Intelligent Analysis Techniques Research Articles

Feature fusion method for pulmonary tuberculosis patient detection based on cough sound.

Since the COVID-19, cough sounds have been widely used for screening purposes. Intelligent analysis techniques have proven to be effective in detecting respiratory diseases. In 2021, there were up to 10 million TB-infected patients worldwide, with an annual growth rate of 4.5%. Most of the patients were from economically underdeveloped regions and countries. The PPD test, a common screening method in the community, has a sensitivity of as low as 77%. Although IGRA and Xpert MTB/RIF offer high specificity and sensitivity, their cost makes them less accessible. In this study, we proposed a feature fusion model-based cough sound classification method for primary TB screening in communities. Data were collected from hospitals using smart phones, including 230 cough sounds from 70 patients with TB and 226 cough sounds from 74 healthy subjects. We employed Bi-LSTM and Bi-GRU recurrent neural networks to analyze five traditional feature sets including the Mel frequency cepstrum coefficient (MFCC), zero-crossing rate (ZCR), short-time energy, root mean square, and chroma_cens. The incorporation of features extracted from the speech spectrogram by 2D convolution training into the Bi-LSTM model enhanced the classification results. With traditional futures, the best TB patient detection result was achieved with the Bi-LSTM model, with 93.99% accuracy, 93.93% specificity, and 92.39% sensitivity. When combined with a speech spectrogram, the classification results showed 96.33% accuracy, 94.99% specificity, and 98.13% sensitivity. Our findings underscore that traditional features and deep features have good complementarity when fused using Bi LSTM modelling, which outperforms existing PPD detection methods in terms of both efficiency and accuracy.

Perspectives on Preparedness for Chemical, Biological, Radiological, and Nuclear Threats in the Middle East and North Africa Region: Application of Artificial Intelligence Techniques.

Over the past 3 decades, the diversity of ethnic, religious, and political backgrounds worldwide, particularly in countries of the Middle East and North Africa (MENA), has led to an increase in the number of intercountry conflicts and terrorist attacks, sometimes involving chemical and biological agents. This warrants moving toward a collaborative approach to strengthening preparedness in the region. In disaster medicine, artificial intelligence techniques have been increasingly utilized to allow a thorough analysis by revealing unseen patterns. In this study, the authors used text mining and machine learning techniques to analyze open-ended feedback from multidisciplinary experts in disaster medicine regarding the MENA region's preparedness for chemical, biological, radiological, and nuclear (CBRN) risks. Open-ended feedback from 29 international experts in disaster medicine, selected based on their organizational roles and contributions to the academic field, was collected using a modified interview method between October and December 2022. Machine learning clustering algorithms, natural language processing, and sentiment analysis were used to analyze the data gathered using R language accessed through the RStudio environment. Findings revealed negative and fearful sentiments about a lack of accessibility to preparedness information, as well as positive sentiments toward CBRN preparedness concepts raised by the modified interview method. The artificial intelligence analysis techniques revealed a common consensus among experts about the importance of having accessible and effective plans and improved health sector preparedness in MENA, especially for potential chemical and biological incidents. Findings from this study can inform policymakers in the region to converge their efforts to build collaborative initiatives to strengthen CBRN preparedness capabilities in the healthcare sector.

Implementation of an intelligence-based framework for anomaly detection on the demand-side of sustainable compressed air systems

The implementation of intelligent techniques produces good results in automating fault finding and predicting future outcomes. These approaches have been on the increase in the past years, especially so to detect faults within Compressed Air Systems (CASs). With the use of intelligent techniques, one could minimise the manual and time-consuming aspect of CAS maintenance, improve the environmental impact of the system, while minimising downtime. This paper proposes a general framework for the implementation of intelligent analysis techniques within a real-world system. Such an approach has been implemented on the demand-side of a CAS. In literature, no open datasets are available for use by artificial intelligence models. Hence, as part of this research, a fault generating and monitoring system has been connected to an existing production machine in a manufacturing site to collect the required data. Two classification machine learning methods were implemented and compared across a number of performance metrics. Both the general framework, and its implementation, provide a stepping stone in integrating smart systems with real-time intelligent data analytics for the demand-side of a CAS. These systems would provide a sustainable CAS operation through the effective detection of anomalies and their timely repair.

