Telecommunication networks are growing exponentially due to their significant role in civilization and industry. As a result of this very significant role, diverse applications have been appeared, which require secured links for data transmission. However, Internet-of-Things (IoT) devices are a substantial field that utilizes the wireless communication infrastructure. However, the IoT, besides the diversity of communications, are more vulnerable to attacks due to the physical distribution in real world. Attackers may prevent the services from running or even forward all of the critical data across the network. That is, an Intrusion Detection System (IDS) has to be integrated into the communication networks. In the literature, there are numerous methodologies to implement the IDSs. In this paper, two distinct models are proposed. In the first model, a custom Convolutional Neural Network (CNN) was constructed and combined with Long Short Term Memory (LSTM) deep network layers. The second model was built about the all fully connected layers (dense layers) to construct an Artificial Neural Network (ANN). Thus, the second model, which is a custom of an ANN layers with various dimensions, is proposed. Results were outstanding a compared to the Logistic Regression algorithm (LR), where an accuracy of 97.01% was obtained in the second model and 96.08% in the first model, compared to the LR algorithm, which showed an accuracy of 92.8%.

Smart cities using the Internet of Things (IoT) can operate various IoT systems with better services that provide intelligent and efficient solutions for various aspects of urban life. With the rapidly growing number of IoT systems, the many smart city services, and their various quality of service (QoS) constraints, servers face the challenge of allocating limited resources across all Internet-based applications to achieve an efficient per-formance. The presence of a cloud in the IoT system of a smart city results in high energy con-sumption and delays in the network. Edge computing is based on a cloud computing framework where computation, storage, and network resources are moved close to the data source. The IoT framework is identical to cloud computing. The critical issue in edge computing when executing tasks generated by IoT systems is the efficient use of energy while maintaining delay limitations. In this paper, we study a multicriteria optimization approach for resource allocation with distributed edge computing in IoT-based smart cities. We present a three-layer network architecture for IoT-based smart cities. An edge resource allocation scheme based on an auctionable approach is proposed to ensure efficient resource computation for delay-sensitive tasks.

Congestion in the world’s traffic systems is a major issue that has far-reaching repercussions, including wasted time and money due to longer commutes and more frequent stops for gas. Modern scholarly challenges arise alongside chances to greatly enhance traffic prediction made possible by the integration of modern technologies into transportation systems. Various techniques have been utilized for the purpose of traffic flow prediction, including statistical, machine learning, and deep neural networks. In this paper, deep neural network architecture based on long short term memory (LSTM), bi-directional version, and gated recurrent units (GRUs) layers have been structured to build the deep neural network, in order to predict the performance of the traffic flow in four distinct junctions which has a great impact on the Internet of vehicles’ applications. The structure comprised of sixteen-layers, five of them are GRU-layers and one bi-directional LSTM-layer. The dataset employed in this work involved four congested junctions. The dataset extended from the first of November 2016 to 30th of June 2017. Cleaning and preprocessing operations were achieved on the dataset before feeding it to the designed deep neural network of this paper. Results show that the suggested method produced a comparable performance with respect to state-of-the art approaches.

Computers and various imaging technologies effectively enhanced the diagnostic procedures of medical imaging, where various medical images enhance the early diagnosis, characterizing and discriminating of different health problems, which lead to efficient healthcare services and treatments. Digital image processing and medical images analysis can significantly support medical diagnosis by providing the necessary tools for automatic detection, extracting significant information and accurate measurement of visible abnormalities. However, there are many challenges related to medical images as well as challenges that arise during the various stages of image processing. This paper provides an overview of different challenges related to medical images and medical image processing. The most prominent and significant challenges are identified and introduced not to discourage the interested researchers in this field of image processing, but to fill the gap of missing a comprehensive overview on these challenges.

The Intrusion Detection System (IDS) is an important feature that should be integrated in high density sensor networks, particularly in wireless sensor networks (WSNs). Dynamic routing information communication and an unprotected public media make them easy targets for a wide variety of security threats. IDSs are helpful tools that can detect and prevent system vulnerabilities in a network. Unfortunately, there is no possibility to construct advanced protective measures within the basic infrastructure of the WSN. There seem to be a variety of machine learning (ML) approaches that are used to combat the infiltration issues plaguing WSNs. The Slime Mould Algorithm (SMA) is a recently suggested ML approach for optimization problems. Therefore, in this paper, SMA will be integrated into an IDS for WSN for anomaly detection. The SMA’s role is to reduce the number of features in the dataset from 41 to five features. The classification was accomplished by two methods, Support Vector Machine with polynomial core and decision tree. The SMA showed comparable results based on the NSL-KDD dataset, where 99.39%, 0.61%, 99.36%, 99.42%, 99.33%, 0.58%, and 99.34%, corresponding to accuracy, error rate, sensitivity, specificity, precision, false positive rate, and F-measure, respectively, are obtained, which are significantly improved values when compared to other works.