Relevant bibliographies by topics / Mood detection

Academic literature on the topic 'Mood detection'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Mood detection"

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Liu, Yu, Kyoung-Don Kang, and Mi Jin Doe. "HADD: High-Accuracy Detection of Depressed Mood." Technologies 10, no. 6 (November 29, 2022): 123. http://dx.doi.org/10.3390/technologies10060123.

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Depression is a serious mood disorder that is under-recognized and under-treated. Recent advances in mobile/wearable technology and ML (machine learning) have provided opportunities to detect the depressed moods of participants in their daily lives with their consent. To support high-accuracy, ubiquitous detection of depressed mood, we propose HADD, which provides new capabilities. First, HADD supports multimodal data analysis in order to enhance the accuracy of ubiquitous depressed mood detection by analyzing not only objective sensor data, but also subjective EMA (ecological momentary assessment) data collected by using mobile devices. In addition, HADD improves upon the accuracy of state-of-the-art ML algorithms for depressed mood detection via effective feature selection, data augmentation, and two-stage outlier detection. In our evaluation, HADD significantly enhanced the accuracy of a comprehensive set of ML models for depressed mood detection.
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Khanolkar, Neil, Ajinkya Sathe, Ketaki Shinde, and Aarti M. Karande. "Mood Detection using Sentiment Analysis." International Journal of Computer Applications 184, no. 26 (August 20, 2022): 16–20. http://dx.doi.org/10.5120/ijca2022922316.

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Howard, Newton, and Mathieu Guidere. "LXIO: The Mood Detection Robopsych." Brain Sciences Journal 1, no. 1 (March 1, 2012): 98–109. http://dx.doi.org/10.7214/brainsciences/2012.01.01.05.

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Gavde, Megna. "Comparative Study on Mood Detection Techniques." International Journal for Research in Applied Science and Engineering Technology 6, no. 4 (April 30, 2018): 1456–57. http://dx.doi.org/10.22214/ijraset.2018.4245.

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Pansare, Ashwini, and Monali Shetty. "Mood Detection based on Facial Expressions." International Journal of Engineering Trends and Technology 48, no. 4 (June 25, 2017): 200–204. http://dx.doi.org/10.14445/22315381/ijett-v48p236.

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Shi, Xiaobo, Yixue Hao, Delu Zeng, Lu Wang, M. Shamim Hossain, Sk Md Mizanur Rahman, and Abdulhameed Alelaiwi. "Cloud-Assisted Mood Fatigue Detection System." Mobile Networks and Applications 21, no. 5 (August 11, 2016): 744–52. http://dx.doi.org/10.1007/s11036-016-0757-x.

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Byrne, Angela, and Michael W. Eysenck. "Trait anxiety, anxious mood, and threat detection." Cognition & Emotion 9, no. 6 (November 1995): 549–62. http://dx.doi.org/10.1080/02699939508408982.

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Pyrovolakis, Konstantinos, Paraskevi Tzouveli, and Giorgos Stamou. "Multi-Modal Song Mood Detection with Deep Learning." Sensors 22, no. 3 (January 29, 2022): 1065. http://dx.doi.org/10.3390/s22031065.

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The production and consumption of music in the contemporary era results in big data generation and creates new needs for automated and more effective management of these data. Automated music mood detection constitutes an active task in the field of MIR (Music Information Retrieval). The first approach to correlating music and mood was made in 1990 by Gordon Burner who researched the way that musical emotion affects marketing. In 2016, Lidy and Schiner trained a CNN for the task of genre and mood classification based on audio. In 2018, Delbouys et al. developed a multi-modal Deep Learning system combining CNN and LSTM architectures and concluded that multi-modal approaches overcome single channel models. This work will examine and compare single channel and multi-modal approaches for the task of music mood detection applying Deep Learning architectures. Our first approach tries to utilize the audio signal and the lyrics of a musical track separately, while the second approach applies a uniform multi-modal analysis to classify the given data into mood classes. The available data we will use to train and evaluate our models comes from the MoodyLyrics dataset, which includes 2000 song titles with labels from four mood classes, {happy, angry, sad, relaxed}. The result of this work leads to a uniform prediction of the mood that represents a music track and has usage in many applications.
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Ferdiana, Ridi, Wiiliam Fajar Dicka, and Faturahman Yudanto. "MOOD DETECTION BASED ON LAST SONG LISTENED ON SPOTIFY." ASEAN Engineering Journal 12, no. 3 (August 31, 2022): 123–27. http://dx.doi.org/10.11113/aej.v12.16834.

