Authors - Samiksha Andeo, Supriya Narad Abstract - How we've been interacting with technology has altered with the presence of chatbots, which have now become a part of our digital society. These artificially intelligent virtual personal assistants are being applied on a vast scale in customer services, health care, and education sectors since they are capable of answering the customers, addressing their queries, and directing them in real-time. In relation to their application in healthcare, in this research study, the his-tory of chatbots is discussed, the technology employed by them, their real-world applications, and the problems faced by them. Modern-day chatbots can understand human language, learn from past conversations, and communicate in a customized way by employing Natural Language Processing (NLP), Machine Learning (ML), and Artificial Intelligence (AI). They may be anything from AI-based chatbots able to engage in meaningful, dynamic conversations to rule-based assistants that utilize pre-defined scripts. With the advent of technology, chatbots are increasingly being applied in the field of health care such as patient assistance, mental assistance, and diet and nutrition guidance, all of which enhance access to health care services. However, despite all their benefits, their potential is also hindered by limitations such as privacy, ethical concerns, and the inability to understand human feelings. As the future of deep learning, IoT connectivity, and more interactive AI is on the horizon, chatbots appear to have a very bright future. The purpose of this research is to give an authoritative and illuminating perspective on how chatbots are changing various businesses and the future of AI communication.
Authors - Sadman Hafiz, Md. Ataur Rahman, Tahmid Zamee, Md Sacklain Hossain, Marufa Akter, Ahmad Mostofa Kamal, Mahady Hasan, M. Rokonuzzaman, Farzana Sadia Abstract - The organic agriculture sector in Bangladesh faces significant challenges due to the absence of a national organic product certification system. As a result, local producers are often forced to rely on expensive international certification agencies, creating barriers to market entry, increasing costs and limiting consumer trust. To solve this, our proposed system is a blockchain-enabled, AI-powered web platform designed to establish a secure, transparent, and efficient organic product certification system tailored for the Bangladeshi context. A key innovation is its hash-based file verification to minimize gas fees to store data on the chain. Farmers or producers submitted product data like images, videos, and documents, which are encrypted (AES-256) and stored locally, while cryptographic hashes (SHA-256) are saved on-chain. Any alteration in a file changes its hash, and QR-code scanning will instantly flag tampering, ensuring product integrity. It also integrates tokenized payment systems using ERC-20 tokens alongside SSLCOMMERZ payment gateway, ensuring secure, traceable, and accessible transactions for all stakeholders. Key features include smart contract automation, real-time certification tracking, a decentralized dispute resolution mechanism, and QR code-based traceability for consumers. This project, therefore, aims to democratize organic product certification, lower operational costs, and build consumer trust, while aligning with global trends in digital food supply chains.
Authors - Wani Tejaswi, Pawar Suvarna Abstract - The number and intensity of car accidents around the world are rising, which makes it even more important to have smart systems that can quickly and correctly process accident pictures. We need more advanced systems to quickly find accidents, label data, and record them. Manual methods take a long time, are prone to mistakes, and are not good enough for real-time apps. This paper tries to solve these problems by suggesting a system that uses advanced machine learning (ML) models to automatically process, label, and report images of car accidents. Convolutional Neural Networks (CNNs) are used for feature extraction, YOLOv8 is used for real-time accident recognition, and Transformer-based models are used for complex multi-object labeling Global Road Accidents Dataset is much better at generalizing models than the Car Crash Dataset and the Road Traffic Accidents Dataset, which are more focused on crash types and vehicle damage. This is because it has more images and a wider range of features, such as location, severity, and weather. It was tested and found that the proposed hybrid model is more accurate than the CNN- based and YOLO-only methods, which got 94.5% and 93.7% accuracy and precision, re-spectively, on the Global Road Accidents Dataset. The combination model also has better accuracy for annotations and fewer fake hits. Comparative research shows that combining Transformer designs with object recognition models makes it easier to understand features and make reports. Findings from this study show that the AI-driven framework can be used to automate accident investigation processes. This is a big step towards smart traffic control and emergency response systems.
Authors - Hasin Mahir, Tahfizul Hasan Zihan, Md. Shirazim Munir, Khondkar Ayaz Rabbani, Rifat Ara Rouf, Ferdows Zahid, Mahady Hasan, Md. Tarek Habib Abstract - Indoor air quality (IAQ) is critical for health, comfort, and cognitive performance in classrooms. Yet, many classrooms in lowermiddle-income countries (e.g., Bangladesh) rely on natural ventilation and lack any warning system for impending air-quality guideline breaches. This study is motivated by the need to maintain healthy learning environments in resource-constrained settings, where students and teachers are often exposed to elevated CO2 and particulate levels. To address this, we present the first comparative evaluation of three forecasting models: Prophet, Random Forest (RF), and Long Short-Term Memory (LSTM), on daily PM2.5 and CO2 time series covering six months of continuous operation in Dhaka, Bangladesh. Our goal is to anticipate IAQ deterioration to enable proactive ventilation or filtration interventions. The nonlinear machine-learning models substantially outperform the Prophet baseline. Random Forest performed best for PM. (RMSE 1.87 μg/m³, R² 0.992), showing its ability to capture complex pollutant dynamics. LSTM excelled at forecasting peak CO2 (RMSE 159.7 ppm, R² 0.693), which is critical for timely interventions. These findings demonstrate the feasibility of accurate, low-cost IAQ forecasting in resource-constrained classrooms and underscore the potential of data-driven forecasting to maintain healthier learning environments.
