Authors - Twinkle K. Patel, Ankit Bhavsar, Arpit Jain Abstract - Automatic Speech Recognition (ASR) systems have become essential tools for the communication in natural human-computer communication across various languages. The growing need for effective communication in public and private sector has increased the need of Natural Language Processing. Gujarat is one of the prominent state in India where most of the official communication takes place in Gujarati language. ASR for Gujarati language has its own importance due to the high usage of Gujarati language. This paper, highlights the emergence of ASR for Gujarati language from the early days of creating ASR commercially to more modern advancement approaches such as deep learning, end-to-end (E2E), and various other methods. Traditional ASR systems utilized statistical methods, most commonly Hidden Markov Models (HMM) and Gaussian Mixture Model (GMM) which often reported low accuracy. With the emergence of deep learning models, including CNN, BiLSTM, and transformer-based models such as Wav2Vec 2.0 and XLSR-53, the recognition accuracy has significantly increased, especially in noisy and spontaneous speech contexts. The paper also provide the comparative study of the work done by various researchers in the field of Gujarati ASR, highlighting that the use of spell correctors as well as hybrid feature extraction methods have reduced phonetic ambiguities and diacritic errors in Gujarati language. The study strongly emphasizes the need for morphological analysis and contextual modeling to adequately address the complexity in the homophones and diacritics of Gujarati language. The paper also reviews the various methods applied to Gujarati ASR and their reported Word Error Rates.
Authors - Prathilothamai M, Nitin Ravi, Rishi Pradeepkumar, Sajith Rajan P, Anurag Nagilla Abstract - Code analysis ensures software quality, readability, and maintenance. Traditional methods such as Static Analysis Tools (SAT) and SZZ algorithms recognize errors and analyze contributions based on predefined rules and historical trends. However, complex code semantics are difficult to understand and often lead to overlooked issues and limited feedback. Despite its benefits, LLM is not practical in some business environments, facing challenges such as high computing costs, slower processing, and data protection concerns. Hybrid models may provide scalable and inexpensive solutions for large enterprise projects. This study contrasts SATs and LLMs, focusing on capabilities to detect bugs and maintain code quality, by investigating hybrid models that fuse the contextual strength of LLMs with the efficiency of SATs. In evaluations with 30 participants, the hybrid model consistently received the highest scores across accuracy, clarity, relevance, and usability (average ratings >8.5/10), outperforming both SATs and AI-alone approaches. Hybrid models may thus provide a scalable and cost-effective solution for problems arising in large-scale corporate enterprise projects. This study supports the use of case studies and benchmarks to assess the effectiveness, cost, and scalability of these various approaches. The findings aim to give practical suggestions for improving traditional code review processes, helping balance rule-based tools with AI-powered insights to enhance software development workflows.
Authors - T. A. Alka, M. Suresh Abstract - The study aims to examine the link between social innovation (SI) and sustainable entrepreneurship (SE). The major focus is to explore how SI is promoted by the SE and what the Sustainable Development Goals (SDGs) are covered by the sustainable entrepreneurship through promoting social innovation identified through a research gap by adopting the TCCM framework. The study is a systematic literature review on the publications in the Scopus database identified through keywords relevant to the theme by adopting the SPAR-4-SLR protocol. The result indicates that the sustainable entrepreneurs promoting SI innovation through innovative business models ensure social well-being. The SI is not only the technological but also non-technological aspects, which are responsible and impactful, having innovative approaches, social practices, and processes. The SEs are developing business models that are socially and sustainability purpose-driven, offering financial advantage and ensuring the social cohesion and the empowerment of the community through their participatory approaches and ensuring the local level stakeholders’ engagement. SE develops partnerships and collaboration among the different actors, which will create knowledge sharing and collaborative innovation. While mapping of SDGs to SI by SEs, the study identifies that the major SDGs covered are: SDG 1, SDG 4, SDG 5, SDG 8, SDG 9, SDG 10, etc. The study offers implications to practitioners, policymakers for actionable strategies to enhance the SI-driven SE ecosystem. This systematic review lacks empirical findings and relies purely on the published works. The limitations will open future research scope avenues by covering more databases, keywords, etc.
