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Artificial intelligence (AI) approaches have been shown to be effective in classifying skin diseases and outperforming dermatologists in diagnosis. Using big data as a dermatological diagnosis service can present several challenges. One challenge is the need to accurately label and classify large amounts of data, such as images of infected skin. This can be time-consuming and resource-intensive. It is important to implement proper safeguards to protect sensitive medical information. Despite these challenges, the use of big data in dermatology can lead to several outcomes. It can improve the accuracy of diagnoses, leading to better patient outcomes. It can also help to identify patterns and trends in skin conditions, allowing for earlier detection and prevention. In addition, big data can be used to identify risk factors for certain conditions, enabling targeted preventative measures. Convolutional neural networks (CNNs) have been widely used for skin lesion classification, and recent advances in machine learning algorithms have led to a decrease in misclassification rates compared to manual categorization by dermatologists. This article utilizes the use of big data for accurate dermatological diagnosis services, it introduces the use of various CNNs for classifying different types of skin cancer. While deep learning and pretrained transfer learning techniques have advantages over traditional methods, they also have limitations and the potential for incorrect identification under certain circumstances. This work discusses these vulnerabilities and employs pretrained models to classify 11 different skin diseases, including monkey pox. The performance of the system is evaluated using accuracy, loss, precision, recall, and F1 score, and the results show that the system is able to diagnose the 11 skin illnesses with an accuracy rate of 97% using transfer learning in Keras and computer vision models. The best model was found to be Inception_ResNetV2 with 50 epochs and the Adam optimizer. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Examining facial expressions is a crucial aspect that garners attention due to its significance in divulging emotional states. This study delves into employing a robust deep learning method for automatically analyzing and identifying facial emotions in images. The chosen technique revolves around the convolutional neural network (CNN) algorithm. A dataset containing images of individuals, each exhibiting distinct facial expressions, was curated. The emotions in these images were categorized into seven groups (angry, disgust, fear, happy, neutral, sad, surprise) based on the depicted emotional states. The approach comprises four primary steps: preprocessing the input facial images, utilizing image adjustments and data augmentation, employing the Viola and Jones technique for face detection and landmark localization, creating a numerical feature vector from the registered image for feature extraction, and inputting the extracted features into the CNN for classification. The proposed CNN model underwent application to classify facial emotions within the image dataset. Additionally, a pretrained VGG-16 model was incorporated into the classification process for facial images. A comparative analysis between the proposed CNN approach and the pretrained VGG-16 model revealed that the latter outperformed the former in terms of accuracy rate and loss function values when determining individuals’ facial expressions. [ABSTRACT FROM AUTHOR]

With 5G technology driving its expansion as the main infrastructure for pervasive connection, the Internet of Things (IoT) symbolises a paradigm-shifting interconnectivity of objects and devices. The increasing integration of IoT devices into our daily lives poses serious security and privacy risks. Every smart object in an urban setting is connected, which increases the vulnerability of IoT-based smart cities to various security risks. It is crucial to guarantee these digital urban settings' security and resilience, especially as cities become more computerised and have a dense population of linked devices. Ensuring the integrity and functionality of smart cities requires immediate attention to detecting and mitigating potential cyberattacks. This research presents an intrusion detection model derived from data extracted by simulating the SYNFLOOD attack scenario, a prominent form of Denial of Service attack in IoT security. The suggested detection model classifies, trains, and validates the imported data using the k-folds method and creates a unique detection model. The proposed model is fast and effectively enables all IoT networks to communicate information without compromising privacy. The model enhances the detection process by employing data preprocessing and balancing. In this work, the experiments' accuracy is stable, proving the model's success for the six used machine learning algorithms resulted in an excellent performance with an accuracy of 92.3% for the Decision Tree and Neural Network. [ABSTRACT FROM AUTHOR]

