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Asian pacific Journal of Reproduction 2023年第1期12卷 10-15页

作者： Muhammed Asif Asha S.Vijay Maheshwari syed fyzullah Usha Rani Swathi R Damodara Gowda KM GarbhaGudi Institute of Reproductive Health and Research(GGIRHR) HanumanthnagarBangaloreKarnataka-560004India Nitte(Deemed to be University) KS Hegde Medical AcademyDepartment of PhysiologyMangalore-575018KarnatakaIndia

Objective:To investigate the association between age and semen parameters among male partners of subfertile couples.Methods:This retrospective study analyzed the semen of 1523 infertile men aged 26 to 50 years.Data were extracted from GarbhaGudi IVF Centre database from January 2019 to September 2020.The basic semen parameters were interpreted according to the WHO manual 2021,6th edition.Semen parameters in different age groups were compared.Results:Total and progressive motile sperms were significantly higher in the age group of 26-30 years compared to other age groups(P46 years(P=0.001).However,sperm head defects,neck and midpiece defects,tail defects,and cytoplasmic droplets showed statistically insignificant difference in all the age groups(P>0.05).Semen viscosity showed no statistical difference in all the age groups compared to the reference age group of 26 to 30 years.Conclusions:Higher age can lead to a significant decrease in normal sperms and motility in subfertile men.Hence,male partner age should be considered as one of the major determining factors for reproductive outcomes.

关键词： Age Semen analysis Sperm count Sperm morphology Motility Spermatozoa Infertility Male

A Framework to Assess Possible Sources of Stakeholder Exploitation:A Developing Country Case

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International Journal of Disaster Risk Science 2024年第1期15卷 32-44页

作者： Kamran Shafique syed Shams Tapan Sarker School of Business University of Southern QueenslandSpringfeldQLD 4300Australia Business School University of QueenslandSt LuciaQLD 4072Australia

Solving complex post-disaster reconstruction challenges requires the altruistic involvement of heterogeneous stakeholder groups.However,small,more organized groups,such as government parastatals,private developers,and contractors often exploit large,unorganized groups,such as affected communities,leaving them more vulnerable to future disasters.Based on data collected from a case study in Pakistan,this study proposed a framework to assess,anticipate,and mitigate the exploitation of vulnerable stakeholders in post-disaster reconstruction projects.The framework draws on influential management theories and utilizes reciprocal relationships between stakeholder attributes(power,legitimacy,and urgency),participation,and exploitation.The study also argued for non-binary treatment of stakeholder attributes.The framework will allow practitioners to address issues around the exploitation of stakeholder interests in future post-disaster reconstruction projects.

关键词： Collective action Pakistan Post-disaster reconstruction-Stakeholder participation Stakeholder exploitation

Automatic Finding of Brain-Tumour Group Using CNN Segmentation and Moth-Flame-Algorithm,Selected Deep and Handcrafted Features

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Computers, Materials & Continua 2024年第5期79卷 2585-2608页

作者： Imad Saud Al Naimi syed Alwee Aljunid syed Junid Muhammad lmran Ahmad K.Suresh Manic Faculty of Electronic Engineering Technology University Malaysia Perils(UniMAP)Pauh Putra CampusArauPerlis02600Malaysia Department of Electrical and Communication Engineering National University of Science and TechnologyAL HailAL Seeb130Sultanate of Oman Department of Research and Innovation Saveetha School of EngineeringSaveetha Institute of Medical and Technical Sciences(SIMATS)ChennaiTamiNadu602105India

Augmentation of abnormal cells in the brain causes brain tumor(BT),and early screening and treatmentwill reduce its harshness in patients.BT’s clinical level screening is usually performed with Magnetic Resonance Imaging(MRI)due to its multi-modality nature.The overall aims of the study is to introduce,test and verify an advanced image processing technique with algorithms to automatically extract tumour sections from brain MRI scans,facilitating improved accuracy.The research intends to devise a reliable framework for detecting the BT region in the twodimensional(2D)MRI slice,and identifying its class with improved accuracy.The methodology for the devised framework comprises the phases of:(i)Collection and resizing of images,(ii)Implementation and Segmentation of Convolutional Neural Network(CNN),(iii)Deep feature extraction,(iv)Handcrafted feature extraction,(v)Moth-Flame-Algorithm(MFA)supported feature reduction,and(vi)Performance evaluation.This study utilized clinical-grade brain MRI of BRATS and TCIA datasets for the investigation.This framework segments detected the glioma(low/high grade)and glioblastoma class BT.This work helped to get a segmentation accuracy of over 98%with VGG-UNet and a classification accuracy of over 98%with the VGG16 scheme.This study has confirmed that the implemented framework is very efficient in detecting the BT in MRI slices with/without the skull section.

