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Enabling Grid Stability: Harnessing μPMU data for data-Driven Analysis of Grid Frequency Events 12th

Enabling Grid Stability: Harnessing μPMU Data for Data-Driv...

引用

12th international conference on Frontiers of intelligent computing: Theory and Applications, FICTA 2024

作者： Singh, Uttam Dey, Maitreyee Patel, Preeti Department of Computer Science and Applied Computing London Metropolitan University 166-220 Holloway Rd LondonN7 8DB United Kingdom

ISBN: (纸本)9789819601462

The surge in distributed energy resources connected to the power grid has significantly heightened challenges for grid operators. While the integration of micro-synchrophasor unit (μPMU) into the power grid infrastructure has facilitated the acquisition of high-resolution data, analyzing such vast datasets presents its own set of challenges. This paper offers insights into fifty days of μPMU data through data analysis techniques focusing on frequency events within the operational and hazardous ranges. Drawing from fifty days of real-world data collected from grid-connected solar farm in England, this paper examines and analyzes multiple frequency events while enhancing comprehension of their diverse patterns. Additionally, this study has explored the isolation forest model for anomalous event detection, with results validated against power quality data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Power grids

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Autism Detection System using Behavioral data 1

Autism Detection System using Behavioral Data

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1st international conference on intelligent systems and Computational Networks, ICISCN 2025

作者： Vijaya Shetty, S. Sowmya, P. Srujana, S.N. Shruthi Shetty, J. Mishra, Aditya Zaid, Mohd Nitte Meenakshi Institute of Technology Department of Cse Bengaluru India

ISBN: (纸本)9798331529246

This study introduces an automated autism spectrum disorder (ASD) detection system employing advanced machine learning techniques. The system integrates behavioral and physiological data from diverse sources. The score parameters of various machine learning models, such as Random Forest, Decision Tree, SVM, and AdaBoost, are compared. Pickling is performed on each model for easy storage and retrieval, and their predictions are aggregated to present a majority decision. Extensive experiments are conducted on a diverse dataset comprising toddlers, children, and adults to assess the system's performance in accurately classifying individuals as ASD or non-ASD. The system aggregates predictions from various models using a majority voting algorithm to present a majority decision, ensuring reliable and efficient early ASD identification across all age groups. The proposed system achieved an accuracy of 97%, with the majority voting algorithm enhancing reliability and efficiency in early ASD identification across all age groups. The novelty of this work lies in integrating behavioral and physiological data from diverse sources, along with the majority voting algorithm, to develop an automated ASD detection system using machine learning techniques. © 2025 ieee.

关键词： Adversarial machine learning

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IntelliSMART: intelligent Semantic Machine-Assisted Research Tool 16th

IntelliSMART: Intelligent Semantic Machine-Assisted Research...

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16th international conference on Social Networks Analysis and Mining

作者： Khatri, Aadyant Egierski, Nicolas Pochamreddy, Ashutosh Alhamadani, Abdulaziz Sarkar, Shailik Lu, Chang-Tien Dept Comp Sci Virginia Tech Falls Church VA 22043 USA Florida Polytech Univ Dept Data Sci & Business Analyt Lakeland FL USA

ISBN: (纸本)9783031785535;9783031785542

The exponential growth of academic literature presents significant challenges for researchers attempting to find relevant information. Traditional keyword-based retrieval systems often fail to address issues such as synonyms, homonyms, and semantic nuances, leading to suboptimal search results. This paper introduces a novel system called IntelliSMART (intelligent Semantic Machine-Assisted Research Tool), which leverages large language models (LLMs) and advanced semantic processing techniques to improve the retrieval of academic literature. Our approach integrates query rewriting, embedding generation, efficient indexing, and complex article retrieval mechanisms to provide highly accurate and contextually relevant results that align with the user's intent. The IntelliSMART system features a user-friendly front end that facilitates intuitive query input, along with a robust back end for handling user queries, generating embeddings, indexing extensive collections of academic papers, and efficiently retrieving the most relevant documents. The proposed system shows significant improvements over conventional methods, highlighting its potential to transform the search experience in academic research.

关键词： data mining Information retrieval Semantic search Large language models Keyword search Text Generation

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FPGA based Vehicle Collision Avoidance and Accident Warning using Sobel Operation and Manhattan Distance Metrics 3

FPGA based Vehicle Collision Avoidance and Accident Warning ...

