented system had a minimum recognition accuracy of 87.83%

An Intelligent Online Diagnostic System With Epidemic Alert

In many parts of the world and especially in developing nations, qualified doctors are overworked. This situation is the direct result of not ensuring that the number of qualified and available doctors keep pace with the exponential population growth rate that is obtainable in developing countries. Despite this, accurate diagnosis of ailments is a must. This paper proposes a novel way to ease the work burden on doctors with an intelligent online diagnosis system that can accurately diagnose diseases and prescribe medications without the need for physical interaction between patient and doctor. The proposed system uses an application programming interface (Infermedica) and has the added advantage of being able to give alerts at the onset of any epidemic

An Analytics Enabled Wireless Anti-Intruder Monitoring and Alarm System

Home intruder detection and alarm system rely on a number of factors to determine if an alarm should be triggered. These factors depend greatly on the type of sensors used and the amount of analytical capability built into the alarm system. Presently, most home intruder detection and alarm systems in the market are highly prone to false alarms because they do not have any analytical capability. In this paper, an analytics enabled wireless anti-intruder monitoring and alarm system that is simple and low in cost is proposed. The proposed alarm system uses still images and the location of sensed motion within the premises of the home to help home owners make informed alarm triggering decisions. The designed security system offers the option of allowing multiple key holders receive security alerts via the cellular network‟s Short Message Service (SMS). The system also gives the option of sending distress messages to the police or trusted neighbours

Development of a Facial Recognition System with Email Identification Message Relay Mechanism

Attendance records play a vital role in the educational sector. It is so vital that students are not allowed to sit for examinations if they do not meet the class attendance benchmark. But students, instead of making sure they attend classes regularly, devise cunny ways of committing attendance fraud. This unpleasant trend has made it necessary to develop systems that can take accurate class attendance records and minimize fraud. The use of biometrics to develop attendance taking systems is becoming quite popular. One of such biometrics is The Face. In this paper, a facial recognition algorithm known as Fisherfaces or Fisher Discriminant Analysis (FDA) which is not sensitive to substantial variation in facial look and illumination is used to develop the facial recognition attendance taking system. The system implemented has a training database of Ten (10) students. Ten (10) facial images of each student are taken with different composures, looks and under different levels of illumination. Tests on nine (9) students in the database yielded accuracies of as low as 70% and as high as 90%. This validates the proof that the more the number of training facial image in the database, the higher the accuracy of Fisherfaces approach. The simple mail transfer protocol (SMTP) was interfaced with the database to send identification messages (name of student identified with time and date of identification) to the email address of the administrator (in this case the lecturer) in realtime to effectively monitor the attendance. The result was found capable of eliminating attendance fraud

Development of an Improved Fingerprint Feature Extraction Algorithm for Personal Verification

New and sophisticated technologies are regularly developed to counter every new wave of breaches in data security. At the heart of some of these technologies is the personal verification system that rests on the oars of biometrics. Biometric systems use unique physical and behavioral traits for identification or verification. In this paper, an improved fingerprint feature extraction algorithm for personal verification is proposed. The improved fingerprint feature extraction algorithm is capable of recognizing authorized individuals and differentiating them from fraudulent imposters. The input images were preprocessed before extracting robust features for matching. Euclidean distance was used for classification. The proposed system was tested using the fingerprint images of fifty registered individuals and thirty imposters. The results obtained were a False Acceptance Rate and False Rejection Rate of 16% and 24% respectively. It is also faster than other feature extraction algorithms by forty (40) seconds Keywords: Fingerprint, biometrics, robust features, division into blocks, ridge pattern, euclidean distance, personal verification, feature extraction, classification

Design and Simulation of a Smart Traffic System in a Campus Community.

Road traffic within campus communities has increased tremendously. More persons are now moving around campuses with vehicles than previously recorded. This development will pose a major traffic challenge if it is not addressed urgently. Standard technologies for traffic management in campus communities do not have a computerized framework that can control traffic based on detected level of congestion. The main purpose of this research is to propose a more efficient and effective system for road traffic management in a campus community. The system is completely automated and can manage the ever mounting traffic in campus communities. The proposed campus traffic management system was simulated using Proteus®. Tests carried out on the simulated reallife campus traffic scenario confirmed that the proposed campus traffic management system was better than conventional traffic control systems in existence on campuses

Automatic Home Appliance Switching Using Speech Recognition Software and Embedded System

In most homes, electrical appliances are controlled and operated manually, this could be difficult and challenging to do when tiredness, handicap, morphological variations (height, aging etc.) and inadequate skill stands in the way as impediment. This study aims to implement a better and more flexible means of controlling home appliances by means of an automated switching mechanism using speech recognition technique. Acoustic signals picked by a microphone controlled by a speech recognition application generate digital signals that are passed to a microcontroller, which in turn dispatches commands that operate the relays to which the appliances in the home are connected. The goal of using speech command to automate the switching of home appliances was achieved and proved to be a more convenient means of switching home appliances

Performance of MPLS-based Virtual Private Networks and Classic Virtual Private Networks Using Advanced Metrics

Multiprotocol Label Switching (MPLS) is effective in managing and utilizing available network bandwidth. It has advanced security features and a lower time delay. The existing literature has covered the performance of MPLS-based networks in relation to conventional Internet Protocol (IP) networks. But, too few literatures exist on the performance of MPLS-based Virtual Private Networks (VPN) in relation to traditional VPN networks. In this paper, a comparison is made between the effectiveness of the MPLS-VPN network and a classic VPN network using simulation studies done on OPNET®. The performance metrics used to carry out the comparison include; End to End Delay, Voice Packet Sent/Received and Label Switched Path’s Traffic. The simulation study was carried out with Voice over Internet Protocol (VoIP) as the test bed. The result of the study showed that MPLS-based VPN networks outperform classic VPN networks