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The challenge of developing an Android malware detection framework that can identify malware in real-world apps is difficult for academicians and researchers. The vulnerability lies in the permission model of Android. Therefore, it has attracted the attention of various researchers to develop an Android malware detection model using permission or a set of permissions. Academicians and researchers have used all extracted features in previous studies, resulting in overburdening while creating malware detection models. But, the effectiveness of the machine learning model depends on the relevant features, which help in reducing the value of misclassification errors and have excellent discriminative power. A feature selection framework is proposed in this research paper that helps in selecting the relevant features. In the first stage of the proposed framework, t-test, and univariate logistic regression are implemented on our collected feature data set to classify their capacity for detecting malware. Multivariate linear regression stepwise forward selection and correlation analysis are implemented in the second stage to evaluate the correctness of the features selected in the first stage. Furthermore, the resulting features are used as input in the development of malware detection models using three ensemble methods and a neural network with six different machine-learning algorithms. The developed models' performance is compared using two performance parameters: F-measure and Accuracy. The experiment is performed by using half a million different Android apps. The empirical findings reveal that malware detection model developed using features selected by implementing proposed feature selection framework achieved higher detection rate as compared to the model developed using all extracted features data set. Further, when compared to previously developed frameworks or methodologies, the experimental results indicates that model developed in this study achieved an accuracy of 98.8%. [ABSTRACT FROM AUTHOR]

UAVs or Drones can be used to support wireless communication by acting as flying or mobile Base Stations for the accumulation of the different types of data to train the models. However, in traditional or DL-based UAVs, the raw data is sent from the devices to the centralized server, which causes problems in terms of the privacy of the devices and the UAVs' communication resources or limited processing. Therefore, the issue with DL-based UAVs is that sending the original data to the centralized body raises questions about security and privacy. The transmission of distributed, unprocessed data from the drones to the cloud, including interactive media information data types, requires a significant amount of network bandwidth and more energy, which has an enormous effect on several trade-offs, including communication rates and computation latencies. Data packet loss caused by asynchronous transmission, which doesn't prevent peer-to-peer communication, is a concern with AFL-based UAVs. Therefore, in order to address the aforementioned issues, we have introduced SFL-based UAVs that focus on creating algorithms in which the models simultaneously update the server as they wait for all of the chosen devices to communicate. The proposed framework enables a variety of devices, including mobile and UAV devices, to train or learn their algorithms for machine learning before updating the models and parameters simultaneously to servers or manned aerial data centers for model buildup without transferring their original private information. This decreases packet loss and privacy threats while also enhancing round effectiveness as well as model accuracy. The comparative analysis of AFL and SFL techniques in terms of accuracy, global rounds, and communication rounds are offered. Simulation findings suggest that the proposed methodology improves in terms of global rounds and accuracy. [ABSTRACT FROM AUTHOR]

The certification of wine quality is essential to the wine industry. The main goal of this work is to develop a machine learning model to forecast wine quality using the dataset. We utilised samples from the red wine dataset (RWD) with eleven distinct physiochemical properties. With the initial RWD, five machine learning (ML) models were trained and put to the test. The most accurate algorithms are Random Forest (RF) and Extreme Gradient Boosting (XGBoost). Using these two ML approaches, the top three features from a total of eleven features are chosen, and ML analysis is performed on the remaining features. Several graphs are employed to demonstrate the feature importance based on the XGBoost model and RF. Wine quality was predicted using relevant characteristics, often referred to as fundamental elements, that were shown to be essential during the feature selection procedure. When trained and tested without feature selection, with feature selection (RF), and with key attributes, the XGBoost classifier displayed 100% accuracy. In the presence of essential variables, the RF classifier performed better. Finally, to assess the precision of their predictions, the authors trained an RF classifier, validated it, and changed its hyperparameters. To address collinearity and decrease the quantity of predictors without sacrificing model accuracy, we have also used cluster analysis. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs), also known as drones, have rapidly gained popularity due to their widely employed applications in various industries and fields, including search and rescue, agriculture, industry, military operations, safety, and more. Additionally, drones assist with tasks such as search and rescue efforts, pandemic virus containment, crisis management, and other critical operations. Due to their unique capabilities in image, video, and information collection, a multi-UAV system plays a crucial role in these activities. However, such images and video data involve individual privacy. Therefore, such multi-UAV applications have an indigenous tradeoff of privacy preservation. We have proposed a Federated Learning (FL) based approach for ensuring privacy in multi-UAV applications. The proposed methodology utilizes a synchronous FL approach and the Convolutional Neural Network (CNN) to ensure security. The model parameters are protected by using a secure aggregation. Results demonstrate that the proposed approach outperforms existing techniques in terms of accuracy and precision. [ABSTRACT FROM AUTHOR]

