SCBC: Smart city monitoring with blockchain using Internet of Things for and neuro fuzzy procedures.

The security of the Internet of Things (IoT) is crucial in various application platforms, such as the smart city monitoring system, which encompasses comprehensive monitoring of various conditions. Therefore, this study conducts an analysis on the utilization of blockchain technology for the purpose of monitoring Internet of Things (IoT) systems. The analysis is carried out by employing parametric objective functions. In the context of the Internet of Things (IoT), it is imperative to establish well-defined intervals for job execution, ensuring that the completion status of each action is promptly monitored and assessed. The major significance of proposed method is to integrate a blockchain technique with neuro-fuzzy algorithm thereby improving the security of data processing units in all smart city applications. As the entire process is carried out with IoT the security of data in both processing and storage units are not secured therefore confidence level of monitoring units are maximized at each state. Due to the integration process the proposed system model is implemented with minimum energy conservation where 93% of tasks are completed with improved security for about 90%

Establishing usability heuristics for heuristics evaluation in a specific domain: is there a consensus?

Heuristics evaluation is frequently employed to evaluate usability. While general heuristics are suitable to evaluate most user interfaces, there is still a need to establish heuristics for specific domains to ensure that their specific usability issues are identified. This paper presents a comprehensive review of 70 studies related to usability heuristics for specific domains. The aim of this paper is to review the processes that were applied to establish heuristics in specific domains and identify gaps in order to provide recommendations for future research and area of improvements. The most urgent issue found is the deficiency of validation effort following heuristics proposition and the lack of robustness and rigour of validation method adopted. Whether domain specific heuristics perform better or worse than general ones is inconclusive due to lack of validation quality and clarity on how to assess the effectiveness of heuristics for specific domains. The lack of validation quality also affects effort in improving existing heuristics for specific domain as their weaknesses are not addressed

Weighted Clustering Ensembles

Clustering is a popular approach to exploratory data analysis and mining. However, clustering faces difficult challenges due to its ill-posed nature. First, it is well known that off-the-shelf clustering methods may discover different patterns in a given set of data, because each clustering algorithm has its own bias resulting from the optimization of different criteria. Second, there is no ground truth against which the clustering result can be validated. High dimensional data also pose a difficult challenge to the clustering process. Various clustering algorithms can handle data with low dimensionality, but as the dimensionality of the data increases, these algorithms tend to break down. In this dissertation, we introduce novel clustering ensemble techniques and novel semi-supervised approaches to address these problems. Clustering ensembles offer a solution to challenges inherent to clustering arising from its ill-posed nature: they can provide more robust and stable solutions by making use of the consensus across multiple clustering results, and they can average out the emergent spurious structures which arise due to the various biases of each participating algorithm, and due to the variance induced by different data samples. We introduce and analyze three new consensus functions for ensembles of subspace clusterings. The ultimate goal of our consensus functions is to provide hard partitions of the data, and weight vectors which convey information regarding the subspaces within which the individual clusters exist. We demonstrate the effectiveness of our three techniques by running experiments with several real datasets, including high dimensional text data, and investigate the issue of diversity and accuracy in our ensemble techniques. We also study scenarios in which limited knowledge on the data (in terms of pair-wise constraints) is available from the user. We develop a methodology to embed such constraints into the ensemble components, so that the desired structure emerges via the consensus clustering. We introduce a mechanism which leverages the ensemble framework to bootstrap informative constraints directly from the data and from the various clusterings, without intervention from the user. We demonstrate the effectiveness of our proposed techniques with experiments using real datasets and other state-of-the-art semi-supervised techniques

Afaneen: The Design and Evaluation of an Interactive Mobile Game to Enhance Arabic Spelling

