Relevant bibliographies by topics / DATA BALANCING TECHNIQUE

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'DATA BALANCING TECHNIQUE'

Author: Grafiati
Published: 11 September 2023

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Journal articles on the topic "DATA BALANCING TECHNIQUE"

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Economou, Nikos, and Antonis Vafidis. "Spectral balancing GPR data using time-variant bandwidth in the t-f domain." GEOPHYSICS 75, no. 3 (May 2010): J19—J27. http://dx.doi.org/10.1190/1.3374464.

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Ground-penetrating radar (GPR) sections encounter a resolution reduction with depth because, for electromagnetic (EM) waves propagating in the subsurface, attenuation is typically more pronounced at higher frequencies. To correct for these effects, we have applied a spectral balancing technique, using the S-transform (ST). This signal-processing technique avoids the drawbacks of inverse [Formula: see text] filtering techniques, namely, the need for estimation of the attenuation factor [Formula: see text] from the GPR section and instability caused by scattering effects that result from methods of dominant frequency-dependent estimation of [Formula: see text]. The method designs and applies a gain in the time-frequency ([Formula: see text]) domain and involves the selection of a time-variant bandwidth to reduce high-frequency noise. This method requires a reference amplitude spectrum for spectral shaping. It performs spectral balancing, which works efficiently for GPR data when it is applied in very narrow time windows. Furthermore, we have found that spectral balancing must be applied prior to deconvolution, instead of being an alternative technique.
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Pattanapairoj, Sirorat, Danaipong Chetchotsak, and Banchar Arnonkijpanich. "Hybrid Balancing Technique Using GRSOM and Bootstrap Algorithms for Classifiers with Imbalanced Data." Advanced Materials Research 931-932 (May 2014): 1375–81. http://dx.doi.org/10.4028/www.scientific.net/amr.931-932.1375.

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To deal with imbalanced data, this paper proposes a hybrid data balancing technique which incorporates both over and under-sampling approaches. This technique determines how much minority data should be grown as well as how much majority data should be reduced. In this manner, noise introduced to the data due to excessive over-sampling could be avoided. On top of that, the proposed data balancing technique helps to determine the appropriate size of the balanced data and thus computation time required for construction of classifiers would be more efficient. The data balancing technique over samples the minority data through GRSOM method and then under samples the majority data using the bootstrap sampling approach. GRSOM is used in this study because it grows new samples in a non-linear fashion and preserves the original data structure. Performance of the proposed method is tested using four data sets from UCI Machine Learning Repository. Once the data sets are balanced, the committee of classifiers is constructed using these balanced data. The experimental results reveal that our proposed data balancing method provides the best performance.
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Chetchotsak, Danaipong, Sirorat Pattanapairoj, and Banchar Arnonkijpanich. "Integrating new data balancing technique with committee networks for imbalanced data: GRSOM approach." Cognitive Neurodynamics 9, no. 6 (July 31, 2015): 627–38. http://dx.doi.org/10.1007/s11571-015-9350-4.

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Muszala, S. P., D. A. Connors, J. J. Hack, and G. Alaghband. "The Promise of Load Balancing the Parameterization of Moist Convection Using a Model Data Load Index." Journal of Atmospheric and Oceanic Technology 23, no. 4 (April 1, 2006): 525–37. http://dx.doi.org/10.1175/jtech1865.1.