Authors - Thuy-Vi Thi Ha, Phuoc-Hung Vo, Thanh-Nghi Do Abstract - In this paper, we propose an enhanced deep learning approach for early-stage rice yield prediction by embedding self-attention layers into established deep neural networks (DNNs) such as VGG-16, DenseNet, MobileNet, ResNet, Inception, and Xception. The addition of self-attention significantly improves the models’ ability to capture longrange dependencies and global context, which traditional convolutional layers often fail to represent adequately due to their inherently local receptive fields. We evaluate the proposed method on a dataset of 18,642 RGB images collected from 47 rice field plots spanning over 28 hectares in An Giang and Tra Vinh provinces. The images were acquired using digital cameras, smartphones, and fixed-wing UAVs during the heading stage of rice growth. Experimental results demonstrate that DNNs enhanced with self-attention layers consistently outperform their original fine-tuned counterparts. Furthermore, these hybrid models also achieve higher prediction accuracy compared to Vision Transformers (ViT), highlighting the effectiveness of integrating self-attention into conventional DNN architectures for agricultural yield forecasting.
Authors - Soham Paithankar, Khushi Khedkar, Supriya Narad Abstract - In this research paper, there is a new forecast on the programme that will be able to predict the weather for agriculture. Enhanced by the Internet-connected weather and climate models, earth observation data, and artificial intelligence algorithms, the system returns to farmers accurate, location-based outlook. It does this by factoring preceding day’s temperature data and other factors to determine the needed plant growth, therefore offering comprehensive and precise directions. Furthermore, the alarm systems help in giving the farmers a prior indication of any future possible weather-related incidents that cause a threat to human and their stock’s safety so that measures could be taken accordingly. It also uses real information especially in agriculture sector thus transmits present information to the farmers. The main objective that the system has for farmers is to provide them with information on crops during a favourable time for crop production and the main idea of achieving this is to boost agriculture sustainability and productivity, this is because climatic conditions are very volatile thus would have a great negative impact on farming. Also, the expected audience becomes more diverse and can principally grow, as we would like to consider farmers, who may not even have a basic computer knowledge, empowered by the platform.
Authors - Ankit Shah, Roshni Rawal Abstract - Artificial intelligence-powered learning analytics (LA) shows great results for assisting teachers with the time-consuming task of providing feedback in context to ethical consideration, leads to fairness, maintaining privacy and transparency in feedback. The study investigated two GPT model versions (3.5 and 4) that produce evaluation advice on students' writing assessment based on assignments in context of data science subject. Researchers evaluated GPT generated advice in comparison with human instructions on the basics of effective-ness, readability, and reliability. The paper concludes that both versions could constantly produce more decipherable advice in comparison with human instructions, but GPT-4 performed much better than GPT 3.5 and human instructors. Researchers talked about the unique blend of ethical contemplation and the impact of LLM’s on automated feedback and data analysis provides a perfect match to the LLMs to achieve ethical horizons and set a new height to be achieved for upcoming generations.
Authors - Shalini S, Mamatha A, S. Sheela, Mala B A, Nagaraj M Lutimath, Koustav Biswas Abstract - Sustainable soil management is essential for ensuring food quality, environmental health and climate resilience. This systematic survey examines the evolution and integration of Artificial Intelligence (AI) and Machine Learning (ML) within green agricultural technologies aimed at enhancing soil health. This paper focuses on sensor networks, remote sensing, Internet of Things (IoT), robotics and data-driven decision-support systems to assess their efficiency in precision soil diagnostics, fertility management and resource conservation. While these green AI technologies show promising benefits, improved resource efficiency, reduced environmental footprint, regenerative soil practices challenges persist, including uneven soil data availability, high implementation costs and limited interpretability of models and lack of standardization.
Authors - Aida Ouedraogo/Rakissga, P Justin Kouraogo Abstract - We're interested of the control plane's fault tolerance and the solution we're proposing is proactive thanks to its modularity. Our solution proposes a distributed SDN (Software-Defined Networking) architecture, comprising a controller specifically dedicated to processing packet_in messages and a generic controller in charge of other network management functions. The goal is to prove the effectiveness of this role specialization with regard to performance in message processing and the ability to resist failures.
Authors - Shyamali Thasale, Seema Kedar, Rutuja Khedkar, Kartik Naphade, Prajakta Gaikwad, Vijay Kale Abstract - Sentiment analysis of tourist reviews examines common points of view to help users choose destinations, accommodations, and services. Provides businesses with information on customer satisfaction and helps travelers make better choices based on a variety of common experiences. The proposed method uses a mix of the Skip-Gram models and Continuous Bag of Words in addition to Word2Vec vectorization to transform textual data into numerical values. This method maintains the sentiment of the text and also captures the context and word relationships. Convolutional neural networks boost the accuracy of classification and feature extraction. Along with CNN, models like Random Forest, AdaBoost, LSTM and CNN-BiLSTM have also been used in order to hold a comparison between them. Among these, the CNN model showed the highest accuracy of 96.3 %. This system provides users with specific recommendations for vacation spots, accommodations, and activities.
