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  1. Article ; Online: Real time anatomical landmarks and abnormalities detection in gastrointestinal tract

    Zeshan Khan / Muhammad Atif Tahir

    PeerJ Computer Science, Vol 9, p e

    2023  Volume 1685

    Abstract: Gastrointestinal (GI) endoscopy is an active research field due to the lethal cancer diseases in the GI tract. Cancer treatments result better if diagnosed early and it increases the survival chances. There is a high miss rate in the detection of the ... ...

    Abstract Gastrointestinal (GI) endoscopy is an active research field due to the lethal cancer diseases in the GI tract. Cancer treatments result better if diagnosed early and it increases the survival chances. There is a high miss rate in the detection of the abnormalities in the GI tract during endoscopy or colonoscopy due to the lack of attentiveness, tiring procedures, or the lack of required training. The procedure of the detection can be automated to the reduction of the risks by identifying and flagging the suspicious frames. A suspicious frame may have some of the abnormality or the information about anatomical landmark in the frame. The frame then can be analysed for the anatomical landmarks and the abnormalities for the detection of disease. In this research, a real-time endoscopic abnormalities detection system is presented that detects the abnormalities and the landmarks. The proposed system is based on a combination of handcrafted and deep features. Deep features are extracted from lightweight MobileNet convolutional neural network (CNN) architecture. There are some of the classes with a small inter-class difference and a higher intra-class differences, for such classes the same detection threshold is unable to distinguish. The threshold of such classes is learned from the training data using genetic algorithm. The system is evaluated on various benchmark datasets and resulted in an accuracy of 0.99 with the F1-score of 0.91 and Matthews correlation coefficient (MCC) of 0.91 on Kvasir datasets and F1-score of 0.93 on the dataset of DowPK. The system detects abnormalities in real-time with the detection speed of 41 frames per second.
    Keywords Medical image analysis ; GI tract diagnostics ; Genetic algorithm ; Threshold selection ; Endoscopic disease detection ; Computer vision ; Electronic computers. Computer science ; QA75.5-76.95
    Subject code 006
    Language English
    Publishing date 2023-12-01T00:00:00Z
    Publisher PeerJ Inc.
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Vehicle images dataset for make and model recognition

    Mohsin Ali / Muhammad Atif Tahir / Muhammad Nouman Durrani

    Data in Brief, Vol 42, Iss , Pp 108107- (2022)

    2022  

    Abstract: Vehicle make and model recognition plays an important role in monitoring traffic in a vehicle surveillance system. Identifying vehicle make and model is a challenging task due to intraclass variation, view-point variation, and different illumination ... ...

    Abstract Vehicle make and model recognition plays an important role in monitoring traffic in a vehicle surveillance system. Identifying vehicle make and model is a challenging task due to intraclass variation, view-point variation, and different illumination conditions (Hassan et al., 2021). In this domain, many datasets regarding car make and model e.g. Stanford Car (Krause et al., 2013), VMMRdB (Tafazzoli et al., 2017, Yang et al., 2015), have already been experimented with by different researchers. However, most of the images in these datasets are high-quality images with no illumination conditions. Further, these images are collected through web crawling or image scraping. This enabled the researchers to achieve good results using deep learning models (Luo et al., 2015). In this article, we have presented an image dataset of 3847 images, designed from high-resolution (1920 1080) videos collected from camera units installed on a highway at different viewpoints with variable frame rates. This helped in collecting images demonstrating a real-world scenario and made this dataset more challenging. Due to consideration of different viewpoints and illumination effects, the dataset will help researchers to evaluate their machine learning models on realworld data (Manzoor et al., 2019).
    Keywords Image data-set ; Vehicle model recognition deep learning ; Machine learning ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Science (General) ; Q1-390
    Subject code 006
    Language English
    Publishing date 2022-06-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: DeepSDC

    Muhammad Hanif / Muhammad Waqas / Amgad Muneer / Ayed Alwadain / Muhammad Atif Tahir / Muhammad Rafi

    Sustainability, Vol 15, Iss 6049, p

    Deep Ensemble Learner for the Classification of Social-Media Flooding Events

    2023  Volume 6049

    Abstract: Disasters such as earthquakes, droughts, floods, and volcanoes adversely affect human lives and valuable resources. Therefore, various response systems have been designed, which assist in mitigating the impact of disasters and facilitating relief ... ...

