Informatics Publishing Limited

Monika Arora ¹, Uma Kanjilal ², Dinesh Varshney ²

Affiliations
1 Department of IT, Apeejay School of Management, Dwarka, New Delhi, IN
2 No Affiliation, IN

Abstract

Recommender systems(RS) are intelligent applications in the field of information retrieval. Information retrieval assists users to take part in decision making process. It assists them in choosing one item from a vast set of alternative products or services. The scope of recommender systems has expanded gradually over the time from 1990s. As the user provides the inputs, these inputs are recorded and used as recommendations. These inputs are further used in the recommender system tool. The inputs received by the user are aggregated by the other people’s inputs and then the system sends them directly to the appropriate recipients(Dean etal.,1995). RS is basically a technology which is based on the important aspects such as collaborative or social filtering. There are many researches already in the area for collaborative filtering and social filtering(Bigus,1996; Rennieand Srebro,2005). The RS can be used in intelligent information retrieval in the field of artificial intelligence. From information retrieval, recommendation technology explores and derives the vision that users are searching. In the process of recommendations, the user is engaged in information searching and the RS automates and collects the content required for matching purposes. Typically the search results are arranged in the form of a ranked list. One of the important phases of artificial intelligence is learning process. This will view the past knowledge, buying behaviour, and interest. The RS are primarily based on two phases that are search phase and user-based interaction model. These phases can be identified as user-model construction and recommendation generation. This paper defines the importance of both the models. The interaction model describes the user needs and preferences. Based on the interactions, during the session they are connected to the same interest group. This paper attempts to define a proposed model for considering the factors of intelligent retrieval.

Keywords

Recommender Systems, Intelligent Applications, Retrieval, Filtration

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Monika Arora ¹, Uma Kanjilal ², Dinesh Varshney ³

Affiliations
1 Department of IT, Apeejay School of Management, Dwarka Institutional Area, New Delhi, IN
2 Department of Library and Information Science, Indira Gandhi National Open University Maidan Garhi, New Delhi, IN
3 School of Physics, Devi Ahilya University, Khandwa Road Campus, Indore, M. P, IN

Abstract

Recommender systems (RS) are intelligent applications in the field of information retrieval. Information retrieval assists users to take part in decision making process. It assists them in choosing one item from a vast set of alternative products or services. The scope of recommender systems has expanded gradually over the time from 1990s. As the user provides the inputs, these inputs are recorded and used as recommendations. These inputs are further used in the recommender system tool. The inputs received by the user are aggregated by the other people's inputs and then the system sends them directly to the appropriate recipients (Dean etal., 1995). RS is basically a technology which is based on the important aspects such as collaborative or social filtering. There are many researches already in the area for collaborative filtering and social filtering (Bigus,1996; Rennieand Srebro, 2005). The RS can be used in intelligent information retrieval in the field of artificial intelligence.

From information retrieval, recommendation technology explores and derives the vision that users are searching. In the process of recommendations, the user is engaged in information searching and the RS automates and collects the content required for matching purposes. Typically the search results are arranged in the form of a ranked list. One of the important phases of artificial intelligence is learning process. This will view the past knowledge, buying behaviour, and interest. The RS are primarily based on two phases that are search phase and user-based interaction model. These phases can be identified as user-model construction and recommendation generation. This paper defines the importance of both the models. The interaction modeldescribes the user needs and preferences. Based on the interactions, during the session they are connected to the same interest group. This paper attempts to define a proposed model for considering the factors of intelligent retrieval.

Keywords

Recommender Systems, Intelligent Applications, Retrieval, Filtration

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Shiv Kumar ¹, R. K. Singh ², Manish Srivastava ³, Dinesh Varshney ⁴, Vijay K. Chaudhari ¹

Affiliations
1 Department of Information Technology, Technocrat Institute of Technology, Bhopal (M.P.), IN
2 MATS University, Raipur (C.G.), IN
3 Department of Computer Science and Engineering, Guru Ghansidas Central University, Bilaspur (C.G.), IN
4 Multimedia Regional Centre, Madhya Pradesh Bhoj (Open) University, Khandwa Road Campus, Indore (M.P.), IN

Abstract

In this paper titled "A New Approach for Tree-Structured Wavelet Transform based Texture Retrieval Analysis (TRA) by Using MatLab" is define Wavelet transform-based texture analysis, as I found in the different research, uses sub-band energy values as features, but not the order of energy values. In fact, a textured image, after a wavelet decomposition, yields an energy distribution which can be rank ordered with respect to the sub-bands. It has been found that the combination of the sub-band energy value and its ranking order leads to a more efficient texture retrieval mechanism.

Keywords

MatLab7.0, Wavelet Toolbox, Image Processing Toolbox, Algorithm.

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Shiv Kumar ¹, Vijay K. Chaudhari ², R. K. Singh ³, Dinesh Varshney ⁴, C. L. Saxena ⁵

Affiliations
1 Department of Information Technology, Technocrat Institute of Technology-Bhopal (M.P.), IN
2 Department of Information Technology, Technocrat Institute of Technology-Bhopal (M.P.), IN
3 MATS University, Raipur (C.G.), IN
4 Multimedia Research Department, Multimedia Regional Center, Madhya Pradesh Bhoj (Open) University, Khandwa Road Campus, Indore (M.P.), IN
5 Technocrats Institute of Technology, Bhopal (M.P.), IN

Abstract

In this project report, (i.e. “Result Analysis to Compute Entropy of Voice Signal (CEVS) SNR using Matlab”) an approach is used to compute the entropy of given voice signal and signal to noise ratio (SNR) with the help of computed entropy. The main goals of this project are:
• To compute the tone of inputted voice signal
• To estimate entropy of tone
• To calculate SNR of entropy
To do this, psychoacoustic model and wavelet toolbox is used. Psychoacoustic model calculates masking threshold. Maximum distortion energy is computed from computed tone of inputted voice signal which defines the CEVS and SNR.

Keywords

Matlab 6.5, Wavelet Toolbox, Psychoacoustic Model, Algorithm.

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