关键词： Brain tumour VGG-UNet VGG16 moth-flame-algorithm classification

An efficient permutation approach for SbPN-based symmetric block ciphers

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Cybersecurity 2024年第2期7卷 113-130页

作者： Mir Nazish M.Tariq Banday Insha syed Sheena Banday Department of Electronics and Instrumentation Technology University ofKashmirSrinagar 190006India

It is challenging to devise lightweight cryptographic primitives efficient in both hardware and software that can provide an optimum level of security to diverse Internet of Things applications running on low-end constrained devices.Therefore,an effcient hardware design approach that requires some specific hardware resource may not be effcient if implemented in software.Substitution bit Permutation Network based ciphers such as PRESENT and GIFT are effcient,lightweight cryptographic hardware design approaches.These ciphers introduce confusion and diffu-sion by employing a 4×4 static substitution box and bit permutations.The bit-wise permutation is realised by sim-ple rerouting,which is most cost-effective to implement in hardware,resulting in negligible power consumption.However,this method is highly resource-consuming in software,particularly for large block-sized ciphers,with each single-bit permutation requiring multiple sub-operations.This paper proposes a novel software-based design approach for permutation operation in Substitution bit Permutation Network based ciphers using a bit-banding fea-ture.The conventional permutation using bit rotation and the proposed approach have been implemented,analysed and compared for GIFT and PRESENT ciphers on ARM Cortex-M3-based LPC1768 development platform with KEIL MDK used as an Integrated Development Environment.The real-time performance comparison between conven-tional and the proposed approaches in terms of memory(RAM/ROM)footprint,power,energy and execution time has been carried out using ULINKpro and ULINKplus debug adapters for various code and speed optimisation sce-narios.The proposed approach substantially reduces execution time,energy and power consumption for both PRE-SENT and GIFT ciphers,thus demonstrating the effciency of the proposed method for Substitution bit Permutation Network based symmetric block ciphers.

关键词： Lightweight cryptography PRESENT GIFT SbPN Cortex-M LPC1768 Bit-band memory

Disease clearance in ulcerative colitis:A new therapeutic target for the future

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World Journal of Gastroenterology 2024年第13期30卷 1801-1809页

作者： syed Adeel Hassan Neeraj Kapur Fahad Sheikh Anam Fahad Somia Jamal Division of Digestive Disease and Nutrition University of KentuckyLexingtonKY 40536United States Department of Pathology and Laboratory Medicine Montefiore Medical CenterAlbert Einstein College of MedicineNew YorkNY 10461United States Division of Primary Care Essen HealthcareNew YorkNY 10457United States Department of Internal Medicine Karachi Medical and Dental CollegeKarachi 74700SindhPakistan

Advancements in murine modeling systems for ulcerative colitis have diversified our understanding of the pathophysiological factors involved in disease onset and progression.This has fueled the identification of molecular targets,resulting in a rapidly expanding therapeutic armamentarium.Subsequently,management strategies have evolved from symptomatic resolution to well-defined objective endpoints,including clinical remission,endoscopic remission and mucosal healing.While the incorporation of these assessment modalities has permitted targeted intervention in the context of a natural disease history and the prevention of complications,studies have consistently depicted discrepancies associated with ascertaining disease status through clinical and endoscopic measures.Current recommendations lack consideration of histological healing.The simultaneous achievement of clinical,endoscopic,and histologic remission has not been fully investigated.This has laid the groundwork for a novel therapeutic outcome termed disease clearance(DC).This article summarizes the concept of DC and its current evidence.

关键词： Inflammatory bowel disease Ulcerative colitis Clinical remission Endoscopic remission Histological remission Mucosal healing Disease clearance

Conservation laws,Lie symmetries,self adjointness,and soliton solutions for the Selkov–Schnakenberg system

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Communications in Theoretical Physics 2024年第2期76卷 29-44页

作者： Kashif Ali Aly R Seadawy syed T R Rizvi Noor Aziz Department of Mathematics COMSATS University IslamabadLahore CampusPakistan Mathematics Department Faculty of ScienceTaibah UniversityAl-Madinah Al-Munawarah41411Saudi Arabia

In this article,we explore the famous Selkov–Schnakenberg(SS)system of coupled nonlinear partial differential equations(PDEs)for Lie symmetry analysis,self-adjointness,and conservation laws.Moreover,miscellaneous soliton solutions like dark,bright,periodic,rational,Jacobian elliptic function,Weierstrass elliptic function,and hyperbolic solutions of the SS system will be achieved by a well-known technique called sub-ordinary differential equations.All these results are displayed graphically by 3D,2D,and contour plots.

