Microorganisms are widely distributed in the human daily living environment. They play an essential role in environmental pollution control, disease prevention and treatment, and food and drug production. The identification, counting, and detection are the basic steps for making full use of different microorganisms. However, the conventional analysis methods are expensive, laborious, and time-consuming. To overcome these limitations, artificial neural networks are applied for microorganism image analysis. We conduct this review to understand the development process of microorganism image analysis based on artificial neural networks. In this review, the background and motivation are introduced first. Then, the development of artificial neural networks and representative networks are introduced. After that, the papers related to microorganism image analysis based on classical and deep neural networks are reviewed from the perspectives of different tasks. In the end, the methodology analysis and potential direction are discussed.

Soniox already offers a very accurate speech recognition transcription engine for English language, and the company now released an audio AI solution that automatically annotates the transcribed audio with real-world entities and their contextual information, providing an augmented representation of the audio stream in real-time and low-latency. Soniox offers this novel technology in Soniox APIs, iOS app and web application. Soniox already offers a very accurate speech recognition transcription engine for English language, and the company now released an audio AI solution that automatically annotates the transcribed audio with real-world entities and their contextual information, providing an augmented representation of the audio stream in real-time and low-latency. Soniox offers this novel technology in Soniox APIs, iOS app and web application. As anyone who as tried transcription engines based on speech recognition, approximately 10% of the resulting accuracy depends on context, which is where all automatic systems fail most spectacularly (automatic closed captions provide the most hilarious and exasperating examples).

In this paper we present a technique for procedurally generating 3D maps using a set of premade meshes which snap together based on designer-specified visual constraints. The proposed approach avoids size and layout limitations, offering the designer control over the look and feel of the generated maps, as well as immediate feedback on a given map's navigability. A prototype implementation of the method, developed in the Unity game engine, is discussed, and a number of case studies are analyzed. These include a multiplayer game where the method was used, together with a number of illustrative examples which highlight various parameterizations and generation methods. We argue that the technique is designer-friendly and can be used as a map composition method and/or as a prototyping system in 3D level design, opening the door for quality map and level creation in a fraction of the time of a fully human-based approach.

Geometric deep learning (GDL), which is based on neural network architectures that incorporate and process symmetry information, has emerged as a recent paradigm in artificial intelligence. GDL bears particular promise in molecular modeling applications, in which various molecular representations with different symmetry properties and levels of abstraction exist. This review provides a structured and harmonized overview of molecular GDL, highlighting its applications in drug discovery, chemical synthesis prediction, and quantum chemistry. Emphasis is placed on the relevance of the learned molecular features and their complementarity to well-established molecular descriptors. This review provides an overview of current challenges and opportunities, and presents a forecast of the future of GDL for molecular sciences.

COVID-19 is the disease evoked by a new breed of coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Recently, COVID-19 has become a pandemic by infecting more than 152 million people in over 216 countries and territories. The exponential increase in the number of infections has rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) and machine learning (ML), which can assist the healthcare sector in providing quick and precise COVID-19 diagnosis. Therefore, this paper provides a comprehensive review of the most recent DL and ML techniques for COVID-19 diagnosis.

For over hundreds of millions of years, sea turtles and their ancestors have swum in the vast expanses of the ocean. They have undergone a number of evolutionary changes, leading to speciation and sub-speciation. However, in the past few decades, some of the most notable forces driving the genetic variance and population decline have been global warming and anthropogenic impact ranging from large-scale poaching, collecting turtle eggs for food, besides dumping trash including plastic waste into the ocean. This leads to severe detrimental effects in the sea turtle population, driving them to extinction. This research focusses on the forces causing the decline in sea turtle population, the necessity for the global conservation efforts along with its successes and failures, followed by an in-depth analysis of the modern advances in detection and recognition of sea turtles, involving Machine Learning and Computer Vision systems, aiding the conservation efforts.