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A Song Is One Medium Used To Express Someone’s Emotion, Whether As A Performer Or Audience. With The Advancement Of Machine Learning And A Deeper Understanding Of Sentiment Analysis, We Decided To Study Mood Detection Based On The Last Song Listened To. One of the direct ways to measure someone's mood is by using a Four-dimensional Mood Scale (FDMS) device. This device categorized mood into four dimensions: low valence, high valence, low arousal, and high arousal. In this article, we used a variation of FDMS adapted to the Indonesian language called FDMS-55 to compare the result from our model. Our model is trained using song data collected from Spotify and Genius using their respective API (Application Programming Interface). We classified manually into a mood class and then processed further using Azure Cognitive Service Text Analytics API. Based on evaluation conducted on the model, the FastTreeOva algorithm produces the highest accuracy both on valence class with 0.8901 and arousal class with 0.9167. The comparison between the model result and respondent's FDMS-55 device result is made with cosine similarity and yields similarity value of 0.770 with 0.103 standard deviation. It is concluded that someone's mood is related to the song they listened to, and our model can precisely predict someone's mood based on the last song they listened to.
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Sundarrajan, Aksharaa, and M. Aneesha. "Survey on Detection of Metal Illnesses by Analysing Twitter Data." International Journal of Engineering & Technology 7, no. 2.24 (April 25, 2018): 37. http://dx.doi.org/10.14419/ijet.v7i2.24.11995.

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Mental illnesses are serious problems that places a burden on individuals, their families and on society in general. Although their symptoms have been known for several years, accurate and quick diagnoses remain a challenge. Inaccurate or delayed diagnoses results in increased frequency and severity of mood episodes, and reduces the benefits of treatment. In this survey paper, we review papers that leverage data from social media and design predictive models. These models utilize patterns of speech and life features of various subjects to determine the onset period of bipolar disorder. This is done by studying the patients, their behaviour, moods and sleeping patterns, and then effectively mapping these features to detect whether they are currently in a prodromal phase before a mood episode or not.
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Dissertations / Theses on the topic "Mood detection"

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Diot, Steven. "La méthode MOOD Multi-dimensional Optimal Order Detection : la première approche a posteriori aux méthodes volumes finis d'ordre très élevé." Toulouse 3, 2012. http://thesesups.ups-tlse.fr/1736/.

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Nous introduisons et développons dans cette thèse un nouveau type de méthodes Volumes Finis d'ordre très élevé pour les systèmes hyperboliques de lois de conservations. Appelée MOOD pour Multidimensional Optimal Order Detection, elle permet de réaliser des simulations très précises en dimensions deux et trois sur maillages non-structurés. La conception d'une telle méthode est rendue délicate par l'apparition de singularités dans la solution (chocs, discontinuités de contact) pour lesquelles des phenomènes parasites (oscillations, création de valeurs non physiques. . . ) sont générés par l'approximation d'ordre élevé. L'originalité de cette thèse réside dans le traitement de ces problèmes. A l'opposé des méthodes classiques qui essaient de contrôler ces phénomènes indésirables par une limitation a priori, nous proposons une approche de traitement a posteriori basée sur une décrémentation locale de l'ordre du schéma. Nous montrons en particulier que ce concept permet très simplement d'obtenir des propriétés qui sont habituellement difficiles à prouver dans le cadre multi-dimensionel non-structuré (préservation de la positité par exemple). La robustesse et la qualité de la méthode MOOD ont été prouvées sur de nombreux tests numériques en 2D et 3D. Une amélioration significative des ressources informatiques (CPU et stockage mémoire) nécessaires à l'obtention de résultats équivalents aux méthodes actuelles a été démontrée
We introduce and develop in this thesis a new type of very high-order Finite Volume methods for hyperbolic systems of conservation laws. This method, named MOOD for Multidimensional Optimal Order Detection, provides very accurate simulations for two- and three-dimensional unstructured meshes. The design of such a method is made delicate by the emergence of solution singularities (shocks, contact discontinuities) for which spurious phenomena (oscillations, non-physical values creation, etc. ) are generated by the high-order approximation. The originality of this work lies in a new treatment for theses problems. Contrary to classical methods which try to control such undesirable phenomena through an a priori limitation, we propose an a posteriori treatment approach based on a local scheme order decrementing. In particular, we show that this concept easily provides properties that are usually difficult to prove in a multidimensional unstructured framework (positivity-preserving for instance). The robustness and quality of the MOOD method have been numerically proved through numerous test cases in 2D and 3D, and a significant reduction of computational resources (CPU and memory storage) needed to get state-of-the-art results has been shown
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East, Rebekah Psychology Faculty of Science UNSW. "Happy and gullible, sad and wise? Mood effects on factual and interpersonal skepticism." Awarded by:University of New South Wales. Psychology, 2006. http://handle.unsw.edu.au/1959.4/24371.

