A variety of screening approaches have been proposed to diagnose epileptic seizures, using Electroencephalography (EEG) and Magnetic Resonance Imaging (MRI) modalities. Artificial intelligence encompasses a variety of areas, and one of its branches is deep learning. Before the rise of deep learning, conventional machine learning algorithms involving feature extraction were performed. This limited their performance to the ability of those handcrafting the features. However, in deep learning, the extraction of features and classification is entirely automated. The advent of these techniques in many areas of medicine such as diagnosis of epileptic seizures, has made significant advances. In this study, a comprehensive overview of the types of deep learning methods exploited to diagnose epileptic seizures from various modalities has been studied. Additionally, hardware implementation and cloud-based works are discussed as they are most suited for applied medicine.

JP Morgan, IP soft, Microsoft Corp., AWS, FUKOKU (Japan), Oracle Corp., Salesforce, IBM Corp., PALANTIR, Google LLC, INBENTA technologies, Intel, Amazon Web Services Inc., NEXT ITSegmental Analysis: -The Artificial Intelligence (AI) Market in BFSI Sector industry is segmented based on the applications, end-users, and type of products and services it offers. The report provides detailed data on the applications which drive the industry's growth. The report also discusses the products and services and end-users which make a significant contribution to the Artificial Intelligence (AI) Market in BFSI Sector industry revenue. The study also talks about new product developments in the industry.Market Breakdown Data by Types:

The latest report on the global Machine Learning in Healthcare market published by the Market Research Store includes an exhaustive research details about the Machine Learning in Healthcare market incorporating the global industrial conditions, value chain structure, market size, forecast details, along with other minute details about the market. In this latest report, the research analysts have tried to cover the current market scenario owing to the outbreak of the pandemic. Each and every market on the global platform has been affected due to COVID-19. Several big changes have been observed in the market conditions which all have been included in the report. Based on this the forecast analysis and future opportunities of the Machine Learning in Healthcare market has been predicted.

The paper presents the approach for the building of consistent and applicable clinical decision support systems (CDSS) using a data-driven predictive model aimed to resolve a problem of low applicability and scalability of CDSS in real-world applications. The approach is based on the three-stage application of domain-specific and data-driven supportive procedures to integrate into clinical business-processes with higher trust and explainability of the prediction results and recommendations. Within the considered three stages, the regulatory policy, data-driven modes, and interpretation procedures are integrated to enable natural domain-specific interaction with decision-makers with sequential narrowing of the intelligent decision support focus. The proposed methodology enables a higher level of automation, scalability, and semantic interpretability of CDSS. The approach was implemented in software solutions and tested within a case study in T2DM prediction, enabling to improve known clinical scales (such as FINDRISK), keeping the problem-specific reasoning interface similar to existing applications. Such inheritance, together with the three-stages approach, provide higher compatibility of the solution and leads to trust, valid, and explainable application of data-driven solution in real-world cases.

The outbreak of the novel coronavirus (COVID-19) is unfolding as a major international crisis whose influence extends to every aspect of our daily lives. Effective testing allows infected individuals to be quarantined, thus reducing the spread of COVID-19, saving countless lives, and helping to restart the economy safely and securely. Developing a good testing strategy can be greatly aided by contact tracing that provides health care providers information about the whereabouts of infected patients in order to determine whom to test. Countries that have been more successful in corralling the virus typically use a ``test, treat, trace, test'' strategy that begins with testing individuals with symptoms, traces contacts of positively tested individuals via a combinations of patient memory, apps, WiFi, GPS, etc., followed by testing their contacts, and repeating this procedure. The problem is that such strategies are myopic and do not efficiently use the testing resources. This is especially the case with COVID-19, where symptoms may show up several days after the infection (or not at all, there is evidence to suggest that many COVID-19 carriers are asymptotic, but may spread the virus). Such greedy strategies, miss out population areas where the virus may be dormant and flare up in the future. In this paper, we show that the testing problem can be cast as a sequential learning-based resource allocation problem with constraints, where the input to the problem is provided by a time-varying social contact graph obtained through various contact tracing tools. We then develop efficient learning strategies that minimize the number of infected individuals. These strategies are based on policy iteration and look-ahead rules. We investigate fundamental performance bounds, and ensure that our solution is robust to errors in the input graph as well as in the tests themselves.

This paper extends recent work on nonlinear Independent Component Analysis (ICA) by introducing a theoretical framework for nonlinear Independent Subspace Analysis (ISA) in the presence of auxiliary variables. Observed high dimensional acoustic features like log Mel spectrograms can be considered as surface level manifestations of nonlinear transformations over individual multivariate sources of information like speaker characteristics, phonological content etc. Under assumptions of energy based models we use the theory of nonlinear ISA to propose an algorithm that learns unsupervised speech representations whose subspaces are independent and potentially highly correlated with the original non-stationary multivariate sources. We show how nonlinear ICA with auxiliary variables can be extended to a generic identifiable model for subspaces as well while also providing sufficient conditions for the identifiability of these high dimensional subspaces. Our proposed methodology is generic and can be integrated with standard unsupervised approaches to learn speech representations with subspaces that can theoretically capture independent higher order speech signals. We evaluate the gains of our algorithm when integrated with the Autoregressive Predictive Decoding (APC) model by showing empirical results on the speaker verification and phoneme recognition tasks.

For many entrepreneurs, starting their startup is the most significant thing they do. For Rana el Kaliouby, it's one achievement in a series of unpredictable things that this self-described "nice Egyptian girl" has accomplished in her life. In her remarkable book Girl Decoded, el Kaliouby shares her inspiring academic and professional journey. Alongside this, it is an intimate meditation about what it took from her personally to accomplish all that she has. Born in Egypt to conservative Egyptian parents, the family spent several years in Kuwait and eventually fled back to Cairo when Iraq invaded Kuwait.

For many entrepreneurs, starting their startup is the most significant thing they do. For Rana el Kaliouby, it's one achievement in a series of unpredictable things that this self-described "nice Egyptian girl" has accomplished in her life. In her remarkable book Girl Decoded, el Kaliouby shares her inspiring academic and professional journey. Alongside this, it is an intimate meditation about what it took from her personally to accomplish all that she has. Born in Egypt to conservative Egyptian parents, the family spent several years in Kuwait and eventually fled back to Cairo when Iraq invaded Kuwait.

When Gulzira Aeulkhan finally fled China for Kazakhstan early last year, she still suffered debilitating headaches and nausea. She didn't know if this was a result of the guards at an internment camp hitting her in the head with an electric baton for spending more than two minutes on the toilet, or from the enforced starvation diet. Maybe it was simply the horror she had witnessed – the sounds of women screaming when they were beaten, their silence when they returned to the cell. Like an estimated 1.5 million other Turkic Muslims, Gulzira had been interned in a "re-education camp" in north-west China. After discovering that she had watched a Turkish TV show in which some of the actors wore hijabs, Chinese police had accused her of "extremism" and said she was "infected by the virus" of Islamism.

The graph represents a network of 4,768 Twitter users whose tweets in the requested range contained "smartcity OR smartcities", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 24 July 2020 at 00:48 UTC. The requested start date was Friday, 24 July 2020 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 5,000. The tweets in the network were tweeted over the 2-day, 20-hour, 23-minute period from Monday, 20 July 2020 at 09:08 UTC to Thursday, 23 July 2020 at 05:32 UTC.