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It is the age of the social web, where people express themselves by giving their opinions about various issues, from their personal life to the world&rsquo ; s political issues. This process generates a lot of opinion data on the web that can be processed for valuable information, and therefore, semantic annotation of opinions becomes an important task. Unfortunately, existing opinion annotation schemes have failed to satisfy annotation challenges and cannot even adhere to the basic definition of opinion. Opinion holders, topical features and temporal expressions are major components of an opinion that remain ignored in existing annotation schemes. In this work, we propose OpinionML, a new Markup Language, that aims to compensate for the issues that existing typical opinion markup languages fail to resolve. We present a detailed discussion about existing annotation schemes and their associated problems. We argue that OpinionML is more robust, flexible and easier for annotating opinion data. Its modular approach while implementing a logical model provides us with a flexible and easier model of annotation. OpinionML can be considered a step towards &ldquo ; information symmetry&rdquo ; . It is an effort for consistent sentiment annotations across the research community. We perform experiments to prove robustness of the proposed OpinionML and the results demonstrate its capability of retrieving significant components of opinion segments. We also propose OpinionML ontology in an effort to make OpinionML more inter-operable. The ontology proposed is more complete than existing opinion ontologies like Marl and Onyx. A comprehensive comparison of the proposed ontology with existing sentiment ontologies Marl and Onyx proves its worth.

1. --Application of drone and sensors in advanced farming: the future smart farming technology --2. --Development and research of a greenhouse monitoring system --3. --A cloud-computing model for implementing smart agriculture --4. --Application of conversational artificial intelligence for farmer's advisory and communication --5. -- Theuse of an intelligent fuzzy logic controller to predict the global warming effect on agriculture: the case of the chickpea cicer arietinum L. --6. --Using machine learning algorithms for mapping soil macronutrient elements variability with digital environmental data in an alluvial plain --7. -- Asmart IoT framework for soil fertility enhancement assisted via deep neural networks --8. --Plant disease detection with the help of advanced imaging sensors --9. --Artificial intelligence-aided phenomics in high throughput stress phenotyping of plants --10. --Plant disease detection using hybrid deep learning architecture in smart agriculture application --11. --Classification of coffee leaf diseases through image processing techniques --12. -- Theuse of artificial intelligence to model oil extraction yields from seeds and nuts --13. --Applications of artificial intelligence in pest management --14. --Applying clustering technique for rainfall received by different district of Maharashtra state --15. --Predicting rainfall for Aurangabad division of Maharashtra by applying auto-regressive moving average model (ARIMA) using Python programming.

According to statistics of the American Cancer Society, in 2015, there are about 91,270 American adults diagnosed with melanoma of the skin. For the European Union, there are over 90,000 new cases of melanoma annually. Although melanoma only accounts for about 1% of all skin cancers, it causes most of the skin cancer deaths. Melanoma is considered one of the fastest-growing forms of skin cancer, and hence the early detection is crucial, as early detection is helpful and can provide strong recommendations for specific and suitable treatment regimens. In this work, we propose a method to detect melanoma skin cancer with automatic image processing techniques. Our method includes three stages: pre-process images of skin lesions by adaptive principal curvature, segment skin lesions by the colour normalisation and extract features by the ABCD rule. We provide experimental results of the proposed method on the publicly available International Skin Imaging Collaboration (ISIC) skin lesions dataset. The acquired results on melanoma skin cancer detection indicates that the proposed method has high accuracy, and overall, a good performance: for the segmentation stage, the accuracy, Dice, Jaccard scores are 96.6%, 93.9% and 88.7%, respectively; and for the melanoma detection stage, the accuracy is up to 100% for a selected subset of the ISIC dataset.

This book focuses on both the design and implementation of AI-based approaches for systems that aid in monitoring and combating pandemics such as occurred with COVID-19. These systems are based on the Internet of Medical Things (IoMT), using sensor networks and other advanced technologies. The book examines different aspects of AI and IoMT.