Breast cancer is the most lethal type of cancer for all women worldwide. At the moment, there are no effective techniques for preventing or curing breast cancer, as the source of the disease is unclear. Early diagnosis is a highly successful means of detecting and managing breast cancer, and early identification may result in a greater likelihood of complete recovery. Mammography is the most effective method of detecting breast cancer early. Additionally, this instrument enables the detection of additional illnesses and may provide information about the nature of cancer, such as benign, malignant, or normal. This article discusses an evolutionary approach for classifying and detecting breast cancer that is based on machine learning and image processing. This model combines image preprocessing, feature extraction, feature selection, and machine learning techniques to aid in the classification and identification of skin diseases. To enhance the image’s quality, a geometric mean filter is used. AlexNet is used for extracting features. Feature selection is performed using the relief algorithm. For disease categorization and detection, the model makes use of the machine learning techniques such as least square support vector machine, KNN, random forest, and Naïve Bayes. The experimental investigation makes use of MIAS data collection. This proposed technology is advantageous for accurately identifying breast cancer disease using image analysis.

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According to the benefits in safeguarding and transferring medical information, illness assessment, evaluation of “Magnetic Resonance Mapping” images, and certain other disciplines, blockchain and machine learning (ML) technology has significantly piqued attention in the healthcare domains. Formerly, those chores have been performed out along with individuals; eventually, individuals acquired attraction because to its precision and efficiency. The proposed study will examine the activities and possible capabilities of learning algorithms and blockchain in the healthcare professions focusing on these fascinating facts. Primary and secondary data analysis has been executed, with primary analysis method consisting of a survey of 150 randomly picked medicine professionals with expertise in machine learning and blockchain. They gave their answers that were being subsequently transferred to figures and employed as response variable in SPSS examining. The length of time where learning and blockchain have been used in medicine is really the independent factor. To better understand the primary and small hurdles of integrating machine learning and blockchain, a correlation investigation was done. Thereafter, secondary methodology is employed to validate the primary study results.

This paper focus on the growth and development of mobile technology research in terms of publication output as reflected in Web of Science database. During 2000-2013 a total of 10,638 publications were published in the field. The average number of publications published per year was 759.86 and the highest number of publications 1495 were published in 2013. Output of total publications, 9037 were produced by multiple authors and 1601 by single authors. Authors from USA have contributed maximum number of publications compared to the other countries and India stood 16th ranking in terms of productivity in this study period.

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