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Online discussion forums are a valuable source of knowledge. Users may share or exchange ideas by posting content in the form of questions and answers. With the increasing volume of online content in the form of forums, finding relevant information in forums can be a challenging task and knowledge management and quality assurance of this content are of critical importance. Although online discussion forums offer search services, in most cases only keyword search is provided. In keyword search techniques, such as cosine similarity, lexical overlap between query and document terms is considered; however, these techniques do not consider the context or meaning of the terms, thus failed to retrieve the relevant documents. Earlier content-based research efforts for improving the performance of thread retrieval were primarily based on cosine similarity technique. Cosine similarity technique assigns term-weights based on term-frequency and inverse-document frequency; however, this technique does not consider discussion semantics which may lead to less effective document retrieval. To address these issues, we have proposed two thread ranking techniques for online discussion forums: (1) threads are ranked on the basis of a semantic similarity score between posts and (2) threads are ranked based on their participants’ reputation and posts’ quality. The proposed work provides a performance comparison between semantic similarity techniques and cosine similarity techniques along with reputation and post quality features in thread ranking process. Experimental results obtained using a real online forum dataset demonstrate that the proposed techniques have significantly improved thread ranking performance.

The intrinsic and liveness detection behavior of electrocardiogram (ECG) signals has made it an emerging biometric modality for the researcher with several applications including forensic, surveillance and security. The main challenge is the low recognition performance with datasets of large populations, including healthy and heart-disease patients, with a short interval of an ECG signal. This research proposes a novel method with the feature-level fusion of the discrete wavelet transform and a one-dimensional convolutional recurrent neural network (1D-CRNN). ECG signals were preprocessed by removing high-frequency powerline interference, followed by a low-pass filter with a cutoff frequency of 1.5 Hz for physiological noises and by baseline drift removal. The preprocessed signal is segmented with PQRST peaks, while the segmented signals are passed through Coiflets’ 5 Discrete Wavelet Transform for conventional feature extraction. The 1D-CRNN with two long short-term memory (LSTM) layers followed by three 1D convolutional layers was applied for deep learning-based feature extraction. These combinations of features result in biometric recognition accuracies of 80.64%, 98.81% and 99.62% for the ECG-ID, MIT-BIH and NSR-DB datasets, respectively. At the same time, 98.24% is achieved when combining all of these datasets. This research also compares conventional feature extraction, deep learning-based feature extraction and a combination of these for performance enhancement, compared to transfer learning approaches such as VGG-19, ResNet-152 and Inception-v3 with a small segment of ECG data.

The spreading of accidental or malicious misinformation on social media, specifically in critical situations, such as real-world emergencies, can have negative consequences for society. This facilitates the spread of rumors on social media. On social media, users share and exchange the latest information with many readers, including a large volume of new information every second. However, updated news sharing on social media is not always true.In this study, we focus on the challenges of numerous breaking-news rumors propagating on social media networks rather than long-lasting rumors. We propose new social-based and content-based features to detect rumors on social media networks. Furthermore, our findings show that our proposed features are more helpful in classifying rumors compared with state-of-the-art baseline features. Moreover, we apply bidirectional LSTM-RNN on text for rumor prediction. This model is simple but effective for rumor detection. The majority of early rumor detection research focuses on long-running rumors and assumes that rumors are always false. In contrast, our experiments on rumor detection are conducted on real-world scenario data set. The results of the experiments demonstrate that our proposed features and different machine learning models perform best when compared to the state-of-the-art baseline features and classifier in terms of precision, recall, and F1 measures.

