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Predicting Age and Gender in Author Profiling: A Multi-Feature Exploration

Computers, Materials & Continua 2024年第5期79卷 3333-3353页

作者： Aiman Muhammad Arshad Bilal Khan Sadique ahmad Muhammad Asim Department of Computer Science City University of Science and Information TechnologyPeshawar25000Pakistan EIAS:Data Science and Blockchain Laboratory College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia School of Computer Science and Technology Guangdong University of TechnologyGuangzhou510006China

Author Profiling (AP) is a subsection of digital forensics that focuses on the detection of the author’s personalinformation, such as age, gender, occupation, and education, based on various linguistic features, e.g., stylistic,semantic, and syntactic. The importance of AP lies in various fields, including forensics, security, medicine, andmarketing. In previous studies, many works have been done using different languages, e.g., English, Arabic, French,***, the research on romanUrdu is not up to the ***, this study focuses on detecting the author’sage and gender based on roman Urdu text messages. The dataset used in this study is Fire’18-MaponSMS. Thisstudy proposed an ensemble model based on AdaBoostM1 and random Forest (AMBrF) for AP using multiplelinguistic features that are stylistic, character-based, word-based, and sentence-based. The proposed model iscontrasted with several of the well-known models fromthe literature, including J48-Decision Tree (J48),na飗e Bays(nB), K nearest neighbor (Knn), and Composite Hypercube on random Projection (CHIrP), nB-Updatable,rF, and AdaboostM1. The overall outcome shows the better performance of the proposed AdaboostM1 withrandom Forest (ABMrF) with an accuracy of 54.2857% for age prediction and 71.1429% for gender predictioncalculated on stylistic features. regarding word-based features, age and gender were considered in 50.5714% and60%, respectively. On the other hand, Knn and CHIrP show the weakest performance using all the linguisticfeatures for age and gender prediction.

关键词： Digital forensics author profiling for security AdaBoostM1 random forest ensemble learning

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Shadows and weak gravitational lensing for black holes within Einstein-Maxwell-scalar theory

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Chinese Physics C 2024年第9期48卷 234-243页

作者： ahmad Al-Badawi Mirzabek Alloqulov Sanjar Shaymatov Bobomurat Ahmedov Department of Physics Al-Hussein Bin Talal UniversityP.O.Box:20Ma'an 71111Jordan Institute of Fundamental and Applied research National Research University TIIAMEKori Niyoziy 39Tashkent 100000Uzbekistan University of Tashkent for Applied Sciences Str.Gavhar 1Tashkent 100149Uzbekistan Institute for Theoretical Physics and Cosmology Zhejiang University of TechnologyHangzhou 310023China Western Caspian University Baku AZ1001Azerbaijan

In this study,we investigated the optical properties of charged black holes within the Einstein-Maxwellscalar(EMS)*** evaluated the shadow cast by these black holes and obtained analytical solutions for both the radius of the photon sphere and that of the *** observed that black hole parametersγandβboth influence the shadow of black *** also found that the photon sphere and shadow radius increase as a consequence of the presence of the parameterγ.Interestingly,the shadow radius decreases first and then remains unchanged owing to the impact of the parameterβ.Finally,we analyzed the weak gravitational lensing and total magnification of lensed images around black *** found that the charge of the black holes and the parameterβboth have a significant impact,reducing the deflection ***,the same behavior for the total magnification was observed,also as a result of the effect of the charge of the black holes and the parameterβ.

关键词： Einstein Maxwell gravity shadow gravitational weak lensing

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Performance comparison of machine learning algorithms for maximum displacement prediction in soldier pile wall excavation

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Underground Space 2024年第3期16卷 301-313页

作者： Danial Sheini Dashtgoli Mohammad Hossein Dehnad Seyed ahmad Mobinipour Michela Giustiniani Department of Mathematics Informaticsand GeosciencesUniversity of TriesteTrieste 34127Italy national Institute of Oceanography and Applied Geophysics-OGS Borgo Grotta Gigante 42/CTriesteSgonico 34010Italy Department of Civil Engineering University of QomQom ***Iran

