Abstract—The modelling and prediction field boasts various practical applications, such as deep learning, which is a powerful tool used in this field. It has been proved that deep learning is a valuable technique for extracting extremely accurate predictions from complex data sources. Recursive neural networks have also demonstrated usefulness in language translation and caption production. However, convolutional neural networks remain the dominant solution for image classification tasks. In addition, deep learning, also known as deep neural networks, involves training models with multiple layers of interconnected artificial neurons. The primary idea of deep learning is to learn data representations through rising levels of abstraction. These strategies are effective but do not explain how the result is produced. Without knowing how a solution is arrived at using deep learning. In the field of artificial intelligence, deep learning and fuzzy logic are two powerful techniques. In addition, fuzzy logic combines deep learning to help deep learning select the desired features and work without supervision, making it possible to develop reliable systems with rich DL information even without hand-labelled data. Fuzzy logic that interprets these features will subsequently explain the system's choice of classification label. This survey highlights the various applications which use fuzzy logic to improve deep learning. .
{"title":"Bridging Techniques: A Review of Deep Learning and Fuzzy Logic Applications","authors":"Dinah Mohammed, Raidah S. Khudeye","doi":"10.52098/airdj.20244314","DOIUrl":"https://doi.org/10.52098/airdj.20244314","url":null,"abstract":"Abstract—The modelling and prediction field boasts various practical applications, such as deep learning, which is a powerful tool used in this field. It has been proved that deep learning is a valuable technique for extracting extremely accurate predictions from complex data sources. Recursive neural networks have also demonstrated usefulness in language translation and caption production. However, convolutional neural networks remain the dominant solution for image classification tasks. In addition, deep learning, also known as deep neural networks, involves training models with multiple layers of interconnected artificial neurons. The primary idea of deep learning is to learn data representations through rising levels of abstraction. These strategies are effective but do not explain how the result is produced. Without knowing how a solution is arrived at using deep learning. In the field of artificial intelligence, deep learning and fuzzy logic are two powerful techniques. In addition, fuzzy logic combines deep learning to help deep learning select the desired features and work without supervision, making it possible to develop reliable systems with rich DL information even without hand-labelled data. Fuzzy logic that interprets these features will subsequently explain the system's choice of classification label. This survey highlights the various applications which use fuzzy logic to improve deep learning.\u0000.","PeriodicalId":145226,"journal":{"name":"Artificial Intelligence & Robotics Development Journal","volume":"4 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141919613","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}
Abstract—The modelling and prediction field boasts various practical applications, such as deep learning, which is a powerful tool used in this field. It has been proved that deep learning is a valuable technique for extracting extremely accurate predictions from complex data sources. Recursive neural networks have also demonstrated usefulness in language translation and caption production. However, convolutional neural networks remain the dominant solution for image classification tasks. In addition, deep learning, also known as deep neural networks, involves training models with multiple layers of interconnected artificial neurons. The primary idea of deep learning is to learn data representations through rising levels of abstraction. These strategies are effective but do not explain how the result is produced. Without knowing how a solution is arrived at using deep learning. In the field of artificial intelligence, deep learning and fuzzy logic are two powerful techniques. In addition, fuzzy logic combines deep learning to help deep learning select the desired features and work without supervision, making it possible to develop reliable systems with rich DL information even without hand-labelled data. Fuzzy logic that interprets these features will subsequently explain the system's choice of classification label. This survey highlights the various applications which use fuzzy logic to improve deep learning. .
