Lahore Garrison University Research Journal of Computer Science and Information Technology
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227 research outputs found
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A Novel Model for Explainable Hostel Recommender System Using Hybrid Filtering
No full textRecommender systems help humans in filtering and finding the right information from the enormous amount of data. Hostels are more famous than hotels for solo travelers, but no prior research related to recommender systems has been conducted in this domain. Hostels allow users to provide multi-criteria ratings and traditional recommender systems are not able to provide effective recommendations in case of multi-dimensionality i.e. contextual information and multi-criteriaratings. So, we have proposed a novel hybrid recommender system (SAFCHERS) that chooses the hostel's features for computation dynamically and provides explainable and better recommendations than the traditional recommender systems
Using Data Mining Technique to Measure the Impact of COVID-19: 1st Wave on the Stock Market of Top Fifteen Affected Countries
No full textThe pandemic of Covid-19 which started in the year 2019 did not just cause an effect on the living of millions of people but in the economic and social sectors of every part of the world as well. It is a challenging task to determine the interrelation between COVID-19 cases concerning the economy in the top affected countries. This paper explores; how severe Impact of COVID-19 1st wave on the economic facets of Pakistan as compared to the Top Fifteen affected countries. Moreover, this paper uses COVID-19 well-known dataset provided by John Hopkins and Stock Market Datasets collectively to carry out the critical analysis successfully. We found a relationship between the cumulative numbers of confirmed cases in each country with a declining state of countries' economies: the higher decline in the stock market indicates a higher number of confirmed cases
A New Robust Multi focus image fusion Method
Get PDFIn today's digital era, multi focus picture fusion is a critical problem in the field of computational image processing. In the field of fusion information, multi-focus picture fusion has emerged as a significant research subject. The primary objective of multi focus image fusion is to merge graphical information from several images with various focus points into a single image with no information loss. We provide a robust image fusion method that can combine two or more degraded input photos into a single clear resulting output image with additional detailed information about the fused input images. The targeted item from each of the input photographs is combined to create a secondary image output. The action level quantities and the fusion rule are two key components of picture fusion, as is widely acknowledged. The activity level values are essentially implemented in either the "spatial domain" or the "transform domain" in most common fusion methods, such as wavelet. The brightness information computed from various source photos is compared to the laws developed to produce brightness / focus maps by using local filters to extract high-frequency characteristics. As a result, the focus map provides integrated clarity information, which is useful for a variety of Multi focus picture fusion problems. Image fusion with several modalities, for example. Completing these two jobs, on the other hand. As a consequence, we offer a strategy for achieving good fusion performance in this study paper. A Convolutional Neural Network (CNN) was trained on both high-quality and blurred picture patches to represent the mapping. The main advantage of this idea is that it can create a CNN model that can provide both the Activity level Measurement" and the Fusion rule, overcoming the limitations of previous fusion procedures. Multi focus image fusion is demonstrated using microscopic images, medical imaging, computer visualization, and Image information improvement is also a benefit of multi-focus image fusion. Greater precision is necessary in terms of target detection and identification. Face recognition" and a more compact work load, as well as enhanced system consistency, are among the new features
Human-Computer User Interface Design for Semiliterate and Illiterate Users
Get PDFInformation and Communication Technology (ICT) has revolutionized the lives of the people. The technology is embedded in daily life of literate or semiliterate/illiterate users. However, the user interface (UI) requirements for semiliterate/illiterate users are different from that of an educated person. The researchers of Human Computer Interaction for Development (HCI4D) face challenges to improve the usability of a UI for the semiliterate users. Therefore, a Systematic Literature Review (SLR) is conducted to provide a set of design factors and guidelines for UI development of semiliterate users. The study is based on extensive research gathered from literature to understand the user-centered design (UCD) approach, enhancing user experience (UX) for semiliterate users. This study analyses fifty two research articles that are published during 2010-2020. The findings shed light on the systematization of UI design guidelines for semiliterate/illiterate users. These guidelines can help in taking advantage of ICT during the COVID-19 pandemic. The analysis shows that seventeen main design factors are indispensable for designing UI of semiliterate users. The most suggested design factors include localization and graphics, which should be incorporated in UI for the target population. Moreover, the lag in the design factors as personalization and consistency open a road for future research
A Spatial Model of K-Nearest Neighbors for Classification of Cotton (Gossypium) Varieties based on Image Segmentation
