Intelligent Judgment Research Articles

Revisiting democratic pedagogy at secondary schools in Nepal

American educator and philosopher Dewey developed a progressive pedagogical philosophy that emphasizes democratic principles. His approach centers on three main dispositions: equality, intelligent judgment and action, and working together. This study aims to investigate how secondary school teachers in Kailali District implement these dispositions in their teaching processes. This study used a qualitative research design. Ten secondary school teachers from Kailali District, Sudurpaschim Province, were selected through purposive and convenient sampling. A two-day seminar with two sessions was held to explore how these teachers implement the three core dispositions of Dewey's democratic pedagogy. The collected information, insights, and views are analyzed and presented through vignettes based on Dewey's three core dispositions. This study finds that secondary school teachers in Nepal encounter several challenges, including overcrowded classrooms, inadequate teacher training, insufficient educational resources, and limited familiarity with contemporary teaching methodologies. While the national curriculum framework highlights the importance of democratic pedagogy, it lacks detailed guidelines for its practical application. Furthermore, the study indicates that secondary school teachers in Nepal continues to predominantly employ traditional teaching methods, underscoring the critical need for reform.

Study on Cavitation Determination of Pump Turbine Model Test

Abstract At present, it is difficult to determine whether cavitation has occurred in the pump turbine model test, with a large human error and no more accurate judgment guidelines, this paper proposes an intelligent algorithm based on image recognition to determine when cavitation has occurred. The method collects images of water bodies in the recognition area, extracts and processes the features of fixed parts, and compares them with database image data to finally realize the recognition of cavitation phenomena. The results show that the method captures the cavitation phenomenon more accurately and efficiently, and achieves the intelligent judgment of the cavitation phenomenon.

Assessing Accented Speech with a Data-Based Assessment Framework: The Key to Evidence-based Accent-Management

Background: Foreign-accent Management continues to be a rapidly growing area. However, due to the vast variability and lack of professional regulation in this area of practice, Speech-language Pathologists and English-as-Second-Language teachers grapple with the basics of assessment, setting goals, planning therapy, and identifying successful outcomes for accent Management. Purpose: The purpose of this paper is to introduce practitioners/teachers to a framework of assessment of accent-related communication concerns. The CAAI assessment framework provides major areas to include in assessment of accented speech, along with methods to assess with key examples. Using a proven assessment framework helps identify the key speech and language issues that interfere with communication, thereby setting an useful baseline for effective intervention, numerically tracking progress along the way, and conducting a systematic post-intervention outcome assessment. Methodology: Twenty-two areas of communication are identified and described in detail as key areas to identify accent-related concerns. Pre- and post-therapy assessment data, intelligibility judgments by independent raters, and the clients’ judgments are used to quantitatively diagnose, set goals in therapy, and measure eventual therapy outcomes. Conclusions: Use of a data-driven, objective assessment method helps clinicians to be evidence-based, systematic, and consistent in helping individuals with accented speech. As a result, clients’ goals of clear communication can be met rapidly and efficiently.

Fuzzy Logic Empowered NetWatch: Revolutionizing Aquaculture with IoT-based Intelligent Monitoring and Management in Bangladesh

The innovative study "Fuzzy Logic Empowered NetWatch: Revolutionizing Aquaculture with IoT-based Intelligent Monitoring and Management in Bangladesh" is a step towards the dawn of a new era in fish farming techniques that emphasize accuracy and efficiency. Using fuzzy logic controllers in the NetWatch system, stakeholders involved in aquaculture can access a degree of intelligence and adaptability that is not possible with standard management techniques. Fuzzy logic techniques are included in NetWatch, allowing it to make intelligent judgments based on the intricate and frequently unpredictable nature of aquaculture systems, in addition to monitoring and controlling environmental parameters and water quality. Because of its dynamic adaptability, the system can mitigate risks and optimize results in real time while successfully responding to changing situations. Furthermore, NetWatch offers a comprehensive picture of the aquaculture ecosystem by combining pond-specific data with more general environmental insights, facilitating better-informed macro and micro decision-making. With this thorough knowledge, fish farmers can allocate resources more efficiently, reduce waste, and sustainably increase productivity. Moreover, Fuzzy Logic Empowered NetWatch's revolutionary potential offers opportunities for the aquaculture industry, transcending the boundaries of individual fish ponds. Bangladesh can establish itself as a global leader in sustainable aquaculture methods and set new benchmarks for production, efficiency, and environmental stewardship using IoT-based intelligent monitoring and management. Fuzzy Logic Empowered NetWatch catalyzes a systemic shift in how we approach aquaculture management rather than merely technology. Bangladesh may achieve previously unattainable levels of sustainability and productivity by utilizing fuzzy logic and the Internet of Things. This would guarantee a better future for the country's aquaculture sector and the communities it serves.

