bluuebunny/crossref_metadata_2025_split huggingface dataset:
A novel αDistance Borderline-ADASYN-SMOTE algorithm for imbalanced data and its application in Alzheimer’s disease classification based on Dense Convolutional Network
Authors: Feng Yun , Li Jingjiao 
Published: September 2021

Abstract
The classification problem of imbalanced data has become a very important issue in the fields of machine learning and data mining. At present, relatively effective oversampling methods for processing imbalanced data include SMOTE, Borderline-SMOTE, and ADASYN. These algorithms have their own advantages; however, they do not adequately consider the distance factor, which is an important factor for balancing data precisely and reducing the misclassification probability of a minority boundary sample. Therefore, a new algorithm, αDistance Borderline-ADASYN-SMOTE algorithm, is proposed in the paper by combining the optimized Borderline-SMOTE algorithm with the optimized ADASYN algorithm. In the new algorithm, both the amount and the distance distribution of the nearest neighbor samples are considered. A few formulas are created to realize the algorithm. After being balanced by the algorithm, the data obtained from ADNI data set is trained, verified and tested by the Dense Convolutional Network. The experimental results show that the new model improves the classification performance of the Alzheimer’s disease.
Construction of Dirichlet Mixture Allocation total probability model based on multiple class text analysis
Authors: Ling Xiaoming , Zhang Feng 
Published: September 2021

Abstract
The LDA model is a total probability generation model for analyzing a large number of documents. It extends PLSA, another text analysis model. In this model, each document is treated as a topic hybrid model, and the topic’s proportional prior distribution is a Dirichlet distribution. The LDA model does not reflect complex dependencies between underlying topics. Based on LDA, this paper introduces a new topic generation model, DMA (Dirichlet Mixture Allocation), which models document collections more accurately than LDA when documents are obtained in multiple classes. In this paper, we build a probabilistic topic model of DMA, use the method of variational inference to approximate each parameter of the model, study the model, and finally solve the estimation of each parameter.
A Data Mining System for Potential Customers Based on One-Class Support Vector Machine
Authors: Mai Weijian , Wu Fengjie , Li Fang , Luo Wenjun , Mai Xiaoting 
Published: September 2021

Abstract
Commodity purchase data is usually severely skewed, which is reflected in the fact that there are far more negative data than positive data. This phenomenon makes it difficult for the binary classification model to obtain satisfactory results. Hence, we transform the binary classification problem into a one-class novelty detection problem. Specifically, this work proposes a potential customer mining system based on the One-class Support Vector Machine (OCSVM) and demonstrates its effectiveness for classification, prediction, and potential customer mining. This system allows merchants to focus on unpurchased customers with the strongest purchase intentions and to change their purchase decisions with minimal sale costs, which enables merchants to maximize their benefits.
Deep learning based news text classification software design
Authors: Xu Xiao , Wang LiJuan , Liu RuFan , Xu TianYu 
Published: September 2021

Abstract
New technologies such as artificial intelligence have developed at a rapid pace in recent years and are increasingly being used in the process of managing news in bulk. The development of deep learning has facilitated unprecedented progress in the field of computing and has opened our eyes to the possibility of using AI for news text classification. In this paper, based on the system requirements analysis, we describe the process of functional modules arising from the requirements analysis, design the internal details of functional modules, including algorithms and detailed principles, and finally obtain a prototype of news text classification software, which results in the pre-design expectations. The research in this paper makes the system development work more concrete, while providing software users, software developers, and analysts and testers with a unified and comprehensive understanding of the system’s functional implementation.
Improving the Relation Classification Using Convolutional Neural Network
Authors: Kamath S , Karibasappa K G , Reddy Anvitha , Kallur Arati M , Priyanka B B , Bhagya B P 
Published: September 2021

