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Legal Analytics is a blooming field of research that draws attraction from various fields such as computational linguistics, natural language processing, machine learning, and data science. Improvement in text analytics has paved the way to a great level in eCommerce, banking, medical and legal sectors after the incoming of deep learning models. The large quantity of digital information in the legal domain and the challenges faced has made this an important area of legal research. Historically, judges and other legal personnel have frequently been required to manually review legal documents to acquire an inclusive judgment justification. This practice is highly challenging also laborious and time-consuming. Most of the existing works predict the legal outcomes based on case facts, but predicting judgment solely on Casefact gives low efficiency. Automation in judgment prediction systems is at the budding stage. Choosing the appropriate word embedding technique in language modeling, Classifier model and loss functions pave way for better results. Selecting the appropriate learning algorithm for text classification on real-time problems is a great challenge as legal documents are lengthy and unstructured. This paper emphasizes the limitations and important aspects of different components of NLP such as tokenization, word embeddings, and classifiers which makes the computer interpret and extract maximum contextual information from documents to provide the best performance for assisting litigants for verdict prediction of countries that use common law system. It also reviews different ideas applicable to legal case facts in a broad manner. [ABSTRACT FROM AUTHOR]

Applications of deep learning to solve complex problems related to computer vision and human level control system has been successfully implemented in real life. On the other hand, deep leaning based systems can also cause harm to the privacy and security of the personnel or a system. One of the threats that is powered by deep learning and AI is deep fake technique which is used to create fake images, video and audios that are not identified by human visual or audio system. This makes it difficult for human to distinguish between the original data and the deep fake data. This paper aims at discussing the techniques or algorithm require to create deepfake and to detect the deepfake in audiodata. [ABSTRACT FROM AUTHOR]

Medical imaging has seen substantial improvements not only in image procurement but also in terms of interpretational methodologies. The practice of using technologies such as image processing and artificial intelligence has shown remarkable results in the detection, classification, and localization of human bone fractures. Breakage or cracks in the bone occur when an external force is applied to a bone that is significantly larger than the bone's tolerance, which may lead to dislocation requiring extensive treatment. However, manual examination by experts is a complex and arduous process with a high chance of false detection. Many approaches under Artificial Intelligence and its subsets are proposed to facilitate fracture diagnosis and prevent misinterpretation, including Deep Learning and Convolutional Neural Networks (CNN). This paper is a comprehensive study of the various techniques and models designed using image processing approaches and the advances in machine learning and Deep Learning that have demonstrated improved performance in the precise detection of fractures in human bones. [ABSTRACT FROM AUTHOR]

Machine Learning (ML) / Deep Learning (DL) has been applied in the development of the healthcare domain in recent years due to its increased potential and performance. It provides better support to clinicians and patients in various aspects, such as automating medical billing, clinical decision support, disease diagnosis, etc. It also plays a vital role in pattern recognition, online recommendation, finance, robotics, information security, etc. It can learn and realize the pattern from the healthcare records and gives better predictions about the disease. The goal of this review paper is to provide an overview of: (i) Machine Learning concepts; (ii) Machine Learning applications, techniques and its purpose in the healthcare domain; (iii) challenges and issues in maintaining health records; and (iv) finally, it helps to minimize the research gap with best decision support system using Machine Learning. [ABSTRACT FROM AUTHOR]

Now a days, air pollution is a critical environmental issue in developing countries due to urbanization and industrialization. The pollution in air causes severe health issues for human beings, affects plants and animals due to acid rain, creates loss in economy, agricultural production and commercial production. In order to prevent or control it, the air quality forecasting necessitates. Accurate air quality prediction is a prominent task to be done to control the pollution level and to take countermeasures for securing the human life. The complex, uncertain and non-linear nature of the air quality pollutants impose complications in achieving accurate forecasting. The advent of artificial intelligence techniques helps to improve the air pollution forecasting results compared to traditional statistical methods at the great extent. This paper focuses the complications of air pollution, significance of air quality prediction, air quality index, and reviews the applications of machine learning and deep learning methods in air quality prediction. Literature shows that the machine learning and deep learning methods along with feature engineering and optimization techniques guarantees a promising air quality prediction. [ABSTRACT FROM AUTHOR]

Environmental conditions continue to debilitate each year on account of advancement of development and expanding messy discharges from enterprises and cars. Notwithstanding the way that air is a fundamental resource always; various people are indifferent with the reality of air tainting or have actually seen the issue. Among different kinds of contaminations, air contamination and water contamination are the most hazardous and extreme, causing environmental change and perilous sicknesses. Air contamination is one among the principle players, prompting worldwide environmental change which has caused abnormalities inside the temperature design, crops creation and water contamination has prompted coming of fresher sicknesses causing broad demolition. In this paper, IOT Device, Data Science and Deep learning Technologies are used to validate the air and water pollution from the factory by the factory management and pollution control board. If any pollution violation occurs in factory, then the IOT device intimate the factory management as well as pollution control board regarding this violation. The pollution control board is provided with each and every detail of pollution emission from factory as a graph. [ABSTRACT FROM AUTHOR]