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Real-Time Analytics for Big Data in Smart City Applications: Transforming Urban Environments with Data-Driven Insights 3

Real-Time Analytics for Big Data in Smart City Applications:...

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3rd IEEE international conference on automation and Computation, AUTOCOM 2025

作者： Khanderi, Avinash Rajaram, Gnanajeyaraman Hussein, Ramy Riad Johri, Prashant Mohanraj, T. Balamurugan, M. Walmart Inc. BentonvilleAR United States Simats Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Cse Chennai India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq School of Computing Science and Engineering Galgotias University Greater Noida India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Excel Engineering College Department of Eee Komarapalayam India

ISBN: (纸本)9798331542375

This paper offers an overview of real-time analytics for smart cities proposing a framework for real-time data processing to improve the urban landscape. In the proposed system, large-scale data-streams can be processed using advanced machine learning algorithms, edge computing, and distributed data storage. Analyzing the outcomes of the evaluation procedure evidences reportable performance enhancements in the areas of smart city applications. Average traffic speed in transportation systems in the same period rose by 60%, concurrency and response to incidents likewise cut down by 40% and 53% respectively. The energy management systems responded with a 16% decrease in maximum energy load and the waste reduction was by 50%, while the costs savings was by 25% increase. Public safety applications alone realised a 60% decrease in the time taken to detect a crime and a 28.6% increase in efficiency on disaster resources with an overall spike in public satisfaction of 25.7%. Such findings improve the understanding of how this framework can increase urban efficiency, sustainability, and quality of life, which makes the solution usable in future smart city projects. © 2025 IEEE.

关键词： Urban transportation

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Detection and Prevention Various Network Attacks Using Soft Computing Approaches: An Overview 1

Detection and Prevention Various Network Attacks Using Soft ...

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1st international conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Deore, Mahendra Gosavi, Chhaya MKSSS's Cummins College of Engineering for women Department of Computer Engineering Pune India

ISBN: (纸本)9798350355611

The Internet of Things (IoT) has emerged as a powerful tool for remote environmental monitoring, attracting considerable interest from the research community. Through IoT-enabled wireless technologies, various applications such as Intelligent Transport Systems (ITS), healthcare, home automation, and precision agriculture have benefited from enhanced monitoring capabilities. The functionality of IoT applications is centred around sensor nodes, which are deployed in the field to collect data and transmit it to a base station (BS) for processing. However, IoT networks that utilize Wireless Sensor Networks (WSN) face significant challenges. Two major issues are energy efficiency, which affects the longevity and performance of the network, and security vulnerabilities, which expose the system to a variety of threats such as sinkhole attacks, black hole attacks, and Denial of Service (DoS) attacks. These security concerns can severely disrupt communication and compromise the integrity of the data being transmitted. This survey paper focuses on examining recent security strategies designed to mitigate such threats in IoT networks. A systematic review was conducted to explore lightweight security solutions, with particular attention to trust-based and probe route-based mechanisms. These mechanisms offer promising approaches to safeguarding IoT networks while maintaining efficiency. The paper also presents a comparative analysis of these methods, evaluating them based on security techniques, types of attacks they address, and key performance metrics such as energy consumption and response times. The findings of this paper serve as a critical evaluation of the existing research, highlighting the strengths and limitations of the security measures reviewed. Through this comparative analysis, the study provides valuable insights into the effectiveness of various security approaches in protecting IoT-enabled networks from evolving cyber threats. © 2025 IEEE.

关键词： Probes

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Intraday economic optimal dispatch of distribution networks based on fuzzy set prediction 9

Intraday economic optimal dispatch of distribution networks ...

