UMT Artificial Intelligence Review最新文献

{"title":"Exploiting Deep Visual Geometry Group Architecture for Fall Detection in the Elderly People","authors":"None Hina Bashir, None Kanwal Majeed, None Sumaira Zafar, None Ghulam Zohra, None Syed Farooq Ali, None Aadil Zia Khan","doi":"10.32350/umtair.31.05","DOIUrl":"https://doi.org/10.32350/umtair.31.05","url":null,"abstract":"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.","PeriodicalId":198719,"journal":{"name":"UMT Artificial Intelligence Review","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136084694","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":"Multi-Modal Data Fusion for Classification of Autism Spectrum Disorder Using Phenotypic and Neuroimaging Data","authors":"Sumaira Kausar, None Adnan Younas, None Muhammad Yousuf Kamal, Samabia Tehsin","doi":"10.32350/umtair.31.01","DOIUrl":"https://doi.org/10.32350/umtair.31.01","url":null,"abstract":"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.","PeriodicalId":198719,"journal":{"name":"UMT Artificial Intelligence Review","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136084693","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 Machine Learning Framework for E. coli Bacteria Detection and Classification","authors":"Bushra Naz, None Shahzad Hyder, None Azlan Ahmed, None Ali Hasnain","doi":"10.32350/umtair.31.02","DOIUrl":"https://doi.org/10.32350/umtair.31.02","url":null,"abstract":"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.","PeriodicalId":198719,"journal":{"name":"UMT Artificial Intelligence Review","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136084549","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":"Comparison of Performance Measures of Pakistani Islamic Mutual Funds using Data Analytics","authors":"Zehra Khan, Muhammad Shahbaz Yaqub, Yasir Ashraf","doi":"10.32350/umtair.31.03","DOIUrl":"https://doi.org/10.32350/umtair.31.03","url":null,"abstract":"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.","PeriodicalId":198719,"journal":{"name":"UMT Artificial Intelligence Review","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136084550","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":"Big Data Framework for Crowd Monitoring in Large Crowded Events","authors":"None Naeem A. Nawaz, Muhammad Abaidullah, Adnan Abid","doi":"10.32350/umtair.31.04","DOIUrl":"https://doi.org/10.32350/umtair.31.04","url":null,"abstract":"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.","PeriodicalId":198719,"journal":{"name":"UMT Artificial Intelligence Review","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136084551","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":"Role of Artificial Intelligence in different aspects of Public Health","authors":"Hassan Anwar, Ifra Chaudhary, Umar Latif, Ali Latif","doi":"10.32350/umtair.22.03","DOIUrl":"https://doi.org/10.32350/umtair.22.03","url":null,"abstract":"In the next decade of disease surveillance research, innovative and novel techniques are required to utilise massive quantities of complex and multi-dimensional data, effectively. Public health is one of the most significant domains of public governance and artificial intelligence has emerged as an innovative problem-solving technique in this domain. Artificial intelligence is a requirement for the early identification of diseases and disasters in order to prevent high mortality rates and reduce economic burden by timely providing appropriate healthcare. This detection is made possible in this research by identifying patterns in the database. This review shows that the use and development of AI techniques has increased in the field of public health over the past few years and most of the existing studies show a positive impact of AI in the domain of public health. This study is divided into three portions. The first portion reviews the role and potential usage of artificial intelligence in epidemics, since it is very important to timely investigate them and AI has the potential to cope with them. The second part of the review provides a detailed discussion about serious game usage in public health. Serious games are used for the training and rehabilitation of the gamer. The third part deals with the management of public health emergencies including evacuation, causality response, and information processing.","PeriodicalId":198719,"journal":{"name":"UMT Artificial Intelligence Review","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116782054","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":"Formal Analysis of Distributed Shared Memory Algorithms","authors":"M. Atif, Mudassar Naseer, Ahmad Salman Khan","doi":"10.32350/umtair.22.02","DOIUrl":"https://doi.org/10.32350/umtair.22.02","url":null,"abstract":"The memory coherence problem occurs while mapping shared virtual memory in a loosely coupled multiprocesses setup. Memory is considered coherent if a read operation provides the same data written in the last write operation. The problem has been addressed in the literature using different algorithms, although the correctness of a distributed algorithm remains questionable. Formal verification is the principal term for a group of techniques that routinely use an analysis established on mathematical transformations to conclude the rightness of the hardware or software behavior in divergence to dynamic verification techniques. The current study employed UPPAAL model checker to model the Dynamic Distributed algorithm for shared virtual memory given by K. Li and P. Hudak. The results showed that the Dynamic Distributed algorithm for shared virtual memory partially fulfils its functional requirements.","PeriodicalId":198719,"journal":{"name":"UMT