Abstract
Relation extraction has been the emerging research topic in the field of Natural Language Processing. The proposed work classifies the relations among the data considering the semantic relevance of words using word2vec embeddings towards training the convolutional neural network. We intended to use the semantic relevance of the words in the document to enrich the learning of the embeddings for improved classification. We designed a framework to automatically extract the relations between the entities using deep learning techniques. The framework includes pre-processing, extracting the feature vectors using word2vec embedding, and classification using convolutional neural networks. We perform extensive experimentation using benchmark datasets and show improved classification accuracy in comparison with the state-of-the-art methodologies using appropriate methods and also including the additional relations.
Application of Natural Betalain Dye from Beetroot for Improved Efficiency in Dye Sensitized Solar Cell
Authors: Soni K , Sheikh A , Jain V , Lakshmi N 
Published: September 2021

Abstract
C, N, S co-doped TiO2 (TU-TiO2) synthesized through solid state method using high energy ball milling was applied onto fluorine doped tin oxide (FTO) glass substrate and sintered at 450° C for half hour for use as a photoanode of dye-sensitized solar cells (DSSCs). Natural Betalain containing dye extracted from beetroot was used as sensitizer, Iodide triodide was used as electrolyte and graphite as counter electrode to obtain FTO/C, N, S-TiO2/Dye/Electrolyte/Graphite/FTO DSSC. X-ray Diffraction analysis established the formation of single phased materials retaining the basic TiO2 structure. Optical properties were enhanced with increase in molar concentration of thiourea in TiO2. Fourier Transform Infrared spectroscopy confirmed the incorporation of sulphur into TiO2. The highest of 94% relative increase is observed in solar to electric conversion efficiency for C, N, S co-doped TiO2 compared to un-doped TiO2. Along with decrease in the band gap making the cell more sensitive to visible light, the improved performance is also due to the rise in charge injection from betalain dye to the C, N, S co-doped TiO2.
Restoration of degraded underwater images with attenuation coefficients as a clue
Authors: Dikshith C S , Vernekar Sameeksha M , Desai Chaitra , Patil Ujwala , Mudenagudi Uma 
Published: September 2021

Abstract
Underwater image restoration methods require the knowledge of wideband attenuation coefficients per color channel. In this paper, a Generative adversarial network(GAN) framework is proposed for underwater image restoration using the attenuation coefficients as an input. Attenuation coefficients like atmospheric light and transmission map are also estimated in order to restore an underwater image. For atmospheric light estimation, a combination of Res-net and Dense-net architecture is modelled and making it a learning based Residual-Dense Network. Through literature it is evident that, the transmission map describes about the portion of the light that is not scattered and reaches the camera. Transmission map is generated using the underwater image formation model. Further, these attenuation coefficients are given as an input to the proposed GAN architecture for underwater image restoration. The GAN architecture is having a generator block and a discriminator block. The result is demonstrated on synthetic and real underwater data-set and then compared with state-of-the-art method.
Design & Simulation of S Band Planar Microwave Filter for Wireless Application
Authors: Gohil Bhumikaba , Chavda Khyati 
Published: September 2021

Abstract
Reducing the size of today’s wireless communication systems has become an important criterion in filter design with minimal possible value of filter parameters. To achieve this goal in wireless communication system, filter design is the most important part of the signal transmission process. Conventional filters are very important for modern wireless communication systems as they do not provide the desired miniaturization and results. Recently, using metamaterials and dielectric resonators, the filter size has been drastically reduced to a very small size for practical applications. This filter is flat and lightweight. It consumes very low power consumption. In this proposed work, a compact S-band microwave band stop filter was designed using a simple technique. These filters are small in size and light in weight. The design covers the S-band and is suitable for the desired frequency for Wi – Fi and wireless LAN application.
Modelling of partial discharge pulse propagation in transformer windings using multi conductor transmission line modelling scheme
Authors: Achankunju Annie , Joseph Jineeth , Varghese Ancy Sara , Nair K R M 
Published: September 2021

