Pub Date : 2023-07-29DOI: 10.15282/ijsecs.9.2.2023.3.0114
Mdurvwa Usiju Ijairi, M. Abdullahi, Ibrahim Hayatu Hassan
The widespread use of social media platforms such as Twitter, Instagram, Facebook, and LinkedIn have had a huge impact on daily human interactions and decision-making. Owing to Twitter's widespread acceptance, users can express their opinions/sentiments on nearly any issue, ranging from public opinion, a product/service, to even a specific group of people. Sharing these opinions/sentiments results in a massive production of user content known as tweets, which can be assessed to generate new knowledge. Corporate insights, government policy formation, decision-making, and brand identity monitoring all benefit from analyzing the opinions/sentiments expressed in these tweets. Even though several techniques have been created to analyze user sentiments from tweets, social media engagements include negation words and emoji elements that, if not properly pre-processed, would result in misclassification. The majority of available pre-processing techniques rely on clean data and machine learning algorithms to annotate sentiment in unlabeled texts. In this study, we propose a text pre-processing approach that takes into consideration negation words and emoji characteristics in text data by translating these features into single contextual words in tweets to minimize context loss. The proposed preprocessor was evaluated on benchmark Twitter datasets using four deep learning algorithms: Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Artificial Neural Network (ANN). The results showed that LSTM performed better than the approaches already discussed in the literature, with an accuracy of 96.36%, 88.41%, and 95.39%. The findings also suggest that pre-processing information like emoji and explicit word negations aids in the preservation of sentimental information. This appears to be the first study to classify sentiments in tweets while accounting for both explicit word negation conversion and emoji translation.
{"title":"Sentiment Classification of Tweets with Explicit Word Negations and Emoji Using Deep Learning","authors":"Mdurvwa Usiju Ijairi, M. Abdullahi, Ibrahim Hayatu Hassan","doi":"10.15282/ijsecs.9.2.2023.3.0114","DOIUrl":"https://doi.org/10.15282/ijsecs.9.2.2023.3.0114","url":null,"abstract":"The widespread use of social media platforms such as Twitter, Instagram, Facebook, and LinkedIn have had a huge impact on daily human interactions and decision-making. Owing to Twitter's widespread acceptance, users can express their opinions/sentiments on nearly any issue, ranging from public opinion, a product/service, to even a specific group of people. Sharing these opinions/sentiments results in a massive production of user content known as tweets, which can be assessed to generate new knowledge. Corporate insights, government policy formation, decision-making, and brand identity monitoring all benefit from analyzing the opinions/sentiments expressed in these tweets. Even though several techniques have been created to analyze user sentiments from tweets, social media engagements include negation words and emoji elements that, if not properly pre-processed, would result in misclassification. The majority of available pre-processing techniques rely on clean data and machine learning algorithms to annotate sentiment in unlabeled texts. In this study, we propose a text pre-processing approach that takes into consideration negation words and emoji characteristics in text data by translating these features into single contextual words in tweets to minimize context loss. The proposed preprocessor was evaluated on benchmark Twitter datasets using four deep learning algorithms: Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Artificial Neural Network (ANN). The results showed that LSTM performed better than the approaches already discussed in the literature, with an accuracy of 96.36%, 88.41%, and 95.39%. The findings also suggest that pre-processing information like emoji and explicit word negations aids in the preservation of sentimental information. This appears to be the first study to classify sentiments in tweets while accounting for both explicit word negation conversion and emoji translation.","PeriodicalId":31240,"journal":{"name":"International Journal of Software Engineering and Computer Systems","volume":"131 4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87620870","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}
Pub Date : 2023-07-29DOI: 10.15282/ijsecs.9.2.2023.1.0112
Noor Aisha Abdul Rahman, Nur Syazana Ahmad Jefiruddin, Z. Ahmad Zukarnain, Nor Asma Mohd Zin
Inventory tracking is one of the most crucial aspects in business strategy. Effective inventory system can help the prevention of stockouts, effective management of different locations, as well as the maintenance of accurate records in a business. Nowadays, digitalization is a critical component of business operations. Digitalization is the process of implementing new digital technology into all aspects of a company's operations, resulting in a significant change in how the business operates. A systematic mapping has been performed on Android-based for smart inventory system by using digitalized technology which is barcoding technology. The mapping are done by conducting systematic mapping process for analyzing related research areas on barcode and inventory system. Two research questions and related keywords are initiated for identifying possible operating system platforms in developing a smart inventory system with barcoding technology for tracking product items.
