Balance Of Accuracy Research Articles

A stochastic convolution kernel transform-based model for evaluating bearing performance degradation

Bearing performance degradation assessment can effectively reduce the accuracy degradation and failure of robotic arms caused by bearing failures. Existing performance degradation models based on deep learning and machine learning models have not yet considered the enhancement of degradation assessment by fault-related decoupling in the signal. Therefore, this paper proposes a random convolution kernel transform-based bearing performance degradation assessment model, which enriches the characterization of bearing degradation trends by decomposing the VMD signal and extracting multi-dimensional sensitive features from the decomposed IMFs. The multi-scale characterization of bearing degradation is enriched by a large number of stochastic convolution kernel transforms. A ridge regression classifier is applied to balance accuracy and computational complexity to achieve an intelligent assessment of bearing degradation trends, and the effectiveness of the proposed method is verified on the XJTU-SY dataset, where the accuracy of the proposed method exceeds that of existing fault degradation trend assessment models.

Developing scalable management information system with big financial data using data mart and mining architecture

The traditional Management Information System (MIS) with Big Financial Data (BFD) for corporate financial diagnosis has many limitations such as the data is not summarized thus these causing increases in query times, and also the complexity in analysis. The creation of a Data Mart (DM) leads to a great summarization of data, such that contains only essential business information. And by using data mining techniques we can be extracting unknown useful information from DM and apply it to make important decisions for the business. Thus, in this paper we are adopting an architecture of six layers; interface layer, analysis layer, extract transformation load layer, data mart layer, data mining layer, and evaluating layer, MIS with BFD using DM and Mining (MIS-BFD-DMM) is proposed, which is not only permits the use of DM and mining technologies in decision support, but also the full utilization of non-financial/financial info held by businesses. This paper offers the benefits of building and integrating DM with mining. Also determines the distinction between DM and a relational database for decision-makers to get information. The test and analysis are achieved in the terms of useful metrics (accuracy, balance accuracy, F-measure, precision, recall, and time). As a result, Data returned from arranged star schema is far faster than ERD. In conclusion, the SVM is best than other algorithms in terms of the parameters of the confusion matrix.

Circuit Game Development: Effects on Balance, Concentration, Muscle Endurance, and Arrow Accuracy

Study purpose. Balance, concentration, muscle endurance, and accuracy are very important for archery athletes, but there are still limited game models to improve balance, concentration, arm muscle endurance, and accuracy in archery athletes. The aim of this research is to develop a circuit game model to improve balance, concentration, arm muscle endurance, and accuracy in proper and effective archery athletes. Materials and methods. The development model used in this study is based on Lee & Owens theory which uses five phases in a cycle, namely ADDIE. The effectiveness test was carried out on archery athletes aged 10–13 years, totaling 30 people. Treatment was given for 18 meetings. Results. The conclusion of the research is that the circuit game model to improve balance, concentration, arm muscle endurance, and accuracy in archery athletes is feasible. The circuit game model is effective in improving balance, concentration, arm muscle endurance, and accuracy in archery athletes. The circuit game model consists of 6 posts. Conclusions. For coaches, the circuit game model can be used to improve balance, concentration, arm muscle endurance, and accuracy in archery athletes. Suggestions for further researchers are that this circuit game model needs to be developed again to be better.

Cascade Active Balance Charging of Electric Vehicle Power Battery Based on Model Prediction Control

As a bi-directional converter, the Buck-Boost converter, which has the advantages of simple structure and taking the SOC of the battery as the balance variable, is adopted as the balance topology in this paper. In view of the shortcomings of traditional balance topology, which can only balance two adjacent batteries, resulting in a long balance time and insufficient balance accuracy, a cascade active balance charging topology that can balance in intra-group and inter-group situations simultaneously is proposed. At the same time, the fuzzy control algorithm and model predictive control are used as the balance control strategies, respectively, to control whether the MOSFET is on or off in the balance topology circuit. The duty cycle is dynamically adjusted to the size of the balance current to achieve the balance of the battery pack. The results show that the cascade Buck-Boost balance topology based on model prediction control can accurately control the balancing current and improve the accuracy and speed of the balance, and it is more suitable for the actual working process.

