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Kim, Tae-Yeun, Hoon Ko, Sung-Hwan Kim y Ho-Da Kim. "Modeling of Recommendation System Based on Emotional Information and Collaborative Filtering". Sensors 21, n.º 6 (12 de marzo de 2021): 1997. http://dx.doi.org/10.3390/s21061997.
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Emotion information represents a user’s current emotional state and can be used in a variety of applications, such as cultural content services that recommend music according to user emotional states and user emotion monitoring. To increase user satisfaction, recommendation methods must understand and reflect user characteristics and circumstances, such as individual preferences and emotions. However, most recommendation methods do not reflect such characteristics accurately and are unable to increase user satisfaction. In this paper, six human emotions (neutral, happy, sad, angry, surprised, and bored) are broadly defined to consider user speech emotion information and recommend matching content. The “genetic algorithms as a feature selection method” (GAFS) algorithm was used to classify normalized speech according to speech emotion information. We used a support vector machine (SVM) algorithm and selected an optimal kernel function for recognizing the six target emotions. Performance evaluation results for each kernel function revealed that the radial basis function (RBF) kernel function yielded the highest emotion recognition accuracy of 86.98%. Additionally, content data (images and music) were classified based on emotion information using factor analysis, correspondence analysis, and Euclidean distance. Finally, speech information that was classified based on emotions and emotion information that was recognized through a collaborative filtering technique were used to predict user emotional preferences and recommend content that matched user emotions in a mobile application.
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Jenkins, Jeffrey. "Detecting emotional ambiguity in text". MOJ Applied Bionics and Biomechanics 4, n.º 3 (25 de mayo de 2020): 55–57. http://dx.doi.org/10.15406/mojabb.2020.04.00134.
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An approach for determining emotional ambiguity in text data is described in this paper. The prediction confidences output from a text classifier are used to measure amount of ambiguity found in target entries. This measure can be used as a filtering mechanism to identify entries that require human feedback. This feedback loop can be implemented in a workflow which retrains a classifier model including newly disambiguated entries and resulting in a boost to classifier accuracy. This emotion ambiguity measure can be utilized to discover concrete emotional content in text data as well as reveal topics which do not have a concrete emotional consensus.
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RULE, R. R., A. P. SHIMAMURA y R. T. KNIGHT. "Orbitofrontal cortex and dynamic filtering of emotional stimuli". Cognitive, Affective, & Behavioral Neuroscience 2, n.º 3 (1 de septiembre de 2002): 264–70. http://dx.doi.org/10.3758/cabn.2.3.264.
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Guerzoni, Michael A. "Vicarious trauma and emotional labour in researching child sexual abuse and child protection: A postdoctoral reflection". Methodological Innovations 13, n.º 2 (mayo de 2020): 205979912092634. http://dx.doi.org/10.1177/2059799120926342.
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Criminology almost inevitably involves the study of sensitive and sorrowful research topics. Consequently, criminologists fall victim to the inherent risks of exposure to vicarious trauma, requiring many to practice emotional labour in the field, in the lecture hall, and perhaps, even along the corridors of the university campus itself. This article offers a reflective account of the experiences of vicarious trauma and the self-imposed, protective practice of emotional labour within doctoral research on child protection initiatives within a religious institution. It explores my experience of self-regulating my emotions in response to the reading of disturbing content, and of the active filtering of points of conversation when asked about my research within professional, familial and social settings, to prevent disturbing the emotions of others. The article encourages potential doctoral students to consider how they might prepare for themselves emotionally, socially and physically, for their inevitable encounter with difficult content, prior to the commencement of candidature, thereby increasing their resilience in facing the difficult components of a doctoral degree tasked with exploring content of a bleak and emotionally unnerving nature.
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Nosshi, Anthony, Aziza Saad Asem y Mohammed Badr Senousy. "Hybrid Recommender System Using Emotional Fingerprints Model". International Journal of Information Retrieval Research 9, n.º 3 (julio de 2019): 48–70. http://dx.doi.org/10.4018/ijirr.2019070104.
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With today's information overload, recommender systems are important to help users in finding needed information. In the movies domain, finding a good movie to watch is not an easy task. Emotions play an important role in deciding which movie to watch. People usually express their emotions in reviews or comments about the movies. In this article, an emotional fingerprint-based model (EFBM) for movies recommendation is proposed. The model is based on grouping movies by emotional patterns of some key factors changing in time and forming fingerprints or emotional tracks, which are the heart of the proposed recommender. Then, it is incorporated into collaborative filtering to detect the interest connected with topics. Experimental simulation is conducted to understand the behavior of the proposed approach. Results are represented to evaluate the proposed recommender.
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Santamaria-Granados, Luz, Juan Francisco Mendoza-Moreno, Angela Chantre-Astaiza, Mario Munoz-Organero y Gustavo Ramirez-Gonzalez. "Tourist Experiences Recommender System Based on Emotion Recognition with Wearable Data". Sensors 21, n.º 23 (25 de noviembre de 2021): 7854. http://dx.doi.org/10.3390/s21237854.
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The collection of physiological data from people has been facilitated due to the mass use of cheap wearable devices. Although the accuracy is low compared to specialized healthcare devices, these can be widely applied in other contexts. This study proposes the architecture for a tourist experiences recommender system (TERS) based on the user’s emotional states who wear these devices. The issue lies in detecting emotion from Heart Rate (HR) measurements obtained from these wearables. Unlike most state-of-the-art studies, which have elicited emotions in controlled experiments and with high-accuracy sensors, this research’s challenge consisted of emotion recognition (ER) in the daily life context of users based on the gathering of HR data. Furthermore, an objective was to generate the tourist recommendation considering the emotional state of the device wearer. The method used comprises three main phases: The first was the collection of HR measurements and labeling emotions through mobile applications. The second was emotional detection using deep learning algorithms. The final phase was the design and validation of the TERS-ER. In this way, a dataset of HR measurements labeled with emotions was obtained as results. Among the different algorithms tested for ER, the hybrid model of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks had promising results. Moreover, concerning TERS, Collaborative Filtering (CF) using CNN showed better performance.
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Prete, Giulia, Bruno Laeng y Luca Tommasi. "Modulating adaptation to emotional faces by spatial frequency filtering". Psychological Research 82, n.º 2 (26 de noviembre de 2016): 310–23. http://dx.doi.org/10.1007/s00426-016-0830-x.
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Wang, Shu, Chonghuan Xu, Austin Shijun Ding y Zhongyun Tang. "A Novel Emotion-Aware Hybrid Music Recommendation Method Using Deep Neural Network". Electronics 10, n.º 15 (24 de julio de 2021): 1769. http://dx.doi.org/10.3390/electronics10151769.
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Emotion-aware music recommendations has gained increasing attention in recent years, as music comes with the ability to regulate human emotions. Exploiting emotional information has the potential to improve recommendation performances. However, conventional studies identified emotion as discrete representations, and could not predict users’ emotional states at time points when no user activity data exists, let alone the awareness of the influences posed by social events. In this study, we proposed an emotion-aware music recommendation method using deep neural networks (emoMR). We modeled a representation of music emotion using low-level audio features and music metadata, model the users’ emotion states using an artificial emotion generation model with endogenous factors exogenous factors capable of expressing the influences posed by events on emotions. The two models were trained using a designed deep neural network architecture (emoDNN) to predict the music emotions for the music and the music emotion preferences for the users in a continuous form. Based on the models, we proposed a hybrid approach of combining content-based and collaborative filtering for generating emotion-aware music recommendations. Experiment results show that emoMR performs better in the metrics of Precision, Recall, F1, and HitRate than the other baseline algorithms. We also tested the performance of emoMR on two major events (the death of Yuan Longping and the Coronavirus Disease 2019 (COVID-19) cases in Zhejiang). Results show that emoMR takes advantage of event information and outperforms other baseline algorithms.