Abstract
Power transformers are considered as an essential component in the electric power systems. They are placed in services in different circumstances like electrical, environmental and mechanical stresses, and through the progress of operation it can affect with various threats and hazards. Transformer failure should be diagnosed earlier in order to avoid its unsafe and unstable operation. The main reason for failure of insulation in power transformer is due to partial discharges. The partial discharge activity occurring in the transformer have to be identified earlier, afore it progresses into a complete discharge, otherwise it results in the maintenance of the device and causes loss of economic cost to the utilities. In this paper, multi conductor transmission line theory is used for the modelling of transformer windings and the partial discharge pulse propagation through the winding is simulated in the MATLAB Simulink. The effect of partial discharge on the windings are also discussed.
Automatic Detection of New Vessels in Retinal Photographs using Machine Learning
Authors: Moorthi M , Gopikrishnan M. , Pooja V , Ragavi Priya M R , Vasanthakumar D K 
Published: September 2021

Abstract
Diabetic Retinopathy is a rare micro-vascular complication that leads to severe ocular disorder or visual impairment. This complication is unidentified due to the mild progression of its symptoms that is associated with Diabetic Mellitus. The disease is categorized by the development of abnormal new vessels developing the optical disc of the retina of the eye. In this paper, an automatic detection system for detecting new vessels in the optical disc region is detected by using image processing techniques in advance. Segmenting the vessels from the optic disc by extracting the desired region of interest. Based on this, the features are extracted and detected as the presence of new vessels in the optic disc by using a Support Vector Machine (SVM) classifier. This automatic system is trained and tested by using 65 normal images that are categorized as 33 normal images and 32 abnormal images. The discrimination performance of the system shows an accuracy of 95.38% in detecting the new vessels. It obtained sensitivity and specificity of 96.8% and 93.9% respectively.
Development of data driven adaptive edge detectors for image processing
Authors: Likhith H G , Nishanth H L , Lohit E , Rudramurthy M S , Sushma S A , Keerthan Kumar T G 
Published: September 2021

Abstract
In most vision processing activities, the early stage involves identifying the features in an image that provide cues to structure and properties of the object in the scene. Most common features in an image or in a scene arel edges. Edges arel significant local changes in intensity within anlimage. Most important goal of edge detection is to produce a line drawing from anlimage representing the scene. The significant features of an image such as line, curve and corners can be extracted from edges. During the stage of discovering and exploring the information from an image of that scene, edge detection is the most important and early-stage activity and as such it is prominent active area in image processing. Most popular edge detection algorithm such as Robert, Sobel, Canny, Prewitt and Laplacian of Gaussian (LoG), etc. are currently in use. This paper emphasis on an experimental study of limitations of conventional edge detectors and to devise a novel approach to resolve the conflicting issues i.e., limitations of these edge detectors in adaptive space utilizing novel methods such as Bi-dimensional Empirical Mode Decomposition (BEMD), Image Empirical Mode Decomposition (IEMD), Complete Ensemble Empirical Mode Decomposition (CEEMD) and Multivariate Decomposition techniques. Further, to study the performance of these modified edge detectors on the images of complex scenes which are of societal and agricultural importance.
Effect of Thermal Barrier Coating in Sustainable Power production of Gas Turbines
Authors: Ghosh S , Saha M , Bakshi S , Mondal S 
Published: September 2021

Abstract
Gas turbine play an important role in producing power for aviation industries. A sustainable operating life with high efficiency of a gas turbine requires very high temperature operation. The increase in thermal conductivity with temperature results in high thermal stress which leads to permanent deformation reducing blade life and efficiency. A thermal barrier coating is crucial to remove such detrimental effects. In the present work, an assessment of gas turbine blade materials has been carried out to study the effect of various coating techniques to improve longevity of blades. Coating methods have been found to play an important role in determining the sustainability of gas turbine performance. It was revealed that such coatings considerably reduce the heat penetration rate and thus increase the service life of gas turbine blades.
In-home medication management by older adults: a modified ethnography study using digital photography walkabouts
Authors: Faisal Sadaf , Ivo Jessica , McMillan Colleen , Grindrod Kelly , Patel Tejal 
Published: September 2021

Abstract

Background
Medication mismanagement can lead to non-optimal management of chronic diseases and poor health outcomes.

