UMT Artificial Intelligence Review

Multi-Modal Data Fusion for Classification of Autism Spectrum Disorder Using Phenotypic and Neuroimaging Data

Sumaira Kausar — 2023-06-23

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that causes disrupted social behaviors and interactions of individuals. Hence, it can adversely affect the social functioning of individuals. Each autistic individual is said to have a sort of unique behavioral pattern. ASD has three major sub-categories, namely autism, Asperger, and pervasive developmental disorder, not otherwise specified. The term spectrum indicates that ASD possesses a large variety of symptoms of severity. Practitioners need to have a vast experience and expertise for the accurate analysis of the symptoms of ASD. These symptoms need to be acquired from a range of modalities. An accurate diagnosis requires the analysis of brain scan and phenotypic data. These aspects present a multifold challenge for computer-aided ASD diagnosis. Most of the existing computer aided ASD diagnosis systems are capable of diagnosing only whether an individual is affected with ASD or not. A detailed categorization into the subcategories of ASD in such diagnosis is missing. Another aspect that is missing in the existing techniques is that symptoms are observed from a single modality. This can adversely affect the accuracy of diagnosis, since different modalities focus on different aspects of symptoms. These challenges and gaps provided the motivation to present a method that covers the variety exhibited in ASD, while considering the dire need of acquiring symptoms from a variety of data sources. The proposed method showed rather encouraging results. Moreover, the achieved results are evident of the efficacy of the proposed method.

A Machine Learning Framework for E. coli Bacteria Detection and Classification

Bushra Naz — 2023-06-23

Water plays an important role in physiological processes, such as the body's thermal equilibrium, the transfer of nutrients to the intended destination through the body, and the lubrication of joints. In Pakistan, the existing water availability is about 79%. Inadequate and adequate drinking water quality is a significant public health concern. In the project, we explain different machine learning techniques which are used to locate exact bacteria in a water sample, their shape, and scale. This technology promises sufficient identification and division. This invention allows for early identification of bacterial water pollution, requires minimal labor, etc. A robotic frame will speed up the treatment period without human power. It will reduce water emissions dramatically. The methods available for bacterial detection are effective but require lengthy waiting periods for results and expensive and laborious equipment. Via images with PYTHON (Its libraries), this research aims to detect bacteria utilizing images. This system tends to be effective and efficient way for water quality monitoring in different sectors in Pakistan. E.g., Wastewater treatment plants, Power plants, Industries, RO plants, and Laboratories.

Comparison of Performance Measures of Pakistani Islamic Mutual Funds using Data Analytics

Zehra Khan — 2023-06-23

The current study attempted to measure and evaluate the performance of 13 Pakistani Shariah compliant mutual funds from the time period (September 2009-August 2017) by using 18 performance measures. It followed the principle that mutual funds are used exclusively for diversification portfolio and mean-variance optimization, following the mutual fund theorem as an investing strategy. The results of few performance measures showed that many funds outperformed the benchmark, while others underperformed. The study also analyzed and compared the performance measures to characterize the relationship between them and investigated if they lead to an identical ranking by using three analysis techniques, namely Pearson’s r, Spearman’s rho, and Kendall’s tau coefficient. The study concluded that there is a high level of correlation among performance measures which indicates that the performance measures classify mutual funds in a similar manner in three sub-periods, that is, 6 months, 1 year, and 3 years. Change of frequency doesn’t disturb their classification ability.

Big Data Framework for Crowd Monitoring in Large Crowded Events

Naeem A. Nawaz — 2023-06-23

The management of large events with hundreds of thousands of individuals has remained a challenge over the years. Crushes and stampedes occurring in the events of mass gathering have swallowed many valuable lives around the world. Considering the substantial advancement in positional tracking, wearable technology, and wireless communication, many event organizers are embracing the use of these technologies to get assistance in managing large events. Intelligent monitoring of crowd movement and timely analysis of evolving conditions may aid in early detection of critical situations. The current research aims to propose a big data resource framework to model, simulate, and visualize the crowd conditions for actual venue settings. A distributed framework has been presented to monitor the movement and interaction of individuals in large crowded events through localized sensing and geospatial analysis of massive positional data. The pilgrimage (Hajj) has been considered as a case study for demonstrating the effectiveness of the proposed framework. The proposed framework has been with the help of synthetic data that covered some useful and frequent scenarios based on the case study of pilgrimage (hajj), which is an annual event involving more than a million people.

Exploiting Deep Visual Geometry Group Architecture for Fall Detection in the Elderly People

Hina Bashir — 2023-06-23

Over the last couple of decades, human fall detection has gained considerable popularity, especially for the elderly. Elderly people need more attention as compared to others in their homes, hospitals, and care centers. Various solutions have been proposed to deal with this problem, yet, many aspects of this problem are still unresolved. The current study proposed an approach for human fall detection based on the Visual Geometry Architecture of deep learning. The presented approach was weighed up with state-of-the-art approaches including ResNet-50 and even ResNet-101 by using MCF and URFD datasets, outperforming them with an accuracy of 98%. The proposed approach also outperformed these deep architectures in terms of performance efficiency.