2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)
This study was conducted for the detection of the types of grain which germinate in India. Every ... more This study was conducted for the detection of the types of grain which germinate in India. Every class of grain has different and unique kind of proteins, carbohydrates and nutrients. The utilization of grains highly depends on their type. The main motive of the pabulum industry today is to fulfil the consumers’ demand. We propose a hybrid deep learning framework composed of the ensemble of CNNs for feature extraction and an integrated Random Forest model for classification. A distinct type of 13 grain types have been classified—Chickpeas, Lentils, Peanuts, Soybeans, Fava Beans, Finger Millets, Fonio, Japanese Millet, Kodo Millet, Barley, Oats, Rice and Wheat. Our proposed framework outperformed (classification accuracy 96.12%) the state of art algorithms for detection of grain types. Index Terms—— Grain, SVM (Support Vector Machine), Deep Learning, CNN (Convolution neural network), RF (Random Forest), KNN (K-Nearest Neighbor).
2019 6th International Conference on Signal Processing and Integrated Networks (SPIN), 2019
Summarizing cricket matches is a labor intensive task that demands a certain level of proficiency... more Summarizing cricket matches is a labor intensive task that demands a certain level of proficiency. The paper proposes a novel method for automatically detecting and summarizing important events in a cricket match. Our model takes into input the video recording of the entire cricket match and returns the most important clips of the game as the output. Techniques like optical character recognition, sound detection and replay detection have been used to extract important events such as boundaries, wickets and other playfield scenarios in a cricket match. These events are then clipped together to form the entire highlight for the cricket match. We performed several qualitative and quantitative experiments to evaluate our model. Our model achieves an accuracy of 89.45% to detect events like wickets, fours and sixes which indicates the usefulness of the model in real life scenarios.
2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART)
This study was conducted for the detection of the types of grain which germinate in India. Every ... more This study was conducted for the detection of the types of grain which germinate in India. Every class of grain has different and unique kind of proteins, carbohydrates and nutrients. The utilization of grains highly depends on their type. The main motive of the pabulum industry today is to fulfil the consumers’ demand. We propose a hybrid deep learning framework composed of the ensemble of CNNs for feature extraction and an integrated Random Forest model for classification. A distinct type of 13 grain types have been classified—Chickpeas, Lentils, Peanuts, Soybeans, Fava Beans, Finger Millets, Fonio, Japanese Millet, Kodo Millet, Barley, Oats, Rice and Wheat. Our proposed framework outperformed (classification accuracy 96.12%) the state of art algorithms for detection of grain types. Index Terms—— Grain, SVM (Support Vector Machine), Deep Learning, CNN (Convolution neural network), RF (Random Forest), KNN (K-Nearest Neighbor).
2019 6th International Conference on Signal Processing and Integrated Networks (SPIN), 2019
Summarizing cricket matches is a labor intensive task that demands a certain level of proficiency... more Summarizing cricket matches is a labor intensive task that demands a certain level of proficiency. The paper proposes a novel method for automatically detecting and summarizing important events in a cricket match. Our model takes into input the video recording of the entire cricket match and returns the most important clips of the game as the output. Techniques like optical character recognition, sound detection and replay detection have been used to extract important events such as boundaries, wickets and other playfield scenarios in a cricket match. These events are then clipped together to form the entire highlight for the cricket match. We performed several qualitative and quantitative experiments to evaluate our model. Our model achieves an accuracy of 89.45% to detect events like wickets, fours and sixes which indicates the usefulness of the model in real life scenarios.
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Papers by Arpit Ahuja