ConfBits: A Web Based Conference Management System

ConfBits is a Web-based Conference Management System (CMS) developed to aid effective organization and management of professional, academic and technical conferences. The web based application is an object-oriented and multi-conferences platform that is made up of four major actors which are authors, reviewers, administrators (otherwise known as Program Committee (PC) chair) and participants. Conference organizers in any Anglophone country can subscribe to the platform via the Internet to access and utilize the different features which include; abstract and full paper submissions, assignment of papers to reviewers, sending email notifications to authors and reviewers, participants management and conference program scheduling. The prototype of the platform is already deployed on the Internet and the trial Universal Resource Locator (URL) is www.cucms.com.ng. From our review of existing online CMSs, ConfBits (although still at a prototype stage) is the first of such system from a developing clime. We hope the platform will serve to bridge the hitherto wide digital divide between the developed and developing nations especially with respect to scholarly online content

Vehicle Accident Alert and Locator (VAAL)

An emergency is a deviation from planned or expected behaviour or a course of event that endangers or adversely affects people, property, or the environment. This paper reports a complete research work in accident (automobile) emergency alert situation. The authors were able to programme a GPS / GSM module incorporating a crash detector to report automatically via the GSM communication platform (using SMS messaging) to the nearest agencies such as police posts, hospitals, fire services etc, giving the exact position of the point where the crash had occurred. This will allow early response and rescue of accident victims; saving lives and properties. The paper reports its experimental results, gives appropriate conclusions and recommendations

Analysis of Capacity Limitation in Nigerian GSM Networks and the Effects on Service Providers and Subscribers

The performance of GSM network is measured in terms of KPIs (Key Performance Indicators) based on statistics generated from the network. The most important of these performance indicators from the operators’ perspective are BER (bit error rate), the FER (frame error rate) and the DCR (dropped call rate). The Dropped Call Rate (DCR) is a measure of the calls dropped in a network as it gives a quick overview of network quality and revenues lost. This makes it one of the most important parameters in network optimization. At the frame level in the NMS (Network Management System), the DCR is measured against the Slow Associated Control Channel (SACCH) frame. If the SACCH frame is not received, then it is considered to be dropped calls. For this work data was acquired form the Network Management System of various GSM operators in Nigeria (e.g. MTN, Celtel, Globacom etc.). The acquired data was analyzed to statistically illustrate the extent of revenue that is lost as a result of dropped calls and the consequent impact on the customers/subscribers

Implementation of XpertMalTyph: An Expert System for Medical Diagnosis of the Complications of Malaria and Typhoid

The dearth of medical experts in the developing world has subjected a large percentage of its populace to preventable ailments and deaths. Also, because of the predominant rural communities, the few medical experts that are available always opt for practice in the few urban cities. This consequently puts the rural communities at a disadvantage with respect to access to quality health care services. In this work, we designed and implemented XpertMalTyph; a novel medical diagnostic expert system for the various kinds of malaria and typhoid complications. A medical diagnostic expert system uses computer(s) to simulate medical doctor skills in diagnosis of ailments and prescription of treatments, hence can be used to provide the same service in the absence of the experts. XpertMalTyph is based on JESS (Java Expert System Shell) programming because of its robust inference engine and rules for implementing expert system

Estimating An Optimal Backpropagation Algorithm for Training An ANN with the EGFR Exon 19 Nucleotide Sequence: An Electronic Diagnostic Basis for Non–Small Cell Lung Cancer(NSCLC)

One of the most common forms of medical malpractices globally is an error in diagnosis. An improper diagnosis occurs when a doctor fails to identify a disease or report a disease when the patient is actually healthy. A disease that is commonly misdiagnosed is lung cancer. This cancer type is a major health problem internationally because it is responsible for 15% of all cancer diagnosis and 29% of all cancer deaths. The two major sub-types of lung cancer are; small cell lung cancer (about 13%) and non-small cell lung cancer (%SCLC- about 87%). The chance of surviving lung cancer depends on its correct diagnosis and/or the stage at the time it is diagnosed. However, recent studies have identified somatic mutations in the epidermal growth factor receptor (EGFR) gene in a subset of non-small cell lung cancer (%SCLC) tumors. These mutations occur in the tyrosine kinase domain of the gene. The most predominant of the mutations in all %SCLC patients examined is deletion mutation in exon 19 and it accounts for approximately 90% of the EGFR-activating mutations. This makes EGFR genomic sequence a good candidate for implementing an electronic diagnostic system for %SCLC. In this study aimed at estimating an optimum backpropagation training algorithm for a genomic based A%% system for %SCLC diagnosis, the nucleotide sequences of EGFR’s exon 19 of a noncancerous cell were used to train an artificial neural network (A%%). Several A%% back propagation training algorithms were tested in MATLAB R2008a to obtain an optimal algorithm for training the network. Of the nine different algorithms tested, we achieved the best performance (i.e. the least mean square error) with the minimum epoch (training iterations) and training time using the Levenberg-Marquardt algorithm

Classification of Eukaryotic Organisms Through Cepstral Analysis of Mitochondrial DNA