One of the common excavation methods in the construction of urban infrastructures as well as water and wastewater facilities is the excavation through soldier pile *** maximum lateral displacement of pile wall is one of the important variables in controlling the stability of the excavation and its adjacent ***,the application of machine learning methods is widely used in engineering sciences due to its low cost and high speed of *** paper utilized three intelligent machine learning algorithms based on the excavation method through soldier pile walls,namely eXtreme gradient boosting(XGBoost),least square support vector regressor(LS-SVr),and random forest(rF),to predict maximum lateral displacement of pile *** results showed that the implemented XGBoost model performed excellently and could make predictions for maximum lateral displacement of pile walls with the mean absolute error of 0.1669,the highest coefficient of determination 0.9991,and the lowest root mean square error *** the LS-SVr,and rF models were less accurate than the XGBoost model,they provided good prediction results of maximum lateral displacement of pile walls for numerical ***,a sensitivity analysis was performed to determine the most effective parameters in the XGBoost *** analysis showed that soil elastic modulus and excavation height had a strong influence on of maximum lateral displacement of pile wall prediction.

关键词： Soldier pile wall Lateral displacements XGBoost Machine learning Artificial intelligence

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network traffic classification:Techniques,datasets,and challenges

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Digital Communications and networks 2024年第3期10卷 676-692页

作者： ahmad Azab Mahmoud Khasawneh Saed Alrabaee Kim-Kwang raymond Choo Maysa Sarsour College of Information Technology and Systems Victorian Institute of TechnologyAustralia College of Engineering Al Ain UniversityAbu DhabiUnited Arab Emirates Information Systems and Security College of ITUnited Arab Emirates UniversityAl Ain15551United Arab Emirates Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78260USA School of Photovoltaic and renewable Energy Engineering University of New South WalesSydneyNSW2052Australia

In network traffic classification,it is important to understand the correlation between network traffic and its causal application,protocol,or service group,for example,in facilitating lawful interception,ensuring the quality of service,preventing application choke points,and facilitating malicious behavior *** this paper,we review existing network classification techniques,such as port-based identification and those based on deep packet inspection,statistical features in conjunction with machine learning,and deep learning *** also explain the implementations,advantages,and limitations associated with these *** review also extends to publicly available datasets used in the ***,we discuss existing and emerging challenges,as well as future research directions.

关键词： network classification Machine learning Deep learning Deep packet inspection Traffic monitoring

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recent advances in the diagnosis of drug-induced liver injury

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World Journal of Hepatology 2024年第2期16卷 186-192页

作者： Taqwa Ahmed Jawad ahmad Department of Medicine Icahn School of Medicine at Mount SinaiNew YorkNY 10029United States Department of recanati-Miller Transplantation Institute Icahn School of Medicine at Mount SinaiNew YorkNY 10029United States

Drug-induced liver injury(DILI)is a major problem in the United States,commonly leading to hospital *** DILI is difficult as it is a diagnosis of exclusion requiring a temporal relationship between drug exposure and liver injury and a thorough work up for other *** addition,DILI has a very variable clinical and histologic presentation that can mimic many different etiologies of liver *** scoring systems can assess the probability that a drug caused the liver injury but liver biopsy findings are not part of the criteria used in these *** review will address some of the recent updates to the scoring systems and the role of liver biopsy in the diagnosis of DILI.

关键词： Drug induced liver injury Liver biopsy Diagnosis rUCAM rECAM

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Artificial ground freezing of underground mines in cold regions using thermosyphons with air insulation

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International Journal of Mining Science and Technology 2024年第5期34卷 643-654页

作者： ahmad F.Zueter Mohammad Zolfagharroshan navid Bahrani Agus P.Sasmito Department of Civil and resource Engineering Dalhousie UniversityHalifax B3H4R2Canada Department of Mining Engineering McGill UniversityMontreal H3A0E8Canada

