Nowadays, social media has become a tremendous source of acquiring user’s opinions. With the advancement of technology and sophistication of the internet, a huge amount of data is generated from ...various sources like social blogs, websites, etc. In recent times, the blogs and websites are the real-time means of gathering product reviews. However, excessive number of blogs on the cloud has enabled the generation of huge volume of information in different forms like attitudes, opinions, and reviews. Therefore, a dire need emerges to find a method to extract meaningful information from big data, classify it into different categories and predict end user’s behaviors or sentiments. Long Short-Term Memory (LSTM) model and Convolutional Neural Network (CNN) model have been applied to different Natural Language Processing (NLP) tasks with remarkable and effective results. The CNN model efficiently extracts higher level features using convolutional layers and max-pooling layers. The LSTM model is capable to capture long-term dependencies between word sequences. In this study, we propose a hybrid model using LSTM and very deep CNN model named as Hybrid CNN-LSTM Model to overcome the sentiment analysis problem. First, we use Word to Vector (Word2Vc) approach to train initial word embeddings. The Word2Vc translates the text strings into a vector of numeric values, computes distance between words, and makes groups of similar words based on their meanings. Afterword embedding is performed in which the proposed model combines set of features that are extracted by convolution and global max-pooling layers with long term dependencies. The proposed model also uses dropout technology, normalization and a rectified linear unit for accuracy improvement. Our results show that the proposed Hybrid CNN-LSTM Model outperforms traditional deep learning and machine learning techniques in terms of precision, recall, f-measure, and accuracy. Our approach achieved competitive results using state-of-the-art techniques on the IMDB movie review dataset and Amazon movie reviews dataset.
Drechslera leaf spot (DLS) is a serious leaf disease of oats chiefly caused by Pyrenophora chaetomioides in the main oat‐producing areas of the world. However, other species of Pyrenophora that may ...cause DLS have not been investigated in China. From 2018 to 2020, a total of 256 strains of Pyrenophora were isolated from nine oat‐producing areas in three provinces in western China. Using a combination of morphological characteristics and phylogenetic analysis of the rDNA internal transcribed spacer (ITS), 28S ribosomal RNA gene (LSU), RNA polymerase second largest subunit region (RPB2), and glyceraldehyde‐3‐phosphate dehydrogenase region (GAPDH), 66% of the isolates were identified as P. chaetomioides and 34% of isolates belonged to P. avenicola. Pathogenicity tests confirmed that P. avenicola also infected oat and caused DLS, but it was less virulent than P. chaetomioides. From our survey, DLS caused by P. avenicola generally occurred in warm areas at lower altitude and the annual average temperature was above 0°C (Huan and Zhenyuan county in Gansu, Huize county in Yunnan), compared to high‐altitude areas (Tianzhu county, Shandan county, Haiyan county). The combination of optimal and maximal temperature indicated that P. avenicola had greater tolerance to high temperature, which was also an important factor for its distribution on oats. Precise identification and understanding of virulence, distribution, and occurrence of these pathogens has significance for control of oat DLS in the future.
In addition to Pyrenophora chaetomioides, P. avenicola is also the main pathogen causing DLS, and was less virulent than P. chaetomioides.
Cardiac disease is the leading cause of death worldwide. Cardiovascular diseases can be prevented if an effective diagnostic is made at the initial stages. The ECG test is referred to as the ...diagnostic assistant tool for screening of cardiac disorder. The research purposes of a cardiac disorder detection system from 12-lead-based ECG Images. The healthcare institutes used various ECG equipment that present results in nonuniform formats of ECG images. The research study proposes a generalized methodology to process all formats of ECG. Single Shoot Detection (SSD) MobileNet v2-based Deep Neural Network architecture was used to detect cardiovascular disease detection. The study focused on detecting the four major cardiac abnormalities (i.e., myocardial infarction, abnormal heartbeat, previous history of MI, and normal class) with 98% accuracy results were calculated. The work is relatively rare based on their dataset; a collection of 11,148 standard 12-lead-based ECG images used in this study were manually collected from health care institutes and annotated by the domain experts. The study achieved high accuracy results to differentiate and detect four major cardiac abnormalities. Several cardiologists manually verified the proposed system’s accuracy result and recommended that the proposed system can be used to screen for a cardiac disorder.
