Pattern Recognition Techniques Research Articles

A novel AI-powered method for robust identification of operational phases in refrigerators

Refrigerators are essential appliances in our daily lives, while the accurate identification of their operational phase is crucial for various applications, such as energy management and maintenance. This paper introduces a novel method to predict the operational phase of refrigerators based on power consumption data, exploiting artificial intelligence (AI) and pattern recognition techniques. Specifically, the proposed method initially identifies the driving speed of the compressor (fixed or variable speed) utilizing an Adaptive Boosting, (AdaBoost) classifier. Following that, a Long Short-Term Memory (LSTM) classifier was designed for each type of compressor based on extracted statistical features. This classifier is used to categorize daily power consumption into the different operational phases. Moreover, the proposed AI-driven diagnostic tool facilitates operational-based prescriptive maintenance by addressing periodic and phase-specific maintenance actions while providing homeowners with informed suggestions on alternative refrigerator models that offer greater cost savings and reduced annual energy consumption. The proposed method was tested on real and simulated data for over 20 years, with the AdaBoost classifier achieving an accuracy close to 97% and an F1 score close to 97% and the LSTM nodes achieving an accuracy of approximately 95% in all cases. Finally, the method demonstrated robustness when applied to new datasets, showing comparable performance on unknown data, rendering it a powerful tool for refrigerator management and maintenance.

An Enhanced System to Detect Cyberbullying and Automate Reporting on Twitter Using Text Based Pattern Recognition Technique

An Enhanced System to Detect Cyberbullying and Automate Reporting on Twitter Using Text Based Pattern Recognition Technique

Exploring Genomic Patterns to Identify Drug-Resistant TB: A Comprehensive Study of Age, Gender, Lineage, and Outcome

Background: Tuberculosis (TB) is a prevalent bacterial infection caused by Mycobacterium tuberculosis (Mtb), primarily affecting the lungs, with rising incidence rates. Drug-resistant TB strains, including multidrug-resistant (MDR) and extensively drug-resistant (XDR) types, pose challenges with poor treatment success rates and increased mortality. Demographic factors and genomic patterns contribute to drug resistance, emphasizing the need for new drugs targeting specific genomic patterns.Methods: This study analyzes the impact of demographics, treatment outcomes, and genomic mutations on drug-resistant TB using pattern identification techniques and whole-genome sequencing to discover therapeutic targets. This study primarily focused on the identification and analysis of MDR and XDR strains, considering various demographic characteristics and treatment outcomes. A dataset comprising 2,602 observations was utilized, and pattern recognition techniques were employed to identify significant features. Additionally, the study employs the whole-genome sequencing (WGS) pipeline to identify single nucleotide polymorphisms (SNPs) that result in new mutations in drug-resistant Mtb strains.Results: The findings of the study revealed that XDR and MDR non-XDR TB were the most prevalent types of drug resistance, and they were associated with unfavorable treatment outcomes, including death or treatment failure. Males exhibited a higher susceptibility to both XDR and MDR non-XDR TB compared to females. The age of onset for both types of resistance was approximately 40 years. Among the observed variants in 31 commonly occurring genes,Conclusion: These findings introduce a novel set of therapeutic targets specific to MDR and XDR Mtb types, which warrant further investigation for potential therapeutic interventions.Keywords: Tuberculosis; Drug Resistance; Multidrug resistance; Demographics; Dataset; Gene; Mutations

Construction of Athletes' Physical Condition Monitoring and Analysis System Using Biometrics Recognition Technology