{"title":"A Systematic Mapping on Android-based Platform for Smart Inventory System","authors":"Noor Aisha Abdul Rahman, Nur Syazana Ahmad Jefiruddin, Z. Ahmad Zukarnain, Nor Asma Mohd Zin","doi":"10.15282/ijsecs.9.2.2023.1.0112","DOIUrl":"https://doi.org/10.15282/ijsecs.9.2.2023.1.0112","url":null,"abstract":"Inventory tracking is one of the most crucial aspects in business strategy. Effective inventory system can help the prevention of stockouts, effective management of different locations, as well as the maintenance of accurate records in a business. Nowadays, digitalization is a critical component of business operations. Digitalization is the process of implementing new digital technology into all aspects of a company's operations, resulting in a significant change in how the business operates. A systematic mapping has been performed on Android-based for smart inventory system by using digitalized technology which is barcoding technology. The mapping are done by conducting systematic mapping process for analyzing related research areas on barcode and inventory system. Two research questions and related keywords are initiated for identifying possible operating system platforms in developing a smart inventory system with barcoding technology for tracking product items.","PeriodicalId":31240,"journal":{"name":"International Journal of Software Engineering and Computer Systems","volume":"101 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85416359","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}
Pub Date : 2023-07-29DOI: 10.15282/ijsecs.9.2.2023.4.0115
Nur Syuhada Mohamad Rodzi, Nur Shahirah Azahari, Nur Ziadah Harun
One of the objectives of information security is to maintain the confidentiality and integrity of the information by ensuring that information is transferred in a way that is secure from any listener or attacker. There was no comparison experiment conducted in earlier studies regarding different level encoding performance towards the multiphoton technique. In Quantum Secure Direct Communication (QSDC), when unpolarized light enters into the polarizer, the light value will be changed into a different value when it hit the Half Wave Plate (HWP) along the quantum communication channel. The multiphoton technique in the earlier study is particular to transmission time for data transfer encoding and extra time for polarizers to change polarization angles, both of which contribute to longer transmission times. With four different sizes of qubits, the three simulation experiments are carried out using Python coding with 2,4 and 8 levels of encoding. Experiment results demonstrate that the most efficient average photon transmission derived from 18 qubit size ranges from 98.71% to 98.73% depending on encoding level. With 18 qubit size, the four-level encoding result has the highest average efficiency, followed by the eight-level and two-level encodings, respectively. 4-level encoding exhibits the highest average photon efficiency between 2 and 8-level encoding.