Algorithm Design and Application of Grammatical Structure and Function Learning in Chinese Writing for Intelligent Analysis

Abstract This study utilizes the Larsen-Freeman three-dimensional theory of grammar teaching to quantitatively analyze the grammatical attributes “form”, “meaning” and “usage” in the Chinese writing process. The study uses the PCFG model to extract the syntactic dependency tree and represents the features of the tree structure with the help of TF-IDF. Meanwhile, the Transformer model extracts the semantic and syntactic features of sentences. At the same time, the positional encoder is removed to ensure that the model obtains information from the syntactic level. Further, an unsupervised method for generating grammatical textual recapitulation for Chinese Writing is proposed, as well as a Grammar Tree Generator for generating recapitulated sentences, which efficiently extracts the features of the input grammatical sequences. In addition, the study also includes writing feature analysis based on grammatical function matching, using a hierarchical clustering algorithm to analyze the similarity of 60 grammatical functions. Finally, validation was performed on 30 Chinese writing text collections, each containing 10 compositions, and the results showed high accuracy of unlabeled grammatical function recognition. The LDA model determined the optimal number of writing topics to be 150. This study highlights the potential application of intelligent analysis techniques in improving the quality of Chinese Writing. It provides new perspectives for an in-depth understanding of the interplay between grammatical structures and functions in the Chinese writing.

토픽모델링을 활용한 상담과정에서 상담자와 내담자 간 언어변화

Objectives The aim of this study is to utilize counseling content between counselors and clients during the coun-seling process using topic modeling, extract key themes, and investigate language changes over time.
 Methods A single counselor conducted individual counseling sessions with Client A and Client B, each lasting 50 minutes, once a week for a total of 5 sessions. The transcripts of these counseling sessions, including a follow-up session, underwent refinement before undergoing frequency analysis and topic modeling.
 Results In the frequency analysis, key keywords in Client A's counseling sessions included ‘thoughts,’ ‘people,’ and ‘emotions,’ while in Client B's counseling sessions, ‘mother,’ ‘child,’ and ‘stories’ predominated. LDA topic modeling revealed that Client A's major topics were ‘thoughts and emotions’ and ‘personal life,’ while topics such as ‘husband and emotions’ and ‘people's emotions’ were extracted for the counselor, each occurring 5 times. For Client B, primary topics included ‘professor's thoughts’ and ‘complimentary stories,’ while for the counselor, top-ics like ‘worrisome stories’ and ‘mother's studies’ were extracted, each occurring 8 times. Additionally, DTM topic modeling results showed that both Client A and B had higher topic appearances at the beginning and end stages of counseling, with lower appearances during the counseling process. In contrast, the counselor had a high topic appearance from the beginning to the end, while in the follow-up session, both the client and the counselor showed lower topic appearances.
 Conclusions Through this study, extracting core keywords and topics during the counseling process allowed for the identification of client interests and themes, as well as the analysis of topic changes over time. This in-formation can assist counselors in focusing on the client's primary concerns, thus improving the quality of the counseling process. Furthermore, the use of artificial intelligence analysis techniques such as topic modeling in analyzing the content of individual counseling sessions is significant in presenting language changes between counselors and clients during the counseling process.

Intelligent Optimization of Gas Flooding Based on Multi-Objective Approach for Efficient Reservoir Management

The efficient development of oil reservoirs mainly depends on the comprehensive optimization of the subsurface fluid flow process. As an intelligent analysis technique, artificial intelligence provides a novel solution to multi-objective optimization (MOO) problems. In this study, an intelligent agent model based on the Transformer framework with the assistance of the multi-objective particle swarm optimization (MOPSO) algorithm has been utilized to optimize the gas flooding injection–production parameters in a well pattern in the Middle East. Firstly, 10 types of surveillance data covering 12 years from the target reservoir were gathered to provide a data foundation for model training and analysis. The prediction performance of the Transformer model reflected its higher accuracy compared to traditional reservoir numerical simulation (RNS) and other intelligent methods. The production prediction results based on the Transformer model were 21, 12, and 4 percentage points higher than those of RNS, bagging, and the bi-directional gated recurrent unit (Bi-GRU) in terms of accuracy, and it showed similar trends in the gas–oil ratio (GOR) prediction results. Secondly, the Pareto-based MOPSO algorithm was utilized to fulfil the two contradictory objectives of maximizing oil production and minimizing GOR simultaneously. After 10,000 iterations, the optimal injection–production parameters were proposed based on the generated Pareto frontier. To validate the feasibility and superiority of the developed approach, the development effects of three injection–production schemes were predicted in the intelligent agent model. In the next 400 days of production, the cumulative oil production increased by 25.3% compared to the average distribution method and 12.7% compared to the reservoir engineering method, while GOR was reduced by 27.1% and 15.3%, respectively. The results show that MOPSO results in a strategy that more appropriately optimizes oil production and GOR compared to some previous efforts published in the literature. The injection–production parameter optimization method based on the intelligent agent model and MOPSO algorithm can help decision makers to update the conservative development strategy and improve the development effect.

