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Book ; Online: COVID-19 Infection Analysis Framework using Novel Boosted CNNs and Radiological Images

Khan, Saddam Hussain

2023

Abstract: COVID-19 is a new pathogen that first appeared in the human population at the end of 2019, and it can lead to novel variants of pneumonia after infection. COVID-19 is a rapidly spreading infectious disease that infects humans faster. Therefore, efficient ...

Abstract	COVID-19 is a new pathogen that first appeared in the human population at the end of 2019, and it can lead to novel variants of pneumonia after infection. COVID-19 is a rapidly spreading infectious disease that infects humans faster. Therefore, efficient diagnostic systems may accurately identify infected patients and thus help control their spread. In this regard, a new two-stage analysis framework is developed to analyze minute irregularities of COVID-19 infection. A novel detection Convolutional Neural Network (CNN), STM-BRNet, is developed that incorporates the Split-Transform-Merge (STM) block and channel boosting (CB) to identify COVID-19 infected CT slices in the first stage. Each STM block extracts boundary and region-smoothing-specific features for COVID-19 infection detection. Moreover, the various boosted channels are obtained by introducing the new CB and Transfer Learning (TL) concept in STM blocks to capture small illumination and texture variations of COVID-19-specific images. The COVID-19 CTs are provided with new SA-CB-BRSeg segmentation CNN for delineating infection in images in the second stage. SA-CB-BRSeg methodically utilized smoothening and heterogeneous operations in the encoder and decoder to capture simultaneously COVID-19 specific patterns that are region homogeneity, texture variation, and boundaries. Additionally, the new CB concept is introduced in the decoder of SA-CB-BRSeg by combining additional channels using TL to learn the low contrast region. The proposed STM-BRNet and SA-CB-BRSeg yield considerable achievement in accuracy: 98.01 %, Recall: 98.12%, F-score: 98.11%, and Dice Similarity: 96.396%, IOU: 98.845 % for the COVID-19 infectious region, respectively. The proposed two-stage framework significantly increased performance compared to single-phase and other reported systems and reduced the burden on the radiologists. Comment: 26 Pages, 11 Figures, 6 Tables. arXiv admin note: text overlap with arXiv:2209.10963
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
Subject code	004
Publishing date	2023-02-06
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Recent Progress on Melatonin-Induced Salinity Tolerance in Plants

İlkay Yavaş / Saddam Hussain

Turkish Journal of Agriculture: Food Science and Technology, Vol 10, Iss 8, Pp 1447-

An Overview

2022 Volume 1454

Abstract: In this context, it is necessary to select and develop salt-tolerant genotypes that can grow in salty soils and have high yields, and formulate strategies which may enhance the plant survival under salinity stress. Melatonin (N-acetyl-5-methoxytryptamine) ...

Abstract	In this context, it is necessary to select and develop salt-tolerant genotypes that can grow in salty soils and have high yields, and formulate strategies which may enhance the plant survival under salinity stress. Melatonin (N-acetyl-5-methoxytryptamine) is an important biological hormone that provides resistance to abiotic stress conditions and can be secreted by plants. Melatonin concentration in plants varies depending on genotype, temperature and growth period. Increase in melatonin concentration is associated with increased SNAT and HIOMAT/ASMT enzyme activity. It plays an important role in gibberellic acid and abscisic acid biosynthesis during the germination and provides plant growth and development. Exogenous application of melatonin significantly alleviates chlorophyll degradation and stomatal closure caused by salt stress, improves photosynthesis and enhances plants' salt tolerance. Besides it significantly reduces the harmful effects of salinity by regulating plant physiology, improving plant morphology, photosynthesis and activities of antioxidant enzymes. The present review discusses the recent studies on the effect of melatonin on plant growth and physiology against salt stress that have important impacts on plant growth and development have been given according to the findings of various researches. It also highlights the mechanim/s of melatonin-induced salinity stress tolerance in plants.
Keywords	antioxidant enzymes ; chlorophyll ; melatonin ; photosynthesis ; salt stress ; Agriculture ; S ; Agriculture (General) ; S1-972
Subject code	580
Language	English
Publishing date	2022-08-01T00:00:00Z
Publisher	Turkish Science and Technology Publishing (TURSTEP)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Malaria Parasitic Detection using a New Deep Boosted and Ensemble Learning Framework

Khan, Saddam Hussain

2022

Abstract: Malaria is a potentially fatal plasmodium parasite injected by female anopheles mosquitoes that infect red blood cells and millions worldwide yearly. However, specialists' manual screening in clinical practice is laborious and prone to error. Therefore, ... ...

