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检索条件"任意字段=2023 International Research Conference on Smart Computing and Systems Engineering, SCSE 2023"

共 8062 条记录，以下是41-50 订阅

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IoT-Based smart Remote Monitoring System for Home Security Using Raspberry Pi 16th

IoT-Based Smart Remote Monitoring System for Home Security U...

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16th international conference on Application of Fuzzy systems, Soft computing and Artificial Intelligence Tools, ICAFS 2023

作者： Imanova, Gunay E. Sesay, Hassan Khalil Azerbaijan State Oil and Industry University 20 Azadlyg Avenue Baku Azerbaijan Research Assistant Area of Information System Freetown Sierra Leone

ISBN: (纸本)9783031725050

Security systems have undergone radical redesigns in recent years, and some have become so comprehensive that they no longer require human intervention. Regardless of its massive advancements, such arrangements have yet to make their way into houses and dwellings, which is partly due to the high cost of the devices and their lack of user-friendliness. This study suggests a smart home security system with addressing the deficiency or weakness of current trade options. In essence, the Raspberry Pi 3 Structure (SoC) device with a Pi camera installed at a front door of a house utilizes image processing and speech identification to recognize would-be visitors and send email notification to the property owner. The Python language was used to write all of the code for running the Raspberry Pi and the entire image processing in this system. The framework accepts known individuals with a kind verbal greeting and opens the front door to let them into the house. The system might start lock down option in the event of unfamiliar face detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： smart homes

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Wireless Networks and the Internet of Things: Empowering Connectivity and smart Applications 5th

Wireless Networks and the Internet of Things: Empowering Con...

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

作者： Rai, Hari Mohan Pal, Aditya Shukla, Kaustubh Kumar Hafeez, Alveera Razaque, Abdul Amanzholova, Saule Department of IT Dronacharya Group of Institutions Greater Noida India Department of Cyber Security International IT University Almaty Kazakhstan Department of ECE Dronacharya Group of Institutions Greater Noida India

ISBN: (纸本)9789819773701

This paper explores the crucial role of wireless networks in facilitating connectivity for smart applications within the Internet of Things (IoT). Objectives include examining wireless network protocols for IoT, addressing security challenges, exploring design considerations, investigating energy efficiency, and analyzing standardization efforts. The study involves an extensive review of literature, research articles, and industry reports. It comprehensively analyzes wireless network protocols in IoT applications, detailing their characteristics. Security challenges are emphasized, advocating robust authentication, encryption, and access control. Design considerations and scalability challenges are explored, with potential solutions discussed. Energy efficiency is investigated, covering power consumption, energy-efficient protocols, and harvesting methods. The study also scrutinizes standardization efforts’ impact on interoperability and IoT adoption. Through these insights, the paper aims to advance the seamless integration of wireless networks and IoT, unlocking the full potential of smart applications in diverse domains. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： IoT Security system smart applications Wireless system

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Deciphering Diabetes: Illuminating Prognostic Insights with an Interpretable Machine Learning Framework 3rd

Deciphering Diabetes: Illuminating Prognostic Insights with ...

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3rd international conference on Intelligent systems and Sustainable computing, ICISSC 2023

作者： Rony, Mohammad Abu Tareq Islam, Mohammad Shariful Saha, Pritom Johora, Fatama Tuz Ahammad, Mejbah Alam, Shah Md Nazmul Rahman, Md Saifur Nag, Anindya Department of Statistics Noakhali Science and Technology University Noakhali3814 Bangladesh Computer Science and Telecommunication Engineering Noakhali Science and Technology University NoakhaliNoakhali-3814 Bangladesh Computer Science and Engineering Daffodil International University Dhaka Bangladesh Computer Science and Engineering University of Chittagong Chittagong Bangladesh Computer Science and Engineering American International University Bangladesh Dhaka Bangladesh Masters of Computer Science University Arizona State University Tempe United States Strategic Management University UniversityoftheCumberlands Williamsburg United States Computer Science and Engineering Khulna University Khulna9208 Bangladesh

