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All Journal PINISI Discretion Review Journal of Computer Networks, Architecture and High Performance Computing International Journal of Applied Research and Sustainable Sciences (IJARSS) Jurnal Ilmiah Computer Science
Ahmad, Ahsan
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Religion Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Social Sciences

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Quality of Driving License Service at the Palu Police Station Haprabu, Wiro; Daswati, Daswati; Ahmad, Ahsan; Salam, Rudi
PINISI Discretion Review Volume 1, Issue 1, September 2017
Publisher : Universitas Negeri Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (294.533 KB) | DOI: 10.26858/pdr.v1i1.12761

Abstract

Service is the process of fulfilling needs through other people's activities directly by becoming one of the indicators of government success towards the level of community satisfaction. This study aims to determine and describe the quality of driving permit services at the Palu Police Station. This study uses a descriptive qualitative research, in which this study attempts to reveal facts that are accurate and factually systematically about the service of a driver's license at the Palu Police station. This study uses the theory of Parasuraman, Zeithmal and Berry with indicators of direct evidence of direct evidence, reliability, responsiveness, assurance, attention. Data collection techniques are carried out through field research which includes observation, interviews and documentation. The technique of determining the informants in this study using purposive. Analysis of the data used is data reduction, data presentation and drawing conclusions. The results of this study indicate that, in general the officers making SIM in the Palu Sat Satas office are relatively fast and precise in providing information, and also in terms of the guarantee of the Palu police SIM management officers very maintaining public trust by not accepting bribery efforts in any form because rules for lawbreakers have been regulated in accordance with the article, although there are still a number of things that need to be addressed by the Palu Traffic Police Unit, such as the lack of instructions or in terms of direct evidence which says the buildings used are small.
BI-RADS Category Prediction from Mammography Images and Mammography Radiology Reports Using Deep Learning: A Systematic Review Shiwlani, Ashish; Ahmad, Ahsan; Umar, Muhammad; Dharejo, Nasrullah; Tahir, Anoosha; Shiwlani, Sheena
Jurnal Ilmiah Computer Science Vol. 3 No. 1 (2024): Volume 3 Number 1 July 2024
Publisher : PT. SNN MEDIA TECH PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jics.v3i1.31

Abstract

Women's health and mortality are significantly threatened by breast cancer, underscoring the importance of timely detection and treatment. Mammograms are an extremely useful and trustworthy diagnostic tool for early detection and screening of breast cancer. Mammograms based CADe systems have helped doctors in predicting BI-RADS categories and make better decisions and have somewhat reduced diagnostic errors. As deep learning algorithms advance, deep learning-based CADe systems become a practical means of resolving these problems and greatly improving the accuracy. The purpose of this review is to discuss the current techniques that have been developed for BI-RADS category classification in the fields of deep learning and convolutional neural networks. Additionally, the paper demonstrates the progression of models introduced in the past ten years. It also discusses the shortcomings of models proposed in the literature for the prediction of BI-RADS categories from mammography radiology reports and mammography images, in addition to summarizing the current challenges. Lastly, it proposes a novel multi-modal approach to predict the BI-RADS categories from radiology reports and mammography images.
Revolutionizing Healthcare: How Deep Learning is poised to Change the Landscape of Medical Diagnosis and Treatment Ahmad, Ahsan; Tariq , Aftab; Hussain , Hafiz Khawar; Yousaf Gill, Ahmad
Journal of Computer Networks, Architecture and High Performance Computing Vol. 5 No. 2 (2023): Article Research Volume 5 Issue 2, July 2023
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v5i2.2350

Abstract

Deep learning has become a significant tool in the healthcare industry with the potential to change the way care is provided and enhance patient outcomes. With a focus on personalised medicine, ethical issues and problems, future directions and opportunities, real-world case studies, and data privacy and security, this review article investigates the existing and potential applications of deep learning in healthcare. Deep learning in personalised medicine holds enormous promise for improving patient care by enabling more precise diagnoses and individualised treatment approaches. But it's important to take into account ethical issues like data privacy and the possibility of bias in algorithms. Deep learning in healthcare will likely be used more in the future to manage population health, prevent disease, and improve access to care for underprivileged groups of people. Case studies give specific examples of how deep learning is already changing the healthcare industry, from discovering rare diseases to forecasting patient outcomes. To fully realize the potential of deep learning in healthcare, however, issues including data quality, interpretability, and legal barriers must be resolved. Remote monitoring and telemedicine are two promising areas where deep learning is lowering healthcare expenses and enhancing access to care. Deep learning algorithms can be used to analyse patient data in real-time, warning medical professionals of possible problems before they worsen and allowing for online discussions with experts. Finally, when applying deep learning to healthcare, the importance of data security and privacy cannot be understated. To preserve patient data and guarantee its responsible usage, the appropriate safeguards and rules must be implemented. Deep learning has the ability to transform the healthcare industry by delivering more individualised, practical, and efficient care. However, in order to fully realize its promise, ethical issues, difficulties, and regulatory barriers must be solved. Deep learning has the potential to significantly contribute to enhancing patient outcomes and lowering healthcare costs with the right safeguards and ongoing innovation
Artificial Intelligence in Stroke Care: Enhancing Diagnostic Accuracy, Personalizing Treatment, and Addressing Implementation Challenges Yahya Abdul Rehman Shah; Qureshi, Sara Mudassir; Hamza Ahmed Qureshi; Saad Ur Rehman Shah; Shiwlani, Ashish; Ahmad, Ahsan
International Journal of Applied Research and Sustainable Sciences Vol. 2 No. 10 (2024): October 2024
Publisher : MultiTech Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59890/ijarss.v2i10.2575

Abstract

Objective: Stroke remains a leading cause of global disability, and with ageing populations, there is a growing need for advanced medical interventions. This literature review aims to assess how Artificial Intelligence (AI) and Machine Learning (ML) technologies have transformed the diagnosis, treatment, and long-term care of stroke patients. Methods: A comprehensive literature review was conducted using databases such as PubMed, IEEE Xplore, and Scopus, covering articles published from January 2018 to August 2024. The review focused on studies related to the application of AI/ML in stroke diagnosis, treatment, and management, including ethical, technical, and regulatory issues. Results: AI and ML technologies have significantly enhanced stroke diagnosis, primarily through advanced deep learning models that analyze imaging data more accurately and rapidly than traditional methods. These AI-based models have demonstrated high precision in detecting ischemic and hemorrhagic strokes, reducing diagnosis time by up to 50% and markedly improving patient outcomes. Predictive models utilizing big data have consistently surpassed traditional risk assessments in forecasting stroke outcomes and customizing treatments. AI-driven decision-support systems have improved patient selection for thrombolysis and mechanical thrombectomy, optimizing treatment strategies. Conclusion: While AI and ML offer substantial advancements in stroke management, including improved diagnosis, personalized therapy, and prognosis, challenges remain. Issues such as data quality, algorithmic transparency, integration into clinical workflows, algorithmic bias, and patient privacy must be addressed. Further research is needed to overcome these technical, ethical, and regulatory obstacles to fully integrate AI and ML into healthcare systems and enhance stroke management and patient outcomes.
Co-Authors Daswati Daswati Dharejo, Nasrullah Hamza Ahmed Qureshi Haprabu, Wiro Hussain , Hafiz Khawar Muhammad Umar Qureshi, Sara Mudassir Rudi Salam Saad Ur Rehman Shah Shiwlani, Ashish Shiwlani, Sheena Tahir, Anoosha Tariq , Aftab Yahya Abdul Rehman Shah Yousaf Gill, Ahmad