2018

Permanent URI for this collection

https://escholar.umt.edu.pk/handle/123456789/7675

Browse

Browsing 2018 by Issue Date

Filter results by year or month
Now showing 1 - 9 of 9
  • Thumbnail Image
    Item
    Predicting employee attrition by using data science
    (UMT, Lahore, 2018) Saud Bin Tahir
    Employee attrition is related to recruitment and selection process of human resource management. Attrition is of two types basically, voluntary and involuntary. First one is a type in which an employee quits the job himself/herself and second is the one on which the employer leaves its employees for what so ever the reason may be. Companies who can afford nowadays trying to develop such a system which is integrated into human resource management system and that can predict different trends especially related to attrition of employees from the company. The purpose of this research is to apply such methods of predictive analysts to solve such human resource management issues. In the end, this research will highlight the tops factors contributing to attrition. This research has tackled the issue in many ways like one of it is to get the data and apply a decision tree algorithm which in turn suggests the important factors contributing to high turnout. We have used C.50 algorithm. Data mining is an emerging field that can answer such questions. It can help us answer the following questions related to turnover analysis, labor planning and recruitment analysis
  • No Thumbnail Available
    Item
    COMPARATIVE ANALYSIS OF LINK PREDICATION TECHNIQUES
    (UMT, Lahore, 2018) Haseeb Ahmad
    In data mining, predication is the most attracting and beneficial in terms of making the right decision. Recently Link predication proofed its importance to the many researches in general and specially in the social network analysis, bio informatics, complex interconnected network, and chemical interconnection network. By finding the missing links many of the complex pattern in the big data had been found that had made the worth of the old data that is present in our archives, by finding the missing links many answer of complex patterns in big data are being answered. Different kind of algorithms had been purposed to find the links from the graph based data which are categories into three main categories maximum likelihood base algorithms, probability base algorithms and similarity base algorithms and each one is best in its own context, as many researchers had done research on link mining or link predication in each one of above mention algorithms category. So in that research I am going to survey purposed algorithms belong to these categories so from their survey result I will do a comparative analysis and will close the survey with the results and discussion and also on survey results will suggest about furthers directions.
  • No Thumbnail Available
    Item
    IMAGE COMPLETION WITH DEEP LEARNING
    (UMT, Lahore, 2018) Aqeel Shamas
    In these days, Image recognition is very important. Image recognition is very widely used in security system to identify and track any object. Most of the time we have incomplete or missing images and we have less quality images as well which are very difficult to process. So in this research, I would try to implement the GANs to complete the missing part of the Images. We also try to look deeply in these network to know how they works and how we can make them more efficient and more accurate. I will use TensorFlow in order to implement these networks using Python. I will interpret images as being samples, generates dummy images and filter the fit fake image for completion using the discriminative network. In this research Generative adversarial networks (GANs) will be used and GANs are one of the Artificial Intelligence approach. These networks are used in unsupervised Machine Learning. These Network was introduced by Ian Goodfellow et al. in 2014. By using these networks/techniques we can generate the photographs which look like superficially authentic to human observers, having many realistic characteristics. These networks use two neural networks which are contesting with each other using the zero-sum game framework. “Zero-sum game is a mathematical equation which represents the situation where each participant loss or gain as compare to the other participants. If one participant gains and then we sum up all the losses by other participants and subtract the loss from gain, we always got zero as sum of the total gains and losses.” In GANs, one neural network generates the output and the other neural network is used to evaluate the output. The second neural network uses the discriminative network discriminates between objects from the true data distribution and outputs produced by the generator.
  • Thumbnail Image
    Item
  • No Thumbnail Available
    Item
    EMOTIONS DETECTION FROM TEXTUAL DATA USING MACHINE LEARNING TECHNIQUES
    (UMT, Lahore, 2018) Muhammad Yar
