Authors - Anis A, Kasi B Anand, Adithyan B, Navaneeth A Nayan, Durgalashmi CV Abstract - The advent of the prospects of using startups especially tech-based having unprecedented significance and continuous severity with regard to Sustainable Development Goals (SDGs) has increased the understanding of how important it is to understand students' knowledge and perceptions in this field. This study is designed for survey to understand and measure students' depths of knowledge regarding startup companies, the SDGs, as well as potentially precautionary attitudes towards startups in achieving sustainable development by 2030. The robust quantitative research design, were a well-designed and systematically developed questionnaire was employed to identify a systematic collection of questionnaires from students who are studying in the different higher educational institutions by incorporating cross-sectional survey methodology data collection technique. In general, the results show that students agree that startups are good for economy, society as a whole and even environment. However, research also mentions that there is low perception about startup doing Sustainable development work along-with moderate awareness and moderate belief in strong government support. These parts together suggest deep necessary work by the policymakers, educators and other stakeholders to raise the level of awareness and support. Furthermore, the study demonstrates the need for systematic integration of sustainability and entrepreneurship education to enhance students' knowledge on sustainability issues as well as their involvement in sustainable development-oriented tasks. The findings of this study offer new and practical lessons to policymakers, educators and researchers facing the continuing challenge of building and reshaping startup ecosystems that reflect or foster their successful fulfilment of sustainable development goals.
Authors - Chethana R.M. and Dr S.P. Manikandan Abstract - The rapid evolution of cyber threats has intensified risks to organisational security, necessitating intelligent, data-driven approaches to threat assessment and mitigation. This study presents a comprehensive analysis of the evolving cyber threat landscape and its impact on organizational security using a dataset of 1,200 cybersecurity incidents reported across major sectors in India from 2019 to 2024. The dataset includes diverse incident categories such as phishing, ransomware, data breaches, online fraud, identity theft, and hacking, along with associated financial losses, geographic distribution, and affected organizational domains. To investigate threat patterns and predict incident behavior, three machine learning models, Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR) were employed for classification and regression tasks. Experimental results reveal significant challenges posed by class imbalance and feature complexity, leading to relatively low classification accuracies, with Random Forest marginally outperforming other models. Regression analysis for predicting financial losses also demonstrated limited explanatory power, indicating the influence of latent factors beyond the available attributes. Despite these constraints, the study identifies important sector-specific vulnerability patterns, highlighting significant financial impacts across healthcare, financial services, and government. The findings emphasize that conventional machine learning models alone may be insufficient for capturing the highly dynamic and nonlinear nature of cyber threats, underscoring the need for advanced threat intelligence frameworks, richer datasets, and adaptive security analytics. This research contributes empirical insights into cyber risk modeling and offers practical implications for policymakers and organizations seeking evidence-based cybersecurity strategies.
Authors - Xamdamov Utkir Raxmatillaevich, Elov Botir Boltayevich, Alavutdinova Nadira Ganiyevna, Malika Suyunova Odil qizi, Sharipov Soxib Salimovich , Narimova Gulnora Abdumanonovna Abstract - In this article, the architecture of an information system for sentiment analysis of Uzbek-language texts and its key components are examined from both scientific and practical perspectives. The system is based on a multi-layered and microservice architecture, consisting of a user interface (front-end) and a server (back-end) that provides services through a REST API. The back-end components, implemented via a Flask-based RESTful API server, carry out the business logic and sentiment classification. Deep learning models, especially transformer-based architectures (BERT, XLM-RoBERTa), were utilized for analyzing Uzbek texts and demonstrated effective results. The system ensures security, provides integration capabilities, and offers a user-friendly interface to enhance user experience. The modular architecture of the system allows broad scalability and integration with various platforms. As a result of scientific and practical experiments, the system achieved high accuracy (90%) and proved effective for real-time sentiment analysis tasks.
