Stock Price Movement Prediction Using Sentiment Analysis and CandleStick Chart Representation.

Determining the price movement of stocks is a challenging problem to solve because of factors such as industry performance, economic variables, investor sentiment, company news, company performance, and social media sentiment. People can predict the price movement of stocks by applying machine learning algorithms on information contained in historical data, stock candlestick-chart data, and social-media data. However, it is hard to predict stock movement based on a single classifier.

Optimization-Based Approaches for Minimizing Deployment Costs for Wireless Sensor Networks with Bounded Estimation Errors.

As wireless sensor networks have become more prevalent, data from sensors in daily life are constantly being recorded. Due to cost or energy consumption considerations, optimization-based approaches are proposed to reduce deployed sensors and yield results within the error tolerance. The correlation-aware method is also designed in a mathematical model that combines theoretical and practical perspectives.

Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database-HF_Lung_V1.

A reliable, remote, and continuous real-time respiratory sound monitor with automated respiratory sound analysis ability is urgently required in many clinical scenarios-such as in monitoring disease progression of coronavirus disease 2019-to replace conventional auscultation with a handheld stethoscope. However, a robust computerized respiratory sound analysis algorithm for breath phase detection and adventitious sound detection at the recording level has not yet been validated in practical applications. In this study, we developed a lung sound database (HF_Lung_V1) comprising 9,765 audio files of lung sounds (duration of 15 s each), 34,095 inhalation labels, 18,349 exhalation labels, 13,883 continuous adventitious sound (CAS) labels (comprising 8,457 wheeze labels, 686 stridor labels, and 4,740 rhonchus labels), and 15,606 discontinuous adventitious sound labels (all crackles).

Multiphase Identification Algorithm for Fall Recording Systems Using a Single Wearable Inertial Sensor.

Fall-related information can help clinical professionals make diagnoses and plan fall prevention strategies. The information includes various characteristics of different fall phases, such as falling time and landing responses. To provide the information of different phases, this pilot study proposes an automatic multiphase identification algorithm for phase-aware fall recording systems.

Optimization-Based Resource Management Algorithms with Considerations of Client Satisfaction and High Availability in Elastic 5G Network Slices.

A combined edge and core cloud computing environment is a novel solution in 5G network slices. The clients' high availability requirement is a challenge because it limits the possible admission control in front of the edge cloud. This work proposes an orchestrator with a mathematical programming model in a global viewpoint to solve resource management problems and satisfying the clients' high availability requirements.

Sensing ecosystem dynamics via audio source separation: A case study of marine soundscapes off northeastern Taiwan.

Remote acquisition of information on ecosystem dynamics is essential for conservation management, especially for the deep ocean. Soundscape offers unique opportunities to study the behavior of soniferous marine animals and their interactions with various noise-generating activities at a fine temporal resolution. However, the retrieval of soundscape information remains challenging owing to limitations in audio analysis techniques that are effective in the face of highly variable interfering sources.

Automatic Functional Shoulder Task Identification and Sub-task Segmentation Using Wearable Inertial Measurement Units for Frozen Shoulder Assessment.

Advanced sensor technologies have been applied to support frozen shoulder assessment. Sensor-based assessment tools provide objective, continuous and quantitative information for evaluation and diagnosis. However, the current tools for assessment of functional shoulder tasks mainly rely on manual operation.

Automatic recognition of murmurs of ventricular septal defect using convolutional recurrent neural networks with temporal attentive pooling.

Recognizing specific heart sound patterns is important for the diagnosis of structural heart diseases. However, the correct recognition of heart murmur depends largely on clinical experience. Accurately identifying abnormal heart sound patterns is challenging for young and inexperienced clinicians.

Fluid Intake Monitoring System Using a Wearable Inertial Sensor for Fluid Intake Management.

Fluid intake is important for people to maintain body fluid homeostasis. Inadequate fluid intake leads to negative health consequences, such as headache, dizziness and urolithiasis. However, people in busy lifestyles usually forget to drink sufficient water and neglect the importance of fluid intake.

A decentralized framework for cultivating research lifecycle transparency.

Research transparency has been advocated as a key means of addressing the current crisis of reproducibility. This article proposes an enhanced form of research transparency, termed lifecycle transparency. Over the entire lifecycle of a research effort, this approach captures the syntactical contexts of artifacts and stakeholders, such as timestamps, agreements, and/or dependency requirements for completing each research phase.

Subtask Segmentation of Timed Up and Go Test for Mobility Assessment of Perioperative Total Knee Arthroplasty.

Total knee arthroplasty (TKA) is one of the most common treatments for people with severe knee osteoarthritis (OA). The accuracy of outcome measurements and quantitative assessments for perioperative TKA is an important issue in clinical practice. Timed up and go (TUG) tests have been validated to measure basic mobility and balance capabilities.

Atypical Frequency Sweep Processing in Chinese Children With Reading Difficulties: Evidence From Magnetoencephalography.

Chinese lexical tones determine word meaning and are crucial in reading development. Reduced tone awareness is widely reported in children with reading difficulties (RD). Lexical-tone processing requires sensitivity to frequency-modulated sound changes.

