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A huge variety of devices accumulates as well distributes a large quantity of data either with the help of wired networks or wireless networks to implement a wide variety of application scenarios. The spectrum resources on the other hand become extremely unavailable with the development of communication devices and thereby making it difficult to transmit data on time.

The spectrum resources on the other hand become extremely unavailable with the development of communication devices and thereby making it difficult to transmit data on time. Therefore, the technology of cognitive radio (CR) is considered as one of the efficient solutions for addressing the drawbacks of spectrum distribution whereas the secondary user (SU) performance is significantly influenced by the spatiotemporal instability of spectrum.

As a result, the technique of the hybrid filter detection network model (HFDNM) is suggested in this research work under various SU relationships in the networks of CR. Furthermore, a technique of hybrid filter detection was recommended in this work to enhance the performance of idle spectrum applications. When compared to other existing techniques, the suggested research work achieves enhanced efficiency with respect to both throughputs as well as delay.

The proposed HFDNM improved the transmission delay at 3 SUs with 0.004 s/message and 0.008 s/message when compared with existing NCNC and NNC methods in case of number of SUs and also improved 0.02 s/message and 0.08 s/message when compared with the existing methods of NCNC and NNC in case of channel loss probability at 0.3.

Road accidents, an inadvertent mishap can be detected automatically and alerts sent instantly with the collaboration of image processing techniques and on-road video surveillance systems. However, to rely exclusively on visual information especially under adverse conditions like night times, dark areas and unfavourable weather conditions such as snowfall, rain, and fog which result in faint visibility lead to incertitude. The main goal of the proposed work is certainty of accident occurrence.

The authors of this work propose a method for detecting road accidents by analyzing audio signals to identify hazardous situations such as tire skidding and car crashes. The motive of this project is to build a simple and complete audio event detection system using signal feature extraction methods to improve its detection accuracy. The experimental analysis is carried out on a publicly available real time data-set consisting of audio samples like car crashes and tire skidding. The Temporal features of the recorded audio signal like Energy Volume Zero Crossing Rate 28ZCR2529 and the Spectral features like Spectral Centroid Spectral Spread Spectral Roll of factor Spectral Flux the Psychoacoustic features Energy Sub Bands ratio and Gammatonegram are computed. The extracted features are pre-processed and trained and tested using Support Vector Machine (SVM) and K-nearest neighborhood (KNN) classification algorithms for exact prediction of the accident occurrence for various SNR ranges. The combination of Gammatonegram with Temporal and Spectral features of the validates to be superior compared to the existing detection techniques.

Temporal, Spectral, Psychoacoustic features, gammetonegram of the recorded audio signal are extracted. A High level vector is generated based on centroid and the extracted features are classified with the help of machine learning algorithms like SVM, KNN and DT. The audio samples collected have varied SNR ranges and the accuracy of the classification algorithms is thoroughly tested.

Denoising of the audio samples for perfect feature extraction was a tedious chore.

The existing literature cites extraction of Temporal and Spectral features and then the application of classification algorithms. For perfect classification, the authors have chosen to construct a high level vector from all the four extracted Temporal, Spectral, Psycho acoustic and Gammetonegram features. The classification algorithms are employed on samples collected at varied SNR ranges.

This paper aims to develop the Hybrid feature descriptor and probabilistic neuro-fuzzy system for attaining the high accuracy in face recognition system. In recent days, facial recognition (FR) systems play a vital part in several applications such as surveillance, access control and image understanding. Accordingly, various face recognition methods have been developed in the literature, but the applicability of these algorithms is restricted because of unsatisfied accuracy. So, the improvement of face recognition is significantly important for the current trend.

This paper proposes a face recognition system through feature extraction and classification. The proposed model extracts the local and the global feature of the image. The local features of the image are extracted using the kernel based scale invariant feature transform (K-SIFT) model and the global features are extracted using the proposed m-Co-HOG model. (Co-HOG: co-occurrence histograms of oriented gradients) The proposed m-Co-HOG model has the properties of the Co-HOG algorithm. The feature vector database contains combined local and the global feature vectors derived using the K-SIFT model and the proposed m-Co-HOG algorithm. This paper proposes a probabilistic neuro-fuzzy classifier system for the finding the identity of the person from the extracted feature vector database.

The face images required for the simulation of the proposed work are taken from the CVL database. The simulation considers a total of 114 persons form the CVL database. From the results, it is evident that the proposed model has outperformed the existing models with an improved accuracy of 0.98. The false acceptance rate (FAR) and false rejection rate (FRR) values of the proposed model have a low value of 0.01.

This paper proposes a face recognition system with proposed m-Co-HOG vector and the hybrid neuro-fuzzy classifier. Feature extraction was based on the proposed m-Co-HOG vector for extracting the global features and the existing K-SIFT model for extracting the local features from the face images. The proposed m-Co-HOG vector utilizes the existing Co-HOG model for feature extraction, along with a new color gradient decomposition method. The major advantage of the proposed m-Co-HOG vector is that it utilizes the color features of the image along with other features during the histogram operation.

To compete and to survive in this era of globalization, organizations, including family businesses, need to have competitive advantage, and innovation and internationalization are some of the ways to achieve this. This paper aims to analyze whether family businesses innovate and internationalize more than non-family businesses and further analyses the type of family businesses “age-wise” and “size-wise” that innovate and internationalize more.

The study is empirical in nature. The period of study is 11 years, from 2005 to 2015 (both years inclusive). The sample is chosen from Bombay Stock Exchange (BSE) 500 index, a broad-based index in India, covering about 20 industries of the economy. The present study uses multiple regression models to find the innovativeness and internationalization of family businesses. The dependent variables are R&D (proxy for innovativeness) and FXINC (proxy for internationalization). The independent variables are FB (variable that defines whether a business is family business or non-family business); FBAGE (variable that defines the age of the family business); and FBSIZE (variable that defines the size of the family business). The other control variables used in in the study are TA (total assets), REV (revenue), CR (current ratio), QR (quick ratio), DER (debt-equity ratio) and RONW (return on net worth). Fixed effects model was used to understand the innovativeness and internationalization of family businesses. Both industry and year fixed effects were used. SPSS 20.0 version is used for the analysis. All results are heteroscedastic consistent using Breusch–Pagan test.

It is found that family businesses are more innovative and internationalized when compared to non-family businesses. The results are consistent with the resource-based theory where it is found that family businesses are entrepreneurial in nature (Salvato, 2004; Zahra et al., 2004; Kellermanns and Eddleston, 2006) which makes them more innovative. It was also found that within the family businesses, younger firms were more innovative and internationalized than older firms. This can be explained by the theory of “learning advantages of newness”, according to which younger firms are more flexible, eager to learn, have less internal resistance and are able to adapt to the changing environment much faster.

During the studies, the authors have found that there is no conclusive evidence on the innovativeness and internationalization of family businesses. Further, there are apparently negligible studies that analyze what type of family businesses, age wise (younger or older firms) and size wise (smaller or larger firms) use the strategy of innovation and internationalization to grow. The present study analyses the innovativeness and internationalization of family businesses when compared to non-family businesses and also studies the type of family businesses (age wise and size wise) that are more innovative and internationalized.

This paper seeks to propose a business intelligence (BI) and enterprise resource planning (ERP) integrated framework that adds value to enterprise systems.

A conceptual approach is taken.

ERP systems integrate all facets of the business and make data available in real time. BI tools are capable of accessing data directly from ERP modules.

The value‐added system proposed allows enterprise‐wide transaction data to be collected and analyzed for organizational decision‐making processes.