Current practice of underground artificial ground freezing(AGF)typically involves huge refrigeration systems of large economic and environmental *** this study,a novel AGF technique is proposed deploying available cold wind in cold *** is achieved by a static heat transfer device called thermosyphon equipped with an air insulation layer.A refrigeration unit can be optionally integrated to meet additional cooling *** introduction of air insulation isolates the thermosyphon from ground zones where freezing is not needed,resulting in:(1)steering the cooling resources(cold wind or refrigeration)towards zones of interest;and(2)minimizing refrigeration *** design is demonstrated using well-validated mathematical models from our previous work based on two-phase enthalpy method of the ground coupled with a thermal resistance network for the *** Canadian mines are considered:the Cigar Lake Mine and the Giant *** results show that our proposed design can speed the freezing time by 30%at the Giant Mine and by two months at the Cigar Lake ***,a cooling load of 2.4 GWh can be saved at the Cigar Lake ***,this study provides mining practitioners with sustainable solutions of underground AGF.

关键词： Artificial ground freezing Underground mining Sustainable mining Thermosyphon Air insulation Cold regions

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Comprehensive analysis of glacier recession(2000-2020)in the nun-Kun Group of Glaciers,northwestern Himalaya

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Journal of Mountain Science 2024年第3期21卷 768-785页

作者： Shakil ahmad rOMSHOO Ummer AMEEn Mustafa Hameed BHAT Tariq ABDULLAH Department of Geoinformatics University of KashmirSrinagarKashmir 190006India Islamic University of Science and Technology AwantiporaKashmir 192122India Centre of Excellence for Glacier Studies in the Western Himalaya University of KashmirSrinagarKashmir 190006India

Himalayan glaciers are shrinking rapidly,especially after *** shrinkage,however,shows a differential pattern in space and time,emphasizing the need to monitor and assess glacier changes at a larger *** this study,changes of 48 glaciers situated around the twin peaks of the nun and Kun mountains in the northwestern Himalaya,hereafter referred to as nun-Kun Group of Glaciers(nKGG),were investigated using Landsat satellite data during *** in glacier area,snout position,Equilibrium Line Altitude(ELA),surface thickness and glacier velocity were assessed using remote sensing data supplemented by field *** study revealed that the nKGG glaciers have experienced a recession of 4.5%±3.4%and their snouts have retreated at the rate of 6.4±1.6 m·a^(-1).Additionally,there was a 41%increase observed in the debris cover area during the observation *** the geodetic approach,an average glacier elevation change of-1.4±0.4 m·a^(-1)was observed between 2000 and *** observed mass loss of the nKGG has resulted in the deceleration of glacier velocity from 27.0±3.7 m·a^(-1)in 2000 to 21.2±2.2 m·a^(-1)in *** ELA has shifted upwards by 83.0±22 m during the *** morphological and topographic factors showed a strong influence on glacier ***,a higher recession of 12.9%±3.2%was observed in small glaciers,compared to 2.7%±3.1%in larger *** debris-covered glaciers showed lower shrinkage(2.8%±1.1%)compared to the clean glaciers(9.3%±5%).The glacier depletion recorded in the nKGG during the last two decades,if continued,would severely diminish glacial volume and capacity to store water,thus jeopardizing the sustainability of water resources in the basin.

关键词： nun-Kun range Glacier recession Glacier snout remote Sensing Himalaya Glacier velocity

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A novel Approach to Breast Tumor Detection: Enhanced Speckle reduction and Hybrid Classification in Ultrasound Imaging

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Computers, Materials & Continua 2024年第5期79卷 1875-1901页

作者： K.Umapathi S.Shobana Anand nayyar Judith Justin r.Vanithamani Miguel Villagómez Galindo Mushtaq ahmad Ansari Hitesh Panchal Department of Biomedical Engineering KIT-Kalaignarkarunanidhi Institute of TechnologyCoimbatore641402India Graduate School Faculty of Information TechnologyDuy Tan UniversityDa Nang550000Viet Nam Department of Biomedical and Instrumentation Engineering Avinashilingam Institute for Home Science and Higher Education forWomenCoimbatore641043India UniversidadMichoacana de Sannicolás de HidalgoAvenida Francisco J.Múgica S/n Ciudad Universitaria MoreliaCP58030Mexico Department of Pharmacology and Toxicology College of PharmacyKing Saud UniversityRiyadh11451Saudi Arabia Department of Mechanical Engineering Gujarat Technological UniveristyNigam NagarChandkhedaAhmedabadGujarat382424India