{"title":"Bridging Techniques: A Review of Deep Learning and Fuzzy Logic Applications","authors":"Dinah Mohammed, Raidah S. Khudeye","doi":"10.52098/airdj.20244314","DOIUrl":"https://doi.org/10.52098/airdj.20244314","url":null,"abstract":"Abstract—The modelling and prediction field boasts various practical applications, such as deep learning, which is a powerful tool used in this field. It has been proved that deep learning is a valuable technique for extracting extremely accurate predictions from complex data sources. Recursive neural networks have also demonstrated usefulness in language translation and caption production. However, convolutional neural networks remain the dominant solution for image classification tasks. In addition, deep learning, also known as deep neural networks, involves training models with multiple layers of interconnected artificial neurons. The primary idea of deep learning is to learn data representations through rising levels of abstraction. These strategies are effective but do not explain how the result is produced. Without knowing how a solution is arrived at using deep learning. In the field of artificial intelligence, deep learning and fuzzy logic are two powerful techniques. In addition, fuzzy logic combines deep learning to help deep learning select the desired features and work without supervision, making it possible to develop reliable systems with rich DL information even without hand-labelled data. Fuzzy logic that interprets these features will subsequently explain the system's choice of classification label. This survey highlights the various applications which use fuzzy logic to improve deep learning.\u0000.","PeriodicalId":145226,"journal":{"name":"Artificial Intelligence & Robotics Development Journal","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141920098","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}
Satellite imagery is employed in many different fields of study. These pictures have serious quality problems. Image enhancement algorithms, however, can improve it in terms of contrast, brightness, feature elimination from noise contents, etc. These algorithms present and analyse the picture's properties by sharpening, focusing, or smoothing the image. Therefore, the specific application determines the goal of picture enhancement. This paper briefly overviews picture enhancement methods that produce optimal and progressive outcomes for satellite images used for secured remote sensing. To do this, various image enhancement techniques are used, which are widely used today to improve image quality across various image processing applications. Some commonly used image enhancement techniques include spatial filtering, contrast stretching, and histogram equalisation. These techniques aim to enhance the visual quality of satellite images by adjusting brightness and contrast and reducing noise. These methods can also improve the interpretability of the images for remote sensing purposes. The enhancement of satellite images finds use in several fields, particularly security. It is essential for security applications, including threat detection, border control, and surveillance. Security professionals may more effectively analyse and understand data to spot any dangers or questionable activity by boosting the visual details and general quality. Keywords: Satellite image analysis; mean filter; secured application; SVM; wavelet transformation
{"title":"Improving Digital Satellite Image for security purposes","authors":"Huda Hamdan Ali","doi":"10.52098/airdj.20244110","DOIUrl":"https://doi.org/10.52098/airdj.20244110","url":null,"abstract":"Satellite imagery is employed in many different fields of study. These pictures have serious quality problems. Image enhancement algorithms, however, can improve it in terms of contrast, brightness, feature elimination from noise contents, etc. These algorithms present and analyse the picture's properties by sharpening, focusing, or smoothing the image. Therefore, the specific application determines the goal of picture enhancement. This paper briefly overviews picture enhancement methods that produce optimal and progressive outcomes for satellite images used for secured remote sensing. To do this, various image enhancement techniques are used, which are widely used today to improve image quality across various image processing applications. Some commonly used image enhancement techniques include spatial filtering, contrast stretching, and histogram equalisation. These techniques aim to enhance the visual quality of satellite images by adjusting brightness and contrast and reducing noise. These methods can also improve the interpretability of the images for remote sensing purposes. The enhancement of satellite images finds use in several fields, particularly security. It is essential for security applications, including threat detection, border control, and surveillance. Security professionals may more effectively analyse and understand data to spot any dangers or questionable activity by boosting the visual details and general quality.\u0000 \u0000Keywords: Satellite image analysis; mean filter; secured application; SVM; wavelet transformation","PeriodicalId":145226,"journal":{"name":"Artificial Intelligence & Robotics Development Journal","volume":"120 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140223813","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}
Deep learning is one of many methods in Artificial Intelligence (AI) that computers can use to process information like text, images, and audio. This manuscript will be focusing on image preprocessing, one of the many different techniques that are used to modify the neural network model training process, and how it affects the training speed and accuracy of the neural network. Six different image preprocessing techniques were picked for use in this study: Grayscale, Smoothing, Unmask Sharpening, Laplacian and Equalization, and Random Cropping and Rotation all of which were implemented using Python and the libraries NumPy, OpenCV, and PyTorch. For the dataset, a batch of 10000 images from the CIFAR10 dataset were used to train the model. This study explored the impact of preprocessing techniques on a deep learning model, employing the RESNET50 architecture. Notable improvements in model accuracy were observed, particularly with normalization and random cropping accompanied by rotation. The efficiency gains attributed to preprocessing were highlighted, leading to a more rapid training process and significant resource savings. This research underscores the importance of thoughtful preprocessing in enhancing the performance of deep learning models, offering valuable insights for practitioners in imageclassification tasks.
