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Item A Bidirectional Long Short Memory Network for Roman Urdu Using Novel Dataset(UMT, Lahore, 2022) Muhammad AwaisThe introduction of the internet made possible the quick and easy dissemination of information about a wide variety of topics, including products, administrations, events, and political hypotheses, among others. Although there has been a rapid increase in the number of research undertaken on sentiment analysis, the majority of these studies have focused on issues associated with the English dialect. It is more challenging to do sentiment analysis in Roman Urdu than it is in English for a number of different reasons. Due to Roman Urdu's lack of distinct lexical resources, there is a possibility that information might get mixed. The primary purpose of this study is to build a large dataset for doing sentiment analysis in Roman Urdu, and a secondary objective is to evaluate several approaches to implementing such analysis by making use of machine learning and deep learning models. The approaches for analysing Roman and Urdu sentiments that are highlighted in this research are the ones that are used most often and extensively. The findings of this research will enhance the resource that is Roman Urdu as well as the methods that are used in sentiment analysis. For the sake of study on Roman Urdu, a dataset is generated. In order to achieve the highest possible levels of accuracy and performance, a combination of machine learning and deep learning algorithms is used. Our proposed approach achieves an accuracy of 83% in machine learning and 70% in deep learning, respectively, on the test dataItem Extrinsic Evaluation of Distributed Sentence Representation Through Recurrent Neural Networks(UMT, Lahore, 2022) Farman AliThere is an enormous amount of textual data on the internet because of the rise of social media and e-commerce. Consequently, the need for an intelligent model to evaluate and extract relevant information is significant. It is necessary to classify a series of texts into one or more specified categories to use NLP applications like sentiment analysis, web search, spam filtering, and information retrieval. The vanishing gradient problem makes learning long-term dependencies with gradient descent in neural network language models difficult. New strategies have been devised to overcome the limitations of current methods. As the number of parameters in the network grows, so does the computational cost, making it increasingly vulnerable to overfitting. As a result, Natural language processing (NLP) systems treat sentences as discrete atomic symbols, allowing the model to use modest amounts of information about the relationships between the made significant. IMDB reviews are being used in this study to test several deep learning algorithms to identify reviewers' opinions effectively. (NLP) Natural language processing and text analytics have a lot in common with the sentiment. It may be used to assess the reviewer's viewpoint toward various issues or the Review's overall polarity.Item Prediction of cardiovascular disease in logistic regression of Artificial Intelligence(UMT, Lahore, 2022) SAMAN PERVEZ MALIKIn contemporary healthcare, cardiovascular disease is one of the most urgent worldwide health ]challenges. The contemporary adage asserts that there has been a tremendous increase in global life expectancy due to cardiovascular disease. If heart abnormalities are discovered early, they are less likely to be life-threatening; nevertheless, if treatment is delayed, they may develop rapidly. Utilizing technology like as body area networks and electronic health records, medical sensors and wearable devices are implanted throughout patients' bodies in order to continually monitor and diagnose their health concerns. As the data generated by body area networks is both continuous and huge in volume, machine learning methods are used to efficiently classify health data. The most challenging aspect, however, is accurately identifying health data with an eye toward early detection of cardiac problems. Consequently, the absence of a more accurate and quick method for identifying cardiac problems is a fundamental drawback of the current methodologies. In this thesis, we create a highly accurate and performance-oriented categorization-based system for the early prediction of heart disease. This dissertation's significant contribution is divided into two sections. First, a technique that has proved useful in the early diagnosis and categorization of cardiovascular disease is described. Then, we provide a medical recommendation model based on the Fourier transform that we think will assist in the early diagnosis of heart disorders. To classify health data, models using the naive bayes classifier are used. Accuracy, sensitivity, precision, and specificity are assessed in conjunction with the results. According to the study, the suggested technique gives more accuracy and prediction measures than the methods currently in use.Item Combining Automation and Analytics to Detect Anti-Money Laundering(UMT, Lahore, 2022) Mariya JavaidThe methods of money laundering are evolving and getting sophisticated day by day. With the advent of cryptocurrency and other methods to easily transfer money around the world, the Anti Money Laundering (AML) professionals are becoming increasingly shorthanded. The financial industry is under pressure to detect and prevent Anti-Money Laundering (AML) due to increasing strictness by Financial Action Task Force (FATF) to increase transaction scanning. Hence they are looking for ways to automate the process and make it more efficient. One way to achieve this is by combining transaction analytics with machine learning. This approach can be used to identify patterns that may indicate money laundering. The machine learning algorithm can be trained to recognize suspicious activity, and then the results can be reviewed by human analysts. In this way, the majority of transactions can be processed quickly and efficiently. Almost all banks now use some form of automation to help detect Anti-Money Laundering (AML). The goal is to combine automation with analytics to get the most accurate results. Financial institutions have been trying to do this for a few years now, but it has been a challenge. The