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Primary care for cancers at diagnosis and follow-up:a narrative review

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Family Medicine and Community Health 2013年第1期1卷 56-67页

作者： CHEW BOON HOW SRI WAHYU taher Department of Family Medicine Universiti Putra MalaysiaMalaysia Klinik Kesihatan Simpang Kuala Alor SetarKedahMalaysia

This paper is concerned about the family physician’s role in early cancer diagnosis and follow-up with his/hers patients who have just been diagnosed with cancer;treatment modalities for cancer;and family physician continuous roles for patients who are under definitive cancer treatment,experiencing side-effects of cancer treatment;some of the effective means to reduce these side-effects during cancer treatment and management of oncologic *** some knowledge on the current cancer therapies would undoubtedly help family physicians to follow up patients with cancer more confidently,to appreciate their side-effects,symptomatic treatment,recognize the limit of primary care and be even useful for counseling and consultation with patients or their family members with a family history of *** searches with terms comprised“cancer”,“malignancy”,“primary care”,“general practice”,“cancer AND diagnosis”and“cancer AND follow-up”were done in the major databases such as Pubmed,ScienceDirect and *** employed selective searches with the above terms and their combination in some of the major journal such as The Lancet Oncology,The Lancet,New England Journal of Medicine,*** were followed by snowballing the relevant articles from the citation of references in those selected *** goal of this narrative review is not to provide exhaustive documentation of sound evidence for practice of primary care for cancer patients at diagnosis and *** mainly aims to provide specific evidence-based information and suggestions that are thought to be relevant for primary care professionals and policymakers.

关键词： Cancers Primary care General practice Office visits Diagnosis Survivors

Predicting carbon nanotube forest attributes and mechanical properties using simulated images and deep learning

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npj Computational Materials 2021年第1期7卷 1214-1224页

作者： taher Hajilounezhad Rina Bao Kannappan Palaniappan Filiz Bunyak Prasad Calyam Matthew R.Maschmann Mechanical&Aerospace Engineering University of MissouriColumbiaMOUSA electrical Engineering and Computer Science University of MissouriColumbiaMOUSA

Understanding and controlling the self-assembly of vertically oriented carbon nanotube(CNT)forests is essential for realizing their potential in myriad *** governing process–structure–property mechanisms are poorly understood,and the processing parameter space is far too vast to exhaustively explore *** overcome these limitations by using a physics-based simulation as a high-throughput virtual laboratory and image-based machine learning to relate CNT forest synthesis attributes to their mechanical *** CNTNet,our image-based deep learning classifier module trained with synthetic imagery,combinations of CNT diameter,density,and population growth rate classes were labeled with an accuracy of>91%.The CNTNet regression module predicted CNT forest stiffness and buckling load properties with a lower root-mean-square error than that of a regression predictor based on CNT physical *** results demonstrate that image-based machine learning trained using only simulated imagery can distinguish subtle CNT forest morphological features to predict physical material properties with high *** paves the way to incorporate scanning electron microscope imagery for high-throughput material discovery.

关键词： carbon properties overcome

IoMT-Based Smart Healthcare of elderly People Using Deep Extreme Learning Machine

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Computers, Materials & Continua 2023年第7期76卷 19-33页

作者： Muath Jarrah Hussam Al Hamadi Ahmed Abu-Khadrah taher M.Ghazal School of Information Technology Skyline University CollegeUniversity City Sharjah1797SharjahUAE College of Computing and Informatics Saudi Electronic UniversityRiyadh11673Saudi Arabia Center for Cyber Security Faculty of Information Science and TechnologyUniversity Kebangsaan Malaysia(UKM)43600BangiSelangorMalaysia Center for Cyber-Physical Systems Khalifa University127788Abu DhabiUAE

The Internet of Medical Things(IoMT)enables digital devices to gather,infer,and broadcast health data via the cloud *** phenomenal growth of the IoMT is fueled by many factors,including the widespread and growing availability of wearables and the ever-decreasing cost of sensor-based *** is a growing interest in providing solutions for elderly people living assistance in a world where the population is rising *** IoMT is a novel reality transforming our daily *** can renovate modern healthcare by delivering a more personalized,protective,and collaborative approach to ***,the current healthcare system for outdoor senior citizens faces new *** healthcare systems are inefficient and lack user-friendly technologies and interfaces appropriate for elderly people in an outdoor ***,in this research work,a IoMT based Smart Healthcare of elderly people using Deep Extreme Learning Machine(SH-EDelM)is proposed to monitor the senior citizens’*** performance of the proposed SH-EDelM technique gives better results in terms of 0.9301 accuracy and 0.0699 miss rate,respectively.

