Agentic AI: A Comprehensive Survey of Technologies, Applications, and Societal Implications
gentic AI represents an advanced stage of artificial intelligence capable of goal-driven behavior, adaptation, and self-improvement. It reviews what makes agentic AI different from traditional AI and explains how abilities like autonomy, memory, and reasoning support more general capabilities. Instead of proposing a new system, the paper provides a conceptual view of how current frameworks are evolving toward agentic AI.
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.
Automated Leak Detection in Drip Irrigation Systems using RGB and Thermal Sensor Fusion
Water leaks are a common issue in surface drip irrigation systems. Visual inspection of irrigation pipelines by humans is the most prevalent method for leak detection. However, this approach is costly and labour-intensive due to the need for frequent on-site visits. This paper describes an AI based sensor fusion algorithm to automatically detect leaks along the drip lines using RGB and thermal images collected from a low-cost ground vision system. The proposed algorithm was tested using images collected from vineyard under various light conditions. Results indicated that proposed sensor fusion detection algorithm is accurate and efficient.
Fly Ash as Sustainable Modifier, Using RSM as Modelling and Optimization Tool
This study uses Response Surface Methodology (RSM) to investigate the effect of using fly Ash type F as partial cement replacement on the compressive strength in concrete pavement. The response surface Methodology (RSM) is increasingly utilized in concrete mix design, as it offers a more effective approach to analyzing and optimizing experimental responses. RSM outperforms traditional experimental design methods in various ways, such as reducing the number of required tests, thus lowering test costs, and identifying optimal input variables based on test results. It can construct a scientific mathematical model and offer insights into the impact of individual factors and factor interactions on test results within the specified numerical boundary.
Additionally, a three-dimensional response surface is created to illustrate the connection between preparation parameters and the response index, allowing for a clearer understanding of the relationship between each factor and the response value. Accordingly, The researcher has utilized the RSM method to assess the influence of various factors on concrete performance
The study also aims to optimize the fly ash percentage in the concrete mix design. To evaluate the impact, the research methodology utilized mathematical modeling and methodical experimentation. The experiment considered various variables, including the amount of fly ash and the length of the curing process. The experiment concluded that the ideal fly ash concrete included a fly ash concentration of 15% and a requirement for an 90 day curing duration to produce a peak compressive strength of 52 MPa, and according to the RSM optimization the fly ash concentration is 14.28% , considering 90 day curing duration achive a peak compressive strength of 51.28 MPa. These results highlight fly ash’s ability to improve concrete performance. These kinds of developments are essential for infrastructure projects such as airports, roads, and infrastructure, where long-term viability and environmental effects are major priorities, It also advances environmentally friendly construction methods by optimizing fly ash concrete mixtures using RSM modelling tool. It offers insightful information on how to maximize concrete strength while reducing environmental impact and points the way for the next improvements in concrete pavement engineering.
Keywords—Optimization, Response Surface Methodology, Concrete pavement, Compressive strength, Airport, Curing time.
Enhancing Lifecycle Sustainability through Optimized Supplier Quality Management in Heating Manufacturing
This research shows that improving supplier quality management for Company A’s wall-hung boilers leads to notable sustainability gains, including a 20% reduction in material defects, a 25% decrease in carbon emissions, and a 30% increase in product durability. These findings highlight the critical role of supplier quality in enhancing product lifecycle sustainability.
Design and Implementation of a High Performance Network Function Virtualization Platform
This study applied parallel processing of incoming packets to reduce processing time. Experimental results show that proposed mechanisms enhanced network performance, allowing efficient resource use, reducing network latency, and ensuring stable packet transmission to fit service requirements.