INTI Institutional Repository
Not a member yet
2178 research outputs found
Sort by
AI as a Learning Partner: Exploring Ethical Awareness of Engineering Students
No full textIn recent years, Artificial Intelligence, particularly generative AI tools, has become increasingly integrated into the daily learning experience of engineering students. These tools provide excellent support for coding, designing, and problem-solving activities, like learning facilitators. However, this tendency of using AI raises important ethical questions: Are students fully aware of when and how the use of AI may cross ethical boundaries, such as plagiarism or over-reliance on technology? This study investigates the ethical awareness of undergraduate engineering students regarding the use of AI tools and examines the challenges they encounter in balancing AI assistance with academic integrity. Using a mixed-methods approach, data were collected from 103 undergraduate engineering students and 10 faculty members through surveys and interviews. Quantitative data were analyzed using descriptive statistics and inferential tests, including the Mann-Whitney U test, to examine differences in students’ verification behaviors based on their awareness levels, while qualitative data were analyzed thematically. The findings showed that while students find AI helpful, many lack understanding of its ethical boundaries. Teachers emphasize the importance of incorporating ethical awareness-related training and establishing clear guidelines within the curriculum to address existing gaps. This study suggests that with appropriate policies and quality education, AI can be used ethically to enhance learning without compromising academic integrity
Small Brands, Big Impact": Harnessing the Long Tail for Market Success
No full textThis book provides an overview of the significant changes in the marketing landscape driven by the advancement of digital technology and shifts in consumer behavior, particularly with the emergence of Generation Z, who prefer unique and personalized products. It also explores how marketing strategies have shifted towards targeting highly specific market segments (niches) that can leverage digital platforms like e-commerce and social media. The goal of this book is to highlight how small businesses and MSMEs, previously constrained by traditional, costly marketing methods and
physical distribution challenges, can now compete effectively in a global market using digital tools. These tools enable affordable content marketing, distribution via marketplaces, and the use of algorithms that help discover niche products.
Ultimately, this book contends that this change presents significant new opportunities for smaller brands to not only survive but to thrive in an increasingly fragmented market. By focusing on changes in the marketing mix—product, price, distribution, and promotion—it offers valuable insights into how marketing has evolved and what MSMEs must do to succeed in this new era
Geospatial Assessment of Road Pavement Distress at Jahangirnagar University Campus
No full textThis study assesses the types, causes, and spatial distribution of pavement distress across six key road segments of the JU campus, aligning with the objectives of SDG 9 (Industry, Innovation, and Infrastructure) and SDG 11 (Sustainable Cities and Communities) to promote resilient and sustainable infrastructure systems. A mixed-method approach was employed, integrating direct field observation, photographic documentation, literature review, and geospatial mapping. Pavement distresses were classified into eight major categories: thermal cracking, warping cracking, longitudinal cracking, bleeding, stripping, raveling, pumping, and blistering. GPS coordinates were recorded using handheld devices to create georeferenced maps illustrating the spatial distribution of distress points. Data from six road sections—covering a total of 987.3 square meters—revealed 206 distress cases, with stripping (47), thermal cracking (39), and raveling (37) as the most prevalent types. These conditions were primarily influenced by temperature fluctuations, drainage inefficiencies, and material aging. Findings emphasize the necessity of regular pavement monitoring, improved drainage design, and the use of durable construction materials to enhance the resilience and longevity of campus road infrastructure. The study contributes to the national and institutional pursuit of sustainable transport and infrastructure management in line with SDG 9 and SDG 11, supporting safer, more sustainable, and inclusive mobility within educational environment
Design of an IoT-based Smart Bio-Toilet Scheme with Hygiene-Preserving
No full textThis research report focuses on designing and implementing an IoT-based bio-toilet system that prioritizes health, hygiene, and eco-friendliness. This work encompasses deep knowledge of IoT, microcontrollers, bio-toilet systems, engineering design issues, and health and hygiene issues. This is an interconnected sub-system or component having a wide range of applications, including a PV system, sensor systems, microcontroller, structural system, etc. In this smart toilet, a proper hygiene maintenance system has been incorporated. Focus was given to reducing the water consumption in this bio-toilet system. Besides, the method of power generation from biogas and human waste in the toilet system was exploited. Also, an effort was made to produce biofertilizers from human waste. In this work, a method of purifying water by absorbing water from waste was used. In this bio-toilet, extra electricity was provided from solar energy, thus preventing wastage of electricity. The Internet of Things (IoT)-based smart bio-toilet with a hygiene-preserving system uses IoT cloud monitoring for centralized and remote monitoring, control, and analysis of several bio-toilets together. The simulation was done with the Proteus software, and hardware implementation was done using an Arduino microcontroller. Both simulation results and experimental results are obtaine
