Pub Date : 2023-01-01DOI: 10.14569/ijacsa.2023.01403101
Nouran Nassibi, Heba A. Fasihuddin, L. Hsairi
—Continued global economic instability and uncertainty is causing difficulties in predicting sales. As a result, many sectors and decision-makers are facing new, pressing challenges. In supply chain management, the food industry is a key sector in which sales movement and the demand forecasting for food products are more difficult to predict. Accurate sales forecasting helps to minimize stored and expired items across individual stores and, thus, reduces the potential loss of these expired products. To help food companies adapt to rapid changes and manage their supply chain more effectively, it is a necessary to utilize machine learning (ML) approaches because of ML’s ability to process and evaluate large amounts of data efficiently. This research compares two forecasting models for confectionery products from one of the largest distribution companies in Saudi Arabia in order to improve the company’s ability to predict demand for their products using machine learning algorithms. To achieve this goal, Support Vectors Machine (SVM) and Long Short-Term Memory (LSTM) algorithms were utilized. In addition, the models were evaluated based on their performance in forecasting quarterly time series. Both algorithms provided strong results when measured against the demand forecasting model, but overall the LSTM outperformed the SVM.
{"title":"Demand Forecasting Models for Food Industry by Utilizing Machine Learning Approaches","authors":"Nouran Nassibi, Heba A. Fasihuddin, L. Hsairi","doi":"10.14569/ijacsa.2023.01403101","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.01403101","url":null,"abstract":"—Continued global economic instability and uncertainty is causing difficulties in predicting sales. As a result, many sectors and decision-makers are facing new, pressing challenges. In supply chain management, the food industry is a key sector in which sales movement and the demand forecasting for food products are more difficult to predict. Accurate sales forecasting helps to minimize stored and expired items across individual stores and, thus, reduces the potential loss of these expired products. To help food companies adapt to rapid changes and manage their supply chain more effectively, it is a necessary to utilize machine learning (ML) approaches because of ML’s ability to process and evaluate large amounts of data efficiently. This research compares two forecasting models for confectionery products from one of the largest distribution companies in Saudi Arabia in order to improve the company’s ability to predict demand for their products using machine learning algorithms. To achieve this goal, Support Vectors Machine (SVM) and Long Short-Term Memory (LSTM) algorithms were utilized. In addition, the models were evaluated based on their performance in forecasting quarterly time series. Both algorithms provided strong results when measured against the demand forecasting model, but overall the LSTM outperformed the SVM.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86537929","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 : 2023-01-01DOI: 10.14569/ijacsa.2023.0140443
Aymen Rhouma, Yahia Said
Renewable energy is becoming a trusted power source. Energy forecasting is an important research field, which is used to provide information about the future power generation of renewable energy plants. Energy forecasting helps to safely manage the power grid by minimizing the operational cost of energy production. Recent advances in energy forecasting based on deep learning techniques have shown great success but the achieved results still too far from the target results. Ordinary deep learning models have been used for time series processing. In this paper, a complex-valued autoencoder was coupled with an LSTM neural network for solar energy forecasting. The complex-valued autoencoder was used to process the time series with the advantage of processing more complex data with more input arguments. The energy value was used as a real value and the weather condition was considered as the imaginary value. Taking into account the weather condition helps to better predict power generation. The proposed approach was evaluated on the Fingrid open data dataset. The mean absolute error (MAE), rootmean-square error (RMSE) and mean absolute percentage error (MAPE) was used to evaluate the performance of the proposed method. A comparison study was performed to prove the efficiency of the proposed approach. Reported results have shown the efficiency of the proposed approach. Keywords—Solar energy forecasting; artificial intelligence; complex-valued autoencoder; long-short term memory; deep
