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All Journal Bulletin of Electrical Engineering and Informatics
Hussein Ali, Adnan
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Electrical & Electronics Engineering

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A radio frequency identification based smart shopping trolley system for automated billing Kamil Naji, Maham; Desher Farhood, Alaa; Fadhil Fahad, Hayder; Hussein Ali, Adnan
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.4490

Abstract

On holidays and weekends, we can notice a large rush at shopping centres in metro areas. This is exacerbated when there are substantial discounts and deals. By applying the notion of internet of thing (IoT) for concerning all things in the grocery store, an automated smart shopping system is created. Each product within such scheme had an economical radio frequency identification (RFID) tag positioned with it. Once an item has been added to a smart cart, the information about the merchandise is quickly read by the RFID reader on the cart. The outcome is, direct invoicing is done out from shopping cart, saving clients from having to wait in a long line at the checkout. The product's expiration date is also displayed and broken products can be determined by their weight. As a result, expired and damaged products will not be taken into account when calculating the cost. Furthermore, this system gains the addition of smart shelves incorporating RFID readers which may check stock and possibly update a central server. Inventory control becomes simpler as a result. Finally, cashier stations could verify a customer's purchase. As a result, billing may be done right in the trolley, saving clients a lot of time.
Behavioral drowsiness detection system execution based on digital camera and MTCNN deep learning Majeed, Ali Hassan; Hussein Ali, Adnan; A. Al-Hilali, Aqeel; Sameer Jabbar, Mohanad; Ghasemi, Safiye; G. S. Al-Safi, Mohammed
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5252

Abstract

Drowsy driving is a major cause of road accidents worldwide, necessitating the development of effective drowsiness detection systems. Each year, there are more accidents and fatalities than ever before for a variety of causes. For instance, there were 22,952 fatalities and 79,545 injuries as a result of nearly 66,500 vehicle accidents in the last 10 years. In this paper, we propose a novel approach for detecting drowsiness based on behavioral cues captured by a digital camera and utilizing the multi-task cascaded convolutional neural network (MTCNN) deep learning algorithm. A high-resolution camera records visual indications like closed or open eye movement to base the technique on the driver's behavior. In order to measure a car user's weariness in the present frame of reference, eyes landmarks are evaluated, which results in the identification of a fresh constraint known as "eyes aspect ratio." A picture with a frame rate of 60 frames per second (f/s) and a resolution of 4,320 eyeballs was used. The accuracy of sleepiness detection was more than 99.9% in excellent lighting and higher than 99.8% in poor lighting, according to testing data. The current study did better in terms of sleepiness detection accuracy than a lot of earlier investigations. 
Co-Authors A. Al-Hilali, Aqeel Desher Farhood, Alaa Fadhil Fahad, Hayder G. S. Al-Safi, Mohammed Ghasemi, Safiye Kamil Naji, Maham Majeed, Ali Hassan Sameer Jabbar, Mohanad