Authors - Pablo Figueroa, Valeria Yunga, Pablo Ramon, Nelson Piedra Abstract - Traditional airport meet-and-greet operations are often characterized by a sea of physical placards and manual, paper-based logging systems. This manual approach not only creates logistical clutter in arrival halls but also leads to significant information lag and frequent data entry errors during the administrative reconciliation process. This paper presents the design and implementation of a centralized digital platform developed to streamline the coordination be-tween airport authorities, hotel representatives, and arriving passengers. Utilizing a responsive web-based architecture, the system eliminates the requirement for native application installations, thereby ensuring immediate accessibility for international travelers and hotel staff through their mobile devices. The platform integrates a multi-tier interface that facilitates real-time booking, automated digital check-ins, and instantaneous data synchronization. By replacing error-prone manual key-in tasks with an automated data pipeline, the system provides airport management with real-time operational visibility and analytics. Preliminary results from the implementation demonstrate a substantial reduction in guest waiting times and a marked improvement in data accuracy. Ultimately, this digital transition enhances terminal space management and provides a more seamless, professional experience for international arrivals, establishing a scalable model for modern airport ground handling services.
Authors - Monali Deshmukh, Payal Shete, Tanvi Pakhale, Pranjal Alhat, Krutika Salve Abstract - Because of their expensive price, large size, and reliance on lab settings, conventional oscilloscopes are inconvenient tools for signal analysis. They have made it necessary to have small, inexpensive, portable devices that can see waveforms outside of typical lab settings. The creation of a portable digital oscilloscope utilizing a 2.8-inch TFT display and an ESP32 microprocessor is detailed in this paper. Because of its autonomous operation, the gadget can record data in real time and display analog signals. Because it runs on batteries, the oscilloscope is affordable, lightweight, and portable. The ESP32 samples analog signals and displays them with user-controlled time-base settings. This oscilloscope has features including a grid display, waveform zooming, and freeze for convenience and readability. Both AC and DC signals can be monitored with an oscilloscope. According to tests, the device accurately displays common waveforms including sine, square, and sawtooth signals, which makes it ideal for embedded system development, simple troubleshooting, and instructional purposes.
Authors - Luis Anthony Hidalgo Ponce, Maricela Pinargote-Ortega Abstract - Technical support management in university environments often faces a high manual operational load due to the constant increase in digital service requests. This paper presents a multi-agent system based on Large Language Models (LLMs) designed to automate the ticket lifecycle, including classification, urgency-based prioritization, and intelligent routing. The proposed solution is built upon a modular architecture coordinated by an orchestrator agent and integrated with Retrieval-Augmented Generation (RAG) techniques to resolve frequent queries without human intervention. The system’s performance was evaluated through a controlled dataset, achieving a classification accuracy of 85.7% and a 100% effectiveness rate in user intent detection. The results demonstrate a significant reduction in response times compared to manual processes, validating the efficacy of generative artificial intelligence to optimize efficiency and user experience within university technology service desks.
Authors - Madhuri Surwase, Trupti Bansode, Jyoti Pawar, Smita Katkar, Vaishali Kalsgonda, Prakash Bansode, Namdev Falake Abstract - Automatic Speech Recognition (ASR) systems have achieved remarkable progress through deep learning and Transformer-based architectures, demonstrating near-human accuracy on clean audio. However, their performance degrades significantly under challenging conditions and specialized domains. This comprehensive study evaluates leading commercial ASR APIs—Google Cloud Speech-to-Text, Microsoft Azure Speech Service, AssemblyAI, Deepgram, OpenAI Whisper, Speechmatics, and others—across multiple dimensions: general speech recognition, low-quality forensic-like audio, domain-specific mathematical notation, and personalized speaker adaptation. Results demonstrate 100% accuracy on clean audio for leading systems (Deepgram, Speechmatics, Webkit SpeechRecognition), but dramatic performance degradation to 10− 81% word error rates on forensic-like audio. Analysis of domain-specific challenges reveals that none of the tested commercial ASR systems natively support direct transcription of mathematical symbols and Greek letters into structured symbolic output (e.g., LaTeX). The study identifies critical limitations in robustness, modularity, and domain adaptation, while highlighting promising customization mechanisms including custom vocabularies, language models, and post-processing integration. Performance improvements through speaker personalization ranged from 3% for natural voices to 10% for synthetic voices. Despite notable advances in end-to-end and Transformer-based approaches, ASR systems remain unsuitable for forensic applications and specialized domains without substantial customization and post-processing. Future research must address low-resource performance, linguistic diversity, robustness in extreme noise, and the integration of Large Language Models for semantic understanding. This paper synthesizes recent advances and critical gaps, providing a roadmap for advancing ASR technology in specialized and challenging acoustic environments.
Authors - Nurul Istiq faroh, Nur Asitah, Amiruddin Hadi Wibowo, Ricky Setiawan, Abdur-Razaq Aliyy Abolaji, Hendratno Abstract - Detecting structural breaks and anticipating volatility regimes in foreign exchange markets remain challenging due to the non-stationary and nonlinear nature of exchange rate dynamics. This study proposes a non-parametric framework for identifying structural breaks in the NZD/ USD exchange rate by integrating sliding-window volatility estimation, concentration bound based change point detection, and wavelet-based time frequency analysis. Volatility is first quantified using a movingwindow approach and compared against a Hoeffding bound to detect extraordinary events. The resulting change points are used to segment the exchange rate series into statistically reliable sequences, which are subsequently analyzed using wavelet scalograms. Empirical results reveal a consistent three-regime structure in the wavelet domain, comprising post-event reaction, stable market behavior, and pre-event escalation phases. Non-parametric statistical tests confirm significant differences in volatility distributions across these regimes, with the pre-event regime exhibiting markedly higher variability and acting as a precursor to structural breaks. The findings demonstrate that wavelet coefficients contain informative signatures of impending market instability. Overall, the proposed framework provides an interpretable and robust approach for analyzing regime-dependent volatility dynamics and offers valuable insights for early warning and risk management in currency markets.
