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Special Features of Metaplastic Breast Cancer in Tunisian Women

Open Journal of Obstetrics and Gynecology 2018年第13期8卷 1310-1323页

作者： Raja Briki Ons Cherif Mouna Derouich Anouar Chaieb Sassi Boughizane Abdeljalil Khlifi hedi khairi Department of Gynecology and Obstetrics Farhat Hached University Hospital Sousse Tunisia

Introduction: Metaplastic breast carcinomas are a rare, heterogeneous group of breast malignancies characterized by an intrinsically aggressive histology and an unfavorable prognosis. Objective: To determine the clinical and pathological characteristics of metaplastic breast cancers in Tunisian patients, and evaluate their impact on its evolution. Methods: A retrospective study of 44 cases of metaplastic cancers archived during a 26-year period in the Cancer Registry of the Tunisian Center. Results: The frequency of metaplastic cancer was 0.97%. Mean age at diagnosis was 55.4 years (range 26 - 84). Average time to diagnosis was 5.5 months. Average clinical tumor size was 4.95 cm (range 1.5 - 15). Axillary ipsilateral adenopathy was present at diagnosis in 45.9%. The clinical stages IIB (31.8%) and IIA (22.7%) predominated. Squamous metaplasia was the most common (68%) followed by the heterologous mesenchymal subtype. Ganglionic invasion was histologically proven in 17 cases, of which 77% had only adenocarcinomatous contingents. Absence of hormone receptor expression and HER2 overexpression predominated. Primary surgery was carried out in 95% of cases. Average follow-up was 40 months (range 2 - 135). Average overall survival (OS) was 74 months, 63% at 5 years and 60% at 10 years. Average progression free survival (PFS) was 29 months (range 3 - 129), 38% at 5 years and 32% at 10 years. Factors significantly influencing OS and PFS were histological lymph node involvement (p = 0.001 and p = 0.002 respectively). Conclusion: Metaplastic breast cancer observed in Tunisian women constitutes a histological type with an unfavorable prognosis whose improvement requires a more adapted therapeutic strategy.

关键词： Breast Metaplastic Cancer Pathology Prognosis

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Embryonal Rhabdomyosarcoma of the Uterine Cervix: Two Cases Report and Literature Review

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Open Journal of Obstetrics and Gynecology 2014年第14期4卷 868-873页

作者： Khlifi Abdeljalil Boukadida Asma Mouna Kouira Hachani Faten Meddeb Sawssen Hidar Samir Bibi Mohamed Essaidi Habib khairi hedi Department of Gynecology and Obstetrics University Teaching Hospital Farhat Hached Sousse Tunisia

Introduction:?Embryonal rhabdomyosarcoma (RMS) of the uterine cervix is a rare and extremely aggressive malignant entity. However, prognosis seems to be improved with the use of multimodality approach treatment. Cases: We report the cases of 50- and 51-year-old women, presenting with vaginal bleeding and mass. The two patients underwent radical surgery. Histological examination revealed RMS of uterine cervix. Treatment was effective in the first case but despite the chemotherapy, the second patient died two months later. Discussion and Conclusions: The prognosis of the cervical embryonal RMS depends on clinical and histological features. The current treatment protocols are based on trials done on pediatric patients. Studies on embryonal RMS treatment and outcomes are limited in women over forty years. In patients with unfavorable prognosis characteristics, the multimodality approach including surgery, adjuvant chemo and radiotherapy can be effective. Otherwise, surgery alone can effectively be proposed.

关键词： Embryonal Rhabdomyosarcoma Uterine Cervix Radiation Therapy Chemotherapy Surgery

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Deep Learning with Natural Language Processing Enabled Sentimental Analysis on Sarcasm Classification

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Computer Systems Science & Engineering 2023年第3期44卷 2553-2567页

作者： Abdul Rahaman Wahab Sait Mohamad khairi Ishak Department of Documents and Archive Center of Documents and Administrative CommunicationKing Faisal UniversityAl HofufAl-Ahsa31982Saudi Arabia School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia

