Crop productivity prediction and recommendation is a significant research area of smart agriculture. This paper proposes an Internet of Things (IoT) framework based on dew computing, edge computing, and federated learning, where soil parameter, weather, and climate data are analysed to predict the crop productivity of a land, and then recommend suitable crop for the land. The dew layer pre-processes and accumulates the received sensor data, and forwards to the edge server. The edge server analyses the sensor data and the climate data, and then forwards the result along with the model characteristics to the cloud for further analysis. The proposed framework is simulated in iFogSim. The theoretical analysis shows that the proposed framework has reduced the delay by 60-70% approximately and power consumption by 70-80% approximately than the conventional IoT-cloud framework. We also observe that the proposed framework has reduced the delay by 12-35% approximately and power consumption by 30-50% approximately than the edge-cloud framework. We compare four machine learning algorithms based on their performance in data analysis in terms of precision, recall, accuracy, and F-Score. We observe that each classifier obtains more than 95% prediction accuracy. An Android application is also proposed for crop recommendation.

Speech is one of the attractive areas of the scientists to research in the field of machine learning and they got maximum success in Automatic Speech Recognition system. ASR system gradually enters its footsteps into space exploration to home automation, education sectors to commercial sector, and various public sectors in our daily life to make it more manageable and comfortable. In our proposed work, we aimed to build a model on isolated Bengali word recognition system based on different colors pronounced in Bengali dialects that provides an audio-visual presentation of the recognized color. In this research work, LPC is used for extracting speech features based on pitch and fundamental frequency and KNN classifier for recognition. The proposed system achieved 94% testing accuracy for the dataset of 1500 audio samples for 15 classes where each class represents a specific color pronounced in Bengali dialect.

No abstract

No abstract

Speech is one of the most natural forms of vocalized communication media. Nowadays with the advancement of machine learning, different doors are opened to us for finding several standard ways to step out in the real world. ASR is just like the door to explore the concept of communication through speech between human and digital devices that can recognize speech. In this paper, we have designed a Hidden Markov Model-based isolated Bangla numerals recognition system where the Short-Term Fourier Transform is used for collecting the feature vectors. The defined system achieved 91.50% accuracy for our own dataset of 2000 uttered samples for 10 classes, which gives a satisfied result for this Bangla numerals recognition. IntroductionStandard communication is always done successfully through speech. Speech helps us to complete a smooth communication among us. Those who are not computer professionals can communicate through speech because of its easiness and coziness in communication purposes. As the people of India live in a semi-illiterate country, so with the help of the application which supports speech recognition they will be more benifitable and can take the advantage of modern science. Speech recognition is the way that helps people to communicate with computer through speech. In speech recognition, isolated words are recognized and after converting it to text format, finally, it is prepared to a machine-readable format. Speech recognition performs a major role in medical unit, home automation, for growing the development of realworld market-based applications, which is used basically for commercial purposes. The application of speech recognition will become more useful in that field where the keyboard operation is not suitable. Speech is really needful to the users who mainly use hands and eyes for their work, like mail-sorter, aircraft pilot, cartographer, etc. as