{"title":"Protocol Efficiency Using Multiple Level Encoding in Quantum Secure Direct Communication Protocol","authors":"Nur Syuhada Mohamad Rodzi, Nur Shahirah Azahari, Nur Ziadah Harun","doi":"10.15282/ijsecs.9.2.2023.4.0115","DOIUrl":"https://doi.org/10.15282/ijsecs.9.2.2023.4.0115","url":null,"abstract":"One of the objectives of information security is to maintain the confidentiality and integrity of the information by ensuring that information is transferred in a way that is secure from any listener or attacker. There was no comparison experiment conducted in earlier studies regarding different level encoding performance towards the multiphoton technique. In Quantum Secure Direct Communication (QSDC), when unpolarized light enters into the polarizer, the light value will be changed into a different value when it hit the Half Wave Plate (HWP) along the quantum communication channel. The multiphoton technique in the earlier study is particular to transmission time for data transfer encoding and extra time for polarizers to change polarization angles, both of which contribute to longer transmission times. With four different sizes of qubits, the three simulation experiments are carried out using Python coding with 2,4 and 8 levels of encoding. Experiment results demonstrate that the most efficient average photon transmission derived from 18 qubit size ranges from 98.71% to 98.73% depending on encoding level. With 18 qubit size, the four-level encoding result has the highest average efficiency, followed by the eight-level and two-level encodings, respectively. 4-level encoding exhibits the highest average photon efficiency between 2 and 8-level encoding.","PeriodicalId":31240,"journal":{"name":"International Journal of Software Engineering and Computer Systems","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134920305","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}
Pub Date : 2023-07-29DOI: 10.15282/ijsecs.9.2.2023.5.0116
S. Usman, Shahnurin Khanam Sanchi, Muhammad Idris, Sadiq Abubakar Zagga
The Internet of Things (IoT) describes the connection of bodily devices as "things" that can communicate with other systems and devices through the Internet and exchange statistics (data or information), facilitating the exchange of data with other systems and devices. These devices have sensors, software, and various components designed to exchange data seamlessly within the IoT network. Securing and protecting the data transmitted over the Internet from unauthorized access is imperative to ensuring the integrity and confidentiality of the information. IoT Smart health monitoring systems, integral components of the IoT landscape, are susceptible to various attacks. These include denial of service (DoS), fingerprint, router, select, forwarding, sensor, and replay attacks, all of which pose significant threats to the security of these systems. As such, there is a pressing need to address and mitigate the vulnerabilities associated with IoT healthcare applications. This paper aims to explore the significant role of IoT devices in healthcare systems and provide an in-depth review of attacks that threaten the security of IoT healthcare applications. The study analyses the existing literature on the vulnerabilities present in smart health monitoring systems and the potential application of blockchain technology as a robust solution to enhance the security of IoT healthcare applications. This research reveals critical vulnerabilities in IoT healthcare applications and highlights blockchain's effectiveness in mitigating them, providing insights for robust security measures and strategic decision-making in secure healthcare systems. This paper provides valuable insight and recommendations for policymakers, researchers, and practitioners involved in the domain of the IoT healthcare system.
{"title":"SECURING IOT HEALTHCARE APPLICATIONS AND BLOCKCHAIN: ADDRESSING SECURITY ATTACKS","authors":"S. Usman, Shahnurin Khanam Sanchi, Muhammad Idris, Sadiq Abubakar Zagga","doi":"10.15282/ijsecs.9.2.2023.5.0116","DOIUrl":"https://doi.org/10.15282/ijsecs.9.2.2023.5.0116","url":null,"abstract":"The Internet of Things (IoT) describes the connection of bodily devices as \"things\" that can communicate with other systems and devices through the Internet and exchange statistics (data or information), facilitating the exchange of data with other systems and devices. These devices have sensors, software, and various components designed to exchange data seamlessly within the IoT network. Securing and protecting the data transmitted over the Internet from unauthorized access is imperative to ensuring the integrity and confidentiality of the information. IoT Smart health monitoring systems, integral components of the IoT landscape, are susceptible to various attacks. These include denial of service (DoS), fingerprint, router, select, forwarding, sensor, and replay attacks, all of which pose significant threats to the security of these systems. As such, there is a pressing need to address and mitigate the vulnerabilities associated with IoT healthcare applications. This paper aims to explore the significant role of IoT devices in healthcare systems and provide an in-depth review of attacks that threaten the security of IoT healthcare applications. The study analyses the existing literature on the vulnerabilities present in smart health monitoring systems and the potential application of blockchain technology as a robust solution to enhance the security of IoT healthcare applications. This research reveals critical vulnerabilities in IoT healthcare applications and highlights blockchain's effectiveness in mitigating them, providing insights for robust security measures and strategic decision-making in secure healthcare systems. This paper provides valuable insight and recommendations for policymakers, researchers, and practitioners involved in the domain of the IoT healthcare system.","PeriodicalId":31240,"journal":{"name":"International Journal of Software Engineering and Computer Systems","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139353943","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}
