Respiratory disorders are one of the common chronic disorders across the world. Any malfunctioning in the respiration process affects speech as speech and respiration go hand in hand. Analysis of speech parameter variation due to respiratory disorders is a prime research area in today’s scenario. In this paper, an efficient approach using a machine learning paradigm was used to detect the speech affected due to respiratory disorders. Various speech parameters were extracted (F1, F2, F3… etc.) using PRAAT software along with MFCC and LPC coefficients. Statistically, significant features were obtained to determine the predominant parameters. The extracted features were applied to different machine learning paradigms then different classification techniques were analyzed and compared to distinguish between normal and affected speech.5-fold,10-fold cross-validation, and hold-out data division protocols were used for evaluating the classification results. To evaluate the performance of the proposed methodology, Accuracy, sensitivity, specificity, and the area under receiver operating characteristics (AUC) were used. The result demonstrates that the speech parameters (F1, F2, F3…etc.) extraction method and the LPC method achieved a significantly higher classification accuracy, sensitivity, and specificity of 100% and AUC of 1, under the holdout method. While MFCC method achieved the highest classification accuracy of 83%, sensitivity of 100%, specificity of 67%, and AUC of 0.83.

Indian mustard [Brassica juncea (L.) Czern & Coss], which is cultivated under the genus Brassica is cultivated all over India and it is throughout the world belongs to family Cruciferae (Brassicaceae). It has 38 to 42 % oil and 24% protein. Among rapeseed and mustard, rai (B. juncea) is very popular among the farmers due to higher yield and greater tolerance against lodging, shattering, drought condition, heat and relative diseases as well as

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An interconnected digital ecosystem, seamlessly blending the virtual and physical worlds, is termed as 'metaverse'. The metaverse offers unlimited virtual space to participants such as corporates and individuals, to explore and design their experiences. The present study showcases the potential of the metaverse to enhance the operations in retail, hospitality & tourism, and entertainment industries etc. The study posits that adoption of metaverse in these industries will alter the consumer decision-making journey, and their subsequent behavior. Corporate leaders by mapping this virtual space, where consumers have their digital doppelganger or avatars representing their digital persona, can reach the consumer across geographies. The study highlights that immersive experiences offered by the metaverse will be the essence of higher customer involvement, leading to value co-creation and seamless exchange of value. The final section of the study provides a perspective on the emerging issues and challenges in the form of privacy, security, racism, and digital hatred in the metaverse world.

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