Main challenges (generation and returned energy) in a deep intelligent analysis technique for renewable energy applications.

In recent years, there has been an increasing demand for Renewable Energy (RE), which refers to energy generated from natural sources such as solar and wind power. Consequently, numerous scientific studies have been conducted to explore various approaches for controlling this type of energy. This work aims to highlight the main challenges associated with the generation and return of RE by employing intelligent data analysis techniques, specifically deep learning. These challenges are examined from different perspectives, including pre-processing, the methodology and techniques used in deep learning, and the evaluation measures employed. Some of the research in this area is focused on predicting the highest amount of energy that can be generated at a particular time and location, while others aim to predict the largest amount of electrical energy that can be returned to the electricity grid to optimize the use of surplus RE resources and maximize their benefits. These efforts are crucial to ensure the effective and continuous operation of the electrical grid. However, despite the efficiency and high accuracy of these models, they are hindered by complex calculations that require considerable time to produce the desired outcomes. Additionally, numerous measures are employed to evaluate the models' performance, including assessing their completion rate, quality of performance, accuracy of results, efficiency, error rate, feasibility of investing in RE, and the largest amount of surplus energy that can be returned to the electricity generation network.

Tool and Workpiece Condition Classification Using Empirical Mode Decomposition (EMD) with Hilbert–Huang Transform (HHT) of Vibration Signals and Machine Learning Models

Existing studies have attempted to determine the tool chipping condition using the indirect method of data capture and intelligent analysis techniques considering machine parameters, and tool conditions using signal processing techniques. Due to the obstructive nature of the machining operation, however, it is daunting to use signal capturing to intelligently capture the condition of the tool as well as that of the workpiece. This study aimed to apply some advanced signal processing techniques to the vibration signals captured experimentally during machining operation for the decision making and analysis of tool and workpiece conditions. Vibration signals were captured during turning operations while using four (4) classes of tools, based on their flank wear. The signals were first pre-processed and decomposed using the Empirical Mode Decomposition (EMD) method. The Hilbert–Huang transform (HHT) was applied to the resulting IMFs obtained to compute the feature vectors used to classify the condition of the tool and workpiece. A total of 12 features, consisting of instantaneous properties such as instantaneous energy, instantaneous frequencies, and amplitudes, were obtained for data training and classification of tool conditions. To optimize the classification process, feature selection was performed using a genetic algorithm (GA) to reduce the number of features from 12 to 4 for data training and classification. The feature vectors were first trained for tool classification with a neural network scaled conjugate gradient (SCG) algorithm. The result showed that the model classification error was 0.102. Two other machine learning models, support vector machine (SVM) and K-Nearest Neighbors (KNN), were also implemented for classifying the tool conditions, from the feature vector, to determine the model that most accurately predicted the condition of the tool. To avoid bias and reduce misclassification errors, the k-fold cross-validation technique was applied with ‘k’ taken as 5 and 10. The computed feature vectors were used as inputs to train the machine learning model using both SVM and KNN models to classify the tool and workpiece condition during machining. The error loss of each model was evaluated and plotted to review the performance. The average overall error loss of 0.5031 was observed for the SVM model with 5-fold cross-validation, whereas the error loss of 0.0318 was observed for the KNN model with 5-fold cross-validation. The average overall error loss of 0.5009 was observed for the SVM model with 10-fold cross-validation when trained using the features selected by a genetic algorithm (GA), while the average overall error loss of 0.0343 was observed for the KNN model. The optimal performance of the SVM model was obtained when all features were used for the training, whereas the KNN model performed better when feature selection was implemented. The error losses of the models were evaluated to be less in KNN models, compared to SVM and SCG. The obtained results also showed that the developed KNN models performed 10 times better than the SVM model in predicting the tool condition from the captured vibration signal during the machining process.

Analisis peran penggalangan intelijen dalam pencegahan konflik disharmoni agama dengan budaya lokal

This study aims to analyze the role of increasing intelligence in an effort to prevent disharmony between religion and local culture. The concepts used in this research are acculturation, religion, culture, and religion as a cultural system. This research uses qualitative research with a case study approach. Data was collected through interviews, literature study and documentation. The data analysis technique uses Miles and Hubberman analysis techniques combined with Intelligence analysis techniques. The results showed that the failure of cultural acculturation led to the emergence of potential threats to the mindset of society, social, culture, economy, and security in the Yogyakarta area, and resulted in the erosion of Pancasila values, Javanese cultural identity, and nationalism. cultural identity. Strategies to anticipate potential threats can be overcome through the implementation of intelligence gathering to create conducive security and protect national interests. Activities carried out as a form of raising intelligence by the Regional Intelligence Agency (Bindan) are coordinating to make the Doa Bersama Lintas Agama event at Tugu Yogyakarta successful. This is done as a form of encouragement to minimize disharmony of religion and local culture. Besides that, as a persuasive step, dialogue and friendship from intelligence gathering carried out by Bindan.