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Abstract The parameterization of physical processes in atmospheric general circulation models contributes to load imbalances among individual processors of message-passing distributed-multiprocessor systems. Load imbalances increase the overall time to completion of a model run and should be eliminated or reduced as much as possible. Presented herein is a new technique that shows promise for load balancing the parameterization of moist convection found in the Community Climate System Model's (CCSM's) Community Atmosphere Model version 3 (CAM3). At the heart of this technique is a load index that is a marker for moist convection (called a model data load index). The marker for moist convection correlates directly to the amount of processing time per model grid cell and can therefore be used to effect a load balance. Spatial locality on the model grid and temporal locality between model time steps exist that allow a decomposition from a load-balancing step to be retained for multiple time steps. The analysis in this paper shows that the load balance does not need to be applied at every time step and that the number of steps in which the previous load balance remains effective is large enough for the overhead to be cost effective. Tests performed indicate that this technique is scalable to higher-resolution models as well as to higher processor counts than those presented. Through the use of the Load Balancing and Scheduling Framework (LBSF), this technique shows promise in reducing (by ∼47%) the time of the unbalanced load of one particular subroutine in CAM3 at the T85 spectral truncation. A maximum of 3.75 s of total execution time is saved over a 2430-time-step simulation. When extrapolated to a 1000-yr simulation, this translates to a potential savings of ∼22 h in that subroutine alone. Similar methods applied to remaining subroutines can add up to a significant savings. These results are encouraging in that a fine-grained load-balancing technique using the evolving characteristics of geophysical data paves the way for load balancing a broad range of physical calculations, both in CAM3 and other scientific applications, where more general techniques are not practical.
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Muktar Yahuza, Yamani Idna Bin Idris, Ainuddin Wahid Bin Abdul Wahab, Mahdi A. Musa, and Adamu Abdullahi Garba. "A LIGHTWEIGHT AUTHENTICATION TECHNIQUE FOR SECURE COMMUNICATION OF EDGE/FOG DATA-CENTERS." Science Proceedings Series 2, no. 1 (April 25, 2020): 76–81. http://dx.doi.org/10.31580/sps.v2i1.1319.

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Edge computing has significantly enhanced the capabilities of cloud computing. Edge data-centres are used for storing data of the end-user devices. Secure communication between the legitimate edge data-centres during the load balancing process has attracted industrial and academic researchers. Recently, Puthal et al. have proposed a technique for authenticating edge datacenters to enable secure load balancing. However, the resource-constraint nature of the edge data-centres is ignored. The scheme is characterized by complex computation and memory intensive cryptographic protocol. It is also vulnerable to key escrow attack because the secret key used for encrypting and decrypting of the communicated messages is been created by the trusted cloud datacenter. Additionally, the key sharing phase of their algorithm is complex. Therefore, to address the highlighted challenges, this paper proposed a lightweight key escrow-less authentication algorithm that will ensure secure communication of resource-constrained edge data-centres during the load balancing process. The security capability of the proposed scheme has been formally evaluated using the automatic cryptographic analytical tool ProVerif. The relatively low computation and communication costs of the proposed scheme compared to the benchmark schemes proved that it is lightweight, thus suitable for resource-constrained edge datacenters.
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Thilagavathi, N., D. Divya Dharani, R. Sasilekha, Vasundhara Suruliandi, and V. Rhymend Uthariaraj. "Energy Efficient Load Balancing in Cloud Data Center Using Clustering Technique." International Journal of Intelligent Information Technologies 15, no. 1 (January 2019): 84–100. http://dx.doi.org/10.4018/ijiit.2019010104.

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Cloud computing has seen tremendous growth in recent days. As a result of this, there has been a great increase in the growth of data centers all over the world. These data centers consume a lot of energy, resulting in high operating costs. The imbalance in load distribution among the servers in the data center results in increased energy consumption. Server consolidation can be handled by migrating all virtual machines in those underutilized servers. Migration causes performance degradation of the job, based on the migration time and number of migrations. Considering these aspects, the proposed clustering agent-based model improves energy saving by efficient allocation of the VMs to the hosting servers, which reduces the response time for initial allocation. Middle VM migration (MVM) strategy for server consolidation minimizes the number of VM migrations. Further, randomization of extra resource requirement done to cater to real-time scenarios needs more resource requirements than the initial requirement. Simulation results show that the proposed approach reduces the number of migrations and response time for user request and improves energy saving in the cloud environment.
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Shivaliya, Shikha, and Vijay Anand. "Design of Load Balancing Technique for Cloud Computing Environment." ECS Transactions 107, no. 1 (April 24, 2022): 2911–18. http://dx.doi.org/10.1149/10701.2911ecst.

