5th Skill in English Language Learning and Teaching: A Pakistani Perspective

This dissertation explores the beliefs of students on the relevance (if at all) of addressing the Fifth skill, culture, in English education in a Pakistani context, with sub question that aims to answer what definitions of the Fifth skill (Tomalin, 2008) could be appropriate to English education in a Pakistani context? So far the research done on English language teaching in Pakistan and anything related to it is based on teachers’ beliefs alone, therefore it was important for me as an English Language teacher to find out what the students attitudes were towards the integration of 5th skill in the classroom. However, I did not limit the research to students’ beliefs alone; my research also includes the perception of the teachers. This is not only to give validity to the research, but also to realize any differences regarding the teachers’ beliefs on the issue in previous researches. To explore the role of 5th skill in teaching English languages in a Pakistani classroom, at the secondary level, this dissertation collected the responses elicited from both the students and the teacher through a semi-structured questionnaire and focus group discussion, and a thematic analysis was carried out. The results of this study highlight a number of issues regarding cultural acceptance, language acceptance and integrating of 5th skill in language teaching. Certain interesting contradictions regarding English culture(s) and English language and their status in Pakistani society also emerged. The findings suggest that students regarded the 5th skill as an essential source for better understanding the concepts and their functional use of English language, as it presents them with real life situations. However, where the 5th skill was seen as an important tool to enhance language competence, the students also supported that both the students’ culture as well as the culture associated with English Language be incorporated in the language class. The students saw integrating 5th skill in a language class as a means to express their ideas, values and experiences, and an opportunity to make others understand them and their point of view, and not restrict the use of Fifth skill to a one way cultural awareness stream only. The dissertation also questioned the current status of English as a second language as perceived by the students and its implications on the future of English Language teaching in Pakistan

Fine-grained sentiment analysis for measuring customer satisfaction using an extended set of fuzzy linguistic hedges

© 2020 The Authors. Published by Atlantis Press SARL. In recent years, the boom in social media sites such as Facebook and Twitter has brought people together for the sharing of opinions, sentiments, emotions, and experiences about products, events, politics, and other topics. In particular, sentiment-based applications are growing in popularity among individuals and businesses for the making of purchase decisions. Fuzzy-based sentiment analysis aims at classifying customer sentiment at a fine-grained level. This study deals with the development of a fuzzy-based sentiment analysis by extending fuzzy hedges and rule-sets for a more efficient classification of customer sentiment and satisfaction. Prior studies have used a limited number of linguistic hedges and polarity classes in their rule-sets, resulting in the degraded efficiency of their fuzzy-based sentiment analysis systems. The proposed analysis of the current study classifies customer reviews using fuzzy linguistic hedges and an extended rule-set with seven sentiment analysis classes, namely extremely positive, very positive, positive, neutral, negative, very negative, and extremely negative. Then, a fuzzy logic system is applied to measure customer satisfaction at a fine-grained level. The experimental results demonstrate that the proposed analysis has an improved performance over the baseline works

Personality classification from text using bidirectional long short-term memory model

A personality is a blend of an individual’s psychological characteristics and qualities, displaying human behaviour. Recently, the development of computational models for personality recognition has received research scientists’ attention. Prior studies on personality trait prediction have used machine and deep learning techniques, which perform feature extraction but do not retain long-term dependencies. In this study, we apply a deep learning model, namely BiLSTM, that can maintain long-term dependencies in both forward and backward directions for personality prediction on a benchmark essay dataset. The suggested model outperforms current strategies in classifying the user’s personality attributes. With this research’s findings, firms may make better judgments about hiring personnel. They may also use the research findings to choose, manage, and optimize their strategies, activities, and commodities

