Towards understanding the role of content-based and contextualized features in detecting abuse on Twitter.

This paper presents a novel approach for detecting abuse on Twitter. Abusive posts have become a major problem for social media platforms like Twitter. It is important to identify abuse to mitigate its potential harm. Many researchers have proposed methods to detect abuse on Twitter. However, most of the existing approaches for detecting abuse look only at the content of the abusive tweet in isolation and do not consider its contextual information, particularly the tweets posted before the abusive tweet. In this paper, we propose a new method for detecting abuse that uses contextual information from the tweets that precede and follow the abusive tweet. We hypothesize that this contextual information can be used to better understand the intent of the abusive tweet and to identify abuse that content-based methods would otherwise miss. We performed extensive experiments to identify the best combination of features and machine learning algorithms to detect abuse on Twitter. We test eight different machine learning classifiers on content- and context-based features for the experiments. The proposed method is compared with existing abuse detection methods and achieves an absolute improvement of around 7%. The best results are obtained by combining the content and context-based features. The highest accuracy of the proposed method is 86%, whereas the existing methods used for comparison have highest accuracy of 79.2%.

Healthcare insurance fraud detection using data mining.

BACKGROUND: Healthcare programs and insurance initiatives play a crucial role in ensuring that people have access to medical care. There are many benefits of healthcare insurance programs but fraud in healthcare continues to be a significant challenge in the insurance industry. Healthcare insurance fraud detection faces challenges from evolving and sophisticated fraud schemes that adapt to detection methods. Analyzing extensive healthcare data is hindered by complexity, data quality issues, and the need for real-time detection, while privacy concerns and false positives pose additional hurdles. The lack of standardization in coding and limited resources further complicate efforts to address fraudulent activities effectively. METHODOLGY: In this study, a fraud detection methodology is presented that utilizes association rule mining augmented with unsupervised learning techniques to detect healthcare insurance fraud. Dataset from the Centres for Medicare and Medicaid Services (CMS) 2008-2010 DE-SynPUF is used for analysis. The proposed methodology works in two stages. First, association rule mining is used to extract frequent rules from the transactions based on patient, service and service provider features. Second, the extracted rules are passed to unsupervised classifiers, such as IF, CBLOF, ECOD, and OCSVM, to identify fraudulent activity. RESULTS: Descriptive analysis shows patterns and trends in the data revealing interesting relationship among diagnosis codes, procedure codes and the physicians. The baseline anomaly detection algorithms generated results in 902.24 seconds. Another experiment retrieved frequent rules using association rule mining with apriori algorithm combined with unsupervised techniques in 868.18 seconds. The silhouette scoring method calculated the efficacy of four different anomaly detection techniques showing CBLOF with highest score of 0.114 followed by isolation forest with the score of 0.103. The ECOD and OCSVM techniques have lower scores of 0.063 and 0.060, respectively. CONCLUSION: The proposed methodology enhances healthcare insurance fraud detection by using association rule mining for pattern discovery and unsupervised classifiers for effective anomaly detection.

Ensemble effort estimation with metaheuristic hyperparameters and weight optimization for achieving accuracy.

Software development effort estimation (SDEE) is recognized as vital activity for effective project management since under or over estimating can lead to unsuccessful utilization of project resources. Machine learning (ML) algorithms are largely contributing in SDEE domain, particularly ensemble effort estimation (EEE) works well in rectifying bias and subjectivity to solo ML learners. Performance of EEE significantly depends on hyperparameter composition as well as weight assignment mechanism of solo learners. However, in EEE domain, impact of optimization in terms of hyperparameter tunning as well as weight assignment is explored by few researchers. This study aims in improving SDEE performance by incorporating metaheuristic hyperparameter and weight optimization in EEE, which enables accuracy and diversity to the ensemble model. The study proposed Metaheuristic-optimized Multi-dimensional bagging scheme and Weighted Ensemble (MoMdbWE) approach. This is achieved by proposed search space division and hyperparameter optimization method named as Multi-dimensional bagging (Mdb). Metaheuristic algorithm considered for this work is Firefly algorithm (FFA), to get best hyperparameters of three base ML algorithms (Random Forest, Support vector machine and Deep Neural network) since FFA has shown promising results of fitness in terms of MAE. Further enhancement in performance is achieved by incorporating FFA-based weight optimization to construct Metaheuristic-optimized weighted ensemble (MoWE) of individual multi-dimensional bagging schemes. Proposed scheme is implemented on eight frequently utilized effort estimation datasets and results are evaluated by 5 error metrices (MAE, RMSE, MMRE, MdMRE, Pred), standard accuracy and effect size along with Wilcox statistical test. Findings confirmed that the use of FFA optimization for hyperparameter (with search space sub-division) and for ensemble weights, has significantly enhanced performance in comparison with individual base algorithms as well as other homogeneous and heterogenous EEE techniques.