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The primary aim of this research was to examine the influence of temporary mood states on factual and interpersonal skepticism. Based on recent affect-cognition theorising and research on credibility judgment, 7 studies predicted that negative moods increase and positive moods decrease skepticism, because of the information-processing consequences of these affective states. First, three studies examined the influence of mood on factual skepticism toward urban myths and legends (Study 1) and novel and familiar general knowledge claims (Studies 2-3). Contrary to predictions, Study 1 found that sad participants were less skeptical than happy participants towards urban legends, possibly due to the negative valence of the claims. Because the feeling of familiarity has been shown to be an important determinant of truth, Studies 2-3 examined the influence of mood and familiarity on skepticism. Consistent with information processing theories of mood, happy participants were more likely than sad participants to give credence to familiar general knowledge claims (Study 2), even when given explicit feedback about their actual truth or falsity during initial exposure to claims (Study 3). The remainder of this thesis extended these findings to interpersonal judgments. Studies 4-5 found that sad participants were more skeptical of the genuineness of facial expressions of emotion compared to happy participants. Studies 6-7 examined whether sad participants might also show greater lie detection accuracy. In Study 6, happy, sad and neutral-mood participants judged the credibility of targets honestly or deceptively describing their emotional reaction to an affectively-laden film, but no evidence was found of mood induced differences in deception detection accuracy. However, in Study 7, sad participants were more skeptical than happy participants about the veracity of videotaped individuals honestly or deceptively denying their involvement in a mock crime (a theft), and showed greater accuracy at discerning lies from truths. This dissertation contributes to the affect-cognition literature by demonstrating that not only may sad moods lead people to be more skeptical, but they may also confer an advantage at detecting deception. The implications of these findings for everyday credibility judgment and for contemporary theories of affect and cognition are considered.
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Hansen, Beau Tanana. "Proteomics Methods for Detection of Modified Peptides." Diss., Tucson, Arizona : University of Arizona, 2005. http://etd.library.arizona.edu/etd/GetFileServlet?file=file:///data1/pdf/etd/azu%5Fetd%5F1083%5F1%5Fm.pdf&type=application/pdf.

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Kim, Yumi. "Chasing the moon /." Online version of thesis, 2009. http://hdl.handle.net/1850/8691.

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Blanco-Meneses, Monica. "Population Biology and Detection of the Tobacco Blue Mold Pathogen, Peronospora tabacina." NCSU, 2009. http://www.lib.ncsu.edu/theses/available/etd-03092009-143022/.