Hybrid Approach with Distance Feature for Multi-Class Imbalanced Datasets

The multi-class imbalance problem has a higher level of complexity when compared to the binary class problem. The difficulty is due to the large number of classes that will present challenges related to overlapping between classes. Many approaches have been proposed to deal with these multi-class problems. One is a hybrid approach combining a data-level approach and an algorithm-level approach. This approach is done by the ensemble on the classifier and also oversampling on the minority class. SMOTE is an oversampling method that provides good performance, but this method is necessary to determine the best sample used in the interpolation process to generate new samples. The need for determining the best sample is related to the overlap between classes that always accompanies the multi-class imbalance problem. The existence of overlap requires efforts to determine the safe region to synthesize the sample in the oversampling process in SMOTE. The safe region is considered the best for synthesizing samples due to the lower tendency of overlapping. It can be done by constructing distance features to determine the safe region. The sample with the best distance and the lowest imbalance ratio will be selected as a sample in the over-sampling process with SMOTE. The main contribution of this research is the proposed method of Hybrid Approach with Distance Feature so that it can determine safe samples, with the main advantage being in addition to handling multi-class imbalances, it is also better for handling overlapping. The results of this study will be compared with Multiple Random Balance (MultiRandBal) which performs a random oversampling process. The results showed that the Augmented R-Value, Class Average Accuracy, Class Balance Accuracy, and Hamming Loss obtained in this method was better than the random oversampling process. These results also show that the Hybrid Approach with Distance Feature provides better results in handling multi-class imbalances when compared to MultiRandBal.

Simulated annealing for balancing covariates.

Covariate balance is one of the fundamental issues in designing experiments for treatment comparisons, especially in randomized clinical trials. In this article, we introduce a new class of covariate-adaptive procedures based on the Simulated Annealing algorithm aimed at balancing the allocations of two competing treatments across a set of pre-specified covariates. Due to the nature of the simulated annealing, these designs are intrinsically randomized, thus completely unpredictable, and very flexible: they can manage both quantitative and qualitative factors and be implemented in a static version as well as sequentially. The properties of the suggested proposal are described, showing a significant improvement in terms of covariate balance and inferential accuracy with respect to all the other procedures proposed in the literature. An illustrative example based on real data is also discussed.

Adenoma Dysplasia Grading of Colorectal Polyps Using Fast Fourier Convolutional ResNet (FFC-ResNet)

Colorectal polyps are precursor lesions of colorectal cancer; hence, early detection and dysplasia grading of polyps are essential for determining cancer risk, the possibility of developing subsequent polyps, and follow-up recommendations. The significant contribution of this study is the development of an enhanced deep-learning model called Fast Fourier Convolutional ResNet (FFC-ResNet) to classify dysplasia grades of polyps. It is based on the ResNet-50 architecture and uses cross-feature fusion, which combines local features extracted using conventional spatial convolution with global features extracted using Fourier convolution. Because of the compensatory effect between local and global features, the learnability and performance of FFC-ResNet have increased. The proposed FFC-ResNet was developed and tested using UniToPatho, a dataset containing 7000 μm and 800 μm hematoxylin-and-eosin (H&E)-stained colorectal images. And a favourable performance of sensitivity 0.95, specificity 0.93, balance accuracy 0.94, precision 0.95, F1 score 0.95 and AUC 0.99 was obtained using 800 μm polyp patches.