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Mičieta, Branislav, Vladimíra Biňasová, Beáta Furmannová, Gabriela Gabajová y Marta Kasajová. "Emotional intelligence as an aspect in the performance of the work of a global manager". SHS Web of Conferences 129 (2021): 12002. http://dx.doi.org/10.1051/shsconf/202112912002.
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Research background: Emotional intelligence is a set of emotional and social abilities and skills of a manager. Nowadays, the environment is global and very complex, and the association between emotional intelligence and performance in enterprises remains an important area of worry for managers and employees' globally. The article focuses on the aspect and abilities of managers dealing with increasing the performance of their subordinates, their relationships in the workplace, division of labour and the overall organization of the team regarding their emotions and individual feeling of importance in the work process. Purpose of the article: The aim of the survey was to find out how today's managers behave in common situations that occur in the daily work of managers. It was also investigated to what extent managers use emotional intelligence and whether they are emotionally stable enough to work as a manager. Methods: A questionnaire survey was attended by managers. The questionnaire contained two parts. Firstly, the filtering questions and secondly, the specific situations in managerial life were analyzed, from which the level of emotional intelligence of the given manager was evaluated. Findings & Value added: These results in the work served to suggest improving awareness and the importance of emotional intelligence in work environments. The knowledge gained from the questionnaire will help in possible further research to create similar activities and improvements to imply emotional intelligence in more efficient operation of the company. A manager with high emotional intelligence can communicate effectively with others, can tolerate, solve problems, and build relationships with and between his employees.
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Kadiri, Sudarsana Reddy y B. Yegnanarayana. "Epoch extraction from emotional speech using single frequency filtering approach". Speech Communication 86 (febrero de 2017): 52–63. http://dx.doi.org/10.1016/j.specom.2016.11.005.
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Denis, Patrice, Vincent Courboulay, Arnaud Revel, Syntyche Gbèhounou, François Lecellier y Christine Fernandez-Maloigne. "Improvement of natural image search engines results by emotional filtering". EAI Endorsed Transactions on Creative Technologies 3, n.º 6 (25 de abril de 2016): 151164. http://dx.doi.org/10.4108/eai.25-4-2016.151164.
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Shanmuga Sundari, P. y M. Subaji. "Integrating Sentiment Analysis on Hybrid Collaborative Filtering Method in a Big Data Environment". International Journal of Information Technology & Decision Making 19, n.º 02 (marzo de 2020): 385–412. http://dx.doi.org/10.1142/s0219622020500108.
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Most of the traditional recommendation systems are based on user ratings. Here, users provide the ratings towards the product after use or experiencing it. Accordingly, the user item transactional database is constructed for recommendation. The rating based collaborative filtering method is well known method for recommendation system. This system leads to data sparsity problem as the user is unaware of other similar items. Web cataloguing service such as tags plays a significant role to analyse the user’s perception towards a particular product. Some system use tags as additional resource to reduce the data sparsity issue. But these systems require lot of specific details related to the tags. Existing system either focuses on ratings or tags based recommendation to enhance the accuracy. So these systems suffer from data sparsity and efficiency problem that leads to ineffective recommendations accuracy. To address the above said issues, this paper proposed hybrid recommendation system (Iter_ALS Iterative Alternate Least Square) to enhance the recommendation accuracy by integrating rating and emotion tags. The rating score reveals overall perception of the item and emotion tags reflects user’s feelings. In the absence of emotional tags, scores found in rating is assumed as positive or negative emotional tag score. Lexicon based semantic analysis on emotion tags value is adopted to represent the exclusive value of tag. Unified value is represented into Iter_ALS model to reduce the sparsity problem. In addition, this method handles opinion bias between ratings and tags. Experiments were tested and verified using a benchmark project of MovieLens dataset. Initially this model was tested with different sparsity levels varied between 0%-100 percent and the results obtained from the experiments shows the proposed method outperforms with baseline methods. Further tests were conducted to authenticate how it handles opinion bias by users before recommending the item. The proposed method is more capable to be adopted in many real world applications
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Li, Shun, Liqing Cui, Changye Zhu, Baobin Li, Nan Zhao y Tingshao Zhu. "Emotion recognition using Kinect motion capture data of human gaits". PeerJ 4 (15 de septiembre de 2016): e2364. http://dx.doi.org/10.7717/peerj.2364.
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Automatic emotion recognition is of great value in many applications, however, to fully display the application value of emotion recognition, more portable, non-intrusive, inexpensive technologies need to be developed. Human gaits could reflect the walker’s emotional state, and could be an information source for emotion recognition. This paper proposed a novel method to recognize emotional state through human gaits by using Microsoft Kinect, a low-cost, portable, camera-based sensor. Fifty-nine participants’ gaits under neutral state, induced anger and induced happiness were recorded by two Kinect cameras, and the original data were processed through joint selection, coordinate system transformation, sliding window gauss filtering, differential operation, and data segmentation. Features of gait patterns were extracted from 3-dimentional coordinates of 14 main body joints by Fourier transformation and Principal Component Analysis (PCA). The classifiers NaiveBayes, RandomForests, LibSVM and SMO (Sequential Minimal Optimization) were trained and evaluated, and the accuracy of recognizing anger and happiness from neutral state achieved 80.5% and 75.4%. Although the results of distinguishing angry and happiness states were not ideal in current study, it showed the feasibility of automatically recognizing emotional states from gaits, with the characteristics meeting the application requirements.
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Murugappan, Murugappan, Waleed Alshuaib, Ali K. Bourisly, Smith K. Khare, Sai Sruthi y Varun Bajaj. "Tunable Q wavelet transform based emotion classification in Parkinson’s disease using Electroencephalography". PLOS ONE 15, n.º 11 (19 de noviembre de 2020): e0242014. http://dx.doi.org/10.1371/journal.pone.0242014.
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Parkinson’s disease (PD) is a severe incurable neurological disorder. It is mostly characterized by non-motor symptoms like fatigue, dementia, anxiety, speech and communication problems, depression, and so on. Electroencephalography (EEG) play a key role in the detection of the true emotional state of a person. Various studies have been proposed for the detection of emotional impairment in PD using filtering, Fourier transforms, wavelet transforms, and non-linear methods. However, these methods require a selection of basis and are confined in terms of accuracy. In this paper, tunable Q wavelet transform (TQWT) is proposed for the classification of emotions in PD and normal controls (NC). EEG signals of six emotional states namely happiness, sadness, fear, anger, surprise, and disgust are studied. Power, entropy, and statistical moments based features are elicited from the highpass and lowpass sub-bands of TQWT. Six features selected by statistical analysis are classified with a k-nearest neighbor, probabilistic neural network, random forest, decision tree, and extreme learning machine. Three performance measures are obtained, maximum mean accuracy, sensitivity, and specificity of 96.16%, 97.59%, and 88.51% for NC and 93.88%, 96.33%, and 81.67% for PD are achieved with a probabilistic neural network. The proposed method proved to be very effective such that it classifies emotions in PD and could be used as a potential tool for diagnosing emotional impairment in hospitals.
