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Online condition monitoring and predictive maintenance are crucial for the safe operation of equipments. This paper highlights an unsupervised statistical algorithm based on principal component analysis (PCA) for the predictive maintenance of industrial induced draft (ID) fan. The high vibration issues in ID fans cause the failure of the impellers and, sometimes, the complete breakdown of the fan-motor system. The condition monitoring system of the equipment should be reliable and avoid such a sudden breakdown or faults in the equipment. The proposed technique predicts the fault of the ID fan-motor system, being applicable for other rotating industrial equipment, and also for which the failure data, or historical data, is not available. The major problem in the industry is the monitoring of each and every machinery individually. To avoid this problem, three identical ID fans are monitored together using the proposed technique. This helps in the prediction of the faulty part and also the time left for the complete breakdown of the fan-motor system. This helps in forecasting the maintenance schedule for the equipment before breakdown. From the results, it is observed that the PCA-based technique is a good fit for early fault detection and getting alarmed under fault condition as compared with the conventional methods, including signal trend and fast Fourier transform (FFT) analysis.

The Identification of brain tumors is a critical step that relies on the expertise and abilities of the physician. In order to enable radiologists to spot brain tumors, an automated tumor arrangement is extremely important. This paper presents a technique for MR brain image segmentation and classification to identify images as normal and abnormal. The proposed technique is a hybrid feature extraction submitted to enhance the classification results and basically consists of three stages. The first stage is used a 3-level of discrete wavelet transform (DWT) to extract image characteristics. In the second stage, the principle component analysis (PCA) is applied to reduce the size of characteristics. Finally, a random forest classifier (RF) was used with a feature selection for identification. 181 MR brain images are collected (81 normal and 100 abnormal), in distinguishing normal and abnormal tissues, the experimental results obtained an accuracy of 98%, the sensitivity achieved is 99.2%, specificity achieved is 97.8%, and showed the effectiveness of the proposed technique compared with many kinds of literature. The results show that the 3L-DWT+PCA+RF still achieved the best classification results. The proposed model could apply to the brain MRI sphere classification, which will help doctors to diagnose a tumor if it is normal or abnormal in certain degrees.

Image classification is getting more attention in the area of computer vision. During the past few years, a lot of research has been done on image classification using classical machine learning and deep learning techniques. Presently, deep learning-based techniques have given stupendous results. The performance of a classification system depends on the quality of features extracted from an image. The better is the quality of extracted features, the more the accuracy will be. Although, numerous deep learning-based methods have shown enormous performance in image classification, still due to various challenges deep learning methods are not able to extract all the important information from the image. This results in a reduction in overall classification accuracy. The goal of the present research is to improve the image classification performance by combining the deep features extracted using popular deep convolutional neural network, VGG19, and various handcrafted feature extraction methods, i.e., SIFT, SURF, ORB, and Shi-Tomasi corner detector algorithm. Further, the extracted features from these methods are classified using various machine learning classification methods, i.e., Gaussian Naive Bayes, Decision Tree, Random Forest, and eXtreme Gradient Boosting (XGBClassifier) classifier. The experiment is carried out on a benchmark dataset Caltech-101. The experimental results indicate that Random Forest using the combined features give 93.73% accuracy and outperforms other classifiers and methods proposed by other authors. The paper concludes that a single feature extractor whether shallow or deep is not enough to achieve satisfactory results. So, a combined approach using deep learning features and traditional handcrafted features is better for image classification.

In this paper, we propose an efficient algorithm for denoising the degraded face image sequence in the principal component analysis (PCA) domain for recognizing this face. We first apply a temporal filter that performs motion compensation combined with a weighted average filter, then an adaptative spatial filter realized by PCA transformation that decomposes the image temporally filtered into two sub-images using a threshold calculated according to the variance of noise and intensity retained by the first eigenvectors. The first sub-image containing a great intensity variance expresses the small features, and the other with high noise level and low intensity variance describes the large feature. Therefore, the spatial filter can be adapted according to the amount of information in each area, so that small features are reprojected in the Kernel PCA domain where image details are reconstructed efficiently, and large features are denoised by an anisotropic diffusion filter to recover the homogenous regions. Finally, the restored image is used in recognition process. The experimental results obtained from the Cohen-Kanade facial expression (CKFE) database tested against different noise levels and three types of blur (Gaussian, motion and pillbox) show better restoration and recognition performance of this algorithm compared to other methods.

This study investigates the relationship between the financial market and the real business cycle in the US from February 1987 to March 2016. Using different monthly time-series as proxies for the financial and macroeconomic cycles, we first specify the determinants and then build two indicators to measure the financial and real business cycles based on principal component analysis. We identify not only the main different cycles for each indicator but also measure the duration of the phases for each indicator. Second, we study the relationship between economic and financial indicators per cycle and per phase using a vector autoregressive model. Our findings show that the economic indicator is useful to forecast the financial, and that there exists a significant relationship between the financial and economic cycles that is actively stronger during the “expansion–growth” phase. This result enables investors and policymakers to better forecast the future dynamics of financial sector using the information provided by the analysis of the real business cycle.

Race/ethnicity may predispose to less favorable prostate cancer characteristics in intermediate risk prostate cancer (IR PCa) patients. We tested this hypothesis in a subgroup of IR PCa patients treated with radical prostatectomy (RP). We relied on the Surveillance, Epidemiology and End Results 2004–2016. The effect of race/ethnicity was tested in univariable and multivariable logistic regression analyses predicting upstaging (pT3+/pN1) and/or upgrading (Gleason Grade Group [GGG] 4–5) at RP. Of 20,391 IR PCa patients, 15,050 (73.8%) were Caucasian, 2857 (14.0%) African-American, 1632 (8.0%) Hispanic/Latino and 852 (4.2%) Asian. Asian patients exhibited highest age (64 year), highest PSA (6.8 ng/ml) and highest rate of GGG3 (31.9%). African-Americans exhibited the highest percentage of positive cores at biopsy (41.7%) and the highest proportion of NCCN unfavorable risk group membership (54.6%). Conversely, Caucasians exhibited the highest proportion of cT2 stage (35.6%). In univariable analyses, Hispanic/Latinos exhibited the highest rates of upstaging/upgrading among all race/ethnicities, in both favorable and unfavorable groups, followed by Asians, Caucasians and African-Americans in that order. In multivariable analyses, Hispanic/Latino race/ethnicity represented an independent predictor of higher upstaging and/or upgrading in favorable IR PCa (odds ratio [OR] 1.27, p

This research proposed an approach that is intended to determine the minimal set of important factors that influence the desire of learning Korean language in the Gulf Cooperation Council (GCC). Those factors will then influence marketing of the Korean language in GCC by guiding interested people to increase their commercial abilities, improve their information about Korean drama, and prepare them to study or travel to Korea. A total of 500 responses out of 526 questionnaires were used for the analysis process. Merging the weight by SVM and the weight guided feature selection (WGFS) techniques were proposed to build a strong hybrid model of reduction for the investigated dataset. Five different classifiers were used to test the results. Empirical results have showed that the generated factors (the reduct) are very significant to test the ability/inability of learning the Korean language. SVM was shown as the best with accuracy value of 94%. This research contributed to the literature by highlighting the importance of the Korean language in the GCC and by presenting the important factors that influence learners of the Korean language: encouragements and obstacles. Moreover, current research presented the best classifier which yields to the high performance of classification.

