In the ever-evolving landscape of media, the demand for efficient and robust analysis of images and videos has intensified. Traditional methods often struggle to keep pace with the scale and complexity of media data. In response, this study introduces a novel approach that integrates Federated Learning (FL) and Bidirectional Long Short-Term Memory (BiLSTM) networks to enhance the analysis of images and videos in media applications. Federated Learning, a decentralized machine learning technique, enables collaborative model training across multiple edge devices without centralized data aggregation, thus addressing privacy concerns and data silo issues inherent in traditional approaches. By leveraging FL, The proposed framework facilitates the aggregation of insights from diverse sources while preserving data privacy. Furthermore, the integration of BiLSTM networks offers enhanced temporal modeling capabilities, allowing for the extraction of contextual information from sequential data such as video frames.Through experimentation on diverse media datasets, including images and videos, demonstrate the effectiveness of approach in tasks such as object recognition, scene understanding, and action recognition. The results showcase significant improvements in accuracy and efficiency compared to baseline methods, highlighting the potential of Federated Learning and BiLSTM integration for advancing image and video analysis in media applications.Overall, This study contributes to the ongoing efforts to innovate media analysis techniques by harnessing the power of decentralized learning and advanced sequential modeling, paving the way for more intelligent and privacy-preserving media analysis systems. This method achieves an accuracy of 97.5% and has been implemented in Python.

This research paper provides innovative approaches to support financial prediction, or it is a different kind of economic prediction that extends over collecting different economic information. Financial prediction is a concept that has been employed. The present study offers a unique approach to predicting finances by integrating many financial issues utilizing a cross-stitch hybrid approach. The method uses information from several financial databases, including market data, corporate reports, and macroeconomic indicators, to create a comprehensive dataset. Employing MinMax normalization the features are equally scaled to provide uniform input for the algorithm. The combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) systems form the basis of the framework. To understand the time-dependent nature of financial information, LSTM networks (long short-term memory) are utilized to record and simulate the temporal interactions and patterns. Concurrently, spatial features are extracted using CNNs; these components help identify patterns that are difficult to identify with conventional techniques. Better handling of risks, more optimal approaches to investing, and more informed decision-making are made possible by the enhanced forecasting potential that this method—which is described above—offers. Potential pilot studies will focus on innovative uses in financial decision-making and advancements in cross-stitching structure. This paper proposes a sophisticated approach that can help stakeholders, such as investors, analysts of data, and other financial intermediaries, traverse the complexities of financial markets

In an increasingly diversified educational landscape, adaptable curriculum development is critical to meeting the varying requirements of students. Traditional curriculum design approaches frequently lack the flexibility to suit individual student variances, resulting in disengagement and poor learning outcomes. Current approaches are mostly based on static evaluations and generic material delivery, and do not take advantage of the wealth of data accessible regarding student achievement and participation. This work provides a unique way for optimizing adaptive curriculum development using Light Gradient Boosting (LightGBM), a machine learning method known for its effectiveness and predictive capacity. By using real-time data analytics, the suggested system enables personalized learning pathways that dynamically alter content based on student progress and preferences. The overall methodology includes collecting data from several educational sources, pre-processing to guarantee quality, and using LightGBM for predictive modelling. The adaptive curriculum’s efficacy is evaluated using key measures such as pupil involvement, rate of retention, and academic performance. A series of case studies from various educational settings throughout the world are used to evaluate performance, comparing traditional curricula to an adaptive model constructed using LightGBM. Preliminary data show considerable gains in student outcomes, including greater engagement and achievement levels

Nowadays with the emergence of Industry 4. 0, there is therefore the need to have sustainable manufacturing in order to increase operational efficiency as well as relieve the pressure on the environment. A potential weakness of conventional approaches is time-dependency while processing real-time data to prevent dysfunctions in the company’s resource management, energy consumption control, and predictive maintenance. Thus, in this work it is proposed an innovative approach to address these problems with the use of Deep Reinforcement Learning (DRL) and Internet of Things (IoT) technology. Energy consumption, machine performance and environmental data are collected by IoT sensors in real-time and fed into a DRL model. This concept effectively eliminates the vices that are characteristic of conventional approaches in that it seeks to optimize production processes by cell design. The approach suggested above attains an unprecedented 98. 2% accuracy rate. This is in a bid to exemplify its capability in predicting these phenomenal and balance forecasts, to improve the overall performance and sustainability of manufacturing. Thereby, the method offers a robust answer to the acute questions arising in front-line manufacturing today and sets a brand-new reference for intelligent and sustainable behavior in the context of the fourth industrial revolution 4.0.