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Belinskiy, Artem Viktorovich, Vazha Mikhailovich Devishvili, Aleksandr Mikhailovich Chernorizov y Mikhail Aleksandrovich Lobin. "Method of Еmotional State Assessment Using a Complex of Psychophysiological and Tensotremorometric Methods". Психология и Психотехника, n.º 1 (enero de 2023): 26–37. http://dx.doi.org/10.7256/2454-0722.2023.1.39849.
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The object of research is emotional tension. The subject of the study is assessment of emotional tension in the process of presentation of emotionally significant stimuli in the form of images and sounds according to the parameters of physiological activity and tensotremorometry in the process of maintaining isometric effort. Particular attention is paid to the consideration of methods of determination of tremor and its connection with emotional tension. Key aspects are the consideration of the key frequency range of physiological tremor analysis 8-16 hertz, methods of tremor measurement such as accelerometry, tensotremorometry, electromyography. Studies of the relationship between tensotremorometry on the frequency range of physiological tremor and emotional tension are considered. We propose a new method for analyzing tremor according to tensotremorometry data using the sliding window method and filtering the frequencies characteristic of physiological tremor. Methods of recording physiological activity in the form of photoplethysmography and skin electrical activity were used for reliability of the results obtained. We described and confirmed the connection between emotional tension during the demonstration of meaningful stimuli and finger tremor, while holding a given force in an isometric condition. The new technique has wide possibilities for use in the psychological and clinical fields, in particular, in detection of hidden information, assessment of the stress resistance of personnel, and determination of the functional state of a person, as well as in the study of the psycho-emotional state of students.
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Cao, Zheng, Heng Xu y Brian Sheng-Xian Teo. "Sentiment of Chinese Tourists towards Malaysia Cultural Heritage Based on Online Travel Reviews". Sustainability 15, n.º 4 (14 de febrero de 2023): 3478. http://dx.doi.org/10.3390/su15043478.
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Analyzing the perception differences and influencing factors of cross-cultural groups in heritage tourism can help heritage sites to formulate differentiated service and improve tourist satisfaction. This research adopted the BERT model to undertake sentiment analysis of 17,555 Chinese online reviews for nine scenic spots in Melaka. Using vocabulary filtering, co-occurrence analysis, and semantic clustering technology, the emotional characteristics of Chinese outbound tourists when they visited heritage sites in Melaka were analyzed, which revealed the factors influencing their positive and negative emotions. Results showed that: 1. The BERT-based deep learning approach can obtain improved sentiment predictive performance. 2. Chinese tourists’ general emotional perceptions of Melaka were positive and they were very interested in heritage sites. 3. The most important reason for the negative emotions of Chinese tourists was a lack of cultural experience in Melaka. This research expands the application of deep learning in the field of tourism, and it helps heritage tourism destinations to improve their marketing plans for Chinese tourists and achieve long-term sustainable development of the destination.
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Georgiewa, Petra, Agnieszka J. Szczepek, Matthias Rose, Burghard F. Klapp y Birgit Mazurek. "Cerebral Processing of Emotionally Loaded Acoustic Signals by Tinnitus Patients". Audiology and Neurotology 21, n.º 2 (2016): 80–87. http://dx.doi.org/10.1159/000443364.
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This exploratory study determined the activation pattern in nonauditory brain areas in response to acoustic, emotionally positive, negative or neutral stimuli presented to tinnitus patients and control subjects. Ten patients with chronic tinnitus and without measurable hearing loss and 13 matched control subjects were included in the study and subjected to fMRI with a 1.5-tesla scanner. During the scanning procedure, acoustic stimuli of different emotional value were presented to the subjects. Statistical analyses were performed using statistical parametric mapping (SPM 99). The activation pattern induced by emotionally loaded acoustic stimuli differed significantly within and between both groups tested, depending on the kind of stimuli used. Within-group differences included the limbic system, prefrontal regions, temporal association cortices and striatal regions. Tinnitus patients had a pronounced involvement of limbic regions involved in the processing of chimes (positive stimulus) and neutral words (neutral stimulus), strongly suggesting improperly functioning inhibitory mechanisms that were functioning well in the control subjects. This study supports the hypothesis about the existence of a tinnitus-specific brain network. Such a network could respond to any acoustic stimuli by activating limbic areas involved in stress reactivity and emotional processing and by reducing activation of areas responsible for attention and acoustic filtering (thalamus, frontal regions), possibly reinforcing negative effects of tinnitus.
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Micucci, Antonia, Vera Ferrari, Andrea De Cesarei y Maurizio Codispoti. "Contextual Modulation of Emotional Distraction: Attentional Capture and Motivational Significance". Journal of Cognitive Neuroscience 32, n.º 4 (abril de 2020): 621–33. http://dx.doi.org/10.1162/jocn_a_01505.
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Emotional stimuli engage corticolimbic circuits and capture attention even when they are task-irrelevant distractors. Whether top–down or contextual factors can modulate the filtering of emotional distractors is a matter of debate. Recent studies have indicated that behavioral interference by emotional distractors habituates rapidly when the same stimuli are repeated across trials. However, little is known as to whether we can attenuate the impact of novel (never repeated) emotional distractors when they occur frequently. In two experiments, we investigated the effects of distractor frequency on the processing of task-irrelevant novel pictures, as reflected in both behavioral interference and neural activity, while participants were engaged in an orientation discrimination task. Experiment 1 showed that, compared with a rare distractor condition (20%), frequent distractors (80%) reduced the interference of emotional stimuli. Moreover, Experiment 2 provided evidence that emotional interference was reduced by distractor frequency even when rare, and unexpected, emotional distractors appeared among frequent neutral distractors. On the other hand, in both experiments, the late positive potential amplitude was enhanced for emotional, compared with neutral, pictures, and this emotional modulation was not reduced when distractors were frequently presented. Altogether, these findings suggest that the high occurrence of task-irrelevant stimuli does not proactively prevent the processing of emotional distractors. Even when attention allocation to novel emotional stimuli is reduced, evaluative processes and the engagement of motivational systems are needed to support the monitoring of the environment for significant events.
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Guitart, Miguel. "Limit Geometries of Architectural Filters: Precise Rationality and Poetic Emotion". ZARCH, n.º 15 (27 de enero de 2021): 222–33. http://dx.doi.org/10.26754/ojs_zarch/zarch.2020154648.
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An architectural filter is a porous material construction that regulates transverse visual relationships, and establishes degrees of connection through the intervention of light and gaze. Filtering boundaries display variable proportions of mass and air, which are instrumental to the production of the spatial experience behind the mediation of matter and geometry. A filter's structural system synthesizes geometric relations with the capacity to cause architectural atmospheres, as a result of the active border that is technically precise and sensorially ambiguous at the same time. The text sustains that the emerging atmospheres behind the filter cannot take place without a previous, precise geometric production; the poetic dimension of filtering strategies originates from its capacity to transform the concrete geometry of its contour conditions and controlled material execution into an unexpected atmosphere of emotional and incommensurable qualities.
