Accepted Papers

Confidence Evaluation Measures for Zero Shot LLM Classification

David Farr1, Iain J. Cruickshank2, Lynnette Hui Xian Ng2, Nico Manzonelli3, Nicholas Clark1, Kate Starbird1, and Jevin West1, 1University of Washington, 2Carnegie Mellon University, 3Cyber Fusion and Innovation Cell.

Abstract

Assessing classification confidence is critical for leveraging Large Language Models (LLMs) in automated labeling tasks, especially in the sensitive domains presented by Computational Social Science (CSS) tasks. In this paper, we apply five different Uncertainty Quantification strategies for three CSS tasks: stance detection, ideology identification and frame detection. We use three different LLMs to perform the classification tasks. To improve the classification accuracy, we propose an ensemble-based UQ aggregation strategy. Our results demonstrate that our proposed UQ aggregation strategy improves upon existing methods and can be used to significantly improve human-in-the-loop data annotation processes.

Keywords

uncertainty quantification, large language models, stance detection, ideology identification, frames detection, ensemble models.

Merging Language and Domain Specific Models: the Impact on Technical Vocabulary Acquisition

Thibault Rousset1, Taisei Kakibuchi2, Yusuke Sasaki2, and Yoshihide Nomura2, 1School of Computer Science, McGill University, 2Fujitsu Ltd.

Abstract

This paper investigates the integration of technical vocabulary in merged language models. We explore the knowledge transfer mechanisms involved when combining a general-purpose language-specific model with a domain-specific model, focusing on the resulting model’s comprehension of technical jargon. Our experiments analyze the impact of this merging process on the target model’s proficiency in handling specialized terminology. We present a quantitative evaluation of the performance of the merged model, comparing it with that of the individual constituent models. The findings offer insights into the effectiveness of different model merging methods for enhancing domain-specific knowledge and highlight potential challenges and future directions in leveraging these methods for crosslingual knowledge transfer in Natural Language Processing.

Keywords

Large Language Models , Knowledge Transfer , Model Merging , Domain Adaptation , Natural Language Processing.

Moving Towards Constructivist Ai Above Epistemic Limitations of Llms Enhancing the Efficacy of Mixed Human-ai Approaches Through Socio-technical Research: Autopoietic Structural Coupling & Consensus Domains of Communities of Practice

Gianni Jacucci, University of Trento, Italy

Abstract

Current AI models, particularly large language models (LLMs), are predominantly grounded in positivist epistemology, treating knowledge as an external, objective entity derived from statistical patterns in data. However, this paradigm fails to capture "facts-in-the-conscience", the subjective, meaning-laden experiences central to human sciences. In contrast, phenomenology hermeneutics and constructivism, as fostered by socio-technical research (16), provide a more fitting foundation for AI development, recognizing knowledge as an intentional, co-constructed process shaped by human interaction and community consensus. Phenomenology highlights the lived experience and intentionality necessary for meaning-making, while constructivism emphasizes the social negotiation of knowledge within communities of practice. This paper argues for an AI paradigm shift integrating second-order cybernetics, enabling recursive interaction between AI and human cognition. Such a shift would make AI not merely a tool for knowledge retrieval but a co-participant in epistemic evolution, supporting more trustworthy, context-sensitive, and meaning-aware AI systems within socio-technical frameworks.

Keywords

AI epistemology, Large Language Models(LLMs), Consensus Domain, Human-AI Interaction, Structural Coupling.