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Peronospora tabacina Adam. is the causal agent of blue mold or downy mildew of tobacco. The pathogen is a fungus-like organism and is a member of the Oomycota. P. tabacina is an obligate parasite restricted to the genus Nicotiana spp. Identification of the pathogen is difficult since symptoms and signs generally occur 6-12 days post inoculation. The spread of the pathogen occurs through aerial long distance dispersal of inoculum and severe epidemics occur yearly in tobacco growing areas of the world. One objective of this work was to develop a real-time Taq Man assay for the detection and quantification of P. tabacina. Optimization of the assay was established at a final concentration of 450nM of primers and 125nM of probe. The assay was useful for detection of the pathogen down to a lower limit of 1fg of DNA. The pathogen could be detected after 4 days post inoculation. The real-time PCR assay was useful for the specific detection of P. tabacina in field samples, artificially inoculated leaves, roots, and systemically infected tobacco seedlings and could be used as a tool for regulatory agencies interested in the detection of the pathogen. A second objective was to examine the genetic structure of the pathogen in North America, Central America, the Caribbean and Europe and determine the direction of migration of the pathogen. The intergenic spacer Igs2 region of the nuclear ribosomal DNA (rDNA) and the Ras-related protein (Ypt1) gene, and the mitochondrial cytochrome c oxidase subunit 2 (cox2 gene) were used. Populations of P. tabacina were characterized by high nuclear diversity, low population division and a possible mixed sexual and asexual reproductive system. Large population size, the mechanism of dispersal, mutation rate and genetic diversity indicate that this pathogen is a high evolutionary risk plant pathogen. An isolation with migration (IM) model was used to study genetic diversity in the U.S./Central America and the Caribbean (CCAM) and the European subpopulations. Results support migration from the CCAM region, Florida and Texas into the northern part of the U.S. including North Carolina. These data validate previous migration reports of the pathogen by the North American Plant Disease Forecasting Center at NCSU. In Europe estimates for the migration of the pathogen from North Central to Western Europe and both these regions to Lebanon support migration reports for the first introductions of the pathogen into Europe. Mitochondrial sequences of P tabacina and Hyaloperonspora parasitica genome were generated using bioinforrmatics approaches and PCR methodology. One quarter of the mitochondrial genome of P. tabacina has been annotated and compared with that of Phytophthora infestans and Hyaloperonospora parasitica. Similarities in direction, arrangement and number of genes and regions have been found. Results from this research will be useful in understanding the evolutionary history of the Oomycetes as well as future population genetics research to investigate how these organisms adapt, survive and attack plants in a changing environment.
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Alolaywi, Haidar. "Electrochemical MoOx/Carbon Nanocomposite Gas Sensor for Formaldehyde Detection at Room Temperature." University of Toledo / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1596821142716346.

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Bunton, Penelope Jessica Claudia. "An evaluation of screening measures for detecting low mood and cognitive impairment in acute stroke patients." Thesis, University of Liverpool, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.589800.

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Objective: To determine the accuracy and appropriateness of screening measures for detecting low mood and cognitive impairment in acute stroke patients, by determination of their sensitivity, specificity and reliability. Methods: 114 English-speaking acute stroke patients completed a depression screening measure from the Mood Assessment Care Pathway (MACP), a battery of mood assessment measures and the Structured Clinical Interview for DSM-IV Axis 1 Disorders (SCID). Participants without significant physical or language impairment additionally completed the Middlesex Elderly Assessment of Mental State (MEAMS), an oral paradigm of the Trail Making Test (OTMT) and detailed neuropsychological assessment with the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), Rule Shift Card Test (BADS) and the Verbal Fluency Test (phonemic task). Results: 106 participants completed all of the mood assessment procedures. Sensitivity of the individual MACP measures was generally high for the detection of major and minor depression. Specificity varied widely between the measures ranging from 18% to 85% but was generally within acceptable levels (greater than 60%). A revised version of the MACP was proposed using optimal cut-off scores. The MACP-revised gave 100% sensitivity and 77% specificity for the detection of major depression and 81% sensitivity and 65% specificity for minor depression. Reliability as assessed by the Kappa coefficient was found to be generally low for either version of the MACP and the individual measures. 91 participants were assessed using the MEAMS. Convergent validity of the MEAMS with full assessment measures was not supported (r < .40). Overall, sensitivity of the MEAMS subtests and the overall MEAMS for the detection of impairment in any of the cognitive domains was generally low, except for the overall MEAMS with Immediate and Delayed Memory Impairment. Specificity was generally higher, but there were only two instances where subtests achieved acceptable levels of both sensitivity and specificity. The overall MEAMS failed to achieve acceptable levels of agreement for any of the cognitive domains assessed. 89 participants were assessed using the OTMT. Sensitivity of the OTMT-B for the detection of impairment in any of the cognitive domains was generally low (less than 60%). Specificity was generally higher (greater than 76%), but there were no instances where acceptable levels of sensitivity and specificity were both achieved. Agreement with full assessment measures was generally poor for any of the cognitive domains assessed. Conclusions: The overall MACP-revised was an acceptably valid screening battery for the assessment of major and minor depression in acute stroke patients and demonstrated a useful way of compiling mood screening batteries which allow the assessment of mood impairment for all stroke patients. Neither the MEAMS nor the OTMT were recommended as a measure for screening for cognitive problems after a stroke due to their low sensitivity. However, further investigation of the psychometric properties of the RBANS was indicated, as the current findings suggest that it can be successfully used to assess acute stroke patients for cognitive impairment.
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Kidron, Matias. "Detecting minimoons in the Earth-Moon system with microsatellite compatible technologies." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-247623.

