· Brief review of machine learning techniques. Machine learning techniques, which integrate artificial intelligence systems, seek to extract patterns learned from historical data – in a process known as training or learning to subsequently make predictions about new data (Xiao, Xiao, Lu, and Wang, , pp. 99–). Empirical studies using machine learning commonly have two main blogger.com by: 75 Ayon Dey. Department of CSE, Gautam Buddha University, Greater Noida, Uttar Pradesh, India. Abstract– In this paper, various machine learning algorithms have been discussed. These algorithms are used for various purposes like data mining, image processing, predictive analytics, etc. to name a few. The main advantage of using machine learning is that, once an algorithm learns what to do with File Size: 1MB · Background and purpose Machine learning (ML) has attracted much attention with the hope that it could make use of large, routinely collected datasets and deliver accurate personalised prognosis. The aim of this systematic review is to identify and critically appraise the reporting and developing of ML models for predicting outcomes after blogger.com by: 9
Top 20 Recent Research Papers on Machine Learning and Deep Learning - KDnuggets
Arshad Ahmad, Chong Feng, Muzammil Khan, Asif Khan, Ayaz Ullah, Shah Nazir, review paper on machine learning, Adnan Tahir, " A Systematic Literature Review on Using Machine Learning Algorithms for Software Requirements Identification on Stack Overflow ", Security and Communication Networksvol. The improvements made in the last couple of decades in the requirements engineering RE processes and methods have witnessed a rapid rise in effectively using diverse machine learning ML techniques to resolve several multifaceted RE issues.
One such challenging issue is the effective identification and classification of the software requirements on Stack Overflow SO for building quality systems. The appropriateness of ML-based techniques to tackle this issue has revealed quite substantial results, much effective than those produced by the usual available natural language processing NLP techniques.
Nonetheless, a complete, systematic, and detailed comprehension of these ML based techniques is considerably scarce. To identify or recognize and classify the kinds of ML algorithms used for software requirements identification primarily on SO. This paper reports a systematic literature review SLR collecting empirical evidence published up to May This SLR study found 2, published papers related to RE and SO. The data extraction process of the SLR showed that 1 Latent Dirichlet Allocation LDA topic modeling is among the widely used ML algorithm in the selected studies and 2 precision and recall are amongst the most commonly utilized evaluation methods for measuring the performance of these ML algorithms.
The RE activity is steered in the very first phase of software development lifecycle and plays a very pivotal role in ensuring the development of quality and secure software systems [ 12 ]. There are various activities i. Normally software requirements are of two types, namely, FRs and NFRs. The research work on the difference between FRs and NFRs is defined and well known; however, the automatic identification and classification of the software requirements stated in different natural language is still a huge challenge [ 14 — 21 ].
In addition, review paper on machine learning, some other reasons which make this task problematic are the diversity of stakeholders, difference in the terminologies used, structures of the sentences, and the language used to specify the same kind of requirements [ 1424 ]. Nonetheless, a complete, systematic, and detailed comprehension of these emerging ML based techniques for identification or recognition of software requirements on SO is currently unavailable in the existing literature [ 13 ].
For this SLR study, we have thoroughly followed the systematic literature review SLR method as our primary research method [ 2526 ], with the aim of identifying and classifying the available empirical evidence about the use of emerging ML methods or algorithms for diverse software requirements identification on the SO platform for developing quality systems.
The SLR method has been used successfully on diverse topics within the area of requirements engineering [ 27 — 30 ]. This work presented a detailed SLR work of 12 primary studies related to the emerging ML based approaches or techniques for software requirements recognition or identification on the SO platform.
The main research goal of this SLR study is to recognize or identify and categorize or classify the type of machine learning algorithms or techniques used for identifying software requirements on the Stack Overflow platform. Our SLR work is aimed at identifying various types of machine learning algorithms or techniques that have been properly utilized to identify the diverse software requirements on the SO, their working, and evaluation mechanisms. These outcomes will ultimately help and enable us to recognize the main complex issues and challenges that need to be properly tackled to enhance the working capabilities of review paper on machine learning different machine learning based techniques.
The specific contributions of our SLR study worth mentioning are as follows. The goal of our SLR work is broad enough so we divided it into the set of different research questions RQs specified as follows. RQ1: what types of software requirements identified or reported in the selected studies? RQ2: what are the types of ML algorithms that have been used for identifying software requirements on SO in the selected studies?
Do the ML based approaches outperform the non-ML based approaches? Are there any ML based techniques that considerably outperform the other ML based techniques? RQ3: what are the types of procedures the reported machine learning algorithms use to identify software requirements on SO? RQ4: what are the methods utilized to assess the performance of the machine learning algorithms applied in the selected studies?