ISBN: (纸本)9789819783540

Diabetes is a chronic disease that impacts millions of people globally. Early detection and management of diabetes can help mitigate potential complications and enhance patient outcomes. Machine learning (ML) algorithms have become more prevalent for predicting diabetes in recent years. The research paper proposed a machine learning-based approach for predicting diabetes utilizing the Diabetes Health indicator dataset and compared the comprehensive performance of multiple ML algorithms. The study used the LIME (Local Interpretable Model-Agnostic Explanations) package to provide interpretable predictions and acquire insights into the factors contributing to the model’s predictions. Although the ML models were interpretable, they also delivered the highest accuracy 86% demonstrated by the XGBoost classifier in prediction. The LIME produces local explanations for individual predictions, emphasizing the most significant features contributing to the prediction. The study found that, for the XGB model, BMI has a negative influence, while Genhlth positively impacts the outcome. Moreover, Age and Income have a positive impact on the outcome of diabetes. The findings of this research can potentially improve patient outcomes and alleviate the global burden of diabetes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Development and Implementation of a Scalable Architecture for smart Healthcare systems Employing Internet of Things 15th

Development and Implementation of a Scalable Architecture fo...

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15th international conference on Soft computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Reddy, A. Srinivasula Singh, Sunil Kumar Babu, P. Raveendra Ram, R. Bhargav Murali, Kanthi Sanaboina, S. V. S. V. Prasad CMR Engineering College Telangana Hyderabad India CMR College of Engineering and Technology Telangana Hyderabad India CMR Technical Campus Telangana Hyderabad India

ISBN: (纸本)9783031810824

Indeed, the traditional healthcare systems had a subservient nature. The patient was required to schedule numerous appointments with the doctor, regardless of minor concerns. The concept of a smarter society has been effectively realized through the Internet of Things (IoT), which has many uses, including in the medical field. The resolution of this issue is being achieved through the advancement of intelligent healthcare systems, resulting in a significant reduction in mortality rates on a global scale. The implementation of Healthcare IoT has the potential to enhance patient well-being and elevate the quality of care provided by healthcare professionals, including doctors, nurses, clinicians, pharmaceutical companies, and the wider population. This article examines the planning and execution of a secured architecture for a smart healthcare system utilizing the Internet of Things (IoT). The efficacy of this intelligent healthcare model is assessed within the MATLAB environment in order to address challenges such as packet loss and other related issues. This research looks at the Cloud Request View Ratio (CRVR), packet loss, and IoT success rate in the healthcare system. Subsequently, proponents of safety and confidentiality solutions engage in a discourse regarding the challenges associated with the implementation of safety mechanisms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Nursing

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A Multi-objective smart Grid Optimization: Integrating Swarm Intelligence with Domain-Specific Heuristics for Superior Energy Efficiency 3rd

A Multi-objective Smart Grid Optimization: Integrating Swarm...

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3rd international conference on Intelligent systems and Sustainable computing, ICISSC 2023

作者： Ammasai, Viswanathan Katbar, Nek Muhammad Kothalanka, Amarendra Vandadi, Venkata Ramana Udhaya Shankar Latha, Sindhu Sharma, Dilip Kumar Sengan, Sudhakar School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632 014 India School of Mathematics and Statistics Central South University Hunan Changsha410083 China Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Department of Computer Science and Engineering KSRM College of Engineering Andhra Pradesh Kadapa516003 India Department of Information Technology V.S.B. College of Engineering Technical Campus Tamil Nadu Coimbatore642109 India Department of Mathematics Jaypee University of Engineering and Technology Madhya Pradesh Guna473226 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli62745 India

