Rytis Maskeliunas

We adopt Bidirectional Long Short-Term Memory (BiLSTM) neural network and Wavelet Scattering Transform with Support Vector Machine (WST-SVM) classifier for detecting speech impairments of patients at the early stage of central nervous system disorders (CNSD). The study includes 339 voice samples collected from 15 subjects: 7 patients with early sta...

We present a model for digital neural impairment screening and self-assessment, which can evaluate cognitive and motor deficits for patients with symptoms of central nervous system (CNS) disorders, such as mild cognitive impairment (MCI), Parkinson’s disease (PD), Huntington’s disease (HD), or dementia. The data was collected with an Android mobile...

With the majority of research, in relation to 3D object reconstruction, focusing on single static synthetic object reconstruction, there is a need for a method capable of reconstructing morphing objects in dynamic scenes without external influence. However, such research requires a time-consuming creation of real world object ground truths. To solv...

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 (S...

Human posture detection allows the capture of the kinematic parameters of the human body, which is important for many applications, such as assisted living, healthcare, physical exercising and rehabilitation. This task can greatly benefit from recent development in deep learning and computer vision. In this paper, we propose a novel deep recurrent...

This study introduces an advanced adaptive sensor clustering technique for environmental monitoring in dynamic forest ecosystems, focusing on optimizing Wireless Sensor Networks (WSNs) for energy efficiency, adaptability, and data accuracy. The framework integrates Quantum Fuzzy C-Means (QFCM) clustering, energy-efficient cluster head selection, an...

The fusion of LiDAR and photogrammetry point clouds is a necessary advancement in 3D-modeling, enabling more comprehensive and accurate representations of physical environments. The main contribution of this paper is the development of an innovative fusion system that combines classical algorithms, such as Structure from Motion (SfM), with advanced...

The objective of this work is to develop a method for tracking human skeletal movements by integrating data from two synchronized video streams. To achieve this, two datasets were created, each consisting of four different rehabilitation exercise videos featuring various individuals in diverse environments and wearing different clothing. The predic...

As a complex ecosystem composed of flora and fauna, the forest has always been vulnerable to threats. Previous researchers utilized environmental audio collections, such as the ESC-50 and UrbanSound8k datasets, as proximate representatives of sounds potentially present in forests. This study focuses on the application of deep learning models for fo...

Leukemia, a life-threatening form of cancer, poses a significant global health challenge affecting individuals of all age groups, including both children and adults. Currently, the diagnostic process relies on manual analysis of microscopic images of blood samples. In recent years, machine learning employing deep learning approaches has emerged as...

Lately, there has been a substantial rise in the number of identified individuals with skin cancer, making it the most widespread form of cancer worldwide. Until now, several machine learning methods that utilize skin scans have been directly employed for skin cancer classification, showing encouraging outcomes in terms of enhancing diagnostic prec...

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 significa...

Featured Application The presented solution can be applied to simplify and hasten the development of gamified programming exercises conforming to the Framework for Gamified Programming Education (FGPE) standard. Abstract Skilled programmers are in high demand, and a critical obstacle to satisfying this demand is the difficulty of acquiring program...

Multimodal neuroimaging, combining data from different sources, has shown promise in the classification of the Alzheimer's disease (AD) stage. Existing multimodal neuroimaging fusion methods exhibit certain limitations, which require advancements to enhance their objective performance, sensitivity, and specificity for AD classification. This study...

Brain tumors are the result of irregular development of cells. It is a major cause of adult demise worldwide. Several deaths can be avoided with early brain tumor detection. Magnetic resonance imaging (MRI) for earlier brain tumor diagnosis may improve the chance of survival for patients. The most common method of diagnosing brain tumors is MRI. Th...

Financial distress identification remains an essential topic in the scientific literature due to its importance for society and the economy. The advancements in information technology and the escalating volume of stored data have led to the emergence of financial distress that transcends the realm of financial statements and its’ indicators (ratios...

This paper presents a novel approach to improving the detection of mild cognitive impairment (MCI) through the use of super-resolved structural magnetic resonance imaging (MRI) and optimized deep learning models. The study introduces enhancements to the perceptual quality of super-resolved 2D structural MRI images using advanced loss functions, mod...

This paper is a systematic literature review of the use of artificial intelligence techniques to detect early dementia. It focuses on multi-modal feature analysis in combination with neuroimaging. The paper examines what past research suggests about issues in the field, what dementia types researchers focus on, what are state-of-the-art methods in...

