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Over the past few years, neutrosophy has gained an exponential growth and has attracted a good number of researchers especially those who focus on soft computing based uncertainty computation. This paper presents the various techniques in neutrosophy. The various techniques are discussed lucidly which help a naïve researcher in this field to understand the on-going researches and establish a strong base. We have summarized the previous work carried out in the field of neutrosophic logic, set, measure, and also classification techniques in neutrosophy and the relevant research work has been discussed. Further, various applications in the field of neutrosophy are elaborated. The major contributions of the existing research in neutrosophy is reviewed and presented from different perspectives. The development of newer algorithms for solving the problems of neutrosophy will provide impetus to the existing research in this field. [ABSTRACT FROM AUTHOR]

Due to the increasing use of information technologies by biomedical experts, researchers, public health agencies, and healthcare professionals, a large number of scientific literatures, clinical notes, and other structured and unstructured text resources are rapidly increasing and being stored in various data sources like PubMed. These massive text resources can be leveraged to extract valuable knowledge and insights using machine learning techniques. Recent advancement in neural network-based classification models has gained popularity which takes numeric vectors (aka word representation) of training data as the input to train classification models. Better the input vectors, more accurate would be the classification. Word representations are learned as the distribution of words in an embedding space, wherein each word has its vector and the semantically similar words based on the contexts appear nearby each other. However, such distributional word representations are incapable of encapsulating relational semantics between distant words. In the biomedical domain, relation mining is a well-studied problem which aims to extract relational words, which associates distant entities generally representing the subject and object of a sentence. Our goal is to capture the relational semantics information between distant words from a large corpus to learn enhanced word representation and employ the learned word representation for various natural language processing tasks such as text classification. In this article, we have proposed an application of biomedical relation triplets to learn word representation through incorporating relational semantic information within the distributional representation of words. In other words, the proposed approach aims to capture both distributional and relational contexts of the words to learn their numeric vectors from text corpus. We have also proposed an application of the learned word representations for text classification. The proposed approach is evaluated over multiple benchmark datasets, and the efficacy of the learned word representations is tested in terms of word similarity and concept categorization tasks. Our proposed approach provides better performance in comparison to the state-of-the-art GloVe model. Furthermore, we have applied the learned word representations to classify biomedical texts using four neural network-based classification models, and the classification accuracy further confirms the effectiveness of the learned word representations by our proposed approach. [ABSTRACT FROM AUTHOR]

• Review on Machine Learning approaches for Cardiovascular Disease. • Enormous Data being generated from various healthcare related devices. • Study of recent development in Healthcare Informatics. • Review of latest literature on diseases pertaining to Cardio. There is a considerable rise in cardiovascular diseases in the world. It is pertinently essential to make cardiovascular prediction accurate to the maximum. A forecast based on machine learning techniques can be beneficial in detecting cardiovascular disease (CVD) with maximum precision and accuracy. The disease's effective prediction helps in early diagnosis, which cuts down the mortality rate. A health history and the causes of heart disease require the efficient detection and prediction of CVD. Data analytics is beneficial for making predictions based on a massive amount of data, and it aids health clinics in disease prognosis. Regularly, a large volume of patient-related data is maintained. The information gathered can be used to forecast the emergence of upcoming diseases. Our study presents a detailed comparative study of Cardiovascular Disease by comparing the various machine learning techniques mainly comprising of classification and predictive algorithms. The study shows an in-depth analysis of around forty-one papers related to cardiovascular disease by using machine learning techniques. This study evaluates the selected publications rigorously and identifies gaps in the available literature, making it useful for researchers to develop and apply in clinical fields, primarily on datasets related to heart disease. The current study will aid medical practitioners in predicting heart threats ahead of time, allowing them to take preventative measures. [ABSTRACT FROM AUTHOR]

