Linear Discriminant Analysis Algorithm Research Articles

A new sub-class linear discriminant for miniature spectrometer based food analysis

The well-known and extensively studied Linear Discriminant Analysis (LDA) can have its performance lowered in scenarios where data is not homoscedastic or not Gaussian. That is, the classical assumptions when LDA models are built are not applicable, and consequently LDA projections would not be able to extract the needed features to explain the intrinsic structure of data and for classes to be separated. As with many real word data sets, data obtained using miniature spectrometers can suffer from such drawbacks which would limit the deployment of such technology needed for food analysis. The solution presented in the paper is to divide classes into subclasses and to use means of sub classes, classes, and data in the suggested between classes scatter metric. Further, samples belonging to the same subclass are used to build a measure of within subclass scatterness. Such a solution solves the shortcoming of the classical LDA. The obtained results when using the proposed solution on food data and on general machine learning datasets show that the work in this paper compares well to and is very competitive with similar sub-class LDA algorithms in the literature. An extension to a Hilbert space is also presented; and the kernel version of the presented solution can be fused with its linear counter parts to yield improved classification rates.

Rapid identification of pediatric brain tumors with differential mobility spectrometry.

Brain tumors are a major source of disease burden in pediatric population, with the most common tumor types being pilocytic astrocytoma, ependymoma and medulloblastoma. In every tumor entity, surgery is the cornerstone of treatment, but the importance of gross-total resection and the corresponding patient prognosis is highly variant. However, real-time identification of pediatric CNS malignancies based on the histology of the frozen sections alone is especially troublesome. We propose a novel method based on differential mobility spectrometry (DMS) analysis for rapid identification of pediatric brain tumors. We prospectively obtained tumor samples from 15 pediatric patients (5 pilocytic astrocytomas, 5 ependymomas and 5 medulloblastomas). The samples were cut into 36 smaller specimens that were analyzed with the DMS. With linear discriminant analysis algorithm, a classification accuracy (CA) of 70% was reached. Additionally, a 75% CA was achieved in a pooled analysis of medulloblastoma vs. gliomas. Our results show that the DMS is able to differentiate most common pediatric brain tumor samples, thus making it a promising additional instrument for real-time brain tumor diagnostics.

Innovative Machine Learning Strategies for Early Detection and Prevention of Pregnancy Loss: The Vitamin D Connection and Gestational Health.

Early pregnancy loss (EPL) is a prevalent health concern with significant implications globally for gestational health. This research leverages machine learning to enhance the prediction of EPL and to differentiate between typical pregnancies and those at elevated risk during the initial trimester. We employed different machine learning methodologies, from conventional models to more advanced ones such as deep learning and multilayer perceptron models. Results from both classical and advanced machine learning models were evaluated using confusion matrices, cross-validation techniques, and analysis of feature significance to obtain correct decisions among algorithmic strategies on early pregnancy loss and the vitamin D serum connection in gestational health. The results demonstrated that machine learning is a powerful tool for accurately predicting EPL, with advanced models such as deep learning and multilayer perceptron outperforming classical ones. Linear discriminant analysis and quadratic discriminant analysis algorithms were shown to have 98 % accuracy in predicting pregnancy loss outcomes. Key determinants of EPL were identified, including levels of maternal serum vitamin D. In addition, prior pregnancy outcomes and maternal age are crucial factors in gestational health. This study's findings highlight the potential of machine learning in enhancing predictions related to EPL that can contribute to improved gestational health outcomes for mothers and infants.

Age and aging process alter the gut microbes.