What are the performance outcomes of the reported ML algorithms? The remainder of the research paper is organized as follows: Section 2 basically describes all the related work. The research methodology followed for this study review paper on machine learning explained comprehensively in Section 3.
In Section 4we briefly presented the main results and discussion of the SLR study. Section 5 briefly summarizes the key findings, limitations, and open challenges identified in our SLR study. The different types of the validity threats of the SLR study are discussed in detail in Section 6. Finally, Section 7 concludes the paper and discusses briefly how the key findings of our SLR study can be further effectively used by the researchers and practitioners in their future research endeavors, review paper on machine learning.
InAhmad et al. It also empowers software programmers to utilize such platforms for the recognized underutilized different tasks of software development lifecycle, e. Similarly, the work of Baltadzhieva and Chrupała [ 31 ] thoroughly reviewed and analyzed various questions quality posted on diverse community question answering CQA websites like SO. Besides, they pointed out the different metrics through which the quality of the posted questions can be identified, which in large ultimately lead to affecting the question quality, review paper on machine learning.
Inreview paper on machine learning, Meth et al. They selected 36 primary papers published between January and March They categorized the identified works through an analysis framework, including tool categorization, technological concepts, and assessment approaches. Later on, Binkhonain and Zaho [ 33 ] conducted an Review paper on machine learning on ML algorithms for identifying and classifying NFRs.
They have selected 24 primary studies published. The key findings of their work revealed that ML approaches could identify and classify NFRs, but they still have many challenges that need more attention. Recently, Iqbal et al. The results revealed that the impact of ML algorithms could be found in different phases of RE lifecycle, e. Unlike the different related works cited above, our study is focused on identifying and classifying ML algorithms or techniques used for identifying diverse software requirements on the SO only.
We have selected 12 primary studies for our SLR work published until May Thus, we are the first to perform a comprehensive SLR study aimed at identifying, reviewing, summarizing, review paper on machine learning, assessing, and thoroughly reporting the diverse works of ML algorithms or techniques for identifying diverse software requirements on the SO.
There has been a rapid surge in the popularity of using review paper on machine learning Evidence-Based Software Engineering EBSE among researchers due to the applicability of systematic literature review SLR in various domains [ 41 — 43 ].
The key goal of the SLR study is to systematically identify, classify, and review paper on machine learning synthesize any new evidence based on the data extracted from the selected research publications. We conducted a comprehensive SLR study, thoroughly following the systematic guidelines defined and stated in [ 252644 — 46 ], with the aim of identifying and classifying the types of machine learning algorithms or techniques used for identifying the software requirements on the Stack Overflow.
In addition, we have also added snowballing search strategy [ 48 ] as a complementary strategy in review paper on machine learning to the automated electronic data sources with the aim of not overlooking any relevant paper. Basically, the conference paper just reported the initial findings with no detailed analysis of the results.
In this work we have deeply assessed all the findings of the research questions, reported the key findings and limitations, and discussed the open challenges for future researchers. The subsequent subsections give comprehensive information regarding the main activities of the SLR protocol used. The key research goal of our SLR study is to recognize or identify and classify or categorize the different types of machine learning algorithms or techniques used for identifying the software requirements on the Stack Overflow.
The goal of our SLR work is broad enough, so we divided it into the set of different research questions RQs specified in Table 1, review paper on machine learning. We also collected evidence to answer some interesting demographic questions DQs as suggested in [ 44 — 46 ] associated with the identification of the most actively participating researchers, organizations affiliations academia or industryand countries, as well as the top publication venues, review paper on machine learning.
Table 2 presents briefly the description of every DQ. The EBSE technique is totally dependent on the approach of identifying, collecting, and summarizing all the existing empirical evidence.
Nonetheless, it is quite hard to fully ensure that all the existing empirical evidence was recognized; we ultimately need to abate the validity threat of not relying on single search strategy [ 49 ]. Therefore, two diverse search approaches, i. The relevant experts on the RE and Stack Overflow areas validated the diverse search strategies. The search was thoroughly performed in four diverse electronic data sources EDSnamely, the ACM Digital Library, IEEE Xplore, Scopus, and Web of Science WoSrespectively [ 5051 ].
Review paper on machine learning different EDS ensure including the diverse main venues i. The automated search strings were developed from the combination of the key terms extracted from our defined research questions, keywords from the different research publications retrieved by a pilot search, and the list of synonyms.
We conducted several search rounds in the diverse EDS until we accomplished the best balance between precision and recall measures. Table 3 presents the set of final selected search strings, adapted to each of the four electronic data sources, respectively.
To start the snowballing process, the 12 primary studies were used as initial seeds that were selected from the automated search strategy.