Формирование структуры влагообмена зоны аэрации орошаемого поля в условиях присутствия капиллярной каймы в толще грунта

Purpose: scientific and technical substantiation of water balance components of an irrigated field in the particular case of the connection of an aquifer and aeration zone through a capillary fringe. Materials and methods. The studies were carried out in Pobeda LLC, Bagaevsky district, Elkin farm in 2018 and 2019. When organizing studies, an integrated approach was used: stationary and experimental plots were used. The climatic conditions of the research objects correspond to the semi-arid steppe zone, the soils are southern chernozems. Results. As a result of processing the field experience data, it was determined that for the conditions of an average dry year in the last third of May, the water table occurrence, calculated according to the radiation balance, is at a depth of 1.0 m, the average daily water consumption is 6.5 mm, groundwater feeding – 2.9 mm, and at a depth of 1.5 and 2.0 m – 2.0 and 1.25 mm, respectively. For the conditions of an average year in the phase of the beginning of alfalfa flowering (in the first days of June), the average daily water consumption is 5 mm, and groundwater recharge is 2.2; 1.38 and 0.81 mm at a depth of groundwater level of 1.0; 1.5 and 2.0 m respectively. The values of water consumption are obtained from the heat balance equation of the active surface, the dynamics of moisture exchange elements in the aeration zone of an irrigated alfalfa field, a comparative assessment of the evaporation obtained by the following methods is given: radiation balance (accuracy 0.89), N. N. Ivanov (accuracy 0.68) and the modified model of S. I. Kharchenko (accuracy 0.88), with the corresponding mathematical processing (coefficient of variation, dispersion, deviation) by the sum of accumulated effective temperatures compared with the actual data. Conclusions: as a reference method, the method of determining the evapotranspiration of agricultural crops according to the radiation balance should be used; the implementation of the required model according to S. I. Kharchenko makes it possible to increase the accuracy of determining the desired value by 30 % compared to the determination model according to N. N. Ivanov.

Optimized cell type signatures revealed from single-cell data by combining principal feature analysis, mutual information, and machine learning

Machine learning techniques are excellent to analyze expression data from single cells. These techniques impact all fields ranging from cell annotation and clustering to signature identification. The presented framework evaluates gene selection sets how far they optimally separate defined phenotypes or cell groups. This innovation overcomes the present limitation to objectively and correctly identify a small gene set of high information content regarding separating phenotypes for which corresponding code scripts are provided. The small but meaningful subset of the original genes (or feature space) facilitates human interpretability of the differences of the phenotypes including those found by machine learning results and may even turn correlations between genes and phenotypes into a causal explanation. For the feature selection task, the principal feature analysis is utilized which reduces redundant information while selecting genes that carry the information for separating the phenotypes. In this context, the presented framework shows explainability of unsupervised learning as it reveals cell-type specific signatures. Apart from a Seurat preprocessing tool and the PFA script, the pipeline uses mutual information to balance accuracy and size of the gene set if desired. A validation part to evaluate the gene selection for their information content regarding the separation of the phenotypes is provided as well, binary and multiclass classification of 3 or 4 groups are studied. Results from different single-cell data are presented. In each, only about ten out of more than 30000 genes are identified as carrying the relevant information. The code is provided in a GitHub repository at https://github.com/AC-PHD/Seurat_PFA_pipeline.

Transformer-Based Long Distance Fiber Channel Modeling for Optical OFDM Systems

The fiber channel model plays an essential role in the simulation and design of optical fiber communication systems. However, it is difficult for conventional model-driven modeling to balance accuracy and efficiency, especially in optical orthogonal frequency division multiplexing (OFDM) systems with complex and long-haul transmission. We introduce the simplified Transformer into optical OFDM systems and combine it with the feature decoupled distributed (FDD) scheme for fast and accurate fiber channel modeling. Unlike the popular Transformer architectures, we remove the Decoder part and cancel the self-attention with quadratic complexity, significantly reducing the computational cost. The modeling performance is investigated from the nonlinear fitting capability, accuracy, and generalization ability. The transmission distance ranges from 80 km to 1600 km. The highly matched four-wave mixing (FWM) power, low error vector magnitudes (EVMs), and similar signal-noise ratios (SNRs) demonstrate the high precision and robustness of the model. Furthermore, the modeling is studied under different transmission rates and is proved to be reliable over a wide transmission bandwidth. Compared to the bidirectional long short-term memory (Bi-LSTM), the Transformer performs better in accuracy and has lower computational and memory costs. For modeling under the same conditions, the required running time of the Transformer is about 60% of Bi-LSTM, less than 1% of the split-step Fourier method (SSFM). The Transformer-based method achieves high precision modeling of the fiber channel in the long-distance and high-rate optical OFDM system and makes a significant breakthrough in complexity.

