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Journal of Materials Science & Technology 2019年第8期35卷 1767-1778页

作者： Rita Maurya abdul rahim siddiqui Prvan Kumar Katiyar Kantesh Balani Materials Science and Engineering Indian Institute of Technology Kanpur Kanpur 208016 India Centre for Material Science & Engineering National Institute of Technology Hamirpur Hamirpur 177005 India China-Canada Institute for Advanced Surface & Interface Science and Engineering Jiangsu University of Technology Changzhou 213001 China

The mechanical,tribological and corrosion protection offered to Mg-9Li-7Al-1Sn and Mg-9Li-5Al-3Sn-1Zn alloys by the epoxy coating containing polyaniline/graphene(PANI/Gr)pigments is undertaken in the current work.PANI/Gr containing coatings were observed to be strongly adherent with a higher scratch hardness(Hs)and plowing hardness(Hp),i.e.Hsof 0.43 GPa,and Hpof 0.61 GPa,respectively when compared to that of neat epoxy coating(Hsof 0.17 GPa,and Hpof 0.40 GPa,respectively).Due to their higher Hsand Hpvalues,PANI/Gr based coatings displayed an enhanced wear resistance(Wear volume,Wv=4.53×10^-3 m^3)than that of neat epoxy coating(Wv=5.15×10^-3 m^3).The corrosion protection efficiency in corrosive environment of 3.5 wt%NaCl solution was obtained to be>99% for PANI/Gr containing coatings when compared to that of neat epoxy coating.The charge-transfer resistance(R(ct))of the PANI/Gr containing coatings were estimated to be>10^6 cm^2,which indicates their highly protective nature when compared to that of neat epoxy coating(R(ct)10^5 Ω cm^2).Hence,PANI/Gr containing coatings can be potentially used for wear resistance and corrosion protection applications in marine environments.

关键词： Mg-Li alloys Scratch hardness Plowing hardness Wear resistance Corrosion protection efficiency Charge transfer resistance

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Secure Transmission of Compressed Medical Image Sequences on Communication Networks Using Motion Vector Watermarking

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Computers, Materials & Continua 2024年第3期78卷 3283-3301页

作者： Rafi Ullah Mohd Hilmi bin Hasan Sultan Daud Khan Mussadiq abdul rahim Computer and Information Sciences Department Universiti Teknologi PETRONASBandar Seri Iskandar Perak32610Malaysia Department of Computer Science National University of TechnologyIslamabad46000Pakistan Interdisciplinary Research Center for Intelligent Secure Systems King Fahd University of Petroleum and MineralsDammam31261Saudi Arabia

Medical imaging plays a key role within modern hospital management systems for diagnostic purposes.Compression methodologies are extensively employed to mitigate storage demands and enhance transmission speed,all while upholding image quality.Moreover,an increasing number of hospitals are embracing cloud computing for patient data storage,necessitating meticulous scrutiny of server security and privacy protocols.Nevertheless,considering the widespread availability of multimedia tools,the preservation of digital data integrity surpasses the significance of compression alone.In response to this concern,we propose a secure storage and transmission solution for compressed medical image sequences,such as ultrasound images,utilizing a motion vector watermarking scheme.The watermark is generated employing an error-correcting code known as Bose-Chaudhuri-Hocquenghem(BCH)and is subsequently embedded into the compressed sequence via block-based motion vectors.In the process of watermark embedding,motion vectors are selected based on their magnitude and phase angle.When embedding watermarks,no specific spatial area,such as a region of interest(ROI),is used in the images.The embedding of watermark bits is dependent on motion vectors.Although reversible watermarking allows the restoration of the original image sequences,we use the irreversible watermarking method.The reason for this is that the use of reversible watermarks may impede the claims of ownership and legal rights.The restoration of original data or images may call into question ownership or other legal claims.The peak signal-to-noise ratio(PSNR)and structural similarity index(SSIM)serve as metrics for evaluating the watermarked image quality.Across all images,the PSNR value exceeds 46 dB,and the SSIM value exceeds 0.92.Experimental results substantiate the efficacy of the proposed technique in preserving data integrity.

