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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 learningbased 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.INDEX TERMS Wireless infrared imaging sensor, infra-red sensor, principal component analysis gray level covariance matrix, machine learning based neural networks.

This paper presents a hybrid ontology-XLNet sentiment analysis classification approach for sentence-level aspects. The main objective of the proposed approach allows discovering user social data considering the extracted in-depth inference about sentiment depending on the context. Thus, in this paper, we investigate the contribution of utilizing the lexicalized ontology to improve the aspect-based sentiment analysis performance through extracting the indirect relationships in user social data. The XLNet model is utilized for extracting the neighboring contextual meaning and concatenating it with each embeddings word to produce a more comprehensive context and enhance feature extraction. In the proposed approach, Bidirectional Long Short Term Memory (Bi-LSTM) networks are used for classifying the aspects in online user reviews. Various experiments considering Adverse Drug Reactions (ADRs) discovery are conducted on six drug-related social data real-world datasets to evaluate the performance of the proposed approach using several measures. Obtained experimental results show that the proposed approach outperformed other tested state-of-the-art related approaches through improving feature extraction of unstructured social media text and accordingly improving the overall accuracy of sentiment classification. A significant accuracy of 98% and F-measure of 96.4% are achieved by the proposed ADRs aspect-based sentiment analysis approach.

A vertebral tumor is a category of spinal tumor which influence the bones or vertebrae of the spine in the human body system. The Spinal tumors that start inside the spinal string or the covering of the spinal line (dura) are called spinal rope tumors. Furthermore, the tumors that influence the vertebrae have frequently spread (metastasized) from the malignant growths in different parts of the body which is erratic with increasing the precision outcomes with the presence of medical instruments. Moreover, there are a few types of tumors that start inside the bones of the spine, for instance, such as chordoma, chondrosarcoma, osteosarcoma, plasmacytoma and Ewing's sarcoma. A vertebral tumor can influence the neurological capacity by pressing on the spinal string or nerve roots closer. In this paper, the vertebral tumor is analysed using Heuristic Hock Transformation Based Gautschi's-Blur Model (HHTGM) as a numerical approach. As these tumors developed inside the bone, they may likewise lead to torment, vertebral breaks or spinal precariousness which has been further examined in this research using Internet of Medical things (IoMT) Platform. Whether harmful or not, a vertebral tumor can be hazardous and cause perpetual incapacity has been tentative and experimentally analysed in accordance with HLC, CTM, LM, and CFRM methods, moreover HHTGM in this research explored the best results with 95.8% efficiency and 98.66 % of precision when compared with these methods. INDEX TERMS Gautschi's-Blur model, IoMT, cervical spine lumbar vertebral, malignant growth.

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Annotation is considered one of the main applications that semantic web applies. The idea beyond annotation focused on adding metadata to existing information which facilitates machines dealing with data that have meanings and can be readable. Semantic annotation is one of the techniques used for the enrichment of web content semantically, which facilitates writing comments and evaluate previously annotated resources that can lead to better search results. Our framework aims to enrich ontology via embedding data directly to ontology in order to have completed and accurate data.

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