Sentiment analysis(SA)is the procedure of recognizing the emotions related to the data that exist in social *** existence of sarcasm in tex-tual data is a major challenge in the efficiency of the *** works on sarcasm detection on text utilize lexical as well as pragmatic cues namely interjection,punctuations,and sentiment shift that are vital indicators of *** the advent of deep-learning,recent works,leveraging neural networks in learning lexical and contextual features,removing the need for handcrafted *** this aspect,this study designs a deep learning with natural language processing enabled SA(DLNLP-SA)technique for sarcasm *** proposed DLNLP-SA technique aims to detect and classify the occurrence of sarcasm in the input ***,the DLNLP-SA technique holds various sub-processes namely preprocessing,feature vector conversion,and ***,the pre-processing is performed in diverse ways such as single character removal,multi-spaces removal,URL removal,stopword removal,and ***,the transformation of feature vectors takes place using the N-gram feature vector ***,mayfly optimization(MFO)with multi-head self-attention based gated recurrent unit(MHSA-GRU)model is employed for the detection and classification of *** verify the enhanced outcomes of the DLNLP-SA model,a comprehensive experimental investigation is performed on the News Headlines Dataset from Kaggle Repository and the results signified the supremacy over the existing approaches.

关键词： Sentiment analysis sarcasm detection deep learning natural language processing n-grams hyperparameter tuning

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Fuzzy logic controller implementation on a microbial electrolysis cell for biohydrogen production and storage

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Chinese Journal of Chemical Engineering 2021年第12期34卷 149-159页

作者： Gabriel Khew Mun Hong Mohd Azlan Hussain Ahmad khairi Abdul Wahab Department of Chemical Engineering Faculty of EngineeringUniversiti Malaya50603 Kuala LumpurMalaysia Department of Biomedical Engineering Faculty of EngineeringUniversiti Malaya50603 Kuala LumpurMalaysia Centre for Separation Science and Technology(CSST) Faculty of EngineeringUniversiti Malaya50603 Kuala LumpurMalaysia

This work presents the implementation of fuzzy logic control(FLC) on a microbial electrolysis cell(MEC).Hydrogen has been touted as a potential alternative source of energy to the depleting fossil fuels. MEC is one of the most extensively studied method of hydrogen production. The utilization of biowaste as its substrate by MEC promotes the waste to energy initiative. The hydrogen production within the MEC system, which involves microbial interaction contributes to the system's nonlinearity. Taking into account of the high complexity of MEC system, a precise process control system is required to ensure a wellcontrolled biohydrogen production flow rate and storage application inside a tank. Proportionalderivative-integral(PID) controller has been one of the pioneer control loop mechanism. However, it lacks the capability to adapt properly in the presence of disturbance. An advanced process control mechanism such as the FLC has proven to be a better solution to be implemented on a nonlinear system due to its similarity in human-natured thinking. The performance of the FLC has been evaluated based on its implementation on the MEC system through various control schemes progressively. Similar evaluations include the performance of Proportional-Integral(PI) and PID controller for comparison purposes. The tracking capability of FLC is also accessed against another advanced controller that is the model predictive controller(MPC). One of the key findings in this work is that the FLC resulted in a desirable hydrogen output via MEC over the PI and PID controller in terms of shorter settling time and lesser overshoot.

关键词： Fuzzy logic control Process control Nonlinear Microbial electrolysis cell Renewable energy Hydrogen

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A Machine Learning Approach for Early COVID-19 Symptoms Identification

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Computers, Materials & Continua 2022年第2期70卷 3803-3820页

作者： Omer Ali Mohamad khairi Ishak Muhammad Kamran Liaquat Bhatti School of Electrical and Electronic Engineering Universiti Sains Malaysia(USM)Nibong Tebal14300PenangMalaysia Department of Electrical Engineering NFC Institute of Engineering and Technology(NFC IET)Multan60000Pakistan

Symptom identification and early detection are the first steps towards a health condition ***-19 virus causes pneumonialike symptoms such as fever,cough,and shortness of ***-19 contraction tests necessitate extensive clinical protocols in medical *** studies help with the accurate analysis of COVID-19,where the virus has already spread to the lungs in most *** majority of existing supervised machine learning-based disease detection techniques are based on clinical data like x-rays and computerized *** is heavily reliant on a larger clinical study and does not emphasize early symptom *** aim of this study is to investigate anomalies in patient physiological data for early COVID-19 symptoms *** this context,two of the most prevalent symptoms,fever and cough,were examined in a two-fold manner utilizing an unsupervised machine *** examine disease progression,physiological features from a chest-worn device were ***,a Single Vector Activity Index(SVAI)parameter is proposed to monitor the breathing and cough ***,the dataset’s variance is examined using the DBSCAN method for clustering and outlier ***,the model accuracy is evaluated to identify outliers on real-time data based on feature dissimilarities,yielding an overall detection accuracy of 90.34%.

