Abstract. In this work, we proposed a method of artificial neural network learning using differential evolutionary(DE) algorithm. DE with global and local neighborhood based mutation(DEGL) algorithm is used to search the synaptic weight coefficients of neural network and to minimize the learning error in the error surface.DEGL is a version of DE algorithm in which both global and local neighborhood-based mutation operator is combined to create donor vector.The proposed method is applied for classification of real-world data and experimental results show the efficiency and effectiveness of the proposed method and also a comparative study has been made with classical DE algorithm.
CLIR techniques searches unrestricted texts and typically extract term and relationships from bilingual electronic dictionaries or bilingual text collections and use them to translate query and/or document representations into a compatible set of representations with a common feature set. In this paper, we focus on dictionary-based approach by using a bilingual data dictionary with a combination to statistics-based methods to avoid the problem of ambiguity also the development of human computer interface aspects of NLP (Natural Language processing) is the approach of this paper. The intelligent web search with regional language like Bengali is depending upon two major aspect that is CLIA (Cross language information access) and NLP. In our previous work with IIT, KGP we already developed content based CLIA where content based searching in trained on Bengali Corpora with the help of Bengali data dictionary. Here we want to introduce intelligent search because to recognize the sense of meaning of a sentence and it has a better real life approach towards human computer interactions.
<p> In this paper, two crop datasets are investigated. First one is numerical data which is downloaded crop dataset from https://github.com/Shrey-B/AV-Janatahack-Machine-Learning-in-Agriculture obtained from US field data collection. The dataset contains 88858 labeled samples, eight features and three classes. Second one is a collection of crop image data. This dataset is downloaded from https://www.kaggle.com/datasets/aman2000jaiswal/agriculture . Crop image dataset contains 1005 samples having five type of images namely maize, wheat, jute, rice and sugarcane. Each sample crop image consists of 224 × 224 pixels) of all category. </p>
<p> In this paper, two crop datasets are investigated. First one is numerical data which is downloaded crop dataset from https://github.com/Shrey-B/AV-Janatahack-Machine-Learning-in-Agriculture obtained from US field data collection. The dataset contains 88858 labeled samples, eight features and three classes. Second one is a collection of crop image data. This dataset is downloaded from https://www.kaggle.com/datasets/aman2000jaiswal/agriculture . Crop image dataset contains 1005 samples having five type of images namely maize, wheat, jute, rice and sugarcane. Each sample crop image consists of 224 × 224 pixels) of all category. </p>
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