An ensemble Kalman filter system with the Stony Brook Parallel Ocean Model v1.0

Abstract. This study develops an ensemble Kalman filter (EnKF)-based regional ocean data assimilation system in which the local ensemble transform Kalman filter (LETKF) is implemented with version 1.0 of the Stony Brook Parallel Ocean Model (sbPOM) to assimilate satellite and in situ observations at a daily frequency. A series of sensitivity experiments are performed with various settings of the incremental analysis update (IAU) and covariance inflation methods, for which the relaxation-to-prior perturbations and spread (RTPP and RTPS, respectively) and multiplicative inflation (MULT) are considered. We evaluate the geostrophic balance and the analysis accuracy compared with the control experiment in which the IAU and covariance inflation are not applied. The results show that the IAU improves the geostrophic balance, degrades the accuracy, and reduces the ensemble spread, and that the RTPP and RTPS have the opposite effect. The experiment using a combination of the IAU and RTPP results in a significant improvement for both balance and analysis accuracy when the RTPP parameter is 0.8–0.9. The combination of the IAU and RTPS improves the balance when the RTPS parameter is ≤0.8 and increases the analysis accuracy for parameter values between 1.0 and 1.1, but the balance and analysis accuracy are not improved significantly at the same time. The experiments with MULT inflating the forecast ensemble spread by 5 % do not demonstrate sufficient skill in maintaining the balance and reproducing the surface flow field regardless of whether the IAU is applied or not. The 11 d ensemble forecast experiments show consistent results. Therefore, the combination of the IAU and RTPP with a parameter value of 0.8–0.9 is found to be the best setting for the EnKF-based ocean data assimilation system.

An efficient smart data mining framework based cloud internet of things for developing artificial intelligence of marketing information analysis

Because of the big volume of marketing data, a human analyst would be unable to uncover any useful information for marketing that could aid in the process of making decision. Smart Data Mining (SDM), which is considered an important field from Artificial Intelligence (AI) is completely assisting in the performance business management analytics and marketing information. In this study, most reliable six algorithms in SDM are applied; Naïve Bayes (NB), Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), ID3, and C4.5 on actual data of marketing for bank that taken from Cloud Internet of Thing (CIoT). The objectives of this study are to build an efficient framework to increase campaign of marketing for banks by identifying main characteristics that affect a success and to test the performance of CIoT and SDM algorithms. This study is expected to enhance the scientific contributions to investigating the marketing information capacities by integrating SDM with CIoT. The performances of SDM algorithms are calculated by eight measures; accuracy, balance accuracy, precision, mean absolute error, root mean absolute error, recall, F1- Score and running time. The experimental findings show that the proposed framework is successful, with higher accuracies and good performance. Results revealed that customer service & marketing tactics are essential for a Company’ success & survival. Also, the C4.5 has accomplished better achievement than the SVM, RF, LR, NB, & ID3. At the end, CIoT Platform was evaluated by response time, request rate & processing of bank data.

An intelligent blockchain-based access control framework with federated learning for genome-wide association studies

Genome-Wide Association Studies (GWAS) aim to find various variations in human disorders and have become one of the most commonly-used methods to find the pathogenesis and genetic mechanisms of complex diseases. However, the GWAS process needs to frequently search the genome-wide data, especially in the calculation process of multi-party participation. The statistical value calculation and interactive search of Single Nucleotide Polymorphisms (SNPs) and model training processes might easily disclose personal information. Therefore, to solve these problems, we propose a Blockchain-based access control Framework for GWAS with Federated Learning-BFGF. Specifically, before training local models, this framework implements Automated Quality Control (AQC) to guarantee the quality of training data. Design the authentication mechanism in blockchain to filter out users who are malicious attackers to protect the security of other users' information initially. To accelerate the speed of cloud model training and resist multiple attacks in federated learning, propose a periodic aggregation method combining differential privacy mechanisms. Finally, simulated experiments have shown that the BFGF framework can protect the security of genetic data and balance availability and accuracy.

