The issue of online fake news is a major concern because many people rely on online platforms for updated information and news. With the massive amount of information on these platforms, fake news identification has become a daunting task. Users often rely on the reactions and comments of others in their social circle on these platforms to judge the credibility of news stories. Many researchers are actively working to detect online fake news by analyzing post titles and headlines. However, there is a considerable research gap as people's comments are currently not being utilized for fake news detection on platforms such as Facebook and Instagram. Integrating people's comments on such news into the fake news detection process is crucial in effectively detecting fake news. To address this challenge, we gather and organize two separate datasets FNID (Fake News Instagram Dataset) and FNFD (Fake News Facebook Dataset) containing news titles and comments. These datasets are specifically created to aid in the detection of online fake news. Subsequently, we propose an innovative hybrid model for news titles and users' comments - DFN-SCNC (Detection of fake news based on social context and news content) - that combines BERT and Bi-GRU models. Our model, which combines BERT for tokenizing and extracting contextual vectors with Bi-GRU for analyzing post content and social interactions (comments), outperforms various state-of-the-art techniques achieving an F1-score of 97% and 91 % on FNID and FNFD datasets respectively.
Electricity demand will rise by $\mathbf{5 0 \%}$ by the year 2035. This will put pressure on the electricity suppliers to shift towards renewable energy sources (RESs) such as wind (WT) and solar (PV). The shift from traditional energy sources is driven by the exploitation of conventional energy and the impact of global warming. This research presents an optimization model integrating RESs like WT and PV with a battery storage system. The main goal of this study is to schedule system sources and optimize the sizing of the system components for smooth system operation and cost-effectiveness. With the existing JAYA and GWO techniques, a new hybrid optimizer setup called the JayaGrey Wolf Optimizer (JGWO) is adapted for unit sizing. The combined objectives of minimizing net annual cost (TAC) and loss of supply power probability (LPSP) are achieved for a standalone (SA) microgrid (MG) environment. The proposed research demonstrates that using a hybrid multi-objective optimization approach, the JGWO results in substantial cost savings and improved grid reliability compared to JAYA and GWO. Hence, this makes JGWO a suitable approach for this research problem.
The study aims to cope with the drawbacks incurred in the conventional User Requirement Analysis (URA) through a new framework that integrates Sentiment Analysis, clustering, and Support Vector Machine (SVM) classifiers. This approach's goal is to optimize URA by categorizing user requirements based on the sentiment of specific target websites. It also demonstrates that these approaches outperform conventional methods in terms of accuracy, recall rates, and speed, enabling more effective consideration of clients' needs in problem-solving processes. In general, this research will explore the potential of machine learning to optimise and enhance the URA process, while also highlighting potential issues that could complicate the requirement gathering process.
Radio Frequency Identification (RFID) is one of the most promising authentication technologies in pervasive computing. Besides identification, the RFID also offers mutual authentication to IoTs (Internet of Things) terminal devices and manages the access control of IoT networks. To decrease the overall cost of the IoT sensors (RFID tags), Ultralightweight Mutual Authentication Protocols (UMAPs) have been proposed. These UMAPs incorporate simple bitwise logical operators (e.g. XOR, AND, OR, Modulo 2 addition, etc.) and extremely low-cost non-triangular primitives in their designs to ensure confi-dentiality, authentication, and integrity. In this paper, we have performed a cryptanalysis of the recently proposed UMAP: Ultralightweight RFID Authentication Protocol (URAP). We have exploited the weak structure of URAP design and identified a desynchronization attack. The proposed attack requires only two (2) sessions to desynchronize the IoT sensor from its network fully. We have also discussed other design flaws of the protocol structure which can lead towards several full disclosure attacks.
The fair management of water shortage and its global distribution posture are substantial issues, with the water situation in Palestine being made worse by the IsraeliPalestinian conflict. The study highlights how critical it is to conduct in-depth research to forecast water demand patterns while accounting for resource limitations and the difficulties posed by conflict. The primary objective is to analyze water usage in Palestine using predictive analytics, with attention to different industries and regions. The research investigates historical data, examining significant aspects such as Year, Period, Memory Number, Consumption, Area No., Area Name, and Type. The scope contains an extensive dataset analysis that recognizes patterns and trends in water usage using cuttingedge statistical and deep learning algorithms. This work is important since it is the first to apply a Hybrid model that combines ARIMA and Multi-Layer Perceptron (MLP). Timeseries data is used in this innovative strategy to get precise predictions. The Hybrid model is applied to predict water usage in Palestine across different geographical areas and customer groups. The proposed hybrid model produced an MAE, MSE, and RMSE value of 0.0663, 0.0063, and 0.0798 respectively.
