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A Double-Branch Xception Architecture for Acute Hemorrhage Detection and Subtype Classification

Computers, Materials & Continua 2023年第9期76卷 3727-3744页

作者： muhammad naeem akram muhammad Usman Yaseen muhammad Waqar muhammad Imran Aftab Hussain Department of Computer Science COMSATS University IslamabadIslamabad45550Pakistan Department of Software Engineering Capital University of Science&TechnologyIslamabad44000Pakistan

This study presents a deep learning model for efficient intracranial hemorrhage(ICH)detection and subtype classification on non-contrast head computed tomography(CT)images.ICH refers to bleeding in the skull,leading to the most critical life-threatening health condition requiring rapid and accurate diagnosis.It is classified as intra-axial hemorrhage(intraventricular,intraparenchymal)and extra-axial hemorrhage(subdural,epidural,subarachnoid)based on the bleeding location inside the skull.Many computer-aided diagnoses(CAD)-based schemes have been proposed for ICH detection and classification at both slice and scan levels.However,these approaches performonly binary classification and suffer from a large number of parameters,which increase storage costs.Further,the accuracy of brain hemorrhage detection in existing models is significantly low for medically critical applications.To overcome these problems,a fast and efficient system for the automatic detection of ICH is needed.We designed a double-branch model based on xception architecture that extracts spatial and instant features,concatenates them,and creates the 3D spatial context(common feature vectors)fed to a decision tree classifier for final predictions.The data employed for the experimentation was gathered during the 2019 Radiologist Society of North America(RSNA)brain hemorrhage detection challenge.Our model outperformed benchmark models and achieved better accuracy in intraventricular(99.49%),subarachnoid(99.49%),intraparenchymal(99.10%),and subdural(98.09%)categories,thereby justifying the performance of the proposed double-branch xception architecture for ICH detection and classification.

关键词： Computed tomography convolutional neural networks intracranial hemorrhage xception architecture

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Nodule Detection Using Local Binary Pattern Features to Enhance Diagnostic Decisions

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Computers, Materials & Continua 2024年第3期78卷 3377-3390页

作者： Umar Rashid Arfan Jaffar muhammad Rashid Mohammed S.Alshuhri Sheeraz akram Faculty of Computer Science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54600Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Department of Radiological Sciences and Medical Imaging College of AppliedMedical SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia

Pulmonary nodules are small, round, or oval-shaped growths on the lungs. They can be benign (noncancerous) or malignant (cancerous). The size of a nodule can range from a few millimeters to a few centimeters in diameter. Nodules may be found during a chest X-ray or other imaging test for an unrelated health problem. In the proposed methodology pulmonary nodules can be classified into three stages. Firstly, a 2D histogram thresholding technique is used to identify volume segmentation. An ant colony optimization algorithm is used to determine the optimal threshold value. Secondly, geometrical features such as lines, arcs, extended arcs, and ellipses are used to detect oval shapes. Thirdly, Histogram Oriented Surface Normal Vector (HOSNV) feature descriptors can be used to identify nodules of different sizes and shapes by using a scaled and rotation-invariant texture description. Smart nodule classification was performed with the XGBoost classifier. The results are tested and validated using the Lung Image Consortium Database (LICD). The proposed method has a sensitivity of 98.49% for nodules sized 3–30 mm.

关键词： Pulmonary nodules segmentation histogram thresholding

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The Spherical q-Linear Diophantine Fuzzy Multiple-Criteria Group Decision-Making Based on Differential Measure

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Computer Modeling in Engineering & Sciences 2024年第2期138卷 1925-1950页

作者： Huzaira Razzaque Shahzaib Ashraf muhammad naeem Yu-Ming Chu Institute of Mathematics Khwaja Fareed University of Engineering&Information TechnologyRahim Yar Khan64200Pakistan Department of Mathematics Deanship of Applied SciencesUmm Al-Qura UniversityMeccaSaudi Arabia Department of Mathematics Huzhou UniversityHuzhou313000China

