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

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 pathologic.Various cardiotocography measures infer wrongly and give wrong predictions because of human error.The traditional way of reading the cardiotocography measures is the time taken and belongs to numerous human errors as well.Fetal condition is very important to measure at numerous stages and give proper medications to the fetus for its well-being.In 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 results.ML techniques play a pivotal role in detecting fetal disease in its early stages.This research article uses Federated machine learning(FML)and ML techniques to classify the condition of the fetus.This study proposed a model for the detection of bio-signal cardiotocography that uses FML and ML techniques to train and test the data.So,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,respectively.So,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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Cervical Cancer Prediction Empowered with Federated Machine Learning

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Computers, Materials & Continua 2024年第4期79卷 963-981页

作者： muhammad umar nasir Omar Kassem Khalil Karamath Ateeq Bassam SaleemAllah Almogadwy M.A.Khan Khan muhammad Adnan Department of Computer Sciences Bahria University Lahore CampusLahore54000Pakistan Faculty of Information Technology Liwa CollegeAbu Dhabi20009UAE Department of Computing Skyline University CollegeSharjah999041UAE Department of Computer Science Taibah UniversityMedina42315Saudi Arabia Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore54000Pakistan Department of Software Faculty of Artificial Intelligence and SoftwareGachon UniversitySeongnam13120Korea

Cervical cancer is an intrusive cancer that imitates various women around the world. Cervical cancer ranks in thefourth position because of the leading death cause in its premature stages. The cervix which is the lower end of thevagina that connects the uterus and vagina forms a cancerous tumor very slowly. This pre-mature cancerous tumorin the cervix is deadly if it cannot be detected in the early stages. So, in this delineated study, the proposed approachuses federated machine learning with numerous machine learning solvers for the prediction of cervical cancer totrain the weights with varying neurons empowered fuzzed techniques to align the neurons, Internet of MedicalThings (IoMT) to fetch data and blockchain technology for data privacy and models protection from hazardousattacks. The proposed approach achieves the highest cervical cancer prediction accuracy of 99.26% and a 0.74%misprediction rate. So, the proposed approach shows the best prediction results of cervical cancer in its early stageswith the help of patient clinical records, and all medical professionals will get beneficial diagnosing approachesfrom this study and detect cervical cancer in its early stages which reduce the overall death ratio of women due tocervical cancer.

关键词： Cervical cancer federated machine learning neurons blockchain technology

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Single and Mitochondrial Gene Inheritance Disorder Prediction Using Machine Learning

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Computers, Materials & Continua 2022年第10期73卷 953-963页

作者： muhammad umar nasir muhammad Adnan Khan muhammad Zubair Taher MGhazal Raed A.Said Hussam Al Hamadi Riphah School of Computing&Innovation Faculty of ComputingRiphah International University Lahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13120GyeonggidoKorea Faculty of Computing Riphah International UniversityIslamabad45000Pakistan School of Information Technology Skyline University CollegeSharjah1797UAE Network and Communication Technology Lab Center for Cyber SecurityFaculty of Information Science and Technology Universiti Kebangsaan Malaysia43600Malaysia Canadian University Dubai00000UAE Cyber-Physical Systems Khalifa UniversityAbu Dhabi127788UAE

One of the most difficult jobs in the post-genomic age is identifying a genetic disease from a massive amount of genetic data.Furthermore,the complicated genetic disease has a very diverse genotype,making it challenging to find genetic markers.This is a challenging process since it must be completed effectively and efficiently.This research article focuses largely on which patients are more likely to have a genetic disorder based on numerous medical parameters.Using the patient’s medical history,we used a genetic disease prediction algorithm that predicts if the patient is likely to be diagnosed with a genetic disorder.To predict and categorize the patient with a genetic disease,we utilize several deep and machine learning techniques such as Artificial neural network(ANN),K-nearest neighbors(KNN),and Support vector machine(SVM).To enhance the accuracy of predicting the genetic disease in any patient,a highly efficient approach was utilized to control how the model can be used.To predict genetic disease,deep and machine learning approaches are performed.The most productive tool model provides more precise efficiency.The simulation results demonstrate that by using the proposed model with the ANN,we achieve the highest model performance of 85.7%,84.9%,84.3%accuracy of training,testing and validation respectively.This approach will undoubtedly transform genetic disorder prediction and give a real competitive strategy to save patients’lives.