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Byun, Jeong y Dong Keun Kim. "Design and Implementation of Location Recommending Services using Personal Emotional Information based on Collaborative Filtering". Journal of the Korea Institute of Information and Communication Engineering 20, n.º 8 (31 de agosto de 2016): 1407–14. http://dx.doi.org/10.6109/jkiice.2016.20.8.1407.
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Ye, Chaoxiong, Qianru Xu, Qiang Liu, Fengyu Cong, Pertti Saariluoma, Tapani Ristaniemi y Piia Astikainen. "The impact of visual working memory capacity on the filtering efficiency of emotional face distractors". Biological Psychology 138 (octubre de 2018): 63–72. http://dx.doi.org/10.1016/j.biopsycho.2018.08.009.
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Dong, Hui. "Modeling and Simulation of English Speech Rationality Optimization Recognition Based on Improved Particle Filter Algorithm". Complexity 2020 (24 de agosto de 2020): 1–10. http://dx.doi.org/10.1155/2020/6053129.
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As one of the most important communication tools for human beings, English pronunciation not only conveys literal information but also conveys emotion through the change of tone. Based on the standard particle filtering algorithm, an improved auxiliary traceless particle filtering algorithm is proposed. In importance sampling, based on the latest observation information, the unscented Kalman filter method is used to calculate each particle estimate to improve the accuracy of particle nonlinear transformation estimation; during the resampling process, auxiliary factors are introduced to modify the particle weights to enrich the diversity of particles and weaken particle degradation. The improved particle filter algorithm was used for online parameter identification and compared with the standard particle filter algorithm, extended Kalman particle filter algorithm, and traceless particle filter algorithm for parameter identification accuracy and calculation efficiency. The topic model is used to extract the semantic space vector representation of English phonetic text and to sequentially predict the emotional information of different scales at the chapter level, paragraph level, and sentence level. The system has reasonable recognition ability for general speech, and the improved particle filter algorithm evaluation method is further used to optimize the defect of the English speech rationality and high recognition error rate Related experiments have verified the effectiveness of the method.
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Patel, Ravikumar y Kalpdrum Passi. "Sentiment Analysis on Twitter Data of World Cup Soccer Tournament Using Machine Learning". IoT 1, n.º 2 (10 de octubre de 2020): 218–39. http://dx.doi.org/10.3390/iot1020014.
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In the derived approach, an analysis is performed on Twitter data for World Cup soccer 2014 held in Brazil to detect the sentiment of the people throughout the world using machine learning techniques. By filtering and analyzing the data using natural language processing techniques, sentiment polarity was calculated based on the emotion words detected in the user tweets. The dataset is normalized to be used by machine learning algorithms and prepared using natural language processing techniques like word tokenization, stemming and lemmatization, part-of-speech (POS) tagger, name entity recognition (NER), and parser to extract emotions for the textual data from each tweet. This approach is implemented using Python programming language and Natural Language Toolkit (NLTK). A derived algorithm extracts emotional words using WordNet with its POS (part-of-speech) for the word in a sentence that has a meaning in the current context, and is assigned sentiment polarity using the SentiWordNet dictionary or using a lexicon-based method. The resultant polarity assigned is further analyzed using naïve Bayes, support vector machine (SVM), K-nearest neighbor (KNN), and random forest machine learning algorithms and visualized on the Weka platform. Naïve Bayes gives the best accuracy of 88.17% whereas random forest gives the best area under the receiver operating characteristics curve (AUC) of 0.97.
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Petridis, Sergios, Theodoros Giannakopoulos y Constantine D. Spyropoulos. "A Low Cost Pupillometry Approach". International Journal of E-Health and Medical Communications 6, n.º 4 (octubre de 2015): 49–61. http://dx.doi.org/10.4018/ijehmc.2015100104.
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The need for low-cost health monitoring is increasing with the continuous increase of the elderly population. In this context, unobtrusive audiovisual monitoring methods can be of great importance. More particularly, the diameter of the pupil is a valuable source of information, since, apart from pathological cases, it can reveal the emotional state, the fatigue and the ageing. To allow for unobtrusive monitoring to gain acceptance, one should seek for efficient methods of monitoring using common low-cost hardware. This paper describes a method for monitoring pupil sizes using a common, low-cost web camera in real time. The proposed approach detects the face and the eyes area at first stage. Subsequently, optimal iris and sclera location and radius, modeled as ellipses, are found using efficient spatial filtering. As a final step, the pupil center and radius is estimated by optimal filtering within the area of the iris. Experimental results show both the efficiency and the effectiveness of our approach.
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Charquero-Ballester, Marina, Jessica G. Walter, Ida A. Nissen y Anja Bechmann. "Different types of COVID-19 misinformation have different emotional valence on Twitter". Big Data & Society 8, n.º 2 (julio de 2021): 205395172110412. http://dx.doi.org/10.1177/20539517211041279.
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The spreading of COVID-19 misinformation on social media could have severe consequences on people's behavior. In this paper, we investigated the emotional expression of misinformation related to the COVID-19 crisis on Twitter and whether emotional valence differed depending on the type of misinformation. We collected 17,463,220 English tweets with 76 COVID-19-related hashtags for March 2020. Using Google Fact Check Explorer API we identified 226 unique COVID-19 false stories for March 2020. These were clustered into six types of misinformation (cures, virus, vaccine, politics, conspiracy theories, and other). Applying the 226 classifiers to the Twitter sample we identified 690,004 tweets. Instead of running the sentiment on all tweets we manually coded a random subset of 100 tweets for each classifier to increase the validity, reducing the dataset to 2,097 tweets. We found that only a minor part of the entire dataset was related to misinformation. Also, misinformation in general does not lean towards a certain emotional valence. However, looking at comparisons of emotional valence for different types of misinformation uncovered that misinformation related to “virus” and “conspiracy” had a more negative valence than “cures,” “vaccine,” “politics,” and “other.” Knowing from existing studies that negative misinformation spreads faster, this demonstrates that filtering for misinformation type is fruitful and indicates that a focus on “virus” and “conspiracy” could be one strategy in combating misinformation. As emotional contexts affect misinformation spreading, the knowledge about emotional valence for different types of misinformation will help to better understand the spreading and consequences of misinformation.
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Ali, Manal Mostafa. "Arabic sentiment analysis about online learning to mitigate covid-19". Journal of Intelligent Systems 30, n.º 1 (1 de enero de 2021): 524–40. http://dx.doi.org/10.1515/jisys-2020-0115.
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Abstract The Covid-19 pandemic is forcing organizations to innovate and change their strategies for a new reality. This study collects online learning related tweets in Arabic language to perform a comprehensive emotion mining and sentiment analysis (SA) during the pandemic. The present study exploits Natural Language Processing (NLP) and Machine Learning (ML) algorithms to extract subjective information, determine polarity and detect the feeling. We begin with pulling out the tweets using Twitter APIs and then preparing for intensive preprocessing. Second, the National Research Council Canada (NRC) Word-Emotion Lexicon was examined to calculate the presence of the eight emotions at their emotional weight. Third, Information Gain (IG) is used as a filtering technique. Fourth, the latent reasons behind the negative sentiments were recognized and analyzed. Finally, different classification algorithms including Naïve Bayes (NB), Multinomial Naïve Bayes (MNB), K Nearest Neighbor (KNN), Logistic Regression (LR), and Support Vector Machine (SVM) were examined. The experiments reveal that the proposed model performs well in analyzing the perception of people about coronavirus with a maximum accuracy of about 89.6% using SVM classifier. From a practical perspective, the method could be generalized to other topical domains, such as public health monitoring and crisis management. It would help public health officials identify the progression and peaks of concerns for a disease in space and time, which enables the implementation of appropriate preventive actions to mitigate these diseases.