关键词： ICT ML FN DelM SH-EDelM

硫化物矿石中金属的提取工艺及使用高锰酸铝[Al(MnO_(4))_(3)]氧化剂去除其中杂质:实验设计和工业建模

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中南大学学报:英文版 2023年第7期30卷 2149-2165页

作者： Alizadeh taher Kadkhodayan HOSSEIN Department of Analytical Chemistry Faculty of ChemistryUniversity College of ScienceUniversity of TehranTehranIran

目前,硫化矿是贵金属的主要来源。在硫化矿处理过程中存在的主要问题之一是其在酸中的溶解度低(K_(sp)<10^(-20)),并产生有毒有害的副产品。在本研究中,使用高锰酸铝[Al(MnO_(4))_(3)]氧化剂溶解硫化矿来提取金属,部分解决了这个问题。... 详细信息

目前,硫化矿是贵金属的主要来源。在硫化矿处理过程中存在的主要问题之一是其在酸中的溶解度低(K_(sp)<10^(-20)),并产生有毒有害的副产品。在本研究中,使用高锰酸铝[Al(MnO_(4))_(3)]氧化剂溶解硫化矿来提取金属,部分解决了这个问题。基于Aspen Hysys模型组装的Taguchi实验设计作为一种从实验到工业规模的新方案,已应用于Al(MnO_(4))_(3)氧化剂对硫化物矿石的溶解研究。最佳结果已被用作Aspen Hysys软件对该过程进行模拟和灵敏度分析的主要数据。研究了pH、停留温度、搅拌速率、停留时间、Al(MnO_(4))_(3)消耗量、浸出密度、粒度和氧压等参数对从硫化矿中萃取金属效率的影响。在优化条件下,锌、铜和铅的萃取率分别达到77%、73%和70%。

关键词：高锰酸铝硫化矿 Aspen Hysys软件 Taguchi实验设计

Automated diagnosis and staging of Fuchs’endothelial cell corneal dystrophy using deep learning

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Eye and Vision 2020年第1期7卷 423-433页

作者： taher eleiwa Amr elsawy EyüpÖzcan Mohamed Abou Shousha Bascom Palmer Eye Institute Miller School of MedicineUniversity of MiamiMiamiFlorida 33136USA Department of Ophthalmology Faculty of MedicineBenha UniversityBenhaEgypt electrical and Computer Engineering University of MiamiCoral GablesFloridaUSA Net Eye Medical Center GaziantepTurkey Biomedical Engineering University of MiamiCoral GablesFloridaUSA

Background:To describe the diagnostic performance of a deep learning algorithm in discriminating early-stage Fuchs’endothelial corneal dystrophy(FECD)without clinically evident corneal edema from healthy and late-stage FECD eyes using high-definition optical coherence tomography(HD-OCT).Methods:In this observational case-control study,104 eyes(53 FECD eyes and 51 healthy controls)received HDOCT imaging(Envisu R2210,Bioptigen,Buffalo Grove,IL,USA)using a 6 mm radial scan pattern centered on the corneal *** was clinically categorized into early(without corneal edema)and late-stage(with corneal edema).A total of 18,720 anterior segment optical coherence tomography(AS-OCT)images(9180 healthy;5400 early-stage FECD;4140 late-stage FECD)of 104 eyes(81 patients)were used to develop and validate a deep learning classification network to differentiate early-stage FECD eyes from healthy eyes and those with clinical *** 5-fold cross-validation on the dataset containing 11,340 OCT images(63 eyes),the network was trained with 80%of these images(3420 healthy;3060 early-stage FECD;2700 late-stage FECD),then tested with 20%(720 healthy;720 early-stage FECD;720 late-stage FECD).Thereafter,a final model was trained with the entire dataset consisting the 11,340 images and validated with a remaining 7380 images of unseen AS-OCT scans of 41 eyes(5040 healthy;1620 early-stage FECD 720 late-stage FECD).Visualization of learned features was done,and area under curve(AUC),specificity,and sensitivity of the prediction outputs for healthy,early and late-stage FECD were ***:The final model achieved an AUC of 0.997±0.005 with 91%sensitivity and 97%specificity in detecting early-FECD;an AUC of 0.974±0.005 with a specificity of 92%and a sensitivity up to 100%in detecting late-stage FECD;and an AUC of 0.998±0.001 with a specificity 98%and a sensitivity of 99%in discriminating healthy corneas from all ***:Deep learning algorithm is an accurate autonomous nove

