Journal articles on the topic 'Absence of training data'
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Böge, M., D. Bulatov, D. Debroize, G. Häufel, and L. Lucks. "EFFICIENT TRAINING DATA GENERATION BY CLUSTERING-BASED CLASSIFICATION." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2022 (May 17, 2022): 179–86. http://dx.doi.org/10.5194/isprs-annals-v-3-2022-179-2022.
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Abstract. Insufficient amount or complete absence of reference data for the training of classifiers is a general topic. Especially the state-of-the-art deep learning approaches have to deal with the availability or adaption of this reference data to produce the reliable results they are designed for. This paper will pursue different approaches according to the absence of training data for land cover classification from aerial images. First, we will analyze the performance of traditional classification in the absence of reference data using clustering techniques and salient features for the assignment of semantic labels. Second, we will transfer the results as training data to a DeepLabv3+ CNN with pre-trained weights to demonstrate the usability of the generated training data. Third, we expand the clustering approaches and combine them with a Random Forest classifier. Finally, if user interaction and manual annotation of training data are still necessary, we also introduce our labeling GUI that enables a simple, fast, and comfortable training data generation with only a few clicks. To evaluate our procedure, we used two datasets, including the Vaihingen benchmark, for which ground truth is available. Without any interactive steps except setting a few algorithm paremeters, we achieved an overall accuracy of 75% using the Deeplab method with image data only.
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Spina, Robert J., Timothy E. Meyer, Linda R. Peterson, Dennis T. Villareal, Morton R. Rinder, and Ali A. Ehsani. "Absence of left ventricular and arterial adaptations to exercise in octogenarians." Journal of Applied Physiology 97, no. 5 (November 2004): 1654–59. http://dx.doi.org/10.1152/japplphysiol.01303.2003.
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Recent evidence suggests that octogenarians exhibit attenuated adaptations to training with a small increase in peak O2 consumption (V̇o2) that is mediated by a modest improvement in cardiac output without an increase in arteriovenous O2 content difference. This study was designed to determine whether diminished increases in peak V̇o2 and cardiac output in the octogenarians are associated with absence of left ventricular and arterial adaptations to exercise training. We studied 22 octogenarians (81.9 ± 3.7 yr, mean ± SD) randomly assigned a group that exercised at an intensity of 82.5 ± 5% of peak heart rate for 9 mo and 14 (age 83.1 ± 4.1) assigned to a control group. Peak V̇o2 increased 12% in the exercise group but decreased slightly (−7%) in the controls. The exercise group demonstrated significant but small decreases in the heart rate (6%, P = 0.002) and the rate-pressure product (9%, P = 0.004) during submaximal exercise at an absolute work rate. Training induced no significant changes in the left ventricular size, geometry (wall thickness-to-radius ratio), mass, and function assessed with two-dimensional echocardiography or in arterial stiffness evaluated with applanation tonometry. Data suggest that the absence of cardiac and arterial adaptations may in part account for the limited gain in aerobic capacity in response to training in the octogenarians.
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Ehsan, Shah Md Azimul, and Zaki Imam. "Analyzing the Effectiveness of Training Programs of BCS Administration Academy: Prospects and Challenges." Journal of Public Administration and Governance 10, no. 4 (November 11, 2020): 73. http://dx.doi.org/10.5296/jpag.v10i4.17740.
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The specific objective of this study was to analyze the effectiveness and the challenges of the training programs imparted by BCSAA, a leading training institution for the BCS administration cadre officials in Bangladesh. The study is qualitative is nature that used data from both primary and secondary sources. Primary data was obtained by interviewing a total of 75 participants from 113th, 114th and 115th Law and Administration Course (LAC) using semi-structured questionnaire and phone in interview. While secondary data was obtained adopting content analysis technique through reviewing books, journal articles, e-resources, unpublished monographs and newspapers. Analyzing the data from both the sources, the study has found that the training program of LAC has been effective to a certain extent as it has helped its participants to achieve some core qualities relevant to their job performance. The findings of the study also suggest that due to the intervention of this training, trainees have improved their knowhow about land management, conducting mobile courts, magisterial duties, e-filing, e-mutation etc. Furthermore, the training has resulted in significant improvement of their communication skills particularly in English, presentation skills, public speaking and in few other areas of their professional needs. However, the respondents raised few concerns about certain aspects of the training which include absence of proper need assessment for training, heavy reliance on guest speakers, huge syllabus compared to the course duration, absence of e-library facilities in the academy, sessions being mostly lecture based, absence of refreshers training etc. Nonetheless, this study has come up with certain pragmatic recommendations drawing on the narratives from the respondents. BCSAA need to address these issues with utmost priority for making this training further effective.
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Somadi, Somadi, and Indani Zulfah. "Analisis Human Error Pada Surat Penyerahan Petikemas (SP2) Dan Warehouse Management System (WMS)." Competitive 16, no. 1 (July 27, 2021): 42–51. http://dx.doi.org/10.36618/competitive.v16i1.1175.
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PT. XYZ is a logistics service company engaged in the shipping service business of sea / air ships (EMKL / EMKU) and warehousing / warehousing. In the operational field of Full Container Load (FCL) at PT XYZ, there are problems, namely Human Error (Human Error) inputting data into the Container Handover Letter (SP2) and Warehouse Management System (WMS) documents. The purpose of this study was to determine the causative factors and to find out the right recommendations for improvement in the event of data input errors in the WMS system and data input errors in the manufacture of SP2. Data collection techniques, namely by observation and interviews. The analysis technique used fishbone diagrams and 5W + 1H analysis. Factors that cause Human Error are lack of training or trainning, the number of other tasks outside the job description, the absence of work shifts, the atmosphere is too busy by EMKL who takes care of documents, and the absence of an RFID Reader. To minimize the occurrence of Human Errors is to hold training and provide motivation so that employees can take part in training in operating the WMS system, socializing and providing additional benefits or money to employees, recruiting new employees at least divided into 2 team work shifts for the WMS operation section, adding service into two parts, will reduce EMKL queues and speed up document input to WMS, conduct socialization to use RFID.
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He, Chaoyang, Emir Ceyani, Keshav Balasubramanian, Murali Annavaram, and Salman Avestimehr. "SpreadGNN: Decentralized Multi-Task Federated Learning for Graph Neural Networks on Molecular Data." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (June 28, 2022): 6865–73. http://dx.doi.org/10.1609/aaai.v36i6.20643.
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Graph Neural Networks (GNNs) are the first choice methods for graph machine learning problems thanks to their ability to learn state-of-the-art level representations from graph-structured data. However, centralizing a massive amount of real-world graph data for GNN training is prohibitive due to user-side privacy concerns, regulation restrictions, and commercial competition. Federated Learning is the de-facto standard for collaborative training of machine learning models over many distributed edge devices without the need for centralization. Nevertheless, training graph neural networks in a federated setting is vaguely defined and brings statistical and systems challenges. This work proposes SpreadGNN, a novel multi-task federated training framework capable of operating in the presence of partial labels and absence of a central server for the first time in the literature. We provide convergence guarantees and empirically demonstrate the efficacy of our framework on a variety of non-I.I.D. distributed graph-level molecular property prediction datasets with partial labels. Our results show that SpreadGNN outperforms GNN models trained over a central server-dependent federated learning system, even in constrained topologies.
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Kulikovsvkikh, I. M. "Reducing computational costs in deep learning on almost linearly separable training data." Computer Optics 44, no. 2 (April 2020): 282–89. http://dx.doi.org/10.18287/2412-6179-co-645.
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Previous research in deep learning indicates that iterations of the gradient descent, over separable data converge toward the L2 maximum margin solution. Even in the absence of explicit regularization, the decision boundary still changes even if the classification error on training is equal to zero. This feature of the so-called “implicit regularization” allows gradient methods to use more aggressive learning rates that result in substantial computational savings. However, even if the gradient descent method generalizes well, going toward the optimal solution, the rate of convergence to this solution is much slower than the rate of convergence of a loss function itself with a fixed step size. The present study puts forward the generalized logistic loss function that involves the optimization of hyperparameters, which results in a faster convergence rate while keeping the same regret bound as the gradient descent method. The results of computational experiments on MNIST and Fashion MNIST benchmark datasets for image classification proved the viability of the proposed approach to reducing computational costs and outlined directions for future research.
