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Integration of Artificial Intelligence and BIM in intelligent Decision Support System for Water Conservancy Projects

Integration of Artificial Intelligence and BIM in Intelligen...

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Electronics, Energy systems and Power Engineering (EESPE), ieee international conference on

作者： Zekun Chang Shandong Vocational College of Industry Zibo China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

A kind of intelligent DSS based on AI and Architecture Information Modeling (BIM) is put forward. The system is composed of several modules, including data collection, processing, intelligence decision-making, feedback and optimizing. The data acquisition and processing module collects environmental data, equipment status and water flow information of water conservancy projects in real time through a variety of sensors and remote sensing technologies; the intelligent decision module uses deep learning algorithms, especially GCN is to model and predict the collected data, so as to make effective risk assessment and scheduling decisions; the feedback and optimization module continuously optimizes the decision process according to real-time feedback information, and improves decision accuracy and response speed. Through a series of experimental simulations, this paper verifies the effectiveness of the system. The GCN-based model improves the forecasting precision by 15% compared with the conventional ANN, and the calculation efficiency is improved 20%. Furthermore, it is proved by experiments that the system has remarkable superiority in dealing with complicated data in real time, data fusion and decision-making.

关键词： Decision support systems Accuracy Graph convolutional networks Building information management Water conservation Predictive models Prediction algorithms Real-time systems Artificial intelligence Water resources

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Social Media Multi-Emotion Detection 2

Social Media Multi-Emotion Detection

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2nd international conference on advanced Innovations in Smart Cities, ICAISC 2025

作者： Ibrahiem, Soha S. El-Sayed Mohammed, Bahy Ibrahim El Sayed Ahmed, Mohammed Ahmed Elbahnasy, Khaled Menofia University Computality R&D System Analyst & Nlp Researcher Faculty of Science Menofia Egypt Web Developer Cairo Egypt Software Engineer Ekad Soft Cairo Egypt Jeddah International College Computer Science and Information Technology Department Jeddah Saudi Arabia

ISBN: (纸本)9798331506995

Natural language in social media is often ambiguous, carrying diverse perspectives that are critical for opinion analysis. With vast amounts of unstructured data, particularly on platforms like Twitter, it is impractical to manually analyse relevant insights. This rapid data growth has highlighted the need for intelligent filtering and mining techniques in decision support systems to help organizations build brand reputation, foster online communities, and enhance profitability. Multi-Emotion detection is key for capturing implicit user opinions and specific emotional states;however, challenges such as text bipolarity, limited representative feature vectors, and inadequate annotated lexicons often hinder accuracy. This research aims to improve multi-emotion detection accuracy by extracting emotion implied tokens and converting them into feature vectors that capture semantic relevance. Using annotated lexicons, word embeddings, unigrams, and term frequency, these vectors are trained across five machine learning models, which are Naïve Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), K- nearest neighbor (KNN), and Multi- layer Percepton (MLP). Finally, two ensemble techniques are applied to refine model outputs, achieving an improvement accuracy over previous work worked. The proposed system, evaluated on a real dataset, achieves a hamming score of 0.55 and an average F1 score of 0.68, demonstrating its potential for effective multi-label emotion detection. © 2025 ieee.

关键词： Tweets

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Optimizing Shortest Paths in Big data Using the Floyd-Warshall Algorithm

Optimizing Shortest Paths in Big Data Using the Floyd-Warsha...

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2025 international conference on intelligent Control, computing and Communications, IC3 2025

作者： Sangeetha, D.P. Sekar, Satheeshkumar Parvathy, Palaniraj Rajidurai Soundarya, R. Ganeshbabu, T.R. Muthulekshmi, M. Sona College of Technology Department of Electronics and Communication Engineering Tamil Nadu Salem India Mphasis Corporation ChandlerAZ United States Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Department of Electronics and Communication Tamil Nadu Namakkal India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331527495

