Chronic Kidney Disease (CKD) is one of the dangerous diseases around the world. Early recognition and appropriate administration are requested for enlarging survivability. According to the UCI informational index, there are 24 qualities for anticipating CKD or non-CKD. At any rate there are 16 qualities need obsessive examinations including more assets, cash, time, and vulnerabilities. The goal of this work is to investigate whether we can anticipate CKD or non-CKD with sensible precision utilizing less number of features. An Intellectual framework advancement approach has been utilized in this investigation. Essential feature determination system to find reduced features that clarify the informational index is introduced. Two insightful paired grouping methods have been received for the legitimacy of the reduced list of capabilities. As recommended from our outcomes, we may more focus on those decreased features for recognizing CKD and along these lines lessens vulnerability, spares time, and reduced costs. The proposed technique uses less features for CKD prediction.

Automatic Speech Recognition (ASR) is a popular research area with many variations in human behaviour functionalities and interactions. Human beings want speech for communication and Conversations. When the conversation is going on, the information or message of the speech utterances is transferred. It also consists of message which includes speaker’s traits like emotion, his or her physiological characteristics and environmental statistics. There is a tremendous number of signals or records that are complex and encoded, but these can be decoded quickly because of human intelligence. Many academics in the domain of Human Computer Interaction (HCI) are working to automate speech generation and the extraction of speech attributes and meaning. For example, ASR can regulate the usage of voice command and maintain dictation discipline while also recognizing and verifying the speech of the speaker. As a result of accent and nativity traits, the speaker's emotional state can be discerned from the speech. In this Paper, we discussed Speech Production System of Human, Research Problems in Speech Processing, SER system Motivation, Challenges and Objectives of Speech Emotion Recognition, so far the work done on Telugu Speech Emotion Databases and their role thoroughly explained. In this Paper, our own Created Database i.e., (DETL) Database for Emotions in Telugu Language and the software Audacity for creating that database is discussed clearly.

Speech Emotion Recognition is always a complicated task in the domain of Speech Processing Research, though many research works have been done. The first and foremost challenge of SER is to selecting the Speech Emotion Database (Corpora), then extracting the related speech features and finally construct an appropriate Classification model. An effort is created during this work to discover the speech prosodies, spectral and combination of features with their dynamism to illustrate and classify the emotions of speech signal. The intrinsic or fine variations of speech samples are combined with the static delivery parameters within the Speech Emotion Recognition (SER) to refine the accuracy. The work in this paper, carried out the experiments on RAVDESS, IIITH IIITH-TEMD and our developed Database of native language DETL (Database for Emotions in Telugu Language) Speech Emotion Databases. This work extracted features like MFCC and Hybrid Features (MFCC+ΔMFCC+ΔΔMFCC) then finally applied those individual features and Combination of Features to different Classification models like SVM and MLP. We have got approximately 75%, 78% and 81% of accuracy for MLP with hybrid combination features on the above Databases respectively.