Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Digital processing of speech signals

Given contrasting evidence in the literature pertaining to the impact of Foreign Direct Investment on the host country’s economy, we take the case of Pakistan and test the said association for this nation. The data used for this study has spanned over the period of 1981 till 2010. Besides FDI, four other variables including Debt, Trade, Inflation and Domestic Investment have been included in the study, to regress upon GDP of this country. The methodology to test the impact of these variables on Pakistan’s economy has been limited to the least squares method. The co-integration of the variables has been ascertained through application of Augmented Dickey Fuller Test and is found to hold in the long run. Our findings indicate that Pakistan’s economic performance is negatively affected by foreign investment while its domestic investment has benefitted its economy. Moreover, the nation’s debt, trade and inflation have found to have negative impact on its GDP.

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Impact of Foreign Direct Investment on Economic Growth of Pakistan

Drowsy driver alert systems have been developed to minimize and prevent car accidents. Existing vision-based systems are usually restricted to using visual cues, depend on tedious parameter tuning, or cannot work under general conditions. One additional crucial issue is the lack of public datasets that can be used to evaluate the performance of different methods. In this paper, we introduce a novel hierarchical temporal Deep Belief Network (HTDBN) method for drowsy detection. Our scheme first extracts high-level facial and head feature representations and then use them to recognize drowsiness-related symptoms. Two continuous-hidden Markov models are constructed on top of the DBNs. These are used to model and capture the interactive relations between eyes, mouth and head motions. We also collect a large comprehensive dataset containing various ethnicities, genders, lighting conditions and driving scenarios in pursuit of wide variations of driver videos. Experimental results demonstrate the feasibility of the proposed HTDBN framework in detecting drowsiness based on different visual cues.

Driver Drowsiness Detection via a Hierarchical Temporal Deep Belief Network

The objective of this study was to compare the financial performance of different ownership structured commercial banks in Nepal based on their financial characteristics and identify the determinants of performance exposed by the financial ratios, which were based on CAMEL Model. Eighteen commercial banks for the period 2005 to 2010 were financially analyzed. In addition, econometric model (multivariate regression analysis) by formulating two regression models was used to estimate the impact of capital adequacy ratio, non-performing loan ratio, interest expenses to total loan, net interest margin ratio and credit to deposit ratio on the financial profitability namely return on assets and return on equity of these banks. The results show that public sector banks are significantly less efficient than their counterpart are; however domestic private banks are equally efficient to foreign-owned (joint-venture) banks. Furthermore, the estimation results reveal that return on assets was significantly influenced by capital adequacy ratio, interest expenses to total loan and net interest margin, while capital adequacy ratio had considerable effect on return on equity.

	 

	Key words: Financial performance, commercial banks, financial ratios analysis, Nepal.

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A comparison of financial performance of commercial banks: A case study of Nepal

Yawning detection has a variety of important applications in a driver fatigue detection, well-being assessment of humans, driving behavior monitoring, operator attentiveness detection, and understanding the intentions of a person with a tongue disability. In all of the above applications, an automatic detection of yawning is one important system component. In this paper, we design and implement such automatic system, using computer vision, which runs on a computationally limited embedded smart camera platform to detect yawning. We use a significantly modified implementation of the Viola-Jones algorithm for face and mouth detections and, then, use a backprojection theory for measuring both the rate and the amount of the changes in the mouth, in order to detect yawning. As proof-of-concept, we have also implemented and tested our system on top of an actual smart camera embedded platform, called APEX from CogniVue Corporation. In our design and implementations, we took into consideration the practical aspects that many existing works ignore, such as real-time requirements of the system, as well as the limited processing power, memory, and computing capabilities of the embedded platform. Comparisons with existing methods show significant improvements in the correct yawning detection rate obtained by our proposed method.

Yawning Detection Using Embedded Smart Cameras

Investment banks are the major contributors in economic development of a country. They have wide impact on capital and credit markets of a country. This study presents comparison of financial performance for the period 2006–2009 by using financial ratios and measures of investment banks working in Pakistan. Financial ratios are divided into three main categories and measures including two indicators. Seven investment banks out of nine are selected for analysis for comparison purpose. This work concludes that the performance of investment banks on the basis of efficiency ratio is different than on the basis of liquidity ratio, capital or leverage ratio and financial measures. Due to the unavailability of data of other two banks, the results are compiled from the data of seven banks. The findings are consistent with the analysis reported by the central bank of Pakistan (State Bank of Pakistan, SBP) in its report for financial sector analysis 2006-09. This study also equips the managers with important information about different activities of banks which have a high impact on the financial performance of investment banks.

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A Comparison of Financial Performance in Investment Banking Sector in Pakistan

The purpose this study was to identify the short-run and long-run relationship between income level and saving behaviour in Pakistan along with inflation and age dependency ratio by using co-integration and error correction model (ECM) for the period of 1985-2009. Auto regressive distributed lag (ARDL) model, bounds testing approach to co-integration were used to delineate the short- run and long –run relationship between income level and saving behavior. It is found that in short and long-run, income levels acquire highly significant and positive relationship with the saving behavior in case of Pakistan. This was the first attempt made by the researcher to estimate the income level effect on saving behaviour in case low income developing country like Pakistan. Given that Pakistan capital market is growing, the empirical assessment has direct policy relevance.

Does Income Level affect Saving Behaviour in Pakistan? An ARDL approach to co-integration for empirical assessment

With the advancement of communication and security technologies, it has become crucial to have robustness of embedded biometric systems. This paper presents the realization of such technologies which demands reliable and error-free biometric identity verification systems. High dimensional patterns are not permitted due to eigen-decomposition in high dimensional feature space and degeneration of scattering matrices in small size sample. Generalization, dimensionality reduction and maximizing the margins are controlled by minimizing weight vectors. Results show good pattern by multimodal biometric system proposed in this paper. This paper is aimed at investigating a biometric identity system using Support Vector Machines(SVMs) and Lindear Discriminant Analysis(LDA) with MFCCs and implementing such system in real-time using SignalWAVE.

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Speech Recognition: Increasing Efficiency of Support Vector Machines

Leasing institutions or companies are the major component of financial system. The intend of this study is to classify the leasing companies on the basis of financial ratios and to make horizontal and as well as vertical analysis among leasing companies for the period 2006-2009. Ten leasing companies out of ten companies are analyzed. This study concludes that ranking of leasing companies on the base of net investment in finance lease is different from the ranking based on return on assets (ROA), return on equity (ROE), return on revenues (ROR) and lease ratio (LR) and the ratios such as ROA, ROE, ROR and LR are negative in 2009 due to the net loss after tax. This study is only and only for academic purposes. Furthermore, all the results of study are fully matched with the results reported by State Bank of Pakistan (central bank of Pakistan).

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Muhammad Farhan

Papers

Yawning Detection Using Embedded Smart Cameras

A Comparison of Financial Performance in Investment Banking Sector in Pakistan

Does Income Level affect Saving Behaviour in Pakistan? An ARDL approach to co-integration for empirical assessment

Speech Recognition: Increasing Efficiency of Support Vector Machines

Leasing Industry in Pakistan: A Comparison of Financial Performance of Leasing Companies