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Article ; Online: Data Visualization to Explore the Countries Dataset for Pattern Creation

Shakir Khan

International Journal of Online and Biomedical Engineering, Vol 17, Iss

2021 Volume 13

Abstract: Data visualization is graph representation of data. It produces interactive graphs that explain the relationships among the data to viewers of the graph. The aim of data visualization is to communicate data value clearly and effectively through graphs [1] ...

Abstract	Data visualization is graph representation of data. It produces interactive graphs that explain the relationships among the data to viewers of the graph. The aim of data visualization is to communicate data value clearly and effectively through graphs [1]. Here we take the advantage of data visualization to explore the countries dataset to provide a holistic and interpretive view about the world. In addition to examine some hypotheses about gross domestic product (GDP) and Literacy and more of the countries effects on different factors showing on the dataset such as the literacy and the migration.
Keywords	Data visualization ; Countries data ; world dataset ; regression analysis ; Computer applications to medicine. Medical informatics ; R858-859.7
Language	English
Publishing date	2021-12-01T00:00:00Z
Publisher	International Association of Online Engineering (IAOE)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Visual Data Analysis and Simulation Prediction for COVID-19 in Saudi Arabia Using SEIR Prediction Model

Shakir Khan

International Journal of Online and Biomedical Engineering, Vol 17, Iss 08, Pp 154-

2021 Volume 167

Abstract: The World Health Organization (WHO) reported the COVID-19 epidemic a global health emergency on January 30 and confirmed its transformation into a pandemic on March 11. China has been the hardest hit since the virus's outbreak, which may date back to ... ...

Abstract	The World Health Organization (WHO) reported the COVID-19 epidemic a global health emergency on January 30 and confirmed its transformation into a pandemic on March 11. China has been the hardest hit since the virus's outbreak, which may date back to late November. Saudi Arabia realized the danger of the Coronavirus in March 2020, took the initiative to take a set of pre-emptive decisions that preceded many countries of the world, and worked to harness all capabilities to confront the outbreak of the epidemic. Several researchers are currently using various mathematical and machine learning-based prediction models to estimate this pandemic's future trend. In this work, the SEIR model was applied to predict the epidemic situation in Saudi Arabia and evaluate the effectiveness of some epidemic control measures, and finally, providing some advice on preventive measures.
Keywords	covid-19 ; coronavirus ; seir model ; pandemic ; saudi arabia ; Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	612
Language	English
Publishing date	2021-08-01T00:00:00Z
Publisher	International Association of Online Engineering (IAOE)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Oxidative Stress in Patients with Type 2 Diabetes Mellitus

Shakir Khan

Journal of Rawalpindi Medical College, Vol 18, Iss

2014 Volume 1

Abstract: Background : To assess the reduced glutathione (GSH) levels as marker of oxidative stress in patients of diabetes mellitus Methods: In this cross sectional study 40 subjects were divided into two groups. One group was designated as control while the ... ...

Abstract	Background : To assess the reduced glutathione (GSH) levels as marker of oxidative stress in patients of diabetes mellitus Methods: In this cross sectional study 40 subjects were divided into two groups. One group was designated as control while the other was diabetic. Glycemic status was measured to confirm their normal and diabetic states respectively. Plasma reduced GSH level was measured by using standard ELISA kits. Results: Level of reduced GSH was decreased in the diabetic group (18.4 ± 1.35) as compared to the control group(27.5 ± 1.38). Conclusion: Levels of reduced GSH were significantly decreased in patients of diabetes mellitus as compared to normal healthy controls.
Keywords	Diabetes mellitus ; oxidative stress ; reduced glutathione marker ; Medicine ; R
Language	English
Publishing date	2014-06-01T00:00:00Z
Publisher	Rawalpindi Medical University
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Oxidative Stress in Patients with Type 2 Diabetes Mellitus

