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Facies development and sedimentology of the Middle Miocene carbonates of the Raghama Formation, northeastern saudi Arabia

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Acta Geochimica 2024年第1期43卷 87-96页

作者： Mansour H.Al-Hashim Abdelbaset S.El-Sorogy Meshal Wadani Department of Geology and Geophysics College of ScienceKing Saud University2455Riyadh 11451Saudi Arabia

Ragahama Formation comprises a siliciclastic continental deposits followed by marine carbonates, representing prograding alluvial fans from adjacent high hinterlands seaward into lagoons and fringing reef environments. The present work aimed to document the facies development and sedimentology of the Raghama carbonates exposed along the eastern coastal plain of the Red Sea, northwestern saudi Arabia. Four stratigraphic sections were measured and sampled(D1–D4) and thin sections and major and trace element analyses were prepared and applied for petrographic and geochemical approaches. The carbonates were subdivided into three successive fore-reef, reef-core, and back-reef depositional facies. Sandy stromatolitic boundstone, microbial laminites, dolomitic ooidal grainstone, bioclastic coralline algal wackestone, sandy bioclastic wackestone, and coral boundstones were the reported microfacies types. Petrographic analysis reveals that the studied carbonates were affected by dissolution, dolomitization, and aggrading recrystallization, which affects both the original micrite matrix and grains or acts as fracture and veinlet filling leading to widespread vuggy and moldic porosity. No evidence of physical compaction, suggesting rapid lithification and recrystallization during early diagenesis and prior to substantial burial and intensive flushing by meteoric waters. Most of the original microstructure of corals were leached and destructed. This is indicated by the higher depletion in Sr and Ca levels and increase in Mg,Na, Fe, and Mn levels, especially in section D1, in comparison with the worldwide carbonates.

关键词： Raghama Formation Miocene Diagenesis Geochemistry saudi Arabia

Unknown Environment Measurement Mapping by Unmanned Aerial Vehicle Using Kalman Filter-Based Low-Cost Estimated Parallel 8-Beam LIDAR

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Computers, Materials & Continua 2024年第9期80卷 4263-4279页

作者： Mohamed Rabik Mohamed Ismail Muthuramalingam Thangaraj Khaja Moiduddin Zeyad Almutairi Mustufa Haider Abidi Department of Mechatronics Engineering SRM Institute of Science and TechnologySRM NagarKattankulathurChengalpattu DistrictTamil Nadu603203India Advanced Manufacturing Institute King Saud UniversityP.O.Box 800Riyadh11421Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Saud UniversityP.O.Box 800Riyadh11421Saudi Arabia

The measurement and mapping of objects in the outer environment have traditionally been conducted using ground-based monitoring systems,as well as *** recently,unmanned aerial vehicles have also been employed for this *** accurate detection and mapping of a target such as buildings,trees,and terrains are of utmost importance in various applications of unmanned aerial vehicles(UAVs),including search and rescue operations,object transportation,object detection,inspection tasks,and mapping ***,the rapid measurement and mapping of the object are not currently achievable due to factors such as the object’s size,the intricate nature of the sites,and the complexity of mapping *** present system introduces a costeffective solution for measurement and mapping by utilizing a small unmanned aerial vehicle(UAV)equipped with an 8-beam Light Detection and Ranging(LiDAR)*** approach offers advantages over traditional methods that rely on expensive cameras and complex algorithm-based *** reflective properties of laser beams have also been *** system provides prompt results in comparison to traditional camerabased surveillance,with minimal latency and the need for complex *** Kalman estimation method demonstrates improved performance in the presence of *** measurement and mapping of external objects have been successfully conducted at varying distances,utilizing different resolutions.

关键词： 8 beam LiDAR UAV measurement mapping Kalman filter

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

GliomaCNN: An Effective Lightweight CNN Model in Assessment of Classifying Brain Tumor from Magnetic Resonance Images Using Explainable AI

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Computer Modeling in Engineering & Sciences 2024年第9期140卷 2425-2448页

作者： Md.Atiqur Rahman Mustavi Ibne Masum Khan Md Hasib M.F.Mridha Sultan Alfarhood Mejdl Safran Dunren Che Department of Computer Science and Engineering Ahsanullah University of Science and TechnologyDhaka1208Bangladesh Department of Computer Science and Software Engineering The University of Western AustraliaPerthWA 6009Australia Department of Computer Science American International University-BangladeshDhaka1229Bangladesh Department of Computer Science College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia School of Computing Southern Illinois UniversityCarbondale62901USA