Authors - Sunandita Adhikary, Dipanwita Chakrabarty, Arunangshu Giri, Shamba Chatterjee, Dibyendu Rath, Soumya Kanti Dhara, Solanki Pattanayak, Samik Bagchi Abstract - The study evaluates how shopping engagement gets influenced by mobile apps in digital retail platform. In e-commerce platform it is important to understand user preference in the context of customization, quality of information, usability and interactivity. The present study investigates how these contextual parameters play a pivotal role in shaping users’ emotional and cognitive reactions. These reactions subsequently influence user engagement and purchase-related decisions. The study has proposed a structured framework to identify the antecedents’ influence on shopping engagement and how it shapes user satisfaction. The findings of the study shows that mobile app plays a crucial role in engaging user in digital retail platform and consequently users’ shopping engagement influence their choice satisfaction. The study has notable contribution for marketers and mobile app developers so that they can enhance users’ satisfaction and can achieve competitive advantage. The study has enriched existing literature as well by extending expectation confirmation theory in the context of shopping engagement through mobile application in digital retail platform.
Authors - Maria Genoveva Moreira Santos, Eric Geovanny Cedeno Zambrano Abstract - Document management in public institutions constitutes a strategic element for improving administrative efficiency and strengthening decision making. In this context, the present study analyzes the implementation of a centralized repository in Ecuador’s Water Secretariat, aimed at the use of business intelligence tools and platforms to optimize access to, control of, and utilization of institutional information. The study was conducted using a quantitative methodology, supported by interviews applied across different departments of the institution, in order to identify needs, limitations, and practices related to records and process management. The results revealed a low adoption of specialized technological solutions and a limited appreciation of their strategic potential within the public sector. A total of 83.7% of the results supported the need to establish clear regulations for document management. It is concluded that the integration of document management systems with business intelligence platforms promotes the generation of timely information, institutional monitoring, and evidence based decision making.
Authors - Eka Dewi Utari, Darma Rika Swaramarinda, Maulana Amirul Adha, Triesninda Pahlevi, Yuliansyah, Dewi Nurmalasari, Agung Kresnamurti Rivai P, Ferry Setyadi Atmadja, Fauzan Fadlullah, Alifah Kusumaningrum, Sabo Hermawan, Renata Rachel Abstract - Academic websites have emerged as critical intelligent digital infrastructures for delivering institutional information and services in higher education. However, existing evaluation frameworks often capture either technical quality dimensions or subjective user experience in isolation. This study proposes and empirically validates an integrated evaluation model combining WebQual 4.0 with the Ease of Use construct from the End User Computing Satisfaction (EUCS) model, applied to the official website of the Faculty of Economics and Business, Universitas Negeri Jakarta (www.feb.unj.ac.id). The integration is motivated by the growing imperative to align academic web services with intelligent service design principles encompassing data-driven content governance, responsive interaction channels, and user centred personalization as foundations for future AI augmented academic portals. A quantitative descriptive design collected data from 124 respondents (93.5% students; 6.5% lecturers) via a 17-item validated questionnaire across four dimensions: Usability, Information Quality, Interaction Quality, and Ease of Use. Multiple linear regression (IBM SPSS 23) revealed that Information Quality (β = 0.419, p < 0.001) and Interaction Quality (β = 0.260, p = 0.002) exerted statistically significant partial effects on user satisfaction, whereas Usability and Content did not reach partial significance. Collectively, the four dimensions explained 70.8% of satisfaction variance (R² = 0.708; F = 72.074; p < 0.001). Bibliometric keyword-network analysis contextualises the study within the broader digital-services literature. The integrated WebQual–EUCS model offers a replicable diagnostic tool for higher education institutions seeking to align web services with intelligent user expectations.
Authors - Anggita Sharon Simanjuntak, Eva Nurhazizah Abstract - This study investigates the impact of cognitive perception on destina tion trust and intention to visit, while examining the moderating role of social media influencers at Taman Impian Jaya Ancol, an urban tourism destination in Indonesia. Utilizing a quantitative approach, data were collected from 385 re spondents via purposive sampling and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS. The results reveal that cognitive perception significantly enhances both destination trust and intention to visit. Similarly, destination trust and social media influencers exhibit a signif icant positive effect on visit intention and destination trust, respectively. How ever, social media influencers do not significantly moderate the cognitive per ception-destination trust relationship. Ultimately, these findings highlight the ne cessity of cultivating positive perceptions and trust, offering strategic insights for destination managers to optimize social media marketing.