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Chen, Mantun, Yongjun Wang, Zhiquan Qin, and Xiatian Zhu. "Few-Shot Website Fingerprinting Attack with Data Augmentation." Security and Communication Networks 2021 (September 15, 2021): 1–13. http://dx.doi.org/10.1155/2021/2840289.
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This work introduces a novel data augmentation method for few-shot website fingerprinting (WF) attack where only a handful of training samples per website are available for deep learning model optimization. Moving beyond earlier WF methods relying on manually-engineered feature representations, more advanced deep learning alternatives demonstrate that learning feature representations automatically from training data is superior. Nonetheless, this advantage is subject to an unrealistic assumption that there exist many training samples per website, which otherwise will disappear. To address this, we introduce a model-agnostic, efficient, and harmonious data augmentation (HDA) method that can improve deep WF attacking methods significantly. HDA involves both intrasample and intersample data transformations that can be used in a harmonious manner to expand a tiny training dataset to an arbitrarily large collection, therefore effectively and explicitly addressing the intrinsic data scarcity problem. We conducted expensive experiments to validate our HDA for boosting state-of-the-art deep learning WF attack models in both closed-world and open-world attacking scenarios, at absence and presence of strong defense. For instance, in the more challenging and realistic evaluation scenario with WTF-PAD-based defense, our HDA method surpasses the previous state-of-the-art results by nearly 3% in classification accuracy in the 20-shot learning case. An earlier version of this work Chen et al. (2021) has been presented as preprint in ArXiv (https://arxiv.org/abs/2101.10063).
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Goldsmith, Rochelle L., David K. Spierer, Lana Tsao, Richard Stein, and Stuart D. Katz. "THE ABSENCE OF GAS EXCHANGE DATA RESULTS IN THE OVERESTIMATION OF THE TRAINING ZONE IN CHF PATIENTS 12." Journal of Cardiopulmonary Rehabilitation 18, no. 5 (September 1998): 349. http://dx.doi.org/10.1097/00008483-199809000-00016.
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Bochkova, E. V., E. A. Avdeeva, and A. V. Usol’tsev. "IS DATA SCIENTIST A PROFESSION OF THE PRESENT OR THE FUTURE?" Scientific Review: Theory and Practice 10, no. 7 (July 30, 2020): 1399–407. http://dx.doi.org/10.35679/2226-0226-2020-10-7-1399-1407.
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Modern society is characterized by rapid increasing the information volume and increasing its role in various fields of activity: economic, political, social and public ones. The rapid spread and introduction of information technologies, the growth of information data flow pose new challenges to the modern society. Global informatization dictates the need to form an infrastructure for processing and managing Big Data - an array of data, the processing of which requires special skills, knowledge and applications. The formation of the digital economy through the penetration of ICT and the digitalization of production processes contributes to the modernization of traditional industries, trade and procurement procedures, related financial and logistics operations, as well as markets with other sources of added value. Big Data as a new tool for analytics, forecasting and management decision making is becoming a leading asset and resource of the state, business and society as a whole. At the same time, the absence of physical boundaries in the digital space opens up access to big data for all participants in the global space. The national program “Digital Economy of the Russian Federation” is aimed at creating the necessary institutional and infrastructural conditions for the development of high-tech businesses, increasing the competitiveness of the national economy, the quality of life of citizens, and ensuring economic growth. One of the federal projects “Human Resources for the Digital Economy” is aimed at ensuring the training of highly qualified personnel for a modern society with high professional qualifications, skills and abilities to develop and introduce competitive products. Therefore, a Data Science specialist is a profession shaped by the needs of the Russian economy. In this regard, new challenges and methodologies for training IT specialists with the necessary knowledge and skills that meet the requirements of high-tech business are being formed.
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Bouhamed, Omar, Manar Amayri, and Nizar Bouguila. "Weakly Supervised Occupancy Prediction Using Training Data Collected via Interactive Learning." Sensors 22, no. 9 (April 21, 2022): 3186. http://dx.doi.org/10.3390/s22093186.
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Accurate and timely occupancy prediction has the potential to improve the efficiency of energy management systems in smart buildings. Occupancy prediction heavily depends on historical occupancy-related data collected from various sensor sources. Unfortunately, a major problem in that context is the difficulty to collect training data. This situation inspired us to rethink the occupancy prediction problem, proposing the use of an original principled approach based on occupancy estimation via interactive learning to collect the needed training data. Following that, the collected data, along with various features, were fed into several algorithms to predict future occupancy. This paper mainly proposes a weakly supervised occupancy prediction framework based on office sensor readings and occupancy estimations derived from an interactive learning approach. Two studies are the main emphasis of this paper. The first is the prediction of three occupancy states, referred to as discrete states: absence, presence of one occupant, and presence of more than one occupant. The purpose of the second study is to anticipate the future number of occupants, i.e., continuous states. Extensive simulations were run to demonstrate the merits of the proposed prediction framework’s performance and to validate the interactive learning-based approach’s ability to contribute to the achievement of effective occupancy prediction. The results reveal that LightGBM, a machine learning model, is a better fit for short-term predictions than known recursive neural networks when dealing with a limited dataset. For a 24 h window forecast, LightGBM improved accuracy from 38% to 50%, which is an excellent result for non-aggregated data (single office).
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Alzahrani, Hani, and Jeffrey Shragge. "Seismic velocity model building using neural networks: Training data design and learning generalization." GEOPHYSICS 87, no. 2 (January 24, 2022): R193—R211. http://dx.doi.org/10.1190/geo2020-0547.1.
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Data-driven artificial neural networks (ANNs) offer several advantages over conventional deterministic methods in a wide range of geophysical problems. For seismic velocity model building, judiciously trained ANNs offer the possibility of estimating high-resolution subsurface velocity models. However, a significant challenge of ANNs is training generalization, which is the ability of an ANN to apply the learning from the training process to test data not previously encountered. In the context of velocity model building, this means learning the relationship between velocity models and the corresponding seismic data from a set of training data, and then using acquired seismic data to accurately estimate unknown velocity models. We have asked the following question: What types of velocity model structures need to be included in the training process so that the trained ANN can invert seismic data from a different (hypothetical) geologic setting? To address this question, we create four sets of training models: geologically inspired and purely geometric, with and without background velocity gradients. We find that using geologically inspired training data produces models with well-delineated layer interfaces and fewer intralayer velocity variations. The absence of a certain geologic structure in training models, however, hinders the ANN’s ability to recover it in the testing data. We use purely geometric training models consisting of square blocks of varying size to demonstrate the ability of ANNs to recover reasonable approximations of flat, dipping, and curved interfaces. However, the predicted models suffer from intralayer velocity variations and nonphysical artifacts. Overall, the results successfully determine the use of ANNs in recovering accurate velocity model estimates and highlight the possibility of using such an approach for the generalized seismic velocity inversion problem.
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Aziz, Arafa Rahman, Budi Warsito, and Alan Prahutama. "PENGARUH TRANSFORMASI DATA PADA METODE LEARNING VECTOR QUANTIZATION TERHADAP AKURASI KLASIFIKASI DIAGNOSIS PENYAKIT JANTUNG." Jurnal Gaussian 10, no. 1 (February 28, 2021): 21–30. http://dx.doi.org/10.14710/j.gauss.v10i1.30933.
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Learning Vector Quantization (LVQ) is a type of Artificial Neural Network with a supervised learning process based on competitive learning. Despite the absence of assumptions in LVQ is an advantage, it can be a problem when the predictor variables have big different ranges.This problems can be overcome by equalizing the range of all variables by data transformation so that all variables have relatively same effect. Heart Disease UCI dataset which used in this study is transformed by several transformation methods, such as minmax, decimal scaling, z-score, mean-MAD, sigmoid, and softmax. The result show that the six transformed data can provide better LVQ classification accuracy than the raw data which has 75.99% for training performance accuracy. LVQ classification accuracy with data transformation of minmax, decimal scaling, z-score, mean-MAD, sigmoid, and softmax are 89.16%, 88.22%, 89.7%, 90.1%, 88.17% and 92.18%. Based on the One-way ANOVA test and DMRT post hoc test known that there are significant differences between the results of the classification with data transformations and raw data in 0,05 significant level of α. It is also known that the best data transformation methods are softmax for training and sigmoid for testing. Keywords: heart disease, neural network, learning vector quantization, classification, data transformation
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Annubaha, Chakim, Aris Puji Widodo, and Kusworo Adi. "Implementation of eigenface method and support vector machine for face recognition absence information system." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 3 (June 1, 2022): 1624. http://dx.doi.org/10.11591/ijeecs.v26.i3.pp1624-1633.
