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Breast Cancer Detection in Saudi Arabian Women Using Hybrid Machine Learning on Mammographic Images

Computers, Materials & Continua 2022年第9期72卷 4833-4851页

作者： Yassir Edrees Almalki Ahmad Shaf Tariq Ali muhammad Aamir Sharifa Khalid Alduraibi Shoayea Mohessen Almutiri muhammad Irfan Mohammad Abd Alkhalik Basha Alaa Khalid Alduraibi Abdulrahman Manaa Alamri muhammad zeeshan azam Khalaf Alshamrani Hassan A.Alshamrani Division of Radiology Department of MedicineMedical CollegeNajran UniversityNajran61441Kingdom of Saudi Arabia Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Department of Radiology College of MedicineQassim UniversityBuraidah52571Kingdom of Saudi Arabia Department of Radiology King Fahad Specialist HospitalBuraydah52571Kingdom of Saudi Arabia Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Kingdom of Saudi Arabia Radiology Department Human Medicine CollegeZagazig UniversityZagazig44631Egypt Department of Surgery College of MedicineNajran UniversityNajran61441Kingdom of Saudi Arabia Department of Computer Science Bahauddin Zakariya UniversityMultan66000Pakistan Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran61441Kingdom of Saudi Arabia

Breast cancer(BC)is the most common cause of women’s deaths *** mammography technique is the most important modality for the detection of *** detect abnormalities in mammographic images,the Breast Imaging Reporting and Data System(BI-RADs)is used as a *** correct allocation of BI-RADs categories for mammographic images is always an interesting task,even for *** this work,to detect and classify the mammogram images in BI-RADs,a novel hybrid model is presented using a convolutional neural network(CNN)with the integration of a support vector machine(SVM).The dataset used in this research was collected from different hospitals in the Qassim health cluster of Saudi *** collection of all categories of BI-RADs is one of the major contributions of this *** significant contribution is the development of a hybrid approach through the integration of CNN and *** proposed hybrid approach uses three CNN models to obtain ensemble CNN model *** ensemble model saves the values to integrate them with *** proposed system achieved a classification accuracy,sensitivity,specificity,precision,and F1-score of 93.6%,94.8%,96.9%,96.6%,and 95.7%,*** proposed model achieved better performance compared to previously available methods.

关键词： Breast cancer CNN SVM BIRADS classification

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Federated Machine Learning Based Fetal Health Prediction Empowered with Bio-Signal Cardiotocography

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Computers, Materials & Continua 2024年第3期78卷 3303-3321页

作者： muhammad Umar Nasir Omar Kassem Khalil Karamath Ateeq Bassam SaleemAllah Almogadwy muhammad Adnan Khan muhammad Hasnain azam Khan muhammad Adnan Department of Computer Sciences Bahria University Lahore CampusLahore54000Pakistan Faculty of Information Technology Liwa CollegeAbu Dhabi00000United Arab Emirates Department of Computing Skyline University CollegeSharjah1797United Arab Emirates Department of Computer Science Taibah UniversityMedina42353Saudi Arabia Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Engineering and Development Dubizzle LabsLahore54000Pakistan Department of Software Faculty of Artificial Intelligence and SoftwareGachon UniversitySeongnam-si13120Korea

Cardiotocography measures the fetal heart rate in the fetus during pregnancy to ensure physical health because cardiotocography gives data about fetal heart rate and uterine shrinkages which is very beneficial to detect whether the fetus is normal or suspect or *** cardiotocography measures infer wrongly and give wrong predictions because of human *** traditional way of reading the cardiotocography measures is the time taken and belongs to numerous human errors as *** condition is very important to measure at numerous stages and give proper medications to the fetus for its *** the current period Machine learning(ML)is a well-known classification strategy used in the biomedical field on various issues because ML is very fast and gives appropriate results that are better than traditional *** techniques play a pivotal role in detecting fetal disease in its early *** research article uses Federated machine learning(FML)and ML techniques to classify the condition of the *** study proposed a model for the detection of bio-signal cardiotocography that uses FML and ML techniques to train and test the ***,the proposed model of FML used numerous data preprocessing techniques to overcome data deficiency and achieves 99.06%and 0.94%of prediction accuracy and misprediction rate,respectively,and parallel the proposed model applying K-nearest neighbor(KNN)and achieves 82.93%and 17.07%of prediction accuracy and misprediction accuracy,***,by comparing both models FML outperformed the KNN technique and achieved the best and most appropriate prediction results as compared with previous studies the proposed study achieves the best and most accurate results.

