There is a drastic need for extracting information from non-linguistic features of the audio sources. It leads to the eminent rise of speech technology over the past few decades. It is termed computational para-linguistics. This research concentrates on extracting and providing a robust feature that examines the characteristics of speech data. The factors are analysed in a spectral way which stimulates the auditory elements. The speech enhancement technological process is being initiated with pre-processing, feature extraction, and classification. Initially, the input data conversion is done with ADC of 16 kHz sampling frequency. The spectral features are extracted with minimal Mean Square Error to enhance the re-construction ability and eliminate the redundancy characteristics. Finally, the deep neural network is adopted for multi-class classification. The simulation is performed in MATLAB 2020a environment, and the empirical outcomes are evaluated with existing approaches. Here, metrics like Mean Square Error, accuracy, Signal-to-Noise ratio (SNR) and features retained are computed efficiently. The anticipated model shows a trade-off in contrast to prevailing approaches. The outcomes demonstrate a better recognition rate and offer significant characteristics in selecting the most influencing features.

Monitoring a driver to detect his distraction is a complex problem that involves physiological and behavioral elements. In order to solve this problem a constant detection system for monitoring driver’s eye movement is to be monitored. Initially, driver’s face is first located in the input video sequence which is then tracked over the subsequent eye movements of the driver are constantly traced. Using Viola and Jones face detection algorithm the sequence of images are trained and classified in such a way that a warning alarm is buzzed if the eyes are constantly closed for a predetermined period amount of time. Hence this reduces the rate of traffic accidents occurring these days. Future work is on how to extend the system to determine the level of vigilance of the driver.

Advanced pictures are difficult to control and alter since the convenience of capable picture management and changing programming. These days, it is believable to contain or push out very important highlights from a picture without leaving any conspicuous hints of altering. As computerized cameras and camcorders supplant their simple partners, the requirement for verifying advanced pictures, approving their substance, and distinguishing frauds will just increment. Most existing systems to identify such altering are for the most part at the cost of higher computational multifaceted nature. Specifically, the attention was given on recognition of an uncommon kind of computerized phony - the Copy-Move assault in which a piece of the picture is reordered on another part for the most part to cover undesirable bits of the picture. Consequently, the fundamental objective of Copy-Move Forgery Detection (CMFD) is to distinguish duplicate move phonies territories that are same or to a great degree comparative. In CMFD a productive and strong way to deal with recognizes such particular sort of phonies is actualized. This takes after piece based coordinating strategy to recognize frauds in an advanced picture. In the first place, the first picture is separated into settled size squares, clustering the pieces by crossing point region among squares and removing comparable bunches. This strategy may effectively distinguish the fashioned part notwithstanding when the replicated region is improved/modified to blend it with the foundation and when the manufactured picture is spared in a noteworthy realistic document organize, for example, JPEG or PNG.

Text summarization plays a crucial role nowadays due to large data available in day to day life. Reduced documents are useful and essential in the busy schedule of our lives. In this paper documents are summarized by four phases. They are preprocessing, feature vector generation, sentence score generation and summary generation. Sentence score is generated by Restricted Boltzmann machine (RBM) to improvise the result accuracy without losing the important information. Each of the sentence in the document undergoes all the phases and final summary generated is better as comparison among the existing (NN +fuzzy) and proposed method (Fuzzy +DL) with a value as 0.25 and beta as 0.75. According to the analysis for precision rate at CR 30%, the Fuzzy+DL method is higher compared to the NN+fuzzy method (Fuzzy+DL)-0.875 and (NN+fuzzy)-0.256). The results shows the comparison graph for recall rate at 30%, the Fuzzy+DL method is higher compared to the NN+fuzzy method ((Fuzzy+DL)-0.777 and (NN+fuzzy)-0.6667) and the result also depicts the comparison graph for F-measure at CR 30%, the Fuzzy+DL method is higher compared to the NN+fuzzy method ((Fuzzy+DL)-0.8235 and (NN+fuzzy)-0.36363.

Purpose– The purpose of this paper is to identify the key determinants of employee engagement and their predictability of the concept. It also studies the impact of employee engagement on employee performance.Design/methodology/approach– Causal study was done to study the impact of relationships. A survey questionnaire was developed and validated using a pilot data (a=0.975). Simple random sampling was used to select the employees from middle and lower managerial levels from small-scale organisations. A total of 700 questionnaires were distributed and 383 valid responses collected. Regression and structural equation modelling were used to predict and estimate the relationships.Findings– It was found that all the identified factors were predictors of employee engagement (r2, 0.672), however, the variables that had major impact were working environment and team and co-worker relationship. Employee engagement had significant impact on employee performance (r2, 0.597).Practical implications– Special focus and effort is required specifically on the factors working-environment and team and co-worker relationship as they have shown significantly higher impact on employee engagement and hence employee performance. Organisations shall focus on presenting a great environment for employees to work and promote programmes that would enhance peer relationships.Social implications– The determinants of employee engagement connote a healthy working atmosphere that reflects on the social impact created by the organisation. Employees would enjoy considerable attention in terms of the determinants being addressed.Originality/value– The research emphasises the growing importance and need for crystallisation of the concept of employee engagement. The research is unique in respect to the comprehensive model that is developed and validated.