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The student <span>attendance system is what is needed in the process of recording attendance in learning and the development of student achievement. Currently several modern educational institutions have implemented a student attendance system using QR codes or fingerprints, but many still use the traditional system by calculating the number of students attending class. Based on these problems, the solution that can be given is to implement a student attendance system through face matching in the Android mobile application with Eigenface algorithm and support vector machine (SVM) algorithm. Eigenface using the principal component analysis (PCA) method can be used to reduce the dimensions of facial images so that they produce fewer variables and are easier to handle. The results obtained are then entered into a pattern classifier to determine the identity of the owner of the face. This study used 100 facial data as test data and training data. The system test results show that the use of Eigenface with SVM as a classifier can provide a fairly high level of accuracy. For facial images that were included in the training, 91% of the identification was correct.</span>
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Munawaroh, AW Rudiastuti, RS Dewi, YH Ramadhani, A. Rahadiati, D. Sutrisno, W. Ambarwulan, et al. "Benthic Habitat Mapping using Sentinel 2A: A preliminary Study in Image Classification Approach in An Absence of Training Data." IOP Conference Series: Earth and Environmental Science 750, no. 1 (May 1, 2021): 012029. http://dx.doi.org/10.1088/1755-1315/750/1/012029.
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Chaoub, Alaaeddine, Christophe Cerisara, Alexandre Voisin, and Benoît Iung. "Deep Learning Representation Pre-training for Industry 4.0." PHM Society European Conference 7, no. 1 (June 29, 2022): 571–73. http://dx.doi.org/10.36001/phme.2022.v7i1.2784.
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Deep learning (DL) approaches have multiple potential advantages that have been explored in various fields, but for prognostic and health management (PHM) applications, this is not the case due to the lack of data in particular applications and also due of the absence of multiple DL-oriented benchmarks as in other fields, which limits the research in this area even though these types of applications will have a strong impact on the industrial world. To introduce the benefits of DL in this area, we should be able to develop models even when we have small data sets, to verify whether or not this is possible, in this thesis we explore the research direction of few shot learning in the context of equipment PHM.
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Tsubota, Miyako, Brian Sumali, Mariko Kai, and Yasue Mitsukura. "Online improvisation training, hybrid improvisation training and on-site improvisation training; are they the same?" Science Progress 105, no. 1 (January 2022): 003685042210806. http://dx.doi.org/10.1177/00368504221080673.
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Background: Since the outbreak of COVID-19 in Wuhan in December 2019, lifestyle has been changing to an online-based learning and working environment rather than on-site, and improvisation training is no exception. However, no research compares the efficacy of online versus on-site training. Although we believed that the most effective way to learn improvisation is an on-site format, it is important to explore how format differences can affect learners. Method: We offer three types of training such as on-site training (n = 6) (Consisting of 1 female with age ≥40 and <50, and 5 males with ages ≥20 and <50), hybrid training (Instructor participates from online and learners participate on-site) (n = 120) (Consisting of 55 female with age ≥15 and <20, and 65 males with ages ≥15 and <50), and online training (n = 20) (Consisting of 4 female with age ≥20 and <30, and 16 males with ages ≥20 and <50) We collected pretest, test, and posttest data by using the Kansei Analyzer, a simplified electroencephalograph (EEG) and Profile of Mood States (POMS) questionnaire. Results: All formats of training displayed an increase in vigor and a decrease in depression, confusion, tension, anger, and fatigue. The online training displayed better results than the on-site training. Regardless of the format, all training displayed an increase in stress during the activities and a decrease in stress after the activity without changes in other indexes. Additionally, on-site training displayed an increase in sleepiness and stress during the activities. Some participants were tested twice but no significant differences were found between the initial results and the secondary results. Conclusion: In this study, we found evidence that online improvisation can lead to the prevention of depressive symptoms and can function as a method for the reduction of stress in conjunction with the increase of individual vigor. However, a future study is required due to the low number of participants and the absence of POMS data for the on-site training. Any future studies should account for these factors while examining other data such as blood pressure, blood sugar, and pulse.
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Money, Annemarie, Mark Hann, Susan Turner, Louise Hussey, and Raymond Agius. "The influence of prior training on GPs’ attitudes to sickness absence certification post-fit note." Primary Health Care Research & Development 16, no. 05 (January 6, 2015): 528–39. http://dx.doi.org/10.1017/s1463423614000577.
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AimTo investigate the attitudes to health and work of general practitioners (GPs) with training in occupational medicine (OM) compared with non-OM trained GPs, since the introduction of the fit note.BackgroundChanges to the UK sickness certification system since 2010 and the introduction of the fit note required GPs to change their focus to what patients can do, rather than what they cannot do in relation to work. In an effort to reduce the UK sickness absence burden, GPs completion of the fit note should help to keep people in work, or assist patients to return to work as quickly as possible after a period of absence.MethodsQuestionnaire data were collected via the 7th National General Practitioner Worklife Survey.FindingsResults indicate that responses from GPs who had undertaken training in OM, and GPs having received some form of work and health training in the 12-month period before the study were associated with significantly more positive attitudes to patients’ returning to work and to the fit note. This study reveals evidence of a difference between trained and non-trained GPs in their attitude to the fit note, and to work and health generally. Further work investigating the effect of specific training in OM on the management and recognition of ill-health by GPs is recommended.
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de Araujo, Juliana Negrini, Vasile Palade, Tabassom Sedighi, and Alireza Daneshkhah. "Improving the Pedestrian Detection Performance in the Absence of Rich Training Datasets: A UK Case Study." Advances in Artificial Intelligence and Machine Learning 02, no. 01 (2022): 315–37. http://dx.doi.org/10.54364/aaiml.2022.1121.
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The World Health Organization estimates that well in excess of one million of lives are lost each year due to road traffic accidents. Since the human factor is the preeminent cause behind the traffic accidents, the development of reliable Advanced Driver Assistance Systems (ADASs) and Autonomous Vehicles (AVs) is seen by many as a possible solution to improve road safety. ADASs rely on the car perception system input that consists of camera(s), LIDAR and/or radar to detect pedestrians and other objects on the road. Hardware improvements as well as advances done in employing Deep Learning techniques for object detection popularized the Convolutional Neural Networks in the area of autonomous driving research and applications. However, the availability of quality and large datasets continues to be a most important contributor to the Deep Learning based model’s performance. With this in mind, this work analyses how a YOLO-based object detection architecture responded to limited data available for training and containing low-quality images. The work focused on pedestrian detection, since vulnerable road user’s safety is a major concern within AV and ADAS research communities. The proposed model was trained and tested on data gathered from Coventry, United Kingdom, city streets. The results show that the original YOLOv3 implementation reaches a 42.18% average precision (AP) and the main challenge was in detecting small objects. Network modifications were made and our final model, based on the original YOLOv3 implementation, achieved 51.6% AP. It is also demonstrated that the employed data augmentation approach is responsible for doubling the average precision of the final model.
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Chibuwe, Albert, and Abioudun Salawu. "Training for English language or indigenous language media journalism: A decolonial critique of Zimbabwean journalism and media training institutions’ training practices." Journal of African Media Studies 12, no. 2 (June 1, 2020): 137–56. http://dx.doi.org/10.1386/jams_00016_1.
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There is growing academic scholarship on indigenous language media in Africa. The scholarship has mostly tended to focus on the content and political economy of indigenous language newspapers. The scholarship also suggests that much needs to be done in inculcating indigenous languages and indigenous language journalism in journalism education. Grounded in decoloniality, this article explores journalism training practices in selected institutions of higher learning in Zimbabwe. The intention is to unravel the absence or existence of training for indigenous journalism and perceptions of lecturers and attitudes of students towards indigenous language media and journalism. The article also seeks to establish whether there are any attempts to de-westernize journalism, media and communication studies. Methodologically, in-depth interviews were used to gather data from lecturers and students of journalism and media studies at colleges and universities in Zimbabwe. Findings show that the colleges surveyed do not offer any indigenous media journalism-specific modules or subjects. The lecturers, who include programme designers in some cases, have a low regard for indigenous language media. This, the article concludes, will have a knock-on effect on journalism students’ and journalists’ misgivings towards a career in indigenous language media.