Object recognition and detection is an area of study, within intelligence and computer vision. It finds applications in fields such as surveillance, detailed activity analysis, robotics and object tracking. The primary focus of research papers in this domain revolves around enhancing the precision of object identification and detection regardless of whether the objects are located indoors or outdoors. To address this challenge, a new approach involving the utilization of SIFT features for information extraction has been proposed. Our approach encompasses two components; the implementation of dimensionality reduction through principal component analysis (PCA) to eliminate redundancies; secondly the incorporation of feature vector encoding using fisher encoding techniques. The RGB-D dataset employed contains 300 objects across scenarios with emphasis on colored aspects rather than depth. The SIFT features are categorized using a support vector machine (SVM) into 7 classes. When compared to using SIFT features integrating them with encoding methods notably enhances recall, precision and F1-score by than 30% through fisher encoding and PCA techniques. The study concludes with an evaluation based on n-cross validation methodology along, with detailed experimental results. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In recent years, there have been many Security vulnerabilities that threaten user security, these threats have led to the finding of user files, so the use of the Internet has become unlimited, and the number of digital network devices has increased,Therefore, maintaining the confidentiality and integrity of information has become an urgent necessity to preserve user information, due to the increase in hackers and intruders, and the innovation of modern methods of penetration every day. Data cryptography has proven to be a secure way to protect a user's data. Many current cryptography algorithms are considered weak regarding data transmission over the Internet, so newly updated algorithms are in high demand. In this paper, we proposed to develop the ancient rotor machine depending on the base64 codding technique, in which we replaced the alphabets of the ancient rotor machine with the alphabets of base64 that contain 64 characters. Furthermore, we proposed a key exchange based on One-time password OTP code via SMS, OTP is mechanism for logging on to a network using unique password that can only be used once, to overcome the static password method that is least secure, and used it to generate the subkeys for rotor machines based on hash and random permutation techniques. MD5 algorithm function is used to authenticate the original message, Finally, we experimented with these techniques of secure sending e-mails by encrypting the contents of them with the proposed technique. However, the proposed security technique got promising results. [ABSTRACT FROM AUTHOR]

The usage of social media and the internet has grown rapidly throughout the world and has assimilated into daily life. Through the internet and social media, people can communicate their feelings, ideas, and thoughts, yet, with the growth of social networking sites, bullying is also growing. Cyberbullying is the term for bullying that involves the use of technology, and the internet may be a source of offensive, damaging, and destructive to other people's content. Bullying can long-term affect young people's capacity to socialize and build enduring friendships. Social media sites offer a great opportunity for bullying and harassment, and young people who use these sites are in danger. This paper's framework was presented for detecting cyberbullying and divided into two main sections. NLP (natural language processing) is the first component, and ML is the second component (machine learning). In the first phase, data are collected from online news sources and Twitter, posts and messages on Facebook, Instagram, WhatsApp, and YouTube comments. Additionally, we collected data from the application that we built to receive complaints and threats that reach students and young people, regardless of the type of threat, whether bullying, intimidation, or even abuse in both English and Arabic. Before using machine learning algorithms on our data, we must clean it up and prepare it for the discovery phase because it contains many extraneous characters. To train the model and determine which model has the highest accuracy, we combine NLP with various machine learning techniques, including support vector machine, Naive Bayes, logistic regression, random forest, extreme gradient boosting algorithm, and convolutional neural networks (CNN). This paper developed an effective technology to detect and avoid bullying in social networks. Many workbooks are used to train and learn about bullying behavior. Evaluation of the proposed model for cyberbullying data shows that CNN performs better than other algorithms used in the study. [ABSTRACT FROM AUTHOR]