Land use and land cover changes occur throughout the world, but none is more concerning than tropical deforestation, much of it for agricultural purposes. Rural-rural frontier migrant farmers such as those colonizing the Sierra del Lacandon National Park in Peten, Guatemala act as a primary direct agent in this land cover conversion. This paper seeks to compare three different algorithms for monitoring changes in forested land cover, making use of freely available remotely sensed Landsat images from two years, 1991 and 2000. In the intervening 9 years, some forested land was converted to cropped, pasture, or fallow land, while other areas experienced no change. This paper contains a detailed description of the methods employed for three different change detection techniques, producing a total of five land change maps: multidate principal components analysis (PCA), normalized difference vegetation index (NDVI ) image differencing, and brightness greenness wetness (BGW) image differencing. Of the five land change maps produced, the Greenness component of the BGW transformation had the highest overall accuracy, at 86%, and is conservative in detecting change. The amount of change detected by this algorithm represents approximately 300 km2 of forest loss, or 11.9% of the area examined.

Purpose To summarize the available evidence on the survival and pathologic outcomes after deferred radical prostatectomy (RP) in men with intermediate- and high-risk prostate cancer (PCa). Methods The PubMed database and Web of Science were searched in November 2020 according to the PRISMA statement. Studies were deemed eligible if they reported the survival and pathologic outcomes of patients treated with deferred RP for intermediate- and high-risk PCa compared to the control group including those patients treated with RP without delay. Results Overall, nineteen studies met our eligibility criteria. We found a significant heterogeneity across the studies in terms of definitions for delay and outcomes, as well as in patients’ baseline clinicopathologic features. According to the currently available literature, deferred RP does not seem to affect oncological survival outcomes, such as prostate cancer-specific mortality and metastasis-free survival, in patients with intermediate- or high-risk PCa. However, the impact of deferred RP on biochemical recurrence rates remains controversial. There is no clear association of deferring RP with any of the features of aggressive disease such as pathologic upgrading, upstaging, positive surgical margins, extracapsular extension, seminal vesicle invasion, and lymph node invasion. Deferred RP was not associated with the need for secondary treatments. Conclusions Owing to the different definitions of a delayed RP, it is hard to make a consensus regarding the safe delay time. However, the current data suggest that deferring RP in patients with intermediate- and high-risk PCa for at least around 3 months is generally safe, as it does not lead to adverse pathologic outcomes, biochemical recurrence, the need for secondary therapy, or worse oncological survival outcomes.

Pandemi corona telah mengubah proses pembelajaran dari yang semula tatap muka secara langsung (offline) menjadi pembelajaran secara daring (online). Pembelajaran daring ini menyebabkan kesulitan dalam pemantauan perilaku siswa oleh guru karena berkurangnya interaksi secara langsung. Lebih dari itu, siswa seringkali merasa terisolasi sehingga jika dibiarkan, situasi ini akan menyebabkan kegagalan dalam prestasi belajarnya. Permasalahan ini mendorong banyak dilakukannya penelitian tentang pemodelan yang berkaitan dengan perilaku siswa. Namun, para peneliti sebelumnya tidak banyak yang fokus pada peningkatan kinerja model atau sistem yang dibangun, padahal kinerja model ini sangat berpengaruh terhadap kualitas hasil pemetaan perilaku siswa. Untuk itu, makalah ini berfokus pada peningkatan kinerja pengklasteran perilaku siswa ketika berinteraksi dengan sistem e-Learning. Peningkatan kinerja dilakukan dengan reduksi dimensi pada data siswa dengan Principal Component Analysis (PCA). Selanjutnya, dua teknik inisialisasi titik pusat klaster dieksplorasi untuk mendapatkan hasil yang optimal, yaitu: random dan K-means++. Untuk pengukuran kualitas klaster, makalah ini menggunakan silhouette index. Hasil pengujian menunjukkan bahwa klaster dengan kualitas tertinggi dicapai oleh penerapan PCA dengan tujuh komponen dan banyaknya klaster tiga sampai empat untuk semua teknik inisialisasi titik pusat. Klaster yang berkualitas ini dapat membantu guru dalam memonitor perilaku siswa pada pembelajaran secara daring.

Defects are the major problems in the current situation and predicting them is also a difficult task. Researchers and scientists have developed many software defects prediction techniques to overcome this very helpful issue. But to some extend there is a need for an algorithm/method to predict defects with more accuracy, reduce time and space complexities. All the previous research conducted on the data without feature reduction lead to the curse of dimensionality. We brought up a machine learning hybrid approach by combining Principal component Analysis (PCA) and Support vector machines (SVM) to overcome the ongoing problem. We have employed PROMISE (CM1: 344 observations, KC1: 2109 observations) data from the directory of NASA to conduct our research. We split the dataset into training (CM1: 240 observations, KC1: 1476 observations) dataset and testing (CM1: 104 observations, KC1: 633 observations) datasets. Using PCA, we find the principal components for feature optimization which reduce the time complexity. Then, we applied SVM for classification due to very native qualities over traditional and conventional methods. We also employed the GridSearchCV method for hyperparameter tuning. In the proposed hybrid model we have found better accuracy (CM1: 95.2%, KC1: 86.6%) than other methods. The proposed model also presents higher evaluation in the terms of other criteria. As a limitation, the only problem with SVM is there is no probabilistic explanation for classification which may very rigid towards classifications. In the future, some other method may also introduce which can overcome this limitation and keep a soft probabilistic based margin for classification on the optimal hyperplane.

Purpose This study aimed to develop a multi-long noncoding RNA (lncRNA) signature for the prediction of gastric cancer (GC) based on differential gene expression between recurrence and nonrecurrence patients. Methods By repurposing microarray expression profiles of RNAs from The Cancer Genome Atlas (TCGA), we performed differential expression analysis between recurrence and nonrecurrence patients. A prognostic risk prediction model was constructed based on data from TCGA database, and its reliability was validated using data from Gene Expression Omnibus database. Furthermore, the lncRNA-associated competing endogenous RNA (ceRNA) network was constructed, namely, DIANA-LncBasev2 and starBase database. Results We identified 363 differentially expressed RNAs (317 mRNAs, 18 lncRNAs, and 28 microRNAs [miRNAs]). Principal component analysis showed that the seven-feature lncRNAs screened by support vector machine–recursive feature elimination algorithm was more informative for predicting recurrence of GC in comparison with the eight-feature lncRNAs screened by random forest–out-of-bag algorithm. Four of the seven-feature lncRNAs including LINC00843, SNHG3, C21orf62-AS1, and MIR99AHG were chosen to develop a four-lncRNA risk score model. This risk score model was able to distinguish patients with high and low risk of recurrence, and was tested in two independent validation sets. The ceRNA network of this four-lncRNA signature included 10 miRNAs and 178 mRNAs. The mRNAs significantly related to the Wnt-signaling pathway and relevant biological processes. Conclusion A useful four-lncRNA signature recurrence was established to distinguish GC patients with high and low risk of recurrence. Regulating the relevant miRNAs and Wnt pathway might partly affect GC metastasisby.