Biometrics recognition technology utilizes unique biological characteristics such as fingerprints, iris patterns, facial features, or voiceprints to identify and authenticate individuals. Through advanced algorithms and pattern recognition techniques, biometric systems capture and analyze these physiological or behavioral traits to verify a person's identity. This technology offers high levels of security and accuracy, making it valuable for various applications including access control, time and attendance tracking, border security, and digital payments. The construction of an athletes' physical condition monitoring and analysis system utilizing biometric recognition technology represents a significant advancement in sports science. By integrating cutting-edge biometric sensors and recognition algorithms, this system provides real-time insights into athletes' physiological parameters and performance metrics. Athlete performance monitoring and analysis play pivotal roles in optimizing training strategies, preventing injuries, and enhancing overall athletic performance. In this paper, we propose a novel approach leveraging the Hybrid Multi-Instance Ensemble Classifier (HMIEC) combined with biometric recognition technology to accurately classify athlete data and assess their physical condition. Our study explores the effectiveness of HMIEC across multiple biometric modalities, including ECG-based, fingerprint-based, and facial recognition, in identifying athletes and monitoring their physical parameters. Through a series of experiments and analyses, demonstrate HMIEC's superior classification performance compared to other classifiers, with high accuracy, sensitivity, specificity, and area under the curve (AUC). The reductions in heart rate from 75 to 65 beats per minute, increase in oxygen saturation levels from 98% to 99%, decreases in blood pressure readings from 120/80 mmHg to 110/70 mmHg, and enhancements in flexibility from 30 to 35 centimeters, muscle strength from 100 lbs to 120 lbs, and endurance capacity from 20 to 25 minutes.

Rationalizing Graphene-ZnO Composites for Gas Sensing via Functionalization with Amines.

The rational design of composites based on graphene/metal oxides is one of the pillars for advancing their application in various practical fields, particularly gas sensing. In this study, a uniform distribution of ZnO nanoparticles (NPs) through the graphene layer was achieved, taking advantage of amine functionalization. The beneficial effect of amine groups on the arrangement of ZnO NPs and the efficiency of their immobilization was revealed by core-level spectroscopy, pointing out strong ionic bonding between the aminated graphene (AmG) and ZnO. The stability of the resulting Am-ZnO nanocomposite was confirmed by demonstrating that its morphology remains unchanged even after prolonged heating up to 350 °C, as observed by electron microscopy. On-chip multisensor arrays composed of both AmG and Am-ZnO were fabricated and thoroughly tested, showing almost tenfold enhancement of the chemiresistive response upon decorating the AmG layer with ZnO nanoparticles, due to the formation of p-n heterojunctions. Operating at room temperature, the fabricated multisensor chips exhibited high robustness and a detection limit of 3.6 ppm and 5.1 ppm for ammonia and ethanol, respectively. Precise identification of the studied analytes was achieved by employing the pattern recognition technique based on linear discriminant analysis to process the acquired multisensor response.

An investigation of the price discovery role of futures markets: A dynamic time warping analysis of the United States corn markets

AbstractFutures markets are critical to price discovery and often dominate spot markets. We analyze the linkages between daily corn futures and spot prices in the United States using dynamic time warping. This nonparametric pattern recognition technique has several advantages over traditional time series methods. First, it can detect multiple changes in the lead‐lag associations between the two prices within short intervals; the duration with which one series leads or lags another is not assumed to be fixed. Second, the method can be applied to time series without regard to their stationarity properties. This greatly expands the scope of this method to accommodate a wide range of time series. Third, it lends itself well to studying small samples, which econometricians encounter routinely. Fourth, the results are presented intelligibly using intuitive visualizations. Our results show that futures markets are critical to price discovery; nevertheless, spot markets dominate futures markets intermittently. We discuss the results in detail, setting them in the proper context. [EconLit Citations: C14, C32, Q02, Q11].

The combination of Fourier-transform infrared spectroscopy with pattern recognition techniques for classification and discrimination of red snapper fish oils

The combination of Fourier-transform infrared spectroscopy with pattern recognition techniques for classification and discrimination of red snapper fish oils

Schrödinger spectrum and slim CNN architecture-based signal quality estimation for Photoplethysmogram signals

