Sentiment Analysis is a Natural Language Processing (NLP) domain related to the identification or extraction of user sentiments or opinions from written language. Although the approaches to achieve the goals may vary, Machine Learning (ML) methods are gradually becoming the preferred method because of their ability to automatically draw useful insight from data regardless of their complexity. However, an important prerequisite for most ML algorithms to learn from text data is to encode them into numerical vectors. Popular approaches to this include word level representation methods TF-IDF, distributed word representations (word2vec) and distributed document representations (doc2vec). Each of these methods has demonstrated remarkable success in representing the encoded text, however we found that no method has been set to be excellence in all tasks. Motivated by this challenge, an improved scheme of pairwise fusion are proposed for sentiment classification of book reviews. In the experimental findings, Artificial Neural Networks (ANN) and Logistic Regression (LR) classifiers showed that the proposed scheme improved the performance compared to the single method vectorization method. We see that TF-IDF-word2vec performed best among other methods with a mean accuracy of 91.0% (ANN) and 92.5% (LR); showed an improvement of 0.7% and 0.2% respectively over TF-IDF which is the best single vector method. Thus, the proposed method can used as a compact alternative to the popular bag-of-n-gram models as it captures contextual information of encoded document with a less sparse data.
{"title":"A Scheme of Pairwise Feature Combinations to Improve Sentiment Classification Using Book Review Dataset","authors":"S. Huspi, Haisal Dauda Abubakar, M. Umar","doi":"10.11113/ijic.v12n1.344","DOIUrl":"https://doi.org/10.11113/ijic.v12n1.344","url":null,"abstract":"Sentiment Analysis is a Natural Language Processing (NLP) domain related to the identification or extraction of user sentiments or opinions from written language. Although the approaches to achieve the goals may vary, Machine Learning (ML) methods are gradually becoming the preferred method because of their ability to automatically draw useful insight from data regardless of their complexity. However, an important prerequisite for most ML algorithms to learn from text data is to encode them into numerical vectors. Popular approaches to this include word level representation methods TF-IDF, distributed word representations (word2vec) and distributed document representations (doc2vec). Each of these methods has demonstrated remarkable success in representing the encoded text, however we found that no method has been set to be excellence in all tasks. Motivated by this challenge, an improved scheme of pairwise fusion are proposed for sentiment classification of book reviews. In the experimental findings, Artificial Neural Networks (ANN) and Logistic Regression (LR) classifiers showed that the proposed scheme improved the performance compared to the single method vectorization method. We see that TF-IDF-word2vec performed best among other methods with a mean accuracy of 91.0% (ANN) and 92.5% (LR); showed an improvement of 0.7% and 0.2% respectively over TF-IDF which is the best single vector method. Thus, the proposed method can used as a compact alternative to the popular bag-of-n-gram models as it captures contextual information of encoded document with a less sparse data.","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82692992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Automatic skull identification systems play a vital role for forensic law authorities to recognize victim identity. Motivated by potential applications of these kinds of systems, this research aims to apply a pre-trained deep convolutional neural network (CNN) for face skull recognition. Basically, the unknown skull image is fed to a pre-trained CNN network to extract a 1D feature vector, and then it will be matched with photos at database agencies to identify the closest match. To validate the proposed skull recognition system, it has been applied for a total of 13 skulls, and the reported results indicated a good was achieved. In addition, various CNN architectures were investigated, including shallow, medium, and deep CNN models. The best performance was reported from the shallow CNN model with a 92% recognition rate.