ISBN: (纸本)9789819783540

The realm of smart Grid systems (SGS) has witnessed unprecedented progress, driven by increasing energy demands and the integration of Renewable Energy Sources (RES). While these grids promise enhanced efficiency and sustainability, their involved and dynamic nature poses significant optimization challenges. Existing solutions, although innovative, often overlook the intricate interplay between diverse factors, leaving room for potential improvements in optimization approaches. Recognizing this gap, the current study delves into developing a novel method that integrates Ant Colony Optimization (ACO) with domain-specific heuristics. By focusing on analytical sections like Load Forecasting, Renewable Energy Integration, Peak Shaving, and Grid Resilience, this method attempts to bridge the divide that existing approaches miss. The proposed method not only ensures optimal energy distribution but also tailors the solution to address the unique features and requirements of modern SGS. A more reliable, effective, and Renewable Energy (RE) future is within reach according to the outcomes of this investigation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Clean energy

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smart and sustainable threat intelligence

Smart and sustainable threat intelligence

引用

international conference on Innovation and Technological Advances for Sustainability, ITAS 2023

作者： Shoukat, Duaa Taimoor Khan, Muhammad Sajjad, Syed Muhammad Akhunzada, Adnan Riphah Institute of Systems Engineering Islamabad Pakistan Doha Qatar

ISBN: (纸本)9781032803722

Distributed Denial of Service (DDoS) attack is known to be the most dangerous attack in cyber-threats which is identically collective in the world of today’s interconnected networks, as it mainly disrupts and makes vital services unavailable. Hackers are skilled in using multi- classification DDoS attacks to evade the detection for exploitation of the targeted networks. A proactive and efficient detection mechanism is much needed to secure the networks from multiclass DDoS attacks. However, implementing DDoS detection schemes is very difficult due to the factors such as cost, complexities, and inflexibility. Most of the work has been done to tackle these attacks;still, the open question is to find out an efficient, smart and sustainable model amongst available choices. Therefore, in this research, the author proposes a novel, lightweight Cuda- powered Deep Neural Network Long ShortTerm Memory (Cu-DNNLSTM) enabled smart and sustainable threat intelligence system for large and distributed enterprise networks and is simply termed as Multiclass DDoS Detection mechanism (MDDM). The proposed approach is implemented using the current state of the art Canadian Institute of Cybersecurity (CIC)-DDoS2019 dataset that is publicly available and has been thoroughly evaluated. The preliminary results achieves 99.60% detection accuracy with a relatively low ratio of False Positives (FP) (i.e., 0.0003). Additionally, the proposed approach has also been compared with two other DL models to show the promising performance of the proposed approach. Finally, the proposed approach is highly scalable, cost effective, flexible and sustainable to be customized for any emerging computational and communication paradigm. © 2025 The Author(s).

关键词： Cyber attacks

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Scalable Real-Time Anomaly Detection in Financial Markets Using a Novel Machine Learning Model 3rd

Scalable Real-Time Anomaly Detection in Financial Markets Us...

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3rd international conference on Intelligent systems and Sustainable computing, ICISSC 2023

作者： Andi, Hari Krishnan Bommisetti, Ravi Kumar Subbarao, Buggavarapu V. S. S. Sonthi, Vijaya Krishna Ray, Samrat Ammasai, Viswanathan Sengan, Sudhakar Centre for Postgraduate Studies Asia Metropolitan University Johor Bahru81750 Malaysia Department of Business Administration and Dean-Research and Development Maris Stella College Vijayawada520008 India Department of MBA Kumaraguru College of Technology Tamil Nadu Coimbatore641049 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Dean Head of International Relations IIMS Pune411033 India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627451 India

ISBN: (纸本)9789819783540

Stakeholder objectives can be put at risk if anomalies are not detected sufficiently promptly in the constantly evolving financial systems. This paper recommends an innovative Machine Learning (ML) method, the Hybrid Temporal-Feature Embedding Model (HTEM), dedicated to real-time Anomaly Detection (AD). Fundamental to this framework is the use of a distributed system to store and process real-time data that has been collected through a strong Data Ingestion Layer. The HTEM framework is a vital component of this layout. The temporal flow of data collected from LSTM networks is precisely combined with the significance of individual features that are analyzed by autoencoders in this technique. By applying this dual embedding, a complete model of financial data sequences is accomplished. By applying a fully connected layer to provide this combined representation, the research affects an accurate anomaly value for each data point. If the overall rating is higher than a particular threshold, the data point is classified as an anomaly. Training this model frequently on known anomalies enables it to be fine-tuned to a higher level. The timely, accurate, and efficient finding of anomalies can be ensured through the incorporation of the HTEM within the model being proposed, protecting the interests of stakeholders in the financial market environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Efficient Temporal Data Mining Technique Using Dynamic Time Warped LSTM for E-Commerce Recommendation systems 3rd

Efficient Temporal Data Mining Technique Using Dynamic Time ...