Photoplethysmography (PPG) signal comprises physiological information related to cardiorespiratory health. However, these signals are often contaminated by motion artifacts, resulting in low-quality noise-enriched signals. Therefore, it is crucial to ensure high-quality signals to extract accurate cardiorespiratory information. Although several rule-based and Machine-Learning (ML) - based approaches exist for PPG signal quality estimation but their effectiveness is questionable during evaluation in diverse datasets. This study proposes a lightweight, slim CNN architecture that utilizes a novel Quantum Pattern Recognition (QPR) technique for signal quality assessment. The proposed algorithm is initially validated on the University of Queensland database (clinical) and a noisy PPG database (non-clinical) collected from the ‘Welltory app’. A total of 28366, 5 s signal segments are preprocessed and transformed into image files of 20 × 500 pixels. The image files and heart rate obtained from the PPG signals are treated as input to the 2D slim CNN architecture. The developed model classifies the PPG signal as ‘good’ or ‘bad’ with an accuracy of 98.5%, 99.2% sensitivity, 97.5% specificity, and 99% F1-score. The classified database is further used for cuffless Blood Pressure estimation, and estimation error is reduced ≥ 1 mmHg w.r.t. reference BP signals. Finally, the proposed scheme is validated in STM32 series board for a resource-constrained wearable implementation. The computational load of the algorithm is reduced by network pruning and quantization for edge-device implementation, and it achieves an accuracy of 94.3%. This extensive analysis concludes that a slim architecture, along with a novel spatio–temporal pattern recognition technique, improves the system’s performance.

Feasibility of UV–Vis spectroscopy combined with pattern recognition techniques to authenticate the medicinal plant material from different geographical areas

The correct identification and authentication of medicinal plants material is a crucial task that ensures quality and prevent adulteration. The use of UV–Vis spectroscopy with principal component analysis (PCA) and discriminant analysis (DA) was proposed for identification/authentication of plant material form different genus and different geographical areas provenience. Hydroalcoholic extracts of samples from twelve genus collected from seven countries (Romania, North Macedonia, Germany, Italy, Serbia, Russia and Kazakhstan) were used. The UV–Vis spectra of the extracts were acquired in the 200–800 nm spectral range, and signal smoothing was used for pre-processing the spectral data. Hierarchical clustering analysis (HCA) with 1-Pearson r distance measurement was used to classify the samples based on the original spectra and different-order derivative spectra, respectively. Data from original spectra and from different-order derivative spectra were evaluated by PCA method. Using the PCA with varimax rotation approach, the spectral ranges with significant contribution for samples classification were revealed for the first time. When the PCA method coupled with DA was applied to the data obtained from the original spectra and the fourth-order derivative spectra, the samples were correctly classified to the respective groups with a 98.04% accuracy. The proposed method can be a useful tool for rapid authentication of plant material derived from different countries.

Magnetic rotor breakage study in permanent magnet synchronous motor at COMSOL multiphysics and fault detection using machine learning

Electric vehicles are one of the most important means in the industrial sector due to their frequent use and depend primarily on electric motors. Electric motors of all types, synchronous and asynchronous, face many faults in the rotor and stator, affecting the performance's reliability. Researchers are seeking to find ways that enable us to detect and diagnose faults in electric motors based on smart and fast methods. Early detection of problems in electric motors is vital, especially in areas such as electric vehicles. This study focuses on magnetic rotor breakage (MRB) in permanent magnet synchronous motors (PMSM). We use a simulation tool such as COMSOL Multiphysics as a simulation tool. This platform is a widely used software for modeling and analyzing complex electromagnetic systems. The study also addresses fault detection using machine learning. This involves using data analysis and pattern recognition techniques to distinguish between normal and defective states of the motor. This is an important step to improve the reliability of motors and identify potential failures in advance. Five different machine learning algorithms such as Extreme Gradient Boosting (XGBoost), AdaBoost, Gradient Boosting (GB), Naive Bayes (NB), and Random Forest (RF) are used in the study. Data from four different cases obtained from the PMSM design were used to train and test the machine-learning models. The results obtained show how accurate the proposed models are in diagnosing PMSM problems, especially MRB.

Model for autism disorder detection using deep learning

<span lang="EN-US">Autism spectrum disorder (ASD) is a neurodegenerative illness that impacts individuals' social abilities. The majority of available approaches rely on structural and resting-state functional magnetic resonance imaging (fMRI) to detect ASD with a small dataset, resulting in high accuracy but low generality. To detect ASD with a limited dataset, the bulk of known technologies involve Machine Learning, pattern recognition, and other techniques, leading to high accuracy but moderate generality. To address this constraint and improve the efficacy of the automated autism diagnosis model, an ASD detection model based on deep learning (DL) is provided in this work. The classification challenge is solved using a convolutional neural network classifier. The suggested model beats state-of-the-art methodologies in terms of accuracy, according to simulation findings. The proposed approach investigates how anatomical and functional connectivity indicators can be used to determine whether or not a person is autistic. The proposed method delivers state-of-the-art results, with the classification of Autistic patients achieving 93.41% accuracy and the localization of the classified data regressed to 0.29 mean absolute error (MAE).</span>