{"title":"Solar Energy Forecasting Based on Complex Valued Auto-encoder and Recurrent Neural Network","authors":"Aymen Rhouma, Yahia Said","doi":"10.14569/ijacsa.2023.0140443","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140443","url":null,"abstract":"Renewable energy is becoming a trusted power source. Energy forecasting is an important research field, which is used to provide information about the future power generation of renewable energy plants. Energy forecasting helps to safely manage the power grid by minimizing the operational cost of energy production. Recent advances in energy forecasting based on deep learning techniques have shown great success but the achieved results still too far from the target results. Ordinary deep learning models have been used for time series processing. In this paper, a complex-valued autoencoder was coupled with an LSTM neural network for solar energy forecasting. The complex-valued autoencoder was used to process the time series with the advantage of processing more complex data with more input arguments. The energy value was used as a real value and the weather condition was considered as the imaginary value. Taking into account the weather condition helps to better predict power generation. The proposed approach was evaluated on the Fingrid open data dataset. The mean absolute error (MAE), rootmean-square error (RMSE) and mean absolute percentage error (MAPE) was used to evaluate the performance of the proposed method. A comparison study was performed to prove the efficiency of the proposed approach. Reported results have shown the efficiency of the proposed approach. Keywords—Solar energy forecasting; artificial intelligence; complex-valued autoencoder; long-short term memory; deep","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82754364","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 : 2023-01-01DOI: 10.14569/ijacsa.2023.0140308
Li Yang
—Recent innovations in the Internet of Things (IoT) have given rise to IoT applications that require quick response times and low latency. Fog computing has proven to be an effective platform for handling IoT applications. It is a significant challenge to deploy fog computing resources effectively because of the heterogeneity of IoT tasks and their delay sensitivity. To take advantage of idle resources in IoT devices, this paper presents an edge computing concept that offloads edge tasks to nearby IoT devices. The IoT-assisted edge computing should meet two conditions, edge services should exploit the computing resources of IoT devices effectively and edge tasks offloaded to IoT devices do not interfere with local IoT tasks. Two main phases are included in the proposed method: virtualization of edge nodes, and task scheduling based on deep reinforcement learning. The first phase offers a layered edge framework. In the second phase, we applied deep reinforcement learning (DRL) to schedule tasks taking into account the diversity of tasks and the heterogeneity of available resources. According to simulation results, our proposed task scheduling method achieves higher levels of task satisfaction and success than existing methods.
{"title":"A New Task Scheduling Framework for Internet of Things based on Agile VNFs On-demand Service Model and Deep Reinforcement Learning Method","authors":"Li Yang","doi":"10.14569/ijacsa.2023.0140308","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140308","url":null,"abstract":"—Recent innovations in the Internet of Things (IoT) have given rise to IoT applications that require quick response times and low latency. Fog computing has proven to be an effective platform for handling IoT applications. It is a significant challenge to deploy fog computing resources effectively because of the heterogeneity of IoT tasks and their delay sensitivity. To take advantage of idle resources in IoT devices, this paper presents an edge computing concept that offloads edge tasks to nearby IoT devices. The IoT-assisted edge computing should meet two conditions, edge services should exploit the computing resources of IoT devices effectively and edge tasks offloaded to IoT devices do not interfere with local IoT tasks. Two main phases are included in the proposed method: virtualization of edge nodes, and task scheduling based on deep reinforcement learning. The first phase offers a layered edge framework. In the second phase, we applied deep reinforcement learning (DRL) to schedule tasks taking into account the diversity of tasks and the heterogeneity of available resources. According to simulation results, our proposed task scheduling method achieves higher levels of task satisfaction and success than existing methods.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82793191","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 : 2023-01-01DOI: 10.14569/ijacsa.2023.0140363
Mohammed Hitham M. H, H. Elkadi, N. Tazi
—Digital transformation has become essential for the majority of organizations, in both public and private sectors. The term "digital transformation" has been used (and misused), so frequently that it is now somewhat ambiguous. It has become imperative to give it some conceptual rigor. The objective of this study is to identify the major elements of digital transformation as well as develop a proper definition for DT in the public and private sectors. For this purpose, 56 different definitions of DT collected from the available literature were analyzed, and we found that they extracted elements from definition of DT manually. So, text mining (TF-IDF and Fp-tree) techniques are used to identify the major constituents and finally consolidate in generic DT definitions. The approach consists of five phases: 1) collecting and classifying DT definitions; 2) detecting synonyms; 3) extracting major elements (terms); 4) discussing and comparing DT elements; 5) formulating DT definitions for different business categories. An evaluation tool was also developed to assess the level of DT elements coverage in various definitions found in the literature, and, as a validation, it was applied to the formulated definitions.
