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Improving Prediction of Chronic Kidney Disease Using KNN Imputed SMOTE Features and TrioNet Model

Computer Modeling in Engineering & Sciences 2024年第6期139卷 3513-3534页

作者： nazik.alturk. Abdulaziz Altamimi Muhammad Umer Oumaima Saidani Amal Alshardan Shtwai Alsubai Marwan Omar Imran Ashraf Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department College of Computer Science and Engineering University of Hafr Al-BatinHafarAl-Batin39524Saudi Arabia Department of Computer Science&Information Technology The Islamia University of BahawalpurP.O.Box 63100BahawalpurPakistan Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityP.O.Box 151Al-Kharj11942Saudi Arabia Information Technology and Management Illinois Institute of TechnologyChicagoIL 60616-3793USA Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

Chronic kidney disease(CKD)is a major health concern today,requiring early and accurate *** learning has emerged as a powerful tool for disease detection,and medical professionals are increasingly using ML classifier algorithms to identify CKD *** study explores the application of advanced machine learning techniques on a CKD dataset obtained from the University of California,UC Irvine Machine Learning *** research introduces TrioNet,an ensemble model combining extreme gradient boosting,random forest,and extra tree classifier,which excels in providing highly accurate predictions for ***,K nearest neighbor(KNN)imputer is utilized to deal withmissing values while synthetic minority oversampling(SMOTE)is used for class-imbalance *** ascertain the efficacy of the proposed model,a comprehensive comparative analysis is conducted with various machine learning *** proposed TrioNet using KNN imputer and SMOTE outperformed other models with 98.97%accuracy for *** in-depth analysis demonstrates the model’s capabilities and underscores its potential as a valuable tool in the diagnosis of CKD.

关键词： Precisionmedicine chronic kidney disease detection SMOTE missing values healthcare KNNimputer ensemble learning

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Efficient and Secure IoT Based Smart Home Automation Using Multi-Model Learning and Blockchain Technology

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Computer Modeling in Engineering & Sciences 2024年第6期139卷 3387-3415页

作者： nazik.alturk. Raed Alharthi Muhammad Umer Oumaima Saidani Amal Alshardan Reemah M.Alhebshi Shtwai Alsubai Ali Kashif Bashir Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Computer Science and Engineering University of Hafr Al-BatinHafar Al-BatinSaudi Arabia Department of Computer Science&Information Technology The Islamia University of BahawalpurBahawalpurPakistan Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterUK Woxsen School of Business Woxsen UniversityHyderabadIndia Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon

The concept of smart houses has grown in prominence in recent *** challenges linked to smart homes are identification theft,data safety,automated decision-making for IoT-based devices,and the security of the device *** home automation systems try to address these issues but there is still an urgent need for a dependable and secure smart home solution that includes automatic decision-making systems and methodical *** paper proposes a smart home system based on ensemble learning of random forest(RF)and convolutional neural networks(CNN)for programmed decision-making tasks,such as categorizing gadgets as“OFF”or“ON”based on their normal routine in *** have integrated emerging blockchain technology to provide secure,decentralized,and trustworthy authentication and recognition of IoT *** system consists of a 5V relay circuit,various sensors,and a Raspberry Pi server and database for managing *** have also developed an Android app that communicates with the server interface through an HTTP web interface and an Apache *** feasibility and efficacy of the proposed smart home automation system have been evaluated in both laboratory and real-time *** is essential to use inexpensive,scalable,and readily available components and technologies in smart home automation ***,we must incorporate a comprehensive security and privacy-centric design that emphasizes risk assessments,such as cyberattacks,hardware security,and other cyber *** trial results support the proposed system and demonstrate its potential for use in everyday life.

关键词： Blockchain Internet of Things(IoT) smart home automation cybersecurity

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Supportive care needs of women with gynecologic cancer and factors affecting in Turkiye: a cross sectional study

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Nursing Communications 2024年第7期8卷 1-7页

作者： Ebru Var Evşen nazik Department of Quality Adana City HospitalAdana 4522Turkiye Department of Obstetrics and Gynecologic Nursing Faculty of Health SciencesÇukurova UniversityAdana 01330Turkiye

Background:Identifying and managing patients'supportive care needs is an essential component of health *** cancer patients'needs are multidimensional and it is important to identify these *** study was conducted to assess supportive care needs of women with gynecologic cancer and affecting ***:This cross-sectional study consisted of 98 women with gynecologic *** data of the study was gathered using“Personal Information Form”and the“James Supportive Care Screening”.Results:The most frequently reported unmet needs of gynecologic cancer patients were emotional issues,including uncertainty(87.6%),fears(85.7%),feeling down(86.7%),worry(87.6%),loss of interest in usual activities(73.3%)and physical symptoms including fatigue/lack of energy(81.9%),weakness(81.9%),lack of appetite(76.2%).The most frequently reported social/practical problems was transportation problems(50%).The mean scores for JSCS was 29.72±*** were a statistically significant difference was found only between family type,stage of the disease,and time of diagnosis,and the total mean scores of the JSCS(p<0.05).Conclusion:It has been determined that patients with gynecological cancer mostly experience emotional and physical *** professionals working in the oncology unit should be aware of unmet *** programs and services to address the identified unmet needs should be provided.

