A number of industrial and marketing entities are beginning to realize that they are members of the wider community and therefore must understand their environmental responsibility. Efforts are being made to achieve environmental objectives as well as profit related objectives. Companies are thus integrating environmental issues into their corporate culture. The green marketing activities run the hazard of misleading the consumers or industry. Such firms have to ensure that they do .not breach any of the regulations or laws dealing with environmental marketing. This article examines how the firms can use their environmental policy as a marketing tool or how can they remain simply environmentally responsible.
Image segmentation is an application area of computer vision and digital image processing that partitions a digital image into multiple image regions or segments. This process involves the extraction of a set of contours from the input digital image in such a manner that pixels belonging to a region share some common characteristics or computed properties, such as color, texture, or intensity. The application domain of image segmentation is widespread, and includes video surveillance, object detection, traffic control systems, and medical imaging. The application of image segmentation techniques in the field of medical imaging can be further subcategorized into virtual surgery simulation, diagnosis, study of anatomical structures, measurement of tissue volumes, location of tumors and other pathologies. In this study, we have proposed two new Convolutional Neural Network (CNN)-based models: (a) S-Net and (b) Attention S-Net (SA-Net) to perform image segmentation tasks in the field of medical imaging, especially to generate segmentation masks for brain tumours if present in brain Medical Resonance Imaging (MRI). Both proposed models were developed by considering U-Net as the base architecture. The newly proposed models have leveraged the concept of 'Merge Block' to infuse both the local and global context, and 'Attention Block' to focus on the region of interest having a specific object. Additionally, it uses techniques, such as data augmentation to utilize the available annotated samples more efficiently. The proposed models achieved a Dice Similarity Coefficient (DSC) measure of 0.78 and 0.80 for the High-Grade Glioma (HGG) and Low-Grade Glioma (LGG) datasets, respectively.
Advancements in computing and storage technologies have significantly contributed to the adoption of deep learning (DL)-based models among machinelearning (ML) experts. Although a generic model can be used in the search fora near-optimal solution in any problem domain, what makes these DL modelscontext-sensitive is the combination of the training data and the hyperparameters. Due to the lack of inherent explainability of DL models the HyperparameterOptimization (HPO) or tuning specific to each model is a combination of art,science, and experience. In this article, we have explored various existing methods or ways to identify the optimal set of values for the hyperparameters specificto the DL models along with the techniques to realize those methods in real-lifesituations. The article also includes a detailed comparative study among variousstate-of-the-art HPO techniques using the Keras Tuner tuning toolkit and highlights the observations describing how the model performance can be improvedby applying various HPO techniques.
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