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Recently, artificial intelligence applications in magnetic resonance imaging have been applied in several clinical studies. The analysis of brain tumors without human intervention is considered a significant area of research because the extracted brain images need to be optimized using a segmentation algorithm that is highly resilient to noise and cluster size sensitivity problems and automatically detects the region of interest (ROI). In this paper, an improved orthogonal gamma distribution-based machine-learning approach is used to analyze the under- segments and over-segments of brain tumor regions to automatically detect abnormalities in the ROI. Further data imbalances due to improper edge matching in the abnormal region is sampled by matching the edge coordinates and sensitivity, and the selectivity parameters are measured using the machine learning algorithm. The benchmark medical image database was collected and analyzed to validate the efficiency and accuracy of the optimal automatic detection in tumor and non-tumor regions. The mean error rate of the algorithm was determined using a mathematical formulation. The system is evaluated based on experimental results that showed the method of orthogonal gamma distribution with the machine learning approach attained an accuracy of 99.55% in detecting brain tumors. This research contributes to the field of brain abnormality detection and analysis without human intervention in the health care sector.

Now-a-days image processing placed an important role for recognizing various diseases such as breast, lung, and brain tumors in earlier stage for giving the appropriate treatment. Presently, most cancer diagnosis worked according to the visual examination process with effectively. Human visual reviewing of infinitesimal biopsy pictures is exceptionally tedious, subjective, and conflicting due to between and intra-onlooker varieties. In this manner, the malignancy and it’s compose will be distinguished in a beginning time for finish treatment and fix. This brain tumor classification system using machine learning-based back propagation neural networks (MLBPNN) causes pathologists to enhance the exactness and proficiency in location of threat and to limit the entomb onlooker variety. Moreover, the technique may assist doctors with analyzing the picture cell by utilizing order and bunching calculations by recoloring qualities of the phones. The different picture preparing steps required for disease location from biopsy pictures incorporate procurement, upgrade, and division; include extraction, picture portrayal, characterization, and basic leadership. In this paper, MLBPNN is analyzed with the help of infra-red sensor imaging technology. Then, the computational multifaceted nature of neural distinguishing proof incredibly diminished when the entire framework is deteriorated into a few subsystems. The features are extracted using fractal dimension algorithm and then the most significant features are selected using multi fractal detection technique to reduce the complexity. This imaging sensor is integrated via wireless infrared imaging sensor which is produced to transmit the tumor warm data to a specialist clinician to screen the wellbeing condition and for helpful control of ultrasound measurements level, especially if there should arise an occurrence of elderly patients living in remote zones.

Currently, reliable data transfer, and energy management have been considered as a significant research challenge in the underwater acoustic sensor networks (UWASN) owing to high packet loss, limited ratio of bandwidth with significant incur of energy, network life time with high propagation delay, less precision with high data hold time and so on. Energy saving and maintaining quality of service (QoS) is more important for UWASN owing to QoS application necessity and limited sensor nodes. To address this issue, several existing algorithms such as adaptive data forwarding algorithms, QoS-based congestion control algorithms and several methodologies were proposed with high throughput and less network lifetime as well as the less utilization of energy in UWASN by choosing sensor nodes data based on data transfer and link reliability. However, all the conventional algorithms have fixed data hold time, which incurs more end-to-end delay with less reliability of data and consumption of high energy due to high data transfer reachability. This high end research proposes adaptive energy aware quality of service (AEA-QoS) algorithm for reliable data delivery by formulating discrete times stochastic control process and deep learning techniques for UWSAN to overcome these issues. The proposed algorithm has been validated with conventional state-of-the-art methods and results show that the proposed approach exhibits its effectiveness in terms of less network overhead and propagation delay with high throughput and less energy consumption for every reliable packet transmission.

According to the survey on various health centres, smart log-based multi access physical monitoring system determines the health conditions of humans and their associated problems present in their lifestyle. At present, deficiency in significant nutrients leads to deterioration of organs, which creates various health problems, particularly for infants, children, and adults. Due to the importance of a multi access physical monitoring system, children and adolescents’ physical activities should be continuously monitored for eliminating difficulties in their life using a smart environment system. Nowadays, in real-time necessity on multi access physical monitoring systems, information requirements and the effective diagnosis of health condition is the challenging task in practice. In this research, wearable smart-log patch with Internet of Things (IoT) sensors has been designed and developed with multimedia technology. Further, the data computation in that smart-log patch has been analysed using edge computing on Bayesian deep learning network (EC-BDLN), which helps to infer and identify various physical data collected from the humans in an accurate manner to monitor their physical activities. Then, the efficiency of this wearable IoT system with multimedia technology is evaluated using experimental results and discussed in terms of accuracy, efficiency, mean residual error, delay, and less energy consumption. This state-of-the-art smart-log patch is considered as one of evolutionary research in health checking of multi access physical monitoring systems with multimedia technology.