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Cloud computing allows for the provision of IT resources on-demand and has various advantages. Because the majority of firms have shifted their activities to the cloud, data centers are frequently flooded with sporadic loads. When dealing with high network traffic in the cloud, it is necessary to balance the load among servers. This is something that load balancing can help with. The primary goal of load balancing is to distribute demand evenly among all available servers such that no server is under or overloaded. Load balancing is the process of dispersing load among several nodes to make the best use of resources when work is overwhelmed. When a node is overburdened to support the load, load balancing is essential. When a node becomes overcrowded, the load is dispersed to the remaining optimal nodes.
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Gui, Chun. "Analysis of imbalanced data set problem: The case of churn prediction for telecommunication." Artificial Intelligence Research 6, no. 2 (June 27, 2017): 93. http://dx.doi.org/10.5430/air.v6n2p93.

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Class-imbalanced datasets are common in the field of mobile Internet industry. We tested three kinds of feature selection techniques-Random Forest (RF), Relative Weight (RW) and Standardized Regression Coefficients (SRC); three kinds of balance methods-over-sampling (OS), under-sampling (US) and synthetic minority over-sampling (SMOTE); a widely used classification method-RF. The combined models are composed of feature selection techniques, balancing techniques and classification method. The original dataset which has 45 thousand records and 22 features were used to evaluate the performances of both feature selection and balancing techniques. The experimental results revealed that SRC combined with SMOTE technique attained the minimum value of Cost = 1085. Through the calculation of the Cost on all models, the most important features for minimum cost of telecommunication were identified. The application of these combined models will have the possibility to maximize the profit with the minimum expenditure for customer retention and help reduce customer churn rates.
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Sapna, P. Fathima, and Dr R. Lal Raja Singh. "Smart Meter Data based Load Analysis Using Clustering Technique." International Academic Journal of Science and Engineering 9, no. 1 (December 6, 2022): 39–48. http://dx.doi.org/10.9756/iajse/v9i1/iajse0918.

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In this study, clustering is investigated as a method for improving energy efficiency based on smart meters for a number of household applications that are both currently available and expected to become available in the near future. These applications include smart thermostats, smart lights, smart water heaters, smart washing machines, and smart refrigerators. We describe a novel approach to load balanced clustering that is founded on the K-means Clustering algorithm. Our algorithm's major goal is to optimize network lifetime while maintaining acceptable sensing coverage in scenarios in which sensor nodes generate either uniform or non-uniform data traffic. This can be accomplished by maintaining acceptable sensing coverage. We also provide a new clustering cost function that takes into consideration not only the volume of traffic but also the amount of work that is required to communicate across substantial geographic distances. This is done so that we can achieve this objective. We demonstrate that our algorithm is able to improve both load analysis as well as load balancing in the domestic area by running extensive simulations that compare the proposed algorithm to leading state-of-the-art clustering approaches and then comparing the results to one another. This allows us to demonstrate that our algorithm is able to improve both load analysis as well as load balancing in the domestic area. In addition to this, it demonstrates the culmination of a stage in the processing of a dataset in order to compute the typical quantity of energy load that is utilized by consumers.
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Ramzan, Bajwa, Kazmi, and Amna. "Challenges in NoSQL-Based Distributed Data Storage: A Systematic Literature Review." Electronics 8, no. 5 (April 30, 2019): 488. http://dx.doi.org/10.3390/electronics8050488.