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Hupont, Isabelle, Eva Cerezo, Sergio Ballano y Sandra Baldassarri. "On the origin of the methodology for the scalable fusion of affective channels in a continuous emotional space and the “emotional kinematics” filtering technique - A correction". Information Fusion 67 (marzo de 2021): 1–2. http://dx.doi.org/10.1016/j.inffus.2020.09.009.
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Cheng, Xinquan y Wenlong Su. "Recommendation Model of Tourist Attractions Based on Deep Learning". Mathematical Problems in Engineering 2022 (28 de agosto de 2022): 1–7. http://dx.doi.org/10.1155/2022/9080818.
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In order to solve the problem of tourism information overload caused by the rapid development of tourism and the Internet era, the author proposes a tourist attraction recommendation model based on deep learning. Convolutional Neural Network (CNN) is used to extract the sentiment of text comments, the Pearson similarity formula is used to calculate similar user groups, and the mean absolute error (MAE) is used to evaluate the resulting error. Compare with traditional collaborative filtering methods. Experimental results show that: the MAE value is smaller than the MAE value of the collaborative filtering method, indicating that considering tourists’ behavioral information, contextual information, and emotional factors in comments can effectively improve the accuracy of recommendation, as the data volume of the test set increased from 250 to 2000; although there was an increase in the MAE value, the overall trend showed a downward trend, indicating that the quality of the model can be more fully verified when the data volume is large. The model proposed by the author can effectively reduce the prediction error and improve the efficiency of tourist attractions recommendation.
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Falfán, Ina, Maite Lascurain-Rangel, Gloria Sánchez-Galván, Eugenia J. Olguín, Arturo Hernández-Huerta y Melissa Covarrubias-Báez. "Visitors’ Perception Regarding Floating Treatment Wetlands in an Urban Green Space: Functionality and Emotional Values". Sustainability 15, n.º 3 (20 de enero de 2023): 2000. http://dx.doi.org/10.3390/su15032000.
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Floating treatment wetlands (FTW) are artificial structures used for water quality improvement through the hydroponic growth of certain macrophytes and their rhizospheric bacteria, with the capacity for pollutant removal. Through the application of face-to-face questionnaires, our study aimed to analyze visitors’ perception of the structure, functionality, and benefits of FTW installed in two ponds of one green space in Xalapa, Veracruz, Mexico, and the emotional experience that these FTW could incite in those same visitors. Visitors identified the plant component of FTW as the most noticeable one, perceived filtering/cleaning water as their principal function, and reported positive and negative emotions in the same proportion. The visitors’ perceptions of FTW varied according to their age, school level, and occupation. Positive and negative perceptions regarding FTW were linked to their maintenance and that of the ponds. Visitors’ awareness of FTW composition and function was associated with the presence of informative signs. The understanding of perception about the FTW can be integrated into management programs for the successful and participative improvement and cleaning of water bodies in urban settings. Along with people’s participation, the municipality of the city must improve the maintenance of these important water bodies given its positive repercussions on visitors’ perception.
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Osório, Neila Barbosa, Deuzivania Carlos de Oliveira, Leda Santana de Noleto y Luiz Sinésio da Silva Neto. "UMANIZING IN TIME OF COVID-19: Quality information". Revista Observatório 6, n.º 3 (1 de mayo de 2020): a1en. http://dx.doi.org/10.20873/uft.2447-4266.2020v6n3a1en.
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This article aims to relate students belonging to the University of Maturity in times of social isolation due to a pandemic of COVID-19, as well as actions carried out by the students of the UMAnizando project, guiding them with quality information, such as activities carried out are being important in the sense of filtering information that is really useful, for the purpose of appropriate prevention at the moment, the project made it possible to guide with safety and protection, so that afflictions can be minimized in a coherent way thinking about the emotional and physical well-being of the elderly, in which he used a descriptive methodology of experience reports.
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Moon, Jayet. "Effect of Emotional Intelligence and Leadership Styles on Risk Intelligent Decision Making and Risk Management". Journal of Engineering, Project, and Production Management 11, n.º 1 (1 de enero de 2021): 71–81. http://dx.doi.org/10.2478/jeppm-2021-0008.
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AbstractIn today’s world, uncertainty abounds. It is therefore incumbent on managers to take decisions using unbiased considerations in dealing with organizational risks. Often, risk decisions are replete with assumptions and biases, leading to incorrect decisions. Leaders who apply emotional intelligence (EI) skills are better poised to challenge internal biases and assumptions to improve decision-making, but limited empirical evidence exists that accounts for the nexus between EI, leadership styles and risk perceptions of managers. The purpose of the paper was to explore the relevance of the theory of EI in risk-based decision-making, while comparing various leadership styles. The research adopted a questionnaire survey administered to 173 employed individuals. The research hypotheses analyzed the mediating roles of EI and leadership styles in risk perceptions using ‘t’ statistic and where applicable, Chi-square testing. The results of the analysis confirmed the role of EI in filtering deleterious internal biases and confirmed EI’s presence as a success factor in leadership and decision-making. Transformational leaders are, however, more emotionally intelligent and less biased. These attributes allow for the generation of a suitable risk attitude and enhance risk-intelligent decisions as compared to transactional leaders. This study, while being descriptive, is exploratory in nature and opens pathways for further targeted research based on specific EI abilities or traits and various situational risk attitudes.
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Placksin, Sally J. "Feature Article—Continuing Education Module—Reimagining Postpartum Support". Journal of Perinatal Education 30, n.º 2 (1 de abril de 2021): 62–70. http://dx.doi.org/10.1891/j-pe-d-21-00004.
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This article introduces the author's emerging new paradigm (“perinatal participation”) that re-imagines postpartum support by helping expectant parents have more peace of mind, confidence, self-compassion, and emotional wellbeing over the course of their perinatal journeys, with special focus on feeling more prepared for all that happens after baby arrives. The author's work rests on the shoulders of her 1992 book, Mothering the New Mother: Women's Feelings and Needs After Childbirth. Perceiving a new urgent need to support expectant parents three decades later (the need to alleviate the high stress levels in expectant parents she was talking to) the author explored filtering the expectant and new parent's experience through what she calls a “peace-of-mind lens.”
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Burch, Gerald F., John H. Batchelor, Jana J. Burch, Shanan Gibson y Bob Kimball. "Microaggression, anxiety, trigger warnings, emotional reasoning, mental filtering, and intellectual homogeneity on campus: A study of what students think". Journal of Education for Business 93, n.º 5 (3 de mayo de 2018): 233–41. http://dx.doi.org/10.1080/08832323.2018.1462137.
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Wang, Kuiqun. "Optimization of the Online Teaching System Based on Streaming Media". Complexity 2021 (22 de febrero de 2021): 1–11. http://dx.doi.org/10.1155/2021/5552168.