main reason it has been difficult is that the data is coming from different sources and needs to be cleansed and normalized before it can be analyzed. This study aims to detect anti money laundering and suspicious transactions from transaction logs using machine learning algorithms and neural network models to provide an optimal solution for suspicious transaction decisions in financial institutions (FIs). In this paper, we have detected suspicious transactions using machine learning and artificial neural network algorithms along with using some network analytics and natural language processing that has achieved more than 95% accuracy. Thus we have tried to implement a solution that can be used along with rule-based models and as a result, reduces false positive suspicious transactions.Item SKIN CANCER BASED HEALTH RECOMMENDATION SYSTEM BY DEEP LEARNING(UMT, Lahore, 2022) MIRHA KAMRANSkin disorders have become one of the most prevalent forms of ailments that folks have had to deal with for a very long time. When an individual with a multicolored disease might be recognized as a person who has skin cancer or is at risk of contracting skin cancer, therefore actions might be done immediately to minimize the patient's chance of having skin cancer or to eradicate the disease when it was established at such a preliminary phase. The diagnosis of skin disorders is primarily dependent on the knowledge and experience of medical professionals in addition to the findings of skin biopsies, which is a time taking operation. It is necessary to have an automated computer-based system for the recognition and classifying of skin disorders utilizing photographs in order to both increase the diagnostic performance and deal with the shortage of human analysts.Item Aspect Based Sentiment Analysis Using Machine learning and Deep learning(UMT, Lahore, 2022) Farhatul-AinIn recent years, the consumption of digital channels, including smart pathways, for government and commercial sector services has been expanded. The government and the commercial sector are trying to create services that can be accessed quickly and conveniently online, leveraging user feedback to construct and expand services and provide the essential values. As life is accelerated and people are seeking a quick and efficient way to consume services, Business owners become concerned in providing easily accessible services through users’ feedback. Thus, it is essential for stakeholders to take into consideration these opinions and comments for the purpose of developing and improving their applications and providing the intended value to their customers. The textual data including reviews and feedback is examined to identify the purpose of emotions, attitudes, and behaviors. Sentiment Analysis (SA) is the process of analyzing textual information and understanding the intent related to emotions, feelings and behavior SA comprises document-level, sentence-level, and aspect-level analysis. Subject of this study is aspect-based sentiment analysis (ABSA) of Yelp reviews. ABSA takes into consideration the study of a number of aspects or components of the inquiry. According to this research, machine learning and deep learning approaches have been applied to increase ABSA's performance in the Yelp reviews domain. Multinomial and SVM are utilized in Machine Learning methods. Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) with word embeddings are employed in our deep learning technique.Item DEEP LEARNING BASED SOCIAL MEDIA RECOMMENDATION SYSTEM(UMT, Lahore, 2022) ERSHA NISARIn past few decades, with the advent of online social networking sites, the field of personalized proposals that take use of the feature of social interactions has emerged as a particularly intriguing issue for researchers to investigate. This trend is expected to continue in the foreseeable future. The classification and suggestion system that is deployed for the purpose of determining the interests of users of social networking sites (SNS) is an important component in a variety of different businesses, particularly advertising. Advertising that is personalized helps firms stand out from the sea of generic internet ads while simultaneously increasing their relevancy to customers and eliciting favorable reaction from those clients. Whereas the vast majority of studies on user interest classification have concentrated on textual data, in this experiment I utilizes the user-generated image posts the model will precisely anticipate the user’s interest. As a consequence, this study categorizes the interests of social networking service users by employing graphics An artificial neural network (ANN) was used to characterize the interests of consumers, and for our user interest classification system, a variety of convolutional neural network (CNN)-based models were evaluated. In this study, neural network (NN) model made use of CNN-based classification models in order to categories photographs taken from users' social networking posts.Item Deep Inside Convolutional Neural Network (DICNN) for Text Classification(UMT, Lahore, 2022) Sohaima InamThe number of complicated text documents including its texts has grown exponentially in current history, necessitating the deeper comprehension of machine learning-based techniques in order to categorize texts in a number of applications effectively. In text processing, numerous deep learning techniques have shown astounding outcomes. Such learning techniques are effective and depend on the ability to comprehend intricate frameworks including non-linear correlations in the available data. Nevertheless, it can be difficult for researchers to locate appropriate text structures, topologies, and methods for textual classification. We provide the novel textual processing framework (Deep inside a convolutional neural network) that works exclusively at the level of the characters but only makes use of short convolutional operations as well as pooling processes. We are capable of demonstrating that the overall performance of the proposed model improves with depth by reporting enhancements above state-of-the-art on the number of open textual categorization activities utilizing up to 49 convolution layers. To the best of our information, deep inside convolutional nets have never been employed for textual processing before.