Background: Fetal heart rate (FHR) extracted from abdominal electrocardiogram (ECG) is a powerful non-invasive method in appropriately assessing the fetus well-being during pregnancy. Despite significant advances in the field of electrocardiography, the analysis of fetal ECG (FECG) signal is considered a challenging issue which is mainly due to low signal to noise ratio (SNR) of FECG. Objective: In this study, we present an approach for accurately locating the fetal QRS complexes in non-invasive FECG. Materials and Methods: In this experimental study, the proposed method included 4 steps. In step 1, comb notching filter was employed to pre-process the abdominal ECG (AECG). Furthermore, low frequency noises were omitted using wavelet decomposition. In next step, principal component analysis (PCA) and signal quality assessment (SQA) were used to obtain an optimal AECG reference channel for maternal R-peaks detection. In step 3, maternal ECG (MECG) was removed from mixture signal and FECG was extracted. In final step, the extracted FECG was first decomposed by discrete wavelet transforms at level 10. Then, by employing details of levels 2, 3, 4, the new FECG signal was reconstructed in which various noises and artifacts were removed and FECG components whose frequency were close to the fetal QRS complexes remained which increased the performance of the method. Results: For evaluation, 15 recordings of PhysioNet Noninvasive FECG database were used and the average F1 measure of 98.77% was obtained. Conclusion: The results indicate that use of both an efficient analysis of major component of AECG along with a signal quality assessment technique has a promising performance in FECG analysis.

The aim of this study is to evaluate prognostic and therapeutic implications of microvessel density (MVD) in the recurrence-free survival (RFS), disease-specific survival (DSS), and overall survival (OS) in prostate cancer (PCa). As of April 2019, EMBASE, PubMed, Cochrane Library, Science Direct/Elsevier, MEDLINE, and CNKI are used for systematic literature retrieval to investigate the correlation between MVD and PCa. Meta-analysis was performed using Review Manager and Stata software. Combined hazard ratio (HR) was identified with 95% confidence intervals (95% CI) in a random or fixed effects model. Thirteen studies were identified in this article. Of which, 8 studies analyzed for the recurrence-free survival (2,399 patients) demonstrated that MVD significantly elevated in the poor recurrence-free survival (HR 2.57, 95% CI 2.21–2.97). Other 2 eligible studies (330 patients) with 3 data sets for the MVD-OS analysis and the pooled HR (HR 1.70, 95% CI 1.27–2.28) suggested a weak risk of overall death rate in patients with high-MVD levels. The last 3 studies for disease-specific survival (220 patients) suggested that the association with high MVD and disease-specific survival may not have statistically significance (HR 1.32, 95% CI 0.49–3.56). This study suggests that high intratumoral MVD appears a significant progenitor for poor recurrence-free survival of PCa.

Background Due to continued advances in sequencing technology, the limitation in understanding biological systems through an “-omics” lens is no longer the generation of data, but the ability to analyze it. Importantly, much of this rich -omics data is publicly available waiting to be further investigated. Although many code-based pipelines exist, there is a lack of user-friendly and accessible applications that enable rapid analysis or visualization of data. Results GECO (Gene Expression Clustering Optimization; http://www.theGECOapp.com) is a minimalistic GUI app that utilizes non-linear reduction techniques to rapidly visualize expression trends in many types of biological data matrices (such as bulk RNA-seq or proteomics). The required input is a data matrix with samples and any type of expression level of genes/protein/other with a unique ID. The output is an interactive t-SNE or UMAP analysis that clusters genes (or proteins/other unique IDs) based on their expression patterns across the multiple samples enabling visualization of expression trends. Customizable settings for dimensionality reduction, data normalization, along with visualization parameters including coloring and filters, ensure adaptability to a variety of user uploaded data. Conclusion This local and cloud-hosted web browser app enables investigation of any -omic data matrix in a rapid and code-independent manner. With the continued growth of available -omic data, the ability to quickly evaluate a dataset, including specific genes of interest, is more important than ever. GECO is intended to supplement traditional statistical analysis methods and is particularly useful when visualizing clusters of genes with similar trajectories across many samples (ex: multiple cell types, time course, dose response). Users will be empowered to investigate -omic data with a new lens of visualization and analysis that has the potential to uncover genes of interest, cohorts of co-regulated genes programs, and previously undetected patterns of expression.

Motion capturing systems produce a large amount of information on the motion of individuals. A growing number of data reduction techniques have been developed to reduce the amount of data while keeping relevant information. An overview that compares and identifies the advantages and disadvantages of these methods on cyclic motion data is, however, lacking. Therefore, this study aims to assess the features of different data reduction techniques by applying them to a large public gait data set. Due to the periodicity of cyclic data, an individual cycle can be isolated and analyzed. The analysis of single cycles requires pre-processing steps to segment and align the individual cycles. The latter is needed to isolate the amplitude variability. Three alignment procedures with different complexity, namely Linear Length Normalization (LLN), Piecewise LNN (PLLN) and Continuous Registration (CR), are assessed based on the amount of resulting variation. Subsequently three data reduction techniques (i.e. Principal Component Analysis (PCA), Principal Polynomial Analysis (PPA) and Multivariate Functional PCA (MFPCA)) are applied to the aligned single gait cycles. The data reduction techniques are evaluated based on the in-sample error, the out-of-sample error, the compactness and the computation time to produce a model. The curves aligned with CR have the lowest remaining variation and thus the lowest amount of remaining phase variation. The differences between the different data reduction techniques appear to be minimal PPA shows to be the most compact and is therefore recommended when compactness is crucial and out-of-sample performance is less essential. The use of MFPCA is advised when one wants to include data from different sources. PCA is suggested when computation time is key. Copyright (C) 2021 The Authors.

This work describes a new method to extract image features using tensor decomposition to model data. Given a set of sample images, we extract patches from images, compute the covariance tensor for all patches, decompose with the Tucker model, and obtain the most critical features from a tensor core. To extract features, we factorize the covariance tensor (CovTen) into its core and propose a new interpretation of the resultant tensor structure, which holds relevant features in a block-wise arrangement (also named filters, weights, or kernels). This tensorial representation allows preserving the spatial structure, learning multichannel filters, and establishing linear dependence between dimensions, reducing the dimensional complexity (the curse of dimensionality). Thus, the proposed method generates filters by a single feed-forward step using a few samples per class as low as 1. Besides, in kernel generation, labels are not needed. The obtained features were extensively tested using a convolutional neural network for classification. All tests were conducted under the VGG architecture conventions. The experiments helped us identify the proposed method’s advantages versus traditional convolutional neural networks in inference capacity and kernels initialization. We also performed experiments to select hyperparameters (nonLinearity, max pooling, samples, filter size) according to their performance. The inference capacity results showed an increased classification accuracy around 67% with CIFAR 10, 64% with CIFAR 100, and 98% with MNIST, using 10,100,1000 samples with a single feed-forward training. On the other hand, the initialization experiments showed the feature extraction capability versus available initializers (He random, He uniform, Glorot, random), confirming linear tensor constraints’ usefulness to generate features. Using the method as kernel initializer returns comparable findings with state of the art around 91% with CIFAR 10, 72% with CIFAR 100, and 99% with MNIST.