Gut microbes and age are both factors that influence the development of disease. The community structure of gut microbes is affected by age. To plot time-dependent gut microbe profiles in individuals over 45 years old and explore the correlation between age and gut microbes. Fecal samples were collected from 510 healthy individuals over 45 years old. Shannon index, Simpson index, Ace index, etc. were used to analyze the diversity of gut microbes. The beta diversity analysis, including non-metric multidimensional scaling (NMDS), was used to analyze community distribution. Linear discriminant analysis (LDA) and random forest (RF) algorithm were used to analyze the differences of gut microbes. Trend analysis was used to plot the abundances of characteristic gut microbes in different ages. The individuals aged 45-49 had the highest richness of gut bacteria. Fifteen characteristic gut microbes, including Siphoviridae and Bifidobacterium breve, were screened by RF algorithm. The abundance of Ligiactobacillus and Microviridae were higher in individuals older than 65 years. Moreover, the abundance of Blautia_A massiliensis, Lubbockvirus and Enterocloster clostridioformis decreased with age and the abundance of Klebsiella variicola and Prevotella increased with age. The functional genes, such as human diseases and aging, were significantly different among different aged individuals. The individuals in different ages have characteristic gut microbes. The changes in community structure of gut microbes may be related to age-induced diseases.

Non-destructive identification of wool and cashmere fibers based on improved LDA using NIR spectroscopy

Abstract With the advancement of near-infrared (NIR) spectroscopy and chemometrics technology, non-destructive qualitative testing has been widely applied in many fields. Both wool and cashmere are keratin protein fibers with many similarities in tissue structure, making it very difficult to distinguish between them. In order to achieve rapid and non-destructive identification of wool and cashmere, an improved linear discriminant analysis (ILDA) algorithm combined with NIR spectroscopy technology is proposed. The proposed method can also be used for the classification of extremely similar fibers and substances, with better classification performance. First, the spectral data of wool and cashmere are collected using an NIR spectrometer so as to reduce the influence of noise in the spectra; data preprocessing methods are used to correct the collected fiber spectra. Then, principal component analysis (PCA), linear discriminant analysis (LDA), and ILDA are used to extract the characteristic variables from the spectral data. Finally, the extracted characteristic variables are input into the machine learning algorithm K-nearest neighbor (K-NN) classifier. In the experimental stage, three dimensionality reduction methods (PCA, LDA, and ILDA) are evaluated using the K-NN classification model. The fiber classification accuracy can reach 97% when using the ILDA method for dimensionality reduction. The results show that the proposed method is effective for the qualitative detection of different types of wool and cashmere fibers.

Initial experience with radiomics of carotid perivascular adipose tissue in identifying symptomatic plaque.

Carotid atherosclerotic ischemic stroke threatens human health and life. The aim of this study is to establish a radiomics model of perivascular adipose tissue (PVAT) around carotid plaque for evaluation of the association between Peri-carotid Adipose Tissue structural changes with stroke and transient ischemic attack. A total of 203 patients underwent head and neck computed tomography angiography examination in our hospital. All patients were divided into a symptomatic group (71 cases) and an asymptomatic group (132 cases) according to whether they had acute/subacute stroke or transient ischemic attack. The radiomic signature (RS) of carotid plaque PVAT was extracted, and the minimum redundancy maximum correlation, recursive feature elimination, and linear discriminant analysis algorithms were used for feature screening and dimensionality reduction. It was found that the RS model achieved the best diagnostic performance in the Bagging Decision Tree algorithm, and the training set (AUC, 0.837; 95%CI: 0.775, 0.899), testing set (AUC, 0.834; 95%CI: 0.685, 0.982). Compared with the traditional feature model, the RS model significantly improved the diagnostic efficacy for identifying symptomatic plaques in the testing set (AUC: 0.834 vs. 0.593; Z = 2.114, p = 0.0345). The RS model of PVAT of carotid plaque can be used as an objective indicator to evaluate the risk of plaque and provide a basis for risk stratification of carotid atherosclerotic disease.

High-precision sensor for glucose solution using active multidimensional feature THz spectroscopy.

