Laryngeal oncosurgery often results in severely impaired speech with unstable voicing and spectral aperiodicity. This paper compares two subdifferential-optimization paradigms within the same clinical pathological speech synthesis backbone to isolate how nonsmooth optimization geometry shapes rehabilitation-relevant outcomes: (i) the Ioffe Subdifferential Optimization Framework (ISOF) for robust long‑form pathological speech synthesis and (ii) the Mordukhovich Subdifferential Optimization (MSO) paradigm for multi‑criteria voice cloning into a single clinical framework. ISOF combines approximate subgradients via minimum‑norm convex combinations and proximal anchoring, MSO balances naturalness, similarity, efficiency, and pathology preservation. ISOF improved AVE, PVF, PVS, HNR, CPP and markedly increased ASVI; no degradation from beginning to end in running speech. MSO achieved high MOS/SMOS and strong objective scores (lower MCD, VDE, GPE, FFE; higher STOI, SII). ISOF+MSO approaches yielded stable, intelligible synthesized speech suitable for rehabilitation, preserving medically plausible signatures.

The impact of brain tumors as a global health concern is due to their aggressive behavior, high mortality, and complexities in their diagnosis.While MRI remains the gold standard for identifying, monitoring, and detecting brain tumors, automated classification methods encounter many complications with respect to the diverse morphologies of tumors, similarities in their imaging features, and the potential variability in imaging conditions.CNNs can capture spatial hierarchies, but cannot generalize effectively and ViTs rely on the context to characterize the image modalities which means that, whilst they address some deficiencies of CNNs, they require extensive data and computational resources.To remedy some of the issues that each approach presents, we present multiscale MoE that leverages CNNs and attention-oriented modules.The proposed architecture uses multi-scale feature extraction, channel-spatial attention, and dynamic expert routing, which adequately collects tumor-specific features efficiently.We applied two different publicly available datasets, namely the Bangladesh Brain Cancer MRI and Figshare Brain Tumor dataset.For the Bangladesh dataset, the proposed model achieved overall accuracy of 96.92% and for FigShare dataset, the highest results achieved 96.42% accuracy.In contrast to state-of-the-art models, multiscale MoE achieved the highest testing accuracy 96.14%, and the lowest Brier score 0.0603.The proposed model has shown to have balanced classification results across the tumor classes and reduced the number of false predictions whilst maintaining efficient computational performance and thus has the potential to provide a valuable resource for clinical practice with respect to real-time applications.

Forest wildfires pose a significant threat to ecosystems, human settlements, and the global environment.Early detection is important for effective mitigation and response.This paper introduces a novel approach to forest wildfire detection by harnessing the unique sound signatures associated with wildfires.Our proposed model combines the strengths of deep learning techniques with heuristic optimization algorithms.The deep learning component focuses on recognizing the intricate patterns in the sound data, while the heuristic optimization, based on a Particle Sworm Optimization (PSO) algorithm, ensured the model's adaptability and efficiency in diverse forest environments.Preliminary results indicate that our hybrid model outperforms traditional methods and existing machine learning models in terms of accuracy, sensitivity, and specificity, demonstrating robustness against ambient forest noise, ensuring fewer false alarms.

This paper presents a novel approach to enhance the spatio-temporal analysis of forest ecosystems using the d'Alembert convolution method, which, integrating elements from wave equation theory and convolutional neural networks, enables the comprehensive analysis of remote sensing images by capturing both spatial and temporal variations.This methodology not only improves feature extraction, but also helps address the challenges associated with traditional image processing techniques, which often overlook the temporal dynamics of forests.The results show significant improvements in the analysis of forest ecosystems.Specifically, the higher performance metrics compared to existing methods, including higher accuracy in classifying various forest types and more effective monitoring of changes over time.

Abstract Personalized health monitoring and prediction are indispensable in advancing healthcare delivery, particularly amidst the escalating prevalence of chronic illnesses and the aging population. Deep learning (DL) stands out as a promising avenue for crafting personalized health monitoring systems adept at forecasting health outcomes with precision and efficiency. As personal health data becomes increasingly accessible, DL‐based methodologies offer a compelling strategy for enhancing healthcare provision through accurate and timely prognostications of health conditions. This article offers a comprehensive examination of recent advancements in employing DL for personalized health monitoring and prediction. It summarizes a diverse range of DL architectures and their practical implementations across various realms, such as wearable technologies, electronic health records (EHRs), and data accumulated from social media platforms. Moreover, it elucidates the obstacles encountered and outlines future directions in leveraging DL for personalized health monitoring, thereby furnishing invaluable insights into the immense potential of DL in this domain.

