Comparative Analysis of Machine Learning Algorithms for Author Age and Gender Identification

Author profiling is part of information retrieval in which different perspectives of the author are observed by considering various characteristics like native language, gender, and age. Different techniques are used to extract the required information using text analysis, like author identification on social media and for Short Text Message Service. Author profiling helps in security and blogs for identification purposes while capturing authors’ writing behaviors through messages, posts, comments, blogs, comments, and chat logs. Most of the work in this area has been done in English and other native languages. On the other hand, Roman Urdu is also getting attention for the author profiling task, but it needs to convert Roman-Urdu to English to extract important features like Named Entity Recognition (NER) and other linguistic features. The conversion may lose important information while having limitations in converting one language to another language. This research explores machine learning techniques that can be used for all languages to overcome the conversion limitation. The Vector Space Model (VSM) and Query Likelihood (Q.L.) are used to identify the author’s age and gender. Experimental results revealed that Q.L. produces better results in terms of accuracy

Hill Climbing-Based Efficient Model for Link Prediction in Undirected Graphs

Link prediction is a key problem in the field of undirected graph, and it can be used in a variety of contexts, including information retrieval and market analysis. By “undirected graphs”, we mean undirected complex networks in this study. The ability to predict new links in complex networks has a significant impact on society. Many complex systems can be modelled using networks. For example, links represent relationships (such as friendships, etc.) in social networks, whereas nodes represent users. Embedding methods, which produce the feature vector of each node in a graph and identify unknown links, are one of the newest approaches to link prediction. The Deep Walk algorithm is a common graph embedding approach that uses pure random walking to capture network structure. In this paper, we propose an efficient model for link prediction based on a hill climbing algorithm. It is used as a cost function. The lower the cost is, the higher the accuracy for link prediction between the source and destination node will be. Unlike other algorithms that predict links based on a single feature, it takes advantage of multiple features. The proposed method has been tested over nine publicly available datasets, and its performance has been evaluated by comparing it to other frequently used indexes. Our model outperforms all of these measures, as indicated by its higher prediction accuracy

Real-World Protein Particle Network Reconstruction Based on Advanced Hybrid Features

Biological network proteins are key operational particles that substantially and operationally cooperate to bring out cellular progressions. Protein links with some other biological network proteins to accomplish their purposes. Physical collaborations are commonly referred to by the relationships of domain-level. The interaction among proteins and biological network reconstruction can be predicted based on various methods such as social theory, similarity, and topological features. Operational particles of proteins collaboration can be indirect among proteins based on mutual fields, subsequently particles of proteins involved in an identical biological progression be likely to harbor similar fields. To reconstruct the real-world network of proteins particles, some methods need only the notations of proteins domain, and then, it can be utilized to multiple species. A novel method we have introduced will analyze and reconstruct the real-world network of protein particles. The proposed technique works based on protein closeness, algebraic connectivity, and mutual proteins. Our proposed method was practically tested over different data sets and reported the results. Experimental results clearly show that the proposed technique worked best as compared to other state-of-the-art algorithms

A Systematic Analysis of Community Detection in Complex Networks

Numerous techniques have been proposed by researchers to uncover the hidden patterns of real-world complex networks. Finding a hidden community is one of the crucial tasks for community detection in complex networks. Despite the presence of multiple methods for community detection, identification of the best performing method over different complex networks is still an open research question. In this article, we analyzed eight state-of-the-art community detection algorithms on nine complex networks of varying sizes covering various domains including animal, biomedical, terrorist, social, and human contacts. The objective of this article is to identify the best performing algorithm for community detection in real-world complex networks of various sizes and from different domains. The obtained results over 100 iterations demonstrated that the multi-scale method has outperformed the other techniques in terms of accuracy. Multi-scale method achieved 0.458 average value of modularity metric whereas multiple screening resolution, unfolding fast, greedy, multi-resolution, local fitness optimization, sparse Geosocial community detection algorithm, and spectral clustering, respectively obtained the modularity values 0.455, 0.441, 0.436, 0.421, 0.368, 0.341, and 0.340.

Efficient Link Prediction Model For Real-World Complex Networks Using Matrix-Forest Metric With Local Similarity Features

Link prediction in a complex network is a difficult and challenging issue to address. Link prediction tries to better predict relationships, interactions and friendships based on historical knowledge of the complex network graph. Many link prediction techniques exist, including the common neighbour, Adamic-Adar, Katz and Jaccard coefficient, which use node information, local and global routes, and previous knowledge of a complex network to predict the links. These methods are extensively used in various applications because of their interpretability and convenience of use, irrespective of the fact that the majority of these methods were designed for a specific field. This study offers a unique link prediction approach based on the matrix-forest metric and vertex local structural information in a real-world complex network. We empirically examined the proposed link prediction method over 13 real-world network datasets obtained from various sources. Extensive experiments were performed that demonstrated the superior efficacy of the proposed link prediction method compared to other methods and outperformed the existing state-of-the-art in terms of prediction accuracy

