Presently, most deaths are caused by heart disease. To overcome this situation, heartbeat sound analysis is a convenient way to diagnose heart disease. Heartbeat sound classification is still a challenging problem in heart sound segmentation and feature extraction. Dataset-B applied in this study that contains three categories Normal, Murmur and Extra-systole heartbeat sound. In the purposed framework, we remove the noise from the heartbeat sound signal by applying the band filter, After that we fixed the size of the sampling rate of each sound signal. Then we applied down-sampling techniques to get more discriminant features and reduce the dimension of the frame rate. However, it does not affect the results and also decreases the computational power and time. Then we applied a purposed model Recurrent Neural Network (RNN) that is based on Long Short-Term Memory (LSTM), Dropout, Dense and Softmax layer. As a result, the purposed method is more competitive compared to other methods.

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User reviews on social networking platforms like Twitter, Facebook, and Google+, etc. have been gaining growing interest on account of their wide usage in sentiment analysis which serves as the feedback to both public and private companies, as well as, governments. The analysis of such reviews not only plays a noteworthy role to improve the quality of such services and products but helps to devise marketing and financial strategies to increase the profit for companies and customer satisfaction. Although many analysis models have been proposed, yet, there is still room for improving the processing, classification, and analysis of user reviews which can assist managers to interpret customers feedback and elevate the quality of products. This study first evaluates the performance of a few machine learning models which are among the most widely used models and then presents a voting classifier Gradient Boosted Support Vector Machine (GBSVM) which is constituted of gradient boosting and support vector machines. The proposed model has been evaluated on two different datasets with term frequency and three variants of term frequency-inverse document frequency including uni-, bi-, and tri-gram as features. The performance is compared with other state-of-the-art techniques which prove that GBSVM outperforms these models.

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The modern age of technology in which most of the customer needs to wait in the supermarket for shopping because it is a highly time-consuming process. A huge crowd in the supermarket at the time of discount offers or weekends makes trouble to wait in long queues because of a barcode-based billing process. In this regard, the Internet of Things (IoT) based Smart Shopping Cart is proposed which consists of Radio Frequency Identification (RFID) sensors, Arduino microcontroller, Bluetooth module, and Mobile application. RFID sensors depend on wireless communication. One part is the RFID tag attached to each product and the other is RFID reader that reads the product information efficiently. After this, each product information shows in the Mobile application. The customer easily manages the shopping list in Mobile application according to preferences. Then shopping information sends to the server wirelessly and automatically generates billing. This experimental prototype is designed to eliminate time-consuming shopping process and quality of services issues. The proposed system can easily be implemented and tested at a commercial scale under the real scenario in the future. That is why the proposed model is more competitive as compared to others.