A screening study of elemental composition in legume (Fabaceae sp.) cultivar from Serbia: Nutrient accumulation and risk assessment

The study is the first analytical approach to evaluate thirteen elements’ profiles of 4 different species (Phaseolus spp., Vicia spp., Pisum spp. and Lathyrus spp.) comprising 38 varieties of legumes cultivated in Serbia. The inductively coupled plasma with an optical emission spectrometer (ICP-OES) was used to determine the levels of macro-, micro- and trace elemental contents, namely, P, K, Ca, Mg, Fe, Cu, Zn, Mn, Cd, Pb, Ni, Cr and As, after microwave-assisted digestion. MANOVA was utilized to reveal significant differences in elemental composition within and between groups, while PCA to reveal the underlying patterns. Among the macroelements, the most abundant was K (8980.7–14177.4 mg kg−1), followed by P, Mg and Ca, being the highest in Phaseolus spp. The data revealed that the studied legumes generally contained a high amount of Zn and Fe, with Lathyrus spp. being the richest in Zn. The mean concentration of trace elements in the analyzed legume samples was in the following order: Ni (24.2–57 mg kg−1) > Cr (0.8–4.1 mg kg−1) > Pb (0.07–1.2 mg kg−1) > Cd (0–0.07 mg kg−1). The determined Pb and Cd contents in all cultivars exceeded the set maximum limits by European and Serbian legislation, having a potential for human health risk. Pattern recognition techniques applied to the data did not distinguish among the species, revealing a similar elemental profile. In conclusion, this study highlights legumes as an extremely valuable source of macro- and microelements, but also the importance of monitoring the level of heavy metals in this commonly consumed foodstuff.

Reconstructing historical urban landscapes from KH-9 HEXAGON mapping camera system imagery: an example of Hangzhou City

ABSTRACT Declassified images from the Keyhole (KH)-9 HEXAGON mapping camera system (MCS) offer fine-scale details of urban regions. However, these images have seldom been utilized in urban research due to challenges in labelling (collecting training samples), having only a single panchromatic band and classification. To tackle these limitations, this paper focuses on developing a multi-stage reconstructed historical fine-scale urban landscape (RHFUL) pipeline for KH-9 HEXAGON MCS. The proposed pipeline first integrates internalized parameters, hierarchical object-based image analysis properties and class variability to synthesize new features, abbreviated to IHC. Second, the pipeline uses a weak semi-automated supervised labelling (WSSL) approach to acquire training samples. Finally, the training samples and generated features are subjected to the SegNet deep learning architecture. The performance of each step was assessed against corresponding state-of-the-art benchmark approaches for each of synthesizing features, labelling and classification. In the proposed RHFUL pipeline, the proposed IHC provided the most salient information for urban classification, WSSL labelled urban features more accurately, and the SegNet architecture classified more accurately the urban features relative to the benchmarks. Considering the potential advantages, but also limitations of KH-9 HEXAGON MCS images, further research should be undertaken, particularly drawing on the current advances in pattern recognition techniques for contemporary digital satellite sensors.

A Characterization Approach for End-of-Life Textile Recovery Based on Short-Wave Infrared Spectroscopy

