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Intelligent Urban Futures: Exploring AI and Machine Learning applications in Smart Cities 5th

Intelligent Urban Futures: Exploring AI and Machine Learning...

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5th international conference on Advances in Information Communication Technology and computing, AICTC 2024

作者： Yadav, Anit Kumar Sharma, Ajay Kumar Patel, Mayank Rathore, Narendra Singh Department of Computer Science Engineering GITS Udaipur India GITS Udaipur India

ISBN: (纸本)9789819761050

the primary goals of smart cities are efficiently managing the rapid urbanization process, energy consumption, environmental protection, citizen economic and living standards, and people's capacity to utilize and accept contemporary information and communication technology (ICT). the development, decision-making, implementation, and delivery of final, beneficial services under the smart city idea all depend on ICT. the aim of the research is to explore the development of smart cities in terms of AI, ML, and DRL. the methods discussed are effectively used to create the best plans of action for a variety of intricate problems pertaining to smart cities. A comprehensive review of prior approaches used in the fields of cyber-security, energy-efficient smart grid utilization, intelligent transportation systems (ITSs), effective use of UAVs to ensure optimal communications services that can even go beyond 5G, and smart cities with health care systems that are smart enough is provided in this survey. Ultimately, a number of research problems and potential future avenues where the aforementioned methodologies can flourish are detailed in order to make smart city concept real. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart city

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A Privacy-Preserving Computer-Aided Diagnosis Framework for Medical applications Using Federated Learning and Homomorphic Encryption 7th

A Privacy-Preserving Computer-Aided Diagnosis Framework for...

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7th international conference on Attacks and Defenses for Internet-of-things, ADIoT 2024

作者： Xiong, Jichao Chen, Jiageng Liu, Hui Zhou, Guangyou Cui, Jianqun Lin, Junyu Jiao, Dian School of Computer Science and Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Central China Normal University Wuhan430079 China School of Central China Normal University Wollongong Joint Institute Central China Normal University Wuhan430079 China

ISBN: (纸本)9783031855924

Skin cancer is one of the most common cancers worldwide, where early detection significantly reduces mortality rates. Integrating Internet of things (IoT) technologies into healthcare enables continuous monitoring and early diagnosis through connected devices, offering new prospects for real-time medical assessment. Advanced machine learning classifiers have shown superior performance over human experts in diagnosing pigmented skin lesions, underscoring their potential for IoT-based applications. However, training high-precision computer-aided diagnosis (CAD) systems requires extensive data, and secure data sharing poses significant challenges due to privacy concerns in IoT healthcare networks. this paper introduces an IoT-oriented skin cancer CAD system combining federated learning and homomorphic encryption to address these issues. Federated learning supports collaborative model training across distributed IoT devices, mitigating data scarcity without compromising data privacy. Homomorphic encryption ensures that patient data remains encrypted during diagnosis, enhancing security within IoT frameworks. To achieve an efficient and high-accuracy model suited for IoT, we implemented Self-Learnable Activation Functions (SLAF), optimized for homomorphic encryption and resource constraints of IoT devices. Our system, validated using the HAM10000 dataset, achieved 94.39% accuracy with dual privacy protection, enhancing both diagnostic precision and data security. Comprehensive evaluations and comparisons with state-of-the-art frameworks confirmed the effectiveness of our approach. Results demonstrate the strong potential and practicality of our IoT-based solution for real-world healthcare, providing a secure, efficient, and accurate diagnostic tool that supports the wider adoption of IoT in medical applications. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Forecasting Urban Growth: Applying Prophet Model to Landsat NDBI Data for Sustainable City Development 12th

Forecasting Urban Growth: Applying Prophet Model to Landsat ...