Authors - Virgel William Afaga, Patrick Andrew Balang, Dana Wynnette Binwag, Emmanuel Paolo Bromeo, Mark John Bumacod, Carl Allan Calsiman, Juliana April Cendana, Roderick Makil,Dulthe Carlo Munar Jr. Abstract - Benguet Province, Cordillera Administrative Region, Philippines, is highly susceptible to landslides due to its rugged topography, complex geology, frequent typhoon tracks, and extensive mining and road construction. Existing hazard maps rely on static statistical methods and coarse rainfall averages that cannot capture the dynamic triggering conditions of individual storm events. This paper presents a dynamic landslide susceptibility model built on Random Forest (RF) and Extreme Gradient Boosting (XGBoost) trained on thirteen environmental conditioning factors across five domains (topographic: elevation, slope, aspect, distance to streams; geological: rock type, soil type; land cover: LULC, NDVI, NDWI; climatic/hydrological: mean annual rainfall, event rainfall, antecedent rainfall; anthropogenic: distance to roads) derived from high-resolution satellite imagery and event-specific rainfall records. Training used a balanced 16,158-sample dataset (50:50 landslide/non-landslide) from the MGB-CAR geohazard inventory, split 60:20:20 for training, validation, and testing. XGBoost outperformed RF on all metrics: AUC-ROC 0.8903, accuracy 81.78%, precision 81.87%, recall 81.62%, and F1 81.75%; the performance difference was statistically significant (McNemar's test: χ² = 6.22, p < .013). Spatial validation via the Seed Cell Area Index (SCAI) confirmed that High and Very High susceptibility classes captured 69.87% of inventoried landslides within only 36.3% of the provincial area. Expert review by four MGB-CAR geoscientists yielded Likert mean scores above 4.0 for conditioning factor appropriateness, inventory quality, and feature importance plausibility. A fully automated monthly update pipeline was deployed—completing the full cycle from remote-sensing data retrieval to web-map publication in approximately 31 minutes—demonstrating operational feasibility for dynamic hazard monitoring using open-source tools and free satellite data.
Authors - Adibhav Agrawal, Nikunj Parikh Abstract - This article is about how a new configuration of devices has been created for a compact, low-cost, real-time monitoring system for measuring electromagnetic fields on dairy farms. The Electromagnetic Field Monitoring System (EMFMS) is composed of an ESP32 micro-controller, MLX90393 three-axis magnetometer, TP4056 based boosting supply module, and a 0.96-inch OLED screen, which are all encased in a unique 3D printed PETG enclosure. The EMFMS can store and transmit wirelessly time-stamped activity levels of the earth’s magnetic field on three axes through MQTT protocol. The EMFMS was placed into three different areas of an operational dairy barn over 28 days where EMF levels of up to 17 times higher were observed between different areas, and a statistical finding was noted between EMF levels and lower levels of milk production (r = −0.61, p = 0.003) and higher levels of cortisol in serum (r = +0.44, p = 0.03) in Holstein-Friesian dairy cows. The findings of this pilot study demonstrate that this method of continuous measurement of electromagnetic fields for animals using IoT technology could serve as a more feasible and low-cost alternative to existing spot measurements.