Efficient Diagnosis of Liver Disease using Deep Learning Technique

The diagnoses a patient receives can have significant repercussions for enhancing patient safety, investigation, and policymaking. Medical practitioners employ a variety of pathologic techniques to arrive at conclusions about their patients\u27 states in clinical information. The field of medical diagnosis has seen renewed efforts from clinicians in recent years. When Artificial Intelligence (AI) and Deep Learning (DL) are used in tandem with clinical data, they can greatly enhance the accuracy of disease diagnoses. The use of computers and internet has made it possible to acquire data and visualize previously inaccessible findings, such as addressing the issue of missing values in clinical research. Decision-making can be aided by problem-specific Deep Learning algorithms. In order to automatically identify illness specimens, effective predictive methods are essential. In this regard, this work employs techniques of deep learning to distinguish liver patients from normal persons. In this research, we make a prediction of liver illness using a Deep Learning model called BiLSTM. This model is able to keep track of long-term relationships in both the forward and the backward direction. The efficiency of the model\u27s predictions came out to be 93.00% overall. According to the findings, the implementation of a hybrid model seems to have enhanced the predictive accuracy

Applying Machine Learning Techniques for Performing Comparative Opinion Mining

In recent times, comparative opinion mining applications have attracted both individuals and business organizations to compare the strengths and weakness of products. Prior works on comparative opinion mining have focused on applying a single classifier, limited comparative opinion labels, and limited dataset of product reviews, resulting in degraded performance for classifying comparative reviews. In this work, we perform multi-class comparative opinion mining by applying multiple machine learning classifiers using an increased number of comparative opinion labels (9 classes) on 4 datasets of comparative product reviews. The experimental results show that Random Forest classifier has outperformed the comparing algorithms in terms of improved accuracy, precision, recall and f-measure

Synthesis, Characterization, and Biological Activity of Novel Schiff and Mannich Bases of 4-Amino-3-(N-phthalimidomethyl)-1,2,4-triazole-5-thione

The present work describes the syntheses and antimicrobial activity studies of a series of novel Schiff bases (4a–4i) and their Mannich bases (5a–5h) starting from 4-amino-3-(N-phthalimido-methyl)-1,2,4-triazole-5-thione (3). All the synthesized compounds were characterized by IR, 1H-NMR, 13C-NMR, and MS. All the synthesized compounds were screened for four Gram-negative strains, one Gram-positive strain of bacteria, and one diploid fungal strain. In general the antimicrobial activity increased remarkably on the introduction of azomethine functionality in parent triazole (3). The antimicrobial activity further improved when morpholine group was added to them except for Enterobacter cloacae, where loss of activity was observed. The results are promising and show that the fine tuning of the structures (5a, 5b, 5e, 5f, and 5h) can lead to some new antimicrobial compounds

Phytochemical composition, antilipidemic and antihypercholestrolemic perspectives of Bael leaf extracts

Abstract Background In recent times, focus on plant research has improved all over the world and essential parts of plants provide bioactive compounds in human diet. The bael (Aegle marmelos) has enormous traditional uses in the treatment of chronic diarrhea, dysentery, peptic ulcers and as a laxative. The main focus of this study was characterization of bael leaf extract for its bioactive constituents, antihypercholestrolemic and antilipidemic perspectives. Methods After proximate composition of bael powder, the aqueous extract of bael leaf was used for phytochemical profiling (alkaloids, total phenolic content and total flavonoid content). Afterwards, normal rats group G0 was administrated basal diet while G1 and G2 normal rat groups were fed diets containing bael leaf extract 125 mg and 250 mg, respectively for consecutive 60 days. In a similar way, hyperlipidemic rats group Gh0 was administrated basal diet while Gh1 and Gh2 hyperlipidemic rat groups were fed diets containing bael leaf extract 125 mg and 250 mg, respectively for consecutive 60 days. The blood drawn on day 0, day 30 and day 60 was analyzed for serum parameters, such as total cholesterol, high-density lipoprotein cholesterol, low–density lipoprotein cholesterol, triglycerides concentration and free and ester cholesterol. Results Bael leaf powder is a rich source of crude fiber (14.50 ± 0.10 g/100 g). Aqueous extract of bael leaf contains alkaloids (15.58 ± 0.05 mg/g), flavonoids (64.00 ± 0.05 mg/g), phenolics (30.34 ± 0.01 GAEmg/g). From the In vivo studies, the lowest weight gain was observed in group G2 and in Gh2 as compared to control of both groups. The decrease in serum TC for G1–15.06%, G2–17.27% while in Gh1–22.46% and Gh2–34.82% after day 60, respectively. The maximum decrease was observed in group G2 (− 14.33%) and in Gh2 (− 24.79%) for triglycerides after 60 days. For HDL-cholesterol, significant increase (11.20%) in G2 and (49.83%) in Gh2 was observed of after 60 days. A trend in decrease of serum LDL–cholesterol in G2 (− 9.63%) and in Gh2 (− 44.65%) was also observed at day 60, and − 19.05% and − 30.06% decrease was noted in G2 and Gh2, respectively and decreasing trend was observed in free and total cholesterol − 22.30% and − 81.49% for groups G2 and Gh2 after day 60. Conclusions The results of the present study demonstrated that the extract contents of bael leaf provide protective role against hypercholesterolemic and hyperlipidemic conditions