Pub Date : 2023-07-29DOI: 10.15282/ijsecs.9.2.2023.2.0113
Nalienaa Muthu, Faieza Abdul Aziz, L. N. Abdullah, M. Mokhtar, Muhd Khaizer Omar, Muhammad Amir Mustaqim Nazar
Teens and young adults may get training in anything from the basics to advanced skills in various workplace and academic settings at Technical and Vocational Education Training and Education (TVET) institutions. Some aspects of teaching and learning in TVET cannot be articulated clearly, and trainees cannot perceive how things fit together. The study was conducted to determine the optimal platform to develop mobile Augmented Reality applications for TVET trainees and, to assess the TVET trainee’s readiness for AR-based mobile application training deployment. An online questionnaire was sent to trainees at Industrial Training Institute in Malaysia via the online system. A marker-based Augmented Reality application was created for the Basic Electronic Components module utilizing Unity software, the Vuforia engine, and C# script. Finally, the trainees were allowed to test the generated application. The trainees were interviewed to obtain data on their responses. The results indicate that 83% of the TVET trainees own and use android as the application platform. The results of the pre-test and post-tests used to gauge the success of the Augmented Reality application show that its usage in the sub-learning module significantly improved memory recalls for the TVET trainees. The outcomes showed that the Augmented Reality application suited the participants' learning needs and improved the effectiveness of their learning. The result from this project will serve as a pre-test for determining the most suitable platform to deploy the Augmented Reality application to be developed in the future.
{"title":"The Mobile Augmented Reality Application for Improving Learning of Electronic Component Module in TVET","authors":"Nalienaa Muthu, Faieza Abdul Aziz, L. N. Abdullah, M. Mokhtar, Muhd Khaizer Omar, Muhammad Amir Mustaqim Nazar","doi":"10.15282/ijsecs.9.2.2023.2.0113","DOIUrl":"https://doi.org/10.15282/ijsecs.9.2.2023.2.0113","url":null,"abstract":"Teens and young adults may get training in anything from the basics to advanced skills in various workplace and academic settings at Technical and Vocational Education Training and Education (TVET) institutions. Some aspects of teaching and learning in TVET cannot be articulated clearly, and trainees cannot perceive how things fit together. The study was conducted to determine the optimal platform to develop mobile Augmented Reality applications for TVET trainees and, to assess the TVET trainee’s readiness for AR-based mobile application training deployment. An online questionnaire was sent to trainees at Industrial Training Institute in Malaysia via the online system. A marker-based Augmented Reality application was created for the Basic Electronic Components module utilizing Unity software, the Vuforia engine, and C# script. Finally, the trainees were allowed to test the generated application. The trainees were interviewed to obtain data on their responses. The results indicate that 83% of the TVET trainees own and use android as the application platform. The results of the pre-test and post-tests used to gauge the success of the Augmented Reality application show that its usage in the sub-learning module significantly improved memory recalls for the TVET trainees. The outcomes showed that the Augmented Reality application suited the participants' learning needs and improved the effectiveness of their learning. The result from this project will serve as a pre-test for determining the most suitable platform to deploy the Augmented Reality application to be developed in the future.","PeriodicalId":31240,"journal":{"name":"International Journal of Software Engineering and Computer Systems","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85345085","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}
Pub Date : 2023-05-19DOI: 10.15282/ijsecs.9.1.2023.6.0110
Muhammad Firdaus B. Mustapha, Nur Maisarah Mohamad, Siti Haslini Ab Hamid
Face classification is a challenging task that is crucial to numerous applications. There are many algorithms for classifying gender, but their ability to evaluate their effectiveness regarding scientific data is constrained. Deep learning is popular among researchers in face classification problems. The detection of many faces is complicated and becomes a necessity in real problems. The proposed research aims to examine the effect of twofold face detection approach on the accuracy of gender classification, as well as the effect of using small datasets on accuracy. In this study, we use a small dataset to classify facial images based on their gender. The following phases involve deep learning methods along with the OpenCV library version 3.4.2 which is recommended to serve as a twofold face detection approach. In the experiments conducted, Phase 1 is the designated training phase, and Phase 2 serves as a testing phase. Two different algorithms are used in the testing phase to detect one face in the image (Experiment 1), while the remaining algorithm detects multiple faces in the image (Experiment 2). The FEI dataset is used to evaluate the accuracy of the proposed research, which results in 84% accuracy for Experiment 2 and 74% for Experiment 1, respectively.
