A Computational Approach to Color Vision Enhancement Using Deep Learning, Tensorflow and Keras

Color vision deficiencies (CVDs) indicate potential genetic variations and can be important biomarkers of acquired impairment in many neuro-ophthalmic diseases. However, CVDs are typically measured with insensitive or inefficient tools that are designed to classify dichromacy subtypes rather than track changes in sensitivity. We introduce FInD (Foraging Interactive D-prime), a novel computer-based, generalizable, rapid, self-administered vision assessment tool and applied it to color vision testing. This signal detection theory-based adaptive paradigm computes test stimulus intensity from d-prime analysis. Stimuli were chromatic gaussian blobs in dynamic luminance noise, and participants clicked on cells that contain chromatic blobs (detection) or blob pairs of differing colors (discrimination). Sensitivity and repeatability of FInD Color tasks were compared against HRR, FM100 hue tests in 19 color-normal and 18 color-atypical, age-matched observers. Rayleigh color match was completed as well. Detection and Discrimination thresholds were higher for atypical observers than for typical observers, with selective threshold elevations corresponding to unique CVD types. Classifications of CVD type and severity via unsupervised machine learning confirmed functional subtypes. FInD tasks reliably detect CVD and may serve as valuable tools in basic and clinical color vision science.

(1) Background: This paper intends to accomplish a comparative study and analysis regarding the multiclass classification of facial thermal images, i.e., in three classes corresponding to predefined emotional states (neutral, happy and sad). By carrying out a comparative analysis, the main goal of the paper consists in identifying a suitable algorithm from machine learning field, which has the highest accuracy (ACC). Two categories of images were used in the process, i.e., images with Gaussian noise and images with “salt and pepper” type noise that come from two built-in special databases. An augmentation process was applied to the initial raw images that led to the development of the two databases with added noise, as well as the subsequent augmentation of all images, i.e., rotation, reflection, translation and scaling. (2) Methods: The multiclass classification process was implemented through two subsets of methods, i.e., machine learning with random forest (RF), support vector machines (SVM) and k-nearest neighbor (KNN) algorithms and deep learning with the convolutional neural network (CNN) algorithm. (3) Results: The results obtained in this paper with the two subsets of methods belonging to the field of artificial intelligence (AI), together with the two categories of facial thermal images with added noise used as input, were very good, showing a classification accuracy of over 99% for the two categories of images, and the three corresponding classes for each. (4) Discussion: The augmented databases and the additional configurations of the implemented algorithms seems to have had a positive effect on the final classification results.

In this paper, we propose content adaptation for visual impairments in MPEG-21. The proposed content adaptation aims to give enhanced visual accessibility to users with visual impairment in MPEG-21. In this paper, we consider two major visual impairments: low vision impairment and color vision deficiency. The proposed method includes description for the visual impairments and content adaptation technique based on it. We have developed a symptom-based description of visual impairment characteristics for users with visual impairment in the context of MPEG-21 digital item adaptation (DIA). To verify usefulness of the proposed method, we performed some experiments with the content adaptation based on the description in MPEG-21. The experiment results showed that the proposed method is effective content adaptation for user with visual impairment and gives enhanced visual accessibility to them.

Large-scale multiparameter screening has become increasingly feasible and straightforward to perform thanks to developments in technologies such as high-content microscopy and high-throughput flow cytometry. The automated toolkits for analyzing similarities and differences between large numbers of tested conditions have not kept pace with these technological developments. Thus, effective analysis of multiparameter screening datasets becomes a bottleneck and a limiting factor in unbiased interpretation of results. Here we introduce compaRe, a toolkit for large-scale multiparameter data analysis, which integrates quality control, data bias correction, and data visualization methods with a mass-aware gridding algorithm-based similarity analysis providing a much faster and more robust analyses than existing methods. Using mass and flow cytometry data from acute myeloid leukemia and myelodysplastic syndrome patients, we show that compaRe can reveal interpatient heterogeneity and recognizable phenotypic profiles. By applying compaRe to high-throughput flow cytometry drug response data in AML models, we robustly identified multiple types of both deep and subtle phenotypic response patterns, highlighting how this analysis could be used for therapeutic discoveries. In conclusion, compaRe is a toolkit that uniquely allows for automated, rapid, and precise comparisons of large-scale multiparameter datasets, including high-throughput screens.

The exponential growth of computer processing power, cloud data storage, and crowdsourcing model of gathering data bring new possibilities to Music Information Retrieval (MIR) field. MIR is no longer music content retrieval only; the area also comprises the discovery of expressing feelings and emotions contained in music, incorporating other than hearing modalities for helping this issue, users’ profiling, merging music with social media and qualitative recommendations in music services. Moreover, 5G telecommunications networks, characterized by “near-instant and everything in the vicinity talks with one another,” with exponentially faster download and upload speeds, may change the existing models and create a new age of interconnectedness. This paper aims at showing some of the already highly exploited technologies and crowdsourcing models applied to music processing. Several studies are discussed in details, such as, e.g., deep learning applied to music, a way to generate an expanded training sets using 2-D data such spectrograms, mel-cepstrograms, chromagrams, and waveform-based representations of the signal instead of feature vectors in machine learning, allowing to retain all nuances related musical articulation in the signal. Also, a discussion is to be outlined, expanding the issue of the impact of these new technologies on the artistic and aesthetic values of music.

