Many datasets used to train artificial intelligence systems to recognize potholes, such as the challenging sequences for autonomous driving (CCSAD) and the Pacific Northwest road (PNW) datasets, do not produce satisfactory results. This is due to the fact that these datasets present complex but realistic scenarios of pothole detection tasks than popularly used datasets that achieve better results but do not effectively represents realistic pothole detection task. In remote sensing, super-resolution generative adversarial networks (GAN), such as enhanced super-resolution generative adversarial networks (ESRGAN), have been employed to mitigate the issues of small-object detection, which has shown remarkable performance in detecting small objects from low-quality images. Inspired by this success in remote sensing, we apply similar techniques with an ESRGAN super-resolution network to improve the image quality of road surfaces, and we use different object detection networks in the same pipeline to detect instances of potholes in the images. The architecture we propose consists of two main components: ESRGAN and a detection network. For the detection network, we employ both you only look once (YOLOv5) and EfficientDet networks. Comprehensive experiments on different pothole detection datasets show better performance for our method compared to similar state-of-the-art methods for pothole detection.

Cloud based health platforms (CBHP) have tremendous capacity to meet patient’s health needs. The benefits inherent in CBHP position it to be relevant for efficient healthcare delivery. Nonetheless, studies have shown that the adoption of new technologies is sometimes a challenge especially in developing nations. This study, therefore, aim to examine, identify and evaluate the factors affecting healthcare professionals’ intention to accept the cloud-based health center (CBHC) in developing countries. The research study focuses on hospitals in North-central of Nigeria. Using questionnaire adopted from related studies, a cross-sectional study was carried out of 300 healthcare professionals selected from medical health institutions in Benue State Nigeria. The study adopted the Unified Theory of Acceptance and use of Technology Extended (UTAUT2). Data analysis was carried out using SPSS (V20.0) and LISREL (V9.30) generally employed in Structural Equation Modeling to examine components and path model. The Socio technical design method was used to develop the CBHC. Findings portrays performance expectancy, cloud based health knowledge, IT infrastructure and social influence to have significant effects on the intentions of healthcare professionals to accept and use the CBHC. These findings, agrees with prior related studies. Our findings impacts the body of knowledge in that it identifies important areas the studies can be useful, especially, to managers and healthcare policy makers in the planning/implementation of health cloud. Research findings from the theoretical acceptance model identifies the factors and barriers towards sustainable cloud based health center solutions to meet the healthcare needs of people in remote communities.

Handwriting and signature biometrics have a long history in the literature, especially in terms of identity recognition and/or verification; nevertheless, it reveals more information therefore provides more opportunities for personal characteristics estimation, particularly, emotional state. However, almost all publicly or commercially available databases do not include contributors’ demographic labels or emotional status in addition to their identification labels, leading these available databases to be only useful for verification and identification based research studies. For this reason, this paper proposes both offline and online handwriting and signature biometric database with a wide range of ground truths (emotional status labels – happy, sad and stress) in addition to the identity labels and tries to predict ones’ emotional state, namely happy, sad and stress, from their online biometric handwriting and signatures. The proposed database comprised total of 134 participants with 804 handwriting and 8040 signature biometric samples. The database presented also includes individuals’ demographic information such as age, gender, handedness, education level and nationality. Subsequently, there are several experiments have been conducted, with different thresholds, which present the usability of the proposed database and preliminary results of emotional state prediction from both signature and handwriting biometrics. The experiments achieved remarkable success especially on stress prediction for handwriting. On the other hand, considering the results from signature biometrics, it is observed that happy and then sad and finally stress class forms the most part of the prediction accuracy.