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The combination of the fields of software engineering, gadgets, and science has stood out among the most revolutionary future innovations. Health issues have been the focus of various engaging and explanatory studies. One such health‐related dilemma is diabetes. Diabetes at its serious stage results in impaired vision. Increase in the glucose level is a critical parameter that could result in hyperglycaemia, hypoglycaemia, massive heart attack, strokes, and aneurysms. Monitoring the glucose level in blood is one of the control measures for diabetes in the affected population. A glucose monitoring framework interminably measures and screens the glucose level in blood. A novel framework for measuring the glucose level is proposed in this study. This study employs nanopellets that evaluate the glucose level. When the glucose level increases or decreases, it is continuously recorded and displayed using a microcontroller (mixed signal processor (MSP) 430). The data are then sent to the physician through global system for mobile communication. The typical blood glucose level of human being ranges from 70 to 110 mg/dl. When the insulin level builds up to certain point, hyperglycaemia occurs. When decreases, hypoglycaemia occurs. Hyperglycaemia leads to cataracts, oedema, hypertension, polyuria, and polydipsia. Hypoglycaemia causes perplexity, energy, insensateness, coma, and death.

Background and objectives Semen analysis is a primary and mandatory procedure to evaluate the infertility during clinical examination. This procedure includes the analysis and classification of normal and abnormal Sperm, selection and efficient tracking of healthy sperm in the sample. Many methods were proposed earlier for the analysis of semen. The fast sperm movement and high dense cluster of sperm is a challenging task for researchers. Methods The paper proposes a novel Faster Region Convolutional Neural Network (FRCNN) with Elliptic Scanning Algorithm (ESA) for classifying human sperm and a Novel Tail to Head movement algorithm (THMA) for the motility analysis and tracking. This proposed method improves the accuracy of computer assisted semen analysis (CASA). Results The proposed method outperforms and provides better results than existing methods. Method provides better accuracy of 97.37%. Sperm detection and identifying the sperm motility in the group is performed with minimum execution time of 1.12 s. Conclusions A novel FRCNN with ESA detection algorithm is proposed for the analysis of human sperm classification. This method provides an accuracy of 97.37%. A Tail head movement-based (THMA) algorithm is explained for the motility analysis.

Karyotyping of Banded Metaphase Chromosomes is one of the preliminary steps used in cytogenetics to analyze the chromosomes for diagnostic purposes. Deep learning is a subfield of machine learning concerned with structure and function of brain. It exploits a way to automate predictive analysis. The key aspect of deep learning is that the layers of features are not designed by human engineers. They are learned from data using a general purpose learning procedure. This paper proposes a convolution based deep learning to classify the chromosomes for automated karyotyping. The developed architecture allows us to train and test images that helps in predicting the chromosome abnormality. The performance analysis is based on loss and accuracy curves and the graphical representation clearly exhibits better classification results for this architecture.