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Multifaceted roles of extracellular RNAs in different diseases

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Military Medical Research 2023年第3期10卷 359-368页

作者： abdullah muhammad Sohail muhammad Babar Khawar Ali Afzal Ali Hassan Sara Shahzaman Ahmed Ali Molecular Medicine and Cancer Therapeutics Lab Department of ZoologyFaculty of SciencesUniversity of Central PunjabLahorePakistan Applied Molecular Biology and Biomedicine Lab Department of ZoologyUniversity of NarowalNarowalPakistan

Extracellular RNAs(exRNAs) are novel circulating factors that can be used as biomarkers in various diseases. Their unique and diverse kinds, as well as their role as biomarkers, make them significant biomarkers. There has been immense work carried out since the discovery of exRNAs in circulation and other biological fluids to catalog and determine whether exRNAs may be utilized as indicators for health and illness. In this review, we aim to understand the current state of exRNAs in relation to various diseases and their potential as biomarkers. We will also review current issues and challenges faced in using exRNAs, with clinical and lab trials, that can be used as viable markers for different diseases.

关键词： Extracellular RNAs(exRNAs) Extracellular vehicles(EVs) Cancer Biomarkers Exosomes

EfficientNet-Based Robust Recognition of Peach Plant Diseases in Field Images

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Computers, Materials & Continua 2022年第4期71卷 2073-2089页

作者： Haleem Farman Jamil Ahmad Bilal Jan Yasir Shahzad muhammad abdullah Atta Ullah Department of Computer Science Islamia College Peshawar25120Pakistan Department of Computer Science FATA University Kohat26100Pakistan Department of Computer Science University of Peshawar25120Pakistan Agriculture Research Institute MingoraSwat19130Pakistan

Plant diseases are a major cause of degraded fruit quality andyield losses. These losses can be significantly reduced with early detection ofdiseases to ensure their timely treatment, particularly in developing *** this regard, an expert system based on deep learning model where the expertknowledge, particularly the one acquired by plant pathologist, is recursivelylearned by the system and is applied using a smart phone application for use inthe target field environment, is being proposed. In this paper, a robust diseasedetection method is developed based on convolutional neural network (CNN),where its powerful features extraction capabilities are leveraged to detectdiseases in images of fruits and leaves. The features extraction pipelines ofseveral state-of-the-art pretrained networks are fine-tuned to achieve optimaldetection performance. A novel dataset is collected from peach orchards andextensively augmented using both label-preserving and non-label-preservingtransformations. The augmented dataset is used to study the effects of finetuning the pretrained networks’ feature extraction pipeline as opposed tokeeping the network parameters unchanged. The CNN models, particularlyEfficientNet exhibited superior performance on the target dataset once theirfeature extraction pipelines are fine-tuned. The optimal model is able toachieve 96.6% average accuracy, 90% sensitivity and precision, and 98%specificity on the test set of images.

关键词： Peach diseases EfficientNet data augmentation transfer learning

Unraveling the role of NiSnPH@OOH/CC perovskite hydroxide for efficient electrocatalytic oxidation of methanol to formate

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Nano Research 2024年第4期17卷 2388-2399页

作者： Jing Shao Yusheng Fang Xiaobing Wu muhammad Imran abdullah Youkun Tao College of Chemistry and Environmental Engineering Shenzhen UniversityShenzhen 518060China School of Science Harbin Institute of TechnologyShenzhen 518055China

The sluggish kinetics of oxygen evolution reaction(OER)is the key tailback for hydrogen production from the water *** OER with thermodynamically auspicious methanol oxidation reaction(MOR)can significantly boost the H_(2) and value-added products ***,it is currently challenging to achieve a synergistic manipulation of product selectivity and performance for MOR ***,we report NiSnPH@OOH/CC(CC=carbon cloth)perovskite hydroxide nanosphere as an efficient MOR electrocatalyst with high activity,stability,and selectivity towards methanol oxidation to formate.A surface amorphous layer of defect rich NiOOH was generated in operando by selective Sn leaching with stable perovskite hydroxide bulk structure,which mitigates the oxidative power and optimizes the local coordination environment of the active NiOOH *** situ Raman combined with electrochemical studies further confirm the key active species,NiOOH,generated in operando enhance the MOR and blocking the over oxidation of methanol to CO_(2).As a result,NiSnPH@OOH/CC effectively masks the OER and attains>99%selectivity with 100%Faradic efficiency for *** results of this study show the advances of NiSnPH@OOH/CC as an efficient electrocatalyst for MOR and also suggest its potential applications for various small organic molecules oxidation.