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Network and related network technology limit the traditional online teaching activities, making teaching activities only limited to asynchronous teaching, limiting the advantages of real-time, interactive, and vivid online teaching. As a new online teaching network technology, streaming media technology can realize flexible and efficient two-way communication between teachers and students, simulate virtual face-to-face teaching environment, and produce enough emotional resonance for both sides in the corresponding time and space. In view of the poor communication quality and flexibility of current streaming media technology, this paper will build a selective streaming media online teaching architecture based on animation media service platform. The system innovatively uses DXSDK filtering technology to realize real-time audio acquisition and filtering processing and solves the communication quality problem of streaming media online teaching. Aiming at the flexibility of streaming media online teaching, the system adds online auxiliary teaching function, which can realize online text communication, file information management, and learning tracking interaction between teaching and learning. The experimental part is applied to the online teaching activities of a training institution. From the experimental results, the stability and flexibility of the system are greatly improved within a certain range of users, which has obvious practical value. It has high stability and application value.
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Awan, Amna Waheed, Syed Muhammad Usman, Shehzad Khalid, Aamir Anwar, Roobaea Alroobaea, Saddam Hussain, Jasem Almotiri, Syed Sajid Ullah y Muhammad Usman Akram. "An Ensemble Learning Method for Emotion Charting Using Multimodal Physiological Signals". Sensors 22, n.º 23 (4 de diciembre de 2022): 9480. http://dx.doi.org/10.3390/s22239480.
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Emotion charting using multimodal signals has gained great demand for stroke-affected patients, for psychiatrists while examining patients, and for neuromarketing applications. Multimodal signals for emotion charting include electrocardiogram (ECG) signals, electroencephalogram (EEG) signals, and galvanic skin response (GSR) signals. EEG, ECG, and GSR are also known as physiological signals, which can be used for identification of human emotions. Due to the unbiased nature of physiological signals, this field has become a great motivation in recent research as physiological signals are generated autonomously from human central nervous system. Researchers have developed multiple methods for the classification of these signals for emotion detection. However, due to the non-linear nature of these signals and the inclusion of noise, while recording, accurate classification of physiological signals is a challenge for emotion charting. Valence and arousal are two important states for emotion detection; therefore, this paper presents a novel ensemble learning method based on deep learning for the classification of four different emotional states including high valence and high arousal (HVHA), low valence and low arousal (LVLA), high valence and low arousal (HVLA) and low valence high arousal (LVHA). In the proposed method, multimodal signals (EEG, ECG, and GSR) are preprocessed using bandpass filtering and independent components analysis (ICA) for noise removal in EEG signals followed by discrete wavelet transform for time domain to frequency domain conversion. Discrete wavelet transform results in spectrograms of the physiological signal and then features are extracted using stacked autoencoders from those spectrograms. A feature vector is obtained from the bottleneck layer of the autoencoder and is fed to three classifiers SVM (support vector machine), RF (random forest), and LSTM (long short-term memory) followed by majority voting as ensemble classification. The proposed system is trained and tested on the AMIGOS dataset with k-fold cross-validation. The proposed system obtained the highest accuracy of 94.5% and shows improved results of the proposed method compared with other state-of-the-art methods.
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Gan, Xiaoli. "On the Causes and Countermeasures of Chinese Learners’ English Reading Anxiety". Journal of Language Teaching and Research 12, n.º 6 (2 de noviembre de 2021): 1034–38. http://dx.doi.org/10.17507/jltr.1206.19.
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Based on Humanistic Psychology and Krashen’s Affective Filtering Hypothesis, this study explores the effects on English reading anxiety among Chinese students, and corresponding countermeasures are put forward to it. English reading anxiety, one of the emotional factors that have a great effect on foreign language learning, mainly refers to the feeling of not being able to achieve desired goal or overcome a mental obstacle. The results indicate that Chinese English learners suffer English reading anxiety in text comprehension, and the psychological, cultural and text factors are the main causes leading to it. Based upon the findings, its advisable to lower the learners’ affective filter and promote their self-confidence as well as cultivate their cross-cultural awareness in future English reading teaching.
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Caran Andrejić, Maca y Roksana Toma. "TEŽINA ONLAJN ZABAVE U SVAKODNEVNOM ŽIVOTU SRPSKIH ADOLESCENATA – ZBOG ČEGA JE BITNO UBEDITI TINEJDŽERE DA SE SPOJE SA SMISLENIM SADRŽAJEM?" ИСХОДИШТА 1, n.º 7 (8 de julio de 2021): 349–54. http://dx.doi.org/10.46630/ish.7.2021.23.
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The content conveyed by the entertainment media is a diverse one, including images with emotional and aggressive content with different effects on adolescents᾽ behaviour. The stage of adolescence and youth within the life span is a stage of knowledge accumulation. Through this paper we intended to explore the informational content of media consumed by adolescents in the investigated group, the daily time allocated to media consumption and their level of empathy. More than 100 adolescents from Serbia completed a self-report questionnaire. The conclusions highlighted an increased consumption and no criteria for filtering the entertainment media. Finally, we propose some research and intervention recommendations for directing the online activities of adolescents in relation with the time spent online but also to the content of the materials covered.
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Li, Jia y YongJian Yang. "Recommender systems based on opinion mining and deep neural networks". MATEC Web of Conferences 173 (2018): 03016. http://dx.doi.org/10.1051/matecconf/201817303016.
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To address rating sparsity problem, various review-based recommender systems have been developed in recent years. Most of them extract topics, opinions, and emotional polarity from the reviews by using the techniques of text analysis and opinion mining. According to existing researches, review-based recommendation methods utilize review elements in rating prediction model, but underuse the actual ratings provided by users. In this paper, we adopt one lexicon-based opinion mining method to extract opinions hidden in reviews, and also, we combine opinions with actual ratings. In addition, we embed deep neural networks model which breaks through the limitation of traditional collaborative filtering. The experimental results based on two public datasets indicate that this personalized model provides an effective recommendation performance.
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Chen, Jingxia, Dongmei Jiang y Yanning Zhang. "A Common Spatial Pattern and Wavelet Packet Decomposition Combined Method for EEG-Based Emotion Recognition". Journal of Advanced Computational Intelligence and Intelligent Informatics 23, n.º 2 (20 de marzo de 2019): 274–81. http://dx.doi.org/10.20965/jaciii.2019.p0274.
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To effectively reduce the day-to-day fluctuations and differences in subjects’ brain electroencephalogram (EEG) signals and improve the accuracy and stability of EEG emotion classification, a new EEG feature extraction method based on common spatial pattern (CSP) and wavelet packet decomposition (WPD) is proposed. For the five-day emotion related EEG data of 12 subjects, the CSP algorithm is firstly used to project the raw EEG data into an optimal subspace to extract the discriminative features by maximizing the Kullback-Leibler (KL) divergences between the two categories of EEG data. Then the WPD algorithm is used to decompose the EEG signals into the related features in time-frequency domain. Finally, four state-of-the-art classifiers including Bagging tree, SVM, linear discriminant analysis and Bayesian linear discriminant analysis are used to make binary emotion classification. The experimental results show that with CSP spatial filtering, the emotion classification on the WPD features extracted with bior3.3 wavelet base gets the best accuracy of 0.862, which is 29.3% higher than that of the power spectral density (PSD) feature without CSP preprocessing, is 23% higher than that of the PSD feature with CSP preprocessing, is 1.9% higher than that of the WPD feature extracted with bior3.3 wavelet base without CSP preprocessing, and is 3.2% higher than that of the WPD feature extracted with the rbio6.8 wavelet base without CSP preprocessing. Our proposed method can effectively reduce the variance and non-stationary of the cross-day EEG signals, extract the emotion related features and improve the accuracy and stability of the cross-day EEG emotion classification. It is valuable for the development of robust emotional brain-computer interface applications.
