May 13 ~ 14, 2023, Virtual Conference
Princy Yadav, Department of Mathematics, Chandigarh University, Mohali, Punjab
Various research fields require different sampling techniques for conducting the research efficiently. It is very essential to choose the adequate technique of sampling. This paper gives an idea on different sampling techniques and their importance in different fields. In this paper, we discuss sampling and various types of sampling techniques. We go through two types of sampling techniques i.e., probability and non- probability sampling techniques. We study their subcategories. Further, we discuss the pros and cons of these techniques. Discussing different sampling techniques and their pros and cons will give the reader better understanding of the sampling techniques. It will help the reader to choose the right sampling technique for particular research. Choosing the correct sampling technique is very important for research. If a researcher knows well about different sampling techniques, he can choose adequate sampling technique for his research work. The aim of the study is fulfilled when a researcher understands which sampling technique is good for his research.
Population, Sampling, Sampling Techniques, Probability Sampling, Non-Probability Sampling.
Rajah Iyer,Microsoft, Redmund, Seattle, USA, R&D
We present herein a new approach to the Continuum hypothesis CH. We will establish a technique for forming a subset K of R, further to this, we will extend the logical premise of Cantor’s Diagonal Argument to devise a means by which the cardinality of K is established between (N,R) respectively.
Diagonal Argument, Continuum Hypothesis CH, Resolution to CH
Jagat Chaitanya Prabhala, Dr. Venkatnareshbabu K, Dr. Ragoju Ravi, Department of Applied Sciences, National Institute of Technology, Goa, India
In this research, we propose a novel approach for speaker diarization, which is the process of determining who spoke when in an audio or video recording that contains unknown amount of speech from unknown speakers and unknown number of speakers. Speaker diarization has several applications in the field of speech processing and is often used as a pre-processing step. However, traditional supervised and unsupervised algorithms for speaker diarization have limitations, such as the high cost of providing exhaustive labeling for training datasets in the case of supervised learning and compromised accuracy when using unsupervised approaches.
To address these limitations, we propose a method that utilizes x-vector embedding, abstract similarity metrics, and a combination of graph theory, matrix algebra, and genetic algorithm. We also introduce the concept of loosely labeled data and demonstrate how our approach effectively clusters temporal segments into unique user segments for speaker diarization.
We evaluate the performance of our proposed algorithm on audio recordings in English, Spanish, and Chinese and compare it with well-known similarity metrics. Our results demonstrate that our approach effectively optimizes the speaker diarization process and outperforms traditional methods. This research has significant implications for various applications in speech processing and has the potential to improve the performance of other related tasks.
signal processing, speaker diarization, discrete optimization, neural networks
Bradford Hansen-Smith, USA
To introduce folding circles at the same time we are drawing pictures and making symbols of them is one thing we can do to enlarge our approach to math and science. It brings 2-D and 3-D together through the movement of folding the circle that is not predictable by either one of them. If it were so, we would already be doing it.
geometry, folding circles, symmetry, unity