关键词： Genetic disorder machine learning deep learning single gene inheritance gene disorder mitochondrial gene inheritance disorder

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Data Fusion Architecture Empowered with Deep Learning for Breast Cancer Classification

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Computers, Materials & Continua 2023年第12期77卷 2813-2831页

作者： Sahar Arooj muhammad Farhan Khan Tariq Shahzad muhammad Adnan Khan muhammad umar nasir muhammad Zubair Atta-ur-Rahman Khmaies Ouahada Faculty of Computing Riphah International UniversityIslamabad45000Pakistan Department of Forensic Sciences University of Health SciencesLahore54000Pakistan Department of Electrical and Electronic Engineering Science University of JohannesburgP.O.Box 524Johannesburg2006South Africa School of Computing Skyline University CollegeUniversity City SharjahSharjah1797United Arab Emirates Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Software Faculty of Artificial Intelligence&SoftwareGachon UniversitySeongnamGyeonggido13120Korea Department of Computer Science Bahria UniversityLahore CampusLahore54000Pakistan Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia

Breast cancer(BC)is the most widespread tumor in females worldwide and is a severe public health issue.BC is the leading reason of death affecting females between the ages of 20 to 59 around the world.Early detection and therapy can help women receive effective treatment and,as a result,decrease the rate of breast cancer disease.The cancer tumor develops when cells grow improperly and attack the healthy tissue in the human body.Tumors are classified as benign or malignant,and the absence of cancer in the breast is considered normal.Deep learning,machine learning,and transfer learning models are applied to detect and identify cancerous tissue like BC.This research assists in the identification and classification of BC.We implemented the pre-trained model AlexNet and proposed model Breast cancer identification and classification(BCIC),which are machine learning-based models,by evaluating them in the form of comparative research.We used 3 datasets,A,B,and C.We fuzzed these datasets and got 2 datasets,A2C and B3C.Dataset A2C is the fusion of A,B,and C with 2 classes categorized as benign and malignant.Dataset B3C is the fusion of datasets A,B,and C with 3 classes classified as benign,malignant,and normal.We used customized AlexNet according to our datasets and BCIC in our proposed model.We achieved an accuracy of 86.5%on Dataset B3C and 76.8%on Dataset A2C by using AlexNet,and we achieved the optimum accuracy of 94.5%on Dataset B3C and 94.9%on Dataset A2C by using proposed model BCIC at 40 epochs with 0.00008 learning rate.We proposed fuzzed dataset model using transfer learning.We fuzzed three datasets to get more accurate results and the proposed model achieved the highest prediction accuracy using fuzzed dataset transfer learning technique.

关键词： Breast cancer classification deep learning machine learning transfer learning learning rate

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Facile in situ synthesis and characterization of Fe@Si/zeolite Na composites with magnetic core–shell structures from natural materials for enhanced curcumin loading capacity

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Nanotechnology and Precision Engineering 2024年第2期7卷 69-86页

作者： Munasir nasir Nuhaa Faaizatunnisa muhammad Naufal Ariesta Lydia Rohmawati Rifqi Aul Department of Physics Faculty of Mathematics and Natural ScienceUniversitas Negeri Surabaya(Unesa)Surabaya60231Indonesia Department of Chemistry Faculty of Science and Data AnalyticsInstitut Teknologi Sepuluh Nopember(ITS)Surabaya 60111Indonesia Department of Chemistry Faculty of Mathematics and Natural SciencesUniversitas Sebelas Maret(UNS)Surakarta57126Indonesia Department of Medicine Faculty of Medical and Health ScienceUniversitas Islam Negeri(UIN)Maulana Malik IbrahimMalang65151Indonesia

Curcumin is a natural polyphenol that is used in various traditional medicines.However,its inherent properties,such as its rapid degradation and metabolism,low bioavailability,and short half-life,are serious problems that must be resolved.To this end,a drug carrier incorporating natural magnetic cores in a zeolite framework was developed and applied to the loading of curcumin in ethanol solutions.In this system,curcumin is encapsulated in a zeolite Na(ZNA)magnetic core–shell structure(Fe@Si/ZNA),which can be easily synthesized using an in situ method.Synthesis of Fe_(3)O_(4) nanoparticles was carried out from natural materials using a co-precipitation method.Analysis of the prepared magnetic core–shell structures and composites was carried out using vibrating-sample magnetometery,Fourier transform infrared spectroscopy,transmission electron microscopy,and x-ray diffraction.The cumulative loading of curcumin in the ZNA composite with 9%nanoparticles was found to reach 90.70%with a relatively long half-life of 32.49 min.Stability tests of curcumin loading in the composite showed that adding magnetic particles to the zeolite framework also increased the stability of the composite structure.Adsorption kinetics and isotherm studies also found that the system follows the pseudo-second-order and Langmuir isotherm models.