The emergence of IoT technology and recent advancement in sensor networks enabled transportation systems to a new dimension called Intelligent Transportation System (ITS). Due to increased usage of vehicles and communication among entities in road traffic scenarios, the amount of raw data generation in ITS is huge. This raw data are to be processed to infer contextual information and provide new services related to different modes of road transport such as traffic signal management, accident prediction, object detection etc.,. To understand the importance of context, this article aims to study context-awareness in the Intelligent Transportation System. We present a review on prominent applications developed in the literature concerning context - awareness in the intelligent transportation system. The objective of this survey article is (i) to highlight context and its features in ITS (ii) to address the applicability of modelling techniques and reasoning approaches in ITS (iii) to shed light on impact of IoT and machine learning in ITS development. I. Introduction The Intelligent Transportation System (ITS) plays a significant role in the transformation of conventional transportation by enhancing user convenience and safety, ensuring efficiency and improving transportation networks. Many countries around the globe have widely accepted and started implementing intelligent transportation systems as a solution to the current road traffic management practices [1]. Research in ITS aims to afford services relating to different modes of road transport and assist users to be better informed on road safety and make safer, coordinated, comfort and smarter use of transportation [2]. The traditional road transportation currently in practise encompasses more of human to human communication. Imparting intelligence to it increases many technical challenges in enabling human to machine communication and machine to machine communication. Yet thanks to advanced technologies such as electronic sensor technology, wireless sensor network, mobile computing, cloud computing, Internet of Things, computer vision, data transmission technology and intelligent control have enabled large scale deployment of intelligent transportation in reality [56].

Much work has been dedicated to the exploration of Multi-Agent Reinforcement Learning (MARL) paradigms implementing a centralized learning with decentralized execution (CLDE) approach to achieve human-like collaboration in cooperative tasks. Here, we discuss variations of centralized training and describe a recent survey of algorithmic approaches. The goal is to explore how different implementations of information sharing mechanism in centralized learning may give rise to distinct group coordinated behaviors in multi-agent systems performing cooperative tasks.

Tourism in India plays a quintessential role in the country's economy with an estimated 9.2% GDP share for the year 2018. With a yearly growth rate of 6.2%, the industry holds a huge potential for being the primary driver of the economy as observed in the nations of the Middle East like the United Arab Emirates. The historical and cultural diversity exhibited throughout the geography of the nation is a unique spectacle for people around the world and therefore serves to attract tourists in tens of millions in number every year. Traditionally, tour guides or academic professionals who study these heritage monuments were responsible for providing information to the visitors regarding their architectural and historical significance. However, unfortunately this system has several caveats when considered on a large scale such as unavailability of sufficient trained people, lack of accurate information, failure to convey the richness of details in an attractive format etc. Recently, machine learning approaches revolving around the usage of monument pictures have been shown to be useful for rudimentary analysis of heritage sights. This paper serves as a survey of the research endeavors undertaken in this direction which would eventually provide insights for building an automated decision system that could be utilized to make the experience of tourism in India more modernized for visitors.

Multimodal deep learning systems which employ multiple modalities like text, image, audio, video, etc., are showing better performance in comparison with individual modalities (i.e., unimodal) systems. Multimodal machine learning involves multiple aspects: representation, translation, alignment, fusion, and co-learning. In the current state of multimodal machine learning, the assumptions are that all modalities are present, aligned, and noiseless during training and testing time. However, in real-world tasks, typically, it is observed that one or more modalities are missing, noisy, lacking annotated data, have unreliable labels, and are scarce in training or testing and or both. This challenge is addressed by a learning paradigm called multimodal co-learning. The modeling of a (resource-poor) modality is aided by exploiting knowledge from another (resource-rich) modality using transfer of knowledge between modalities, including their representations and predictive models. Co-learning being an emerging area, there are no dedicated reviews explicitly focusing on all challenges addressed by co-learning. To that end, in this work, we provide a comprehensive survey on the emerging area of multimodal co-learning that has not been explored in its entirety yet. We review implementations that overcome one or more co-learning challenges without explicitly considering them as co-learning challenges. We present the comprehensive taxonomy of multimodal co-learning based on the challenges addressed by co-learning and associated implementations. The various techniques employed to include the latest ones are reviewed along with some of the applications and datasets. Our final goal is to discuss challenges and perspectives along with the important ideas and directions for future work that we hope to be beneficial for the entire research community focusing on this exciting domain.