Terahertz waves are known for their bio-safety and spectral fingerprinting features, and terahertz spectroscopy technology holds great potential for both qualitative and quantitative identification in the biomedical field. There has been a substantial amount of research utilizing this technology in conjunction with machine learning algorithms for substance identification. However, due to the strong absorption of water for terahertz waves, the single-dimensional features of the sample become indistinct, thereby diminishing the efficiency of the algorithmic recognition. Building upon this, we propose a method that employs terahertz time-domain spectroscopy (THz-TDS) in conjunction with multidimensional feature spectrum identification for the detection of blood sugar and glucose mixtures. Our research indicates that combining THz-TDS with multidimensional feature spectrum and linear discriminant analysis (LDA) algorithms can effectively identify glucose concentrations and detect adulteration. By integrating the multidimensional feature spectrum, the identification success rate increased from 68.9% to 96.0%. This method offers an economical, rapid, and safe alternative to traditional methods and can be applied in blood sugar monitoring, sweetness assessment, and food safety.

Fusion of Coherent and Non-Coherent Pol-SAR Features for Land Cover Classification

Remote Sensing plays a fundamental role in acquiring crucial information about the Earth’s surface from a distance, especially through fully polarimetric data, which offers a rich source of information for diverse applications. However, extracting meaningful insights from this intricate data necessitates sophisticated techniques. In addressing this challenge, one predominant trend that has emerged is known as target decomposition techniques. These techniques can be broadly classified into coherent and non-coherent methods. Each of these methods provides high-quality information using different procedures. In this context, this paper introduces innovative feature fusion techniques, amalgamating coherent and non-coherent information. While coherent techniques excel in detailed exploration and specific feature extraction, non-coherent methods offer a broader perspective. Our feature fusion techniques aim to harness the strengths of both approaches, providing a comprehensive and high-quality fusion of information. In the first approach, features derived from Pauli coherent decomposition, Freeman–Durden non-coherent technique, and the Symmetry criterion from Cameron’s stepwise algorithm are combined to construct a sophisticated feature vector. This fusion is achieved using the well-established Fisher Linear Discriminant Analysis algorithm. In the second approach, the Symmetry criterion serves as the basis for fusing coherent and non-coherent coefficients, resulting in the creation of a new feature vector. Both approaches aim to exploit information simultaneously extracted from coherent and non-coherent methods in feature extraction from Remote Sensing data through fusion at the feature level. To evaluate the effectiveness of the feature generated by the proposed fusion techniques, we employ a land cover classification procedure. This involves utilizing a basic classifier, achieving overall accuracies of approximately 82% and 86% for each of the two proposed techniques. Furthermore, the accuracy in individual classes surpasses 92%. The evaluation aims to gauge the effectiveness of the fusion methods in enhancing feature extraction from fully polarimetric data and opens avenues for further exploration in the integration of coherent and non-coherent features for remote sensing applications.

Predicting academic achievement from the collaborative influences of executive function, physical fitness, and demographic factors among primary school students in China: ensemble learning methods

BackgroundElevated levels of executive function and physical fitness play a pivotal role in shaping future quality of life. However, few studies have examined the collaborative influences of physical and mental health on academic achievement. This study aims to investigate the key factors that collaboratively influence primary school students' academic achievement from executive function, physical fitness, and demographic factors. Additionally, ensemble learning methods are employed to predict academic achievement, and their predictive performance is compared with individual learners.MethodsA cluster sampling method was utilized to select 353 primary school students from Huai'an, China, who underwent assessments for executive function, physical fitness, and academic achievement. The recursive feature elimination cross-validation method was employed to identify key factors that collaboratively influence academic achievement. Ensemble learning models, utilizing eXtreme Gradient Boosting and Random Forest algorithms, were constructed based on Bagging and Boosting methods. Individual learners were developed using Support Vector Machine, Decision Tree, Logistic Regression, and Linear Discriminant Analysis algorithms, followed by the establishment of a Stacking ensemble learning model.ResultsOur findings revealed that sex, body mass index, muscle strength, cardiorespiratory function, inhibition, working memory, and shifting were key factors influencing the academic achievement of primary school students. Moreover, ensemble learning models demonstrated superior predictive performance compared to individual learners in predicting academic achievement among primary school students.ConclusionsOur results suggest that recognizing sex differences and emphasizing the simultaneous development of cognition and physical well-being can positively impact the academic development of primary school students. Ensemble learning methods warrant further attention, as they enable the establishment of an accurate academic early warning system for primary school students.