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12th international conference on Frontiers of Intelligent computing: theory and applications, FICTA 2024

作者： Mohanty, Vidya Panda, Amiya Ranjan Behera, Dayal Kumar Swetanisha, Subhra School of Computer Engineering KIIT Deemed to be University Bhubaneswar India School of Computing Trident Academy of Technology Bhubaneswar India

ISBN: (纸本)9789819601387

Urban growth forecasting is a critical component of sustainable city development, enabling policymakers to make informed decisions about land use and infrastructure planning. this study presents a comparative analysis of two prominent forecasting models, Auto ARIMA and Prophet, applied to time series data derived from the Landsat Normalized Difference Built-up Index (NDBI) for the Bhubaneswar-Cuttack twin city region. the dataset spans from January 1, 2000, to January 1, 2024, providing a comprehensive view of urban expansion over nearly two and a half decades. the Auto ARIMA model, known for its robustness in time series forecasting, was first employed to predict future urban growth patterns. Subsequently, the Prophet model, a decomposable time series model that accommodates seasonal fluctuations was applied to the same dataset. the performance of both models was evaluated based on their prediction accuracy and computational efficiency. Results indicate that the Prophet model outperformed Auto ARIMA in forecasting urban growth in the Bhubaneswar-Cuttack region. the findings serve as a foundation for further exploration into the integration of remote sensing data and predictive modeling for urban planning and management. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Sustainable city

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Application of Simulated LIDAR Methodology in Autonomous Aerial Platforms

Application of Simulated LIDAR Methodology in Autonomous Aer...

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Mobile computing and Sustainable Informatics (ICMCSI), international conference on

作者： E. Ajitha E. Hephzibai S. Ilakkiya Department of Computer Science and Engineering St. Joseph's Institute of Technology Tamil Nadu India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

the real-time obstacle identification and precise distance calculation is necessary for drone deliveries but the traditional LIDAR sensors are expensive and their weight poses significant challenges. the high financial investment required to procure and integrate LIDAR into drones also adds significant weight to drones, which impacts their performance. this project offers a low-cost substitute for LIDAR sensors in drone applications that require pre-pixel depth estimation. A great deal of testing confirms the effectiveness of depth estimation. the object's distance is yield by a disparity map. this demonstrates how depth information can be accurately determined with the help of stereo vision by using block-matching and semi-global block-matching algorithms. According to our research, integrating pseudo-LIDAR systems offers a viable and affordable alternative to LIDAR sensors for drone applications.

关键词： Laser radar Depth measurement Sensor systems and applications real-time systems Stereo vision Informatics Mobile computing Drones Testing Investment

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Graph Transformer Hawkes Processes for Causal Structure Learning in Telecom Networks 18th

Graph Transformer Hawkes Processes for Causal Structure Lear...

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18th international conference on Wireless Artificial Intelligent computing systems and applications, WASA 2024

作者： Yin, Shiwei Li, Ying Wang, Jiandong Kong, Yu College of Computer Science and Technology Qingdao University Qingdao266701 China

ISBN: (纸本)9783031714634

the event sequence in telecommunications networks covers important data such as communication records and system alarms, which is crucial for fault detection and network management. However, existing causality discovery algorithms have challenges on casual discovery in telecom networks. To this end, we design the Graph Transformer Hawkes Process (GthP) model to reveal the causal relationships behind event sequences in telecom networks. Our proposed model is contributed to allow the use of transformer's self-attention mechanism with Hawkes process for casual discovery. To begin with, the proposed GthP injects graph structure information into Transformer's self-attention by graph convolution networks to construct a graph transformer block, which effectively captures the long-range casual dependencies between event types. We then combine the graph transformer block with Hawkes process, in which the intensities of multiple event types evolve based on a continuous-time transformer mechanism. the extensive experiments are implemented for measuring the performance of GthP in real-world telecom data. It demonstrates that GthP has an obvious improvement in comparison with the baselines. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Continuous time systems

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RISC-V Accelerators, Enablement and applications for Automotive and Smart Home in the ISOLDE Project 24th

RISC-V Accelerators, Enablement and Applications for Automot...

