Miracle-dz/UniFAR-FTUs huggingface dataset:
Design And Implementing Heart Disease Prediction Using Naives Bayesian

Data mining, a great developing technique that revolves around exploring and digging out significant information from massive collection of data which can be further beneficial in examining and drawing out patterns for making business related decisions. Talking about the Medical domain, implementation of data mining in this field can yield in discovering and withdrawing valuable patterns and information which can prove beneficial in performing clinical diagnosis. The research focuses on heart disease diagnosis by considering previous data and information. To achieve this SHDP (Smart Heart Disease Prediction) is built via Navies Bayesian in order to predict risk factors concerning heart disease. The speedy advancement of technology has led to remarkable rise in mobile health technology that being one of the web application. The required data is assembled in a standardized form. For predicting the chances of heart disease in a patient, the following attributes are being fetched from the medical profiles, these include: age, BP, cholesterol, sex, blood sugar etc… The collected attributes acts as input for the Navies Bayesian classification for predicting heart disease. The dataset utilized is split into two sections, 80% dataset is utilized for training and rest 20% is utilized for testing. The proposed approach includes following stages: dataset collection, user registration and login (Application based), classification via Navies Bayesian, prediction and secure data transfer by employing AES (Advanced Encryption Standard). Thereafter result is produced. The research elaborates and presents multiple knowledge abstraction techniques by making use of data mining methods which are adopted for heart disease prediction. The output reveals that the established diagnostic system effectively assists in predicting risk factors concerning heart diseases.
A Novel Feature Extraction Framework for Four Class Motor Imagery Classification using Log Determinant Regularized Riemannian Manifold

Brain-Computer Interface (BCI) systems allow the person in communicating with the external world using Electroencephalography (EEG). Motor Imagery (MI) based BCI systems play a vital role in interacting with the external environment. In this paper, we propose a novel robust feature extraction and classification framework for four class MI classification to improve the classification accuracy. The proposed architecture is developed using log-determinant (log-det) based Regularized Riemannian mean (LDRRM) and linear SVM. The robustness of features extracted from the four class MI data is improved to the outliers and noise by using the proposed LDRRM framework. We evaluated the performance of the proposed LDRRM classification framework on publicly available four class MI dataset 2a of BCI competition IV. The performance results show that the proposed LDRRM classification architecture obtained a mean classification accuracy of 69.12%, also achieved 1.54% higher classification accuracy when compared with the existing studies.
Tracking Changes in Brain Network Connectivity under Transcranial Current Stimulation

Noninvasive transcranial brain stimulation has been widely used in experimental and clinical applications to perturb the brain activity, aiming at promoting synaptic plasticity or enhancing functional connectivity within targeted brain regions. However, there are different types of neurostimulations and various choices of stimulation parameters; how these choices influence the intermediate neurophysiological effects and brain connectivity remain incompletely understood. We propose several quantitative methods to investigate the brain connectivity of an epileptic patient before and after transcranial alternating/direct current stimulation (tACS/tDCS). The neuro-feedback derived from our analyses may provide useful cues for the effectiveness of neurostimulation.
A Transition Probability Based Classification Model for Enhanced N1 Sleep stage Identification During Automatic Sleep Stage Scoring

Automatic sleep staging provides a cheaper, faster and more accessible alternative for evaluating sleep patterns and quality compared with manual hypnogram scoring performed by a clinician. Traditionally, classification methods treat sleep stages independently of their temporal order, despite sleep patterns themselves being highly sequential. Such independent sleep stage classification can result in poor sensitivity and precision, in particular when attempting to classify the sleep stage N1, otherwise known as the transition stage of sleep which links periods of wakefulness to periods of deep sleep. To this end, we propose a novel transition sleep classification method which aims to improve classification accuracy. This is achieved by utilising both the temporal information of previous stages and treating the transitions between stages as classes in their own right. Simulations on publicly available polysomnography (PSG) data and a comprehensive performance comparison with standard classifiers demonstrate a marked improvement achieved by the proposed method in both N1 sensitivity and precision across all considered classifiers. This includes an increase in N1 precision from 0.01% to 36.75% in an MLP classifier, and an increase in both accuracy and Cohen’s kappa value in two of the three classifiers. Overall best mean performance is obtained by transition classification with a random forest classifier (RF) which achieved a kappa value of κ = 0.75 (substantial agreement), and an N1 stage precision of 58%.
Wireless Low–Cost Bioimpedance Measurement Device for Lung Capacity Screening

This paper presents the development and implementation at a prototype level of a wireless, low–cost system for the measurement of the electrical bio-impedance of the chest with two channels using the AD5933 in a bipolar electrode configuration to measure lung volume variation. 15 volunteers were measured with the prototype, and the acquired signal presents the phases of the respiratory cycle, useful for the breathing rate calculation and for possible screening applications.
Verification of Medical Measuring Device on Therapeutic Ultrasound Using Metrological Traceability