Brain tumors pose a significant threat to human lives and have gained increasing attention as the tenth leading cause of global *** study addresses the pressing issue of brain tumor classification using Magnetic resonance imaging(MRI).It focuses on distinguishing between Low-Grade Gliomas(LGG)and High-Grade Gliomas(HGG).LGGs are benign and typically manageable with surgical resection,while HGGs are malignant and more *** research introduces an innovative custom convolutional neural network(CNN)model,*** stands out as a lightweight CNN model compared to its *** research utilized the BraTS 2020 dataset for its *** with the gradient-boosting algorithm,GliomaCNN has achieved an impressive accuracy of 99.1569%.The model’s interpretability is ensured through SHapley Additive exPlanations(SHAP)and Gradient-weighted Class Activation Mapping(Grad-CAM++).They provide insights into critical decision-making regions for classification *** challenges in identifying tumors in images without visible signs,the model demonstrates remarkable performance in this critical medical application,offering a promising tool for accurate brain tumor diagnosis which paves the way for enhanced early detection and treatment of brain tumors.

关键词： Deep learning magnetic resonance imaging convolutional neural networks explainable AI boosting algorithm ablation

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 *** facilitating human-computer interaction(HCI),hand gesture recognition(HGRoc)technology is crucial for seamless and error-free *** technology is pivotal in healthcare and communication for the deaf *** 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 ***,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 ***,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 *** 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%,*** results demonstrate the effectiveness of CNN as a promising HGRoc *** 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

Ionotropic receptors mediate olfactory learning and memory in Drosophila

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Insect Science 2024年第4期31卷 1249-1269页

作者： Md Zeeshan Ali Anushree Aarif Ahsan Mohammad Shamsul Ola Rizwanul Haque Jawaid Ahsan Department of Biotechnology Central University of South BiharGayaBiharIndia Department of Radiation Oncology University of MichiganAnn ArborMichiganUSA Department of Biochemistry College of ScienceKing Saud UniversityRiyadhSaudi Arabia

Phenylacetaldehyde(PAH),an aromatic compound,is present in a diverse range of fruits including overripe bananas and prickly pear cactus,the two major host fruits for Drosophila *** acts as a potent ligand for the ionotropic receptor 84a(IR84a)in the adult fruit fly and it is detected by the IR84a/IR8a heterotetrameric *** role in the male courtship behavior through IR84a as an environmental aphrodisiac is of additional *** ***,two distinct kinds of olfactory receptors,that is,odorant receptors(ORs)and ionotropic receptors(IRs),perceive the odorant *** display unique structural,molecular,and functional characteristics in addition to having different evolutionary ***,olfactory cues detected by the ORs such as ethyl acetate,1-butanol,isoamyl acetate,1-octanol,4-methylcyclohexanol,*** as aliphatic esters and alcohols have been employed in olfactory classical conditioning using fruit *** underlines the participation of OR-activated olfactory pathways in learning and memory *** study elucidates that likewise ethyl acetate(EA)(an OR-responsive odorant),PAH(an IR-responsive aromatic compound)too can form learning and memory when associated with an appetitive gustatory *** association of PAH with sucrose(PAH/SUC)led to learning and formation of the long-term memory(LTM).Additionally,the Orco^(1),Ir84a^(MI00501),and Ir8a^(1)mutant flies were used to confirm the exclusive participation of the IR84a/IR8a complex in PAH/SUC olfactory associative *** results highlight the involvement of IRs via an IR-activated pathway in facilitating robust olfactory behavior.

关键词： appetitive conditioning Drosophila melanogaster ethyl acetate ionotropic receptors learning and memory phenylacetaldehyde

Identification of Software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features

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Computers, Materials & Continua 2024年第3期78卷 4379-4397页

作者： Qazi Mazhar ul Haq Fahim Arif Khursheed Aurangzeb Noor ul Ain Javed Ali Khan Saddaf Rubab Muhammad Shahid Anwar Department of Computer Science and Engineering and International Bachelor’s Program in Informatics Yuan Ze UniversityZhongliTaoyuan320315Taiwan Department of Computer Science National University of PakistanRawalpindiPunjab46000Pakistan Department of Computer Software Engineering MCSNational University of Sciences and TechnologyIslamabad44000Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Department of Computer Science School of PhysicsEngineering and Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates Department of AI and Software Gachon UniversitySeongnam-SI13120South Korea

Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty *** are exploring machine learning to predict software bugs,but a more precise and general approach is *** bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning ***,these studies are not generalized and efficient when extended to other ***,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification *** methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a *** National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were *** reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.

