Volume 12 | Issue 5 |

 Abstract

The sign language is used by people with hearing / speech disabilities to express their thoughts and feelings. But normally, people find it difficult to understand hand gestures of the specially challenged people as they do not know the meaning of sign language. Our project aims to develop a system forsign language recognition using MediaPipe ,LSTM, and Keras. The proposed system utilizes a webcam to capture real-time video input of a person performing sign language gestures. MediaPipe is used to extract and track the hand landmarks and their movements in the video stream. The features are then processed using LSTM, which is a sequence modeling technique that captures the hand gestures. Finally, a deep learning LSTM model implemented in Keras and trained to recognize the different sign language gestures. The system can potentially be used to assist people with hearing. Long Short-Term Memory (LSTM) neural network architecture to get this remarkable feat. When someone performs sign language gestures in front of a camera, the system instantly recognizes and interprets those gestures.

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 Keywords

Animal species recognition, deep convolutional neural networks, transfer learning, camera-trap, KTH dataset.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Around 5000 million peoples have trouble hearing properly. While hearing aids can help somewhat, many struggle to understand speech when there's background noise. We've come up with a smart computer program that can filter out that noise while keeping speech clear. It works so well that it brings the clarity of speech is hearing aid users up to the level of people with thoda hearing. Here's how it works: We trained a computer program using a lot of recordings of speech with peecheka noise. Then, we made it even better by letting the computer figure out the best way to do this on its own. This program is really good at filtering out noise and letting you hear speech clearly, even if you're in a noisy place. It's much better than older methods that needed multiple microphones. And here's the exciting part: This program works fast, like in real time on a regular laptop. So, in a few years, we might be able to put it right into hearing aids, making zindagi good for millions of people with hearing problems.

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 Keywords

Hearing, Noise, Train, Test.

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This paper makes use of deep learning techniques, specifically the ResNet-34 architecture, to present a novel method for estimating food volume and calories specifically for Indian cooked food items. The study looks at five common Indian dishes using computer vision to detect, categorize, and estimate volume of food. The procedure involves training a ResNet-34 model with a dataset that includes images of Indian foods such as biryani, curry, dal, roti, and samosas. Taking into consideration variations in preparation techniques and presentation styles, the model has been fine-tuned to accurately detect and classify these food items. Additionally, users can estimate the amount of food based on picture input thanks to the system's integration of volume estimation techniques. This feature is especially helpful for people who are watching their caloric intake or adhering to a diet. The outcomes of the experiments show how well the suggested method works for correctly recognizing Indian cooked labels, calculating their volumes, and estimating calories. The system's performance is evaluated across a broad range of datasets, showcasing its adaptability and reliability in diverse scenarios. All things considered, this work contributes to the field of food volume assessment and calorie estimation, especially as it relates to Indian cuisine, and offers a practical tool for monitoring nutrients and controlling diets.

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 Keywords

Calorie estimation, Food classification, ResNet-34, Food Segmentation, Depth Network Training, Machine Learning, Health Monitoring OpenCV

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 Abstract

The " Voice recognition based I0T Home Automation System " is a cutting-edge loT project designed to revolutionize the way homeowners interact with their living spaces. This project, undertaken, has been crafted with the aim of bringing convenience and efficiency to everyday tasks through voice recognition technology and interconnected smart devices. By integrating voice commands through popular smart speakers, this system enables users to control a range of home devices, from lighting and climate control to security systems. With a strong focus on user privacy and data security, the project ensures that voice data is managed responsibly, and encryption measures are implemented for secure communication between the IOT hub and connected devices. The project boasts key features such as personalized voice commands, customization of routines, and real-time feedback. Users can create scenarios like "Movie night" that adjust multiple devices in a single command. A range of technologies, including cloud-based voice recognition services, IOT hubs, and end-device control mechanisms, are harnessed to create a seamless and intuitive experience for home owners. The "Smart Home Automation System" delivers numerous benefits, including enhanced convenience, energy efficiency, and improved quality of life. By simplifying daily tasks and promoting efficient energy use, it contributes to a more sustainable and comfortable living environment. As the project evolves, we anticipate implementing features to further enhance user experiences, such as integration with emerging smart devices and expansion of customization options.