Cross-Subject Motor Imagery Decoding by Transfer Learning of Tactile ERD.

For Brain-Computer Interface (BCI) based on motor imagery (MI), the MI task is abstract and spontaneous, presenting challenges in measurement and control and resulting in a lower signal-to-noise ratio. The quality of the collected MI data significantly impacts the cross-subject calibration results. To address this challenge, we introduce a novel cross-subject calibration method based on passive tactile afferent stimulation, in which data induced by tactile stimulation is utilized to calibrate transfer learning models for cross-subject decoding. During the experiments, tactile stimulation was applied to either the left or right hand, with subjects only required to sense tactile stimulation. Data from these tactile tasks were used to train or fine-tune models and subsequently applied to decode pure MI data. We evaluated BCI performance using both the classical Common Spatial Pattern (CSP) combined with the Linear Discriminant Analysis (LDA) algorithm and a state-of-the-art deep transfer learning model. The results demonstrate that the proposed calibration method achieved decoding performance at an equivalent level to traditional MI calibration, with the added benefit of outperforming traditional MI calibration with fewer trials. The simplicity and effectiveness of the proposed cross-subject tactile calibration method make it valuable for practical applications of BCI, especially in clinical settings.

Rapid testing of irradiation dose in beef and potatoes by reaction-based optical sensing technique

Food irradiation is becoming increasingly popular in many countries for preserving and extending the shelf life of foods, which creates a demand for express methods for the detection of absorbed doses. This paper proposes an innovative method for the estimation of the dose absorbed by X-rayed beef samples using a reaction-based optical sensing technique that was proposed earlier by our team for the estimation of the dose absorbed by raw potatoes. Potato and beef samples were exposed to X-ray irradiation at 100 and 1000 Gy and then extracted by water at 23 °C for 24 h or 60/70 °C for 1 h. The resulting solutions were introduced to the reaction mixtures of dyes (carbocyanines, Rhodamines, or Crystal Violet) with oxidants (hypochlorite, bromate, or hydrogen peroxide). The fluorescence intensity and absorbance of the mixtures were periodically measured photographically using visualizers. The data were processed using linear discriminant analysis (LDA) and k-nearest neighbors algorithm (kNN). Using the most efficient individual reactions, the doses can be recognized with a 90–100% accuracy, and the combinations of up to 5 reactions can improve the accuracy to 100%. The protocol is simple and rapid: sample extraction time and indicator reactions take not more than 1 h each. The proposed method potentially has a wide area of applications ranging from plants to animal products.

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Age-related decline in sensory inputs in elderly people leads to postural instability that increases irregularity of postural sway. This study aimed to examine the effect of visual or somatosensory inputs on postural sway irregularity in the elderly by using machine learning (ML). The feature set was extracted from entropy measurements including sample, fuzzy, distribution, conditional, and permutation. Then, the variables were classified by ML including support vector machines (SVM), k-nearest neighbors (k-NN), and linear discriminant analysis (LDA) algorithms. Classification performances were compared with the confusion matrix. For the elderly, in the eyes closed condition on an unstable surface, the SVM algorithm achieved higher accuracy (77%), sensitivity (72%), specificity (85%), and precision (83%) for the cv dataset. For young, SVM also achieved high accuracy (86%), sensitivity (87%), specificity (84%), and precision (84%). For the elderly, under the eyes open on unstable surface conditions, the SVM exhibited an accuracy of 79%, sensitivity of 75%, specificity of 72%, and precision of 75%. However, for young, it did not reveal good results for both surfaces. In conclusion, the findings suggest that older people adapt their postural control mechanisms, relying more on somatosensory inputs. ML algorithms with entropy-based features can give insights into age-related differences in postural control.