关键词： Natural language processing software bug prediction transfer learning ensemble learning feature selection

Use of waste plastic oil as a fuel in reactivity-controlled compression ignition engines:a bibliometric investigation from 2017-23

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Clean Energy 2024年第3期8卷 206-222页

作者： Sumit Kanchan Sarbjot Singh Sandhu Department of Mechanical Engineering Dr B R Ambedkar National Institute of TechnologyJalandharPunjabIndia School of Mechanical Engineering Lovely Professional UniversityJalandharPunjabIndia

Waste plastic oil,generated through the pyrolysis of plastic without oxygen,known as plastic-to-fuel conversion,stands as a promising alternative *** research employs bibliometric analysis,a quantitative method,to comprehensively assess publications in the domain of waste plastic oil integration within reactivity-controlled compression ignition *** Scopus as the primary repository post-2017,the study categorizes information into pivotal aspects,including influential journals,countries,authors,affiliations,document types,thematic areas,sponsors and *** analysis reveals significant contributions from diverse nations,prominently India,China,Turkey and *** is the prevalence of Energy,Engineering and Chemical Engineering as thematic focal points,with the journal Fuel leading the count followed by the journal Energy emerging as a prominent *** contributions are prolific,led by Gandhi Institute of Technology and Management university and Vellore Institute of *** authors‘Gugulothu S.K’and‘He Z’play a pivotal role in shaping the *** National Natural Science Foundation of China stands out as a primary *** bibliometric analysis provides a structured understanding of waste plastic oil integration in reactivity-controlled compression ignition engines,laying the groundwork for future research *** study identifies potential research platforms beyond Scopus,including Web of Science,CrossRef,Science Direct and Google Scholar,as valuable repositories for further ***,after careful observation of the research articles published by the top 10 authors as found in the study,it was found that the authors share a few common characteristics that helped them in advancing the understanding and application of waste plastic oil in reactivity-controlled compression ignition *** are also summarized in the present study with supporting data.

关键词： bibliometric analysis plastic-to-fuel waste plastic oil RCCI engine Scopus database

Soil erosion susceptibility mapping of Hangu Region,Kohat Plateau of Pakistan using GIS and RS-based models

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Journal of Mountain Science 2024年第8期21卷 2547-2561页

作者： Fakhrul ISLAM Liaqat Ali WASEEM Tehmina BIBI Waqar AHMAD Muhammad SADIQ Matee ULLAH Walid SOUFAN Aqil TARIQ Department of Geology Khushal Khan Khattak UniversityKarak 27200Pakistan Department of Geography Government College University FaisalabadPunjab 38000Pakistan Institute of Geology The University of Azad Jammu&KashmirMuzaffarabad 13100Pakistan Department of Earth Sciences Changan UniversityXi’an 710003China NCEG University of PeshawarPeshawar 25120Pakistan Faculty of Earth Sciences Geography and AstronomyUniversity of ViennaVienna 1090Austria Plant Production Department College of Food and Agriculture SciencesKing Saud UniversityRiyadh 11451Saudi Arabia Department of Wildlife Fisheries and AquacultureCollege of Forest ResourcesMississippi State UniversityMS 39762USA

Soil erosion is a crucial geo-environmental hazard worldwide that affects water quality and agriculture,decreases reservoir storage capacity due to sedimentation,and increases the danger of flooding and ***,this study uses geospatial modeling to produce soil erosion susceptibility maps(SESM)for the Hangu region,Khyber Pakhtunkhwa(KPK),*** Hangu region,located in the Kohat Plateau of KPK,Pakistan,is particularly susceptible to soil erosion due to its unique geomorphological and climatic ***,the Hangu region is characterized by a combination of steep slopes,variable rainfall patterns,diverse land use,and distinct soil types,all of which contribute to the complexity and severity of soil erosion *** factors necessitate a detailed and region-specific study to develop effective soil conservation *** this research,we detected and mapped 1013 soil erosion points and prepared 12 predisposing factors(elevation,aspect,slope,Normalized Differentiate Vegetation Index(NDVI),drainage network,curvature,Land Use Land Cover(LULC),rainfall,lithology,contour,soil texture,and road network)of soil erosion using GIS ***,GIS-based statistical models like the weight of evidence(WOE)and frequency ratio(FR)were applied to produce the SESM for the study *** SESM was reclassified into four classes,i.e.,low,medium,high,and very high *** results of WOE for SESM show that 16.39%,33.02%,29.27%,and 21.30%of areas are covered by low,medium,high,and very high zones,*** contrast,the FR results revealed that 16.50%,24.33%,35.55%,and 23.59%of the areas are occupied by low,medium,high,and very high ***,the reliability of applied models was evaluated using the Area Under Curve(AUC)*** validation results utilizing the area under curve showed that the success rate curve(SRC)and predicted rate curve(PRC)for WOE are 82%and 86%,respectively,while SRC and PRC for FR are 85%and 96%,res

关键词： Soil erosion Geospatial technology Statistical models Hangu Pakistan