    Emotions detection from textual data is a comparatively new classification job. Peoples are identified through his expressions and emotions. Emotions can be expressed through various modalities including face, voice, body language, physiology, brain imaging and text. The objective of this thesis is to identify emotions from text using Machine Learning techniques. Text can be a sentence, a paragraph, a book, a news article, a written speech and any text can be detect through emotions. According to science, human have twenty seven different types of emotions. In this research we have made emotions vocabularies by itself because the available emotions vocabularies are only two to four types which is not sufficient for valuable results nor satisfactory for further research work. We have get text of ten speeches of different personalities. We have applied and compare of five different types of classifiers; Naïve Bayes Classifier (NBC), Support Vector Machine Classifier (SVMC), Linear Support Vector Machine Classifier (LSVMC), Logistic Regression Classifier (LRC) and Stochastic Gradient Descent Classifier (SGDC) on these speeches one by one. In this research we have found that the highest accuracy is the Linear Support Vector Machine Classifier (LSVMC) which is 59.70% and the second highest accuracy of Logistic Regression Classifier (LRC) which is 59.49%.
  • No Thumbnail Available
    Item
    FAKE NEWS DETECTION USING MACHINE LEARNING TECHNIQUES
    (UMT, Lahore, 2018) ADNAN HAIDER
    With the expansion of social media, fake news detection topic gains a lot of popularity for the researchers in the world. The fabricated and false information is spread on the online network to manipulate the views of the people. Using misleading words, individuals can get contaminated by the fake news effectively and can share them without verification. The widespread of false information in current years increases great concerns. Fake news increased significant consideration in the 2016 United States Presidential Elections. To eliminate the bad impact of fake news, it is necessary to make a plan or system to stop such kinds of misinformation on the online networks. In our research, we purpose a systematic identification of fake news using machine learning techniques. We obtain fake news data from Kaggle and real news data from popular news agencies websites. We implement and compare results of six different machine learning algorithms and two different feature extraction techniques. We extract the sentiment features form the dataset and find a correlation in all sentiments of each news. Our research results find that support vector machine classifier is the best classifier, on the basis of obtain accuracy is 93% and F1 score is 94%. The results show that TF-IDF is the good technique for feature extraction from text.
  • No Thumbnail Available
    Item
    Study on temperature rise and weather changes in Lahore, Pakistan.
    (UMT, Lahore, 2018) Fakhar Abbas Mehar
    Latest study and research tells us that temperature of earth is increasing day by day. Temperature of Planet earth was low in 1980s as we have today. In this study I will try to learn temperature patterns through statistical analysis and I will also try to identify the most important variable which are influencing directly to the temperature in Lahore, Pakistan. In this study I got weather data for last 30 years. For this investigation monthly, mean minimum and maximum temperatures have been examined. Rain, Humidity, Cloud amount, Atmospheric pressure, Vapour pressure and Wind speed have also been studied in this research.
  • Thumbnail Image
    Item
    RESUME PARSER AUTOMATION USING NATURAL LANGUAGE PROCESSING
    (UMT, Lahore, 2018) Muneeb Ul Haque
    Recruitment is a tough and tiring procedure for HR team of any company. The whole HR team has to devote their time and efforts for the recruitment of only once vacancy sometimes. It made the task difficult which makes the existence of mistakes inevitable. In such a situation, resume parser could be a blessing for the company as it does not only saves time but also money as fewer resources are required now for the recruitment process. Resume is being parsed by separating every section through applying multiple techniques notably NLTK, PDF minor, NLP library etc. This process is done by computer through an automation process by dividing the features of resume made by human into sub sections; analysing the job description requirements and shortlisting the most appropriate candidates within few minutes. Hence, this resume parser will convert the recruitment process task into the project of mere few days overall
  • No Thumbnail Available
    Item
    Impact of suicide attacks on Karachi Stock Exchange
    (UMT, Lahore, 2019) Irum Noor
    Suicide attacks in Pakistan have adversely affected its economic system as well as its overall reputation in the world. This thesis is aimed at identifying the impact of major suicide attacks events took place in from 2010 - 2018 and their impact on the performance of Karachi stock index. A variety of quantitative methods have been used to identify the impact and to what extent these attacks damaged the overall performance of KSE.