Authors - Shaveta Thakral, JyotiVerma, Pratima Manhas, Shankar Amalraj, Neha Shahare, Monali Tingane, Aparna Mahesh Torade Abstract - Urbanization and increasing housing density have necessitated innovative solutions to address both space constraints and accessibility for individuals with limited mobility. This paper presents a novel concept of an integrated lift-toilet system, designed specifically for elderly and disabled individuals living in multi-story buildings. By merging the functionalities of vertical transportation and sanitary facilities, the proposed system aims to provide dignity, convenience, and emergency usability, particularly in high-rise residences and healthcare environments. The research encompasses a multidisciplinary approach, including mechanical design, sanitary engineering, user-centric ergo-nomics, and real-world application assessments.
Authors - Jyotika R. Yadav, Arpit A. Jain, Ankit Bhavsar Abstract - Efficient traffic management is a major focus in smart city projects. Intelligent Transportation Systems (ITS) are revolutionize the way transportation networks are managed, to improve safety, efficiency, and sustainability. Machine learning (ML) has been developed as a powerful means for advancing ITS by enabling data-driven decision-making, real-time traffic management, predictive analytics, and automation. This paper offers a thorough summary of the current trends and advancements in the utilization of ML techniques in ITS. It explores key areas such as traffic flow prediction, autonomous vehicles, congestion management, smart mobility solutions, and road anomaly detection.
Authors - Saranya P., Rejikumar G. Abstract - This study employs a systematic literature review (SLR) to investigate the changing landscape of smart labelling in the food industry. As Customer demand for transparency, traceability, and ethical consumption increases, conventional food labels are evolving into interactive communication instruments. Smart labels facilitated by technologies such as QR codes, RFID, and intelligent packaging are now integral to real time customer interaction and business decision making. Despite increasing scholarly interest, a thorough delineation of research trends, deficiencies, and prospective directions has been absent. To tackle this, adoption of SPAR-4-SLR protocol was done to examine 43 peer reviewed papers (2015-2025) from Scopus utilizing Biblioshiny and Vosviewer. The investigation identified few predominant topics like food safety, customer behaviors, and technological integration while also highlighting under discovered topics like RFID and packaging designs. Result indicated a shift from a compliance orientated labeling to an interactive digital approach that foster consumer trust. This review provides essential in-sights for scholars, legislators, and industry executives seeking to create data-driven, customer-focused labelling systems in the digital era.
Authors - Abhishek Passwan, Srushti Karki, B M Bandgar Abstract - Accurately predicting stock prices remains a challenging task, primarily due to the volatile, dynamic, and non-linear nature of financial markets. This study introduces a robust deep learning-based framework designed to forecast the closing prices of companies listed on the Nifty 50 index. We explore and compare the predictive performance of several state-of-the-art neural network architectures, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Recurrent Neural Networks (RNN), and Convolutional Neural Networks (CNN), alongside two novel hybrid models: StockAugNet-f and StockAugNet-c. Historical stock data were retrieved from Yahoo Finance and pre-processed using Min-Max normalization. To effectively model temporal dependencies, we applied a sliding window technique for sequence construction during training. Model performance was assessed using key evaluation metrics: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). To enhance usability and support real-time forecasting, we developed an interactive Streamlit-based application, allowing users to dynamically visualize model outputs. Experimental findings indicate that the GRU and RNN model give better accuracy over the hybrid models accuracy and resilience for the NIFTY 50 index stocks.
Authors - Kumar Gaurav, Sudhir Agarmore, Akshay Kumar, Akshat Jha, Edidiong Akpabio, Chandan Kumar Abstract - Network architecture is examined in-depth in terms of the structural design or the underlying protocols that help in the facilitation of data transmission between networks. The paper discusses the classic models of network architecture, which include P2P, client-server, and hybrid models, and outlines the latest advancements in cloud computing, edge computing, and IoT. The paper further discusses the different types of network topologies and other devices, such as the OSI and TCP/IP models, in the shaping of data communication. This also encompasses new emerging trends, such as 5G, IoT, quantum networking, and zero-trust security, among many others. These will detail discussions of the concepts involved as well as their impacts on network protocols and architecture. As demand for faster, more secure, and scalable networks grows, the integration of these modern technologies opens the way to new opportunities and challenges of future network infrastructure.