关键词： Cardiotocography ML FML fetal disease bio-signal

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Enhanced Topic-Aware Summarization Using Statistical Graph Neural Networks

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Computers, Materials & Continua 2024年第8期80卷 3221-3242页

作者： Ayesha Khaliq Salman Afsar Awan Fahad Ahmad muhammad azam Zia muhammad Zafar Iqbal Department of Computer Science University of Agriculture FaisalabadFaisalabadPunjab37300Pakistan School of Computing Faculty of TechnologyUniversity of PortsmouthSouthseaPortsmouthPO12UPUK Department of Mathematics and Statistics University of Agriculture FaisalabadFaisalabadPunjab37300Pakistan

The rapid expansion of online content and big data has precipitated an urgent need for efficient summarization techniques to swiftly comprehend vast textual documents without compromising their original *** approaches in Extractive Text Summarization(ETS)leverage the modeling of inter-sentence relationships,a task of paramount importance in producing coherent *** study introduces an innovative model that integrates Graph Attention Networks(GATs)with Transformer-based Bidirectional Encoder Representa-tions from Transformers(BERT)and Latent Dirichlet Allocation(LDA),further enhanced by Term Frequency-Inverse Document Frequency(TF-IDF)values,to improve sentence selection by capturing comprehensive topical *** approach constructs a graph with nodes representing sentences,words,and topics,thereby elevating the interconnectivity and enabling a more refined understanding of text *** model is stretched to Multi-Document Summarization(MDS)from Single-Document Summarization,offering significant improvements over existing models such as THGS-GMM and Topic-GraphSum,as demonstrated by empirical evaluations on benchmark news datasets like Cable News Network(CNN)/Daily Mail(DM)and *** results consistently demonstrate superior performance,showcasing the model’s robustness in handling complex summarization tasks across single and multi-document *** research not only advances the integration of BERT and LDA within a GATs but also emphasizes our model’s capacity to effectively manage global information and adapt to diverse summarization challenges.

关键词： Summarization graph attention network bidirectional encoder representations from transformers Latent Dirichlet Allocation term frequency-inverse document frequency

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AGGREGATE SPECIAL FUNCTIONS TO APPROXIMATE PERMUTING TRI-HOMOMORPHISMS AND PERMUTING TRI-DERIVATIONS ASSOCIATED WITH A TRI-ADDITIVEψ-FUNCTIONAL INEQUALITY IN BANACH ALGEBRAS

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Acta Mathematica Scientia 2024年第1期44卷 311-338页

作者： Safoura Rezaei ADERYANI azam AHADI Reza SAADATI Hari M.SRIVASTAVA School of Mathematics Iran University of Science and TechnologyNarmakTehranIran Department of Mathematics and Statistics Universtity of VictoriaVictoriaCanada Department of Medical Research China Medical University HospitalChina Medical UniversityTaichung40402China Department of Mathematics and Informatics Azerbaijan University71 Jeyhun Hajibeyli StreetAZ1007BakuAzerbaijan Section of Mathematics International TelematicUniversity UninettunoI-00186RomeItaly

In this paper,we define a new class of control functions through aggregate special *** class of control functions help us to stabilize and approximate a tri-additiveψ-functional inequality to get a better estimation for permuting tri-homomorphisms and permuting tri-derivations in unital C*-algebras and Banach algebras by the vector-valued alternative fixed point theorem.

