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 paper presents a systematic benchmark for depth-assisted single-person pose estimation pipelines in three consumer RGB-D cameras.We introduce a lightweight optimization that adjusts only the relative depth coordinates of predicted joints so that their inter-joint depth gaps match those observed in the depth sensor image.The proposed approach is fully differentiable, sensor-agnostic, and light enough for realtime edge deployment, making it immediately applicable to sports coaching, workplace ergonomics, and mixed reality mobile systems.Experiments on a controlled motion capture dataset demonstrate performance trade-offs in accuracy, speed, and robustness under challenging viewing geometries.The findings provide practical guidance on which depth technology best complements state-of-the-art vision models and establish relative depth matching as an effective computationally trivial alternative for laboratory calibration.

Accurate blood glucose prediction is critical for effective diabetes management, yet existing models struggle with individual variability, data sparsity, and non-linearity.We propose an attention-based multi-task learning (MTL) model integrated with proximal policy optimization (PPO) reinforcement learning (RL) to enhance forecasting accuracy and adaptability.MTL captures shared patterns across multiple prediction tasks, while PPO dynamically refines predictions based on patientspecific glucose trends.By incorporating explainability techniques such as SHAP analysis and Monte Carlo dropout, our approach not only achieves state-of-the-art predictive accuracy but also enhances trust in AI-driven decision support systems for diabetes care.Evaluated on the BrisT1D blood glucose dataset, our model achieves a R 2 score of 0.85, MSE of 0.0017, RMSE of 0.0419, and MAE of 0.0310, significantly surpassing conventional methods.This work advances personalized real-time glucose forecasting, offering a promising step toward AI-powered glycemic management in clinical settings.

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.

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.

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 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 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 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.