TREES RECOMMENDATION IN AGROFORESTRY ECOSYSTEM USING NLP

Agroforestry farming is one of the challenging sectors to grow the crops or farming or variety of trees from the ancient days due to erosion and desertification. This Culminating Experience Project explored how recommendation system can be developed and used in agroforestry. The research questions are Q1. What methods can be used to improve the accuracy and reliability of soil-based agroforestry tree species recommendation systems? Q2. How can agroforestry tree species recommendation systems be tailored to the needs of different stakeholders, such as smallholder farmers or agribusinesses? Q3. What will be the top three, tree recommendations using natural language processing based on varying soil content? Data was collected from two datasets the Agroforestry Database and the European Commission\u27s extension of the periodic Land Use/Land Cover Area Frame Survey. The findings are: 1) Various Natural language processing techniques such as cosine similarity, count vectorization, and TF-IDF can significantly enhance the system\u27s ability to analyze and process large amounts of Data collection, validation, and monitoring to improve the accuracy and reliability of soil-based agroforestry tree species recommendation systems. 2) Cosine similarity achieve to recommend tree species based on soil test report data collected by the European Commission\u27s extension of the periodic Land Use/Land Cover Area Frame Survey and tailored based on various soil properties helps the smallholders, stake holders, farmers to best decisions to increase their growth. 3) Natural language processing techniques such as cosine similarity, count vectorization, and TF-IDF can be employed to analyze soil data and identify the tree species that are most appropriate for different soil types.The conclusions are: 1) The system\u27s ability to analyze and process large volumes of data accurately, and the recommendations provided by the system can become more effective and reliable. 2) The system\u27s recommendations can become more relevant, practical, and acceptable, leading to higher adoption rates and better outcomes.3) Develop The proposed agroforestry tree species recommendation system provides top three trees recommendations using cosine similarity, TFIDF and Count vectorization techniques. Furthermore, areas for future research that emerged from this study include the need to improve the sustainability and productivity of agroforestry practices, enhance ecosystem services, and promote economic, social benefits and identify additional strategies for improving the accuracy and reliability by getting additional feedback about the trees recommendation from the stakeholders and farmers directly in design and development

CosSIF: Cosine similarity-based image filtering to overcome low inter-class variation in synthetic medical image datasets

Crafting effective deep learning models for medical image analysis is a complex task, particularly in cases where the medical image dataset lacks significant inter-class variation. This challenge is further aggravated when employing such datasets to generate synthetic images using generative adversarial networks (GANs), as the output of GANs heavily relies on the input data. In this research, we propose a novel filtering algorithm called Cosine Similarity-based Image Filtering (CosSIF). We leverage CosSIF to develop two distinct filtering methods: Filtering Before GAN Training (FBGT) and Filtering After GAN Training (FAGT). FBGT involves the removal of real images that exhibit similarities to images of other classes before utilizing them as the training dataset for a GAN. On the other hand, FAGT focuses on eliminating synthetic images with less discriminative features compared to real images used for training the GAN. Experimental results reveal that employing either the FAGT or FBGT method with modern transformer and convolutional-based networks leads to substantial performance gains in various evaluation metrics. FAGT implementation on the ISIC-2016 dataset surpasses the baseline method in terms of sensitivity by 1.59% and AUC by 1.88%. Furthermore, for the HAM10000 dataset, applying FABT outperforms the baseline approach in terms of recall by 13.75%, and with the sole implementation of FAGT, achieves a maximum accuracy of 94.44%.Comment: 18 pages, 20 figure

Towards Stable Co-saliency Detection and Object Co-segmentation

In this paper, we present a novel model for simultaneous stable co-saliency detection (CoSOD) and object co-segmentation (CoSEG). To detect co-saliency (segmentation) accurately, the core problem is to well model inter-image relations between an image group. Some methods design sophisticated modules, such as recurrent neural network (RNN), to address this problem. However, order-sensitive problem is the major drawback of RNN, which heavily affects the stability of proposed CoSOD (CoSEG) model. In this paper, inspired by RNN-based model, we first propose a multi-path stable recurrent unit (MSRU), containing dummy orders mechanisms (DOM) and recurrent unit (RU). Our proposed MSRU not only helps CoSOD (CoSEG) model captures robust inter-image relations, but also reduces order-sensitivity, resulting in a more stable inference and training process. { Moreover, we design a cross-order contrastive loss (COCL) that can further address order-sensitive problem by pulling close the feature embedding generated from different input orders.} We validate our model on five widely used CoSOD datasets (CoCA, CoSOD3k, Cosal2015, iCoseg and MSRC), and three widely used datasets (Internet, iCoseg and PASCAL-VOC) for object co-segmentation, the performance demonstrates the superiority of the proposed approach as compared to the state-of-the-art (SOTA) methods

Image Cosegmentation via Saliency-Guided Constrained Clustering with Cosine Similarity

Cosegmentation jointly segments the common objects from multiple images. In this paper, a novel clustering algorithm, called Saliency-Guided Constrained Clustering approach with Cosine similarity (SGC3), is proposed for the image cosegmentation task, where the common foregrounds are extracted via a one-step clustering process. In our method, the unsupervised saliency prior is utilized as a partition-level side information to guide the clustering process. To guarantee the robustness to noise and outlier in the given prior, the similarities of instance-level and partition-level are jointly computed for cosegmentation. Specifically, we employ cosine distance to calculate the feature similarity between data point and its cluster centroid, and introduce a cosine utility function to measure the similarity between clustering result and the side information. These two parts are both based on the cosine similarity, which is able to capture the intrinsic structure of data, especially for the non-spherical cluster structure. Finally, a K-means-like optimization is designed to solve our objective function in an efficient way. Experimental results on two widely-used datasets demonstrate our approach achieves competitive performance over the state-of-the-art cosegmentation methods