International Journal of Trend in Scientific Research and Development

Acoustic Scene Classification ASC is classified audio signals to imply about the context of the recorded environment. Audio scene includes a mixture of background sound and a variety of sound events. In this paper, we present the combination of maximal overlap wavelet packet transform MODWPT level 5 and six sets of time domain and frequency domain features are energy entropy, short time energy, spectral roll off, spectral centroid, spectral flux and zero crossing rate over statistic values average and standard deviation. We used DCASE Challenge 2016 dataset to show the properties of machine learning classifiers. There are several classifiers to address the ASC task. We compare the properties of different classifiers K nearest neighbors KNN , Support Vector Machine SVM , and Ensembles Bagged Trees by using combining wavelet and spectral features. The best of classification methodology and feature extraction are essential for ASC task. In this system, we extract at level 5, MODWPT energy 32, relative energy 32 and statistic values 6 from the audio signal and then extracted feature is applied in different classifiers. 

The major contributing factor to biometric technological advancements is fingerprints. Biometric authentication technology works completely for worker time management combined with the ability to acknowledge distinctive behavioral features. The growing popularity for period and enrollment of biometrics based platforms provides several advantages. These terminals can not only display fingerprints, but also some are intended to display the distinctive speech style, finger pattern, head form, or iris of a person. Image processing methods were the finest option for human administration to define fingerprint images. This article provides a study of present image processing research by reviewing methods used to distinguish fingerprints and computer training models used to classify fingerprints. The papers primary objective is to demonstrate the present suggested feature extraction duties for image processing and fingerprint identification computer training methods. This evaluation document will be essential to other fingerprint identification scientists operating in the field of image processing 

Face recognition is a kind of automatic human identification from face images has been performed widely research in image processing and machine learning. Face image, facial information of the person is presented and unique information for each person even two person possessed the same face. We propose a methodology for automatic human classification based on Binary Robust Invariant Scalable Keypoints BRISK feature of face images and the normal distribution model. In our proposed methodology, the normal distribution model is used to represent the statistical information of face image as a global feature. The human name is the output of the system according to the input face image. Our proposed feature is applied with Artificial Neural Networks to recognize face for human identification. The proposed feature is extracted from the face image of the Extended Yale Face Database B to perform human identification and highlight the properties of the proposed feature. 

Education is perceived in this piece as a process of learning in which the knowledge, skills, and habits of a group of people are transferred from one generation to the next through teaching, training, support and research. ICT has a very significant place in the education process that involves teachers and instruction. Information and communication technology have become within a very short time, one of the basic building blocks of modern society including Science and technology teachers. The purpose of the study is to determine the status of ICT training and support for science and technology teacher educators in colleges of education within Southeast Nigeria. The study adopted a survey research design. The population of the study consisted of 454 science and technology teacher educators in colleges of education within Southeast state Nigeria. The instrument was subjected to face validation by three experts. The reliability coefficients of the instrument for the study is 0.98 using Cronbachs Alpha formula. Five research questions that guided the study were answered using mean and standard deviation. Five research hypotheses were tested at 0.5 level of significance using one way ANOVA. Among the findings of the study were that, there is low ICT training, support and competence for science and technology teacher educators. It was concluded, among others, that government should help. 

Video analytics framework detection performance is worked at cloud. Object detection and classification are the basic tasks in video analytics and become the initial point for other complex submissions. Old-fashioned video analytics approaches are manual and time consuming. These are particular due to the very participation of human factor. 

This paper present a cloud based video analytics framework for accessible and robust analysis of video streams. The framework enables an operative by programing the object detection and classification process from recorded video streams. An operative only specifies an analysis criteria and period of video streams to analyze. The streams are then realized from cloud storage, cracked and analyzed on the cloud. The framework performs compute severe parts of the analysis to CPU powered servers in the cloud. 

Vehicle and face finding are accessible as two case studies for assessing the framework, with one month of data and a 15 node cloud. The framework consistently performed object detection and classification on the data, comprising of 21,600 video streams and 175 GB in size, in 6.52 hours. The GPU enabled placement of the framework took 3 hours to perform analysis on the same number of video streams, thus making it at least double as fast than the cloud deployment Without GPUs. The analysis framework is high. 

Examination misconduct has consistently remained the bane of Nigeria's educational system. This cuts across all the educational sectors, including the tertiary. This paper examined the concept of educational misconduct, and the various types that were listed in two state-owned Universities in Northern Nigerian Students' Handbook. It goes further to highlight those forms of examination misconducts that resulted from the development of technologies. Majority of current examination misconduct cases were those that involves the usage of technologies, most especially, the mobile phones. 

The paper further examined various measures that the University has put in place to curb examination misconduct. However, these measures have yielded little or no positive result. This paper therefore highlighted various technologies that can be used to minimise or possibly eradicate examination misconduct issues. The challenges associated with the integration were discussed. The prospects were also enumerated. The paper further highlighted how some of these technologies could be incorporated and effectively used to curb examination misconducts in the University. Suggestions were made on how to achieve these. 

This paper concluded by hypothesising that if technologies are adopted to curb examination misconducts, the quality and standard of education would be enhanced. 

by Dr. Peter Ajelabi Ph.D "Towards the Utilisation of Technologies in Curbing Examination Misconduct in a Nigerian University" 

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-6 , October 2018,