{"title":"Convolutional Neural Network for Skull Recognition","authors":"Hussein Samma, Bader Lahasan","doi":"10.11113/ijic.v12n1.347","DOIUrl":"https://doi.org/10.11113/ijic.v12n1.347","url":null,"abstract":"Automatic skull identification systems play a vital role for forensic law authorities to recognize victim identity. Motivated by potential applications of these kinds of systems, this research aims to apply a pre-trained deep convolutional neural network (CNN) for face skull recognition. Basically, the unknown skull image is fed to a pre-trained CNN network to extract a 1D feature vector, and then it will be matched with photos at database agencies to identify the closest match. To validate the proposed skull recognition system, it has been applied for a total of 13 skulls, and the reported results indicated a good was achieved. In addition, various CNN architectures were investigated, including shallow, medium, and deep CNN models. The best performance was reported from the shallow CNN model with a 92% recognition rate. ","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87230250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nur Sabrina Azmi, H. Hashim, L. Hong, A. A. Samah, H. Majid, Z. A. Shah, Nuraina Syaza Azman
Protease is a proteolytic enzyme that hydrolyzes the amino acid where the cleavage only occurs at specific sites of the amino acid substrate. By discovering the nick site, the prediction on the function of proteases can be identified and enable humans to control the protein's hydrolysis by their corresponding protease. It is very contributed to controlling protein production especially viral protein. The experts may alter the production of viral protein by reducing the viral proteases to undergo proteolysis. With the rise of computational methods in this era, deep learning is becoming more famous and applied in every field of study, including the biological area. Conventional techniques such as mass spectrometry and two-dimensional gel electrophoresis are being replaced by computational methods due to time-consuming. Thus, this study improves the deep learning algorithm by proposing the Hybrid model of Random Forest + Deep Neural Network (Hybrid RF+DNN) to classify nick sites. The classification in this study is compared with the other machine learning algorithms such as Random Forest (RF), Support Vector Machine (SVM), and Deep Neural Network (DNN). The proposed method is believed to enhance the classification results in identifying the positive and negative nick sites. The RF is a feature-selector that gathers the most important feature before entering the DNN classifier. This approach reduces the data dimensionality and speeds up the execution time of the training process. The performance of the models was measured by confusion matrix, specificity, sensitivity, etc. However, the proposed method is not the best performer among the mentioned classifiers from the result. The proposed method may become the best performer as the parameter tuning is done more precisely, even after the feature selection by the RF algorithm. Thus, the proposed method with the enhancement appears to be an alternative to the researcher discovering nick site.
{"title":"An Improved Deep Neural Network Algorithm for the Prediction of Limited Proteolysis in Native Protein","authors":"Nur Sabrina Azmi, H. Hashim, L. Hong, A. A. Samah, H. Majid, Z. A. Shah, Nuraina Syaza Azman","doi":"10.11113/IJIC.V12N1.351","DOIUrl":"https://doi.org/10.11113/IJIC.V12N1.351","url":null,"abstract":"Protease is a proteolytic enzyme that hydrolyzes the amino acid where the cleavage only occurs at specific sites of the amino acid substrate. By discovering the nick site, the prediction on the function of proteases can be identified and enable humans to control the protein's hydrolysis by their corresponding protease. It is very contributed to controlling protein production especially viral protein. The experts may alter the production of viral protein by reducing the viral proteases to undergo proteolysis. With the rise of computational methods in this era, deep learning is becoming more famous and applied in every field of study, including the biological area. Conventional techniques such as mass spectrometry and two-dimensional gel electrophoresis are being replaced by computational methods due to time-consuming. Thus, this study improves the deep learning algorithm by proposing the Hybrid model of Random Forest + Deep Neural Network (Hybrid RF+DNN) to classify nick sites. The classification in this study is compared with the other machine learning algorithms such as Random Forest (RF), Support Vector Machine (SVM), and Deep Neural Network (DNN). The proposed method is believed to enhance the classification results in identifying the positive and negative nick sites. The RF is a feature-selector that gathers the most important feature before entering the DNN classifier. This approach reduces the data dimensionality and speeds up the execution time of the training process. The performance of the models was measured by confusion matrix, specificity, sensitivity, etc. However, the proposed method is not the best performer among the mentioned classifiers from the result. The proposed method may become the best performer as the parameter tuning is done more precisely, even after the feature selection by the RF algorithm. Thus, the proposed method with the enhancement appears to be an alternative to the researcher discovering nick site.","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78826335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Increase in the adoption of ICT for service provision renders higher educational institutions in Nigeria vulnerable to numerous security challenges. The information security threats addressed in this paper encompasses identity theft and identification frauds. The high number of students patronizing these institutions seeking for knowledge results in the challenges of identifying authentic students. The use of students’ identification number and password is no longer sufficient for authentication of students. Therefore, this paper proposed a centralized authentication model for higher education system (CAMHES) for Nigeria. The model uses multi-factor authentication combining students’ identification number, students’ fingerprint biometric and smartcard technology for students’ authentication. The solution authenticates the identity of genuine students to eliminate impersonation in Nigerian higher education systems. Although, this solution provides authentication that is extremely difficult to replicate, it is recommended that the students’ biometric data captured must be strictly kept safe.