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Minimoons, Earth’s temporarily-captured orbiters, are excellent candidates for asteroidmining technology demonstrations and general asteroid studies because of their relativelylong stay in the vicinity of Earth. In this thesis, microsatellite compatible surveillancetechnologies are discussed and the suitability of various locations in the Earth-Moon systemfor minimoon surveillance is examined. This is done to acquire knowledge on whichtype of an orbit a minimoon-surveying-microsatellite could be placed on.The instantaneous visible fraction of the minimoon steady-state population is the figureof merit when comparing surveillance systems and locations. The visible fraction is estimatedby simulating the distribution of visible minimoons in the sky-plane. The objectsin the simulated sky-plane are synthetic minimoons, which are generated in large numbersaccording to the geocentric 6-dimensional-residence-time-distribution of minimoons, andthus, the bin values of the sky-plane distribution can be thought of as instantaneous probabilitiesfor containing a detectable minimoon within certain ecliptic latitude-longituderange.The visible fractions are estimated for various locations with given surveillance systemperformance. Multiple microsatellite compatible surveillance technology configurationsare examined as well as the e↵ect of limiting magnitude and maximum angular velocity.Minimoons are faint and fast moving objects and thus the use of synthetic tracking algorithmis beneficial and considered. Only visual band surveillance systems with aperturesizes less than 0.30 m and minimoons with diameter sizes larger than 0.50 m are consideredin the simulations.
Minimånar, jordens temporärt fångade satelliter, är utmärkta kandidater för demonstra-tioner av asteroidbrytningteknologi och för allmänna asteroidstudier på grund av deras relativt långa vistelse i närheten av jorden. I den här avhandlingen, diskuteras mikrosatellit kompatibla övervakningsteknologier och därtill undersökes lämpligheten av olika platser i jord-måne-systemet för övervakning av minimånar. Det här görs för att ska↵a kunskap om vilken typ av omloppsbana en mikrosatellit för minimåneövervakning kunde placeras på.Den momentana synliga fraktionen av den jämviktstillstånd minimånepopulationen är den merit som används vid jämförelse av övervakningssystem och platser i rymden. Den synliga fraktionen uppskattas genom att simulera fördelningen av synliga minimånar i skyplanet. Föremålen i det simulerade skyplanet är syntetiska minimånar, vilka genereras i stort antal enligt den geocentriska 6-dimensionella-uppehållstid-distributionen av minimånarna, och sålunda kan värdena i den diskretiserade skyplanfördelningen betraktas som momentana sannolikheter för att innehålla en observerbar minimåne inom det specifiserade ecliptiska latitudinella-longitudinella området.De synliga fraktionerna beräknas för olika platser med det givna övervakningssystemets parametrar. Flera mikrosatellit-kompatibla övervakningsteknologikonfigurationer undersöks, såväl som e↵ekterna av begränsande magnitud och maximal vinkelhastighet. Minimånar är dunkla och snabba rörliga föremål, och således är användningen av synthetic tracking fördelaktig och övervägd. Endast övervakningssystem som fungerar i visuellt bandmed en bländarstorlek mindre än 0,30 m och minimånar med en diameter större än 0,50m beaktas i simuleringarna.
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Maurer, Andreas. "Methods for Multisensory Detection of Light Phenomena on the Moon as a Payload Concept for a Nanosatellite Mission." Thesis, Luleå tekniska universitet, Rymdteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-80785.

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For 500 years transient light phenomena (TLP) have been observed on the lunar surface by ground-based observers. The actual physical reason for most of these events is today still unknown. Current plans of NASA and SpaceX to send astronauts back to the Moon and already successful deep-space CubeSat mission will allow in the future research nanosatellite missions to the cislunar space. This thesis presents a new hardware and software concept for a future payload on such a nanosatellite. The main task was to develop and implement a high-performance image processing algorithm which task is to detect short brightening flashes on the lunar surface. Based on a review of historic reported phenomena, possible explanation theories for these phenomena and currently active and planed ground- or space-based observatories possible reference scenarios were analyzed. From the presented scenarios one, the detection of brightening events was chosen and requirements for this scenario stated. Afterwards, possible detectors, processing computers and image processing algorithms were researched and compared regarding the specified requirements. This analysis of available algorithm was used to develop a new high-performance detection algorithm to detect transient brightening events on the Moon. The implementation of this algorithm running on the processor and the internal GPU of a MacMini achieved a framerate of 55 FPS by processing images with a resolution of 4.2 megapixel. Its functionality and performance was verified on the remote telescope operated by the Chair of Space Technology of the University of Würzburg. Furthermore, the developed algorithm was also successfully ported on the Nvidia Jetson Nano and its performance compared with a FPGA based image processing algorithm. The results were used to chose a FPGA as the main processing computer of the payload. This concept uses two backside illuminated CMOS image sensor connected to a single FPGA. On the FPGA the developed image processing algorithm should be implemented. Further work is required to realize the proposed concept in building the actual hardware and porting the developed algorithm onto this platform.
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Hallakoun, N., (许偲艺) S. Xu, D. Maoz, T. R. Marsh, V. D. Ivanov, V. S. Dhillon, M. C. P. Bours, et al. "Once in a blue moon: detection of ‘bluing' during debris transits in the white dwarf WD 1145+017." OXFORD UNIV PRESS, 2017. http://hdl.handle.net/10150/625505.