Shakir Khan

Journal of Rawalpindi Medical College, Vol 18, Iss

2014 Volume 1

Abstract	Background : To assess the reduced glutathione (GSH) levels as marker of oxidative stress in patients of diabetes mellitus Methods: In this cross sectional study 40 subjects were divided into two groups. One group was designated as control while the other was diabetic. Glycemic status was measured to confirm their normal and diabetic states respectively. Plasma reduced GSH level was measured by using standard ELISA kits. Results: Level of reduced GSH was decreased in the diabetic group (18.4 ± 1.35) as compared to the control group(27.5 ± 1.38). Conclusion: Levels of reduced GSH were significantly decreased in patients of diabetes mellitus as compared to normal healthy controls
Keywords	Diabetes mellitus ; oxidative stress ; reduced glutathione marker ; Medicine ; R
Language	English
Publishing date	2014-06-01T00:00:00Z
Publisher	Rawalpindi Medical University
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Fault Tolerance Byzantine Algorithm for Lower Overhead Blockchain

Riyad Almakki / Lulwah AlSuwaidan / Shakir Khan / Abdul Rauf Baig / Samad Baseer / Manmohan Singh

Security and Communication Networks, Vol

2022 Volume 2022

Abstract: A new algorithm for practical Byzantine fault tolerance (PBFT), called score-PBFT or S-PBFT, is proposed to solve the problems of high communication overhead and low algorithm efficiency. This algorithm is based on the characteristics of the consortium ... ...

Abstract	A new algorithm for practical Byzantine fault tolerance (PBFT), called score-PBFT or S-PBFT, is proposed to solve the problems of high communication overhead and low algorithm efficiency. This algorithm is based on the characteristics of the consortium chain. The scoring mechanism for nodes is added. All the nodes are broken up into consensus nodes, candidate nodes, and early nodes. To make sure the consensus nodes are as reliable as possible, the nodes are changed dynamically based on how each node is behaving. Improved: the election method for the controller node has been changed. The node’s score and behaviour are used as the election basis to make the algorithm more stable. In this paper, we want to improve the consensus protocol’s execution process, cut down on how many nodes are involved in the consensus process, simplify it, and make it more efficient. Results show that, when compared with the PBFT algorithm, the S-PBFT algorithm has a shorter consensus delay, less communication overhead and throughput, and better consensus node reliability.
Keywords	Technology (General) ; T1-995 ; Science (General) ; Q1-390
Subject code	000
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Hindawi-Wiley
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Multichannel CNN Model for Biomedical Entity Reorganization

Ajay Kumar Singh / Ihtiram Raza Khan / Shakir Khan / Kumud Pant / Sandip Debnath / Shahajan Miah

BioMed Research International, Vol

2022 Volume 2022

Abstract: Biomedical researchers and biologists often search a large amount of literature to find the relationship between biological entities, such as drug-drug and compound-protein. With the proliferation of medical literature and the development of deep ... ...

Abstract	Biomedical researchers and biologists often search a large amount of literature to find the relationship between biological entities, such as drug-drug and compound-protein. With the proliferation of medical literature and the development of deep learning, the automatic extraction of biological entity interaction relationships from literature has shown great potential. The fundamental scope of this research is that the approach described in this research uses technologies like dynamic word vectors and multichannel convolution to learn a larger variety of relational expression semantics, allowing it to detect more entity connections. The extraction of biological entity relationships is the foundation for achieving intelligent medical care, which may increase the effectiveness of intelligent medical question answering and enhance the development of precision healthcare. In the past, deep learning methods have achieved specific results, but there are the following problems: the model uses static word vectors, which cannot distinguish polysemy; the weight of words is not considered, and the extraction effect of long sentences is poor; the integration of various models can improve the sample imbalance problem, the model is more complex. The purpose of this work is to create a global approach for eliminating different physical entity links, such that the model can effectively extract the interpretation of the expression relationship without having to develop characteristics manually. To this end, a deep multichannel CNN model (MC-CNN) based on the residual structure is proposed, generating dynamic word vectors through BERT (Bidirectional Encoder Representation from Transformers) to improve the accuracy of lexical semantic representation and uses multihead attention to capture the dependencies of long sentences and by designing the Ranking loss function to replace the multimodel ensemble to reduce the impact of sample imbalance. Tested on multiple datasets, the results show that the proposed method has good performance.
Keywords	Medicine ; R
Subject code	006
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Hindawi Limited
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Cyclic GAN Model to Classify Breast Cancer Data for Pathological Healthcare Task

