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| 1 | BitmapAligner:Bit-Parallelism String Matching with MapReduce and Hadoop显示文摘Advancements in next-generation sequencer(NGS)platforms have improved NGS sequence data production and reduced the cost involved,which has resulted in the production of a large amount of genome data.The downstream analysis of multiple associated sequences has become a bottleneck for the growing genomic data due to storage and space utilization issues in the domain of bioinformatics.The traditional string-matching algorithms are efficient for small sized data sequences and cannot process large amounts of data for downstream analysis.This study proposes a novel bit-parallelism algorithm called BitmapAligner to overcome the issues faced due to a large number of sequences and to improve the speed and quality of multiple sequence alignment(MSA).The input files(sequences)tested over BitmapAligner can be easily managed and organized using the Hadoop distributed file system.The proposed aligner converts the test file(the whole genome sequence)into binaries of an equal length of the sequence,line by line,before the sequence alignment processing.The Hadoop distributed file system splits the larger files into blocks,based on a defined block size,which is 128 MB by default.BitmapAligner can accurately process the sequence alignment using the bitmask approach on large-scale sequences after sorting the data.The experimental results indicate that BitmapAligner operates in real time,with a large number of sequences.Moreover,BitmapAligner achieves the exact start and end positions of the pattern sequence to test the MSA application in the whole genome query sequence.The MSA’s accuracy is verified by the bitmask indexing property of the bit-parallelism extended shifts(BXS)algorithm.The dynamic and exact approach of the BXS algorithm is implemented through the MapReduce function of Apache Hadoop.Conversely,the traditional seeds-and-extend approach faces the risk of errors while identifying the pattern sequences’positions.Moreover,the proposed model resolves the largescale data challenges that are covered through MapReduce in the Hadoop framework.Hive,Yarn,HBase,Cassandra,and many other pertinent flavors are to be used in the future for data structuring and annotations on the top layer of Hadoop since Hadoop is primarily used for data organization and handles text documents. | Mary Aksa Junaid Rashid Muhammad Wasif Nisar Toqeer Mahmood Hyuk-Yoon Kwon Amir Hussain | 2021 | Computers, Materials & Continua2021，，9： | 0 |
| 2 | An Artificial Neural Network-Based Model for Effective Software Development Effort Estimation显示文摘In project management,effective cost estimation is one of the most cru-cial activities to efficiently manage resources by predicting the required cost to fulfill a given task.However,finding the best estimation results in software devel-opment is challenging.Thus,accurate estimation of software development efforts is always a concern for many companies.In this paper,we proposed a novel soft-ware development effort estimation model based both on constructive cost model II(COCOMO II)and the artificial neural network(ANN).An artificial neural net-work enhances the COCOMO model,and the value of the baseline effort constant A is calibrated to use it in the proposed model equation.Three state-of-the-art publicly available datasets are used for experiments.The backpropagation feed-forward procedure used a training set by iteratively processing and training a neural network.The proposed model is tested on the test set.The estimated effort is compared with the actual effort value.Experimental results show that the effort estimated by the proposed model is very close to the real effort,thus enhanced the reliability and improving the software effort estimation accuracy. | Junaid Rashid Sumera Kanwal Muhammad Wasif Nisar Jungeun Kim Amir Hussain | 2023 | Computer Systems Science & Engineering2023，44，2： | 0 |
| 3 | Heart Disease Diagnosis Using the Brute Force Algorithm and Machine Learning Techniques显示文摘Heart disease is one of the leading causes of death in the world today.Prediction of heart disease is a prominent topic in the clinical data processing.To increase patient survival rates,early diagnosis of heart disease is an important field of research in the medical field.There are many studies on the prediction of heart disease,but limited work is done on the selection of features.The selection of features is one of the best techniques for the diagnosis of heart diseases.In this research paper,we find optimal features using the brute-force algorithm,and machine learning techniques are used to improve the accuracy of heart disease prediction.For performance evaluation,accuracy,sensitivity,and specificity are used with split and cross-validation techniques.The results of the proposed technique are evaluated in three different heart disease datasets with a different number of records,and the proposed technique is found to have superior performance.The selection of optimized features generated by the brute force algorithm is used as input to machine learning algorithms such as Support Vector Machine(SVM),Random Forest(RF),K Nearest Neighbor(KNN),and Naive Bayes(NB).The proposed technique achieved 97%accuracy with Naive Bayes through split validation and 95%accuracy with Random Forest through cross-validation.Naive Bayes and Random Forest are found to outperform other classification approaches when accurately evaluated.The results of the proposed technique are compared with the results of the existing study,and the results of the proposed technique are found to be better than other state-of-the-artmethods.Therefore,our proposed approach plays an important role in the selection of important features and the automatic detection of heart disease. | Junaid Rashid Samina Kanwal Jungeun Kim Muhammad Wasif Nisar Usman Naseem Amir Hussain | 2022 | Computers, Materials & Continua2022，，8： | 0 |
| 4 | A Hybrid Model for Improving Software Cost Estimation in Global Software Development显示文摘Accurate software cost estimation in Global Software Development(GSD)remains challenging due to reliance on historical data and expert judgments.Traditional models,such as the Constructive Cost Model(COCOMO II),rely heavily on historical and accurate data.In addition,expert judgment is required to set many input parameters,which can introduce subjectivity and variability in the estimation process.Consequently,there is a need to improve the current GSD models to mitigate reliance on historical data,subjectivity in expert judgment,inadequate consideration of GSD-based cost drivers and limited integration of modern technologies with cost overruns.This study introduces a novel hybrid model that synergizes the COCOMO II with Artificial Neural Networks(ANN)to address these challenges.The proposed hybrid model integrates additional GSD-based cost drivers identified through a systematic literature review and further vetted by industry experts.This article compares the effectiveness of the proposedmodelwith state-of-the-artmachine learning-basedmodels for software cost estimation.Evaluating the NASA 93 dataset by adopting twenty-six GSD-based cost drivers reveals that our hybrid model achieves superior accuracy,outperforming existing state-of-the-artmodels.The findings indicate the potential of combining COCOMO II,ANN,and additional GSD-based cost drivers to transform cost estimation in GSD. | Mehmood Ahmed Noraini B.Ibrahim Wasif Nisar Adeel Ahmed Muhammad Junaid Emmanuel Soriano Flores Divya Anand | 2024 | Computers, Materials & Continua2024，78，1： | 0 |
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