Artificial Intelligence Review","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131827900","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 Deep Learning based Smart Navigational Stick for Blind People","authors":"Muhammad Sulaman, S.U.Bazai, Muhammad AKram, Muhammad Akram Khan","doi":"10.32350/umtair.22.05","DOIUrl":"https://doi.org/10.32350/umtair.22.05","url":null,"abstract":"Blind and visually impaired people find difficulty in detecting obstacles and recognizing people in their way, which makes it dangerous for them to walk, to work, or to go in a crowded area/place. They have to be cautious all the time to move, while avoiding any solid obstacles in their way. Typically, they use different aid devices to reach their destination or to accomplish their daily task. The normal stick is useless for blind and visually impaired people since it cannot detect barriers or people's faces. Visually impaired individuals are unable to distinguish between different types of objects in front of them. They are unable to gauge the size of an object or its distance from them. Several works have been done by public individuals and scientific investigators but their work is dearth in technological aspect. This technological aspect need to be addressed by adding artificial intelligence (AI). This prototype aims to help blind and visually impaired individuals in several aspects to simply obtain/perform everyday tasks and help these individuals to live with the same confidence as sighted people live.Therefore, this study inclined deep learning Mobile-Net Single Shot MultiBox detection (SSD) algorithm for object recognition and Dlib library for face recognition. Subsequently, the proposed solution is using an Open CV and Python. Additionally, Ultrasonic sensors are used for distance measurement, which can be a great help for visually impaired people. These components are grouped together to work effectively and efficiently for the development of visually impaired people. The recognition procedure was revealed through headphones, which notifies the visually impaired when face or any object get recognized. Inclusively, the innovative solution would be a great aid for the blind and visually impaired individuals. As a result, to test and validate the accuracy of the smart navigational stick, several experiments have been conducted on a range of objects and faces. Hence, this study’s modified navigational system was adequate and valid for visually impaired people.","PeriodicalId":198719,"journal":{"name":"UMT Artificial Intelligence Review","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123419047","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":"Multiclass Light Weight Brain Tumor Classification and Detection using Machine Learning Model Yolo 5","authors":"A. Raza, Usman Amjad, Muhammad Abubakr, Dr.Asad Abbasi, Humera Azam, Asher Ali","doi":"10.32350/umtair.22.04","DOIUrl":"https://doi.org/10.32350/umtair.22.04","url":null,"abstract":"Early brain tumor identification is a critical challenge for neurologists and radiologists. Manually identifying brain tumors through magnetic resonance imaging (MRI) is difficult and prone to mistakes. The diagnosis of tumor is a complex job when performed in a traditional manner. Brain abnormalities can be fatal, lowering a patient's quality of life and adversely harming their overall health. Brain tumors vary in nature based on where they are situated and how rapidly they develop inside the skull. Tumors are a proliferation of abnormal nerve cells that form a mass. Some brain tumors begin in the cells that support the brain's nerve cells. This paper proposes a machine learning algorithm known as YOLO v5 SSD (single shot detection) to detect and classify such tumors namely meningioma, glioma, and pituitary gland with 88% accuracy. For this purpose, data augmentation was applied to the publically available dataset from Kaggle. MRI of different classes including 396 glioma images, 397 meningioma, 380 no tumor, and 399 images of pituitary tumors were employed. The current study presents false negative, true positive false positive, and true negative, which were used to test the YOLO v5 (You Only Look Once) classifier performance. It was determined that the YOLO v5 model is giving 88% accuracy.","PeriodicalId":198719,"journal":{"name":"UMT Artificial Intelligence Review","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131790360","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":"Role of GraphDB in FinTech, Blockchain Ledgers","authors":"Hassan Kaleem, Sundas Rukhsar, Waqar Ahmad, Hafiz Ali Haris","doi":"10.32350/umtair.21.003","DOIUrl":"https://doi.org/10.32350/umtair.21.003","url":null,"abstract":"GrpahDB stores data in nodes and edges, nodes represent entities and edges represents the relationship between entities. The role of GraphDB in the blockchain is described as blockchain uses blocks and these blocks are connected through hashcode to store the data. In cipher language, hash is the irreversible conversion of data which makes it impossible to decrypt. Blockchain also uses proof of work system, in which data is entered only if maximum people allows verifies it. And once anything entered into ledger, it cannot be altered or deleted. The paper has provided how hashing & indexing, query processing, transaction management, data management and data distribution is done for GraphDB into ledger, with previously done work and libraries to build and manage GraphDB blockchain.","PeriodicalId":198719,"journal":{"name":"UMT Artificial Intelligence Review","volume":"701 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116118767","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}