Lahore Garrison University Research Journal of Computer Science and Information Technology最新文献

{"title":"Roman Urdu Sentiment Analysis of Reviews on PSL Anthems","authors":"M. Qureshi, Muhammad Asif, Mujahid Bashir, Hafiz Muhammad Zain, Muhammad Shoaib","doi":"10.54692/lgurjcsit.2022.0603351","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2022.0603351","url":null,"abstract":"Due to the easy access of internet and smart devices, people are becoming habitual to give their feedback on what they hear or watch, online. These reviews are very valuable for all sorts of users. Due to the widespread online activities, the count of these reviews has raised tremendously. This fact makes it humanly impossible to analyse them manually. So it needs time that reviews to be analysed and use patterns to be found and explored through the automated channel. This led to a new field of research known as Sentiment Analysis. This paper is targeting to design a model to perform sentiment analysis of Roman Urdu text using the reviews of Pakistan Super League’s official song. To perform this analysis five different techniques-- Naïve Bayes Kernal, Random Forest, Logistic Regression, K-Nearest Neighbour and Artificial Neural Network, are applied. Naïve Bayes Kernal and Logistic Regression correctly predicted 97.00% reviews.","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116602668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Intelligent Healthcare system for detecting diabetes using machine learning algorithms","authors":"Hassan Kaleem, Saman Liaqat, Malik Tahir Hassan, Aneela Mehmood, Umer Ahmad, A. Ditta","doi":"10.54692/lgurjcsit.2022.0603327","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2022.0603327","url":null,"abstract":"\u0000 \u0000 \u0000 \u0000The human disease prediction is specifically a struggling piece of work for an accurate and on time treatment. Around the world, diabetes is a hazardous disease. It affects the various essential organs of the human body, for example, nerves, retinas, and eventually heart. By using models of machine learning algorithms, we can recommend and predict diabetes on various healthcare datasets more accurately with the assistance of an intelligent healthcare recommendation system. Not long ago, for the prediction of diabetes, numerous models and methods of machine learning have been introduced. But despite that, enormous multi-featured healthcare datasets cannot be handled by those systems appropriately. By using Machine Learning, an intelligent healthcare recommendation system is introduced for the prediction of diabetes. Ultimately, the model of machine learning is trained to predict this disease along with K-Fold Cross validation testing.  The evaluation of this intelligent and smart recommendation system is depending on datasets of diabetes and its execution is differentiated from the latest development of previous literatures. Our system accomplished 99.0% of efficiency with the shortest time of 12 Milliseconds, which is highly analyzed by the previous existing models of machine learning. Consequently, this recommendation system is superior for the prediction of diabetes than the previous ones. This system enhances the performance of automatic diagnosis of this disease. Code is available at (https://github.com/RaoHassanKaleem/Diebetes-Detection-using-Machine-Learning-Algorithms). \u0000 \u0000 \u0000 \u0000 \u0000 ","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124656608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Descriptive Analysis of Human Emotions Based on Eye pupils","authors":"Muhammad Abdullah Sarwar, Sajid Ali, , Muhammad Sharoze khan, Muhammad Asad mera, Malik Mubashir Hussain, Salman Qadri","doi":"10.54692/lgurjcsit.2022.0602265","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2022.0602265","url":null,"abstract":"Facial emotional expressions are viewed as the most descriptive way to understand the human’s state of temperament during confronting communication. In this work numerous statistical approaches have been applied on human eye pupil with static images of Chicago face dataset (CFD) to analyze and classify the considered categories for emotions which are Happy, Fear, Anger and Neutral. The aim of this study is to develop the specific architecture for image processing domain after applying different enhancement techniques on human eye pupil for analysis & recognition of the facial expressions. This work is divided into three phases initially in the first phase data preprocessing is performed to prepare according to the requirement of work and also the color images are converted in to negative by applying the pixel intensity controlled mechanism. Second phase define the boundary to compute the feature by using Circular Hough Transform algorithm. Lastly statistical approaches are applied on extracted features to corporate the central point of pupil. This corporation the central point presents the effects of emotions. While comparing peoples of different Age groups it is concluded that pupil constricted on Anger at different levels on different age groups. If further it is discussed about cross cultural and gender wise comparison then Happy Emotion effects most and resulted towards dilated pupil same like that Anger emotion effects most on constricting the pupil size.","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132882766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MM-Wave HetNet in 5G and beyond Cellular Networks Reinforcement Learning Method to improve QoS and Exploiting Path Loss Model","authors":"Khawar Bashir, Asad Ali, Amir Ali, Muhammad Waseem Razzaq","doi":"10.54692/lgurjcsit.2022.0602272","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2022.0602272","url":null,"abstract":"This paper presents High density heterogeneous networks (HetNet) which are the most promising technology for the fifth generation (5G) cellular network. Since 5G will be available for a long time, previous generation networking systems will need customization and updates. We examine the merits and drawbacks of legacy and Q-Learning (QL)-based adaptive resource allocation systems. Furthermore, various comparisons between methods and schemes are made for the purpose of evaluating the solutions for future generation. Microwave macro cells are used to enable extra high capacity such as Long-Term Evolution (LTE), eNodeB (eNB), and Multimedia Communications Wireless technology (MC), in which they are most likely to be deployed. This paper also presents