Research on Intelligent Airbag Technology for Snowboarding Suits based on Arduino and Embedded Systems

With the popularity of skiing, the safety protection of participants has become an important issue. Traditional skiing equipment often overlooks flexibility and instant response capabilities while providing protection. This study aims to explore the application of embedded systems and Arduino in improving the safety of snowboarding suits. By integrating MPU6050 accelerometer and gyroscope sensor, real-time monitoring and intelligent judgment of skiers' sports status are realized. Based on the Arduino platform, a set of intelligent airbag buffer device is designed, which can automatically inflate when dangerous movements such as falling are detected, thereby reducing impact force and protecting skiers from serious injuries. The experimental results show that the system can effectively recognize the falling action and complete the inflation and deflation of the airbag in a short time, verifying the feasibility and effectiveness of the system. This study not only provides a new safety protection scheme for skiing suit design, but also provides a reference for the development of other sports safety equipment.

SelANet: decision-assisting selective sleep apnea detection based on confidence score

BackgroundOne of the most common sleep disorders is sleep apnea syndrome. To diagnose sleep apnea syndrome, polysomnography is typically used, but it has limitations in terms of labor, cost, and time. Therefore, studies have been conducted to develop automated detection algorithms using limited biological signals that can be more easily diagnosed. However, the lack of information from limited signals can result in uncertainty from artificial intelligence judgments. Therefore, we performed selective prediction by using estimated respiratory signals from electrocardiogram and oxygen saturation signals based on confidence scores to classify only those sleep apnea occurrence samples with high confidence. In addition, for samples with high uncertainty, this algorithm rejected them, providing a second opinion to the clinician.MethodOur developed model utilized polysomnography data from 994 subjects obtained from Massachusetts General Hospital. We performed feature extraction from the latent vector using the autoencoder. Then, one dimensional convolutional neural network—long short-term memory (1D CNN-LSTM) was designed and trained to measure confidence scores for input, with an additional selection function. We set a confidence score threshold called the target coverage and performed optimization only on samples with confidence scores higher than the target coverage. As a result, we demonstrated that the empirical coverage trained in the model converged to the target coverage.ResultTo confirm whether the model has been optimized according to the objectives, the coverage violation was used to measure the difference between the target coverage and the empirical coverage. As a result, the value of coverage violation was found to be an average of 0.067. Based on the model, we evaluated the classification performance of sleep apnea and confirmed that it achieved 90.26% accuracy, 91.29% sensitivity, and 89.21% specificity. This represents an improvement of approximately 7.03% in all metrics compared to the performance achieved without using a selective prediction.ConclusionThis algorithm based on selective prediction utilizes confidence measurement method to minimize the problem caused by limited biological information. Based on this approach, this algorithm is applicable to wearable devices despite low signal quality and can be used as a simple detection method that determine the need for polysomnography or complement it.

Effects of multitasker babble noise on speech intelligibility in children with velopharyngeal insufficiency (VPI).