Older age, lack of vaccination and infection with variants other than Omicron associated with severity of COVID-19 and in-hospital mortality in Pakistan

Objectives: We investigated factors associated with COVID-19 disease severity and in-hospital mortality in a low-middle income setting.Methods: Records of 197 adult COVID-19 patients admitted to the Aga Khan University Hospital, Karachi between April 2021 and February 2022 were reviewed. Clinical data including, that of SARS-CoV-2 variants was collected.Results: The median age of the patients was 55 years and 51.8% were males. 48.2 % of patients had non-severe disease, while 52.8% had severe/critical disease. Hypertension (48%) and diabetes mellitus (41.3%) were most common comorbid conditions. Omicron (55.3%), Beta (14.7%), Alpha (13.7%), Delta (12.7%) and Gamma (3.6%) were identified in patients. The risk of severe disease was higher in those aged above 50 years (OR 5.73; 95%CI [2.45-13.7]) and in diabetics (OR 4.24; 95% CI[1.82-9.85]). Full vaccination (OR 0.25; 95%CI [0.11-0.58]) or infection with Omicron variants (OR 0.42; 95% CI[0.23-0.74]) reduced disease severity. Age \u3e 50 (OR 5.07; 95%CI [1.92-13.42]) and presence of myocardial infarction (OR 5.11; 95% CI[1.45-17.93]) was associated with increased mortality, but infection with Omicron (OR 0.22 95% CI 0.10-0.53]) reduced risk.Conclusions: Vaccination was found to protect against severe COVID-19 regardless of the infecting variant and is recommended especially, in those aged over 50 years and with co-morbid conditions

D-PFA: A Discrete Metaheuristic Method for Solving Traveling Salesman Problem Using Pathfinder Algorithm

The Traveling Salesman Problem (TSP) which is a theoretical computer science and operations research problem, has several applications even in its purest formulation, such as the manufacture of microchips, planning, and logistics. There are many methods proposed in the literature to solve TSP with gains and losses. We propose a discrete metaheuristic method called D-PFA to solve this problem more efficiently. Initially, the Pathfinder Algorithm (PFA) was presented to handle issues involving continuous optimization, where it worked effectively. In recent years, there have been various published variants of PFA, and it has been frequently employed to address engineering challenges. In this study, the original PFA algorithm is broken into four sub-algorithms and every sub-algorithm is discretized and coupled to form a new algorithm. The proposed algorithm has a high degree of flexibility, a quick response time, strong exploration and exploitation. To validate the significant advantages of the proposed D-PFA, 34 different instances with different sizes are used in simulation results. The proposed method was also compared with 12 State-of-the-Art algorithms. Results indicate that the suggested approach is more competitive and resilient in solving TSP than other algorithms in different aspects. A conclusion and an outlook on future studies and applications are given at the end of the paper