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Item Selecting A Better Classifier Using Machine Learning For COVID-19(UMT, Lahore, 2019) MUHAMMAD IMRANNow a day’s world is confronting a severe issue identified as Coronavirus. Its officially declare as COVID-19. In this infection we don’t use clinically approved vaccines and medicines. Antibiotics give a relief to the effected patients because proper vaccination is not discovered. COVID-19 has resemblance like pervious infectious diseases such as Middle East Respiratory Syndrome (MERS) and Sever Acute Respiratory Syndrome (SARS). World need quick and rapid precautionary measures to handle this outbreak. Wuhan, Chinese city is the hub of this infection. To achieve the outcomes and future forecasting of COVID-19, we analyze the records and datasets of COVID-19 through Machine Learning algorithms. For this purpose, we used various algorithms to construct classifiers such as: Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbor (K-NN), Naïve Bayes and Random Forecast. These algorithms apply on different software Python. In our research we discussed two types of classification: Binary and Multinomial. Support Vector Machine and Decision Tree give us precise results. Other classifier models gave satisfactory outcomes. Above algorithms directly apply on datasets in Python and programming Language. The outcomes may be helping to predict the future circumstances of COVID-19.Item iTSP-PseAAC: Identify Tumor Suppressor Proteins by Using Fully Connected Neural Network and PseAAC(UMT, Lahore, 2019) Muhammad AwaisThe tumor suppressor genes (TSG), are like normal genes, controllers of cells related function from cell production to the death of the cell, if they are working properly, they can control the cell division, repairing of DNA mistakes and many other functions. There is a number of other tumor suppressor proteins that suppress the gene to not encode and produce cells. The gene, to act and perform like tumor suppression, undergo to transcription and translation process and produced the relevant proteins which bind with DNA and perform the tumor suppression activities to control the unwanted growth of cell or activities that are the part of tumor production. This study aims to propose a new and more accurate tumor suppressor proteins predictor and make it, easy to use, user-friendly and publicly available to the experimental biologist to get their desired results. The predictor model has used input features vector (IFV) calculated form the physiochemical properties of proteins based on FCNN to compute the accuracy, sensitivity, specificity, and MCC. The proposed model was validated against different exhaustive validation techniques i.e. self-consistency and cross-validation. Using self- consistency, the accuracy is 99%, for cross-validation and independent testing has 99.80% and 100% accuracy respectively. The overall accuracy of the proposed model is 99%, sensitivity value 98% and specificity 99% and F1-score was 0.99. It concludes, the proposed model for prediction of the tumor suppressor proteins has the ability to predict the tumor suppressor proteins efficiently, but it still has space for improvements in computational ways as the protein sequences may rapidly increase, day by day.Item Sequence-Based Identification of DNA Replication Proteins and DNA Replication Inhibitors using Statistical Moments and PseAAC(UMT, Lahore, 2019) Muhammad Arqam AminDNA is undoubtedly important for all living beings and a DNA molecule that holds a lot of information about heritage, also predicts if they are at risk for certain diseases. Double helix DNA consists of two integrated branches. These strings are separated during the copy process. Next, each strand of the original DNA molecule functions as a template and generates its counterpart. This is a process known as semi-conservative iteration. Because of the semi- conservative replication, the new coil is composed of both the original DNA strand and the newly synthesized strand. Cell error correction and error-checking mechanisms ensure almost complete commitment to DNA replication. DNA replication can also be performed in the laboratory. DNA synthesis can be initiated from a known sequence of template DNA molecules using DNA polymers isolated from cells and artificial DNA primers. Examples of polymerase chain reaction, ligase chain reaction and transcription-mediated amplification, but it can be very costly and time-consuming. Similarly, the identification of DNA replication proteins and DNA replication inhibitor proteins is somewhat extremely crucial that requires the reliable and comprehensive computational method that can precisely predict and discriminate the proteins. In this study, identification of DNA replication proteins and DNA replication inhibitors was aimed. This study is totally followed by Chou’s 5 step rule and different types of techniques used to get efficient prediction results by using an artificial neural network algorithm. This study comprehends the construction of novel prediction model to serve the proposed purpose. A prediction model was developed based on the artificial neural network by integrating the position relative features and sequence statistical moments in PseAAC for training neural networks. 10-fold cross-validation and Leave-one- out method was opted by validating at different levels like overall accuracy, sensitivity, and specificity. The study results recommend that the proposed strategy may play a fundamental part in the other existing strategies for DNA replication inhibitors and proteins prediction. Hence the proposed prediction method can offer assistance in foreseeing the DNA replication proteins and inhibitors in a productive and exact way. Our astonishing experimental results demonstrated that the proposed predictor surpass the existing models that can be served as a xi time and cost-effective stratagem for designing novel to identify DNA replication proteins and inhibitors.Item Spatial Data Analysis of Vehicle Accidents in Victoria Australia from 2006 to 2016 using ANOVA and T- Statistics(UMT, Lahore, 2019) Zain AsifAccidents and the analysis of accidents has always been an area of interest in the present age and is of prime interest to not only passengers but also the manufacturers of those vehicles and the government. The analysis of accidents helps expose the relationship between different types of attributes that are involved in causing the accidents. Accidents can be of airplanes, ships, road accidents etc but for our thesis we will be considering road accidents for different vehicles and focusing on cars. The analysis of various types of accidents using the given dataset we can gather information and contribute to finding the attributes which can cause accidents and how are can use these attributes to decrease the number of accidents. In our study we will be using statistical data analysis on spatial data on the State of Victoria in the Australia region. There have been laws made to reduce the speed limit of the vehicles over the past decades and our focus of this research would be to find out what vehicles are most prone to accidents and the driving rules and policies made by the government to control them. Accident are something which everyone tries to avoid and incase of the mishap what policies and safety measures can be taken to prevent them. Our research is an extension of work of data exploration [4] and finding the vehicles most prone to accidents and then finding out the government policies [3] that were applied to those vehicles and if there was any decrease in the accidents as a result of those policies.Item Leveraging Data Analytics to Maximize Business Outcomes; a Comparison across ICT SMEs in Pakistan(UMT, Lahore, 2019) Rukham BashirData analytics and its contribution of improving business performance is creating a hype in discussion, research and practice. With the emergence of IT enabled services, consumer generated data on online platforms is increasing day by day and henceforth bringing up challenges of measurement and analysis. This study identifies that measuring marketing performance to track business result remained a challenge for marketers to prove their abilities in highlighting campaign performance to scale business gains, especially in resource restrained SME sector. This study aims to explore the contributions of digital marketing efforts with application of data analytics approach to maximize business outcomes in terms of measurement and its impact on business outcomes. A qualitative approach is taken to analyze data from 20 respondents companies in different ICT services. This study concludes that measurement process with a refined approach to meet targets can lead to standardize the metrics that reduces market tensions. The study suggest the components for a data driven approach i.e. integration of data sources, appropriateness of measurement techniques, selection of metrics and analysis of impact can influence overall business performance. Therefore, analysis of marketing efforts is divided into four stages. This study identifies the similar practices in measuring digital marketing efforts with data techniques and tools and identifies the gap of data driven mindset in light of results obtained. It was found that ICT SMEs are growing with a data driven mindset and planning for future improvements, however, e-realty in Pakistan is lagging behind other industries in terms of knowledge and approach to data driven marketing that is limited to target settings for acquisition rather than creation of personalized marketing for long-term retention. The future directions and implications of this study are discussed in last where most important is the testing of the model with another research method and on large population for refining results and improving validity of findings.