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Scichilone, Nicola, Giuseppe Morici, Daniele Zangla, Laura Chimenti, Eva Davì, Simona Reitano, Alessandra Paternò, et al. "Effects of exercise training on airway responsiveness and airway cells in healthy subjects." Journal of Applied Physiology 109, no. 2 (August 2010): 288–94. http://dx.doi.org/10.1152/japplphysiol.01200.2009.
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Airway responsiveness to methacholine (Mch) in the absence of deep inspirations (DIs) is lower in athletes compared with sedentary individuals. In this prospective study, we tested the hypothesis that a training exercise program reduces the bronchoconstrictive effect of Mch. Ten healthy sedentary subjects (M/F: 3/7; mean ± SD age: 22 ± 3 yr) entered a 10-wk indoor rowing exercise program on rowing ergometer and underwent Mch bronchoprovocation in the absence of DIs at baseline, at weeks 5 and 10, as well as 4–6 wk after the training program was completed. Exercise-induced changes on airway cells and markers of airway inflammation were also assessed by sputum induction and venous blood samples. Mean power output during the 1,000 m test was 169 ± 49 W/stroke at baseline, 174 ± 49 W/stroke at 5 wk, and 200 ± 60 W/stroke at 10 wk of training ( P < 0.05). The median Mch dose used at baseline was 50 mg/ml (range 25–75 mg/ml) and remained constant per study design. At the pretraining evaluation, the percent reduction in the primary outcome, the inspiratory vital capacity (IVC) after inhalation of Mch in the absence of DIs was 31 ± 13%; at week 5, the Mch-induced reduction in IVC was 22 ± 19%, P = 0.01, and it further decreased to 15 ± 11% at week 10 ( P = 0.0008). The percent fall in IVC 4–6 wk after the end of training was 15 ± 11% ( P = 0.87 vs. end of training). Changes in airway cells were not associated with changes in airway responsiveness. Our data show that a course of exercise training can attenuate airway responsiveness against Mch inhaled in the absence of DIs in healthy subjects and suggest that a sedentary lifestyle may favor development of airways hyperresponsiveness.
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Humphrey, A., W. Kuberski, J. Bialek, N. Perrakis, W. Cools, N. Nuyttens, H. Elakhrass, and P. A. C. Cunha. "Machine-learning classification of astronomical sources: estimating F1-score in the absence of ground truth." Monthly Notices of the Royal Astronomical Society: Letters 517, no. 1 (October 16, 2022): L116—L120. http://dx.doi.org/10.1093/mnrasl/slac120.
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ABSTRACT Machine-learning based classifiers have become indispensable in the field of astrophysics, allowing separation of astronomical sources into various classes, with computational efficiency suitable for application to the enormous data volumes that wide-area surveys now typically produce. In the standard supervised classification paradigm, a model is typically trained and validated using data from relatively small areas of sky, before being used to classify sources in other areas of the sky. However, population shifts between the training examples and the sources to be classified can lead to ‘silent’ degradation in model performance, which can be challenging to identify when the ground-truth is not available. In this letter, we present a novel methodology using the nannyml Confidence-Based Performance Estimation (CBPE) method to predict classifier F1-score in the presence of population shifts, but without ground-truth labels. We apply CBPE to the selection of quasars with decision-tree ensemble models, using broad-band photometry, and show that the F1-scores are predicted remarkably well (${\rm MAPE} \sim 10{{\ \rm per\ cent}}$; R2 = 0.74–0.92). We discuss potential use-cases in the domain of astronomy, including machine-learning model and/or hyperparameter selection, and evaluation of the suitability of training data sets for a particular classification problem.
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Getman, Aleksandr Igorevich, Maxim Nikolaevich Goryunov, Andrey Georgievich Matskevich, and Dmitry Aleksandrovich Rybolovlev. "Methodology for Collecting a Training Dataset for an Intrusion Detection Model." Proceedings of the Institute for System Programming of the RAS 33, no. 5 (2021): 83–104. http://dx.doi.org/10.15514/ispras-2021-33(5)-5.
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The paper discusses the issues of training models for detecting computer attacks based on the use of machine learning methods. The results of the analysis of publicly available training datasets and tools for analyzing network traffic and identifying features of network sessions are presented sequentially. The drawbacks of existing tools and possible errors in the datasets formed with their help are noted. It is concluded that it is necessary to collect own training data in the absence of guarantees of the public datasets reliability and the limited use of pre-trained models in networks with characteristics that differ from the characteristics of the network in which the training traffic was collected. A practical approach to generating training data for computer attack detection models is proposed. The proposed solutions have been tested to evaluate the quality of model training on the collected data and the quality of attack detection in conditions of real network infrastructure.
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Wang, Shiqiang, Caiyun Gao, Chang Luo, Huiyong Zeng, Guimei Zheng, Qin Zhang, Juan Bai, and Binfeng Zong. "Deep Feature Autoextraction Method for Intrapulse Data of Radar Emitter Signal." Mobile Information Systems 2021 (August 26, 2021): 1–6. http://dx.doi.org/10.1155/2021/6870938.
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Concerned with the problems that the extracted features are the absence of objectivity for radar emitter signal intrapulse data because of relying on priori knowledge, a novel method is proposed. First, this method gets the sparse autoencoder by adding certain restrain to the autoencoder. Second, by optimizing the sparse autoencoder and confirming the training scheme, intrapulse deep features are autoextracted with encoder layer parameters. The method extracts the eigenvectors of six typical radar emitter signals and uses them as inputs to a support vector machine classifier. The experimental results show that the method has higher accuracy in the case of large signal-to-noise ratio. The simulation verifies that the extracted features are feasible.
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Danilovskii, K. N., and Loginov G. N. "Lateral scanning logging while drilling data processing using convolutional neural networks." Russian Journal of Geophysical Technologies, no. 2 (January 13, 2022): 24–35. http://dx.doi.org/10.18303/2619-1563-2021-2-24.
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This article discusses a new approach to processing lateral scanning logging while drilling data based on a combination of three-dimensional numerical modeling and convolutional neural networks. We prepared dataset for training neural networks. Dataset contains realistic synthetic resistivity images and geoelectric layer boundary layouts, obtained based on true values of their spatial orientation parameters. Using convolutional neural networks two algorithms have been developed and programmatically implemented: suppression of random noise and detection of layer boundaries on the resistivity images. The developed algorithms allow fast and accurate processing of large amounts of data, while, due to the absence of full-connection layers in the neural networks’ architectures, it is possible to process resistivity images of arbitrary length.
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Kalashnikova, D. A., and V. V. Buryachenko. "IDENTIFICATION OF NATURAL OBJECTS USING DEEP LEARNING AND ADDITIONAL DATA PREPROCESSING." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-2/W3-2023 (May 12, 2023): 95–101. http://dx.doi.org/10.5194/isprs-archives-xlviii-2-w3-2023-95-2023.
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Abstract. The classification of natural objects in the wild is a popular task in the field of tourism and remote sensing. The key problem is the requirement for system performance in the absence of Internet access and a small amount of available resources, such as a mobile phone. In this regard, to solve the classification problem, it is required to use fairly simple neural networks and rely on a small amount of training data. The paper presents an image preprocessing method for object recognition in the the “Stolby National Park” in Krasnoyarsk city using a neural network. The approach involves applying a set of methods to expand the original training set. To analyze the effectiveness, several different neural networks based on MobileNET V2 are used, which makes it possible to compare test results on the original and extended data sets. We also evaluate the quality of objects identification on open datasets, such as Animals-10 and Landscape Pictures. The results of the experiments show the efficiency of data preprocessing, as well as the high performance of the modified neural network structure for the task of classifying natural objects in the environment.
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Lim, Weng Zhen, Norasmiza Mohd, Anwar P. P. Abdul Majeed, Mohd Azraai Mohd Razman, and Yin Goon Koon. "Screw Absence Classification on Aluminum Plate via Features Based Transfer Learning Models." MEKATRONIKA 5, no. 1 (April 21, 2023): 62–66. http://dx.doi.org/10.15282/mekatronika.v5i1.9408.