关键词： Block matching algorithm(BMA) compression full-search algorithm motion vectors ultrasound image sequence watermarking

Graphene oxide-coated microbottle resonators for relative humidity sensing

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Optoelectronics Letters 2024年第7期20卷 400-405页

作者： Huda Adnan Zain Malathy Batumalay Md Ashadi Md Johari Hazli Rafis abdul rahim Sulaiman Wadi Harun Department of Electrical Engineering Faculty of Engineering University of Malaya Faculty of Data Science and Information Technology INTI International University Faculty of Electrical and Electronic Engineering Technology Universiti Teknikal Malaysia Melaka Faculty of Electronic and Computer Engineering Universiti Teknikal Malaysia Melaka

Graphene oxide(GO) is a 2D coating material used to improve fiber optics sensors' response to relative humidity. Microbottle resonators(MBRs) have garnered more attention as sensing media structures. An MBR with a 190 μm diameter was coated with GO. Then, tapered fiber light coupling was used to investigate the relative humidity sensing performance in the range of 35—70%RH at 25 °C. The MBR showed a higher Q factor before and after GO coating. The sensitivity of 0.115 d B/%RH was recorded with the 190 μm GO-coated MBR sample compared to a sensitivity of 0.022 d B/%RH for the uncoated MBR sample. These results show that the MBR can be used in fiber optic sensing applications for environmental sensing.

关键词： A

A review of salivary composition changes induced by fasting and its impact on health

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Food Science and Human Wellness 2024年第1期13卷 50-64页

作者： Nurul Fadhilah Kamalul Aripin NIdayu Zahid Mohd Aizat abdul rahim Hashim Yaacob Parvez I.Haris Zubaidah Haji Abdrahim Rauzah Hashim School of Chemical Engineering College of EngineeringUniversiti Teknologi MARAShah Alam 40450Malaysia Centre for Fundamental and Frontier Sciences in Nanostructure Self-Assembly Department of ChemistryFaculty of ScienceUniversiti MalayaKuala Lumpur 50603Malaysia Department of Oral and Craniofacial Sciences Faculty of DentistryUniversiti MalayaKuala Lumpur 50603Malaysia UMSC Dental Specialist Clinic UM Dentistry TowerUniversiti MalayaMedical CentreKuala Lumpur 50603Malaysia Leicester School of Allied Health Sciences De Montfort UniversityLeicester LE19BHUnited Kingdom Chancellery(Research and Innovation) Universiti MalayaKuala Lumpur 50603Malaysia

Human saliva is an indispensable fluid that maintains a healthy oral cavity which otherwise can lead to oral diseases(dental caries and periodontitis).In addition,salivary metabolites and microbiome profile provide early detection of systemic diseases such as cancer and obesity.Salivary diagnostic has gained popularity due to its non-invasive sampling technique.Fasting(abstinence from food or drink or both)research for weight loss and improve health is common,but studies using fasting saliva are scarce.Some metabolites in fasting saliva have been reported with interesting results,which can be enhanced by considering different confounding factors.For example,fasting saliva contains higher salivary nitrite,which is related to nitric oxide(NO).NO is a vasodilator supporting the healthy function of endothelial cells and its deficiency is connected to many diseases.The timely supply of NO through exogenous and endogenous means is highlighted and the potential advantage of fasting salivary composition changes in relation to COVID-19 infection is speculated.This review aims to provide a general discussion on the salivary composition,properties,and functions of the whole saliva,including the health benefits of fasting.

