E-commerce has become very important in our daily lives. Many business transactions are made easier on this platform. Sellers and consumers are the two main parties that gain a lot of benefits from it. Although many sellers are attracted to set up their businesses on this online platform, it also causes challenges such as a highly competitive business environment and unpredictable sales. Thus, we propose a data analytics approach for short-term sales forecasts using limited information in the ecommerce marketplace. Product details are scraped from the e-commerce marketplace using a content scraping tool. Since the information in the e-commerce marketplace is limited and essential, scraped product details are pre-processed and constructed into meaningful data. These data are used in the computation of the forecasting methods. Three types of quantitative forecasting methods are computed and compared. These are simple moving average, dynamic linear regression and exponential smoothing. Three different evaluation metrics, namely mean absolute deviation, mean absolute percentage error and mean squared error, are used for the performance evaluation in order to determine the most suitable forecasting method. In our experiment, we found that the simple moving average has the best forecasting accuracy among other forecasting methods. Therefore, the application of the simple moving average forecasting method is suitable and can be used in the e-commerce marketplace for sales forecasting.
Wireless Sensor Network (WSN) is a type of wireless network that is fast getting a lot of attention in scientific and industrial applications, and it is a network of decentralized autonomous standalone sensor devices. However, WSN is easily prone to malicious attacks as anyone can access the server through the node without a proper security authentication. In this paper, we proposed a secure AODV based multi-factor authentication scheme for WSN to mitigate physical attack, offline guessing attack and replay attack. Our proposed scheme is preferred to keep the scheme lightweight while providing enough security that requires smart card, user identity, password, and OTP. Our proposed scheme has relatively lower computational cost with a total of 10Th than the other compared schemes except for Adil et al.’s scheme. However, we have around 8288 bits of authentication overhead due to the nature of packet and the addition of factors. Hence, our scheme is outperformed from computational cost perspective, but the scheme is slightly higher on authentication overhead perspective. In the future, multiple device authentication, implementation of biometric feature can be added to improve the scheme.
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