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• Traditional health care system fails to predict the exact patient health information. • For the above issue, an IoT sensor with AI is used to predict exact patient details. • Healthcare mobile application is used to collect the patient's information easily. • The gathered patient details are processed according to the IGDBN. Internet of Things (IoT) and Artificial Intelligence (AI) play a vital role in the upcoming years to improve the assistance systems. The IoT devices utilize several sensor devices that able to collect a large volume of data in different domains which is processed by AI techniques to make the decision about the assistance problems. Among several applications, in this work, IoT with AI is used to examine the healthcare sectors to improve patient assistance and patient care in the future direction. Traditional health care assistance system fails to predict the exact patient health information and needs which reduces the accuracy of patient assistance process. For these issues, an IoT sensor with AI is used to predict the exact patient details such as fitness tracker, medical reports, health activity, body mass, temperature, and other health care information which helps to choose the right assistance process. Healthcare mobile application is used to achieve this goal and collect the patient's information. This information is shared in the cloud environment, which is accessed and processed by applying the optimized machine learning techniques. The gathered patient details are processed according to the iterative golden section optimized deep belief neural network (IGDBN). The introduced network examines the patient's details from the previous health information which helps to predict the exact patient health condition in the future direction. The efficiency of IoT sensor with an AI-based health assistance prediction process is developed using MATLAB tool. Excellence is determined in terms of precision (99.87), loss error (0.045), simple matching coefficient (99.71%), Matthews correlation coefficient (99.10%) and accuracy (99.86%). [ABSTRACT FROM AUTHOR]

• Now a days the major oral health issues of man is crucial an area of research. • This paper traces classification, segmentation and pre-processing of dental images. • Histogram based adaptive approach is used in the radiographic X-ray 2d dental Image. • This is useful to distinguish the foreground teeth & the regions of background bones. In the present scenario the major oral health issues of man is crucial an area of research. The Data mining techniques, image processing, and Computational intelligence techniques are playing a vital role in biomedical research. Dental image processing helps to improve the early detection and classification of the diagnostic process to make accurate decisions. The radiographic 2d dental image is widely utilized for analytic thinking of several dental disorders. In this paper traces the complete steps such as classification and segmentation as well as pre-processing of dental images has been carried out. In the pre-processing, histogram based on adaptive approach is used to stretch the contrast and equalize the brightness throughout the radiographic X-ray 2d dental Image. This operation is useful to distinguish the foreground teeth and the regions of background bones. Separation of dental 2d images into regions corresponding to the objects is a fundamental step of segmentation. The hybrid graph cut segmentation is used to segment the oral cavity and its tissues. In this research deep learning based convolution neural network (CNN) has been used to process the dental image and shows promising outcomes with 97.07% accuracy. [ABSTRACT FROM AUTHOR]

• To analyze the tooth decay and acid erosion from European teeth biomedical data. • To collect information from children having age 5. • The teeth decay activities are monitored by EMOCA algorithm with RTRFNN. • To analyze the changes & characteristics of children biomedical teeth data. Now-a-days most of the children faced tooth decay and acid erosion problem in their teeth because of continuous bacterial infection, acid segregation, presents of food particles in teeth and so on. Especially, children are more affected by tooth decay, that leads to create severe problem like gingivitis, teeth loss and teeth pain. Due to the importance of tooth decay it needs to predict in earlier condition for eliminating children teeth problem such anorexia and bulimia disorders. Hence the bacterial infection of teeth is critical to be predicted from affected teeth. So, in this paper we analyze the tooth decay and acid erosion from European teeth biomedical data portal which collects information from children having age 5. The teeth decay activities are monitored by evolutionary multi-objective cuckoo feature selection (EMOCA) algorithm with Ruzzo–Tompa optimized regulatory feedback neural network (RTRFNN) that successfully analyze the changes and characteristics of children teeth biomedical teeth data. The introduced method effectively evaluates children tooth data before making the final decision about tooth decay and acid erosion. Then the excellence of the system is evaluated with the help of the experimental results, Ruzzo–Tompa optimized regulatory feedback neural network recognize the abnormal dental features with 99.22% of accuracy. [ABSTRACT FROM AUTHOR]

• This paper presents the HHMRF to predict the vertebral tumor for the early detection. • The importance of this research is to implement a strategy for detection of tumors. • It has been used to implement the threshold techniques in MRI images on the IoMT. • This method attains the better performance on segmentation of lumbar spinal stenosis. Recently, prediction evaluation of the metastatic spine tumors in therapy is considered a significant area of research. Further, for spinal clinical diagnoses, a large amount of image data from different modalities is often used and interchangeably analyzed based on the automatic vertebra identification. It includes recognition of vertebral positions and recognition in several image modalities. Due to the differences in MR or CT images appearance or shape/size of the vertebras, the identification is however difficult in the present conventional medical research. The segmentation of vertebral tumors that are manually performed by MRI is an important and time-consuming process by the conventional research algorithms. The accuracy of identification of the size and location of spine tumors plays a major role in effective tumor diagnosis and treatment. Therefore, this paper presents the Hierarchical Hidden Markov Random Field Model (HHMRF) to predict the vertebral tumor for the early detection and diagnosis treatment in an effective and efficient manner. The importance of this research is to implement a state-of-the-art strategy for detection of tumors using HHMRF and threshold techniques in MRI images on the Internet of Medical Things Platform (IoMT). HHMRF can coordinate the final section of vertebral tumor homogeneous areas of tissue while preserving the edges between different tissue constituents more effectively using mathematical computation. The proposed method attains the state-of-the-art performance on the diagnosis and segmentation of lumbar spinal stenosis using deep neural network and experimentally analyzed with 97.44% accuracy and 97.11% efficiency ratio on IoMT platform whereas proposed HHMRF achieves 98.5% high precision ratio compared to other existing TDCN (78.2%), DLA (81.6%), M-CNN (78.9%), and DCE-MRI (80.2%) methods. [ABSTRACT FROM AUTHOR]