关键词： Selkov-Schnakenberg system Lie symmetry analysis conservation laws adjointness integrability

Safety of tranexamic acid in surgically treated isolated spine trauma

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World Journal of Orthopedics 2024年第4期15卷 346-354页

作者： Wajiha Zahra Sandeep Krishan Nayar Ashwin Bhadresha Vinay Jasani syed Aftab Trauma and Orthopedics Department University Hospital of North Midlands NHS TrustStoke-on-Trent ST46QGUnited Kingdom Trauma and Orthopedics Department Royal London HospitalBarts Health InstituteLondon E11BBUnited Kingdom Craniospinal Services University Hospital of North Midlands NHS TrustStokeon-Trent ST46QGUnited Kingdom Spine Department Royal London HospitalBarts Health InstituteLondon E11BBUnited Kingdom

BACKGROUND Tranexamic acid(TXA),a synthetic antifibrinolytic drug,effectively reduces blood loss by inhibiting plasmin-induced fibrin breakdown.This is the first study in the United Kingdom to investigate the effectiveness of TXA in the surgical management of isolated spine trauma.AIM To assess the safety of TXA in isolated spine trauma.The primary and secondary outcomes are to assess the rate of thromboembolic events and to evaluate blood loss and the incidence of blood transfusion,respectively.METHODS This prospective observational study included patients aged≥17 years with isolated spine trauma requiring surgical intervention over a 6-month period at two major trauma centers in the United Kingdom.RESULTS We identified 67 patients:26(39%)and 41(61%)received and did not receive TXA,respectively.Both groups were matched in terms of age,gender,American Society of Anesthesiologists grade,and mechanism of injury.A higher proportion of patients who received TXA had a subaxial cervical spine injury classification or thoracolumbar injury classification score>4(74%vs 56%).All patients in the TXA group underwent an open approach with a mean of 5 spinal levels involved and an average operative time of 203 min,compared with 24 patients(58%)in the non TXA group who underwent an open approach with an average of 3 spinal levels involved and a mean operative time of 159 min.Among patients who received TXA,blood loss was<150 and 150–300 mL in 8(31%)and 15(58%)patients,respectively.There were no cases of thromboembolic events in any patient who received TXA.CONCLUSION Our study demonstrated that TXA is safe for isolated spine trauma.It is challenging to determine whether TXA effectively reduces blood loss because most surgeons prefer TXA for open or multilevel cases.Further,larger studies are necessary to explore the rate,dosage,and mode of administration of TXA.

关键词： Tranexamic acid Infection Trauma Thromboembolic disease Minimally invasive Percutaneous

Enhancing Skin Cancer Diagnosis with Deep Learning:A Hybrid CNN-RNN Approach

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Computers, Materials & Continua 2024年第4期79卷 1497-1519页

作者： syeda Shamaila Zareen Guangmin Sun Mahwish Kundi syed Furqan Qadri Salman Qadri Faculty of Information Technology Beijing University of TechnologyBeijing100124China Computer Science International Engineering Collage Maynooth UniversityKildareW23 F2H6Irland Research Center for Data Hub and Security Zhejiang LabHangzhou311121China Computer Science Department MNS University of AgricultureMultan59220Pakistan

Skin cancer diagnosis is difficult due to lesion presentation variability. Conventionalmethods struggle to manuallyextract features and capture lesions spatial and temporal variations. This study introduces a deep learning-basedConvolutional and Recurrent Neural Network (CNN-RNN) model with a ResNet-50 architecture which usedas the feature extractor to enhance skin cancer classification. Leveraging synergistic spatial feature extractionand temporal sequence learning, the model demonstrates robust performance on a dataset of 9000 skin lesionphotos from nine cancer types. Using pre-trained ResNet-50 for spatial data extraction and Long Short-TermMemory (LSTM) for temporal dependencies, the model achieves a high average recognition accuracy, surpassingprevious methods. The comprehensive evaluation, including accuracy, precision, recall, and F1-score, underscoresthe model’s competence in categorizing skin cancer types. This research contributes a sophisticated model andvaluable guidance for deep learning-based diagnostics, also this model excels in overcoming spatial and temporalcomplexities, offering a sophisticated solution for dermatological diagnostics research.