{"title":"TWOFOLD FACE DETECTION APPROACH IN GENDER CLASSIFICATION USING DEEP LEARNING","authors":"Muhammad Firdaus B. Mustapha, Nur Maisarah Mohamad, Siti Haslini Ab Hamid","doi":"10.15282/ijsecs.9.1.2023.6.0110","DOIUrl":"https://doi.org/10.15282/ijsecs.9.1.2023.6.0110","url":null,"abstract":"Face classification is a challenging task that is crucial to numerous applications. There are many algorithms for classifying gender, but their ability to evaluate their effectiveness regarding scientific data is constrained. Deep learning is popular among researchers in face classification problems. The detection of many faces is complicated and becomes a necessity in real problems. The proposed research aims to examine the effect of twofold face detection approach on the accuracy of gender classification, as well as the effect of using small datasets on accuracy. In this study, we use a small dataset to classify facial images based on their gender. The following phases involve deep learning methods along with the OpenCV library version 3.4.2 which is recommended to serve as a twofold face detection approach. In the experiments conducted, Phase 1 is the designated training phase, and Phase 2 serves as a testing phase. Two different algorithms are used in the testing phase to detect one face in the image (Experiment 1), while the remaining algorithm detects multiple faces in the image (Experiment 2). The FEI dataset is used to evaluate the accuracy of the proposed research, which results in 84% accuracy for Experiment 2 and 74% for Experiment 1, respectively.","PeriodicalId":31240,"journal":{"name":"International Journal of Software Engineering and Computer Systems","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82972664","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}
Pub Date : 2023-05-02DOI: 10.15282/ijsecs.9.1.2023.5.0109
Matthew Willetts, A. Atkins
Big Data Analytics is widely adopted by large companies but to a lesser extent by small to medium-sized enterprises (SMEs). SMEs comprise 99% of all businesses in the UK (6 million), employ 61% of the country’s workforce and generate over half of the turnover of the UK’s private sector (£2.1 trillion). SMEs represent 99% of all businesses in Europe and 90% worldwide. Therefore, assisting them to gain competitive advantage by the adoption of technology, such as Big Data Analytics is an important business initiative. The aim of this paper is to outline the process in which a positioning tool based on theoretical frameworks has been developed to help SMEs analyse their readiness to adopt Big Data Analytics using a case study. Previous work has identified 21 barriers to adoption and a methodology based on theoretical frameworks was developed to produce a positioning tool Holistic Big Data Analytics Framework for UK SMEs (HBDAF-UKSMEs). The paper outlines a case study based on a software development company to utilise this HBDAF-UKSMEs framework to assess the readiness using the proposed scoring tool for the adoption of Big Data Analytics based on three stages: pre-data analytics, business intelligence and Big Data Analytics.