This work investigates the efficiency of the process of load disaggregation, considering only the values of active power. To perform the task, we use data collected from the NILM (Non-Intrusive Load Monitoring) measurement method, presented in the Rainforest Automation Energy Dataset (RAE) and Reference Energy Disagreggation Dataset (REDD) database. A strategy of assigning labels using combinations of equipment in use, by status ON/OFF, and also by choosing an appropriate temporal data window is discussed. Also, the performance of very well-known machine learning algorithms such as k-Nearest Neighbor (kNN), Decision Tree, and Random Forest are evaluated. The results show a very efficient and low computer complexity strategy presenting values of F1-Score above 95%, for RAE and REDD database. As presented in table I, the proposed approach presents the highest F1-Score, compared to other methods in the literature, considering all appliances in the REDD database. The greatest benefit of the approach consists in the possibility of applying the disaggregation process in a household without smart outlets, under the restriction that the training and test houses hold identical or similar appliances.

Much attention has been paid to large data technologies in the past few years mainly due to its capability to impact business analytics and data mining practices, as well as the possibility of influencing an ambit of a highly effective decision-making tools. With the current increase in the number of modern applications (including social media and other web-based and healthcare applications) which generates high data in different forms and volume, the processing of such huge data volume is becoming a challenge with the conventional data processing tools. This has resulted in the emergence of big data analytics which also comes with many challenges. This paper introduced the use of principal components analysis (PCA) for data size reduction, followed by SVM parallelization. The proposed scheme in this study was executed on the Spark platform and the experimental findings revealed the capability of the proposed scheme to reduce the classifiers’ classification time without much influence on the classification accuracy of the classifier.

Introduction: Lymph node metastases are a key prognostic factor in prostate cancer (PCa), but detecting lymph node lesions from PET/CT images is a subjective process resulting in inter-reader variability. Artificial intelligence (AI)-based methods can provide an objective image analysis. We aimed at developing and validating an AI-based tool for detection of lymph node lesions. Methods: A group of 399 patients with biopsy-proven PCa who had undergone 18F-choline PET/CT for staging prior to treatment were used to train (n = 319) and test (n = 80) the AI-based tool. The tool consisted of convolutional neural networks using complete PET/CT scans as inputs. In the test set, the AI-based lymph node detections were compared to those of two independent readers. The association with PCa-specific survival was investigated. Results: The AI-based tool detected more lymph node lesions than Reader B (98 vs. 87/117; p =.045) using Reader A as reference. AI-based tool and Reader A showed similar performance (90 vs. 87/111; p =.63) using Reader B as reference. The number of lymph node lesions detected by the AI-based tool, PSA, and curative treatment was significantly associated with PCa-specific survival. Conclusion: This study shows the feasibility of using an AI-based tool for automated and objective interpretation of PET/CT images that can provide assessments of lymph node lesions comparable with that of experienced readers and prognostic information in PCa patients.

The individual clinical significance of a positive surgical margin (PSM) after radical prostatectomy has remained controversial. Studies have suggested that the Gleason grade (GG) at the PSM could improve predictive accuracy and decision making. Our objective was to systematically review the reported data to determine the effect of the GG at the PSM on the prognosis after radical prostatectomy. A systematic review was conducted by searching MEDLINE/PubMed for studies reported by June 2019 in accordance with the Preferred Reporting Items for Systematic Review statement. The keywords used included prostate cancer, radical prostatectomy, positive surgical margin, Gleason score, and/or Gleason grade. After a systematic literature review, 10 studies were included, comprising 14,108 patients, of whom 2454 (17.4%) had a PSM and 428 (14%) eventually experienced biochemical recurrence (BCR) within a median follow-up of 18 to 156 months. Data on neoadjuvant or adjuvant therapy were not estimable. In a meta-analysis, GG4 at PSM was significantly associated with BCR compared with GG3 (pooled hazard ratio, 1.87; 95% confidence interval, 1.53-2.28; z = 6.16). The Cochrane Q test (χ2 = 5.88; P = .318) and I2 test (I2 = 15.0%) showed that no significant heterogeneity was present. GG4 at a PSM is a feature of biologically and clinically aggressive prostate cancer that is associated with a significant increase risk of BCR. GG at PSM should be recorded in each pathological report. Given this adverse prognostic value patients with GG4 at the PSM should be considered for multimodal therapy such as radiotherapy.

Purpose The study aimed to determine normative values for the Tonsillectomy Outcome Inventory 14 (TOI-14) in a healthy middle-European cohort. We also compared these generated values with TOI-14 scores from a patient population with recurrent tonsillitis (RT) and explored the factorial structure of the TOI-14. Methods We systematically studied the responses of healthy individuals (reference cohort) and patients with RT (clinical cohort) to the TOI-14 survey. The reference cohort contained 1000 participants, who were recruited using the Respondi panel for market and social science research. This subsample was quoted to the population distribution of the German Microcensus and selected from a non-probability panel. Tonsillitis patients were assessed before and 6 and 12 months after tonsillectomy. Data were analysed using principal component and exploratory factor analyses. Results The PCA revealed three TOI-14 domains (physiological, psychological and socio-economic), which explained 73% of the total variance. The reference cohort perceived a good quality of life (QOL) with a TOI-14 total score of 11.8 (physiological: 8.0, psychological: 5.8, and socio-economic subscale score: 13.9). TOI-14 scores were higher in the patient cohort, indicating that the TOI-14 discriminates between patients with RT and healthy individuals with no RT. Age and female gender significantly influenced the total TOI-14 score, especially in the psychological (age) and socio-economic (gender) subscales. Conclusion We have developed a set of normative values that, together with the TOI-14, can determine the disease burden indicating tonsillectomy.

Introduction Post-operative pain control remains unsatisfactory in patients after laparotomy. This study aimed to evaluate the efficacy, safety, and quality of life with a single dose of extended-release dinalbuphine sebacate (ERDS) pre-operatively to intravenous patient-controlled analgesia (PCA) with fentanyl in patients undergoing laparotomy. Methods This was a prospective, open-label, randomized controlled study. Of 110 randomized patients, 107 completed all assessments. The area under the curve (AUC) of visual analogue scale (VAS) from baseline to 48 h after surgery, VAS throughout 7 days after surgery, post-operative analgesics use, quality of life, satisfaction, and safety were evaluated. Results The AUC of VAS from baseline to 48 h after surgery were 118.6 [97.5% confidence interval (CI) 95.6–141.6] in ERDS group and 176.13 (97.5% CI 150.8–201.4) in PCA group, which showed the non-inferiority because the upper limit of the 97.5% CIs of ERDS group was lower than the lower limit of PCA group (P

Ganga River water is very much stressed with the rapidly increasing population, climate change and water pollution that increase domestic, agricultural and industrial needs. This study assesses the surface water quality of the River Ganga in India, using NSFWQI, OIP and multivariate techniques. During the current study, water samples from Ganga River were collected for the assessment of 19 physico-chemical determinants from 20 sampling locations. Water quality indices (WQIs) is used to classify the overall impact of different variables of water. Multivariate techniques were utilized to assess the water conditions for productive management of fresh water quality. The WQI results showed that surface water quality varied at the selected sampling sites among medium and good categories. The PCA generates the 6 principle components which highly contributes (80.3%) in influencing the hydro-chemistry of river water. Agricultural waste runoff, untreated effluents and many other anthropogenic activities were identified as main contributor in decreasing the water quality of the River Ganga. To maintain and protect this fresh water resources against contamination, the usage of stringent policies and rules are expected to preserve fresh water resources for people in the future.