关键词： perovskite hydroxide small organic molecules nickel oxy(hydroxide) value-added products methanol oxidation reaction

A Novel Hybrid Ensemble Learning Approach for Enhancing Accuracy and Sustainability in Wind Power Forecasting

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Computers, Materials & Continua 2024年第5期79卷 3373-3395页

作者： Farhan Ullah Xuexia Zhang Mansoor Khan muhammad Abid abdullah Mohamed School of Electrical Engineering Southwest Jiaotong UniversityChengdu611756China College of Intelligent Manufacturing and Control Engineering Qilu Institute of TechnologyJinan250200China College of Aerospace and Civil Engineering Harbin Engineering UniversityHarbin150001China Research Centre Future University in EgyptNew Cairo11835Egypt

Accurate wind power forecasting is critical for system integration and stability as renewable energy reliance *** approaches frequently struggle with complex data and non-linear connections. This article presentsa novel approach for hybrid ensemble learning that is based on rigorous requirements engineering *** approach finds significant parameters influencing forecasting accuracy by evaluating real-time Modern-EraRetrospective Analysis for Research and Applications (MERRA2) data from several European Wind farms usingin-depth stakeholder research and requirements elicitation. Ensemble learning is used to develop a robust model,while a temporal convolutional network handles time-series complexities and data gaps. The ensemble-temporalneural network is enhanced by providing different input parameters including training layers, hidden and dropoutlayers along with activation and loss functions. The proposed framework is further analyzed by comparing stateof-the-art forecasting models in terms of Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE),respectively. The energy efficiency performance indicators showed that the proposed model demonstrates errorreduction percentages of approximately 16.67%, 28.57%, and 81.92% for MAE, and 38.46%, 17.65%, and 90.78%for RMSE for MERRAWind farms 1, 2, and 3, respectively, compared to other existingmethods. These quantitativeresults show the effectiveness of our proposed model with MAE values ranging from 0.0010 to 0.0156 and RMSEvalues ranging from 0.0014 to 0.0174. This work highlights the effectiveness of requirements engineering in windpower forecasting, leading to enhanced forecast accuracy and grid stability, ultimately paving the way for moresustainable energy solutions.

关键词： Ensemble learning machine learning real-time data analysis stakeholder analysis temporal convolutional network wind power forecasting

Optimization of Sentiment Analysis Using Teaching-Learning Based Algorithm

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Computers, Materials & Continua 2021年第11期69卷 1783-1799页

作者： abdullah muhammad Salwani abdullah Nor Samsiah Sani Center for Artificial Intelligence Technology Faculty of Information Science&TechnologyUniversiti KebangsaanKajang43000Malaysia

Feature selection and sentiment analysis are two common studies that are currently being conducted;consistent with the advancements in computing and growing the use of social *** dimensional or large feature sets is a key issue in sentiment analysis as it can decrease the accuracy of sentiment classification and make it difficult to obtain the optimal subset of the ***,most reviews from social media carry a lot of noise and irrelevant ***,this study proposes a new text-feature selection method that uses a combination of rough set theory(RST)and teaching-learning based optimization(TLBO),which is known as *** framework to develop the proposed RSTLBO includes numerous stages:(1)acquiring the standard datasets(user reviews of six major ***)which are used to validate search result feature selection methods,(2)preprocessing of the dataset using text processing *** involves applying text processing methods from natural language processing techniques,combined with linguistic processing techniques to produce high classification results,(3)employing the RSTLBO method,and(4)using the selected features from the previous process for sentiment classification using the Support Vector Machine(SVM)*** show an improvement in sentiment analysis when combining natural language processing with linguistic processing for text *** importantly,the proposed RSTLBO feature selection algorithm is able to produce an improved sentiment analysis.