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Key-Value stores (KVSs) are the most flexible and simplest model of NoSQL databases, which have become highly popular over the last few years due to their salient features such as availability, portability, reliability, and low operational cost. From the perspective of software engineering, the chief obstacle for KVSs is to achieve software quality attributes (consistency, throughput, latency, security, performance, load balancing, and query processing) to ensure quality. The presented research is a Systematic Literature Review (SLR) to find the state-of-the-art research in the KVS domain, and through doing so determine the major challenges and solutions. This work reviews the 45 papers between 2010–2018 that were found to be closely relevant to our study area. The results show that performance is addressed in 31% of the studies, consistency is addressed in 20% of the studies, latency and throughput are addressed in 16% of the studies, query processing is addressed in 13% of studies, security is addressed in 11% of the studies, and load balancing is addressed in 9% of the studies. Different models are used for execution. The indexing technique was used in 20% of the studies, the hashing technique was used in 13% of the studies, the caching and security techniques were used together in 9% of the studies, the batching technique was used in 5% of the studies, the encoding techniques and Paxos technique were used together in 4% of the studies, and 36% of the studies used other techniques. This systematic review will enable researchers to design key-value stores as efficient storage. Regarding future collaborations, trust and privacy are the quality attributes that can be addressed; KVS is an emerging facet due to its widespread popularity, opening the way to deploy it with proper protection.
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Dissertations / Theses on the topic "DATA BALANCING TECHNIQUE"

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Lee, Young-Jun. "Routing and Efficient Evaluation Techniques for Multi-hop Mobile Wireless Networks." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/7455.

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In this dissertation, routing protocols, load-balancing protocols, and efficient evaluation techniques for multi-hop mobile wireless networks are explored. With the advancements made in wireless communication and computer technologies, a new type of mobile wireless network, known as a mobile ad hoc network (MANET), has drawn constant attention. In recent years, several routing protocols for MANETs have been proposed. However, there still remains the need for mechanisms for better scalability support with respect to network size, traffic volume, and mobility. To address this issue, a new method for multi-hop routing in MANETs called Dynamic NIx-Vector Routing (DNVR) is proposed. DNVR has several distinct features compared to other existing on-demand routing protocols, which lead to more stable routes and better scalability. Currently, ad hoc routing protocols lack load-balancing capabilities. Therefore they often fail to provide good service quality, especially in the presence of a large volume of network traffic since the network load concentrates on some nodes, resulting in a highly congested environment. To address this issue, a novel load-balancing technique for ad hoc on-demand routing protocols is proposed. The new method is simple but very effective in achieving load balance and congestion alleviation. In addition, it operates in a completely distributed fashion. To evaluate and verify wireless network protocols effectively, especially to test their scalability properties, scalable and efficient network simulation methods are required. Usually simulation of such large-scale wireless networks needs a long execution time and requires a large amount of computing resources such as powerful CPUs and memory. Traditionally, to cope with this problem, parallel network simulation techniques with parallel computing capabilities have been considered. This dissertation explores a different type of method, which is efficient and can be achieved with a sequential simulation, as well as a parallel and distributed technique for large-scale mobile wireless networks.
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Sonnert, Adrian. "Predicting inter-frequency measurements in an LTE network using supervised machine learning : a comparative study of learning algorithms and data processing techniques." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148553.

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With increasing demands on network reliability and speed, network suppliers need to effectivize their communications algorithms. Frequency measurements are a core part of mobile network communications, increasing their effectiveness would increase the effectiveness of many network processes such as handovers, load balancing, and carrier aggregation. This study examines the possibility of using supervised learning to predict the signal of inter-frequency measurements by investigating various learning algorithms and pre-processing techniques. We found that random forests have the highest predictive performance on this data set, at 90.7\% accuracy. In addition, we have shown that undersampling and varying the discriminator are effective techniques for increasing the performance on the positive class on frequencies where the negative class is prevalent. Finally, we present hybrid algorithms in which the learning algorithm for each model depends on attributes of the training data set. These algorithms perform at a much higher efficiency in terms of memory and run-time without heavily sacrificing predictive performance.
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JAWA, MISHA. "COMPARISION OF ENSEMBLE LEARNING MODELS AND IMPACT OF DATA BALANCING TECHNIQUE FOR SOFTWARE EFFORT ESTIMATION." Thesis, 2022. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19229.