关键词： COVID-19 symptoms identification machine learning dbscan

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A Novel Handcrafted with Deep Features Based Brain Tumor Diagnosis Model

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Intelligent Automation & Soft Computing 2023年第2期35卷 2057-2070页

作者： Abdul Rahaman Wahab Sait Mohamad khairi Ishak Department of Documents and Archive Center of Documents and Administrative CommunicationKing Faisal UniversityAl Hofuf31982Al-AhsaSaudi Arabia School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong Tebal14300PenangMalaysia

In healthcare sector,image classiﬁcation is one of the crucial problems that impact the quality output from image processing *** purpose of image classiﬁcation is to categorize different healthcare images under various class labels which in turn helps in the detection and management of *** Resonance Imaging(MRI)is one of the effective non-invasive strate-gies that generate a huge and distinct number of tissue contrasts in every imaging *** technique is commonly utilized by healthcare professionals for Brain Tumor(BT)*** recent advancements in Machine Learning(ML)and Deep Learning(DL)models,it is possible to detect the tumor from images automatically,using a computer-aided *** current study focuses on the design of automated Deep Learning-based BT Detection and Classiﬁcation model using MRI images(DLBTDC-MRI).The proposed DLBTDC-MRI techni-que aims at detecting and classifying different stages of *** proposed DLBTDC-MRI technique involves medianﬁltering technique to remove the noise and enhance the quality of MRI ***,morphological operations-based image segmentation approach is also applied to determine the BT-affected regions in brain MRI ***,a fusion of handcrafted deep features using VGGNet is utilized to derive a valuable set of feature ***,Artiﬁcial Fish Swarm Optimization(AFSO)with Artiﬁcial Neural Network(ANN)model is utilized as a classiﬁer to decide the presence of *** order to assess the enhanced BT classiﬁcation performance of the proposed model,a comprehensive set of simulations was performed on benchmark dataset and the results were vali-dated under several measures.

关键词： Brain tumor medical imaging image classiﬁcation handcrafted features deep learning parameter optimization

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Early COVID-19 Symptoms Identification Using Hybrid Unsupervised Machine Learning Techniques

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Computers, Materials & Continua 2021年第10期69卷 747-766页

作者： Omer Ali Mohamad khairi Ishak Muhammad Kamran Liaquat Bhatti School of Electrical and Electronic Engineering Universiti Sains Malaysia(USM)Nibong Tebal14300Malaysia Department of Electrical Engineering NFC Institute of Engineering and Technology(NFC IET)Multan60000Pakistan

The COVID-19 virus exhibits pneumonia-like symptoms,including fever,cough,and shortness of breath,and may be *** COVID-19 contraction experiments require comprehensive clinical procedures at medical *** studies help to make a correct diagnosis of COVID-19,where the disease has already spread to the organs in most *** and early diagnosis is indispensable for providing patients with the possibility of early clinical diagnosis and slowing down the disease ***,clinical investigations in patients with COVID-19 have revealed distinct patterns of breathing relative to other diseases such as flu and cold,which are worth *** supervised Machine Learning(ML)based techniques mostly investigate clinical reports such as X-Rays and Computerized Tomography(CT)for disease *** strategy relies on a larger clinical dataset and does not focus on early symptom *** this end,an innovative hybrid unsupervised ML technique is introduced to uncover the probability of COVID-19 occurrence based on the breathing patterns and commonly reported symptoms,fever,and ***,various metrics,including body temperature,breathing and cough patterns,and physical activity,were considered in this ***,a lightweight ML algorithm based on the K-Means and Isolation Forest technique was implemented on relatively small data including 40 *** proposed technique shows an outlier detection with an accuracy of 89%,on average.

关键词： COVID-19 symptoms identification machine learning isolation forest k-means

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Enhanced Best Fit Algorithm for Merging Small Files

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Computer Systems Science & Engineering 2023年第7期46卷 913-928页

作者： Adnan Ali Nada Masood Mirza Mohamad khairi Ishak School of Electrical and Electronic Engineering Universiti Sains Malaysia(USM)Nibong TebalPulau Pinang14300Malaysia University College United Arab Emirates UniversityAl AinUAE

In the Big Data era,numerous sources and environments generate massive amounts of *** enormous amount of data necessitates specialized advanced tools and procedures that effectively evaluate the information and anticipate decisions for future *** is used to process this kind of *** is known to handle vast volumes of data more efficiently than tiny amounts,which results in inefficiency in the *** study proposes a novel solution to the problem by applying the Enhanced Best Fit Merging algorithm(EBFM)that merges files depending on predefined parameters(type and size).Implementing this algorithm will ensure that the maximum amount of the block size and the generated file size will be in the same *** primary goal is to dynamically merge files with the stated criteria based on the file type to guarantee the efficacy and efficiency of the established *** procedure takes place before the files are available for the Hadoop ***,the files generated by the system are named with specific keywords to ensure there is no data loss(file overwrite).The proposed approach guarantees the generation of the fewest possible large files,which reduces the input/output memory burden and corresponds to the Hadoop framework’s *** findings show that the proposed technique enhances the framework’s performance by approximately 64%while comparing all other potential performance-impairing *** proposed approach is implementable in any environment that uses the Hadoop framework,not limited to smart cities,real-time data analysis,etc.