{"title":"Centralized Students’ Authentication for Higher Education Systems","authors":"M. Ahmed","doi":"10.11113/ijic.v12n1.325","DOIUrl":"https://doi.org/10.11113/ijic.v12n1.325","url":null,"abstract":"Increase in the adoption of ICT for service provision renders higher educational institutions in Nigeria vulnerable to numerous security challenges. The information security threats addressed in this paper encompasses identity theft and identification frauds. The high number of students patronizing these institutions seeking for knowledge results in the challenges of identifying authentic students. The use of students’ identification number and password is no longer sufficient for authentication of students. Therefore, this paper proposed a centralized authentication model for higher education system (CAMHES) for Nigeria. The model uses multi-factor authentication combining students’ identification number, students’ fingerprint biometric and smartcard technology for students’ authentication. The solution authenticates the identity of genuine students to eliminate impersonation in Nigerian higher education systems. Although, this solution provides authentication that is extremely difficult to replicate, it is recommended that the students’ biometric data captured must be strictly kept safe.","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81037847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Information Retrieval has been in existence since the 1940s and is impossible to do without. However, current information retrieval systems are known to be ineffective. The reason that seems to encompass this ineffectiveness is design-focused. Designers and developers of Information Retrieval Systems are known to be system focused rather than user-focused. They provide the same information to users even when they are in different contexts and have diverse preferences. Using a restaurant use case, we propose a conceptual user-centric multi-context hybrid reasoning Information Retrieval model to improve the accuracy of retrieved results.
{"title":"A User-Centric Multi-Context Hybrid Reasoning Information Retrieval Model","authors":"S. E. Nnebe, E. Okoro, F. Sadiq, B. Abara","doi":"10.11113/ijic.v12n1.337","DOIUrl":"https://doi.org/10.11113/ijic.v12n1.337","url":null,"abstract":"Information Retrieval has been in existence since the 1940s and is impossible to do without. However, current information retrieval systems are known to be ineffective. The reason that seems to encompass this ineffectiveness is design-focused. Designers and developers of Information Retrieval Systems are known to be system focused rather than user-focused. They provide the same information to users even when they are in different contexts and have diverse preferences. Using a restaurant use case, we propose a conceptual user-centric multi-context hybrid reasoning Information Retrieval model to improve the accuracy of retrieved results.","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77056626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Advancements made in consumer and readily available RGB-D capturing devices have sparked researcher interest in 3D reconstruction, particularly in dynamic scenes, as well as the quality performance and its speed. The recent advancement in such devices supports the developments of various applications such as teleportation, gaming, volumetric video, and CG films. Real-time 3D reconstruction methods review in a dynamic scene of virtual environment is depicted in this paper. This provides an insight view on how real-time 3D reconstruction beneficial achievement further enables reconstruction systems to be managed in real-time technology such as virtual reality or augmented reality application.
{"title":"A Review on Real-Time 3D Reconstruction Methods in Dynamic Scene","authors":"M. N. A. Nor’a, F. E. Fadzli, A. W. Ismail","doi":"10.11113/ijic.v12n1.317","DOIUrl":"https://doi.org/10.11113/ijic.v12n1.317","url":null,"abstract":"Advancements made in consumer and readily available RGB-D capturing devices have sparked researcher interest in 3D reconstruction, particularly in dynamic scenes, as well as the quality performance and its speed. The recent advancement in such devices supports the developments of various applications such as teleportation, gaming, volumetric video, and CG films. Real-time 3D reconstruction methods review in a dynamic scene of virtual environment is depicted in this paper. This provides an insight view on how real-time 3D reconstruction beneficial achievement further enables reconstruction systems to be managed in real-time technology such as virtual reality or augmented reality application.","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78993872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Over the years, people have tried to advance 3D display technology and researchers as well as developers have created different innovations in recent decades. there are many other different types of 3D display technology that can be classified into stereoscopic, auto stereoscopic, holographic and volumetric 3D displays. This paper, however, discusses the 3D display technology that have been implemented in the telepresence system, which can be divided into two main devices, projectors and head mounted display (HMD). From these two devices, the 3D display technology using projector devices are on-stage hologram, auto stereoscopic display, and holographic projection; while for HMD can be divided into MR headset and VR HMD. This paper provides a review on these 3D display for telepresence. Finally, we make a comparison based on the features of the 3D display technologies such as life-size capability, viewable from different perspectives, headset-free experience number of viewers per device, level of ease of setup and the nausea of discomfort level. To choose the best 3D display technology for a telepresence system, we must first identify the number of users who will be projected and who will be viewed. The goal and activity of using telepresence technology will also define the appropriate type of 3D display.