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2024 9th international Seminar on computer Technology, Mechanical and Electrical engineering, ISCME 2024

作者： Zhang, Chunyu Zhang, Maosong Wu, Di Yang, Jie Zhao, Hongsheng Zhang, Luyao School of Electrical Engineering and Automation Anhui University Anhui Hefei230601 China Economic and Technical Research Institute of State Grid Hubei Electric Power Co Wuhan430000 China China Renewable Energy Engineering Institute Beijing100120 China

Aiming at the uncertainty problem of electric vehicle charging and discharging, this paper proposes an intraday scheduling model and its solution method based on fuzzy set theory and an improved subtraction optimizer algorithm. The charging station dispatchable resource prediction model is constructed based on event-wise fuzzy set theory. On this basis, the intraday optimal scheduling model is constructed by comprehensively utilizing the dispatchable resources of the charging station to collaborate with the photovoltaic and energy storage outputs and is solved by the improved subtractive optimizer algorithm to realize the optimal scheduling to minimize the operating cost. Finally, the effectiveness of the proposed method is demonstrated by simulation analysis based on the IEEE-33 node arithmetic system. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Charging stations

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Enhancing Customer Retention: A Federated Machine Learning Framework for Banking Churn Prediction 3

Enhancing Customer Retention: A Federated Machine Learning F...

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3rd IEEE international conference on automation and Computation, AUTOCOM 2025

作者： Vanitha, M. Faritha Banu, J. Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Bharathi Salai Ramapuram Tamil Nadu Chennai India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542375

Customer attrition has become the significant challenge for the bank, making large volume of customers to migrate to other banks, as the banks keeps providing multiple benefits to the incoming customers. The loss due to migration of the existing customer to the competitive bankers creates the banking churn, means of loss of customer relationship with the bank, and affects the development, business and the profitability of the corresponding bank. It is essential to predict the banking churn with a primary objective of retaining the customers, considered as a critical task. To achieve this objective, banking sectors employ the customer behavioral analysis to determine the rate of customer churn, resulting in incorrect diagnosis of the churn rate. To over this concern, this research manuscript proposes a novel federated Machine Learning (ML) framework for the prediction of the customer churn, directly contributes for the enhancement of the customer retention. This proposed novel framework offers significant advancements for the banking prediction on customer retention, thus provides an accurate tool for the analysis of customer relationships. The proposed prediction framework is analyzed in terms of accuracy, precision, recall, F1 score and the performance is compared with the state of the art prediction methodologies. © 2025 IEEE.

关键词： Federated learning

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Constructing a Hierarchical Model Based on the Blockchain in Health Care 5th

Constructing a Hierarchical Model Based on the Blockchain in...

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5th international conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Singh, Ravendra Singh, Anupam Singh, Raj Pal Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur India Department of Computer Science and Engineering Graphic Era Hill University Uttarakhand Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to be University Uttarakhand Dehradun India

ISBN: (纸本)9789819773701

Building a hierarchical conceptual structure for smart health care, identifying the effects of distributed ledger technology on clever medical treatment, and then developing a system of applications for smart health care via blockchain that utilizes the theory of involvement of stakeholders are the goals of the research. However, besides the essential characteristics and the connections between different features and characteristics, such a hierarchy conceptual framework should also consider the involvement of several stakeholders. The study offers decision-making including analytical lab (DEMATEL) to control the intricate relationships between various features and attributes. Finally, a hierarchical conceptual framework is created by splitting the hierarchy employing Interpretive Structural Modeling (ISM). The results show that the top-level organization, medical records leadership, and general doctor management are among the system's core reasons. A specific application of blockchain in the field of healthcare automation requires a decentralized agreement that depends on how health information is handled within system constraints. The secret to updating a system is application optimization. Environmental control, health insurance, and internal and external regulation all contribute to the system's development and the successful protection of stakeholder interests. The financing layer, data layer, as well as stakeholder layer must form the basis of a blockchain-based application structure for smart health care. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decentralized finance

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Next-Gen Energy Management: IoT Gateway for Smart Monitoring and Appliance Control 6

Next-Gen Energy Management: IoT Gateway for Smart Monitoring...

