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Spark-Based Parallel Deep Neural Network Model for classification of Large Scale RNAs into piRNAs and Non-piRNAs

IEEE ACCESS 2020年 8卷 136978-136991页

作者： Khan, Salman Khan, Mukhtaj Iqbal, Nadeem Li, Maozhen Khan, Dost Muhammad Abdul Wali Khan Univ Mardan Dept Comp Sci Mardan 23200 Pakistan Brunel Univ London Dept Elect & Comp Engn London UB8 3PN England

With recent advancement in computational biology, high throughput next generation sequencing technology has become a de facto standard technology for genes expression studies including DNAs, RNAs and proteins. As a promising technology, it has significant impact on medical sciences and genomic research. However, it generates several millions of short DNA and RNA sequences with several petabytes size in single run. In addition, the raw sequencing datasets such as RNAs are increasing exponentially leading to a big data analytics issue in computational biology. Due to the explosive growth of RNA sequences, the timely classification of RNAs sequence into piRNAs and non-piRNAs have become a challenging issue for traditional technology and conventional machine learning algorithms. Parallel and distributed computing models along with deep neural network have become a major computing platform for big data analytics now required in the field of computational biology. This paper presents a computational model based on parallel deep neural network for timely classification of large number of RNAs sequence into piRNAs and non-piRNAs, taking advantages of parallel and distributed computing platform. The performance of the proposed model was extensively evaluated using two-fold performance metrics. In the first fold, the performance of the proposed model was assessed using accuracy-based metrics such as accuracy, specificity, sensitivity and Matthews's correlation coefficient. In the second fold, computational-based metrics such as computation times, speedup and scalability were observed. Moreover, initially the performance of the proposed model was assessed using real benchmark dataset and subsequently the performance was assessed using replicated benchmark dataset. The evaluation results in both cases showed that the proposed model improved computation speedup in order of magnitude in comparison with sequential approach without affected accuracy level.

关键词： Computational modeling RNA Biological system modeling Data models Mathematical model Feature extraction Sparks Deep neural network spark big data piRNA classification algorithm artificial intelligence

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Smart Healthcare Data Acquisition System 3rd

Smart Healthcare Data Acquisition System

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International Conference on Computational Vision and Bio-Inspired Computing (ICCVBIC)

作者： Ghole, Tanvi Karande, Shruti Mondkar, Harshita Kulkarni, Sujata Sardar Patel Inst Technol Dept Elect & Telecommun Mumbai Maharashtra India

ISBN: (纸本)9783030372187;9783030372170

A general healthcare system monitors the parameters and simply display the readings. The records are maintained on paper. This paper aims in building an independent offline system for maintaining records in regions where network connectivity is an issue. After providing basic identification information it automatically logs vital parameters of patients acquired from the sensors. The records are maintained digitally and the system does a personalized data analysis for each patient, as "normality" is subjective to every patient. This system is trained to classify such minute details and create a cluster for each patient individually. As and when patient visit for checkup the data is added to his cluster and analysis is performed on it. Various suggestions are provided to the patient in order to avoid worsening the condition. It is a platform that provides the doctor with the patient's entire history when they visit a non-digitized area.

关键词： Personalized healthcare Data analysis Machine learning Clustering of data classification algorithm

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Towards an optimal personalization strategy in MOOCs

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SMART LEARNING ENVIRONMENTS 2020年第1期7卷 1-18页

作者： Qaffas, Alaa A. Kaabi, Kaouther Shadiev, Rustam Essalmi, Fathi Univ Jeddah Coll Business Management Informat Syst Dept Jeddah Saudi Arabia Univ Kairouan Higher Inst Comp Sci & Management Kairouan ISIGK Kairouan Tunisia Nanjing Normal Univ Sch Educ Sci Nanjing Peoples R China Univ Tunis Higher Sch Sci & Technol Tunis ESSTT Res Lab Technol Informat & Commun & Elect Engn La Tunis Tunisia

Several successful initiatives have resulted from the OER movement. One of them is Massive Open Online Courses (MOOCs) which is a popular learning mode as it offers an affordable and flexible way to learn. However, the evolution of the MOOCs has some challenges. One of the major problems of MOOCs is the diversity of learners and the need to personalize the content as well as the way of delivering it. The origin of this problem is the one size does not fit all. In fact, learners have different characteristics such as their learning styles, levels of knowledge, and so on. The selection of the most suitable parameters (set of complementary learners' characteristics) to be considered in learner's profile is not easy in the presence of a considerable number of learners in MOOCs. One reason is because a course can be attended by many learners with varied profiles from different regions of the world. This plurality of learner profiles makes it important to develop content that can meet the needs and objectives of each learner in MOOCS. Our solution to solve this problem consists of personalizing the content of MOOC for each learner. We propose a new approach which allows to optimize the selection of the personalization parameters and to apply the appropriate personalization strategy, based on a classification algorithm. The proposed approach aims to improve the retention rate and the quality of learning in MOOCs. This approach is validated by experiments which test its success when applied to many combinations of strategies and learner profiles.