In recent decades the global effects of climate change have requested for a more sustainable approach in thinking and planning of our cities, making them more inclusive, safe and resilient. In terms of consumption of natural resources and pollution, cities are seen as entities with most significant impact to the natural environment. Strategic policies focused on tackling the challenges induced by climate change suggest in fact the necessity to start from the management and operating models of the cities themselves. This study illustrates an initial evaluation of parameters for purposes of urban generation studies using optical multi-spectral satellite imagery from Landsat-5, Landsat-8 and Sentinel-2 missions. The changes in land occupation and urban density are the first aspects chosen to be examined for the period 1985–2020. The focus was given on possible modifications occurred in occasion of Milano Expo 2015. The paper firstly explores the known best band combination for observation of urban fabric. Suggestions derived have then been calibrated with reference to ground truth data, while the image pairs over the 35 years span were then build with selected bands. Finally, all image pairs have been processed for Principal Component Analysis in order to identify possible “hot-spots” of significant changes. The results found on the image pair 2006–2015 have been explored in detail and checked upon official orthophotos. Monitoring of changes in urban fabric using multispectral optical imagery can provide valuable insights for further evaluation of single urban generation interventions. Such contributions could be considered in the processes of urban planning policies in a more systematic manner.

Background Digital image technology has made great progress in the field of foreign body detection and classification, which is of great help to drug purity extraction and impurity analysis and classification. Objective The detection and classification of foreign bodies in lyophilized powder are important. The method which can obtain a higher accuracy of recognition needs to be proposed. Methods We used digital image technology to detect and classify foreign bodies in lyophilized powder, and studied the process of image preprocessing, median filtering, Wiener filtering and average filtering balance to better detect and classify foreign bodies in lyophilized powder. Results Through industrial small sample data simulation, test results show that in the process of image preprocessing, 3 × 3 median filtering is best. In the aspect of foreign body recognition, the recognition based on principal component analysis (PCA) and support vector machine (SVM) algorithm and the recognition based on PCA and Third-Nearest Neighbor classification algorithm are compared and results show that the PCA+SVM algorithm is better. Conclusion We demonstrated that integrating PCA and SVM to classify foreign bodies in lyophilized powder. Our proposed method is effective for the prediction of essential proteins.

Fabric quality and performance is assessed subjectively by the customer using an important and complex phenomenon of fabric hand. Objectively, it is evaluated with complicated and expensive instruments, such as Kawabata Evaluation System for Fabrics (KES-F) and Fabric Assurance with Simple Testing (FAST). The present research explores a non-touch objective approach, i.e., three-dimensional (3D) drape model to estimate fabric hand. Fabric hand prediction was testified on different commercial fabrics spanning a wide range of areal weight, thickness, yarn count, and fabric density. Fabric objective ranks based on drape indicators using principal component analysis (PCA) were compared with subjective ranks of fabric hand. Additionally, fabric drape is evaluated three dimensionally and a new drape indicator drape height (DH) is proposed. The cosine similarity results have proved fabric drape as an objective alternate to fabric hand.

A potential eco-friendly method without organic solvents is presented by integrating a chromatographic fingerprint and multivariate control chart based on Q residuals to differentiate grape juices from different farming practices. The sample preparation was only water dilution, and the mobile phase was water acidified with sulfuric acid, which can be readily neutralized before its disposal. The proposed method is shown to be a simple way to distinguish between organic and non-organic grape juices in a non-target way, successfully evaluating an external validation data set, where organic and non-organic samples were correctly assigned. Through the chromatographic profile, it is possible to suggest that one of the species responsible for this distinction may be from the anthocyanins class.

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works, The lack of resolution has a negative impact on the performance of image-based biometrics. While many generic super-resolution methods have been proposed to restore low-resolution images, they usually aim to enhance their visual appearance. However, an overall visual enhancement of biometric images does not necessarily correlate with a better recognition performance. Reconstruction approaches thus need to incorporate the specific information from the target biometric modality to effectively improve recognition performance. This paper presents a comprehensive survey of iris super-resolution approaches proposed in the literature. We have also adapted an eigen-patches’ reconstruction method based on the principal component analysis eigen-transformation of local image patches. The structure of the iris is exploited by building a patch-position-dependent dictionary. In addition, image patches are restored separately, having their own reconstruction weights. This allows the solution to be locally optimized, helping to preserve local information. To evaluate the algorithm, we degraded the high-resolution images from the CASIA Interval V3 database. Different restorations were considered, with 15 × 15 pixels being the smallest resolution evaluated. To the best of our knowledge, this is the smallest resolutions employed in the literature. The experimental framework is complemented with six publicly available iris comparators that were used to carry out biometric verification and identification experiments. The experimental results show that the proposed method significantly outperforms both the bilinear and bicubic interpolations at a very low resolution. The performance of a number of comparators attains an impressive equal error rate as low as 5% and a Top-1 accuracy of 77%–84% when considering the iris images of only 15 × 15 pixels. These results clearly demonstrate the benefit of using trained super-resolution techniques to improve the quality of iris images prior to matching, This work was supported by the EU COST Action under Grant IC1106. The work of F. Alonso-Fernandez and J. Bigun was supported in part by the Swedish Research Council, in part by the Swedish Innovation Agency, and in part by the Swedish Knowledge Foundation through the CAISR/SIDUS-AIR projects. The work of J. Fierrez was supported by the Spanish MINECO/FEDER through the CogniMetrics Project under Grant TEC2015-70627-R. The authors acknowledge the Halmstad University Library for its support with the open access fees

This study seeks to confirm whether lesions in posterior regions of the brain involved in visuo-spatial processing are of functional relevance to the processing of words with spatial meaning. We investigated whether patients with Posterior Cortical Atrophy (PCA), an atypical form of Alzheimer’s Disease which predominantly affects parieto-occipital brain regions, is associated with deficits in working memory for spatial prepositions. Case series of patients with PCA and matched healthy controls performed tests of immediate and delayed serial recall on words from three lexico-semantic word categories: number words (twelve), spatial prepositions (behind) and function words (e.g., shall). The three word categories were closely matched for a number of psycholinguistic and semantic variables including length, bi-/tri-gram frequency, word frequency, valence and arousal. Relative to controls, memory performance of PCA patients on short word lists was significantly impaired on spatial prepositions in the delayed serial recall task. These results suggest that lesions in posterior parieto-occipital regions specifically impair the processing of spatial prepositions. Our findings point to a pertinent role of posterior cortical regions in the semantic processing of words with spatial meaning and provide strong support for modality-specific semantic theories that recognize the necessary contributions of sensorimotor regions to conceptual semantic processing.