关键词： Feature selection sentiment analysis rough set theory teachinglearning optimization algorithms text processing

Human Gait Recognition for Biometrics Application Based on Deep Learning Fusion Assisted Framework

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Computers, Materials & Continua 2024年第1期78卷 357-374页

作者： Ch Avais Hanif muhammad Ali Mughal muhammad Attique Khan Nouf abdullah Almujally Taerang Kim Jae-Hyuk Cha Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea

The demand for a non-contact biometric approach for candidate identification has grown over the past ten *** on the most important biometric application,human gait analysis is a significant research topic in computer *** have paid a lot of attention to gait recognition,specifically the identification of people based on their walking patterns,due to its potential to correctly identify people far *** recognition systems have been used in a variety of applications,including security,medical examinations,identity management,and access *** systems require a complex combination of technical,operational,and definitional *** employment of gait recognition techniques and technologies has produced a number of beneficial and well-liked *** proposes a novel deep learning-based framework for human gait classification in video *** framework’smain challenge is improving the accuracy of accuracy gait classification under varying conditions,such as carrying a bag and changing *** proposed method’s first step is selecting two pre-trained deep learningmodels and training fromscratch using deep transfer ***,deepmodels have been trained using static hyperparameters;however,the learning rate is calculated using the particle swarmoptimization(PSO)***,the best features are selected from both trained models using the Harris Hawks controlled Sine-Cosine optimization *** algorithm chooses the best features,combined in a novel correlation-based fusion ***,the fused best features are categorized using medium,bi-layer,and tri-layered neural *** the publicly accessible dataset known as the CASIA-B dataset,the experimental process of the suggested technique was carried out,and an improved accuracy of 94.14% was *** achieved accuracy of the proposed method is improved by the recent state-of-the-art techniques that show the significance of this wor

关键词： Gait recognition covariant factors biometric deep learning fusion feature selection

Enhanced Object Detection and Classification via Multi-Method Fusion

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Computers, Materials & Continua 2024年第5期79卷 3315-3331页

作者： muhammad Waqas Ahmed Nouf abdullah Almujally Abdulwahab Alazeb Asaad Algarni Jeongmin Park Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Engineering Tech University of Korea237 Sangidaehak-roSiheung-siGyeonggi-do15073South Korea

Advances in machine vision systems have revolutionized applications such as autonomous driving,robotic navigation,and augmented *** substantial progress,challenges persist,including dynamic backgrounds,occlusion,and limited labeled *** address these challenges,we introduce a comprehensive methodology toenhance image classification and object detection *** proposed approach involves the integration ofmultiple methods in a complementary *** process commences with the application of Gaussian filters tomitigate the impact of noise *** images are then processed for segmentation using Fuzzy C-Meanssegmentation in parallel with saliency mapping techniques to find the most prominent *** Binary RobustIndependent Elementary Features(BRIEF)characteristics are then extracted fromdata derived fromsaliency mapsand segmented *** precise object separation,Oriented FAST and Rotated BRIEF(ORB)algorithms *** Algorithms(GAs)are used to optimize Random Forest classifier parameters which lead toimproved *** method stands out due to its comprehensive approach,adeptly addressing challengessuch as changing backdrops,occlusion,and limited labeled data concurrently.A significant enhancement hasbeen achieved by integrating Genetic Algorithms(GAs)to precisely optimize *** minor adjustmentnot only boosts the uniqueness of our system but also amplifies its overall *** proposed methodologyhas demonstrated notable classification accuracies of 90.9%and 89.0%on the challenging Corel-1k and MSRCdatasets,***,detection accuracies of 87.2%and 86.6%have been *** ourmethod performed well in both datasets it may face difficulties in real-world data especially where datasets havehighly complex *** these limitations,GAintegration for parameter optimization shows a notablestrength in enhancing the overall adaptability and performance of our system.