Authors - Janani Vijayan, Bala Subramanian R Abstract - This study explores the growing body of research on leader’s gratitude, emphasizing its significance in organizational settings. Gratitude, a complex and multifaceted positive emotion, plays a crucial role in enhancing interpersonal relationships, promoting well-being, and fostering prosocial behavior. The review categorizes gratitude into trait, state, and collective forms, examining their unique characteristics and implications. It highlights key theoretical frameworks such as the Find-Remind-and-Bind Theory, Broaden-and-Build Theory, and Cognitive Emotion Theory to explain how gratitude functions within leadership dynamics. The review further delves into the antecedents of leader’s gratitude, including team error management, feedback, and appreciation programs and its consequences, such as enhanced social intelligence, team innovation, improved leader-member relationships, and increased organizational commitment. Moreover, the paper discusses the broader social impacts of leader gratitude, showing how it fosters a positive work culture. Identified limitations include the scarcity of causal studies and underrepresentation of cultural factors. Future research directions are proposed, such as exploring gender differences in gratitude expression, cultural variations, and the effectiveness of different gratitude expressions. Overall, the review underscores that leader’s gratitude is a powerful yet underutilized tool in cultivating effective leadership and healthy organizational environments.
Authors - S. Kirthica, K. Subha, S. Arul Murugan, V. Deepak Kumar, M. Dhivagar, S. Kanishka Abstract - In contemporary society, effective grievance redressal is vital for transparency, accountability, and public trust. Traditional systems often face issues like manual processing, delays, and poor user engagement. To overcome these challenges, we propose VOICE, an AI-driven grievance management platform that automates the complaint lifecycle—from classification and similarity detection to assignment and tracking. Leveraging natural language processing (NLP), speech-to-text, and automated prioritization, VOICE reduces human intervention and improves response times. It also incorporates community-driven upvoting, real-time notifications, and performance monitoring to enhance user engagement and system transparency. Designed for scalability and ease of use, VOICE can be deployed across institutions, communities, and national systems. By combining automation, intelligence, and user-centric features, VOICE represents a significant advancement in modern grievance redressal.
Authors - And Julie D. Rosal Abstract - This study checks on emerging research trends and conducts a bibliographic analysis on smart waste bins as foundational elements for cost-effective, automated waste management solutions. The escalating urbanization and environmental pressures necessitate advanced waste management technologies, where smart trash bins equipped with IoT (Internet of Things), AI (Artificial Intelligence), and robotics offer transformative solutions such as automated segregation, real-time monitoring, and optimized collection routes. Employing a systematic literature review facilitated by Scopus AI, the study identifies consistent, rising, and novel themes in smart bin research. IoT-enabled systems consistently emerged, underscoring their role in operational efficiency and sustainability. Rising trends prominently featured AI integration, robotic automation, and advanced waste segregation methods, indicating a clear shift towards intelligent automation. Security and privacy issues constitute novel themes, reflecting the growing importance of ensuring trust and reliability in technology adoption. The bibliometric analysis revealed significant global research contributions with notable peaks aligning with heightened global sustainability goals. Additionally, the study proposes a conceptual prototype integrating sensors, AI-driven categorization, and autonomous collection mechanisms, validated through simulation testing. Findings demonstrated improved operational efficiency, higher recycling accuracy, and substantial cost savings. Addressing integration challenges, infrastructure compatibility, and public acceptance, the study provides critical insights for urban planners, policymakers, and stake-holders. This work significantly contributes to the discourse on sustainability and smart city initiatives, highlighting technological innovation as pivotal in solving contemporary environmental management issues.
Authors - Akheel Mohammed, Sameera Khanam, Ayesha, Mohammed Shah Alam Rasool Khan Abstract - The proliferation of cloud computing presents a significant challenge for enterprises: entrusting mission-critical data to remote service providers. The widespread adoption of cloud services is fundamentally hampered by a lack of robust and reliable trust evaluation mechanisms. To address this critical obstacle, this paper introduces a novel reputation-based trust model designed to accurately assess the trustworthiness of Cloud Service Providers (CSPs). Our proposed model uniquely integrates three key metrics: direct customer feedback, historical server rejection rates, and real-time server workload. A specialized trust evaluation algorithm processes these inputs to generate a comprehensive reputation score. Experimental results validate the efficacy of our approach, demonstrating a more efficient and accurate evaluation of CSP trustworthiness compared to existing models.