In recent years, the application of various recommendation algorithms on over-the-top (OTT) platforms such as Amazon Prime and Netflix has been explored, but the existing recommendation systems are less effective because either they fail to take an advantage of exploiting the inherent user relationship or they are not capable of precisely defining the user relationship. On such platforms, users generally express their preferences for movies and TV shows and also give ratings to them. For a recommendation system to be effective, it is important to establish an accurate and precise relationship between the users. Hence, there is a scope of research for effective recommendation systems that can define a relationship between users and then use the relationship to enhance the user experiences. In this research article, we have presented a hybrid recommendation system that determines the degree of friendship among the viewers based on mutual liking and recommendations on OTT platforms. The proposed enhanced model is an effective recommendation model for determining the degree of friendship among viewers with improved user experience. [ABSTRACT FROM AUTHOR]

In this paper the authors introduce the "Theory of α-Sustainable Development" which is developed using fuzzy set theory. The existing concept and definition of "Sustainable Development" is correct and well understood by the world, but is not very precisely defined. In our "Theory of α-Sustainable Development" we say that every development (D) is a sustainable development up to certain extent depending upon the fuzzy measure 'α' of the amount of sustainability of D. We propose double categorization of every hybrid-pillar (HP) Development for better evaluation: Categorization in Type-1 and Categorization in Type-2. For a single-pillar development D the value of the fuzzy measure α is a non-negative number in [0,1], and the same is also true for a hybrid pillar (2P or 3P) development D while categorizing under Type-2. However, while categorizing the HP-Developments under Type-1, the value of α is an ordered pair (α 1 , α 2) where α 1 and α 2 are in [0,1]. Every development D is graded to be qualified as one of the five categories of sustainable development: SSD, GSD, PSD, WSD and NSD. In 'Theory of α-Sustainable Development' the existing core notion of 'Sustainable Development' is neither compromised nor diluted. The theory initially discusses about 'single-pillar Sustainable Development' and then about 'multi-pillar Sustainable Development' (also called by Hybrid Pillar Sustainable Development). The notion of 'α-Sustainable Development' will be very much useful to the corresponding regulatory bodies to improve the practices of dealing a development D with the existing notion of Yes-No type of sustainable development. Mathematically, the core aim of this paper is to grade a development D in the continuous range [0,1] instead of the existing notion of grading in the discrete range {0, 1}. This work deals only with those type of developments which are completely constructed, not about reviewing the interim progress of them. The "Theory of α-Sustainable Development" is an open proposal to the 2030 Agenda meeting, and will surely enrich the policy of ESD for educating the present and future generation people and thus to retain the sustainability of our planet as a whole. [ABSTRACT FROM AUTHOR]

The highly infectious COVID-19 critically affected the world that has stuck millions of citizens in their homes to avoid possible spreading of the disease. Researchers in different fields are continually working to develop vaccines and prevention strategies. However, an accurate forecast of the outbreak can help control the pandemic until a vaccine is available. Several machine learning and deep learning-based approaches are available to forecast the confirmed cases, but they lack the optimized temporal component and nonlinearity. To enhance the current forecasting frameworks' capability, we proposed optimized long short-term memory networks (LSTM) to forecast COVID-19 cases and reduce mean absolute error. For the optimization of LSTM, we applied bat algorithm. Furthermore, to tackle the premature convergence and local minima problem of BA, we proposed an enhanced variant of BA. The proposed version utilized Gaussian adaptive inertia weight to control the individual velocity in the entire swarm. In addition, we substitute random walk with the Gaussian walk to observe the local search mechanism. The proposed LSTM examines the personal best solution with the swarm's local best and preserves the optimal solution by combining the Gaussian walk. To evaluate the optimized LSTM, we compared it with the non-optimal version of LSTM, recurrent neural network, gated recurrent units, and other recent state-of-the-art algorithms. The experimental results prove the superiority of the optimized LSTM over other recent algorithms by obtaining 99.52 % accuracy. [ABSTRACT FROM AUTHOR]