Phytochemical profiling and anti-inflammatory, analgesic activity of Ficus religiosa L. and molecular docking study against iNOS, TNF-α enzyme.

Ficus religiosa L., a member of the Moraceae family, is a medicinal plant having a number of pharmacological properties. The anti-inflammatory and analgesic actions of an ethanolic extract of F. religiosa bark FRE (at 100 and 200mg/kg dosages) and the biomarker component quercetin QC (at 5 and 10mg/kg doses) were investigated. The estimate of quercetin was carried by using an HPTLC analysis of FRE. Additionally, qualitative and quantitative screening for key important phytocomponents was done using dried, ground plant stem barks. By using molecular docking, the molecular interaction profile with several anti-inflammatory drug targets was examined. Both the FRE as well as QC showed a substantial decline in paw volume when compared with the relevant control groups (p<0.01 & p<0.001). Following the administration of acetic acid to mice, the FRE and QC both demonstrate a substantial lengthening of the paw licking or leaping towards Eddy's hot plate as well as a decrease in the number of writhes (p<0.01 & p<0.001). This study supports the use of these herbs in conventional medicine to treat pain and inflammation by through similar mechanism as compound quercetin (QC).

Prediction of antifreeze proteins using machine learning.

Living organisms including fishes, microbes, and animals can live in extremely cold weather. To stay alive in cold environments, these species generate antifreeze proteins (AFPs), also referred to as ice-binding proteins. Moreover, AFPs are extensively utilized in many important fields including medical, agricultural, industrial, and biotechnological. Several predictors were constructed to identify AFPs. However, due to the sequence and structural heterogeneity of AFPs, correct identification is still a challenging task. It is highly desirable to develop a more promising predictor. In this research, a novel computational method, named AFP-LXGB has been proposed for prediction of AFPs more precisely. The information is explored by Dipeptide Composition (DPC), Grouped Amino Acid Composition (GAAC), Position Specific Scoring Matrix-Segmentation-Autocorrelation Transformation (Sg-PSSM-ACT), and Pseudo Position Specific Scoring Matrix Tri-Slicing (PseTS-PSSM). Keeping the benefits of ensemble learning, these feature sets are concatenated into different combinations. The best feature set is selected by Extremely Randomized Tree-Recursive Feature Elimination (ERT-RFE). The models are trained by Light eXtreme Gradient Boosting (LXGB), Random Forest (RF), and Extremely Randomized Tree (ERT). Among classifiers, LXGB has obtained the best prediction results. The novel method (AFP-LXGB) improved the accuracies by 3.70% and 4.09% than the best methods. These results verified that AFP-LXGB can predict AFPs more accurately and can participate in a significant role in medical, agricultural, industrial, and biotechnological fields.

A retrospective evaluation of side-effects associated with the booster dose of Pfizer-BioNTech/BNT162b2 COVID-19 vaccine among females in Eastern Province, Saudi Arabia.

