Image segmentation has proved its importance and plays an important role in various domains such as health systems and satellite-oriented military applications. In this context, accuracy, image ...quality, and execution time deem to be the major issues to always consider. Although many techniques have been applied, and their experimental results have shown appealing achievements for 2D images in real-time environments, however, there is a lack of works about 3D image segmentation despite its importance in improving segmentation accuracy. Specifically, HMM was used in this domain. However, it suffers from the time complexity, which was updated using different accelerators. As it is important to have efficient 3D image segmentation, we propose in this paper a novel system for partitioning the 3D segmentation process across several distributed machines. The concepts behind distributed multimedia network segmentation were employed to accelerate the segmentation computational time of training Hidden Markov Model (HMMs). Furthermore, a secure transmission has been considered in this distributed environment and various bidirectional multimedia security algorithms have been applied. The contribution of this work lies in providing an efficient and secure algorithm for 3D image segmentation. Through a number of extensive experiments, it was proved that our proposed system is of comparable efficiency to the state of art methods in terms of segmentation accuracy, security and execution time.
Plant diseases represent one of the critical issues which lead to a major decrease in the quantity and quality of crops. Therefore, the early detection of plant diseases can avoid any losses or ...damage to these crops. This paper presents an image processing and a deep learning-based automatic approach that classifies the diseases that strike the apple leaves. The proposed system has been tested using over 18,000 images from the Apple Diseases Dataset by PlantVillage, including images of healthy and affected apple leaves. We applied the VGG-16 architecture to a pre-trained unlabeled dataset of plant leave images. Then, we used some other deep learning pre-trained architectures, including Inception-V3, ResNet-50, and VGG-19, to solve the visualization-related problems in computer vision, including object classification. These networks can train the images dataset and compare the achieved results, including accuracy and error rate between those architectures. The preliminary results demonstrate the effectiveness of the proposed Inception V3 and VGG-16 approaches. The obtained results demonstrate that Inception V3 achieves an accuracy of 92.42% with an error rate of 0.3037%, while the VGG-16 network achieves an accuracy of 91.53% with an error rate of 0.4785%. The experiments show that these two deep learning networks can achieve satisfying results under various conditions, including lighting, background scene, camera resolution, size, viewpoint, and scene direction.
Diabetes is a metabolic disorder in which the body is unable to properly regulate blood sugar levels. It can occur when the body does not produce enough insulin or when cells become resistant to ...insulin’s effects. There are two main types of diabetes, Type 1 and Type 2, which have different causes and risk factors. Early detection of diabetes allows for early intervention and management of the condition. This can help prevent or delay the development of serious complications associated with diabetes. Early diagnosis also allows for individuals to make lifestyle changes to prevent the progression of the disease. Healthcare systems play a vital role in the management and treatment of diabetes. They provide access to diabetes education, regular check-ups, and necessary medications for individuals with diabetes. They also provide monitoring and management of diabetes-related complications, such as heart disease, kidney failure, and neuropathy. Through early detection, prevention and management programs, healthcare systems can help improve the quality of life and outcomes for people with diabetes. Current initiatives in healthcare systems for diabetes may fail due to lack of access to education and resources for individuals with diabetes. There may also be inadequate follow-up and monitoring for those who have been diagnosed, leading to poor management of the disease and lack of prevention of complications. Additionally, current initiatives may not be tailored to specific cultural or demographic groups, resulting in a lack of effectiveness for certain populations. In this study, we developed a diabetes prediction system using a healthcare framework. The system employs various machine learning methods, such as K-nearest neighbors, decision tree, deep learning, SVM, random forest, AdaBoost and logistic regression. The performance of the system was evaluated using the PIMA Indians Diabetes dataset and achieved a training accuracy of 82% and validation accuracy of 80%.
Virtual reality is an important technology that is fast gaining global attention in different spheres of life particularly in the education sector. In view of this, this study designs a distance ...learning system for spoken English based on virtual reality, firstly, the overall design of the teaching system and the hardware and software of the system are designed, then a double-supervised signal convolutional neural network algorithm is proposed for the speech data recognition function of the system, and finally the testing of the system performance and the simulation analysis of the algorithm are carried out. The results show that the step response curve of the system designed in this study is gradually stabilized after 11s of operation, although there are certain fluctuations in the initial stage; the speaking scoring function of the system is more influenced by the sampling period T. When T is at 3 and 4, the speaking scoring speed of the teaching system is 33s~42s, which is significantly better than other intervals. The number of information submission and feedback was approximately the same and the interaction activity was very high after students used the system designed in this study, reflecting that student were more motivated to learn spoken English after using the system. The final loss rate using Goog Le Net is smaller and more convergent compared to the loss rate of the other three CNN models trained. The convolutional neural network algorithm constructed in this study has a very high accuracy rate in the recognition of English speech data, which is significantly better than other recognition models. To a certain extent, this study can provide guidance for the construction of English-speaking distance learning system, and more needs of users can be considered in future research.