Breast cancer detection heavily relies on medical imaging, particularly ultrasound, for early diagnosis and effectivetreatment. This research addresses the challenges associated with computer-aided diagnosis (CAD) of breastcancer fromultrasound images. The primary challenge is accurately distinguishing between malignant and benigntumors, complicated by factors such as speckle noise, variable image quality, and the need for precise segmentationand classification. The main objective of the research paper is to develop an advanced methodology for breastultrasound image classification, focusing on speckle noise reduction, precise segmentation, feature extraction, andmachine learning-based classification. A unique approach is introduced that combines Enhanced Speckle reducedAnisotropic Diffusion (SrAD) filters for speckle noise reduction, U-nET-based segmentation, Genetic Algorithm(GA)-based feature selection, and random Forest and Bagging Tree classifiers, resulting in a novel and efficientmodel. To test and validate the hybrid model, rigorous experimentations were performed and results state thatthe proposed hybrid model achieved accuracy rate of 99.9%, outperforming other existing techniques, and alsosignificantly reducing computational time. This enhanced accuracy, along with improved sensitivity and specificity,makes the proposed hybrid model a valuable addition to CAD systems in breast cancer diagnosis, ultimatelyenhancing diagnostic accuracy in clinical applications.

关键词： Ultrasound images breast cancer tumor classification segmentation deep learning lesion detection

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A review of Privacy and Security of Edge Computing in Smart Healthcare Systems: Issues, Challenges, and research Directions

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Tsinghua Science and Technology 2024年第4期29卷 1152-1180页

作者： ahmad Alzu’bi Ala’a Alomar Shahed Alkhaza’leh Abdelrahman Abuarqoub Mohammad Hammoudeh Department of Computer Science Jordan University of Science and TechnologyIrbid 22110Jordan Cardiff School of Technologies Cardiff Metropolitan UniversityCardiff CF52YBUK Department of Information and Computer Science King Fahd University of Petroleum and MineralsDhahran 31261Kingdom of Saudi Arabia

The healthcare industry is rapidly adapting to new computing environments and technologies. With academics increasingly committed to developing and enhancing healthcare solutions that combine the Internet of Things (IoT) and edge computing, there is a greater need than ever to adequately monitor the data being acquired, shared, processed, and stored. The growth of cloud, IoT, and edge computing models presents severe data privacy concerns, especially in the healthcare sector. However, rigorous research to develop appropriate data privacy solutions in the healthcare sector is still lacking. This paper discusses the current state of privacy-preservation solutions in IoT and edge healthcare applications. It identifies the common strategies often used to include privacy by the intelligent edges and technologies in healthcare systems. Furthermore, the study addresses the technical complexity, efficacy, and sustainability limits of these methods. The study also highlights the privacy issues and current research directions that have driven the IoT and edge healthcare solutions, with which more insightful future applications are encouraged.

关键词： edge computing fog computing data privacy healthcare systems intelligent edges

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Computational and bioinformatics tools for understanding disease mechanisms

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BIOCELL 2024年第6期48卷 935-944页

作者： MOHD ATHAr AnU MAnHAS nISArG rAnA ahmad IrFAn Physics Department University of CagliariCittadella UniversitariaMonserrato(CA)09042Italy Department of Chemistry School of Energy TechnologyPandit Deendayal Energy UniversityGandhinagar382426India Department of Chemistry College of ScienceKing Khalid UniversityAbha61413Saudi Arabia

Computational methods have signiﬁcantly transformed biomedical research,offering a comprehensive exploration of disease mechanisms and molecular protein *** article reviews a spectrum of computational tools and network analysis databases that play a crucial role in identifying potential interactions and signaling networks contributing to the onset of disease *** utilization of protein/gene interaction and genetic variation databases,coupled with pathway analysis can facilitate the identiﬁcation of potential drug *** bridging the gap between molecular-level information and disease understanding,this review contributes insights into the impactful utilization of computational methods,paving the way for targeted interventions and therapeutic advancements in biomedical research.

关键词： Interaction database Disease mechanisms Protein function network analysis Bioinformatics Genetic variations Protein-protein interactions Signaling pathways

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