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Spam is some unwanted material that may be put in the form of images. While many machine learning approaches are effective at detecting textual spam, this is not true for image spam. In this paper, a new framework for identifying image spams is proposed. The images are divided into two categories: undesirable material contained in the form of images which is referred to as a spam image, whereas anything else is referred to as a ham image. Our proposed methodology is based on applying different pre-trained deep learning models, including InceptionV3, Densely Connected Convolutional Networks 121(DenseNet121), Residual Networks (ResNet50), Visual Geometry Group (VGG16) and MobileNetV2, to filter out the unwanted spam images. Different standard test datasets such as Dredze Dataset, Image Spam Hunter (ISH) Dataset and Improved Dataset are utilized in this paper for performance testing. Furthermore, transfer learning and data augmentation are employed to address the issue of a shortage of labeled data. In our implementation, the fully connected (FC) layer in the aforementioned pre-trained models is replaced with a Support Vector Machine (SVM) classifier, resulting in an improved accuracy. The obtained results reveal that ResNet50 model yields the best performance achieving 99.87% accuracy, 99.88% area under the curve (AUC), 99.98% sensitivity, 99.79% precision, 98.99% F1 score and a computational testing time of in the order of one to two seconds for the ISH dataset.

The tendency towards carbon dioxide reduction greatly stimulates the popularity of electric vehicles against conventional vehicles. However, electric vehicle chargers represent a huge electric burden, which affects the performance and stability of the grid. Various optimization methodologies have been proposed in literature to enhance the performance of the distribution grids. However, existing techniques handle the raised issues from individual perspectives and/or with limited scopes. Therefore, this paper aims to develop a distributed controller-based coordination scheme in both medium and low voltage networks to handle the electric vehicles’ charging impact on the power grid. The scope of this work covers improving the network voltage profile, reducing the total active and reactive power, reducing the load fluctuations and total charging cost, while taking into consideration the random arrivals/departures of electric vehicles and the vehicle owners’ preferred charging time zones with vehicle-to-grid technology. Simulations are carried out to prove the success of the proposed method in improving the performance of IEEE 31-bus 23 kV system with several 415 V residential feeders. Additionally, the proposed method is validated using Controller Hardware-in-the-Loop. The results show that the proposed method can significantly reduce the issues that appear in the electric power grid during charging with minor changes in the existing grid. The results prove the successful implementation of different types of charging, namely, ultra-fast, fast, moderate, normal and vehicle-to-grid charging with minimum charging cost to enhance the owner’s satisfaction level.

The use of capacitors in power systems has many well-known benefits that include improvement of the system power factor, improvement of the system voltage profile, increasing the maximum flow through cables and transformers and reduction of losses due to the compensation of the reactive component of power flow. By decreasing the flow through cables, the systems’ loads can be increased without adding any new cables or overloading the existing cables. These benefits depend greatly on how capacitors are placed in the system. In this paper, the problem of how to optimally determine the locations to install capacitors and the sizes of capacitors to be installed in the buses of radial distribution systems is addressed. The proposed methodology uses loss sensitivity factors to identify the buses requiring compensation and then a discrete particle swarm optimization algorithm (PSO) is used to determine the sizes of the capacitors to be installed. The proposed algorithm deals directly with discrete nature of the design variables. The results obtained are superior to those reported in Prakash and Sydulu (2007).

Effective utilization plans for various resources at a container terminal are essential to reducing the turnaround time of cargo vessels. Among the scarcest resources are the berth and its associated cranes. Thus, two important optimization problems arise, which are the berth allocation and quay crane assignment problems. The berth allocation problem deals with the generation of a berth plan, which determines where and when a ship has to berth alongside the quay. The quay crane assignment problem addresses the problem of determining how many and which quay crane(s) will serve each vessel. In this paper, an integrated heuristics-based solution methodology is proposed that tackles both problems simultaneously. The preliminary experimental results show that the proposed approach yields high quality solutions to such an NP-hard problem in a reasonable computational time suggesting its suitability for practical use.