DL based analysis of movie reviews

Undoubtedly, social media are brainstormed by a tremendous volume of stories, feedback, reviews, and reactions expressed in various languages and idioms, even though some are factually incorrect. These motifs make assessing such data challenging, time-consuming, and vulnerable to misinterpretation. This paper describes a classification model for movie reviews founded on deep learning approaches. Almost 500KB pairs of balanced data from the IMDb movie review databases are employed to train the model. People's perspectives regarding movies were classified using both the long short-term memory (LSTM) and convolutional neural network (CNN) strategies. According to the findings, the CNN algorithm's prediction accuracy rate would be almost 97.4%. Furthermore, the model trained by LSTM resulted in accuracies of around and applying 99.2% within the Keras library. The model is investigated more by modification of model parameters. According to the outcomes, LTSM outperforms CNN in assessing IMDb movie reviews and is computationally less costly than LSTM

A Fault Detection Scheme Utilizing Convolutional Neural Network for PV Solar Panels with High Accuracy

Solar energy is one of the most dependable renewable energy technologies, as it is feasible almost everywhere globally. However, improving the efficiency of a solar PV system remains a significant challenge. To enhance the robustness of the solar system, this paper proposes a trained convolutional neural network (CNN) based fault detection scheme to divide the images of photovoltaic modules. For binary classification, the algorithm classifies the input images of PV cells into two categories (i.e. faulty or normal). To further assess the network's capability, the defective PV cells are organized into shadowy, cracked, or dusty cells, and the model is utilized for multiple classifications. The success rate for the proposed CNN model is 91.1% for binary classification and 88.6% for multi-classification. Thus, the proposed trained CNN model remarkably outperforms the CNN model presented in a previous study which used the same datasets. The proposed CNN-based fault detection model is straightforward, simple and effective and could be applied in the fault detection of solar panel

ANFIS-based prediction of power generation for combined cycle power plant

This paper presents the application of an adaptive neuro-fuzzy inference system (ANFIS) to predict the generated electrical power in a combined cycle power plant. The ANFIS architecture is implemented in MATLAB through a code that utilizes a hybrid algorithm that combines gradient descent and the least square estimator to train the network. The Model is verified by applying it to approximate a nonlinear equation with three variables, the time series Mackey-Glass equation and the ANFIS toolbox in MATLAB. Once its validity is confirmed, ANFIS is implemented to forecast the generated electrical power by the power plant. The ANFIS has three inputs: temperature, pressure, and relative humidity. Each input is fuzzified by three Gaussian membership functions. The first-order Sugeno type defuzzification approach is utilized to evaluate a crisp output. Proposed ANFIS is cable of successfully predicting power generation with extremely high accuracy and being much faster than Toolbox, which makes it a promising tool for energy generation applications

Application of artificial intelligence in fault detection and classification of solar power plants and prediction of power generation of combined cycled power plants

Solar energy is one of the most dependable renewable energy technologies, as it is feasible almost everywhere globally and is environmentally friendly. Photovoltaic-based renewable energy systems are highly susceptible to power grid transients. Their operation may suffer drastically during faults in the solar arrays, power electronics, and the inverter. Thus, it is vital to develop an intelligent mechanism to detect any type of fault or abnormalities within the shortest possible time that will increase reliability and decrease the maintenance cost of the solar system. To accomplish that, in this research, different artificial intelligence (AI) techniques are utilized to develop classification, fault detection, and optimization algorithms for solar photovoltaic (PV) panels. Initially, a convolutional neural network (CNN) model was designed to detect and classify PV modules based on the images taken from the solar panels. It is found that the proposed CNN model can identify the fault with an accuracy of 91.1% for binary (i.e., healthy and faulty) and 88.6% for multi-classification (i.e. cracked, shadowy, dusty and normal). However, sometimes the fault in the solar panel may not be detectable from the images of the solar panels. That is why an adaptive neuro-fuzzy inference system (ANFIS) model is developed to detect and classify the defects of PV systems based on the signals collected from the solar panels. The performance of the developed defect detection and classification algorithms was tested using real-life solar farm datasets. The performance of the proposed ANFIS-based fault detection scheme has been compared with different machine learning algorithms. It is found from the comparative results that the proposed ANFIS-based fault detection technique is robust and straightforward. Thus, the developed ANFISbased intelligent technique will enhance the reliability of the PV system by minimizing the maintenance cost and saving energy. Finally, another ANFIS model is developed to predict the power generation in a combined cycle power plant. The codes were written in MATLAB, and their validity is confirmed with the available ANFIS toolboxes in MATLAB. The proposed ANFIS is found capable of successfully predicting power generation with extremely high accuracy and being much faster than the built-in ANFIS of MATLAB Toolbox. Thus, the developed ANFIS model could be utilized as a promising tool for energy generation applications