关键词： Zeolite Na Magnetic core–shell nanoparticles(MNPs) ZNA Adsorption Curcumin

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Tuning structural,electrical,dielectric and magnetic properties of Mg-Cu-Co ferrites via dysprosium(Dy^(3+))doping

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Journal of Rare Earths 2024年第1期42卷 137-146,I0005页

作者： Maria Akhtar M.S.Hasan nasir Amin N.A.Morley muhammad Imran Arshad Department of Physics Government College UniversityFaisalabad38000Pakistan Department of Physics The University of Lahore53700Pakistan Department of Materials Science and Engineering The University of SheffieldS13JDUK Department of Physics&Astronomy University College LondonUK

In current research work,Dy3+substituted Mg0.5Cu0.25Co0.25Fe2-xDyxO4(0.0≤x≤0.04 with the step interval of 0.01)soft ferrites were synthesized by the sol-gel auto combustion method.The prepared samples were characterized by the techniques using X-ray diffraction(XRD),scanning electron microscopy(SEM),energy dispersive X-ray spectroscopy(EDS),Fourier transform infrared(FTIR)spectroscopy,curre nt-voltage(Ⅰ-Ⅴ)measurement,LCR meter,vibrating sample magnetometer(VSM)and Raman.XRD data reveal that the average crystallite size is 49.71 nm and the lattice constant is 0.83703 nm for sample x=0.03.The non-uniform grain growth was demonstrated by micrographs and impurity-free elemental composition was observed from EDX analysis.The DC resistivity has an increasing and decreasing trend in ferromagnetic and paramagnetic regions with an increase in temperature.Moreover,the high resistivity is observed with the order of 1010Ω·cm,and the activation energy is 0.944 eV for samples x=0.03.The dielectric parameters including dielectric constant,dielectric losses,and impedance gradually decrease with the increase in frequency from 8 Hz to 8 MHz.The minimum dielectric loss at high frequency is found for sample x=0.03.The coercivity(Hc)and saturation magnetization(Ms)are found in the ranges of 520.82-544.02 Oe and 20.5841-21.1473 emu/g,respectively.These observations confirm that dysprosium(x=0.03)doped MCC-soft ferrites may be applicable in transformer cores,microwave absorbance,and telecommunication devices.

关键词： Soft ferrites DC resistivity Dielectric loss Impedance Coercivity Rare earths

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Identifying the stability of housekeeping genes to be used for the quantitative real-time PCR normalization in retinal tissue of streptozotocin-induced diabetic rats

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International Journal of Ophthalmology(English edition) 2024年第5期17卷 794-805页

作者： muhammad Zulfiqah Sadikan Nurul Alimah Abdul nasir Mohammad Johari Ibahim Igor Iezhitsa Renu Agarwal Department of Pharmacology Faculty of MedicineManipal University College Malaysia(MUCM)Bukit BaruMelaka 75150Malaysia Centre for Neuroscience Research(NeuRon) Faculty of MedicineUniversiti Teknologi MARASungai Buloh CampusSungai BulohSelangor 47000Malaysia Department of Medical Education Faculty of MedicineUniversiti Teknologi MARASungai Buloh CampusSungai BulohSelangor 47000Malaysia Department of Biochemistry and Molecular Medicine Faculty of MedicineUniversiti Teknologi MARASungai Buloh CampusSungai BulohSelangor 47000Malaysia School of Medicine International Medical UniversityBukit JalilKuala Lumpur 57000Malaysia Department of Pharmacology and Bioinformatics Volgograd State Medical UniversityVolgograd 400131Russia

AIM:To investigate the stability of the seven housekeeping genes:beta-actin(ActB),glyceraldehyde-3-phosphate dehydrogenase(GAPDH),18s ribosomal unit 5(18s),cyclophilin A(CycA),hypoxanthine-guanine phosphoribosyl transferase(HPRT),ribosomal protein large P0(36B4)and terminal uridylyl transferase 1(U6)in the diabetic retinal tissue of rat model.METHODS:The expression of these seven genes in rat retinal tissues was determined using real-time quantitative reverse transcription polymerase chain reaction(RT-qPCR)in two groups;normal control rats and streptozotocininduced diabetic rats.The stability analysis of gene expression was investigated using geNorm,NormFinder,BestKeeper,and comparative delta-Ct(ΔCt)algorithms.RESULTS:The 36B4 gene was stably expressed in the retinal tissues of normal control animals;however,it was less stable in diabetic retinas.The 18s gene was expressed consistently in both normal control and diabetic rats’retinal tissue.That this gene was the best reference for data normalisation in RT-qPCR studies that used the retinal tissue of streptozotocin-induced diabetic rats.Furthermore,there was no ideal gene stably expressed for use in all experimental settings.CONCLUSION:Identifying relevant genes is a need for achieving RT-qPCR validity and reliability and must be appropriately achieved based on a specific experimental setting.

