A total of 8.46 million tons of date fruit are produced annually around the world. The date fruit is considered a high-valued confectionery and fruit crop. The hot arid zones of Southwest Asia, North Africa, and the Middle East are the major producers of date fruit. The production of dates in 1961 was 1.8 million tons, which increased to 2.8 million tons in 1985. In 2001, the production of dates was recorded at 5.4 million tons, whereas recently it has reached 8.46 million tons. A common problem found in the industry is the absence of an autonomous system for the classification of date fruit, resulting in reliance on only the manual expertise, often involving hard work, expense, and bias. Recently, Machine Learning (ML) techniques have been employed in such areas of agriculture and fruit farming and have brought great convenience to human life. An automated system based on ML can carry out the fruit classification and sorting tasks that were previously handled by human experts. In various fields, CNNs (convolutional neural networks) have achieved impressive results in image classification. Considering the success of CNNs and transfer learning in other image classification problems, this research also employs a similar approach and proposes an efficient date classification model. In this research, a dataset of eight different classes of date fruit has been created to train the proposed model. Different preprocessing techniques have been applied in the proposed model, such as image augmentation, decayed learning rate, model checkpointing, and hybrid weight adjustment to increase the accuracy rate. The results show that the proposed model based on MobileNetV2 architecture has achieved 99% accuracy. The proposed model has also been compared with other existing models such as AlexNet, VGG16, InceptionV3, ResNet, and MobileNetV2. The results prove that the proposed model performs better than all other models in terms of accuracy.

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With all the recent advancements in the electronic world, hardware is becoming smaller, cheaper and more powerful; while the software industry is moving towards service-oriented integration technologies. Hence, service oriented architecture is becoming a popular platform for the development of applications for distributed embedded real-time system (DERTS). With rapidly increasing diversity of services on the internet, new demands have been raised concerning the efficient discovery of heterogeneous device services in the dynamic environment of DERTS. Context-awareness principles have been widely studied for DERTS; hence, it can be used as an additional set of service selection criteria. However, in order to use context information effectively, it should be presented in an unambiguous way and the dynamic nature of the embedded and real-time systems should be considered. To address these challenges, the authors present a service discovery framework for DERTS which uses context-aware ontology of embedded and real-time systems and a semantic matching algorithm to facilitate the discovery of device services in embedded and real-time system environments. The proposed service discovery framework also considers the associated priorities with the requirements posed by the requester during the service discovery process.

Surveillance of crowded places can benefit from improved techniques of anomaly detection in crowd videos. Several existing methods have detected various types of crowd abnormal behaviors by using spatial and temporal information got from videos. So far as real-time detection of anomalies is concerned, special attention must be given to reducing the model complexity that leads to computational and memory loads. This paper proposes a low computational cost approach to detect crowd anomalies. The proposed approach avoids the expensive optical flow calculations by adopting a pre-trained 2D convolutional neural network (CNN) for motion information and implements a lighter form of the 2D CNN to achieve high recognition accuracy at low computational cost. Experiments on public datasets show that the proposed model outperforms the existing approaches in terms of detection accuracy alongside providing better performance in generating input frames.