Purpose The purpose of this paper is to understand Indian tourists’ perceptions of Airbnb compared to other hospitality options, and the factors driving their purchase intentions. Design/methodology/approach An integrated model for purchase intention was conceptualized based on the theory of planned behavior and social exchange theory. Constructs such as trust, authenticity, travel innovativeness, price sensitivity and effort expectancy were included based on a survey of the literature. Structural equation models were built using survey data. Respondent ranking of different criteria for Airbnb vs its competitors were aggregated using Borda count method. Findings Price is the most important criteria across hospitality choices, including Airbnb, except high-end hotels. Facilities, home-like feeling, trust and friendly service were important for Airbnb. Consumer expectations from Airbnb are similar to homestays, mid-range and budget hotels and different from resorts and high-range hotels. In the theory of planned behavior model, trust in Airbnb and perceived authenticity had large significant positive effects on purchase intention, mediated by attitude. Social norms and effort expectancy had direct positive effects on behavioral intentions. Price sensitivity had a direct small negative effect on purchase intention. Overall, fit of the model was within acceptable parameters. Originality/value Despite being an important emerging market, Airbnb in India has not been covered by studies of consumer behavior. This paper fills that research gap. Airbnb’s main competitors are home-stays and mid-range hotels. Building trust, creating authentic experiences and ensuring price competitiveness will drive adoption.

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Face image recognition has been widely used and implemented in many aspects of life, such as in the field of investigation or security. However, research in this area is still rarely done. Source images in this paper are taken directly from 41 students with a total of 131 faces in JPG format, each with a dimension of 256×256. By applying Discrete Wavelet Transform and Discrete Cosine Transform, an image can be represented as a number of DCT coefficients efficiently. Recognition process is done using Radial Basis Function Neural network. The experiment results show that the best configuration for RBF is 8×41×41 with recognition rate of student faces is 100% and 98% of the sample face images are identified perfectly.  Index Terms-discrete wavelet transform, discrete cosine transform, radial basis function neural network

Fire is a type of disaster that can occur anytime and anywhere as a result of any accidental or intentional causes. Without exception, houses are also very vulnerable to fire. To anticipate the catastrophic effects of fire that can destroy houses, advanced technology, such as the Internet of Things (IoT) can be utilized to detect the smoke and fire. This study aims to design an early warning fire detection system for home monitoring using smoke detection sensors based on Arduino microcontroller together with NodeMCU ESP8266. This early warning fire detection system is expected to function by notifying homeowners when detecting the presence of smoke in their homes. With the aid of this detection system, the issue of potential damage, death, or material loss caused by fire can be significantly reduced. The results and testing of the designed system will be discussed in the paper.

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Abstract. Optical Mark Recognition (OMR) is often used by teachers to help mark students' test in the form of multiple-choice test. This article describes how to build an offline OMR system which run in Android based smartphone. The input is the digital image of student's answer sheet which was taken from smartphone's camera. The system implements image processing techniques to detect and determine not only student's handwritten for his/her ID, name, and form's ID, but also for the answer of each test number. Gradient feature is used as features for the handwritten parts, while pixel density and area are used for the marked parts. Radial Basis Function Neural Network (RBFNN) is implemented to recognize of the students' handwriting. The experiment result shows that the system has successfully identified the answers with 99.69% success rates, but only has 82.28% and 72.25% success rates for digits and uppercase letters recognition respectively.

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