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Article ; Online: Revisiting the environmental Kuznets curve: assessing the impact of climate policy uncertainty in the Belt and Road Initiative.

Huang, Yi / Rahman, Saif Ur / Meo, Muhammad Saeed / Ali, Muhammad Sibt E / Khan, Sarwar

Environmental science and pollution research international

2024 Volume 31, Issue 7, Page(s) 10579–10593

Abstract: Climate change repercussions such as temperature shifts and more severe weather occurrences are felt globally. It contributes to larger-scale challenges, such as climate change and biodiversity loss in food production. As a result, the purpose of this ... ...

Abstract	Climate change repercussions such as temperature shifts and more severe weather occurrences are felt globally. It contributes to larger-scale challenges, such as climate change and biodiversity loss in food production. As a result, the purpose of this research is to develop strategies to grow the economy without harming the environment. Therefore, we revisit the environmental Kuznets curve (EKC) hypothesis, considering the impact of climate policy uncertainty along with other control variables. We investigated yearly panel data from 47 Belt and Road Initiative (BRI) nations from 1998 to 2021. Pooled regression, fixed effect, and the generalized method of moment (GMM) findings all confirmed the presence of inverted U-shaped EKC in BRI counties. Findings from this paper provide policymakers with actionable ideas, outlining a framework for bringing trade and climate agendas into harmony in BRI countries. The best way to promote economic growth and reduce carbon dioxide emissions is to push for trade and climate policies to be coordinated. Moreover, improving institutional quality is essential for strong environmental governance, as it facilitates the adoption of environmentally friendly industrialization techniques and the efficient administration of climate policy uncertainties.
MeSH term(s)	Conservation of Natural Resources ; Uncertainty ; Environmental Policy ; Economic Development ; Industrial Development ; Carbon Dioxide
Chemical Substances	Carbon Dioxide (142M471B3J)
Language	English
Publishing date	2024-01-10
Publishing country	Germany
Document type	Journal Article
ZDB-ID	1178791-0
ISSN	1614-7499 ; 0944-1344
ISSN (online)	1614-7499
ISSN	0944-1344
DOI	10.1007/s11356-023-31471-y
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Article ; Online: Towards Adversarial Robustness for Multi-Mode Data through Metric Learning.

Khan, Sarwar / Chen, Jun-Cheng / Liao, Wen-Hung / Chen, Chu-Song

Sensors (Basel, Switzerland)

2023 Volume 23, Issue 13

Abstract: Adversarial attacks have become one of the most serious security issues in widely used deep neural networks. Even though real-world datasets usually have large intra-variations or multiple modes, most adversarial defense methods, such as adversarial ... ...

Abstract	Adversarial attacks have become one of the most serious security issues in widely used deep neural networks. Even though real-world datasets usually have large intra-variations or multiple modes, most adversarial defense methods, such as adversarial training, which is currently one of the most effective defense methods, mainly focus on the single-mode setting and thus fail to capture the full data representation to defend against adversarial attacks. To confront this challenge, we propose a novel multi-prototype metric learning regularization for adversarial training which can effectively enhance the defense capability of adversarial training by preventing the latent representation of the adversarial example changing a lot from its clean one. With extensive experiments on CIFAR10, CIFAR100, MNIST, and Tiny ImageNet, the evaluation results show the proposed method improves the performance of different state-of-the-art adversarial training methods without additional computational cost. Furthermore, besides Tiny ImageNet, in the multi-prototype CIFAR10 and CIFAR100 where we reorganize the whole datasets of CIFAR10 and CIFAR100 into two and ten classes, respectively, the proposed method outperforms the state-of-the-art approach by 2.22% and 1.65%, respectively. Furthermore, the proposed multi-prototype method also outperforms its single-prototype version and other commonly used deep metric learning approaches as regularization for adversarial training and thus further demonstrates its effectiveness.
MeSH term(s)	Learning ; Neural Networks, Computer
Language	English
Publishing date	2023-07-05
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s23136173
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Book ; Online: CapST

Ahmad, Wasim / Peng, Yan-Tsung / Chang, Yuan-Hao / Ganfure, Gaddisa Olani / Khan, Sarwar / Shahzad, Sahibzada Adil

An Enhanced and Lightweight Model Attribution Approach for Synthetic Videos

2023

Abstract: Deepfake videos, generated through AI faceswapping techniques, have garnered considerable attention due to their potential for powerful impersonation attacks. While existing research primarily focuses on binary classification to discern between real and ... ...