Summary: Recently, there has been a massive rise in the popularity of Instagram, which connects individuals globally and allows videos and images to be uploaded and exchanged, and communicated over social media. Instagram is also an online playground of deceit. The use of filters, lighting, and cunning angles transforms the mundane into something spectacular. Automated spam accounts and fake profiles use this to their malicious advantage for executing attacks targeting high‐profile executives. Creating fake Instagram identities is easy to reproduce the idea of being accepted by many fans on social media. Fake accounts are used in the marketing of fake services and products. This research focused on designing and training a unique neural network model and proposed a new algorithm for detecting automated spam and fake Instagram account profiles. The precision and accuracy of the proposed method were achieved at 93% and 91%, respectively. [ABSTRACT FROM AUTHOR]

Individuals with pre-existing diabetes seem to be vulnerable to the COVID-19 due to changes in blood sugar levels and diabetes complications. As observed globally, around 20–50% of individuals affected by coronavirus had diabetes. However, there is no recent finding that diabetic patients are more prone to contract COVID-19 than nondiabetic patients. However, a few recent findings have observed that it could be at least twice as likely to die from complications of diabetes. Considering the multifold mortality rate of COVID-19 in diabetic patients, this study proposes a COVID-19 risk prediction model for diabetic patients using a fuzzy inference system and machine learning approaches. This study aimed to estimate the risk level of COVID-19 in diabetic patients without a medical practitioner's advice for timely action and overcoming the multifold mortality rate of COVID-19 in diabetic patients. The proposed model takes eight input parameters, which were found as the most influential symptoms in diabetic patients. With the help of the various state-of-the-art machine learning techniques, fifteen models were built over the rule base. CatBoost classifier gives the best accuracy, recall, precision, F1 score, and kappa score. After hyper-parameter optimization, CatBoost classifier showed 76% accuracy and improvements in the recall, precision, F1 score, and kappa score, followed by logistic regression and XGBoost with 75.1% and 74.7% accuracy. Stratified k-fold cross-validation is used for validation purposes. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) or Drones technology has a huge potential for supporting different efficient solutions for the smart applications in our world. The applications include smart things, smart transportation, smart cities, smart healthcare, smart personal care, smart house, smart industries, and so on. Due to the sensitive applications of UAVs, the security has become a major concern, and therefore, efficient techniques are required to protect captured data from hackers and the fictitious activities from illegitimate users. Machine learning (ML) techniques play a vital role in improving UAVs' security intelligently, while blockchain is recent technology for decentralized UAVs and security. Furthermore, watermarking guarantees digital media to be authenticated, protected, and copyright. Therefore, we provide a comprehensive survey of optimal techniques, which are used for securing UAVs applications in terms of blockchain, ML, and watermarking. Furthermore, we introduce each technique with the advantages and suitably used for securing UAVs collaboration applications. This survey contributes to a better understanding of the blockchain, ML, and watermarking techniques for securing UAVs and sheds new light on challenges and opportunities on subject applications. [ABSTRACT FROM AUTHOR]

Over the past few years, the awful COVID-19 pandemic effect has become a lethal sickness. The processing of the gathered samples requires extra time due to the use of medical diagnostic equipment, methodologies, and clinical testing procedures for the early diagnosis of infected individuals. An innovative multimodal paradigm for the early diagnosis and precise categorization of COVID-19 is put up as a solution to this issue. To extract distinguishing features from the prepared chest X-ray picture and cough (audio) database, chest X-ray-based and cough-based model are used here. Other public chest X-ray image datasets, and the Coswara cough (audio) dataset containing 92 COVID-19 positive, and 1079 healthy subjects (people) using the deep Uniform-Net, and Convolutional Neural Network (CNN). The weighted sum-rule fusion method and ensemble deep learning algorithms are utilized to further combine the extracted features. For the early diagnosis of patients, the framework offers an accuracy of 98.67%. [Display omitted] • A novel multimodal framework is proposed for the early diagnosis and accurate classification of COVID-19. • The extracted features are fused using the weighted sum-rule fusion technique for early diagnosis and accurate prediction. • The framework provided an accuracy of 98.67% (X-ray based) and 86.53% (cough based diagnosis) for early diagnosis of patients. [ABSTRACT FROM AUTHOR]