关键词： Online learning OER MOOCs Personalization strategies Retention classification algorithm Learner characteristics Educational games

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A DDoS Attack Mitigation Framework for IoT Networks using Fog Computing

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Procedia Computer Science 2021年 182卷 13-20页

作者： Muhammad Aminu Lawal Riaz Ahmed Shaikh Syed Raheel Hassan Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21589 Saudi Arabia

The advent of 5G which strives to connect more devices with high speed and low latencies has aided the growth IoT network. Despite the benefits of IoT, its applications in several facets of our lives such as smart health, smart homes, smart cities, etc. have raised several security concerns such as Distributed Denial of Service (DDoS) attacks. In this paper, we propose a DDoS mitigation framework for IoT using fog computing to ensure fast and accurate attack detection. The fog provides resources for effective deployment of the mitigation framework, this solves the deficits in resources of the resource-constrained IoT devices. The mitigation framework uses an anomaly-based intrusion detection method and a database. The database stores signatures of previously detected attacks while the anomaly-based detection scheme utilizes k-NN classification algorithm for detecting the DDoS attacks. By using a database containing the attack signatures, attacks can be detected faster when the same type of attack is executed again. The evaluations using a DDoS based dataset show that the k-NN classification algorithm proposed for our framework achieves a satisfactory accuracy in detecting DDoS attacks.

关键词： Internet of Things (IoT) Anomaly mitigation DDoS Fog computing classification algorithm

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Gingivitis detection by Fractional Fourier Entropy and Biogeography-based Optimization 13

Gingivitis detection by Fractional Fourier Entropy and Bioge...

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13th IEEE/ACM International Conference on Utility and Cloud Computing (UCC)

作者： Yan, Yan Univ Leicester Sch Informat Univ Rd Leicester LE1 7RH Leics England Liming Vocat Univ Educ Informat Ctr Quanzhou 362000 Fujian Peoples R China

ISBN: (数字)9780738123943

ISBN: (纸本)9780738123943

As people keep a watch eye on the oral health, more people choose to go to professional dental hospitals for the regular dental examinations and diagnosis. It is well known that the dental diagnosis and treatment require excellent nursing skills and extensive experience by the dentists. Nervously, the number of experts is limited. However, the rapid increase in the number of diagnoses and the small number of professional dentists resulted in an increase in the daily diagnostic frequency of dentists, and the overworked working hours seriously affected the energy and diagnostic efficiency of dentists. This study for the sake of reduce the burden of dental diagnosis, proposes a computer-aided diagnosis method. This method classifies gingivitis images by using the image feature extraction method of fractional Fourier entropy (FRFE) and biogeography-based optimization (BBO) algorithm. The FRFE coefficient extracted from the image was used as the input feature vector, and the classification was carried out by the BBO algorithm with the optimal scheme of automatic screening. After 10-fold cross-validation, more effective healthy and pathological gingival image classification results were obtained compared with the latest methods.

关键词： fractional Fourier entropy detection of gingivitis biogeography-based optimization Multi-Layer Perception classification algorithm

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Establishing Classifiers With Clinical Laboratory Indicators to Distinguish COVID-19 From Community-Acquired Pneumonia: Retrospective Cohort Study

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JOURNAL OF MEDICAL INTERNET RESEARCH 2021年第2期23卷 e23390页

作者： Dai, Wanfa Ke, Pei-Feng Li, Zhen-Zhen Zhuang, Qi-Zhen Huang, Wei Wang, Yi Xiong, Yujuan Huang, Xian-Zhang Gong An Cty Peoples Hosp Dept Respirat Jingzhou Peoples R China Guangzhou Univ Chinese Med Affiliated Hosp 2 Dept Lab Med 111 Dade Rd Guangzhou 510210 Peoples R China Guangdong Prov Key Lab Res Emergency Tradit Chine Guangzhou Peoples R China Guangzhou Univ Chinese Med Clin Med Coll 2 Guangzhou Peoples R China