Reusing and recycling End-Of-Life (EoL) textiles is a successful approach to develop sustainable and circular strategies in the apparel industry. Textile reuse and recycling can help to reduce the environmental impact of the fashion and textile industry by preserving natural resources and reducing waste. Textile fibers recognition and sorting, according to material composition, are of primary importance for the implementation of efficient and sustainable recycling strategies. In this work, Short-Wave InfraRed (SWIR: 1000–2500 nm) spectroscopy was applied to extract information regarding the fabric composition of different EoL textiles in order to set up a hierarchical classification procedure able to recognize different type of textile. In more detail, Partial Least Squares-Discriminant Analysis (PLS-DA) pattern recognition technique was used and classifications were performed in two steps: (1) recognition of the fiber origin [i.e. plant-derived, animal-derived, artificial textiles such as synthetic and/or Man-Made Cellulosic Fibers (MMCFs)] and, (2) discrimination of fabrics according to the material classes (i.e. silk, cotton, wool, viscose, linen, jute, polyester and blends). The proposed chemometric technique successfully classified textiles based on their spectral properties. The acquired results are highly promising and provide important insight into the EoL textile recycling business. These analytical techniques have the potential to be utilized to successfully automate the recycling process, either in addition to or as a replacement for manual processes, hence improving sorting procedures.Graphical

Literature Survey Paper on Epilepsy and Autism Spectrum Disorder Detection and Analysis Using Machine Learning

The detection and cure of epilepsy and autism spectrum disorder (ASD) are significantly complicated by their co-occurrence. This survey research investigates an integrated method for identifying ASD using behavioural characteristic questionnaires and epilepsy using EEG corpus inside a single system. We provide an overview of all the relevant research, emphasizing the difficulties in diagnosing each of these disorders separately and in combination. Our suggested approach combines behavioural questionnaire assessments for ASD with EEG-based analysis for epilepsy detection in an effort to improve diagnostic accuracy and expedite the evaluation process. This study examines the approaches, difficulties, and developments in both domains, providing perspectives on possible overlaps and prospects for further investigation. So, an attempt has been made to review on the pattern detection methods for epilepsy seizure detection from EEG signals. More than 150 research papers have been discussed to determine the techniques for detecting epileptic seizures. Further, the literature review confirms that the pattern recognition techniques required to detect epileptic seizures varies across the electroencephalogram (EEG) datasets of different conditions. This is mostly owing to the fact that EEG detected under different conditions have different characteristics.

Multimedia Identification and Analysis Algorithm of Piano Performance Music Based on Deep Learning

In the biometric biomedical applications, the study presents a pioneering research endeavor focused on the development of a multimedia identification and analysis algorithm tailored for piano performance music. With the power of deep learning techniques, this algorithm has been designed to address the intricate challenges posed by the convergence of musical data analysis and biometrics. The core objectives encompass the extraction, recognition, and interpretation of biometric information from piano performances, exploring the intricate patterns, nuances, and individualistic characteristics inherent to musicians. The study focuses on the development of advanced biometric authentication and identification systems capable of capturing and analyzing a user's unique behavioral patterns across diverse modalities. These biometric modalities offer the potential for highly secure and non-intrusive user identification. Hence, this paper developed an architecture of Marker Controlled Point (MCP) Estimation for the computation of the gesture in biometric-based applications. This architecture utilizes markers or reference points to precisely track and analyze user gestures, resulting in accurate and reliable biometric data. The research details the architecture's implementation, integrating advanced deep-learning techniques for feature extraction, pattern recognition, and authentication. This system finds versatile applications in various domains, from healthcare and cybersecurity to finance, where secure and user-friendly authentication is paramount. Experimental results underscore the system's effectiveness and robustness, demonstrating its potential for enhancing biometric authentication. The proposed model represents a significant stride in biometric technology, offering secure, non-intrusive user identification through the synergy of behavior biometrics and emerging modalities.

Fuzzy Evolutionary Algorithm in Construction Project for Valuation

Clustering algorithms has emerged as an important data mining technique for pattern recognition, data analysis and dimensionality reduction. Clustering is usually used in all fields to merge the same feature objects into a single group. The clustering method is incorporated with search algorithms to search the dataset from the databases. For large databases, there is a need of good clustering algorithm with high accuracy. Despite its high performance, the existing methods show some limitations. This paper focused on optimize the clustering method with a search structure for large multidimensional databases with dynamic indexes for the effective validation of construction projects. In the construction project validation to improve the clustering methods using Evolutionary Constrained Differential Optimization (ECDO) and Distributed Weighted Fuzzy C-Means algorithm to improve clustering performance than the centralized clustering approaches. The proposed methodology includes ECDO which presents new cluster tree indexing approach with cluster speed improvement in the validation of the construction projects. Distributed Fuzzy Possibilistic C-Means algorithm is proposed in improvement of cluster centre to overcome coincidence cluster problems. The comparative analysis stated that proposed ECDO model exhibits ~6% reduced memory utilization ~11% reduced scalability and ~8% minimized selectivity than the conventional WPCM model.