Therapeutic ultrasound is regularly used in healthcare system with the aim of treatment disease. Considering, the significance of these device in daily activities of medical professionals and impact of their measurements on the health of patients, safety and accuracy of their measurements is becoming the topic of interest. Therefore, the testing guideline of medical device on ultrasonic physiotherapy had been published in Thailand. This guideline was carried out to develop the reference procedure for acceptance or periodic testing of therapeutic ultrasound, which meet the requirements from the authorities in Thailand. The ultrasound power meter (UPM) is used to investigate the actual power of therapeutic devices to be confirmed the traceability. Before the testing procedure was revealed, two categories of UPM calibration were determined to confirm which proper techniques will be the best traceability to check in the power system. Two methods, which are mass calibration and radiation force balance (RFB) system, shall be determined by carried out from 0.5 W to 10 W over the frequency range of 0.95 MHz and of 3 MHz. From the results, RFB method according to IEC 61161 can be determined the output power at desired frequencies with various powers in the real use, whereas the mass method can be calibrated only in terms of power. Moreover, the deviation results of RFB method are within ±30% tolerance of the device indication, which has been agreed performance with the criterion. However, the UPM shall be calibrated has the accuracy of≤10%.
Rehand II: Wire-Driven Five-Fingered Electric Prosthetic Hand Utilizing Elasticity of a Cosmetic Glove

Five-fingered electric prosthetic hands have been commercialized to assist the activities of forearm amputees in their daily life. Since the five-fingered electric prosthetic hands use a complicated mechanism and several actuators, the total weight is 1 kg or more. Although some hands are covered with a dedicated silicon glove, the appearance of them is not realistic compared to a general cosmetic hand. In this paper, we report a wire-driven five-fingered electric prosthetic hand utilizing elasticity of a cosmetic glove termed as Rehand II. The five fingers are flexed by pulling wires with a single servo motor and are extended by the elasticity of the cosmetic glove. In addition, a fitting mechanism in the hand allows the hand to fit the shape of various objects. With this approach, we developed an electric prosthetic hand with a simple grasping function that solves appearance and weight problems in conventional hands. The total weight of the developed hand was 562 g. The results of evaluation tests via Southampton Hand Assessment Procedure (SHAP) and grasping test of daily necessities in which a forearm amputee participated demonstrated that the developed prosthesis exhibited function to manipulate various objects used in daily activities.
Experimental and Mathematical Verification of Permanent Magnet Synchronous Motor Using Modified Simulink

Permanent Magnet motor has significant importance in the area of motors because it's more reliable than DC motors and the reason for this that PM motor doesn't have a commutator. With the comparison of Alternate Current motor, the PM synchronous motor has a significant advantage like PM synchronous generates rotor magnetic flux with rotor magnets and due to this, the gained efficiency is very high. In a latter part of this paper we have explained that how we applied and simulation results are compared under different section like mathematical modeling, experimental validation, simulation toll and result. In the second last part of this paper that is results, the simulation results are presented, then we modified the simulation and results are gained which are presented in the nest sub heading of experiment result.
A Critical Comparison of Pipelines for Structural Brain Network Analysis

The human brain could be understood as a vast network composed of interconnected regions forming hierarchical and overlapping subnetworks. Cortical thickness (CT) correlations, as measure of structural connectedness, can be transformed into structural networks. Those can be analyzed via cluster detection algorithms to investigate their organization. An analysis pipeline from CT to links, networks, clusters and beyond is composed of a lot of consecutive reasoned or just arbitrary steps. How much can different pipeline components alter the result? Which step is to be considered most influential? In order to give a sufficient answer, we critically compare 96 different pipelines. The results of this study are to some extent surprising as the choice of a specific CT correlation and correction procedure can lead to more diverse results than the decision between taking only absolute CT correlations or ignoring all negative ones. Even more crucial, exemplary cluster detectors were found to pair-wisely correlate from r = 0.98 to even r = −0.20 on the same data. Thus, a summary of multiple detector results with different but suited properties is highly advisable until a theory based neuroscientific recommendation for the best approach will be found.
The Role of Haptic Stimuli on Affective Reading: a Pilot Study

