NPS Australia Submission System
A Conceptual Framework for the Buying Intention of Energy Efficient LED Bulbs: Evidence from A Developing Country

Environmental challenges and their risks to human health have drawn the attention of academics, policymakers, and industry leaders. This study examines the factors influencing purchase intentions for energy-efficient LED bulbs in the context of sustainable development, which are relevant from business, marketing, and economic perspectives. Moreover, energy-efficient LED bulbs contribute to the attainment of the Sustainable Development Goals (SDGs) through their direct and indirect impacts. By promoting clean energy and sustainable urban growth, energy-efficient LED bulbs are consistent with SDGs 7, 11, and 13.
Theoretically, the research extends UTAUT2 to explain consumer behavior in the context of green technology adoption. Practically, it provides insights for segmentation, targeting, and brand positioning in emerging markets. Managers and stakeholders are encouraged to focus on product design, packaging, quality, energy efficiency, and cost to enhance adoption. Social factors, including family and peer influence, play a key role, suggesting strategies such as social proof, influencer marketing, and media campaigns to boost engagement and loyalty.

From a societal perspective, promoting energy-efficient LED bulbs supports sustainable consumption, energy savings, and community well-being. Economically, the findings guide producers and marketers in building brand value, competitive pricing, and using digital platforms to educate consumers. At the national level, the study emphasizes government support in regulating fair pricing, ensuring sustainable production, and offering financial incentives such as subsidies, tax breaks, and loans. Policymakers can align environmental objectives with national strategies, foster global collaborations, and promote adoption to reduce carbon emissions.Overall, the study contributes theoretically by enhancing UTAUT2, offers actionable insights for business and policy, and supports Bangladesh’s progress toward sustainable development through widespread adoption of energy-efficient technologies.

Application of Artificial intelligence for prediction of Brucellosis in Bangladesh

This research pioneers the application of artificial intelligence for predicting brucellosis in dairy cattle in Bangladesh by developing a highly accurate deep learning model (up to 93.94%). A significant contribution is the use of the SMOTE technique to effectively manage imbalanced veterinary data, a common challenge in disease diagnostics. Furthermore, the study identifies and ranks critical clinical risk factors, establishing that a retained placenta is the most significant predictor. By creating association rules to clarify the interplay between these factors, this work provides veterinarians and farmers with a powerful and practical tool for early diagnosis, paving the way to mitigate substantial economic losses in the dairy industry.

Design and Modelling of a Small-Scale Automated Biogas Digester with Monitoring and Control Capabilities

This research presents the integration of IoT and Mechatronic Systems in a compact, automated biogas digester designed for Small-Scale household use. The system enhances biogas yield efficiency through control of key parameters and real-time monitoring. The system offers a scalable solution for decentralized renewable energy and sustainable waste management in Small Island Developing States.

Smart Biophilic system with an AI based plant monitoring system

This research introduces a modular hydroponic system with robotic integration for automated indoor plant care, supported by a CNN-based disease detection model. The system also monitors indoor air quality and controls ventilation, providing both health benefits and energy efficiency. It offers a sustainable alternative to traditional air purifiers, aligning with biophilic design to enhance wellbeing.

Eco Pad: From Roots to Relief

Menstrual hygiene is really a concerning issue and using commercial sanitary napkins during this period not only effects on maintaining hygiene but also effects our environment. We tried to innovate one more available waste in pads to make it biodegradable and also cost effective so that the feminine can learn about menstrual hygiene and use this pad which is affordable for them and the feminine of Bangladesh and India who thinks its a matter of shy as the pad will not be biodegradable(commercially sells);this biodegradable pads will remove their concern as the aerial root and bamboo is available in this country.

Transforming Oil Palm Empty Fruit Bunches (OPEFBs) into Sustainable Ceramic Membranes for Microbial Fuel Cells

integration of OPEFBs for ceramic membrane in MFC to produce electricity

Development of an IoT-Enabled Biogas Digester for Optimizing Anaerobic Digestion and Methane Production

Integration of IoT to anaerobic digestion to measure operational performance

Innovative Biofilter Design with OPEFB-Activated Carbon for Sustainable Tofu Wastewater Treatment

This research provides significant contribution on valorizing waste for energy production

The Impact of Greenwashing on Job Applicant’s Choice of Company

The purpose of this study is to present a theoretical framework for identifying the impact of greenwashing on job applicants’ choice of company and to test its usefulness in a laboratory experiment using eye tracking. The objective of this paper is to elucidate the influence of greenwashing on decision-making processes by delineating the cognitive mechanisms underlying the evaluation of corporate information by job applicants when selecting a prospective employer. The findings of this study indicate that the presentation of environmentally-oriented information can influence the selection of prospective employers by job applicants.

Privacy preserving medical image classification and steganography using deep learning architecture: A pipeline

This study addresses significant data privacy issues in telemedicine by proposing a secure method for transmitting sensitive medical information over unsecured networks. The novel pipeline combines state-of-the-art image classification with image steganography to integrate patient diagnosis and personal information into medical images, ensuring data privacy and protection from unauthorized access. The approach uses fine-tuned convolutional models for classification and an encoder-decoder architecture for steganography, safeguarding the transmission of medical data while maintaining image quality.