ORCID Profile
0000-0003-4012-553X
Current Organisations
Umm Al-Qura University
,
Universitätsklinik Balgrist
,
Technical University of Munich
,
Technische Universität München
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Publisher: TuEngr Group
Date: 2022
Publisher: MDPI AG
Date: 05-06-2023
DOI: 10.3390/DIAGNOSTICS13111964
Abstract: The accurate and timely diagnosis of skin cancer is crucial as it can be a life-threatening disease. However, the implementation of traditional machine learning algorithms in healthcare settings is faced with significant challenges due to data privacy concerns. To tackle this issue, we propose a privacy-aware machine learning approach for skin cancer detection that utilizes asynchronous federated learning and convolutional neural networks (CNNs). Our method optimizes communication rounds by iding the CNN layers into shallow and deep layers, with the shallow layers being updated more frequently. In order to enhance the accuracy and convergence of the central model, we introduce a temporally weighted aggregation approach that takes advantage of previously trained local models. Our approach is evaluated on a skin cancer dataset, and the results show that it outperforms existing methods in terms of accuracy and communication cost. Specifically, our approach achieves a higher accuracy rate while requiring fewer communication rounds. The results suggest that our proposed method can be a promising solution for improving skin cancer diagnosis while also addressing data privacy concerns in healthcare settings.
Publisher: TuEngr Group
Date: 2022
Publisher: MDPI AG
Date: 28-02-2022
DOI: 10.3390/INFO13030120
Abstract: Social media platforms such as Twitter have been used by political leaders, heads of states, political parties, and their supporters to strategically influence public opinions. Leaders can post about a location, a state, a country, or even a region in their social media accounts, and the posts can immediately be viewed and reacted to by millions of their followers. The effect of social media posts by political leaders could be automatically measured by extracting, analyzing, and producing real-time geospatial intelligence for social scientists and researchers. This paper proposed a novel approach in automatically processing real-time social media messages of political leaders with artificial intelligence (AI)-based language detection, translation, sentiment analysis, and named entity recognition (NER). This method automatically generates geospatial and location intelligence on both ESRI ArcGIS Maps and Microsoft Bing Maps. The proposed system was deployed from 1 January 2020 to 6 February 2022 to analyze 1.5 million tweets. During this 25-month period, 95K locations were successfully identified and mapped using data of 271,885 Twitter handles. With an overall 90% precision, recall, and F1score, along with 97% accuracy, the proposed system reports the most accurate system to produce geospatial intelligence directly from live Twitter feeds of political leaders with AI.
Publisher: MDPI AG
Date: 23-12-2022
Abstract: Three decades after the first set of work on Medical Augmented Reality (MAR) was presented to the international community, and ten years after the deployment of the first MAR solutions into operating rooms, its exact definition, basic components, systematic design, and validation still lack a detailed discussion. This paper defines the basic components of any Augmented Reality (AR) solution and extends them to exemplary Medical Augmented Reality Systems (MARS). We use some of the original MARS applications developed at the Chair for Computer Aided Medical Procedures and deployed into medical schools for teaching anatomy and into operating rooms for telemedicine and surgical guidance throughout the last decades to identify the corresponding basic components. In this regard, the paper is not discussing all past or existing solutions but only aims at defining the principle components and discussing the particular domain modeling for MAR and its design-development-validation process, and providing exemplary cases through the past in-house developments of such solutions.
Publisher: MDPI AG
Date: 09-05-2022
DOI: 10.20944/PREPRINTS202205.0095.V1
Abstract: Tropical cyclones devastate large areas, take numerous lives and damage extensive property in Bangladesh. Research on landfalling tropical cyclones affecting Bangladesh has primarily focused on events occurring since AD1960 with limited work examining earlier historical records. We rectify this gap by developing a new tornado catalogue that include present and past records of tornados across Bangladesh maximizing use of available sources. Within this new tornado database, 119 records were captured starting from 1838 till 2020 causing 8,735 deaths and 97,868 injuries leaving more than 1,02,776 people affected in total. Moreover, using this new tornado data, we developed an end-to-end system that allows a user to explore and analyze the full range of tornado data on multiple scenarios. The user of this new system can select a date range or search a particular location, and then, all the tornado information along with Artificial Intelligence (AI) based insights within that selected scope would be dynamically presented in a range of devices including iOS, Android, and Windows. Using a set of interactive maps, charts, graphs, and visualizations the user would have a comprehensive understanding of the historical records of Tornados, Cyclones and associated landfalls with detailed data distributions and statistics.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Computers, Materials and Continua (Tech Science Press)
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: MDPI AG
Date: 11-07-2023
DOI: 10.3390/DIAGNOSTICS13142340
Abstract: Healthcare professionals consider predicting heart disease an essential task and deep learning has proven to be a promising approach for achieving this goal. This research paper introduces a novel method called the asynchronous federated deep learning approach for cardiac prediction (AFLCP), which combines a heart disease dataset and deep neural networks (DNNs) with an asynchronous learning technique. The proposed approach employs a method for asynchronously updating the parameters of DNNs and incorporates a temporally weighted aggregation technique to enhance the accuracy and convergence of the central model. To evaluate the effectiveness of the proposed AFLCP method, two datasets with various DNN architectures are tested, and the results demonstrate that the AFLCP approach outperforms the baseline method in terms of both communication cost and model accuracy.
