Forecasting meteorological analysis using machine learning algorithms.

Weather prediction is gaining up ubiquity quickly in the current period of Machine learning and Technologies. It is fundamental to foresee the temperature of the climate for quite a while. Decision trees, K-NN, Random Forest algorithms are an integral asset which has been utilized in several prediction works for instance, flood prediction, storm detection etc. In this paper, a simple approach for weather prediction of future years by utilizing the past data analysis is proposed by the decision tree, K-NN and random forest algorithm calculations and showing the best accuracy result of these three algorithms. Weather prediction plays a significant job in everyday applications and in this paper the prediction is done based on the temperature changes of the certain area. All these algorithms calculate the mean values, median, confidence values, probability and show the difference between plots of all the three algorithms etc. Finally, using these algorithms in this work we can predict whether the temperature increases or decreases, is it a rainy day or not. The dataset is completely based on the weather of certain area including few objects like year, month, and temperature, predicted values and so on.

Speech to text translation enabling multilingualism.

Speech acts as a barrier to communication between two individuals and helps them in expressing their feelings, thoughts, emotions, and ideologies among each other. The process of establishing a communicational interaction between the machine and mankind is known as Natural Language processing. Speech recognition aids in translating the spoken language into text. We have come up with a Speech Recognition model that converts the speech data given by the user as an input into the text format in his desired language. This model is developed by adding Multilingual features to the existent Google Speech Recognition model based on some of the natural language processing principles. The goal of this research is to build a speech recognition model that even facilitates an illiterate person to easily communicate with the computer system in his regional language

Cognitive model for object detection based on speech-to-text conversion.

The goal of this paper is to develop a model which is the integrated version of both SpeechRecognition and Object detection. This model is developed after undergoing the literature survey and the existing models that are related to Object Detection and Speech Recognition. There are several types of Speech Recognition and Object Detection models available so far. In addition to the existing models, this paper proposes a new model named "Cognitive Model for Object Detection based on Speech-to-Text Conversion, "which is an integrated version of both Speech Recognition and Object Detection models. Firstly, A speech command is provided as an input to the model, it takes the command and processes the data, and then it detects the specified object from a source of images. The detected object is represented with a rectangular box. This approach is implemented with the help of Google Speech Recognition and YOLO object detection models utilizing the Darknet and OpenCV frameworks