Comparison of Novel Recurrent Neural Network Over Artificial Neural network in Predicting Email spammers with improved accuracy

The main aim is to compare Novel Recurrent Neural Network over Artificial Neural Network in predicting Email spammers with improved accuracy. Material and Methods : This research study contains two groups namely Novel Recurrent Neural Network and Artificial Neural Network. Each group consists of a sample size of 10 and the study parameters are calculated using clincalc with preset parameters as alpha 0.8, beta 0.2 and CI as 90%. Results and Discussion : The Novel Recurrent Neural Network has the highest accuracy 97.96% when compared to Artificial Neural Network it has 93.79% accuracy in Electronic Mail spam prediction with significance value p=0.000(p<0.05) that is significantly better. The G-power value is 80%. When used as a spam predictor for electronic mail, the Novel Recurrent Neural Network performance analysis outperforms the best results than the Artificial Neural Network performance

Optimización mediante el diseño Box Behnken del proceso de extracción con dióxido de carbono supercrítico (SC-CO2) de aceite de germen de trigo en relación al rendimiento, contenido de fósforo y tocoles

The supercritical carbon dioxide (SC-CO2) extraction technique has emerged as one of the best possible alternatives to organic solvent (hexane) extraction. However, very limited information is available on process optimization for this extraction technique and the lack of available engineering data is causing the slow growth of this technique. In the present investigation, SC-CO2 extraction was carried out to extract the oil from wheat germ under various operating conditions and the oil samples were characterized for properties such as phosphorous and tocol contents (vitamin E). A three-level Box Behnken design from response surface methodology was applied to optimize the SC-CO2 extraction parameters such as pressure, temperature and CO2 flow rate with an objective to obtain high oil yield, rich tocol contents and low phosphorous content. The process parameters were maintained between 30 to 50 MPa, 40 to 60 °C and a flow rate of 10 to 30 g·min-1 in a Box Behnken design matrix. Three different second order polynomial models were obtained for oil yield, phosphorous content and tocol contents with high R2 values. The optimum conditions were found to be 50 M Pa, 60 °C and 30 g·min-1 where the predicted oil yield, phosphorous content and tocol contents were found to be 8.87%, 31.86 mg·Kg-1 and 2059.92 mg·Kg-1 respectively. Under the optimum conditions, the experimental oil yield, phosphorous content and tocol contents obtained were found to be very close to the values predicted by the model.La técnica de extracción mediante dióxido de carbono supercrítico (SC-CO2) ha surgido como una de las mejores alternativas posibles a la extracción con solventes orgánicos (hexano). Sin embargo, se dispone de información muy limitada sobre la optimización del proceso y la falta de disponibilidad de datos de ingeniería es la causa del lento crecimiento de esta técnica. En la presente investigación, la extracción con SC-CO2 se llevó a cabo para obtener aceite de germen de trigo en diversas condiciones operacionales. Los aceites se caracterizaron mediante sus contenidos en fósforo y tocoles (vitamina E). Se aplicó el diseño Box Behnken de tres niveles a partir de la metodología de superficie de respuesta para optimizar los parámetros de la extracción, presión, temperatura y flujo de CO2 para obtener un alto rendimiento de aceite, alto contenido de tocoles y bajo contenido de fósforo. Los parámetros del proceso se mantuvieron entre 30 - 50 MPa, de 40 a 60 °C y de 10 a 30 g·min-1 de caudal de CO2 en la matriz de diseño Box Behnken. Se obtuvieron tres modelos polinomiales de segundo orden diferentes para rendimiento de aceite, contenido de fósforo y contenido de tocoles, con altos valores de R2. Las condiciones óptimas fueron: 50 M Pa, 60 °C y 30 g·min-1 donde el rendimiento de aceite, el contenido de fósforo y el contenido de tocoles previstos fueron 8.87%, 31,86 mg·Kg-1 y 2059,92 mg·Kg-1 respectivamente. Bajo las condiciones óptimas, el rendimiento de aceite, el contenido de fósforo y el contenido de tocoles presentaron valores muy cercanos a los predichos por el modelo