Adaptive Multiscale Superpixel Embedding Convolutional Neural Network for Land Use Classification

Currently, large quantities of remote sensing images with different resolutions are available for earth observation and land monitoring, which are inevitably demanding intelligent analysis techniques for accurately identifying and classifying land use (LU). This article proposes an adaptive multi-scale superpixel embedding convolutional neural network architecture (AMUSE-CNN) for tackling land use classification. Initially, the images are parsed via the superpixel representation so that the object based analysis (via a superpixel embedding CNN scheme) can be carried out with the pixel context and neighborhood information. Then, a multi-scale convolutional neural network (MS-CNN) is proposed to classify the superpixel based images by identifying object features across a variety of scales simultaneously in which multiple window sizes are used to fit to the various geometries of different LU classes. Furthermore, a proposed adaptive strategy is applied to best exert the classification capability of MS-CNN. Subsequently two modules are developed to fully implement the AMUSE-CNN architecture. More specifically, Module I is to determine the most suitable classes for each window size (scale) by applying majority voting to a series of MS-CNNs. Module II carries out the classification of the classes identified in Module I for the given scale used in MS-CNN and therefore complete the LU classification of the entire classes. The proposed AMUSE-CNN architecture is both quantitatively and qualitatively validated using remote sensing data collected from two cities, Kano and Lagos in Nigeria due to the spatially complex land use distribution. Experimental results show the superior performance of our approach against several state-of-the-art techniques.

Dimensionality analysis in machine learning failure detection models. A case study with LNG compressors

The correct selection of the subset of features for the design of a CBM (Condition Based Maintenance) strategy may result in models working faster and producing more accurate predictions. This must be done avoiding a phenomenon known as the curse of dimensionality, that appears in Machine Learning when algorithms must learn from an ample feature volume with abundant values within each one.This paper deals precisely with feature selection problem when dealing with compressors failure modes detection, using machine learning (ML) models. To that end, several feature selection ranking (FSR) methods are considered. These methods are basically algorithms which include wrappers and filters and they are able to provide a ranking about all the analysed features. A very important issue of these methods, is to realise the feature selection unconstrained of the Machine Learning algorithm to be later applied, and that will be tested in this paper. Stability and scalability of these methods will be also defined and discussed in the paper.The paper case study evaluates the possibility of detecting and therefore diagnosing the rod drop failure mode appearance in Liquid Natural Gas (LNG) cryogenic reciprocating compressors by using artificial intelligence analysis techniques. This failure mode implies unavailability of this equipment, which are critic in the LNG industry due to the cost of flaring or, less common and desirable, venting of the boil-off gas (BOG) recovered by its compression in order to send it out or use as fuel.More than 90.000 running hours and thirteen representative features are evaluated as well as thirteen FSR methods. Three most-used classifiers have been employed in order to assess the feature rankers’ effect over the models development to diagnose the rod drop failure.Conclusions are about the possibility, not only to diagnose the appearance of a failure mode like rod drop, but also to do it with considering a reduced number of features.

Data aggregation and indexing support from multiple sources using graph model in medical expert system databases

One of the key problems in developing and integrating expert systems for medical research is the problem of data aggregation. Most of the times, general information about the patient and data about undergone research procedures exist as part of several disconnected information systems, each using its own schema for presenting and storing information. The paper proposes a solution to aggregate research and patient data in medical establishments using formal projections mechanism, which allows to unify data extraction from separate data sources. Graph-based patient and research record representation is introduced, which allows to support and optimize complex queries for single patient and for a set of historical data from single research. Proposed representation mechanism is also shown to be effective for centralized processing using various data mining and intelligent analysis techniques.