Abstract	Deepfake videos, generated through AI faceswapping techniques, have garnered considerable attention due to their potential for powerful impersonation attacks. While existing research primarily focuses on binary classification to discern between real and fake videos, however determining the specific generation model for a fake video is crucial for forensic investigation. Addressing this gap, this paper investigates the model attribution problem of Deepfake videos from a recently proposed dataset, Deepfakes from Different Models (DFDM), derived from various Autoencoder models. The dataset comprises 6,450 Deepfake videos generated by five distinct models with variations in encoder, decoder, intermediate layer, input resolution, and compression ratio. This study formulates Deepfakes model attribution as a multiclass classification task, proposing a segment of VGG19 as a feature extraction backbone, known for its effectiveness in imagerelated tasks, while integrated a Capsule Network with a Spatio-Temporal attention mechanism. The Capsule module captures intricate hierarchies among features for robust identification of deepfake attributes. Additionally, the video-level fusion technique leverages temporal attention mechanisms to handle concatenated feature vectors, capitalizing on inherent temporal dependencies in deepfake videos. By aggregating insights across frames, our model gains a comprehensive understanding of video content, resulting in more precise predictions. Experimental results on the deepfake benchmark dataset (DFDM) demonstrate the efficacy of our proposed method, achieving up to a 4% improvement in accurately categorizing deepfake videos compared to baseline models while demanding fewer computational resources.
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	006 ; 004
Publishing date	2023-11-07
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Hybrid Sharpening Transformation Approach for Multifocus Image Fusion Using Medical and Nonmedical Images.

Khan, Sarwar Shah / Khan, Muzammil / Alharbi, Yasser / Haider, Usman / Ullah, Kifayat / Haider, Shahab

Journal of healthcare engineering

2021 Volume 2021, Page(s) 7000991

Abstract: In this study, we introduced a preprocessing novel transformation approach for multifocus image fusion. In the multifocus image, fusion has generated a high informative image by merging two source images with different areas or objects in focus. Acutely ... ...

Abstract	In this study, we introduced a preprocessing novel transformation approach for multifocus image fusion. In the multifocus image, fusion has generated a high informative image by merging two source images with different areas or objects in focus. Acutely the preprocessing means sharpening performed on the images before applying fusion techniques. In this paper, along with the novel concept, a new sharpening technique, Laplacian filter + discrete Fourier transform (LF + DFT), is also proposed. The LF is used to recognize the meaningful discontinuities in an image. DFT recognizes that the rapid change in the image is like sudden changes in the frequencies, low-frequency to high-frequency in the images. The aim of image sharpening is to highlight the key features, identifying the minor details, and sharpen the edges while the previous methods are not so effective. To validate the effectiveness the proposed method, the fusion is performed by a couple of advanced techniques such as stationary wavelet transform (SWT) and discrete wavelet transform (DWT) with both types of images like grayscale and color image. The experiments are performed on nonmedical and medical (breast medical CT and MRI images) datasets. The experimental results demonstrate that the proposed method outperforms all evaluated qualitative and quantitative metrics. Quantitative assessment is performed by eight well-known metrics, and every metric described its own feature by which it is easily assumed that the proposed method is superior. The experimental results of the proposed technique SWT (LF + DFT) are summarized for evaluation matrices such as RMSE (5.6761), PFE (3.4378), MAE (0.4010), entropy (9.0121), SNR (26.8609), PSNR (40.1349), CC (0.9978), and ERGAS (2.2589) using clock dataset.
MeSH term(s)	Algorithms ; Entropy ; Humans ; Magnetic Resonance Imaging ; Tomography, X-Ray Computed/methods ; Wavelet Analysis
Language	English
Publishing date	2021-12-11
Publishing country	England
Document type	Journal Article
ZDB-ID	2545054-2
ISSN	2040-2309 ; 2040-2295
ISSN (online)	2040-2309
ISSN	2040-2295
DOI	10.1155/2021/7000991
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Book ; Online: Classification of Macromolecule Type Based on Sequences of Amino Acids Using Deep Learning

Khan, Sarwar / Ghaffar, Faisal / Ali, Imad / Mazhar, Qazi

2019

Abstract: The classification of amino acids and their sequence analysis plays a vital role in life sciences and is a challenging task. This article uses and compares state-of-the-art deep learning models like convolution neural networks (CNN), long short-term ... ...

