Mobile Learning Business Model Framework

M-learning has become more and more important but still is a young educational and economical (Edu-Eco) technology. M-learning strategies are aimed at economic, academic and technological objectives, however they lack in modeling ensured economic measurements in the sense of profitable products. Throughout this paper, we discuss the prime categories of elements that participate in the m-learning value net and give an overview of their business models. In addition to considering the mobile and e-learning business, business models, we deconstruct the m-learning value net, and also use a literature review in order to identify different actors in a business model for M-learning

Exchange rate Fluctuation and Sustainable Economic growth in Nigeria: VAR Approach

The essence of this research is to ascertain the relationship between real exchange rate and economic growth applying those variables that adjudged to make up equilibrium exchange rate thereby defining how interrelated are RER, GDP, EXP, IMP, FER and FDI. The major aim was to define how exchange rate fluctuation stimulates economic development in Nigeria from 2004 to 2014. Analysing the data using VAR technique, based on the prevailing situation in Nigerian economy within these period, one can envisage that RER fluctuation was significantly controlled by its positive relation with real import as well as its negative relation to real GDP and foreign direct investment. Similarly, GDP are positively controlled by depreciating exchange rate, increasing previous GDP, FER and FDI. Nigerian economic growth within these period were characterised by sustainable growth enhanced by sustainable increase in these factors. The inference being those investors, policy-makers and others of common interest should understand that Nigeria however, benefited from currency depreciation. Key words: VAR, Real exchange rate, Nigeria, FD

The Determinants of Voluntary Disclosure in Emerging Markets: The Case of Egypt

This paper estimates the extent of voluntary disclosure and the impact of a comprehensive set of corporate governance attributes (firm size, firm age, firm profitability, firm leverage, board independent, the existence of audit committee, director ownership, block-holder ownership, Auditor Specialization and Auditor Type) on the extent of voluntary disclosure in Egypt. It is based on the measurement of disclosure to the published data generated from a checklist of 54 items to measure the levels of voluntary disclosure, which had been collected from reviewing manually the financial statements and electronic sites a sample of Egyptian companies listed on the Egyptian Stock Exchange (EGX). We also have level standard ordinary least square (OLS) regression analysis to test for our sample of 100 observations to companies listed on the Egyptian Stock Exchange in 2016. We find that the average level of voluntary disclosure is 18.38%. Our analysis also shows that the size of the firm, firm age, profitability of the firm, auditor specialisation, and ownership of director have a positive impact on voluntary disclosure. However, we find a negative relationship between firm leverage and voluntary disclosure. Our analysis also shows that board independence, audit committee, Block-holder ownership and Auditor Type have no impact on voluntary disclosure. The empirical evidence from this study improves the understanding of the voluntary corporate disclosure environment in Egypt as one of the emerging markets in the Middle East. Keywords: Corporate Governance, Firm characteristics, Voluntary Disclosure

Deep Learning Based Sound Event Detection and Classification

Hearing sense has an important role in our daily lives. During the recent years, there has been many studies to transfer this capability to the computers. In this dissertation, we design and implement deep learning based algorithms to improve the ability of the computers in recognizing the different sound events. In the first topic, we investigate sound event detection, which identifies the time boundaries of the sound events in addition to the type of the events. For sound event detection, we propose a new method, AudioMask, to benefit from the object-detection techniques in computer vision. In this method, we convert the question of identifying time boundaries for sound events, into the problem of identifying objects in images by treating the spectrograms of the sound as images. AudioMask first applies Mask R-CNN, an algorithm for detecting objects in images, to the log-scaled mel-spectrograms of the sound files. Then we use a frame-based sound event classifier trained independently from Mask R-CNN, to analyze each individual frame in the candidate segments. Our experiments show that, this approach has promising results and can successfully identify the exact time boundaries of the sound events. The code for this study is available at https://github.com/alireza-nasiri/AudioMask. In the second topic, we present SoundCLR, a supervised contrastive learning based method for effective environmental sound classification with state-of-the-art performance, which works by learning representations that disentangle the samples of each class from those of other classes. We also exploit transfer learning and strong data augmentation to improve the results. Our extensive benchmark experiments show that our hybrid deep network models trained with combined contrastive and cross-entropy loss achieved the state-of-the-art performance on three benchmark datasets ESC-10, ESC-50, and US8K with validation accuracies of 99.75%, 93.4%, and 86.49% respectively. The ensemble version of our models also outperforms other top ensemble methods. Finally, we analyze the acoustic emissions that are generated during the degradation process of SiC composites. The aim here is to identify the state of the degradation in the material, by classifying its emitted acoustic signals. As our baseline, we use random forest method on expert-defined features. Also we propose a deep neural network of convolutional layers to identify the patterns in the raw sound signals. Our experiments show that both of our methods are reliably capable of identifying the degradation state of the composite, and in average, the convolutional model significantly outperforms the random forest technique

SyMPox: An Automated Monkeypox Detection System Based on Symptoms Using XGBoost

Monkeypox is a zoonotic disease. About 87000 cases of monkeypox were confirmed by the World Health Organization until 10th June 2023. The most prevalent methods for identifying this disease are image-based recognition techniques. Still, they are not too fast and could only be available to a few individuals. This study presents an independent application named SyMPox, developed to diagnose Monkeypox cases based on symptoms. SyMPox utilizes the robust XGBoost algorithm to analyze symptom patterns and provide accurate assessments. Developed using the Gradio framework, SyMPox offers a user-friendly platform for individuals to assess their symptoms and obtain reliable Monkeypox diagnoses

Aid for Trade and Export Performance of Developing Countries

Developing countries face a compliance gap with regard to trading rules at multilateral and bilateral negotiations and despite greater trade openness and the increasingly interdependence nature of global economy, these countries face internal constraints perpetuated by limited resources required to invest in the elements that would boost exports. This study examines the impact of Aid-for-Trade (AfT) on export performance of developing countries using panel data consisting of 131 countries from 2000 to 2013. In order to reveal the impact of AfT on export performance, the study uses regression analysis where the model specification includes variables such as Gross Domestic Product (GDP), money supply, exchange rate, trade openness, transparency and corruption and regulatory environment which are important in trade and have been used in regressions to explore the impact of aid on exports of receiving countries. The study reveals surprising results of the impact of AfT on export performance. Empirical results demonstrate that the impact of AfT on export performance is insignificant despite having a positive coefficient. Keywords: Aid for Trade, Exports, developing countries