VINAY YADAV

Canine personality and behavioural characteristics have a significant influence on relationships between domestic dogs and humans as well as determining the suitability of dogs for specific working roles. As a result, many researchers have attempted to develop reliable personality assessment tools for dogs. Most previous work has analysed dogs’ behavioural patterns collected via questionnaires using traditional statistical analytic approaches. Artificial Intelligence has been widely and… Show more

Canine personality and behavioural characteristics have a significant influence on relationships between domestic dogs and humans as well as determining the suitability of dogs for specific working roles. As a result, many researchers have attempted to develop reliable personality assessment tools for dogs. Most previous work has analysed dogs’ behavioural patterns collected via questionnaires using traditional statistical analytic approaches. Artificial Intelligence has been widely and successfully used for predicting human personality types. However, similar approaches have not been applied to data on canine personality. In this research, machine learning techniques were applied to the classification of canine personality types using behavioural data derived from the C-BARQ project. As the dataset was not labelled, in the first step, an unsupervised learning approach was adopted and K-Means algorithm was used to perform clustering and labelling of the data. Five distinct categories of dogs emerged from the K-Means clustering analysis of behavioural data, corresponding to five different personality types. Feature importance analysis was then conducted to identify the relative importance of each behavioural variable’s contribution to each cluster and descriptive labels were generated for each of the personality traits based on these associations. The five personality types identified in this paper were labelled: “Excitable/Hyperattached”, “Anxious/Fearful”, “Aloof/Predatory”, “Reactive/Assertive”, and “Calm/Agreeable”. multiple ML models has been implemented and through CV method results has been demonstrated that decision tree is performing well with 99% accuracy.The novel AI-based methodology in this research may be useful in the future to enhance the selection and training of dogs for specific working and non-working roles. Show less

Aim of noise suppression techniques is to get enhanced speech from noisy speech signal.
There are ample of applications of Noise removal algorithms like speaker recognition, voice
identification, voice communication, and voice compression and in the digital gadgets. Various noise
suppression techniques were analyzed for different kind of noise. Different speech enhancement
algorithms like spectral subtraction, wiener filtering, and statistical estimation based methods… Show more

Aim of noise suppression techniques is to get enhanced speech from noisy speech signal.
There are ample of applications of Noise removal algorithms like speaker recognition, voice
identification, voice communication, and voice compression and in the digital gadgets. Various noise
suppression techniques were analyzed for different kind of noise. Different speech enhancement
algorithms like spectral subtraction, wiener filtering, and statistical estimation based methods were
implemented successfully on noisy speech. Total eight different kind of noisy environment were used
for analysis of noise suppression algorithm. Quality of noise removal technique has been evaluated
by various quality measurements as Perceptual Evaluation of Speech Quality (PESQ), Signal to
Noise Ratio (SNR) and segmental SNR (segSNR). Show less