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Many studies have demonstrated the sensitivities of crop yield to a changing climate, a major challenge for the agricultural research community is to relate these findings to the wider societal concern with food security. Apart from few exceptions, the likely impacts of climate change on agricultural sector in the future are not understood in any great depth. There are many concerns as to how changes in temperature, rainfall and atmospheric Carbon Dioxide concentrations will interact in relation to agricultural productivity. The present article is an attempt to distil about the likely effects of climate change on food security and nutrition in coming decades. The consequences of climate change on various important aspects of agriculture are discussed and summarized. The article also discusses the analysis on the possible mitigation measures and adaptations for agriculture production in the future climate change scenarios.

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Breast cancer is the most common kind of cancer in people. The term cancer was first used to describe the illness in Egypt around 1600 BC. Despite the fact that since then, research and studies have been conducted to mitigate the effects of this illness, it is still regarded as one of the most lethal diseases of all time due to mortality from breast cancer. Modern medicine has recently created numerous methods and strategies for the early detection of breast cancer. Various datasets, hybrid machine learning techniques, decision trees, KNN, SVM, naive bays, etc. are used. Most of technology rely on state-of-the-art equipment like deep learning algorithms and medical image processing, as well as machine learning techniques. Many previous machine learning experiments have been done. Decision trees, KNN, SVM, naive bays, and other machine learning techniques perform better in their own fields. The classification of breast cancer, however, is currently being done using a recently created approach. Deep learning is an emerging method. Machine learning's limitations are surmounted using deep learning. Deep learning techniques, including convolution neural networks, recurrent neural networks, deep belief networks, and others, are frequently employed in data science. Performance-wise, deep learning algorithms beat machine learning methods. The most appealing aspects of the images are captured. CNN is employed in our investigation to categorize the photographs. In essence, CNN is the most widely used method for categorizing photographs. The goal of this study is to identify the most effective breast cancer early detection and life-saving paradigm.

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