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Fostering Inclusive Education in Asia: Insights from a Systematic Literature Review
No full textThe strong communication strategies for inclusive education policies are intended to offer equality
across the board for access to education for all students, including those with special needs. This
systematic review used ROSES (Reporting Standards for Systematic Evidence Syntheses)
methodology to investigate the use of communication strategies for inclusive education by
countries in Asia. The articles were based exclusively on these three databases in conjunction with
Google Scholar: Scopus, ProQuest, and ERIC, subjected to a series of screening, data collection,
and evaluation of quality processes as guided by ROSES protocols. Criteria for inclusion included
research on events on communication strategies regarding policy concerning inclusive education
in Asia. Themes that emerged included the negotiation of partnerships, channels for
communication, and cultural sensitivity. The paper identifies communication strategies that are
culturally and contextually relevant towards the successful implementation of inclusive education
policies. This will add to the body of knowledge in practice and provide directions for future
research, policy development, and practical implementation
Impact of Macroeconomic Factors towards Price Index Performance in New York Stock Exchange Market
No full textThe NYSE is the world's biggest exchange market. Performance of the stock market has a big
impact on how wealthy the economy is and how people live their daily lives. The stock price index,
which serves as a benchmark for stock market performance, is a crucial tool for assessing each
nation's economic situation. As a result, a significant amount of study has been done to determine
how macroeconomic factors affect the performance of stock price indices. However, the literature
does not support a consistent conclusion, but rather offers contradictory results. This study's
objective is to dispel uncertainty in the literature by presenting fresh ideas that can improve
descriptions of and understandings of stock price index performance. The aim of this research is
to examine the New York Stock Exchange index for a span of four years that is between 2017 and
2021. Also, to examine stock price index performance and macroeconomics indicators. The NYSE
Composite Index (NYA), which measures stock price index performance over the Nyse period
2017 to 2021, serves as the study's dependent variable (DV), and the GDP, rate of interest, inflation
rate, and exchange rate serve as its independent variables (IVs). All of the companies listed and
operating on the New York Stock Exchange (NYSE), as represented by NYA, are the study's target
population. Secondary data were employed in this investigation. The DV data was acquired from
NYSE websites, whilst the IVs were taken from reliable sources like the World Bank and
International Money Fund. Regression analysis, descriptive statistics, and the reliability test were
the only data analysis techniques used in this study
Enhanced Semantic Image Segmentation Through Convolutional and Atrous Convolution Techniques
No full textEnhanced Image content classification has improved dramatically with the advent of CNNs.
This paper presents an enhanced method for semantic partitioning through merging traditional
convolutional level and atrous (extended) convolution techniques. Our approach takes
advantage of the hierarchical feature extraction capabilities of CNNs, while incorporating atrous
convolutions to capture multi-scale contextual information without increasing the
computational load. The proposed feature combines standard diffraction layers for detailed
feature extraction that broadens the perceptive field, thus improving segmentation accuracy,
especially on multiscale features Extensive testing on the datasets including PASCAL VOC
2012 and Cityscapes
Data-Driven Expert System for Tuberculosis (TB) Diagnosis Using the Forward Chaining Method
No full textTuberculosis (TBC) is a disease caused by Mycobacterium tuberculosis, one of the oldest
known diseases affecting humans. While it primarily affects the lungs, about one-third of cases
involve other organs, underscoring the importance of early detection and accurate diagnosis. To
address this, a data-driven expert system has been developed to assist in diagnosing tuberculosis
and providing relevant information to users. An expert system is a form of intelligent software
that leverages data and expert knowledge to solve complex problems. In this study, the Forward
Chaining method is applied, utilizing a rule-based approach to process data and conclusions
from known facts. This method iteratively matches facts to rules, deriving new insights until a
conclusion is reached or no further matches are found. If the premise satisfies the conditions
(evaluated as TRUE), the system generates a decision. The system is designed to simplify the
recognition of tuberculosis symptoms by analyzing user-provided data to produce accurate
diagnostic results and actionable solutions. Findings indicate that the data-driven approach
enhances the system's ability to provide precise diagnoses and recommendations, ensuring
reliability and effectiveness. This work demonstrates the value of integrating data-driven
methodologies in expert systems to improve healthcare delivery, particularly in the early
detection and management of tuberculosis
Automatic Textile Stain Detection Using Yolo Algorithm
No full textAutomatic textile stain detection is essential for optimizing the quality control process within the
textile industry. Traditional hands-on inspection methods are time-consuming, not immune to
errors, and expensive. This research paper proposes a novel approach for automatic textile stain
detection using the YOLO (You Only Look Once) algorithm, a state-of-the-art object detection
model. The proposed system utilizes a YOLOv5 model trained on a diverse dataset of stained
textile images to accurately identify and localize stains in real-time. The model's performance is
evaluated based on standard metrics such as precision, recall, and mean average precision (mAP).