    Abstract Disasters such as earthquakes, droughts, floods, and volcanoes adversely affect human lives and valuable resources. Therefore, various response systems have been designed, which assist in mitigating the impact of disasters and facilitating relief activities in the aftermath of a disaster. These response systems require timely and accurate information about affected areas. In recent years, social media has provided access to high-volume real-time data, which can be used for advanced solutions to numerous problems, including disasters. Social-media data combines two modalities (text and associated images), and this information can be used to detect disasters, such as floods. This paper proposes an ensemble learning-based Deep Social Media Data Classification (DeepSDC) approach for social-media flood-event classification. The proposed algorithm uses datasets from Twitter to detect the flooding event. The Deep Social Media Data Classification (DeepSDC) uses a two-staged ensemble-learning approach which combines separate models for textual and visual data. These models obtain diverse information from the text and images and combine the information using an ensemble-learning approach. Additionally, DeepSDC utilizes different augmentation, upsampling and downsampling techniques to tackle the class-imbalance challenge. The performance of the proposed algorithm is assessed on three publically available flood-detection datasets. The experimental results show that the proposed DeepSDC is able to produce superior performance when compared with several state-of-the-art algorithms. For the three datasets, FRMT, FCSM and DIRSM, the proposed approach produced F1 scores of 46.52, 92.87, and 92.65, respectively. The mean average precision (MAP@480) of 91.29 and 98.94 were obtained on textual and a combination of textual and visual data, respectively.
    Keywords flood classification ; disaster response ; social media ; two-staged ensemble learning ; deep learning ; Environmental effects of industries and plants ; TD194-195 ; Renewable energy sources ; TJ807-830 ; Environmental sciences ; GE1-350
    Subject code 006
    Language English
    Publishing date 2023-03-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: NeuPD—A Neural Network-Based Approach to Predict Antineoplastic Drug Response

    Muhammad Shahzad / Muhammad Atif Tahir / Musaed Alhussein / Ansharah Mobin / Rauf Ahmed Shams Malick / Muhammad Shahid Anwar

    Diagnostics, Vol 13, Iss 2043, p

    2023  Volume 2043

    Abstract: With the beginning of the high-throughput screening, in silico-based drug response analysis has opened lots of research avenues in the field of personalized medicine. For a decade, many different predicting techniques have been recommended for the ... ...

    Abstract With the beginning of the high-throughput screening, in silico-based drug response analysis has opened lots of research avenues in the field of personalized medicine. For a decade, many different predicting techniques have been recommended for the antineoplastic (anti-cancer) drug response, but still, there is a need for improvements in drug sensitivity prediction. The intent of this research study is to propose a framework, namely NeuPD, to validate the potential anti-cancer drugs against a panel of cancer cell lines in publicly available datasets. The datasets used in this work are Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE). As not all drugs are effective on cancer cell lines, we have worked on 10 essential drugs from the GDSC dataset that have achieved the best modeling results in previous studies. We also extracted 1610 essential oncogene expressions from 983 cell lines from the same dataset. Whereas, from the CCLE dataset, 16,383 gene expressions from 1037 cell lines and 24 drugs have been used in our experiments. For dimensionality reduction, Pearson correlation is applied to best fit the model. We integrate the genomic features of cell lines and drugs’ fingerprints to fit the neural network model. For evaluation of the proposed NeuPD framework, we have used repeated K-fold cross-validation with 5 times repeats where K = 10 to demonstrate the performance in terms of root mean square error (RMSE) and coefficient determination (R 2 ). The results obtained on the GDSC dataset that were measured using these cost functions show that our proposed NeuPD framework has outperformed existing approaches with an RMSE of 0.490 and R 2 of 0.929.
    Keywords NeuPD ; cell lines ; gene expression ; machine learning ; neural networks ; drug response prediction ; Medicine (General) ; R5-920
    Subject code 006
    Language English
    Publishing date 2023-06-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: MAJORITY VOTING APPROACH FOR THE IDENTIFICATION OF DIFFERENTIALLY EXPRESSED GENES TO UNDERSTAND GENDER-RELATED SKELETAL MUSCLE AGING

    Abdouladeem Dreder / Muhammad Atif Tahir / Huseyin Seker / Muhammad Naveed Anwar

    Computer Science & Information Technology, Vol 6, Iss 6, Pp 237-

    2016  Volume 244

    Abstract: Understanding gene function (GF) is still a significant challenge in system biology. Previously, several machine learning and computational techniques have been used to understand GF. However, these previous attempts have not produced a comprehensive ... ...

    Abstract Understanding gene function (GF) is still a significant challenge in system biology. Previously, several machine learning and computational techniques have been used to understand GF. However, these previous attempts have not produced a comprehensive interpretation of the relationship between genes and differences in both age and gender. Although there are several thousand of genes, very few differentially expressed genes play an active role in understanding the age and gender differences. The core aim of this study is to uncover new biomarkers that can contribute towards distinguishing between male and female according to the gene expression levels of skeletal muscle (SM) tissues. In our proposed multi-filter system (MFS), genes are first sorted using three different ranking techniques (t-test, Wilcoxon and ROC). Later, important genes are acquired using majority voting based on the principle that combining multiple models can improve the generalization of the system. Experiments were conducted on Micro Array gene expression dataset and results have indicated a significant increase in classification accuracy when compared with existing system.
    Keywords Multi-Filter System ; Filter Techniques ; Micro Array Gene Expression ; Skeletal muscle ; Electronic computers. Computer science ; QA75.5-76.95 ; Instruments and machines ; QA71-90 ; Mathematics ; QA1-939 ; Science ; Q
    Subject code 612
    Language English
    Publishing date 2016-05-01T00:00:00Z
    Publisher Academy & Industry Research Collaboration Center (AIRCC)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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