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24th international conference on Embedded Computer systems: Architectures, Modeling, and Simulation

作者： Ciobanu, Catalin Bogdan Galmeanu, Honorius Puscasu, Alexandru Gologanu, Mihai Buiu, Octavian Antonescu, Mihai Serbu, Vlad-Gabriel Moise, Vasile-Madalin Axinte, Cristian-Tiberius Popovici, Alexandru-Tudor Uleru, George-Iulian Stan, Andrei Munteanu, Mihai Drimbarean, Alexandru Nemeti, Csaba Rotar, Danut Comma Grosu, Daniel Moisa, Cosmin Ditu, Bogdan Trusca, Petre Cristian Antache, Marius Costinescu, Simona Sachian, Mari-Anais Suciu, George Gheorghe, Cristian Tudor, Cristina Koci, Kejsi Natl Inst Res & Dev Microtechnol IMT Bucharest Romania Transilvania Univ Brasov Brasov Romania Natl Univ Sci & Technol Politeh Bucharest Bucharest Romania Gheorghe Asachi Tech Univ Iasi Iasi Romania FotoNat SRL Brasov Romania Continental Automot Romania Timisoara Romania NXP Semicond Romania Bucharest Romania BEIA Consult Int Bucharest Romania FotoNat Ltd Galway Ireland

ISBN: (纸本)9783031783791;9783031783807

With Artificial Intelligence applications becoming ubiquitous, both industrial and consumer applications require more computing availability. the increasing need for power-constrained Artificial Intelligence computing asks for new and innovative solutions, on both hardware design as well as on software toolchains that leverage them. At the same time, the challenge of EU technological sovereignty encourages an industry trend oriented to developing its own solutions for power efficient High Performance computing (HPC). In this context, as partners in the ISOLDE project started in 2023, we propose the development of new power efficient embedded HPC accelerators, HPC design platforms, as well as High Level Synthesis (HLS) approaches and toolchain technology designs for RISC-V based applications. Specifically, we envisage applications such as Fast Fourier Transform, Number theoretic Transforms, Post Quantum computing and artificial intelligence, as well as designing ONNX targeted toolchains based on the LLVM compiler, open solutions for Neural Networks (NN) accelerators and new OpenMP extensions. Within the ISOLDE framework, the envisaged applications to prove the readiness of these technologies are the Automotive and the Smart Home demonstrators.

关键词： RISC-V HPC Polymorphic Register File PolyMem AI Accelerator HLS AI Compiler ONNX LLVM OpenMP

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Lightweight Intrusion Detection for IoT systems Using Artificial Neural Networks 19th

Lightweight Intrusion Detection for IoT Systems Using Artifi...

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19th international conference on Security and Privacy in Communication Networks

作者： Saleh, Radhwan A. A. Al-Awami, Louai Ghaleb, Mustafa Abudaqa, Anas A. King Fahd Univ Petr & Minerals KFUPM Dept Comp Engn Dhahran 31261 Saudi Arabia KFUPM Interdisciplinary Res Ctr Intelligent Secure Syst Dhahran 31261 Saudi Arabia

ISBN: (纸本)9783031649530;9783031649547

Internet of things (IoT) systems, due to their vulnerability to a plethora of security attacks, suffer significant detrimental impacts on their reliability. Additionally, the inherent constraints in IoT devices necessitate the incorporation of lightweight security schemes endowed with the capacity to identify intrusions and serve as a robust line of defense. this study presents a lightweight Intrusion Detection System (IDS) that leverages the power of Artificial Neural Network (ANN) while addressing the challenges within IoT systems. the effectiveness of the proposed IDS has been validated through empirical testing by utilizing the ToN IoT Telemetry dataset. the proposed IDS exhibits superior performance compared to other Machine Learning-based IDS solutions identified in the literature, particularly in terms of binary classification where the feature set is continuous. the proposed IDS consistently exceeds expected benchmarks in multi-class classification, with metrics including recall, precision, accuracy, and F-score ranging between 91% and 100%. More importantly, its high accuracy and low time complexity make it an ideal choice for real-time applications, offering a superior alternative to existing AI-based IDS.