Abstract	The classification of amino acids and their sequence analysis plays a vital role in life sciences and is a challenging task. This article uses and compares state-of-the-art deep learning models like convolution neural networks (CNN), long short-term memory (LSTM), and gated recurrent units (GRU) to solve macromolecule classification problems using amino acids. These models have efficient frameworks for solving a broad spectrum of complex learning problems compared to traditional machine learning techniques. We use word embedding to represent the amino acid sequences as vectors. The CNN extracts features from amino acid sequences, which are treated as vectors, then fed to the models mentioned above to train a robust classifier. Our results show that word2vec as embedding combined with VGG-16 performs better than LSTM and GRU. The proposed approach gets an error rate of 1.5%. Comment: under review
Keywords	Quantitative Biology - Biomolecules ; Computer Science - Machine Learning
Subject code	006 ; 612
Publishing date	2019-06-30
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Macromolecule Classification Based on the Amino-acid Sequence

Ghaffar, Faisal / Khan, Sarwar / O., Gaddisa / Yu-jhen, Chen

2020

Abstract: Deep learning is playing a vital role in every field which involves data. It has emerged as a strong and efficient framework that can be applied to a broad spectrum of complex learning problems which were difficult to solve using traditional machine ... ...

Abstract	Deep learning is playing a vital role in every field which involves data. It has emerged as a strong and efficient framework that can be applied to a broad spectrum of complex learning problems which were difficult to solve using traditional machine learning techniques in the past. In this study we focused on classification of protein sequences with deep learning techniques. The study of amino acid sequence is vital in life sciences. We used different word embedding techniques from Natural Language processing to represent the amino acid sequence as vectors. Our main goal was to classify sequences to four group of classes, that are DNA, RNA, Protein and hybrid. After several tests we have achieved almost 99% of train and test accuracy. We have experimented on CNN, LSTM, Bidirectional LSTM, and GRU. Comment: arXiv admin note: substantial text overlap with arXiv:1907.03532
Keywords	Quantitative Biology - Biomolecules ; Computer Science - Machine Learning
Subject code	612
Publishing date	2020-01-06
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Detection of Diabetic Anomalies in Retinal Images using Morphological Cascading Decision Tree

Ghaffar, Faisal / Khan, Sarwar / Uyyanonvara, Bunyarit / Sinthanayothin, Chanjira / Kaneko, Hirohiko

2020

Abstract: This research aims to develop an efficient system for screening of diabetic retinopathy. Diabetic retinopathy is the major cause of blindness. Severity of diabetic retinopathy is recognized by some features, such as blood vessel area, exudates, ... ...

Abstract	This research aims to develop an efficient system for screening of diabetic retinopathy. Diabetic retinopathy is the major cause of blindness. Severity of diabetic retinopathy is recognized by some features, such as blood vessel area, exudates, haemorrhages and microaneurysms. To grade the disease the screening system must efficiently detect these features. In this paper we are proposing a simple and fast method for detection of diabetic retinopathy. We do pre-processing of grey-scale image and find all labelled connected components (blobs) in an image regardless of whether it is haemorrhages, exudates, vessels, optic disc or anything else. Then we apply some constraints such as compactness, area of blob, intensity and contrast for screening of candidate connectedcomponent responsible for diabetic retinopathy. We obtain our final results by doing some post processing. The results are compared with ground truths. Performance is measured by finding the recall (sensitivity). We took 10 images of dimension 500 * 752. The mean recall is 90.03%.
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition
Subject code	006
Publishing date	2020-01-07
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Small Gut Volvulus, A Rare Twist, In The Setting Of An Even Rarer Entity; Multiple Giant Jejuno Ileal Diverticula.