Adaptive Timestepping, Linearization, and A Posteriori Error Control for Multiphase Flow of Immiscible Fluids in Porous Media with Wells

Summary This work focuses on the development of adaptive timesteps, stopping criteria, and error control strategies for reservoir simulations with fully implicit (FIM) solvers. Using a rigorous error control framework, an adaptive time selector combined with nonlinear stopping criteria is used to control nonlinear iterations as well as to balance accuracy and robustness for challenging nonlinear simulations. In reservoir simulation, efficiently solving a system of nonlinear equations arising from the FIM method can be computationally burdensome for complex recovery processes. Theoretically, an FIM reservoir simulator has no stability limit on the timestep size. In practice, standard Newton’s method often fails to converge for large timestep sizes and must therefore cut the timestep multiple times to achieve convergence, resulting in a large number of unnecessary iterations. Another cause of nonlinear convergence issues is the presence of wells, which are often presented as singular point/line sources strongly coupled to the reservoir model, posing additional restrictions on the timestep choice. Here, we use a posteriori error estimators to avoid unnecessary nonlinear iterations and timestep cuts when solving immiscible multiphase flow. First, we estimate error components (e.g., spatial, temporal, and nonlinear) and then apply these to balancing criteria, providing us with dynamic and adaptive strategies to control timestep and nonlinear iterations. The error estimators are fully and locally computable, inexpensive to use, and target the various error components, including well singularities. The method provides an adaptive criterion for stopping the nonlinear iteration process whenever the linearization error does not significantly affect the overall error. Simultaneously, timesteps are adapted to maintain a constant size of the temporal discretization error with respect to the total error. Altogether, this avoids using unnecessary linearization iterations, wasteful timestep cuts, and too small timesteps. To demonstrate the effectiveness of these adaptive features, we present results for a suite of cases, covering both standard benchmarks and conceptual problems incorporating highly heterogeneous media with multiple wells. The proposed timestep selector cooperates with the new stopping criteria to improve nonlinear solver performance and increases robustness for cases with high nonlinearity. Perhaps most important, the adaptive features ensure balanced temporal and spatial errors while maintaining sufficiently small nonlinear errors, which ensures solution accuracy by accurately reproducing saturation fronts, production plateau, and breakthrough times.

Vitamin D levels and Vitamin D-related gene polymorphisms in Chinese children with type 1 diabetes.

Low vitamin D levels may play a role in type 1 diabetes (T1D) susceptibility. Since 25(OH)D synthesis is genetically regulated, single nucleotide polymorphisms (SNPs) of important genes have also been shown to modulate the risk of T1D, so this study aimed to investigate the relationship between five SNPs in CYP2R1, DHCR7, CYP24A1, VDR genes, serum 25(OH)D levels and T1D in Chinese children. This case-control study included 141 T1D patients and 200 age-matched healthy children.25 (OH) D concentration was determined, genotyping was performed by High resolution melting (HRM). There was a significant difference in the prevalence of vitamin D deficiency, insufficiency, and sufficiency between T1D and healthy controls. (χ2 = 10.86, p = 0.004), however no evidence of the association between any group of SNPs and circulating 25(OH) D levels was observed. The allele distribution of CYP2R1(rs1993116) was significantly different between T1D and control group (p = 0.040), and the C allele carriers of rs1993116 had a higher risk of T1D than the T allele carriers, Carriers of the CC and CT genotypes of rs1993116 have higher T1D risk than those carrying the TT genotype. GMDR analysis revealed a significant interaction between CYP2R1(rs12794714) and CYP2R1(rs1993116) in the risk of T1D with a maximum testing balance accuracy of 60.39%.