Intelligibility measurement is influenced by the characteristics of a speaker, listener and contextual factors. This study addresses the clinical problem of measuring speech intelligibility in children with velopharyngeal insufficiency (VPI) in real-world conditions. The purpose of the study was to investigate the effects of background noise on speech intelligibility in speakers with velopharyngeal insufficiency (VPI) compared to typical speech. The study further determined the contribution of nasalance and articulation accuracy in judgments of intelligibility. Fifteen speakers diagnosed with VPI and their typical peers provided audio recordings of 20 sentences from the Hearing in Noise Test. Speech samples were presented over quiet and noise (+5dB signal-to-noise ratio) conditions to 70 naïve listeners. Intelligibility scores from naïve listeners' orthographic transcriptions were obtained as the percentage of correctly identified words. A repeated-measures analysis of variance showed diagnosis of VPI (F(1, 28) = 13.44, p = 0.001, and presence of noise (F(1, 28) = 39.18, p < 0.001) significantly affected the intelligibility scores. There was no interaction between the diagnosis of VPI and noise (F(1, 28) = 0.06, p = 0.80). The multivariate regression analysis indicated that nasalance and articulation accuracy explain a significant amount of variance in the intelligibility scores of VPI speakers in quiet (F(2, 12) = 7.11, p < 0.05, R2 = 0.55, R2 Adjusted = 0.47) and noise (F(2, 12) = 6.32, p < 0.05, R2 = 0.51, R2 Adjusted = 0.43), but the significance mainly came from the effect of percentage of consonants correct (β = 0.97, t(12) = 2.90, p = 0.01). Percentage of consonants correct significantly increased the speech intelligibility in either with or without noise conditions. The current work suggests that background noise will significantly affect reductions in intelligibility in both groups; the effect is more prominent in VPI speech. It was also further noted that articulation accuracy significantly affected intelligibility in quiet and noise rather than nasalance scores. What is already known on the subject Intelligibility measurement is influenced by the characteristics of a speaker, listener and contextual factors. Accordingly, it is essential to determine the degree to which speech assessments in the clinic can predict communication difficulties in the presence of background noise in real life. Background noise can adversely cause speech intelligibility degradation in individuals with speech disorders. What this study adds The study examined the effects of background noise on speech intelligibility in speakers with velopharyngeal insufficiency (VPI) secondary to cleft palate compared to typical speech. The study results suggested that the presence of background noise will significantly affect reductions in intelligibility in both groups; however, the effect is more prominent in VPI speech. What are the clinical implications of this work? We found out that the intelligibility of VPI speech is lower in the presence of background noise, and therefore, assessments of speech intelligibility in clinical settings should take this into account. To ensure effective communication in noisy environments, recommended strategies include selecting quiet locations, eliminating potential distractions and supplementing communication with nonverbal cues. It is important to recognize that the effectiveness of these strategies may vary depending on the individual and the specific communication context.

Data-Oriented Intelligent Judgment Platform for Red Tourism Industry in Northeast Guangdong Based on Tourism Big Data Clustering Algorithm

Defining big data in the context of tourism forecasting, summarizes the changes it brings to tourism business decision-making. The experimental part deals with the tourism metadata shared by the website, and applies a suitable clustering algorithm to generate the density pattern of the most frequently visited places by tourists. At the same time, it proposes a method to obtain local core categories through the discovery of maximal cliques, and proposes a parallelization of the maximal clique discovery algorithm. strategy, and then propose a parallel strategy for the entire algorithm and experiment on real datasets. Realize the intelligence, intelligence, and semantics of the video surveillance network, so that the police can be freed from the labor of watching video surveillance. Through the extraction and correlation of data feature attributes, the collision can obtain "object laws", so as to make efficient and accurate decisions.

Democratic faith. A philosophical profile of Richard J. Bernstein

Democratic faith. A philosophical profile of Richard J. Bernstein

Collective intelligence improves probabilistic diagnostic assessments.

Collective intelligence, the "wisdom of the crowd," seeks to improve the quality of judgments by aggregating multiple individual inputs. Here, we evaluate the success of collective intelligence strategies applied to probabilistic diagnostic judgments. We compared the performance of individual and collective intelligence judgments on two series of clinical cases requiring probabilistic diagnostic assessments, or "forecasts". We assessed the quality of forecasts using Brier scores, which compare forecasts to observed outcomes. On both sets of cases, the collective intelligence answers outperformed nearly every individual forecaster or team. The improved performance by collective intelligence was mediated by both improved resolution and calibration of probabilistic assessments. In a secondary analysis looking at the effect of varying number of individual inputs in collective intelligence answers from two different data sources, nearly identical curves were found in the two data sets showing 11-12% improvement when averaging two independent inputs, 15% improvement averaging four independent inputs, and small incremental improvements with further increases in number of individual inputs. Our results suggest that the application of collective intelligence strategies to probabilistic diagnostic forecasts is a promising approach to improve diagnostic accuracy and reduce diagnostic error.