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Screw is one of the important elements in every industry. Present of screw play an important role in which it holds the product in its own position and prevent loosen or collision with the case which will cause the small components or compartment fall off from its original position and lead to product failure. With the rise of revolution 4.0 in the industry, it helps to reduce the labor cost and human error. The main purpose of this study is to create a robust classification model used for machine vision detection – absence and present of screw, which could be adapted into respective robotics application system. 6 degree of freedom UR robot, Universal Robot is used to collect the custom dataset in TT Vision Technologies Sdn Bhd. The collected dataset is then classified into two categories, named as absent and present. Pretrained dataset, ImageNet is used to ease the training process in this research. Transfer learning model is used to extract the features which used to feed into different machine learning models. Each machine learning models undergoes hyperparameters tunning to achieve best classification accuracy. Samling ratio of 60:20:20 is used to separate the data in training, validation and testing respectively before fed into different ml models
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Progonov, Dmytro. "Detection of Stego Images with Adaptively Embedded Data by Component Analysis Methods." Advances in Cyber-Physical Systems 6, no. 2 (December 17, 2021): 146–54. http://dx.doi.org/10.23939/acps2021.02.146.
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Ensuring the effective protection of personal and corporate sensitive data is topical task today. The special interest is taken at sensitive data leakage prevention during files transmission in communication systems. In most cases, these leakages are conducted by usage of advance adaptive steganographic methods. These methods are aimed at minimizing distortions of cover files, such as digital images, during data hiding that negatively impact on detection accuracy of formed stego images. For overcoming this shortcoming, it was proposed to pre-process (calibrate) analyzed images for increasing stego- to-cover ratio. The modern paradigm of image calibration is based on usage of enormous set of high-pass filters. However, selection of filter(s) that maximizes the probability of stego images detection is non-trivial task, especially in case of limited a prior knowledge about embedding methods. For solving this task, we proposed to use component analysis methods for image calibration, namely principal components analysis. Results of comparative analysis of novel maxSRMd2 cover rich model and proposed solution showed that principal component analysis allows increasing detection accuracy up to 1.5% even in the most difficult cases (low cover image payload and absence of cover- stego images pairs in training set).
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Tao, Huawei, Shuai Shan, Ziyi Hu, Chunhua Zhu, and Hongyi Ge. "Strong Generalized Speech Emotion Recognition Based on Effective Data Augmentation." Entropy 25, no. 1 (December 30, 2022): 68. http://dx.doi.org/10.3390/e25010068.
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The absence of labeled samples limits the development of speech emotion recognition (SER). Data augmentation is an effective way to address sample sparsity. However, there is a lack of research on data augmentation algorithms in the field of SER. In this paper, the effectiveness of classical acoustic data augmentation methods in SER is analyzed, based on which a strong generalized speech emotion recognition model based on effective data augmentation is proposed. The model uses a multi-channel feature extractor consisting of multiple sub-networks to extract emotional representations. Different kinds of augmented data that can effectively improve SER performance are fed into the sub-networks, and the emotional representations are obtained by the weighted fusion of the output feature maps of each sub-network. And in order to make the model robust to unseen speakers, we employ adversarial training to generalize emotion representations. A discriminator is used to estimate the Wasserstein distance between the feature distributions of different speakers and to force the feature extractor to learn the speaker-invariant emotional representations by adversarial training. The simulation experimental results on the IEMOCAP corpus show that the performance of the proposed method is 2–9% ahead of the related SER algorithm, which proves the effectiveness of the proposed method.
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Nasution, Sri Lestari. "EFEKTIFITAS PELATIHAN KECERDASAN EMOSIONAL TERHADAP PENINGKATAN KECERDASAN EMOSIONAL PERAWAT DI RSU ROYAL PRIMA MEDAN." Jurnal Maternitas Kebidanan 5, no. 2 (October 2, 2020): 1–10. http://dx.doi.org/10.34012/jumkep.v5i2.1165.
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Emotional intelligence also determines a person's success. Emotional intelligence is an ability such as self-motivating ability, enduring to frustration, regulating mood so that stress loads don't paralyze thinking, and empathize. The purpose of this research is to analyze the effectiveness of emotional intelligence training on the enhancement of nurse emotional intelligence at Royal Prima Medan Hospital. This research is a quasi-experimental research with pretest-posttest design. The population in the study was the nurse of Royal Prima Medan Hospital and sampled as many as 38 respondents with provisions 19 for the experimental group and 19 for the control group. The data analysis methods used in this study are descriptive statistics and inferential statistics. The results showed: 1) Absence of increased or absence of emotional intelligence differences without emotional intelligence training (P = 0,310), 2) The emotional intelligence of nurses at Royal Prima Medan Hospital after given emotional intelligence training showed There is an increase or difference (P = 0,000).
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Saleem Khasawneh, Mohamad Ahmad. "Training program on developing reading skills in the english language among students with learning difficulties." Revista EDUCARE - UPEL-IPB - Segunda Nueva Etapa 2.0 25, no. 1 (April 29, 2021): 84–101. http://dx.doi.org/10.46498/reduipb.v25i1.1445.
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This study aimed at developing an electronic training program for the development of reading skills among students with reading difficulties in English language at the primary stage in Aseer region. The study used the reading difficulties assessment battery, the achievement test, and the training program in developing reading skills. The study sample included (29) students from the third and fourth grades, who are studying in resources rooms of students with learning difficulties. The ANCOVA analysis was used to analyze the data. The results revealed that the experimental outperformed the control groups in developing reading skills. The results also indicated the absence of significant differences between the experimental and control groups according to gender. Finally, the results also revealed the absence of significant differences between the two groups according to the grade (third/fourth).
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Browning, Dawn M., and Michael C. Duniway. "Digital Soil Mapping in the Absence of Field Training Data: A Case Study Using Terrain Attributes and Semiautomated Soil Signature Derivation to Distinguish Ecological Potential." Applied and Environmental Soil Science 2011 (2011): 1–12. http://dx.doi.org/10.1155/2011/421904.
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Spatially explicit data for soil properties governing plant water availability are needed to understand mechanisms influencing plant species distributions and predict plant responses to changing climate. This is especially important for arid and semiarid regions. Spatial data representing surrogates for soil forming factors are becoming widely available (e.g., spectral and terrain layers). However, field-based training data remain a limiting factor, particularly across remote and extensive drylands. We present a method to map soils with Landsat ETM+ imagery and high-resolution (5 m) terrain (IFSAR) data based on statistical properties of the input data layers that do not rely on field training data. We then characterize soil classes mapped using this semiautomated technique. The method distinguished spectrally distinct soil classes that differed in subsurface rather than surface properties. Field evaluations of the soil classification in conjunction with analysis of long-term vegetation dynamics indicate the approach was successful in mapping areas with similar soil properties and ecological potential.
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Sánchez Torrejón, Begoña, Alejandro Granero Andújar, and Jesús Esteban Mora. "Absence of Transgender Identities in Primary Education Teachers’ Training and Its Implications in the Classroom: A Phenomenological Study." Education Sciences 13, no. 8 (August 7, 2023): 809. http://dx.doi.org/10.3390/educsci13080809.
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The main objective of this article is to acquire in-depth knowledge of the training primary education teachers receive regarding transgender identities, as well as the resulting consequences in school realities. A phenomenological qualitative research approach was used to accomplish this purpose. The data were collected using a semi-structured interview technique. The participants were 38 primary education teachers from different public schools in the province of Cádiz, Spain. They acted as key informants, allowing us to gain knowledge, understanding, and meaning regarding our object of study. Among the results obtained, the absence of transgender identities in pre-service and in-service teacher training is observed. As a result, a severe lack of knowledge and confusion about the subject, as well as discriminatory values, are perceived in teachers. The need for transgender identities to be present in initial and continuous teacher training is stressed in order to see to the social and educational needs of transgender students in primary education and avoid reproducing the invisibility of transgender identities and the transmission of inegalitarian values.
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Zhu, Ming, Karthik Suresh, and Chandan K. Reddy. "Multilingual Code Snippets Training for Program Translation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (June 28, 2022): 11783–90. http://dx.doi.org/10.1609/aaai.v36i10.21434.