Authors - Viraj Bhatt, Rajvi Bhimani, Bhupendra Fataniya, Dhaval Shah Abstract - Cross-border security remains a critical concern for global stability, particularly in jungle or forested terrains where soldiers face significant risks. Military camouflage is engineered to blend in with natural surroundings using advanced concealment techniques that match local textures and color patterns. Consequently, the identification of concealed threats is a challenging task where human observation is prone to error due to poor visibility and fatigue. Traditional surveillance methods often rely on optical sensors which may fail to efficiently detect modern military camouflage. To address this, an automated detection model was developed using the YOLOv8-Nano architecture and deployed on NVIDIA Jetson Nano hardware. The framework was validated using a 5- fold cross-validation strategy to ensure robust and reliable performance. Experimental results yielded a peak mean Average Precision (mAP) at an Intersection over Union (IoU) threshold of 0.5 of 0.955 and an average mAP of 94.8%. The model was further optimized into a TensorRT engine using FP16 quantization, achieving a final footprint of 5.9 MB. These results demonstrate that low-power, portable hardware can effectively perform real-time surveillance as an edge-AI system. This also results in minimizing risks to human lives and directly supporting the core mission of Sustainable Development Goal-16 (SDG-16).
Authors - Kapil Mohan, Ritu Chauhan, Harleen Kaur Abstract - ESG (Environment, Social and Governance) rating in today’s financial world is becoming a good indicator for investors in decision making and risk analysis. There has been stress on E and S in the recent past as Governments and Regulators stress these parameters and benefits to those who are working towards this improvement rating. The rating is a clear indicator of sustainability and promising business and thus is gaining popularity. The analytics firms have combined this indicator and have come up with this calculation using certain scientific and mathematical models from the published data and/or requested data that are provided exclusively to do this calculation for the indicator. These ratings are published annually by analytics firms like Sustain analytics and Bloomberg ESG data service for global but limited firms. This study’s focus is to fit financial data of firms on machine learning models and predict ESG rating with changing market fundamentals and firm’s business value indicators. The result can be com-pared to passed ratings, category averages, deviation and outliers which can benefit venture capitalists and investors to refine their investment strategies. The re-search also captures and compares this output and suggests the approach that best suits this problem by building an architecture that can update the model and can predict live data from the market.
Authors - Anuj Kothawade, Ishan Patra, Pravin Chavare, N. S. Shirude Abstract - The rapid digital transformation of higher education emphasizes the need for robust, data-driven platforms to monitor and enhance student learning. However, many institutions rely on closed, third-party learning management systems that restrict direct access to raw educational data and limit customized analytical capabilities. To address this gap, this paper proposes a scalable educational assessment and learning analytics platform that grants educators complete data sovereignty. Built on a modern stack of TypeScript, React and Tailwind CSS over an owned, directly accessible Firebase backend, the system enables secure, unhindered access for granular data mining. The platform monitors a range of college assessment activities, targeting quizzes and practical coding tests, and uses role-based authentication and custom data-fetching hooks to process student interactions into comprehensive performance metrics. A distinguishing feature is its integrated client-side PDF generation, which instantly produces detailed analytical score reports that serve a dual pedagogical purpose: empowering teachers with actionable insights to adapt instruction, while giving students personalized, self-reflective feedback for continuous improvement. Validated on a controlled pilot, the system achieved 95% overall accuracy, an 85% quiz-evaluation accuracy, a 28% improvement in student engagement, a 40% reduction in report-generation time, and a 92% system-usability score.
Authors - Aleta Fabregas, Nathanael Almazan, Jordan Garcia, Shaina Laman, Paolo Morato, Armin Coronado, Montaigne Molejon, Mariel Leo Violeta Abstract - The Philippines is frequently affected by tropical storms, typhoons, and flooding events that threaten communities located near major river systems. Accurate river level forecasting is essential for improving disaster preparedness and reducing flood-related risks. This study proposes RIVERCAST, a forecasting system that utilizes an Auto-Regressive Transformer with Kernel Principal Component Analysis (Kernel PCA) and Euclidean Kernel to predict Marikina River water levels across the Nangka, Sto. Niño, and Montalban monitoring stations. Meteorological, hydrological, and topographical datasets were collected from PAGASA, MMDA, DPWH, and OpenWeather API records from January 2012 to January 2023. Eighty percent of the collected records were allocated for training while the remaining twenty percent were utilized for testing. The pro-posed model was compared with the Transformer model developed by Xu et al. (2023) using rolling window testing and mean absolute error analysis. Results revealed that the proposed Auto-Regressive Transformer with Kernel PCA and Euclidean Kernel achieved an overall forecasting accuracy of 93.19%, outperforming the Bidirectional Transformer model, which obtained 92.57% accuracy. Findings further indicated that precipitation, rainfall intensity, and temperature significantly influenced forecasting performance, while humidity exhibited the least contribution. The developed model demonstrated reliable twelve-hour river level forecasting capability and may support flood preparedness and early warning initiatives within flood-prone communities along the Marikina River.