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3rd international conference on intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kumar, Vishal Pranao Amarnath Bhattacharjee, Sanjana Kumar, Harsh Mal, Rupam Ravichandran, Vaarshini Sivasankaran, K. School of Electronics Engineering VIT University Vellore India

ISBN: (纸本)9798331527549

A collision avoidance system in vehicles is a safety technology which is designed to prevent or reduce accidents caused due to any obstacles such as other vehicles or pedestrians. It utilizes cameras, or other sensors to monitor the vehicle's surroundings and alerting the driver to any potential danger. advanced driver assistance systems (ADAS) and collision avoidance works hand in hand as this enhances overall vehicle safety, reduces any driver errors and also contributes to overall safety on road. This paper presents the design and implementation of a real-time collision avoidance system using Field-Programmable Gate Arrays (FPGAs). The proposed system processes the video input from a vehicle's camera and converts the video's frame to grayscale and applies Sobel edge detection on it which helps to calculate the distance between the two vehicles using Manhattan distance formula and hence effectively detects any potential collision. It utilizes FPGA's parallel processing capabilities, reconfigurability and low power to enhance the efficiency and speed of the collision detection and response in autonomous vehicles. The experiments were performed real-time on Altera Cyclone IV-E FPGA which demonstrated effectiveness of the system as the results indicated high accuracy and reliability in distance measurement and collision warning by analyzing parameters like dynamic color variation and background noise reduction for improving the safety and performance in autonomous vehicles. © 2025 ieee.

关键词： advanced driver assistance systems

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Hybrid data Fusion for Elegant Emotion Recognition in Cloud-Based Big data systems

Hybrid Data Fusion for Elegant Emotion Recognition in Cloud-...

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2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Jin, Hulin Sun, Xun Jin, Zhiran Kim, Yong-Guk School of Computer Science and Technology Anhui University Hefei230031 China Foothill Preparatory School Temple CityCA91780 United States Sejong University Department of Computer Engineering Seoul05006 Korea Republic of

ISBN: (纸本)9798331509828

In the era of intelligent information, video, audio, and text information are growing explosively. How to extract emotional factors from this information has become very important. This study proposes the novel hybrid data fusion framework based on cloud computing big data system for elegant emotion recognition. This model can effectively solve the multimodal data fusion and recognition tasks. Initially, as the cloud system are always encrypted, data verification mechanism based on linked list index structure is integrated and the novel data extraction methodology is designed. In the image emotion recognition (IER) part, a structure based on a lightweight neural network is proposed, and combined with the Transformer model, spatial and temporal features are extracted to enhance the accuracy of the emotion recognition. In the audio emotion recognition (AER) part, an innovative emotion vector space analysis method is proposed, which converts the emotional expression in music into vector representation and uses the Transformer encoder structure to capture temporal relationship in music features. In the hybrid data fusion aspect, this study uses the Lukasiewicz T-norm to achieve the fusion of image and audio data. Through the experiment, recognition accuracy is tested and performance is evaluated. © 2025 ieee.

关键词： Spatio-temporal data

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Leveraging Large Language Models for Smart Pharmacy systems: Enhancing Drug Safety and Operational Efficiency 19

Leveraging Large Language Models for Smart Pharmacy Systems:...

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19th international conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Osheba, Abdelrahman Abou-El-Ela, Ahmed Adel, Osama Maaod, Youssef Abuhmed, Tamer El-Sappagh, Shaker Faculty of Computer Science and Engineering Galala University Egypt College of Computing and Informatics Sungkyunkwan University Korea Republic of

ISBN: (纸本)9798331507817

Adverse drug reactions (ADRs) remain a crucial challenge in healthcare systems, highly contributing to patient mortality. We present an innovative smart pharmacy system that utilizes advanced large language models (LLMs) to enhance drug safety and pharmacy operational efficiency. Our system integrates real-time data from patient's prescriptions, medication databases, and electronic health records (EHR) to automate the detection of potential drug interactions, optimizing clinical decision-making and reducing ADR-related risks. The system's architecture is designed to easily manage prescription processing for patients, inventory management and control, and Pharmacist consultations through an intelligent chatbot interface. Key features include real-time tracking of medication expiration and inventory levels, an interaction checker API for identifying and mitigating risky drug combinations, and an LLM-powered chatbot for accurate data analysis and visualization. By combining advanced computational techniques with AI-driven insights, our smart pharmacy system not only improves medication safety but also eases pharmacy operations. This transformative approach holds the potential to significantly reduce ADR-related hospital admissions and enhance overall healthcare delivery. Our research underscores the vital role of AI and LLMs in modern pharmacy practice, offering an inclusive solution that integrates easily into existing healthcare infrastructures. The proposed smart pharmacy system can be plugged into hospital EHR systems to automatically track patient medications. © 2025 ieee.

关键词： Electronic health record

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Integrating the dynamic optimization strategies of Adam and AdaGrad 13

Integrating the dynamic optimization strategies of Adam and ...