Before the internet, people acquired their news from the radio, television, and newspapers. With the internet, the news moved online, and suddenly, anyone could post information on websites such as Facebook and Twitter. The spread of fake news has also increased with social media. It has become one of the most significant issues of this century. People use the method of fake news to pollute the reputation of a well-reputed organization for their benefit. The most important reason for such a project is to frame a device to examine the language designs that describe fake and right news through machine learning. This paper proposes models of machine learning that can successfully detect fake news. These models identify which news is real or fake and specify the accuracy of said news, even in a complex environment. After data-preprocessing and exploration, we applied three machine learning models; random forest classifier, logistic regression, and term frequency-inverse document frequency (TF-IDF) vectorizer. The accuracy of the TFIDF vectorizer, logistic regression, random forest classifier, and decision tree classifier models was approximately 99.52%, 98.63%, 99.63%, and 99.68%, respectively. Machine learning models can be considered a great choice to find reality-based results and applied to other unstructured data for various sentiment analysis applications.

This article investigates how consumer attitudes toward green buying and subjective norms influence the network buying with the mediation of behavioral intentions and moderation of product type. Due to the increasing environmental concerns, network buying has positively changed human interactions with the environment. Based on a sample of 392 Chinese consumers, this study reveals that consumer attitudes toward green buying and subjective norms significantly influence network buying. The empirical results provide strong evidence for the mediating role of behavioral intentions in the relationship between the attitudes toward green buying and network buying. This study empirically tests a model, including the moderating effect of product type toward green products through network buying. Green consumption through network buying will help consumers to live a healthy life together with achieving environmental sustainability and consumer well-being at large. Interestingly, the current study suggests that network buying can drive sustainable consumption.

This article provides an overview of some recent findings on consumer attitudes and other vital antecedents of purchase intention of branded meat. Based on a sample of 349 respondents in Pakistan, this study tests a model including factors associated with branded meat purchase intentions. SPSS, version 21.0 (Statistical Package for Social Sciences) and AMOS (Analysis of Moment Structure) were used for data analysis. Structural equation modeling (SEM) is a statistical modeling method of analysis that enables the testing of a series of separate yet, interrelated constructs and regression equations, allowing for the study of multiple relationships at once. Consumer attitude, subjective norms, and perceived behavioral control are the most critical factors in forming consumers’ intentions toward buying and consuming branded meat. The overall mediating role of brand trust is less driven by subjective norms and perceived behavioral control, more by consumer attitude than branded meat products. The findings can help public policymakers and managers to understand consumers’ branded meat purchase tendencies and help promote healthier consumption habits.

Several studies investigate the various aspects of tobacco at the processing and cigarette manufacturing levels, but the profitability, supply response, and input demand of tobacco farms in Pakistan remain unknown. Our study fills this gap by examining farm-level profitability, input demand, and output supply using survey data of 140 tobacco farms by employing a profit function approach. The results show that tobacco production is not very lucrative at the farm level and farmers are responsive to changes in market prices for the inputs and output. The price of tobacco is the most important determinant of the output supply and demand for inputs, and farmers' response to increasing tobacco prices is positive but inelastic in the study area. The use of variable inputs such as fertilizers, labor, mechanical power, pesticides, and farmyard manure is important in resource allocation decisions in tobacco production. As a result, a price increase for green tobacco leaves would significantly increase the demand for farm inputs such as fertilizers, labor, mechanical power, pesticides, and farmyard manure. Tobacco production is negatively affected by the increasing input prices in the study area. Among the fixed factors, land area has a significant impact on tobacco productivity in the province. The present study is the first to quantify the farm-level input demand and output supply; therefore, based on the findings, the increasing tobacco production requires higher output prices and reasonable input prices.

