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.

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.

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.

Optical Character Recognition (OCR) have an importance in the research based on image processing and recognizing the pattern. It serves as an automatic technique for identifying the various patterns in diverse applications. The recognition technique effectively explores the characters, images, and even the handwriting of an individual. Much research concerning OCR involves every deep learning, machine learning, and artificial intelligence algorithm. A segmentation-free approach has been introduced in this paper that combines with Hybrid Network Model (HNM) that works on collaborating convolutional neural network and recurrent neural network for minimizing the time in processing and improving the accuracy. In hybrid model we consider CNN in the input layer, the middle layer is LSTM and MLP is the layer generated as an output. The length of sequences in the input and output can vary it need not be specific they are managed by the encoder and decoders present in LSTM.

Nowadays Machine Learning is the most useful area for students and researchers for classification and prediction by using the machine learning algorithms. In this four kinds of algorithms are available. Those are supervised, unsupervised, semi-supervised and reinforcement algorithms. In this paper is compared the already existed KNN algorithm and a new Dynamic KNN algorithm with some case studies in order to show the accuracy. Dynamic KNN algorithm is an improvised version of KNN only. One of the significant factors that affect the accuracy of the model is the value of K. Fundamentally; the value of K is fixed for all data points. In this study, the value is calculated at the local level for all test points. This study performs a comparative study between suggested model and the traditional KNN model.

The best biometric information processes is a face recognition device, its applicability is simpler and its working range is broader than other methods like fingerprinting, iris scanning and signature. Face Detection is one of the kinds of bio-metric strategies that immediately apply to facial recognition by computerized devices through staring at the facial. It is a common feature used in bio analytics, digital cameras, and social labeling. Main applications of facial recognition algorithms that concentrate on recognition of face include environments, artifacts, and other parts of humans. Face-detection systems uses learning algorithms which are part of machine learning that can be used to identify subject faces inside big size pictures in order to function.

Two typical methods are used to choose a stock. The first is an essential analysis and the second is a study of technical aspects. Basic analysis and technical analysis, however, use totally distinct ways to collect stocks. Fundamental analysis and technical analysis may be used to evaluate whether or not an investment in the stock is lucrative and to predict future stock trends further. Basic and technical analysis to produce a comprehensive trading strategy may be blended. The aim of this document is to provide the top basic analysis and stock valuation methods for selecting stocks of actively traded stock portfolios used by everyday equity traders. Daily equity traders utilise mainly technical charts and other tools to identify patterns which may promote business perspective without evaluating the fundamental worth of a company to take trade choices. The examination of charts is designed to identify highly anticipated probability results in trades with precise price objectives. The objective of this technical paper is to highlight the importance of fundamental analysis in the everyday investment decisions of traders. The fundamental analysis focuses on important comparisons between the intrinsic value of an inventory and the current market price. If the intrinsic value of the stock exceeds the price marker, the acquisition of the stock by a fundamental investor/trader makes sense. This research supports the idea that equities traders should use both investing techniques to make more successful investment decisions.

There are two commonly held beliefs when it comes to picking stocks. Fundamental analysis is the initial step in making an investment decision, while technical analysis is the process used in making that decision. Investing involves using two separate analytical tools: fundamental analysis and technical analysis. Fundamental analysis and technical analysis can both tell whether an investment in a company is appealing or unattractive, and then go on to speculate on what the future trends of stocks will be. The combination of fundamental and technical research may provide a complete trading strategy. Artificial recurrent neural network (RNN) architecture, long short-term memory (LSTM) networks. The large-sequence-processing capabilities of LSTMs apply to many data sets. The vast quantity of data that is produced every day in the stock market is ideal for use in artificial intelligence applications. We want to use LSTM to build a financial market forecasting network that uses Technical and Fundamental analysis of businesses to predict the stock prices the next day. To do both kinds of analysis, the input data is pre-processed to contain necessary variables and then trained on LSTM& GRU. In this work we proposed Clustered Gradient Descent Adam optimizer usually perform better than models with Adam optimizer. The GRU model beats the LSTM model when it comes to overall accuracy.

In the world, every country depending on the economy. Economy depending upon the population activities. One of the most vital activities in these activities is selling, buying, and Investing money. So, Investment of the money on different aspects of the economy. So, Investment of the money common man to a rich person thinking about it. Above all the things are call share market. So many people are daily investing the daily money basis and long term basis also. Every significant business factor is risk analysis and trends of the share prices. In the share market, a massive amount of data generated day by day. So In this paper objective is to predict the low price and high price situations. This situation to predict using machine learning regression techniques is Linear, Lasso, Bayesian ridge, ridge, and Embaded Stack Regression algorithms. And predict the classes of high and low prices in time sequences and find out the performance attributes of the regression and compare them with the new embed stacking algorithm.

We often meet product development requirements which need to analyse, interpret or transform data from different business sources. Most manufacturing industries use their product manufacturing purposes for high-cost equipment. The impact of a failure cannot be afforded and production will be suspended. The machine is subject to preventive maintenance, often time-in-service inspections and repairs, to avoid such an enormous loss. The challenge of proper planning is increasing with the complexity of machinery: working together and effecting the lives of one another in a system with several components. Predictive maintenance is aimed at developing models that evaluate the risk of failure of a machine at any time and use these analytics to enhance the maintenance schedule. The achievement of predictive maintenance frameworks relies on various principal components: that the right data are available, that the problem is properly framed and predictions are evaluated appropriately. This paper will provide insights on how to choose the best modelling technique for the daily usage, service life of the machine and how to predict certain breakthrough points.

The recent growth in data collection and management processes have made the financial or transactional data availability for every modelling process such as forecasting. The financial forecasting replies on data modelling on the transactional financial data and which again relies on traditional data mining processes. The data mining process, in spite of multiple advantages such as modelling flexibilities, the sub-processes must be customized for fitting into the financial modelling. A good number of research attempts were made to predict the financial data points based on time series transactional data. However, most of the parallel research outcomes are criticised for higher complexity. Also, the outcome of the predictive or forecasting processes rely on the data cleaning methods, thus, the search for the less complex and composite data cleaning method is continuing as a focus of recent researches. The data cleaning method primarily concerns the outlier or noise detection and reduction, anomaly detection and reduction, missing value detection and reduction with the scope for dimensionality reductions. The parallel research outcomes for data cleaning is highly criticised for higher complexity. Thus, this work proposes a novel method for data cleaning with the help of composite data cleaning coefficient method. As a result, this work demonstrates nearly 100% accurate for any modelling technique with the proposed cleaning method, which is nearly 6% improvement to each modelling technique. In the course of establishing the novel method, this work also showcases the mapping of traditional data mining process into the financial forecasting models.

The Bitcoin market is filled with vast amounts of incomprehensible data. This work aims to provide a concise and simple representation of the data so that even the oblivious eye can make conclusions through the concepts of data mining and machine learning. Trading on Bitcoins is growing at a fast pace and is playing a vital role in data analysis and in the field of economics. On the other hand, Machine Learning is a part of data science that focuses on designing algorithm, such that the algorithm will understand the data provided by the user and predicts the value accordingly. This paper focuses on the market trends for popular bitcoins from various established sources like quandle, predicting for a future period of time using machine learning algorithm named Vector Auto Regression (VAR). This is highly advantageous as the investor, not only look at different stocks simultaneously but is also provided with highly accurate data, with an accuracy of 90%. We plan to take this to the next step by providing insight into the future market trends and allowing the investor to compare all the currencies in one platform and make his investment accordingly.