Pooja Chopra / N. Junath / Sitesh Kumar Singh / Shakir Khan / R. Sugumar / Mithun Bhowmick

BioMed Research International, Vol

2022 Volume 2022

Abstract: An algorithm framework based on CycleGAN and an upgraded dual-path network (DPN) is suggested to address the difficulties of uneven staining in pathological pictures and difficulty of discriminating benign from malignant cells. CycleGAN is used for color ...

Abstract	An algorithm framework based on CycleGAN and an upgraded dual-path network (DPN) is suggested to address the difficulties of uneven staining in pathological pictures and difficulty of discriminating benign from malignant cells. CycleGAN is used for color normalization in pathological pictures to tackle the problem of uneven staining. However, the resultant detection model is ineffective. By overlapping the images, the DPN uses the addition of small convolution, deconvolution, and attention mechanisms to enhance the model’s ability to classify the texture features of pathological images on the BreaKHis dataset. The parameters that are taken into consideration for measuring the accuracy of the proposed model are false-positive rate, false-negative rate, recall, precision, and F1 score. Several experiments are carried out over the selected parameters, such as making comparisons between benign and malignant classification accuracy under different normalization methods, comparison of accuracy of image level and patient level using different CNN models, correlating the correctness of DPN68-A network with different deep learning models and other classification algorithms at all magnifications. The results thus obtained have proved that the proposed model DPN68-A network can effectively classify the benign and malignant breast cancer pathological images at various magnifications. The proposed model also is able to better assist the pathologists in diagnosing the patients by synthesizing the images of different magnifications in the clinical stage.
Keywords	Medicine ; R
Subject code	006
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Hindawi Limited
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Synthesis and applications of graphitic carbon nitride (g-C3N4) based membranes for wastewater treatment

Muhammad Azam Qamar / Mohsin Javed / Sammia Shahid / Mohammad Shariq / Mohammed M. Fadhali / Syed Kashif Ali / Mohd. Shakir Khan

Heliyon, Vol 9, Iss 1, Pp e12685- (2023)

A critical review

2023

Abstract: Semiconducting membrane combined with nanomaterials is an auspicious combination that may successfully eliminate diverse waste products from water while consuming little energy and reducing pollution. Creating an inexpensive, steady, flexible, and ... ...

Abstract	Semiconducting membrane combined with nanomaterials is an auspicious combination that may successfully eliminate diverse waste products from water while consuming little energy and reducing pollution. Creating an inexpensive, steady, flexible, and diversified business material for membrane production is a critical challenge in membrane technology development. Because of its unusual structure and high catalytic activity, graphitic carbon nitride (g-C3N4) has come out as a viable material for membranes. Furthermore, their great durability, high permanency under challenging environments, and long-term use without decrease in flux are significant advantages. The advanced material techniques used to manage the molecular assembly of g-C3N4 for separation membrane were detailed in this review work. The progress in using g-C3N4-based membranes for water treatment has been detailed in this presentation. The review delivers an updated description of g-C3N4 based membranes and their separation functions and new ideas for future enhancements/adjustments to address their weaknesses in real-world situations. Finally, the ongoing problems and promising future research directions for g-C3N4-based membranes are discussed.
Keywords	g-C3N4 ; Antifouling ; Water purification ; Separation membrane ; Photodegradation ; Nanofiltration ; Science (General) ; Q1-390 ; Social sciences (General) ; H1-99
Subject code	660
Language	English
Publishing date	2023-01-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A Study of CNN and Transfer Learning in Medical Imaging