关键词： Saliva Fasting Nitrate Nitrite Nitric oxide

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An Enhanced Hybrid Model Based on CNN and BiLSTM for Identifying Individuals via Handwriting Analysis

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Computer Modeling in Engineering & Sciences 2024年第8期140卷 1689-1710页

作者： Md.Abdur rahim Fahmid Al Farid Abu Saleh Musa Miah Arpa Kar Puza Md.Nur Alam Md.Najmul Hossain Sarina Mansor Hezerul abdul Karim Department of Computer Science and Engineering Pabna University of Science and TechnologyPabna6600Bangladesh Faculty of Engineering Multimedia UniversityCyberjaya63100Malaysia Department of Computer Science and Engineering Bangladesh Army University of Science and TechnologySaidpur5311Bangladesh Department of Mathematics Pabna University of Science and TechnologyPabna6600Bangladesh Department of Electrical Electronic and Communication EngineeringPabnaUniversity of Science and TechnologyPabna6600Bangladesh Department of Electrical and Communication Engineering Pabna University of Science and TechnologyPabna6600Bangladesh

Handwriting is a unique and significant human feature that distinguishes them from one another.There are many researchers have endeavored to develop writing recognition systems utilizing specific signatures or symbols for person identification through verification.However,such systems are susceptible to forgery,posing security risks.In response to these challenges,we propose an innovative hybrid technique for individual identification based on independent handwriting,eliminating the reliance on specific signatures or symbols.In response to these challenges,we propose an innovative hybrid technique for individual identification based on independent handwriting,eliminating the reliance on specific signatures or symbols.Our innovative method is intricately designed,encompassing five distinct phases:data collection,preprocessing,feature extraction,significant feature selection,and classification.One key advancement lies in the creation of a novel dataset specifically tailored for Bengali handwriting(BHW),setting the foundation for our comprehensive approach.Post-preprocessing,we embarked on an exhaustive feature extraction process,encompassing integration with kinematic,statistical,spatial,and composite features.This meticulous amalgamation resulted in a robust set of 91 features.To enhance the efficiency of our system,we employed an analysis of variance(ANOVA)F test and mutual information scores approach,meticulously selecting the most pertinent features.In the identification phase,we harnessed the power of cutting-edge deep learning models,notably the Convolutional Neural Network(CNN)and Bidirectional Long Short-Term Memory(BiLSTM).These models underwent rigorous training and testing to accurately discern individuals based on their handwriting characteristics.Moreover,our methodology introduces a groundbreaking hybrid model that synergizes CNN and BiLSTM,capitalizing on fine motor features for enhanced individual classifications.Crucially,our experimental results underscore the superiority of our approach.The CNN,B

关键词： Bengali handwriting(BHW) person identification convolutional neural network(CNN) BiLSTM

A Hybrid and Lightweight Device-to-Server Authentication Technique for the Internet of Things

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Computers, Materials & Continua 2024年第3期78卷 3805-3823页

作者： Shaha Al-Otaibi rahim Khan Hashim Ali Aftab Ahmed Khan Amir Saeed Jehad Ali Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Abdul Wali Khan University MardanMardan23200Pakistan Department of Computer Science and IT UET PeshawarJalozai Campus PeshawarPeshawar24240Pakistan Department of AI Convergence Network Ajou UniversitySuwon16499South Korea

The Internet of Things(IoT)is a smart networking infrastructure of physical devices,i.e.,things,that are embedded with sensors,actuators,software,and other technologies,to connect and share data with the respective server module.Although IoTs are cornerstones in different application domains,the device’s authenticity,i.e.,of server(s)and ordinary devices,is the most crucial issue and must be resolved on a priority basis.Therefore,various field-proven methodologies were presented to streamline the verification process of the communicating devices;however,location-aware authentication has not been reported as per our knowledge,which is a crucial metric,especially in scenarios where devices are mobile.This paper presents a lightweight and location-aware device-to-server authentication technique where the device’s membership with the nearest server is subjected to its location information along with other measures.Initially,Media Access Control(MAC)address and Advance Encryption Scheme(AES)along with a secret shared key,i.e.,λ_(i) of 128 bits,have been utilized by Trusted Authority(TA)to generate MaskIDs,which are used instead of the original ID,for every device,i.e.,server and member,and are shared in the offline phase.Secondly,TA shares a list of authentic devices,i.e.,server S_(j) and members C_(i),with every device in the IoT for the onward verification process,which is required to be executed before the initialization of the actual communication process.Additionally,every device should be located such that it lies within the coverage area of a server,and this location information is used in the authentication process.A thorough analytical analysis was carried out to check the susceptibility of the proposed and existing authentication approaches against well-known intruder attacks,i.e.,man-in-the-middle,masquerading,device,and server impersonations,etc.,especially in the IoT domain.Moreover,proposed authentication and existing state-of-the-art approaches have been simulated in the real environment of IoT to verif