Online Social Networks (OSN) such as Facebook, Instagram, Twitter, and others have seen rapid growth in recent years. Such applications provide attractive online social networks and communications with the opportunity to connect with relatives and acquaintances, meet new people, enter communities, talk, exchange photos, organize events, and network with others who are close to real-life; unfortunately, on the other hand, they also raise privacy and security issues. We identified OSN threats in this paper and recommended a digital face-processing authentication method as a double-factor authentication after entering the password using Matlab. After applying deep learning classification by attending to a real dataset from the live webcam to train the model, we achieved the best accuracy rate of 95%. However, such methods have yet to be deployed to all social networks, so we also mentioned the problem of fake accounts, which is one of the most significant problems in OSN. These are effective tools for executing spam campaigns and spreading malware and phishing attacks. Fake accounts could lead to the loss of money for businesses, loss of reputation, stealing information for malicious purposes, and much more. This study is related to detecting fake and legitimate profiles on OSN. For this purpose, we chose two datasets that contain fake and legitimate accounts on Facebook and Instagram. Each contains different features after applying machine learning using Naive Bayes, Logistic Regression, Support Vector Machines, K-Nearest Neighbour, Boosted Tree, Neural Networks, SVM Kernal, and Logistec Regression Kernal. SVM achieved the highest classification accuracy for the Fake Profiles detection datasets with 97.1%. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Satisfaction Detection is one of the most common issues that impact the business world. So, this study aims to suggest an application that detects the Satisfaction tone that leads to customer happiness for Big Data that came out from online businesses on social media, in particular, Facebook and Twitter, by using two famous methods, machine learning and deep learning (DL) techniques.There is a lack of datasets that are involved with this topic. Therefore, we have collected the dataset from social media. We have simplified the concept of Big Data analytics for business on social media using three of the most famous Natural Language Processing tools, stemming, normalization, and stop word removal. To evaluate the performance of the classifiers, we calculated F1-measure, Recall, and Precision measures. The result showed superiority for the Random Forest classifier the highest value of F1-measure with (99.1%). The best result achieved without applying pre-processing techniques, through Support Vector Machine with F1-measure (93.4%). On the other hand, we apply DL techniques, and we apply the feature extraction method, which includes Word Embedding and Bag of Words on the dataset. The results showed superiority for the Deep Neural Networks DNN algorithm. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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%. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Chest and lung diseases are among the most serious chronic diseases in the world, and they occur as a result of factors such as smoking, air pollution, or bacterial infection, which would expose the respiratory system and chest to serious disorders. Chest diseases lead to a natural weakness in the respiratory system, which requires the patient to take care and attention to alleviate this problem. Countries are interested in encouraging medical research and monitoring the spread of communicable diseases. Therefore, they advised researchers to perform studies to curb the diseases' spread and urged researchers to devise methods for swiftly and readily detecting and distinguishing lung diseases. In this paper, we propose a hybrid architecture of contrast-limited adaptive histogram equalization (CLAHE) and deep convolutional network for the classification of lung diseases. We used X-ray images to create a convolutional neural network (CNN) for early identification and categorization of lung diseases. Initially, the proposed method implemented the support vector machine to classify the images with and without using CLAHE equalizer. The obtained results were compared with the CNN networks. Later, two different experiments were implemented with hybrid architecture of deep CNN networks and CLAHE as a preprocessing for image enhancement. The experimental results indicate that the suggested hybrid architecture outperforms traditional methods by roughly 20% in terms of accuracy. [ABSTRACT FROM AUTHOR]

Emotional intelligence is the automatic detection of human emotions using various intelligent methods. Several studies have been conducted on emotional intelligence, and only a few have been adopted in education. Detecting student emotions can significantly increase productivity and improve the education process. This paper proposes a new deep learning method to detect student emotions. The main aim of this paper is to map the relationship between teaching practices and student learning based on emotional impact. Facial recognition algorithms extract helpful information from online platforms as image classification techniques are applied to detect the emotions of student and/or teacher faces. As part of this work, two deep learning models are compared according to their performance. Promising results are achieved using both techniques, as presented in the Experimental Results Section. For validation of the proposed system, an online course with students is used; the findings suggest that this technique operates well. Based on emotional analysis, several deep learning techniques are applied to train and test the emotion classification process. Transfer learning for a pre-trained deep neural network is used as well to increase the accuracy of the emotion classification stage. The obtained results show that the performance of the proposed method is promising using both techniques, as presented in the Experimental Results Section. [ABSTRACT FROM AUTHOR]

People worldwide suffer from fake news in many life aspects, healthcare, transportation, education, economics, and many others. Therefore, many researchers have considered seeking techniques for automatically detecting fake news in the last decade. The most popular news agencies use e-publishing on their websites; even websites can publish any news they want. However, thus before quotation any news from a website, there should be a close look at news resource ranking by using a trusted websites classifier, such as the website world rank, which reflects the repute of these websites. This paper uses the world rank of news websites as the main factor of news accuracy by using two widespread and trusted websites ranking. Moreover, a secondary factor is proposed to compute the news accuracy similarity by comparing the current news with fakes news and getting the possible news accuracy. Experiments results are conducted on several benchmark datasets. The results showed that the proposed method got promising results compared to other comparative methods in defining the news accuracy. [ABSTRACT FROM AUTHOR]

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 [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The online version of the original article can be found at https://doi.org/10.1007/s11042-022-13914-9 B Correction to: Multimedia Tools and Applications b https://doi.org/10.1007/s11042-022-13914-9 The original publication of this article contains incorrect affiliations of the author "Laith Abualigah". Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. [Extracted from the article]

The original article can be found online at https://doi.org/10.1007/s10586-021-03397-y. The affiliation details for Author Ala Mughaid were incorrectly given as 'Computer Science Department, The Hashemite University, Zarqa, Jordan' but should have been 'Department of Computer science and application, Faculty of prince Al-Hussien bin Abdullah II for IT, The Hashemite University, Zarqa, Jordan'. Correction to: Cluster Computing https://doi.org/10.1007/s10586-021-03397-y The original version of this article unfortunately contained a mistake. [Extracted from the article]