Abstract During voice calls through a mobile phone,there are a large number of unwanted sounds that transmitted with our speech signals, such as the noise of cars on the street, the machines in industrial places, group conversations, the sounds of people around us like children’s screams and other sounds. These sounds are unwanted noise, because it affects the quality of the... During voice calls through a mobile phone,there are a large number of unwanted sounds that transmitted with our speech signals, such as the noise of cars on the street, the machines in industrial places, group conversations, the sounds of people around us like children’s screams and other sounds. These sounds are unwanted noise, because it affects the quality of the speech signal on the one hand. On the other hand, it provides information about where we are located now and what is environment around us. This is a private information,we do not want them be known by those we talk to. In this paper, we propose to isolate the speech signal that transmitted through mobile phone from the surrounding sounds, without affecting the quality of the signal and any delay time. We have implemented many algorithms. That take input as a sound signal with noise, and then the system estimates the random noise signal and deletes it from the input signal in real-time. That will improve the quality of the transmitted or recorded speech signal. Read More Read Less

Abstract Corporations have always desired prompt customer experience feedback about their products for amending current pricing and policies to stay ahead of their competitors. A positive customer experience can be created by analyzing customer sentiments and acting on them promptly. Social networks like Twitter represent collective intelligence and opinion of the general public and hence can be harnessed for real-time feedback. They... Corporations have always desired prompt customer experience feedback about their products for amending current pricing and policies to stay ahead of their competitors. A positive customer experience can be created by analyzing customer sentiments and acting on them promptly. Social networks like Twitter represent collective intelligence and opinion of the general public and hence can be harnessed for real-time feedback. They have evolved as a resource for extracting sentiments for applications in various fields. Sentiment analysis can be used to obtain the overall customer experience of a large customer base on real-time. In this research, a total of 108814 distinct tweets for Twitter handles of three popular telecom brands in India: Vodafone India, Reliance Jio and Bharti Airtel were extracted for three months. The results indicate positive customer sentiments about the brand which they prefer, reflected by the number of new subscribers added with that brand in the study period. The sentiment analysis results can be used by managements to take timely actions for improving the future customer experience. Read More Read Less

Abstract The main result of the paper provides a lower bound on sufficient number of randomly generated formal concepts to correctly predict all important positive test examples with given confidence level. The technique coincides with modern approach to the famous theorem of V.N. Vapnik and A.Ya. Chervonenkis. However the situation is dual to the classical one: in our case test examples... The main result of the paper provides a lower bound on sufficient number of randomly generated formal concepts to correctly predict all important positive test examples with given confidence level. The technique coincides with modern approach to the famous theorem of V.N. Vapnik and A.Ya. Chervonenkis. However the situation is dual to the classical one: in our case test examples correspond to fixed subsets and probabilistically generated formal concepts must fall into selected areas of sufficient large volume. Read More Read Less