Recognition Problem Research Articles

Flexible Label-Induced Manifold Broad Learning System for Multiclass Recognition.

Broad learning system (BLS), which emerges as a lightweight network paradigm, has recently attracted great attention for recognition problems due to its good balance between efficiency and accuracy. However, the supervision mechanism in BLS and its variants generally relies on the strict binary label matrix, which imposes limitations on approximation and fails to adequately align with the data distribution. To address this issue, in this article, two novel flexible label-induced BLS models with the manifold manner are proposed, whose notable characteristics are as follows. First, two proposed label relaxation strategies can both enlarge the margins between different categories and simultaneously enhance the diversity within labels. Second, the integration of manifold geometrical criterion enables the models to capture local feature structures, ensuring the obtained flexible labels align better with the similarity between samples. Third, the proposed models can be optimized efficiently with the alternating direction method of multipliers. Each iteration benefits from a closed-form solution, facilitating the optimization process. Extensive experiments and thorough theoretical analysis are intended to show the advantages of our proposed models compared to other state-of-the-art recognition algorithms.

Human Behavior Recognition Based on CNN-LSTM Hybrid and Multi-Sensing Feature Information Fusion

To address the human activity recognition problem and its application in practical situations, a CNN-LSTM hybrid neural network model capable of automatically extracting sensor data features and memorizing temporal activity data is designed and improved by integrating CNN and gated recurrent units as a variant of RNN. A multi-channel spatiotemporal fusion network-based two-person interaction behavior recognition method is proposed for two-person skeletal sequential behavior recognition. Firstly, a viewpoint invariant feature extraction method is used to extract two-player skeleton features, then a two-layer cascaded spatiotemporal fusion network model is designed, and finally, a multi-channel spatiotemporal fusion network is used to learn multiple sets of two-player skeleton features separately to obtain multi-channel fusion features, and the fusion features are used to recognize the interaction behavior, and the weights are shared among the channels. Applying the algorithm in the paper to the UCF101 dataset for experiments, the accuracy of the two-person cross-object experiment can reach 96.42% and the accuracy of the cross-view experiment can reach 97.46%. The method in the paper shows better performance in two-player interaction behavior recognition compared to typical methods in this field.

On the Similarity over the Ring of Integers of Certain Nilpotent Matrices of Maximal Rank

This paper is devoted to the problem of matrix similarity recognition over the ring of integers for some families of matrices. Namely, nilpotent upper triangular matrices of maximal rank are considered such that only first and second superdiagonals of these matrices are non-zero. Several necessary conditions are obtained for similarity of such matrices to matrices of the form superdiag(a1,a2,…,an−1) with a single non-zero superdiagonal, that is a generalization of the Jordan cell Jn(0)=superdiag(1,1,…,1) . These conditions are formulated in simple terms of divisibility and greatest common divisors of matrix elements. The result is obtained by reducing the problem of similarity recognition to the problem of solving in integers a system of linear equations and applying the known necessary similarity conditions for arbitrary matrices. Under some additional conditions on the elements a1,a2,…,an−1 of the first superdiagonal of matrix A , it is proven that the matrix A is similar to matrix superdiag(a1,a2,…,an−1) regardless of the values of the elements of the second superdiagonal. Moreover, for the considered matrices of the third and the fourth orders, easily verifiable necessary and sufficient similarity conditions are obtained describing their similarity to a matrix of the form superdiag(a1,a2,…,an−1) .

АВТОМАТИЗАЦИЯ ПРОЦЕДУР ОБНАРУЖЕНИЯ ЛЕСНЫХ ПОЖАРОВ ПО РЕЗУЛЬТАТАМ ОБРАБОТКИ ВИДЕО

The results of developing a proposal for automating forest fire detection procedures based on pattern recognition methods are presented. The mathematical problem of constructing a feature space and the problem of pattern recognition based on the results of comparing feature vectors have been formulated and posed. As part of the solution to the problem under consideration, it is proposed to form a feature space from two vectors characterizing the image of the current and previous frames. The choice of histograms characterizing the distribution of image brightness intensity as feature vectors is justified. Experimental results are presented that confirm the validity of the theoretical conclusions and recommendations for automating detection procedures 
 are formulated.

