Peoples’ Perception towards Telemedicine: A Case Study on Rural Area of Bangladesh

Telemedicine is a two-way, real time interactive session between the patient and the physician or practitioner at the remote location for the purpose of providing consultation. Telemedicine has enormous prospect to reduce health care service gap between rural area and urban area of Bangladesh. The aim of this paper is to demonstrate the peoples’ perception towards telemedicine and the factors influencing peoples’ perception in telemedicine. The chi-square analysis and descriptive statistics have been used as statistical tools to test the hypotheses. A self-administered questionnaire has been developed and snowball sampling method is used to collect data from the study area. In addition to the traditional health care system, urban specialist doctors provide medical consultation in rural area by telemedicine. The findings of this study have revealed that age, gender, educational qualification, trust, privacy and confidentiality, awareness, service quality, existing equipment status, proper coordination, treatment cost and IT infrastructure have significant influence on the perception of telemedicine. The findings and recommendations may be taken by policymakers to make effective decisions regarding telemedicine services. Keywords: Telemedicine, Urban area, Peoples’ perception, Bangladesh

The Impact of Sound Industrial Relations on Job Satisfaction: A Case of Different Manufacturing Industries in Bangladesh

Comparing to the enormous changes in the infrastructure and communication with the dramatic diversity at the workplace the norms, values and personal thinking of the workers have been changing drastically and the effect of those mental revolution influences at the workplace. As Bangladesh is a flourishing economy, the industrial sector faces different challenges at workplace. The focus of this study is to find out the current industrial relations practices and the relative job satisfaction at different manufacturing industrial sector in Bangladesh. The study is based on field survey where randomly selected twelve manufacturing industries in Barisal division through observing and talking with workers and managers information are collected through ninety seven respondents. It is found that for maintaining harmonious relationship at industrial premises twelve factors are much  influential such as Job security, work environment, healthy and safely measures, Fair practices of Labor court, Wages & monetary benefit, Grievance handling procedures, Industrial democracy, Participation in Decision making, training and development, structured conflict resolution procedures, strong collective bargaining agent, Existence of Trade union and have positive influences with job satisfaction. Through this study it is found that there is a positive relationship exists between sound industrial relations and job satisfaction and the study developed by using hypothesis with multiple regression models. Finally the research finished on the conclusion that workers job satisfaction is the result of sound industrial relations. Keywords: Industrial Relations, job Satisfaction, Manufacturing industrial sector, multiple regression mode

Guiding Software Developers using Automated Adaptation of Object Ensembles Plug-in

Software developing process has been improving day by day The development process can be affected through different ways like changing the development environment strategies and upcoming technologies In order to save valuable times and to speed up the process we can guide programmer during the development time through providing relevant recommendations There are some strategies that suggest related code snippets and API-items to the software programmers There are some techniques that apply general code searching approaches and some techniques that employ online based repository mining process But it is kind of difficult tasks to guide programmers when they need specific type conversion like adapting existing interfaces from the previously used types as per their demands One of the familiar approaches to guide developers in such a situation is to adapt collections and arrays through automated adaptation of object ensembles But how does it help a novice developer in real time software development that is not explicitly exemplified In this paper we have tried to introduce a system that works as a plug-in tool incorporated with a data mining integrated environment to recommend the relevant interfaces while they look for a type conversion We have a mined repository of respective adapter classes and related APIs from where programmers search their query and get their result using the relevant transformer classes The system that recommends developers entitled automated objective ensembles AOE plug-in From the investigation that we have done we can see that our approach works much better than some of the existing approache

BiofilmScanner: A Computational Intelligence Approach to Obtain Bacterial Cell Morphological Attributes from Biofilm Image

Desulfovibrio alaskensis G20 (DA-G20) is utilized as a model for sulfate-reducing bacteria (SRB) that are associated with corrosion issues caused by microorganisms. SRB-based biofilms are thought to be responsible for the billion-dollar-per-year bio-corrosion of metal infrastructure. Understanding the extraction of the bacterial cells' shape and size properties in the SRB-biofilm at different growth stages will assist with the design of anti-corrosion techniques. However, numerous issues affect current approaches, including time-consuming geometric property extraction, low efficiency, and high error rates. This paper proposes BiofilScanner, a Yolact-based deep learning method integrated with invariant moments to address these problems. Our approach efficiently detects and segments bacterial cells in an SRB image while simultaneously invariant moments measure the geometric characteristics of the segmented cells with low errors. The numerical experiments of the proposed method demonstrate that the BiofilmScanner is 2.1x and 6.8x faster than our earlier Mask-RCNN and DLv3+ methods for detecting, segmenting, and measuring the geometric properties of the cell. Furthermore, the BiofilmScanner achieved an F1-score of 85.28% while Mask-RCNN and DLv3+ obtained F1-scores of 77.67% and 75.18%, respectively.Comment: Submitted to Pattern Recognitio

Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer Learning Method

This research paper focuses on Acute Lymphoblastic Leukemia (ALL), a form of blood cancer prevalent in children and teenagers, characterized by the rapid proliferation of immature white blood cells (WBCs). These atypical cells can overwhelm healthy cells, leading to severe health consequences. Early and accurate detection of ALL is vital for effective treatment and improving survival rates. Traditional diagnostic methods are time-consuming, costly, and prone to errors. The paper proposes an automated detection approach using computer-aided diagnostic (CAD) models, leveraging deep learning techniques to enhance the accuracy and efficiency of leukemia diagnosis. The study utilizes various transfer learning models like ResNet101V2, VGG19, InceptionV3, and InceptionResNetV2 for classifying ALL. The methodology includes using the Local Interpretable Model-Agnostic Explanations (LIME) for ensuring the validity and reliability of the AI system's predictions. This approach is critical for overcoming the "black box" nature of AI, where decisions made by models are often opaque and unaccountable. The paper highlights that the proposed method using the InceptionV3 model achieved an impressive 98.38% accuracy, outperforming other tested models. The results, verified by the LIME algorithm, showcase the potential of this method in accurately identifying ALL, providing a valuable tool for medical practitioners. The research underscores the impact of explainable artificial intelligence (XAI) in medical diagnostics, paving the way for more transparent and trustworthy AI applications in healthcare

Design and Hardware Implementation Considerations of Modified Multilevel Cascaded H-Bridge Inverter for Photovoltaic System

Inverters are an essential part in many applications including photovoltaic generation. With the increasing penetration of renewable energy sources, the drive for efficient inverters is gaining more and more momentum. In this paper, output power quality, power loss, implementation complexity, cost, and relative advantages of the popular cascaded multilevel H-bridge inverter and a modified version of it are explored. An optimal number of levels and the optimal switching frequency for such inverters are investigated, and a five-level architecture is chosen considering the trade-offs. This inverter is driven by level shifted in-phase disposition pulse width modulation technique to reduce harmonics, which is chosen through deliberate testing of other advanced disposition pulse width modulation techniques. To reduce the harmonics further, the application of filters is investigated, and an LC filter is applied which provided appreciable results. This system is tested in MATLAB/Simulink and then implemented in hardware after design and testing in Proteus ISIS. The general cascaded multilevel H-bridge inverter design is also implemented in hardware to demonstrate a novel low-cost MOSFET driver build for this study. The hardware setups use MOSFETs as switching devices and low-cost ATmega microcontrollers for generating the switching pulses via level shifted in-phase disposition pulse width modulation. This implementation substantiated the effectiveness of the proposed design

Nanotechnology for high-performance textiles: A promising frontier for innovation

Nanotechnology embodies a groundbreaking innovation for the textile and apparel industry, facilitating enhancements to the functionality and performance of textiles, including durability, resistance to water, odor, flame, stain, UV-protection, and antimicrobial properties. Nanotechnology also enables biosensing, drug delivery, energy generation, and storage in textiles. Here, we present a comprehensive overview of the possibilities offered by nanotechnology in the context of high-performance textiles providing a roadmap for future research and development in this exciting field. We scrutinize the current research on nanotechnology in textiles, exploring various types of nanomaterials and their properties, the methods of incorporating nanomaterials into textiles, and the numerous applications of high-performance textiles across critical industries such as healthcare, military, sports, fashion, and wearable electronics. We conclude the review with an analysis of the potential health and environmental concerns arising from the use of nanotechnology in textiles, emphasizing the importance of further research in these areas

Performance Analysis of YOLO-based Architectures for Vehicle Detection from Traffic Images in Bangladesh

The task of locating and classifying different types of vehicles has become a vital element in numerous applications of automation and intelligent systems ranging from traffic surveillance to vehicle identification and many more. In recent times, Deep Learning models have been dominating the field of vehicle detection. Yet, Bangladeshi vehicle detection has remained a relatively unexplored area. One of the main goals of vehicle detection is its real-time application, where `You Only Look Once' (YOLO) models have proven to be the most effective architecture. In this work, intending to find the best-suited YOLO architecture for fast and accurate vehicle detection from traffic images in Bangladesh, we have conducted a performance analysis of different variants of the YOLO-based architectures such as YOLOV3, YOLOV5s, and YOLOV5x. The models were trained on a dataset containing 7390 images belonging to 21 types of vehicles comprising samples from the DhakaAI dataset, the Poribohon-BD dataset, and our self-collected images. After thorough quantitative and qualitative analysis, we found the YOLOV5x variant to be the best-suited model, performing better than YOLOv3 and YOLOv5s models respectively by 7 & 4 percent in mAP, and 12 & 8.5 percent in terms of Accuracy.Comment: Accepted in 25th ICCIT (6 pages, 5 figures, 1 table