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Добірка наукової літератури з теми "Human Activity Prediction"

Автор: Grafiati

Опубліковано: 15 березня 2025

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Статті в журналах з теми "Human Activity Prediction"

Dönmez, İlknur. "Human Activity Analysis and Prediction Using Google n-Grams." International Journal of Future Computer and Communication 7, no. 2 (June 2018): 32–36. http://dx.doi.org/10.18178/ijfcc.2018.7.2.516.

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Yan, Aixia, Zhi Wang, Jiaxuan Li, and Meng Meng. "Human Oral Bioavailability Prediction of Four Kinds of Drugs." International Journal of Computational Models and Algorithms in Medicine 3, no. 4 (October 2012): 29–42. http://dx.doi.org/10.4018/ijcmam.2012100104.

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In the development of drugs intended for oral use, good drug absorption and appropriate drug delivery are very important. Now the predictions for drug absorption and oral bioavailability follow similar approach: calculate molecular descriptors for molecules and build the prediction models. This approach works well for the prediction of compounds which cross a cell membrane from a region of high concentration to one of low concentration, but it does not work very well for the prediction of oral bioavailability, which represents the percentage of an oral dose which is able to produce a pharmacological activity. The models for bioavailability had limited predictability because there are a variety of pharmacokinetic factors influencing human oral bioavailability. Recent study has shown that good quantitative relationship could be obtained for subsets of drugs, such as those that have similar structure or the same pharmacological activity, or those that exhibit similar absorption and metabolism mechanisms. In this work, using MLR (Multiple Linear Regression) and SVM (Support Vector Machine), quantitative bioavailability prediction models were built for four kinds of drugs, which are Angiotensin Converting Enzyme Inhibitors or Angiotensin II Receptor Antagonists, Calcium Channel Blockers, Sodium and Potassium Channels Blockers and Quinolone Antimicrobial Agents. Explorations into subsets of compounds were performed and reliable prediction models were built for these four kinds of drugs. This work represents an exploration in predicting human oral bioavailability and could be used in other dataset of compounds with the same pharmacological activity.

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D., Manju, and Radha V. "A survey on human activity prediction techniques." International Journal of Advanced Technology and Engineering Exploration 5, no. 47 (October 21, 2018): 400–406. http://dx.doi.org/10.19101/ijatee.2018.547006.

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Keshinro, Babatunde, Younho Seong, and Sun Yi. "Deep Learning-based human activity recognition using RGB images in Human-robot collaboration." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 66, no. 1 (September 2022): 1548–53. http://dx.doi.org/10.1177/1071181322661186.

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In human-robot interaction, to ensure safety and effectiveness, robots need to be able to accurately predict human intentions. Hidden Markov Model, Bayesian Filtering, and deep learning methods have been used to predict human intentions. However, few studies have explored deep learning methods to predict variant human intention. Our study aims to evaluate the performance of the human intent recognition inference algorithm, and its impact on the human-robot team for collaborative tasks. Two deep learning algorithms ConvLSTM and LRCN were used to predict human intention. A dataset of 10 participants performing Pick, Throw, Wave, and Carry actions was used. The ConvLSTM method had a prediction accuracy of 74%. The LRCN method had a lower prediction accuracy of 25% compared to ConvLSTM. This result shows that deep learning methods using RGB images can predict human intent with high accuracy. The proposed method is successful in predicting human intents underlying human behavior.

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Bragança, Hendrio, Juan G. Colonna, Horácio A. B. F. Oliveira, and Eduardo Souto. "How Validation Methodology Influences Human Activity Recognition Mobile Systems." Sensors 22, no. 6 (March 18, 2022): 2360. http://dx.doi.org/10.3390/s22062360.

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In this article, we introduce explainable methods to understand how Human Activity Recognition (HAR) mobile systems perform based on the chosen validation strategies. Our results introduce a new way to discover potential bias problems that overestimate the prediction accuracy of an algorithm because of the inappropriate choice of validation methodology. We show how the SHAP (Shapley additive explanations) framework, used in literature to explain the predictions of any machine learning model, presents itself as a tool that can provide graphical insights into how human activity recognition models achieve their results. Now it is possible to analyze which features are important to a HAR system in each validation methodology in a simplified way. We not only demonstrate that the validation procedure k-folds cross-validation (k-CV), used in most works to evaluate the expected error in a HAR system, can overestimate by about 13% the prediction accuracy in three public datasets but also choose a different feature set when compared with the universal model. Combining explainable methods with machine learning algorithms has the potential to help new researchers look inside the decisions of the machine learning algorithms, avoiding most times the overestimation of prediction accuracy, understanding relations between features, and finding bias before deploying the system in real-world scenarios.

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Giri, Pranit. "Human Activity Recognition System." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 6671–73. http://dx.doi.org/10.22214/ijraset.2023.53135.

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Abstract: Almost every university has its management system to manage the students' records. Currently, even though there is a student management system that manages the students' records in Universiti Malaysia Sarawak (UNIMAS), no permission is provided for lecturers to access the system. This is because the access permission is only to top management such as Deans and Deputy Deans of Undergraduate and Student Development due to its privacy setting. Thus, this project proposes a system named Student Performance Analysis System (SPAS) to keep track of students' results in the Faculty of Computer Science and Information Technology (FCSIT). The proposed system offers a predictive system that can predict the student's performance in the course "TMC1013 System Analysis and Design", which in turn assists the lecturers from the Information System department to identify students that are predicted to have bad performance in the course "TMC1013 System Analysis and Design". The proposed system offers student performance prediction through the rules generated via the data mining technique. The data mining technique used in this project is classification, which classifies the students based on students' grades. Keywords- Student performance; student analysis; data mining; student performance analysis; classification; prediction; system

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Bhambri, Pankaj, Sachin Bagga, Dhanuka Priya, Harnoor Singh, and Harleen Kaur Dhiman. "Suspicious Human Activity Detection System." December 2020 2, no. 4 (October 31, 2020): 216–21. http://dx.doi.org/10.36548/jismac.2020.4.005.

