Drivers face many challenges when driving under unfamiliar traffic regulations, which may lead to a reduction in road safety. The need to adjust to different traffic rules could be a major factor toward a safer drive. Gamification is a promising way to enhance the user engagement in non-game tasks. In this paper, we hypothesize that gamification can improve driving performance and minimize the number of driving errors when driving under unfamiliar traffic regulations and thus enhance road safety. A game was designed to provide gamification elements in a simulated driving environment with unfamiliar traffic regulations where the players were motivated to reach the target with no errors. In the experiments, 14 participants who were not familiar with the designed traffic regulations were asked to drive a car simulator in two scenarios. The first scenario had no gamification elements, whereas the second one included gamification elements. The results indicated that gamification significantly helped the participants to drive in the correct traffic flow with the proper use of vehicle configuration. Our findings show that gamified simulation is a reasonable method to adjust the required driving performance and behavior to safely drive under unfamiliar traffic regulations.

Machine learning has evolved from a lab curiosity to a widely used technology that is fundamentally reliant on ground truth data for model training and evaluation. This research addresses the challenges in obtaining accurate ground truth data due to limited domain expertise, sparse and unrepresentative datasets, and the high costs associated with data acquisition. The quality of this data significantly influences the reliability of machine learning models, prompting research into methods to improve ground-truth reliability. This research proposes a framework that utilize blockchain-based crowdsourcing for ground-truth data annotation. Blockchain technology, with its decentralized immutable ledger system, offers a secure method for data verification and collection from decentralized entities. The proposed framework was implemented in an Ethereum network environment using blockchain technology and smart contracts. Next, we evaluated the collected ground truth by measuring the inter-rater agreement among the participants. The experimental results indicate that blockchain can enhance annotation consistency, showing a higher reliability of crowd-sourced data compared to expert opinions. Most annotator pairs demonstrated moderate to strong agreement, confirming the potential of blockchain technology in improving ground truth data annotation.

Leukemia is a category of cancer that is normally found in blood and bone marrow, and which causes rapid abnormal development in the making of white blood cells than the required amount. The produced white blood cells could be ineffective to fight against harmful infections and can even prejudice or restrict the capability of the bone marrow to generate red blood cells and blood platelets. If this is not diagnosed in the earlier stage, it may start to affect the function of the internal organs and cause death. Normally, entire blood counts image analysis and diagnosis are done manually which is an inaccurate and time-intensive process. In this proposed method the classification is tested with two Machine Learning algorithms which are Hybrid Fuzzy C Means (FCM) and Random Forest algorithm (RF) and Support Vector Machine for the detection and classification of Acute Leukemia disease and their performance was evaluated. Experimental results convey that Hybrid FCM and RF Algorithm attained an accuracy of 99.06%, a sensitivity of 99.4%, and a specificity of 97.8% respectively, and the ROC (Receiver Operating Characteristic) curve shows that the result produced by the Hybrid FCM & RF based Classifier is best suitable in diagnosing the classification of the Acute Leukemia disease. The tool used for developing the proposed method was Matlab R2018 software.

