Continuous-time series is essential for different modern application areas, e.g. healthcare, automobile, energy, finance, Internet of things (IoT) and other related areas. Different application needs to process as well as analyse a massive amount of data in time series structure in order to determine the data-driven result, for example, financial trend prediction, potential probability of the occurrence of a particular event occurrence identification, patient health record processing and so many more. However, modeling real-time data using a continuous-time series is challenging since the dynamical systems behind the data could be a differential equation. Several research works have tried to solve the challenges of modelling the continuous-time series using different neural network models and approaches for data processing and learning. The existing deep learning models are not free from challenges and limitations due to diversity among different attributes, behaviour, duration of steps, energy, and data sampling rate. This paper has described the general problem domain of time series and reviewed the challenges of modelling the continuous time series. We have presented a comparative analysis of recent developments in deep learning models and their contribution to solving different difficulties of modelling the continuous time series. We have also identified the limitations of the existing neural network model and open issues. The main goal of this review is to understand the recent trend of neural network models used in a different real-world application with continuous-time data.

The healthcare sector experienced transition as a result of COVID-19, and this shift will last for years to come. Despite industry obstacles, the pandemic has led to a growing acceptance of new technology among patients, providers, and healthcare practitioners. These technologies lessen workplace stress and improved patient care. But there is still hope for change. Many medical schools now include the use of technology in their curriculum; the new generation of medical practitioners has a distinct relationship with technology.

If you are looking for what's next in ML, here are a few recent trends in machine learning research & industry. Machine Learning has become quite popular and everyone in tech is aware of it by now. It is even being adopted into the industry at a fast pace in different fields. To put it into perspective, here are some numbers from a research article. The global machine learning market is projected to grow from $15.50 billion in 2021 to $152.24 billion in 2028 at a CAGR of 38.6% in the forecast period.

The increasing population of elderly people is associated with the need to meet their increasing requirements and to provide solutions that can improve their quality of life in a smart home. In addition to fear and anxiety towards interfacing with systems; cognitive disabilities, weakened memory, disorganized behavior and even physical limitations are some of the problems that elderly people tend to face with increasing age. The essence of providing technology-based solutions to address these needs of elderly people and to create smart and assisted living spaces for the elderly; lies in developing systems that can adapt by addressing their diversity and can augment their performances in the context of their day to day goals. Therefore, this work proposes a framework for development of a Personalized Intelligent Assistant to help elderly people perform Activities of Daily Living (ADLs) in a smart and connected Internet of Things (IoT) based environment. This Personalized Intelligent Assistant can analyze different tasks performed by the user and recommend activities by considering their daily routine, current affective state and the underlining user experience. To uphold the efficacy of this proposed framework, it has been tested on a couple of datasets for modelling an average user and a specific user respectively. The results presented show that the model achieves a performance accuracy of 73.12% when modelling a specific user, which is considerably higher than its performance while modelling an average user, this upholds the relevance for development and implementation of this proposed framework.

The population of elderly people has been increasing at a rapid rate over the last few decades and their population is expected to further increase in the upcoming future. Their increasing population is associated with their increasing needs due to problems like physical disabilities, cognitive issues, weakened memory and disorganized behavior, that elderly people face with increasing age. To reduce their financial burden on the world economy and to enhance their quality of life, it is essential to develop technology-based solutions that are adaptive, assistive and intelligent in nature. Intelligent Affect Aware Systems that can not only analyze but also predict the behavior of elderly people in the context of their day to day interactions with technology in an IoT-based environment, holds immense potential for serving as a long-term solution for improving the user experience of elderly in smart homes. This work therefore proposes the framework for an Intelligent Affect Aware environment for elderly people that can not only analyze the affective components of their interactions but also predict their likely user experience even before they start engaging in any activity in the given smart home environment. This forecasting of user experience would provide scope for enhancing the same, thereby increasing the assistive and adaptive nature of such intelligent systems. To uphold the efficacy of this proposed framework for improving the quality of life of elderly people in smart homes, it has been tested on three datasets and the results are presented and discussed.

"The global Cloud Machine Learning market is a comprehensive and trustworthy study of various market viewpoints, including key economies, major …

Quality healthcare is one of the most important factors, how individuals perceive their quality of life. In most countries, alongside the economy, it is one of the major political issues. In some countries, the healthcare delivery organization is a part of the national identity. Currently, the healthcare industry is going through a transformation, and to succeed in the increasingly competitive environment, organizations need to make significant investments in processes and technologies to cut down costs, increase access to care delivery, and improve medical care. Globally, majority of economies are facing similar challenges such as rising healthcare costs, changing demographics, resource gap (i.e. even the demand is increasing, there is a global shortage of clinicians), increasing focus on quality, etc. Driving current healthcare trends are the costs of providing care and the outcome of this care.

Global Machine Learning Market was valued US$ 2.5 Bn in 2017 and is expected to reach US$ 12.3 Bn by 2026, at a CAGR of 22.4 % during forecast period. Global Machine learning Market includes a complete range of services, solutions and techniques interconnected closely to artificial intelligence, which is performing statistical analysis of input data to recognize its current and future relationship and performance. Machine learning is making use of huge amount of input data to deliver better analytical output while enhancing workflow for different industry verticals, Machine learning is incorporating variety of services which offers machine learning tools by cloud computing services. Global machine learning market is influenced by numerous factors which includes growth in demand for improved application areas, development associated with artificial intelligence & cognitive computing market, lack of trained professionals, and effect of developing economies. All these factors are collectively creating opportunities for market growth, each factor is expected to have its certain impact on the machine learning market share. Increasing automation and advanced technology are expected to boost the market growth during the forecast period.

In the recent times, automatic detection of human personality traits has received a lot of attention. Specifically, multimodal personality trait prediction has emerged as a hot topic within the field of affective computing. In this paper, we give an overview of the advances in machine learning based automated personality detection with an emphasis on deep learning techniques. We compare various popular approaches in this field based on input modality, the computational datasets available and discuss potential industrial applications. We also discuss the state-of-the-art machine learning models for different modalities of input such as text, audio, visual and multimodal. Personality detection is a very broad topic and this literature survey focuses mainly on machine learning techniques rather than the psychological aspect of personality detection.

In a timely new paper, Young and colleagues discuss some of the recent trends in deep learning based natural language processing (NLP) systems and applications. The focus of the paper is on the review and comparison of models and methods that have achieved state-of-the-art (SOTA) results on various NLP tasks such as visual question answering (QA) and machine translation. In this comprehensive review, the reader will get a detailed understanding of the past, present, and future of deep learning in NLP. In addition, readers will also learn some of the current best practices for applying deep learning in NLP. Natural language processing (NLP) deals with building computational algorithms to automatically analyze and represent human language. NLP-based systems have enabled a wide range of applications such as Google's powerful search engine, and more recently, Amazon's voice assistant named Alexa.