This paper studies the use of three different approaches to reduce the dimensionality of a type of spectral&ndash, temporal features, called motion picture expert group (MPEG)-7 audio signature descriptors (ASD). The studied approaches include principal component analysis (PCA), independent component analysis (ICA), and factor analysis (FA). These approaches are applied to ASD features obtained from audio items with or without distortion. These low-dimensional features are used as queries to a dataset containing low-dimensional features extracted from undistorted items. Doing so, we may investigate the distortion-resistant capability of each approach. The experimental results show that features obtained by the ICA or FA reduction approaches have higher identification accuracy than the PCA approach for moderately distorted items. Therefore, to extract features from distorted items, ICA or FA approaches should also be considered in addition to the PCA approach.

Made available in DSpace on 2022-04-28T19:46:38Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-11-01 Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) In this paper, we investigate the influence of holidays and community mobility on the transmission rate and death count of COVID-19 in Brazil. We identify national holidays and hallmark holidays to assess their effect on disease reports of confirmed cases and deaths. First, we use a one-variate model with the number of infected people as input data to forecast the number of deaths. This simple model is compared with a more robust deep learning multi-variate model that uses mobility and transmission rates (R0, Re ) from a SEIRD model as input data. A principal components model of community mobility, generated by the principal component analysis (PCA) method, is added to improve the input features for the multi-variate model. The deep learning model architecture is an LSTM stacked layer combined with a dense layer to regress daily deaths caused by COVID-19. The multi-variate model incremented with engineered input features can enhance the forecast performance by up to 18.99% compared to the standard one-variate data-driven model. Pós-Graduação em Engenharia Elétrica e de Computação Universidade Federal do Rio Grande do Norte, Av. Salgado Filho, 3000, Lagoa Nova Faculdade de Ciências Farmacêuticas Universidade Estadual Paulista “Júlio Mesquita Filho”, Rodovia Araraquara-Jaú, Km 1, Campus Ville Faculdade de Ciências Farmacêuticas Universidade Estadual Paulista “Júlio Mesquita Filho”, Rodovia Araraquara-Jaú, Km 1, Campus Ville CNPq: 311640/2018-4 CAPES: 88881.506890/2020-01

Today, the threat of crude oil pollution globally, has been a resounding epoch of ecosystem degradation. This affects the lives of flora and fauna in the ecosystem as well as influence the human's economic needs. The aim of this study is to assess the heavy metals and petroleum hydrocarbons concentrations (crude oil pollutants) in agricultural soils of selected communities in Niger-Delta region. Models were used to assess the crude oil pollution loads and its source apportionment. The results from the principal components analysis (PCA) for the heavy metals (HMs) and total petroleum hydrocarbons (TPHs) in soils showed high loading factors > 0.32. The source allotment were both of crustal and human origin. The cluster analysis for the physical and chemical parameters of the HMs and TPHs showed that stations 1, 5, 2, 4, 6 and 7 and 2, 6, 7 and 9 were similar respectively. The outcome of the ecological risk status of the sampled soils, showed that the metals in the nine sampling stations were at the slight risk level with Cd being the main donor. The results of the health risk for both non-carcinogenic and carcinogenic pathways for the metals were

Unhealthy dietary patterns (DPs) can lead to cardiovascular and other chronic diseases. We assessed the effects of a community-focused intervention with a traditional Atlantic diet on changes in DPs in families and the associations of these changes with weight loss. The Galiat study is a randomized, controlled trial conducted in 250 families (720 adults and children) and performed at a primary care setting with the cooperation of multiple society sectors. Over 6 months, families randomized to the intervention group received educational sessions, cooking classes, written supporting material, and foods that form part of the Atlantic diet, whereas those randomized to the control group followed their habitual lifestyle. At baseline, five DPs that explained 30.1% of variance were identified: “Caloric”, “Frieds”, “Fruits, vegetables, and dairy products”, “Alcohol”, and “Fish and boiled meals.” Compared to the controls, the intervention group showed significant improvements in “Fruits, vegetables, and dairy products” and “Fish and boiled meals” and reductions in the “Caloric” and “Frieds”. Changes in bodyweight per unit increment of “Frieds” and “Fruits, vegetables, and dairy products” scores were 0.240 kg (95% CI, 0.050–0.429) and −0.184 kg (95% CI, −0.379–0.012), respectively. We found that a culturally appropriate diet improved DPs associated with weight loss.

Low plasma levels of branched chain amino acids (BCAA) in liver cirrhosis are associated with hepatic encephalopathy (HE). We aimed to identify a metabolic signature of minimal hepatic encephalopathy (MHE) in malnourished cirrhotic patients and evaluate its modification with oral nutritional supplements (ONS) enriched with ß-Hydroxy-ß-methylbutyrate (HMB), a derivative of the BCAA leucine. Post hoc analysis was conducted on a double-blind placebo-controlled trial of 43 individuals with cirrhosis and malnutrition, who were randomized to receive, for 12 weeks, oral supplementation twice a day with either 220 mL of Ensure® Plus Advance (HMB group, n = 22) or with 220 mL of Ensure® Plus High Protein (HP group, n = 21). MHE evaluation was by psychometric hepatic encephalopathy score (PHES). Compared to the HP group, an HMB-specific treatment effect led to a larger increase in Val, Leu, Phe, Trp and BCAA fasting plasma levels. Both treatments increased Fischer’s ratio and urea without an increase in Gln or ammonia fasting plasma levels. MHE was associated with a reduced total plasma amino acid concentration, a reduced BCAA and Fischer´s ratio, and an increased Gln/Glu ratio. HMB-enriched ONS increased Fischer´s ratio without varying Gln or ammonia plasma levels in liver cirrhosis and malnutrition, a protective amino acid profile that can help prevent MHE. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Surface waters are very important for society, as they are a source of potable water, as well a water supply for agricultural, industrial and recreational purposes. This paper presents changes in the quality of the water in the Utrata River, along its entire length, as a function of the catchment area’s land use. Water-quality measurements were carried out once a month for a total period of one year (April 2019–March 2020) at 24 measurement points. The concentrations of the following compounds were measured: total phosphorus, ammonia nitrogen and nitrate nitrogen, dissolved oxygen, and chemical oxygen demand. The results were compared with the limit values specified in the Regulation of the Minister of Maritime Economy and Inland Navigation. In order to determine the impact of land use on water quality in the Utrata River, principal component analyses (PCA) were conducted. The research demonstrated a considerably negative impact of agricultural land use and the presence of urban areas on the water quality of the Utrata River, with elevated concentrations of total phosphorus, ammonia nitrogen, nitrate nitrogen and COD, and decreasing concentrations of dissolved oxygen. The presented results point to the need for effective strategies to mitigate the adverse impact of agriculture and urbanisation on the environment and surface waters.

The gait assessment is instrumental for evaluating the efficiency of rehabilitation of persons with a motor impairment of the lower extremities. The protocol for quantifying the gait performance needs to be simple and easy to implement; therefore, a wearable system and user-friendly computer program are preferable. We used the Gait Master (instrumented insoles) with the industrial quality ground reaction forces (GRF) sensors and 6D inertial measurement units (IMU). WiFi transmitted 10 signals from the GRF sensors and 12 signals from the accelerometers and gyroscopes to the host computer. The clinician was following in real-time the acquired data to be assured that the WiFi operated correctly. We developed a method that uses principal component analysis (PCA) to provide a clinician with easy to interpret cyclograms showing the difference between the recorded and healthy-like gait performance. The cyclograms formed by the first two principal components in the PCA space show the step-to-step reproducibility. We suggest that a cyclogram and its orientation to the coordinate system PC1 vs. PC2 allow a simple assessment of the gait. We show results for six healthy persons and five patients with hemiplegia.