{"title":"Consolidated Definition of Digital Transformation by using Text Mining","authors":"Mohammed Hitham M. H, H. Elkadi, N. Tazi","doi":"10.14569/ijacsa.2023.0140363","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140363","url":null,"abstract":"—Digital transformation has become essential for the majority of organizations, in both public and private sectors. The term \"digital transformation\" has been used (and misused), so frequently that it is now somewhat ambiguous. It has become imperative to give it some conceptual rigor. The objective of this study is to identify the major elements of digital transformation as well as develop a proper definition for DT in the public and private sectors. For this purpose, 56 different definitions of DT collected from the available literature were analyzed, and we found that they extracted elements from definition of DT manually. So, text mining (TF-IDF and Fp-tree) techniques are used to identify the major constituents and finally consolidate in generic DT definitions. The approach consists of five phases: 1) collecting and classifying DT definitions; 2) detecting synonyms; 3) extracting major elements (terms); 4) discussing and comparing DT elements; 5) formulating DT definitions for different business categories. An evaluation tool was also developed to assess the level of DT elements coverage in various definitions found in the literature, and, as a validation, it was applied to the formulated definitions.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82932680","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 : 2023-01-01DOI: 10.14569/ijacsa.2023.0140243
Asep Id Hadiana, S. K. Baharin, Zahriah Othman
—Indoor navigation is crucial, particularly during indoor disasters such as fires. However, current spatial subdivision models struggle to adapt to the dynamic changes that occur in such situations, making it difficult to identify the appropriate navigation space, and thus reducing the accuracy and efficiency of indoor navigation. This study presents a new framework for indoor navigation that is specifically designed for first responders, with a focus on improving their response time and safety during rescue operations in buildings. The framework is an extension of previous research and incorporates the combustibility factor as a critical variable to consider during fire disasters, along with definitions of safe and unsafe areas for first responders. An algorithm was developed to accommodate the framework and was evaluated using Pyrosim and Pathfinder software. The framework calculates walking speed factors that affect the path and walking speed of first responders, enhancing their chances of successful evacuation. The framework captures dynamic changes, such as smoke levels, that may impact the navigation path and walking speed of first responders, which were not accounted for in previous studies. The experimental results demonstrate that the framework can identify suitable navigation paths and safe areas for first responders, leading to successful evacuation in as little as 148 to 239 seconds. The proposed framework represents a significant improvement over previous studies and has the potential to enhance the safety and effectiveness of first responders during emergency situations.