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Logunova, E. V. y Yu E. Shelepin. "Study of the role of spatial-frequency filtering of images when evaluating the age and interpreting the emotional expression of faces". Journal of Optical Technology 82, n.º 10 (1 de octubre de 2015): 694. http://dx.doi.org/10.1364/jot.82.000694.
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Drigas, Athanasios, Eleni Mitsea y Charalampos Skianis. "Subliminal Training Techniques for Cognitive, Emotional and Behavioural Balance. The role of Emerging Technologies". Technium Social Sciences Journal 33 (9 de julio de 2022): 164–86. http://dx.doi.org/10.47577/tssj.v33i1.6881.
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Traditionally, metacognition & higher mental abilities are thought to be exclusively linked to consciousness. However, a growing number of researchers support the idea that nonconscious processes may hold the keys to higher forms of intelligence. Subliminal messages expose individuals to visual or/and auditory stimuli below the threshold of perception. The current review aims to explore the effectiveness of subliminal cues on fundamental aspects of metacognition such as higher cognitive and emotional meta-abilities, affective and behavioral regulation, and academic achievement. In this context, we search for and classify the existing subliminal training techniques, while evaluating the usability of ICTs such as virtual reality, mobile apps, intelligent tutoring systems, and software in subliminal learning and training. The results of this review revealed that subliminal techniques improve all those aspects that assure metacognitive improvements in terms of self- & emotional regulation, higher mental abilities, and behavioral modification. Subliminal cues lower people's shields and update filtering mechanisms enabling people to focus on positive rather than negative interpretations. Subliminal techniques are under the umbrella of metacognitive strategies, since they can used consciously to increase self-regulation capacity as wells as expand the horizons of consciousness. Subliminal teaching techniques can be used by teachers and parents in general and special education to instill higher-level needs & motives, accelerate students’ performance and unfold students’ existing but underdeveloped abilities. Therapists can also utilize these methods to help patients with phobia, anxiety and depression to overcome fear. Subliminal techniques can be also used as a strategy by leaders, mentors, and employees to build trust, inspire and provide humanity with innovative ideas. ICTs provide the ideal environment for implementing subliminal training. However, more research is needed.
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Khulusi, Richard, Stephanie Billib y Stefan Jänicke. "Exploring Life in Concentration Camps through a Visual Analysis of Prisoners’ Diaries". Information 13, n.º 2 (21 de enero de 2022): 54. http://dx.doi.org/10.3390/info13020054.
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Diaries are private documentations of people’s lives. They contain descriptions of events, thoughts, fears, and desires. While diaries are usually kept in private, published ones, such as the diary of Anne Frank, show that they bear the potential to give personal insight into events and into the emotional impact on their authors. We present a visualization tool that provides insight into the Bergen-Belsen memorial’s diary corpus, which consists of dozens of diaries written by concentration camp prisoners. We designed a calendar view that documents when authors wrote about concentration camp life. Different modes support quantitative and sentiment analyses, and we provide a solution for historians to create thematic concepts that can be used for searching and filtering for specific diary entries. The usage scenarios illustrate the importance of the tool for researchers and memorial visitors as well as for commemorating the Holocaust.
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H. Londoño, Nora, Erika B. Jiménez, Fernando Juárez y Carlos A. Marín. "The components of cognitive vulnerability to generalized anxiety disorder". International Journal of Psychological Research 3, n.º 2 (30 de diciembre de 2010): 43–54. http://dx.doi.org/10.21500/20112084.811.
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The components of cognitive vulnerability to generalized anxiety disorder (GAD) were identified. We performed a comparative analysis between the cognitive profile of patients diagnosed with GAD (69 adults) and a control group with no diagnosis (69 adults). They were completed the MINI International Neuropsyquiatric Interview, the Young Schemes Questionnaire -YSQ-, the Core Beliefs Questionnaire for Personality Disorders -CCE-TP-, the Inventory of Automatic Thoughts -IPA-, and the Coping Strategies Questionnaire -EEC-M-. The cognitive profile of GAD comprised patterns of abandonment, mistrust/abuse, uncompromising standards and insufficient self-control/self-discipline. Associated personality disorders were dependent, paranoid, avoidant, schizotypal, borderline and antisocial. Cognitive distortions were filtering or selective abstraction (low scores), and significantly higher scores in polarized thinking, overgeneralization, interpretation of thought, catastrophic vision, fallacy of control, emotional reasoning and fallacy of change. Coping strategies were high aggressive reaction, expression of coping difficulty, denial, and low positive reappraisal.
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Zhou, Xiaoxi, Hui’e Liang y Zhiya Dong. "A personalized recommendation model for online apparel shopping based on Kansei engineering". International Journal of Clothing Science and Technology 29, n.º 1 (6 de marzo de 2017): 2–13. http://dx.doi.org/10.1108/ijcst-12-2015-0137.
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Purpose Today clothing has become the largest category in online shopping in China, and even in Asia-Pacific. The satisfaction degree of apparel online shopping can be improved by effective personalized recommendation. The purpose of this paper is to propose a personalized recommendation model and algorithm based on Kansei engineering, traditional filtering algorithm and the knowledge relating to apparel. Design/methodology/approach Users’ perceptual image and the design elements of apparel based on Kansei engineering are discussed to build the mapping relation between the design elements and user ratings employing verbal protocol, semantic differential and partial least squares. The implicit knowledge and emotional needs pertaining to users are accessed using analytic hierarchy process. A personalized recommendation model for apparel online shopping is established and the algorithm for the personalized recommendation process is proposed. To present the personalized recommendation model, men’s plaid shirts are taken as the example, and the recommendations of apparel for online shopping were implemented and ranked in the context of differing users’ emotional needs. A comparison between the traditional model and this model is made to verify the effectiveness. Findings The recommendation model is capable of analyzing data and information effectively, and providing fast, personalized apparel recommendation services in accordance with users’ emotional needs. The experimental results suggest that the model is effective. Originality/value Similar researches of recommendation mainly focus on the field of computer science, the basic idea of which is using users’ history accessing records or the preferences of other similar users for determination of users’ preferences. Since the attributes of apparel products are not factored in the approach referred above, the issue of personalized recommendation cannot be solved in a really effective way. Combining Kansei engineering and recommendation algorithm, a framework for apparel product recommendation is presented and it is a new way for improvement of recommendations for apparel products on shopping sites.
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Grani, Giorgio, Andrea Lenzi y Paola Velardi. "Supporting Personalized Health Care With Social Media Analytics: An Application to Hypothyroidism". ACM Transactions on Computing for Healthcare 3, n.º 1 (31 de enero de 2022): 1–28. http://dx.doi.org/10.1145/3468781.