Direct conversion of lignocellulose to biofuel without pretreatment always results in a low-ethanol yield owing to its highly rigid structure. The current study was performed to improve the ...effectiveness of two different chemical pretreatments, alkaline and acidic, prior to the enzymatic hydrolysis of cotton stalk, and the yeast fermentation process for ethanol production. Alkaline pretreatments used alkaline hydrogen peroxide (AHP) and sodium hydroxide (NaOH), while acidic pretreatments used sulfuric acid (H2SO4) and phosphoric acid (H3PO4) at concentrations of 1.0%, 3.0%, 5.0%, and 7.0%. The highest bioethanol production (3.956 g/L) was observed in the 1.0% AHP pretreated sample, while the reducing sugar yield after enzymatic hydrolysis was 178 mg/g. The highest reducing sugar yield after enzymatic hydrolysis was obtained from the samples pretreated with 7.0% NaOH and 7.0% AHP, which yielded 241 mg/g and 238 mg/g, respectively, and provided 3.798 g/L and 3.739 g/L of ethanol, respectively. Scanning electron microscopy and Fourier-transform infrared analyses of the biomass showed that the structure of the alkali-treated cotton stalk was more disrupted and distorted than the acid-treated cotton stalk. Therefore, alkaline chemical pretreatment is more effective for breaking down lignocellulose and enhancing the yield of reducing sugars and bioethanol production from cotton stalk.
•Alkali pretreatment was an effective approach for pretreatment of cotton stalk (CS).•SEM and FT-IR analysis showed that the structure of CS was deteriorated after pretreatment.•TRS was increased by 2.34-folds compared to control after enzymatic hydrolysis.•TRS were effectively consumed by Saccharomyces cerevisiae during fermentation.
•Unsupervised learning approaches are widely employed for keyword extraction.•Recent use of deep neural networks has significantly improved abstractive summarization.•Deep learning frameworks are ...less applied for keyword extraction.•For reliability on deep learning models, their interpretability is of immense importance.•Scarcity of datasets for ill-structured and informal data has resulted into limited progress in relevant domains.•Existing evaluation metrics have limited applicability to determine semantic equivalence between machine and human generated summary
With the advent of Web 2.0, there exist many online platforms that results in massive textual data production such as social networks, online blogs, magazines etc. This textual data carries information that can be used for betterment of humanity. Hence, there is a dire need to extract potential information out of it. This study aims to present an overview of approaches that can be applied to extract and later present these valuable information nuggets residing within text in brief, clear and concise way. In this regard, two major tasks of automatic keyword extraction and text summarization are being reviewed. To compile the literature, scientific articles were collected using major digital computing research repositories. In the light of acquired literature, survey study covers early approaches up to all the way till recent advancements using machine learning solutions. Survey findings conclude that annotated benchmark datasets for various textual data-generators such as twitter and social forms are not available. This scarcity of dataset has resulted into relatively less progress in many domains. Also, applications of deep learning techniques for the task of automatic keyword extraction are relatively unaddressed. Hence, impact of various deep architectures stands as an open research direction. For text summarization task, deep learning techniques are applied after advent of word vectors, and are currently governing state-of-the-art for abstractive summarization. Currently, one of the major challenges in these tasks is semantic aware evaluation of generated results.
Electrocardiogram (ECG) is the most common and low-cost diagnostic tool used in healthcare institutes for screening heart electrical signals. The abnormal heart signals are commonly known as ...arrhythmia. Cardiac arrhythmia can be dangerous, or in most cases, it can cause death. The arrhythmia can be of different types, and it can be detected by an ECG test. The automated screening of arrhythmia classification using ECG beats is developed for ages. The automated systems that can be adapted as a tool for screening arrhythmia classification play a vital role not only for the patients but can also assist the doctors. The deep learning-based automated arrhythmia classification techniques are developed with high accuracy results but still not adopted by healthcare professionals as the generalized approach. The primary concerns that affect the success of the developed arrhythmia detection systems are (i) manual features selection, (ii) techniques used for features extraction, and (iii) algorithm used for classification and the most important is the use of imbalanced data for classification. This study focuses on the recent trends in arrhythmia classification techniques, and through extensive simulations, the performance of the various arrhythmia classification and detection models has been evaluated. Finally, the study presents insights into arrhythmia classification techniques to overcome the limitation in the existing methodologies.