Objective
The purpose of this study was to better understand meanings associated with in-home medication management and storage practices of older adults with chronic diseases.

Methods
A modified ethnographic approach using digital photography walkabouts, observation protocols and field notes were used to document in-home medication organisation and storage locations. Thematic analysis was used to generate themes and sub-themes.

Results
Data from multiple home visits of 10 participants (mean age = 76 years; 80% females) including 30 photographs, 10 observation protocols and field notes were analysed. The average number of medications used was reported to be 11.1 (range: 5–20). Themes and sub-themes include choice of storage location (sub-themes: impact on medication behaviour, visibility of medications and storage with other items), knowledge regarding appropriate medication storage conditions (sub-themes: impact on safety of patient and impact on stability of medications) and systems to manage in-home medication intake.

Discussion
In-home medication management reflects older adults perspectives regarding privacy, medication taking routine, knowledge about safe and effective storage and organisation systems. The lack of knowledge causing inappropriate medication storage not only impacts the stability of medications, but also increases risk of medication errors and safety, ultimately affecting medication intake behaviours.
Sodium hyperaccumulators in the Caryophyllales are characterized by both abnormally large shoot sodium concentrations and [Na]shoot/[Na]root quotients greater than unity
Authors: Neugebauer Konrad , Broadley Martin R , El-Serehy Hamed A , George Timothy S , Graham Neil S , Thompson Jacqueline A , Wright Gladys , White Philip J 
Published: September 2021

AbstractBackground and AimsSome Caryophyllales species accumulate abnormally large shoot sodium (Na) concentrations in non-saline environments. It is not known whether this is a consequence of altered Na partitioning between roots and shoots. This paper tests the hypotheses (1) that Na concentrations in shoots ([Na]shoot) and in roots ([Na]root) are positively correlated among Caryophyllales, and (2) that shoot Na hyperaccumulation is correlated with [Na]shoot/[Na]root quotients.MethodsFifty two genotypes, representing 45 Caryophyllales species and 4 species from other angiosperm orders, were grown hydroponically in a non-saline, complete nutrient solution. Concentrations of Na in shoots and in roots were determined using inductively coupled plasma mass spectrometry (ICP-MS).Key ResultsSodium concentrations in shoots and roots were not correlated among Caryophyllales species with normal [Na]shoot, but were positively correlated among Caryophyllales species with abnormally large [Na]shoot. In addition, Caryophyllales species with abnormally large [Na]shoot had greater [Na]shoot/[Na]root than Caryophyllales species with normal [Na]shoot.ConclusionsSodium hyperaccumulators in the Caryophyllales are characterized by abnormally large [Na]shoot, a positive correlation between [Na]shoot and [Na]root, and [Na]shoot/[Na]root quotients greater than unity.
Two Ecological Tools for Testing Slowness of Information Processing in Italian Patients with Moderate-to-Severe Traumatic Brain Injury
Authors: Valentini Federica , Fabio Valentina , Boccia Maddalena , Tanzilli Antonio , Iannetti Manuela , Cinelli Maria Cristina , De Angelis Carmela , Fasotti Luciano , Formisano Rita , Guariglia Cecilia , Ciurli Maria Paola 
Published: September 2021