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Project management is a critical component of every software project's success. Estimating the cost and effort of software development at the outset of the project is one of the most important responsibilities in software project management. Estimating effort allows project managers to more effectively manage resources and activities. The primary purpose of this study was to construct and compare the usage of two common ensemble approaches (bagging and boosting) to improve estimator accuracy and to study the impact of Synthetic Minority Over-Sampling Technique for Regression (SMOTER) to predict effort estimation by using machine learning algorithms. Random forest, support vector regression, elastic net, decision tree regressor, linear regression, lasso regression, and ridge regression are some of the machine learning techniques we've implemented. For our study we used Albrecht, China, COCOMO81, Desharnais and Maxwell dataset. We also performed feature selection and considered only those features that have strong correlation with target feature i.e., effort. The two-performance metrics Mean Magnitude Relative Error (MMRE) and PRED(25) results demonstrate that utilising elastic net as the base learner for AdaBoost outperforms the other models and there is a significant decrease in error of each model after applying SMOTER.
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Books on the topic "DATA BALANCING TECHNIQUE"

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1961-, Choudhary Alok N., and United States. National Aeronautics and Space Administration., eds. Novel techniques for data decomposition and load balancing for parallel processing of vision systems: Implementation and evaluation using a motion estimation system. Urbana, IL: Coordinated Science Laboratory, College of Engineering, University of Illinois at Urbana-Champaign, 1990.

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Book chapters on the topic "DATA BALANCING TECHNIQUE"

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Vani, K. A., J. Prathima Mabel, and K. N. Rama Mohan Babu. "A Switch-Prioritized Load-Balancing Technique in SDN." In Data Analytics and Learning, 39–49. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2514-4_4.

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Sharma, Deepak Kumar, Kartik Kwatra, Manan Manwani, Nimit Arora, and Aarti Goel. "Optimized Resource Allocation Technique Using Self-balancing Fast MinMin Algorithm." In Data Analytics and Management, 473–87. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8335-3_36.

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Bano, Hamida, Nadeem Javaid, Komal Tehreem, Kainat Ansar, Maheen Zahid, and Tooba Nazar. "Cloud Computing Based Resource Allocation by Random Load Balancing Technique." In Lecture Notes on Data Engineering and Communications Technologies, 28–39. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02613-4_3.

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Mohammed, Rafiq Ahmed, Kok Wai Wong, Mohd Fairuz Shiratuddin, and Xuequn Wang. "Classification of Multi-class Imbalanced Data Streams Using a Dynamic Data-Balancing Technique." In Communications in Computer and Information Science, 279–90. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63823-8_33.

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Ansar, Kainat, Nadeem Javaid, Maheen Zahid, Komal Tehreem, Hamida Bano, and Momina Waheed. "A Hybrid HS-Mean Technique for Efficient Load Balancing in Cloud Computing." In Lecture Notes on Data Engineering and Communications Technologies, 40–48. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02613-4_4.

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Xiong, Xuanrui, Yufan Zhang, Huijun Zhang, Yi Chen, Hailing Fang, Wen Xu, Weiqing Lin, and Yuan Zhang. "Data Balancing Technique Based on AE-Flow Model for Network Instrusion Detection." In Communications and Networking, 174–90. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-34790-0_14.

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de Hollander, J. A. "Application of a metabolic balancing technique to the analysis of microbial fermentation data." In Quantitative Aspects of Growth and Metabolism of Microorganisms, 275–92. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2446-1_11.

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Rahman, Md Hasibur, Chowdhury Rafeed Rahman, Ruhul Amin, Md Habibur Rahman Sifat, and Afra Anika. "A Hybrid Approach Towards Two Stage Bengali Question Classification Utilizing Smart Data Balancing Technique." In Cyber Security and Computer Science, 454–64. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52856-0_36.