{"title":"First Responders Space Subdivision Framework for Indoor Navigation","authors":"Asep Id Hadiana, S. K. Baharin, Zahriah Othman","doi":"10.14569/ijacsa.2023.0140243","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140243","url":null,"abstract":"—Indoor navigation is crucial, particularly during indoor disasters such as fires. However, current spatial subdivision models struggle to adapt to the dynamic changes that occur in such situations, making it difficult to identify the appropriate navigation space, and thus reducing the accuracy and efficiency of indoor navigation. This study presents a new framework for indoor navigation that is specifically designed for first responders, with a focus on improving their response time and safety during rescue operations in buildings. The framework is an extension of previous research and incorporates the combustibility factor as a critical variable to consider during fire disasters, along with definitions of safe and unsafe areas for first responders. An algorithm was developed to accommodate the framework and was evaluated using Pyrosim and Pathfinder software. The framework calculates walking speed factors that affect the path and walking speed of first responders, enhancing their chances of successful evacuation. The framework captures dynamic changes, such as smoke levels, that may impact the navigation path and walking speed of first responders, which were not accounted for in previous studies. The experimental results demonstrate that the framework can identify suitable navigation paths and safe areas for first responders, leading to successful evacuation in as little as 148 to 239 seconds. The proposed framework represents a significant improvement over previous studies and has the potential to enhance the safety and effectiveness of first responders during emergency situations.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86744294","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 : 2023-01-01DOI: 10.14569/ijacsa.2023.0140555
S. Fuada, T. Adiono, Prasetiyo -, Harthian Widhanto, Shorful Islam, Tri Chandra Pamungkas
—This study aims to design and develop Wi-Fi tracker system that utilizes RSSI-based distance parameters for crowd-monitoring applications in indoor settings. The system consists of three main components, namely 1) an embedded node that runs on Raspberry-pi Zero W, 2) a real-time localization algorithm, and 3) a server system with an online dashboard. The embedded node scans and collects relevant information from Wi-Fi-connected smartphones, such as MAC data, RSSI, timestamps, etc. These data are then transmitted to the server system, where the localization algorithm passively determines the location of devices as long as Wi-Fi is enabled. The mentioned devices are smartphones, tablets, laptops, while the algorithm used is a Non-Linear System with Lavenberg–Marquart and Unscented Kalman Filter (UKF). The server and online dashboard (web-based application) have three functions, including displaying and recording device localization results, setting parameters, and visualizing analyzed data. The node hardware was designed for minimum size and portability, resulting in a consumer electronics product outlook. The system demonstration in this study was conducted to validate its functionality and performance.
-本研究旨在设计和开发基于rssi距离参数的Wi-Fi跟踪系统，用于室内环境下的人群监控应用。该系统由三个主要部分组成，即1)一个运行在Raspberry-pi Zero W上的嵌入式节点，2)一个实时定位算法，以及3)一个带有在线仪表板的服务器系统。嵌入式节点扫描并收集wi - fi连接的智能手机的相关信息，如MAC数据、RSSI、时间戳等。然后将这些数据传输到服务器系统，只要启用Wi-Fi，定位算法就会被动地确定设备的位置。提到的设备是智能手机，平板电脑，笔记本电脑，而使用的算法是一个非线性系统与拉文伯格-马夸特和Unscented卡尔曼滤波器(UKF)。服务器和在线仪表板(基于web的应用程序)具有显示和记录设备本地化结果、设置参数和可视化分析数据三个功能。节点硬件的设计是为了最小的尺寸和便携性，导致消费电子产品的前景。本研究进行了系统演示，以验证其功能和性能。
{"title":"A Consumer Product of Wi-Fi Tracker System using RSSI-based Distance for Indoor Crowd Monitoring","authors":"S. Fuada, T. Adiono, Prasetiyo -, Harthian Widhanto, Shorful Islam, Tri Chandra Pamungkas","doi":"10.14569/ijacsa.2023.0140555","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140555","url":null,"abstract":"—This study aims to design and develop Wi-Fi tracker system that utilizes RSSI-based distance parameters for crowd-monitoring applications in indoor settings. The system consists of three main components, namely 1) an embedded node that runs on Raspberry-pi Zero W, 2) a real-time localization algorithm, and 3) a server system with an online dashboard. The embedded node scans and collects relevant information from Wi-Fi-connected smartphones, such as MAC data, RSSI, timestamps, etc. These data are then transmitted to the server system, where the localization algorithm passively determines the location of devices as long as Wi-Fi is enabled. The mentioned devices are smartphones, tablets, laptops, while the algorithm used is a Non-Linear System with Lavenberg–Marquart and Unscented Kalman Filter (UKF). The server and online dashboard (web-based application) have three functions, including displaying and recording device localization results, setting parameters, and visualizing analyzed data. The node hardware was designed for minimum size and portability, resulting in a consumer electronics product outlook. The system demonstration in this study was conducted to validate its functionality and performance.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90132610","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 : 2023-01-01DOI: 10.14569/ijacsa.2023.0140888
Md. Sujan Ali, Jannatul Ferdous
—The synchronization of neural activity in the human brain has great significance for coordinating its various cognitive functions. It changes throughout time and in response to frequency. The activity is measured in terms of brain signals, like an electroencephalogram (EEG). The time-frequency (TF) synchronization among several EEG channels is measured in this research using an efficient approach. Most frequently, the windowed Fourier transforms-short-time Fourier transform (STFT), as well as wavelet transform (WT), and are used to measure the TF coherence. The information provided by these model-based methods in the TF domain is insufficient. The proposed synchro squeezing transform (SST)-based TF representation is a data-adaptive approach for resolving the problem of the traditional one. It enables more perfect estimation and better tracking of TF components. The SST generates a clearly defined TF depiction because of its data flexibility and frequency reassignment capabilities. Furthermore, a non-identical smoothing operator is used to smooth the TF coherence, which enhances the statistical consistency of neural synchronization. The experiment is run using both simulated and actual EEG data. The outcomes show that the suggested SST-dependent system performs significantly better than the previously mentioned traditional approaches. As a result, the coherences dependent on the suggested approach clearly distinguish between various forms of motor imagery movement. The TF coherence can be used to measure the interdependencies of neural activities.
