Glaucoma is a major threatening cause, in which it affects the optical nerve to lead to a permanent blindness to individuals. The major causes of Glaucoma are high pressure to eyes, family history, irregular sleeping habits, and so on. These kinds of causes lead to Glaucoma easily, and the effect of such disease leads to heavy damage to the internal optic nervous system and the affected person will get permanent blindness within few months. The major problem with this disease is that it is incurable; however, the affection stages can be reduced and the same level of effect as that for the long period can be maintained but this is possible only in the earlier stages of identification. This Glaucoma causes structural effect to the eye ball and it is complex to estimate the cause during regular diagnosis. In medical terms, the Cup to Disc Ratio (CDR) is minimized to the Glaucoma patients suddenly and leads to harmful damage to one’s eye in severe manner. The general way to identify the Glaucoma is to take Optical Coherence Tomography (OCT) test, in which it captures the uncovered portion of eye ball (backside) and it is an efficient way to visualize diverse portions of eyes with optical nerve visibility shown clearly. The OCT images are mainly used to identify the diseases like Glaucoma with proper and robust accuracy levels. In this work, a new methodology is introduced to identify the Glaucoma in earlier stages, called Depth Optimized Machine Learning Strategy (DOMLS), in which it adapts the new optimization logic called Modified K-Means Optimization Logic (MkMOL) to provide best accuracy in results, and the proposed approach assures the accuracy level of more than 96.2% with least error rate of 0.002%. This paper focuses on the identification of early stage of Glaucoma and provides an efficient solution to people in case of effect by such disease using OCT images. The exact position pointed out is handled by using Region of Interest- (ROI-) based optical region selection, in which it is easy to point the optical cup (OC) and optical disc (OD). The proposed algorithm of DOMLS proves the accuracy levels in estimation of Glaucoma and the practical proofs are shown in the Result and Discussions section in a clear manner.

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Technology advancement, industrialisation, and globalisation have been significant reasons for air pollution outdoors and indoors. It may surprise us that we spend about 80% of our time indoors breathing toxic, stale, polluted air, making us sluggish and fretful. In contrast to outdoor air, indoor air does not recycle consistently. It traps and builds pollutants from wood and coal stoves, furniture and building materials, paints and solvents, cigarette smoke, and cleaning supplies. The prolonged exposure to these hidden pollutants can prompt respiratory disorders such as lung disease, pneumonitis, asthma, pulmonary hypertension, and chronic obstructive pulmonary disease (COPD). It is enduring and is not curable, which has been a threat to humanity for ages. COPD’s major cause is airborne particulate matter and other toxic compounds emitted from indoor and outdoor sources. Outdoor air pollution can be controlled only by acquiring changes in the vast population, wherein for indoor, every individual may create a major impact on improving air purity, thereby promoting health. The proposed design model for monitoring indoor air quality was tested in a normal and stimulating environment where we live. Parameters tested included temperature, humidity, amount of PM2.5, and the concentration of CO, CO 2 , and NH 3 . These parameters were monitored for five to 6 h per day for 8 days. Results indicate that the total air quality lies in the moderate range. Further study will be helpful to utilise this module as an effective Indoor air quality (IAQ) monitoring system. Highlights • A simple, effective, inexpensive integrated gas sensor module (IGSM) has been proposed in this study to monitor the indoor air quality index (IAQI). • Indoor air quality was tested in a normal and stimulating environment for 8 days. • The integrated gas sensor module (IGSM) was composed of sensors in series, and the outputs for the respective parameter were measured easily. • Among the parameters tested, CO and PM2.5 lie in the moderate range, while other pollutants within the normal range reveal that the tested air quality is moderate. Graphical Abstract

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