Information Retrieval vs Cache Augmented Generation vs Fine Tuning: A Comparative Study on Urdu Medical Question Answering

Ahmad Mahmood1, Zainab Ahmad1, Iqra Ameer2, and Grigori Sidorov1, 1Instituto Politécnico Nacional (IPN), Centro de InvestigaciónenComputación(CIC), Mexico City, Mexico, 2Division of Science and Engineering, The Pennsylvania State University, Abington, USA

Abstract

The development of medical question-answering (QA) systems has predominantly focused on high-resource languages, leaving a significant gap for low-resource languages like Urdu. This study proposed a novel corpus designed to advance medical QA research in Urdu, created by translating the benchmark MedQuAD corpus into Urdu using the Generative AI-based translation technique. The proposed corpus is evaluated using three approaches: (i) Information Retrieval (IR), (ii) Cache-Augmented Generation (CAG), and (iii) Fine-Tuning (FT). We conducted two experiments, one on a 500-instance subset and another on the complete 3,152-question corpus, to assess retrieval effectiveness, response accuracy, and computational efficiency. Our results show that JinaAIembeddings outperformed other IR models, while OpenAI 4o mini, FT achieved the highest response accuracy (BERTScore: 70.6%) but is computationally expensive. CAG eliminates retrieval latency but requires high resources. Findings suggest that IR is optimal for real-time QA, Fine-Tuning ensures accuracy, and CAG balances both. This research advances Urdu medical AI, bridging healthcare accessibility gaps.

Keywords

Information retrieval, retrieval-augmented generation, cache-augmented generation, fine-tuning, Urdu medical question-answering.

Enhancing Road Sign Detection for Autonomous Driving using Yolov8 and Multisensory Vision Integration

Yang Liu1, Soroush Mirzaee2, 1Esperanza High School, 1830 Kellogg Dr, Anaheim, CA 92807, 2California State Polytechnic University, USA.

Abstract

StopCV aims to improve road sign detection for autonomous and assisted driving systems [1]. One of the main challenges is that current systems struggle with poor lighting, weather conditions, and obstructed signs. To address these issues, we developed StopCV, a vision-based detection system using a Raspberry Pi 5, a high-quality camera, and a custom-trained YOLOv8 model for real-time recognition [2]. Additional sensors, such as ultrasonic and LiDARreplicating systems, enhance object detection accuracy. Through multiple experiments, including real-world testing and public perception surveys, we identified limitations in low-visibility conditions and obscured signs. We mitigated these issues using improved image processing, infrared cameras, and AI training using different datasets. Our results show that enhanced sensor and AI integration can significantly improve accuracy [3]. Ultimately, StopCV demonstrates the potential of AI-driven vision systems to help improve driving safety, and further testing paves the way for autonomous driving applications.

Keywords

Road Sign Detection, YOLOv8, Autonomous Driving, Multisensory Vision System.

A Smart RPG Game-based English Learning Platform using Generative Artificial Intelligence and Nature Language Processing

Hongjia Meng1, Moddwyn Andaya2, 1Kantonale Mittelschule Uri, Gotthardstrasse 59, 6460 Altdorf, Uri, 2California State Polytechnic University, USA.

Abstract

Language barriers continue to challenge immigrants as they adapt to new environments, often limiting their confidence and social integration. Many existing language-learning applications fail to provide immersive, adaptive, and emotionally supportive experiences—particularly for beginners. This game is about learning language by talking and interacting with NPCs in real life scenes [1]. The aim is to remove the language barrier of a newcomer who doesn’t have enough motivation and bravery to talk in real life by simulating real life in a game, where the player only talks to NPCs. This paper introduces an AI-powered language learning game designed to simulate real-world conversations in a safe and engaging virtual environment. Players navigate through a city, interact with dynamic NPCs, and receive language support through a personalized guide who speaks their native language. This guide gradually shifts to the target language, helping users build confidence and fluency over time. The system leverages OpenAIs GPT-4o model to deliver context-aware, level-appropriate dialogue, ensuring that players are neither overwhelmed nor under-challenged. A user study showed that the game effectively fosters engagement and supports language acquisition, though it also highlighted areas for improvement in navigation and user interface design. Compared to traditional apps, this game offers a richer, more supportive learning experience by combining adaptive AI, immersive storytelling, and real-time conversational practice. Ongoing development will enhance usability and explore features such as multiplayer interaction to further support language learners.