Clean mucosal area detection of gastroenterologists versus artificial intelligence in small bowel capsule endoscopy.

Studies comparing the detection of clean mucosal areas in capsule endoscopy (CE) using human judgment versus artificial intelligence (AI) are rare. This study statistically analyzed gastroenterologist judgments and AI results. Three hundred CE video clips (100 patients) were prepared. Five gastroenterologists classified the video clips into 3 groups (≥75% [high], 50%-75% [middle], and < 50% [low]) according to their subjective judgment of cleanliness. Visualization scores were calculated using an AI algorithm based on the predicted visible area, and the 5 gastroenterologists' judgments and AI results were compared. The 5 gastroenterologists evaluated CE clip video quality as "high" in 10.7% to 36.7% and as "low" in 28.7% to 60.3% and 29.7% of cases, respectively. The AI evaluated CE clip video quality as "high" in 27.7% and as "low" in 29.7% of cases. Repeated-measures analysis of variance (ANOVA) revealed significant differences in the 6 evaluation indicators (5 gastroenterologists and 1 AI) (P < .001). Among the 300 judgments, 90 (30%) were consistent with 5 gastroenterologists' judgments, and 82 (91.1%) agreed with the AI judgments. The "high" and "low" judgments of the gastroenterologists and AI agreed in 95.0% and 94.9% of cases, respectively. Bonferroni's multiple comparison test showed no significant difference between 3 gastroenterologists and AI (P = .0961, P = 1.0000, and P = .0676, respectively) but a significant difference between the other 2 with AI (P < .0001). When evaluating CE images for cleanliness, the judgments of 5 gastroenterologists were relatively diverse. The AI produced a relatively universal judgment that was consistent with the gastroenterologists' judgements.

Traits of highly innovative people

Purpose: The aim of the paper is to analyze the most important traits of highly innovative people. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: The publication concentrate on problems connected with various aspects of traits of highly inventive people. In the paper there is an analysis of the very important role of cognition in innovativeness. In the paper there is an analysis of various aspects of the role of cognition like: general intelligence, genius, cognitive abilities and observer judgments of intelligence. On the basis of the analysis following main traits of highly innovative peoples can be distinguished: sensitive, not motivated by money, sense of destiny, adaptable, tolerant of ambiguity, observant, perceive world differently, see possibilities, question asker, ability to synthetize, able to fantasize, flexible, fluent, imaginative, intuitive, original, ingenious, energetic, sense of humor, self-actualizing, self-disciplined, self-knowledgeable, specific interests, divergent thinker, curious, open-ended, independent, severely critical, non-conforming, confident, risk taker. Originality/value: Detailed analysis of all subjects related to the problems connected traits of highly innovative people. Keywords: Industry 4.0; innovation, industrial enterprise, innovative traits, highly innovative people. Category of the paper: literature review.

Conditional Generative Adversarial Network Approach for Autism Prediction

Autism Spectrum Disorder (ASD) requires a precise diagnosis in order to be managed and rehabilitated. Non-invasive neuroimaging methods are disease markers that can be used to help diagnose ASD. The majority of available techniques in the literature use functional magnetic resonance imaging (fMRI) to detect ASD with a small dataset, resulting in high accuracy but low generality. Traditional supervised machine learning classification algorithms such as support vector machines function well with unstructured and semi structured data such as text, images, and videos, but their performance and robustness are restricted by the size of the accompanying training data. Deep learning on the other hand creates an artificial neural network that can learn and make intelligent judgments on its own by layering algorithms. It takes use of plentiful low-cost computing and many approaches are focused with very big datasets that are concerned with creating far larger and more sophisticated neural networks. Generative modelling, also known as Generative Adversarial Networks (GANs), is an unsupervised deep learning task that entails automatically discovering and learning regularities or patterns in input data in order for the model to generate or output new examples that could have been drawn from the original dataset. GANs are an exciting and rapidly changing field that delivers on the promise of generative models in terms of their ability to generate realistic examples across a range of problem domains, most notably in image-to-image translation tasks and hasn't been explored much for Autism spectrum disorder prediction in the past. In this paper, we present a novel conditional generative adversarial network, or cGAN for short, which is a form of GAN that uses a generator model to conditionally generate images. In terms of prediction and accuracy, they outperform the standard GAN. The proposed model is 74% more accurate than the traditional methods and takes only around 10 min for training even with a huge dataset.