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The first transiting planetesimal orbiting a white dwarf was recently detected in K2 data of WD 1145+017 and has been followed up intensively. The multiple, long and variable transits suggest the transiting objects are dust clouds, probably produced by a disintegrating asteroid. In addition, the system contains circumstellar gas, evident by broad absorption lines, mostly in the u' band, and a dust disc, indicated by an infrared excess. Here we present the first detection of a change in colour of WD 1145+017 during transits, using simultaneous multiband fast-photometry ULTRACAM measurements over the u'g'r'i' bands. The observations reveal what appears to be 'bluing' during transits; transits are deeper in the redder bands, with a u' - r' colour difference of up to similar to-0.05 mag. We explore various possible explanations for the bluing, including limb darkening or peculiar dust properties. 'Spectral' photometry obtained by integrating over bandpasses in the spectroscopic data in and out of transit, compared to the photometric data, shows that the observed colour difference is most likely the result of reduced circumstellar absorption in the spectrum during transits. This indicates that the transiting objects and the gas share the same line of sight and that the gas covers the white dwarf only partially, as would be expected if the gas, the transiting debris and the dust emitting the infrared excess are part of the same general disc structure (although possibly at different radii). In addition, we present the results of a week-long monitoring campaign of the system using a global network of telescopes.
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Books on the topic "Mood detection"

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Coyne, James C. Screening for depression: A practical guide for detection and diagnosis of mood disorders. Oxford: Oxford University Press, 2009.

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McDonald, Megan. Judy Moody, girl detective. Somerville, Mass: Candlewick Press, 2010.

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McDonald, Megan. Judy Moody, Girl Detective. Somerville, Mass: Candlewick Press, 2010.

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McDonald, Megan. Judy Moody es detective. Doral, FL: Alfaguara Infantil, 2011.

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Nabb, Magdalen. Moord in Florence: Een Salvatore Guarnaccia detective. Amsterdam: Sirene, 2003.

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The invention of murder: How the Victorians revelled in death and detection and created modern crime. London: HarperPress, 2011.

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Jefferson, M. T. In the mood for murder. New York: Berkley, 2000.

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Lourey, Jess. August moon. Woodbury, Minn: Midnight Ink, 2008.

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David, Cole. Stalking moon. New York: Avon Books, 2002.

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Hawk moon. New York: Simon & Schuster Books for Young Readers, 1996.

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Book chapters on the topic "Mood detection"

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Shaikh, Salman Ahmed, and Hiroyuki Kitagawa. "MOOD: Moving Objects Outlier Detection." In Web Technologies and Applications, 666–69. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11116-2_66.

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Priyanka Tyagi, Abhishek Mehrotra, Shanu Sharma, and Sushil Kumar. "Audio Pattern Recognition and Mood Detection System." In Advances in Intelligent Systems and Computing, 321–32. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0448-3_26.

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Mukherjee, Deep, Ishika Raj, and Sushruta Mishra. "Song Recommendation Using Mood Detection with Xception Model." In Cognitive Informatics and Soft Computing, 491–501. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8763-1_40.

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Kindra, Madhav, Harshit Garg, Srishti Jhunthra, Vikrant Dixit, and Vedika Gupta. "Song Recommendation Using Computational Techniques Based on Mood Detection." In Computational Intelligence for Information Retrieval, 75–91. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003134138-6.