Machine learning to support social media empowered patients in cancer care and cancer treatment decisions

BackgroundA primary variant of social media, online support groups (OSG) extend beyond the standard definition to incorporate a dimension of advice, support and guidance for patients. OSG are complementary, yet significant adjunct to patient journeys. Machine learning and natural language processing techniques can be applied to these large volumes of unstructured text discussions accumulated in OSG for intelligent extraction of patient-reported demographics, behaviours, decisions, treatment, side effects and expressions of emotions. New insights from the fusion and synthesis of such diverse patient-reported information, as expressed throughout the patient journey from diagnosis to treatment and recovery, can contribute towards informed decision-making on personalized healthcare delivery and the development of healthcare policy guidelines.Methods and findingsWe have designed and developed an artificial intelligence based analytics framework using machine learning and natural language processing techniques for intelligent analysis and automated aggregation of patient information and interaction trajectories in online support groups. Alongside the social interactions aspect, patient behaviours, decisions, demographics, clinical factors, emotions, as subsequently expressed over time, are extracted and analysed. More specifically, we utilised this platform to investigate the impact of online social influences on the intimate decision scenario of selecting a treatment type, recovery after treatment, side effects and emotions expressed over time, using prostate cancer as a model. Results manifest the three major decision-making behaviours among patients, Paternalistic group, Autonomous group and Shared group. Furthermore, each group demonstrated diverse behaviours in post-decision discussions on clinical outcomes, advice and expressions of emotion during the twelve months following treatment. Over time, the transition of patients from information and emotional support seeking behaviours to providers of information and emotional support to other patients was also observed.ConclusionsFindings from this study are a rigorous indication of the expectations of social media empowered patients, their potential for individualised decision-making, clinical and emotional needs. The increasing popularity of OSG further confirms that it is timely for clinicians to consider patient voices as expressed in OSG. We have successfully demonstrated that the proposed platform can be utilised to investigate, analyse and derive actionable insights from patient-reported information on prostate cancer, in support of patient focused healthcare delivery. The platform can be extended and applied just as effectively to any other medical condition.

Architecture of Maritime Awareness System Supplied with External Information

Abstract In this paper, we discuss a software architecture, which has been developed for the needs of the System for Intelligent Maritime Monitoring (SIMMO). The system bases on the state-of-the-art information fusion and intelligence analysis techniques, which generates an enhanced Recognized Maritime Picture and thus supports situation analysis and decision- making. The SIMMO system aims to automatically fuse an up-to-date maritime data from Automatic Identification System (AIS) and open Internet sources. Based on collected data, data analysis is performed to detect suspicious vessels. Functionality of the system is realized in a number of different modules (web crawlers, data fusion, anomaly detection, visualization modules) that share the AIS and external data stored in the system’s database. The aim of this article is to demonstrate how external information can be leveraged in maritime awareness system and what software solutions are necessary. A working system is presented as a proof of concept.

Интеллектуальный метод анализа для автоматизированного прогнозирования состояния КА

Интеллектуальный метод анализа для автоматизированного прогнозирования состояния КА

The Use of Hidden Markov Models for Anomaly Detection in Nuclear Core Condition Monitoring

Unplanned outages can be especially costly for generation companies operating nuclear facilities. Early detection of deviations from expected performance through condition monitoring can allow a more proactive and managed approach to dealing with ageing plant. This paper proposes an anomaly detection framework incorporating the use of the Hidden Markov Model (HMM) to support the analysis of nuclear reactor core condition monitoring data. Fuel Grab Load Trace (FGLT) data gathered within the UK during routine refueling operations has been seen to provide information relating to the condition of the graphite bricks that comprise the core. Although manual analysis of this data is time consuming and requires considerable expertise, this paper demonstrates how techniques such as the HMM can provide analysis support by providing a benchmark model of expected behavior against which future refueling events may be compared. The presence of anomalous behavior in candidate traces is inferred through the underlying statistical foundation of the HMM which gives an observation likelihood averaged along the length of the input sequence. Using this likelihood measure, the engineer can be alerted to anomalous behaviour, indicating data which might require further detailed examination. It is proposed that this data analysis technique is used in conjunction with other intelligent analysis techniques currently employed to analyse FGLT to provide a greater confidence measure in detecting anomalous behaviour from FGLT data.

Understanding Social and Organizational Aspects of the Work Domain Using Techniques and Technologies from Intelligence Analysis

Methods for the analysis of social and organizational aspects of the work domain are increasingly relevant as new technologies enable users to collect, process, and study entities and their relationships to design support systems. Cognitive Work Analysis (CWA) practitioners can leverage analytic methods and tools from military intelligence analysis to address the need to understand complex organizations and interactions. These communities are increasingly integrating Social Science methods with computational tools to navigate and understand the network of human related data. This paper provides a brief overview of how CWA tools could be used in conjunction with intelligence analysis techniques and technologies for the analysis of social and organizational aspects of the work domain.