Knowledge Graph Construction and Representation Method for Potato Diseases and Pests

Potato diseases and pests have a serious impact on the quality and yield of potatoes, and timely prevention and control of potato diseases and pests is essential. A rich knowledge reserve of potato diseases and pests is one of the most important prevention and control measures; however, valuable knowledge is buried in the massive data of potato diseases and pests, making it difficult for potato growers and managers to obtain and use it in a timely manner and to develop the potential of knowledge. Therefore, this paper explores the construction method of a knowledge graph for automatic knowledge extraction, which extracts the knowledge of potato diseases and pests scattered in heterogeneous data from multiple sources, organises it into a semantically related knowledge base, and provides potato growers with professional knowledge and timely guidance to effectively prevent and control potato diseases and pests. In this paper, a data corpus on potato diseases and pests, called PotatoRE, is first constructed. Then, a model of ALBert-BiLSTM-Self_Att-CRF is designed to extract knowledge from the corpus to form a triplet structure, which is imported into the Neo4j graph database for storage and visualisation. Furthermore, the performance of the model constructed in this paper is compared and verified using the datasets PotatoRE and People’s Daily. The results show that compared to the SOTA models of ALBert BiLSTM-CRF and ALBert BiGRU-CRF, the accuracy of our model has been improved by 2.92% and 3.12%, respectively, using PotatoRE. Compared to the Bert BiLSTM-CRF model on two datasets, our model not only improves the accuracy, recall, and F1 values, but also has a higher efficiency. The model in this paper solves the problem of the difficult recognition of nested entities. On this basis, through comparative experiments, the TransH model is used to effectively represent the constructed knowledge graph, which lays the foundation for achieving inference, extension, and automatic updating of the knowledge base. The achievements of the thesis have made certain contributions to the automatic construction of large-scale knowledge bases.

Design of Internet Platform for English Teaching in Colleges and Universities under the Background of Big Data

Abstract This paper constructs an English teaching platform based on a speech recognition algorithm and Internet technology, adopts a three-tier architecture to design the web application, and combines the server and browser technology to realize the operation of data interaction with the SQL database. A rectangular bandpass filter is selected for narrow-band information extraction by introducing the sinc function to improve the feature extraction efficiency of speech signals at each frequency. CTC is used to obtain input feature-to-state mapping to solve the sequence problem in speech recognition and achieve end-to-end speech recognition. To achieve the final audio recognition results, the speech decoding algorithm is employed to select the optimal speech output. The results show that in the listening score, the score of the experimental group is 9, and the score of the control is 6. The completion rate of the topics among students who studied on the English teaching platform was higher than that among students who were taught in ordinary English. The completion rate is 0.82 for English questions of grammar type and 0.98 for English questions of listening type, and the average number of passes is the least number of passes, and the number of times is 2.

Open set recognition fault diagnosis framework based on convolutional prototype learning network for nuclear power plants

As an Open Set Recognition (OSR) problem, nuclear power plant fault diagnosis requires not only the correct classification of known faults, but also the effective identification of unknown faults, which cannot be satisfied by most previously developed models. In this study, a novel nuclear power plant OSR fault diagnosis framework based on Convolutional Prototype Learning (CPL) is proposed, in which CPL is used to extract discriminative fault features from raw nuclear power plant data. Besides, two classification methods, One-Class Support Vector Machine (OCSVM) method and Prototype Matching by Distance (PMD) method, are developed to complete the fault diagnosis of open space for the framework. To verify the feasibility and effectiveness of the proposed OSR framework, numerical experiments on 24 OSR tasks with high-dimensional and strong-nonlinear complex nuclear power plant simulation data are conducted, and the OSR performance evaluate by normalized accuracy and Youden's index, feature visualization and convergence rates are analyzed. Results show that compared with adopting traditional Convolutional Neural Network (CNN), the proposed CPL-based framework can significantly boost the OSR diagnostic performance on nuclear power plant fault diagnosis tasks, which benefits from the excellent ability of CPL to extract intra-class compact and inter-class separable feature representation.