Khan, Ayesha / Khan, Faheem / Rafique, Kashif / Khan, Sarwar / Majid, Anam / Khan, Khalid / Ullah, Asad

Journal of Ayub Medical College, Abbottabad : JAMC

2017 Volume 29, Issue 1, Page(s) 147–149

Abstract: Small gut volvulus with multiple Jejuno-ileal diverticulosis is an unusual pathology of the small intestine with a scarce number of cases reported so far. It usually goes unnoticed because it is often asymptomatic but complications like diverticulitis, ... ...

Abstract	Small gut volvulus with multiple Jejuno-ileal diverticulosis is an unusual pathology of the small intestine with a scarce number of cases reported so far. It usually goes unnoticed because it is often asymptomatic but complications like diverticulitis, perforation, bleeding or intestinal obstruction can occur in 10-30% of the cases. Mechanical obstruction, if it occurs, can be caused by adhesions or stenosis due to diverticulitis, intussusception at the site of the diverticulum and volvulus of the segment containing the diverticula. Acute volvulus of the small bowel is a serious abdominal emergency that poses a difficulty in diagnosis and delayed operative intervention can lead to dire consequences. We herein report the case of a 42-yearold man presented at the emergency department with acute abdominal pain, absolute constipation and vomiting. Preoperative investigations followed by laparotomy revealed small gut volvulus and multiple giant jejunal and ileal diverticula.
MeSH term(s)	Abdominal Pain/etiology ; Adult ; Diverticulum/diagnostic imaging ; Diverticulum/pathology ; Diverticulum/surgery ; Emergency Service, Hospital ; Humans ; Ileal Diseases/diagnostic imaging ; Ileal Diseases/pathology ; Ileal Diseases/surgery ; Intestinal Volvulus/diagnostic imaging ; Intestinal Volvulus/pathology ; Intestinal Volvulus/surgery ; Jejunal Diseases/diagnostic imaging ; Jejunal Diseases/pathology ; Jejunal Diseases/surgery ; Laparotomy ; Male ; Vomiting/etiology
Language	English
Publishing date	2017-09-04
Publishing country	Pakistan
Document type	Case Reports ; Journal Article
ZDB-ID	2192473-9
ISSN	1025-9589
ISSN	1025-9589
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Article ; Online: Anhedonia in cocaine use disorder is associated with inflammatory gene expression.

Fries, Gabriel Rodrigo / Khan, Sarwar / Stamatovich, Sydney / Dyukova, Elena / Walss-Bass, Consuelo / Lane, Scott D / Schmitz, Joy M / Wardle, Margaret C

PloS one

2018 Volume 13, Issue 11, Page(s) e0207231

Abstract: Treatments for Cocaine Use Disorder (CUD) are variably effective, and there are no FDA-approved medications. One approach to developing new treatments for CUD may be to investigate and target poor prognostic signs. One such sign is anhedonia (i.e. a loss ...