Keywords

Language Learning Gamification, NPC Interaction, Immersive Language Acquisition, Real-Life Simulation.

Integrating Large Language Models in Financial Investments and Market Analysis: A Survey

SedighehMahdavi, Kristin Chen, Pradeep Kumar Joshi, Lina HuertasGuativa, and Upmanyu Singh, AI Research Lab, Blend360, Columbia, USA

Abstract

Large Language Models (LLMs) have been employed in financial decision making, offering enhanced analytical capabilities for investment strategies. Traditional investment strategies often utilize quantitative models, fundamental analysis, and technical indicators. However, LLMs have introduced new capabilities to process and analyze large volumes of structured and unstructured data, extract meaningful insights, and enhance decision-making in real-time. This survey provides a structured overview of recent research on LLMs within the financial domain, categorizing research contributions into four main frameworks: LLM-based Frameworks and Pipelines, Hybrid Integration Methods, Fine-Tuning and Adaptation Approaches, and Agent-Based Architectures. This study provides a structured review of recent LLMs research on applications in stock selection, risk assessment, sentiment analysis, algorithmic trading, and financial forecasting. By reviewing the existing literature, this study highlights the capabilities, challenges, and potential directions of LLMs in financial markets.

Keywords

Large Language Models, Financial Decision-Making, Investment Strategies, Fine-Tuning, Multi-Agent Systems, Portfolio Optimization, Stock Market Prediction.

Enhancing Road Sign Detection for Autonomous Driving using Yolov8 and Multisensory Vision Integration

Yang Liu1, Soroush Mirzaee2, 1Esperanza High School, 1830 Kellogg Dr, Anaheim, CA 92807, 2California State Polytechnic University, Pomona, CA, 91768, Irvine, CA 92620

Abstract

StopCV aims to improve road sign detection for autonomous and assisted driving systems [1]. One of the main challenges is that current systems struggle with poor lighting, weather conditions, and obstructed signs. To address these issues, we developed StopCV, a vision-based detection system using a Raspberry Pi 5, a high-quality camera, and a custom-trained YOLOv8 model for real-time recognition [2]. Additional sensors, such as ultrasonic and LiDARreplicating systems, enhance object detection accuracy. Through multiple experiments, including real-world testing and public perception surveys, we identified limitations in low-visibility conditions and obscured signs. We mitigated these issues using improved image processing, infrared cameras, and AI training using different datasets. Our results show that enhanced sensor and AI integration can significantly improve accuracy [3]. Ultimately, StopCV demonstrates the potential of AI-driven vision systems to help improve driving safety, and further testing paves the way for autonomous driving applications.

Keywords

Road Sign Detection, YOLOv8, Autonomous Driving, Multisensory Vision System.

A Smart RPG Game-based English Learning Platform using Generative Artificial

Hongjia Meng1, Moddwyn Andaya2, 1Kantonale Mittelschule Uri, Gotthardstrasse 59, 6460 Altdorf, Uri, 2California State Polytechnic University, Pomona, CA, 91768, Irvine, CA 92620

Abstract

Language barriers continue to challenge immigrants as they adapt to new environments, often limiting their confidence and social integration. Many existing language-learning applications fail to provide immersive, adaptive, and emotionally supportive experiences—particularly for beginners. This game is about learning language by talking and interacting with NPCs in real life scenes [1]. The aim is to remove the language barrier of a newcomer who doesn’t have enough motivation and bravery to talk in real life by simulating real life in a game, where the player only talks to NPCs. This paper introduces an AI-powered language learning game designed to simulate real-world conversations in a safe and engaging virtual environment. Players navigate through a city, interact with dynamic NPCs, and receive language support through a personalized guide who speaks their native language. This guide gradually shifts to the target language, helping users build confidence and fluency over time. The system leverages OpenAIs GPT-4o model to deliver context-aware, level-appropriate dialogue, ensuring that players are neither overwhelmed nor under-challenged. A user study showed that the game effectively fosters engagement and supports language acquisition, though it also highlighted areas for improvement in navigation and user interface design. Compared to traditional apps, this game offers a richer, more supportive learning experience by combining adaptive AI, immersive storytelling, and real-time conversational practice. Ongoing development will enhance usability and explore features such as multiplayer interaction to further support language learners.