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In collaboration with machine learning and artificial intelligence, anomaly detection systems are vastly used in behavioral analysis so that you can help in identity and prediction of prevalence of anomalies. It has applications in enterprise, from intrusion detection to system fitness tracking, and from fraud detection in credit score card transactions to fault detection in running environments. With the growing crime charges and human lack of confidence globally, majority of the countries are adopting precise anomaly detection systems to approach closer to a comfy area. Visualizing the Indian crime index which stands at 42. 38, the adoption of anomaly detection structures is an alarming want of time. Our own cannot be prevented with the aid of CCTV installations. These systems not simplest lead to identification on my own, but their optimized versions can help in prediction of unusual activities as properly.

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Xu-Nan Tan, Xu-Nan Tan. "Human Activity Recognition Based on CNN and LSTM." 電腦學刊 34, no. 3 (June 2023): 221–35. http://dx.doi.org/10.53106/199115992023063403016.

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<p>Human activity recognition (HAR) based on wearable devices is an emerging field of great interest. HAR can provide additional information on a human subject’s physical status. Utilising new technologies for HAR will become very meaningful with the development of deep learning. This study aims to mine deep learning models for HAR prediction with the highest accuracy on the basis of time-series data collected by mobile wearable devices. To this end, convolutional neural networks (CNN) and long short-term memory neural networks (LSTM) are combined in a deep network model to extract behavioural facts. The proposed CNN model contains two convolutional layers and a maximum pooling layer, and batch normalisation is added after each convolutional layer to improve convergence speed and avoid overfitting. This structure yields significant results in terms of performance. The model is evaluated on the MHEALTH dataset with a test set accuracy of 99.61% and can be used for the intelligent recognition of human activity. The results of this study show that the proposed model has better robustness and motion pattern detection capability compared to other models.</p> <p> </p>

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Esther, Ekemeyong, and Teresa Zielińska. "Predicting Human Activity – State of the Art." Pomiary Automatyka Robotyka 27, no. 2 (June 16, 2023): 31–46. http://dx.doi.org/10.14313/par_248/31.

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Predicting human actions is a very actual research field. Artificial intelligence methods are commonly used here. They enable early recognition and classification of human activities. Such knowledge is extremely needed in the work on robots and other interactive systems that communicate and cooperate with people. This ensures early reactions of such devices and proper planning of their future actions. However, due to the complexity of human actions, predicting them is a difficult task. In this article, we review state-of-the-art methods and summarize recent advances in predicting human activity. We focus in particular on four approaches using machine learning methods, namely methods using: artificial neural networks, support vector machines, probabilistic models and decision trees. We discuss the advantages and disadvantages of these approaches, as well as current challenges related to predicting human activity. In addition, we describe the types of sensors and data sets commonly used in research on predicting and recognizing human actions. We analyze the quality of the methods used, based on the prediction accuracy reported in scientific articles. We describe the importance of the data type and the parameters of machine learning models. Finally, we summarize the latest research trends. The article is intended to help in choosing the right method of predicting human activity, along with an indication of the tools and resources necessary to effectively achieve this goal.

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Liu, Zhenguang, Kedi Lyu, Shuang Wu, Haipeng Chen, Yanbin Hao, and Shouling Ji. "Aggregated Multi-GANs for Controlled 3D Human Motion Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 3 (May 18, 2021): 2225–32. http://dx.doi.org/10.1609/aaai.v35i3.16321.

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Human motion prediction from historical pose sequence is at the core of many applications in machine intelligence. However, in current state-of-the-art methods, the predicted future motion is confined within the same activity. One can neither generate predictions that differ from the current activity, nor manipulate the body parts to explore various future possibilities. Undoubtedly, this greatly limits the usefulness and applicability of motion prediction. In this paper, we propose a generalization of the human motion prediction task in which control parameters can be readily incorporated to adjust the forecasted motion. Our method is compelling in that it enables manipulable motion prediction across activity types and allows customization of the human movement in a variety of fine-grained ways. To this aim, a simple yet effective composite GAN structure, consisting of local GANs for different body parts and aggregated via a global GAN is presented. The local GANs game in lower dimensions, while the global GAN adjusts in high dimensional space to avoid mode collapse. Extensive experiments show that our method outperforms state-of-the-art. The codes are available at https://github.com/herolvkd/AM-GAN.

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Більше джерел

Дисертації з теми "Human Activity Prediction"

Coen, Paul Dixon. "Human Activity Recognition and Prediction using RGBD Data." OpenSIUC, 2019. https://opensiuc.lib.siu.edu/theses/2562.

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Being able to predict and recognize human activities is an essential element for us to effectively communicate with other humans during our day to day activities. A system that is able to do this has a number of appealing applications, from assistive robotics to health care and preventative medicine. Previous work in supervised video-based human activity prediction and detection fails to capture the richness of spatiotemporal data that these activities generate. Convolutional Long short-term memory (Convolutional LSTM) networks are a useful tool in analyzing this type of data, showing good results in many other areas. This thesis’ focus is on utilizing RGB-D Data to improve human activity prediction and recognition. A modified Convolutional LSTM network is introduced to do so. Experiments are performed on the network and are compared to other models in-use as well as the current state-of-the-art system. We show that our proposed model for human activity prediction and recognition outperforms the current state-of-the-art models in the CAD-120 dataset without giving bounding frames or ground-truths about objects.

