International Journal of Artificial Intelligence & Mathematical Sciences

Performance Analysis for the Diagnosis of COVID-19 Prediction by Mathematical Modeling & Simulation

Fri, 20 Oct 2023 06:40:32 Pakistan Daylight Time

Machine learning is a type of artificial intelligence (AI) method. The system can be conscious and adaptable to changes in concepts when emphatically programmed for presence based on prior experience. The study built and examined several sorts of ML algorithms in this work by creating a classifier depending on a predefined model to estimate COVID-19. The ML approach might be used in any sector of medicine, including diagnostics. COVID-19 forecast research is still needed, and there is a need to improve the diagnostic COVID-19 methodology. Several investigations have varied objectives regarding COVID-19 estimation methods that employ techniques. The purpose of this effort was to provide an integrative strategy to enhance the accuracy, precision, and recall of the COVID-19 proposed method. This study supports the utilization of ML and DL approaches. It is an essential element in the analysis of ML approaches such as K-Means and is applied to both supervised and unsupervised learning concepts. Several of these ML algorithms use various approaches to examine and perform the Decision Tree ,Naive Bayes and Multi-Layer Perceptron algorithms. Equivalent data sets were examined for clustering and classification approaches, including K-Means techniques. It was demonstrated that outcomes could be foreseen that were efficient, effective, and accurate.The best algorithmic outcome for COVID-19 case detection was achieved by combining K-Means with Multi-Layer Perceptrons (MLPs), resulting in an accuracy of 99.88%.

Character Recognition of Auslan Sign Language using Neural Network

Mateen Mureed, Mir Atif, Fahad Ali Abbasi — Fri, 20 Oct 2023 06:51:59 Pakistan Daylight Time

People which have inability to speak use different modes of communication to convey their message the most
common one is sign language they use hand gestures and orientation of arms with facial expressions. To make it easy
for the normal people to communicate with deaf people we use neural networks to build an AI model application. We
use Densely Connected Convolutional Neural Networks to capture the features of image dataset with label of sign
languages. The goal of this work is to take the first crucial step in using sign language to overcome the communication
gap between hearing people and deaf and people. For this we use image dataset consists of 1300 images. By using
Convolutional Neural Network, we got an accuracy of 92%.

MCQ’s Evaluation using Python OCR: An Algorithmic Implementation and Design Approach

Fri, 20 Oct 2023 07:04:53 Pakistan Daylight Time

Today, Multiple Choice Questions (MCQs) are being widely used as an effective way to assess or to grade
high school and university students.MCQs has become a fast and reliable method for national entrance examination
over the world, particularly in Pakistan because it can assess students with the broad range of knowledge coverage
within a limited time. However, when the use of MCQs is more demanded, a manual grading solution seems harder.
Although technology for automatic grading of multiple-choice questions has existed for several decades, it is not yet
as widely available or affordable as it should be. The main reasons preventing this adoption are in the cost and the
complexity of the setup procedures. Hence, in this research, we propose a cheaper alternative to grade multiple choice
questions, an OCR based solution built in python, which will use desktop scanners for image acquisition. OCR
Algorithm takes advantage of Python programming language for its cross-platform, open source, and communitybased environment. In this system desktop scanners will be used, as they are cheaper and widely available. In this
algorithm, OpenCV (Open-Source Computer Vision Library), an open-source computer vision and machine learning
software library will be utilized, to OCR scanned images of answer sheets.

A visual analytics framework for discovering temporal patterns in large scale artistic collections

Fri, 20 Oct 2023 07:26:18 Pakistan Daylight Time

In this research work we have come up with a framework for the visual analysis of temporal large scale artistic images. The goal of the research paper is to understand and discover the patterns in collections of art. The key technical aspect will be to adapt a standard model/framework and applying it on the specific art collection. Spatial consistency between different feature matches will be used. This will lead to having a more accurate style-invariant matching, and identification of patterns. The approach will be evaluated on selected artistic images. The findings have given us the understanding as to how the visualization of the large-scale paint image dataset can be made while finding relationships among different paintings through similarities and semantic information. This included tracking patterns, finding features with elements and principles in images by using image features. These observations have led to the understanding of influences of artists on each other based on patterns and origins of the paintings.

Revolutionizing Urban traffic control with Artificial Intelligence: A Review

Wardah Manzoor, Abdullah Farooq, Haresh Kumar — Fri, 20 Oct 2023 07:33:54 Pakistan Daylight Time

Artificial intelligence (AI) has the potential to revolutionize traffic flow management and road safety. In this
piece of examination, we present a traffic signal framework that uses computer-based intelligence to upgrade the
progression of vehicles in metropolitan regions. A machine learning algorithm is used in our system to analyze realtime traffic conditions data and make predictions about the best routes for vehicles and how to control intersections.
We exhibit the viability of our framework through reproductions and contrast its presentation with conventional traffic
signal techniques. In light of our discoveries, the computer-based intelligence based traffic signal framework is
prepared to do essentially shortening travel times and expanding wellbeing, bringing about a more useful and
pleasurable driving experience for drivers.