关键词： Big data Hadoop MapReduce small file HDFS

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An Optimal Method for Speech Recognition Based on Neural Network

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Intelligent Automation & Soft Computing 2023年第5期36卷 1951-1961页

作者： Mohamad khairi Ishak DagØivind Madsen Fahad Ahmed Al-Zahrani School of Electrical and Electronic Engineering Universiti Sains MalaysiaNibong Tebal14300Malaysia University of South-Eastern Norway Bredalsveien 143511HønefossNorway Computer Engineering Department Umm Al-Qura UniversityMecca24381Saudi Arabia

Natural language processing technologies have become more widely available in recent years,making them more useful in everyday *** learning systems that employ accessible datasets and corporate work to serve the whole spectrum of problems addressed in computational linguistics have lately yielded a number of promising *** methods were particularly advantageous for regional languages,as they were provided with cut-ting-edge language processing tools as soon as the requisite corporate information was *** bulk of modern people are unconcerned about the importance of *** aloud,on the other hand,is an effective technique for nour-ishing feelings as well as a necessary skill in the learning *** paper pro-posed a novel approach for speech recognition based on neural *** attention mechanism isﬁrst utilized to determine the speech accuracy andﬂuency assessments,with the spectrum map as the feature extraction *** increase phoneme identiﬁcation accuracy,reading precision,for example,employs a new type of deep *** makes use of the exportchapter tool,which provides a corpus,as well as the TensorFlow framework in the experimental *** experimentalﬁndings reveal that the suggested model can more effectively assess spoken speech accuracy and readingﬂuency than the old model,and its evalua-tion model’s score outcomes are more accurate.

关键词： Machine learning neural networks speech recognition signal processing learning process ﬂuency and accuracy

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Dual-specificity phosphatase 6(DUSP6): a review of its molecular characteristics and clinical relevance in cancer

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Cancer Biology & Medicine 2018年第1期15卷 14-28页

作者： Muhammad khairi Ahmad Nur Ainina Abdollah Nurul Husna Shafie Narazah Mohd Yusof Siti Razila Abdul Razak Oncological and Radiological Sciences Cluster Advanced Medical and Dental Institute Universiti Sains Malaysia Department of Nutrition and Dietetics Faculty of Medicine and Health SciencesUniversiti Putra Malaysia Regenerative Medicine Cluster Advanced Medical and Dental Institute Universiti Sains Malaysia

Mitogen-activated protein kinases(MAPKs) are the main regulators of cellular proliferation, growth, and survival in physiological or pathological conditions. Aberrant MAPK signaling plays a pivotal role in carcinogenesis, which leads to development and progression of human cancer. Dual-specificity phosphatase 6(DUSP6), a member of the MAPK phosphatase family, interacts with specifically targeted extracellular signal-regulated kinase 1/2 via negative feedback regulation in the MAPK pathway of mammalian cells. This phosphatase functions in a dual manner, pro-oncogenic or tumor-suppressive, depending on the type of cancer. To date, the tumor-suppressive role of DUSP6 has been demonstrated in pancreatic cancer, non-small cell lung cancer, esophageal squamous cell and nasopharyngeal carcinoma, and ovarian cancer. Its pro-oncogenic role has been observed in human glioblastoma, thyroid carcinoma, breast cancer, and acute myeloid carcinoma. Both roles of DUSP6 have been documented in malignant melanoma depending on the histological subtype of the cancer. Loss-or gain-of-function effects of DUSP6 in these cancers highlights the significance of this phosphatase in carcinogenesis. Development of methods that use the DUSP6 gene as a therapeutic target for cancer treatment or as a prognostic factor for diagnosis and evaluation of cancer treatment outcome has great potential. This review focuses on molecular characteristics of the DUSP6 gene and its role in cancers in the purview of development, progression, and cancer treatment outcome.

关键词： Dual-specificity phosphatase 6 MAPK signaling cancer chemoresponsiveness chemoresistance

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