Abstract	Malaria is a potentially fatal plasmodium parasite injected by female anopheles mosquitoes that infect red blood cells and millions worldwide yearly. However, specialists' manual screening in clinical practice is laborious and prone to error. Therefore, a novel Deep Boosted and Ensemble Learning (DBEL) framework, comprising the stacking of new Boosted-BR-STM convolutional neural networks (CNN) and the ensemble ML classifiers, is developed to screen malaria parasite images. The proposed Boosted-BR-STM is based on a new dilated-convolutional block-based split transform merge (STM) and feature-map Squeezing-Boosting (SB) ideas. Moreover, the new STM block uses regional and boundary operations to learn the malaria parasite's homogeneity, heterogeneity, and boundary with patterns. Furthermore, the diverse boosted channels are attained by employing Transfer Learning-based new feature-map SB in STM blocks at the abstract, medium, and conclusion levels to learn minute intensity and texture variation of the parasitic pattern. The proposed DBEL framework implicates the stacking of prominent and diverse boosted channels and provides the generated discriminative features of the developed Boosted-BR-STM to the ensemble of ML classifiers. The proposed framework improves the discrimination ability and generalization of ensemble learning. Moreover, the deep feature spaces of the developed Boosted-BR-STM and customized CNNs are fed into ML classifiers for comparative analysis. The proposed DBEL framework outperforms the existing techniques on the NIH malaria dataset that are enhanced using discrete wavelet transform to enrich feature space. The proposed DBEL framework achieved Accuracy (98.50%), Sensitivity (0.9920), F-score (0.9850), and AUC (0.997), which suggest it to be utilized for malaria parasite screening. Comment: 26 pages, 10 figures, 9 Tables
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
Subject code	006
Publishing date	2022-12-05
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Biotransformation of toxic lignin and aromatic compounds of lignocellulosic feedstock into eco-friendly biopolymers by Pseudomonas putida KT2440.

Mohammad, Saddam Hussain / Bhukya, Bhima

Bioresource technology

2022 Volume 363, Page(s) 128001

Abstract: Lignin and its derivatives are the most neglected compounds in bio-processing industry due to their toxic and recalcitrant nature. Considering this, the present study aimed at valorizing these toxic compounds by employing Pseudomonas putida KT2440. ... ...

Abstract	Lignin and its derivatives are the most neglected compounds in bio-processing industry due to their toxic and recalcitrant nature. Considering this, the present study aimed at valorizing these toxic compounds by employing Pseudomonas putida KT2440. Acclimatization resulted in improved tolerance with considerable lag phase reduction and aromatics degradation. Glucose as co-substrate enhanced growth and degradation in the toxic environment. The strain was able to degrade 30 % (1.60 g·L
MeSH term(s)	Benzoates/metabolism ; Biotransformation ; Catechols ; Glucose/metabolism ; Lignin/chemistry ; Organic Chemicals/metabolism ; Palmitic Acid/metabolism ; Phenols/metabolism ; Polyhydroxyalkanoates/metabolism ; Pseudomonas putida/metabolism ; Spectroscopy, Fourier Transform Infrared
Chemical Substances	Benzoates ; Catechols ; Organic Chemicals ; Phenols ; Polyhydroxyalkanoates ; lignocellulose (11132-73-3) ; Palmitic Acid (2V16EO95H1) ; Lignin (9005-53-2) ; Glucose (IY9XDZ35W2)
Language	English
Publishing date	2022-09-20
Publishing country	England
Document type	Journal Article
ZDB-ID	1065195-0
ISSN	1873-2976 ; 0960-8524
ISSN (online)	1873-2976
ISSN	0960-8524
DOI	10.1016/j.biortech.2022.128001
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Article ; Online: Biotransformation of toxic lignin and aromatic compounds of lignocellulosic feedstock into eco-friendly biopolymers by Pseudomonas putida KT2440