{"title":"Enhancing The Accuracy of Image Classification Using Deep Learning and Preprocessing Methods","authors":"Mohammed J Yousif","doi":"10.52098/airdj.2023348","DOIUrl":"https://doi.org/10.52098/airdj.2023348","url":null,"abstract":"Deep learning is one of many methods in Artificial Intelligence (AI) that computers can use to process information like text, images, and audio. This manuscript will be focusing on image preprocessing, one of the many different techniques that are used to modify the neural network model training process, and how it affects the training speed and accuracy of the neural network. Six different image preprocessing techniques were picked for use in this study: Grayscale, Smoothing, Unmask Sharpening, Laplacian and Equalization, and Random Cropping and Rotation all of which were implemented using Python and the libraries NumPy, OpenCV, and PyTorch. For the dataset, a batch of 10000 images from the CIFAR10 dataset were used to train the model. This study explored the impact of preprocessing techniques on a deep learning model, employing the RESNET50 architecture. Notable improvements in model accuracy were observed, particularly with normalization and random cropping accompanied by rotation. The efficiency gains attributed to preprocessing were highlighted, leading to a more rapid training process and significant resource savings. This research underscores the importance of thoughtful preprocessing in enhancing the performance of deep learning models, offering valuable insights for practitioners in imageclassification tasks.","PeriodicalId":145226,"journal":{"name":"Artificial Intelligence & Robotics Development Journal","volume":"64 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139451533","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}
One of the most important applications of medical data mining is the early diagnosis of diseases with high accuracy. In the meantime, timely diagnosis of cancer as one of the main causes of death is of special importance. However, the classification and diagnosis of cancer is challenging due to the unbalanced nature of related data. In the data related to cancer disease, there is usually a minority class (patient samples) and a majority class (healthy people samples), which diagnoses the disease from the minority samples, and this is a challenge for the classifiers. This work investigated the problem of classifying the imbalanced data related to cancer disease using a machine learning approach based on the K-Nearest Neighbor (KNN) clustering technique. In this method, the insignificant samples of the majority class are removed, and the data are balanced. The proposed method is simulated and evaluated on 15 cancer datasets selected from the general SEER database. The simulation results approve a high classification of cancer type based on the average detecting accuracy criterion of more than 90%. Moreover, the current result is more efficient and improves classification accuracy compared to the methods proposed by other researchers in the literature survey.
{"title":"Machine Learning Approaches for Detecting and Classifying the Cancer type using Imbalanced Data Downsampling","authors":"F. Kiyoumarsi, Sara Wisam","doi":"10.52098/airdj.2023332","DOIUrl":"https://doi.org/10.52098/airdj.2023332","url":null,"abstract":"One of the most important applications of medical data mining is the early diagnosis of diseases with high accuracy. In the meantime, timely diagnosis of cancer as one of the main causes of death is of special importance. However, the classification and diagnosis of cancer is challenging due to the unbalanced nature of related data. In the data related to cancer disease, there is usually a minority class (patient samples) and a majority class (healthy people samples), which diagnoses the disease from the minority samples, and this is a challenge for the classifiers. This work investigated the problem of classifying the imbalanced data related to cancer disease using a machine learning approach based on the K-Nearest Neighbor (KNN) clustering technique. In this method, the insignificant samples of the majority class are removed, and the data are balanced. The proposed method is simulated and evaluated on 15 cancer datasets selected from the general SEER database. The simulation results approve a high classification of cancer type based on the average detecting accuracy criterion of more than 90%. Moreover, the current result is more efficient and improves classification accuracy compared to the methods proposed by other researchers in the literature survey.","PeriodicalId":145226,"journal":{"name":"Artificial Intelligence & Robotics Development Journal","volume":"186 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132752964","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}
Strengthening cultural heritage is very important in countries with rich heritage. Oman is one of those countries with a large and large cultural heritage. There is a variety of archaeological sites in Oman from castles, forts, and others, with rapid developments in modern technologies is important to attract visitors and tourism at archaeological sites. One of the latest technologies that can be used for this purpose is enhanced reality applications. This proposal aims to create a reliable and intelligent system based on enhanced reality technology to display the shapes , paintings, and other things in archaeological sites from castles and forts. The proposed system is allowed to provide a lot of information about the object or location For visitors. In this project we offer an enhanced reality that can be published in archaeological sites in Oman . The project is a motive for a major increase in Omani national income by increasing visitors to these cultural sites.