关键词： Internet of things authenticity security location communication

DNNBoT: Deep Neural Network-Based Botnet Detection and Classification

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Computers, Materials & Continua 2022年第4期71卷 1729-1750页

作者： Mohd Anul Haq Mohd abdul rahim Khan Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah 11952Saudi Arabia

The evolution and expansion of IoT devices reduced human efforts,increased resource utilization, and saved time;however, IoT devices createsignificant challenges such as lack of security and privacy, making them morevulnerable to IoT-based botnet attacks. There is a need to develop efficientand faster models which can work in real-time with efficiency and stability. The present investigation developed two novels, Deep Neural Network(DNN) models, DNNBoT1 and DNNBoT2, to detect and classify well-knownIoT botnet attacks such as Mirai and BASHLITE from nine compromisedindustrial-grade IoT devices. The utilization of PCA was made to featureextraction and improve effectual and accurate Botnet classification in IoTenvironments. The models were designed based on rigorous hyperparameterstuning with GridsearchCV. Early stopping was utilized to avoid the effects ofoverfitting and underfitting for both DNN models. The in-depth assessmentand evaluation of the developed models demonstrated that accuracy andefficiency are some of the best-performed models. The novelty of the presentinvestigation, with developed models, bridge the gaps by using a real datasetwith high accuracy and a significantly lower false alarm rate. The results wereevaluated based on earlier studies and deemed efficient at detecting botnetattacks using the real dataset.

关键词： Botnet network monitoring machine learning deep neural network IoT threat

Development of PCCNN-Based Network Intrusion Detection System for EDGE Computing

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Computers, Materials & Continua 2022年第4期71卷 1769-1788页

作者： Mohd Anul Haq Mohd abdul rahim Khan Talal AL-Harbi Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah 11952Saudi Arabia

Intrusion Detection System(IDS)plays a crucial role in detecting and identifying the DoS and DDoS type of attacks on IoT devices.However,anomaly-based techniques do not provide acceptable accuracy for efficacious intrusion detection.Also,we found many difficulty levels when applying IDS to IoT devices for identifying attempted attacks.Given this background,we designed a solution to detect intrusions using the Convolutional Neural Network(CNN)for Enhanced Data rates for GSM Evolution(EDGE)Computing.We created two separate categories to handle the attack and non-attack events in the system.The findings of this study indicate that this approach was significantly effective.We attempted both multiclass and binary classification.In the case of binary,we clustered all malicious traffic data in a single class.Also,we developed 13 layers of Sequential 1-D CNN for IDS detection and assessed them on the public dataset NSL-KDD.Principal Component Analysis(PCA)was implemented to decrease the size of the feature vector based on feature extraction and engineering.The approach proposed in the current investigation obtained accuracies of 99.34%and 99.13%for binary and multiclass classification,respectively,for the NSL-KDD dataset.The experimental outcomes showed that the proposed Principal Component-based Convolution Neural Network(PCCNN)approach achieved greater precision based on deep learning and has potential use in modern intrusion detection for IoT systems.

关键词： IDS edge computing machine learning NSL-KDD IoT

Elemental Assessments in Paddy Soil for Geographical Traceability of Rice from Peninsular Malaysia

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Rice science 2023年第5期30卷 486-498页

作者： Nazaratul Ashifa abdullah SALIM Norlida Mat DAUD Julieta GRIBOFF abdul rahim HARUN Department of Food Sciences Faculty of Science and TechnologyUniversiti Kebangsaan Malaysia43600 BangiSelangorMalaysia Malaysian Nuclear Agency 43000 KajangSelangorMalaysia Innovation Centre for Confectionery Technology Faculty of Science and TechnologyUniversiti Kebangsaan Malaysia43600 BangiSelangorMalaysia National Council for Scientific and Technical Research/Centre for Research in Clinical Biochemistry Immunology/Clinical Biochemistry Department Faculty of Chemical ScienceNational University of Córdoba/Ciudad UniversitariaMedina Allende esq.Haya de la Torre s/n5000 CórdobaArgentina