Breast cancer prediction with transcriptome profiling using feature selection and machine learning methods

BackgroundWe used a hybrid machine learning systems (HMLS) strategy that includes the extensive search for the discovery of the most optimal HMLSs, including feature selection algorithms, a feature extraction algorithm, and classifiers for diagnosing breast cancer. Hence, this study aims to obtain a high-importance transcriptome profile linked with classification procedures that can facilitate the early detection of breast cancer.MethodsIn the present study, 762 breast cancer patients and 138 solid tissue normal subjects were included. Three groups of machine learning (ML) algorithms were employed: (i) four feature selection procedures are employed and compared to select the most valuable feature: (1) ANOVA; (2) Mutual Information; (3) Extra Trees Classifier; and (4) Logistic Regression (LGR), (ii) a feature extraction algorithm (Principal Component Analysis), iii) we utilized 13 classification algorithms accompanied with automated ML hyperparameter tuning, including (1) LGR; (2) Support Vector Machine; (3) Bagging; (4) Gaussian Naive Bayes; (5) Decision Tree; (6) Gradient Boosting Decision Tree; (7) K Nearest Neighborhood; (8) Bernoulli Naive Bayes; (9) Random Forest; (10) AdaBoost, (11) ExtraTrees; (12) Linear Discriminant Analysis; and (13) Multilayer Perceptron (MLP). For evaluating the proposed models' performance, balance accuracy and area under the curve (AUC) were used.ResultsFeature selection procedure LGR + MLP classifier achieved the highest prediction accuracy and AUC (balanced accuracy: 0.86, AUC = 0.94), followed by an LGR + LGR classifier (balanced accuracy: 0.84, AUC = 0.94). The results showed that achieved AUC for the LGR + LGR classifier belonged to the 20 biomarkers as follows: TMEM212, SNORD115-13, ATP1A4, FRG2, CFHR4, ZCCHC13, FLJ46361, LY6G6E, ZNF323, KRT28, KRT25, LPPR5, C10orf99, PRKACG, SULT2A1, GRIN2C, EN2, GBA2, CUX2, and SNORA66.ConclusionsThe best performance was achieved using the LGR feature selection procedure and MLP classifier. Results show that the 20 biomarkers had the highest score or ranking in breast cancer detection.

Generalized Simultaneous Localization and Mapping (G-SLAM) as unification framework for natural and artificial intelligences: towards reverse engineering the hippocampal/entorhinal system and principles of high-level cognition.

Simultaneous localization and mapping (SLAM) represents a fundamental problem for autonomous embodied systems, for which the hippocampal/entorhinal system (H/E-S) has been optimized over the course of evolution. We have developed a biologically-inspired SLAM architecture based on latent variable generative modeling within the Free Energy Principle and Active Inference (FEP-AI) framework, which affords flexible navigation and planning in mobile robots. We have primarily focused on attempting to reverse engineer H/E-S “design” properties, but here we consider ways in which SLAM principles from robotics may help us better understand nervous systems and emergent minds. After reviewing LatentSLAM and notable features of this control architecture, we consider how the H/E-S may realize these functional properties not only for physical navigation, but also with respect to high-level cognition understood as generalized simultaneous localization and mapping (G-SLAM). We focus on loop-closure, graph-relaxation, and node duplication as particularly impactful architectural features, suggesting these computational phenomena may contribute to understanding cognitive insight (as proto-causal-inference), accommodation (as integration into existing schemas), and assimilation (as category formation). All these operations can similarly be describable in terms of structure/category learning on multiple levels of abstraction. However, here we adopt an ecological rationality perspective, framing H/E-S functions as orchestrating SLAM processes within both concrete and abstract hypothesis spaces. In this navigation/search process, adaptive cognitive equilibration between assimilation and accommodation involves balancing tradeoffs between exploration and exploitation; this dynamic equilibrium may be near optimally realized in FEP-AI, wherein control systems governed by expected free energy objective functions naturally balance model simplicity and accuracy. With respect to structure learning, such a balance would involve constructing models and categories that are neither too inclusive nor exclusive. We propose these (generalized) SLAM phenomena may represent some of the most impactful sources of variation in cognition both within and between individuals, suggesting that modulators of H/E-S functioning may potentially illuminate their adaptive significances as fundamental cybernetic control parameters. Finally, we discuss how understanding H/E-S contributions to G-SLAM may provide a unifying framework for high-level cognition and its potential realization in artificial intelligences.