Authors - Syafira Aulia Azzahra, Christina Angelica Himawan, Brigitta Vellia, Indra Kusumawardhana Abstract - The hospitality industry is increasingly shaped by smart technology, integrated systems, and subscription-based service models that require consistent and personalized guest experiences. In Indonesia, particularly in the Greater Jakarta area, hotels are adopting Internet of Things-based devices, cloud-based property management systems, and data-driven service platforms to improve guest convenience and strengthen customer retention. This study examines the effects of smart technology devices and integrated systems on customer satisfaction in subscription-based hospitality, with service personalization positioned as a central mechanism in the guest experience. A quantitative cross-sectional survey was conducted with 400 hotel users in the Greater Jakarta area who had experience using smart technology in hotel services. The data were analyzed using Partial Least Squares Structural Equation Modeling. The findings show that smart technology devices and integrated systems positively influence customer satisfaction and service personalization. Service personalization also emerges as the strongest predictor of customer satisfaction, indicating that technology creates greater value when it enables relevant, adaptive, and individualized services. The study contributes to hospitality technology and customer intelligence literature by explaining how digital infrastructure and system integration support personalized subscription-based hotel experiences. Practically, the findings suggest that hotel managers should prioritize investment in interoperable systems, guest data integration, and personalization capabilities to improve satisfaction and sustain long-term customer relationships in technology-enabled hospitality services.
Authors - Aye Mya Mya Win, Ah Nge Htwe Abstract - In recent years, optical flow-based deep learning methods have pro vided evidence of impressive performance in recognizing human behavioral movements from video sequences, revealing high applicability for fall detection functions. This paper analyzes GMFlow-based architectures by experimenting with three different approaches that merge TCN, Attention, and CNN compo nents. These methods are GMFlow-TCN, GMFlow-TCN-Attention, and GMFlow-CNN-TCN-Attention. The experiments were executed on URFD Da taset, Le2i Dataset, and a combined, URFD-Le2i dataset to analyze and evalu ate their performance. According to the experimental results, the method that combines GMFlow-CNN-TCN-Attention achieved better performance than the other proposed models. This model obtained test accuracies of 100% on the URFD dataset, 92% on the Le2i dataset, and 94% on URFD-Le2i dataset. These results point out that the presented method is capable of effectively cap turing both spatial features and temporal features required for fall detection. This approach provides useful insights for developing effective real-time vi sion-based fall detection applications.
Authors - Al John A. Villareal, Jaime M. Samaniego Abstract - Potholes significantly impact road safety, vehicle performance, and infrastructure maintenance, particularly in developing countries where monitoring systems remain largely manual. This study presents the design and implementation of an Android-based mobile application that utilizes sensor fusion and deep learning for real-time pothole detection and severity analysis. The system integrates a YOLO-based object detection model with smartphone sensors, including accelerometer, gyroscope, and Global Positioning System (GPS), enabling simultaneous visual and motion-based detection. A dataset consisting of 9,253 road surface images containing 16,123 pothole annotations was used for training and evaluation using a 70:20:10 dataset split for training, validation, and testing. Among the evaluated models, YOLO11s achieved the highest mAP@50– 95 value of 54.2%. However, YOLO26n was selected and implemented in the developed Android application due to its competitive detection performance, compact 5.2 MB model size, and suitability for real-time mobile deployment. Field testing across four road segments covering 18.97 kilometers resulted in 130 detections, of which 84 were verified potholes and 46 were false detections, yielding a verification rate of 64.62% and a false detection rate of 35.38%. The system recorded an average detection density of 6.85 potholes per kilometer. Results demonstrate that integrating deep learning and sensor fusion in a mobile platform provides a scalable and cost-effective solution for automated road condition monitoring and intelligent transportation systems.