关键词： Optical coherence tomography Descemet’s membrane Corneal endothelium Fuchs endothelial dystrophy

Data Fusion-Based Machine Learning Architecture for Intrusion Detection

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Computers, Materials & Continua 2022年第2期70卷 3399-3413页

作者： Muhammad Adnan Khan taher M.Ghazal Sang-Woong Lee Abdur Rehman Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebansaan Malaysia(UKM)43600BangiSelangorMalaysia School of Information Technology Skyline University CollegeUniversity City Sharjah1797SharjahUAE School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan

In recent years,the infrastructure of Wireless Internet of Sensor Networks(WIoSNs)has been more complicated owing to developments in the internet and devices’*** effectively prepare,control,hold and optimize wireless sensor networks,a better assessment needs to be *** field of artificial intelligence has made a great deal of progress with deep learning systems and these techniques have been used for data *** study investigates the methodology of Real Time Sequential Deep Extreme LearningMachine(RTS-DelM)implemented to wireless Internet of Things(IoT)enabled sensor networks for the detection of any intrusion *** fusion is awell-knownmethodology that can be beneficial for the improvement of data accuracy,as well as for the maximizing of wireless sensor networks *** also suggested an approach that not only makes the casting of parallel data fusion network but also render their computations more *** using the Real Time Sequential Deep Extreme Learning Machine(RTSDelM)methodology,an excessive degree of reliability with a minimal error rate of any intrusion activity in wireless sensor networks is *** results show that wireless sensor networks are optimized effectively to monitor and detect any malicious or intrusion activity through this proposed ***,threats and a more general outlook are explored.

关键词： Wireless internet of sensor networks machine learning deep extreme learning machine artificial intelligence data fusion

Personality Assessment Based on Natural Stream of Thoughts Empowered with Machine Learning

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Computers, Materials & Continua 2023年第7期76卷 1-17页

作者： Mohammed Salahat Liaqat Ali taher M.Ghazal Haitham M.Alzoubi College of Engineering and Technology University of Science and Technology of FujairahFujairahUAE School of Information Technology Skyline University CollegeUniversity City Sharjah1797UAE Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia(UKM)43600Malaysia School of Business Skyline University CollegeUniversity City Sharjah1797SharjahUAE

Knowing each other is obligatory in a multi-agent collaborative *** may develop the desired know-how of each other in various aspects such as habits,job roles,status,and *** different distinguishing characteristics related to a person,personality traits are an effective predictive tool for an individual’s behavioral *** has been observed that when people are asked to share their details through questionnaires,they intentionally or unintentionally become *** knowingly or unknowingly provide enough information in much-unbiased comportment in open writing about *** writings can effectively assess an individual’s personality traits that may yield enormous possibilities for applications such as forensic departments,job interviews,mental health diagnoses,*** of consciousness,collected by James Pennbaker and Laura King,is one such way of writing,referring to a narrative technique where the emotions and thoughts of the writer are presented in a way that brings the reader to the fluid through the mental states of the ***-over,computationally,various attempts have been made in an individual’s personality traits assessment through deep learning algorithms;however,the effectiveness and reliability of results vary with varying word embedding *** article proposes an empirical approach to assessing personality by applying convolutional networks to text *** Encoder Representations from Transformers(BERT)word embedding technique is used for word vector generation to enhance the contextual meanings.