The study contains the dataset of ECG images of Cardiac and COVID-19 patients. This rare dataset contains 1937 distinct patient records, data is collected using ECG Device ‘EDAN SERIES-3’ installed ...in Cardiac Care and Isolation Units of different health care institutes across Pakistan. The collected ECG images data were manually reviewed by medical professors using Telehealth ECG diagnostic system, under the supervision of senior medical professionals with experience in ECG interpretation. The manual reviewing process of ECG images took several months to review the five distinct categories (COVID-19, Abnormal Heartbeat, Myocardial Infarction (MI), Previous History of MI, and Normal Person). The collected data contains 12 leads-based ECG images dataset can be used by Data Scientist, IT Professional and Medical Research Institutes to design, compare, fine-tune classical techniques and Deep learning methods in studies focused on COVID-19, Arrhythmia, and other cardiovascular conditions. The dataset contains rare categories of patients that may be used for the development of automatic diagnosis tool for healthcare institutes.
In last few decades, with the advent of World Wide Web (WWW), world is being overloaded with huge data. This huge data carries potential information that once extracted, can be used for betterment of ...humanity. Information from this data can be extracted using manual and automatic analysis. Manual analysis is not scalable and efficient, whereas, the automatic analysis involves computing mechanisms that aid in automatic information extraction over huge amount of data. WWW has also affected overall growth in scientific literature that makes the process of literature review quite laborious, time consuming and cumbersome job for researchers. Hence a dire need is felt to automatically extract potential information out of immense set of scientific articles to automate the process of literature review. Therefore, in this study, aim is to present the overall progress concerning automatic information extraction from scientific articles. The information insights extracted from scientific articles are classified in two broad categories i.e. metadata and key-insights. As available benchmark datasets carry a significant role in overall development in this research domain, existing datasets against both categories are extensively reviewed. Later, research studies in literature that have applied various computational approaches applied on these datasets are consolidated. Major computational approaches in this regard include Rule-based approaches, Hidden Markov Models, Conditional Random Fields, Support Vector Machines, Naïve-Bayes classification and Deep Learning approaches. Currently, there are multiple projects going on that are focused towards the dataset construction tailored to specific information needs from scientific articles. Hence, in this study, state-of-the-art regarding information extraction from scientific articles is covered. This study also consolidates evolving datasets as well as various toolkits and code-bases that can be used for information extraction from scientific articles.
Abstract
Leaf blight disease (LBD) caused by
Pantoea agglomerans
is a newly emerged oat disease in China that causes great economic and yield losses of oat production. Laboratory and field ...experiments were conducted to evaluate the efficacy of 13 antimicrobial agents against
P
.
agglomerans
and recommend agents with better control efficacy for use. The antibacterial potential of four concentrations of each agent was evaluated under in vitro conditions. Zhongshengmycin and ethylicin were the most effective in inhibiting the growth of
P
.
agglomerans
; their bacteriostatic rates (85.0% and 84.8%, respectively) were higher while EC
50
lower (0.31 and 0.90, respectively) than other agents. The greenhouse and field experiment suggested that the greatest fresh weight per plant (57.26 g and 57.93 g), forage yield (22.53 t/ha and 22.21 t/ha) and control efficacy (>75%) were obtained after spraying twice with zhongshengmycin or ethylicin in the LBD occurrence phase, with lower yields and control by kasugamycin + dicopper chloride trihydroxide, kasugamycin + oxine copper, benziothiazolinone, copper hydroxide, phenanthrenecarboxylicacid, copper, picoxystrobin, chloroisobromine cyanuric acid, oxathiapiprolin or azoxystrobin. Foliar application of zhongshengmycin or ethylicin can therefore be used for the effective management of LBD of oat.
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