Human Posture Recognition (HPR) has garnered growing interest given the possibility of its use in various applications, including healthcare and sports fitness. Interestingly, achieving accurate pose recognition on mobile devices with very little computing power is still tricky. The precise identification of human posture on mobile devices with con...

The study aimed to investigate and compare the accuracy and robustness of the multiparametric acoustic voice indices (MAVIs), namely the Dysphonia Severity Index (DSI), Acoustic Voice Quality Index (AVQI), Acoustic Breathiness Index (ABI), and Voice Wellness Index (VWI) measures in differentiating normal and dysphonic voices. The study group consis...

Research into acoustic signal-based failure detection has developed into a subject that has attracted the attention of many researchers in recent years. Acoustic signal data collection can be performed without having to interrupt or stop the operation of the machine to be inspected. Therefore, it is very beneficial for the development of nondestruc...

Forests established through afforestation are one of the most precious natural resources, especially in harsh and desert-biased conditions. Trees are often exposed to various threats that need to be addressed. Some of the threats are igniting fires, illegal lumberjacking, hunting, using, and crossing prohibited areas, etc. This article delves into...

Sarcasm and irony represent intricate linguistic forms in social media communication, demanding nuanced comprehension of context and tone. In this study, we propose an advanced natural language processing methodology utilizing long short-term memory with an attention mechanism (LSTM-AM) to achieve an impressive accuracy of 99.86% in detecting and i...

Multimodal neuroimaging has gained traction in Alzheimer’s Disease (AD) diagnosis by integrating information from multiple imaging modalities to enhance classification accuracy. However, effectively handling heterogeneous data sources and overcoming the challenges posed by multiscale transform methods remains a significant hurdle. This article prop...

Magnetic resonance imaging (MRI) is a technique that is widely used in practice to evaluate any pathologies in the human body. One of the areas of interest is the human brain. Naturally, MR images are low-resolution and contain noise due to signal interference, the patient’s body’s radio-frequency emissions and smaller Tesla coil counts in the mach...

In recent years, Alzheimer’s disease (AD) has been a serious threat to human health. Researchers and clinicians alike encounter a significant obstacle when trying to accurately identify and classify AD stages. Several studies have shown that multimodal neuroimaging input can assist in providing valuable insights into the structural and functional c...

This paper is poised to inform educators, policy makers and software developers about the untapped potential of PWAs in creating engaging, effective, and personalized learning experiences in the field of programming education. We aim to address a significant gap in the current understanding of the potential advantages and underutilisation of Progre...

The problem of cleaning impaired speech is crucial for various applications such as speech recognition, telecommunication, and assistive technologies. In this paper, we propose a novel approach that combines Pareto-optimized deep learning with non-negative matrix factorization (NMF) to effectively reduce noise in impaired speech signals while prese...

Alzheimer’s disease (AD) is a neurological condition that gradually weakens the brain and impairs cognition and memory. Multimodal imaging techniques have become increasingly important in the diagnosis of AD because they can help monitor disease progression over time by providing a more complete picture of the changes in the brain that occur over t...

Purpose Alzheimer’s disease (AD) is a progressive, incurable human brain illness that impairs reasoning and retention as well as recall. Detecting AD in its preliminary stages before clinical manifestations is crucial for timely treatment. Magnetic Resonance Imaging (MRI) provides valuable insights into brain abnormalities by measuring the decrease...

The aim of the study was to develop a universal-platform-based (UPB) application suitable for different smartphones for estimation of the Acoustic Voice Quality Index (AVQI) and evaluate its reliability in AVQI measurements and normal and pathological voice differentiation. Our study group consisted of 135 adult individuals, including 49 with norma...

The aim of the study was to develop the universal-platform-based (UPB) application suitable for different smartphones for estimation of the Acoustic Voice Quality Index (AVQI) and evaluate its reliability in AVQI measurements and normal and pathological voice differentiation. Our study group consisted of 135 adult individuals, including 49 with nor...

The paper presents an evaluation of a Pareto-optimized FaceNet model with data preprocessing techniques to improve the accuracy of face recognition in the era of mask-wearing. The COVID-19 pandemic has led to an increase in mask-wearing, which poses a challenge for face recognition systems. The proposed model uses Pareto optimization to balance acc...

Advancements in artificial intelligence are leading researchers to find use cases that were not as straightforward to solve in the past. The use case of simulated autonomous driving has been known as a notoriously difficult task to automate, but advancements in the field of reinforcement learning have made it possible to reach satisfactory results....