four scenarios for 5G mm-Wave implementation, including proposed system architectures. The WL algorithm allocates optimal power to the small cell base station (SBS) to satisfy the minimum necessary capacity of macro cell user equipment (MUEs) and small cell user equipment (SCUEs) in order to provide quality of service (QoS) (SUEs). The challenges with dense HetNet and the massive backhaul traffic they generate are discussed in this study. Finally, a core HetNet design based on clusters is aimed at reducing backhaul traffic. According to our findings, MM-wave HetNet and MEC can be useful in a wide range of applications, including ultra-high data rate and low latency communications in 5G and beyond. We also used the channel model simulator to examine the directional power delay profile with received signal power, path loss, and path loss exponent (PLE) for both LOS and NLOS using uniform linear array (ULA) 2X2 and 64x16 antenna configurations at 38 GHz and 73 GHz mmWave bands for both LOS and NLOS (NYUSIM). The simulation results show the performance of several path loss models in the mmWave and sub-6 GHz bands. The path loss in the close-in (CI) model at mmWave bands is higher than that of open space and two ray path loss models because it considers all shadowing and reflection effects between transmitter and receiver. We also compared the suggested method to existing models like Amiri, Su, Alsobhi, Iqbal, and greedy (non adaptive), and found that it not only enhanced MUE and SUE minimum capacities and reduced BT complexity, but it also established a new minimum QoS threshold. We also talked about 6G researches in the future. When compared to utilizing the dual slope route loss model alone in a hybrid heterogeneous network, our simulation findings show that decoupling is more visible when employing the dual slope path loss model, which enhances system performance in terms of coverage and data rate.","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122308235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative Analysis of Machine Learning Techniques for Predicting Air Pollution","authors":"M. U. Ashraf, Farwa Akram, Sardar Usman","doi":"10.54692/lgurjcsit.2022.0602270","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2022.0602270","url":null,"abstract":"The modern and motorized way of life has cultured air pollution.  Air pollution has become the biggest rival of robust living. This situation is becoming more lethal in developing countries and so in Pakistan.  Hence, this inquiry was carried out to propose an architecture design that could make real-time prediction of air pollution with another purpose of scanning the frequently adopted algorithm in past investigations. In addition, it was also intended to narrate the toxic effects of air pollution on human health. So, this research was carried out on a large dataset of Seoul as an adequate dataset of Pakistan was not attainable. The dataset consisted of three years (2017-2019) including 647,512 instances and 11 attributes. The four distinctive algorithms termed Random Forest, Linear Regression, Decision Tree and XGBoosting were employed. It was inferred that XGB is more promising and feasible in predicting concentration level of NO2, O3, SO2, PM10, PM2.5 and CO with the lowest RMSE and MAE values of 0.0111, 0.0262, 0.0168, 49.64, 41.68 and 0.1856 and 0.0067, 0.0096, 0.0017, 12.28, 7.63 and 0.0982 respectively. Furthermore, it was found out as well that the Random Forest was preferred mostly in the previous studies related to air pollution prophecy while many probes supported that air pollution is very detrimental to human health especially long-lasting exposure causes lung cancer, respiratory and cardiovascular diseases.","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131883554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Estimation of outliers in cognitive networks spectrum sensing","authors":"Awais Salman Qazi, S. Mahmood, A. U. Rehman, Waqas Ahmad","doi":"10.54692/lgurjcsit.2022.0602284","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2022.0602284","url":null,"abstract":"The choice of this topic was influenced from the concept that statistical analysis of different attributes representing certain endpoints of behavior during radio communication in cognitive networks was necessary to study the outliers occurring in those parameters. The importance of cognitive radio is explained in detail in the literature review section of this paper. The purpose of this report is to do an overview of emerging patterns in cognitive radio networks and seek an understanding of data by learning what kind of attributes that display outliers during estimation. During the course of this research, it has come to light that study of outliers require preprocessing of data during which certain anomalies of data are studied and then removed thus optimizing the dataset. In the process, two major attributes SNR and Lambda have emerged and statistically shown a pattern that helped with the estimation of outliers. \u0000Key words: SNR, Lambda, Outliers, PU, SU, CRs.","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125423561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of an efficient Computational Model for classification of Tissue remodeling","authors":"Zarsha Nazim, Dr.Sajid Mahmood, Kiran Amjad","doi":"10.54692/lgurjcsit.2022.0602282","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2022.0602282","url":null,"abstract":"Tissue remodeling is one of the most important and crucial biological process. Process in which tissue reorganization and renovation takes place is called tissue remodeling. Mean of recovery in human beings is tissue remodeling in which damaged tissue are replaced completely with new tissue or through tissue repairmen types physiological and pathological tissue remodeling are two derivatives of Tissue remodeling. Normal Tissue remodeling is referred to as Physiological tissue remodeling, however abnormal process which may lead to a disease is known as pathological tissue remodeling. \u0000From past till now different techniques like histopathology and chemicals were being used to identify abnormality in tissues. Which is a time taking and costly processes. There is no such computational method which can be used for the identification of the physiological and pathological tissue remodeling. The current article aims to develop a classification model which has ability to classify weather the given sequence is physiological or pathological process. Three