Education Occupation Mismatch in Developing countries

This paper contributes to the literature highlighting the cost of education-occupation mismatch in the labor market. Most of the existing literature analyzing the education-occupation mismatch has been focused on the developed economies. Using the Skill Towards Employment and Productivity (STEP) data, this paper analyzes the loss in income as a result of the education-occupation mismatch in developing countries. The results suggest that vertically mismatched (over-educated as well as under-educated) workers earn significantly less than the "matched" workers, whereas there is no significant penalty for being a horizontally mismatched worker. This paper also found the over-educated and horizontally mismatched workers to be significantly less satisfied with their life as compared to the "matched"

Education Occupation Mismatch in Developing countries

This paper contributes to the literature highlighting the cost of education-occupation mismatch in the labor market. Most of the existing literature analyzing the education-occupation mismatch has been focused on the developed economies. Using the Skill Towards Employment and Productivity (STEP) data, this paper analyzes the loss in income as a result of the education-occupation mismatch in developing countries. The results suggest that vertically mismatched (over-educated as well as under-educated) workers earn significantly less than the "matched" workers, whereas there is no significant penalty for being a horizontally mismatched worker. This paper also found the over-educated and horizontally mismatched workers to be significantly less satisfied with their life as compared to the "matched"

Sentiment classification of Roman-Urdu opinions using Naïve Bayesian, Decision Tree and KNN classification techniques

AbstractSentiment mining is a field of text mining to determine the attitude of people about a particular product, topic, politician in newsgroup posts, review sites, comments on facebook posts twitter, etc. There are many issues involved in opinion mining. One important issue is that opinions could be in different languages (English, Urdu, Arabic, etc.). To tackle each language according to its orientation is a challenging task. Most of the research work in sentiment mining has been done in English language. Currently, limited research is being carried out on sentiment classification of other languages like Arabic, Italian, Urdu and Hindi. In this paper, three classification models are used for text classification using Waikato Environment for Knowledge Analysis (WEKA). Opinions written in Roman-Urdu and English are extracted from a blog. These extracted opinions are documented in text files to prepare a training dataset containing 150 positive and 150 negative opinions, as labeled examples. Testing data set is supplied to three different models and the results in each case are analyzed. The results show that Naïve Bayesian outperformed Decision Tree and KNN in terms of more accuracy, precision, recall and F-measure

Advanced Formulation of QoT-Estimation for Un-established Lightpaths Using Cross-train Machine Learning Methods

Planning tools with excellent accuracy along with precise and advance estimation of the quality of transmission (QoT) of lightpaths (LPs) have techno-economic importance for a network operator. The QoT metric of LPs is defined by the generalized signal-to-noise ratio (GSNR) which includes the effect of both amplified spontaneous emission (ASE) noise and non-linear interference (NLI) accumulation. Typically, a considerable number of analytical models are available for the estimation of QoT but all of them require the exact description of system parameters. Thus, the analytical models are impractical in case of un-used network scenarios. In this study, we exploit an alternative approach based on three machine learning (ML) techniques for QoT estimation (QoT-E). The proposed ML based techniques are cross-trained on the characteristic features extracted from the telemetry data of the already in-service network. This new approach provides a reliable QoT-E and consequently assists the network operator in network planning and also enables the reliable low-margin LP deployment

Cross-Train: Machine Learning Assisted QoT-Estimation in Un-used Optical Networks

The quality of transmission (QoT) estimation of lightpaths (LPs) has both technological and economic significance from the operator’s perspective. TThe quality of transmission (QoT) estimation of lightpaths (LPs) has both technological and economic significance from the operator’s perspective. Typically, the network administrator configures the network element (NE) working point according to the specified nominal values given by vendors. These operational NEs experienced some variation from the given nominal working point and thus put up uncertainty during their operation, resulting in the introduction of uncertainty in estimating LP QoT. Consequently, a substantial margin is required to avoid any network outage. In this context, to reduce the required margin provisioning, a machine learning (ML) based framework is proposed which is cross-trained using the information retrieved from the fully operational network and utilized to support the QoT estimation unit of an un-used sister network

Assessment of Cross-train Machine Learning Techniques for QoT-Estimation in agnostic Optical Networks

With the evolution of 5G technology, high definition video, virtual reality, and the internet of things (IoT), the demand for high capacity optical networks has been increasing dramatically. To support the capacity demand, low-margin optical networks engage operator interest. To engross this techno-economic interest, planning tools with higher accuracy and accurate models for the quality of transmission estimation (QoT-E) are needed. However, considering the state-of-the-art optical network’s heterogeneity, it is challenging to develop such an accurate planning tool and low-margin QoT-E models using the traditional analytical approach. Fortunately, data-driven machine-learning (ML) cognition provides a promising path. This paper reports the use of cross-trained ML-based learning methods to predict the QoT of an un-established lightpath (LP) in an agnostic network based on the retrieved data from already established LPs of an in-service network. This advanced prediction of the QoT of un-established LP in an agnostic network is a key enabler not only for the optimal planning of this network but it also provides the opportunity to automatically deploy the LPs with a minimum margin in a reliable manner. The QoT metric of the LPs are defined by the generalized signal-to-noise ratio (GSNR), which includes the effect of both amplified spontaneous emission (ASE) noise and non-linear interference (NLI) accumulation. The real field data is mimicked by using a well reliable and tested network simulation tool GNPy. Using the generated synthetic data set, supervised ML techniques such as wide deep neural network, deep neural network, multi-layer perceptron regressor, boasted tree regressor, decision tree regressor, and random forest regressor are applied, demonstrating the GSNR prediction of an un-established LP in an agnostic network with a maximum error of 0.40 dB

Cognitive Radio and Dynamic Spectrum Access Using Fuzzy Logic

Cognitive Radio is artificially intelligent radio and dynamic spectrum. This research paper presents an application of Cognitive Radio and Dynamic Spectrum Access with the help of fuzzy logic considering the inputs: radio and satellite frequency and outputs: adjust power and modulation. This research paper shows the real approach of comparing the simulation and design algorithm result and its successful use

Working Capital Management and Performance of SME Sector

The study investigates the influence of working capital management (WCM) on performance of small medium enterprises (SME’s) in Pakistan. The duration of the study is seven years from 2006 to 2012. The data used in this study was taken from different sources i.e. SMEDA, Karachi Stock Exchange, tax offices, company itself and Bloom burgee business week. Data of SME’s acquired from these sources forms the foundation of our calculation and then interpretation. As the data was gathered for a period of seven years i.e. 2006-2012, the reason for choosing this period was because of the availability of the latest data. The dependent variable of the study is Return on assets which is used as a proxy for profitability.  Independent variables were number of days account receivable, number of day’s inventory, cash conversion cycle (CCC) and number of days account payable. In addition to these variables some other variables were used which includes firm size, leverage and growth. Panal data technique is used to study the influence of WCM on profitability of SME’s. Results suggest that number of day’s accounts payable has positive association with profitability whereas average collection period, inventory turnover and CCC have inverse relation with performance. On the other hand the variable size and growth in sales has positive influence on profitability. In contrast debt ratio has negative impact on profitability. Keywords: Cash Conversion Cycle, Working Capital Management, SME’