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Bergelin, Victor. "Human Activity Recognition and Behavioral Prediction using Wearable Sensors and Deep Learning." Thesis, Linköpings universitet, Matematiska institutionen, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-138064.

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When moving into a more connected world together with machines, a mutual understanding will be very important. With the increased availability in wear- able sensors, a better understanding of human needs is suggested. The Dart- mouth Research study at the Psychiatric Research Center has examined the viability of detecting and further on predicting human behaviour and complex tasks. The field of smoking detection was challenged by using the Q-sensor by Affectiva as a prototype. Further more, this study implemented a framework for future research on the basis for developing a low cost, connected, device with Thayer Engineering School at Dartmouth College. With 3 days of data from 10 subjects smoking sessions was detected with just under 90% accuracy using the Conditional Random Field algorithm. However, predicting smoking with Electrodermal Momentary Assessment (EMA) remains an unanswered ques- tion. Hopefully a tool has been provided as a platform for better understanding of habits and behaviour.

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Baldo, Fatima Magdi Hamza. "Integrating chemical, biological and phylogenetic spaces of African natural products to understand their therapeutic activity." Thesis, University of Cambridge, 2019. https://www.repository.cam.ac.uk/handle/1810/289714.

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This research aims to utilise ligand-based target prediction to (i) understand the mechanism of action of African natural products (ANPs), (ii) help identify patterns of phylogenetic use in African traditional medicine and (iii) elucidate the mechanism of action of phenotypically active small molecules and natural products with anti-trypanosomal activity. In Chapter 2 the objective was to utilise ligand-based target prediction to understand the mechanism of action of natural products (NPs) from African medicinal plants used against cancer. The Random Forest classifier used in this work compares the similarity of the input compounds from the natural product dataset with compound-target combinations in the training set. The more similar they are in structure, the more likely they are to modulate the same target. Natural products from plants used against cancer in Africa were predicted to modulate targets and pathways directly associated with the disease, thus understanding their mechanism of action e.g. "flap endonuclease 1" and "Mcl-1". The "Keap1-Nrf2 Pathway" and "apoptosis modulation by HSP70", two pathways previously linked to cancer (which are not currently targeted by marketed drugs, but have been of increasing interest in recent years) were predicted to be modulated by ANPs. In Chapter 3, we aimed to identify phylogenetic patterns in medicinal plant use and the role this plays in predicting medicinal activity. We combined chemical, predicted target and phylogenetic information of the natural products to identify patterns of use for plant families containing plant species used against cancer in African, Malay and Indian (Ayurveda) traditional medicine. Plant families that are close phylogenetically were found to produce similar natural products that act on similar targets regardless of their origin. Additionally, phylogenetic patterns were identified for African traditional plant families with medicinal species used against cancer, malaria and human African trypanosomiasis (HAT). We identified plant families that have more medicinal species than would statistically be expected by chance and rationalised this by linking their activity to their unique phyto-chemistry e.g. the napthyl-isoquinoline alkaloids, uniquely produced by Acistrocladaceae and Dioncophyllaceae, are responsible for anti-malarial and anti-trypanosome activity. In Chapter 4, information from target prediction and experimentally validated targets was combined with orthologue data to predict targets of phenotypically active small molecules and natural products screened against Trypanosoma brucei. The predicted targets were prioritised based on their essentiality for the survival of the T. brucei parasite. We predicted orthologues of targets that are essential for the survival of the trypanosome e.g. glycogen synthase kinase 3 (GSK3) and rhodesain. We also identified the biological processes predicted to be perturbed by the compounds e.g. "glycolysis", "cell cycle", "regulation of symbiosis, encompassing mutualism through parasitism" and "modulation of development of symbiont involved in interaction with host". In conclusion, in silico target prediction can be used to predict protein targets of natural products to understand their molecular mechanism of action. Phylogenetic information and phytochemical information of medicinal plants can be integrated to identify plant families with more medicinal species than would be expected by chance.

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Snyder, Kristian. "Utilizing Convolutional Neural Networks for Specialized Activity Recognition: Classifying Lower Back Pain Risk Prediction During Manual Lifting." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1583999458096255.

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Mehdi, Nima. "Approches probabilistes pour la perception et l’interprétation de l’activité humaine." Electronic Thesis or Diss., Université de Lorraine, 2024. http://www.theses.fr/2024LORR0202.

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Анотація:

