Data pre-processing is considered as the core stage in machine learning and data mining. Normalization, discretization, and dimensionality reduction are well-known techniques in data pre-processing. This research paper seeks to examine the effects of Min-max, Z-score, Decimal Scaling, and Logarithm to the base 2 on the accuracy of J48 classifier using the NSL-KDD dataset. Experiments were conducted using the above-listed methods and their individual results were compared to each other. Principal component analysis (PCA) and Linear Discriminant Analysis (LDA) were tested for dimensionality reduction; furthermore, a hybrid combination of PCA and LDA was attempted and the performance showed an improved classification accuracy compared to the individual methods.

The recent advances in technology, the increased dependence on electrical energy and the emergence of the fourth industrial revolution (Industry 4.0) were all factors in the increased need for smart, efficient and reliable energy systems. This introduced the concept of the Smart Grid (SG). A SG is a potential replacement for older power grids, capable of adapting and distributing energy based on demand. SG systems are complex. They combine various components and have high requirements for real time reliable operation. This paper attempts to provide an overview of SG systems, by outlining SG architecture and various components. It also introduces communication technologies, integration and network management tools that are involved in SG systems. In addition, the paper highlights challenges and issues that need to be addressed for a successful implementation of SG. Finally, we provide suggestions for future research directions.

The Internet of Things (IoT) concept has recently been presented as the next revolution and a part of the internet of the future. IoT consists of billions of uniquely identified smart objects ‘things’ with communication ability over the internet. These smart objects generate useful information for the provision of better services and improve the quality of life in different domains. However, there is a general lack of an integrated vision or a plan for selection and implementation of these smart objects. This paper presents the required tools and technologies (such as sensors, RFID, processors, actuators and communication models) that are essential in the IoT paradigm. The major objective of this review is to provide more detailed information about IoT smart objects, the building block of IoT, and conduct a comprehensive overview of IoT architecture with different scenarios. Moreover, this paper gives insight into IoT networking and the new type of low power wide area network (LPWAN) leading technologies in the licensed and unlicensed spectrum. Finally, this paper provides perspectives for future research and developments.

We conduct relatively extensive investigations of automatic hate speech (HS) detection using different State-of-The-Art (SoTA) baselines across 11 subtasks spanning six different datasets. Our motivation is to determine which of the recent SoTA models is best for automatic hate speech detection and what advantage methods, such as data augmentation and ensemble, may have on the best model, if any. We carry out six cross-task investigations. We achieve new SoTA results on two subtasks—macro F1 scores of 91.73% and 53.21% for subtasks A and B of the HASOC 2020 dataset, surpassing previous SoTA scores of 51.52% and 26.52%, respectively. We achieve near-SoTA results on two others—macro F1 scores of 81.66% for subtask A of the OLID 2019 and 82.54% for subtask A of the HASOC 2021, in comparison to SoTA results of 82.9% and 83.05%, respectively. We perform error analysis and use two eXplainable Artificial Intelligence (XAI) algorithms (Integrated Gradient (IG) and SHapley Additive exPlanations (SHAP)) to reveal how two of the models (Bi-Directional Long Short-Term Memory Network (Bi-LSTM) and Text-to-Text-Transfer Transformer (T5)) make the predictions they do by using examples. Other contributions of this work are: (1) the introduction of a simple, novel mechanism for correcting Out-of-Class (OoC) predictions in T5, (2) a detailed description of the data augmentation methods, and (3) the revelation of the poor data annotations in the HASOC 2021 dataset by using several examples and XAI (buttressing the need for better quality control). We publicly release our model checkpoints and codes to foster transparency.

As an effective mechanism acting on the intermediate nodes to support end-to-end congestion control, Active Queue Management (AQM) takes a trade-off between link utilization and delay experienced by data packets. In this paper a linear quadratic optimal controller was designed based on linear control theory for TCP/AQM router. The design specifications, depends on choosing weighting matrices Q and R. One must carry out a trial- and- error process to choose the weighting matrices that can satisfy the design specifications. To overcome this difficulty we employ the Genetic Algorithm (GA) to find the proper weighting matrices. This idea gives a new alternative procedure in time varying feedback control to improve the stability performance. The controller simulation results show the efficiency of the proposed controller.

Cerebral palsy (CP) is a common reason for human motor ability limitations caused before birth, through infancy or early childhood. Poor head control is one of the most important problems in children with level IV CP and level V CP, which can affect many aspects of children’s lives. The current visual assessment method for measuring head control ability and cervical range of motion (CROM) lacks accuracy and reliability. In this paper, a HeadUp system that is based on a low-cost, 9-axis, inertial measurement unit (IMU) is proposed to capture and evaluate the head control ability for children with CP. The proposed system wirelessly measures CROM in frontal, sagittal, and transverse planes during ordinary life activities. The system is designed to provide real-time, bidirectional communication with an Euler-based, sensor fusion algorithm (SFA) to estimate the head orientation and its control ability tracking. The experimental results for the proposed SFA show high accuracy in noise reduction with faster system response. The system is clinically tested on five typically developing children and five children with CP (age range: 2–5 years). The proposed HeadUp system can be implemented as a head control trainer in an entertaining way to motivate the child with CP to keep their head up.