{"title":"SOFTWARE POSITIONING TOOL TO SUPPORT SMES IN ADOPTION OF BIG DATA ANALYTICS USING A CASE STUDY APPLICATION","authors":"Matthew Willetts, A. Atkins","doi":"10.15282/ijsecs.9.1.2023.5.0109","DOIUrl":"https://doi.org/10.15282/ijsecs.9.1.2023.5.0109","url":null,"abstract":"Big Data Analytics is widely adopted by large companies but to a lesser extent by small to medium-sized enterprises (SMEs). SMEs comprise 99% of all businesses in the UK (6 million), employ 61% of the country’s workforce and generate over half of the turnover of the UK’s private sector (£2.1 trillion). SMEs represent 99% of all businesses in Europe and 90% worldwide. Therefore, assisting them to gain competitive advantage by the adoption of technology, such as Big Data Analytics is an important business initiative. The aim of this paper is to outline the process in which a positioning tool based on theoretical frameworks has been developed to help SMEs analyse their readiness to adopt Big Data Analytics using a case study. Previous work has identified 21 barriers to adoption and a methodology based on theoretical frameworks was developed to produce a positioning tool Holistic Big Data Analytics Framework for UK SMEs (HBDAF-UKSMEs). The paper outlines a case study based on a software development company to utilise this HBDAF-UKSMEs framework to assess the readiness using the proposed scoring tool for the adoption of Big Data Analytics based on three stages: pre-data analytics, business intelligence and Big Data Analytics.","PeriodicalId":31240,"journal":{"name":"International Journal of Software Engineering and Computer Systems","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81990654","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}
Pub Date : 2023-01-31DOI: 10.15282/ijsecs.9.1.2023.4.0108
Nurhilyana Anuar, Zamri Abu Bakar, Normaly Kamal Ismail
Stemming is an important process in text processing especially in Natural Language Processing (NLP). It could extract root word from the affix words in the text. In addition, it helps in extracting useful information that contributes to many area of research study such as Information Retrieval. Several stemming algorithms have been discussed in previous studies. However, there are limited studies on Malay stemming process and the number of experimental data used. In this study, we focus on stemming process of Malay stemming algorithm by using rule-based algorithm for a larger dataset of Malay language text. The syntactic linguistic rule-based method was used in the stemming process involves of removing prefixes, suffixes and, prefixes and suffixes. Training dataset was used in this study which consisted of 3233 sentences from e-khutbah text. The result of the experimental evaluation was done by measuring the precision, recall and f-measure. It was found that the algorithm used in this study showed a promising result based on total of dataset used for each test. The value of precision, recall and F-measure increase to 95%, 97% and 97% respectively. The enhancement of the stemming process has shown a significant impact on Malay text processing which in general improved the performance of NLP applications.
{"title":"Extraction of Malay Root Word that Starts with Letter P in Malay e-Khutbah using Rule Based","authors":"Nurhilyana Anuar, Zamri Abu Bakar, Normaly Kamal Ismail","doi":"10.15282/ijsecs.9.1.2023.4.0108","DOIUrl":"https://doi.org/10.15282/ijsecs.9.1.2023.4.0108","url":null,"abstract":"Stemming is an important process in text processing especially in Natural Language Processing (NLP). It could extract root word from the affix words in the text. In addition, it helps in extracting useful information that contributes to many area of research study such as Information Retrieval. Several stemming algorithms have been discussed in previous studies. However, there are limited studies on Malay stemming process and the number of experimental data used. In this study, we focus on stemming process of Malay stemming algorithm by using rule-based algorithm for a larger dataset of Malay language text. The syntactic linguistic rule-based method was used in the stemming process involves of removing prefixes, suffixes and, prefixes and suffixes. Training dataset was used in this study which consisted of 3233 sentences from e-khutbah text. The result of the experimental evaluation was done by measuring the precision, recall and f-measure. It was found that the algorithm used in this study showed a promising result based on total of dataset used for each test. The value of precision, recall and F-measure increase to 95%, 97% and 97% respectively. The enhancement of the stemming process has shown a significant impact on Malay text processing which in general improved the performance of NLP applications.","PeriodicalId":31240,"journal":{"name":"International Journal of Software Engineering and Computer Systems","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87464664","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}