Spherical q-linearDiophantine fuzzy sets(Sq-LDFSs)provedmore effective for handling uncertainty and vagueness in multi-criteria decision-making(MADM).It does not only cover the data in two variable parameters but is also beneficial for three parametric data.By Pythagorean fuzzy sets,the difference is calculated only between two parameters(membership and non-membership).According to human thoughts,fuzzy data can be found in three parameters(membership uncertainty,and non-membership).So,to make a compromise decision,comparing Sq-LDFSs is essential.Existing measures of different fuzzy sets do,however,can have several flaws that can lead to counterintuitive results.For instance,they treat any increase or decrease in the membership degree as the same as the non-membership degree because the uncertainty does not change,even though each parameter has a different implication.In the Sq-LDFSs comparison,this research develops the differentialmeasure(DFM).Themain goal of the DFM is to cover the unfair arguments that come from treating different types of FSs opposing criteria equally.Due to their relative positions in the attribute space and the similarity of their membership and non-membership degrees,two Sq-LDFSs formthis preference connectionwhen the uncertainty remains same in both sets.According to the degree of superiority or inferiority,two Sq-LDFSs are shown as identical,equivalent,superior,or inferior over one another.The suggested DFM’s fundamental characteristics are provided.Based on the newly developed DFM,a unique approach tomultiple criterion group decision-making is offered.Our suggestedmethod verifies the novel way of calculating the expert weights for Sq-LDFSS as in PFSs.Our proposed technique in three parameters is applied to evaluate solid-state drives and choose the optimum photovoltaic cell in two applications by taking uncertainty parameter zero.The method’s applicability and validity shown by the findings are contrasted with those obtained using various other existing approaches.To assess its stabilit

关键词： Multi-criteria group decision-making spherical q-linear Diophantine fuzzy sets differencemeasures photovoltaic cells medical diagnosis

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Diabetes mellitus:Is Pakistan the epicenter of the next pandemic?

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Chronic Diseases and Translational Medicine 2024年第1期10卷 75-77页

作者： muhammad Bilal Shahid Mahnoor Saeed Hamza naeem Usha Kumari King Edward Medical University LahorePakistan Dow University of Health Sciences KarachiPakistan

To the Editor,Diabetes mellitus(DM)is an endocrine disorder of chronic hyperglycemia diagnosed by fasting blood sugar levels of≥126 mg/dL or glycated hemoglobin(HbA1c)levels of≥6.5%.Type 1 DM involves a lack of insulin secretion by the pancreas,whereas the human body's resistance to insulin combined with impaired function of beta cells causes type 2 DM.

关键词： impaired Diabetes mellitus

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Enhanced Steganalysis for Color Images Using Curvelet Features and Support Vector Machine

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Computers, Materials & Continua 2024年第1期78卷 1311-1328页

作者： Arslan akram Imran Khan Javed Rashid Mubbashar Saddique muhammad Idrees Yazeed Yasin Ghadi Abdulmohsen Algarni Faculty of Computer Science and Information Technology Department of Computer ScienceSuperior UniversityLahore54000Pakistan Machine Learning Code(MLC)Lab Meharban HouseHouse Number 209Zafar ColonyOkara56300Pakistan Information Technology Services University of OkaraOkara56310Pakistan Department of Computer Science&Engineering University of Engineering&Technology LahoreNarowal CampusNarowal51601Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain11671Abu Dhabi Department of Computer Science King Khalid UniversityAbha61421Saudi Arabia

Algorithms for steganography are methods of hiding data transfers in media files.Several machine learning architectures have been presented recently to improve stego image identification performance by using spatial information,and these methods have made it feasible to handle a wide range of problems associated with image analysis.Images with little information or low payload are used by information embedding methods,but the goal of all contemporary research is to employ high-payload images for classification.To address the need for both low-and high-payload images,this work provides a machine-learning approach to steganography image classification that uses Curvelet transformation to efficiently extract characteristics from both type of images.Support Vector Machine(SVM),a commonplace classification technique,has been employed to determine whether the image is a stego or cover.The Wavelet Obtained Weights(WOW),Spatial Universal Wavelet Relative Distortion(S-UNIWARD),Highly Undetectable Steganography(HUGO),and Minimizing the Power of Optimal Detector(MiPOD)steganography techniques are used in a variety of experimental scenarios to evaluate the performance of the proposedmethod.Using WOW at several payloads,the proposed approach proves its classification accuracy of 98.60%.It exhibits its superiority over SOTA methods.

关键词： Curvelets fast fourier transformation support vector machine high pass filters steganography

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Mid-term outcomes of a kinematically designed cruciate retaining total knee arthroplasty

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World Journal of Orthopedics 2024年第2期15卷 118-128页

作者： Jonathan L Katzman akram A Habibi muhammad A Haider Casey Cardillo Ivan Fernandez-Madrid Morteza Meftah Ran Schwarzkopf Department of Orthopedic Surgery Division of Adult Reconstructive SurgeryNYU Langone Orthopedic HospitalNYU Langone HealthNew YorkNY 10010United States