The affective role of touch has opened new perspectives in human-machine interaction. This paper presents an emotion recognition algorithm to investigate the role of tactile stimuli conveyed through a wearable haptic system during affective reading. To this end, a group of 32 healthy volunteers underwent an emotional stimulation by reading affective texts, with and without the concurrent presence of pleasant haptic stimuli. Throughout the experiment, autonomic nervous system dynamics was quantified through heart rate variability (HRV) and electrodermal activity (EDA) analyses. EDA and HRV features were then used as input of a SVM-RFE learning algorithm for an automatic recognition of neutral and arousing texts. The affective recognition of the reading was performed in the presence or absence of the haptic stimulation. Results show that the affective perception induced by the neutral and arousing reading were discriminated with a significantly improved accuracy (+14.5%) when a caress-like haptic stimulus was conveyed to the user.
Epileptic States Recognition Using Transfer Learning

Automatic recognition of electroencephalogram (EEG) signals plays a major role in epilepsy diagnosis and assessment. However, the recognition accuracy of conventional methods is usually not satisfactory because of the inconsistent distribution of training and testing data in practical applications. To overcome this problem, we used cross-domain mean joint approximation embedding (CMJAE) transductive transfer learning method to realize the knowledge transfer from the training data to the testing data by measuring the distribution difference between them. We combined the subspace learning and joint distribution to adapt the marginal and conditional distribution discrepancy. Our method was able to effectively learn a model for the testing data from training data with different distribution at a low computational complexity cost. On a public dataset, an ad-hoc cross-validation scheme of the proposed method exhibited that the average recognition accuracy, sensitivity, specificity of different states was 97.5%, 94.3%, 92.7% respectively, much better than conventional machine learning or deep learning methods, which may serve as a promising strategy for epileptic states recognition algorithms.
A Baseline Evaluation Method Based on Principal Component Analysis for Software Test Case Design

Software has been widely applied in the field of aerospace telemetry telecontrol & communication (TT&C). Testing is one of the most important means to ensure the quality of TT&C software, and one of the most critical links of software testing is test case design. The adequacy of test case design determines the effectiveness of testing. This paper proposes a baseline evaluation method for test cases of space TT&C software based on principal component analysis. This method extracts the principal components of the factors (software key level, program circle complexity, interface complexity, etc.) that affect test case design in the test project of historical space TT&C software, builds a regression evaluation model, and forms a standard baseline for evaluating the adequacy of test case design. This baseline evaluation method is applied to a real case. The validity of this method is illustrated in one practical case.
Strategy preference in complex dynamical tasks: preliminary results*

Many complex activities of daily living are characterized by instabilities. From a motor control point of view, there are two alternative stabilization mechanisms that require to integrate multi-joint coordination aspects: stiffness and positional stabilization strategies. The aim of this work is to understand how the central nervous system can switch between the two control paradigms, depending on the dynamic properties of the environment. This study reports the performance of healthy participants trained to master both strategies and, in particular, it shows how it is possible to quantitatively assess the individual strategy preference when specific environmental factors are introduced.
Agricultural growth, efficiency and family agriculture in Paraguay

Between 2001 and 2012, Latin America and the Caribbeanâ€™s (LAC) agriculture saw its best performance of the last 30 years. What were the implications of this growth for family agriculture (FA) in the region? This study contributes to answer this question by looking at the case of Paraguay, a country with one of the fastest growing agricultural sectors in the region during this period. At the center of the development challenges faced by this country is the debate on the role of family agriculture and smallholders in a future growth strategy. Between 1991 and 2008 the number of family workers in agriculture decreased significantly, while the total area of FA crops decreased to only 48 percent of its level in 1991. As some authors argued in the past, the 2000s represent a turning point for FA development in Paraguay, given that until 2002, the total area of farms of less than 20 hectares was still increasing, a trend that reversed after this year. Are these changes, part of a process of impoverishment of the rural population resulting from displacement of FA by the commercial sector as is normally assumed in previous studies? Evidence from this study shows that rural poverty decreased almost by half between 2003 and 2015; that the reduction of output of crops traditionally produced by FA was not the result of competition with the commercial sector, but mostly a consequence of the collapse of cotton production, a failure of a government program for FA; and that in regions with high proportion of FA, commercial crop production expanded by displacing inefficient extensive livestock farmers and not FA agriculture. We conclude that the situation of FA in Paraguay is much more diverse and complex than the simple claims of decomposition and disappearance as the result of the expansion of capitalist farmers. In this context, there are options for the government to promote the development of FA with the goal of increasing employment opportunities in rural areas while achieving a much-needed diversification of agricultural production and exports.
Automated Melanoma Staging in Lymph Node Biopsy Image using Deep Learning

Melanoma is the most dangerous type of skin cancer with aggressive and unpredictable behavior which can spread to any part of the body. The Proliferation Index (PI) in lymph node biopsy is an important indicator to determine the spread of the cancer in the human body. In this paper, we propose a technique for automatic measurement of PI values in Ki-67 stained biopsy image using deep learning algorithm. This technique segments the nuclei and measures the PI by using a trained convolutional neural network (CNN) model. The CNN model is trained using the manually nuclei segmentation of Ki-67 images. Experimental results show that the proposed segmentation technique can robustly segment and classify the nuclei with low computational complexity and has an average error rate less than 0.7%.
Automated Technique for Segmentation of Brain Tumor in MR Images