The COVID-19 pandemic has highlighted the critical importance of efficient and effective vaccine distribution in responding to global health emergencies. However, the complex and rapidly changing nature of the pandemic has made it challenging for traditional methods of vaccine allocation and delivery to keep up. Reinforcement learning (RL) has emer...

One of the most difficult components of stroke therapy is regaining hand mobility. This research describes a preliminary approach to robot-assisted hand motion therapy. Our objectives were twofold: First, we used machine learning approaches to determine and describe hand motion patterns in healthy people. Surface electrodes were used to collect ele...

Multiparametric indices offer a more comprehensive approach to voice quality assessment by taking into account multiple acoustic parameters. Artificial intelligence technology can be utilized in healthcare to evaluate data and optimize decision-making processes. Mobile devices provide new opportunities for remote speech monitoring, allowing the use...

Technology-assisted diagnosis is increasingly important in healthcare systems. Brain tumors are a leading cause of death worldwide, and treatment plans rely heavily on accurate survival predictions. Gliomas, a type of brain tumor, have particularly high mortality rates and can be further classified as low- or high-grade, making survival prediction...

As the topic of sustainable development continues to prominence in global affairs, the case for renewable energy has never been stronger. To be regarded as a perfect alternative to conventional (non-renewable) energy sources in many climes, renewable energy, such as solar and wind, shows promise when considering concepts like grid parity. A signifi...

Human beauty evaluation is a particularly difficult task. This task can be solved using deep learning methods. We propose a new method for determining the attractiveness of a face by using the generation of synthetic data. Our approach uses the generative adversarial network (GAN) to generate an artificial face and then predict the facial beauty of...

Alzheimer’s disease (AD) has become a serious hazard to human health in recent years, and proper screening and diagnosis of AD remain a challenge. Multimodal neuroimaging input can help identify AD in the early mild cognitive impairment (EMCI) and late mild cognitive impairment (LMCI) stages from normal cognitive development using magnetic resonanc...

Because of its benefits in providing an engaging and mobile environment, virtual reality (VR) has recently been rapidly adopted and integrated in education and professional training. Augmented reality (AR) is the integration of VR with the real world, where the real world provides context and the virtual world provides or reconstructs missing infor...

Eye gaze interfaces are an emerging technology that allows users to control graphical user interfaces (GUIs) simply by looking at them. However, using gaze-controlled GUIs can be a demanding task, resulting in high cognitive and physical load and fatigue. To address these challenges, we propose the concept and model of an adaptive human-assistive h...

A serious game is a type of game that is designed for a primary purpose other than entertainment. Instead, serious games are intended to achieve specific goals, such as education, training, or health promotion. The goal of serious games is to engage players in a way that is both enjoyable and effective in achieving the intended learning or behavior...

The research introduces a unique deep-learning-based technique for remote rehabilitative analysis of image-captured human movements and postures. We present a ploninomial Pareto-optimized deep-learning architecture for processing inverse kinematics for sorting out and rearranging human skeleton joints generated by RGB-based two-dimensional (2D) ske...

Remote patient monitoring is one of the most reliable choices for the availability of health care services for the elderly and/or chronically ill. Rehabilitation requires the exact and medically correct completion of physiotherapy activities. This paper presents BiomacVR, a virtual reality (VR)-based rehabilitation system that combines a VR physica...

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 whi...

To monitor and handle big data obtained from electrical, electronic, electro-mechanical, and other equipment linked to the power grid effectively and efficiently, it is important to monitor them continually to gather information on power line integrity. We propose that data transmission analysis and data collection from tools like digital power met...

This paper describes a serious game based on a knowledge transfer model using deep reinforcement learning, with an aim to improve the caretakers’ knowledge and abilities in post-stroke care. The iTrain game was designed to improve caregiver knowledge and abilities by providing non-traditional training to formal and informal caregivers who deal with...

Purpose We present a systematic literature review of dialogue agents for Artificial Intelligence (AI) and agent-based conversational systems dealing with cognitive disability of aged and impaired people including dementia and Parkinson’s disease. We analyze current applications, gaps, and challenges in the existing research body, and provide guidel...

The study presents a systematic review of 232 studies on various aspects of the use of artificial intelligence methods for identification of financial distress (such as bankruptcy or insolvency). We follow the guidelines of the PRISMA methodology for performing the systematic reviews. The study discusses bankruptcy-related financial datasets, data...