Internet of Things (IoT) devices are becoming increasingly ubiquitous, and their adoption is growing at an exponential rate. However, they are vulnerable to security breaches, and traditional security mechanisms are not enough to protect them. The massive amounts of data generated by IoT devices can be easily manipulated or stolen, posing significant privacy concerns. This paper is to provide a comprehensive overview of the integration of blockchain and IoT technologies and their potential to enhance the security and privacy of IoT systems. The paper examines various security issues and vulnerabilities in IoT and explores how blockchain-based solutions can be used to address them. It provides insights into the various security issues and vulnerabilities in IoT and explores how blockchain can be used to enhance security and privacy. The paper also discusses the potential applications of blockchain-based IoT (B-IoT) systems in various sectors, such as healthcare, transportation, and supply chain management. The paper reveals that the integration of blockchain and IoT has the potential to enhance the security, privacy, and trustworthiness of IoT systems. The multi-layered architecture of B-IoT, consisting of perception, network, data processing, and application layers, provides a comprehensive framework for the integration of blockchain and IoT technologies. The study identifies various security solutions for B-IoT, including smart contracts, decentralized control, immutable data storage, identity and access management (IAM), and consensus mechanisms. The study also discusses the challenges and future research directions in the field of B-IoT.

Recent advances in Artificial Intelligence (AI) have revolutionized the area of biomedical imaging, providing unprecedented prospects for precision diagnoses. This special issue offers an overview of the integration of AI into biomedical imaging systems and its tremendous impact on improving diagnostic accuracy and efficiency. The combination of AI and biomedical imaging has resulted in intelligent systems capable of deciphering complex medical pictures with amazing precision. Deep learning algorithms, particularly convolutional neural networks (CNNs), have shown exceptional capabilities in recognising patterns and extracting meaningful information from a variety of imaging modalities, including magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) [1].

With the growing proliferation of mobile devices, the interest in mobile learning environments increases. While many of them are provided as web apps, with limited effort they can be converted to Progressive Web Apps, providing user experience more resembling native mobile apps. In this work, we investigate, using the case of an open-source gamified programming learning environment FGPE PLE, how the user experience of its PWA version accessed on a mobile device compares to the user experience of the same learning environment yet accessed via a desktop browser.

Abstract Hunger Games Search (HGS) is a newly developed swarm-based algorithm inspired by the cooperative behavior of animals and their hunting strategies to find prey. However, HGS has been observed to exhibit slow convergence and may struggle with unbalanced exploration and exploitation phases. To address these issues, this study proposes a modified version of HGS called mHGS, which incorporates five techniques: (1) modified production operator, (2) modified variation control, (3) modified local escaping operator, (4) modified transition factor, and (5) modified foraging behavior. To validate the effectiveness of the mHGS method, 18 different benchmark datasets for dimensionality reduction are utilized, covering a range of sizes (small, medium, and large). Additionally, two Parkinson’s disease phonation datasets are employed as real-world applications to demonstrate the superior capabilities of the proposed approach. Experimental and statistical results obtained through the mHGS method indicate its significant performance improvements in terms of Recall, selected attribute count, Precision, F-score, and accuracy when compared to the classical HGS and seven other well-established methods: Gradient-based optimizer (GBO), Grasshopper Optimization Algorithm (GOA), Gray Wolf Optimizer (GWO), Salp Swarm Algorithm (SSA), Whale Optimization Algorithm (WOA), Harris Hawks Optimizer (HHO), and Ant Lion Optimizer (ALO).

COVID-19 is the disease evoked by a new breed of coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Recently, COVID-19 has become a pandemic by infecting more than 152 million people in over 216 countries and territories. The exponential increase in the number of infections has rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) and machine learning (ML), which can assist the healthcare sector in providing quick and precise COVID-19 diagnosis. Therefore, this paper provides a comprehensive review of the most recent DL and ML techniques for COVID-19 diagnosis. The studies are published from December 2019 until April 2021. In general, this paper includes more than 200 studies that have been carefully selected from several publishers, such as IEEE, Springer and Elsevier. We classify the research tracks into two categories: DL and ML and present COVID-19 public datasets established and extracted from different countries. The measures used to evaluate diagnosis methods are comparatively analysed and proper discussion is provided. In conclusion, for COVID-19 diagnosing and outbreak prediction, SVM is the most widely used machine learning mechanism, and CNN is the most widely used deep learning mechanism. Accuracy, sensitivity, and specificity are the most widely used measurements in previous studies. Finally, this review paper will guide the research community on the upcoming development of machine learning for COVID-19 and inspire their works for future development. This review paper will guide the research community on the upcoming development of ML and DL for COVID-19 and inspire their works for future development.