Mohammad, Saddam Hussain / Bhukya, Bhima

Bioresource Technology. 2022 Nov., v. 363 p.128001-

2022

Abstract	Lignin and its derivatives are the most neglected compounds in bio-processing industry due to their toxic and recalcitrant nature. Considering this, the present study aimed at valorizing these toxic compounds by employing Pseudomonas putida KT2440. Acclimatization resulted in improved tolerance with considerable lag phase reduction and aromatics degradation. Glucose as co-substrate enhanced growth and degradation in the toxic environment. The strain was able to degrade 30 % (1.60 g·L⁻¹) lignin, 45 mM benzoate, 40 mM p-coumarate, 35 mM ferulate, 10 mM phenol, 10 mM pyrocatechol and 8 mM aromatics mixture. The strain synthesized biopolymers using these compounds under feast and famine conditions. Characterization using GC–MS, FT-IR, H¹ NMR revealed them to be Polyhydroxyalkanoate (PHA) heteropolymers. All the analyzed PHAs contained versatile monomers with Hexadecanoic acid being the major one. This is a novel attempt towards lignin and aromatics degradation coupled with biopolymers synthesis without any genetic manipulation of the strain.
Keywords	Pseudomonas putida ; acclimation ; bioprocessing ; biotransformation ; feedstocks ; genetic engineering ; glucose ; industry ; lignin ; lignocellulose ; palmitic acid ; phenol ; polyhydroxyalkanoates ; pyrocatechol ; toxicity ; Biotrasformation ; GC–MS ; Biopolymers
Language	English
Dates of publication	2022-11
Publishing place	Elsevier Ltd
Document type	Article ; Online
ZDB-ID	1065195-0
ISSN	1873-2976 ; 0960-8524
ISSN (online)	1873-2976
ISSN	0960-8524
DOI	10.1016/j.biortech.2022.128001
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: An Efficient Optimized DenseNet Model for Aspect-Based Multi-Label Classification

Nasir Ayub / Tayyaba / Saddam Hussain / Syed Sajid Ullah / Jawaid Iqbal

Algorithms, Vol 16, Iss 12, p

2023 Volume 548

Abstract: Sentiment analysis holds great importance within the domain of natural language processing as it examines both the expressed and underlying emotions conveyed through review content. Furthermore, researchers have discovered that relying solely on the ... ...

Abstract	Sentiment analysis holds great importance within the domain of natural language processing as it examines both the expressed and underlying emotions conveyed through review content. Furthermore, researchers have discovered that relying solely on the overall sentiment derived from the textual content is inadequate. Consequently, sentiment analysis was developed to extract nuanced expressions from textual information. One of the challenges in this field is effectively extracting emotional elements using multi-label data that covers various aspects. This article presents a novel approach called the Ensemble of DenseNet based on Aquila Optimizer (EDAO). EDAO is specifically designed to enhance the precision and diversity of multi-label learners. Unlike traditional multi-label methods, EDAO strongly emphasizes improving model diversity and accuracy in multi-label scenarios. To evaluate the effectiveness of our approach, we conducted experiments on seven distinct datasets, including emotions, hotels, movies, proteins, automobiles, medical, news, and birds. Our initial strategy involves establishing a preprocessing mechanism to obtain precise and refined data. Subsequently, we used the Vader tool with Bag of Words (BoW) for feature extraction. In the third stage, we created word associations using the word2vec method. The improved data were also used to train and test the DenseNet model, which was fine-tuned using the Aquila Optimizer (AO). On the news, emotion, auto, bird, movie, hotel, protein, and medical datasets, utilizing the aspect-based multi-labeling technique, we achieved accuracy rates of 95%, 97%, and 96%, respectively, with DenseNet-AO. Our proposed model demonstrates that EDAO outperforms other standard methods across various multi-label datasets with different dimensions. The implemented strategy has been rigorously validated through experimental results, showcasing its effectiveness compared to existing benchmark approaches.
Keywords	classification ; multi-labeling ; natural language processing ; deep learning ; optimization method ; sentiment analysis ; Industrial engineering. Management engineering ; T55.4-60.8 ; Electronic computers. Computer science ; QA75.5-76.95
Subject code	006
Language	English
Publishing date	2023-11-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Heavy Metal Toxicity in Plants

