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Submitted by: Muhammad Arif Mahmood
Additive manufacturing with an emphasis on 3D printing has recently become popular due to its exceptional advantages over conventional manufacturing processes. However, 3D printing process parameters are challenging to optimize, as they influence the properties and usage time of printed parts. Therefore, it is a complex task to develop a correlation between process parameters and printed parts’ properties via traditional optimization methods. A machine-learning technique was recently validated to carry out intricate pattern identification and develop a deterministic relationship, eliminating the need to develop and solve physical models. In machine learning, artificial neural network (ANN) is the most widely utilized model, owing to its capability to solve large datasets and strong computational supremacy.
Submitted by: Jose Garcia Rodriguez
Dementia is a syndrome that is characterised by the decline of different cognitive abilities. A high rate of deaths and high cost for detection, treatments, and patients care count amongst its consequences. Although there is no cure for dementia, a timely diagnosis helps in obtaining necessary support, appropriate medication, and maintenance, as far as possible, of engagement in intellectual, social, and physical activities. The early detection of Alzheimer Disease (AD) is considered to be of high importance for improving the quality of life of patients and their families. In particular, Virtual Reality (VR) is an expanding tool that can be used in order to assess cognitive abilities while navigating through a Virtual Environment (VE).
Submitted by: Hongtian Chen
For ensuring the safety and reliability of high-speed trains, fault diagnosis (FD) technique plays an important role. Benefiting from the rapid developments of artificial intelligence, intelligent FD (IFD) strategies have obtained much attention in the field of academics and applications, where the qualitative approach is an important branch.
Submitted by: Stefanus Tao Hwa Kieu
The recent developments of deep learning support the identification and classification of lung diseases in medical images.
The problem of autonomous navigation of a ground vehicle in unstructured environments is both challenging and crucial for the deployment of this type of vehicle in real-world applications. We present a review on the recent contributions in the roboticsliterature adopting learning-based methods to solve the problem of environment perception andinterpretation with the final aim of the autonomous context-aware navigation of ground vehicles inunstructured environments.
Submitted by: Theodosios Sapounidis
Simulators are part of educational robotics, which easily and quickly enable the user to engage virtually with the development and programming of robots through GUIs. Using a simulator means that it is not necessary to deal exclusively with real robots that might have a significant cost. Hence, simulators are a useful tool that might save resources and assist the educational process.
Submitted by: Sanobar Dar
When people communicate, information is sent, received, and interpreted between the sender  and the receiver. The information exchange often results in a closed loop, where a back and forth information transfer happens between sender and receiver . This, we refer to as an “interaction”. Rather than over a single, sequential channel, this information is often transmitted using multiple channels at once. The multiplicity of channels reduces the risk of interruptions, more so since the information channels are used in parallel, thereby increasing the seamlessness of the process. The redundancy afforded by multichannel communication increases the overall reliability of the communication, and because the decoding is distributed over a larger number of modalities and decoding modules such as sight, sound, and touch, the overall effort is reduced, even though in physical interaction there is a greater number of channels to decode.
Submitted by: Ievgeniia Kuzminykh
Over the last decade, the area of electroencephalography (EEG) witnessed a progressive move from high-end large measurement devices, relying on accurate construction and providing high sensitivity, to miniature hardware, more specifically wireless wearable EEG devices. While accurate, traditional EEG systems need a complex structure and long periods of application time, unwittingly causing discomfort and distress on the users. Given their size and price, aside from their lower sensitivity and narrower spectrum band(s), wearable EEG devices may be used regularly by individuals for continuous collection of user data from non-medical environments. This allows their usage for diverse, nontraditional, non-medical applications, including cognition, BCI, education, and gaming. Given the reduced need for standardization or accuracy, the area remains a rather incipient one, mostly driven by the emergence of new devices that represent the critical link of the innovation chain.
Submitted by: Mohammed Elmogy
Histopathology refers to the examination by a pathologist of biopsy samples. Histopathology images are captured by a microscope to locate, examine, and classify many diseases, such as different cancer types. They provide a detailed view of different types of diseases and their tissue status. These images are an essential resource with which to define biological compositions or analyze cell and tissue structures. This imaging modality is very important for diagnostic applications.The analysis of histopathology images is a prolific and relevant research area supporting disease diagnosis. In this paper, the challenges of histopathology image analysis are evaluated. An extensive review of conventional and deep learning techniques that have been applied in histological image analyses is presented. This entry summarizes many current datasets and highlights important challenges and constraints with recent deep learning techniques, alongside possible future research avenues. Despite the progress made in this research area so far, it is still a significant area of open research because of the variety of imaging techniques and disease-specific characteristics. 
Submitted by: Patricia Takako Endo
As part of SDG, the members of the UN aim to end epidemics of neglected tropical diseases by 2030. These include wide range communicable diseases that prevail in tropical and subtropical conditions. These diseases are present in over 149 countries worldwide and are a significant burden on health systems and economies. One major category of neglected tropical disease are arthropod-borne viruses or arboviruses including West Nile virus, yellow fever, dengue, chikungunya and Zika. Arboviruses spread rapidly and as they present very similar symptoms, it is hard to diagnose and select the best treatment. The use of machine learning for the diagnosis and prognosis of these diseases has become increasingly common however there is a paucity of research on deep learning and associated decision support platforms for frontline staff. 
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