Classroom Management for Teaching English at Tertiary Colleges in Bangladesh: Challenges and Solutions

The study investigates the challenges and solutions of English classroom management at tertiary level colleges in Bangladesh through the case study of X college Though the instructional strategy of tertiary colleges shifted from teacher-centered to student-centered learning still the tertiary colleges are following the traditional classroom management system For teaching English the learners are rarely asked to get engaged in a communicative exercise in the classroom However to investigate the research problem the study follows mixed method technique It finds out the learners fondness opinions learning preferences and atmosphere and lecturers instruction systems are involved as substantial for study It also investigates lecturers awareness of classroom management and their current practices regarding the issue After analysing all data collected from teachers and students as well as correlating with other literatures it is found that teachers are overlooking the realities of classroom management such as seating grouping activities teachers control over students appropriate opening and conclusion of the lesson time management keeping discipline problem management using suitable tools and methods instruction nursing etc However the study finds that learners at tertiary colleges in Bangladesh do not get the benefits of the English classroom

REPORTING DISCLOSURE LEVELS AND COMPLIANCE WITH BB, AAOIFI, B/IFRS AND SEC OF ISLAMIC FINANCIAL INSTITUTIONS IN BANGLADESH

Compliance with financial reporting guidelines/standards promulgated by Regulatory Bodies has become a crucial issue of the day after a series of corporate debacles over a few years. Regulators, professional bodies and researchers throughout the world have expressed their concern about the need for improved accounting pronouncements and compliance for providing better information than previously required for the preparation and presentation of corporate financial reporting. The present study primarily focuses on the reporting disclosure levels and compliance with Bangladesh Bank (BB) Guidelines, Accounting and Auditing Organization for Islamic Financial Institutions (AAOIFI) Accounting Standard, Bangladesh/International Financial Reporting Standard (B/IFRS) and Securities and Exchange Commission (SEC) Rules of Islamic Financial Institutions in Bangladesh. Annual reports of (08) eight Islamic banks in Bangladesh have been examined for the year ending 2015. The results showed that the Islamic banks significantly followed the selected accounting guidelines/standards under review and did bring remarkable changes in the financial reporting practices made by the Islamic banks in Bangladesh. The study attempted to examine empirically the levels of disclosure in corporate annual reports of Islamic banks in Bangladesh. The study recommended increasing the level of compliance to make their financial reports more informative. The study also tries to ascertain the regulatory necessary requirements in preparing the financial statements of banks under Islamic shariah and tries to display the compliance status of these banks with legislations. The average compliance rate is 93.28% for BB guidelines, 46.54% for AAOIFI Accounting Standard, 48.50% for B/IFRS and 51.99% for SEC rules considering all required aspects of financial reports. Compiling all of the requirements regarding financial reports of regulatory bodies will be helpful for banks to make financial reports convenient

A Precise Evolutionary Approach to Solve Multivariable Functional Optimization

Genetic Algorithm (GA) is a stochastic search andoptimization method imitating the metaphor of naturalbiological evolution. GA manages population of solutionsinstead of a single solution to find an optimal solution to agiven problem. Although GA draws attention for functionaloptimization, it may search same point again due to itsprobabilistic operations that hinder its performance. In thisstudy, we make a novel approach of standard GeneticAlgorithm (sGA) to achieve better performance. Themodification of sGA is investigated in selection andrecombination stages and proposed Precise Genetic Algorithm(PGA). PGA searches the target space efficiently and it showsseveral potential advantages over the conventional GA whentested for solving functions having multiple independentvariables

Hybrid CNN-SVD Based Prominent Feature Extraction and Selection for Grading Diabetic Retinopathy Using Extreme Learning Machine Algorithm

Funding Information: This work was supported in part by the National Research Foundation of Korea-Grant funded by the Government of Korea (Ministry of Science and ICT) under Grant NRF-2020R1A2B5B02002478, and in part by Sejong University through the Faculty Research Program under Grant 20212023Peer reviewedPublisher PD

Effectiveness of different elicitors in inducing resistance in chilli (Capsicum annuum L.) against pathogen infection.

