Thermal emissions from skin are an intrinsic property independent of illumination. Therefore the face images captured using thermal IR sensors will be nearly invariant to changes in ambient illumination. The illumination variation problem is one of the well-known problems in face recognition in uncontrolled environment. Infrared imaging sensors have become an area of growing interest. Thermal IR imagery has been suggested as an alternative source of information for detection and recognition of faces, while visual cameras measure the ultra-magnetic energy in the visible spectrum range(0.4-0.7um), sensor in IR cameras respond to the thermal radiation in the infrared spectrum range at 0.72-14.0um. While sacrificing colour recognition, thermal IR face recognition techniques can be used to identify faces when there is little or no control over lighting conditions. To address this serious limitation of IR it is proposedto fusing IR with other forms of facial recognition techniques by combining IR with hyper-spectral face recognition for a better identification system.
AI techniques have sent vast waves in the field of healthcare and medical science, even fueling an active discussion of whether AI doctors will eventually replace human physicians in the future. Our belief is that human doctors and researchers will not be replaced by machines in the future, but AI can definitely assist physicians to make better clinical decisions or even replace human judgement in certain functional fields of healthcare (e.g. radiology). The increasing availability of healthcare data and rapid development of big data analytic methods has made possible the recent successful applications of AI in healthcare. Advised by possible clinical questions, powerful artificial intelligence techniques will not ravel clinically relevant information hidden in the massive amount of data, which in turn can assist clinical decision making. This study centers on how computer-based decision procedures, under the vast umbrella of artificial intelligence (AI), can assist in improving health and health care. Although advanced statistics and machine learning provide the base for AI, there are currently revolutionary advances underway in the sub-field of neural networks. This has created huge excitement in many fields of science, including in medicine and public health. First demonstrations have already appear showing that deep neural networks can perform as well as the best human clinicians in well-defined diagnostic tasks. In addition, AI-based tools are already appearing in health based apps that can be employed on handheld, networked devices such as smart phones. AI is actually a software means computer programs with the capacity to perform operations analogous to learning and decisions-making in humans. This tool is being increasingly applied in the pharmaceutical, medical device, and healthcare sectors to aid various stages of research and development, as well as treatment of patients. AI as a software, and in particular software that incorporates machine learning, which provides the ability to learn from data without rule-based programming, may streamline the process of translating a molecule from original inception to a market-ready product.
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