Inhibition of Long-Term Variability in Decoding Forelimb Trajectory Using Evolutionary Neural Networks With Error-Correction Learning.

In brain machine interfaces (BMIs), the functional mapping between neural activities and kinematic parameters varied over time owing to changes in neural recording conditions. The variability in neural recording conditions might result in unstable long-term decoding performance. Relevant studies trained decoders with several days of training data to make them inherently robust to changes in neural recording conditions.

Forecasting Air Quality in Taiwan by Using Machine Learning.

This study proposes a gradient-boosting-based machine learning approach for predicting the PM concentration in Taiwan. The proposed mechanism is evaluated on a large-scale database built by the Environmental Protection Administration, and Central Weather Bureau, Taiwan, which includes data from 77 air monitoring stations and 580 weather stations performing hourly measurements over 1 year. By learning from past records of PM and neighboring weather stations' climatic information, the forecasting model works well for 24-h prediction at most air stations.

SQUAT: a Sequencing Quality Assessment Tool for data quality assessments of genome assemblies.

Background: With the rapid increase in genome sequencing projects for non-model organisms, numerous genome assemblies are currently in progress or available as drafts, but not made available as satisfactory, usable genomes. Data quality assessment of genome assemblies is gaining importance not only for people who perform the assembly/re-assembly processes, but also for those who attempt to use assemblies as maps in downstream analyses. Recent studies of the quality control, quality evaluation/ assessment of genome assemblies have focused on either quality control of reads before assemblies or evaluation of the assemblies with respect to their contiguity and correctness.

A Mobile Phone-Based Approach for Hearing Screening of School-Age Children: Cross-Sectional Validation Study.

Background: Pure-tone screening (PTS) is considered as the gold standard for hearing screening programs in school-age children. Mobile devices, such as mobile phones, have the potential for audiometric testing.

Objective: This study aimed to demonstrate a new approach to rapidly screen hearing status and provide stratified test values, using a smartphone-based hearing screening app, for each screened ear of school-age children.

Auditory identification of frequency-modulated sweeps and reading difficulties in Chinese.

Background: In Chinese Mandarin, lexical tones play an important role of providing contrasts in word meaning. They are pitch patterns expressed by frequency-modulated (FM) signals. Yet, few studies have looked at the relationship between low-level auditory processing of frequency signals and Chinese reading skills.

Demographic and Symptomatic Features of Voice Disorders and Their Potential Application in Classification Using Machine Learning Algorithms.

Background: Studies have used questionnaires of dysphonic symptoms to screen voice disorders. This study investigated whether the differential presentation of demographic and symptomatic features can be applied to computerized classification.

Methods: We recruited 100 patients with glottic neoplasm, 508 with phonotraumatic lesions, and 153 with unilateral vocal palsy.

Specifying the diffusion MRI connectome in Chinese-speaking children with developmental dyslexia and auditory processing deficits.

Background: Lexical tone identification has a unique role in the perceptual processes of Chinese readers. Reduced lexical tone awareness, along with poor word-decoding abilities, is frequently observed in Chinese-speaking children with developmental dyslexia. However, whether this deficit is linked to reduced auditory processing and interrupted structural connectivity in the brain requires further investigation.

Effective Pneumothorax Detection for Chest X-Ray Images Using Local Binary Pattern and Support Vector Machine.

Automatic image segmentation and feature analysis can assist doctors in the treatment and diagnosis of diseases more accurately. Automatic medical image segmentation is difficult due to the varying image quality among equipment. In this paper, the automatic method employed image multiscale intensity texture analysis and segmentation to solve this problem.

Comparison of passive acoustic soniferous fish monitoring with supervised and unsupervised approaches.

Passive acoustics has been used to investigate behavior and relative abundances of soniferous fish. However, because of noise interferences, it remains challenging to accurately analyze acoustic activities of soniferous fish. This study proposes a multi-method approach, which combines rule-based detector, periodicity-coded non-negative matrix factorization, and Gaussian mixture models.

Detection of Pathological Voice Using Cepstrum Vectors: A Deep Learning Approach.

Objectives: Computerized detection of voice disorders has attracted considerable academic and clinical interest in the hope of providing an effective screening method for voice diseases before endoscopic confirmation. This study proposes a deep-learning-based approach to detect pathological voice and examines its performance and utility compared with other automatic classification algorithms.

Methods: This study retrospectively collected 60 normal voice samples and 402 pathological voice samples of 8 common clinical voice disorders in a voice clinic of a tertiary teaching hospital.

Off-Line Evaluation of Mobile-Centric Indoor Positioning Systems: The Experiences from the 2017 IPIN Competition.

The development of indoor positioning solutions using smartphones is a growing activity with an enormous potential for everyday life and professional applications. The research activities on this topic concentrate on the development of new positioning solutions that are tested in specific environments under their own evaluation metrics. To explore the real positioning quality of smartphone-based solutions and their capabilities for seamlessly adapting to different scenarios, it is needed to find fair evaluation frameworks.