Nowadays, the economy of countries highly depends on the agriculture productivity which has a great effect on the development of human civilization. Sometimes, plant diseases cause a major reduction ...in agricultural products. This paper proposes a new approach for the automatic detection and classification of plant leaf diseases based on using the ELM deep learning algorithm on a real dataset of plant leaf images. The proposed approach uses the k-means clustering algorithm for image segmentation and applies the GLCM for feature extraction. The BDA optimization algorithm is employed for feature selection, and lastly the ELM algorithm is used for plant leaf diseases classification. The presented approach optimizes the input weights and hidden biases for ELM. The dataset used in this study includes 73 plant leaf images, such that we tested our approach on four diseases that usually affect plants, including: Alternaria alternata, Anthracnose, Bacterial blight, and Cercospora leaf spot. The experimental results show that the proposed approach has achieved encouraging results in terms of these classification measures: accuracy, error rate, recall, F score, and AUC which are 94%, 6%, 92%, 95%, and 96% respectively. Babu
Recently, phishing attacks have become one of the most prominent social engineering attacks faced by public internet users, governments, and businesses. In response to this threat, this paper ...proposes to give a complete vision to what Machine learning is, what phishers are using to trick gullible users with different types of phishing attacks techniques and based on our survey that phishing emails is the most effective on the targeted sectors and users which we are going to compare as well. Therefore, more effective phishing detection technology is needed to curb the threat of phishing emails that are growing at an alarming rate in recent years, thus will discuss the techniques of mitigation of phishing by Machine learning algorithms and technical solutions that have been proposed to mitigate the problem of phishing and valuable awareness knowledge users should be aware to detect and prevent from being duped by phishing scams. In this work, we proposed a detection model using machine learning techniques by splitting the dataset to train the detection model and validating the results using the test data , to capture inherent characteristics of the email text, and other features to be classified as phishing or non-phishing using three different data sets, After making a comparison between them, we obtained that the most number of features used the most accurate and efficient results achieved. the best ML algorithm accuracy were 0.88, 1.00, and 0.97 consecutively for boosted decision tree on the applied data sets.
Non Orthogonal Multiple Access (NOMA) successfully drew attention to the deployment of 5th Generation (5G) wireless communication systems, and it is now considered a significant technology in 5G ...communications. The primary enhancement in 5G is the speed, which may be 100 times faster than 4G. Due to the rising number of internal or external attacks on the Network, wireless intrusion detection systems are a vital aspect of any system connected to the Internet, and 5G will demand considerable improvements in data rate and security. In this paper, we have built a simulator for NOMA and applied a dropping attack to extract a dataset from the simulation model. The accuracy for detecting dropping attacks using the extracted data after applying ML algorithms was 95.7% for LR. Furthermore, this work suggests a methodology for wireless cyberattack detection in 5G networks based on applying several ML and DL techniques such as Decision Trees, KNN, Multi-class Decision Jungle, Multi-class Decision Forest, and Multi-class Neural Network. The proposed work is implemented and tested using a comprehensive Wi-Fi network benchmark dataset. The conducted experiments resulted in an outstanding performance with an accuracy of 99% for the KNN algorithm and 93% for DF and Neural Network.
This paper aims to address the detection of COVID-19 by developing an accurate and efficient diagnostic system using chest X-ray images. The research utilizes open-source Kaggle data comprising four ...categories: COVID-19, Lung-Opacity, Normal, and Viral Pneumonia. The proposed system employs convolutional neural networks (CNNs), including VGG19, RNN-LSTM, and inceptionv3. Results vary among the methodologies, with VGG19 achieving 26% accuracy, RNN-LSTM attaining 25% accuracy (28% with preprocessing), and inceptionv3 with histogram equalization achieving 83% accuracy. A CNN designed from scratch demonstrates the highest performance, with an accuracy of 93% (96% with histogram equalization). The findings emphasize the potential of AI techniques in enhancing disease diagnosis, particularly in distinguishing COVID-19 from other conditions, thereby facilitating timely and effective interventions.
Users of computer networks may benefit from cloud computing, which is a fairly new abstraction that offers features like processing as well as the sharing and storing of data. As a result of the ...services it provides, cloud computing is drawing significant investments from across the world. Despite this, Cloud Computing Security continues to be one of the most important issues for businesses and consumers that use cloud computing systems. A few of the security flaws that are associated with cloud computing were passed down from earlier computer systems. In contrast, the other flaws were brought about by the distinctive qualities and design of cloud computing. The newly developed platform has measures that restrict data access to just those users who are authorized to do so. Using the user’s identification and authentication/authorization information, a third-party service is responsible for managing access to the data. This service checks on all requests. Sensitive information and facts pertaining to users are encrypted both while in transit and while being stored. The platform was put into operation, analysed, and compared to other cloud platforms that were already in existence in terms of how effective it was in comparison to other platforms. When compared to the other security platforms, the findings demonstrated that this platform performed as anticipated in a relatively short amount of time and offered robust protection against the acts of an intruder.
PDF