Background: The initial symptoms of patients with COVID-19 are very much like those of patients with community-acquired pneumonia (CAP);it is difficult to distinguish COVID-19 from CAP with clinical symptoms and imaging examination. Objective: The objective of our study was to construct an effective model for the early identification of COVID-19 that would also distinguish it from CAP. Methods: The clinical laboratory indicators (CLIs) of 61 COVID-19 patients and 60 CAP patients were analyzed retrospectively. Random combinations of various CLIs (ie, CLI combinations) were utilized to establish COVID-19 versus CAP classifiers with machine learning algorithms, including random forest classifier (RFC), logistic regression classifier, and gradient boosting classifier (GBC). The performance of the classifiers was assessed by calculating the area under the receiver operating characteristic curve (AUROC) and recall rate in COVID-19 prediction using the test data set. Results: The classifiers that were constructed with three algorithms from 43 CLI combinations showed high performance (recall rate >0.9 and AUROC >0.85) in COVID-19 prediction for the test data set. Among the high-performance classifiers, several CLIs showed a high usage rate;these included procalcitonin (PCT), mean corpuscular hemoglobin concentration (MCHC), uric acid, albumin, albumin to globulin ratio (AGR), neutrophil count, red blood cell (RBC) count, monocyte count, basophil count, and white blood cell (WBC) count. They also had high feature importance except for basophil count. The feature combination (FC) of PCT, AGR, uric acid, WBC count, neutrophil count, basophil count, RBC count, and MCHC was the representative one among the nine FCs used to construct the classifiers with an AUROC equal to 1.0 when using the RFC or GBC algorithms. Replacing any CLI in these FCs would lead to a significant reduction in the performance of the classifiers that were built with them. Conclusions: The classifiers constr

关键词： COVID-19 clinical laboratory indicators community-acquired pneumonia classifier classification algorithm

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Building an accurate hardware Trojan detection technique from inaccurate simulation models and unlabelled ICs

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IET COMPUTERS AND DIGITAL TECHNIQUES 2019年第4期13卷 348-359页

作者： Xue, Mingfu Bian, Rongzhen Wang, Jian Liu, Weiqiang Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Jiangsu Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 210016 Jiangsu Peoples R China

Most of prior hardware Trojan detection approaches require golden chips for references. A classification-based golden chips-free hardware Trojan detection technique has been proposed in the authors' previous work. However, the algorithm in that work is trained by simulated ICs without considering a shift between the simulation and silicon fabrication. In this study, a co-training based hardware Trojan detection method by exploiting inaccurate simulation models and unlabeled fabricated ICs is proposed to provide reliable detection capability when facing fabricated ICs, which eliminates the need of golden chips. Two classification algorithms are trained using simulated ICs. These two algorithms can identify different patterns in the unlabelled ICs during test-time, and thus can label some of these ICs for the further training of the other algorithm. Moreover, a statistical examination is used to choose ICs labelling for the other algorithm. A statistical confidence interval based technique is also used to combine the hypotheses of the two classification algorithms. Furthermore, the partial least squares method is used to preprocess the raw data of ICs for feature selection. Both EDA experiment results and field programmable gate array (FPGA) experiment results show that the proposed technique can detect unknown Trojans with high accuracy and recall.

关键词： classification-based golden chips-free hardware Trojan detection technique invasive software pattern classification statistical confidence interval based technique embedded systems electronic design automation simulation labelled ICs fabricated ICs authors ICs labelling inaccurate simulated models detection performance Hardware Trojans classification algorithm field programmable gate arrays unlabelled ICs prior hardware Trojan detection approaches reliable detection capability integrated circuit industry co-training based hardware Trojan detection method simulated ICs IC co-training method final detection decision unknown Trojans electronic design automation inaccurate simulation models accurate hardware Trojan detection technique silicon fabrication

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AI4CDI: Introducing a novel machine learning approach to demonstrate feasibility of timely and early identification of at-risk populations for Clostridioides difficile infections