A Deep Learning Method of Human Identification from Radar Signal for Daily Sleep Health Monitoring.

Radar signal has been shown as a promising source for human identification. In daily home sleep-monitoring scenarios, large-scale motion features may not always be practical, and the heart motion or respiration data may not be as ideal as they are in a controlled laboratory setting. Human identification from radar sequences is still a challenging task. Furthermore, there is a need to address the open-set recognition problem for radar sequences, which has not been sufficiently studied. In this paper, we propose a deep learning-based approach for human identification using radar sequences captured during sleep in a daily home-monitoring setup. To enhance robustness, we preprocess the sequences to mitigate environmental interference before employing a deep convolution neural network for human identification. We introduce a Principal Component Space feature representation to detect unknown sequences. Our method is rigorously evaluated using both a public data set and a set of experimentally acquired radar sequences. We report a labeling accuracy of 98.2% and 96.8% on average for the two data sets, respectively, which outperforms the state-of-the-art techniques. Our method excels at accurately distinguishing unknown sequences from labeled ones, with nearly 100% detection of unknown samples and minimal misclassification of labeled samples as unknown.

Distance and similarity measures of Hesitant bi-fuzzy set and its applications in renewable energy systems

The concept of Hesitant bi-fuzzy set (HBFS) is an extension of dual hesitant fuzzy set, which play an important role in minimizing uncertainty in an efficient way. Distance and Similarity measures are an important tool for illustrating and integrating the differences between elements and its class and have a great importance in decision making, medical diagnosis and pattern recognition. To enhance and expand the applicability of HBFS, in this study, we have defined a series of distance and similarity measures of HBFS in both continuous and discrete environments. Since the information in day-to-day life may be biased, therefore, to show the importance of each factor/criterion weighted measures are also defined. Due to global warming and climate change, the utilization of renewable energy systems not only plays a significant role but also decreases greenhouse gas emissions. Choosing the right energy system affects each nation's economic, social, and environmental development. To show the efficiency of proposed measures, these measures are implemented in renewable energy systems. An algorithm for hesitant bi-fuzzy TOPSIS has also been developed to demonstrate the application of these measures in decision- making. Finally, a comparative study is done in pattern recognition problem, to show the supremacy and efficiency of the proposed measures.

Проблема цифрового распознавания поведения личности в условиях современных вызовов: социально-философский и теологический анализ

Introduction. The article examines the problem of digital personality recognition as one of the dimensions of artificial intelligence from the perspective of socio-philosophical and theological analysis. Theoretical analysis. One of the distinctive features of the scientific and technological development of modern society is that various artificial intelligence (AI) systems have moved from the category of science fiction, utopian projects, innovative developments, prototypes and market innovations to the category of familiar and little-noticed elements of everyday life. This technology, also known as the Internet of Behavior, involves identifying and assessing an individual’s behavior both from the point of view of compliance with requirements and from the point of view of identifying patterns of individual behavior that can become the basis for his identification. The prerequisites and conditions for the emergence of the phenomenon of “golemization” of the individual are analyzed, i.e. its reduction to a set of external physical actions and manifestations without taking into account internal motives as one of the risks that require social philosophical and theological reflection. Conclusion. Within the framework of socio-philosophical and theological discourse, digital recognition of personal behavior can have a positive meaning, because increases attention to responsibility for one’s actions and potentially leads to a decrease in crime rates, as well as a negative value due to the possibility of creating virtual identities that differ from a person’s true identity.