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Program translation aims to translate source code from one programming language to another. It is particularly useful in applications such as multiple-platform adaptation and legacy code migration. Traditional rule-based program translation methods usually rely on meticulous manual rule-crafting, which is costly both in terms of time and effort. Recently, neural network based methods have been developed to address this problem. However, the absence of high-quality parallel code data is one of the main bottlenecks which impedes the development of program translation models. In this paper, we introduce CoST, a new multilingual Code Snippet Translation dataset that contains parallel data from 7 commonly used programming languages. The dataset is parallel at the level of code snippets, which provides much more fine-grained alignments between different languages than the existing translation datasets. We also propose a new program translation model that leverages multilingual snippet denoising auto-encoding and Multilingual Snippet Translation (MuST) pre-training. Extensive experiments show that the multilingual snippet training is effective in improving program translation performance, especially for low-resource languages. Moreover, our training method shows good generalizability and consistently improves the translation performance of a number of baseline models. The proposed model outperforms the baselines on both snippet-level and program-level translation, and achieves state-of-the-art performance on CodeXGLUE translation task. The code, data, and appendix for this paper can be found at https://github.com/reddy-lab-code-research/MuST-CoST.
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Muhammad, Waqar, Maria Mushtaq, Khurum Nazir Junejo, and Muhammad Yaseen Khan. "SENTIMENT ANALYSIS OF PRODUCT REVIEWS IN THE ABSENCE OF LABELLED DATA USING SUPERVISED LEARNING APPROACHES." Malaysian Journal of Computer Science 33, no. 2 (April 24, 2020): 118–32. http://dx.doi.org/10.22452/mjcs.vol33no2.3.
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With the growing pace of internet usage, there is a vast variety of diverse individual opinions and thoughts avail-able online. Consumer reviews can act as a feedback and as well as a pool of ideas for which they can be of immense importance to any business. With the growth and popularity of opinion-rich resources such as online review sites and personal blogs, people now can and do, actively use information technology to seek out and un-derstand the opinions of others to decide whether to buy a product or not. Social media websites such as Facebook, Twitter, and e-commerce websites such as eBay, Amazon, etc. are being widely used to communicate viewpoints effectively. Assigning a positive or a negative sentiment to these reviews can help companies understand their users and also help users to make better decisions. Sentiment analysis being a challenging task can be tackled using supervised machine learning techniques or through unsupervised lexicon based approaches if labelled data is unavailable. In this study, we show that in absence of labelled product reviews of a particular website, labelled product reviews from a different website can be effectively used to train the supervised techniques to achieve a comparable performance to the unsupervised lexicon based approaches. This approach also benefits by covering all of the product reviews which the lexicon based approaches fail to do so. We deduce this by comparing five su-pervised approached and three lexicon based approaches on iPhone 5s reviews gathered from Amazon, Facebook, and Reevoo blog. Furthermore, we also found that unigram features combined with bigram features give the best results, and the effect of varying the training data size on the performance of ML classifiers in some cases was significant whereas in other cases it did not have any effect. Our results also suggest that reviews from Amazon are easiest to classify, followed by reviews from Reevo, and Facebook reviews are the hardest to classify.
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Banjoko, Alabi Waheed, and Kawthar Opeyemi Abdulazeez. "Efficient Data-Mining Algorithm for Predicting Heart Disease Based on an Angiographic Test." Malaysian Journal of Medical Sciences 28, no. 5 (October 26, 2021): 118–29. http://dx.doi.org/10.21315/mjms2021.28.5.12.
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Background: The computerised classification and prediction of heart disease can be useful for medical personnel for the purpose of fast diagnosis with accurate results. This study presents an efficient classification method for predicting heart disease using a data-mining algorithm. Methods: The algorithm utilises the weighted support vector machine method for efficient classification of heart disease based on a binary response that indicates the presence or absence of heart disease as the result of an angiographic test. The optimal values of the support vector machine and the Radial Basis Function kernel parameters for the heart disease classification were determined via a 10-fold cross-validation method. The heart disease data was partitioned into training and testing sets using different percentages of the splitting ratio. Each of the training sets was used in training the classification method while the predictive power of the method was evaluated on each of the test sets using the Monte-Carlo cross-validation resampling technique. The effect of different percentages of the splitting ratio on the method was also observed. Results: The misclassification error rate was used to compare the performance of the method with three selected machine learning methods and was observed that the proposed method performs best over others in all cases considered. Conclusion: Finally, the results illustrate that the classification algorithm presented can effectively predict the heart disease status of an individual based on the results of an angiographic test.
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Cesar Santos da Silva, Julio, and Laélia Portela Moreira. "PROFESSORES INICIANTES EM CURSOS DE COMUNICAÇÃO SOCIAL DO RIO DE JANEIRO." COLLOQUIUM HUMANARUM 15, no. 3 (September 1, 2018): 79–93. http://dx.doi.org/10.5747/ch.2018.v15.n3.h375.
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The absence of policies aimed at the training of teachers working in Higher Education Institutions (HEIs) in Brazil represents an important point of attention, and several authors have more recently called attention to the importance of teacher training who work at this level of education, both public and private HEIs. The article presents the results of a research that sought to analyze the main challenges at the beginning of the teaching activity faced by teachers of Social Communication courses hired because of their professional expertise, without having, however, pedagogical training. This is a qualitative research, whose analysis was based on data obtained through semi-structured interviews with professors from two private HEIs in Rio de Janeiro. The results indicated several types of difficulties faced by teachers in the teaching practice, which, in the absence of institutional policies and initiatives geared to pedagogical training, relied only on the informal help of their peers, developed their own strategies for the exercise of their function, and in many cases, their professional expertise, engendering different strategies for coping with the difficulties presented in the daily routine of the classroom.
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Hu, Xiaowei, Xi Yin, Kevin Lin, Lei Zhang, Jianfeng Gao, Lijuan Wang, and Zicheng Liu. "VIVO: Visual Vocabulary Pre-Training for Novel Object Captioning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (May 18, 2021): 1575–83. http://dx.doi.org/10.1609/aaai.v35i2.16249.
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It is highly desirable yet challenging to generate image captions that can describe novel objects which are unseen in caption-labeled training data, a capability that is evaluated in the novel object captioning challenge (nocaps). In this challenge, no additional image-caption training data, other than COCO Captions, is allowed for model training. Thus, conventional Vision-Language Pre-training (VLP) methods cannot be applied. This paper presents VIsual VOcabulary pre-training (VIVO) that performs pre-training in the absence of caption annotations. By breaking the dependency of paired image-caption training data in VLP, VIVO can leverage large amounts of paired image-tag data to learn a visual vocabulary. This is done by pre-training a multi-layer Transformer model that learns to align image-level tags with their corresponding image region features. To address the unordered nature of image tags, VIVO uses a Hungarian matching loss with masked tag prediction to conduct pre-training. We validate the effectiveness of VIVO by fine-tuning the pre-trained model for image captioning. In addition, we perform an analysis of the visual-text alignment inferred by our model. The results show that our model can not only generate fluent image captions that describe novel objects, but also identify the locations of these objects. Our single model has achieved new state-of-the-art results on nocaps and surpassed the human CIDEr score.
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Vidhya, Gowri, D. Nirmala, and T. Manju. "Quality challenges in Deep Learning Data Collection in perspective of Artificial Intelligence." Journal of Information Technology and Computing 4, no. 1 (June 27, 2023): 46–58. http://dx.doi.org/10.48185/jitc.v4i1.725.
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With reinforcement learning powered by big data and computer infrastructure, data-centric AI is driving a fundamental shift in the way software is developed. To treat data as a first-class citizen on par with code, software engineering must be rethought in this situation. One surprise finding is how much time is spent on data preparation throughout the machine learning process. Even the most powerful machine learning algorithms will struggle to perform adequately in the absence of high-quality data. Advanced technologies that are data-centric are being used more frequently as a result. Unfortunately, a lot of real-world datasets are small, unclean, biased, and occasionally even tainted. In this study, we focus on the scientific community for data collecting and data quality for deep learning applications. Data collection is essential since modern algorithms for deep learning rely mostly on large-scale data collecting than classification techniques. To enhance data quality, we investigate data validation, cleaning, and integration techniques. Even if the data cannot be completely cleaned, robust model training strategies enable us to work with imperfect data during training the model. Furthermore, despite the fact that that these issues have gotten less attention in conventional data management studies, bias and fairness are significant themes in modern application of machine learning. In order to prevent injustice, we investigate controls for fairness and strategies for doing so before, during, and after model training. We believe the information management community is in a good position to address these problems.