Improved secure PCA and LDA algorithms for intelligent computing in IoT‐to‐cloud setting

AbstractThe rapid development of new technologies such as artificial intelligence and big data analysis requires the simultaneous development of cloud computing technology. The application of IoT‐to‐cloud setting has been fully applied in various industry sectors, such as sensor‐cloud system which is composed of wireless sensor network and cloud computing technology. With the increasing amount and types of collected data, companies need to reduce the dimension of massive data in cloud servers for obtaining data analysis reports rapidly. Due to frequent cloud server data leaks, companies must adequately protect the privacy of some confidential data. To this end, we designed a dimension reduction method for ciphertext data in the sensor‐cloud system based on the CKKS encryption scheme, principal component analysis (PCA) and linear discriminant analysis (LDA) dimension reduction algorithm. As data cannot be directly calculated using traditional PCA and LDA algorithm after encryption, we add some interactive operations and iterative calculations to replace some steps in traditional algorithms. Finally, we select the classification dataset IRIS which is commonly used in machine learning, and screen out the best encryption and calculation parameters, and efficiently realize the dimension reduction method of ciphertext data through a large number of experiments.

Plant Leaf Disease Detection Using Efficient Image Processing and Machine Learning Algorithms

India is often described as a country of villages, where a majority of the population depends on agriculture for their livelihood. The landscape of Indian agriculture is approximately 159.7 million hectares. Agriculture plays a pivotal role in India's Gross Domestic Product (GDP), accounting for about 18% of the nation's economic output. Diseases and pests can have detrimental effects on crops, leading to reduced yields. These challenges can include the spread of plant diseases, infestations by insects or other pests, and the overall degradation of crop health. Early detection of diseases in crops is crucial for several reasons. Detecting diseases at an early stage allows for prompt intervention, such as applying appropriate pesticides or taking preventive measures. The main aim of this study is to develop a highly effective method for plant leaf disease detection using computer vision techniques. Here, leaf disease detection comprises histogram equalization, denoising, image color threshold masking, feature descriptors such as Haralick textures, Hu moments, and color histograms to extract the salient features of leaf images. These features are then used to classify the images by training Logistic Regression, Linear Discriminant Analysis, K-nearest neighbor, decision tree, Random Forest, and Support Vector Machine algorithms using K-fold validation. K-fold validation is used to separate the validation samples from the training samples, and the K indicates the number of times this is repeated for the generalization. The training and validation processes are performed in two approaches. The first approach uses default hyperparameters with segmented and non-segmented images. In the second approach, all hyperparameters of the models are optimized to train segmented datasets. The classification accuracy improved by 2.19% by utilizing segmentation and hyperparameter tuning further improved by 0.48%. The highest average classification accuracy of 97.92% is achieved using the Random Forest classifier to classify 40 classes of 10 different plant species. Accurate detection of plant disease leads to the sustained growth of plants throughout the growing span of the plants.

OGC P44 Biospectrometry as a non invasive tool in the diagnosis of oesophageal cancer

Abstract Background Oesophageal adenocarcinoma (OAC) typically presents at an advanced stage with resultant poor survival rates. The 2022 oesophago-gastric (OG) audit suggests only 35.9% of patients diagnosed with OG cancer were treated with curative intent. Raman spectroscopy of serum is a non-invasive and rapid diagnostic technique which has proved promising in cancer diagnostics. A proof-of-concept study of 124 samples undertaken by our team to determine the accuracy of Raman spectral discrimination in detecting oesophageal transformation of Barrett’s to adenocarcinoma revealed sensitivity and specificity of over 95% when using serum to diagnose OAC. Methods Our research group has conducted a further study to validate the aforementioned findings by re-analysing 60 samples from the proof of concept study in a separate laboratory. Chemometric techniques, coupled with principal component analysis, were employed to identify discriminant spectral peaks. Subsequently, a prediction model was developed using a linear discriminant analysis algorithm to accurately classify serum samples from cancer patients. Results Spectral peaks representing methylene deformation at 1450 cm-1 and C-O stretch of ribose at 1012 cm-1 were identified as discriminant spectral peaks in identifying OAC in both analyses, inferring that these results are reproducible. A specificity and sensitivity of 72% and 71% respectively was calculated from chemometric spectral analysis. Conclusions We conclude that Raman spectroscopy has potential applications as a non-invasive screening tool for patients with suspected OAC.

