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3.3. Inclusion and exclusion criteria

Table 2 3.3. Inclusion and exclusion criteria

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3.2. Research strategy
3.2. Research strategy
Table 3. Inclusion and exclusion criteria Academic papers that failed to meet the mentioned criteria were excluded from research. The initial screening was done on all the extracted 790 records in order to narrow the result. These records were
Table 3. Inclusion and exclusion criteria Academic papers that failed to meet the mentioned criteria were excluded from research. The initial screening was done on all the extracted 790 records in order to narrow the result. These records were
compared against pre-selection criteria that included refining the search fields to engineering and computer science, limiting the results to the no older than ten years. This resulted in the evaluated numbers to decrease to 413 papers, which were assessed by the selection criteria mentioned. Figure 1 represents the process for both the pre-selection and the selection criteria. This process resulted in 16 papers to be included in the systematic review in sequence-to-sequence neural network. 3.4, Quality assessment
compared against pre-selection criteria that included refining the search fields to engineering and computer science, limiting the results to the no older than ten years. This resulted in the evaluated numbers to decrease to 413 papers, which were assessed by the selection criteria mentioned. Figure 1 represents the process for both the pre-selection and the selection criteria. This process resulted in 16 papers to be included in the systematic review in sequence-to-sequence neural network. 3.4, Quality assessment
A systematic review on sequence-to-sequence learning with neural network and its models (Hana Yousuf) OMS I SS, RP SS review [35-39]. The assessment quality assurance checklist for our systematic review consists of 6 questions for the 16 chosen papers, as shown in Table 4. The scoring of this process is done based on the work of [40] as: a 'Yes' to the question of the quality assessment is indicated by a 1, a 'No' was indicated by a 0, and a 'Partially' was indicated by a 0.5. As seen by the results in Table 5, all the chosen papers have passed the quality assessment.
A systematic review on sequence-to-sequence learning with neural network and its models (Hana Yousuf) OMS I SS, RP SS review [35-39]. The assessment quality assurance checklist for our systematic review consists of 6 questions for the 16 chosen papers, as shown in Table 4. The scoring of this process is done based on the work of [40] as: a 'Yes' to the question of the quality assessment is indicated by a 1, a 'No' was indicated by a 0, and a 'Partially' was indicated by a 0.5. As seen by the results in Table 5, all the chosen papers have passed the quality assessment.
By studying all the 16 research papers included in the systematic review, a classification system was comprised based on their contribution to answering the research questions. Markings were made if the main focus of the paper was related to a particular category. Most papers, for example, talked in brief about the different applications of sequence-to-sequence (seq2seq) models. However, only studies that were mainly focused on a certain application or discussed several applications in depth were categorized as 'A pplications of seq2seq'. The classification results can be seen in Table 6. Additionally, each study was analyzed in detail, and the results of this detailed study are outlined in Table 7 (see in Appendix). Figure 2 shows distributions country wise. As the results show, there has been an increase in interest in this last two decades, as indicated by the increase in a number of publications since 1990. Most of focused on the US, as mentioned. the publication topic over the this research is
By studying all the 16 research papers included in the systematic review, a classification system was comprised based on their contribution to answering the research questions. Markings were made if the main focus of the paper was related to a particular category. Most papers, for example, talked in brief about the different applications of sequence-to-sequence (seq2seq) models. However, only studies that were mainly focused on a certain application or discussed several applications in depth were categorized as 'A pplications of seq2seq'. The classification results can be seen in Table 6. Additionally, each study was analyzed in detail, and the results of this detailed study are outlined in Table 7 (see in Appendix). Figure 2 shows distributions country wise. As the results show, there has been an increase in interest in this last two decades, as indicated by the increase in a number of publications since 1990. Most of focused on the US, as mentioned. the publication topic over the this research is
A, RESULTS AND DISCUSSION Utilizing the research procedure outlined in the previous section, the research questions that were ended to each paper are classified and analyzed based on their contribution to each question.
A, RESULTS AND DISCUSSION Utilizing the research procedure outlined in the previous section, the research questions that were ended to each paper are classified and analyzed based on their contribution to each question.
a. RQ1. What are the different applications of the sequence-to-sequence neural network model? As seen from Error! Reference source not found., 11, or 68.75%, of the studies were relevant to the applications of sequence-to-sequence neural network models, indicating not only the relevance of this question but also its widespread interest in the field. The general consensus was that sequence-to-sequence models were best utilized for speech recognition and general linguistics, as suggested by [16, 31, 48]. In addition, sequence-to-sequence models can be used for video to text conversion [46] and handling large vocabularies, optimizing translation performance, and multi-lingual learning [27]. b. RQ2. How has this model been implemented and developed?
a. RQ1. What are the different applications of the sequence-to-sequence neural network model? As seen from Error! Reference source not found., 11, or 68.75%, of the studies were relevant to the applications of sequence-to-sequence neural network models, indicating not only the relevance of this question but also its widespread interest in the field. The general consensus was that sequence-to-sequence models were best utilized for speech recognition and general linguistics, as suggested by [16, 31, 48]. In addition, sequence-to-sequence models can be used for video to text conversion [46] and handling large vocabularies, optimizing translation performance, and multi-lingual learning [27]. b. RQ2. How has this model been implemented and developed?
Table 7. Analysis of the literature review Int] Elec & Comp Eng, Vol. 11, No. 3, June 2021: 2315 - 2326
Table 7. Analysis of the literature review Int] Elec & Comp Eng, Vol. 11, No. 3, June 2021: 2315 - 2326
A systematic review on sequence-to-sequence learning with neural network and its models (Hana Yousuf)
A systematic review on sequence-to-sequence learning with neural network and its models (Hana Yousuf)
BIOGRAPHIES OF AUTHORS
BIOGRAPHIES OF AUTHORS
Related topics:
Recurrent Neural NetworksSequence-to-Sequence Models
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