Keywords

Language Learning Gamification, NPC Interaction, Immersive Language Acquisition, Real-Life Simulation.

Integrating Large Language Models in Financial Investments and Market Analysis: A Survey

SedighehMahdavi, Kristin Chen, Pradeep Kumar Joshi, Lina HuertasGuativa, and Upmanyu Singh, AI Research Lab, Blend360, Columbia, USA.

Abstract

Large Language Models (LLMs) have been employed in financial decision making, offering enhanced analytical capabilities for investment strategies. Traditional investment strategies often utilize quantitative models, fundamental analysis, and technical indicators. However, LLMs have introduced new capabilities to process and analyze large volumes of structured and unstructured data, extract meaningful insights, and enhance decision-making in real-time. This survey provides a structured overview of recent research on LLMs within the financial domain, categorizing research contributions into four main frameworks: LLM-based Frameworks and Pipelines, Hybrid Integration Methods, Fine-Tuning and Adaptation Approaches, and Agent-Based Architectures. This study provides a structured review of recent LLMs research on applications in stock selection, risk assessment, sentiment analysis, algorithmic trading, and financial forecasting. By reviewing the existing literature, this study highlights the capabilities, challenges, and potential directions of LLMs in financial markets.

Keywords

Large Language Models, Financial Decision-Making, Investment Strategies, Fine-Tuning, Multi-Agent Systems, Portfolio Optimization, Stock Market Prediction.

A Context-Aware Mobile App to Support Early Disease Detection and Education using GPT-4 and Visual Symptom Surveys

Qiaoman Cai1, Qiaoqian Cai2, Rodrigo Onate3, 1Crean Lutheran High School, 12500 Sand Canyon Ave, Irvine, CA 92618, 2Crean Lutheran High School, 12500 Sand Canyon Ave, Irvine, CA 92618, 3California State Polytechnic University, Pomona, CA, 91768.

Abstract

This research project addresses the growing gap in accessible healthcare, particularly for women in underserved communities [1]. Rising healthcare costs and delayed diagnosis contribute to higher mortality, particularly in low income areas. To address this, we developed Care Bridge Health, a mobile app that uses AI (GPT-4) to simulate medical reasoning based on structured user input [2]. The app features a body diagram for symptom selection, guided surveys for pain description, and access to disease education through multimedia content. The system is divided into three components: the Ask Page for diagnosis, the Learn Page for information retrieval, and the History Page for recordkeeping. Through two experiments, we identified that both symptom clarity and AI consistency can affect diagnosis quality [3]. Despite these limitations, the app provides a scalable, low-cost way to raise health awareness and guide users toward early symptom identification. With further refinement and medical validation, Care Bridge Health could become a valuable tool for community-level health empowerment.

Keywords

Artificial Intelligence in Healthcare, Symptom Checker App, GPT-4 Diagnosis Assistance, Womens Health Accessibility, Mobile Health Technology.

Multi-Domain ABSA Conversation Dataset Generation via LLMs for Real-World Evaluation and Model Comparison

Tejul Pandit1, Meet Raval2, and Dhvani Upadhyay3, 1Palo Alto Networks, Santa Clara, USA, 3University of Southern California, Los Angeles, USA, 3Dhirubhai Ambani University, Gandhinagar, India.