Teachers of English and their Perceptions towards ESL Errors

This study entitled 'Teachers of English and their Perceptions towards ESL Errors' aimed at evaluating and determining gravity of grammatical errors in terms of acceptability and intelligibility judgments. Quantitative research methodology was utilized in this study. The data was gathered with the aid of a questionnaire prepared on the basis of the result of error analysis conducted earlier. The questionnaires were mailed to English teachers of different private and public schools, colleges and universities of Nepal and worldwide. Two hundred twenty (220) useable surveys were collected altogether for the study: 100 surveys from Nepali English teachers, 100 surveys from native English speaking teachers, 20 each from Britain, Australia, New Zealand, Canada and America, and likewise, 20 surveys from non-Nepali English teachers. Received responses were analyzed and explained descriptively. The findings of the study showed that the native English teachers evaluated the errors far more leniently than Nepali and non- Nepali English teachers. There is no any significant difference in evaluation of errors found in between Nepali and non-Nepali English teachers, but compared to native English teachers, they were found statistically and significantly different in their judgments of errors in acceptability and intelligibility both. On the other hand, while comparing judgments of learners' errors in between the native English teachers; it was revealed that there were not any significant differences found there. It is recommended that the native English speaking teachers’ perception of errors be rightly explored and accordingly evaluation scales be developed and the teachers be made aware of such universal rating scales of grammatical errors while evaluating learners' errors.

Intelligent Detection Algorithm of Medium and Low Voltage Switch Performance Based on the Improved K-means Clustering

The performance of medium and low voltage switches will directly affect the reliability of medium and low voltage distribution networks. The medium and low voltage switch algorithm’s failure form is mainly mechanical. In order to realize its intelligent detection, a medium and low voltage switch detection algorithm based on the improved K-means clustering algorithm is proposed. Optimizing the algorithm’s clustering center selection establishes a model of the switch working state under different vibration signals and an intelligent detection algorithm for medium and low voltage switches with intelligent judgment, adaptive adjustment, and remote fault diagnosis. Experiments show that: the detection algorithm of the medium and low voltage switch controller based on the improved K-means algorithm can more accurately identify the fault of the switch algorithm according to the vibration signals, which provides a new idea for the fault identification and monitoring of the medium and low voltage switch.

DC Series Arc Fault Detection Method in Photovoltaic System Based on Multiple Frequency Selections for Common-Mode Conductive Voltage

DC series arc faults are one of the main causes of fire hazards in photovoltaic power systems. The common method of the traditional dc series arc fault detection uses wideband current sensors to obtain the arc current signal, extract arc characteristic frequency components, and make intelligent judgments based on numerous samples. This kind of detection method requires high-performance hardware and has a high cost. This article takes the common-mode conductive voltage (CMCV) signal, which is produced by arc faults, as the object and confirms the feature frequency band of the arc CMCV signal based on the spectral analysis. To obtain the feature frequency component and perform frequency reduction at the hardware level, multiple frequency selection and detection circuits were designed, and the feature quantities of the time and frequency domains were extracted from the output of the circuits. Subsequently, a two-dimensional arc fault feature plane was constructed, and an arc/normal similarity criterion and arc fault judgment algorithm were proposed. Finally, this article discusses or experimentally verifies the effectiveness, reliability, and anti-interference ability of the proposed method. The proposed method is low cost, simple, reliable, and effective and provides a novel idea and approach for dc series arc fault detection.