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Halimu, Chongomweru, and Asem Kasem. "Split Balancing (sBal)—A Data Preprocessing Sampling Technique for Ensemble Methods for Binary Classification in Imbalanced Datasets." In Lecture Notes in Electrical Engineering, 241–57. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4069-5_21.

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Sui, Zhiquan, and Shrideep Pallickara. "A Survey of Load Balancing Techniques for Data Intensive Computing." In Handbook of Data Intensive Computing, 157–68. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1415-5_6.

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Conference papers on the topic "DATA BALANCING TECHNIQUE"

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Vibha, M. B., P. Rakshitha Kiran, R. Raju Gondkar, and Poornima Nataraja. "Load balancing streamed healthcare data using MR technique." In 2017 International Conference on Innovative Mechanisms for Industry Applications (ICIMIA). IEEE, 2017. http://dx.doi.org/10.1109/icimia.2017.7975540.

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Shrivastava, Adarsh, Rushikesh Pupale, and Pradeep Singh. "Enhancing Aggression Detection using GPT-2 based Data Balancing Technique." In 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2021. http://dx.doi.org/10.1109/iciccs51141.2021.9432283.

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Paul, Debdeep, and Wen-De Zhong. "Price and renewable aware geographical load balancing technique for data centres." In 2013 9th International Conference on Information, Communications & Signal Processing (ICICS). IEEE, 2013. http://dx.doi.org/10.1109/icics.2013.6782783.

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Gupta, Ekta, and Vidya Deshpande. "A Technique Based on Ant Colony Optimization for Load Balancing in Cloud Data Center." In 2014 International Conference on Information Technology (ICIT). IEEE, 2014. http://dx.doi.org/10.1109/icit.2014.54.

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Mounika, G., and G. Murali. "A communication aware load balancing technique for cluster file systems based on distributed hash tables (DHTs)." In 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS). IEEE, 2017. http://dx.doi.org/10.1109/icecds.2017.8390136.

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Anjali, Nidhi, Kumkum Dubey, Prince Rajpoot, Amit Kumar Singh, Ajeet Kumar, and Ritika Yaduvanshi. "Fuzzy based Technique for Nodes Coverage with Load Balancing Data collection using Multiple Conflicting Factors." In 2019 International Conference on Communication and Electronics Systems (ICCES). IEEE, 2019. http://dx.doi.org/10.1109/icces45898.2019.9002069.

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Kaushik, Vaibhav. "The Enhanced Load Balancing Technique to Optimize the Data Diffusion Swiftness Using Hybrid Context Modeling." In 2023 World Conference on Communication & Computing (WCONF). IEEE, 2023. http://dx.doi.org/10.1109/wconf58270.2023.10234973.

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Shaw, Subhadra Bose, Chandan Kumar, and Anil Kumar Singh. "Use of time-series based forecasting technique for balancing load and reducing consumption of energy in a cloud data center." In 2017 International Conference on Intelligent Computing and Control (I2C2). IEEE, 2017. http://dx.doi.org/10.1109/i2c2.2017.8321782.

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Xie, Jiarui, Katherine Schmidt, Nausica Budeanu, Vincent Letendre, and Yaoyao Fiona Zhao. "Combining Feature Learning and Transfer Learning in Balancing Anomaly Detection for Gas Turbine Engine Vibration Analysis." In ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/detc2022-88223.