This investigation aimed to establish the geographical traceability of Malaysian rice by assessing the elemental composition in paddy soil.Multi-element determination in combination with a chemometric approach was applied to evaluate the elemental concentrations of paddy soil from granaries cultivated with the same rice variety and to assess the relationship between elements in the soil and rice(SAR) system.A total of 29 elements(aluminum,arsenic,barium,bromine,calcium,chlorine,cobalt,chromium,cesium,europium,iron,gallium,hafnium,potassium,lanthanum,lutetium,magnesium,manganese,sodium,rubidium,antimony,scandium,samarium,thorium,titanium,uranium,vanadium,ytterbium and zinc) were successfully determined in paddy soil from Kedah,Selangor and Langkawi by neutron activation analysis.A significant difference(P < 0.05) between 18 elements in the soil samples was obtained.The chemometric approaches of principal component and linear discriminant analyses demonstrated clear discrimination and highly corrected classification(100%) of the soil samples.A high classification(98.1%) was also achieved by assessing 10 elements(aluminum,arsenic,bromine,chlorine,potassium,magnesium,manganese,sodium,rubidium and zinc),which similarly applied to rice geographical origin determination.Similar elements in SAR were also observed for differences in the pattern of correlation and bioaccumulation factor between the granaries.Furthermore,the generalized Procrustes analysis showed a 98% consensus between SAR and clear differences between the studied regions.The canonical correlation analysis demonstrated a significant correlation between the chemical profile of SAR(r~2 = 0.88,P < 0.001).Therefore,the current work model provides a reliable assessment to establish rice provenance.

关键词： soil rice multi-element correlation geographical origin

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Fusion-Based Deep Learning Model for Hyperspectral Images Classification

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Computers, Materials & Continua 2022年第7期72卷 939-957页

作者： Kriti Mohd Anul Haq Urvashi Garg Mohd abdul rahim Khan V.Rajinikanth Department of Computer Science and Engineering Chandigarh UniversityMohali140413India Department of Computer Science College of Computer Science and Information ScienceMajmaah UniversityAL-Majmaah11952Saudi Arabia Department of Electronics and Instrumentation Engineering St.Joseph’s College of EngineeringChennai600119Tamil NaduIndia

A crucial task in hyperspectral image(HSI)taxonomy is exploring effective methodologies to effusively practice the 3-D and spectral data delivered by the statistics cube.For classification of images,3-D data is adjudged in the phases of pre-cataloging,an assortment of a sample,classifiers,post-cataloging,and accurateness estimation.Lastly,a viewpoint on imminent examination directions for proceeding 3-D and spectral approaches is untaken.In topical years,sparse representation is acknowledged as a dominant classification tool to effectually labels deviating difficulties and extensively exploited in several imagery dispensation errands.Encouraged by those efficacious solicitations,sparse representation(SR)has likewise been presented to categorize HSI’s and validated virtuous enactment.This research paper offers an overview of the literature on the classification of HSI technology and its applications.This assessment is centered on a methodical review of SR and support vector machine(SVM)grounded HSI taxonomy works and equates numerous approaches for this matter.We form an outline that splits the equivalent mechanisms into spectral aspects of systems,and spectral–spatial feature networks to methodically analyze the contemporary accomplishments in HSI taxonomy.Furthermore,cogitating the datum that accessible training illustrations in the remote distinguishing arena are generally appropriate restricted besides training neural networks(NNs)to necessitate an enormous integer of illustrations,we comprise certain approaches to increase taxonomy enactment,which can deliver certain strategies for imminent learnings on this issue.Lastly,numerous illustrative neural learning-centered taxonomy approaches are piloted on physical HSI’s in our experimentations.

关键词： Hyperspectral images feature reduction(FR) support vector machine(SVM) semi supervised learning(SSL) markov random fields(MRFs) composite kernels(CK) semi-supervised neural network(SSNN)