{"title":"3D Display for 3D Telepresence: A Review","authors":"F. E. Fadzli, M. N. A. Nor’a, A. W. Ismail","doi":"10.11113/ijic.v12n1.318","DOIUrl":"https://doi.org/10.11113/ijic.v12n1.318","url":null,"abstract":"Over the years, people have tried to advance 3D display technology and researchers as well as developers have created different innovations in recent decades. there are many other different types of 3D display technology that can be classified into stereoscopic, auto stereoscopic, holographic and volumetric 3D displays. This paper, however, discusses the 3D display technology that have been implemented in the telepresence system, which can be divided into two main devices, projectors and head mounted display (HMD). From these two devices, the 3D display technology using projector devices are on-stage hologram, auto stereoscopic display, and holographic projection; while for HMD can be divided into MR headset and VR HMD. This paper provides a review on these 3D display for telepresence. Finally, we make a comparison based on the features of the 3D display technologies such as life-size capability, viewable from different perspectives, headset-free experience number of viewers per device, level of ease of setup and the nausea of discomfort level. To choose the best 3D display technology for a telepresence system, we must first identify the number of users who will be projected and who will be viewed. The goal and activity of using telepresence technology will also define the appropriate type of 3D display.","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82161109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Speaker recognition is an ability to identify speaker’s characteristics based from spoken language. The purpose of this study is to identify gender of speakers based on audio recordings. The objective of this study is to evaluate the accuracy rate of this technique to differentiate the gender and also to determine the performance rate to classify even when using self-acquired recordings. Audio forensics uses voice recordings as part of evidence to solve cases. This study is mainly conducted to provide an easier technique to identify the unknown speaker characteristics in forensic field. This experiment is fulfilled by training the pattern classifier using gender dependent data. In order to train the model, a speech database is obtained from an online speech corpus comprising of both male and female speakers. During the testing phase, apart from the data from speech corpus, audio recordings of UTM students will too be used to determine the accuracy rate of this speaker identification experiment. As for the technique to run this experiment, Mel Frequency Cepstrum Coefficient (MFCC) algorithm is used to extract the features from speech data while Gaussian Mixture Model (GMM) is used to model the gender identifier. Noise removal was not used for any speech data in this experiment. Python software is used to extract using MFCC coefficients and model the behavior using GMM technique. Experiment results show that GMM-MFCC technique can identify gender regardless of language but with varying accuracy rate.
{"title":"Study on Gender Identification Based on Audio Recordings Using Gaussian Mixture Model and Mel Frequency Cepstrum Coefficient Technique","authors":"Thurgeaswary Rokanatnam, Hazinah Kutty Mammi","doi":"10.11113/ijic.v11n2.343","DOIUrl":"https://doi.org/10.11113/ijic.v11n2.343","url":null,"abstract":"Speaker recognition is an ability to identify speaker’s characteristics based from spoken language. The purpose of this study is to identify gender of speakers based on audio recordings. The objective of this study is to evaluate the accuracy rate of this technique to differentiate the gender and also to determine the performance rate to classify even when using self-acquired recordings. Audio forensics uses voice recordings as part of evidence to solve cases. This study is mainly conducted to provide an easier technique to identify the unknown speaker characteristics in forensic field. This experiment is fulfilled by training the pattern classifier using gender dependent data. In order to train the model, a speech database is obtained from an online speech corpus comprising of both male and female speakers. During the testing phase, apart from the data from speech corpus, audio recordings of UTM students will too be used to determine the accuracy rate of this speaker identification experiment. As for the technique to run this experiment, Mel Frequency Cepstrum Coefficient (MFCC) algorithm is used to extract the features from speech data while Gaussian Mixture Model (GMM) is used to model the gender identifier. Noise removal was not used for any speech data in this experiment. Python software is used to extract using MFCC coefficients and model the behavior using GMM technique. Experiment results show that GMM-MFCC technique can identify gender regardless of language but with varying accuracy rate.","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84231340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Enterprise Resource Planning (ERP) is a widely known type of software that eases the managerial aspect in enterprises. It increases their efficiency and productivity which helps them to exponentially grow in a short span of time compared to organizations that are not using it. However, as much as productive it is, implementing it does not often succeed. Majority of ERP implementations ends up failing due to different types of factors. Spotting the light on technical aspects showed that several factors contribute to this failure. Starting from pre-implementation phase with Business Process Reengineering (BPR) execution failure, or during the implementation phase due to miscommunication or incapable project members. The research amount in this field, particularly in critical failure factors is not sufficient to learn from and avoid future implementations, hence this topic provides insights about this specific issue. Quantitative method is used to analyse the data collected from a survey questionnaire for those who got involved in ERP or BPR implementations. The research process goes through objectives from problem identification to an in-detail explanation about its causes and effects, to how it is going to be addressed, how the data is going to be collected and analysed, and finally the proposed approach with a technical evaluation for it. The final objective of the research results in developing an approach that minimises the negative contribution of two failure factors, poor BPR and ineffective communication on the mentioned implementations, or prevent them entirely. The reason these two were chosen were due to their high occurrence frequency and lack of research regarding why they are considered failure factors. Concluding the research, the mentioned enhanced approach is being evaluated showing its potential to solve these factors, as they are relying on each other, with additional suggestions to further improve the approach in future work.