Abstract

Aspect-Based Sentiment Analysis (ABSA) offers granular insights into opinions but often suffers from the scarcity of diverse, labeled datasets that reflect real-world conversational nuances. This paper presents an approach for generating synthetic ABSA data using Large Language Models (LLMs) to address this gap. We detail the generation process aimed at producing data with consistent topic and sentiment distributions across multiple domains using GPT-4o. The quality and utility of the generated data were evaluated by assessing the performance of three state-of-the-art LLMs (Gemini 1.5 Pro, Claude 3.5 Sonnet, and DeepSeek-R1) on topic and sentiment classification tasks. Our results demonstrate the effectiveness of the synthetic data, revealing distinct performance trade-offs among the models: DeepSeekR1 showed higher precision, Gemini 1.5 Pro and Claude 3.5 Sonnet exhibited strong recall, and Gemini 1.5 Pro offered significantly faster inference. We conclude that LLM-based synthetic data generation is a viable and flexible method for creating valuable ABSA resources, facilitating research and model evaluation without reliance on limited or inaccessible real-world labeled data.

Keywords

Aspect-Based sentiment analysis (ABSA), Synthetic Data Generation, Large Language Models, GPT-4o, Gemini 1.5 Pro, Claude 3.5 Sonnet, Deepseek-R1, Comparative analysis of LLMs.

Self-explaining Emotion Classification Through Preference-aligned Large Language Models

Muhammad HammadFahim Siddiqui and Diana Inkpen, University of Ottawa, Canada.

Abstract

Recent advancements in large language models (LLMs) have shown promise for NLP applications, yet producing accurate explanations remains a challenge. In this work, we introduce a self-explaining model for classifying emotions in X posts and construct a novel preference dataset using chain-of-thought prompting in GPT-4o. Using this dataset, we guide GPT-4o with preference alignment via the Direct Preference Optimization (DPO). Beyond GPT-4o, we adapt smaller models such as LLaMA 3 (8B) and DeepSeek (32B distilled) through preference tuning using Odds Ratio Preference Optimization (ORPO), significantly boosting their classification accuracy and explanation quality. Our approach achieves state-ofthe-art performance (68.85%) on the SemEval 2018 E-c multilabel emotion classification benchmark, exhibits comparable results on the DAIR AI multiclass dataset and attains a high sufficiency score—indicating the standalone effectiveness of the generated explanations. These findings highlight the impact of preference alignment for improving interpretability and enhancing classification.

Keywords

LLMs, preference alignment, emotion classification.

IOT Security and Privacy

NikithaMerilenaJonnada, University of the Cumberlands, USA.

Abstract

In this paper, the author discusses the importance of IoT, its security measures, and device protection. IoT devices have become a trend as they allow users to easily use and understand the devices. IoT has become a widely used technique within many industries like banking, agriculture, health care, and others. It made the users experience easy. IoT without AI has been a good investment for many users as its connectivity helps them use multiple devices from a single device and sometimes with a single click.

Keywords

Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), Security, Hacking, Risks.

The Smart Garbage Bin Management Using Iot& Mobile Application with Cloud Databases

LakmaliKarunarathne, York St John University, UK.

Abstract

Smart garbage bins which are automatically opened the bins doors when the person is standing in front of the smart bins are the perfectly innovated garbage bins by the IT industries and developers. The IR sensor is used to sense the waste and then its identified the which category the waste is by the support of sensors like metal proximity sensor, capacitive proximity sensor and the inductive proximity sensor. The expected services are aimed to provide by this entire system. The entire project is included things are, identifying the bins category, dispose the waste based on that category, send notifications and provide the reports for the purpose of getting awareness about the users garbage management. The IOT product is combined with the SMART GARBAGE MASTER (SGM) mobile application to interact with the entire IOT system via the cloud based to provide effective and efficient service to the users who use this system. The data is sent the Arduino for taking the decision that the garbage is either metal or non- metal.

Keywords

Smart, garbage, segregation, plastic, paper, sensors, ultrasonic sensor, IR sensor, bin, level, percentage, IOT, Arduino, Cloud Databases.