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Shcheglov, B. O. "Application of the Electronic Atlas of Personal Anatomy SkiaAtlas in Teaching Medical Students." Virtual Technologies in Medicine 1, no. 3 (September 17, 2021): 181–82. http://dx.doi.org/10.46594/2687-0037_2021_3_1380.
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This article discusses the logic and aspects of creating a web application for the accumulation and processing of incoming data of radiation diagnostics and their transformation into information that can be used by students of medical educational institutions in training in the absence or lack of anatomical materials. In prospective analysis, it is possible to use this software product not only as a means for simulation training, but also in medical applied practice in preoperative surgical planning, modeling various mechanical characteristics of implants.
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Iatan, Iuliana, Mihăiţă Drăgan, Silvia Dedu, and Vasile Preda. "Using Probabilistic Models for Data Compression." Mathematics 10, no. 20 (October 17, 2022): 3847. http://dx.doi.org/10.3390/math10203847.
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Our research objective is to improve the Huffman coding efficiency by adjusting the data using a Poisson distribution, which avoids the undefined entropies too. The scientific value added by our paper consists in the fact of minimizing the average length of the code words, which is greater in the absence of applying the Poisson distribution. Huffman Coding is an error-free compression method, designed to remove the coding redundancy, by yielding the smallest number of code symbols per source symbol, which in practice can be represented by the intensity of an image or the output of a mapping operation. We shall use the images from the PASCAL Visual Object Classes (VOC) to evaluate our methods. In our work we use 10,102 randomly chosen images, such that half of them are for training, while the other half is for testing. The VOC data sets display significant variability regarding object size, orientation, pose, illumination, position and occlusion. The data sets are composed by 20 object classes, respectively: aeroplane, bicycle, bird, boat, bottle, bus, car, motorbike, train, sofa, table, chair, tv/monitor, potted plant, person, cat, cow, dog, horse and sheep. The descriptors of different objects can be compared to give a measurement of their similarity. Image similarity is an important concept in many applications. This paper is focused on the measure of similarity in the computer science domain, more specifically information retrieval and data mining. Our approach uses 64 descriptors for each image belonging to the training and test set, therefore the number of symbols is 64. The data of our information source are different from a finite memory source (Markov), where its output depends on a finite number of previous outputs. When dealing with large volumes of data, an effective approach to increase the Information Retrieval speed is based on using Neural Networks as an artificial intelligent technique.
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O'Brien, JK, S. Heffernan, PC Thomson, and PD McGreevy. "Effect of positive reinforcement training on physiological and behavioural stress responses in the hamadryas baboon (Papio hamadryas)." Animal Welfare 17, no. 2 (May 2008): 125–38. http://dx.doi.org/10.1017/s0962728600027639.
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AbstractBehavioural and salivary Cortisol responses were measured in hamadryas baboons (Papio hamadryas) (n = 5) undergoing positive reinforcement training (PRT). Compliance was assessed by collecting behavioural data on desirable and undesirable responses during each training session (33-46 training sessions per male). Saliva was collected before implementation of the training programme (3-4 baseline samples per male) and immediately before and ten minutes after a training session (24-53 saliva samples per male). During training, the incidence of leaving the training area, vocalising and threat displays changed across time. Performance of the desired behaviour (holding a target for increasing increments of time) improved for all males during the study period. Concentrations of salivary cortisol were similar for pre-training and post-training collection times, but both were significantly lower than baseline concentrations. The overall decline in undesirable behaviours and the absence of constantly elevated salivary cortisol suggest that PRT had no adverse effects on animal welfare.
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Jegorova, Marija, Antti Ilari Karjalainen, Jose Vazquez, and Timothy Hospedales. "R2D2-GAN: Unlimited Resolution Image Generation for Acoustic Data." Marine Technology Society Journal 55, no. 4 (July 1, 2021): 99–107. http://dx.doi.org/10.4031/mtsj.55.4.11.
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Abstract In this paper, we present a novel simulation technique for generating high-quality images of any predefined resolution. This method can be used to synthesize sonar scans of size equivalent to those collected during a full-length mission, with across-track resolutions of any chosen magnitude. In essence, our model extends generative adversarial network (GAN)-based architecture into a conditional recursive setting that facilitates the continuity of the generated images. The data produced are continuous and realistically looking and can also be generated at least two times faster than the real speed of acquisition for the sonars with higher resolutions, such as EdgeTech. The seabed topography can be fully controlled by the user. The visual assessment tests demonstrate that humans cannot distinguish the simulated images from real ones. Moreover, experimental results suggest that, in the absence of real data, the autonomous recognition systems can benefit greatly from training with the synthetic data, produced by the double-recursive double-discriminator GANs (R2D2-GANs).
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Lestari, Indah Rahayu, Haryatih Haryatih, and Hestyaningsih Hestyaningsih. "MANAGEMENT TRAINING INVENTORIES IN SMEE FOOD SECTOR (STUDY ON UMKM FOOD SECTOR IN TANGERANG CITY)." ICCD 1, no. 1 (December 19, 2018): 461–66. http://dx.doi.org/10.33068/iccd.vol1.iss1.68.
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Based on survey and problem analysis faced by SMEs Food Field in Tangerang City in cooperation with Tangerang City Health Office, is problem of supply. MSME owners often experience excess stock in the warehouse for a particular product, or otherwise the lack of availability of goods that cause disruption of the production process. The causes of the deficiency or excess inventory include: 1) The amount of raw materials ordered only based on previous historical data and owners of MSMEs do not forecast sales and production, so it is not clear about the production plan and raw material needs plan that can be used as reference bookings materials, 2) the absence of safety stocks for each material used, causing the owners of MSMEs to lack or surplus inventory; 3) the absence of a clear inventory management system, in the absence of evidence of record keeping, valuation or inventory monitoring. So based on the analysis, the Dedication Team provides solutions in the form of knowledge management improvement for MSME owners in Tangerang City. Where in Community Service Training is held in the form of how to perform calculations, ranging from purchases, production to sales. Also provided training on product storage and efficient cost calculation in carrying out production. Community Service Activities on the training of inventory management and continued by training in making inventory cards using Microsoft Excel. Furthermore, will be provided assistance to SMEs for other fields such as making simple financial reports for MSMEs, computerized financial reports, as well as financial management and strategic management.
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Bhandari, Sundeep. "Lessons Learnt from a Pneumonia Outbreak in a Naval Training Establishment." Epidemiology International 07, no. 02 (September 7, 2022): 1–5. http://dx.doi.org/10.24321/2455.7048.202204.
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Introduction: In August 2014, an unusually large number of cases of pneumonia was reported from amongst Naval Recruits in a Naval Training Establishment by the co-located Indian Naval Hospital Ship. Material and Methods: The study was descriptive observational (retrospective) in nature, which was carried out at Indian Naval Training Establishment and Indian Naval Hospital Ship. The following information was collected: (a) Batch-wise comparison of data of recruits at Indian Naval Training establishment (where two major batches undergo training every year) was done for the occurrence year (2014) and the preceding 03 years i.e 2011, 2012 and 2013. Further, data on cases of pneumonia was collected from Indian Naval Hospital Ship as recorded by the hospital for last 03 years (2011, 2012 & 2013) and the year 2014, using an epidemiological format. Results: (a) Overcrowding. (b) Confirmation of existence of Outbreak. (c) Distribution of cases as per symptomatology and X-ray findings. (d) Manmade overcrowding, physical and psychological stress of military training exposes non-immune persons to several pathogens. The study confirms overcrowding with per capita standard for floor space and air space being 5 sqm and 18 m 3. The recommended distance between two adjacent beds is 1.8m 1 Infact, they were even below the standard of 4 sqm recommended by WHO 4. A positive Correlation is known to exist between overcrowding and incidence of pneumonia 5. Conclusion: Batch-wise comparison of cases and incidence of LRTI/ Pneumonia confirmed the outbreak. Further, there were no cases of Pneumonia amongst training staff (officers & sailors) and other civilian in adjoining areas. Consequent to increase in induction pattern at Naval Training Establishment, coupled with absence of accompanying increase. Consequently, analysis of living conditions established that overcrowding existed in dormitories, dining areas and all facilities related to training.