Publisher: MDPI AG
Date: 09-08-2022
DOI: 10.3390/SU14169830
Abstract: Tropical cyclones take precious lives, damage critical infrastructure, and cause economic losses worth billions of dollars in Australia. To reduce the detrimental effect of cyclones, a comprehensive understanding of cyclones using artificial intelligence (AI) is crucial. Although event records on Australian tropical cyclones have been documented over the last 4 decades, deep learning studies on these events have not been reported. This paper presents automated AI-based regression, anomaly detection, and clustering techniques on the largest available cyclone repository covering 28,713 records with almost 80 cyclone-related parameters from 17 January 1907 to 11 May 2022. Experimentation with both linear and logistic regression on this dataset resulted in 33 critical insights on factors influencing the central pressure of cyclones. Moreover, automated clustering determined four different clusters highlighting the conditions for low central pressure. Anomaly detection at 70% sensitivity identified 12 anomalies and explained the root causes of these anomalies. This study also projected parameterization and fine-tuning of AI-algorithms at different sensitivity levels. Most importantly, we mathematically evaluated robustness by supporting an enormous scenario space of 4.737 × 108234. A disaster strategist or researcher can use the deployed system in iOS, Android, or Windows platforms to make evidence-based policy decisions on Australian tropical cyclones.
Publisher: Computers, Materials and Continua (Tech Science Press)
Date: 2021
Publisher: Taru Publications
Date: 2023
DOI: 10.47974/JIOS-1461
Publisher: University North
Date: 11-05-2022
DOI: 10.31803/TG-20220124143638
Abstract: This study was aimed to evaluate the application of software metrics used by the software enterprises present in Saudi Arabia. Extensive literature reviews were conducted to comprehend the current body of knowledge on the use of software metrics in Saudi enterprises. These literature review and studies elapsed approximately two decades. Based on the drawbacks, shortcomings, and fallacies of the existing studies a series of interview questionnaires were developed. Interviews were conducted for collection of real-time and actual data. Around seven Saudi enterprises were selected, and each enterprise was considered and regarded as a unit, and the manager of the enterprise was acting as a unit for our case study. 40 managers were interviewed, and their responses were analyzed. Respondents' responses indicate that the software is useful enough to support business processes. In an attempt to assess the complexity of implementing this software, effective feedback was received, suggesting that there is a lack of communication between the developers and managements’ intent. Moreover, the findings of this study showed that the organization need to give more attention to quality and productivity management. In addition, the results indicate that when agile development is undertaken through software effectiveness, the enterprise's services are implemented appropriately.
Publisher: University North
Date: 11-05-2022
DOI: 10.31803/TG-20220124142802
Abstract: With the daily use of social media, the cybersecurity and privacy are challenging for most communities. This research is aimed to explore and evaluate the awareness of cybersecurity in Saudi Arabia. It is also designed to discover how the ordinary users performances in using internet security while using social media. This research employed mixed methods. The researcher sent the online questionnaire to random people who used the social media to participate in this study. At the end of the online survey, one more question was attached to ask participants to involve in the second round of data collection (interview). This research found that social media users' awareness about internet security is different from users based on their gender. In addition, the ways of contact were affecting the reaction of users to share their information with others. The results also indicated that when the level of awareness is high the way of dealing with others is different. This study also confirmed the users' belief in internet security and most of them knew the strengths of internet security enabled them to protect their devices and personal information from outside intruders. Moreover, the findings of this study showed that the users of social media need to give more attention to all types of security threats. The findings of this paper can be used theoretically and practically by identifying the level of security awareness based on social media usage and the purpose of use.