{"title":"Motor Imagery EEG Signals Marginal Time Coherence Analysis for Brain-Computer Interface","authors":"Md. Sujan Ali, Jannatul Ferdous","doi":"10.14569/ijacsa.2023.0140888","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140888","url":null,"abstract":"—The synchronization of neural activity in the human brain has great significance for coordinating its various cognitive functions. It changes throughout time and in response to frequency. The activity is measured in terms of brain signals, like an electroencephalogram (EEG). The time-frequency (TF) synchronization among several EEG channels is measured in this research using an efficient approach. Most frequently, the windowed Fourier transforms-short-time Fourier transform (STFT), as well as wavelet transform (WT), and are used to measure the TF coherence. The information provided by these model-based methods in the TF domain is insufficient. The proposed synchro squeezing transform (SST)-based TF representation is a data-adaptive approach for resolving the problem of the traditional one. It enables more perfect estimation and better tracking of TF components. The SST generates a clearly defined TF depiction because of its data flexibility and frequency reassignment capabilities. Furthermore, a non-identical smoothing operator is used to smooth the TF coherence, which enhances the statistical consistency of neural synchronization. The experiment is run using both simulated and actual EEG data. The outcomes show that the suggested SST-dependent system performs significantly better than the previously mentioned traditional approaches. As a result, the coherences dependent on the suggested approach clearly distinguish between various forms of motor imagery movement. The TF coherence can be used to measure the interdependencies of neural activities.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89148871","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 : 2023-01-01DOI: 10.14569/ijacsa.2023.0140607
J. Young, M. Shishido
—Lack of opportunities is a significant hurdle for English as a Foreign Language (EFL) for students during their learning journey. Previous studies have explored the use of chatbots as learning partners to address this issue. However, the success of chatbot implementation depends on the quality of the reference dialogue content, yet research focusing on this subject is still limited. Typically, human experts are involved in creating suitable dialogue materials for students to ensure the quality of such content. Research attempting to utilize artificial intelligence (AI) technologies for generating dialogue practice materials is relatively limited, given the constraints of existing AI systems that may produce incoherent output. This research investigates the potential of leveraging OpenAI's ChatGPT, an AI system known for producing coherent output, to generate reference dialogues for an EFL chatbot system. The study aims to assess the effectiveness of ChatGPT in generating high-quality dialogue materials suitable for EFL students. By employing multiple readability metrics, we analyze the suitability of ChatGPT-generated dialogue materials and determine the target audience that can benefit the most. Our findings indicate that ChatGPT's dialogues are well-suited for students at the Common European Framework of Reference for Languages (CEFR) level A2 (elementary level). These dialogues are easily comprehensible, enabling students at this level to grasp most of the vocabulary used. Furthermore, a substantial portion of the dialogues intended for CEFR B1 (intermediate level) provides ample stimulation for learning new words. The integration of AI-powered chatbots in EFL education shows promise in overcoming limitations and providing valuable learning resources to students.