关键词： permuting tri-homomorphism in Banach algebra permuting tri-derivation on C*-algebra fixed point theorem Ulam-Hyers-Rassias stability aggregate special functions tri-additiveψ-functional inequality

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Analysis of b→cτν¯_(τ)anomalies using weak effective Hamiltonian with complex couplings and their impact on various physical observables

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Chinese Physics C 2024年第8期48卷 56-75页

作者： muhammad Arslan Tahira Yasmeen Saba Shafaq Ishtiaq Ahmed muhammad Jamil Aslam Department of Physics Quaid-i-Azam UniversityIslamabad 45320Pakistan Department of Physics International Islamic UniversityIslamabad 44000Pakistan National Center for Physics Islamabad 44000Pakistan

Recently,the experimental measurements of the branching ratios and different polarization asymmetries for processes occurring through flavor-changing-charged current(b→cτν¯_(τ))transitions by BABAR,Belle,and LHCb have revealed some significant differences from the corresponding Standard Model(SM)*** has triggered an interest to search for physics beyond the SM in the context of various new physics(NP)models and using the model-independent weak effective Hamiltonian(WEH).Assuming left-handed neutrinos,we add the dimension-six vector,(pseudo-)scalar,and tensor operators with complex Wilson coefficients(WCs)to the SM *** 60%,30%,and 10%constraints resulting from the branching ratio of B_(c)→τν¯_(τ),we reassess the parametric space of these new physics WCs accommodating the current anomalies based on the most recent HFLAV data of R_(τ/μ,e)(D)and R_(τ/μ,e)(D^(*))and Belle data of F_(L)(D^(*))and P_(τ)(D^(*)).We find that the allowed parametric region of left-handed scalar couplings strongly depends on the constraints of the B_(c)→τν¯_(τ)branching ratio,and the maximum pull from the SM predictions results from the<60%branching ratio ***,the parametric region changes significantly if we extend the analysis by adding LHCb data of R_(τ/μ)(J/ψ)and R_(τ/ℓ)(Λ_(c)).Furthermore,due to the large uncertainties in the measurements of R_(τ/μ)(J/ψ)and R_(τ/ℓ)(X_(c)),we derive the sum rules which complement them with R_(τ/μ,e)(D)and R_(τ/μ,e)(D^(*)).Using the best-fit points of the new complex WCs along with the latest measurements of R_(τ/μ,e)(D(^(*))),we predict the numerical values of the observable R_(τ/ℓ)(Λ_(c)),R_(τ/μ)(J/ψ),and R_(τ/ℓ)(X_(c))from the sum *** simultaneous dependence of abovementioned physical observables on the NP WCs is established by plotting their correlation with R_(D) and R_(D^(*)),which are useful to discriminate between various NP *** find that the most significant impact of NP results from the WC C^(S)_(L)=4C^(T).Fi

关键词： B meson anomalies new physics complex coupling HFLAV

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Natural resource scarcity,fossil fuel energy consumption,and total greenhouse gas emissions in top emitting countries

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Geoscience Frontiers 2024年第2期15卷 85-99页

作者： Jiannan Wang Waseem azam College of Economics and Management South China Agricultural UniversityGuangzhouChina Groupe Ecole de Commerce de Lyon LyonFrance Institute Supérieur de l’Environnement-ISE LYON France

Natural resource scarcity,fossil fuel energy consumption,and total greenhouse gas emissions are critical issues that require immediate *** the global population steadily increasing and economies expanding,the demand for natural resources,particularly fossil fuels,has experienced an unprecedented *** surge in consumption is directly linked to the alarming rise in greenhouse gas *** study examines the nexus between agricultural nitrous oxide emissions and natural resource scarcity,taking into account the dynamics of agriculture,forestry,fishing value addition,fossil fuels,and total greenhouse gas emissions in top-emitting countries between 1971 and *** resource scarcity positively correlates with agriculture,forestry,fishing,fossil fuel energy consumption,and total greenhouse gas *** is a decrease in natural resource scarcity in countries that emit agricultural nitrous oxide,forestry,fishing emissions,fossil fuel energy consumption,and greenhouse gas ***-makers may promote sustainable development,mitigate climate change,and ensure the longterm viability of agricultural systems by addressing the dynamics of agriculture,forestry,and fishing value addition in top-emitting *** strategic policy interventions,supported by technology transfer,capacity building,and market-based instruments,the agricultural,forestry,and fishing sector can achieve a more sustainable future while addressing the challenges of natural resource scarcity.