Recent years have witnessed significant advancements in the field of character recognition, thanks to the revolutionary introduction of machine learning techniques. Among various types of character recognition, offline Handwritten Character Recognition (HCR) is comparatively more challenging as it lacks temporal information, such as stroke count and direction, ink pressure, and unexpected handwriting variability. These issues contribute to a poor level of precision, which calls for the adoption of anomaly detection techniques to enhance Optical Character Recognition (OCR) schemes. Previous studies have not researched unsupervised anomaly detection using MLP for handwriting recognition. Therefore, this study proposes a novel approach for enhanced English letter identification based on a Multi-Layer Perceptron (MLP) neural network and an anomaly detection method. The MLP neural network is trained on a massive dataset of letters, enabling it to achieve high accuracy in recognizing letters from different font styles. Additionally, to further enhance the performance of the system, an anomaly detection method is employed to identify and eliminate any outliers or anomalies in the recognition process. The combination of these two techniques results in a highly accurate and robust letter recognition system that can be applied to a wide range of practical applications. The hybrid approach aims to increase the accuracy of English letter identification while minimizing misclassification, i.e., classification issues associated with recognizing English letters in images. To identify handwritten letters based on their underlying patterns, discriminant samples with extracted attributes were fed into a neural network, which was then used to categorize the data. This paper achieved significant improvements in English letter identification by coupling the anomaly detection algorithm and the multilayer perceptron neural network. When tested on the UCI-English letters dataset, this hybrid technique boosted the accuracy from 84.11% using the multiple neural networks to an impressive 92.84% with the proposed method. Moreover, the experimental results of the study have demonstrated that the hybrid method outperforms modern classification techniques for handwriting letter recognition. Overall, the proposed approach has tremendous potential to advance the field of character recognition, especially for offline handwritten character recognition, and can pave the way for developing more accurate and efficient OCR systems in the future.

Healthcare applications, such as remote patient monitoring systems (RPMSs), utilize various Internet of Things (IoT) devices, including headsets, pulse oximeters, EEG monitors, ECG monitors, and smartwatches, to remotely gather critical patient data, such as heart rate, body temperature, and blood pressure. This technology enables healthcare professionals to collect patient information without needing in-person visits, streamlining data collection. Once the data is collected, these IoT devices transmit the information to smartphones, which serve as edge devices. Given the limited processing capabilities of smartphones, data preprocessing is necessary before transferring the data to the fog layer, where it can be analyzed using advanced algorithms to generate treatment recommendations and alerts. However, when the fog nodes overload, the computational tasks may need to be shifted to the cloud, which can impact the quality of service (QoS), especially in situations where patients require immediate attention. To maintain high levels of user satisfaction and ensure QoS, efficient fog resource management is crucial. This includes the reduction of job response time and cloud service dependency, optimization of the network utilization, and moderation of service cost. Currently, conventional methods using cloud computing or the brute force algorithm (BFA) fail to meet the desired QoS standards. This study involved the simulation of an IoT setting for resource management using the Sparrow Search Algorithm (SSA) and its comparison with both the BFA and Particle Swarm Optimization (PSO). The results indicate that compared to the BFA, the SSA enhances the response time by 20% and provides better resource load balancing. Additionally, compared to PSO, the SSA demonstrated a 10% reduction in network usage and improved efficiency in fog resource utilization.

Situation awareness (SA) describes an individual’s understanding of their surroundings and actions in the near future based on the individual’s comprehension and understanding of the surrounding inputs. SA measurements can be applied to improve system performance or human effectiveness in many fields of study, including driving. However, in some scenarios drivers might need to drive in unfamiliar traffic regulations (UFTRs), where the traffic rules and vehicle configurations are a bit different from what the drivers are used to under familiar traffic regulations. Such driving conditions require drivers to adapt their attention, knowledge, and reactions to safely reach the destination. This ability is influenced by the degree of handedness. In such tasks, mixed-/left-handed people show better performance than strong right-handed people. This paper aims to explore the influence of the degree of handedness on SA when driving under UFTRs. We analyzed the SA of two groups of drivers: strong right-handed drivers and mixed-/left-handed drivers. Both groups were not familiar with driving in keep-left traffic regulations. Using a driving simulator, all participants drove in a simulated keep-left traffic system. The participants’ SA was measured using a subjective assessment, named the Participant Situation Awareness Questionnaire PSAQ, and performance-based assessment. The results of the study indicate that mixed-/left-handed participants had significantly higher SA than strong right-handed participants when measured by performance-based assessment. Also, in the subjective assessment, mixed-/left-handed participants had significantly higher PSAQ performance scores than strong right-handed participants. The findings of this study suggest that advanced driver assistance systems (ADAS), which show improvement in road safety, should adapt the system functionality based on the driver’s degree of handedness when driving under UFTRs.