Reassessment of Bitcoin Mining: the Utilization of Excessive Energy and Promotion of Green Energy Technologies

Tanja Steigner and Mohammad Ikbal Hossain, Emporia State University, USA

Abstract

Bitcoin mining, often criticized for its substantial energy consumption, holds significant potential to drive energy innovation and sustainability. This paper reevaluates Bitcoin minings environmental impact, focusing on its ability to utilize surplus and renewable energy sources. Mining operations absorb excess energy, such as curtailed wind and solar power, that would otherwise go to waste, contributing to grid efficiency and renewable energy integration. The increasing shift toward renewables, now accounting for over 50% of mining’s energy mix, underscores the industrys progress toward sustainability. Through the analysis of industry data, this paper highlights Bitcoin minings dual role as both a flexible energy consumer and a catalyst for green energy investments. Despite challenges like e-waste and the industrys reliance on energy-intensive proof-of-work mechanisms, the findings demonstrate how targeted policies, and technological advancements can transform Bitcoin mining into a force for environmental and economic benefits. The study emphasizes the need for collaborative efforts among stakeholders to unlock Bitcoin minings full potential in supporting the global energy transition.

Keywords

Bitcoin mining, renewable energy, grid stabilization, green energy investments, proof-of-work (PoW), carbon footprint reduction, e-waste management, decentralized energy systems.

IOT Experimental Results and Deployment Scenario for Tactical Battle Area

Avnish Kumar Singh and RachitAhulwalia, India

The Synergy of AI and IoT: Unlocking New Frontiers in Automation and Innovation

Gawande Krishna Ashok1 and Vandana B. Patil1, Gawande Krishna Ashok1 and Vandana B. Patil2, D. Y. Patil International University, India

A Smart Innovative Emergency Response System for Managing Outdoor Autism-focused using Artificial Intelligence and IOT System (Internet of Things)

Ruibo Song and Andrew Park, California State Polytechnic University, USA

Framework for Data-Driven Spirulina Cultivation and Recommendations

AakaashKurunth, Adithya S Gurikar, Tejas B, Sean Sougaijam and KamatchiPriya L, PES University, India

Biobit: ML Secured Supply Chain Management and Drug Authentication ThroughBlockchain

Vaani Bansal, R Navaneeth Krishnan, PunithAnand, Aditya Kumar Sinha, and Prof.Sheela Devi, , PES University, India

Optimizing Graph Neural Networks Hyperparameters for Molecular Property Prediction Using Nature-Inspired Metaheuristic Algorithms

Ali Ebadi, Yaser Al Mtawa, and Qian Liu, The University of Winnipeg, Canada

Operations Research-guided Graph Neural Networks for Multi-property Regression in Materials Science

ManpreetKuar, Yaser AI Mtawa, Qian Liu, The University of Winnipeg, Winnipeg, Canada

The Role of Power BI in Enterprise Reporting: a Testers Perspective

SwethaTalakola, Quality Engineer III at Walmart Inc, USA

Kubernetes Meets Legacy Systems: A Migration Playbook for Modern Infrastructure

Ali Asghar Mehdi Syed, IT Engineer at World Bank Group, USA

Leveraging Merge Request Data to Analyze Devops Practices: Insights From a Networking Software Solution Company

Samah Kansab1, Matthieu Hanania1, Francis Bordeleau1, and Ali Tizghadam2, Canada

Regulatory and Policy Discussions on LLM Auditing: Challenges, Frameworks, and Future Directions

Kailash Thiyagarajan, Independent Researcher, USA

Enhancing LLM-assisted Translation: Optimizing Contextual Prompting and Pivot Strategies for Low-resource Languages with a Focus on Korean-to-english News Translation

WANG Wei and ZHOU Weihong, Beijing International Studies University, China

AI-enhanced Interactive Simulation for Scalable CPR and Lifeguard Training in Aquatic Emergency Response

Evan Mikai Lu and Tyler Boulom, California State Polytechnic University, USA

An Evolved Model for Online Content Filtering With Real-time AI Identification and Imagery Recognition

Chenghao Feng and Andrew Park, California State Polytechnic University, USA

Evaluating Speech Recognition Algorithms for Linguistic Analysis in Hearing-Impaired Childrens Environments

Rafael Pintoa, Pedro Moraisa, Gustavo Tomaza, Shawn N Frasere, Ricardo Valentima and JoseliBrazorottoa, Federal University of Rio Grande do Norte, Brazil.