Authors - Ashwini Matange, Jibi Abraham Abstract - Major component of research is searching for relevant publications in the domain of research from varied sources, exploring them, organizing them and finding interconnections between them. Different platforms ranging from Google Scholar, PubMed, IEEE Xplore, ScienceDirect, Springer are to be visited and explored to find research papers in the relevant field of the researchers. Research Rabbit is a free to use literature mapping tool that provides a one stop venue to locate papers of interest effortlessly with effective visualizations and smooth navigation.
Authors - Seema Shrawne, Aishwarya Jadhav, Saniya Gupte, Sayali Panch, Sakshi Thombre, Vaibhav Dhore, Vijay Sambhe Abstract - Accurately detecting and counting fruits is crucial for improving yield estimates in Precision Agriculture. However, challenges such as varying fruit types, overlapping objects, and cluttered backgrounds make this task difficult. In this study, we use RetinaNet, a deep learning model known for detecting dense objects, to address these issues. By replacing its ResNet50 backbone with SEResNet18, we improve both accuracy and speed, increasing mAP by 1.65%at IoU 0.5 and 1.79%at IoU 0.7. We also fine-tune the model using Differential Evolution to optimize anchors, further boosting mAP by 0.61% and 7.3% at IoU 0.5 and 0.7, respectively. Our enhanced model performs better on KFuji apples, mangoes, and holly fruits, while the default model slightly outperforms ours on strawberries and oranges at higher IoU thresholds.
Authors - Meeradevi, Maria Rufina P, Prathik B, C S Parthasarathy, Janya V, Neeha Ritvika Mandava Abstract - PathoVision is an AI-powered framework that enhances cancer diagnosis by integrating histopathology images with clinical text using multimodal learning. Unlike traditional systems focused solely on image analysis, it employs the PLIP model to generate combined visual-text embeddings for more accurate and contextual understanding. A custom decoder pro- cesses these embeddings to classify cancer subtypes, while Grad- CAM provides interpretable heatmaps, ensuring transparency in decision-making. Designed for use in clinical diagnostics, research, and education, PathoVision addresses the limitations of manual pathology—such as subjectivity and time constraints—by offering a reliable, interpretable, and accessible solution. This framework represents a significant advancement in computational pathology, blending modern AI with traditional diagnostics to support expert pathologists and improve patient outcomes.
Authors - And Julie D. Rosal, David Livingstone T. Ramos, Randy Joy M. Ventayen Abstract - This study explores the quality assurance (QA) practices of Pangasinan State University (PSU) from 2016 to 2025, emphasizing key strategies that enhanced its national and global reputation. Using a multi-method approach—including literature review, surveys, and interviews, examines stake-holder perceptions, institutional challenges, and achievements in quality assurance recognition, and improved international rankings. The findings highlight the importance of strong leadership, continuous improvement, faculty development, and stakeholder engagement in fostering a culture of quality. These elements have strengthened PSU’s quality management system and its responsive-ness to academic and institutional demands. As a forward strategy, the study presents a five-year roadmap for 2026–2030 focused on sustaining quality through digital transformation, global benchmarking, research-driven planning, and performance-based evaluation. This roadmap is intended to solidify PSU’s status as a globally competitive university and offers practical insights for other state universities aiming to institutionalize QA and improve their academic standing at both national and international levels.