关键词： BRIEF features saliency map fuzzy c-means object detection object recognition

Adaptation of Federated Explainable Artificial Intelligence for Efficient and Secure E-Healthcare Systems

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Computers, Materials & Continua 2024年第3期78卷 3413-3429页

作者： Rabia Abid muhammad Rizwan Abdulatif Alabdulatif abdullah Alnajim Meznah Alamro Mourade Azrour Department of Computer Science Kinnaird College for WomenLahorePakistan College of Science and Engineering University of DerbyDerbyDE221GBUK Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia STI Laboratory IDMS TeamFaculty of Sciences and TechniquesMoulay Ismail University of MeknèsMeknesMorocco

Explainable Artificial Intelligence(XAI)has an advanced feature to enhance the decision-making feature and improve the rule-based technique by using more advanced Machine Learning(ML)and Deep Learning(DL)based *** this paper,we chose e-healthcare systems for efficient decision-making and data classification,especially in data security,data handling,diagnostics,laboratories,and *** Machine Learning(FML)is a new and advanced technology that helps to maintain privacy for Personal Health Records(PHR)and handle a large amount of medical data *** this context,XAI,along with FML,increases efficiency and improves the security of e-healthcare *** experiments show efficient system performance by implementing a federated averaging algorithm on an open-source Federated Learning(FL)*** experimental evaluation demonstrates the accuracy rate by taking epochs size 5,batch size 16,and the number of clients 5,which shows a higher accuracy rate(19,104).We conclude the paper by discussing the existing gaps and future work in an e-healthcare system.

关键词： Artificial intelligence data privacy federated machine learning healthcare system security

Enhancing Security and Privacy in Distributed Face Recognition Systems through Blockchain and GAN Technologies

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Computers, Materials & Continua 2024年第5期79卷 2609-2623页

作者： muhammad Ahmad Nawaz Ul Ghani Kun She muhammad Arslan Rauf Shumaila Khan Javed Ali Khan Eman abdullah Aldakheel Doaa Sami Khafaga School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu611731China Department of Computer Science University of Science&TechnologyBannu28100Pakistan Department of Computer Science University of HertfordshireHatfieldAL109ABUK Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The use of privacy-enhanced facial recognition has increased in response to growing concerns about data securityand privacy in the digital age. This trend is spurred by rising demand for face recognition technology in a varietyof industries, including access control, law enforcement, surveillance, and internet communication. However,the growing usage of face recognition technology has created serious concerns about data monitoring and userprivacy preferences, especially in context-aware systems. In response to these problems, this study provides a novelframework that integrates sophisticated approaches such as Generative Adversarial Networks (GANs), Blockchain,and distributed computing to solve privacy concerns while maintaining exact face recognition. The framework’spainstaking design and execution strive to strike a compromise between precise face recognition and protectingpersonal data integrity in an increasingly interconnected environment. Using cutting-edge tools like Dlib for faceanalysis,Ray Cluster for distributed computing, and Blockchain for decentralized identity verification, the proposedsystem provides scalable and secure facial analysis while protecting user privacy. The study’s contributions includethe creation of a sustainable and scalable solution for privacy-aware face recognition, the implementation of flexibleprivacy computing approaches based on Blockchain networks, and the demonstration of higher performanceover previous methods. Specifically, the proposed StyleGAN model has an outstanding accuracy rate of 93.84%while processing high-resolution images from the CelebA-HQ dataset, beating other evaluated models such asProgressive GAN 90.27%, CycleGAN 89.80%, and MGAN 80.80%. With improvements in accuracy, speed, andprivacy protection, the framework has great promise for practical use in a variety of fields that need face recognitiontechnology. This study paves the way for future research in privacy-enhanced face recognition systems, emphasizingt

关键词： Facial recognition privacy protection blockchain GAN distributed systems