PurposeThis paper examines the impact of communication technology and human-related factors on teacher, student and course performance, in particular on teacher's clarity, perceived value of the course and student learning.Design/methodology/approachCommunication technology factors are analyzed in terms of social media use, while human-related factors are considered through teacher expertise and communication skills, more specifically non-verbal communication (NVC). The research model is tested on a sample of 303 students from two European universities, one in Southwestern and another in Southeastern Europe.FindingsFindings reveal dominance of human factors over technology ones. Culture moderates some of the relationships examined.Research limitations/implicationsNotwithstanding the relatively limited sample of students according to their national culture, this work offers valuable insights into the impact of technology use in classroom and teacher communication skills.Practical implicationsTeacher NVC and expertise override their use of technology-mediated communication in classroom. Findings have raised important questions whether social media applications should actually have place in nowadays education systems. What seems clear from this research is that technology advancements cannot replace teachers, although further research is necessary to re-examine their impact on different student outcomes and in different cultural contexts.Originality/valueThe originality of this paper lies in comparing the impact of technology vs human-related factors on classroom performance. An additional contribution is provided by considering the moderating role of national culture within the proposed research model.

The construction field is proving to be one of the most dynamic sectors not only on a global scale, but also in India. With a sector subject to extreme technicalities and complexities, it is only rational to infer that disputes arising out of construction activities would also call for distinct expertise and in depth understanding of the issues surrounding construction activities. The construction sector has seen positive growth in the application of Technology. Technological applications have become more independent in the development of problem-solving processes. Despite its incredible contributions, for example the decision-making processes faster, cheaper, and more predictable. Among the biggest beneficiaries of the advancement in technology within the recent past has been the construction industry, which is also complicated by its procedures and disputes. Advancement in technology helps in resolution of the problems of construction through the identification of the digital tools that enhance efficiency, transparency, and fairness. Key technologies such as Building Information Modelling (BIM), blockchain technology, artificial intelligence (AI) and machine learning, drone, project management software, virtual and augmented reality (VR/AR) internet of things (IOT) real-time monitoring and simulation alter traditional approaches to the solution of problems not only does the technology support dispute resolution, but also helps in lowering the level of conflict by providing better management and communication. By using technology in a careful and responsible way, we can help to build trust in digital solutions and make sure that everyone benefits from the amazing potential of this technology. The construction field is riddled with disputes due to its size mainly involving the builders or maybe even between the employees and employer. However, all of this affect the owners, hence there is a need to ensure that the disputes are solved amicably. This abstract examines the Impact of Technology in Construction Dispute Resolution.

Aim. To assess the prospects of using artificial intelligence technologies in predicting the outcomes and risks of cardiovascular diseases (CVD) in patients with hypertension (HTN).Material and methods. A software application was created for data mining from respondent profiles in a semi-automatic mode; libraries with data preprocessing were analyzed. We analyzed the main and additional parameters (35) of CVD risk factors in 2131 people as a part of ESSE-RF study (2014-2019). To create a forecasting model, a high-level language Python 2.7 was used using object-oriented programming and exception handling with multithreading support. Using randomization, learning (n=488) and test (n=245) samples were formed, which included data from patients with an established diagnosis of HTN.Results. The prevalence of HTN among subjects was 34,39%. There were following significant factors for predicting CVD: anthropometric parameters, smoking, biochemical profile (total cholesterol, ApoA, ApoB, glucose, D-dimer, C-reactive protein). As a result of a 5-year follow-up, CVD was found in 235 people (32,06%) with HTN and 187 people (13,38%) without HTN; mortality rates were 1,27% in subjects with HTN and 1,12% — without HTN. The absolute mortality risk among participants with HTN (0,037) was significantly higher (p<0,05) than in patients without HTN (0,017). To create a neural network (NN), the basic Sequential model from the Keras library was used. During machine learning, 26 variables important for the CVD development were used as input and 9 neurons — as output, which corresponded to the number of established cardiovascular events. The created NN had a predictive value of up to 97,9%, which exceeded the SCORE value (34,9%).Conclusion. The data obtained indicate the importance of risk factor phenotyping using anthropometric markers and biochemical profile for determining their significance in the top 20 predictors of CVD. The Python-based machine learning provides CVD prediction according to standard risk assessments.

