Artificial intelligence can assist providers in a variety of patient care and intelligent health systems. Artificial intelligence techniques ranging from machine learning to deep learning are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Numerous medical data sources are required to perfectly diagnose diseases using artificial intelligence techniques, such as ultrasound, magnetic resonance imaging, mammography, genomics, computed tomography scan, etc. Furthermore, artificial intelligence primarily enhanced the infirmary experience and sped up preparing patients to continue their rehabilitation at home. This article covers the comprehensive survey based on artificial intelligence techniques to diagnose numerous diseases such as Alzheimer, cancer, diabetes, chronic heart disease, tuberculosis, stroke and cerebrovascular, hypertension, skin, and liver disease. We conducted an extensive survey including the used medical imaging dataset and their feature extraction and classification process for predictions. Preferred reporting items for systematic reviews and Meta-Analysis guidelines are used to select the articles published up to October 2020 on the Web of Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, and Psychology Information for early prediction of distinct kinds of diseases using artificial intelligence-based techniques. Based on the study of different articles on disease diagnosis, the results are also compared using various quality parameters such as prediction rate, accuracy, sensitivity, specificity, the area under curve precision, recall, and F1-score.

Artificial intelligence has aided in the advancement of healthcare research. The availability of open-source healthcare statistics has prompted researchers to create applications that aid cancer detection and prognosis. Deep learning and machine learning models provide a reliable, rapid, and effective solution to deal with such challenging diseases in these circumstances. PRISMA guidelines had been used to select the articles published on the web of science, EBSCO, and EMBASE between 2009 and 2021. In this study, we performed an efficient search and included the research articles that employed AI-based learning approaches for cancer prediction. A total of 185 papers are considered impactful for cancer prediction using conventional machine and deep learning-based classifications. In addition, the survey also deliberated the work done by the different researchers and highlighted the limitations of the existing literature, and performed the comparison using various parameters such as prediction rate, accuracy, sensitivity, specificity, dice score, detection rate, area undercover, precision, recall, and F1-score. Five investigations have been designed, and solutions to those were explored. Although multiple techniques recommended in the literature have achieved great prediction results, still cancer mortality has not been reduced. Thus, more extensive research to deal with the challenges in the area of cancer prediction is required.

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It is concluded that Gore-Tex E-PTFE is a new thread augmentation advance in the solution of facial skin irregularities that yields satisfactory and permanent results.

License plate recognition is a fully automated real time technique that has been widely used for identification, theft control and security validation of vehicles. For recognition and extraction of desired regions of the number plate of the vehicle, different algorithms are used. An image processing technology based on license plate recognition (LPR) that is being used to identify vehicles, using neural networks and image co-relation was developed by K. Yilmaz [2]. In this paper, a different novel approach has been presented to increase the quality of the image and to enhance the results for extracting license plate from dull and low intensity images. In the previous technique the recognition rate (percentage of image recognized) reached was 96.64% [2], but now using multithresholding and neural pattern recognition (NPR) techniques together with artificial neural networks, a higher recognition rate of 98.40% has been achieved. Certain problems related to neural networks in the previous research methodology such as blobs extraction, segmentation and character recognition, that inhibit complete extraction of features from number plate of the vehicle were analyzed in this approach. The proposed technique helps to improve the quality of the images and detect the characters or digits of the number plate with a better recognition rate.

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Digital predistortion (DPD) techniques are widely used to linearize of RF power amplifiers. In this article, a memory polynomial-based power has been modeled with memory order of 5 and nonlinearity order of 9. These specifications had been found suitable for advanced long-term evolution (LTE) wireless systems. A suitable DPD model was created based on the least square error minimization. Results for the complementary cumulative distribution function and normalized mean squared error were also computed. The adjacent channel leakage power ratio and error vector magnitude calculations showed that the proposed DPD for LTE system performed within the required limits as desired.

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