{"title":"Smart system Based on Augmented Reality for Displaying Cultural Heritage in Oman","authors":"M. Al-Bahri","doi":"10.52098/airdj.202367","DOIUrl":"https://doi.org/10.52098/airdj.202367","url":null,"abstract":"Strengthening cultural heritage is very important in countries with rich heritage. Oman is one of those countries with a large and large cultural heritage. There is a variety of archaeological sites in Oman from castles, forts, and others, with rapid developments in modern technologies is important to attract visitors and tourism at archaeological sites. One of the latest technologies that can be used for this purpose is enhanced reality applications. This proposal aims to create a reliable and intelligent system based on enhanced reality technology to display the shapes , paintings, and other things in archaeological sites from castles and forts. The proposed system is allowed to provide a lot of information about the object or location For visitors. In this project we offer an enhanced reality that can be published in archaeological sites in Oman . The project is a motive for a major increase in Omani national income by increasing visitors to these cultural sites.","PeriodicalId":145226,"journal":{"name":"Artificial Intelligence & Robotics Development Journal","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115546057","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}
In recent years, it has become increasingly common for individuals to connect with their relatives and friends, read the most recent news, and discuss various social topics by using online social platforms such as Twitter and Facebook. As a consequence of this, anything that is considered spam can quickly spread among them. The identification of spam is widely regarded as one of the most significant problems involved in text analysis. Previous studies on the detection of spam concentrated primarily on English-language content and paid little attention to other languages. The information gathered by the University of California; Irvine served as the basis for the development of our spam detection technology (UCI). In this research study, the effectiveness of various supervised machine learning algorithms, such as the J48, KNN, and DT, in identifying spam and ham communications is investigated. SMS spam is becoming more widespread as the number of individuals who use the internet continues to rise and an increasing number of businesses disclose their customers' personal information. E-mail spam filtering is the progenitor of SMS spam filtering, which inherits a significant number of its features. We evaluate the proposed method based on accuracy, recall, and precision. Experiments showed that DT has obtained higher accuracy compared to other machine learning classifiers.
{"title":"A Method for SMS Spam Message Detection Using Machine Learning","authors":"Vaman Ashqi Saeed","doi":"10.52098/airdj.202366","DOIUrl":"https://doi.org/10.52098/airdj.202366","url":null,"abstract":" In recent years, it has become increasingly common for individuals to connect with their relatives and friends, read the most recent news, and discuss various social topics by using online social platforms such as Twitter and Facebook. As a consequence of this, anything that is considered spam can quickly spread among them. The identification of spam is widely regarded as one of the most significant problems involved in text analysis. Previous studies on the detection of spam concentrated primarily on English-language content and paid little attention to other languages. The information gathered by the University of California; Irvine served as the basis for the development of our spam detection technology (UCI). In this research study, the effectiveness of various supervised machine learning algorithms, such as the J48, KNN, and DT, in identifying spam and ham communications is investigated. SMS spam is becoming more widespread as the number of individuals who use the internet continues to rise and an increasing number of businesses disclose their customers' personal information. E-mail spam filtering is the progenitor of SMS spam filtering, which inherits a significant number of its features. We evaluate the proposed method based on accuracy, recall, and precision. Experiments showed that DT has obtained higher accuracy compared to other machine learning classifiers.","PeriodicalId":145226,"journal":{"name":"Artificial Intelligence & Robotics Development Journal","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122813456","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}
AQI (Air Quality Index) is the standard degree that guides us to measure air pollution levels such as PM2.5, O3, NO2, and SO2 to show the state of air quality. Polluted gas causes much damage and problems to people, plants, and the environment because of its negative impact. Data mining successfully examines an enormous cluster of data to recognize associations, determine relations between variables, and predict future values. In this paper, an experimental study was performed on analyzing the previous dataset of (PM2.5 and O3) for accurately predicting AQI using deep learning Feedforward Neural network techniques. The deep learning (Feedforward Neural Network (FFNN) predicting models are employed to evaluate based on R, R², MSE, MAE, and RMSE criteria using historical data from (the Ministry of Environment-Oman). Different epochs and a different number of hidden layers are deployed to improve and boost performance. In FFNN, the epochs number increase by 50,100 and 500 while the hidden layer utilized to 1,5 and 10. This optimization technique exceeds the performance from R=0.892 to R=0.992 in predicting the level of (PM2.5) and the (O3) from R=0.864 to R=0.999. The results show that the Sohar Region in a safe level of AQI.