De l'industrie aux services, les systèmes intelligents sont amenés à observer, interagir ou encore coopérer avec l'humain. Cette thèse s'inscrit ainsi dans le contexte des méthodes de perception intelligente pour l'analyse de l'humain en utilisant la pose et l'activité qui lui sont associées. En raison de la nature variable et changeante de l'humain, il est difficile d'obtenir une représentation précise des processus guidant ses mouvements et ses actions. Ces difficultés sont accrues lorsqu'il s'agit d'estimer ou de prédire les mouvements ou les activités. Afin de considérer l'incertitude inhérente à l'humain, nous proposons une approche bayésienne pour la perception et l'analyse de l'activité humaine. La première contribution est consacrée à l'estimation simultanée de la pose et de la posture humaine. À l'aide d'une caméra monoculaire et de capteurs portés, nous cherchons à estimer la pose 3D humaine en temps réel. Pour une estimation robuste, une approche de fusion multimodale est proposée en incorporant les mesures de capteurs inertiels portés aux observations caméra. De cette manière, nous outrepassons les ambiguïtés de mesure liées à la caméra et à la dérive inertielle due aux centrales inertielles. Afin de tenir compte de la nature non déterministe du mouvement humain et du caractère non gaussien de la posture, nous choisissons d'utiliser un filtre particulaire. Dans le but de réduire le coût du calcul, nous proposons une architecture composée de deux filtres consécutifs. Un premier filtre estime la posture de manière factorisée uniquement à partir des observations inertielles. Puis un second filtre estime la pose complète à partir de la caméra, en incorporant l'estimation du premier filtre. Notre approche réalise la fusion de manière originale par la construction de la distribution d'échantillonnage du second filtre. Cette architecture permet ainsi d'estimer la pose et la posture de manière simultanée avec un coût calculatoire réduit tout en étant robuste aux occultations et dérives. La seconde contribution s'intéresse à la prédiction de l'activité humaine. Les modèles de Markov cachés se sont montrés efficaces pour l'analyse de l'activité humaine à travers des tâches de segmentation et de reconnaissance de l'activité. Cependant, ils présentent des limites en termes de modélisation les rendant insuffisants pour la prédiction. Nous proposons donc l'utilisation de modèles semi-markoviens pour la prédiction. Ces modèles étendent la définition des modèles de Markov en modélisant de manière explicite la durée passée dans chaque état. Cette modélisation explicite de la durée permet une meilleure modélisation des processus non stationnaires et améliore la capacité prédictive de ces modèles. Notre étude démontre ainsi l'utilité de tels modèles pour la prédiction d'activité avec prise en compte de l'incertitude
From industry to services, intelligent systems are required to observe, interact with, or cooperate with humans. This thesis is therefore set in the context of intelligent perception methods for the analysis of humans, using the pose and activity associated with them. Due to the variable and changing nature of humans, it is difficult to obtain an accurate representation of theprocesses guiding their movements and actions. These difficulties are compounded when it comes to estimating or predicting movements or activities. In order to take account of the uncertainty inherent in humans, we propose a Bayesian approach to the perception and analysis of human activity. The first contribution is dedicated to the simultaneous estimation of human pose and posture. Using a monocular camera and wearable sensors, we aim to estimate human 3D pose in real time. For robust estimation, a multimodal fusion approach is suggested, incorporating measurements from wearable inertial sensors with camera observations. In this way, we overcome measurement ambiguities related to the camera and inertial drift due to inertial units. We use a particle filter so as to take into account the non-deterministic nature of human motion and thenon-Gaussian nature of posture. In order to reduce the computational cost, we put forward an architecture composed of two consecutive filters. A first filter estimates the posture in a factorized way from inertial observations only. Then a second filter estimates the complete pose from the camera, incorporating the estimation of the first filter. Our approach achieves fusion by constructing the sampling distribution of the second filter. This architecture makes it possible to estimate pose and posture simultaneously, at low computational cost, and is robust to cloaking and drift. The second contribution pertains to the prediction of human activity. Hidden Markov models have proved effective for the analysis of human activity through segmentation and activity recognition tasks. However, they have modeling limitations that make them insufficient for prediction. We therefore propose the use of semi-Markovian models for prediction. These models extend the definition of Markov models by explicitly modeling the duration spent in each state. This explicit modeling of duration enables better modeling of non-stationary processes and improves the predictive capability of these models. Our study thus demonstrates the usefulness of such models for activity prediction while taking uncertainty into account

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Rozman, Peter Andrew. "Multi-Unit Activity in the Human Cortex as a Predictor of Seizure Onset." Thesis, Harvard University, 2015. http://nrs.harvard.edu/urn-3:HUL.InstRepos:15821597.

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Epilepsy is a neurological disorder affecting 50 million people worldwide. It consists of a large number of syndromes, all of which are characterized by a predisposition to recurrent, unprovoked seizures, while differing by degree of focality, clinical manifestation and many other factors. Despite the prevalence of this disorder, relatively little is known about the basic physiological mechanisms that underlie the seizures themselves. Additionally, roughly 25% of patients are refractory to existing therapies. The need for more highly targeted therapies for focal epilepsies has driven decades of research on seizure prediction. While most of these studies have relied on scalp or intracranial EEG, more recent studies have taken advantage of electrodes that capture single- or multi-unit activity. We utilized a linear microelectrode array to capture multi-unit activity in humans with refractory epilepsy with the expectation that such microscale activity may provide a signal in advance of changes on electroencephalography. Twelve patients underwent long-term monitoring with both clinical electrocorticography (ECoG) and the laminar microelectrode array, which consists of linearly arranged contacts that sample all layers of the human cortex. Multi-unit (300-5000 Hz) power was compared between thirty-minute preictal and interictal time windows. Several parameters characterizing the multi-unit power were compared between preictal and interictal time windows. Parameters included proximity to seizure focus, depth of recording, and directionality of changes in multi-unit power. Optimization of these parameters resulted in a best-performing classifier with sensitivity and specificity of 0.70 and 0.80, respectively. These results demonstrate reproducible increases and decreases in multi-unit activity prior to seizure onset and suggest that multi-unit information may be useful in the development of future seizure prediction systems.

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Karst, Gregory Mark. "Multijoint arm movements: Predictions and observations regarding initial muscle activity at the shoulder and elbow." Diss., The University of Arizona, 1989. http://hdl.handle.net/10150/184920.