BACKGROUND Advances in implant material and design have allowed for improvements in total knee arthroplasty(TKA)outcomes.A cruciate retaining(CR)TKA provides the least constraint of TKA designs by preserving the native posterior cruciate ligament.Limited research exists that has examined clinical outcomes or patient reported outcome measures(PROMs)of a large cohort of patients undergoing a CR TKA utilizing a kinematically designed implant.It was hypothesized that the studied CR Knee System would demonstrate favorable outcomes and a clinically significant improvement in pain and functional scores.AIM To assess both short-term and mid-term clinical outcomes and PROMs of a novel CR TKA design.METHODS A retrospective,multi-surgeon study identified 255 knees undergoing a TKA utilizing a kinematically designed CR Knee System(JOURNEY™II CR;Smith and Nephew,Inc.,Memphis,TN)at an urban,academic medical institution between March 2015 and July 2021 with a minimum of two-years of clinical follow-up with an orthopedic surgeon.Patient demographics,surgical information,clinical outcomes,and PROMs data were collected via query of electronic medical records.The PROMs collected in the present study included the Knee Injury and Osteoarthritis Outcome Score for Joint Replacement(KOOS JR)and Patient-Reported Outcomes Measurement Information System(PROMIS■)scores.The significance of improvements in mean PROM scores from preoperative scores to scores collected at six months and two-years postoperatively was analyzed using Independent Samples t-tests.RESULTS Of the 255 patients,65.5%were female,43.8%were White,and patients had an average age of 60.6 years.Primary osteoarthritis(96.9%)was the most common primary diagnosis.The mean surgical time was 105.3 minutes and mean length of stay was 2.1 d with most patients discharged home(92.5%).There were 18 emergency department(ED)visits within 90 d of surgery resulting in a 90 d ED visit rate of 7.1%,including a 2.4%orthopedic-related ED visit rate and a 4.7%non-orthopedic-related ED visit rat

关键词： Total knee arthroplasty Cruciate retaining Kinematic design Survivorship Bearing material Prosthetic design Clinical outcomes Patient-reported outcome measures

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Smart Energy Management System Using Machine Learning

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Computers, Materials & Continua 2024年第1期78卷 959-973页

作者： Ali Sheraz akram Sagheer Abbas muhammad Adnan Khan Atifa Athar Taher M.Ghazal Hussam Al Hamadi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Department of Computer Science COMSATS University IslamabadLahore CampusLahore54000Pakistan School of Information Technology Skyline University CollegeSharjah1797United Arab Emirates Network and Communication Technology Lab Center for Cyber SecurityFaculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia College of Engineering and IT University of DubaiDubaiUnited Arab Emirates

Energy management is an inspiring domain in developing of renewable energy sources.However,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and demand.The major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization,minimize energy costs without affecting production,and minimize environmental effects.Modern energy management is an essential and complex subject because of the excessive consumption in residential buildings,which necessitates energy optimization and increased user comfort.To address the issue of energy management,many researchers have developed various frameworks;while the objective of each framework was to sustain a balance between user comfort and energy consumption,this problem hasn’t been fully solved because of how difficult it is to solve it.An inclusive and Intelligent Energy Management System(IEMS)aims to provide overall energy efficiency regarding increased power generation,increase flexibility,increase renewable generation systems,improve energy consumption,reduce carbon dioxide emissions,improve stability,and reduce energy costs.Machine Learning(ML)is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy(IoE)network.The IoE network is playing a vital role in the energy sector for collecting effective data and usage,resulting in smart resource management.In this research work,an IEMS is proposed for Smart Cities(SC)using the ML technique to better resolve the energy management problem.The proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy,and 7.89% miss-rate.

关键词： Intelligent energy management system smart cities machine learning

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Variable range hopping transport and dielectric relaxation mechanism in GdCrO3 rare-earth orthochromite perovskite

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Journal of Rare Earths 2024年第7期42卷 1304-1316页

作者： muhammad Javed Ayaz Arif Khan Jamal Kazmi naeem Akbar Said Nasir Khisro Amanullah Dar Aleem Dad Khan Tareen Mohd Ambri Mohamed Department of Physics The University of Azad Jammu and Kashmir Department of Physics University of Kotli Azad Jammu and Kashmir Institute of Microengineering and Nanoelectronics Universiti Kebangsaan Malaysia Department of Mathematics University of Kotli Azad Jammu and Kashmir