Pub Date : 2023-01-31DOI: 10.15282/ijsecs.9.1.2023.2.0106
Goh Wei Sheng, Wan Isni Sofiah Wan Din, Quadri Waseem, A. Zabidi
Crack detection in old buildings has been shown to be inefficient, with many technical challenges such as physical inspection and difficult measurements. It is important to have an automatic, fast visual inspection of these building components to detect cracks by evaluating their conditions (impact) and the level of their risk. Unmanned Aerial Vehicles (UAV) can automate, avoid visual inspection, and avoid other physical check-ups of these buildings. Automated crack detection using Machine Learning Algorithms (MLA), especially a Conventional Neural Network (CNN), along with an Unmanned Aerial Vehicle (UAV), can be effective and both can efficiently work together to detect the cracks in buildings using image processing techniques. The purpose of this research project is to evaluate currently available crack detection systems and to develop an automated crack detection system using Aggregate Channel Features (ACF)that can be used with unmanned aerial vehicles (UAV). Therefore, we conducted a real-world experiment of crack detection at Hospital Raja Permaisuri Bainun using DJI Mavic Air (Drone Hardware) and DJI GO 4(Drone Software) using CNN through MATLAB software with CNN-SVM method with the accuracy rate of3.0 percent increased from 82.94%to 85.94%. in comparison with other ML algorithms like CNN Random Forest (RF), Support Vector Machine (SVM) and Artificial Neural Network (ANN).
老旧建筑的裂缝检测效率低下，存在许多技术挑战，如物理检测和测量困难。重要的是要对这些建筑构件进行自动、快速的视觉检查，通过评估它们的状况(影响)和风险水平来检测裂缝。无人驾驶飞行器(UAV)可以自动化，避免目视检查，并避免对这些建筑物进行其他物理检查。使用机器学习算法(MLA)，特别是传统神经网络(CNN)以及无人驾驶飞行器(UAV)进行自动裂缝检测是有效的，两者可以有效地协同工作，使用图像处理技术检测建筑物中的裂缝。本研究项目的目的是评估目前可用的裂缝检测系统，并利用聚合通道特征(ACF)开发一种可与无人机(UAV)一起使用的自动裂缝检测系统。因此，我们使用大疆Mavic Air (Drone Hardware)和大疆GO 4(Drone Software)，通过MATLAB软件使用CNN，采用CNN- svm方法，在Raja permanisuri Bainun医院进行了真实的裂缝检测实验，准确率从82.94%提高到85.94%，提高了3.0%。与CNN随机森林(RF)、支持向量机(SVM)和人工神经网络(ANN)等其他ML算法相比。
{"title":"Investigation and Analysis of Crack Detection using UAV and CNN: A Case Study of Hospital Raja Permaisuri Bainun","authors":"Goh Wei Sheng, Wan Isni Sofiah Wan Din, Quadri Waseem, A. Zabidi","doi":"10.15282/ijsecs.9.1.2023.2.0106","DOIUrl":"https://doi.org/10.15282/ijsecs.9.1.2023.2.0106","url":null,"abstract":"Crack detection in old buildings has been shown to be inefficient, with many technical challenges such as physical inspection and difficult measurements. It is important to have an automatic, fast visual inspection of these building components to detect cracks by evaluating their conditions (impact) and the level of their risk. Unmanned Aerial Vehicles (UAV) can automate, avoid visual inspection, and avoid other physical check-ups of these buildings. Automated crack detection using Machine Learning Algorithms (MLA), especially a Conventional Neural Network (CNN), along with an Unmanned Aerial Vehicle (UAV), can be effective and both can efficiently work together to detect the cracks in buildings using image processing techniques. The purpose of this research project is to evaluate currently available crack detection systems and to develop an automated crack detection system using Aggregate Channel Features (ACF)that can be used with unmanned aerial vehicles (UAV). Therefore, we conducted a real-world experiment of crack detection at Hospital Raja Permaisuri Bainun using DJI Mavic Air (Drone Hardware) and DJI GO 4(Drone Software) using CNN through MATLAB software with CNN-SVM method with the accuracy rate of3.0 percent increased from 82.94%to 85.94%. in comparison with other ML algorithms like CNN Random Forest (RF), Support Vector Machine (SVM) and Artificial Neural Network (ANN).","PeriodicalId":31240,"journal":{"name":"International Journal of Software Engineering and Computer Systems","volume":"72 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88151394","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}
