Artificial Intelligence & Robotics Development Journal最新文献

Bridging Techniques: A Review of Deep Learning and Fuzzy Logic Applications 桥梁技术：深度学习和模糊逻辑应用综述

Artificial Intelligence & Robotics Development Journal Pub Date : 2024-08-10 DOI: 10.52098/airdj.20244314

Dinah Mohammed, Raidah S. Khudeye

{"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}

引用次数: 0

Bridging Techniques: A Review of Deep Learning and Fuzzy Logic Applications 桥梁技术：深度学习和模糊逻辑应用综述

Artificial Intelligence & Robotics Development Journal Pub Date : 2024-08-10 DOI: 10.52098/airdj.20244314

Dinah Mohammed, Raidah S. Khudeye

{"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}

引用次数: 0

Improving Digital Satellite Image for security purposes 为安全目的改进数字卫星图像

Artificial Intelligence & Robotics Development Journal Pub Date : 2024-03-21 DOI: 10.52098/airdj.20244110

Huda Hamdan Ali

{"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}

引用次数: 0

Enhancing The Accuracy of Image Classification Using Deep Learning and Preprocessing Methods 利用深度学习和预处理方法提高图像分类的准确性

Artificial Intelligence & Robotics Development Journal Pub Date : 2024-01-03 DOI: 10.52098/airdj.2023348

Mohammed J Yousif

{"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}

引用次数: 0

Machine Learning Approaches for Detecting and Classifying the Cancer type using Imbalanced Data Downsampling 利用不平衡数据下采样检测和分类癌症类型的机器学习方法

Artificial Intelligence & Robotics Development Journal Pub Date : 2023-08-23 DOI: 10.52098/airdj.2023332

F. Kiyoumarsi, Sara Wisam

{"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}

引用次数: 0

Smart system Based on Augmented Reality for Displaying Cultural Heritage in Oman 基于增强现实的阿曼文化遗产展示智能系统

Artificial Intelligence & Robotics Development Journal Pub Date : 2023-05-28 DOI: 10.52098/airdj.202367

M. Al-Bahri

引用次数: 0

A Method for SMS Spam Message Detection Using Machine Learning 一种基于机器学习的短信垃圾信息检测方法

Artificial Intelligence & Robotics Development Journal Pub Date : 2023-02-14 DOI: 10.52098/airdj.202366

Vaman Ashqi Saeed

{"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}

引用次数: 0

Deep learning Feedforward Neural Network in predicting model of Environmental risk factors in the Sohar region 深度学习前馈神经网络在苏哈尔地区环境风险因素预测模型中的应用

Artificial Intelligence & Robotics Development Journal Pub Date : 2023-01-01 DOI: 10.52098/airdj.202257

Yusra Khamis, Jabar H. Yousif

{"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}

引用次数: 2

VR/AR Environment for Training Students on Engineering Applications and Concepts 培养学生工程应用和概念的VR/AR环境

Artificial Intelligence & Robotics Development Journal Pub Date : 2022-07-02 DOI: 10.52098/airdj.202254

M. Yousif

引用次数: 2

Soft Computing implementation in Tourism Sector: A review 软计算在旅游领域的应用综述

Artificial Intelligence & Robotics Development Journal Pub Date : 2022-04-02 DOI: 10.52098/airdj.202248

Sara Ali AL MAZRUII, Basil Ibrahim, Sara Wisam Hassan

引用次数: 1

Artificial Intelligence &amp; Robotics Development Journal最新文献

Artificial Intelligence & Robotics Development Journal最新文献