The polycrystalline GdCrO3（GCO） perovskite orthochromite was synthesized by the sol-gel autocombustion method.The Rietveld refined X-ray diffraction（XRD） profile of GCO confirms that the orthorhombic phase crystallizes in perovskite crystal structure with space group Pbnm.The transmission electron microscopy and selected area electron diffraction divulge the polycrystalline nature of the perovskite chromite.The field emission scanning electron microscopy（FESEM） images reveal the micro structural polyhedral growth of grains separated by sharp grain boundaries.The chemical states of the ions present in the compound were investigated by X-ray photoelectron spectroscopy（XPS） studies,and the electrical pathways were identified.Complex impedance studies were carried out in the experimental frequency and temperature ranges of 40 Hz-4 MHz and 178-408 K,respectively.The equivalent circuit model（RGQG）(RGBQGB) resolves the impedance response into two contributions originating from the intra-grain and inter-granular micro-constituents of the material.The electrical conduction mechanism is based on the Mott variable range hopping model.The complex electric modulus studies demonstrate the short-and long-range mobility of the charge carriers and rule out the presence of electrode effects on the dielectric relaxation mechanism of the material.The decreasing pattern of dielectric permittivity with frequency is explained by the Maxwell-Wagner type of polarization.The frequency dependence of the loss factor exhibits the presence of thermally activated relaxation time distribution in the material.

关键词： Auto-combustion Dielectric Electrical transport Electro-ceramic Impedance Perovskite

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Mechanical sensor PDLIM5 promotes the osteogenesis of human adipose-derived stem cells through microfilament alterations

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Genes & Diseases 2024年第3期11卷 136-139页

作者： Yuchao Yang Shutong Wu Rongmei Qu Congrong Wang Jinyang Wang Asmat Ullah Khan You Pan Wenqing Liu Jinhui Zhu muhammad akram Khan Chujiang Xu Jingxing Dai Jun Ouyang Guangdong Provincial Key Laboratory of Medical Biomechanics&National Key Discipline of Human Anatomy Schoolof BasicMedical SciencesSouthern Medical UniversityGuangzhouGuangdong 510515China Department of Laboratory Medicine Nanfang Hospital of Southern Medical UniversityGuangzhouGuangdong 510515China Department of Pathology Guangdong Provincial People's HospitalGuangdong Academy of Medical SciencesGuangzhouGuangdong 510080China Department of Breast Surgery Guangxi Medical University Cancer HospitalNanningGuangxi 530000China Department of Veterinary Pathology Faculty of Veterinary and Animal SciencesPMAS Arid Agriculture UniversityRawalpindi 46300Pakistan Department of Orthopedics TCM-Integrated HospitalSouthern Medical UniversityGuangzhouGuangdong 510000China

Bone regeneration is a multifaceted,abstract,and wellcoordinated physiological progression of bone formation that participates in continuous regeneration and remodeling throughout life.However,when it comes to complex clinical situations requiring extensive bone regeneration,such as massive bone defects caused by injuries,infection,or tumor removal,traditional methods do not often yield good treatment strategies or protocols due to their limitations.

关键词： alterations injuries massive

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Analysis of Genetic Alterations in TP53 Gene in Breast Cancer - A Secondary Publication

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Proceedings of Anticancer Research 2024年第3期8卷 25-35页

作者： Baqaur Rehman muhammad Abubakar muhammad naeem Kiani Rooma Ayyoub Department of Biosciences COMSATS University IslamabadPark RdIslamabad Capital Territory 45550Pakistan Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging School of Biomedical EngineeringShenzhen University Medical SchoolShenzhen UniversityShenzhen 518060China

Tumor protein p53 (TP53) mediates DNA repair and cell proliferation in growing cells. The TP53 gene is a tumor suppressor that regulates the expression of target genes in response to multiple cellular stress factors. Key target genes are involved in crucial cellular events such as DNA repair, cell cycle regulation, apoptosis, metabolism, and senescence. TP53 genetic variants and the activity of the wild-type p53 protein (WT-p53) have been linked to a wide range of tumorigenesis. Various genetic and epigenetic alterations, including germline and somatic mutations, loss of heterozygosity, and DNA methylation, can alter TP53 activity, potentially resulting in cancer initiation and progression. This study was designed to screen three reported mutations in the DNA-binding domain of the p53 protein in breast cancer, to evaluate the relative susceptibility and risk associated with breast cancer in the local population. Genomic DNA was isolated from 30 breast tumor tissues along with controls. Tetra and Tri ARMS PCR were performed to detect mutations in the TP53 coding region. For SNPs c.637C>T and c.733C>T, all analyzed cases were homozygous for the wild-type allele ‘C,’ while for SNP c.745A>G, all cases were homozygous for the wild-type allele ‘A.’ These results indicate no relevance of these three SNPs to cancer progression in our study cohort. Additionally, the findings from whole exon sequencing will help to predict more precise outcomes and assess the importance of TP53 gene mutations in breast cancer patients.

关键词： Breast cancer p53 Gene expression Mutation SNPs

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