classifiers RF, ANN and SVM will be used for practice and evaluation of proposed classification model.","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130854797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comparative Analysis of COVID Forecasting by Using Various Machine Learning Methods","authors":"Jamaluddin Mir","doi":"10.54692/lgurjcsit.2022.0601278","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2022.0601278","url":null,"abstract":"Covid-19 emerged as one of the most infectious diseases in the history of mankind, affecting nearly 250 million people all over the world in just a short period. The pandemic which started in China, has now spread all over the world, taking about 5 million lives globally. This has also severely affected the economies of countries and has proved to be a burden on health care systems. Due to these reasons, forecasting the spread of the disease has become critical so that concerned government authorities in countries can have the chance to mitigate the spread and plan health care resources efficiently and properly. This makes it more important to have a reliable forecast so that resources can be planned ahead of time. In the present work, linear regression is used for time forecasting the spread of Covid-19 in Pakistan. Statistical parameters and metrics have been used to evaluate and validate the model. The results show that linear regression results are highly reliable, time efficient and accurate.  ","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":"151 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122967024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying Optimal Parameters And Their Impact For Predicting Credit Card Defaulters Using Machine-Learning Algorithms","authors":"Muhammad Qasim Idrees, Humaira Naeem, Muhammad Imran, Asma Batool, Nadia Tabassum","doi":"10.54692/lgurjcsit.2022.0601260","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2022.0601260","url":null,"abstract":"Data mining and Machine learning are the emerging technologies that are rapidly spreading in every field of life due to their beneficial aspects. The financial sector also makes use of these technologies. Many research studies regarding banking data analysis have been performed using machine learning techniques. These research studies also have many Problems as the main focus of these studies was to achieve high accuracy and some of them only perform comparative analysis of different classifier's performance. Another major drawback of these studies was that they do not identify any optimal parameters and their impact. In this research, we have identified optimal parameters. These parameters are valuable for performing the credit scoring process and might also be used to predict credit card defaulters. We also find their impact on the results. We have used feature selection and classification techniques to identify optimal parameters and their impact on credit card defaulters identification. We have introduced three classifiers which are Kstar, SMO and Multilayer perceptron and repeat the process of classification and feature selection for every classifier. First, we apply feature selection techniques to our dataset with each classifier to find out possible optimal parameters and In the next phase, we use classification to find the impact of possible optimal parameters and proved our findings. In each round of classification, we have used different parameters available in the dataset every time we include and exclude some parameters and noted the results of each run of classification with each classifier and in this way, we identify the optimal parameters and their impact on the results Whereas we also analyze the performance of classifiers. To perform this research study, we use the “credit card defaults” dataset which we obtained from UCI Machine learning online repository. We use two feature selection techniques that include ranker approach and evolutionary search method and after that, we also apply classification techniques on the dataset. This research can help to reduce the complexities of the credit scoring process. Through this study, we identify up to six optimal parameters and also find their impact on the performance of classifiers. Further We also identify that multilayer perceptron was the best performing classifier out of three. This research work can also be extended to other fields in the future where we use this mechanism to find out optimal parameters and their impact can help us to predict the  results.  \u0000 ","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122079130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Urdu News Content Classification Using Machine Learning Algorithms","authors":"Khawar Iqbal Malik","doi":"10.54692/lgurjcsit.2022.0601274","DOIUrl":"https://doi.org/10.54692/lgurjcsit.2022.0601274","url":null,"abstract":"As the world has become a global village, the flow of news in terms of volume and speed increases. It is necessary to engage computing machines for assisting people in dealing with this massive data. The availability of different types of news and such material on the Internet serves as a source of information for billions of users. Millions of people in our subcontinent speak and understand Urdu. There are several classification techniques that are available and are applied to classify English news like political, Education, Medical, etc. Plenty of research work has been done in multiple languages but Urdu is still to be worked on due to a lack of resources. This research evaluates the performance of twelve (12) different Machine learning classifiers for the Urdu News text Classification problem. The analysis was performed on a relatively big and recent collection of Urdu text that contains over 0.15 million (153,050) labeled instances of eight different classes. In addition, after applying pre-processing techniques, the TF-IDF weighting technique was adopted for feature selection and data extraction. After evaluating various machine learning methods, the SVM outperforms the other eleven algorithms with an accuracy of 91.37 %. We also compare its results with other classifiers like linear SVM, Logistic regression, SGD, Naïve bays, ridge regression, and a few others.","PeriodicalId":197260,"journal":{"name":"Lahore Garrison University Research Journal of Computer Science and Information Technology","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114887077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}