Juicy or Dry? A Comparative Study of user Engagement and Information Retention in Interactive Infographics

Bruno Campos, Department of Design, MacEwan University, Canada.

A Convenient Scooper with Sensors and Application to Help with Dog Waste Picking and Environmental Responsibility Management

Shilei Cao1, Jonathan Sahagun2, 1Arnold O. Beckman High School, 2California State Polytechnic University, Pomona, USA.

The Signal is the System Scaling Real-time Systems for Planetary Intelligence

Stephen W. Marshall1 and Jurgen Valckenaere2, 1ora.systems, 2University of Western Australia.

A Sophisticated Mobile Application to Determine Hairstyle and Locate Barbers using Artificial Intelligence Models and Web Scraping

Michael Zhang1, Rodrigo Onate2, 1Oakton High School, 2900 Sutton Rd, Vienna, VA 22181, 2California State Polytechnic University,USA.

Quantum-consistent Adelic Integration and Structure of Egyptian Fractions

Julian Del Bel, Independent Researcher, Canada.

Secure API-Driven Workforce Data Pipelines: Leveraging Oauth 2.0 and S3 for Real-time Compliance and Forecasting

Abdul JabbarMohammad ,UKG Lead Technical Consultant at Metanoia Solutions Inc, USA.

Data to Decisions: Using Powerbi and Spss for Real-time Quality Metrics in Pbm and Insurance Systems

Varun Varma Sangaraju, Senior QA Engineer at Cognizant, USA.

Service Cloud Optimization for Claims Processing: a Developer’s Perspective

Vasanta Kumar Tarra, Lead engineer at Guidewire software.

Blockchain-backed Sla Enforcement in Multi-tenant Cloud Infrastructures

ParthJani, Project manager at Molina Healthcare, USA.

Cross-domain Vulnerability Management Using Unified Dashboards: a Metrics-based Approach to Compliance and Risk Remediation

Pavan Paidy1 and Krishna Chaganti2, 1AppSec Lead At FINRA, USA, 2Associate Director at S & P Global, USA.

Multilingual Information Retrieval: Building a Scalable Social Media Search Engine with Apache Solr

Sai Prasad Veluru1 and Mohan Krishna Manchala2, 1Software Engineer at Apple Inc, USA.

Signal-based Anomaly Detection in Cloud Operations using Log Insights and Prometheus Metrics

LalithSriramDatla,Cloud Engineer, USA.

Driving Automation in Oracle ERP With FBDI, HDL, and Custom BI Reports

Anusha Atluri1 and Teja Puttamsetti2, 1SR Oracle Techno Functional consultant at Oracle, USA, 2Senior Integration Specialist at Caesar’s Entertainment, USA.

A Comprehensive B2b2b Multi-tenant Saas Solution for Agency and Client Management with Stripe Integration

Rahul Ambekar, AtharvAgharkar, LalitBagul, Niraj Bade, Department of Computer Engineering, A. P. Shah Institute of Technology, Thane, India.

Lessons Learned and Achievements in the Development and Testing of on-board Software for the Nahid 2 Satellite

ShirinRanjbaran and ShahrookhJalilian, Satellite Research Institute, Iranian Space Research Centre, Tehran, Iran.

An AI-powered Mobile App to Democratize Tennis Skill Development Through Pose Estimation and Video Comparison

Yixuan Liu1, Jonathan Sahagun2, California State Polytechnic University, USA.

Smart Diagnostics: Integrating AI-powered Biosensors for Early Detection in Oncology

Varun Varma Sangaraju, Senior QA Engineer at Cognizant, USA.

Venue

Hilton Vancouver Downtown