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

作者： Kabra, Sohum Nadagouda, Prithvi Nikam, Yash Kavedia, Manoj S. Thadomal Shahani Engineering College Mumbai India Thadomal Shahani Engineering College Mumbai India

ISBN: (纸本)9798331522667

This research study proposes an IoT-based solution for intelligent energy management, designed to monitor and optimize power consumption in both residential and industrial environments. The system will integrate an AI meter for real-time tracking of voltage, current, and power usage, alongside environmental sensors that measure parameters like temperature, humidity, light intensity, and motion. These data inputs are processed by an IoT gateway based on an ESP32 microcontroller and Raspberry pi, which will handle the data locally or transmits it to a cloud-based or edge machine learning model. The machine learning model analyzes real-time sensor data combined with historical consumption patterns to predict and implement optimal appliance control strategies. The system aims to reduce power consumption by up to 20-25%, contributing to energy efficiency without compromising operational performance and delays. Appliances are controlled remotely using IR Blasters and relays, allowing for dynamic, real-time adjustments based on environmental factors. The solution is scalable, making it applicable for both household energy management and large-scale warehouse automation. By utilizing predictive analytics and intelligent control, the system optimizes energy usage, offering a sustainable, automated solution for modern smart homes and industries. This integration of IoT and machine learning provides a significant step toward more efficient and sustainable energy management practices. © 2025 IEEE.

关键词： Gateways (computer networks)

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Design Algorithms for Lifelong Learning That Maintain Performance on Old Tasks While Learning New Ones 3

Design Algorithms for Lifelong Learning That Maintain Perfor...

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3rd IEEE international conference on Industrial Electronics: Developments and Applications, ICIDeA 2025

作者： Sharma, Madhav Saini, Hukum Chand Sikarwal, Pushpendra kumar Saxena, Akash Agarwal, Samiksha Institute of Engineering and Technology Department of CSE-IoT Poornima Jaipur India Jagannath University Department of Engineering & Technology Jaipur India JECRC University Department of Computer science Rajasthan Jaipur India CIITM Department of CSE Jaipur India

ISBN: (纸本)9798331533205

Lifelong studying, or persistent learning, seeks to enable synthetic intelligence (AI) systems to accumulate knowledge and skills over the years even as preserving performance on formerly found out tasks. A number one challenge in lifelong learning is the stability-plasticity catch 22 situation: how to strike a stability among retaining old knowledge (stability) and integrating new understanding (plasticity). This paper explores algorithmic strategies for lifelong studying, that specialize in mitigating catastrophic forgetting and improving understanding switch across tasks. We present a complete evaluate of current strategies, suggest novel strategies, and compare their effectiveness in benchmark environments. By addressing center challenges, this study contributes to advancing the improvement of robust and adaptive AI structures able to lifelong mastering. © 2025 IEEE.

关键词： automation Environmental layout Machine Predictability

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Advancing Brain-computer Interfaces: A Deep Learning Approach for Enhanced Neural Signal Processing 3

Advancing Brain-Computer Interfaces: A Deep Learning Approac...

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3rd IEEE international conference on automation and Computation, AUTOCOM 2025

作者： Dharmendra, Kaku Riya Verma, Deepak Kumar Vats, Satvik Marwadi University Department of Computer Engineering Rajkot India Madan Mohan Malaviya University of Technology Department of Computer Science and Engineering U.P. Gorakhpur India