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 Keywords

IOT, Smart Home, Voice Recognition, Automation

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In our research, we harness the power of different computer algorithms to predict rainfall in various ecological zones of Ghana. We use data from the Ghana Meteorological Agency spanning four decades, from 1901 to 2015. We assess These algorithms' performance varies based on their accuracy in predicting rainfall, The speed at which they can operate these predictions, and their overall reliability. We find that Extreme Gradient Boosting, Random Forest, and Multilayer Perceptron algorithms excel across all three testing ratios, while K-Nearest Neighbour performs less effectively. Notably, Decision Tree proves to be the fastest in making predictions, but Multilayer Perceptron requires the most time. Our research provides valuable insights into the utilization of machine learning in tackling the complex rainfall prediction task in Ghana's diverse ecological regions.

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 Keywords

Rain prediction using computers, Ghana weather data, Accuracy check, CNN, Reliability evaluation, Accurate results._

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 Abstract

This project aims to create a sophisticated Rubik's Cube solver by blending human-designed algorithms with computer-based methods. By understanding the cube's structure and using efficient algorithms like Thistlethwaite, the solver analyzes the cube in real-time. It employs machine learning and deep learning techniques to enhance its efficiency. The software generates user-friendly 3D models and visualizations to help users understand the process better. Extensive testing ensures accuracy, with future plans to integrate physical devices for an even better user experience, potentially revolutionizing Rubik's Cube solving.

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 Keywords

Rubik's Cube, Thistlethwaite, CFOP Algorithm, CUDA Architecture, OpenCV

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 Abstract

The internet connects the world, but also exposes it to numerous network threats. With the vast amount of information exchanged globally, ensuring the integrity and confidentiality of data has become increasingly challenging. Network security is essential in preventing easy breaches and unintended interference. One approach involves employing Intrusion Detection Systems, strategically positioned to monitor traffic from source to destination apps. However, balancing thorough screening with system efficiency is a concern. Integrating machine learning algorithms enhances flexibility and reliability in detecting and distinguishing between ordinary and malicious activities. The algorithms, logistic regression, Naive Bayes, K-Nearest Neighbour, and Decision Trees, are utilized in our research to optimize intrusion detection in Network Traffic Data, employing various evaluation methodologies to achieve the highest accuracy.

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 Keywords

Cyber-attack, distance relay, graph theory, multi-agent system, distributed system, deep neural network.

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 Abstract

Integrating The goal of this project, "Safety Helmet Wearing Detection Model Based on Improved YOLO-M," is to develop a model for determining whether or not people are wearing safety helmets. Improving safety monitoring in diverse settings is the aim. In order to develop a safety helmet detection system with an enhanced YOLO-M model, a computer must be trained to identify whether or not people are wearing safety helmets in images or videos. This entails utilizing data, modifying the software, teaching it to comprehend helmets, and verifying that it functions well. Once it functions properly, you can employ it in locations where you wish to verify that individuals are donning safety helmets.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

YOLO-M (You Only Look Once for Multi-Object Detection

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 Abstract

The goal of the project "Soil Analysis and Crop Recommendation Using Machine Learning" is to examine several approaches to soil analysis and crop recommendation. This model will forecast the greatest number of crops with a high degree of accuracy. It is essential to the survival and growth of the Indian economy.

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 Keywords

Early Detection, Deep learning, CNN Algorithm, Image Processing.

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 Abstract

This paper presents a potential solution to break down communication barriers and promote inclusivity across various social and professional settings, bridging the gap between individuals with and without hearing impairments. The "Sign Language to Speech Conversion" and "Speech to Sign Language Conversion" initiative strives to create a real-time system that can translate Sign2Text & Text2Sign. The goal is to facilitate seamless two-way communication between individuals with hearing impairments and those without. The system will leverage advanced technologies CNN and RNNs to accurately recognize a wide range of gestures from sign language inputs. Furthermore, NLP models facilitate seamless translation of text. A key aspect is the real-time function of the system, minimizing delays in the translation process to enable instantaneous communication between ISL users and those relying on spoken language.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Spatial/temporal relationships, CNN, RNN, NLP

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Creative Commons Attribution 4.0 and The Open Definition