Improving the efficacy and precision of route computations in massive datasets is the goal of optimizing big data shortest routes using the Floyd- Warshall algorithm. The algorithm's ability to determine the shortest paths between all pairs of vertices in a graph allows for optimal route design, network optimization, and urban transportation solutions. Creating a solid foundation that can analyze large amounts of data and provide trustworthy outcomes for decision-making is the main objective. In addressing these challenges, concerns related to scalability, slow convergence rates, and potential errors in route calculations need resolution. The objective is to improve the efficiency and decision-making abilities of intricate systems by adjusting the Floyd- Warshall algorithm for extensive data environments. This will lead to a scalable and efficient solution that can be used in transportation, network routing, and urban planning, among other areas. In one example, the intermediate matrix displays the nodes from A to D, while in the other, the results from the road network show the initial adjacency matrix, where the distances between points A and D vary from 10 to 35 kilometers. In the final matrix, for the nodes A-D, the distance in kilometers varies from 10-35 for the shortest distance traveled;in the same data set, this range is 10-40. © 2025 ieee.

关键词： Urban transportation

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A Hybrid Deep Learning Model for Optimizing Electric Vehicle Battery and Navigation systems

A Hybrid Deep Learning Model for Optimizing Electric Vehicle...

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2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Arul Jose, R. Ebby Darney, P. Santhana Krishnan, R. Relin Francis Raj, J. Jansi Rani, D. Sundaravadivel, P. Rajarajeswari College of Engineering Department of Electrical and Electronics Engineering Bengaluru India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331505745

This paper proposes a deep learning-based optimization framework for Electic Vehicle that leverages LSTM, CNN, and DRL to enhance battery management, autonomous navigation, and energy efficiency. The LSTM model predicts the State of Charge (SoC) and State of Health (SoH) of the EV battery, utilizing temporal patterns in sensor data to improve predictive maintenance. The CNN-based model focuses on real-time obstacle detection and fault recognition in autonomous driving systems, processing visual data from onboard cameras, LiDAR, and radar sensors. Finally, the DRL model optimizes energy consumption and route planning by training an agent to navigate traffic and road conditions efficiently while minimizing power usage. data for the system is collected from various sensors embedded in the vehicle, including battery data, traffic information, GPS, and driver behavior. The data is preprocessed and augmented to ensure the models are robust and generalizable. Training involves GPU-accelerated computing platforms to enhance model efficiency. The performance of the models is evaluated through metrics such as R2 score, accuracy, and energy efficiency improvements, with results showing a 20-30% enhancement in energy consumption and battery life. The proposed system has practical applications in battery management, autonomous driving, and real-time energy optimization, contributing to the advancement of intelligent EV technologies. The work presents a step toward integrating deep learning techniques into EVs for improved performance and operational efficiency. © 2025 ieee.

关键词： State of charge

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Optimizing Battery Performance in Electric Vehicles Through Predictive Modeling

Optimizing Battery Performance in Electric Vehicles Through ...

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2025 international conference on intelligent Control, computing and Communications, IC3 2025

作者： Kumar, C. Siva Sagar, P. Lakshmi Venkata Bhanu Pratap, B. Geethamadhuri, Gurramkonda Kumar, Banda Sai Ram Dinesh, Bommineni Mukesh, Pallaputhimmannagari Sree Vidyanikethan Engineering College Dept of Csse Andhra Pradesh Tirupati India Dept of Data Science Karakambadi A.P Tirupati India BORiTS Private Limited Telangana Hyderabad India Ug School Dept of Csbs Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati India

ISBN: (纸本)9798331527495

The fast-paced electrification of the automobile sector has intensified an immediate need for reliable battery management systems capable of sustaining long-term performance. One of the most important indicators when it comes to battery health is State of Health (SOH), which indicates how much capacity is left and how long a certain battery will endure. Reliable estimation of SOH is critical to enable the optimal performance of vehicles, reduce service costs, avoid unanticipated failures. In this project, we propose a machine learning approach to predict the SOH of EV batteries based on the two widely used algorithms, XGBoost and LGBM. In order to cover and analyze as many process variables available as possible to build a database for the training and evaluation of the models, we measured multiple battery parameters, such as voltage, current, temperature and historical charge/discharge cycles. The train test split this dataset in such a way that the greater part of the dataset is used for the machine learning operations. Individual models, such as XGBoost and LGBM, are used to be train due to its strength in handling large data and complex data with a high precision. This study attempts to find out the better of the two algorithms for real-time SOH prediction in practice by analyzing the accuracy and performance metrics of both algorithms. Results of this analysis can help manufacturers and operators make data-informed decisions regarding battery usage, maintenance schedules and replacement, which will contribute to improved vehicle reliability and extended battery life. © 2025 ieee.