Abstract Images captured through a visual sensory system are degraded in a foggy scene, which negatively influences recognition, tracking, and detection of targets. Efficient tools are needed to detect, pre‐process, and enhance foggy scenes. Machine learning (ML) has a significant role in image defogging domain for tackling adverse issues. Unfortunately, regardless of contributions that were made by ML, little attention has been attributed to this topic. This paper summarizes the role of ML methods and relevant aspects in the image defogging research area. Also, the basic terms and concepts are highlighted in image defogging topic. Feature extraction approaches with a summary of advantages and disadvantages are described. ML algorithms are also summarized that have been used for applications related to image defogging, that is, image denoising, image quality assessment, image segmentation, and foggy image classification. Open datasets are also discussed. Finally, the existing problems of the image defogging domain in general and, specifically related to ML which need to be further studied are discussed. To the best knowledge, this the first review paper which sheds a light on the role of ML and relevant aspects in the image defogging domain.

Abstract High resolution remote sensing image cloud removal can bring a lot of convenience for human activities. However, the existing cloud removal algorithms have a variety of disadvantages. First of all, they have the disadvantages of long computing time and large consumption of computing resources. Secondly, the effect of recovery needs to be improved. In order to improve the above two points, a near real‐time effective algorithm is proposed, namely MSAR‐Defognet (multiple scale attention residual network using for cloud remove), which consumes less computing power and space and has superior cloud removal effect. On the one hand, several different large‐scale filters are chosen to extract the weak information effectively, while can save the computing power and shorten the image processing time. On the other hand, the fine‐grained convolution residual block with channel attention mechanism is used to enhance the network's ability to extract cloud features. In addition, a data set which is closer to the real cloud shape and has higher richness to train the cloud removal network, so that the parameters obtained by training have stronger robustness and can adaptively remove clouds with different thickness. Experiments show that, compared with other advanced network models, the network not only has the advantage of fast processing speed, but also has better image restoration effect in high‐resolution remote sensing image restoration. It can meet the requirements of many hard real‐time tasks, so that remote sensing images can play a greater value for human activities.

Abstract The third type of neural network called spiking is developed due to a more accurate representation of neuronal activity in living organisms. Spiking neural networks have many different parameters that can be difficult to adjust manually to the current classification problem. The analysis and selection of coefficients’ values in the network can be analyzed as an optimization problem. A practical method for automatic selection of them can decrease the time needed to develop such a model. In this paper, we propose the use of a heuristic approach to analyze and select coefficients with the idea of collaborative working. The proposed idea is based on parallel analyzing of different coefficients and choosing the best of them or average ones. This type of optimization problem allows the selection of all variables, which can significantly affect the convergence of the accuracy. Our proposal was tested using network simulators and popular databases to indicate the possibilities of the described approach. Five different heuristic algorithms were tested and the best results were reached by Cuckoo Search Algorithm, Grasshopper Optimization Algorithm, and Polar Bears Algorithm.

Abstract Facial symmetry is a key component in quantifying the perception of beauty. In this paper, we propose a set of facial features computed from facial landmarks which can be extracted at a low computational cost. We quantitatively evaluated the proposed features for predicting perceived attractiveness from human portraits on four benchmark datasets (SCUT-FBP, SCUT-FBP5500, FACES and Chicago Face Database). Experimental results showed that the performance of the proposed features is comparable to those extracted from a set with much denser facial landmarks. The computation of facial features was also implemented as an augmented reality (AR) app developed on Android OS. The app overlays four types of measurements and guidelines over a live video stream, while the facial measurements are computed from the tracked facial landmarks at run time. The developed app can be used to assist plastic surgeons in assessing facial symmetry when planning reconstructive facial surgeries.