关键词： gynecologic cancer unmet needs supportive care nursing

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Pattern of Potential Laboratory Markers for COVID-19 in Eastern Sudan

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Open Journal of Epidemiology 2024年第3期14卷 508-516页

作者： nazik Sir El Khatim Bakhit Suliman Mohammed Omer Abaker Gibreel Mervat Sir El Khatim Bakhit Suliman Department of Clinical Chemistry College of Medical Laboratory Science Eastern Sudan University of Medical Science and Technology Port Sudan Sudan Dean of the Secretariat of Academic Affairs Port Sudan Ahlia University Port Sudan Sudan Cardiology Department Cath Lab Fujaira Hospital Fujaira UAE

Background: Coronavirus disease 2019 (COVID-19) is a recent global health crisis. One of the major issues of COVID-19 is its unpredictable manifestations and serious outcomes. Many hematological parameters are thought to change dramatically during the course of the disease. These include white blood cells, red blood cells, and platelets. This study aimed at evaluating certain laboratory results;peripheral blood lymphopenia, relative neutrophilia, high neutrophil-lymphocyte ratio, and elevated C-reactive protein as potential laboratory markers of COVID-19 in Eastern Sudanese patients. Methods: We, retrospectively, aimed at the evaluation of peripheral blood leucocytes count, neutrophil-lymphocyte ratio NLR and C-reactive protein (CRP) levels in confirmed COVID-19 eastern Sudanese patients during the course of the disease. Results: The mean total leucocytes count, % neutrophils count, absolute neutrophils count and C-reactive protein (CRP) were significantly higher (P. value = 0.000) in COVID-19 patients than in the control group while the mean % lymphocytes count and % mixed cells count were found to be significantly lower in COVID-19 patients than in the control group (P. value 0.000). Conclusion: Peripheral blood leucocyte alterations (simultaneous presence of lymphopenia, relative neutrophilia and high neutrophil lymphocyte ratio (NLR) along with elevated CRP levels may be valuable biomarkers associated with COVID-19 in Port Sudan city, Red Sea state, Sudan. These markers might be important in prediction, inspection of disease progression and prognosis.

关键词： COVID-19 SARS 2 WBCs CRP NLR Port Sudan

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Colliding Bodies Optimization with Machine Learning Based Parkinson’s Disease Diagnosis

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Computer Systems Science & Engineering 2023年第3期44卷 2195-2207页

作者： Ashit Kumar Dutta nazik M.A.Zakari Yasser Albagory Abdul Rahaman Wahab Sait Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Nursing College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

Parkinson’s disease(PD)is one of the primary vital degenerative diseases that affect the Central Nervous System among elderly *** affect their quality of life drastically and millions of seniors are diagnosed with PD every year *** models have been presented earlier to detect the PD using various types of measurement data like speech,gait patterns,*** identification of PD is important owing to the fact that the patient can offer important details which helps in slowing down the progress of *** recently-emerging Deep Learning(DL)models can leverage the past data to detect and classify *** this motivation,the current study develops a novel Colliding Bodies Optimization Algorithm with Optimal Kernel Extreme Learning Machine(CBO-OKELM)for diagnosis and classification of *** goal of the proposed CBO-OKELM technique is to identify whether PD exists or ***-OKELM technique involves the design of Colliding Bodies Optimization-based Feature Selection(CBO-FS)technique for optimal subset of *** addition,Water Strider Algorithm(WSA)with Kernel Extreme Learning Machine(KELM)model is also developed for the classification of *** algorithm is used to elect the optimal set of fea-tures whereas WSA is utilized for parameter tuning of KELM model which alto-gether helps in accomplishing the maximum PD diagnostic *** experimental analysis was conducted for CBO-OKELM technique against four benchmark datasets and the model portrayed better performance such as 95.68%,96.34%,92.49%,and 92.36%on Speech PD,Voice PD,Hand PD Mean-der,and Hand PD Spiral datasets respectively.