The CORSMAL benchmark for the prediction of the properties of containers

13 pages, 6 tables, 7 figures, Pre-print submitted to IEEE AccessAuthors' post-print accepted for publication in IEEE Access, see https://doi.org/10.1109/ACCESS.2022.3166906 . 14 pages, 6 tables, 7 figuresThe contactless estimation of the weight of a container and the amount of its content manipulated by a person are key pre-requisites for safe human-to-robot handovers. However, opaqueness and transparencies of the container and the content, and variability of materials, shapes, and sizes, make this estimation difficult. In this paper, we present a range of methods and an open framework to benchmark acoustic and visual perception for the estimation of the capacity of a container, and the type, mass, and amount of its content. The framework includes a dataset, specific tasks and performance measures. We conduct an in-depth comparative analysis of methods that used this framework and audio-only or vision-only baselines designed from related works. Based on this analysis, we can conclude that audio-only and audio-visual classifiers are suitable for the estimation of the type and amount of the content using different types of convolutional neural networks, combined with either recurrent neural networks or a majority voting strategy, whereas computer vision methods are suitable to determine the capacity of the container using regression and geometric approaches. Classifying the content type and level using only audio achieves a weighted average F1-score up to 81% and 97%, respectively. Estimating the container capacity with vision-only approaches and estimating the filling mass with audio-visual multi-stage approaches reach up to 65% weighted average capacity and mass scores. These results show that there is still room for improvement on the design of new methods. These new methods can be ranked and compared on the individual leaderboards provided by our open framework

A practical approach to adult-onset white matter diseases, with illustrative cases

Aim. To evaluate five illustrative cases and perform a literature review to identify and describe a working approach to adult-onset white matter diseases (WMD).State of the art. Inherited WMD are a group of disorders often seen in childhood. In adulthood, progressive WMDs are rare, apart from the common nonspecific causes of hypertension and other cerebrovascular diseases. The pattern of WMDs on neuroimaging can be an important clue to the final diagnosis. Due to the adoption of a combined clinical-imaging-laboratory approach, WMD is becoming better recognised, in addition to the rapidly evolving field of genomics in this area.Clinical implications. While paediatric WMDs have a well-defined and literature-based clinical-laboratory approach to diagnosis, adult-onset WMDs remain an important, pathologically diverse, radiographic phenotype, with different and distinct neuropathologies among the various subtypes of WMD. Adult-onset WMDs comprise a wide collection of both acquired and inherited aetiologies. While severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) neurological complications are emerging, we are as yet unaware of it causing WMD outside of post-anoxic changes. It is important to recognise WMD as a potentially undefined acquired or genetic syndrome, even when extensive full genome testing reveals variants of unknown significance.Future directions. We propose a combined clinical-imaging-laboratory approach to WMD and continued exploration of acquired and genetic factors. Adult-onset WMD, even given this approach, can be challenging because hypertension is often comorbid. Therefore, we propose that undiagnosed patients with WMD be entered into multicentre National Organisation for Rare Diseases registries to help researchers worldwide make new discoveries that will hopefully translate into future cures

Comparison of Novel Recurrent Neural Network Over Artificial Neural network in Predicting Email spammers with improved accuracy

The main aim is to compare Novel Recurrent Neural Network over Artificial Neural Network in predicting Email spammers with improved accuracy. Material and Methods : This research study contains two groups namely Novel Recurrent Neural Network and Artificial Neural Network. Each group consists of a sample size of 10 and the study parameters are calculated using clincalc with preset parameters as alpha 0.8, beta 0.2 and CI as 90%. Results and Discussion : The Novel Recurrent Neural Network has the highest accuracy 97.96% when compared to Artificial Neural Network it has 93.79% accuracy in Electronic Mail spam prediction with significance value p=0.000(p<0.05) that is significantly better. The G-power value is 80%. When used as a spam predictor for electronic mail, the Novel Recurrent Neural Network performance analysis outperforms the best results than the Artificial Neural Network performance

The Impact of Social Determinants of Health, Namely Financial Assistance, on Overall Survival in Advanced-Stage Non-Small Cell Lung Cancer Patients.