Ahmad Waleed Salehi / Shakir Khan / Gaurav Gupta / Bayan Ibrahimm Alabduallah / Abrar Almjally / Hadeel Alsolai / Tamanna Siddiqui / Adel Mellit

Sustainability, Vol 15, Iss 5930, p

Advantages, Challenges, Future Scope

2023 Volume 5930

Abstract: This paper presents a comprehensive study of Convolutional Neural Networks (CNN) and transfer learning in the context of medical imaging. Medical imaging plays a critical role in the diagnosis and treatment of diseases, and CNN-based models have ... ...

Abstract	This paper presents a comprehensive study of Convolutional Neural Networks (CNN) and transfer learning in the context of medical imaging. Medical imaging plays a critical role in the diagnosis and treatment of diseases, and CNN-based models have demonstrated significant improvements in image analysis and classification tasks. Transfer learning, which involves reusing pre-trained CNN models, has also shown promise in addressing challenges related to small datasets and limited computational resources. This paper reviews the advantages of CNN and transfer learning in medical imaging, including improved accuracy, reduced time and resource requirements, and the ability to address class imbalances. It also discusses challenges, such as the need for large and diverse datasets, and the limited interpretability of deep learning models. What factors contribute to the success of these networks? How are they fashioned, exactly? What motivated them to build the structures that they did? Finally, the paper presents current and future research directions and opportunities, including the development of specialized architectures and the exploration of new modalities and applications for medical imaging using CNN and transfer learning techniques. Overall, the paper highlights the significant potential of CNN and transfer learning in the field of medical imaging, while also acknowledging the need for continued research and development to overcome existing challenges and limitations.
Keywords	deep learning ; transfer learning ; medical imaging ; CNN ; machine 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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Article ; Online: BiCHAT

Shakir Khan / Mohd Fazil / Vineet Kumar Sejwal / Mohammed Ali Alshara / Reemiah Muneer Alotaibi / Ashraf Kamal / Abdul Rauf Baig

Journal of King Saud University: Computer and Information Sciences, Vol 34, Iss 7, Pp 4335-

BiLSTM with deep CNN and hierarchical attention for hate speech detection

2022 Volume 4344

Abstract: Online social networks(OSNs) face the challenging problem of hate speech, which should be moderated for the growth of OSNs. The machine learning approaches dominate the existing set of approaches for hate speech detection. In this study, we introduce ... ...

Abstract	Online social networks(OSNs) face the challenging problem of hate speech, which should be moderated for the growth of OSNs. The machine learning approaches dominate the existing set of approaches for hate speech detection. In this study, we introduce BiCHAT: a novel BiLSTM with deep CNN and Hierarchical ATtention-based deep learning model for tweet representation learning toward hate speech detection. The proposed model takes the tweets as input and passes through a BERT layer followed by an attention-aware deep convolutional layer. The convolutional encoded representation further passes through an attention-aware Bidirectional LSTM network. Finally, the model labels the tweet as hateful or normal through a softmax layer. The proposed model is trained and evaluated over the three benchmark datasets extracted from Twitter and outperforms the state-of-the-art (SOTA) (Khan et al., 2022; Roy et al., 2020; Ding et al., 2019) and baseline methods with an improvement of 8%,7% and 8% in terms of precision, recall, and f-score, respectively. BiCHAT also demonstrates good performance considering training and validation accuracy with an improvement of 5% and 9%, respectively. We also examined the impact of different constituting neural network components on the model. On analysis, we observed that the exclusion of the deep convolutional layer has the highest impact on the performance of the proposed model. We also investigated the efficacy of different embedding techniques, activation function, batch size, and optimization algorithms on the performance of the BiCHAT model.
Keywords	Hate speech detection ; Deep learning ; Social network security ; Twitter data analysis ; BiCHAT ; Electronic computers. Computer science ; QA75.5-76.95
Subject code	006
Language	English
Publishing date	2022-07-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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