Derivation of Context-free Stochastic L-Grammar Rules for Promoter Sequence Modeling Using Support Vector Machine

Formal grammars can used for describing complex repeatable structures such as DNA sequences. In this paper, we describe the structural composition of DNA sequences using a context-free stochastic L-grammar. L-grammars are a special class of parallel grammars that can model the growth of living organisms, e.g. plant development, and model the morphology of a variety of organisms. We believe that parallel grammars also can be used for modeling genetic mechanisms and sequences such as promoters. Promoters are short regulatory DNA sequences located upstream of a gene. Detection of promoters in DNA sequences is important for successful gene prediction. Promoters can be recognized by certain patterns that are conserved within a species, but there are many exceptions which makes the promoter recognition a complex problem. We replace the problem of promoter recognition by induction of context-free stochastic L-grammar rules, which are later used for the structural analysis of promoter sequences. L-grammar rules are derived automatically from the drosophila and vertebrate promoter datasets using a genetic programming technique and their fitness is evaluated using a Support Vector Machine (SVM) classifier. The artificial promoter sequences generated using the derived L- grammar rules are analyzed and compared with natural promoter sequences

Gamification Patterns for Gamification Applications

AbstractRecently, gamification has gained popularity in the development of enterprise information systems. Gamification is usually implemented using game elements combined with game mechanics that encourage competition between game players trying to reach some objectives or quantifiable outcome. Most games contain certain common aspects that are frequently created or reinvented for each new game. Solutions to these aspects may vary system to system but they have many commonalities. The concept of design patterns, which so far have proven successful in object-oriented design and software engineering, seeks to communicate these solutions in an easy to understand manner. We extract gamification patterns from known gamified systems and describe them using the Machinations modelling tool and the pattern description scheme. A case study shows how patterns can be used in practice

3D Object Reconstruction from Imperfect Depth Data Using Extended YOLOv3 Network

State-of-the-art intelligent versatile applications provoke the usage of full 3D, depth-based streams, especially in the scenarios of intelligent remote control and communications, where virtual and augmented reality will soon become outdated and are forecasted to be replaced by point cloud streams providing explorable 3D environments of communication and industrial data. One of the most novel approaches employed in modern object reconstruction methods is to use a priori knowledge of the objects that are being reconstructed. Our approach is different as we strive to reconstruct a 3D object within much more difficult scenarios of limited data availability. Data stream is often limited by insufficient depth camera coverage and, as a result, the objects are occluded and data is lost. Our proposed hybrid artificial neural network modifications have improved the reconstruction results by 8.53 which allows us for much more precise filling of occluded object sides and reduction of noise during the process. Furthermore, the addition of object segmentation masks and the individual object instance classification is a leap forward towards a general-purpose scene reconstruction as opposed to a single object reconstruction task due to the ability to mask out overlapping object instances and using only masked object area in the reconstruction process

Analiza produkcji opartej na odnawialnych źródłach energii w Afryce Zachodniej – studium przypadku Nigerii

