The automated identification of colors and their intensity from sensor images is a significant interest in field deployable and cost-effective smartphone-based water monitoring solutions. artificial Intelligence (AI) ...
The automated identification of colors and their intensity from sensor images is a significant interest in field deployable and cost-effective smartphone-based water monitoring solutions. artificial Intelligence (AI) has been extensively used in automated imageprocessingapplications specifically when there is no recognizable pattern in the image data. AI considerably outperforms conventional detection techniques using image analysis in such scenarios. In the present work, we have developed an artificial Intelligence (AI) based mobile application platform, that can capture the sensor image using an inbuilt smartphone camera, identify the presence of sensing parameters and classify the level of the same based on color intensity recognized in the training sets of the captured image using deep convolutional neuralnetworks (CNN). As a test case, we have implemented the developed AI-based mobile application platform to monitor the water quality for bacterial contamination where the sensor images are classified into the presence or absence of bacteria based on visual appearance. Our method is seen to detect the presence with a ~99.99% accuracy which is an improvement in the detection accuracy of the already established method in this regard where manual visual inspection is carried out to classify the sensor images. The considerable enhancement in detection accuracy can be attributed to the elimination of subjective decision making which inevitable consequence of human intervention in the reported test case.
Face recognition is one of the most active areas of research from the past two decades. Attempts are being made to understand how a human recognizes another human face. It is widely accepted that facial recognition ca...
Face recognition is one of the most active areas of research from the past two decades. Attempts are being made to understand how a human recognizes another human face. It is widely accepted that facial recognition can be based on structural information and nonstructural / spatial details. In the present study, he is applying differential observations using Eigen / docking characteristics of many built-in facial features and artificialneuralnetworks. The proposed method aims to obtain a facial feature by reducing facial features such as eyes, nose, mouth, and face depending on the importance of facial features. The face recognition system developed in this paper will inform the human face and assess the current percentage of accuracy. Therefore, this work is for human facial recognition and includes a percentage of facial expressions. The implementation of this function also offers many applications such as photography, bio-metric in bank Lockers, etc.
数千年来，舌诊在中医医学（TCM，Traditional Chinese Medicine）中一直都拥有举足轻重的作用。舌质特徵，与身体器官的健康有关，对辨证和治疗选择有很大的帮助。一般民众通常并不具备辨别舌头异常状况的专业能力，大多数人认为给医...
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数千年来，舌诊在中医医学（TCM，Traditional Chinese Medicine）中一直都拥有举足轻重的作用。舌质特徵，与身体器官的健康有关，对辨证和治疗选择有很大的帮助。一般民众通常并不具备辨别舌头异常状况的专业能力，大多数人认为给医生看诊就是唯一且精确的方法。不过，医师若仅是以传统主观的判断看诊流程，有可能不易进行精确的诊断；近年来，随着深度卷积神经网络（CNN）应用、人工智慧框架技术的急速发展，¬¬更完善的方式－应该是设计一套可以「藉由图像来检测舌苔及舌质各式样貌并判断症状的系统」。有监於前述所探讨，本计画所开发的系统采用开放式平台来实现，期望透过使用者的网路摄像头自行拍摄出的影像利用网际网路，将影像回传伺服端进行分析，先筛选出「舌象部分」再加以分析，并结合事先所蒐集的中医相关资讯，再将分析结果回传至使用者端，当作病例的参考，同时将其资料储存进伺服端，以供未来进行纪录的调阅以及追踪使用。使用者端可利用任何具有浏览器功能的装置，连接本计画所开发的网站做使用。此计画所开发的网页将采用响应式网页设计（Responsive Web Design，RWD），使得使用者无论在行动装置或电脑，都能得到最佳的视觉体验。
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