Parental consanguinity increases the risk of congenital malformations

Introduction: Congenital malformation is a physical or structural abnormality present either prenatally or after birth. These anomalies are either primary or secondary malformation. Primary malformations adversely affect body functions, however, the secondary malformations are the structural defects which may have less or no effect on body functions. Primary congenital anomalies show marked variations globally with respect to prevalence. The aim of the current study was to further add to the scientific evidences on the pattern and prevalence of congenital anomalies in cousins and non-cousins’ marriages in Khyber Pakhtunkhwa. Material and Methods: Data of 200 patients (divided into two groups) was collected by convenience sampling through cross-sectional survey. Group-I consisted of 100 gravidas who were diagnosed with anomalous foetus either hydrocephalous, anencephaly or cleft lip/palate and Group-II comprised of infants with inborn heart defects were selected. Results: The study shows 68% consanguineous and 32% non-consanguineous marriages. Hydrocephalous shows the highest rate of incidence (55%) followed by anencephaly (40%) cleft lip/palate (5%), Ventricular Septal Defect (43%), Atrial Septal Defect (29%), Patent Ductus Arteriosus (16%) and Tetralogy of Fallot (12%). The relative risk of hydrocephalus and anencephaly in consanguineous and non-consanguineous marriage was 0.98 while the relative risk of Ventricular Septal Defect and Patent Ductus Arteriosus was 1.1. Rate of miscarriages was comparatively high in cousin marriages. Frequency of CM was higher in multigravida compared to primigravida. Detection rate of hydrocephalus was highest in second trimester, cleft lip/palate in third trimester and anencephaly in first trimester. Conclusion: Parental consanguinity is one of the major risk factors for structural, neurological and cardiac anomalies

Effect of additivized biodiesel blends on diesel engine performance, emission, tribological characteristics, and lubricant tribology

Abstract: This research work focuses on investigating the lubricity and analyzing the engine characteristics of diesel–biodiesel blends with fuel additives (titanium dioxide (TiO2) and dimethyl carbonate (DMC)) and their effect on the tribological properties of a mineral lubricant. A blend of palm–sesame oil was used to produce biodiesel using ultrasound-assisted transesterification. B30 (30% biodiesel + 70% diesel) fuel was selected as the base fuel. The additives used in the current study to prepare ternary fuel blends were TiO2 and DMC. B30 + TiO2 showed a significant reduction of 6.72% in the coefficient of friction (COF) compared to B30. B10 (Malaysian commercial diesel) exhibited very poor lubricity and COF among all tested fuels. Both ternary fuel blends showed a promising reduction in wear rate. All contaminated lubricant samples showed an increment in COF due to the dilution of combustible fuels. Lub + B10 (lubricant + B10) showed the highest increment of 42.29% in COF among all contaminated lubricant samples. B30 + TiO2 showed the maximum reduction (6.76%) in brake-specific fuel consumption (BSFC). B30 + DMC showed the maximum increment (8.01%) in brake thermal efficiency (BTE). B30 + DMC exhibited a considerable decline of 32.09% and 25.4% in CO and HC emissions, respectively. The B30 + TiO2 fuel blend showed better lubricity and a significant improvement in engine characteristics

Effect of palm-sesame biodiesel fuels with alcoholic and nanoparticle additives on tribological characteristics of lubricating oil by four ball tribo-tester

Dilution of engine oil with unburned fuels alters its lubricity and tribological properties. In this research paper, SAE-40 lubricating oil samples were contaminated with known percentages (5%) of fuels (diesel, palm-sesame biodiesel blend (B30), B30 + ethanol, B30 + dimethyl carbonate, B30 + carbon nanotubes and, B30 + titanium oxide). The effect of all these fuels on wear and frictional characteristics of lubricating oil was determined by using a 4-ball tribo tester and wear types on worn surfaces were analyzed by using SEM. Lubricating oil diluted with B10 (commercial diesel) showed highest COF (42.95%) with severe abrasive and adhesive wear than mineral lubricant among other fuels. Lubricating oil diluted with palm-sesame biodiesel (B30 blend) with alcoholic additives showed comparatively less COF, less wear scar diameter and polishing wear due to presence of ester molecules. Lub + B30 + Eth exhibited increment in COF value (35.81%) compared to SAE-40 mineral lubricant. While lubricating oil contaminated with B30 with nanoparticles showed least frictional characteristics with abrasive wear. Lub + B30 + TiO2 showed least increment in COF value (13.78%) among all other contaminated fuels compared to SAE-40 mineral lubricant. It is concluded that nanoparticles in biodiesel blends (B30) helps in reducing degradation of lubricants than alcoholic fuel additives and commercial diesel. (C) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study

Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world. Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231. Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001). Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication