Hast Mudra: Hand Sign Gesture Recognition Using LSTM

Even though using the most natural way of communication is sign language, deaf and mute people find it challenging to socialize. A language barrier is erected between regular people and D&M individuals due to the structure of sign language, which is distinct from text. They converse by using vision-based communication as a result. The gestures can be easily understood by others if there is a standard interface that transforms sign language to visible text. As a result, R&D has been done on a vision-based interface system that will allow D&M persons to communicate without understanding one another's languages. In this project, first gathered     and acquired data and created a dataset, after which extracting useful data from the images. Keywords After verification and trained data and model using the (LSTM) algorithm, TensorFlow, and Keras technology, classified the gestures according to alphabet. Using our own dataset, this system achieved an accuracy of around 86.75% in an experimental test. system uses the (LSTM) algorithm to process images and data

A Systematic and Comparative Analysis of Semantic Search Algorithms

Users often struggle to discover the information they need online because of the massive volume of data that is readily available as well as being generated every day in the today’s digital age. Traditional keyword-based search engines may not be able to handle complex queries, which could result in irrelevant or insufficient search results. This issue can be solved by semantic search, which utilises machine learning and natural language processing to interpret the meaning and context of a user's query. In this paper we focus on analyzing the BM-25 algorithm, Mean of Word Vectors approach, Universal Sentence Encoder model, and Sentence-BERT model on the CISI Dataset for Semantic Search Task. The results indicate that, the Finetuned SBERT model performs the best

Machine Learning Based Fluid-Transportation Monitoring and Controlling

The discipline of fluid mechanics is developing quickly, propelled by previously unheard-of data volumes from experiments, field measurements, and expansive simulations at various spatiotemporal scales. The field of machine learning (ML) provides a plethora of methods for gleaning insights from data that can be used to inform our understanding of the fluid dynamics at play. As an added bonus, ML algorithms can be used to automate duties associated with flow control and optimization, while also enhancing domain expertise. This article provides a review of the background, current state, and potential future applications of ML in fluid mechanics. We provide an introduction to the most fundamental ML approaches and describe their applications to the study, modelling, optimization, and management of fluid flows. From the standpoint of scientific inquiry, which treats data as an integral aspect of modelling, experiments, and simulations, the benefits and drawbacks of these approaches are discussed. Since ML provides a robust information-processing framework, it can supplement and potentially revolutionize conventional approaches to fluid mechanics study and industrial applications. &nbsp

Enhancing Auction Systems with Blockchain Technology

This research paper examines the use of blockchain technology in auction systems. Traditional auction systems face issues related to trust, transparency, and security. Blockchain offers a decentralized and immutable solution that can enhance the efficiency, security, and transparency of auctions. The paper provides an overview of blockchain technology and identifies the challenges in traditional auctions that blockchain can address. It explores existing blockchain-based auction systems and evaluates their effectiveness in mitigating issues such as bid manipulation and fraud. The impact of blockchain on auction participants is also discussed, including benefits like increased trust and reduced transaction costs, as well as challenges related to adoption and scalability. The paper considers both theoretical and practical aspects, analyzing case studies and implementation challenges. It concludes by summarizing the key findings and suggesting future research directions to advance the application of blockchain in auction systems. The auction contract allows users to place bids and determine the highest bidder within a specified time period. The contract also provides functionality for canceling the auction and finalizing it by transferring the funds to the appropriate recipients

Mind Control Robotic Arm: Augmentative and Alternative Communication in the Classroom Environment

In recent years, technological advancements have greatly benefited the field of prosthetics. A large number of disabled people depend on prosthetics because they are an important technology. In order to provide augmentative and alternative methods of communication to these disabled people with various neuromuscular disorders, we must make sure they are provided with appropriate equipment to express themselves. Different types of arms are evaluated under robotic technology in terms of resistance, usability, flexibility, cost, and potential (such as robotic, surgical, bionic, prosthetic, and static arms). The main problems with these techniques are their high cost, the difficulty of installing and maintaining them, and the possibility of requiring surgery may arise. As a result, this paper is going to provide a description of the idea for combining an EEG controlled smart prosthetic arm with a smart robotic hand. An electrode headset is used to capture the signals from the robotic hand in order to control the device. Creating a robot arm that can help disabled people lead a more independent life is the main objective of this paper