HACR-MDL: HANDWRITTEN ARABIC CHARACTER RECOGNITION MODEL USING DEEP LEARNING

Despite the enormous effort and prior research, Arabic handwritten character recognition still has a deep, wide-ranging, and untapped scope for study owing to the enormous challenges faced in this research area. The reason for such challenges is that the Arabic script comprises 28 alphabets, each of which can be written in two to four different forms depending on where it appears in a word—beginning, middle, end, or isolated. The Convolutional Neural Network (CNN or ConvNet) is a subtype of neural network that is commonly used in image classification, speech recognition, video processing, object detection, and segmentation because its built-in convolutional layer reduces the high dimensionality of images without losing significant information. Hence, the scope of this study is to examine the classification performance of various deep CNN models on offline handwritten Arabic character recognition. Based on the experimental comparative studies, this research proposes a Handwritten Arabic Character Recognition Model using Deep Learning (HACR-MDL), a modified CNN model. The proposed model is trained and tested using the AHCD dataset achieving an accuracy of 98.54%. The results achieved showed that HACR outperformed the recent research offline handwritten Arabic character recognition in terms of model complexity, speed, model parameters, and performance metrics

A Reliable and Efficient Tracking System Based on Deep Learning for Monitoring the Spread of COVID-19 in Closed Areas

Since 2020, the world is still facing a global economic and health crisis due to the COVID-19 pandemic. One approach to fighting this global crisis is to track COVID-19 cases by wireless technologies, which requires receiving reliable, efficient, and accurate data. Consequently, this article proposes a model based on Lagrange optimization and a distributed deep learning model to assure that all required data for tracking any suspected COVID-19 patient is received efficiently and reliably. Finding the optimum location of the Radio Frequency Identifier (RFID) reader relevant to the base station results in the reliable transmission of data. The proposed deep learning model, developed using the one-dimensional convolutional neural network and a fully connected network, resulted in lower mean absolute squared errors when compared to state-of-the-art regression benchmarks. The proposed model based on Lagrange optimization and deep learning algorithms is evaluated when changing different network parameters, such as requiring signal-to-interference-plus-noise-ratio, reader transmission power, and the required system quality-of-service. The analysis of the obtained results, which indicates the appropriate transmission distance between an RFID reader and a base station, shows the effectiveness and the accuracy of the proposed approach, which leads to an easy and efficient tracking system

Enhancing the Reliability of Communication between Vehicle and Everything (V2X) Based on Deep Learning for Providing Efficient Road Traffic Information

Developing efficient communication between vehicles and everything (V2X) is a challenging task, mainly due to the characteristics of vehicular networks, which include rapid topology changes, large-scale sizes, and frequent link disconnections. This article proposes a deep learning model to enhance V2X communication. Various channel conditions such as interference, channel noise, and path loss affect the communication between a vehicle (V) and everything (X). Thus, the proposed model aims to determine the required optimum interference power to enhance connectivity, comply with the quality of service (QoS) constraints, and improve the communication link reliability. The proposed model fulfills the best QoS in terms of four metrics, namely, achievable data rate (Rb), packet delivery ratio (PDR), packet loss rate (PLR), and average end-to-end delay (E2E). The factors to be considered are the distribution and density of vehicles, average length, and minimum safety distance between vehicles. A mathematical formulation of the optimum required interference power is presented to achieve the given objectives as a constrained optimization problem, and accordingly, the proposed deep learning model is trained. The obtained results show the ability of the proposed model to enhance the connectivity between V2X for improving road traffic information efficiency and increasing road traffic safety

Effect of cow colostrum on the performance and survival rate of local newborn piglets in Benin Republic

peer reviewedaudience: researcher, professional, studentThe effect of bovine colostrum, including its thermally labile compounds, on the survival and growth performance of local breed piglets reared by their mother, in Benin, was evaluated over a 49-day trial. Three groups of 16 piglets, stemming from two primiparous sows belonging to a unique traditional farm, were respectively fed for the first 48 h of life with either bovine colostrum heated to 85 °C for 30 min, or thawed bovine colostrum, or colostrum from the mother. Thereafter, the animals that received bovine colostrum turned back to their mother. At day 21, almost all piglets from the group that received heated colostrum died. The highest total weight gain was obtained in the group that received thawed bovine colostrum (P ˂ 0.01), followed by the group left with the mother. Corresponding average daily gains (ADGs) were 56, 34 and 2 g/day, respectively (P ˂ 0.05). At the end of the trial, the treatment effect was highly significant on the survival of piglets (100% in the thawed colostrum group vs. 00 and 50%, respectively, in the heated colostrum group and in the group left with the mother). At day 49, numerically higher weight and ADGs were obtained in the group that received thawed cow colostrum. Thawed bovine colostrum improved the growth performance and piglet survival in the local pig breed in Benin, probably owing to thermally labile components. Bovine colostrum may be used in our farms in order to reduce pre-weaning mortality, improve the profitability of livestock farmers, and ensure survival of traditional farms. The use of bovine colostrum on farms could be facilitated by collaboration between pig farmers and bovine farmers. It could also be facilitated by the creation of a colostrum bank