Authors - Syeda Zaina Rohana Sneha, Mohammad Shamsul Arefin, M. M. Musharaf Hussain Abstract - This study details the development and evaluation of a web-based digital health platform that uses Optical Character Recognition (OCR) and Artificial Intelligence (AI) to automate the reading of medication labels and manage appointments. Users photograph medication labels and appointment slips, and the system automatically extracts and organizes relevant data to generate medication schedules, appointment calendars, and reminders with minimal manual effort. Designed with a user-centered approach to lessen cognitive load, the platform was tested with 35 users. Three experts verified the content validity of the assessment tool via the Item Objective Congruence (IOC) index. User satisfaction analysis indicates high approval, particularly for reducing the memory burden associated with medication routines and appointments. The results indicate that integrating OCR and AI can support continuous care, enhance usability, and increase patient engagement in the sustainable management of chronic diseases.
Authors - Tirupathi Rao Dockara, Manisha Malhotra Abstract - The prediction of cardiovascular disease (CVD) risk by machine learning is frequently impeded by duplicated and associated clinical characteristics, leading to complex and less robust models. Feature selection is therefore essential to improve model compactness while maintaining predictive performance. This study presents a systematic evaluation of meta-heuristic-based feature selection for CVD risk modeling under a standardized experimental setting. Feature selection is formulated as a wrapper-based optimization problem and evaluated using representative population-based meta-heuristic algorithms from multiple families. All methods are assessed using the XGBoost Histogram classifier on a public cardiovascular dataset comprising approximately 70,000 records with 13 clinical features. Experimental results show that meta-heuristic feature selection consistently reduces the number of input features by more than 60% while achieving comparable predictive performance across different algorithmic families. In addition, SHAP analysis is employed to examine the contributions of the selected features and support model interpretability.
Authors - Md. Shahidul Islam, Ronobir Chandra Sarker Abstract - The widespread adoption of artificial intelligence (AI) and automation is emerging as a central driver of productivity growth in European firms. Yet identifying the causal impact of AI adoption on firm productivity is complicated by endogeneity, selection bias, and heterogeneous treatment effects. This paper analyzes the productivity effects of AI and automation adoption using a unified framework that combines traditional econometric techniques with causal machine learning methods. Using firm-level data from Orbis merged with industry-level productivity and ICT capital measures from EU KLEMS for the period 2010–2023, we estimate both average and heterogeneous treatment effects. Double Machine Learning yields a robust average productivity gain of approximately 4.5 percent, while Causal Forests reveal substantial heterogeneity across industries, firm size, human capital, and digital maturity. The results provide credible causal evidence that AI adoption enhances firm productivity and highlight the importance of complementary capabilities in realizing its economic benefits.
Authors - Sonia Kuwelkar, Veena Gauns, Rohit Sopan, Sonia Shetkar, Dinanath Usgaonkar Abstract - Prompt engineering has emerged as an essential paradigm in leveraging desired behaviors from large language models (LLMs) without altering their parameters. Although the majority of the current literature has revolved around the introduction of novel prompt engineering strategies, there has been comparatively less emphasis on the contribution of the evaluation and optimization of prompts in concrete systems. In this paper, we offer a specialized review of prompt engineering from an evaluation/optimization centric viewpoint with a larger nod to conceptual developments and illumination rather than detailing the comparisons of approaches. Furthermore, we attempt to establish the concrete importance of prompt engineering via a real-life application, which resulted in improved performances in tasks through the process of prompt refinement and informal evaluations without the need to change the architecture and weights of the models. The paper will also introduce the deficiencies in prompt engineering in the realms of re-producibility, robustness, and the unavailability of standardized approaches in the aspect of concrete evaluations.
Authors - Domenico Vito, Carol Maione, Gabriela Fernandez, Catia Algieri, Sudip Chakraborty Abstract - The demand for long-endurance, intelligent drone systems is growing across diverse domains including defense, sports analytics, and industrial inspection. This paper presents the design and implementation of a solar-powered drone platform equipped with an autonomous, image-based range scoring system. Leveraging high-efficiency monocrystalline photovoltaic panels and Silicon- Carbide (SiC)-based lithium-ion batteries, the drone achieves extended flight durations while maintaining energy reliability. A centralized Energy Management System (EMS), featuring Maximum Power Point Tracking (MPPT) control, optimizes real-time energy harvesting and distribution. The platform also integrates an AI-enhanced thermal imaging module for precise target impact detection and scoring, with results computed using a multi-parameter range scoring model. An interactive Ground Control Station (GCS) interface enables intuitive mission planning, telemetry visualization, and data export. Experimental evaluations demonstrate significant gains in energy efficiency and scoring precision, underscoring the system’s potential for sustainable, autonomous aerial operations in real-world conditions.