关键词： Resource scarcity Non-renewable energy CO_(2)emissions Top-emitting countries

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Contemporary Study for Detection of COVID-19 Using Machine Learning with Explainable AI

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Computers, Materials & Continua 2024年第7期80卷 1075-1104页

作者： Saad Akbar Humera azam Sulaiman Sulmi Almutairi Omar Alqahtani Habib Shah Aliya Aleryani Department of Computer Science University of KarachiKarachi75270Pakistan Department of Computing Faculty of Engineering Science and TechnologyHamdard UniversityKarachi75540Pakistan Department of Health Informatics College of Applied Medical SciencesQassim UniversityQassim51452Saudi Arabia Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia

The prompt spread of COVID-19 has emphasized the necessity for effective and precise diagnostic *** this article,a hybrid approach in terms of datasets as well as the methodology by utilizing a previously unexplored dataset obtained from a private hospital for detecting COVID-19,pneumonia,and normal conditions in chest X-ray images(CXIs)is proposed coupled with Explainable Artificial Intelligence(XAI).Our study leverages less preprocessing with pre-trained cutting-edge models like InceptionV3,VGG16,and VGG19 that excel in the task of feature *** methodology is further enhanced by the inclusion of the t-SNE(t-Distributed Stochastic Neighbor Embedding)technique for visualizing the extracted image features and Contrast Limited Adaptive Histogram Equalization(CLAHE)to improve images before extraction of ***,an AttentionMechanism is utilized,which helps clarify how the modelmakes decisions,which builds trust in artificial intelligence(AI)*** evaluate the effectiveness of the proposed approach,both benchmark datasets and a private dataset obtained with permissions from Jinnah PostgraduateMedical Center(JPMC)in Karachi,Pakistan,are *** 12 experiments,VGG19 showcased remarkable performance in the hybrid dataset approach,achieving 100%accuracy in COVID-19 *** classification and 97%in distinguishing normal ***,across all classes,the approach achieved 98%accuracy,demonstrating its efficiency in detecting COVID-19 and differentiating it fromother chest disorders(Pneumonia and healthy)while also providing insights into the decision-making process of the models.

关键词： COVID-19 detection deep neural networks support vector machine CXIs InceptionV3 VGG16 VGG19 t-SNE embedding CLAHE attention mechanism XAI

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Examination of the Effective Factors on the Multiple Sclerosis Diseases

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Open Journal of Internal Medicine 2024年第2期14卷 213-227页

作者： Rakeeh Ghaderi azam Alikhademi Sampad Organization Farzanegan High School Tehran Iran Faculty of Foreign Language Islamic Azad University Tehran Iran

This study was an attempt to examine the effective factors of the Multiple Sclerosis diseases. The participants of the study were selected from among a total number of 45 men and women who were treated in a health center in Azarbayegan and Damavand in Iran. In order to study, the researchers applied various procedures to collect the data of the study. The participants were interviewed and filled out the questionnaires. After categorizing and classifying the collected information and data, it was processed and analyzed and the results are found. To test the research questions, a one-sample T-test was used to analyze the data. The role of hypo vitamin D as a possible risk factor for multiple sclerosis was reviewed. First, it was emphasized that hypo vitamin could be only one of the risk factors for multiple sclerosis and that numerous other environmental and genetic risk factors appear to interact and combine to trigger the disease. The main aim of this study was to examine the effective factors of Multiple Sclerosis diseases. The methodology of this research was to test the research questions;one-sample T-test was used to analyze the data. The findings of this study revealed that the factors of gender, cold weather, vitamin D deficiency, and age (between 30 - 59) were effective on the Multiple Sclerosis diseases.