Background and Objectives: Recently, many studies have focused on the early detection of Parkinson’s disease (PD). This disease belongs to a group of neurological problems that immediately affect brain cells and influence the movement, hearing, and various cognitive functions. Medical data sets are often not equally distributed in their classes and this gives a bias in the classification of patients. We performed a Hybrid feature selection framework that can deal with imbalanced datasets like PD. Use the SOMTE algorithm to deal with unbalanced datasets. Removing the contradiction from the features in the dataset and decrease the processing time by using Recursive Feature Elimination (RFE), and Principle Component Analysis (PCA). Materials and Methods: PD acoustic datasets and the characteristics of control subjects were used to construct classification models such as Bagging, K-nearest neighbour (KNN), multilayer perceptron, and the support vector machine (SVM). In the prepressing stage, the synthetic minority over-sampling technique (SMOTE) with two-feature selection RFE and PCA were used. The PD dataset comprises a large difference between the numbers of the infected and uninfected patients, which causes the classification bias problem. Therefore, SMOTE was used to resolve this problem. Results: For model evaluation, the train–test split technique was used for the experiment. All the models were Grid-search tuned, the evaluation results of the SVM model showed the highest accuracy of 98.2%, and the KNN model exhibited the highest specificity of 99%. Conclusions: the proposed method is compared with the current modern methods of detecting Parkinson’s disease and other methods for medical diseases, it was noted that our developed system could treat data bias and reach a high prediction of PD and this can be beneficial for health organizations to properly prioritize assets.

Determination of heavy metal concentrations in vegetables and agricultural soils is crucial because high levels of heavy metals could affect soil quality, crop production and safe consumption of crops. A field study was conducted to determine the heavy metal concentrations and their transfer from agricultural soils to different parts (leaf, stem, and root) of Brassica juncea (L.) Czern. In addition, potential health risks of contamination in the vegetables grown in the field were evaluated. Acid digestion method USEPA 3050B in combination with ICP-OES were used to analyze heavy metal (Al, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb and Zn) contents in both pre- and post-harvest soils and vegetable samples. Results showed that none of the heavy metals in soils had concentrations above the maximum safety limits based on the WHO, USEPA and CCME guidelines. Calculated metal transfer factor (MTF >1) showed B. juncea accumulated Cd, Co, Ni, Pb and Zn in leaves, stems and roots, but Cu and Mn, as well as Cr were only accumulated in stems and roots, respectively. There were variations in heavy metal contents between the different parts of B. juncea, but only Cd and Pb contents were above the maximum allowable limit recommended by FAO/WHO. PCA analysis was able to identify 4 major components corresponding to 38.38%, 28.98%, 14.39% and 10.67% of the total variance and PC1 was clearly associated to leaves of B. juncea. Based on the MTF values, only Cd was found to have a value of HRI >1 compared to the other heavy metals, implying potential health risk associated with long-term ingestion of the vegetable., Brassicaceae; DIM; EDEM; HRI; MTF; PCA

Artificial neural network (ANN) is one of the techniques in artificial intelligence, which has been widely applied in many fields for prediction purposes, including wind speed prediction. The aims of this research is to determine the topology of neural network that are used to predict wind speed. Topology determination means finding the hidden layers number and the hidden neurons number for corresponding hidden layer in the neural network. The difference between this research and previous research is that the objective function of this research is regression, while the objective function of previous research is classification. Determination of the topology of the neural network using principal component analysis (PCA) and K-means clustering. PCA is used to determine the hidden layers number, while clustering is used to determine the hidden neurons number for corresponding hidden layer. The selected topology is then used to predict wind speed. Then the performance of topology determination using PCA and clustering is then compared with several other methods. The results of the experiment show that the performance of the neural network topology determined using PCA and clustering has better performance than the other methods being compared. Performance is determined based on the RMSE value, the smaller the RMSE value, the better the neural network performance. In future research, it is necessary to apply a correlation or relationship between input attribute and output attribute and then analyzed, prior to conducting PCA and clustering analysis.

A 76-year-old man with dyspnea (initial prostate-specific antigen [PSA]: 216 ng/mL) underwent 18F-FDG PET/CT, with uptake in the prostate, lymph nodes, fifth thoracic vertebra (T5), and cricoid cartilage. A biopsy revealed prostate adenocarcinoma (Gleason score 4 + 5, cT4 N1 M1). On initiation of combined androgen blockade therapy, PSA value decreased. However, 4 years later, in a castration-resistant state (PSA 2.14 ng/mL), CT and bone scintigraphy revealed a duodenal tumor and T5 metastasis. 18F–prostate-specific membrane antigen–1007 PET/CT showed uptake in the already known T5 metastasis (SUVmax, 33.55) and even in the duodenal tumor (16.55). The latter was histologically diagnosed as duodenal adenocarcinoma.

Telecom Companies logs customer’s actions which generate a huge amount of data that can bring important findings related to customer’s behavior and needs. The main characteristics of such data are the large number of features and the high sparsity that impose challenges to the analytics steps. This paper aims to explore dimensionality reduction on a real telecom dataset and evaluate customers’ clustering in reduced and latent space, compared to original space in order to achieve better quality clustering results. The original dataset contains 220 features that belonging to 100,000 customers. However, dimensionality reduction is an important data preprocessing step in the data mining process specially with the presence of curse of dimensionality. In particular, the aim of data reduction techniques is to filter out irrelevant features and noisy data samples. To reduce the high dimensional data, we projected it down to a subspace using well known Principal Component Analysis (PCA) decomposition and a novel approach based on Autoencoder Neural Network, performing in this way dimensionality reduction of original data. Then K-Means Clustering is applied on both-original and reduced data set. Different internal measures were performed to evaluate clustering for different numbers of dimensions and then we evaluated how the reduction method impacts the clustering task.

Encrypted traffic classification plays a vital role in cybersecurity as network traffic encryption becomes prevalent. First, we briefly introduce three traffic encryption mechanisms: IPsec, SSL/TLS, and SRTP. After evaluating the performances of support vector machine, random forest, naïve Bayes, and logistic regression for traffic classification, we propose the combined approach of entropy estimation and artificial neural networks. First, network traffic is classified as encrypted or plaintext with entropy estimation. Encrypted traffic is then further classified using neural networks. We propose using traffic packet’s sizes, packet's inter‐arrival time, and direction as the neural network's input. Our combined approach was evaluated with the dataset obtained from the Canadian Institute for Cybersecurity. Results show an improved precision (from 1 to 7 percentage points), and some application classification metrics improved nearly by 30 percentage points.

The shelf life of bakery products highly depends on the environment and it may get spoiled earlier than its expiry which results in food-borne diseases and may affect human health or may get wasted beforehand. The traditional spoilage detection methods are time-consuming and destructive in nature due to the time taken to get microbiological results. To the best of the author’s knowledge, this work presents a novel method to automatically predict the microbial spoilage and detect its spatial location in baked items using Hyperspectral Imaging (HSI) range from $395-1000\,\,nm$ . A spectral preserve fusion technique has been proposed to spatially enhance the HSI images while preserving the spectral information. Furthermore, to automatically detect the spoilage, Principal Component Analysis (PCA) followed by K-means and SVM has been used. The proposed approach can detect the spoilage almost 24 hours before it started appearing or visible to a naked eye with 98.13% accuracy on test data. Furthermore, the trained model has been validated through external dataset and detected the spoilage almost a day before it started appearing visually.