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Abstract Rotor imbalance is a vital measure that indicates the health state of a gas turbine (GT). Abnormal balancing patterns will lead to excessive vibration and gradually compromise structural integrity. This paper presents the construction of anomaly detection (AD) models that recognize abnormal balancing patterns for two aeroderivative GTs, AGT-A and AGT-B, from Siemens Energy. Such a diagnostic tool can predict at an early stage whether a high vibration would occur during the vibration test and avoid engine reject for re-balance. Machine learning (ML) algorithms have been extensively utilized to conduct GT design space exploration and condition monitoring. However, ML has not been implemented to improve the efficiency of GT manufacturing processes, mainly due to data scarcity. The authors propose a combined feature learning and transfer learning technique to leverage the data resources of GT manufacturing processes. The physical and operational similarities between GTs belonging to the same series imply the transferability of features between models. The normal balancing patterns of the data-rich AGT-A were first learned by a sparse autoencoder to detect balancing anomalies. Then, the learned features were used to initialize the balancing AD model for the data-poor AGT-B. The test accuracy of the AGT-B AD model was increased from 75% to 92% with transfer learning. The presented methodology can facilitate and enable various data-driven analysis tasks for the manufacturing processes of original equipment manufacturers.
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Maia, Lucas Bezerra, Alan Carlos Lima, Pedro Thiago Cutrim Santos, Nigel da Silva Lima, João Dallyson Sousa De Almeida, and Anselmo Cardoso Paiva. "Evaluation of Melanoma Diagnosis using Imbalanced Learning." In XVIII Simpósio Brasileiro de Computação Aplicada à Saúde. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/sbcas.2018.3680.

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Melanoma is the most lethal type of skin cancer when compared to others, but patients have high recovery rates if the disease is discovered in its early stages. Several approaches to automatic detection and diagnosis have been explored by different authors. Training models with the existing data sets has been a difficult task due to the problem of imbalanced data. This work aims to evaluate the performance of machine learning algorithms combined with imbalanced learning techniques, regarding the task of melanoma diagnosis. Preliminary results have shown that features extracted with ResNet Convolutional Neural Network, along with Random Forest, achieved an improvement of sensibility of approximately 21%, after balancing the training data with Synthetic Minority Oversampling TEchnique (SMOTE) and Edited Nearest Neighbor (ENN) rule.
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Reports on the topic "DATA BALANCING TECHNIQUE"

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McDougall, Robert. Entropy Theory and RAS are Friends. GTAP Working Paper, September 2000. http://dx.doi.org/10.21642/gtap.wp06.

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Presented at the Second Annual Conference on Global Economic Analysis Recent research in applications of entropy theory to matrix balancing problems in eco-nomics has put powerful new tools in the hands of data base developers, but overshadowed some previous findings. We recall earlier findings that the RAS is an entropy-theoretic model. Investigating the properties of a more recently proposed entropy-theoretic model, we find that in general the RAS remains preferable. We show further that the RAS can be obtained also as a generalised cross-entropy model. Finally, we present examples illus-trating how entropy-theoretic techniques can extend the RAS to handle a wider range of problems.
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Brunner, Huschenbett, and Beshouri. PR-336-06206-R01 Engine Control for Legacy Engines - Cylinder and Cycle Level Control. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), May 2014. http://dx.doi.org/10.55274/r0010041.

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The reduction or even elimination of cylinder to cylinder combustion variation and cycle-to-cycle combustion instability may result in further nitrous oxides (NOx) reductions at the same engine average air/fuel ratio (and hence turbocharger operating point) or allow operation at a leaner air/fuel ratio before the onset of instability. Reductions in imbalance and instability can also reduce carbon pollutant emissions, reduce break specific fuel consumption (BSFC) and improve engine operability. Achieving these goals requires a better understanding of the sources and impacts of that imbalance and instability and then the development techniques to reduce if not eliminate both. This effort focuses on combustion instability of legacy large bore pipeline reciprocating engines operating at very high air/fuel ratios to meet higher emission standards. The report provides a detailed cycle-to-cycle analysis of acquired pressure and ion current data of four test engines in-cluding Clark T-series family and Cooper Bessemer GMV/W engines. The possibility of upcom-ing misfire or poor combustion prediction is investigated and discussed. The document further investigates advanced cylinder balancing strategies as well as the benefits of improved pre-combustion chamber performance.
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