Analysis of the trade-offs between parametric and non-parametric classifiers for classification of faults in pneumatic actuator. Case: DAMADICS

This paper disseminates an extra dimension of substantial analysis demonstrating the trade-offs between the performance of Parametric (P) and Non-Parametric (NP) classification algorithms when applied to classify faults occurring in pneumatic actuators. Owing to the criticality of the actuator failures, classifying faults accurately may lead to robust fault tolerant models. In most cases, when applying machine learning, the choice of existing classifier algorithms for an application is random. This work, addresses the issue and quantitatively supports the selection of appropriate algorithm for non-parametric datasets. For the case study, popular parametric classification algorithms namely: Naïve Bayes (NB), Logistic Regression (LR), Linear Discriminant Analysis (LDA), Perceptron (PER) and non-parametric algorithms namely: Multi-Layer Perceptron (MLP), k Nearest Neighbor (kNN), Support Vector Machine (SVM), Decision Tree (DT) and Random Forest (RF) are implemented over a non-parametric, imbalanced synthetic dataset of a benchmark actuator process. Upon using parametric classifiers, severe adultery in results is witnessed which misleads the interpretation towards the accuracy of the model. Experimentally, about 20% improvement in accuracy is obtained on using non-parametric classifiers over the parametric ones. The robustness of the models is evaluated by inducing label noise varying between 5% to 20%. Triptych analysis is applied to discuss the interpretability of each machine learning model. The trade-offs in choice and performance of algorithms and the evaluating metrics for each estimator are analyzed both quantitatively and qualitatively. For a more cogent reasoning through validation, the results obtained for the synthetic dataset are compared against the industrial dataset of the pneumatic actuator of the sugar refinery, Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems (DAMADICS). The efficiency of non-parametric classifiers for the pneumatic actuator dataset is well proved.

Dynamic modeling of photoacoustic sensor data to classify human blood samples.

The photoacoustic effect is an attractive tool for diagnosis in several biomedical applications. Analyzing photoacoustic signals, however, is challenging to provide qualitative results in an automated way. In this work, we introduce a dynamic modeling scheme of photoacoustic sensor data to classify blood samples according to their physiological status. Thirty-five whole human blood samples were studied with a state-space model estimated by a subspace method. Furthermore, the samples are classified using the model parameters and the linear discriminant analysis algorithm. The classification performance is compared with time- and frequency-domain features and an autoregressive-moving-average model. As a result, the proposed analysis can predict five blood classes: healthy women and men, microcytic and macrocytic anemia, and leukemia. Our findings indicate that the proposed method outperforms conventional signal processing techniques to analyze photoacoustic data for medical diagnosis. Hence, the method is a promising tool in point-of-care devices to detect hematological diseases in clinical scenarios.

A hybrid linear discriminant analysis and genetic algorithm to create a linear model of aging when performing motor tasks through inertial sensors positioned on the hand and forearm