关键词： housekeeping genes stability real-time reverse transcription polymerase chain reaction retinal tissue streptozotocin-induced diabetic rats

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Long-term outcomes of titanium-nitride-oxide coated stents and drug-eluting stents in acute coronary syndrome:A systematic review and meta-analysis

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World Journal of Cardiology 2024年第5期16卷 293-305页

作者： muhammad Ahmed Ali Fahim Afia Salman Hira Anas Khan Syed muhammad Hasan Muskan FatimaBhojani Sarah Aslam Amna Zia Ul Haq Vishal Reddy Bejugam Beena Muntaha nasir Wajiha Gul Abdul Moeed Abdelrahman S Abdalla muhammad Majid muhammad Sohaib Asghar Md Al Hasibuzzaman Department of Internal Medicine Dow University of Health SciencesKarachi 74200Pakistan Department of Internal Medicine North Central Bronx HospitalBronxNY 10467United States Department of Urology Center for Academic MedicineStanford University School of MedicinePalo AltoCA 33872United States Department of Cardiovascular Imaging Cleveland ClinicClevelandOH 44195United States Division of Nephrology and Hypertension Mayo Clinic RochesterRochesterMN 55905United States Institute of Nutrition and Food Science University of DhakaDhaka 1000Bangladesh

BACKGROUND In severe cases of coronary artery disease,percutaneous coronary intervention provide promising results.The stent used could be a drug-eluting stent(DES)or a titanium-nitride-oxide coated stent(TiNOS).AIM To compare the 5-year effectiveness and safety of the two stent types.METHODS The following systematic review and meta-analysis was conducted in accordance with the preferred reporting items for systematic reviews and meta-analysis guidelines,and PubMed/MEDLINE,Scopus,and Cochrane Central were searched from inception till August 2023.Primary outcomes were major adverse cardiac events(MACE),cardiac death,myocardial infarction(MI),cardiac death or MI,and ischemia-driven total lesion revascularization(ID-TLR).RESULTS Four randomized controlled trials(RCT),which analyzed a sum total of 3045 patients with acute coronary syndrome(ACS)after a median follow-up time of 5 years were included.Though statistically insignificant,an increase in the ID-TLR was observed in patients receiving TiNOSs vs DESs.In addition,MI,cardiac death and MI,and definite stent thrombosis(DST)were significantly decreased in the TiNOS arm.Baseline analysis revealed no significant results with meta-regression presenting non-ST elevated MI(NSTEMI)as a statistically significant covariate in the outcome of MACE.CONCLUSION TiNOS was found to be superior to DES in terms of MI,cardiac death or MI,and DST outcomes,however,the effect of the two stent types on ID-TLR and MACE was not significant.A greater number of studies are required to establish an accurate comparison of patient outcomes in TiNOS and DES.

关键词： Stents Drug-eluting Major adverse cardiac events All-cause death Meta-analysis

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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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Isolation, characterization and identification of plant growth-promoting rhizobacteria from the rhizosphere of Acacia mangium at sandy BRIS soil

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Frontiers of Agricultural Science and Engineering 2024年第3期11卷 499-513页

作者： Zakiah MUSTAPHA Radziah OTHMAN Nik Nurnaeimah NIK muhammad nasir Dhiya Dalila ZAWAWI Mohd Khairi CHE LAH Hafizan JUAHIR School of Agriculture Science and Biotechnology Faculty of Bioresources and Food Industry Universiti Sultan Zainal Abidin Besut Campus Department of Land Management Faculty of Agriculture Universiti Putra Malaysia Faculty of Plantation and Agrotechnology Universiti Technologi MARA Cawangan Pahang

This study has isolated, characterized, and identified potential plant growthpromoting rhizobacteria（PGPR） with multiple PGP characteristics（N2-fixation,P-and K-solubilization, IAA, and siderophores production） from the rhizosphere BRIS soil of Acacia mangium. A total of 24 pure colonies were isolated and only 8 colonies were selected for further evaluation of the growth rate in 5% organic molasses medium supplemented with 2% KNO3. Based on the biochemical, potential PGP characteristics and growth performance, 3 superior PGPR strains were selected and identified as Paraburkholderia unamae（UA1）, Bacillus amyloliquefaciens（UA6）, and Enterobacter asburiae（UAA2） by partial sequencing of the 16S rRNA gene. The selected bacterial strains either in single or mixed（UA1 + UA6 + UAA2） cultures have shown a significant biochemical estimation of the PGP characteristics. Each strain has its own PGPR traits superiority with UA1 showing the best PGP characteristic followed by UA6 and UAA2. The use of mixed bacterial strains was beneficial as it showed the best performance in N2-fixation, siderophores production, and significant effect on corn phenology, growth and yield compared to using a single strain. These types of microbes showed potential to be used as biofertilizer and should be exploited more.

关键词： Biofertilizer corn organic molasses me- dium PGP characteristic phytohormone potassium nitrate siderophores

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