Abstract Medical images play a fundamental role in disease screening, and automated evaluation of these images is widely preferred in hospitals. Recently, Convolutional Neural Network (CNN) supported medical data assessment is widely adopted to inspect a set of medical imaging modalities. Extraction of the leukocyte section from a thin blood smear image is one of the essential procedures during the preliminary disease screening process. The conventional segmentation needs complex/hybrid procedures to extract the necessary section and the results achieved with conventional methods sometime tender poor results. Hence, this research aims to implement the CNN-assisted image segmentation scheme to extract the leukocyte section from the RGB scaled hematological images. The proposed work employs various CNN-based segmentation schemes, such as SegNet, U-Net, and VGG-UNet. We used the images from the Leukocyte Images for Segmentation and Classification (LISC) database. In this work, five classes of the leukocytes are considered, and each CNN segmentation scheme is separately implemented and evaluated with the ground-truth image. The experimental outcome of the proposed work confirms that the overall results accomplished with the VGG-UNet are better (Jaccard-Index = 91.5124%, Dice-Coefficient = 94.4080%, and Accuracy = 97.7316%) than those of the SegNet and U-Net schemes Finally, the merit of the proposed scheme is also confirmed using other similar image datasets, such as Blood Cell Count and Detection (BCCD) database and ALL-IDB2. The attained result confirms that the proposed scheme works well on hematological images and offers better performance measure values.

Abstract Improvement of deep learning algorithms in smart agriculture is important to support the early detection of plant diseases, thereby improving crop yields. Data acquisition for machine learning applications is an expensive task due to the requirements of expert knowledge and professional equipment. The usability of any application in a real‐world setting is often limited by unskilled users and the limitations of devices used for acquiring images for classification. We aim to improve the accuracy of deep learning models on low‐quality test images using data augmentation techniques for neural network training. We generate synthetic images with a modified colour value distribution to expand the trainable image colour space and to train the neural network to recognize important colour‐based features, which are less sensitive to the deficiencies of low‐quality images such as those affected by blurring or motion. This paper introduces a novel image colour histogram transformation technique for generating synthetic images for data augmentation in image classification tasks. The approach is based on the convolution of the Chebyshev orthogonal functions with the probability distribution functions of image colour histograms. To validate our proposed model, we used four methods (resolution down‐sampling, Gaussian blurring, motion blur, and overexposure) for reducing image quality from the Cassava leaf disease dataset. The results based on the modified MobileNetV2 neural network showed a statistically significant improvement of cassava leaf disease recognition accuracy on lower‐quality testing images when compared with the baseline network. The model can be easily deployed for recognizing and detecting cassava leaf diseases in lower quality images, which is a major factor in practical data acquisition.

Abstract Effective recognition of fruit leaf diseases has a substantial impact on agro‐based economies. Several fruit diseases exist that badly impact the yield and quality of fruits. A naked‐eye inspection of an infected region is a difficult and tedious process; therefore, it is required to have an automated system for accurate recognition of the disease. It is widely understood that low contrast images affect identification and classification accuracy. Here a parallel framework for real‐time apple leaf disease identification and classification is proposed. Initially, a hybrid contrast stretching method to increase the visual impact of an image is proposed and then the MASK RCNN is configured to detect the infected regions. In parallel, the enhanced images are utilized for training a pre‐trained CNN model for features extraction. The Kapur's entropy along MSVM (EaMSVM) approach‐based selection method is developed to select strong features for the final classification. The Plant Village dataset is employed for the experimental process and achieve the best accuracy of 96.6% on the ensemble subspace discriminant analysis (ESDA) classifier. A comparison with the previous techniques illustrates the superiority of the proposed framework.

Abstract New mobile applications need to estimate user activities by using sensor data provided by smart wearable devices and deliver context-aware solutions to users living in smart environments. We propose a novel hybrid data fusion method to estimate three types of daily user activities (being in a meeting, walking, and driving with a motorized vehicle) using the accelerometer and gyroscope data acquired from a smart watch using a mobile phone. The approach is based on the matrix time series method for feature fusion, and the modified Better-than-the-Best Fusion (BB-Fus) method with a stochastic gradient descent algorithm for construction of optimal decision trees for classification. For the estimation of user activities, we adopted a statistical pattern recognition approach and used the k-Nearest Neighbor (kNN) and Support Vector Machine (SVM) classifiers. We acquired and used our own dataset of 354 min of data from 20 subjects for this study. We report a classification performance of 98.32 % for SVM and 97.42 % for kNN.