The limited supply of fossil fuels, constant rise in the demand of energy and the importance of reducing greenhouse emissions has brought about the adoption of renewable energy sources for generation of electrical power. In this paper, the impact of renewable energy generation in Nigeria is explored. A review of renewable deposits in Nigeria with a focus on Solar, Biomass, Hydropower, Pumped Storage Hydro and Ocean energy is detailed. The impact of renewable energy-based generation is assessed from three different dimensions: Economic Impact, Social Impact and Environmental Impact. In accessing economic impact; the conditions are employment and job creation, gross domestic product (GDP) growth and increase in local research and development. To analyze the social impact; renewable energy education, renewable energy businesses, ministries and institutes, renewable energy projects and investments as well as specific solar and wind projects across Nigeria were considered. Also, environmental issues were discussed. Similarly, policy imperatives for renewable energy generation in Nigeria was provided. This paper would be useful in accessing the successes Nigeria has experienced so far in the area of sustainable development and the next steps to achieving universal energy for all in Nigeria in 2030.Ograniczona podaż paliw kopalnych, stały wzrost zapotrzebowania na energię oraz konieczność ograniczenia emisji gazów cieplarnianych pociągnęły za sobą konieczność stosowania odnawialnych źródeł energii do wytwarzania energii elektrycznej. W artykule zbadano wpływ wytwarzania energii odnawialnej w Nigerii. Szczegółowy przegląd zasobów energii odnawialnej w Nigerii, ze szczególnym uwzględnieniem energii słonecznej, biomasy, energii wodnej i elektrowni szczytowo-pompowych i energii oceanicznej. Produkcja energii ze źródeł odnawialnych została przeanalizowana w trzech różnych wymiarach: wpływ na gospodarkę, wpływ na społeczeństwo i wpływ na środowisko. W aspekcie wpływu na gospodarkę wzięto pod uwagę: zatrudnienie i tworzenie miejsc pracy, wzrost produktu krajowego brutto (PKB) oraz wzrost lokalnych badań i rozwój. Analiza wpływu społecznego objęła: edukację w zakresie energii odnawialnej, przedsiębiorstwa, ministerstwa i instytuty zajmujące się energią odnawialną, projekty i inwestycje w zakresie energii odnawialnej, a także konkretne projekty dotyczące energii słonecznej i wiatrowej w całej Nigerii. Omówiono również kwestie środowiskowe. W podobny sposób przedstawiono imperatywy polityczne dotyczące wytwarzania energii odnawialnej w Nigerii. Ten artykuł wskazuje na istotny dla Nigerii cel zrównoważonego rozwoju, jakim jest odnawialna energia dla wszystkich.  Celem jest  osiągnięcie w tym kraju powszechnej odnawialnej energii dla wszystkich w 2030 r

Machine learning with multimodal neuroimaging data to classify stages of Alzheimer’s disease: a systematic review and meta-analysis

publishedVersio

Usability and Security Testing of Online Links: A Framework for Click-Through Rate Prediction Using Deep Learning

CC BY 4.0The user, usage, and usability (3U’s) are three principal constituents for cyber security. The effective analysis of the 3U data using artificial intelligence (AI) techniques allows to deduce valuable observations, which allow domain experts to design practical strategies to alleviate cyberattacks and ensure decision support. Many internet applications, such as internet advertising and recommendation systems, rely on click-through rate (CTR) prediction to anticipate the possibility that a user would click on an ad or product, which is key for understanding human online behaviour. However, online systems are prone to click on fraud attacks. We propose a Human-Centric Cyber Security (HCCS) model that additionally includes AI techniques targeted at the key elements of user, usage, and usability. As a case study, we analyse a CTR prediction task, using deep learning methods (factorization machines) to predict online fraud through clickbait. The results of experiments on a real-world benchmark Avazu dataset show that the proposed approach outpaces (AUC is 0.8062) other CTR forecasting approaches, demonstrating the viability of the proposed framework

Ontologies in Cloud Computing - Review and Future Directions

Cloud computing as a technology has the capacity to enhance cooperation, scalability, accessibility, and offers discount prospects using improved and effective computing, and this capability helps organizations to stay focused. Ontologies are used to model knowledge. Once knowledge is modeled, knowledge management systems can be used to search, match, visualize knowledge, and also infer new knowledge. Ontologies use semantic analysis to define information within an environment with interconnecting relationships between heterogeneous sets. This paper aims to provide a comprehensive review of the existing literature on ontology in cloud computing and defines the state of the art. We applied the systematic literature review (SLR) approach and identified 400 articles; 58 of the articles were selected after further selection based on set selection criteria, and 35 articles were considered relevant to the study. The study shows that four predominant areas of cloud computing—cloud security, cloud interoperability, cloud resources and service description, and cloud services discovery and selection—have attracted the attention of researchers as dominant areas where cloud ontologies have made great impact. The proposed methods in the literature applied 30 ontologies in the cloud domain, and five of the methods are still practiced in the legacy computing environment. From the analysis, it was found that several challenges exist, including those related to the application of ontologies to enhance business operations in the cloud and multi-cloud. Based on this review, the study summarizes some unresolved challenges and possible future directions for cloud ontology researchers.publishedVersio

Pareto Optimized Large Mask Approach for Efficient and Background Humanoid Shape Removal