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Understanding the control strategies that underlie multijoint limb movements is important to researchers in motor control, robotics, and medicine. Due to dynamic interactions between limb segments, choosing appropriate muscle activations for initiating multijoint arm movements is a complex problem, and the rules by which the nervous system makes such choices are not yet understood. The aim of the dissertation studies was to evaluate some proposed initiation rules based on their ability to correctly predict which shoulder and elbow muscles initiated planar, two-joint arm movements in various directions. Kinematic and electromyographic data were collected from thirteen subjects during pointing movements involving shoulder and elbow rotations in the horizontal plane. One of the rules tested, which is based on statics, predicted that the initial muscle activity at each joint is chosen such that the hand exerts an initial force in the direction of the target, while another rule, based on dynamics, predicted initial muscle activity such that the initial acceleration of the hand is directed toward the target. For both rules, the data contradict the predicted initial shoulder muscle activity for certain movement directions. Moreover, the effects of added inertial loads predicted by the latter rule were not observed when a 1.8 kg mass was added to the limb. The results indicated, however, that empirically derived rules, based on ψ, the target direction relative to the distal segment, could predict which muscles would be chosen to initiate movement in a given direction. Furthermore, the relative timing and magnitude of initial muscle activity at the shoulder and elbow varied systematically with ψ. Thus, the target direction relative to the forearm may be an important variable in determining initial muscle activations for multijoint arm movements. These findings suggest a control scheme for movement initiation in which simple rules suffice to launch the hand in the approximate direction of the target by first specifying a basic motor output pattern, then modulating the relative timing and magnitude of that pattern.

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Cheradame, Stéphane. "Biomodulation du 5-fluorouracile par l'acide folinique et recherche des facteurs de prédiction de la sensibilité tumorale à cette association." Université Joseph Fourier (Grenoble ; 1971-2015), 1996. http://www.theses.fr/1996GRE10252.

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Анотація:

Le principal effet cytotoxique du 5-fluorouracile (5fu) s'exerce par inhibition de la thymidylate synthetase (ts). La formation d'un complexe ternaire intracellulaire entre la ts, un anabolite du 5fu le fluorodeoxyuridine monophosphate (fdump) et un folate reduit, le 5,10-methylenetetrahydrofolate (ch2fh4), bloque la synthese de thymidine et donc la formation d'adn. L'acide folinique (af) potentialise l'effet du 5fu en augmentant le pool intracellulaire de ch2fh4. Une concentration optimale de ch2fh4 sous forme polyglutamatee via la folylpolyglutamate synthetase (fpgs) est necessaire pour une inhibition maximale de la ts. Le 5fu est catabolise par la dihydropyrimidine deshydrogenase (dpd), qui diminue la concentration intratumorale de fdump. Les objectifs de cette etude etaient de tester sur des lignees cellulaires tumorales et des biopsies tumorales de patients, la valeur predictive des activites ts, dpd, fpgs et du ch2fh4 vis a vis de la sensibilite au 5fu et a l'af. Dans les lignees cellulaires, la fpgs est le seul facteur predictif de la sensibilite au 5fu seul ou en presence d'af. L'effet potentialisateur de l'af sur le 5fu est d'autant plus important que le taux de ch2fh4 de base et l'activite fpgs basale sont eleves. Le ch2fh4 intratumoral n'est pas le facteur limitant de l'effet potentialisateur. Dans les tumeurs orl, les patients repondeurs au 5fu ont un taux de ch2fh4 plus eleve et une activite dpd normalisee (dpdtumorale/dpdtissu sain) plus faible que les patients resistants. Au dessus de 1,6 pmole/mg de proteine de ch2fh4, tous les patients sont repondeurs au traitement. Au dessous de 1,6 pmole/min/mg de proteine 52% des patients sont resistants au traitement. Dans le cas des metastases hepatiques de cancers colorectaux, les patients resistants au 5fu ont une activite fpgs plus faible que les repondeurs. 96% des metastases hepatiques dont l'activite fpgs < 1,1 pmole/min/mg de proteine ou dont l'activite ts > 0,32 pmole/min/mg de proteine, sont resistantes a une chimiotherapie a base de 5fu - af

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Silva, Joana. "Smartphone Based Human Activity Prediction." Dissertação, 2013. http://hdl.handle.net/10216/74272.

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Silva, Joana Raquel Cerqueira da. "Smartphone based human activity prediction." Master's thesis, 2013. http://hdl.handle.net/10216/72620.

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Книги з теми "Human Activity Prediction"

Fu, Yun, ed. Human Activity Recognition and Prediction. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27004-3.

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Human Activity Recognition and Prediction. Springer, 2016.

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Fu, Yun. Human Activity Recognition and Prediction. Springer, 2018.

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Fu, Yun. Human Activity Recognition and Prediction. Springer London, Limited, 2015.

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Andersson, Jenny. The Future as Social Technology. Prediction and the Rise of Futurology. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198814337.003.0005.

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Chapter 5 examines the experiments at RAND with a new future science, a “general theory of the future” capable of explaining human behavior and developments in the world system. The chapter also proposes that futurology was ultimately a failure, as forms of prediction encountered criticism and led to a discussion within RAND about the epistemological limits of prediction. As RAND researchers came to the conclusion that prediction was logically and empirically impossible, they shifted their interest from predicting actual future developments, to prediction as a “social technology”—a means of actively intervening into the future and shape desirable developments. The chapter zeroes in on the so called Delphi technology, the purpose of which was to conduct an expert driven reflection on a possible wide array of social futures, produce judgments on desirable and undesirable futures, and choose the optimal future.