Background During the aging process, cognitive functions and performance of the muscular and neural system show signs of decline, thus making the elderly more susceptible to disease and death. These alterations, which occur with advanced age, affect functional performance in both the lower and upper members, and consequently human motor functions. Objective measurements are important tools to help understand and characterize the dysfunctions and limitations that occur due to neuromuscular changes related to advancing age. Therefore, the objective of this study is to attest to the difference between groups of young and old individuals through manual movements and whether the combination of features can produce a linear correlation concerning the different age groups.Methods This study counted on 99 participants, these were divided into 8 groups, which were grouped by age. The data collection was performed using inertial sensors (positioned on the back of the hand and on the back of the forearm). Firstly, the participants were divided into groups of young and elderly to verify if the groups could be distinguished through the features alone. Following this, the features were combined using the linear discriminant analysis (LDA), which gave rise to a singular feature called the LDA-value that aided in verifying the correlation between the different age ranges and the LDA-value.Results The results demonstrated that 125 features are able to distinguish the difference between the groups of young and elderly individuals. The use of the LDA-value allows for the obtaining of a linear model of the changes that occur with aging in the performance of tasks in line with advancing age, the correlation obtained, using Pearson’s coefficient, was 0.86.Conclusion When we compare only the young and elderly groups, the results indicate that there is a difference in the way tasks are performed between young and elderly individuals. When the 8 groups were analyzed, the linear correlation obtained was strong, with the LDA-value being effective in obtaining a linear correlation of the eight groups, demonstrating that although the features alone do not demonstrate gradual changes as a function of age, their combination established these changes.

A customized deep learning-based framework for classification and analysis of social media posts to enhance the Hajj and Umrah services

Each year, millions of pilgrims come to Saudi Arabia to perform Hajj and Umrah. To help pilgrims with their difficulties and make plans to enhance the Hajj and Umrah services, this article offers a deep learning-based framework to categorize and analyze pilgrims’ posts on social media networks X (formerly Twitter), Facebook and Instagram. We extracted Arabic posts related to Hajj/Umrah then tokenized and pre-processed the dataset to remove unnecessary parts such as punctuation. Posts are manually labeled into five classes: Health, Organization, Security, Services, and Worship. Labeled posts are collected to build a Long Short-Term Memory (LSTM) network classifier for deep learning. To increase the prediction accuracy, we customized the LSTM classification layer and used the sum of squares error (SSE) loss function instead of the default cross-entropy loss function. A rigorous simulation is run to assess the system’s effectiveness. Every time a simulation round occurs, the trained network is tasked with identifying the classes of unlabeled posts (testing data). Similar tests are performed using the K-Nearest Neighbors (KNN) and Linear Discriminant Analysis (LDA) algorithms for comparative analysis. Accuracy, confusion matrix, precision, sensitivity, specificity, and F1 score are used to quantify the system performance. The maximum mean accuracy of the proposed framework, KNN and LDA are 85.65%, 68.47%, and 59.06% respectively. The proposed framework needs to be integrated into a larger system called the Intelligent Pilgrim Service System (IPSS). We described the IPSS architecture, which comprises modules for data generation, data storage (on the cloud), and a control center. We described a hashtag mechanism as a part of the IPSS. This hashtag mechanism will provide both general and focused advice to millions of pilgrims related to their social media posts. We discussed scenarios of how the IPSS can enhance the Hajj and Umrah services.

Global subclass discriminant analysis

Linear discriminant analysis (LDA) is a powerful supervised dimensionality reduction method for analysing high-dimensional data. However, LDA cannot use locality information in data, which makes LDA degrade dramatically in performance on multimodal data. A number of LDA variants have been proposed to exploit locality information in data, including subclass-based LDAs. We discover a problem with these variants, which is that subclasses are selected on a within-class basis without considering other classes. This causes the loss of important information at class boundaries. In this paper, we present a novel variant of subclass-based LDA, Global Subclass Discriminant Analysis (GSDA). Unlike other subclass-based LDAs, GSDA selects subclasses from global clusters that may cross class boundaries, thus utilising within-class information and between-class information. More specifically, GSDA applies an effective clustering algorithm to the whole data to construct global clusters. It then utilises the local structure refining strategy on these global clusters to construct subclasses. Finally, GSDA learns a representative data subspace by maximising inter-subclass distance and minimising intra-subclass distance simultaneously. GSDA is extensively evaluated on a wide range of public datasets through comparison with the state-of-the-art LDA algorithms. Experimental results demonstrate its superiority in terms of accuracy and run times.