Semi-development and Performance Evaluation of a Pronunciation Judgment System Using Free Machine Learning Services
No full textWith the recent development of the global community, communication skills in languages other than one's native tongue have become essential. While attending school is an effective method of language learning, it is often difficult due to time and financial constraints. If it becomes possible to acquire language pronunciation through self-study, it is expected that the learning process can be significantly shortened. Furthermore, building such a practice system without specialized IT knowledge, such as programming, would be a great benefit to educational settings. This paper describes a pronunciation assessment system for language learning that utilizes a free machine learning service. The target language is Japanese. By providing machine learning with speech data of homonyms that are difficult for non-native speakers to distinguish, we build a system that can assess the accuracy of pronounced words. For machine learning, we use Google Teachable Machine, a free service that allows system building without specialized IT knowledge. Experiments using this method demonstrate that we have constructed a system that can assess the accuracy of native speaker pronunciation with a very high probability
Analyzing the Efficacy of Anti-Gravity Treadmill in Enhancing Gait and Balance among Patients with Spastic Cerebral Palsy - A Critical Review
No full textBackground: In the pediatric population, cerebral palsy is the most prevalent neurological
condition. As the result of prenatal or perinatal events, the cause of this complex physical disability
is cast over the congenital fragility. There is often a challenge in gait and balance for individuals
with spastic cerebral palsy. Innovative interventions for spastic cerebral palsy such as the anti-gravity treadmill which simulates partial weight-bearing have been sparked in innovative
rehabilitation technologies. This study aims to assess published studies on anti-gravity treadmill
training, evaluating their strengths and weaknesses, considering study design, sample size,
outcomes, and limitations. This critical review will make recommendations for future research and
address the limitations identified in the current body of literature.
Methods: A quantitative research critical review form is used to analyze the eleven studies with
different study designs, including 6 Randomized Control Trials, 2 Experimental Study, 1
Prospective Study, 1 Systematic review and meta-analysis and 1 study where not clearly
mentioned about their study design. This review includes studies obtained from Google Scholar,
PubMed, Cochrane, and ResearchGate databases. Our primary focus revolves around the studies
with populations of spastic cerebral palsy and outcomes associated with balance and gait.
Conclusion: This critical review concludes that the anti-gravity treadmill enhances gait and
balance for children with spastic cerebral palsy. There were positive outcomes despite variations
in methodologies and limited studies, including improved gait patterns and enhanced balance. In
spite of the necessity for standardized research, anti-gravity treadmills may be an effective way to
address the motor challenges of those with spastic cerebral palsy. The long-term efficacy of this
intervention needs to be confirmed and compared with alternatives through further robust studies
Fintech Adoption: A Study of Users in the Guangdong-Macao In-Depth Cooperation Zone in Hengqin (China)
No full textIntended as an economic and development hub, the Hengqin Cooperation Zone aims to foster
collaboration and integration between mainland China, Hong Kong, and Macao, serving as a
platform for economic development and innovation among the three regions. The zone's
development has increased demand for financial services, often offered through fintech. There is,
however, a lack of interoperability between the fintech services currently used in Macao and
Hengqin. This may hinder Macao users' adoption of the technology. Thus, our research objective
is to identify the factors determining Macao residents' adoption of fintech services in the area and
provide insights for service providers, developers, and policymakers. A framework based on the
Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB) was used for this
purpose. The responses of 103 Macao residents provided evidence that ease of use significantly
and positively impacts the usefulness of the technology. This in turn influences attitudes towards
fintech usage. Subjective norms and perceived behavioral control positively impact fintech
adoption intentions. The fintech industry and the governments of Macao and Hengqin can work
on improving technology's ease of use and usefulness. They can also promote them to Macao users,
and provide the resources required for better access to fintech in the zon
Assessing the Efficacy of Intermittent Intensive Physical Therapy in Pediatric Cerebral Palsy
No full textBackground: Cerebral palsy (CP) is a prevalent neurological disorder affecting children
worldwide, particularly boys, with spastic CP being the most common form. Despite its
prevalence, effective rehabilitation strategies for severely disabled children with CP remain
limited.