Experimental results Showcase the impact of the YOLO-based approach in achieving high
accuracy and efficiency in stain detection, significantly outperforming traditional methods. This
research contributes to the advancement of automation in the textile industry, ultimately leading
to improved quality control, reduced costs, and enhanced productivity
Qualitative Review on the Negative Effects of Facebook Towards Mental Health
No full textThis study investigates the negative impacts of Facebook use on mental health, with a focus on its
implications for achieving Sustainable Development Goal 3 (SDG 3): ensuring good health and
well-being for all. As a dominant force in social media, Facebook has profoundly influenced
societal behaviors and individual lifestyles since its inception. However, its pervasive use has been
linked to mental health outcomes, including increased anxiety, depression, and feelings of social
isolation. Through an exploration of behavioral patterns and psychological responses associated
with excessive Facebook use, this study aims to uncover the subtle yet significant ways in which
the platform may undermine mental well-being. The findings are expected to offer actionable
insights into how prolonged social media engagement contributes to deteriorating mental health,
particularly among vulnerable populations. Additionally, this study examines how demographic
factors, such as age, mediating the relationship between Facebook use and mental health,
highlighting disparities that challenge the universality of SDG 3. By raising awareness of the
potential mental health risks posed by social media, this research underscores the urgency of
fostering digital literacy and promoting healthier online habits as part of a broader effort to achieve
mental well-being globally. Ultimately, the study advocates for a more balanced integration of
social media into daily life to support the overarching goals of SDG 3
Proceedings of the 2nd International Conference on Green Sustainable Technology and Management 2023
No full textComparison of Recursive Feature Elimination and Boruta as Feature Selection in Greenhouse Gas Emission Data Classification
No full textClassification analysis is a supervised learning method that can be utilized to categorize levels of greenhouse gas emissions. Regular monitoring of greenhouse gas emissions is essential for relevant agencies to devise prevention and mitigation programs that address climate change. In classification analysis, enhancing model performance is correlated with the number of features or variables utilized, thus necessitating feature selection in its application. This study compares feature selection methods for classifying greenhouse gas emission levels, specifically wrapper feature selection, recursive feature elimination, and boruta. The Support Vector Machine (SVM) algorithm is employed to evaluate classification performance, focusing on binary classification into "high" and "low" categories in this study. The results indicate that classification performance improves with feature selection and recursive feature elimination compared to scenarios without feature selection or with Boruta feature selection. By employing three out of the thirty-nine features, accuracy, sensitivity, and specificity of 98.95%, 99%, and 97% were achieved, respectively
Blockchain-based Management for Organ Donation and Transplantation
No full textOrgan donation and transplantation systems now face a variety of requirements and obstacles in terms of registration, donor-recipient matching, organ removal, organ delivery, and transplantation, all of which are hampered by legal, clinical, ethical, and technical restrictions. As a result, a comprehensive organ donation and transplantation system is essential to provide a fair and efficient procedure that improves patient experience and confidence. In this work, we present a private Ethereum blockchain-based system for managing organ donation and transplantation in a completely decentralised, secure, traceable, auditable, private, and trustworthy manner. We create smart contracts and offer six algorithms, along with information on their implementation, testing, and validation. We assess the performance of the suggested solution by conducting privacy, security, and confidentiality assessments and comparing it to current solutions
EasyLearnify - A Student Study Portal
No full textEasyLearnify is web-based online research developed using Django framework of Python. Django is a high-level Python web framework that encourages rapid development and clean, pragmatic design. The research is designed to facilitate and enhance student's learning experience. The research mainly provides 8 different features for the students. It consists of Notes, Homework, YouTube, To Do List, Books, Dictionary, Wikipedia and Conversions. The main objective of EasyLearnify is to provide all in one study portal where students can manage all their study related works. The admin can also add and assign task to the students. Admin can upload notes which the students can download and assign homework for the students. Basically, it is a centralized hub for students to access various educational resources