Recently, CNN-based image denoising has been investigated and shows better performance than conventional vision based techniques. However, there are still a couple of limits that are weak partly in restoring image details like textured regions or produce other artifacts. In this paper, we introduce noise-separable orthogonal transform features into a neural denoising framework. We specifically choose wavelet and PCA as an orthogonal transform, which achieved a good denoising performance conventionally. In addition to spatial image signals, the orthogonal transform features (OTFs) are fed into a denoising network. For the guide of the denoising process, we also concatenate OTFs from the image denoised by the existing method. This can play a role of prior for learning a denoising process. It has been confirmed that our proposed multi-input network can achieve better denoising performance than other single-input networks.

Society and individuals are negatively influenced both politically and socially by the widespread increase of fake news either way generated by humans or machines. In the era of social networks, the quick rotation of news makes it challenging to evaluate its reliability promptly. Therefore, automated fake news detection tools have become a crucial requirement. To address the aforementioned issue, a hybrid Neural Network architecture, that combines the capabilities of CNN and LSTM, is used with two different dimensionality reduction approaches, Principle Component Analysis (PCA) and Chi-Square. This work proposed to employ the dimensionality reduction techniques to reduce the dimensionality of the feature vectors before passing them to the classifier. To develop the reasoning, this work acquired a dataset from the Fake News Challenges (FNC) website which has four types of stances: agree, disagree, discuss, and unrelated. The nonlinear features are fed to PCA and chi-square which provides more contextual features for fake news detection. The motivation of this research is to determine the relative stance of a news article towards its headline. The proposed model improves results by ~4% and ~20% in terms of $Accuracy$ and $F1-score$ . The experimental results show that PCA outperforms than Chi-square and state-of-the-art methods with 97.8% accuracy.

Pellet quality including chemical composition, physical properties and metallurgical performance of three parts, its quality and mineral composition, properties and structure of the pellets has the close relation, studies show that the mineralogical micro structure and distribution of pellets had significant effects on the metallurgical properties, so the analysis and determination of pellets of mineral composition and micro structure is very important to improve the quality of pellets. Paper to pellets micro mineral as the research object, mainly studies the CNN and PCA two kinds of image processing algorithm, in the heart of the traditional model of CNN structure characteristics obtained by convolution PCA dimension reduction at a time, will be the main features of PCA to extract into the depth of the CNN learning, realize the mineral phase of shallow and deep information of the image to do effective fusion, in a larger extent, reflects the mineralogical characteristics, thus more intuitive response metallurgical properties. The location and alkalinity of the ore phase were identified by the extracted deep fusion feature, and the results were compared with those of the traditional CNN algorithm. It was found that the accuracy of location and alkalinity recognition of PCA and CNN coupling algorithm was 93.82% and 91.26%, respectively, which were higher than the accuracy of traditional CNN algorithm 92.73% and 88.93%, which verified the accuracy of PCA and CNN coupling model and its applicability in mineral phase recognition.

Brain image classification is one of the most useful and widely needed processes in the medical system, and it is a highly challenging field. This paper presents a new method for selecting a significant subset of features as the input to the classifier, called mutual information-accelerated singular value decomposition (MI-ASVD). This novel algorithm is exploited to design an intelligent system for classifying MRI brain images into three classes: healthy, high-grade glioma, and low-grade glioma. The proposed system has six stages: pre-processing, clustering, tumour localization, feature extraction, MI-ASVD and classification. First, the MR images are smoothed by using enhancement techniques such as Gaussian kernel filters. Then, local difference in intensity-means (LDI-Means) clustering is employed to segment and detect suspicious regions. The grey-level run-length matrix (GLRLM), texture, and colour intensity features are used for tumour feature extraction. Later, a special method including a summation of feature selection and dimensionality reduction, MI-ASVD, is applied to select the most useful features for the classification process. Finally, the simplified residual neural network technique is implemented to classify the MR brain images. Using MI-ASVD provided accurate and more efficacious results in classification compared with the original feature space and with two other standard dimensionality reduction methods, principal component analysis (PCA) and singular value decomposition (SVD). It achieved a classification accuracy of 94.91%, which is better than the two state-of-the-art techniques as well as methods from similar published studies.

Deep learning models hold state of the art performance in many fields, yet their design is still based on heuristics or grid search methods that often result in overparametrized networks. This work proposes a method to analyze a trained network and deduce an optimized, compressed architecture that preserves accuracy while keeping computational costs tractable. Model compression is an active field of research that targets the problem of realizing deep learning models in hardware. However, most pruning methodologies tend to be experimental, requiring large compute and time intensive iterations of retraining the entire network. We introduce structure into model design by proposing a single shot analysis of a trained network that serves as a first order, low effort approach to dimensionality reduction, by using PCA (Principal Component Analysis). The proposed method simultaneously analyzes the activations of each layer and considers the dimensionality of the space described by the filters generating these activations. It optimizes the architecture in terms of number of layers, and number of filters per layer without any iterative retraining procedures, making it a viable, low effort technique to design efficient networks. We demonstrate the proposed methodology on AlexNet and VGG style networks on the CIFAR-10, CIFAR-100 and ImageNet datasets, and successfully achieve an optimized architecture with a reduction of up to 3.8X and 9X in the number of operations and parameters respectively, while trading off less than 1% accuracy. We also apply the method to MobileNet, and achieve 1.7X and 3.9X reduction in the number of operations and parameters respectively, while improving accuracy by almost one percentage point., Comment: To be Published in IEEE Access, Volume 8, 2020

Stefano Turi, Francesco Deni, Gaetano Lombardi, Marilena Marmiere, Francesco Giuseppe Nisi, Luigi BerettaDipartimento di Anestesia e Rianimazione, Ospedale San Raffaele, Milano, ItalyBackground: The Sufentanil Sublingual Tablet System (SSTS) is a new, pre-programmed, noninvasive, handheld system for patient-controlled analgesia (PCA) which may allow a faster postoperative recovery compared with standard PCA. The efficacy of SSTS in controlling pain after open abdominal surgery has already been documented. However, to our knowledge SSTS has never been investigated in patients undergoing major surgery within an Enhanced Recovery After Surgery (ERAS) protocol.Methods: This observational, retrospective analysis included consecutive patients undergoing elective major abdominal and gynecological surgery. All patients received the SSTS device once they were fully awake and had a good control of pain at the end of the surgery. We analyzed changes in pain intensity according to the numerical rating scale (NRS) throughout the treatment as well as its duration, the number of administrations, and possible related adverse events. Patients were also interviewed to assess their quality of sleep and overall satisfaction with the SSTS device.Results: The study included 308 patients. Compared to the first SSTS administration, pain intensity decreased from a median NRS of 6 to 0 at day 3, for an overall reduction of 79%. Results were already statistically significant at postoperative day 1 (p