In the snowballing process, it is vital to consider only review paper on machine learning suitable or relevant research studies, so to ensure this, we adopted a top-down sequential process to include only the relevant research papers for each stage of the new iteration. To guarantee the relevance, we also performed the data extraction with the aim that the preselected papers were suitable for answering the defined research questions.
All those papers were selected as the new seeds for the next stage or iteration of the snowballing process which passed the data extraction criterion. Finally, the snowballing process retrieved 1, papers and ended the process at the third iteration with no new primary papers found. To minimize the possible biasness, two of the authors individually conducted the selection of the papers, and a third author carefully reviewed the data review paper on machine learning from every iteration, integrated the individual outcomes, and thoroughly assessed them for any disagreements.
Tables 4 and 5respectively, present the outputs of the two adopted search approaches for the SLR study. The selection process primarily consists of two tasks: a principally perfect definition of both inclusion and exclusion criteria and truly applying these definite benchmarks to select the pertinent primary research studies [ 5556 ]. As inclusion and exclusion are principally two conflicting activities, we chose to categorically focus our efforts on the exclusion criterion, review paper on machine learning, by outlining a clear set of criteria, both objectively and subjectively appropriate.
The former one does not cause any sort of threat to the validity, and, henceforth, its application is much easier and simpler. While applying the very first exclusion criterion, specifically, those related to the language and duplicity assisted us to remove irrelevant data quite rapidly. For this SLR study, the following objective exclusion criteria were applied to all the retrieved papers: a Exclusion criterion 1: research papers not written in English language b Exclusion criterion 2: short research papers less than four pages in length c Exclusion criterion 3: research papers not published in peer-reviewed publication venues d Exclusion criterion 4: research papers that are not a primary research study secondary and tertiary research studies e Exclusion criterion 5: any kind of grey literature books, presentations, poster sessions, forewords, talks, review paper on machine learning, editorials, tutorials, panels, keynotes, etc.
f Exclusion criterion 6: all sorts of research thesis whether Ph, review paper on machine learning. or master or bachelor theses. It is obvious that subjective criteria are very complex to address adequately in any SLR study including this one. They are prone to create biasness into the SLR study, and, thus, a predefined protocol principally needs to be applied with the aim of minimizing this threat. On the contrary, applying these criteria might also leads to a substantial reduction in the number of research papers to consider as being relevant.
For this Review paper on machine learning study, the authors applied the two exclusion criteria described as follows: a Not focus: research studies not related to any of the RE activities on the Stack Overflow b Out of scope: research studies not related to any of the RE phases of software development lifecycle. Any research paper not excluded by the aforementioned criteria was deemed relevant and included in the set of final selected primary research studies.
The authors primarily adopted a top-down method to the application of these criteria on the research papers, review paper on machine learning. In the first stage, some metadata information such as the title, abstract, and keywords of the research paper was taken into consideration.
If these data were not sufficient to exclude any research paper at hand, then the authors reviewed review paper on machine learning full text of the research publication, more specifically the introduction problems and contributions of the research studythe results, and conclusions sections of the research study.
To handle appropriately with any disagreements, the authors primarily followed the inclusive review paper on machine learning as systematically suggested in [ 44 ] and described in detail in Table 6. The complete diagrammatic flow of both the searches performed EDS and Snowballingdetailed systematic selection processes and the outcome of every task of the SLR study are reflected in Figure 1.
A final set of 12 research papers was selected for this SLR study the detailed list with full bibliographic references is provided in Table 7. Besides, the details of the quality assessment criterion review paper on machine learning for the SLR study are described in the next section. For any research publication to pass the defined selection phase, a comprehensive quality assessment criterion was defined.
The DEF see Table 9 was mainly used to extract and store the data for each of the selected research studies.
A Literature Review on Machine Learning in Materials Science - AISC
, time: 31:19Book Review: Machine Learning for Kids - insideBIGDATA
Recently, Deep Learning (a surrogate of Machine Learning) have won several contests in pattern recognition and machine learning. This review comprehensively summarises relevant studies, much of A Review Paper on Machine Learning Based Recommendation System 1Bhumika Bhatt, 2Prof. Premal J Patel, 3Prof. Hetal Gaudani 1M.E.C.E., 2HOD, 2Associate Professor 1,2Department of Computer Engineering, IIET, Dharmaj 3Department of Computer Engineering, GCET, Vallabh Vidhyanagar · Background and purpose Machine learning (ML) has attracted much attention with the hope that it could make use of large, routinely collected datasets and deliver accurate personalised prognosis. The aim of this systematic review is to identify and critically appraise the reporting and developing of ML models for predicting outcomes after blogger.com by: 9
No comments:
Post a Comment