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Anaerobe 2025年 94卷 102978页

作者： Anastasia Karatzia Danai Aristeridou Wawi Kantz A Carmine Colavecchia Harish Madhava Mohammad Ateya Carole Czudek Patrick H Kelly Kate Halsby Data Science and Advanced Analytics Pfizer Digital New York New York USA. Electronic address: Anastasia.Karatzia@***. Data Science and Advanced Analytics Pfizer Digital New York New York USA. Electronic address: aristeridoudanai@***. Data Science and Advanced Analytics Pfizer Digital Vienna Austria. Electronic address: Waltraud.Kantz@***. Global Medical Affairs Pfizer New York New York USA. Electronic address: Anthony.Colavecchia@***. Global Vaccines and Antivirals Chief Medical Affairs Office Pfizer London UK. Electronic address: Harish.Madhava@***. Clinical Informatics Medical Digital Health Chief Medical Affairs Office Pfizer New York New York USA. Electronic address: Mohammad.Ateya@***. Vaccines Medical Affairs Pfizer Ltd UK. Electronic address: Carole.Czudek@***. Global Vaccines and Antivirals Chief Medical Affairs Office Pfizer New York New York USA. Electronic address: Patrick.Kelly2@***. Global Vaccines and Antivirals Chief Medical Affairs Office Pfizer London UK. Electronic address: kate.halsby@***.

OBJECTIVE:We evaluated machine learning (ML) model feasibility to predict Clostridioides difficile infection (CDI) six months prior to onset and to identify early predictors over a longer period. METHODS:A retrospective analysis was performed using electronic health records data from US adults (Optum Market Clarity). Cases with CDI and non-CDI controls were identified. A 1:1 coarsened exact matching algorithm was applied, with final analysis cohorts of 4736 cases and 4732 controls. CDI-relevant features were identified from the published literature, and information was extracted for >900 features. The final model was trained on 597 mostly binary features. Feature information during the 6 months prior to date of first CDI diagnosis was hidden to the model to identify patients at risk for CDI with a longer time horizon. Sensitivity analysis was conducted on cases aged 65-80 years. RESULTS:Median age was 65 years (19-88) in case and control cohorts. The Gradient Boosted Trees ML model had an Area Under the Curve Receiver Operating Characteristic (AUC-ROC) of 0.79. Post-model bias evaluation revealed disparities in sensitivity (race). Long-term predictors included hospitalization days. While some predictors were exclusive to the 65-80 years model, others were more strongly associated with CDI in the overall model. CONCLUSIONS:We developed a ML model that can identify patient groups at increased risk for primary CDI. While the predictive capability of this ML model is promising, validation is needed before exploring its readiness for use in healthcare settings to inform preventive measures for CDI.

关键词： CDI Clostridioides difficile infection classification algorithm machine learning predictive model

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Solving customer insurance coverage recommendation problem using a two-stage clustering-classification model

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INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING MANAGEMENT 2019年第1期14卷 9-19页

作者： Khalili-Damghani, Kaveh Abdi, Farshid Abolmakarem, Shaghayegh Islamic Azad Univ South Tehran Branch Dept Ind Engn Tehran Iran

Insurance coverage recommendation problem (ICRP) in which the most suitable coverage for customers is suggested is an essential issue for an insurance company. ICRP helps insurance companies to give suitable services to their customers. In ICRP, the insurance company tried to mine the features and records of data associated with the customers to suggest them the most economic and fitted insurance plan. The insurance companies have large databases which are considered as a proper infrastructure to analyze, model and predict the customer behavior. In this paper, a two-stage clustering-classification model is proposed to suggest suitable insurance coverage for customers. The first stage addresses a data pre-screening phase and clustering of customers based on the record of insurance coverage. Well-known clustering algorithms are employed. The superior clustering algorithm is selected based on Davies-Bouldin metric. In the second stage, several filter and wrapper methods are implemented to select proper features. The selected features are assumed as inputs of K-nearest neighbor classification algorithm. The proposed approach is applied in a real case study for clustering the customers and recommend insurance coverage. The results show that the model is capable of suggesting suitable insurance coverage based on customers' characteristics. [GRAPHICS] .

关键词： Knowledge engineering consumer behavior modeling data mining clustering algorithm classification algorithm insurance coverage recommendation

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Flexible Job Shop Scheduling Rules Mining Based on Random Forest

Flexible Job Shop Scheduling Rules Mining Based on Random Fo...

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作者： Yizhong Wang School of Aerospace Transport and Manufacturing Cranfield University

With the development of the global economy and customization,the manufacturing scheduling problem is increasingly *** job shops(FJSs) have to be more flexible and dynamic to handle these complex and various manufacturing *** at the dynamic scheduling problem of FJS,a method of mining scheduling rules from scheduling related historical data with industrial big data characteristics is *** the mining of scheduling rules,an improved random forest algorithm is proposed,which is suitable for mining scheduling rules from historical data related to large-scale,high-dimensional,and noisy *** results show that the scheduling rules obtained by the mining method have good performance in terms of scheduling performance and computational efficiency.

关键词： Flexible Job Shop scheduling rules mining random forest classification algorithm

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