Spelling is an important skill for children learning to strengthen their knowledge of a language and enhance their reading and writing comprehension. However, many young Arabic learners nowadays lack spelling competency, which affects their overall learning process. Traditional spelling instruction, which is usually based on the rote memorization of words, has its drawbacks, and this might be one of the reasons for the incompetency. In addition, there is a paucity of technology-based aids for facilitating spelling skills tailored for the specific intricacies of the Arabic language. This paper describes the design and development of an interactive mobile spelling game "Afaneen". The application targets Arab students at the elementary and higher levels, and aims to enhance their Arabic spelling ability. In the game, the learner can listen to words and is required to type the correct spelling for these words in order to move to the next level. Immediate feedback is presented to the learners, and they can access the spelling rules at any time to check their understanding. To evaluate the usefulness of the spelling game, three evaluation approaches were used: a case study; think aloud sessions; and interviews. The results demonstrate an overall positive attitude toward the game, and reveal areas for further improvement and development

Deep Feature Fusion and Optimization-Based Approach for Stomach Disease Classification

Cancer is the deadliest disease among all the diseases and the main cause of human mortality. Several types of cancer sicken the human body and affect organs. Among all the types of cancer, stomach cancer is the most dangerous disease that spreads rapidly and needs to be diagnosed at an early stage. The early diagnosis of stomach cancer is essential to reduce the mortality rate. The manual diagnosis process is time-consuming, requires many tests, and the availability of an expert doctor. Therefore, automated techniques are required to diagnose stomach infections from endoscopic images. Many computerized techniques have been introduced in the literature but due to a few challenges (i.e., high similarity among the healthy and infected regions, irrelevant features extraction, and so on), there is much room to improve the accuracy and reduce the computational time. In this paper, a deep-learning-based stomach disease classification method employing deep feature extraction, fusion, and optimization using WCE images is proposed. The proposed method comprises several phases: data augmentation performed to increase the dataset images, deep transfer learning adopted for deep features extraction, feature fusion performed on deep extracted features, fused feature matrix optimized with a modified dragonfly optimization method, and final classification of the stomach disease was performed. The features extraction phase employed two pre-trained deep CNN models (Inception v3 and DenseNet-201) performing activation on feature derivation layers. Later, the parallel concatenation was performed on deep-derived features and optimized using the meta-heuristic method named the dragonfly algorithm. The optimized feature matrix was classified by employing machine-learning algorithms and achieved an accuracy of 99.8% on the combined stomach disease dataset. A comparison has been conducted with state-of-the-art techniques and shows improved accuracy

Eye-Blink Event Detection Using a Neural-Network-Trained Frame Segment for Woman Drivers in Saudi Arabia

Women have been allowed to drive in Saudi Arabia since 2018, revoking a 30-year ban that also adhered to the traffic rules provided in the country. Conventional drivers are often monitored for safe driving by monitoring their facial reactions, eye blinks, and expressions. As driving experience and vehicle handling features have been less exposed to novice women drivers in Saudi Arabia, technical assistance and physical observations are mandatory. Such observations are sensed as images/video frames for computer-based analyses. Precise computer vision processes are employed for detecting and classifying events using image processing. The identified events are unique to novice women drivers in Saudi Arabia, assisting with their vehicle usage. This article introduces the Event Detection using Segmented Frame (ED-SF) method to improve the abnormal Eye-Blink Detection (EBD) of women drivers. The eye region is segmented using variation pixel extraction in this process. The pixel extraction process requires textural variation identified from different frames. The condition is that the frames are to be continuous in the event detection. This method employs a convolution neural network with two hidden layer processes. In the first layer, continuous and discrete frame differentiations are identified. The second layer is responsible for segmenting the eye region, devouring the textural variation. The variations and discrete frames are used for training the neural network to prevent segment errors in the extraction process. Therefore, the frame segment changes are used for Identifying the expressions through different inputs across different texture luminosities. This method applies to less-experienced and road-safety-knowledge-lacking woman drivers who have initiated their driving journey in Saudi-Arabia-like countries. Thus the proposed method improves the EBD accuracy by 9.5% compared to Hybrid Convolutional Neural Networks (HCNN), Long Short-Term Neural Networks (HCNN + LSTM), Two-Stream Spatial-Temporal Graph Convolutional Networks (2S-STGCN), and the Customized Driving Fatigue Detection Method CDFDM