Speech impairment analysis and processing technologies have evolved substantially in recent years, and the use of voice as a biomarker has gained popularity. We have developed an approach for clinical speech signal processing to demonstrate the promise of deep learning-driven voice analysis as a screening tool for Parkinson’s Disease (PD), the worl...

Posture detection targets toward providing assessments for the monitoring of the health and welfare of humans have been of great interest to researchers from different disciplines. The use of computer vision systems for posture recognition might result in useful improvements in healthy aging and support for elderly people in their daily activities...

Human posture classification (HPC) is the process of identifying a human pose from a still image or moving image that was recorded by a digicam. This makes it easier to keep a record of people’s postures, which is helpful for many things. The intricate surroundings that are depicted in the image, such as occlusion and the camera view angle, make HP...

The article focuses on utilizing unmanned aerial vehicles (UAV) to capture and classify building façades of various forms of cultural sites and structures. We propose a Pareto-optimized deep learning algorithm for building detection and classification in a congested urban environment. Outdoor image processing becomes difficult in typical European m...

With the advancement in pose estimation techniques, human posture detection recently received considerable attention in many applications, including ergonomics and healthcare. When using neural network models, overfitting and poor performance are prevalent issues. Recently, convolutional neural networks (CNNs) were successfully used for human postu...

The purpose of this research was to develop an artificial intelligence-based method for evaluating substitution voicing (SV) and speech following laryngeal oncosurgery. Convolutional neural networks were used to analyze spoken audio sources. A Mel-frequency spectrogram was employed as input to the deep neural network architecture. The program was t...

Semantic segmentation is the task of clustering together parts of an image that belong to the same object class. Semantic segmentation of webpages is important for inferring contextual information from the webpage. This study examines and compares deep learning methods for classifying webpages based on imagery that is obscured by semantic segmentat...

Binary object segmentation is a sub-area of semantic segmentation that could be used for a variety of applications. Semantic segmentation models could be applied to solve binary segmentation problems by introducing only two classes, but the models to solve this problem are more complex than actually required. This leads to very long training times,...

Background and Objectives: Clinical diagnosis has become very significant in today’s health system. The most serious disease and the leading cause of mortality globally is brain cancer which is a key research topic in the field of medical imaging. The examination and prognosis of brain tumors can be improved by an early and precise diagnosis based...

The COVID-19 pandemic is one of the most disruptive outbreaks of the 21st century considering its impacts on our freedoms and social lifestyle. Several methods have been used to monitor and diagnose this virus, which includes the use of RT-PCR test and chest CT/CXR scans. Recent studies have employed various crowdsourced sound data types such as co...

The usage of techniques of the artificial neural networks (ANNs) in the field of microwave devices has recently increased. The advantages of ANNs in comparison with traditional full-wave methods are that the prediction speed when the traditional time-consuming iterative calculations are not required and also the complex mathematical model of the mi...

Federated learning (FL) is a scheme in which several consumers work collectively to unravel machine learning (ML) problems, with a dominant collector synchronizing the procedure. This decision correspondingly enables the training data to be distributed, guaranteeing that the individual device’s data are secluded. The paper systematically reviewed t...

One of the most important strategies for preventative factory maintenance is anomaly detection without the need for dedicated sensors for each industrial unit. The implementation of sound-data-based anomaly detection is an unduly complicated process since factory-collected sound data are frequently corrupted and affected by ordinary production nois...

In today’s healthcare setting, the accurate and timely diagnosis of breast cancer is critical for recovery and treatment in the early stages. In recent years, the Internet of Things (IoT) has experienced a transformation that allows the analysis of real-time and historical data using artificial intelligence (AI) and machine learning (ML) approaches...

Simple Summary A total laryngectomy involves the full and permanent separation of the upper and lower airways, resulting in the loss of voice and inability to interact vocally. To identify, extract, and evaluate replacement voicing following laryngeal oncosurgery, we propose employing convolutional neural networks for categorization of speech repre...

The identification of human activities from videos is important for many applications. For such a task, three-dimensional (3D) depth images or image sequences (videos) can be used, which represent the positioning information of the objects in a 3D scene obtained from depth sensors. This paper presents a framework to create foreground–background mas...

Pedestrian occurrences in images and videos must be accurately recognized in a number of applications that may improve the quality of human life. Radar can be used to identify pedestrians. When distinct portions of an object move in front of a radar, micro-Doppler signals are produced that may be utilized to identify the object. Using a deep-learni...

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,...