Katie’s Closet: An On-campus Experiential Learning Project

Two faculty and one undergraduate student embarked on an applied learning experience to determine the feasibility of starting a free professional clothing shop for University students called Katie\u27s Closet. All aspects of conceptualizing and testing the Katie\u27s Closet pop-up-store concept began from a feasibility study. With the support of faculty mentors, the undergraduate student researched, analyzed, and executed the project. The action research project started with informational interviews and a focus group for gathering qualitative data on the student population\u27s needs. These findings led to more research to explore potential collaborative partnerships with other University departments. Competitive benchmarking also supported developing the entrepreneurial project concept and offered insightful information to help with the start-up. The data provided the foundational knowledge to create and market a Katie\u27s Closet pop-up store on campus. This article is a collaborative effort written by both the undergraduate student intern and her faculty mentors

Callers’ attitudes and experiences of UK breastfeeding helpline support

Background: Breastfeeding peer support, is considered to be a key intervention for increasing breastfeeding duration rates. Whilst a number of national organisations provide telephone based breastfeeding peer support, to date there have been no published evaluations into callers’ experiences and attitudes of this support. In this study we report on the descriptive and qualitative insights provided by 908 callers as part of an evaluation of UK-based breastfeeding helpline(s). Methods: A structured telephone interview, incorporating Likert scale responses and open-ended questions was undertaken with 908 callers over May to August, 2011 to explore callers’ experiences of the help and support received via the breastfeeding helpline(s). Results: Overall satisfaction with the helpline was high, with the vast majority of callers’ recalling positive experiences of the help and support received. Thematic analysis was undertaken on all qualitative and descriptive data recorded during the evaluation, contextualised within the main areas addressed within the interview schedule in terms of ‘contact with the helplines’; ‘experiences of the helpline service’, ‘perceived effectiveness of support provision’ and ‘impact on caller wellbeing’. Conclusion: Callers valued the opportunity for accessible, targeted, non-judgmental and convenient support. Whilst the telephone support did not necessarily influence women’s breastfeeding decisions, the support they received left them feeling reassured, confident and more determined to continue breastfeeding. We recommend extending the helpline service to ensure support can be accessed when needed, and ongoing training and support for volunteers. Further advertising and promotion of the service within wider demographic groups is warranted

Surgical management of life threatening events caused by intermittent aortic insufficiency in a native valve: case report

We describe a case of a patient admitted with apparent life threatening events characterized by hypotension and bradycardia. The patient was ultimately found to have intermittent severe aortic insufficiency. Upon surgical exploration, abnormalities were discovered in the aortic valve, which had a small left coronary cusp with absence of the nodulus of Arantius. Following surgical repair of the valve, aimed at preventing the small cusp from becoming stuck in the open position, the patient has remained episode free for over one year

Preterm birth a long distance from home and its significant social and financial stress

The present paper reports a retrospective cohort of preterm infants admitted to our hospital who delivered outside the normal geographical catchment area of the mother's local level three neonatal nursery. Nineteen mothers had 21 preterm infants (23.1-34.9 weeks, 500-2330 g born) where 14 infants required ventilation (median 57 h, range 3-428). Eighteen survivors had a median length of stay of 41 days (range 3-91). Twelve of 19 mothers were interviewed: all described isolation, loneliness, poor social support and significant financial hardship related to getting their infants back to a local hospital or home. To avoid these problems, we recommend confining travel to within a short distance from home or local maternity unit after 22 weeks