关键词： Parkinson’s disease colliding bodies optimization algorithm feature selection metaheuristics classification kelm model

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A New S-Box Design System for Data Encryption Using Artificial Bee Colony Algorithm

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Computers, Materials & Continua 2023年第10期77卷 781-797页

作者： Yazeed Yasin Ghadi Mohammed SAlshehri Sultan Almakdi Oumaima Saidani nazik.alturk. Fawad Masood Muhammad Shahbaz Khan Department of Computer Science Al Ain UniversityAbu Dhabi112612United Arab Emirates Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Electrical Engineering Institute of Space TechnologyIslamabad4400Pakistan School of Computing Engineering and the Built EnvironmentEdinburgh Napier UniversityEdinburghEH105DTUK

Securing digital image data is a key concern in today’s information-driven *** encryption techniques are required to protect sensitive image data,with the Substitution-box(S-box)often playing a pivotal role in many symmetric encryption *** study introduces an innovative approach to creating S-boxes for encryption *** proposed S-boxes are tested for validity and non-linearity by incorporating them into an image encryption *** nonlinearity measure of the proposed S-boxes is *** qualities significantly enhance its resistance to common cryptographic attacks,ensuring high image data ***,to assess the robustness of the S-boxes,an encryption system has also been proposed and the proposed S-boxes have been integrated into the designed encryption *** validate the effectiveness of the proposed encryption system,a comprehensive security analysis including brute force attack and histogram analysis has been *** addition,to determine the level of security during the transmission and storage of digital content,the encryption system’s Number of Pixel Change Rate(NPCR),and Unified Averaged Changed Intensity(UACI)are *** results indicate a 99.71%NPCR and 33.51%*** results demonstrate that the proposed S-boxes offer a significant level of security for digital content throughout its transmission and storage.

关键词： S-box chaos artificial bee colony image encryption

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LexDeep:Hybrid Lexicon and Deep Learning Sentiment Analysis Using Twitter for Unemployment-Related Discussions During COVID-19

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计算机、材料和连续体（英文） 2023年第4期75卷 1577-1601页

作者： Azlinah Mohamed Zuhaira Muhammad Zain Hadil Shaiba nazik.alturk. Ghadah Aldehim Sapiah Sakri Saiful Farik Mat Yatin Jasni Mohamad Zain Institute for Big Data Analytics and Artificial Intelligence Universiti Teknologi MARA(UiTM)Shah AlamMalaysia College of Computer and Information Sciences Princess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA(UiTM)Shah AlamMalaysia

The COVID-19 pandemic has spread globally,resulting in financialinstability in many countries and reductions in the per capita grossdomestic *** analysis is a cost-effective method for acquiringsentiments based on household income loss,as expressed on social ***,limited research has been conducted in this domain using theLexDeep *** study aimed to explore social trend analytics usingLexDeep,which is a hybrid sentiment analysis technique,on Twitter to capturethe risk of household income loss during the COVID-19 ***,tweet data were collected using Twint with relevant keywords before(9 March2019 to 17 March 2020)and during(18 March 2020 to 21 August 2021)***,the tweets were annotated using VADER(lexiconbased)and fed into deep learning classifiers,and experiments were conductedusing several embeddings,namely simple embedding,Global Vectors,andWord2Vec,to classify the sentiments expressed in the *** performanceof each LexDeep model was evaluated and compared with that of a supportvector machine(SVM).Finally,the unemployment rates before and duringCOVID-19 were analysed to gain insights into the differences in unemploymentpercentages through social media input and *** resultsdemonstrated that all LexDeep models with simple embedding outperformedthe *** confirmed the superiority of the proposed LexDeep modelover a classical machine learning classifier in performing sentiment analysistasks for domain-specific *** terms of the risk of income loss,the unemployment issue is highly politicised on both the regional and globalscales;thus,if a country cannot combat this issue,the global economy will alsobe *** research should develop a utility maximisation algorithmfor household welfare evaluation,given the percentage risk of income lossowing to COVID-19.

关键词： Sentiment analysis sentiment lexicon machine learning imbalanced data deep learning method unemployment rate

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Privacy Preserving Image Encryption with Deep Learning Based IoT Healthcare Applications

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Computers, Materials & Continua 2022年第10期73卷 1159-1175页

作者： Mohammad Alamgeer Saud S.Alotaibi Shaha Al-Otaibi nazik.alturk. Anwer Mustafa Hilal Abdelwahed Motwakel Ishfaq Yaseen Mohamed I.Eldesouki Department of Information Systems College of Science&Art at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