Since the emergence of COVID-19, thousands of people undergo chest X-ray and computed tomography scan for its screening on everyday basis. This has increased the workload on radiologists, and a number of cases are in backlog. This is not only the case for COVID-19, but for the other abnormalities needing radiological diagnosis as well. In this work, we present an automated technique for rapid diagnosis of COVID-19 on computed tomography images. The proposed technique consists of four primary steps: (1) data collection and normalization, (2) extraction of the relevant features, (3) selection of the most optimal features and (4) feature classification. In the data collection step, we collect data for several patients from a public domain website, and perform preprocessing, which includes image resizing. In the successive step, we apply discrete wavelet transform and extended segmentation-based fractal texture analysis methods for extracting the relevant features. This is followed by application of an entropy controlled genetic algorithm for selection of the best features from each feature type, which are combined using a serial approach. In the final phase, the best features are subjected to various classifiers for the diagnosis. The proposed framework, when augmented with the Naive Bayes classifier, yields the best accuracy of 92.6%. The simulation results are supported by a detailed statistical analysis as a proof of concept.

The lack of dopamine in the human brain is the cause of Parkinson disease (PD) which is a degenerative disorder common globally to older citizens.However, late detection of this disease before the first clinical diagnosis has led to increased mortality rate.Research effort towards the early detection of PD has encountered challenges such as: small dataset size, class imbalance, overfitting, high false detection rate, model complexity, etc.This paper aims to improve early detection of PD using machine learning through data augmentation for very small datasets.We propose using Spline interpolation and Piecewise Cubic Hermite Interpolating Polynomial (Pchip) interpolation methods to generate synthetic data instances.We further investigate on reducing dimensionality of features for effective and real-time classification while considering computational complexity of implementation on real-life mobile phones.For classification we use Bidirectional LSTM (BiLSTM) deep learning network and compare the results with traditional machine learning algorithms like Support Vector Machine (SVM), Decision Tree, Logistic regression, KNN and Ensemble bagged tree.For experimental validation we use the Oxford Parkinson disease dataset with 195 data samples, which we have augmented with 571 synthetic data samples.The results for BiLSTM shows that even with a holdout of 90%, the model was still able to effectively recognize PD with an average accuracy for ten rounds experiment using 22 features as 82.86%, 97.1%, and 96.37% for original, augmented (Spline) and augmented (Pchip) datasets, respectively.Our results show that proposed data augmentation schemes have significantly (p < 0.001) improved the accuracy of PD recognition on a small dataset using both classical machine learning models and BiLSTM.

Huntington's disease is a hereditary condition in which brain nerve cells rupture over time. This work proposes a new method for the detection of Huntington's disease using digitised voice signals by diseased and healthy volunteers while they were reading Lithuanian poems. In this approach, the produced features by voice signals suffer a dimensionality reduction to optimize the prediction stage. The performance evaluation regarded 186 speech exams and 24 volunteers, combining twelve audio signal feature extractors with classification models. The results indicate an excellent performance, reaching precision and accuracy over 99 percent with prediction time below 1 second. This approach shows promising results indicating its usability to improve the medical diagnosis via computer-aided diagnosis.

BACKGROUND AND OBJECTIVE: New technology enables constant boost to the powers of mobile devices, which in the previous years have transformed from simple mobile phones to smart phones. Computational powers of these electronics enable actions that previously were possible only for computers. By the use of special applications, we may benefit from sensors and multimedia capabilities of operating systems. Therefore, a new era for devoted implementations opens, in which a smart application can take a role of computing system to estimate the symptoms of diseases by evaluating signals coming from a human body. METHODS: We propose a model of an application implemented for mobile android systems, which can be used for examination of central nervous system motor disorders occurring in patients suffering from Huntington (HD), Alzheimer, or Parkinson diseases. In particular, the model tracks tremors (involuntary movements), and cognitive (memory loss or dementia) impairments using touch and visual stimulus modalities. The proposed model interprets the symptoms from human bodies that indicate one of the diseases of the nervous system. Pre-processing of collected data for feature extraction is executed on a mobile device by using core functionality and methods provided in android's application programming interface. The information is evaluated by a back-propagation neural network classifier and the result is presented to the end user. The system is able to contact medical supervision and provide an assistance from the clinic. RESULTS: The system uses a collected dataset of 1928 records, taken from 11 HD patients and 11 healthy persons in Lithuania, to gather statistics about examinations and presents the results as medical evaluation with prediction on the state of health. The accuracy of recognition of early, prodromal symptoms for central nervous system motor disorders is 86.4% (F-measure 0.859). The app (available on Google Play) is easy to use and is efficient tool for decision support in medical examinations. CONCLUSIONS: The use of intelligent apps which can help to evaluate neurodegenerative disorders is an important enhancement to medical diagnosis. The developed smartphone app supports the doctor with additional results that are easy to compare with other examinations. This kind of examination is a nice change from classic stereotypes, especially for younger age patients, who are used to various aspects of information technology.