The past few decades have witnessed a steep increase in image data analysis for lung cancer, leading to huge repositories in the area of research in the medical sector. Content Based medical Image Retrieval (CBMIR) methods for lung cancer have been tried with the objective of facilitating access to image data. Many research works have been developed in content based medical image retrieval. But the techniques have the drawback of low efficiency and high computation cost. Image segmentation, extraction and classification methods of various kinds was taken upusing traditional methodswhich involves extraction of a specific region of interest and given to medical experts for diagnosis. The extracted region of interest region provides information useful for the for diagnosis of the disease. But the segmentation methods have some limitations such as flat valleys, noise sensitive and computational expensive which lead to reduction in the entire system performance. This is addressed by animproved watershed histogram thresholding using the probabilistic neural networks (IWHT-PNN) approach. The algorithm introduced outperforms the existing techniques improving the segmentation ratio and recognition accuracy of lung cancer which can be validated using experimental analysis. [ABSTRACT FROM AUTHOR]

Alzheimer's Neurodegenerative Disease is understood neurological issue, which is influenced cerebrum cells are causes the intellectual decrease and memory misfortune, the sickness begins gentle and deteriorates. There is a serious necessity to identify Alzheimer's disease at an earlier stage, therefore that the appropriate treatment can initiate early. The foremost causes for Alzheimer's diseases are low blood flow and brain activity. So, the serious Alzheimer disease has been recognized by several existing methodologies but they are fails to recognize the disease in the earlier stage. Thus, in this work use Single Photon Emission Nuclear Tomographic Imaging(SPECT) for find the Alzheimer's diseases. The collected SPECT image noise is removed by applying Lucy-Richardson method and affected region is segmented by using Prolong adaptive exclusive analytical Atlas (PAEA). After this segmentation process Regional Atrophy Analysis (RAA) is apply for find the values of Gray Matters (GM) and White Matters (WM) regions in SPECT image. Finally Artificial Neural Network is applying for classification process. The experimental results shows that the promising outcomes in term of accuracy, sensitivity, and specificity. [ABSTRACT FROM AUTHOR]

• The proposed framework incorporates the IoT paradigm for transmission and classifying redundancies. • Data handling process is classified under transmission by regulating transmission flow regardless of the density and time. • Both processes are assimilated for handling unpredictable sensitive medical data transmission using IoT devices. The Internet of Things (IoT), uses communication technologies and intelligent computing to improve data analysis and computation accuracy, which is essential to the healthcare industry. The sensitivity and requirement for precision in managing and processing medical data make it a difficult task for diagnosing patients. To handle medical data in a healthcare scenario based on the IoT, a Data Classification and Analysis Framework (DCAF) is proposed in this research. The proposed framework seeks to improve the precision and dependability of handling and processing medical data, which are essential components of biomedical signal processing. By reducing errors and accelerating the processing of medical data, the framework can help in improving the accuracy of medical diagnosis and therapy, which is a key objective of biomedical signal processing and control. The goal of this framework is to increase the reliability of message delivery by concentrating on data classification and analysis. The earlier procedure is in charge of decreasing the volume of medical data transfers and ensuring the accuracy and delivery of consent data. The framework's latter process is in charge of allotting suitable processing time slots to increase delivery speed and lower data handling faults. A linear decision-making process based on accessibility, sequence order, and accuracy constraints and conditions supports the framework's joint process. Utilizing measures such as an accuracy ratio 0f 93.09 %, message delivery rate of 0.949 %, error with a range of 0.052 %, classification of 73.81 ms, and analysis time over fluctuating flow instances and analysis slots, the suggested framework's performance is validated. [ABSTRACT FROM AUTHOR]

In the recent past, Internet of Things (IoT) plays a significant role in different applications such as health care, industrial sector, defense and research etc.… It provides effective framework in maintaining the security, privacy and reliability of the information in internet environment. Among various applications as mentioned health care place a major role, because security, privacy and reliability of the medical information is maintained in an effective way. Even though, IoT provides the effective protocols for maintaining the information, several intermediate attacks and intruders trying to access the health information which in turn reduce the privacy, security and reliability of the entire health care system in internet environment. As a result and to solve the issues, in this research Learning based Deep-Q-Networks has been introduced for reducing the malware attacks while managing the health information. This method examines the medical information in different layers according to the Q-learning concept which helps to minimize the intermediate attacks with less complexity. The efficiency of the system has been evaluated with the help of experimental results and discussions. [ABSTRACT FROM AUTHOR]