The financial sector has greatly impacted the monetary well-being of consumers, traders, and financial institutions. In the current era, artificial intelligence is redefining the limits of the financial markets based on state-of-the-art machine learning and deep learning algorithms. There is extensive use of these techniques in financial instrument price prediction, market trend analysis, establishing investment opportunities, portfolio optimization, etc. Investors and traders are using machine learning and deep learning models for forecasting financial instrument movements. With the widespread adoption of AI in finance, it is imperative to summarize the recent machine learning and deep learning models, which motivated us to present this comprehensive review of the practical applications of machine learning in the financial industry. This article examines algorithms such as supervised and unsupervised machine learning algorithms, ensemble algorithms, time series analysis algorithms, and deep learning algorithms for stock price prediction and solving classification problems. The contributions of this review article are as follows: (a) it provides a description of machine learning and deep learning models used in the financial sector; (b) it provides a generic framework for stock price prediction and classification; and (c) it implements an ensemble model—“Random Forest + XG-Boost + LSTM”—for forecasting TAINIWALCHM and AGROPHOS stock prices and performs a comparative analysis with popular machine learning and deep learning models.

Secure communication is crucial in today’s increasingly interconnected world, especially in distributed contexts where several parties must cooperate while maintaining confidentiality and authenticity. In order to overcome these difficulties, this work introduces a novel Secure Group Key Agreement Protocol (SGKAP) strengthened with Elliptic Curve Secret Sharing (ECSS).The proposed SGKAP uses the strength of elliptic curve cryptography to provide a shared secret key across participants, assuring data transmission confidentiality. Contrary to conventional methods, SGKAP integrates ECSS as a second layer of security to verify participant identity, thus boosting the protocol’s resistance to malevolent adversaries.To create a shared group key, members exchange their elliptic curve public keys at the start of the protocol. During this stage, ECSS is used, and participants create distinctive authentication tokens using their personal secrets. Participants exchange and verify these tokens among themselves, ensuring that only those with the proper permissions have access to the group key.In addition to guaranteeing data confidentiality, SGKAP also verifies the group members’ participation. Furthermore, by mandating secret-sharing-based authentication, ECSS strengthens the protocol’s resistance to insider attacks.We carried out a comprehensive set of simulations and experiments in various distributed contexts to assess the performance and security of SGKAP with ECSS.

To become a successful organization it is the most important aspect to consider is the customer satisfaction. A business can grow only when the customers enjoy their product/service and how much they recommend it. For the same, companies collect reviews and ratings from its customers to understand their perspective better. Amazon is also such a giant, which values its customers’ opinions. It is very difficult or nearly impossible to have a detailed manual scan of each and every review for each and every product available on the website. This is where NLP (Natural language processing) comes into picture. It processes human language from text to arrays of numbers in order to find sense in the text so that the machine learning algorithms can process them further and classify the texts. This project focuses on processing the amazon reviews of various categories and products and then classify them into 5 ratings, from 1 to 5, worst to best respectively. The supervised machine learning algorithms used to process these reviews are Support Vector machines (SVM), Naïve Bayes classifier and Decision Tree classifier. The analysis of comparison of these algorithms has also been discussed using accuracy score bar charts.

Nowadays tremendous research is carried on document analysis and classification digitally. Old manuscripts like magazines, novels, study material, business cards, and post cards can be straightforwardly construed to computer via these OCRs. Segmentation and Identification of the handwritten or typed lettering is thorny due to a variety of aspect. Words need to be fragmented perfectly and with more precision, else the recognition rate decreases. At this point the projected methodology is presented which performs the job of line, word and character fragmentation. For line segmentation customized horizontal histogram approach has been designed. Words are split up using the regionprops and bounding box method which provide the accuracy in the segmentation. Characters are fragmented by using the vertical projection amid the upper and lower modifier fragmentation using the cut line concept. Lines and Words are also split with good precision and accuracy rate in typed documents, but in handwritten document it also splits the separated or broken characters as extraneous words. Further study need to be done to cover up many more issues, which are not identified and solved during this work.