{"title":"Preventing Enterprise Resource Planning Failure Through an Enhanced Approach to Solve Ineffective Communication","authors":"Mazen Ahmed Kabbary, Dayang N. A. Jawawi","doi":"10.11113/ijic.v11n2.320","DOIUrl":"https://doi.org/10.11113/ijic.v11n2.320","url":null,"abstract":"Enterprise Resource Planning (ERP) is a widely known type of software that eases the managerial aspect in enterprises. It increases their efficiency and productivity which helps them to exponentially grow in a short span of time compared to organizations that are not using it. However, as much as productive it is, implementing it does not often succeed. Majority of ERP implementations ends up failing due to different types of factors. Spotting the light on technical aspects showed that several factors contribute to this failure. Starting from pre-implementation phase with Business Process Reengineering (BPR) execution failure, or during the implementation phase due to miscommunication or incapable project members. The research amount in this field, particularly in critical failure factors is not sufficient to learn from and avoid future implementations, hence this topic provides insights about this specific issue. Quantitative method is used to analyse the data collected from a survey questionnaire for those who got involved in ERP or BPR implementations. The research process goes through objectives from problem identification to an in-detail explanation about its causes and effects, to how it is going to be addressed, how the data is going to be collected and analysed, and finally the proposed approach with a technical evaluation for it. The final objective of the research results in developing an approach that minimises the negative contribution of two failure factors, poor BPR and ineffective communication on the mentioned implementations, or prevent them entirely. The reason these two were chosen were due to their high occurrence frequency and lack of research regarding why they are considered failure factors. Concluding the research, the mentioned enhanced approach is being evaluated showing its potential to solve these factors, as they are relying on each other, with additional suggestions to further improve the approach in future work.","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83902175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Training evaluation can be defined as a way of measuring how well users learn and adapt to a system or software. Various methods have been developed to carry out training evaluations of systems or software over the past few decades. A systematic literature review report on the assessment training model was conducted to give different views on the usability aspects of the proposed approach. This study provides a current systematic review of training evaluation on skill-based system or software. The particular purpose of the review is to explore the research as preliminary step that helps in choosing the right type of training evaluation model for skill-based E-learning system or software. There is a lack of appropriate models available through the specific gaps in literature and finding especially for skill-based E-learning system evaluation.
{"title":"Training Evaluation Models for Skill-Based E-learning System: A Systematic Literature Review","authors":"Muneswary a/p Saminathan, Norhaida Mohd Suaib","doi":"10.11113/ijic.v11n2.323","DOIUrl":"https://doi.org/10.11113/ijic.v11n2.323","url":null,"abstract":"Training evaluation can be defined as a way of measuring how well users learn and adapt to a system or software. Various methods have been developed to carry out training evaluations of systems or software over the past few decades. A systematic literature review report on the assessment training model was conducted to give different views on the usability aspects of the proposed approach. This study provides a current systematic review of training evaluation on skill-based system or software. The particular purpose of the review is to explore the research as preliminary step that helps in choosing the right type of training evaluation model for skill-based E-learning system or software. There is a lack of appropriate models available through the specific gaps in literature and finding especially for skill-based E-learning system evaluation.","PeriodicalId":50314,"journal":{"name":"International Journal of Innovative Computing Information and Control","volume":null,"pages":null},"PeriodicalIF":1.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87506605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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