关键词： Personality traits convolutional neural network deep learning word embedding

Local Consolidation Therapy versus Observation for Oligometastatic Non Small Cell Lung Cancer Patients: Phase II Randomized Trial

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Journal of Cancer Therapy 2018年第11期9卷 925-940页

作者： Ghada Ezzat eladawei Rasha Mohamed Abdellatif Sheref Mohamed el-taher Clinical Oncology and Nuclear Medicine Department Mansoura University Mansoura Egypt Public Health & Community Medicine Department Benha University Benha Egypt

Background and Objective: Based on retrospective trials, most progression sites after first line systemic therapy for metastatic non small cell lung cancer (NSCLC) were the primary disease sites rather than new sites. Therefore we conducted phase II randomized study to determine whether oligometastatic NSCLC without disease progression after first line chemotherapy, have prolonged progression free survival when treated with local consolidation therapy of residual disease followed by surveillance compared with no local consolidation therapy (observation). Patients and Methods: Forty eight eligible patients were randomized to either immediate or no local consolidation radiotherapy. 26 patients of immediate local consolidation radiotherapy received 3 D-conformal radiation therapy to primary tumor site and metastatic sites of disease. 22 patients were followed up by observation. Results: Patients in local consolidation arm had significantly better progression free survival (PFS) compared with patients in observation group. Median PFS was 9.5 months (95% CI 7.8 - 11.08) in local consolidation arm and 4.5 months (95%CI 3.9 - 5.7) in observation arm. Patients in local consolidation arm had longer median time to appearance of new metastatic sites (10 months CI 9.3 - 12.6) than those patients in observation arm (4.5 months CI 4.2 - 6.9). Median overall survival (OS) of patients in local consolidation arm was 12 months (95% CI 12.1 - 18.01) and in observation arm 10 months (95% CI 8.7 - 13.8). One year OS rate was 42.3% in local consolidation arm and 31.8% in observation arm;2 year OS rate was 23.1% in local consolidation arm and only 4.5% in observation arm. Conclusion: Local consolidation radiotherapy is simple, safe, efficient, and not expensive treatment for oligometastatic non small cell lung cancer after upfront chemotherapy. Local consolidation radiotherapy achieved significantly prolonged progression free survival and delayed appearance of new metastatic sites. Phase

关键词： Lung Cancer Oligometastatic Conformal Thoracic Radiotherapy

Cardiac Arrhythmia Disease Classifier Model Based on a Fuzzy Fusion Approach

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Computers, Materials & Continua 2023年第5期75卷 4485-4499页

作者： Fatma taher Hamoud Alshammari Lobna Osman Mohamed elhoseny Abdulaziz Shehab Eman elayat College of Technological Innovation Zayed UniversityDubaiUAE Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaSaudi Arabia Department of electronics and Communications Engineering Delta Higher Institute for Engineering&TechnologyMansouraEgypt College of Computing and Informatics University of SharjahSharjahUnited Arab Emirates Department of Information Systems Mansoura UniversityMansoura35516Egypt Department of Teacher Preparation Faculty of Specific EducationMansoura UniversityMansouraEgypt

Cardiac diseases are one of the greatest global health *** to the high annual mortality rates,cardiac diseases have attracted the attention of numerous researchers in recent *** article proposes a hybrid fuzzy fusion classification model for cardiac arrhythmia *** fusion model is utilized to optimally select the highest-ranked features generated by a variety of well-known feature-selection *** ensemble of classifiers is then applied to the fusion’s *** proposed model classifies the arrhythmia dataset from the University of California,Irvine into normal/abnormal classes as well as 16 classes of ***,at the preprocessing steps,for the miss-valued attributes,we used the average value in the linear attributes group by the same class and the most frequent value for nominal ***,in order to ensure the model optimality,we eliminated all attributes which have zero or constant values that might bias the results of utilized *** preprocessing step led to 161 out of 279 attributes(features).Thereafter,a fuzzy-based feature-selection fusion method is applied to fuse high-ranked features obtained from different heuristic feature-selection *** short,our study comprises three main blocks:(1)sensing data and preprocessing;(2)feature queuing,selection,and extraction;and(3)the predictive *** proposed method improves classification performance in terms of accuracy,F1measure,recall,and precision when compared to state-of-the-art *** achieves 98.5%accuracy for binary class mode and 98.9%accuracy for categorized class mode.

关键词： Cardiac arrhythmia preprocessing missing values classification model fusion