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Topic Review
Extreme Learning Machine
In the learning paradigms of artificial neural networks, classical algorithms such as back-propagation aim to approach the theories of biological learning through an iterative adjustment of all the hyper-parameters of the hidden layers for each sequence of data. Therefore, without looking at relearning, in actual problems, the tuning process could take days or months under the use of the available ordinary computers for generalization of the neural network over all of the selected samples. But, the fact that a microprocessor is faster than a brain by about twelve million times, denies that these algorithms are capable of responding to the thought of the human brain which takes less seconds to classify or restore new images or sensations. In 2004, ELM gave birth to new learning rules for artificial neural networks. ELM learning rules remove barriers between human biological thought and artificial neural networks by addressing the fact that: "the parameters of the hidden layer do not need to be adjusted, and the only element responsible for the "universal approximation and generalization are the output weights ". Consequently, ELM has been studied through several applications and extends to a multitude of paradigms such as the ensemble, the hybrid and the deep learning and achieved an excellent reputation. Therefore, The aim of this review will be introducing basic theories of ELM.
  • 1.6K
  • 30 Sep 2021
Topic Review
NER&RE Techniques on Clinical Texts
Out of the various text mining tasks and techniques, our goal in this paper is to review the current state-of-the-art in Clinical Named Entity Recognition (NER) and Relationship Extraction (RE)-based techniques. Clinical NER is a natural language processing (NLP) method used for extracting important medical concepts and events i.e., clinical NEs from the data. Relationship Extraction (RE) is used for detecting and classifying the annotated semantic relationships between the recognized entities.
  • 518
  • 30 Sep 2021
Topic Review
Assistive Technology for Dementia People
There has been a significant increase in the number of people diagnosed with dementia (PLWD). With diminishing public health and social care resources, there is substantial need for assistive technology-based devices that support independent living. The term assistive technology (AT) is used to describe electronic devices that can be used to support PLWD’s lifestyles. These devices can improve the living standards of PLWD, encourage independence, and may decrease hospital admission rates. Furthermore, assistive technology can reduce the stress of caring for PLWD. AT devices that support remote assistance of PLWD can play a vital role in mitigating loneliness and stress caused by pandemics, reducing the need for home visits and hospitalization, thus reducing the costs associated with caregiver services. Reducing the risks of virus transmission within care homes are also a major consideration.
  • 416
  • 30 Sep 2021
Topic Review
Artificial Intelligence Marketing for Customer-Relationships
Artificial intelligence marketing (AIM), which is an interdisciplinary research topic, is a disruptive technology that enables machines to automate the process of collecting and processing a massive amount of data and information to create knowledge related to marketing mix. This capability is essential to manifest personalization at scale, which has been impossible through human effort alone. This paper synthesizes the literature and develops an AIM framework to create a quantum leap in customer relationship enhancement, including customer trust, satisfaction, commitment, engagement, and loyalty.
  • 496
  • 29 Sep 2021
Topic Review
The DESMOS Project
“DESMOS”, a novel ecosystem for the interconnection of smart infrastructures, mobile and wearable devices, and applications, to provide a secure environment for visitors and tourists. The presented solution brings together state-of-the-art IoT technologies, crowdsourcing, localization through BLE, and semantic reasoning, following a privacy and security-by-design approach to ensure data anonymization and protection. Despite the COVID-19 pandemic, the solution was tested, validated, and evaluated via two pilots in almost real settings—involving a fewer density of people than planned—in Trikala, Thessaly, Greece. 
  • 597
  • 29 Sep 2021
Topic Review
Data-Driven Predictive Maintenance
Cyber-physical systems in Industry 4.0 are reforming conventional decision-making processes, mainly through integrating entities and functionalities via telecommunication systems and intelligent data processing approaches. This reformulation brings new challenges and increases complexity. Nevertheless, these advancements might provide new solutions for typical problems, such as system failures, and thus, for maintenance approaches. Predictive Maintenance (PdM) is a data-based approach that emerged as a prominent field of research among many existing maintenance approaches. We have three main categories in PdM: model-based prognosis, knowledge-based prognosis, and data-driven prognosis. Data-driven PdM strategies appeared with great prominence and importance both in industry and academia.
  • 809
  • 28 Sep 2021
Topic Review
Machine Learning Approaches in SCRM
Machine learning (ML) has already been implemented within the supply chain risk management (SCRM) field, both in theory and in practice. The applied examples relate primarily to the early identification of production, transport, and supply risks in order to counteract potential supply chain problems quickly. Through the analyzed case studies, we were able to identify the added value that ML integration can bring to the SCRM.
  • 782
  • 28 Sep 2021
Topic Review
AI-Assisted Design-on-Simulation for Life Prediction
Many researchers have adopted the finite-element-based design-on-simulation (DoS) technology for the reliability assessment of electronic packaging. DoS technology can effectively shorten the design cycle, reduce costs, and effectively optimize the packaging structure. However, the simulation analysis results are highly dependent on the individual researcher and are usually inconsistent between them. Artificial intelligence (AI) can help researchers avoid the shortcomings of the human factor. 
  • 1.0K
  • 28 Sep 2021
Topic Review
Simulation of Sunspot Cycles
Numerous systems in nature exhibit oscillatory dynamics suggesting common underlying processes. Without knowing an exact interacting mechanism, predictive modelling applied to known count data of a system can provide a non-statistical solution defining its evolution. A recursive difference equation is used to describe the evolution of sunspots and solar cycles in the discrete time domain. Sunspot count for solar cycle 21 is pulse-like over an 11-year period, definable by the product of a pair of growth and decay logistic difference equations. Oscillatory behaviour of multiple solar cycles 22 to 24 up to 2010 are modelled by stabilizing a delayed logistic difference equation.
  • 768
  • 28 Sep 2021
Topic Review
Radiomics/Deep Learning for Nasopharyngeal Carcinoma
Nasopharyngeal carcinoma (NPC) is one of the most common malignant tumours of the head and neck, and improving the efficiency of its diagnosis and treatment strategies is an important goal. With the development of the combination of artificial intelligence (AI) technology and medical imaging in recent years, an increasing number of studies have been conducted on image analysis of NPC using AI tools, especially radiomics and artificial neural network methods.
  • 703
  • 28 Sep 2021
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