Accurate classification of organisms into taxonomical hierarchies based on genomic sequences is currently an open challenge, because majority of the traditional techniques have been found wanting. In this study, we employed mitochondrial DNA (mtDNA) genomic sequences and Digital Signal Processing (DSP) for accurate classification of Eukaryotic organisms. The mtDNA sequences of the selected organisms were first encoded using three popular genomic numerical representation methods in the literature, which are Atomic Number (AN), Molecular Mass (MM) and Electron-Ion Interaction Pseudopotential (EIIP). The numerically encoded sequences were further processed with a DSP based cepstral analysis to obtain three sets of Genomic Cepstral Coefficients (GCC), which serve as the genomic descriptors in this study. The three genomic descriptors are named AN-GCC, MM-GCC and EIIP-GCC. The experimental results using the genomic descriptors, backpropagation and radial basis function neural networks gave better classification accuracies than a comparable descriptor in the literature. The results further show that the accuracy of the proposed genomic descriptors in this study are not dependent on the numerical encoding methods

Improved Classification of Lung Cancer Using Radial Basis Function Neural Network with Affine Transforms of Voss Representation

Lung cancer is one of the diseases responsible for a large number of cancer related death cases worldwide. The recommended standard for screening and early detection of lung cancer is the low dose computed tomography. However, many patients diagnosed die within one year, which makes it essential to find alternative approaches for screening and early detection of lung cancer. We present computational methods that can be implemented in a functional multi-genomic system for classification, screening and early detection of lung cancer victims. Samples of top ten biomarker genes previously reported to have the highest frequency of lung cancer mutations and sequences of normal biomarker genes were respectively collected from the COSMIC and NCBI databases to validate the computational methods. Experiments were performed based on the combinations of Z-curve and tetrahedron affine transforms, Histogram of Oriented Gradient (HOG), Multilayer perceptron and Gaussian Radial Basis Function (RBF) neural networks to obtain an appropriate combination of computational methods to achieve improved classification of lung cancer biomarker genes. Results show that a combination of affine transforms of Voss representation, HOG genomic features and Gaussian RBF neural network perceptibly improves classification accuracy, specificity and sensitivity of lung cancer biomarker genes as well as achieving low mean square erro

Improved Classification of Lung Cancer Using Radial Basis Function Neural Network with Affine Transforms of Voss Representation

Lung cancer is one of the diseases responsible for a large number of cancer related death cases worldwide. The recommended standard for screening and early detection of lung cancer is the low dose computed tomography. However, many patients diagnosed die within one year, which makes it essential to find alternative approaches for screening and early detection of lung cancer. We present computational methods that can be implemented in a functional multi-genomic system for classification, screening and early detection of lung cancer victims. Samples of top ten biomarker genes previously reported to have the highest frequency of lung cancer mutations and sequences of normal biomarker genes were respectively collected from the COSMIC and NCBI databases to validate the computational methods. Experiments were performed based on the combinations of Z-curve and tetrahedron affine transforms, Histogram of Oriented Gradient (HOG), Multilayer perceptron and Gaussian Radial Basis Function (RBF) neural networks to obtain an appropriate combination of computational methods to achieve improved classification of lung cancer biomarker genes. Results show that a combination of affine transforms of Voss representation, HOG genomic features and Gaussian RBF neural network perceptibly improves classification accuracy, specificity and sensitivity of lung cancer biomarker genes as well as achieving low mean square erro

Lung Cancer Prediction Using Neural Network Ensemble with Histogram of Oriented Gradient Genomic Features

This paper reports an experimental comparison of artificial neural network (ANN) and support vector machine (SVM) ensembles and their “nonensemble” variants for lung cancer prediction. These machine learning classifiers were trained to predict lung cancer using samples of patient nucleotides with mutations in the epidermal growth factor receptor, Kirsten rat sarcoma viral oncogene, and tumor suppressor p53 genomes collected as biomarkers from the IGDB.NSCLC corpus. The Voss DNA encoding was used to map the nucleotide sequences of mutated and normal genomes to obtain the equivalent numerical genomic sequences for training the selected classifiers. The histogram of oriented gradient (HOG) and local binary pattern (LBP) state-of-the-art feature extraction schemes were applied to extract representative genomic features from the encoded sequences of nucleotides. The ANN ensemble and HOG best fit the training dataset of this study with an accuracy of 95.90% and mean square error of 0.0159. The result of the ANN ensemble and HOG genomic features is promising for automated screening and early detection of lung cancer. This will hopefully assist pathologists in administering targeted molecular therapy and offering counsel to early stage lung cancer patients and persons in at risk populations

Development of e-SIWES Portal: A Web based Platform for Student Industrial Work Experience Scheme (SIWES) Management

We developed the e-SIWES portal in order to enhance the manual task of carrying out SIWES activities such as registration, dissemination of information, filling of log book for students’ day-to-day activities and supervision/assessment by lecturers and industry based supervisors. The portal is web-based and allows all tasks to be carried out using the personal computer and the Internet. We digitized the SIWES logbook and assessment forms for filling by students and grading by the supervisors electronically. This will allow supervisors to be assigned immediately the students commence their industrial training and facilitate their monitoring in real-time. With the e-SIWES portal, important messages can be broadcast to all students at once and on a prompt and regular basis