Search

Your search keyword '"Khan, Muhammad Adnan"' showing total 572 results
Start Over Author "Khan, Muhammad Adnan"
572 results on '"Khan, Muhammad Adnan"'

26. Smart Vision Transparency: Efficient Ocular Disease Prediction Model Using Explainable Artificial Intelligence.

Author

Abbas, Sagheer, Qaisar, Adnan, Farooq, Muhammad Sajid, Saleem, Muhammad, Ahmad, Munir, and Khan, Muhammad Adnan

Abstract

The early prediction of ocular disease is certainly an obligatory concern in the domain of ophthalmic medicine. Although modern scientific discoveries have shown the potential to treat eye diseases by using artificial intelligence (AI) and machine learning, explainable AI remains a crucial challenge confronting this area of research. Although some traditional methods put in significant effort, they cannot accurately predict the proper ocular diseases. However, incorporating AI into diagnosing eye diseases in healthcare complicates the situation as the decision-making process of AI demonstrates complexity, which is a significant concern, especially in major sectors like ocular disease prediction. The lack of transparency in the AI models may hinder the confidence and trust of the doctors and the patients, as well as their perception of the AI and its abilities. Accordingly, explainable AI is significant in ensuring trust in the technology, enhancing clinical decision-making ability, and deploying ocular disease detection. This research proposed an efficient transfer learning model for eye disease prediction to transform smart vision potential in the healthcare sector and meet conventional approaches' challenges while integrating explainable artificial intelligence (XAI). The integration of XAI in the proposed model ensures the transparency of the decision-making process through the comprehensive provision of rationale. This proposed model provides promising results with 95.74% accuracy and explains the transformative potential of XAI in advancing ocular healthcare. This significant milestone underscores the effectiveness of the proposed model in accurately determining various types of ocular disease. It is clearly shown that the proposed model is performing better than the previously published methods. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

34. Corn leaf disease: insightful diagnosis using VGG16 empowered by explainable AI.

Author

Tariq, Maria, Ali, Usman, Abbas, Sagheer, Hassan, Shahzad, Naqvi, Rizwan Ali, Khan, Muhammad Adnan, and Jeong, Daesik

Subjects
CORN diseases, IMAGE recognition (Computer vision), ARTIFICIAL intelligence, ECONOMIC security, CROP yields, CORN
Abstract

The agricultural sector is pivotal to food security and economic stability worldwide. Corn holds particular significance in the global food industry, especially in developing countries where agriculture is a cornerstone of the economy. However, corn crops are vulnerable to various diseases that can significantly reduce yields. Early detection and precise classification of these diseases are crucial to prevent damage and ensure high crop productivity. This study leverages the VGG16 deep learning (DL) model to classify corn leaves into four categories: healthy, blight, gray spot, and common rust. Despite the efficacy of DL models, they often face challenges related to the explainability of their decision-making processes. To address this, Layer-wise Relevance Propagation (LRP) is employed to enhance the model's transparency by generating intuitive and human-readable heat maps of input images. The proposed VGG16 model, augmented with LRP, outperformed previous state-of-the-art models in classifying corn leaf diseases. Simulation results demonstrated that the model not only achieved high accuracy but also provided interpretable results, highlighting critical regions in the images used for classification. By generating human-readable explanations, this approach ensures greater transparency and reliability in model performance, aiding farmers in improving their crop yields. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

37. Sensing Time Optimization Using Genetic Algorithm in Cognitive Radio Networks

Author

Ali, Muhammad Nadeem, Naveed, Iqra, Khan, Muhammad Adnan, Nasir, Ayesha, Mushtaq, M. Tahir, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Washio, Takashi, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Bajwa, Imran Sarwar, editor, Kamareddine, Fairouz, editor, and Costa, Anna, editor

Published
2019
Full Text
View/download PDF

46. Accelerated Assistant to SubOptimum Receiver for Multi Carrier Code Division Multiple Access System

Author

Khan, Muhammad Adnan, Umair, Muhammad, and Choudry, Muhammad Aamer Saleem

Subjects
Computer Science - Information Theory
Abstract

The Multiple Input Multiple output system are considered to be the strongest candidate for the maximum utilization of available bandwidth. In this paper, the MIMO system with the combination of Multi-Carrier Code Division Multiple Access and Space Time Coding using the Alamoutis scheme is considered. A Genetic Algorithm based receiver with an exceptional relationship between filter weights while detecting symbols is proposed. This scheme has better Convergence Rate and Bit Error Rate than the Fast-LMS Adaptive receiver.

Published
2014

47. Development of antibacterial and remineralising composite bone cements

Author

Khan, Muhammad Adnan, Young, A. M., and Spratt, D.

Subjects
617.6
Abstract

The thesis aim is to develop composite cement with (1) optimized curing time/ high reaction rate to prevent cement leakage (1) high monomer conversion to provide good strength, (2) water-sorption to compensate shrinkage and promote anti-bacterial release, (3) release of ions and antimicrobial to remineralise the bone structure and prevent infection, (5), assess the adhesive properties of composites and (4) antibacterial efficacy against various strains of Staphylococcus aureus. Curing time, reaction rate, monomer conversion/ shrinkage was assessed using FTIR and Raman. Water-sorption and anti-bacterial (polylysine and gentamicin) release into deionized water (DW) and simulated body fluid (SBF) were assessed using gravimetrical studies, and UV/ HPLC spectroscopy respectively. Flexural strength, modulus and compressive strength were assessed up to 1 months of SBF storage. Antibacterial efficacy was assessed via bacterial growth in suspension and biofilm formation on disc. Adhesive properties were assessed via pushout and shaerbond test. One way Anova, Two way Anova, Kruskal–Wallis one-way analysis of variance, regression analysis, factorial analysis were used to analyze the data. Increasing initiator and activator concentrations decreased curing time. Furthermore, they enhanced monomer conversion and strength. Addition of calcium phosphate fillers and antimicrobials decreased the curing time and monomer conversion. The major factors enhancing water-sorption were calcium filler and antimicrobial level. Calcium ion and gentamicin (Gen) release was enhanced by the use of deionized water instead of SBF, calcium fillers and polylysine. Higher polylysine (PLS) release was seen with low levels of antibacterial in the filler phase. Flexural strength, modulus and compressive strength were decreased with the addition of calcium fillers and antimicrobials. Lastly, PLS showed reduced bacterial growth in surrounding medium and on set material discs. These materials are therefore promising antibacterial injectable bone composites that could remineralise the bone structure and may prevent postoperative infection. These cements can be used for load bearing areas (vertebroplasty, Hip arthroplasty, dental filling) and non-load bearing areas (bone augmentation etc.).

Published
2015

Catalog

Books, media, physical & digital resources
See catalog results