The excessive application of pesticides for agricultural production in Malaysia has raised quite some concern about environmental safety and sustainability. To reduce environmental impact of pesticide overuse, there is an increasing interest in using different elicitors to induce disease resistance in crop. Chilli (Capsicum annuum L.), which is an important vegetable cum spice crop in Malaysia, was used to compare the effectiveness of two natural elicitors (jasmonic acid and salicylic acid) with conventional pesticide application as control. The experimental results indicated that pesticide-treated plants showed rapid reduction in disease severity after application while elicitors perform slowly and its effectiveness increase gradually over time. Among the tested elicitors, jasmonic acid was found most effective regarding disease severity and yield of chilli compared with salicylic acid. Although used elicitors was not best performing treatment compared with conventional pesticide, some physiological parameters (relative chlorophyll content, chlorophyll fluorescence and photosynthesis rate) and disease severity in chilli plants treated with jasmonic acid was very close to conventional pesticide. Therefore, jasmonic acid could be a potential elicitor for inducing disease resistance in chilli and salicylic acid may not an appropriate elicitor for chilli

A Review on Brain Tumor Segmentation Based on Deep Learning Methods with Federated Learning Techniques

Brain tumors have become a severe medical complication in recent years due to their high fatality rate. Radiologists segment the tumor manually, which is time-consuming, error-prone, and expensive. In recent years, automated segmentation based on deep learning has demonstrated promising results in solving computer vision problems such as image classification and segmentation. Brain tumor segmentation has recently become a prevalent task in medical imaging to determine the tumor location, size, and shape using automated methods. Many researchers have worked on various machine and deep learning approaches to determine the most optimal solution using the convolutional methodology. In this review paper, we discuss the most effective segmentation techniques based on the datasets that are widely used and publicly available. We also proposed a survey of federated learning methodologies to enhance global segmentation performance and ensure privacy. A comprehensive literature review is suggested after studying more than 100 papers to generalize the most recent techniques in segmentation and multi-modality information. Finally, we concentrated on unsolved problems in brain tumor segmentation and a client-based federated model training strategy. Based on this review, future researchers will understand the optimal solution path to solve these issues

Multi-Scale CNN: An Explainable AI-Integrated Unique Deep Learning Framework for Lung-Affected Disease Classification

Lung-related diseases continue to be a leading cause of global mortality. Timely and precise diagnosis is crucial to save lives, but the availability of testing equipment remains a challenge, often coupled with issues of reliability. Recent research has highlighted the potential of Chest X-Ray (CXR) images in identifying various lung diseases, including COVID-19, fibrosis, pneumonia, and more. In this comprehensive study, four publicly accessible datasets have been combined to create a robust dataset comprising 6650 CXR images, categorized into seven distinct disease groups. To effectively distinguish between normal and six different lung-related diseases (namely, bacterial pneumonia, COVID-19, fibrosis, lung opacity, tuberculosis, and viral pneumonia), a Deep Learning (DL) architecture called a Multi-Scale Convolutional Neural Network (MS-CNN) is introduced. The model is adapted to classify multiple numbers of lung disease classes, which is considered to be a persistent challenge in the field. While prior studies have demonstrated high accuracy in binary and limited-class scenarios, the proposed framework maintains this accuracy across a diverse range of lung conditions. The innovative model harnesses the power of combining predictions from multiple feature maps at different resolution scales, significantly enhancing disease classification accuracy. The approach aims to shorten testing duration compared to the state-of-the-art models, offering a potential solution toward expediting medical interventions for patients with lung-related diseases and integrating explainable AI (XAI) for enhancing prediction capability. The results demonstrated an impressive accuracy of 96.05%, with average values for precision, recall, F1-score, and AUC at 0.97, 0.95, 0.95, and 0.94, respectively, for the seven-class classification. The model exhibited exceptional performance across multi-class classifications, achieving accuracy rates of 100%, 99.65%, 99.21%, 98.67%, and 97.47% for two, three, four, five, and six-class scenarios, respectively. The novel approach not only surpasses many pre-existing state-of-the-art (SOTA) methodologies but also sets a new standard for the diagnosis of lung-affected diseases using multi-class CXR data. Furthermore, the integration of XAI techniques such as SHAP and Grad-CAM enhanced the transparency and interpretability of the model’s predictions. The findings hold immense promise for accelerating and improving the accuracy and confidence of diagnostic decisions in the field of lung disease identification