Artificial Consciousness: Misconception(s) of a Self-Fulfilling Prophecy Nobody Wants

The rise of Artificial Intelligence (AI) has produced prophets and prophecies announcing that the age of artificial consciousness is near. Not only does the mere idea that any machine could ever possess the full potential of human consciousness suggest that AI could replace the role of God in the future, it also puts into question the fundamental human right to freedom and dignity. This position paper takes the stand that, in the light of all we currently know about brain evolution and the never-stopping formation of adaptive neural circuitry for learning, memory, decision making and, ultimately, fully conscious reasoning and creativity in the human species, the idea of an artificial consciousness appears misconceived. The paper highlights some of the major reasons why. While awareness to external stimuli for processes such as perception, recognition, and operational problem solving is under the direct control of functionally specific brain networks associated with sensory and cognitive functions across animal species, consciousness is a unique property of the human mind. Potentiated by brain evolution, consciousness has come to be when humans became able to represent, and reflect on, the Self in relation to past, present and future, and to project these representations into possible worlds by drawing and other forms of conceptual and creative expression. Epigenetically determined, shaped by experience, capable of representing real and non-real world states, consciousness is enabled by context-dependent adaptive brain circuits that have evolved on the grounds of self-organizing functional interactions at different levels of integration in a from-local-to global functional brain design. The evolution of the latter being continuous, the limits of consciousness are unpredictable. If cracking the computational code to human consciousness were possible, the resulting algorithms would have to be able to generate temporal activity patterns simulating long-distance signal reverberation across the brain and the de-correlation of spatial signal contents from their temporal signatures. In the light of scientific evidence for complex interactions between implicit (non-conscious) and explicit (conscious) representations in learning, memory, and the construction of conscious representation, the code would have to be capable of making all implicit processing explicit. Algorithms would have to be capable of a progressive, less and less arbitrary selection of temporal activity patterns in a continuously developing neural network structure akin to that of the human brain, from synapses to higher cognitive functional integration. The code would have to possess the self-organizing capacities that generate the brain signatures of phenomenal consciousness. In the biological brain, consolidation or extinction of these temporal brain signatures is driven by external event probabilities according to the principles of Hebbian learning. Consciousness is constantly fed by such learning, capable of generating stable representations despite an incommensurable amount of variability in input data, across time and across individuals, for life-long integration of experience data. Artificial consciousness would require probabilistic adaptive computations capable of emulating all the dynamics of human learning and memory that enable human intelligence and creativity. No AI is likely to ever have such potential.

Interpreting and processing side-scan sonar data with the objective of further automation

One of the most effective tools of remote sensing and visualization of underwater surfaces and objects are acoustic devices, in particular side-scan sonars (SSS). Recently, largely due to the emergence of affordable devices, the geography and scope of application of this device has been significantly expanded. Meanwhile, despite certain progress achieved in terms of improving and minimizing the SSS hardware, the software used remains, in general, at a basic level, providing the operator mainly with a simple tool for visualizing benthic environments and data recording for further post-processing. Existing experience in SSS exploitation reveals that the key issue of interpreting acoustic images lies in the physical essence of their acquisition. Arguably, attempts to implement methods of automated interpretation of optical images have no perspective. Hence, the objective of this paper is to provide a theoretical and practical background of SSS data interpretation and processing. A layout for the potential automation of this process is provided with the objective of eliminating human operation in the process of conducting survey and search operations. The authors consider the conditions of SSS exploitation including special attention to such issues as the vastness of search areas, which, as argued is the key problem of data and data pattern recognition. SSS image recognition is an issue relevant for a wide range of academic topics such as geometric distortion, image recognition, and navigation satellite system target localization.

A novel ant lion optimizer-salp swarm algorithm for inverse heat conduction problem in pipeline fluid temperature recognition

A novel ant lion optimizer-salp swarm algorithm for inverse heat conduction problem in pipeline fluid temperature recognition

Partial and Simultaneous Transitive Orientations via Modular Decompositions

A natural generalization of the recognition problem for a geometric graph class is the problem of extending a representation of a subgraph to a representation of the whole graph. A related problem is to find representations for multiple input graphs that coincide on subgraphs shared by the input graphs. A common restriction is the sunflower case where the shared graph is the same for each pair of input graphs. These problems translate to the setting of comparability graphs where the representations correspond to transitive orientations of their edges. We use modular decompositions to improve the runtime for the orientation extension problem and the sunflower orientation problem to linear time. We apply these results to improve the runtime for the partial representation problem and the sunflower case of the simultaneous representation problem for permutation graphs to linear time. We also give the first efficient algorithms for these problems on circular permutation graphs.