İlkay Yavaş / Shafaqat Ali / Zohaib Abbas / Saddam Hussain

Turkish Journal of Agriculture: Food Science and Technology, Vol 10, Iss 8, Pp 1468-

An Overview on Tolerance Mechanisms and Management Strategies

2022 Volume 1481

Abstract: Heavy metals are one of the factors that pollute the environment and significantly affect soil fertility, plant physiology, development, and productivity. The tolerance of plants to toxicity depends on the species and tissue, element type, and duration ... ...

Abstract	Heavy metals are one of the factors that pollute the environment and significantly affect soil fertility, plant physiology, development, and productivity. The tolerance of plants to toxicity depends on the species and tissue, element type, and duration of exposure to stress. Some special signal molecules such as nitric oxide (NO), hydrogen peroxide (H2O2), beneficial ions, hyperaccumulating plants, stress hormones, nanoparticles, organic compounds, and microbial applications can be recommended to alleviate the stress effects caused by toxic heavy metals in plants. Induction of other promising techniques like seed priming, active involvement of plant growth regulator, use of osmoprotectants, successful plant microbes’ crosstalk and recent utilization of nanoparticles are worth using strategies in mitigation of heavy metal stress in plants. These practices effectively regulate the activities of antioxidant enzymes for the alleviation of stress in plants, creditably improving the plant tolerance via preserving cell homeostasis and amending the adversative effects of heavy metal stress in plants. These inventive strategies offer an enriched understanding of how to boost crop productivity under heavy metal stress in order to decrease the risk to global food security.
Keywords	chelation ions ; heavy metal stress ; stress tolerance ; seed priming ; plant microbes ; Agriculture ; S ; Agriculture (General) ; S1-972
Subject code	580
Language	English
Publishing date	2022-08-01T00:00:00Z
Publisher	Turkish Science and Technology Publishing (TURSTEP)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Resistant anemia in a kidney transplant recipient: Pure red cell aplasia due to parvovirus B19 infection.

Choudry, Hassan / Chattah, Fateh / Shalla, Hilal / Ghulam, Farooq / Abbasi, Saddam Hussain / Jesus-Silva, Jorge

Qatar medical journal

2024 Volume 2024, Issue 1, Page(s) 8

Abstract: Anemia in kidney transplant recipients can stem from a diverse array of etiologies, including dietary deficiencies, inflammatory processes, allograft dysfunction, as well as viral and bacterial infections. We present a case of refractory anemia in a 49- ... ...

Abstract	Anemia in kidney transplant recipients can stem from a diverse array of etiologies, including dietary deficiencies, inflammatory processes, allograft dysfunction, as well as viral and bacterial infections. We present a case of refractory anemia in a 49-year-old male patient occurring within the initial month following a kidney transplant, which persisted despite numerous transfusions, posing a formidable challenge. The patient was maintained on the standard immunosuppressant regimen-Tacrolimus, Mycophenolate, and Prednisolone. Diagnostic evaluations eliminated well-established causes such as dietary deficiencies, gastrointestinal losses, and prevalent infections. Subsequently, after viral PCR testing, a diagnosis of Pure Red Cell Aplasia (PRCA) due to infection with parvovirus B19 was made. Although the patient had a reduction in the immunosuppression drugs and received a course of Intravenous Immunoglobulins (IVIG) on two separate occasions spanning two months, the anemia relapsed. Subsequently, after an additional dose of IVIG with further modification and reduction of the immunosuppressant regimen, including stopping the mycophenolate and switching tacrolimus with cyclosporine, the patient ultimately achieved successful resolution of his symptoms and a significant decrease in viral load. Our case highlights the significance of unconventional etiologies when confronted with anemia in the setting of kidney transplantation. Furthermore, it also provides further insights into therapeutic avenues for addressing PRCA in kidney transplant recipients.
Language	English
Publishing date	2024-02-10
Publishing country	Qatar
Document type	Case Reports
ZDB-ID	3031075-1
ISSN	2227-0426 ; 0253-8253
ISSN (online)	2227-0426
ISSN	0253-8253
DOI	10.5339/qmj.2024.8
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Book ; Online: A New Deep Boosted CNN and Ensemble Learning based IoT Malware Detection

