In this paper the various driving style analysis solutions are investigated. An in-depth investigation is performed to identify the relevant machine learning and artificial intelligence algorithms utilised in current driver behaviour and driving style analysis systems. This review therefore serves as a trove of information, and will inform the specialist and the student regarding the current state of the art in driver style analysis systems, the application of these systems and the underlying artificial intelligence algorithms applied to these applications. The aim of the investigation is to evaluate the possibilities for unique driver identification utilizing the approaches identified in other driver behaviour studies. It was found that Fuzzy Logic inference systems, Hidden Markov Models and Support Vector Machines consist of promising capabilities to address unique driver identification algorithms if model complexity can be reduced.

Objectives: The objective of this study was to examine the uptake, user characteristics, and performance of the free WHO smartphone hearing screening test ( hearWHO) as a global hearing health promotion initiative.Method: We retrospectively examined the data of 242 626 tests conducted by adults (> 18 years) on the hearWHO app between February 2019 and May 2021. Test uptake was evaluated by country, WHO world region, test date, and demographics of age and gender.Results: The hearWHO test was completed in nearly every country globally ( n = 179/195), with the greatest uptake seen in China and India. Uptake was greatest in the Western Pacific (32.9%) and European (24.8%) WHO regions. There was a high uptake of tests (44%) by young adults under the age of 30 years. Referral rates were typically higher for older age groups in most WHO regions, except for the African and Eastern Mediterranean regions, where overall hearWHO test uptake was lowest. Most testing (49%) took place in March (2019–2021) coinciding with World Hearing Day (3rd of March) each year.Conclusions: Digital mhealth tools provide many benefits in healthcare, including health promotion, access to information, and services for hearing loss. The hearWHO test was mainly reaching younger adults, positioning it as an important measure for public health advocacy to prevent hearing loss. Since hearing loss is primarily age related, more targeted campaigns or community-based initiatives should be directed toward older adults.

Exact state-of-charge estimation is necessary for every application related to energy storage systems to protect the battery from deep discharging and overcharging. This leads to an improvement in discharge efficiency and extends the battery lifecycle. Batteries are a main source of energy and are usually monitored by management systems to achieve optimal use and protection. Coming up with effective methods for battery management systems that can adequately estimate the state-of-charge of batteries has become a great challenge that has been studied in the literature for some time. Hence, this paper analyses the different energy storage technologies, highlighting their merits and demerits. The various estimation methods for state-of-charge are discussed, and their merits and demerits are compared, while possible applications are pointed out. Furthermore, factors affecting the battery state-of-charge and approaches to managing the same are discussed and analysed. The different modelling tools used to carry out simulations for energy storage experiments are analysed and discussed. Additionally, a quantitative comparison of different technical and economic modelling simulators for energy storage applications is presented. Previous research works have been found to lack accuracy under varying conditions and ageing effects; as such, integrating hybrid approaches for enhanced accuracy in state-of-charge estimations is advised. With regards to energy storage technologies, exploring alternative materials for improved energy density, safety and sustainability exists as a huge research gap. The development of effective battery management systems for optimisation and control is yet to be fully exploited. When it comes to state-of-the-art simulators, integrating multiscale models for comprehensive understanding is of utmost importance. Enhancing adaptability across diverse battery chemistries and rigorous validation with real-world data is essential. To sum up the paper, future research directions and a conclusion are given.

Internet of things (IoT)-enabled wireless body area network (WBAN) is an emerging technology that combines medical devices, wireless devices, and non-medical devices for healthcare management applications. Speech emotion recognition (SER) is an active research field in the healthcare domain and machine learning. It is a technique that can be used to automatically identify speakers’ emotions from their speech. However, the SER system, especially in the healthcare domain, is confronted with a few challenges. For example, low prediction accuracy, high computational complexity, delay in real-time prediction, and how to identify appropriate features from speech. Motivated by these research gaps, we proposed an emotion-aware IoT-enabled WBAN system within the healthcare framework where data processing and long-range data transmissions are performed by an edge AI system for real-time prediction of patients’ speech emotions as well as to capture the changes in emotions before and after treatment. Additionally, we investigated the effectiveness of different machine learning and deep learning algorithms in terms of performance classification, feature extraction methods, and normalization methods. We developed a hybrid deep learning model, i.e., convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM), and a regularized CNN model. We combined the models with different optimization strategies and regularization techniques to improve the prediction accuracy, reduce generalization error, and reduce the computational complexity of the neural networks in terms of their computational time, power, and space. Different experiments were performed to check the efficiency and effectiveness of the proposed machine learning and deep learning algorithms. The proposed models are compared with a related existing model for evaluation and validation using standard performance metrics such as prediction accuracy, precision, recall, F1 score, confusion matrix, and the differences between the actual and predicted values. The experimental results proved that one of the proposed models outperformed the existing model with an accuracy of about 98%.

Objective: COVID-19 has been prohibitive to traditional audiological services. No- or low-touch audiological assessment outside a sound-booth precludes test batteries including bone conduction audiometry. This study investigated whether conductive hearing loss (CHL) can be differentiated from sensorineural hearing loss (SNHL) using pure-tone air conduction audiometry and a digits-in-noise (DIN) test. Design: A retrospective sample was analysed using binomial logistic regressions, which determined the effects of pure tone thresholds or averages, speech recognition threshold (SRT), and age on the likelihood that participants had CHL or bilateral SNHL. Study sample: Data of 158 adults with bilateral SNHL (n = 122; PTA_0.5–4 kHz > 25 dB HL bilaterally) or CHL (n = 36; air conduction PTA_{0.5–4 kHz} > 25 dB HL and ≥20 dB air bone gap in the affected ears) were included. Results: The model which best discriminated between CHL and bilateral SNHL used low-frequency pure-tone average (PTA), diotic DIN SRT, and age with an area under the ROC curve of 0.98 and sensitivity and specificity of 97.2 and 93.4%, respectively. Conclusion: CHL can be accurately distinguished from SNHL using pure-tone air conduction audiometry and a diotic DIN. Restrictions on traditional audiological assessment due to COVID-19 require lower touch audiological care which reduces infection risk.

The use of Internet of Things (IoT) technology in military settings has introduced the notion of “Internet of Battle Things” (IoBT), transforming modern warfare by interconnecting various equipment and systems essential for battlefield operations. This connectivity facilitates real-time communication, data sharing, and collaboration among military assets, enhancing situational awareness, decision-making processes, and overall operational effectiveness. The domain for IoBT encompasses a broad range of military assets, from drones and ground vehicles to soldier-worn wearables, sensors, and munitions. These assets are capable of collecting and transmitting critical information from the battlefield, including location data, status updates, environmental conditions, and the movements of adversaries. IoBT networks depend on robust communication networks, secure data transmission protocols, advanced data analytics for processing vast datasets, and seamless integration with command-and-control infrastructures. However, IoBT devices and systems function in dynamic and challenging battlefield conditions which present unique communication challenges. This study aims to review research efforts that provide current state-of-the-art solutions, their limitations, and emerging technologies. We classify these challenges into interoperability, power and energy management, security, and network resilience, while also discussing future research directions to improve communication in IoBT networks.