Pub Date : 2023-01-31DOI: 10.15282/ijsecs.9.1.2023.3.0107
K. Shobowale, Z. Mukhtar, B. Yahaya, Y. Ibrahim, M. O. Momoh
The roll out of the deployment of the 5G technology has been ongoing globally. The deployment of the technologies associated with 5G has seen mixed reaction as regards its prospects to improve communication services in all spares of life amid its security concerns. The security concerns of 5G network lies in its architecture and other technologies that optimize the performance of its architecture. There are many fractions of 5G security architecture in the literature, a holistic security architectural structure will go a long way in tackling the security challenges. In this paper, the review of the security challenges of the 5G technology based on its architecture is presented along with their proposed solutions. This review was carried out with some keywords relating to 5G securities and architecture; this was used to retrieve appropriate literature for fitness of purpose. The 5G security architectures are majorly centered around the seven network security layers; thereby making each of the layers a source of security concern on the 5G network. Many of the 5G security challenges are related to authentication and authorization such as denial-of-service attacks, man in the middle attack and eavesdropping. Different methods both hardware (Unmanned Aerial Vehicles, field programmable logic arrays) and software (Artificial intelligence, Machine learning, Blockchain, Statistical Process Control) has been proposed for mitigating the threats. Other technologies applicable to 5G security concerns includes: Multi-radio access technology, smart-grid network and light fidelity. The implementation of these solutions should be reviewed on a timely basis because of the dynamic nature of threats which will greatly reduce the occurrence of security attacks on the 5G network.
{"title":"Latest Advances on Security Architecture for 5GTechnology and Services","authors":"K. Shobowale, Z. Mukhtar, B. Yahaya, Y. Ibrahim, M. O. Momoh","doi":"10.15282/ijsecs.9.1.2023.3.0107","DOIUrl":"https://doi.org/10.15282/ijsecs.9.1.2023.3.0107","url":null,"abstract":"The roll out of the deployment of the 5G technology has been ongoing globally. The deployment of the technologies associated with 5G has seen mixed reaction as regards its prospects to improve communication services in all spares of life amid its security concerns. The security concerns of 5G network lies in its architecture and other technologies that optimize the performance of its architecture. There are many fractions of 5G security architecture in the literature, a holistic security architectural structure will go a long way in tackling the security challenges. In this paper, the review of the security challenges of the 5G technology based on its architecture is presented along with their proposed solutions. This review was carried out with some keywords relating to 5G securities and architecture; this was used to retrieve appropriate literature for fitness of purpose. The 5G security architectures are majorly centered around the seven network security layers; thereby making each of the layers a source of security concern on the 5G network. Many of the 5G security challenges are related to authentication and authorization such as denial-of-service attacks, man in the middle attack and eavesdropping. Different methods both hardware (Unmanned Aerial Vehicles, field programmable logic arrays) and software (Artificial intelligence, Machine learning, Blockchain, Statistical Process Control) has been proposed for mitigating the threats. Other technologies applicable to 5G security concerns includes: Multi-radio access technology, smart-grid network and light fidelity. The implementation of these solutions should be reviewed on a timely basis because of the dynamic nature of threats which will greatly reduce the occurrence of security attacks on the 5G network.","PeriodicalId":31240,"journal":{"name":"International Journal of Software Engineering and Computer Systems","volume":"63 2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89677017","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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