Abstract
Slowness of Information Processing (SIP) is frequently experienced after traumatic brain injury (TBI); however, the impact of SIP on everyday functioning may be underestimated by standard neuropsychological tests. Objective: we aimed to adapt two ecological instruments assessing SIP in Italian patients with moderate-to-severe TBI, as formerly proposed by Winkens and colleagues for persons with stroke, testing also its possible relation with other neuropsychological processes and functional outcomes. Method: we performed an observational study on 37 patients with moderate-to-severe TBI and 35 demographically matched healthy controls, who underwent the Mental Slowness Observation Test (MSOT) and the Mental Slowness Questionnaire (MSQ), which had been adapted through a pilot study on independent sample of participants; extensive neuropsychological and functional evaluations were performed as well. Results: We found good clinical and psychometric properties of the Italian adaptation of the MSOT and MSQ; also, performance on MSOT significantly correlated with executive functions. Moreover, patients with TBI are significantly slower and less accurate than healthy controls on the MSOT, in particular in tasks with time limits. Even if the subjective feeling of SIP does not differ between patients and controls, we found a significant correlation between MSQ and MSOT. Finally, the performances on the MSOT correlated with measures of functional outcome and community integration. Conclusions: the results support the use of the MSOT and the MSQ to measure SIP in an ecological fashion in patients with TBI, so that specific treatments for persons with acquired brain injury can be prescribed.
Impact of Psychiatric Illnesses and Selective Serotonin Reuptake Inhibitor Medications on Baseline Neurocognitive Testing
Authors: Asfaw Zerubabbel K , Hannah Theodore C , Ali Muhammad , Li Adam Y , Spiera Zachary , Marayati Naoum Fares , Kalagara Roshini , Dreher Nickolas , Schupper Alexander J , Gometz Alex , Lovell Mark R , Choudhri Tanvir 
Published: September 2021

AbstractIntroductionNeurocognitive tests are an integral component of sport-related concussion (SRC) workup. A history of psychiatric illness (HPI) is common among young athletes. Investigations of factors that influence athletes’ baseline neurocognitive function are crucial for an accurate assessment of SRC.ObjectiveIn this study, we aim to elucidate the effect of HPI and selective-serotonin reuptake inhibitor (SSRI) medication use on baseline neurocognitive performance in young athletes.MethodsWe conducted a retrospective cross-sectional study of Immediate Post-Concussion Assessment and Cognitive Testing assessments. A total of 268 athletes with HPI and a control group of 6,364 athletes were included. The outcomes were total symptom score based on post-concussion symptom scale, verbal memory, visual memory, visual motor, reaction time, and impulse control scores with self-reported HPI status and SSRI use.ResultsAthletes with HPI had an elevated symptom score in both univariate analysis (p &lt; .0001) and multivariate analysis (p &lt; .0001). HPI influence on visual memory score was not robust to multivariate analysis (p = .24). Athletes with HPI who reported SSRI medication use had the same baseline neurocognitive performance as other athletes with HPI. HPI influences athletes’ baseline neurocognitive performance by elevating symptom scores. HPI does not alter any of the objective neurocognitive composite scores in contrast to previous work.ConclusionsClinicians should consider the impact of HPI on baseline neurocognitive performance during the assessment of a suspected SRC. Additional research is required to bolster our findings on SSRI use and ascertain the effects of other drug classes on baseline neurocognitive performance.
Disease category-specific annotation of variants using an ensemble learning framework
Authors: Cao Zhen , Huang Yanting , Duan Ran , Jin Peng , Qin Zhaohui S , Zhang Shihua 
Published: September 2021

AbstractUnderstanding the impact of non-coding sequence variants on complex diseases is an essential problem. We present a novel ensemble learning framework—CASAVA, to predict genomic loci in terms of disease category-specific risk. Using disease-associated variants identified by GWAS as training data, and diverse sequencing-based genomics and epigenomics profiles as features, CASAVA provides risk prediction of 24 major categories of diseases throughout the human genome. Our studies showed that CASAVA scores at a genomic locus provide a reasonable prediction of the disease-specific and disease category-specific risk prediction for non-coding variants located within the locus. Taking MHC2TA and immune system diseases as an example, we demonstrate the potential of CASAVA in revealing variant-disease associations. A website (http://zhanglabtools.org/CASAVA) has been built to facilitate easily access to CASAVA scores.