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Cook, Diane J., and Narayanan C. Krishnan. Activity Learning: Discovering, Recognizing, and Predicting Human Behavior from Sensor Data. Wiley & Sons, Incorporated, John, 2015.

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Cook, Diane J., and Narayanan C. Krishnan. Activity Learning: Discovering, Recognizing, and Predicting Human Behavior from Sensor Data. Wiley & Sons, Incorporated, John, 2015.

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Cook, Diane J., and Narayanan C. Krishnan. Activity Learning: Discovering, Recognizing, and Predicting Human Behavior from Sensor Data. Wiley & Sons, Limited, John, 2015.

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Activity Learning: Discovering, Recognizing, and Predicting Human Behavior from Sensor Data. Wiley, 2015.

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Andersson, Jenny. The Future of the World. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198814337.001.0001.

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The book is devoted to the intriguing post-war activity called—with different terms—futurism, futurology, future research, or futures studies. It seeks to understand how futurists and futurologists imagined the Cold War and post-Cold War world and how they used the tools and methods of future research to influence and change that world. Forms of future research emerged after 1945 and engaged with the future both as an object of science and as an object of the human imagination. The book carefully explains these different engagements with the future, and inscribes them in the intellectual history of the post-war period. Futurists were a motley crew of Cold War warriors, nuclear scientists, journalists, and peace activists. Futurism also drew on an eclectic range of repertoires, some of which were deduced from positivist social science, mathematics, and nuclear physics, and some of which came from new strands of critical theory in the margins of the social sciences or sprung from alternative forms of knowledge in science fiction, journalism, or religion. Different forms of prediction lay very different claims to how, and with what accuracy, futures could be known, and what kind of control could be exerted over coming and not yet existing developments. Not surprisingly, such different claims to predictability coincided with radically different notions of human agency, of morality and responsibility, indeed of politics.

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Частини книг з теми "Human Activity Prediction"

Kong, Yu, and Yun Fu. "Activity Prediction." In Human Activity Recognition and Prediction, 107–22. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27004-3_6.

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Li, Kang, and Yun Fu. "Actionlets and Activity Prediction." In Human Activity Recognition and Prediction, 123–51. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27004-3_7.

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Kong, Yu, and Yun Fu. "Action Recognition and Human Interaction." In Human Activity Recognition and Prediction, 23–48. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27004-3_2.

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Kong, Yu, and Yun Fu. "Introduction." In Human Activity Recognition and Prediction, 1–22. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27004-3_1.

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Jia, Chengcheng, and Yun Fu. "Subspace Learning for Action Recognition." In Human Activity Recognition and Prediction, 49–69. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27004-3_3.

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Jia, Chengcheng, Wei Pang, and Yun Fu. "Multimodal Action Recognition." In Human Activity Recognition and Prediction, 71–85. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27004-3_4.

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Jia, Chengcheng, Yu Kong, Zhengming Ding, and Yun Fu. "RGB-D Action Recognition." In Human Activity Recognition and Prediction, 87–106. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27004-3_5.

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Li, Kang, Sheng Li, and Yun Fu. "Time Series Modeling for Activity Prediction." In Human Activity Recognition and Prediction, 153–74. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27004-3_8.

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Friedrich, Björn, and Andreas Hein. "Ensemble Classifier for Nurse Care Activity Prediction Based on Care Records." In Human Activity and Behavior Analysis, 323–32. Boca Raton: CRC Press, 2024. http://dx.doi.org/10.1201/9781003371540-22.

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Piergiovanni, A. J., Anelia Angelova, Alexander Toshev, and Michael S. Ryoo. "Adversarial Generative Grammars for Human Activity Prediction." In Computer Vision – ECCV 2020, 507–23. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58536-5_30.

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Тези доповідей конференцій з теми "Human Activity Prediction"

Shete, Amar, Aashita Gupta, Ajay Waghumbare, Upasna Singh, Triveni Dhamale, and Kiran Napte. "Human Activity Prediction Using Generative Adversarial Networks." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10726013.

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Sukanya, K., Addagatla Prashanth, and Ugendhar Addagatla. "Development of Human Activity Prediction Systems in Smart Homes." In 2024 IEEE International Conference on Smart Power Control and Renewable Energy (ICSPCRE), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/icspcre62303.2024.10675115.

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Nirmala, S., and R. A. Priya. "A Human Activity Determination Predicting Abnormality Using SVM Approach for Mining Field Workers." In 2024 7th International Conference on Circuit Power and Computing Technologies (ICCPCT), 1659–63. IEEE, 2024. http://dx.doi.org/10.1109/iccpct61902.2024.10673253.

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Mansoor, Zara, Mustansar Ali Ghazanfar, Syed Muhammad Anwar, Ahmed S. Alfakeeh, and Khaled H. Alyoubi. "Pain Prediction in Humans using Human Brain Activity Data." In Companion of the The Web Conference 2018. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3184558.3186348.

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Karthikeyan, M. V., Mohamed Faisal M, and Jithesh R. "Public Human Assault Prediction Using Human Activity Recognition with AI." In 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS). IEEE, 2024. http://dx.doi.org/10.1109/adics58448.2024.10533461.

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Ziaeefard, Maryam, Robert Bergevin, and Jean-Francois Lalonde. "Deep Uncertainty Interpretation in Dyadic Human Activity Prediction." In 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2017. http://dx.doi.org/10.1109/icmla.2017.00-55.

