Convolutional neural networks (CNNs) have shown outstanding image classification performance, having been successfully applied in several real‐world applications. However, there are performance limitations encountered in CNNs and few works have pointed out these limitations across the literature. Therefore, this survey aims to provide a comprehensive explanation of both the importance and performance limitations of CNNs on image classification tasks. In this paper, we start with a brief CNN background and follow the main CNN architectures along with their contributions and benchmark datasets. We propose a classification of the CNNs performance limitations, which are grouped into four categories: labeled datasets, translation invariance, adversarial attacks, and spatial relationship. In addition, we describe some approaches that are currently being developed to overcome these performance limitations. Finally, we introduce specific ongoing research to address these performance limitations through capsule networks (CapsNets) and CNNs combined with CapsNets.

Recent advancements in cybersecurity threats and malware have brought into question the safety of modern software and computer systems. As a direct result of this, artificial intelligence-based solutions have been on the rise. The goal of this paper is to demonstrate the efficacy of memory-optimized machine learning solutions for the task of static analysis of software metadata. The study comprises an evaluation and comparison of the performance metrics of three popular machine learning solutions: artificial neural networks (ANN), support vector machines (SVMs), and gradient boosting machines (GBMs). The study provides insights into the effectiveness of memory-optimized machine learning solutions when detecting previously unseen malware. We found that ANNs shows the best performance with 93.44% accuracy classifying programs as either malware or legitimate even with extreme memory constraints.

Service robots are expected to help us in different tasks in various environments such as homes, hospitals and offices. This work presents the first steps towards building a service robot at our university. The robot is provided with a topological map of a building floor (environmental map). Using this map the robot learns a path from one location to another by means of reinfocerment learning. During execution the robot is provided with a navigation map, related to the environmental map. We make use of a comercial Roomba robot and show how to create an interface in order to control it. Service robots are expected to help us in different tasks in various environments such as homes, hospitals and offices. This work presents the first steps towards building a service robot at our university. The robot is provided with a topological map of a building floor (environmental map). Using this map the robot learns a path from one location to another by means of reinfocerment learning. During execution the robot is provided with a navigation map, related to the environmental map. We make use of a comercial Roomba robot and show how to create an interface in order to control it.