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In this study, the Chaotic Sinusoidal Map (CSM)-enhanced Zebra Optimization Algorithm (CZOA) is introduced. CZOA combines CSM's integration strengths with ZOA's optimization skills. ZOA already exhibits great optimization capabilities, but the addition of CSM increases its potential even more. This addition greatly strengthens ZOA's exploration and exploitation skills and increases its flexibility for various optimization tasks. CZOA outperforms both the original ZOA and contemporary optimisation methods on 23 benchmark functions, including high-dimensional (FD), multimodal (MM), and unimodal (UM) challenges. Using the chaos of CSM to investigate regional optimal and determine better convergence and exploration-exploitation equilibrium are shown by CZOA, which also shows more profitable solution locations. CZOA demonstrates its resilience and versatility through multiple benchmark activities, underscoring its potential as an adaptable optimisation tool. CZOA becomes a potent metaheuristic by combining biological inspiration and chaotic dynamics to solve difficult optimisation problems. Inspired by the natural behaviour of zebras, the Zebra Optimisation Algorithm (ZOA) is a relatively new optimisation technique. It makes use of a herd behaviour mechanism and the ideas of leadership and following, in which members of the population—zebras in this case—cooperate to solve optimisation issues in the best possible ways

Research on DeepFake detection using deep neural networks (DNNs) has gained more attention in an effort to detect and categorize DeepFakes. In essence, DeepFakes are regenerated content made by changing particular DNN model elements. In this study, a summary of DeepFake detection methods for images and videos involving faces will be given based on their effectiveness, outcomes, methodology, and type of detection method. We will analyze and categorize the many DeepFake-generating techniques now in use into five primary classes. DeepFake datasets are frequently used to train and test DeepFake models. We will also cover the latest developments in DeepFake dataset trends that are currently accessible. We will also examine the problems in building a generalized DeepFake detection model. Lastly, the difficulties in creating and identifying DeepFakes will be covered. [ABSTRACT FROM AUTHOR]

Sharing traffic information on the vehicular network can help in the implementation of intelligent traffic management, such as car accident warnings, road construction notices, and driver route changes to reduce traffic congestion earlier. If the exposed traffic incident is incorrect, the driving route will be misleading, and the driving response may be in danger. The blockchain ensures the correctness of data and tampers resistance in the consensus mechanism, which can solve such similar problems. The traffic data are collected through the roadside units, and the passing vehicles will verify the correctness when receiving the event notification. We employ data collection, preprocessing, sentiment analysis, geospatial analysis, and machine learning techniques to automatically identify and categorize traffic events, such as accidents, congestion, or road closures, based on information shared by users on various social media platforms. The framework aims to provide accurate and timely insights into traffic conditions, enabling better urban planning and incident response. [ABSTRACT FROM AUTHOR]

Our method is to keep a close eye on the patient's health and inform the person responsible for their care on a regular basis. Since it is not possible for an individual to always watch over a single patient in a hospital setting, the primary objective is to monitor the patient around-the-clock. There is going to be aWe employed an apparatus that continuously observed the patient and sent frequent updates to the attending physician, regardless of the physician's location. This device also has an extra feature that allows it to sound an alarm to notify hospital staff members of a patient emergency. [ABSTRACT FROM AUTHOR]