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13th international conference on intelligent Control and Information Processing, ICICIP 2025

作者： Huang, Zhixuan Luo, Xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9798331516147

Artificial intelligence (AI) has been a key research area since the 1950s, initially focused on using logic and reasoning to create systems that understand language, control robots, and offer expert advice. With the rise of big data and deep learning, AI has advanced in applications like recommendation systems, image recognition, and machine translation, primarily through optimizing loss functions in deep neural networks to improve accuracy and reduce training *** descent is the core optimization method but faces challenges like slow convergence and local minima. To overcome these, algorithms like Momentum, AdaGrad, RMSProp, Adadelta, Adam, and Nadam have been developed, introducing momentum and adaptive learning rates to accelerate convergence. This paper presents a new optimization algorithm that combines the strengths of Adam and AdaGrad, offering better adaptability to different learning rates. © 2025 ieee.

关键词： Contrastive Learning

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SupplyRx: intelligent Pharmaceutical Inventory and Supply Monitoring System 1

SupplyRx: Intelligent Pharmaceutical Inventory and Supply Mo...

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1st international conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Singh, Dhruvveer Singh, Harkamal Kumar, Shubham Garg, Vanshit Kumar, Vijay Chahar, Narendra Kumar Dr B R Ambedkar National institute of Technology Department of Information Technology Jalandhar India

ISBN: (纸本)9798331518578

To guarantee the availability of necessary medications and reduce waste in healthcare systems, effective management of drug inventory and supply chains is vital. The goal of the Drug Inventory and Supply Chain Tracking System is to make the distribution, storage, monitoring, and acquisition of pharmaceuticals more efficient Utilizing cutting-edge technologies, this system offers automated inventory updates, real-time tracking, and demand forecasting predictive analytics. By keeping precise records of medication movements and expirations, it improves regulatory compliance, guarantees supply chain transparency, and lowers the possibility of stockouts or overstocking. The architecture of the system incorporates modules for point-of-sale tracking, transportation logistics, warehouse operations, and supplier management. To further protect sensitive data, it integrates access control and user authentication and access control systems to protect private information. This technology helps healthcare organizations save money, enhance patient outcomes, and run more efficiently by promoting data-driven decision-making and resource optimization. © 2025 ieee.

关键词： Access control

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A Comparative Analysis of Rule-based and AI-Driven systems for Improving Customer Satisfaction and Engagement in E-Commerce using Chatbots Powered by Artificial Intelligence 6

A Comparative Analysis of Rule-based and AI-Driven Systems f...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Deshmukh, Santoshi Gundewar, Swapnil Department of Artificial Intelligence and Data Science Datta Meghe Institute of Higher Education and Research Wardha India Department of Artificial Intelligence and Machine Learning Datta Meghe Institute of Higher Education and Research Wardha India

ISBN: (纸本)9798331522667

Chatbots: Chatbots, as robots, are the most recent and intelligent live chat communication devices. Furthermore, these days, it is commonly one of the customer service methods as it is also perfect for providing an array o f services, like personalized offers, individual communication, responsive customer support, etc. The interaction between banks and other financial institutions and their consumers has been entirely transformed due to the marriage of financial technology and artificial intelligence (AI). This scientific investigation deals with artificial intelligence (AI) technology for better communication among people;its special focus is on the effective use of chatbots and natural language processing (NLP) in banking support. Banks are increasingly turning their efforts to AI-driven chatbots as valuable means of enhancing customer support while channeling communication effectively. We are now at the point where human creativity and technology will combine and reimagine finance principles. © 2025 ieee.

关键词： Customer satisfaction

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Design of Daily Life Ability Training System Based on intelligent Evaluation Algorithm 14th

Design of Daily Life Ability Training System Based on Intell...

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14th international conference on Frontier computing, FC 2024

作者： Tan, Yue Zhang, Baoying School of Health and Welfare Changchun Humanities and Sciences College Jilin Changchun China

ISBN: (纸本)9789819627936

With the increasing aging population, the significance of daily living ability training systems for the elderly has become increasingly important in their daily *** overall goal of this project is to learn different data mining methods and find ways to improve the detection accuracy of human daily behavior, especially for falling behavior. Specifically, this article mainly analyzes the results of three existing data mining methods applied to specified datasets, and greatly improves the detection accuracy of human daily behavior through new experimental methods. Experimental results have shown that the improved learning algorithm outperforms existing machine learning algorithms to a large extent. At the same time, this article will organize this into a set of training and application methods, which not only includes the training data and classifier acquisition part, but also includes the algorithm part of applying the classifier, making the new method universal. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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