关键词： Multiple Sclerosis Disease Effective Factors

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Glacier area change (1993-2019) and its relationship to debris cover, proglacial lakes, and morphological parameters in the Chandra-Bhaga Basin, Western Himalaya, India

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Journal of Mountain Science 2024年第4期21卷 1287-1306页

作者： VATSAL Sarvagya azam Mohd Farooq BHARDWAJ Anshuman MANDAL Arindan BAHUGUNA Ishmohan RAMANATHAN Alagappan RAJU N.Janardhana TOMAR Sangita Singh Department of Civil Engineering Indian Institute of Technology IndoreSimrol 453552India School of Geosciences University of AberdeenKing’s CollegeAberdeen AB243FXUnited Kingdom Interdisciplinary Centre for Water Research Indian Institute of ScienceBengaluru 560012India Space Application Centre Ahmedabad 380015India School of Environmental Sciences Jawaharlal Nehru UniversityNew Delhi 110067India Department of Geography Norwegian University of Science and TechnologyTrondheim No-7491Norway

Glacier inventories serve as critical baseline data for understanding the impacts of climate change on *** present study maps the outlines of glaciers in the Chandra-Bhaga Basin(western Himalaya)for the years 1993,2000,2010,and 2019 using Landsat Thematic Mapper(TM),Enhanced Thematic Mapper(ETM),and Operational Land Imager(OLI)datasets.A total of 251 glaciers,each having an area above 0.5 km^(2),were identified,which include 216 clean-ice and 35 debris-covered *** changes are estimated for three periods:1993-2000,2000-2010,and *** total glacierized area was 996±62 km^(2) in 1993,which decreased to 973±70 km^(2) in *** mean rate of glacier area loss was higher in the recent decade(2010-2019),at 0.036 km^(2),compared to previous decades(0.029 km^(2) in 2000-2010 and 0.025 km^(2) in 1993-2000).Supraglacial debris cover changes are also mapped over the period of 1993 and *** is found that the supraglacial debris cover increased by 14.12±2.54 km^(2)(15.2%)during *** field surveys on Chhota Shigri,Panchi II,Patsio,Hamtah,Mulkila,and Yoche Lungpa glaciers were carried out to validate the glacier outlines and supraglacial debris cover estimated using satellite *** of various morphological parameters on retreat were also *** is observed that small,clean ice,south oriented glaciers,and glaciers with proglacial lakes are losing area at faster rates than other glaciers in the basin.

关键词： Glacier Area change Debris cover Morphology Proglacial lake

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High-Performance Flow Classification of Big Data Using Hybrid CPU-GPU Clusters of Cloud Environments

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Tsinghua Science and Technology 2024年第4期29卷 1118-1137页

作者： azam Fazel-Najafabadi Mahdi Abbasi Hani H.Attar Ayman Amer Amir Taherkordi Azad Shokrollahi Mohammad R.Khosravi Ahmed A.Solyman Department of Computer Engineering Faculty of EngineeringBu-Ali Sina UniversityHamedan ***Iran Department of Energy Engineering Zarqa UniversityZarqa 13132Jordan Department of Informatics University of OsloOslo 0316Norway Department of Computer Science MalmöUniversityMalmö20506Sweden Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyWeifang 261100China Department of Electrical and Electronics Engineering NişantaşıUniversityIstanbul 34481742Türkiye

The network switches in the data plane of Software Defined Networking (SDN) are empowered by an elementary process, in which enormous number of packets which resemble big volumes of data are classified into specific flows by matching them against a set of dynamic rules. This basic process accelerates the processing of data, so that instead of processing singular packets repeatedly, corresponding actions are performed on corresponding flows of packets. In this paper, first, we address limitations on a typical packet classification algorithm like Tuple Space Search (TSS). Then, we present a set of different scenarios to parallelize it on different parallel processing platforms, including Graphics Processing Units (GPUs), clusters of Central Processing Units (CPUs), and hybrid clusters. Experimental results show that the hybrid cluster provides the best platform for parallelizing packet classification algorithms, which promises the average throughput rate of 4.2 Million packets per second (Mpps). That is, the hybrid cluster produced by the integration of Compute Unified Device Architecture (CUDA), Message Passing Interface (MPI), and OpenMP programming model could classify 0.24 million packets per second more than the GPU cluster scheme. Such a packet classifier satisfies the required processing speed in the programmable network systems that would be used to communicate big medical data.

关键词： OpenMP Compute Unified Device Architecture(CUDA) Message Passing Interface(MPI) packet classification medical data tuple space algorithm Graphics Processing Unit(GPU)cluster

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