Authors - Nu Yin Khaing, Win Lelt Lelt Phyu Abstract - Water quality monitoring is essential for sustainable aquaculture management and fish health assessment. However, monitoring a large number of physicochemical parameters in creases sensor deployment costs and system complexity. While traditional machine learning ap proaches struggle with complex, nonlinear relationships among water quality variables, feature reduction can optimize system efficiency. This study proposes a hybrid machine learning and deep learning framework to achieve accurate, cost-effective water quality classification using the Water Quality Dataset (WQD). The framework integrates Random Forest (RF) and XGBoost for feature selection with Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) models as classifiers, evaluating four hybrid combinations (XGBoost+SVM, XGBoost+LSTM, RF+SVM, and RF+LSTM) across subsets of 10, 7, and 5 features. Experimental results demon strate that hybrid deep learning architectures consistently outperform traditional machine learn ing methods. Specifically, XGBoost+LSTM and RF+LSTM achieved the highest classification accuracy of 96.05% using 10 selected features, while maintaining reliable performance at lower dimensions. Furthermore, Shapley Additive explanations (SHAP) analysis was applied to en hance model interpretability, identifying Dissolved Oxygen (DO), Turbidity, BOD, H2S, and Nitrite as the most important attributes. Ultimately, the proposed framework minimizes sensor requirements and provides an accurate, interpretable, and economically viable solution for aqua culture water quality monitoring systems.
<b>Authors - </b>Mahi Shah, Sachin Pande, Sumitra Jakhete, Emmanuel Mark<br /> <b>Abstract - </b>Brain-Computer Interfaces (BCIs) operate as systems that translate brain signals into digital commands. They provide a non-muscular channel of communication for individuals with profound motor disabili ties. Cerebral Palsy (CP) is a neurological condition that impairs move ment and muscle tone, frequently making physical or verbal expression difficult. This paper reviews the current state of BCI technology and, building upon these insights, introduces a framework for a non-invasive, low-cost BCI communication system tailored specifically for children with CP, addressing the limited accessibility of assistive communication technologies in low-resource environments. The proposed seven-stage framework targets these ongoing challenges by incorporating OpenBCI hardware, adaptive signal processing, and gamified interfaces. This processing pipeline converts neural signals into structured communication outputs, enhancing accessibility and engagement for CP children. To assess the feasibility of the proposed framework, an offline analysis was conducted using a publicly available EEG dataset. A Linear Discriminant Analysis (LDA) classifier y a classification accu racy of 62.5% and an Information Transfer Rate (ITR) of 11.4 bits/min, demonstrating the computational viability of the approach. The modular design offers scalability, though its efficacy requires further validation in real-world pediatric settings. In summary, this work bridges theoretical insights with practical innovation, offering a promising step toward empowering CP children. While limitations in real-world testing remain, the framework lays a foundation for future refinements. Successful implementation could significantly improve independence and quality of life, marking a milestone in inclusive assistive technology.
Authors - Apolinar P. Datu, Barnard J. Maraon, Rommel H. Orquiza, Cristopher T. Takano, Olivia L. Yosa, Mark Joseph G. Cruz, Jeferson D. Talisayon Abstract - This study examines the integration of multilingual customer service skills into workforce development programs for hospitality management students. In today’s globalized environment, the hospitality industry serves guests from diverse cultural and linguistic backgrounds, making effective communication an essential skill. Using a quantitative approach, the study gathered data from 100 hospitality students to assess their communication skills, performance, and confidence in multilingual settings. The findings reveal that most students already possess basic multilingual abilities, particularly in English and Filipino, which serve as a strong foundation for further development. Results also show that the current curriculum includes elements of multilingual training, contributing to students’ overall competence. However, while students demonstrate satisfactory performance and confidence, there is still a need for increased practical exposure and strengthened training programs. Furthermore, the study highlights that students generally meet industry expectations but may benefit from more structured and continuous training to enhance their real-world readiness. Overall, integrating multilingual customer service skills significantly supports the development of hospitality students, preparing them for the demands of a culturally diverse workforce and improving their competitiveness in the global hospitality industry.
