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.
Internet of Things (IoT) has become an information bridge between societies. Wireless sensor networks (WSNs) are one of the emergent technologies that work as the main force in IoT. Applications based on WSN include environment monitoring, smart healthcare, user legitimacy authentication, and data security. Recently, many multifactor user authentication schemes for WSNs have been proposed using smart cards, passwords, as well as biometric features. Unfortunately, these schemes are shown to be susceptible towards several attacks and these includes password guessing attack, impersonation attack, and Man-in-the-middle (MITM) attack due to non-uniform security evaluation criteria. In this paper, we propose a lightweight multifactor authentication scheme using only hash function of the timestamp (TS) and One Time Password (OTP). Furthermore, public key and private key is incorporated to secure the communication channel. The security analysis shows that the proposed scheme satisfies all the security requirement and insusceptible towards some wellknown attack (password guessing attack, impersonation attack and MITM).
Garbage-man-in-the-middle (type 1) attack is an attack exploit the polynomial structure of LUC-type cryptosystems and depends on the possibility to get the faulty plaintext in the bin of the receiver. This paper reports an investigation for LUC-type cryptosystems under garbage-man-in-the middle (type 1) attack. Among all LUC-type cryptosystems, LUC, LUC3, and LUC4, 6 are selected to analyze their security. Results show that the attack fully success into the selected LUC-type cryptosystems under certain conditions.
2D surface flow models are useful to understand and predict the flow through breach, over a dyke or over the floodplains. This paper is aimed at the surface flows to study the behavior of flood waves. The open channel water flow in drains and rivers is considered in view of the fact that such flows are the source of flash flood. In order to predict and simulate the flood behavior, a mathematical model with the initial and boundary conditions is established using 2D Saint-Venant partial differential equations. Next, the corresponding model is discretized by using the explicit finite difference method and implemented on MATLAB. For the testing and implementation purpose a simple rectangular flow channel is considered. The output parameters like height or depth of water z (m), the fluid velocity u (m/s) and the volumetric flow rate Q (m3/sec) are simulated numerically and visualized for the different time steps. The initial simulation results are useful to understand and predict the flood behavior at different locations of flow channel at specific time steps and can be helpful in early flood warning systems. It is also suggested that the coupling of the subsurface flow with the surface flow may provide even better approximations for the flood circulation.
Human Activity Recognition (HAR) focuses on detecting people's daily regular activities based on time-series recordings of their actions or motions. Due to the extensive feature engineering and human feature extraction required by traditional machine learning algorithms, they are timeconsuming to develop. To identify complicated human behaviors, deep learning approaches are more suited since they can automatically learn the features from the data. In this paper, a feature-fusion concept on handcrafted features and deep learning features is proposed to increase the recognition accuracy of diverse human physical activities using wearable sensors. The deep learning model Long-Short Term Memory based Deep Recurrent Neural Network (LSTM-DRNN) will be used to extract deep features. By fusing the handcrafted produced features with the automatically extracted deep features through the use of deep learning, the performance of the HAR model can be improved, which will result in a greater level of accuracy in the HAR model. Experiments conducted on two publicly available datasets show that the proposed feature fusion achieves a high level of classification accuracy.
LUC-type cryptosystems are asymmetric key cryptosystems based on the Lucas sequence that is extended from RSA. The security challenge is comparable to RSA, which is based on the intractability of factoring a large number. This paper analysed the security of LUC, LUC3, and LUC4,6 cryptosystems using a common modulus attack. For a common modulus attack to be successful, a message must be transmitted to two distinct receivers with the same modulus. The strengths and limitations of the LUC, LUC3, and LUC4,6 cryptosystems when subjected to a common modulus attack were discussed as well. The results reveal that the LUC4,6 cryptosystem provides greater security than the LUC and LUC3.
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