Melt electrowriting (MEW) is a solvent‐free (i.e., no volatile chemicals), a high‐resolution three‐dimensional (3D) printing method that enables the fabrication of semi‐flexible structures with rigid polymers. Despite its advantages, the MEW process is sensitive to changes in printing parameters (e.g., voltage, printing pressure, and temperature), which can cause fluid column breakage, jet lag, and/or fiber pulsing, ultimately deteriorating the resolution and printing quality. In spite of the commonly used error‐and‐trial method to determine the most suitable parameters, here, we present a machine learning (ML)‐enabled image analysis‐based method for determining the optimum MEW printing parameters through an easy‐to‐use graphical user interface (GUI). We trained five different ML algorithms using 168 MEW 3D print samples, among which the Gaussian process regression ML model yielded 93% accuracy of the variability in the dependent variable, 0.12329 on root mean square error for the validation set and 0.015201 mean square error in predicting line thickness. Integration of ML with a control feedback loop and MEW can reduce the error‐and‐trial steps prior to the 3D printing process, decreasing the printing time (i.e., increasing the overall throughput of MEW) and material waste (i.e., improving the cost‐effectiveness of MEW). Moreover, embedding a trained ML model with the feedback control system in a GUI facilitates a more straightforward use of ML‐based optimization techniques in the industrial section (i.e., for users with no ML skills).

Graphical User Interfaces (GUIs) are amongst the most common user interfaces, enabling interactions with applications through mouse movements and key presses. Tools for automated testing of programs through their GUI exist, however they usually rely on operating system or framework specific knowledge to interact with an application. Due to frequent operating system updates, which can remove required information, and a large variety of different GUI frameworks using unique underlying data structures, such tools rapidly become obsolete, Consequently, for an automated GUI test generation tool, supporting many frameworks and operating systems is impractical. We propose a technique for improving GUI testing by automatically identifying GUI widgets in screen shots using machine learning techniques. As training data, we generate randomized GUIs to automatically extract widget information. The resulting model provides guidance to GUI testing tools in environments not currently supported by deriving GUI widget information from screen shots only. In our experiments, we found that identifying GUI widgets in screen shots and using this information to guide random testing achieved a significantly higher branch coverage in 18 of 20 applications, with an average increase of 42.5% when compared to conventional random testing.

Advances in technology are making health research increasingly complex. Artificial intelligence is widely used in this research. Convolutional neural networks are one of the most common and optimal algorithms for working with images. Image recognition results are used to analyze the results of medical examinations of patients. The subject of the research – analysis of the human brain computed tomography results using a convolutional neural network based on the Keras library.

Round-ended concrete-filled steel tubes (RECFSTs) present very different performances between the primary and secondary axes, which renders them particularly suitable for use as bridge piers and arches. In recent years, research into RECFST heavily relies on experimental procedures restricting the parameter range under consideration, which narrows the far-reaching applicability of RECFST. This study employs advanced machine learning methods to predict the axial load-bearing capacity of RECFST with a wide parameter range. Firstly, a machine learning database comprising 2400 RECFSTs is established, which covers a wider range of commonly used material strengths and cross-sectional dimensions. Three machine learning prediction models of this database are then developed, respectively, using different algorithms. The robustness of the machine learning models is evaluated by predicting the axial load-bearing capacity of 60 RECFST specimens from existing references. The results demonstrated that the machine learning models provided superior predictive accuracy compared to theoretical or code-based formulas. A graphical user interface (GUI) is ultimately developed based on the machine learning prediction models to predict the axial load-bearing capacity of RECFST. This tool facilitates rapid and accurate RECFST design.

Prediction of thermal diffusivity of volcanic rocks using machine learning and genetic algorithm hybrid strategy

Image processing and computer vision algorithms are very computationally intensive and could not be implemented on low power microcontroller used in embedded and small IoT devices. Performing computation on the cloud is not practical for always-on real-time operations and deploying a high-end microcontroller or a microprocessor consume too much power and are generally too expensive to fit into these small systems. In this paper, we propose a new programming workflow using reusable hardware modules and a graphical programming interface for implementing a complete image processing system on an FPGAs which overcomes the steep learning curve of tradition FPGAs design tool. The design can be deployed onto the low-ended FPGAs in a similar price point as a mid-range microcontroller. To demonstrate our proposed framework, we have implemented a number of image transformation operations on an FPGAs development board with Lattice iCE40 Ultra Plus FPGA and a tiny camera module. Results have shown that our designs can fit in a low-ended FPGAs while performing 5.5-345x faster and consuming 2.4-4x less power compared to current state-of-the-art microcontroller used in small embedded and IoT devices.

Our Plant disease detection project presents a Convolutional Neural Network (CNN) model for the classification of plant diseases based on image data. The dataset comprises images of various plant diseases and healthy plants obtained from the "PlantVillage" database. The images are preprocessed by resizing them to a standard size and applying augmentation techniques. The CNN model is built using the Keras library and consists of multiple convolutional layers followed by pooling, batch normalization, and dropout layers. The model is trained using the Adam optimizer and evaluated on a test set. The training and validation accuracy and loss are plotted over the epochs to analyze the model's performance. The trained model achieves a certain accuracy on the test set, indicating its potential for accurately identifying plant diseases. The saved model can be utilized for real-world applications in plant disease detection and management, providing valuable assistance to farmers and researchers.