Impact of a Digital Balancing Tool on Femur and Tibial First Total Knee Arthroplasty: A Prospective Nonrandomized Controlled Trial

BackgroundRecent developments in intra-operative sensor technology provide surgeons with predictive and real-time feedback on joint balance. It remains unknown, however, whether these technologies are better suited to femur-first or tibia-first workflows. This study investigates the balance accuracy, precision and early patient outcomes between the femur-first and tibial-first workflows using a digital gap-balancing tool. MethodsOne-hundred six patients had posterior cruciate ligament sacrificing total knee arthroplasty using a digital joint tensioner. The participants were divided into 4 groups with different visibility to balance data 1) Femur-first blinded data, 2) Femur-first not blinded data, 3) Tibia-first blinded data, 4) Tibia-first not blinded data with predictive balancing. Knee Injury and Osteoarthritis Outcome Score and University of California at Los Angeles activity level were recorded at 1-year. ResultsGroup 4 reported less midflexion imbalance (40°) compared to all other groups (1: 1.5 mm, 2: 1.7 mm, 3: 1.6 mm, 4: 1.0 mm, P < .031) and reduced variance compared to all other groups at 40° and 90° (P < .012), resulting in an increased frequency of joints balanced within 2 mm throughout flexion in group 4 (1: 69%, 2: 65%, 3: 67%, 4: 91%, P < .006). No differences were found between 3-month, 6-month, or 1-year outcome scores between technique. ConclusionsImprovements in balance were observed in midflexion instability and balance variability throughout flexion when a tibia-first approach in combination with a digital balancing tool was used. The combination of a digital balancing tool and a tibia-first approach allowed a target joint balance to be achieved more accurately compared to a non-sensor augmented or femur-first approach.

Design and Implementation of an Efficient Electronic Bank Management Information System Based Data Warehouse and Data Mining Processing

The quantity of electronic bank data grows exponentially with development of Information Technology (IT). The size of these data is impossible for traditional database and human analyst to come up with interesting information that will help in process of decision making. Management Information System (MIS) based Data warehouse (DW) and Data Mining (DM) techniques support the development of IT and process of management decision-making. But the traditional DW size make the query complex, which may cause unacceptable delay in decision support queries. Thus, in this paper an Efficient Electronic Bank MIS based DW and Mining Processing (EEBMIS-DWMP) was developed with cluster and non-cluster indexed view to provide decision-makers with both best response time and precise information. Also, analysis of the multilayer perception neural network, naïve Bayes, random forest, logistic regression, support vector machine and C5.0 on a real-world data of bank was done to improve effectiveness for campaign by analyzing the most useful features that influence campaign success. Results offer how the proposed EEBMIS-DWMP developed bank organizations by comparing performance of system with and without index view in terms of balance accuracy, accuracy, precision, recall, mean absolute error, root mean square error, F measure and running time. Conclusions from results offers that EEBMIS-DWMP can construct a database for each customer, a storage system that integrates data from a variety of sources into a single unified framework, decrease errors and time required to prepare financial reports, quickly access for information, analysis of data in multivariate, accurate prediction of competent, profitability segmentation.

Error prediction of balancing machine calibration based on machine learning method

This paper proposes a method of compensating for brake disc balance error by using a machine learning algorithm. Automobile brake discs in the production process will inevitably produce unbalance. The unevenness produced by the uneven mass distribution will produce high-frequency vibration in the process of high-speed rotation, which seriously affects the safety of the vehicle and the personal safety of the occupants. The key measure to solve this problem is to correct the unbalance with higher precision before the brake disc leaves the factory. The traditional correction method is to improve the detection accuracy of unbalance to achieve balance accuracy. In this paper, we hope to improve the balance accuracy by compensating for the errors generated in the correction process. We use the random forest model, decision tree model, and support vector machine model to predict the errors of the balancing machine during the calibration process. The main idea is to take the parameters of the brake disc and the features in the milling process as input and the error amplitude as output. The results show that the stochastic forest model has higher prediction accuracy than the decision tree model and support vector machine model. This method can also predict errors from other sources, such as thermal errors.