The purpose of automated video object removal is to not only detect and remove the object of interest automatically, but also to utilize background context to inpaint the foreground area. Video inpainting requires to fill spatiotemporal gaps in a video with convincing material, necessitating both temporal and spatial consistency; the inpainted part must seamlessly integrate into the background in a variety of scenes, and it must maintain a consistent appearance in subsequent frames even if its surroundings change noticeably. We introduce deep learning-based methodology for removing unwanted human-like shapes in videos. The method uses Pareto-optimized Generative Adversarial Networks (GANs) technology, which is a novel contribution. The system automatically selects the Region of Interest (ROI) for each humanoid shape and uses a skeleton detection module to determine which humanoid shape to retain. The semantic masks of human like shapes are created using a semantic-aware occlusion-robust model that has four primary components: feature extraction, and local, global, and semantic branches. The global branch encodes occlusion-aware information to make the extracted features resistant to occlusion, while the local branch retrieves fine-grained local characteristics. A modified big mask inpainting approach is employed to eliminate a person from the image, leveraging Fast Fourier convolutions and utilizing polygonal chains and rectangles with unpredictable aspect ratios. The inpainter network takes the input image and the mask to create an output image excluding the background humanoid shapes. The generator uses an encoder-decoder structure with included skip connections to recover spatial information and dilated convolution and squeeze and excitation blocks to make the regions behind the humanoid shapes consistent with their surroundings. The discriminator avoids dissimilar structure at the patch scale, and the refiner network catches features around the boundaries of each background humanoid shape. The efficiency was assessed using the Structural Learned Perceptual Image Patch Similarity, Frechet Inception Distance, and Similarity Index Measure metrics and showed promising results in fully automated background person removal task. The method is evaluated on two video object segmentation datasets (DAVIS indicating respective values of 0.02, FID of 5.01 and SSIM of 0.79 and YouTube-VOS, resulting in 0.03, 6.22, 0.78 respectively) as well a database of 66 distinct video sequences of people behind a desk in an office environment (0.02, 4.01, and 0.78 respectively).publishedVersio

Detection of Macula and Recognition of Aged-Related Macular Degeneration in Retinal Fundus Images

In aged people, the central vision is affected by Age-Related Macular Degeneration (AMD). From the digital retinal fundus images, AMD can be recognized because of the existence of Drusen, Choroidal Neovascularization (CNV), and Geographic Atrophy (GA). It is time-consuming and costly for the ophthalmologists to monitor fundus images. A monitoring system for automated digital fundus photography can reduce these problems. In this paper, we propose a new macula detection system based on contrast enhancement, top-hat transformation, and the modified Kirsch template method. Firstly, the retinal fundus image is processed through an image enhancement method so that the intensity distribution is improved for finer visualization. The contrast-enhanced image is further improved using the top-hat transformation function to make the intensities level differentiable between the macula and different sections of images. The retinal vessel is enhanced by employing the modified Kirsch's template method. It enhances the vasculature structures and suppresses the blob-like structures. Furthermore, the OTSU thresholding is used to segment out the dark regions and separate the vessel to extract the candidate regions. The dark region and the background estimated image are subtracted from the extracted blood vessels image to obtain the exact location of the macula. The proposed method applied on 1349 images of STARE, DRIVE, MESSIDOR, and DIARETDB1 databases and achieved the average sensitivity, specificity, accuracy, positive predicted value, F1 score, and area under curve of 97.79 %, 97.65 %, 97.60 %, 97.38 %, 97.57 %, and 96.97 %, respectively. Experimental results reveal that the proposed method attains better performance, in terms of visual quality and enriched quantitative analysis, in comparison with eminent state-of-the-art methods

Evaluation of MyRelief Serious Game for Better Self-Management of Health Behaviour Strategies on Chronic Low-Back Pain

Low back pain is a leading cause of disability worldwide, putting a significant strain on individual sufferers, their families, and the economy as a whole. It has a significant economic impact on the global economy because of the costs associated with healthcare, lost productivity, activity limitation, and work absence. Self-management, education, and adopting healthy lifestyle behaviors, such as increasing physical activity, are all widely recommended treatments. Access to services provided by healthcare professionals who provide these treatments can be limited and costly. This evaluation study focuses on the application of the MyRelief serious game, with the goal of addressing such challenges by providing an accessible, interactive, and fun platform that incorporates self-management, behavior change strategies, and educational information consistent with recommendations for managing low-back pain, based on self-assessment models implemented through ontology-based mechanics. Functional disability measured using the Oswestry Disability Questionnaire showed the statistically significant (p < 0.001) improvement in subjects’ self-evaluation of their health status. System Usability Scale (SUS) test score of 77.6 also suggests that the MyRelief serious game can potentially influence patient enablement