We address the Lithuanian author profiling task in two dimensions (AGE and GENDER) using two deep learning methods (i.e., Long Short-Term Memory -LSTM) and Convolutional Neural Network -CNN) applied on the top of Lithuanian neural word embeddings.We also investigate an impact of the training dataset size on the author profiling accuracy.The best results are achieved with the largest datasets, containing 5,000 instances in each class.Besides, LSTM was more effective on the smaller datasets, and CNN -on the larger ones.We compare the deep learning methods with the traditional machine learning methods (in particular, Naive Bayes Multinomial and Support Vector Machine), and frequencies of elements as the feature representation).The comparison revealed that the deep learning is not the best solution for our author profiling task.

This paper reports comparative authorship attribution results obtained on the Internet comments of the morphologically complex Lithuanian language.We have explored the impact of machine learning and similarity-based approaches on the different author set sizes (containing 10, 100, and 1,000 candidate authors), feature types (lexical, morphological, and character), and feature selection techniques (feature ranking, random selection).The authorship attribution task was complicated due to the used Lithuanian language characteristics, nonnormative texts, an extreme shortness of these texts, and a large number of candidate authors.The best results were achieved with the machine learning approaches.On the larger author sets the entire feature set composed of word-level character tetra-grams demonstrated the best performance.

Internet can be misused by cyber criminals as a platform to conduct illegitimate activities (such as harassment, cyber bullying, and incitement of hate or violence) anonymously.As a result, authorship analysis of anonymous texts in Internet (such as emails, forum comments) has attracted significant attention in the digital forensic and text mining communities.The main problem is a large number of possible of authors, which hinders the effective identification of a true author.We interpret open class author attribution as a process of expert recommendation where the decision support system returns a list of suspected authors for further analysis by forensics experts rather than a single prediction result, thus reducing the scale of the problem.We describe the task formally and present algorithms for constructing the suspected author list.For evaluation we propose using a simple Winner-Takes-All (WTA) metric as well as a set of gain-discount model based metrics from the information retrieval domain (mean reciprocal rank, discounted cumulative gain and rank-biased precision).We also propose the List Precision (LP) metric as an extension of WTA for evaluating the usability of the suspected author list.For experiments, we use our own dataset of Internet comments in Lithuanian language and consider the use of language-specific (Lithuanian) lexical features together with general lexical features derived from English language.For classification we use one-class Support Vector Machine (SVM) classifier.The results of experiments show that the usability of open class author attribution can be improved considerably by using a set of language-specific lexical features together with general lexical features, while the proposed method can be used to reduce the number of suspected authors thus alleviating the work of forensic linguists.

We propose a novel noise cancellation method based on the scale-adaptive remixing and demixing of Intrinsic Mode Functions (IMFs) constructed using Empirical Mode Decomposition (EMD). The method addresses the problem of mode mixing in the EMD by performing mode demixing. An illustrative example using noisy random binary sequence is presented. The proposed approach allows achieving better denoising results than the classic first IMF discarding approach.

Complexity of Internet of Things IoT applications and difficulty to predict their behaviour in wireless communications make modelling of IoT units an important research topic. The IoT unit is considered here as the two-node IoT model that supports bi-directional wireless communications. There are many approaches to model security and energy awareness; however, little is known about the synergistic effect of those factors at the application level under the influence of environmental factors such as noise. The paper introduces a modelling framework to model the security-energy-environment issues as main attributes to allow defining quality of service QoS for the IoT-based applications. Among others, the healthcare ones are regarded as predominant now. We model IoT units using the feature-based modelling methodology adopted from the software engineering domain. The result of modelling is a set of feature models with valid configurations that describe the energy-security-environment-performance relationships and possible constraints to support various IoT applications. Having feature models, we can select a configuration that is best suited for a given IoT application with respect to QoS requirements. As feature models represent abstract relationships of domain factors, we need additionally to provide experiments to determine concrete values of modelled features. We describe the experimental system to measure energy consumption under the influencing factors. Both models abstract and concrete allow reasoning about QoS of the IoT applications. Copyright © 2016 John Wiley & Sons, Ltd.