Khan, Saddam Hussain / Ullah, Wasi

2022

Abstract: Security issues are threatened in various types of networks, especially in the Internet of Things (IoT) environment that requires early detection. IoT is the network of real-time devices like home automation systems and can be controlled by open-source ... ...

Abstract	Security issues are threatened in various types of networks, especially in the Internet of Things (IoT) environment that requires early detection. IoT is the network of real-time devices like home automation systems and can be controlled by open-source android devices, which can be an open ground for attackers. Attackers can access the network credentials, initiate a different kind of security breach, and compromises network control. Therefore, timely detecting the increasing number of sophisticated malware attacks is the challenge to ensure the credibility of network protection. In this regard, we have developed a new malware detection framework, Deep Squeezed-Boosted and Ensemble Learning (DSBEL), comprised of novel Squeezed-Boosted Boundary-Region Split-Transform-Merge (SB-BR-STM) CNN and ensemble learning. The proposed STM block employs multi-path dilated convolutional, Boundary, and regional operations to capture the homogenous and heterogeneous global malicious patterns. Moreover, diverse feature maps are achieved using transfer learning and multi-path-based squeezing and boosting at initial and final levels to learn minute pattern variations. Finally, the boosted discriminative features are extracted from the developed deep SB-BR-STM CNN and provided to the ensemble classifiers (SVM, MLP, and AdabooSTM1) to improve the hybrid learning generalization. The performance analysis of the proposed DSBEL framework and SB-BR-STM CNN against the existing techniques have been evaluated by the IOT_Malware dataset on standard performance measures. Evaluation results show progressive performance as 98.50% accuracy, 97.12% F1-Score, 91.91% MCC, 95.97 % Recall, and 98.42 % Precision. The proposed malware analysis framework is robust and helpful for the timely detection of malicious activity and suggests future strategies Comment: 20 pages, 10 figures, 6 tables; Corresponding saddamhkhan@ueas.edu.pk
Keywords	Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Artificial Intelligence
Subject code	006
Publishing date	2022-12-15
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Brain Tumor MRI Classification using a Novel Deep Residual and Regional CNN

Zahoor, Mirza Mumtaz / Khan, Saddam Hussain

2022

Abstract: Brain tumor classification is crucial for clinical analysis and an effective treatment plan to cure patients. Deep learning models help radiologists to accurately and efficiently analyze tumors without manual intervention. However, brain tumor analysis ... ...

Abstract	Brain tumor classification is crucial for clinical analysis and an effective treatment plan to cure patients. Deep learning models help radiologists to accurately and efficiently analyze tumors without manual intervention. However, brain tumor analysis is challenging because of its complex structure, texture, size, location, and appearance. Therefore, a novel deep residual and regional-based Res-BRNet Convolutional Neural Network (CNN) is developed for effective brain tumor (Magnetic Resonance Imaging) MRI classification. The developed Res-BRNet employed Regional and boundary-based operations in a systematic order within the modified spatial and residual blocks. Moreover, the spatial block extract homogeneity and boundary-defined features at the abstract level. Furthermore, the residual blocks employed at the target level significantly learn local and global texture variations of different classes of brain tumors. The efficiency of the developed Res-BRNet is evaluated on a standard dataset; collected from Kaggle and Figshare containing various tumor categories, including meningioma, glioma, pituitary, and healthy images. Experiments prove that the developed Res-BRNet outperforms the standard CNN models and attained excellent performances (accuracy: 98.22%, sensitivity: 0.9811, F-score: 0.9841, and precision: 0.9822) on challenging datasets. Additionally, the performance of the proposed Res-BRNet indicates a strong potential for medical image-based disease analyses. Comment: 21 pages, 11 figures, 4 tables
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
Subject code	006
Publishing date	2022-11-29
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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