SE-Lightweight YOLO: Higher Accuracy in YOLO Detection for Vehicle Inspection

Against the backdrop of ongoing urbanization, issues such as traffic congestion and accidents are assuming heightened prominence, necessitating urgent and practical interventions to enhance the efficiency and safety of transportation systems. A paramount challenge lies in realizing real-time vehicle monitoring, flow management, and traffic safety control within the transportation infrastructure to mitigate congestion, optimize road utilization, and curb traffic accidents. In response to this challenge, the present study leverages advanced computer vision technology for vehicle detection and tracking, employing deep learning algorithms. The resultant recognition outcomes provide the traffic management domain with actionable insights for optimizing traffic flow management and signal light control through real-time data analysis. The study demonstrates the applicability of the SE-Lightweight YOLO algorithm, as presented herein, showcasing a noteworthy 95.7% accuracy in vehicle recognition. As a prospective trajectory, this research stands poised to serve as a pivotal reference for urban traffic management, laying the groundwork for a more efficient, secure, and streamlined transportation system in the future. To solve the existing vehicle detection problems in vehicle type recognition, recognition and detection accuracy need to be improved, alongside resolving the issues of slow detection speed, and others. In this paper, we made innovative changes based on the YOLOv7 framework: we added the SE attention transfer mechanism in the backbone module, and the model achieved better results, with a 1.2% improvement compared with the original YOLOv7. Meanwhile, we replaced the SPPCSPC module with the SPPFCSPC module, which enhanced the trait extraction of the model. After that, we applied the SE-Lightweight YOLO to the field of traffic monitoring. This can assist transportation-related personnel in traffic monitoring and aid in creating big data on transportation. Therefore, this research has a good application prospect.

Material Recognition Technology of Internal Loose Particles in Sealed Electronic Components Based on Random Forest

<div>Sealed electronic components are the basic components of aerospace equipment, but the issue of internal loose particles greatly increases the risk of aerospace equipment. Traditional material recognition technology has a low recognition rate and is difficult to be applied in practice. To address this issue, this article proposes transforming the problem of acquiring material information into the multi-category recognition problem. First, constructing an experimental platform for material recognition. Features for material identification are selected and extracted from the signals, forming a feature vector, and ultimately establishing material datasets. Then, the problem of material data imbalance is addressed through a newly designed direct artificial sample generation method. Finally, various identification algorithms are compared, and the optimal material identification model is integrated into the system for practical testing. The results show that the proposed material identification technology achieves an accuracy rate of 85.7% in distinguishing between metal and nonmetal materials, and an accuracy rate of 73.8% in identifying specific materials. This result surpasses the accuracy rates achieved by all currently known identification techniques. At the same time, this technology represents the latest expansion in the field of loose particles detection and holds significant practical value for improving system robustness. The proposed technique theoretically can be widely applied to other fault diagnosis fields with similar signal generation mechanisms.</div>

U-net based vortex detection in Bose–Einstein condensates with automatic correction for manually mislabeled data

Quantum vortices in Bose–Einstein condensates (BECs) are essential phenomena in condensed matter physics, and precisely locating their positions, especially the vortex core, is a precondition for studying their properties. With the rise of machine learning, there is a possibility to expedite the localization process and provide accurate predictions. However, traditional machine learning requires particular considerable amount of manual data annotation, leading to uncontrollable accuracy. In this paper, we utilize the U-Net method to detect vortex positions accurately at the pixel level and propose an Automatic Correction Labeling (ACL) approach to optimize the acquisition of data sets for vortex localization in BECs. This approach addresses inaccuracies in the labeled vortex positions and improves the accuracy of vortex localization, especially the vortex core positions, while enhancing the tolerance for human mislabeling. The main process involves Rough Labeling →\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\rightarrow $$\\end{document} Machine Learning →\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\rightarrow $$\\end{document} Probability Region Search →\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\rightarrow $$\\end{document} Data Relabeling →\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\rightarrow $$\\end{document} Machine Learning again. The objective of ACL is to secure more accurate labeled data for model retraining. Through vortex localization experiments conducted in a two-dimensional Bose-Einstein condensate, our results establish the following: 1. Even under conditions of biased and missing manual annotations, U-Net can still accurately locate vortex positions; 2. Vortices exhibit certain regularities, and training U-Net with a small number of samples yields excellent predictive consequences; 3. The machine learning vortex locator based on the ACL method effectively corrects errors in manually annotated data, significantly improving the model’s performance metrics, thus enhancing the precision and metrics of vortex localization. This substantial advancement in the application of machine learning in vortex localization provides an effective way for vortex dynamics localization. Furthermore, this method of obtaining more accurate positions of approximate human labels through machine learning offers new insights for machine learning in other types of image recognition problems.