Objective: This study aimed to evaluate the feasibility and effectiveness of a 6-month
rehabilitation program for severely disabled children with CP, utilizing alternating intense therapy
sessions and rest periods, to enhance gross motor function.
Methods: Employing a multiple-baseline design, the study assessed changes in motor performance
using the Gross Motor Function Measure (GMFM). Visual and statistical analyses, including
descriptive statistics and paired t-tests, were conducted to evaluate outcomes. Results: Participants
received an average of 60 treatments over the 20-week trial, exceeding expectations. Seven out of
ten children exhibited significant increases in GMFM scores, with a mean improvement of 9.2%
(range 3 to 15%; p < 0.05). Importantly, all participants maintained motor function during rest
periods, with a high compliance rate of 93.1% during intense therapy.
Conclusion: The study highlights the potential of the proposed rehabilitation program to improve
gross motor function in severely disabled children with CP. These findings provide valuable
insights for developing more effective and sustainable rehabilitation strategies for this population
Traffic Sign Board Recognition and Voice Alert System using CNN
No full textStreet to guarantee a secure and efficient flow of traffic. Street accidents sometimes occur on account of carelessness in reading traffic signs incorrectly. The suggested framework aids in recognizing the stop sign and giving a voice warning to the motorist for the speaker to make their point, and crucial decisions. The proposed framework is prepared Using a Convolutional Neural Network (CNN), which aids with the recognition and arranging of rush hour congestion sign pictures. To increase precision, a number are of classes generated and characterized on a particular dataset. Utilized was the German Traffic Sign Benchmarks Dataset, which includes 51,900 pictures of road signage in 43 classifications. Around 98.52 percent during execution was precise. After the framework recognizes the sign, the driver is informed through a voice alarm issued through the speaker. The suggested framework also includes a section where drivers are warned about nearby traffic signs so they can keep track of which laws to follow while on a highway. The system’s goal is to protect the driver, passengers, and pedestrians from harm
Prediction of Fetal Health Status Using Machine Learning
No full textThe goal of this promising area of study is to enhance prenatal care and lower fetal morbidity and mortality by utilizing machine learning to anticipate fetal disease. In this study, we present a machine learning-based strategy for predicting fetal diseases from clinical data. First, we gathered a sizable collection of clinical information from expectant mothers with various fetal disorders. Using clinical guidelines, we pre-processed the data and retrieved pertinent features. We integrated a range of machine learning algorithms, including logistic regression, support vector machines, decision trees, and random forests, to train and test our model. We evaluated the performance of our model using several factors, such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC). The results of this study demonstrate how machine learning algorithms can accurately forecast fetal health status. The developed models achieve good accuracy and AUC-ROC ratings to distinguish between healthy and at-risk fetuses. The interpretability study identifies key clinical characteristics that have a significant impact on the prediction, providing medical practitioners with useful information when making decisions about prenatal care. Through the provision of more unbiased and precise assessments of fetal health status, machine learning techniques incorporated into prenatal care have the potential to transform the industry. By providing accurate and early projections, this technology can assist healthcare professionals in identifying high-risk pregnancies and carrying out the necessary procedures, improving mother and fetal outcomes. Future research should concentrate on verifying and improving predictive models on larger and more varied datasets to ensure real-world applicability and reliabilit