No full textIn this study, we describe a technique that used a machine learning (ML) approach to classify four (4) different cotton leaf varieties namely; BS-15, S-32, Z-31, and Z-32. Each variety of cotton leaves were collected from 500 Farmers. These image datasets are captured by using the cell phone camera in the open agricultural field area, and every image was captured from both sides (Front and Back) of the cotton leaf. Each variety of cotton has used over 300 (150 Front Side and 150 Back Side of the leaves) leaf images and the total calculated cotton leaves are 1200 (300 x 4) as leaf image samples. These sample datasets have analyzed through image preprocessing and image segmentation process. Each image was employing four different non-over-lapping regions of interest (ROI’s) and calculated a total of 4800 (1200 x 4) ROI’s. The acquired datasets are employed different machine learning features such as Scalability, Texture, Spectral, Binary, Histogram, Rotational, and translational (R-S-T). A total of fifty-seven (57) machine learning features were evaluated on each ROI and a total calculated 273,600 (4800 x 57) features. Furthermore, the Correlation-Based Feature Selection (CFS) genetic algorithm technique was employed for feature optimization. It has been evaluated 22 optimized features and applying different machine learning (M-L) classifiers namely; K-Nearest Neighbor (K-NN), K*, Random Forest (RF) Tree, and Naive Bayes (NB) Tree. The resulting accuracy produced by K-NN presented is 98.9167% on (512 x 512) ROI’s. The individually overall result accuracy dataset values by using K-NN classifier on the four varieties of cotton leaf namely; BS-15, S-32, Z-31, and Z-32 were evaluated 97.83%, 99.50%, 99%, and 99.33%, respectively
Template Matching Based Probabilistic Optical Character Recognition for Urdu Nastaliq Script
No full textThis paper presents a technique for optical recognition of Urdu characters using template matching based on a probabilistic N-Gram language model. Dataset used has the collection of both printed and typed text. This model is able to perform three types of segmentations including line, ligature and character using horizontal projection, connected component labeling, corners and pointers techniques, respectively. A separate stochastic lexicon is built from a collected corpus, which contains the probability values of grams. By using template matching and the N-Gram language model, our study predicts complete segmented words with the promising result, particularly in case of bigrams. It outperforms three out of four existing models with an accuracy rate of 97.33%. Results achieved on our test dataset are encouraging in one perspective but provide direction to work for further improvement in this model
Business Process Model for IOT Based Systems Operations
Get PDFThe internet of things (IoT) is an innovative and advanced high-level IT development that provides the connection between a large network of devices equipped with numerous computing capabilities, actuation, and sensing with the help of internet connection, consequently providing multifarious novel services regarding smart systems. All around the globe the attractive big data analytics and IoT services are allowing initiatives regarding smart systems. Business processes are commonly executed inside the application systems where computers, objects of IoT as well as humans participate. However, for the system-supported processes, the use of IoT technology is still facing the problem of the absence of a standard system architecture that is essential to manage the coordination in a smart IoT environment. Business process management (BPM) is regarded as a substantial technique for designing, controlling, and improving the processes of a system. This article introduces a BPM modeling approach for IoT-based systems operation exploits IoT using BPM by adopting an IoT framework architecture and considering IoT data for interaction in a defined process model. The methodology has been carried out on top of current BPM modeling notions and system techniques for formal representations of the system and also to get through the challenges of collaboration and connection
Data Classification Using Decision Trees J48 Algorithm for Text Mining of Business Data
No full textThe business industry is generating a lot of data on daily business deals and financial transactions. These businesses are generating intensive-data like they need customer satisfaction on top priority, fulfilling their needs, etc. In every step, Data is being produced. This Data has a great value that is hidden from regular users. Data analytics is used to unhide those values. In our project, we are using a business-related dataset that contains strings and their class (0 or 1). 0 or 1 denotes the positive or negative string labels. To analyze this data, we are using a decision tree classification algorithm (J48 exceptionally) to perform text mining (classification) on our target dataset. Text mining comes under supervised learning (type). In-text mining, generally, we use two datasets. One is used to train the model, and the second dataset is used to predict the missing class labels in the second dataset based on this training model generated using the first dataset
An Efficient Classification Model using Fuzzy Rough Set Theory and Random Weight Neural Network
Get PDFIn the area of fuzzy rough set theory (FRST), researchers have gained much interest in handling the high-dimensional data. Rough set theory (RST) is one of the important tools used to pre-process the data and helps to obtain a better predictive model, but in RST, the process of discretization may loss useful information. Therefore, fuzzy rough set theory contributes well with the real-valued data. In this paper, an efficient technique is presented based on Fuzzy rough set theory (FRST) to pre-process the large-scale data sets to increase the efficacy of the predictive model. Therefore, a fuzzy rough set-based feature selection (FRSFS) technique is associated with a Random weight neural network (RWNN) classifier to obtain the better generalization ability. Results on different dataset show that the proposed technique performs well and provides better speed and accuracy when compared by associating FRSFS with other machine learning classifiers (i.e., KNN, Naive Bayes, SVM, decision tree and backpropagation neural network)
Usability Enhancement of SMS Interface for Illiterate Users
Get PDFThis article analyzes several User Interface (UI) designs and puts forward some more general design principles for interfaces designed for low-literate users. The results of this study highlight the importance of text-free interfaces compared to text-based interfaces for the illiterate and low-literate population. The study developed a Short Message Service (SMS) interface consisting of many design elements, including graphical icons, voice, and text reduction. The participants were more satisfied with the designed SMS interface as compared to the traditional text-based interface of SMS. We believe that if the user interface is appropriately designed, users will not need formal literacy, computer skills, or any external help to operate the application. It has been shown that an interface with minimal or no text but one or more graphics, audio, and digital components is helpful for users with low literacy rates