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Topic Review
Application of Machine Learning in Traumatic Brain Injury
One of the main challenges in traumatic brain injury (TBI) patients is to achieve an early and definite prognosis. Despite the recent development of algorithms based on artificial intelligence for the identification of these prognostic factors relevant for clinical practice, the literature lacks a rigorous comparison among classical regression and machine learning (ML) models. The utility of comparing traditional regression modeling to ML is highlighted here, particularly when using a small number of reliable predictor variables after TBI. The dataset of clinical data used to train ML algorithms will be publicly available to other researchers for future comparisons. 
  • 679
  • 21 Mar 2022
Topic Review
Building a Super Smart Nation
Globally, countries are increasingly facing challenges regarding their national future post the COVID-19 pandemic with respect to decreasing and aging populations; dwindling workforces; trade wars due to restricted movement of goods, people, and services; and overcoming economic development and societal problems. 
  • 722
  • 21 Mar 2022
Topic Review
Hyperspectral Remote Sensing
Hyperspectral imaging is an incorporation of the modern imaging system and traditional spectroscopy technology. Unmanned aerial vehicle (UAV) hyperspectral imaging techniques have recently emerged as a valuable tool in agricultural remote sensing, with tremendous promise for many application such as weed detection and species separation
  • 8.0K
  • 21 Mar 2022
Topic Review
Near-Infrared Spectroscopy Coupled to Hyperspectral Imaging
Near-infrared (800–2500 nm; NIR) spectroscopy coupled to hyperspectral imaging (NIR-HSI) has greatly enhanced its capability and thus widened its application and use across various industries. This non-destructive technique that is sensitive to both physical and chemical attributes of virtually any material can be used for both qualitative and quantitative analyses.
  • 1.2K
  • 18 Mar 2022
Topic Review
Deep Learning Algorithms in Agriculture
The field of agriculture is one of the most important fields in which the application of deep learning still needs to be explored, as it has a direct impact on human well-being. In particular, there is a need to explore how deep learning models can be used as a tool for optimal planting, land use, yield improvement, production/disease/pest control, and other activities. The vast amount of data received from sensors in smart farms makes it possible to use deep learning as a model for decision-making in this field. In agriculture, no two environments are exactly alike, which makes testing, validating, and successfully implementing such technologies much more complex than in most other industries. 
  • 945
  • 18 Mar 2022
Topic Review
Deep Learning Algorithms and Their Applications
Deep learning is a type of machine learning that uses artificial neural networks to mimic the human brain. It uses machine learning methods such as supervised, semi-supervised, or unsupervised learning strategies to learn automatically in deep architectures and has gained much popularity due to its superior ability to learn from huge amounts of data.
  • 1.8K
  • 17 Mar 2022
Topic Review
Theoretical Background of Explainable Artificial Intelligence
Artificial intelligence (AI) and machine learning (ML) have recently been radically improved and are now being employed in almost every application domain to develop automated or semi-automated systems. To facilitate greater human acceptability of these systems, explainable artificial intelligence (XAI) has experienced significant growth over the last couple of years with the development of highly accurate models but with a paucity of explainability and interpretability. XAI methods are mostly developed for safety-critical domains worldwide, deep learning and ensemble models are being exploited more than other types of AI/ML models, visual explanations are more acceptable to end-users and robust evaluation metrics are being developed to assess the quality of explanations.
  • 1.6K
  • 16 Mar 2022
Topic Review
Internet of Things in the Industry Revolution 4.0
Researchers offers a wide range of information on Industry 4.0, finds research gaps and recommends future directions. Seven research questions are addressed: (i) What are the contributions of WSN in IR 4.0? (ii) What are the contributions of IoT in IR 4.0? (iii) What are the types of WSN coverage areas for IR 4.0? (iv) What are the major types of network intruders in WSN and IoT systems? (v) What are the prominent network security attacks in WSN and IoT? (vi) What are the significant issues in IoT and WSN frameworks? and (vii) What are the limitations and research gaps in the existing work? Researchers mainly focuses on the solutions and new techniques to automate Industry 4.0 and analyzed over 130 articles from 2014 until 2021. The entry covers several aspects of Industry 4.0, from the designing phase to security needs, from the deployment stage to the classification of the network, the difficulties, challenges, and future directions.
  • 3.7K
  • 16 Mar 2022
Topic Review
Agri-Food Traceability System User Intention
Scientists believed the outbreak of COVID-19 could be linked to the consumption of wild animals, so food safety and hygiene have become the top concerns of the public. An agri-food traceability system becomes very important in this context because it can help the government to trace back the entire production and delivery process in case of food safety concerns. The traceability system is a complicated digitalized system because it integrates information and logistics systems. Previous studies used the technology acceptance model (TAM), information systems (IS) success model, expectation confirmation model (ECM), or extended model to explain the continuance intention of traceability system users.
  • 1.3K
  • 16 Mar 2022
Topic Review
Developing IoT Artifacts in a MAS Platform
The Internet of Things (IoT) is a computational paradigm where a massive number (perhaps billions) of ordinary objects are endowed with interconnection capabilities, making them able to communicate and cooperate with other (surrounding) devices, generally via the Internet.. The Internet of Things (IoT) is a growing computational paradigm where all kinds of everyday objects are interconnected, forming a vast cyberphysical environment at the edge between the virtual and the real world. Since the emergence of the IoT, Multi-Agent Systems (MAS) technology has been successfully applied in this area, proving itself to be an appropriate paradigm for developing distributed, intelligent systems containing sets of IoT devices. However, this technology still lacks effective mechanisms to integrate the enormous diversity of existing IoT devices systematically.
  • 734
  • 15 Mar 2022
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