Authors - Sanjana Priyadarsini, Choudhary Aman Kumar Roy, Ashlesha Shree Bajpai, Rajdeep Banerjee, Shivali Sharma, Ranjita Kumari Dash Abstract - Today, machine learning methods are quickly being adopted in healthcare. In numerous instances, it has been observed that datadriven approaches have increased reliance of medical data analysis and disease detection by about 60-70%. It is important to diagnose cardiac arrhythmias early using electrocardiogram (ECG) analysis, as timely diagnosis can prevent severe complications and loss of life. Most ECG datasets are however not balanced, with normal beats by far outnumbering abnormal ones and causing the models to underperform on rare but significant cases. In this work, Logistic Regression is used as a baseline model. To correct this imbalance, Class weighting and Synthetic Minority over-sampling Technique (SMOTE) are applied. These techniques help the model detect rare heartbeat patterns more reliably and miss fewer abnormalities. This paper shows that addressing class imbalance can make ECG-based classification systems more accurate and clinically valid.
Authors - Windy Permata Suyono, Dwi Handarini, Eka Septariana Puspa, Surya Anugrah, Nuramalia Hasanah, Ratna Anggraini, Sabo Hermawan, Rio Firnanda Abstract - This study aims to develop an AI-Based Digital Audit Maturity Frame work integrated with SPIP to support Smart SME Governance and Sustainable Development Goal 9 (SDG 9). The study employs a systematic literature review approach by analyzing 50 relevant articles published between 2020 and 2026 re lated to artificial intelligence in auditing, digital audit maturity, internal control systems, smart governance, SMEs, and sustainable innovation. The findings in dicate that artificial intelligence technologies significantly improve audit effec tiveness, governance transparency, operational efficiency, and organizational re silience. The study also reveals that digital audit maturity and SPIP-based internal control systems play important roles in supporting sustainable digital transfor mation and adaptive governance within SMEs. Based on the literature synthesis, this study proposes a conceptual framework consisting of input factors, digital transformation processes, digital audit maturity levels, SPIP-based internal con trol systems, smart SME governance, and SDG 9 achievement. The proposed framework contributes theoretically by integrating technological capability, gov ernance systems, internal control mechanisms, and sustainability perspectives into a unified governance model. Practically, the framework provides guidance for SMEs, policymakers, auditors, and digital transformation practitioners in strengthening sustainable AI-driven governance implementation.
Authors - Milind Nemade, Khush Chheda, Rahul Dhanak, Durgeshkumar Dubey Abstract - The problem of meeting productivity continues to prevail in the current era in multilingual environments due to frequent language switching between speakers. Most of the existing frameworks for meeting intelligence primarily focus on automatic transcription and lack significant support for Indic languages, speaker identification, and task extraction. Additionally, many of these frameworks depend on metadata associated with specific platforms and, therefore, cannot be used in any offline environment or even on other platforms. In this work, we present a scalable and platform-agnostic framework for meeting intelligence which can automatically an alyze meetings post factors by leveraging speech recognition, speaker identification, and contex tual analysis. The system leverages multilingual and code-switched transcription capabilities of Sarvam AI, generates speaker embeddings using ECAPA-TDNN, and then uses Large Language Models for context-based analysis. Two different strategies for speaker identification are dis cussed in this paper such that they do not need any platform-based metadata while improving the attribution accuracy. We have further developed an asynchronous framework for extracting tasks, assigning tasks, and notifying about them. Experimental results indicate enhanced transcription accuracy as well as speaker identification accuracy in Hindi-English code-switching cases. Fu ture work will focus on implementing advanced privacy protection and end-to-end encryption mechanisms for secure storage and processing of meeting recordings and metadata.
