Distraction while driving occurs when a driver is engaged in non-driving activities. These activities reduce the driver’s attention and focus on the road, therefore increasing the risk of accidents. As a consequence, the number of accidents increases and infrastructure is damaged. Cars are now equipped with different safety precautions that ensure driver awareness and attention at all times. The first step for such systems is to define whether the driver is distracted or not. Different methods are proposed to detect such distractions, but they lack efficiency when tested in real-life situations. In this paper, four machine learning classification methods are implemented and compared to identify drivers’ behavior and distraction situations based on real data corresponding to different behaviors such as aggressive, drowsy and normal. The data were randomized for a better application of the methods. We demonstrate that the gradient boosting method outperforms the other used classifiers.

In this paper, a new modified particle swarm optimization, m-PSO, is proposed, in which the novelty consists of proposing a fitness-based particle swarm optimization algorithm, PSO, which adapts the particles’ behavior rather than the PSO parameters and where particles evolve differently considering their level of optimality. A multi-objective optimization, MO, approach is then built based on m-PSO. In the proposed method, particles with fitness better than the mean local best are only updated toward the global best, while others keep moving in a classical manner. The proposed m-PSO and its multi-objective version MO-m-PSO are then employed to solve the inverse kinematics of a 5-DOF robotic arm which is 3D-printed for educational use. In the MO-m-PSO approach of inverse kinematics, the arm IK problem is formulated as a multi-objective problem searching for an appropriate solution that takes into consideration the end-effector position and orientation with a Pareto front strategy. The IK problem is addressed as the optimization of the end-effector position and orientation based on the forward kinematics model of the systems which is built using the Denavit–Hartenberg approach. Such an approach allows to avoid classical inverse kinematics solvers challenges such as singularities, which may simply harm the existence of an inverse expression. Experimental investigations included the capacity of the proposal to handle random single points in the workspace and also a circular path planning with a specific orientation. The comparative analysis showed that the mono-objective m-PSO is better than the classical PSO, the CSA, and SSA. The multi-objective variants returned accurate results, fair and better solutions compared to multi-objective variants of MO-PSO, MO-JAYA algorithm, and MO-CSA. Even if the proposed method were applied to solve the inverse kinematics of and educational robotics arms for a single point as well as for a geometric shape, it may be transposed to solve related industrial robotized arms withthe only condition of having their forward kinematics model.

In recent years, Optical character recognition (OCR) has experienced a resurgence of interest especially for contemporary Arabic data. In fact, OCR development for printed and handwritten Arabic script is still a challenging task. These challenges are due to the specific characteristics of the Arabic script. In this work, we attempt to address these challenges by creating a deep learning OCR for Arabic document recognition called ADOCRNet. It is a novel deep learning framework whose architecture is built of layers of Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (BLSTM) trained by the so-called Connectionist Temporal Classification (CTC) algorithm. In order to assess the performance of our OCR, the proposed system is performed on two printed text datasets which are P-KHATT (text line images) and APTI (word images). It’s also evaluated on a handwritten Arabic text dataset IFN/ENIT (word images). According to the practical tests, the conceived model achieves strength recognition rates on the three datasets. ADOCRNet reaches a Character Error Rate (CER) of 0.01% on the P-KHATT dataset, 0.03% on the APTI dataset and a Word Error Rate (WER) of 1.09% on the IFN/ENIT dataset, which considerably outperforms the outcomes of the current systems.

Satisfying the world’s rapidly increasing demands in energy via the optimized management of available resources is becoming one of the most important research trends worldwide. When it comes to energy, it is very important to talk about decentralization, security, traceability and transparency. Thus, over the last few years, numerous research works have presented blockchain technology as the best novel business platform enabling a secure, transparent and tamper-proof energy management solution. In this paper, we conducted a systematic literature review (SLR) using the PRISMA framework of the different existing research studies related to the use of the blockchain technology in the energy sector, published between 2008 and 2021. We identified a total of 769 primary studies after intensive manual analysis and filtering, which we thoroughly assessed using various criteria to address six main research questions that covered the blockchain types, applications and platforms in the energy sector, the energy source types for which blockchain platforms are implemented, the emergent technologies that are combined to blockchain solutions, and the types of consensuses used in energy blockchains. Based on the collected survey data, we built a database to categorize the existing research works, identify research trends, and highlight knowledge gaps and potential areas for additional field study.

The increasing age of our society is connected to a rising number of people suffering from disorders. One such disorder is Parkinson’s disease (PD). Predictions indicate that the number of individuals affected by PD will more, than double in the future. Neurologists and data scientists consider handwriting as one of the motor symptoms of PD and recognize it as a valuable resource for detecting this disorder. Within this framework, we introduce an innovative system for Parkinson’s disease detection, which encompasses several key stages. The process commences with data augmentation and preprocessing, subsequently leading to the segmentation of online handwriting into Beta strokes. Following that, feature extraction is carried out utilizing the Beta-elliptical approach and the fuzzy perceptual detector. Finally, we employ bidirectional long short-term memory (BLSTM) for the classification task. To assess the performance of our system, we created a new online Arabic handwriting dataset designed for detecting Parkinson’s disease. The results we obtained affirm the efficacy of our proposed system. Through comprehensive evaluations conducted on the PaHaW dataset, we achieved good accuracy, thereby highlighting that our system surpasses the performance of existing systems.

Currently, numerous machine learning (ML) techniques are being applied in the field of renewable energy (RE). These techniques may not perform well if they do not have enough training data. Additionally, the main assumption in most of the ML algorithms is that the training and testing data are from the same feature space and have similar distributions. However, in many practical applications, this assumption is false. Recently, transfer learning (TL) has been introduced as a promising machine-learning framework to mitigate these issues by preparing extra-domain data so that knowledge may be transferred across domains. This learning technique improves performance and avoids the resource expensive collection and labeling of domain-centric datasets; furthermore, it saves computing resources that are needed for re-training new ML models from scratch. Lately, TL has drawn the attention of researchers in the field of RE in terms of forecasting and fault diagnosis tasks. Owing to the rapid progress of this technique, a comprehensive survey of the related advances in RE is needed to show the critical issues that have been solved and the challenges that remain unsolved. To the best of our knowledge, few or no comprehensive surveys have reviewed the applications of TL in the RE field, especially those pertaining to forecasting solar and wind power, load forecasting, and predicting failures in power systems. This survey fills this gap in RE classification and forecasting problems, and helps researchers and practitioners better understand the state of the art technology in the field while identifying areas for more focused study. In addition, this survey identifies the main issues and challenges of using TL for REs, and concludes with a discussion of future perspectives.