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Dönnebrink, Robin, Fernando Moya Rueda, Rene Grzeszick, and Maximilian Stach. "Miss-placement Prediction of Multiple On-body Devices for Human Activity Recognition." In iWOAR 2023: 8th international Workshop on Sensor-Based Activity Recognition and Artificial Intelligence. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3615834.3615838.

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Dong-Gyu Lee and Seong-Whan Lee. "Human activity prediction based on Sub-volume Relationship Descriptor." In 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE, 2016. http://dx.doi.org/10.1109/icpr.2016.7899939.

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Rodrigues, Royston, Neha Bhargava, Rajbabu Velmurugan, and Subhasis Chaudhuri. "Multi-timescale Trajectory Prediction for Abnormal Human Activity Detection." In 2020 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2020. http://dx.doi.org/10.1109/wacv45572.2020.9093633.

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Nagpal, Diana, and Shikha Gupta. "Human Activity Recognition and Prediction: Overview and Research Gaps." In 2023 IEEE 8th International Conference for Convergence in Technology (I2CT). IEEE, 2023. http://dx.doi.org/10.1109/i2ct57861.2023.10126458.

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Звіти організацій з теми "Human Activity Prediction"

Allen-Dumas, Melissa, Kuldeep Kurte, Haowen Xu, Jibonananda Sanyal, and Guannan Zhang. A Spatiotemporal Sequence Forecasting Platform to Advance the Predictionof Changing Spatiotemporal Patterns of CO2 Concentrationby Incorporating Human Activity and Hydrological Extremes. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1769653.

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Harris, Virginia, Gerald C. Nelson, and Steven Stone. Spatial Econometric Analysis and Project Evaluation: Modeling Land Use Change in the Darién. Inter-American Development Bank, November 1999. http://dx.doi.org/10.18235/0008801.

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The Program for the Sustainable Development of Darién Province in Panama is a $70 million operation approved in 1998 and a major component of the program involves the resurfacing of the Pan American highway, which runs roughly north south through the province to a point about 70 kilometers from the Colombian border. The paper illustrates the use of spatial analysis techniques to predict the land use changes that would occur after the road is resurfaced and other project interventions completed. The predictions are based on a spatial econometric model relating categories of land use to geophysical and socioeconomic variables, including transportation costs and distance from markets. The results of this model are used to predict the spatially explicit effects of road resurfacing on economic activities. The methods explored in this paper offer a promising way to combine behavioral models of human activity with geographic information to realistically assess the prospective land use changes induced by development projects.

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Alter, Ross, Michelle Swearingen, and Mihan McKenna. The influence of mesoscale atmospheric convection on local infrasound propagation. Engineer Research and Development Center (U.S.), February 2024. http://dx.doi.org/10.21079/11681/48157.

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Infrasound—that is, acoustic waves with frequencies below the threshold of human hearing—has historically been used to detect and locate distant explosive events over global ranges (≥1,000 km). Simulations over these ranges have traditionally relied on large-scale, synoptic meteorological information. However, infrasound propagation over shorter, local ranges (0–100 km) may be affected by smaller, mesoscale meteorological features. To identify the effects of these mesoscale meteorological features on local infrasound propagation, simulations were conducted using the Weather Research and Forecasting (WRF) meteorological model to approximate the meteorological conditions associated with a series of historical, small-scale explosive test events that occurred at the Big Black Test Site in Bovina, Mississippi. These meteorological conditions were then incorporated into a full-wave acoustic model to generate meteorology-informed predictions of infrasound propagation. A series of WRF simulations was conducted with varying degrees of horizontal resolution—1, 3, and 15 km—to investigate the spatial sensitivity of these infrasound predictions. The results illustrate that convective precipitation events demonstrate potentially observable effects on local infrasound propagation due to strong, heterogeneous gradients in temperature and wind associated with the convective events themselves. Therefore, to accurately predict infrasound propagation on local scales, it may be necessary to use convection-permitting meteorological models with a horizontal resolution ≤4 km at locations and times that support mesoscale convective activity.

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Saville, Alan, and Caroline Wickham-Jones, eds. Palaeolithic and Mesolithic Scotland : Scottish Archaeological Research Framework Panel Report. Society for Antiquaries of Scotland, June 2012. http://dx.doi.org/10.9750/scarf.06.2012.163.