{"title":"Investigating OpenAI’s ChatGPT Potentials in Generating Chatbot's Dialogue for English as a Foreign Language Learning","authors":"J. Young, M. Shishido","doi":"10.14569/ijacsa.2023.0140607","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140607","url":null,"abstract":"—Lack of opportunities is a significant hurdle for English as a Foreign Language (EFL) for students during their learning journey. Previous studies have explored the use of chatbots as learning partners to address this issue. However, the success of chatbot implementation depends on the quality of the reference dialogue content, yet research focusing on this subject is still limited. Typically, human experts are involved in creating suitable dialogue materials for students to ensure the quality of such content. Research attempting to utilize artificial intelligence (AI) technologies for generating dialogue practice materials is relatively limited, given the constraints of existing AI systems that may produce incoherent output. This research investigates the potential of leveraging OpenAI's ChatGPT, an AI system known for producing coherent output, to generate reference dialogues for an EFL chatbot system. The study aims to assess the effectiveness of ChatGPT in generating high-quality dialogue materials suitable for EFL students. By employing multiple readability metrics, we analyze the suitability of ChatGPT-generated dialogue materials and determine the target audience that can benefit the most. Our findings indicate that ChatGPT's dialogues are well-suited for students at the Common European Framework of Reference for Languages (CEFR) level A2 (elementary level). These dialogues are easily comprehensible, enabling students at this level to grasp most of the vocabulary used. Furthermore, a substantial portion of the dialogues intended for CEFR B1 (intermediate level) provides ample stimulation for learning new words. The integration of AI-powered chatbots in EFL education shows promise in overcoming limitations and providing valuable learning resources to students.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89176538","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 : 2023-01-01DOI: 10.14569/ijacsa.2023.0140725
Maher Abuhamdeh, O. Qtaish, Hasan Kanaker, Ahmad Alshanty, Nidal Yousef, A. Alali
—This study investigates the current state of mobile shopping in Jordan and the integration of big data and AI technologies in this context. A mixed-methods approach, combining qualitative and quantitative data collection techniques, utilized to gather comprehensive insights. The survey questionnaire distributed to 105 individuals engaged in mobile shopping in Jordan. The findings highlight the popularity of mobile shopping and the preference for mobile apps as the primary platform. Personalized product recommendations emerged as a crucial factor in enhancing the mobile shopping experience. Privacy concerns regarding data sharing were present among respondents. Trust in AI-powered virtual assistants varied, indicating the potential for leveraging AI technologies. Respondents recognized the potential of big data and AI in improving the mobile shopping experience. The study concludes that businesses can enhance mobile shopping by utilizing AI-powered virtual assistants and prioritizing data security. The findings contribute to understanding mobile shopping dynamics and provide guidance for businesses and policymakers in optimizing mobile shopping experiences and driving economic growth in Jordan's digital economy. Future research and implementation efforts are encouraged to harness the potential of big data and AI in the mobile shopping landscape.