BACKGROUNDS: The development of several types of vaccines to avert COVID-19 has taken place. Despite several reports of undesirable reactions noted post-COVID-19 vaccine administration, later remains one of the best prevention and management tools in fighting the spread of the virus and its variants and reducing the harshness of this viral attack. The purpose of the current paper was to explore the side-effects experienced by the females in the Eastern Province of Saudi Arabia directly after receiving the booster dose of the Pfizer-BioNTech/BNT162b2 COVID-19 vaccine. METHODS: A descriptive cross-sectional study among adults living in the East-ern Province, Saudi Arabia was applied. A survey link was, distributed through WhatsApp, SMS, or e-mail to community members. Respondent's demographic information was acquired, as well as information about any local and systemic side-effects reported following booster dose of BioNTech/BNT162b2 COVID-19 vaccine. RESULTS: A total of 72.36% (432/597) of the respondents who participated in this study reported at least one side-effect. Pain and redness at the injection site (75.93%), myalgia (71.99%), headache (53.24%), fever (33.56%), and fatigue (43.78%) were the highest frequently stated side-effects. Furthermore, 9.25% of the respondents had to see a physician due to side effects, plus merely four participants were admitted to the hospital. The respondents working in the non-healthcare-related sector had a 1.677-fold more possibility of side effects in comparison with the other respondents (adjusted odds ratio = 1.677; 95% CI = 1.363, 2.064). CONCLUSIONS: All reported side-effects were mild to moderate. These findings might persuade pessimists and refusers to get the COVID-19 vaccine. Myalgia and pain or redness at the site of injection were the most common reported side-effects in our study.

Target-DBPPred: An intelligent model for prediction of DNA-binding proteins using discrete wavelet transform based compression and light eXtreme gradient boosting.

DNA-protein interaction is a critical biological process that performs influential activities, including DNA transcription and recombination. DBPs (DNA-binding proteins) are closely associated with different kinds of human diseases (asthma, cancer, and AIDS), while some of the DBPs are used in the production of antibiotics, steroids, and anti-inflammatories. Several methods have been reported for the prediction of DBPs. However, a more intelligent method is still highly desirable for the accurate prediction of DBPs. This study presents an intelligent computational method, Target-DBPPred, to improve DBPs prediction. Important features from primary protein sequences are investigated via a novel feature descriptor, called EDF-PSSM-DWT (Evolutionary difference formula position-specific scoring matrix-discrete wavelet transform) and several other multi-evolutionary methods, including F-PSSM (Filtered position-specific scoring matrix), EDF-PSSM (Evolutionary difference formula position-specific scoring matrix), PSSM-DPC (Position-specific scoring matrix-dipeptide composition), and Lead-BiPSSM (Lead-bigram-position specific scoring matrix) to encapsulate diverse multivariate features. The best feature set from the features of each descriptor is selected using sequential forward selection (SFS). Further, four models are trained using Adaboost, XGB (eXtreme gradient boosting), ERT (extremely randomized trees), and LiXGB (Light eXtreme gradient boosting) classifiers. LiXGB, with the best feature set of EDF-PSSM-DWT, has attained 6.69% and 15.07% higher performance in terms of accuracies using training and testing datasets, respectively. The obtained results verify the improved performance of our proposed predictor over the existing predictors.

Pharmacological Evaluation of Safoof-e-Pathar Phori- A Polyherbal Unani Formulation for Urolithiasis.

Safoof-e-Pathar phori (SPP) is an Unani poly-herbomineral formulation, which has for a long time been used as a medicine due to its antiurolithiatic activity, as per the Unani Pharmacopoeia. This powder formulation is prepared using six different plant/mineral constituents. In this study, we explored the antiurolithiatic and antioxidant potentials of SPP (at 700 and 1,000 mg/kg) in albino Wistar rats with urolithiasis induced by 0.75% ethylene glycol (EG) and 1% ammonium chloride (AC). Long-term oral toxicity studies were performed according to the Organization for Economic Co-operation and Development (OECD) guidelines for 90 days at an oral dose of 700 mg/kg of SPP. The EG urolithiatic toxicant group had significantly higher levels of urinary calcium, serum creatinine, blood urea, and tissue lipid peroxidation and significantly (p < 0.001 vs control) lower levels of urinary sodium and potassium than the normal control group. Histopathological examination revealed the presence of refractile crystals in the tubular epithelial cell and damage to proximal tubular epithelium in the toxicant group but not in the SPP treatment groups. Treatment of SPP at 700 and 1,000 mg/kg significantly (p < 0.001 vs toxicant) lowered urinary calcium, serum creatinine, blood urea, and lipid peroxidation in urolithiatic rats, 21 days after induction of urolithiasis compared to the toxicant group. A long-term oral toxicity study revealed the normal growth of animals without any significant change in hematological, hepatic, and renal parameters; there was no evidence of abnormal histology of the heart, kidney, liver, spleen, or stomach tissues. These results suggest the usefulness of SPP as an antiurolithiatic and an antioxidant agent, and long-term daily oral consumption of SPP was found to be safe in albino Wistar rats for up to 3 months. Thus, SPP may be safe for clinical use as an antiurolithiatic formulation.