关键词： Skin cancer classification deep learning Convolutional Neural Network(CNN) RNN ResNet-50

Model Agnostic Meta-Learning(MAML)-Based Ensemble Model for Accurate Detection of Wheat Diseases Using Vision Transformer and Graph Neural Networks

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Computers, Materials & Continua 2024年第5期79卷 2795-2811页

作者： Yasir Maqsood syed Muhammad Usman Musaed Alhussein Khursheed Aurangzeb Shehzad Khalid Muhammad Zubair Department of Creative Technologies Faculty of Computing and Artificial IntelligenceAir UniversityIslamabad44000Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Department of Computer Engineering Bahria School of Engineering and Applied SciencesBahria UniversityIslamabad44000Pakistan Laboratory for Neuro&Psychophysiology KU LeuvenLeuven3000Belgium

Wheat is a critical crop,extensively consumed worldwide,and its production enhancement is essential to meet escalating demand.The presence of diseases like stem rust,leaf rust,yellow rust,and tan spot significantly diminishes wheat yield,making the early and precise identification of these diseases vital for effective disease management.With advancements in deep learning algorithms,researchers have proposed many methods for the automated detection of disease pathogens;however,accurately detectingmultiple disease pathogens simultaneously remains a challenge.This challenge arises due to the scarcity of RGB images for multiple diseases,class imbalance in existing public datasets,and the difficulty in extracting features that discriminate between multiple classes of disease pathogens.In this research,a novel method is proposed based on Transfer Generative Adversarial Networks for augmenting existing data,thereby overcoming the problems of class imbalance and data scarcity.This study proposes a customized architecture of Vision Transformers(ViT),where the feature vector is obtained by concatenating features extracted from the custom ViT and Graph Neural Networks.This paper also proposes a Model AgnosticMeta Learning(MAML)based ensemble classifier for accurate classification.The proposedmodel,validated on public datasets for wheat disease pathogen classification,achieved a test accuracy of 99.20%and an F1-score of 97.95%.Compared with existing state-of-the-art methods,this proposed model outperforms in terms of accuracy,F1-score,and the number of disease pathogens detection.In future,more diseases can be included for detection along with some other modalities like pests and weed.

关键词： Wheat disease detection deep learning vision transformer graph neural network model agnostic meta learning

Deep Learning Approach for Hand Gesture Recognition:Applications in Deaf Communication and Healthcare

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Computers, Materials & Continua 2024年第1期78卷 127-144页

作者： Khursheed Aurangzeb Khalid Javeed Musaed Alhussein Imad Rida syed Irtaza Haider Anubha Parashar Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Kingdom of Saudi Arabia Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates Laboratory Biomechanics and Bioengineering University of Technology of CompiegneCompiegne60200France Department of Computer Science and Engineering Manipal University JaipurJaipur303007India

Hand gestures have been used as a significant mode of communication since the advent of human civilization.By facilitating human-computer interaction(HCI),hand gesture recognition(HGRoc)technology is crucial for seamless and error-free HCI.HGRoc technology is pivotal in healthcare and communication for the deaf community.Despite significant advancements in computer vision-based gesture recognition for language understanding,two considerable challenges persist in this field:(a)limited and common gestures are considered,(b)processing multiple channels of information across a network takes huge computational time during discriminative feature extraction.Therefore,a novel hand vision-based convolutional neural network(CNN)model named(HVCNNM)offers several benefits,notably enhanced accuracy,robustness to variations,real-time performance,reduced channels,and scalability.Additionally,these models can be optimized for real-time performance,learn from large amounts of data,and are scalable to handle complex recognition tasks for efficient human-computer interaction.The proposed model was evaluated on two challenging datasets,namely the Massey University Dataset(MUD)and the American Sign Language(ASL)Alphabet Dataset(ASLAD).On the MUD and ASLAD datasets,HVCNNM achieved a score of 99.23% and 99.00%,respectively.These results demonstrate the effectiveness of CNN as a promising HGRoc approach.The findings suggest that the proposed model have potential roles in applications such as sign language recognition,human-computer interaction,and robotics.

关键词： Computer vision deep learning gait recognition sign language recognition machine learning