Bearing fault diagnosis of rail vehicles traction motor based on adaptive periodic segment matrix and singular value decomposition

The early fault characteristics of rolling bearings are relatively weak and accompanied by large amounts of noise, resulting in the collected bearing vibration signal having an extremely low signal-to-noise ratio, hence creating challenges in diagnosing faults in rail vehicle bearings. This paper proposes a method based on an adaptive period segment matrix and singular value decomposition to extract fault information from bearing fault impulse signals, in response to the problem of unstable period recognition in existing rolling bearing fault diagnosis methods due to strong background noise. Firstly, a periodic segment estimation method based on the average envelope spectrum is proposed. By calculating the envelope spectrum of the vibration signal, a method is proposed for assessing period length and period constraint F in segments. Then this method is applied to pinpoint the fault impact peak and the position of the vibration signal. Utilizing the fault impact peak and the calculated period constraint F, a strict rank-1 adaptive period segment matrix (APSM) is introduced. Finally, singular value decomposition is performed on this matrix, the bearing impulse vibration signal can be reconstructed through the first maximum singular value. The simulation and experimental analysis results of rolling bearings signals demonstrate the performance of the proposed method, even under severe background noise conditions(−20 dB), and the calculated envelope spectrum signal-to-noise ratio further supports the adaptive periodic segment matrix has an excellent ability to identify and extract periodic components, thus validating its usefulness as a fault diagnosis approach for rolling bearings.

Multiscale Dynamic Graph Representation for Biometric Recognition with Occlusions.

Occlusion is a common problem with biometric recognition in the wild. The generalization ability of CNNs greatly decreases due to the adverse effects of various occlusions. To this end, we propose a novel unified framework integrating the merits of both CNNs and graph models to overcome occlusion problems in biometric recognition, called multiscale dynamic graph representation (MS-DGR). More specifically, a group of deep features reflected on certain subregions is recrafted into a feature graph (FG). Each node inside the FG is deemed to characterize a specific local region of the input sample, and the edges imply the co-occurrence of non-occluded regions. By analyzing the similarities of the node representations and measuring the topological structures stored in the adjacent matrix, the proposed framework leverages dynamic graph matching to judiciously discard the nodes corresponding to the occluded parts. The multiscale strategy is further incorporated to attain more diverse nodes representing regions of various sizes. Furthermore, the proposed framework exhibits a more illustrative and reasonable inference by showing the paired nodes. Extensive experiments demonstrate the superiority of the proposed framework, which boosts the accuracy in both natural and occlusion-simulated cases by a large margin compared with that of baseline methods. The source code is available here, or you can visit this website: https://github.com/RenMin1991/Dyamic-Graph-Representation.

Machine learning-based fault diagnosis for high-voltage transmission lines

In order to comply with the speed and diversification process of human economic and technological development, the power system gradually develops in the direction of intelligence and complexity, and it is urgent to combine transmission line fault diagnosis methods with machine learning pattern recognition problems in order to reflect the representative characteristics of transmission lines under different operating conditions and different operation modes. In this paper, a convolutional neural network (CNN) model is established for transmission line fault diagnosis, and a series of parameters such as weights, bias, learning rate, batch size and loss function of the proposed CNN model are debugged through experimental comparison, and the best CNN model structure is determined through experimental comparison and debugging, and the recognition accuracy is higher than other traditional machine learning algorithms, which can be as high as 98.38%, with validity, practicality and advancedness.