Machine learning (ML) approaches cover several aspects of daily life tasks, including knowledge representation, data analysis, regression, classification, recognition, clustering, planning, reasoning, text recommendation, and perception. The ML approaches enable applications to learn and adapt with or without being directly programmed from previous data or experience. The ML techniques, coupled with current technologies, provide a range of solutions, starts from vision-based applications to text-generation applications. To this end, this article presents a comprehensive overview of the approaches of ML, including supervised, unsupervised, semi-supervised, reinforcement, and self-learning. This review critically examines the roles performed by these aforementioned approaches in terms of their weaknesses and strengths. Furthermore, within this study, a new comparative analysis is conducted by reviewing existing studies and evaluating ML techniques using metrics including data requirement, accuracy, complexity, interpretability, scalability, applications, and challenges. Thereafter, the implemented ML techniques are classified, and their key findings are examined. The comprehensive review demonstrates that neither standalone nor hybrid ML techniques can completely satisfy all of the evaluated metrics, the necessity of customized solutions based on the requirements of particular applications.

People gender and activities recognition are becoming a hot topic in our daily applications through gait information. The very well-known applications are safety-health, security, entertainment and billing. Numerous data mining and machine learning algorithms have been proposed for such issue. Equally, many technologies could be used to observe the people activities to identify their gender and activities. However, the existing solutions and applications suffer from privacy and cost to deploy the solution and their obtained accuracy. For example, when the CCTV camera or Kinect sensors technology are used to identify people, such technologies will violate the privacy since most of the people do not want to take their pictures or videos during their daily activities. Therefore, to tackle such issue, this paper presents a new scheme to identify the gender of the people via onboard Smartphone sensors including accelerometer sensor. Such a scheme requires little interaction with the people; individuals would simply have to hold his/her smartphone and walk normally. Four different data mining techniques and machine learning algorithms are used to identify people gender including: Decision Tree (DT), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN) and Deep learning algorithm (recurrent-neural-network long-short-term-memory ‘RNN-LSTM’). Further, a set of experiments are conducted via Android-based smartphones (to read smartphone accelerometer sensor) and MATALB-2018a packages used to perform the validity of the scheme. The obtained results from the experiments show that the accuracy of the gender identification is about 94.11% via deep learning algorithm (RNN-LSTM) and is around 83.75% by using DT algorithm.

The number of connected mobile devices and Internet of Things (IoT) is growing around us, rapidly. Since, most of the people daily activities are relying on these connected things or devices. Specifically, this past year (with COVID-19) changed daily life in abroad and this is increased the use of IoT enabled technologies in health sector, work, and play. Further, the most common service via using these technologies is the localization/positioning service for different applications including: geo-tagging, billing, contact tracing, health-care system, point-of-interest recommendations, social networking, security, and more. Despite the availability of a large number of localization solutions in the literature, the precision of localization cannot meet the needs of consumers. For that reason, this paper provides an in-depth investigation of the existing technologies and techniques in the localization field, within the IoT era. Furthermore, the benefits and drawbacks of each technique with enabled technologies are illustrated and a comparison between the utilized technologies in the localization is made. The paper as a guideline is also going through all of the metrics that may be used to assess the localization solutions. Finally, the state-of-the-art solutions are examined, with challenges and perspectives regarding indoors/outdoors environments are demonstrated.