Authors - David Livingstone T. Ramos Abstract - Quality assurance (QA) in higher education is a critical mechanism for promoting academic excellence, institutional ac-countability, and societal impact. State Universities and Colleges (SUCs) in the Philippines serve as gateways to education for under-served regions but face significant challenges in maintaining and im-proving quality due to the absence of standardized frameworks, reliance on manual processes, and limited technological integration. These issues hinder their efficiency, compliance with accreditation standards, and ability to respond to evolving educational demands. This study examines the role of a Quality Assurance Information System (QAIS) as a solution for addressing these challenges. Employing a literature review methodology, it evaluates current QA practices in SUCs and identifies gaps, such as fragmented processes, inconsistent standards, and insufficient data management capabilities. The research emphasizes the potential of QAIS to streamline processes, automate routine tasks, and provide real-time data for evidence-based decision-making. Key components of an effective QAIS include modular design for scalability, compliance with international standards, user-friendly interfaces, and robust analytics for monitoring and continuous improvement. By implementing QAIS, SUCs can align their practices with global benchmarks, enhance stakeholder engagement, and improve institutional performance. The study also highlights challenges in adopting QAIS, including resource limitations and resistance to change, and proposes strategies to mitigate these barriers. The findings offer actionable recommendations for SUCs to implement and scale QAIS effectively, aligning with national and international quality standards while addressing local needs. By leveraging QAIS, SUCs can enhance their competitive-ness, improve educational outcomes, and contribute to broader national development goals.
Authors - Pravitha N R, Sumedha N Varma Abstract - This study reflects the pivotal role of financial technology (Fin-tech) in reshaping the IT and Corporate sectors while playing a role in achieving sustainable development. Fintech has evolved into a significant role as a game changer, driving innovation in financial contribution through digital platforms, blockchain technology, artificial technology, and big data analytics. In the IT sector, Fintech facilitates secure, efficient transactions and enhances digital infrastructure, leading to improved service delivery and client engagement. Within the corporate domain, it streamlines operations, increases financial inclusion, and supports strategic decision-making. In addition, the integration of Fintech services with principles of sustainability is enhancing green finance and promoting efficient digital solutions. This paper studies the factors affecting the adoption of fin-tech services and how sustainable development goals are achieved by adopting fin-tech services. By using regression analysis, the study expresses that Digital Financial Literacy and Perceived Performance Expectancy have a positive impact on the Behavioural Intention in adopting fintech services. The analysis further states that the actual use of fin-tech services plays a vital role in promoting sustainable development.
Authors - Sasini Gandhari Dabare, Indra Mahakalanda, Prasanga Jayathunga, Nathali Fernando Abstract - Media Mix Modeling (mMM) is a data-driven analytical approach that quantifies the influence of multiple advertising channels on sales performance while adjusting for external factors such as seasonality and economic conditions. This study explores machine learning-based mMM to predict weekly sales performance in the Sri Lankan home appliances industry by combining ad spending variables with external factors such as exchange rates and holidays. The study applies time series decomposition, ad stock transformation, and data normalization to capture delayed and nonlinear media effects. TimeSeriesSplit cross-validation was used to test four predictive models: Linear Regression, XGBoost Regression, Support Vector Regression (SVR), and Bayesian Ridge Regression, along with key metrics for performance (MSE, RMSE, R² Score, MAPE). The tuned XGBoost model had the best accuracy (R² = 0.9145; MAPE = 2.87%), making it most useful for predicting. The findings demonstrate the importance of machine learning-enhanced MMM as an effective strategy for optimizing media spending and increasing advertising efficiency in competitive, budget-constrained markets.
Authors - Hera Khairunnisa, Nayla Nandhita Nuril Hadi, Surya Anugrah, Rochma Sudiati, Windy Permata Suyono Abstract - First objective of this study is evaluating MSMEs tax compliance with Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis. Second objective of this study is evaluating the role of Core Tax Administration System (CTAS) for MSME. The study uses a descriptive qualitative approach, with semi-structured interviews with three tax consultants with extensive experience in MSME taxation. This study also employs the literature review approach. From the interview, this study shows that MSMEs typically have straightforward cost structures given their simple business operations. Government support, such as the reduction of the final tax rate for MSMEs from 1% to 0.5%, is viewed as an opportunity in taxation. However, the study highlights ongoing challenges in MSME operators' tax competence, particularly their understanding of recent tax regulations and literacy in tax matters. This study also shows that the implementation of the Core Tax System serves as a strategic advancement in enhancing tax administration for MSMEs in Indonesia. By leveraging digital integration and automation, the system aims to promote greater efficiency, transparency, and ac-countability in tax processes.
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.