ISBN: (纸本)9798331542375

Brain-to-computer interfaces (BCIs) are a developing technology that has led to a straight link among the human brain and various systems. The complexity and lack of predictability of brain signals make the development of a trustworthy, accurate BCI system. In this study, we look into how advanced deep learning approaches like Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Graph Isomorphism Networks (GIN), and Convolutional Neural Network (CNN) can be used to improve neural signal processing performance in BCI. LSTM and GRU are good at discovering the temporally related EEG sequences, while CNNs extract the spatial feature of raw EEG data very well. We model and analyze graph-structured data from functional brain networks using GIN, revealing insights into spatial dynamics and inter-channel connections. Both graph-based and spatiotemporal features are integrated as proposed in the framework, and a deep learning architecture is constructed for signal decoding and classification that allows for a higher signal decoding and classification accuracy. Our experimental results on publicly available EEG datasets demonstrate that this approach outperforms standalone deep learning models and traditional machine learning approaches. It designs its solutions to attain higher accuracy, robustness, and the ability to generalize across various BCI tasks, including cognitive state recognition and motor imagery classification. The result indicates that our model LSTM is 96.92%, CNN is 95.95%, GRU is 95.40%, and GIN is 88.68%. Finally, this study demonstrates how advanced deep learning techniques can address the current limitations of BCI systems, opening the door for more reliable and successful real-world applications. © 2025 IEEE.

关键词： Convolutional neural networks

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Analyzing Generative AI’s Impact on Graph Theory and Software Testing: A Comparative Study

Analyzing Generative AI’s Impact on Graph Theory and Softwa...

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international conference on Smart Business and technology, ICSBT 2024

作者： Elasri, Chaimae Kharmoum, Nassim Saoiabi, Fadwa Lagmiri, Souad Najoua Ziti, Soumia IPSS Research Team Faculty of Sciences Mohammed V University in Rabat Rabat Morocco Rabat Morocco Laboratory Higher Institute of Management Administration and Computer Engineering Rabat Morocco

ISBN: (纸本)9783031866975

Generative AI has been considered to fit Science and Technology especially in Graph Theory and Software Testing and as such the subject has attracted some attention. Based on the literature, this paper reviews previous works concerning the effect of Generative AI in these fields owing to the call to build more knowledge, address challenges, and improve practices. Graph theory which is a branch of discrete mathematics is vital in computer science and operation research. For this field, generative AI would help in the discovery of graphs, algorithms as well as solutions. With this proposal, this study examines how the existing GANs and VAEs are applied to graph generation, node prediction, and anomaly detection before pointing out the existing research gaps and future research avenues. Likewise, Software Testing essential in system development is evolving with Generative AI. The conventional ways are thus more cumbersome and very often inaccurate. In terms of testing, generative AI can come up with test cases, predict failures, and generate smart data therefore changing the test techniques. This paper discusses and analyze models of AI used for test automation and identification of defects for test automation. Consequently, comparing the literature, the paper elucidates that Generative AI transforms Graph Theory and Software Testing while promoting interdisciplinary cooperation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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Implementation of YOLOv9-Based Real-Time Object Detection for Robotic Finger Applications 5

Implementation of YOLOv9-Based Real-Time Object Detection fo...

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5th IEEE international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Duraisamy, Premkumar Deepika, A. Dhanushiya, S. Karthik, S. Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Hindusthan College of Engineering & Technology Department of Information Technology Coimbatore India

ISBN: (纸本)9798350356236

The project investigates the integration of YOLO v9, the latest iteration of the 'You Only Look Once' model, into robotic fingers to enhance their precision, responsiveness, and versatility in object handling. YOLO v9's superior object detection capabilities - especially its real-time processing speed and high accuracy - make it an ideal candidate for advancing robotic manipulation. By training YOLO v9 on an extensive dataset encompassing diverse object shapes, sizes, and textures, this project enables robotic fingers to recognize and respond to various physical characteristics with greater nuance. Through this approach, robotic systems gain an adaptive edge, adjusting their grip and interaction based on specific object features, thus expanding their utility in precision-demanding fields such as manufacturing, healthcare, and logistics. The research not only explores technical integration but also assesses the impact of this enhanced object recognition on robotics' overall reliability and efficiency in handling real-world tasks. The findings could contribute to the development of more intelligent, responsive, and adaptable robotic systems, redefining automation standards in critical industries. © 2025 IEEE.

关键词： Intelligent robots

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