Ambient Assisted Living (AAL) aims to improve the quality of daily life for all humans in different periods of life.Neural-Computer Interface (NCI) can be used within AAL environments to provide alternative communication means for impaired persons bypassing the need for speech and other motor activities.By monitoring, analyzing and responding to muscular activity (EMG signals) of users, NCI systems are able to monitor, diagnose and respond to the cognitive, emotional and physical states of users in real time.In this paper we analyze and develop a speller application based on the EMG interface.We analyze requirements for developing interfaces for disabled users and interfaces of known speller applications, and describe the development of the EMG-based speller as a benchmark application.The developed speller has adaptive stimulus rate and allows word selection from dictionary.We evaluate performance and usability of the developed speller using a set of empirical (accuracy, information transfer speed, input speed), ergonomic (NASA-TLX scale) and conceptual (humanistic intelligence) attributes.

The success of learning objects (LO) is limited by the ability to integrate them into coherent teaching courses in order to enable the educational goal-oriented creation of competency. LO sequences must be defined that are adapted to different types of learners, their personal needs and knowledge states to allow for effective personalized learning. In this paper, we propose a method for specification of LO sequences using Sequence Feature Diagrams and describe the generation of LO sequences from Generative LOs using metaprogramming techniques.

Modern e-learning systems are mostly implemented by computer science specialists, who often lack awareness of the pedagogical/psychological aspects of e-learning processes. However, every approach to e-learning implementation must be based on sound pedagogical background. Currently, the development of adaptive and personalised learning environments is a hot topic in the e-learning domain, nevertheless, the majority of e-learning approaches still ignores learning context as an important source of knowledge. In this paper, we focus on the development of a conceptual model of learning context for e-learning environment, which captures the technological, subject domain, pedagogical, psychological aspects of learning situation. We propose a Learning Context Model (LCM) aimed for further use while developing technological implementations of adaptive, personalised learning environments.

To achieve better software quality, to shorten software development time and to lower development costs, software engineers are adopting generative reuse as a software design process. The usage of generic components allows increasing reuse and design productivity in software engineering. Generic component design requires systematic domain analysis to identify similar components as candidates for generalization. However, component feature analysis and identification of components for generalization usually is done ad hoc. In this paper, we propose to apply a data visualization method, called Multidimensional Scaling (MDS), to analyze software components in the multidimensional feature space. Multidimensional data that represent syntactical and semantic features of source code components are mapped to 2D space. The results of MDS are used to partition an initial set of components into groups of similar source code components that can be further used as candidates for generalization. STRESS value is used to estimate the generalizability of a given set of components. Case studies for Java Buffer and Geom class libraries are presented.

Recognition of specific functionally-important DNA sequence fragments is considered one of the most important problems in bioinformatics. One type of such fragments is splice-junction (intron-exon or exon-intron) sites. Detection of splice-junction sites in DNA sequences is important for successful gene prediction. In this paper, Support Vector Machine (SVM) is used for classification of DNA sequences and splice-site recognition. For optimal classification, four position-independent k-mer frequency based methods for mapping DNA sequences into SVM feature space are analyzed. Classification is performed using SVM power series kernels. Kernel parameters are optimized using a modification of the Nelder-Mead (downhill simplex) optimization method. Precision of classification is evaluated using F-measure, which is a combination of precision and recall metrics. Best classification results are achieved using 4-mers for exon-intron dataset (78%) and 6-mers for intron-exon dataset (70%) using 4-nucleotide frequencies.

Design patterns, which encapsulate common solutions to the recurring design problems, have contributed to the increased reuse, quality and productivity in software design. We argue that hardware design patterns could be used for customizing and integrating the Intellectual Property (IP) components into System-on-Chip designs. We formulate the role of design patterns in HW design, and describe their implementation using metaprogramming. We propose a Wrapper design pattern for adapting the behavior of the soft IPs, and demonstrate its application to the communication interface synthesis.

We consider the one- and two-language approaches (1LA & 2LA) for developing generic components (GCs) for VHDL generators. By 1LA & 2LA we mean a generalization using "pure" VHDL, or using the VHDL abstractions mixed with Open PROMOL, the external scripting language we have developed for building GCs and generators, respectively. We present the evaluation of both approaches.