{"title":"Leveraging Big Data and AI in Mobile Shopping: A Study in the Context of Jordan","authors":"Maher Abuhamdeh, O. Qtaish, Hasan Kanaker, Ahmad Alshanty, Nidal Yousef, A. Alali","doi":"10.14569/ijacsa.2023.0140725","DOIUrl":"https://doi.org/10.14569/ijacsa.2023.0140725","url":null,"abstract":"—This study investigates the current state of mobile shopping in Jordan and the integration of big data and AI technologies in this context. A mixed-methods approach, combining qualitative and quantitative data collection techniques, utilized to gather comprehensive insights. The survey questionnaire distributed to 105 individuals engaged in mobile shopping in Jordan. The findings highlight the popularity of mobile shopping and the preference for mobile apps as the primary platform. Personalized product recommendations emerged as a crucial factor in enhancing the mobile shopping experience. Privacy concerns regarding data sharing were present among respondents. Trust in AI-powered virtual assistants varied, indicating the potential for leveraging AI technologies. Respondents recognized the potential of big data and AI in improving the mobile shopping experience. The study concludes that businesses can enhance mobile shopping by utilizing AI-powered virtual assistants and prioritizing data security. The findings contribute to understanding mobile shopping dynamics and provide guidance for businesses and policymakers in optimizing mobile shopping experiences and driving economic growth in Jordan's digital economy. Future research and implementation efforts are encouraged to harness the potential of big data and AI in the mobile shopping landscape.","PeriodicalId":13824,"journal":{"name":"International Journal of Advanced Computer Science and Applications","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83134844","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 : 2023-01-01DOI: 10.14569/ijacsa.2023.0140592
Jayanti Dansana, M. R. Kabat, P. Pattnaik
— With the rise in technology, a huge volume of data is being processed using data mining, especially in the healthcare sector. Usually, medical data consist of a lot of personal data, and third parties utilize it for the data mining process. Perturbation in health care data highly aids in preventing intruders from utilizing the patient’s privacy. One of the challenges in data perturbation is managing data utility and privacy protection. Medical data mining has certain special properties compared with other data mining fields. Hence, in this work, the machine learning (ML) based perturbation approach is introduced to provide more privacy to healthcare data. Here, clustering and IGDP-3DR processes are applied to improve healthcare privacy preservation. Initially, the dataset is pre-processed using data normalization. Then, the dimensionality is reduced by SVD with PCA (singular value decomposition with Principal component analysis). Then, the clustering process is performed by IFCM (Improved Fuzzy C means). The high-dimensional data are divided into several segments by IFCM, and every partition is set as a cluster. Then, improved Geometric Data Perturbation (IGDP) is used to perturb the clustered data. IGDP is a combination of GDP with 3D rotation (3DR). Finally, the perturbed data are classified using a machine learning (ML) classifier - kernel Support Vector Machine- Horse Herd Optimization (KSVM-HHO) to classify the perturbed data and ensure better accuracy. The overall evaluation of the proposed KSVM-HHO is carried out in the Python platform. The performance of the IGDP-KSVM-HHO is compared over the two benchmark datasets, Wisconsin prognostic breast cancer (WBC) and Pima Indians Diabetes (PID) dataset. For the WBC dataset, the proposed method obtains an overall accuracy of 98.08% perturbed data, and for the PID dataset, the proposed method obtains an overall accuracy of 98.04%.
-随着技术的发展，正在使用数据挖掘处理大量数据，特别是在医疗保健领域。通常，医疗数据由大量个人数据组成，第三方利用这些数据进行数据挖掘。在医疗保健数据的扰动高度有助于防止入侵者利用病人的隐私。数据扰动的挑战之一是管理数据效用和隐私保护。与其他数据挖掘领域相比，医疗数据挖掘具有一定的特殊性。因此，在这项工作中，引入了基于机器学习(ML)的扰动方法来为医疗保健数据提供更多隐私。在这里，应用聚类和IGDP-3DR流程来改进医疗保健隐私保护。首先，使用数据规范化对数据集进行预处理。然后，利用主成分分析的奇异值分解(singular value decomposition with Principal component analysis)进行SVD降维。然后，通过IFCM(改进模糊C均值)进行聚类处理。IFCM将高维数据分成若干段，并将每个分区设置为一个聚类。然后，采用改进的几何数据摄动(IGDP)对聚类数据进行摄动。IGDP是GDP与3D旋转(3DR)的组合。最后，使用机器学习(ML)分类器-核支持向量机-马群优化(KSVM-HHO)对扰动数据进行分类，以确保更好的精度。提出的KSVM-HHO的总体评估是在Python平台上进行的。IGDP-KSVM-HHO的性能在两个基准数据集上进行了比较，威斯康星州预后乳腺癌(WBC)和皮马印第安人糖尿病(PID)数据集。对于WBC数据集，所提方法得到的扰动数据总体准确率为98.08%，对于PID数据集，所提方法得到的扰动数据总体准确率为98.04%。
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