Retraction Note: Research on the intelligent judgment of traffic congestion in intelligent traffic based on pattern recognition technology

Retraction Note: Research on the intelligent judgment of traffic congestion in intelligent traffic based on pattern recognition technology

Assessment of L2 intelligibility: Comparing L1 listeners and automatic speech recognition

AbstractAn increasing number of studies are exploring the benefits of automatic speech recognition (ASR)–based dictation programs for second language (L2) pronunciation learning (e.g. Chen, Inceoglu &amp; Lim, 2020; Liakin, Cardoso &amp; Liakina, 2015; McCrocklin, 2019), but how ASR recognizes accented speech and the nature of the feedback it provides to language learners is still largely under-researched. The current study explores whether the intelligibility of L2 speakers differs when assessed by native (L1) listeners versus ASR technology, and reports on the types of intelligibility issues encountered by the two groups. Twelve L1 listeners of English transcribed 48 isolated words targeting the /ɪ-i/ and /æ-ε/ contrasts and 24 short sentences that four Taiwanese intermediate learners of English had produced using Google’s ASR dictation system. Overall, the results revealed lower intelligibility scores for the word task (ASR: 40.81%, L1 listeners: 38.62%) than the sentence task (ASR: 75.52%, L1 listeners: 83.88%), and highlighted strong similarities in the error types – and their proportions – identified by ASR and the L1 listeners. However, despite similar recognition scores, correlations indicated that the ASR recognition of the L2 speakers’ oral productions mirrored the L1 listeners’ judgments of intelligibility in the word and sentence tasks for only one speaker, with significant positive correlations for one additional speaker in each task. This suggests that the extent to which ASR approaches L1 listeners at recognizing accented speech may depend on individual speakers and the type of oral speech.

A classification model of power operation inspection defect texts based on graph convolutional network

Aiming at the problems of diversification, complexity and islanding of power operation and inspection data and the high dependence of operation and inspection operations on expert experience and normative information, the key technology research of intelligent judgment of defect types of power operation inspection equipment is carried out. For the field of power operation and inspection, the defect text classification algorithm based on graph convolutional neural network is proposed. And the practical tests in a large defect text network diagram built by main transformer defect reports are performed. And the proposed model achieves better classification results than 7 benchmark models in the defect text classification task. Specifically, the Accuracy, Weighed-Precision, and Weighed-F1 indicators reach 73.39, 72.42, and 72.21 respectively, which improves the model’s ability to identify defect types to a greater extent and plays an important role in improving the intelligence and digitalization of power operation and inspection work.

Prediction of Tea Polyphenols, Free Amino Acids and Caffeine Content in Tea Leaves during Wilting and Fermentation Using Hyperspectral Imaging.

The withering and fermentation degrees are the key parameters to measure the processing technology of black tea. The traditional methods to judge the degree of withering and fermentation are time-consuming and inefficient. Here, a monitoring model of the biochemical components of tea leaves based on hyperspectral imaging technology was established to quantitatively judge the withering and fermentation degrees of fresh tea leaves. Hyperspectral imaging technology was used to obtain the spectral data during the withering and fermentation of the raw materials. The successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and uninformative variable elimination (UVE) are used to select the characteristic bands. Combined with the support vector machine (SVM), random forest (RF), and partial least square (PLS) methods, the monitoring models of the tea polyphenols (TPs), free amino acids (FAA) and caffeine (CAF) contents were established. The results show that: (1) CARS performs the best among the three feature band selection methods, and PLS performs the best among the three machine learning models; (2) the optimal models for predicting the content of the TPs, FAA, and CAF are CARS-PLS, SPA-PLS, and CARS-PLS, respectively, and the coefficient of determination of the prediction set is 0.91, 0.88, and 0.81, respectively; and (3) the best models for quantitatively judging the withering and fermentation degrees are FAA-SPA-PLS and TPs-CARS-PLS, respectively. The model proposed in this study can improve the monitoring efficiency of the biochemical components of tea leaves and provide a basis for the intelligent judgment of the withering and fermentation degrees in the process of black tea processing.