Publisher: Elsevier BV
Date: 09-2023
Publisher: MDPI AG
Date: 28-06-2023
DOI: 10.3390/DIAGNOSTICS13132195
Abstract: Congenital heart disease (CHD) is a critical global public health concern, particularly when it comes to newborn mortality. Low- and middle-income countries face the highest mortality rates due to limited resources and inadequate healthcare access. To address this pressing issue, machine learning presents an opportunity to develop accurate predictive models that can assess the risk of death from CHD. These models can empower healthcare professionals by identifying high-risk infants and enabling appropriate care. Additionally, machine learning can uncover patterns in the risk factors associated with CHD mortality, leading to targeted interventions that prevent or reduce mortality among vulnerable newborns. This paper proposes an innovative machine learning approach to minimize newborn mortality related to CHD. By analyzing data from infants diagnosed with CHD, the model identifies key risk factors contributing to mortality. Armed with this knowledge, healthcare providers can devise customized interventions, including intensified care for high-risk infants and early detection and treatment strategies. The proposed diagnostic model utilizes maternal clinical history and fetal health information to accurately predict the condition of newborns affected by CHD. The results are highly promising, with the proposed Cardiac Deep Learning Model (CDLM) achieving remarkable performance metrics, including a sensitivity of 91.74%, specificity of 92.65%, positive predictive value of 90.85%, negative predictive value of 55.62%, and a miss rate of 91.03%. This research aims to make a significant impact by equipping healthcare professionals with powerful tools to combat CHD-related newborn mortality, ultimately saving lives and improving healthcare outcomes worldwide.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: RGN Publications
Date: 22-05-2022
Publisher: MDPI AG
Date: 05-07-2023
DOI: 10.3390/SYM15071369
Abstract: Skin cancer represents one of the most lethal and prevalent types of cancer observed in the human population. When diagnosed in its early stages, melanoma, a form of skin cancer, can be effectively treated and cured. Machine learning algorithms play a crucial role in facilitating the timely detection of skin cancer and aiding in the accurate diagnosis and appropriate treatment of patients. However, the implementation of traditional machine learning approaches for skin disease diagnosis is impeded by privacy regulations, which necessitate centralized processing of patient data in cloud environments. To overcome the challenges associated with data privacy, federated learning emerges as a promising solution, enabling the development of privacy-aware healthcare systems for skin cancer diagnosis. This paper presents a comprehensive review that examines the obstacles faced by conventional machine learning algorithms and explores the integration of federated learning in the context of privacy-conscious skin cancer prediction healthcare systems. It provides discussion on the various datasets available for skin cancer prediction and provides a performance comparison of various machine learning and federated learning techniques for skin lesion prediction. The objective is to highlight the advantages offered by federated learning and its potential for addressing privacy concerns in the realm of skin cancer diagnosis.
Publisher: Springer Science and Business Media LLC
Date: 02-10-2023
DOI: 10.1007/S13369-022-07250-1
Abstract: Negative events are prevalent all over the globe round the clock. People demonstrate psychological affinity to negative events, and they incline to stay away from troubled locations. This paper proposes an automated geospatial imagery application that would allow a user to remotely extract knowledge of troubled locations. The autonomous application uses thousands of connected news sensors to obtain real-time news pertaining to all global troubles. From the captured news, the proposed application uses artificial intelligence-based services and algorithms like sentiment analysis, entity detection, geolocation decoder, news fidelity analysis, and decomposition tree analysis to reconstruct global threat maps representing troubled locations interactively. The fully deployed system was evaluated for full three months of summer 2021, during which the autonomous system processed above 22 k news from 2397 connected news sources involving BBC, CNN, NY Times, Government websites of 192 countries, and all possible major social media sites. The study revealed 11,668 troubled locations classified successfully with outstanding precision, recall, and F1-score, all evaluated in ubiquitous environment covering mobile, tablet, desktop, and cloud platforms. The system generated interesting global threat maps for robust scenario set of $$3.71 \\times {10}^{29}$$ 3.71 × 10 29 , to be reported as original fully autonomous remote sensing application of this kind. The research discloses attractive news and global threat-maps with trusted overall classification accuracy.
Publisher: TuEngr Group
Date: 2021
Publisher: Monash University
Date: 2022
DOI: 10.26180/19566853.V1
Publisher: IEEE
Date: 2016
Publisher: MDPI AG
Date: 22-05-2022
DOI: 10.3390/SU14106303
Abstract: Tropical cyclones devastate large areas, take numerous lives and damage extensive property in Bangladesh. Research on landfalling tropical cyclones affecting Bangladesh has primarily focused on events occurring since AD1960 with limited work examining earlier historical records. We rectify this gap by developing a new Tornado catalogue that include present and past records of Tornados across Bangladesh maximizing use of available sources. Within this new Tornado database, 119 records were captured starting from 1838 till 2020 causing 8735 deaths and 97,868 injuries leaving more than 102,776 people affected in total. Moreover, using this new Tornado data, we developed an end-to-end system that allows a user to explore and analyze the full range of Tornado data on multiple scenarios. The user of this new system can select a date range or search a particular location, and then, all the Tornado information along with Artificial Intelligence (AI) based insights within that selected scope would be dynamically presented in a range of devices including iOS, Android, and Windows. Using a set of interactive maps, charts, graphs, and visualizations the user would have a comprehensive understanding of the historical records of Tornados, Cyclones and associated landfalls with detailed data distributions and statistics.
Publisher: TuEngr Group
Date: 2021
Publisher: Springer International Publishing
Date: 2022
No related grants have been discovered for Musleh Alsulami.