PURPOSE: Non-small cell lung cancer (NSCLC) is the most prevalent form of lung cancer. Studies have evaluated the association of social determinants of health (SDH) with outcomes in early-stage NSCLC. These studies have shown statistically and clinically significant associations between overall survival (OS) and other SDH (e.g marital status, educational attainment).The aim of our study was to better understand the role of various SDH on OS in advanced-stage NSCLC patients in a community oncology practice in Florida. Methods: In this retrospective study, 125 patients with stage III and IV NSCLC were identified between January 1, 2014, and December 31, 2018. We performed Pearson\u27s chi-square and Kruskal-Wallis test to evaluate the association between median OS and several independent variables, including; gender, race, marital status, insurance status, living status, receiving financial assistance (FA), alcohol use, and smoking histories. OS is defined as the date of diagnosis up to the date of death. Other confounders that were analyzed included histology, treatment modality, comorbidities, and performance status of the patients. Results: Our results demonstrated that patients receiving FA had nearly a two-fold increase in median OS compared to patients without FA (median OS = 1.01 years vs. 0.545 years, respectively; p = 0.012). CONCLUSION: Overall, this study highlighted the importance of reducing the financial burden of advanced-stage NSCLC on patients and how FA impacts patient outcomes. However, future prospective cohort studies with a larger sample size are warranted to identify other SDH, as well as the underlying mechanisms affecting median OS, in patients with advanced-stage NSCLC

Traditional processing methods for quality enhancement of indigenous basil seeds and formulation of functional flours

The changing food habits and lifestyle led to consumption of faulty diets with increased prevalence of life style diseases in India. This has spurred health consciousness among food consumers and enhanced the demand for functional foods. The indigenous underutilised clove and sweet basil seeds being rich source of fibre (36.23, 28.85%), protein (9.16, 8.55%), polyphenols (17.28, 17.71 mg GAE/g extract) and antioxidants (266.13 and 344.63 mg TE/g extract), exhibited vast potential for formulation of functional flours. Traditional processing methods such as roasting, fermentation and germination have significantly enhanced the nutritional and antioxidant properties of both the seeds. Among them, germination was found to be ideal processing technique with relatively higher fibre, protein, total mineral, phenolic contents, antioxidant capacity and less fat content. Henceforth, processing of basil seeds in a traditional way could significantly enhance their quality and promote their utilisation as functional ingredients for designing healthy foods.

Predilection of chewing side preferences and clinical assessment of its impact on temporomandibular joint

Statement of the Problem: Normal mastication in humans generally favors one side and then the other, but most people chew more on a particular side, which means they have a preferred chewing side (PCS). The relationship between the use of one habitual chewing side and the peripheral factors involved in temporomandibular disorders (TMDs) is not understood yet. Aims and Objectives: The objective of the study is to explore the effect of chewing side preference on temporomandibular joint (TMJ) in adult population. Methods: One hundred and seventy healthy dentate subjects (148 female and 22 male) were selected and clinically examined for this cross-sectional study. Chewing side preference test and TMJ clinical examination were conducted. Subjects were classified into unilateral and bilateral chewers, signs and symptoms of TMDs were recorded. Results: Statistical analysis was performed to evaluate the difference between the subjects regarding preferable chewing side, distribution of subjects with respect to signs and symptoms of TMDs, and frequency of symptoms in habitual chewers using Chi-square test, at 5% significance level. The results showed that among all the subjects, 80% preferred unilateral chewing side and there was a significant correlation with asymmetric factors of TMJ with masticatory side. Conclusion: Within the limitations of the study, it may be concluded that the presence of a PCS affects the morphology and parameters of TMJ. This signifies that it is not only sufficient enough to maintain anatomic health but also dynamic and functional factors should be considered to avoid TMDs