P180 Novel biomarkers associated with inflammatory bowel disease

Abstract Background Inflammatory bowel disease (IBD) is a chronic multi-factorial disease characterized by inflammation of the gastrointestinal tract. Currently, the mechanisms underlying the disease are far from being elucidated. Thus, the study of proteins involved in its pathogenesis would allow further insights into the molecular mechanisms underlying IBD. Methods The serum and intestinal mucosa (ileum and left colon) proteomic profiles of patients with Crohn´s disease (CD) and ulcerative colitis (UC) and healthy controls (HC) were analyzed using a label-free quantitative proteomics approach (Table 1). Data mining and pattern recognition techniques were performed to identify hidden relationships that are not detectable using classical linear classifiers, and thus identify potential markers able to discriminate between the different study groups. Six candidate biomarkers were selected for further validation using enzyme-linked immunosorbent assay (ELISA) and immunohistochemistry (IHC) in serum and intestinal tissue samples, respectively (Table 2). Results Comparison of serum proteins between UC patients and HC revealed statistically lower levels of protein 1 and 2 and higher levels of protein 3. The comparison of potential biomarkers between CD and HC group showed lower levels of protein 1 and higher levels of protein 3. In addition, protein 4 was differentially upregulated in CD patients compared to UC (Figure 1A). Regarding the intestinal biopsies, protein 5 showed higher intensity in the IHC staining of ileum from CD patients compared to HC, whereas protein 6 was significantly decreased in left colon and ileum from CD and UC patients compared to HC (Figure 1B). These proteins are involved in thyroxine transport, regulation of chemotactic chemokine activity, inflammatory processes, triglyceride homeostasis, oxidative stress and regulation of NF-kappa-B activation in the cytosol. Conclusion In this study, we have identified a panel of six potential biomarkers that could help to elucidate mechanisms involved in IBD pathogenesis. Their roles in the pathophysiology need to be clarified in further experiments.

Discrimination/Classification of Edible Vegetable Oils from Raman Spatially Solved Fingerprints Obtained on Portable Instrumentation.

Currently, the combination of fingerprinting methodology and environmentally friendly and economical analytical instrumentation is becoming increasingly relevant in the food sector. In this study, a highly versatile portable analyser based on Spatially Offset Raman Spectroscopy (SORS) obtained fingerprints of edible vegetable oils (sunflower and olive oils), and the capability of such fingerprints (obtained quickly, reliably and without any sample treatment) to discriminate/classify the analysed samples was evaluated. After data treatment, not only unsupervised pattern recognition techniques (as HCA and PCA), but also supervised pattern recognition techniques (such as SVM, kNN and SIMCA), showed that the main effect on discrimination/classification was associated with those regions of the Raman fingerprint related to free fatty acid content, especially oleic and linoleic acid. These facts allowed the discernment of the original raw material used in the oil's production. In all the models established, reliable qualimetric parameters were obtained.

Unsupervised Learning for Public Transport Delay Pattern Analysis

To analyze inherent and diverse patterns within line-based public transport daily delay occurrences, we introduce a data-driven exploratory analysis focused on the spatial-temporal distribution of these delays. Our approach relies on the utilization of the image pattern recognition technique and k-means clustering algorithm. We extract daily punctuality information from the automatic vehicle location data for a singular public transport route. This information is then translated into a visual representation through aggregated daily delay distribution profile images, offering insights into the spatial and temporal distribution of delays. The delay distribution finds expression in the arrangement of pixels within these profile images. The essence of these images is further distilled through image pattern recognition using the neural network architecture of ResNet50. Employing the k-means algorithm, we cluster these images based on their similarity, revealing five distinct daily delay patterns. The analysis of these patterns offers insight into their unique characteristics, yielding noteworthy outcomes. These findings hold the potential to provide public transport operators with an enriched comprehension of the dynamics of delays occurring on a specific line.