关键词： Decision making

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intelligent Video Surveillance systems With Violence Detection

Intelligent Video Surveillance Systems With Violence Detecti...

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data Science, Agents & Artificial Intelligence (ICDSAAI), international conference on

作者： Jeevan R Avanthika B R Subash Computing Technology SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computing Technologies SRM Institute of Science and Technologym Kattankulathur Chennai India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Modern video surveillance systems capable of identifying and responding to security threats and violent crimes in real time have become increasingly common in recent years. This study presents a novel approach to enhancing the capabilities of video surveillance systems through the integration of YOLO (You Only Look Once), an advanced object detection algorithm, specifically for human violence detection. The proposed intelligent Video Surveillance System utilizes YOLOv8’s speed and accuracy to detect violent activity in real-time video feeds. YOLO is particularly well-suited for real-time surveillance applications due to its ability to classify and identify objects in a single network pass. The system leverages a YOLOv8 model that has been fine-tuned on a comprehensive dataset containing both violent and nonviolent activities, allowing it to distinguish between various forms of human violence, including physical altercations, aggressive gestures, and hostile poses. Key components of the system include video feed acquisition, preprocessing, YOLOv8-based object detection, and post-processing techniques to reduce false positives. When compared to traditional surveillance methods, the proposed system offers numerous advantages. By automating the detection process, ensuring continuous monitoring, and responding promptly to potentially harmful situations, it significantly reduces the burden on human operators. Furthermore, the integration of YOLOv8 allows for scalability and flexibility, making the system suitable for diverse surveillance environments, such as public spaces, transportation hubs, and private facilities.

关键词： YOLO Accuracy Scalability Transportation Streaming media Video surveillance Real-time systems Public security Feeds Security

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A Unified IoT-based Smart Home System: Integration of Security and Convenience Features for Consumer 3

A Unified IoT-based Smart Home System: Integration of Securi...

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3rd international conference on intelligent systems, advanced computing, and Communication, ISACC 2025

作者： Oeishee, Moslema Hoque Ahammad Muyeed, Munyem Alvi, Mishkat Mehedi, Rejoan Shariar Emon Shaikat, M. Molla, Md Nayan Brac University Electrical and Electronic Engineering Department Dhaka Bangladesh United International University Computer Science and Engineering Department Dhaka Bangladesh Brac University Computer Science and Engineering Department Dhaka Bangladesh

ISBN: (纸本)9798331523893

Home automation is growing rapidly in the Fourth Industrial Revolution (4IR), providing users with unwavering convenience and enhanced security. This paper presents a comprehensive Internet of Things (IoT) smart home automation solution with a focus on energy management, user-friendliness, and security. Our system includes real-time integrated dashboards accessible via web and mobile interfaces, smart irrigation for indoor gardening, passkeys for door security, voice commands, fingerprint access, night security lighting, and morning alarms. The hardware consists of an Arduino MEGA microcontroller, various sensors (smoke, temperature, humidity, and soil), an ESP Wi-Fi module for connectivity, and additional components. The system demonstrated a satisfactory effectiveness level with an accuracy of 78% based on various tests. The voice assistant bot and accessible dashboard significantly improve user experience. Despite the complexity of home automation processes, increased affordability and accessibility are the aims of this paper. Future work will focus on enhancing real-time capabilities, integrated circuits, data security, and web-based functionalities to improve system performance, scalability, and user accessibility. © 2025 ieee.

关键词： data security home appliances control IoT sensor integration smart home user-friendly dashboard

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Smart Transducers, Robotics and Artificial Intelligence in Industry 4.0 26

Smart Transducers, Robotics and Artificial Intelligence in I...