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Social media analytics can considerably contribute to understanding health conditions beyond clinical practice, by capturing patients’ discussions and feelings about their quality of life in relation to disease treatments. In this article, we propose a methodology to support a detailed analysis of the therapeutic experience in patients affected by a specific disease, as it emerges from health forums. As a use case to test the proposed methodology, we analyze the experience of patients affected by hypothyroidism and their reactions to standard therapies. Our approach is based on a data extraction and filtering pipeline, a novel topic detection model named Generative Text Compression with Agglomerative Clustering Summarization ( GTCACS ), and an in-depth data analytic process. We advance the state of the art on automated detection of adverse drug reactions ( ADRs ) since, rather than simply detecting and classifying positive or negative reactions to a therapy, we are capable of providing a fine characterization of patients along different dimensions, such as co-morbidities, symptoms, and emotional states.
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Stratigi, Maria, Haridimos Kondylakis y Kostas Stefanidis. "Multidimensional Group Recommendations in the Health Domain". Algorithms 13, n.º 3 (28 de febrero de 2020): 54. http://dx.doi.org/10.3390/a13030054.
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Providing useful resources to patients is essential in achieving the vision of participatory medicine. However, the problem of identifying pertinent content for a group of patients is even more difficult than identifying information for just one. Nevertheless, studies suggest that the group dynamics-based principles of behavior change have a positive effect on the patients’ welfare. Along these lines, in this paper, we present a multidimensional recommendation model in the health domain using collaborative filtering. We propose a novel semantic similarity function between users, going beyond patient medical problems, considering additional dimensions such as the education level, the health literacy, and the psycho-emotional status of the patients. Exploiting those dimensions, we are interested in providing recommendations that are both high relevant and fair to groups of patients. Consequently, we introduce the notion of fairness and we present a new aggregation method, accumulating preference scores. We experimentally show that our approach can perform better recommendations to small group of patients for useful information documents.
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Shen, Ruyue, Venice S. W. Li, Mandy O. M. Wong y Poemen P. M. Chan. "Pediatric Glaucoma—From Screening, Early Detection to Management". Children 10, n.º 2 (18 de enero de 2023): 181. http://dx.doi.org/10.3390/children10020181.
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Pediatric glaucoma (PG) covers a rare and heterogeneous group of diseases with variable causes and presentations. Delayed diagnosis of PG could lead to blindness, bringing emotional and psychological burdens to patients’ caregivers. Recent genetic studies identified novel causative genes, which may provide new insight into the etiology of PG. More effective screening strategies could be beneficial for timely diagnosis and treatment. New findings on clinical characteristics and the latest examination instruments have provided additional evidence for diagnosing PG. In addition to IOP-lowering therapy, managing concomitant amblyopia and other associated ocular pathologies is essential to achieve a better visual outcome. Surgical treatment is usually required although medication is often used before surgery. These include angle surgeries, filtering surgeries, minimally invasive glaucoma surgeries, cyclophotocoagulation, and deep sclerectomy. Several advanced surgical therapies have been developed to increase success rates and decrease postoperative complications. Here, we review the classification and diagnosis, etiology, screening, clinical characteristics, examinations, and management of PG.
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Chen, Yulin. "Information Clues and Emotional Intentions: A Case Study of the Regional Image of the Cultural and Creative Community". Administrative Sciences 9, n.º 2 (24 de mayo de 2019): 39. http://dx.doi.org/10.3390/admsci9020039.
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In order to capture the value of cultural creativity, this study explored regional cultural creativities with different creative forms to understand how people interpret and interact with various regional cultural creative images. This was done by analyzing the abstract (performance) type of cultural creativity and the figurative (commodity) type of cultural creativity, in order to understand how existing regional cultural creativities operate information threads in social media, and how the different forms of content may lead to different levels of participation and feedback. The Cloud Gate Dance Theater can be taken as an example of an abstract cultural creation (performance type), and Green-in-hand as an example of a figurative cultural creativity (commodity type). In this study, all user page content for the period 1 January 2011, to 31 December 2018, and the number of user comments for each post were analyzed, for a total of 4784 posts. Computer science, data mining, big data, and social network analysis were combined to verify the findings of the analyses. Through an application programming interface (API), data and information in social media is extracted. Then data filtering, storage, and analysis is performed with meaningful information extracted for interpretation and for use in text mining to explore the relationship with the public based on content attributes. This study first verifies that the regional image is consistent with the social image location. Second, the information cues results found that information cues could be organized into region personality through any direct or indirect contact. Third, emotional clues can evoke emotions and self-expression, which is seen as an important clue to region emotions. In addition, this study also provides a conceptual framework for understanding how different forms of information, in regards to social management of existing regional cultural creativities, leads to varying levels of participation. Understanding the form of information is a key factor in the acceptance of information by the public. It is a reminder for cultural and creative institutions of the importance of text and images, and of figurative and abstract information planning in social content. In order to improve the competitiveness of the destination, using content interaction through social media to create and enhance a strong brand image is important.
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Hao, Yange y Na Song. "Key Technologies and Discrete Dynamic Modeling Analysis of Online Travel Planning System Based on Big Data Scenario Aware Service". Discrete Dynamics in Nature and Society 2021 (7 de diciembre de 2021): 1–10. http://dx.doi.org/10.1155/2021/3244179.
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The key technology of online travel recommendation system has been widely concerned by many Internet experts. This paper studies and designs a scenario aware service model in online travel planning system and proposes an online travel planning recommendation model which integrates collaborative filtering and clustering personalized recommendation algorithm. At the same time, the algorithm performance test method and model evaluation index are given. The results show that CTTCF algorithm can find more neighbor users than UCF algorithm, and the smaller the search space is, the more significant the advantage is. The number of neighbors is 5, 10, 15, 20, and 25, respectively, and the corresponding average absolute error values are about 0.815, 0.785, 0.765, 0.758, and 0.755, respectively. The scores of the six emotional travel itinerary recommendation schemes are all higher than 142 points. Only the two schemes have no obvious rendering effect. The proposed online travel itinerary planning scheme has potential value and important significance in the application of follow-up recommendation system. It solves the problem of low scene perception satisfaction in the key technologies of online tourism planning system.
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Linh, Tran Hoai. "A SOLUTION FOR IMPROVEMENT OF ECG ARRHYTHMIA RECOGNITION USING RESPIRATION INFORMATION". Vietnam Journal of Science and Technology 56, n.º 3 (11 de junio de 2018): 335. http://dx.doi.org/10.15625/2525-2518/56/3/10779.
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Electrocardiogram (ECG) and respiration signals are two basic and important and valuable biomedical signals as source of information used to determine a person's health status. However, ECG signals are usually of small amplitude and are susceptible to various noises such as: the 50Hz grid noise, poor electrodes’ contacts with the patient's skin, the patient's emotional variations, the respiration and movement of the patient... The idea in this paper by filtering out the effect of the respiration in the ECG signal or by incorporating the information of breathing stage into the ECG signal classification the we can improve the reliability and accuracy of the arrythmia classification. This paper proposes a solution, which uses wavelet filter to reduce the effect of respiration in the ECG signals and will use additional information from the breathing rhythm (when available) to help better classifying the arrythmias. As the main nonlinear classifier we use the classical neuro-fuzzy TSK network. The proposed solution will be tested with data from the MIT-BIH and the MGH/MF databases.