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Why research Palaeolithic and Mesolithic Scotland? Palaeolithic and Mesolithic archaeology sheds light on the first colonisation and subsequent early inhabitation of Scotland. It is a growing and exciting field where increasing Scottish evidence has been given wider significance in the context of European prehistory. It extends over a long period, which saw great changes, including substantial environmental transformations, and the impact of, and societal response to, climate change. The period as a whole provides the foundation for the human occupation of Scotland and is crucial for understanding prehistoric society, both for Scotland and across North-West Europe. Within the Palaeolithic and Mesolithic periods there are considerable opportunities for pioneering research. Individual projects can still have a substantial impact and there remain opportunities for pioneering discoveries including cemeteries, domestic and other structures, stratified sites, and for exploring the huge evidential potential of water-logged and underwater sites. Palaeolithic and Mesolithic archaeology also stimulates and draws upon exciting multi-disciplinary collaborations. Panel Task and Remit The panel remit was to review critically the current state of knowledge and consider promising areas of future research into the earliest prehistory of Scotland. This was undertaken with a view to improved understanding of all aspects of the colonization and inhabitation of the country by peoples practising a wholly hunter-fisher-gatherer way of life prior to the advent of farming. In so doing, it was recognised as particularly important that both environmental data (including vegetation, fauna, sea level, and landscape work) and cultural change during this period be evaluated. The resultant report, outlines the different areas of research in which archaeologists interested in early prehistory work, and highlights the research topics to which they aspire. The report is structured by theme: history of investigation; reconstruction of the environment; the nature of the archaeological record; methodologies for recreating the past; and finally, the lifestyles of past people – the latter representing both a statement of current knowledge and the ultimate aim for archaeologists; the goal of all the former sections. The document is reinforced by material on-line which provides further detail and resources. The Palaeolithic and Mesolithic panel report of ScARF is intended as a resource to be utilised, built upon, and kept updated, hopefully by those it has helped inspire and inform as well as those who follow in their footsteps. Future Research The main recommendations of the panel report can be summarized under four key headings:  Visibility: Due to the considerable length of time over which sites were formed, and the predominant mobility of the population, early prehistoric remains are to be found right across the landscape, although they often survive as ephemeral traces and in low densities. Therefore, all archaeological work should take into account the expectation of Palaeolithic and Mesolithic ScARF Panel Report iv encountering early prehistoric remains. This applies equally to both commercial and research archaeology, and to amateur activity which often makes the initial discovery. This should not be seen as an obstacle, but as a benefit, and not finding such remains should be cause for question. There is no doubt that important evidence of these periods remains unrecognised in private, public, and commercial collections and there is a strong need for backlog evaluation, proper curation and analysis. The inadequate representation of Palaeolithic and Mesolithic information in existing national and local databases must be addressed.  Collaboration: Multi-disciplinary, collaborative, and cross- sector approaches must be encouraged – site prospection, prediction, recognition, and contextualisation are key areas to this end. Reconstructing past environments and their chronological frameworks, and exploring submerged and buried landscapes offer existing examples of fruitful, cross-disciplinary work. Palaeolithic and Mesolithic archaeology has an important place within Quaternary science and the potential for deeply buried remains means that geoarchaeology should have a prominent role.  Innovation: Research-led projects are currently making a substantial impact across all aspects of Palaeolithic and Mesolithic archaeology; a funding policy that acknowledges risk and promotes the innovation that these periods demand should be encouraged. The exploration of lesser known areas, work on different types of site, new approaches to artefacts, and the application of novel methodologies should all be promoted when engaging with the challenges of early prehistory.  Tackling the ‘big questions’: Archaeologists should engage with the big questions of earliest prehistory in Scotland, including the colonisation of new land, how lifestyles in past societies were organized, the effects of and the responses to environmental change, and the transitions to new modes of life. This should be done through a holistic view of the available data, encompassing all the complexities of interpretation and developing competing and testable models. Scottish data can be used to address many of the currently topical research topics in archaeology, and will provide a springboard to a better understanding of early prehistoric life in Scotland and beyond.

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Eparkhina, Dina. EuroSea Legacy Report. EuroSea, 2023. http://dx.doi.org/10.3289/eurosea_d8.12.

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EuroSea is a holistic large-scale project encompassing the full value chain of marine knowledge, from observations to modelling and forecasting and to user-focused services. This report summarizes the legacy of EuroSea as planned and measured through a dedicated impact monitoring protocol, a holistic assessment of the project's successes in advancing and integrating European ocean observing and forecasting systems. Since its start, EuroSea has been analysing how well the project progresses towards the identified areas of impact. Impact assessment is not performance evaluation. These terms overlap but are distinct: performance relates to the efficient use of resources; impact relates to the transformative effect on the users. The EuroSea legacy report is presented through an aggregation and analysis of the EuroSea work towards achieving its impacts. Overall, over 100 impacts have been identified and presented on the website and in a stand-alone impact report. The legacy report sheds light on 32 most powerful impacts (four impacts in each of the eight EuroSea impact areas). EuroSea Impact Areas: 1. Strengthen the European Ocean Observing System (EOOS), support the Global Ocean Observing System (GOOS) and the GOOS Regional Alliances; 2. Increase ocean data sharing and integration; 3. Deliver improved climate change predictions; 4. Build capacity, internally in EuroSea and externally with EuroSea users, in a range of key areas; 5. Develop innovations, including exploitation of novel ideas or concepts; shorten the time span between research and innovation and foster economic value in the blue economy; 6. Facilitate methodologies, best practices, and knowledge transfer in ocean observing and forecasting; 7. Contribute to policy making in research, innovation, and technology; 8. Raise awareness of the need for a fit for purpose, sustained, observing and forecasting system in Europe. Ocean observing and forecasting is a complex activity brining about a variety of technologies, human expertise, in water and remote sensing measurements, high-volume computing and artificial intelligence, and a high degree of governance and coordination. Determining an impact on a user type or an area, therefore, requires a holistic assessment and a clear strategic overview. The EuroSea impact monitoring protocol has been the first known such attempt in a European ocean observing and forecasting project. The project’s progress has been followed according to the identified impact areas, through consortium workshops, stakeholder webinars, tracking, and reporting. At the end of EuroSea, we are able to demonstrate how well we have responded to the European policy drivers set out in the funding call and the grant agreement of our project, signed between the European Commission and 53 organizations, members of the EuroSea consortium. The project's impact is diverse, spanning areas from strengthening ocean observing governance to contributing to policymaking or boosting ocean research, innovation, and technology. Each impact area underscores EuroSea's commitment to a sustainable and informed approach to ocean observing and forecasting for enhanced marine knowledge and science-based sustainable blue economy and policies. (EuroSea Deliverable, D8.12)

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