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26th international conference on Industrial Technology, ICIT 2025

作者： Da Rocha, Helbert Chen, Gang Li, En Salvado, José A. Espirito-Santo, Antonio University of Beira Interior It - Institute of Telecommunications Dept. of Electromechanical Engineering Covilhã Portugal College of Mechanical Engineering Jiaxing University Jiaxing314000 China Institute of Automation Chinese Academy of Sciences Engineering Laboratory of Industrial Intelligent Vision Beijing100190 China

ISBN: (纸本)9798331521950

This paper evaluates the possibility of implementing a production line that incorporates the strategies of Industry 4.0 into its operation system, which includes the use of the Internet of Things (IoT), artificial intelligence (AI), big data, cloud computing, and robotics to develop a smart factory. These factories have a high level of connectivity and integration of systems, allowing an effective sharing of information in real-time. Topics include the function of smart transducers in real-time management and the improvement of process interoperability through the application of the ieee 1451 standard. This is achieved through the integration of robotics with AI to enable the adaptive and precise control of production variables, thus improving the efficiency and quality of products. The paper also discusses the issues and scenarios of using these technologies where standardization plays a major role. © 2025 ieee.

关键词： intelligent robots

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Rise of Diversity of Internet of Things (IoT): A Review

Rise of Diversity of Internet of Things (IoT): A Review

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2025 international conference on intelligent Control, computing and Communications, IC3 2025

作者： Sobana, P. Yaiaman, Gummagatta Vybhavi Kumar, A. Pradeep Karthi, S. Almusawi, Muntather Rani, Hema Sethu Institute of Technology Department of Computer Science Engineering Pulloor Tamil Nadu Kariapatti626115 India Gokaraju Rangaraju Institute of Engineering and Technology Department of Cse Telangana Hyderabad India Cmr College of Engineering & Technology Department of Ece Telanagana Hyderabad India Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil nadu Chennai India College of technical engineering The Islamic University Department of computers Techniques engineering Najaf Iraq Centre for Managament studies Gitarattan International Business School Delhi India

ISBN: (纸本)9798331527495

It has been established for a long time and has evolved correspondingly that actuators and sensor systems may be connected to the internet for the purpose of continuously monitoring and controlling biological phenomena. The term 'Internet of Things (IoT)' was first used to combine these methods into a coherent framework a little more than a decade earlier. The IoT based idea has evolved along with science, adopting new vocabulary that is suited for technical advancements and many applications sectors. The IoT has undergone modifications since its inception, and this study examines studies on how technical advancements have transformed it as well and encouraged the emergence of modified terms appropriate for certain fields. A three separate study of the literature was done using games companies and classification data warehouses. First step involved looking for proposed changes on the Iot devices principle & evaluating them to compute the similarity, disparities, and functionality that enable us to generate a number of links its progression. The 1st step involved identifying and briefly assessing the much more common names handed to it IoT for multiple areas. As underpinning innovation continues to advance and software update areas are put out, the research supports the assertion that a consensus on the IoT concept is still not established. With information and information solutions playing an important role in the panorama, recent developments have been somewhat restrained and its variants on possible applications are distinct. © 2025 ieee.

关键词： Labeled data

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From Sensor to Insight: A Comprehensive Overview of data acquisition Methods in IoT

From Sensor to Insight: A Comprehensive Overview of Data Acq...

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Cognitive computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI), international conference on

作者： Qudsia Shahab Mohd. Faizan Farooqui Department of Computer Application Integral University Lucknow India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

The Internet of Things (IoT) has revolutionized data collection by enabling ubiquitous sensing and real-time monitoring across various domains. This paper provides a comprehensive overview of data acquisition techniques and methods employed in IoT deployments. We begin by outlining the core components of an IoT data acquisition system, including sensors, signal conditioning units, and Analog-to-Digital Converters (ADCs). We then delve into various data acquisition methods, categorized by factors like data type (analog vs. digital) and transmission protocols (wired vs. wireless). Common techniques explored include direct sensor readings, image/audio capture, and RFID tags for identification. Furthermore, the paper addresses considerations specific to the IoT environment, such as resource constraints on devices and energy efficiency. We discuss data filtering and compression techniques to optimize data transmission and storage. Additionally, the importance of data security and privacy in the context of large-scale data acquisition is highlighted. This overview paper aims to equip readers with a foundational understanding of the diverse data acquisition techniques that empower the vast potential of the Internet of Things. By exploring these methods, we can unlock the power of real-time data for intelligent decision-making and innovation across various industries.

关键词： Wireless communication Wireless sensor networks Technological innovation Protocols Filtering data acquisition Real-time systems Sensors Internet of Things Sensor arrays

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