The altered cast impression technique is commonly used for the mandibular distal extension partially edentulous arches. The technique is relatively simple and improves the residual ridge to dentition relationship of the prosthesis. Due to the displaceability of the mucosa, free end saddles get displaced under occlusal load. A 45-year-female patient reported to the Department Of Prosthodontics and Crown-Bridge, College of Dental Surgery, B.P. Koirala Institute of Health Sciences, Nepal with a chief complaint of missing bilateral lower posterior teeth. Clinical examination revealed missing teeth w.r.t 36, 37, 45, 46, and 47. The treatment plan was made to rehabilitate the patient with removable partial denture using functional impression along with the altered cast technique. The altered cast technique is the best alternative technique in the case of distal extension cases because better tissue adaptability, proper extension of a denture base, equal stress distribution, better support, and stability can be achieved. Finally, increased patient comfort and function are obtained.
The pandemic caused due to COVID-19, has seen things going online. People tired of typing prefer to give voice commands. Most of the voice based applications and devices are not prepared to handle the native languages. Moreover, in a party environment it is difficult to identify a voice command as there are many speakers. The proposed work addresses the Cocktail party problem of Indian language, Gujarati. The voice response systems like, Siri, Alexa, Google Assistant as of now work on single voice command. The proposed algorithm G- Cocktail would help these applications to identify command given in Gujarati even from a mixed voice signal. Benchmark Dataset is taken from Microsoft and Linguistic Data Consortium for Indian Languages(LDC-IL) comprising single words and phrases. G-Cocktail utilizes the power of CatBoost algorithm to classify and identify the voice. Voice print of the entire sound files is created using Pitch, and Mel Frequency Cepstral Coefficients (MFCC). Seventy percent of the voice prints are used to train the network and thirty percent for testing. The proposed work is tested and compared with K-means, Naïve Bayes, and LightGBM.
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