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Ripon, Shamim H., Sarwar Kamal, Saddam Hossain, and Nilanjan Dey. "Theoretical Analysis of Different Classifiers under Reduction Rough Data Set." International Journal of Rough Sets and Data Analysis 3, no. 3 (July 2016): 1–20. http://dx.doi.org/10.4018/ijrsda.2016070101.
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Rough set plays vital role to overcome the complexities, vagueness, uncertainty, imprecision, and incomplete data during features analysis. Classification is tested on certain dataset that maintain an exact class and review process where key attributes decide the class positions. To assess efficient and automated learning, algorithms are used over training datasets. Generally, classification is supervised learning whereas clustering is unsupervised. Classifications under mathematical models deal with mining rules and machine learning. The Objective of this work is to establish a strong theoretical and manual analysis among three popular classifier namely K-nearest neighbor (K-NN), Naive Bayes and Apriori algorithm. Hybridization with rough sets among these three classifiers enables enable to address larger datasets. Performances of three classifiers have tested in absence and presence of rough sets. This work is in the phase of implementation for DNA (Deoxyribonucleic Acid) datasets and it will design automated system to assess classifier under machine learning environment.
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Veronice, Veronice, Farid Azel, and Benny Warman. "Analysis of Agricultural Explanation Centers in the Implementation of the Agricultural Development Strategy Command Program (Konstratani)." International Journal of Multicultural and Multireligious Understanding 9, no. 12 (December 10, 2022): 79. http://dx.doi.org/10.18415/ijmmu.v9i12.4191.
Full textAbstract:
Konstratani (Farmers Strategic Command) is a sub-district agricultural development reform movement, through optimizing the tasks, functions and roles of the Agricultural Extension Center in realizing the success of agricultural development. This research is to analyze the constraint program. The research was conducted at the Agricultural Extension Center (BPP) in Fifty Cities District, West Sumatra. The research method that will be used is a survey. The results of the study prove that socialization of the Konstratani Program to extension workers, mayors and related agencies was 73%,b. Information Technology training Activities and agricultural data/information management and program Report application training 66%, implementation or monitoring of training and mentoring activities by 59%, the constraint program promoted by the government is still constrained, such as the absence of clear directions regarding the technical implementation of activities at the sub-district level. Konstratani still focuses on data collection, coordination or training through the Zoom meeting application because it has been facilitated by the internet and counseling media.
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Miskir, Yihun, and Solomon Emishaw. "Determinants of Nursing Process Implementation in North East Ethiopia: Cross-Sectional Study." Nursing Research and Practice 2018 (September 6, 2018): 1–9. http://dx.doi.org/10.1155/2018/7940854.
Full textAbstract:
Nursing process is a framework used to provide an effective, coordinated, and organized quality care for patients. Effective implementation of this framework leads to improved quality of care and decreases potential complication, hospital length of stay, and the cost of care. To assess implementation of nursing process and its hindering factors, a quantitative cross-sectional study was conducted among nurses in Afar region hospitals from October 2016 to December 2016. The data were collected from 102 nurses using primary Brooking’s ward nurses’ self-report questionnaire and with some newly prepared questions. The collected data were entered using Epi-Data version 3.1 and analyzed by SPSS version 20 and then presented by tables, graphs, and figures. Forty-three (42.1%) nurses were implementing nursing process at the time of data collection. Assessment and diagnosis were carried out by 57 (56.9%) nurses, planning by 46% of nurses, implementation by 38.2% of nurses, and evaluation by 36.2% of nurses in Afar region. Among the hindering factors towards nursing process implementation, lack of preparedness or knowledge about the nursing process or some part of it (83.3%) and absence of in-service training pertinent to nursing process (75.5%) were the most mentioned ones. Generally, nursing process was poorly implemented in Afar region mainly due to lack of knowledge and absence of in service training. Therefore, giving emphasis for cognitive parts of students about nursing process during their school time and refreshing nurse staffs with continuous training will definitively improve level of nursing process implementation.
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Amaratunga, Vinushi, Lasini Wickramasinghe, Anushka Perera, Jeevani Jayasinghe, and Upaka Rathnayake. "Artificial Neural Network to Estimate the Paddy Yield Prediction Using Climatic Data." Mathematical Problems in Engineering 2020 (July 18, 2020): 1–11. http://dx.doi.org/10.1155/2020/8627824.
Full textAbstract:
Paddy harvest is extremely vulnerable to climate change and climate variations. It is a well-known fact that climate change has been accelerated over the past decades due to various human induced activities. In addition, demand for the food is increasing day-by-day due to the rapid growth of population. Therefore, understanding the relationships between climatic factors and paddy production has become crucial for the sustainability of the agriculture sector. However, these relationships are usually complex nonlinear relationships. Artificial Neural Networks (ANNs) are extensively used in obtaining these complex, nonlinear relationships. However, these relationships are not yet obtained in the context of Sri Lanka; a country where its staple food is rice. Therefore, this research presents an attempt in obtaining the relationships between the paddy yield and climatic parameters for several paddy grown areas (Ampara, Batticaloa, Badulla, Bandarawela, Hambantota, Trincomalee, Kurunegala, and Puttalam) with available data. Three training algorithms (Levenberg–Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugated Gradient (SCG)) are used to train the developed neural network model, and they are compared against each other to find the better training algorithm. Correlation coefficient (R) and Mean Squared Error (MSE) were used as the performance indicators to evaluate the performance of the developed ANN models. The results obtained from this study reveal that LM training algorithm has outperformed the other two algorithms in determining the relationships between climatic factors and paddy yield with less computational time. In addition, in the absence of seasonal climate data, annual prediction process is understood as an efficient prediction process. However, the results reveal that there is an error threshold in the prediction. Nevertheless, the obtained results are stable and acceptable under the highly unpredicted climate scenarios. The ANN relationships developed can be used to predict the future paddy yields in corresponding areas with the future climate data from various climate models.
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Frybort, Jan, Pavel Suk, and Filip Fejt. "Designing Stainless Steel Reflector at VR-1 Training Reactor." EPJ Web of Conferences 239 (2020): 17009. http://dx.doi.org/10.1051/epjconf/202023917009.
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Light-water reactor cores are commonly surrounded by a stainless steel and water reflector. The reflectors are improving power distribution in the core, reducing the leakage of neutrons and thus also protecting the pressurized vessel from the neutron irradiation and the following embrittlement. Contrary to the standard procedures utilized for generation of the fuel assembly data, the reflector elements require a special approach. The major difficulty with the reflectors is represented by an absence of neutron sources in the reflector elements. Some artificial neutron source simulating the realistic source of neutrons from neutron leakage from the surrounding fuel assemblies must be added in the calculation model. The reflector data in the full-core calculations have a great impact on the power distribution in the core. The research in this field is usually focused on the square geometry, and therefore the accurate data for the hexagonal geometry are lacking. Improvements in this area are needed. Training Reactor VR-1 is used for measurements related to nuclear engineering. Department of Nuclear Reactors operating this reactor at the Czech Technical University in Prague is currently designing reflector elements containing stainless steel in order to provide measurable characteristics that can be compared to calculations realized by either Monte-Carlo codes or macroscopic core simulators. This article summarizes the methodology of development of the reflector assemblies to improve their similarity with the VVER-1000 reflector. The impact of the evaluated nuclear data is assessed. Further improvements of the proposed design is necessary to reach better agreement with the neutron spectrum in VVER-1000 reactor reflectors. The influence of evaluated data on the global characteristics was found negligible.
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Guerrero Ortiz, Carolina, and Rita Borromeo Ferri. "Pre-service teachers’ challenges in implementing mathematical modelling: Insights into reality." PNA. Revista de Investigación en Didáctica de la Matemática 16, no. 4 (June 23, 2022): 309–41. http://dx.doi.org/10.30827/pna.v16i4.21329.
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Modelling has become mandatory in the school curricula in many countries across the globe, often without providing teachers the training needed to address this challenge. With a qualitative case study, we analyzed the tasks designed by secondary mathematics pre-service teachers. We recognized how participants manage their knowledge for teaching modelling in the absence of training. Elements of knowledge for teaching such as translation between languages, recognizing unknown data, covariation, and usefulness of representations for understanding and solving problems, were identified. Our results also reveal that future teachers have a tendency to create word problems when first attempting to teach modelling.