{"title":"Deep learning Feedforward Neural Network in predicting model of Environmental risk factors in the Sohar region","authors":"Yusra Khamis, Jabar H. Yousif","doi":"10.52098/airdj.202257","DOIUrl":"https://doi.org/10.52098/airdj.202257","url":null,"abstract":"AQI (Air Quality Index) is the standard degree that guides us to measure air pollution levels such as PM2.5, O3, NO2, and SO2 to show the state of air quality. Polluted gas causes much damage and problems to people, plants, and the environment because of its negative impact. Data mining successfully examines an enormous cluster of data to recognize associations, determine relations between variables, and predict future values. In this paper, an experimental study was performed on analyzing the previous dataset of (PM2.5 and O3) for accurately predicting AQI using deep learning Feedforward Neural network techniques. The deep learning (Feedforward Neural Network (FFNN) predicting models are employed to evaluate based on R, R², MSE, MAE, and RMSE criteria using historical data from (the Ministry of Environment-Oman). Different epochs and a different number of hidden layers are deployed to improve and boost performance. In FFNN, the epochs number increase by 50,100 and 500 while the hidden layer utilized to 1,5 and 10. This optimization technique exceeds the performance from R=0.892 to R=0.992 in predicting the level of (PM2.5) and the (O3) from R=0.864 to R=0.999. The results show that the Sohar Region in a safe level of AQI.","PeriodicalId":145226,"journal":{"name":"Artificial Intelligence & Robotics Development Journal","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121609796","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}
Modern technologies in virtual reality (VR) and augmented reality (AR) provide unique features that can be used to facilitate tasks in everyday life. Several courses can be built using augmented reality, such as engine maintenance, computer maintenance, chemistry lab, etc. Augmented reality technologies provide dynamic and interactive instructions to resolve a problem or present required concepts. Building an educational system based on augmented reality is not an easy task due to some difficulties and challenges, such as the cost of augmented reality tools and other hardware and software required. Also, training students with engineering concepts and precise parts involves a lot of analysis and practice to know problems and then design solutions. The paper aims to develop a virtual educational environment for training students in engineering sectors in practical laboratory sessions based on AR/VR techniques. The proposed system provides a safe and low-cost environment to train the student different concepts in engineering sector, such as basic concepts in electrical, mechanical and renewable energy engineering.
{"title":"VR/AR Environment for Training Students on Engineering Applications and Concepts","authors":"M. Yousif","doi":"10.52098/airdj.202254","DOIUrl":"https://doi.org/10.52098/airdj.202254","url":null,"abstract":"Modern technologies in virtual reality (VR) and augmented reality (AR) provide unique features that can be used to facilitate tasks in everyday life. Several courses can be built using augmented reality, such as engine maintenance, computer maintenance, chemistry lab, etc. Augmented reality technologies provide dynamic and interactive instructions to resolve a problem or present required concepts. Building an educational system based on augmented reality is not an easy task due to some difficulties and challenges, such as the cost of augmented reality tools and other hardware and software required. Also, training students with engineering concepts and precise parts involves a lot of analysis and practice to know problems and then design solutions. The paper aims to develop a virtual educational environment for training students in engineering sectors in practical laboratory sessions based on AR/VR techniques. The proposed system provides a safe and low-cost environment to train the student different concepts in engineering sector, such as basic concepts in electrical, mechanical and renewable energy engineering.","PeriodicalId":145226,"journal":{"name":"Artificial Intelligence & Robotics Development Journal","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133318617","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}
Sara Ali AL MAZRUII, Basil Ibrahim, Sara Wisam Hassan
The Corona virus epidemic has had numerous detrimental repercussions on the tourism industry. Domestic production and trade have been impeded by the global epidemic and the mitigation efforts implemented when the disease broke out, according to the World Health Organization in particular. The current study looks at how neural networks and soft computing techniques can help provide accurate and effective predictions for the tourism industry, preventing future losses. The study will compare a number of strategies and procedures in order to determine the most successful technology in the tourism industry.
冠状病毒疫情对旅游业产生了许多不利影响。据世界卫生组织(World Health Organization)称，全球疫情以及疫情爆发时实施的缓解措施，阻碍了国内生产和贸易。目前的研究着眼于神经网络和软计算技术如何帮助为旅游业提供准确有效的预测，防止未来的损失。这项研究将比较一些策略和程序，以确定旅游业中最成功的技术。
{"title":"Soft Computing implementation in Tourism Sector: A review","authors":"Sara Ali AL MAZRUII, Basil Ibrahim, Sara Wisam Hassan","doi":"10.52098/airdj.202248","DOIUrl":"https://doi.org/10.52098/airdj.202248","url":null,"abstract":"The Corona virus epidemic has had numerous detrimental repercussions on the tourism industry. Domestic production and trade have been impeded by the global epidemic and the mitigation efforts implemented when the disease broke out, according to the World Health Organization in particular. The current study looks at how neural networks and soft computing techniques can help provide accurate and effective predictions for the tourism industry, preventing future losses. The study will compare a number of strategies and procedures in order to determine the most successful technology in the tourism industry.","PeriodicalId":145226,"journal":{"name":"Artificial Intelligence & Robotics Development Journal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129962961","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：