关键词： Intrusion Detection System Lightweight Internet of things Machine Learning Artificial Neural Network

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Inventory Management using Near Field Communication (NFC)

Inventory Management using Near Field Communication (NFC)

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Mobile computing and Sustainable Informatics (ICMCSI), international conference on

作者： Abhay Chopde Vedant Keche Apoorv Karmalkar Manas Kasodekar Department of E&TC Engineering Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

this research creates an NFC-inclusive website-based inventory management system that enhances tracking and management of stock. Scanning these tags allows users to obtain a specific product description without wasting time searching through the inventory. the solution is designed to enhance accuracy, efficiency, and user-friendliness for the convenience of the business through enhanced real time tracking and management ofinventories.

关键词： Accuracy Databases Near field communication Medical services Manuals Inventory management real-time systems Mobile applications Manufacturing Mobile computing

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From Concept to reality: A Systematic Review of Urban Digital Twin and their applications in Smart Cities 20th

From Concept to Reality: A Systematic Review of Urban Digita...

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20th international conference on computing in Civil and Building Engineering, ICCCBE 2024

作者： Huang, Hao Tsou, Jin Yeu Department of Architecture and Civil Engineering City University of Hong Kong Kowloon Hong Kong

ISBN: (纸本)9783031842078

Metropolises comprise six primary systems that serve distinct domains: governance, environment, mobility, economy, living, and society. these systems work together to create a macrosystem that constitutes a city. In contemporary urban planning, Smart Cities (SCs) have emerged as a means of effectively managing and integrating critical infrastructure, optimizing resource allocation, coordinating maintenance activities, and addressing complex urban challenges while prioritizing security. Over the past few years, cutting-edge technologies like Wireless Sensor Networks, Building Information Modeling, Internet of things (IoT), Virtual reality, Big Data Analytic, Artificial Intelligence (AI), Cloud computing, Blockchain, and Cyber-Physical systems have paved the way for intelligent and groundbreaking solutions in a variety of industries. Built on the knowledge gained from developing SCs and implementing Digital Twins (DTs) in other sectors, the Urban Digital Twin (UDT) has rapidly expanded in just a few years. UDT technology utilizes real-time data, simulation, machine learning, and reasoning to provide valuable assistance in decision-making processes. However, more than informatization and digitalization practices are required to embrace UDT fully. there has yet to be a comprehensive analysis of how to apply these features of DTs to manage SCs better. this review paper analyzed 150 papers that focused on DT-supported SCs. We aim to identify the drivers, challenges, and future directions for applying UDT-supported technologies. We systematically investigated the characteristics and mechanisms of DTs, categorized different technologies based on their application stages, and identified the key challenges, benefits, and open issues. the findings inform stakeholders in urban management to establish a foundation for future design, development, and widespread adoption of UDT. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Resource allocation

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Character Recognition Using Convolutional Neural Networks Based on CUDA

Character Recognition Using Convolutional Neural Networks Ba...

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international Youth conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Set Paing Tun Applied Mathematics and Artificial Intelligence Department National Research University “MPEI” Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

the article explores character recognition using convolutional neural networks (CNNs) optimized with the CUDA platform to enhance computational efficiency. It outlines the CNN architecture, methods for leveraging GPU-based parallel data processing, and presents experimental results derived from the MNIST dataset. the study highlights that implementing CUDA drastically reduces processing time while maintaining a high level of predictive accuracy. the findings emphasize the potential of GPU acceleration in handling intensive computational tasks, making it a promising approach for real-time applications in image recognition and machine learning.

关键词： Deep learning Training Energy consumption Graphics processing units Parallel processing real-time systems Energy efficiency Convolutional neural networks Character recognition Optimization

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