Abstract	Treatments for Cocaine Use Disorder (CUD) are variably effective, and there are no FDA-approved medications. One approach to developing new treatments for CUD may be to investigate and target poor prognostic signs. One such sign is anhedonia (i.e. a loss of pleasure or interest in non-drug rewards), which predicts worse outcomes in existing CUD treatments. Inflammation is thought to underlie anhedonia in many other disorders, but the relationship between anhedonia and inflammation has not been investigated in CUD. Therefore, we assessed peripheral genome-wide gene expression in n = 48 individuals with CUD with high (n = 24) vs. low (n = 24) levels of anhedonia, defined by a median split of self-reported anhedonia. Our hypothesis was that individuals with high anhedonia would show differential gene expression in inflammatory pathways. No individual genes were significantly different between the low and high anhedonia groups when using t-tests with a stringent false discovery rate correction (FDR-corrected p < 0.05). However, an exploratory analysis identified 166 loci where t-tests suggested group differences at a nominal p < 0.05. We used DAVID, a bioinformatics tool that provides functional interpretations of complex lists of genes, to examine representation of this gene list in known pathways. It confirmed that mechanisms related to immunity were the top significant associations with anhedonia in the sample. Further, the two top differentially expressed genes in our sample, IRF1 and GBP5, both have primary inflammation and immune functions, and were significantly negatively correlated with total scores on our self-report of anhedonia across all 48 subjects. These results suggest that prioritizing development of anti-inflammatory medications for CUD may pay dividends, particularly in combination with treatment-matching strategies using either phenotypic measures of anhedonia or biomarkers of inflammatory gene expression to individualize treatment.
MeSH term(s)	Adult ; Anhedonia/physiology ; Cocaine-Related Disorders/complications ; Cocaine-Related Disorders/genetics ; Cocaine-Related Disorders/psychology ; Computational Biology ; Female ; GTP-Binding Proteins/genetics ; GTP-Binding Proteins/immunology ; Gene Expression ; Genome-Wide Association Study ; Humans ; Inflammation/complications ; Inflammation/genetics ; Inflammation/immunology ; Interferon Regulatory Factor-1/genetics ; Interferon Regulatory Factor-1/immunology ; Male ; Middle Aged ; Pleasure ; Reward ; Self Report
Chemical Substances	GBP5 protein, human ; IRF1 protein, human ; Interferon Regulatory Factor-1 ; GTP-Binding Proteins (EC 3.6.1.-)
Language	English
Publishing date	2018-11-08
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0207231
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Article ; Online: Anti-inflammatory, analgesic and antipyretic activities of Physalis minima Linn.

Khan, Murad Ali / Khan, Haroon / Khan, Sarwar / Mahmood, Tahira / Khan, Pir Mohammad / Jabar, Abdul

Journal of enzyme inhibition and medicinal chemistry

2009 Volume 24, Issue 3, Page(s) 632–637

Abstract: In our present investigation, the crude methanol extract and chloroform fraction of the whole plant of Physalis minima Linn (Solanaceae) was investigated for anti-inflammatory, analgesic and antipyretic activities in NMRI mice and Wistar rats of either ... ...

Abstract	In our present investigation, the crude methanol extract and chloroform fraction of the whole plant of Physalis minima Linn (Solanaceae) was investigated for anti-inflammatory, analgesic and antipyretic activities in NMRI mice and Wistar rats of either sex at 200 and 400 mg/kg, respectively. Various established in-vivo model's were used during the study. Both crude extract and chloroform fraction showed marked anti-inflammatory and analgesic activities as compared to a control at tested doses. The antipyretic potential of the crude extract and chloroform were insignificant in the Brewer's yeast fever model. Therefore, the whole plant of Physalis minima Linn could be considered as a potential candidate for bioactivity-guided isolation of natural anti-inflammatory and analgesic agents.
MeSH term(s)	Analgesics/isolation & purification ; Analgesics/pharmacology ; Analgesics/therapeutic use ; Analgesics, Non-Narcotic/isolation & purification ; Analgesics, Non-Narcotic/pharmacology ; Analgesics, Non-Narcotic/therapeutic use ; Animals ; Anti-Inflammatory Agents/isolation & purification ; Anti-Inflammatory Agents/pharmacology ; Anti-Inflammatory Agents/therapeutic use ; Chloroform/chemistry ; Disease Models, Animal ; Edema/drug therapy ; Edema/pathology ; Female ; Inflammation/drug therapy ; Male ; Methanol/chemistry ; Mice ; Pain/drug therapy ; Pain Measurement/drug effects ; Physalis/chemistry ; Phytotherapy ; Plant Extracts/isolation & purification ; Plant Extracts/pharmacology ; Plant Extracts/therapeutic use ; Plant Preparations/isolation & purification ; Plant Preparations/pharmacology ; Plant Preparations/therapeutic use ; Rats ; Rats, Wistar ; Time Factors
Chemical Substances	Analgesics ; Analgesics, Non-Narcotic ; Anti-Inflammatory Agents ; Plant Extracts ; Plant Preparations ; Chloroform (7V31YC746X) ; Methanol (Y4S76JWI15)
Language	English
Publishing date	2009-06
Publishing country	England
Document type	Journal Article
ZDB-ID	2082578-X
ISSN	1475-6374 ; 1475-6366
ISSN (online)	1475-6374
ISSN	1475-6366
DOI	10.1080/14756360802321120
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