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Tu, Wei, Lei Wei, Wenyan Hu, Zhengguo Sheng, Hasen Nicanfar, Xiping Hu, Edith C. H. Ngai, and Victor C. M. Leung. "A Survey on Mobile Sensing Based Mood-Fatigue Detection for Drivers." In Smart City 360°, 3–15. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33681-7_1.

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Manni, Andrea, Andrea Caroppo, Alessandro Leone, and Pietro Siciliano. "Video-Based Contactless Mood Detection Combining Heart Rate and Facial Expressions." In Lecture Notes in Electrical Engineering, 307–13. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08136-1_48.

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Capodieci, Antonio, Pascal Budner, Joscha Eirich, Peter Gloor, and Luca Mainetti. "Dynamically Adapting the Environment for Elderly People Through Smartwatch-Based Mood Detection." In Studies on Entrepreneurship, Structural Change and Industrial Dynamics, 65–73. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74295-3_6.

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Jome Yazdian, Payam, and Hadi Moradi. "User Mood Detection in a Social Network Messenger Based on Facial Cues." In Ubiquitous Computing and Ambient Intelligence, 778–88. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67585-5_75.

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Schleusing, O., Ph Renevey, M. Bertschi, St Dasen, and R. Paradiso. "Detection of Mood Changes in Bipolar Patients though Monitoring of Physiological and Behavioral Signals." In IFMBE Proceedings, 1106–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23508-5_287.

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Ragheb, Waleed, Jérôme Azé, Sandra Bringay, and Maximilien Servajean. "Language Modeling in Temporal Mood Variation Models for Early Risk Detection on the Internet." In Lecture Notes in Computer Science, 248–59. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28577-7_21.

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Conference papers on the topic "Mood detection"

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Savas, Muhammed Emin, Isa Ahmet Guney, Nazli Nakeeb Tokatli, Berk Kisinbay, and Gurhan Kucuk. "iMODE (interactive MOod Detection Engine) Processor." In 2019 4th International Conference on Computer Science and Engineering (UBMK). IEEE, 2019. http://dx.doi.org/10.1109/ubmk.2019.8907005.

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Lietz, Rebecca, Meaghan Harraghy, James Brady, Diane Calderon, Joe Cloud, and Fillia Makedon. "A wearable system for unobtrusive mood detection." In PETRA '19: The 12th PErvasive Technologies Related to Assistive Environments Conference. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3316782.3322743.

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Lin, Ziqian, Sreya Dutta Roy, and Yixuan Li. "MOOD: Multi-level Out-of-distribution Detection." In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2021. http://dx.doi.org/10.1109/cvpr46437.2021.01506.

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Nashed, Nader N., Christine Lahoud, Marie-Helene Abel, Frederic Andres, and Bernard Blancan. "Mood detection ontology integration with teacher context." In 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2021. http://dx.doi.org/10.1109/icmla52953.2021.00272.

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Miyoshi, Masato, Satoru Tsuge, Tadahiro Oyama, Momoyo Ito, and Minoru Fukumi. "Feature selection method for music mood score detection." In 2011 Fourth International Conference on Modeling, Simulation and Applied Optimization (ICMSAO). IEEE, 2011. http://dx.doi.org/10.1109/icmsao.2011.5775562.

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Sukamto, Rosa Ariani, Munir, and Siswo Handoko. "Learners mood detection using Convolutional Neural Network (CNN)." In 2017 3rd International Conference on Science in Information Technology (ICSITech). IEEE, 2017. http://dx.doi.org/10.1109/icsitech.2017.8257079.

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Lee, Jong In, Dong-Gyu Yeo, Byeong Man Kim, and Hae-Yeoun Lee. "Automatic Music Mood Detection through Musical Structure Analysis." In 2009 2nd International Conference on Computer Science and its Applications (CSA). IEEE, 2009. http://dx.doi.org/10.1109/csa.2009.5404218.

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Lietz, Rebecca, Meaghan Harraghy, Diane Calderon, James Brady, Eric Becker, and Fillia Makedon. "Survey of mood detection through various input modes." In PETRA '19: The 12th PErvasive Technologies Related to Assistive Environments Conference. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3316782.3321543.

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Ruvolo, Paul, Ian Fasel, and Javier Movellan. "Auditory mood detection for social and educational robots." In 2008 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2008. http://dx.doi.org/10.1109/robot.2008.4543754.

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Lahoti, Madhav, Sanchit Gajam, Aditya Kasat, and Nataasha Raul. "Music Recommendation System Based on Facial Mood Detection." In 2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT). IEEE, 2022. http://dx.doi.org/10.1109/icicict54557.2022.9917956.