The physiological and biochemical characterization of the “Halloween” genes has fundamental importance in the biosynthesis pathway of ecdysteroids. These genes were found to catalyze the final phases of ecdysteroid biosynthesis from dietary cholesterol to the molting hormone 20-hydroxyecdysone. We report the characterization of the Cs-Phm in a major insect pest in agriculture, the rice striped stem borer, Chilo suppressalis (C. suppressalis). A full-length transcript of Cs-Phm was amplified with an open reading frame (ORF) of 478 amino acids through 5′ and 3′ RACE. Cs-Phm shows five insect-conserved P450 motifs: Helix-C, Helix-I, Helix-K, PERF, and heme-binding motifs. Phylogenetic analysis clearly shows high similarity to Lepidoptera and evolutionary conservation in insects. The relative spatial and temporal transcript profile shows that Cs-Phm is highly expressed in the prothoracic glands and appears throughout the larval development, but with low expression at the start of the larval instar. It seems to peak in 3–4 days and decreases again before the larvae molt. Double-stranded RNA (dsRNA) injection of Cs-Phm at the larval stage efficiently knocked down the target gene and decreased its expression level. The dsRNA-treated group showed significantly decreased ecdysteroid titers, which leads to delayed larval development and higher larval mortality. Negative effects of larval development were rescued by treating 20E in the dsRNA-treated group. Thus, in conclusion, our results suggest that Cs-Phm is functionally conserved in C. suppressalis and encodes functional CYP that contributes to the biogenesis of 20E.

With the increase in users of social media websites such as IMDb, a movie website, and the rise of publicly available data, opinion mining is more accessible than ever. In the research field of language understanding, categorization of movie reviews can be challenging because human language is complex, leading to scenarios where connotation words exist. Connotation words have a different meaning than their literal meanings. While representing a word, the context in which the word is used changes the semantics of words. In this research work, categorizing movie reviews with good F-Measure scores has been investigated with Word2Vec and three different aspects of proposed features have been inspected. First, psychological features are extracted from reviews positive emotion, negative emotion, anger, sadness, clout (confidence level) and dictionary words. Second, readablility features are extracted; the Automated Readability Index (ARI), the Coleman Liau Index (CLI) and Word Count (WC) are calculated to measure the review’s understandability score and their impact on review classification performance is measured. Lastly, linguistic features are also extracted from reviews adjectives and adverbs. The Word2Vec model is trained on collecting 50,000 reviews related to movies. A self-trained Word2Vec model is used for the contextualized embedding of words into vectors with 50, 100, 150 and 300 dimensions.The pretrained Word2Vec model converts words into vectors with 150 and 300 dimensions. Traditional and advanced machine-learning (ML) algorithms are applied and evaluated according to performance measures: accuracy, precision, recall and F-Measure. The results indicate Support Vector Machine (SVM) using self-trained Word2Vec achieved 86% F-Measure and using psychological, linguistic and readability features with concatenation of Word2Vec features SVM achieved 87.93% F-Measure.

Sucking pests of cotton (Gossypium hirsutum L.), such as thrips, or Thrips tabaci Lindeman, and jassid, or Amrasca biguttula Ishida, are among the most threatening insect pests to young cotton plants in Pakistan. New chemical insecticides have been trialed to control their damage in commercial fields. Formulations that show good suppression of these pest’s populations, while sparing bio-controlling agents, are always preferred for obtaining better crop yield. Six different commercially available insecticides, namely Fountain® (fipronil and imidacloprid), Movento Energy® (spirotetramat and imidacloprid), Oshin® (dinotefuran), Concept Plus® (pyriproxyfen, fenpyroximate, and acephate), Maximal® (nitenpyram), and Radiant® (spinetoram) were evaluated in the present study to shortlist the best available insecticide against targeted pests. Harmful impacts of selected insecticides were also evaluated against naturally occurring predators, such as spiders and green lacewings (Chrysoperla carnea). Radiant® (spinetoram) and Movento Energy®, respectively, were best at controlling thrips (with 61% and 56% mortality, respectively) and jassid (62% and 57% mortality, respectively) populations during 2018 and 2019. Radiant® proved itself as the best option and showed minimal harmful effects on both major arthropod predators of cotton fields i.e., spiders (with 8–9% mortality) and green lacewings (with 12–16% mortality). Movento Energy® also showed comparatively less harmful effects (with 15–18% mortality) towards natural predatory fauna of cotton crops, as compared to other selective insecticides used in the study. The findings of current study suggest that the judicious use of target-oriented insecticides can be an efficient and predator-friendly management module in cotton fields. However, the impact of these chemicals is also depended on their timely application, keeping in consideration the ETL of pests and the population of beneficial arthropods.