Latest developments in computing and communication technologies are enabled the design of connected healthcare system which are mainly based on IoT and Edge ***,data encryption,and deep learning(DL)models can be utilized to design efficient security solutions for IoT healthcare *** this aspect,this article introduces a Blockchain with privacy preserving image encryption and optimal deep learning(BPPIEODL)technique for IoT healthcare *** proposed BPPIE-ODL technique intends to securely transmit the encrypted medical images captured by IoT devices and performs classification process at the cloud *** proposed BPPIE-ODL technique encompasses the design of dragonfly algorithm(DFA)with signcryption technique to encrypt the medical images captured by the IoT ***,blockchain(BC)can be utilized as a distributed data saving approach for generating a ledger,which permits access to the users and prevents third party’s access to encrypted *** addition,the classification process includes SqueezeNet based feature extraction,softmax classifier(SMC),and Nadam based hyperparameter *** usage of Nadam model helps to optimally regulate the hyperparameters of the SqueezeNet *** examining the enhanced encryption as well as classification performance of the BPPIE-ODL technique,a comprehensive experimental analysis is carried *** simulation outcomes demonstrate the significant performance of the BPPIE-ODL technique on the other techniques with increased precision and accuracy of 0.9551 and 0.9813 respectively.

关键词： Internet of things healthcare decision making privacy preserving blockchain deep learning

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LMP1 Immunohistochemistry in Non-Hodgkin’s Lymphoma of Sudanese Cases

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Open Journal of Pathology 2016年第2期6卷 79-87页

作者： Amal Ismail Ihsan Osman nazik Elmalaika Husain Medical Doctorate Student Pathology Department Khartoum University Khartoum Sudan Pathology Department Alzaiem Alazhari University Khartoum Sudan Pathology Department Omdurman Islamic University Omdurman Sudan

Back ground: Non-Hodgkin’s lymphoma (NHL) with its different subtypes is strongly related to Epstein Bar virus (EBV) infection mainly Burkitt’s lymphoma in Africa. Studies proved the role of EBV in tumor-genesis and linked it to prognosis and therapy of patients. Objectives: To determine the frequency of EBV in non-Hodgkin lymphomas using EBV latent membrane protein 1 (EBV-LMP1) immunohistochemical stain. Methods: This cross-sectional study was conducted at radio-isotope centre of Khartoum (2012-2014). A total of 75 cases of non-Hodgkin lymphoma paraffin embedded sections were stained for EBV LMP1 antibody. Data were analyzed by SPSS 16 and statistical cross linking of the results of immune staining with other data was done. Results: Out of 75 patients of non Hodgkin’s lymphoma (74.7%) were males. EBV-LMP1 Immune-staining was positive in (17.3%) with predominance of Burkitt’s lymphoma (33.3%), followed by diffuse large B cell lymphoma (17.9%). Conclusion: Burkitt’s lymphoma expressed the highest percentage of non-Hodgkin’s lymphoma positive cases (46.2%) out of the total (17.3%) positive cases. Different methods need to be used in studying Burkitt’s lymphoma expression of EBV and its effects on the treatment and prognosis of cases.

关键词： Epstein Bar Virus Latent Membrane Protein 1 LMP1 Non-Hodgkin’s Lymphoma Immunohistochemistry

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A qualitative study of the Omani physicians’ lived experience with truth disclosure to cancer patient

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Open Journal of Nursing 2013年第1期3卷 130-137页

作者： Ahmed Bait Amer nazik AL-Zakri Behavioral Medicine Sultan Qaboose University Hospital Muscat Oman nzakari@***

Objectives: The purpose of this phenomenological Qualitative Study is to gain an in depth understanding of the nature and meaning of the experiences of physicians in relation to truth disclosure and ethics of veracity when diagnosing cancer. Methods: A qualitative phenomenological study using semi-structured interviews was conducted at Sultan Qaboos university hospital (SQUH), l to explore the Omani Physicians’ lived experiences with truth disclosure in patients who were diagnosed with cancer. The target population for this study is Omani physicians working in SQUH. The total of four participants was involved in this study. Results: Three essential themes were generated through the analysis of the participants’ descriptions of their perceptions and lived experiences as the following with their subthemes: the first theme is the clinical aspect of the experiences with three subthemes: 1) The ethical aspect and the physician’s attitude;2) The strategy of breaking bad news;3) Training and breaking bad news. The second theme is Cultural diversity with three subthemes: 1) Relatives preference;2) The patient education;3) The hospital setting. The third theme is the emotional aspect of the experiences with five subthemes: 1) Emotional sadness;2) Personal grief;3) Other emotions;4) The positive emotions;5) Control of the emotions. Conclusion: This study recommends: to build up appropriate measures and guidelines on the subject of truth telling and medical ethics, and to provide more training for the health care providers in the context of breaking bad news. In addition to add to the curriculum of medical colleges the basic ethical principles relevant to medical practice. Finally, to establish medical social work departments in SQUH, and other healthcare institutions in Oman.

关键词： Telling the Truth Ethics Cancer Communication Oman Health Care Provider

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