Genetic Engineering Approaches for Enhanced Insect Pest Resistance in Sugarcane.

Sugarcane (Saccharum officinarum), a sugar crop commonly grown for sugar production all over the world, is susceptible to several insect pests attack in addition to bacterial, fungal and viral infections leading to substantial reductions in its yield. The complex genetic makeup and lack of resistant genes in genome of sugarcane have made the conventional breeding a difficult and challenging task for breeders. Using pesticides for control of the attacking insects can harm beneficial insects, human and other animals and the environment as well. As alternative and effective strategy for control of insect pests, genetic engineering has been applied for overexpression of cry proteins, vegetative insecticidal proteins (vip), lectins and proteinase inhibitors (PI). In addition, the latest biotechnological tools such as host-induced gene silencing (HIGS) and CRISPR/Cas9 can be employed for sustainable control of insect pests in sugarcane. In this review overexpression of the cry, vip, lectins and PI genes in transgenic sugarcane and their disease resistance potential is described.

The possible impact of socioeconomic, income, and educational status on adverse effects of drug and their therapeutic episodes in patients targeted with a combination of tuberculosis interventions.

First-line antituberculosis (anti-TB) compounds have been considered as proven components of the Directly Observed Treatment-Short course (DOTS). Drug therapy against tuberculosis has been categorized as I, II, or III following the Revised National Tuberculosis Control Program guidelines. Anti-TB are drugs are quite common and show limited adverse effects. However, first-line anti-TB compounds mediated DOTS therapy and were found with several complications. Thus, those drugs have been discontinued. Therefore, the present study was designed to find out the possible impact of socioeconomic, income, and educational status on the adverse effects of drugs and their therapeutic episodes in patients targeted with a combination of tuberculosis intervention. This study found that an increased incidence of tuberculosis was found in patients who have finished high school, contributing to a high percentage of adverse effects. Notably, adverse events were shown maximally in poor patients compared with rich- or high-income patients. On the contrary, a high prevalence of adverse events was shown to be increased in partially skilled workers compared with full-skilled workers. Consequently, adversely considerable events were implicated to be raised in patients associated with minimal socioeconomic class. Such interesting factors would help in monitoring such events in experimental patients.

MIIB: A Metric to Identify Top Influential Bloggers in a Community.

Social networking has revolutionized the use of conventional web and has converted World Wide Web into the social web as users can generate their own content. This change has been possible due to social web platforms like forums, wikis, and blogs. Blogs are more commonly being used as a form of virtual communication to express an opinion about an event, product or experience and can reach a large audience. Users can influence others to buy a product, have certain political or social views, etc. Therefore, identifying the most influential bloggers has become very significant as this can help us in the fields of commerce, advertisement and product knowledge searching. Existing approaches consider some basic features, but lack to consider some other features like the importance of the blog on which the post has been created. This paper presents a new metric, MIIB (Metric for Identification of Influential Bloggers), based on various features of bloggers' productivity and popularity. Productivity refers to bloggers' blogging activity and popularity measures bloggers' influence in the blogging community. The novel module of BlogRank depicts the importance of blog sites where bloggers create their posts. The MIIB has been evaluated against the standard model and existing metrics for finding the influential bloggers using dataset from the real-world blogosphere. The obtained results confirm that the MIIB is able to find the most influential bloggers in a more effective manner.