Cerebrum Tumor is the unusual or unrestrained cell division within the brain. The remedial problems are serious if tumors are not diagnosed at early stage inevitably most timely examination is necessary. This paper deals with detection of tumor region by Morphological Operators based segmentation approach. It contains upgrade, partition and positioning stages. To extract the tumor region the images are partitioned and order by categorizing them into Benign and Malignant. In case if there are more number of images, doctors could save time by using this approach.
Neural Offset Time Evaluation in Surface Respiratory Signals during Controlled Respiration

The electrical activity of the diaphragm measured by surface electromyography (sEMGdi) provides indirect information on neural respiratory drive. Moreover, it allows evaluating the ventilatory pattern from the onset and offset (ntoff) estimation of the neural inspiratory time. sEMGdi amplitude variation was quantified using the fixed sample entropy (fSampEn), a less sensitive method to the interference from cardiac activity. The detection of the ntoff is controversial, since it is located in an intermediate point between the maximum value and the cessation of sEMGdi inspiratory activity, evaluated by the fSampEn. In this work ntoff detection has been analyzed using thresholds between 40% and 100 % of the fSampEn peak. Furthermore, fSampEn was evaluated analyzing the r parameter from 0.05 to 0.6, using a m equal to 1 and a sliding window size equal to 250 ms. The ntoff has been compared to the offset time (toff) obtained from the airflow during a controlled respiratory protocol varying the fractional inspiratory time from 0.54 to 0.18 whilst the respiratory rate was constant at 16 bpm. Results show that the optimal threshold values were between 66.0 % to 77.0 % of the fSampEn peak value. r values between 0.25 to 0.50 were found suitable to be used with the fSampEn.
Using Blockchains to Enable Big Data Analysis of Private Information

Blockchains were initially introduced in 2009, and since then, they have evolved into a revolutionary technology that has affected, not only the digital world but also our everyday lives and activities. Blockchain systems and architectures continue to grow at a fast rate, constantly introducing innovative solutions across multiple research topics. However, despite their wide adoption by the research community, the technology is still not mature enough to address the problems of business use cases. Issues like scalability, complexity, cryptocurrencies' market fluctuations etc. prevent the proposed solutions from being widely accepted. Based on this observation this paper introduces an innovative solution to enable Big Data analysis of private information using blockchains. This solution is based on the Hyperledger Fabric blockchain and is able support the strict privacy and security requirements of operational and business environments.
A Deep Neural Network Based Hierarchical Multi-Label Classifier for Protein Function Prediction

Hierarchical classification approaches have been shown to be effective for protein function prediction problem. Traditionally, a set of simple classifiers are used for each hierarchical level separately. In this paper, we introduce a deep neural network (DNN) model with multiple heads and multiple ends to realize the whole set of classifiers. The DNN model consists of three parts: the body part, the multi-end part and the multi-head part. The body part is a deep multilayer perceptron (MLP) shared by different classifiers for feature mapping. The multi-end part performs feature fusion transforming the input vectors of different classifiers to feature vectors with the same length so as to share the feature mapping part. The multi-head part is a set of linear multi-label classifiers. By sharing a deep MLP with multiple classifiers, we are able to construct more powerful classifiers for each level with limited training samples, and expecting to have better classification performance. Experiment results on benchmark datasets show that the proposed method significantly outperforms these traditional methods.
Evolutionary-learning framework: improving automatic swarm robotics design

PurposeThe purpose of this paper is to review the current state of proceedings in the research area of automatic swarm design and discusses possible solutions to advance swarm robotics research.Design/methodology/approachFirst, this paper begins by reviewing the current state of proceedings in the field of automatic swarm design to provide a basic understanding of the field. This should lead to the identification of which issues need to be resolved in order to move forward swarm robotics research. Then, some possible solutions to the challenges are discussed to identify future directions and how the proposed idea of incorporating learning mechanism could benefit swarm robotics design. Lastly, a novel evolutionary-learning framework for swarms based on epigenetic function is proposed with a discussion of its merits and suggestions for future research directions.FindingsThe discussion shows that main challenge which is needed to be resolved is the presence of dynamic environment which is mainly caused by agent-to-agent and agent-to-environment interactions. A possible solution to tackle the challenge is by incorporating learning capability to the swarm to tackle dynamic environment.Originality/valueThis paper gives a new perspective on how to improve automatic swarm design in order to move forward swarm robotics research. Along with the discussion, this paper also proposes a novel framework to incorporate learning mechanism into evolutionary swarm using epigenetic function.