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Reports on the topic "Mood detection"

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Bounds, John Alan. Mod 1 ICS TI Report: ICS Conversion of a 140% HPGe Detector. Office of Scientific and Technical Information (OSTI), July 2016. http://dx.doi.org/10.2172/1261788.

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Haider, Huma. Malaria, HIV and TB in Mozambique: Epidemiology, Disease Control and Interventions. Institute of Development Studies, January 2022. http://dx.doi.org/10.19088/k4d.2022.035.

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Malaria, HIV and tuberculosis (TB) are significant public health concerns in Mozambique. Malaria was the fourth leading cause of death in the country in 2019, accounting for 42% of deaths among children under 5 years of age (Mugabe et al., 2021; USAID, 2018). Mozambique is among the top eight countries with the highest HIV prevalence; with the second highest mother-to-child transmission (MTCT) rate in the world (Fuente-Soro et al., 2021; Nacarapa et al., 2021). The incidence of TB is rising, with pediatric TB cases almost tripling in recent years (WHO, 2020b; Nguenha et al., 2018; Orlando et al., 2018). Mozambique has one of the highest global incidence of malaria-HIV and TB-HIV co-infection, which raises the likelihood of poor clinical outcomes (Moon et al., 2019; USAID, 2018). This rapid literature review highlights key aspects of the epidemiology of malaria, HIV and TB in Mozambique and challenges in prevention, detection and treatment; and surveys select interventions that seek to address these challenges. This is part of a series of reports looking into Epidemiology of Malaria, human immune deficiency virus (HIV) and tuberculosis (TB) across a set of African Nations.
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Haider, Huma. Malaria, HIV and TB in Mozambique: Epidemiology, Disease Control and Interventions. Institute of Development Studies, January 2022. http://dx.doi.org/10.19088/k4d.2022.035.

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Malaria, HIV and tuberculosis (TB) are significant public health concerns in Mozambique. Malaria was the fourth leading cause of death in the country in 2019, accounting for 42% of deaths among children under 5 years of age (Mugabe et al., 2021; USAID, 2018). Mozambique is among the top eight countries with the highest HIV prevalence; with the second highest mother-to-child transmission (MTCT) rate in the world (Fuente-Soro et al., 2021; Nacarapa et al., 2021). The incidence of TB is rising, with pediatric TB cases almost tripling in recent years (WHO, 2020b; Nguenha et al., 2018; Orlando et al., 2018). Mozambique has one of the highest global incidence of malaria-HIV and TB-HIV co-infection, which raises the likelihood of poor clinical outcomes (Moon et al., 2019; USAID, 2018). This rapid literature review highlights key aspects of the epidemiology of malaria, HIV and TB in Mozambique and challenges in prevention, detection and treatment; and surveys select interventions that seek to address these challenges. This is part of a series of reports looking into Epidemiology of Malaria, human immune deficiency virus (HIV) and tuberculosis (TB) across a set of African Nations.
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Haider, Huma. Malaria, HIV and TB in Mozambique: Epidemiology, Disease Control and Interventions. Institute of Development Studies, January 2022. http://dx.doi.org/10.19088/k4d.2022.035.

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Abstract:
Malaria, HIV and tuberculosis (TB) are significant public health concerns in Mozambique. Malaria was the fourth leading cause of death in the country in 2019, accounting for 42% of deaths among children under 5 years of age (Mugabe et al., 2021; USAID, 2018). Mozambique is among the top eight countries with the highest HIV prevalence; with the second highest mother-to-child transmission (MTCT) rate in the world (Fuente-Soro et al., 2021; Nacarapa et al., 2021). The incidence of TB is rising, with pediatric TB cases almost tripling in recent years (WHO, 2020b; Nguenha et al., 2018; Orlando et al., 2018). Mozambique has one of the highest global incidence of malaria-HIV and TB-HIV co-infection, which raises the likelihood of poor clinical outcomes (Moon et al., 2019; USAID, 2018). This rapid literature review highlights key aspects of the epidemiology of malaria, HIV and TB in Mozambique and challenges in prevention, detection and treatment; and surveys select interventions that seek to address these challenges. This is part of a series of reports looking into Epidemiology of Malaria, human immune deficiency virus (HIV) and tuberculosis (TB) across a set of African Nations.
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