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3rd international conference on Intelligent systems and Sustainable computing, ICISSC 2023

作者： Rajassekharan, Dinesh Subramanian, Jayasundar Lazar, Arokia Jesu Prabhu Pichuka Veera Venkata Satya, Srinivas Ray, Samrat Ammasai, Viswanathan Sengan, Sudhakar Faculty of AI Computing and Multimedia Lincoln University College Petaling Jaya47301 Malaysia Department of Computer Science and Engineering Idhaya Engineering College For Women Tamil Nadu Chinnasalem606201 India Department of Computer Science and Engineering CMR Institute of Technology Hyderabad501401 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Dean Head of International Relations IIMS Pune411033 India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627451 India

ISBN: (纸本)9789819783540

The rapid surge of the electronic commerce (Ecomm) industry has ushered in intensified competition, with platforms vying for customer attention and loyalty. Many Ecomm sites have been employing various tactics to stand out, with Recommendation systems (RS at the forefront of their strategies. However, traditional RS faces challenges, mainly when interactions occur at irregular intervals. This article introduces the DTW + LSTM framework, a methodology combining the merits of Long Short-Term Memory Networks and Dynamic Time Warping to improve this work. This method requires consideration of DTW's time series data alignment capabilities to account for interaction interval variability. The LSTM is then designed to accurately record the key elements of user interactions in sequential order. Combining the outcomes of these two methodologies in the DTW + LSTM model presents a more flexible and extensive approach to generating online shopping recommendations. The design guarantees that forecasts continue to be reliable and accurate despite the inherent temporal deviations that can be observed in user data. This paper investigates the context of this approach, its benefits, and prospective developments, demonstrating its possibilities in the dynamic Ecomm market. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Accuracy CNN E-commerce F1-score GRU LSTM Machine learning

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smart Mobility Solutions for Urban Transportation in ASEAN: A Bibliometric Study of Trends and Innovations 20th

Smart Mobility Solutions for Urban Transportation in ASEAN: ...

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20th international conference on the Economics of Grids, Clouds, systems, and Services, GECON 2024

作者： Pambudi, Dany Hwang, Junseok Technology Management Economics and Policy Program College of Engineering Seoul National University Seoul08826 Korea Republic of Integrated Major in Smart City Global Convergence Seoul National University Seoul08826 Korea Republic of

ISBN: (纸本)9783031812255

The ASEAN region faces unique challenges in urban transportation due to rapid urbanization, increasing population densities, and the growing demand for sustainable mobility solutions. This study does a thorough bibliometric analysis of the patterns and advancements in smart mobility, with the objective of tackling these difficulties between 2013 and 2023, focusing on 1,249 peer-reviewed publications. The analysis shows a consistent increase in research effort, with publications reaching their highest point in the past two years. The study identified six main research areas: Intelligent Transportation systems, Public Transportation, smart City, Urban Mobility, Autonomous Vehicle, and Advanced Mobility Technologies. The findings suggest a transition from conventional transportation systems to integrated, technologically enhanced solutions prioritizing sustainability and efficiency. The study emphasizes the importance of innovation in addressing urban transportation issues in ASEAN cities and proposes potential areas for further research, including incorporating emerging technology and analyzing socio-economic effects. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Sustainable city

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Efficient Electricity Theft Management: A smart Energy Meter Approach for Real-Time Detection and Prevention

Efficient Electricity Theft Management: A Smart Energy Meter...

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international conference on Computational Intelligence and smart Technologies in Electrical engineering, CISTEE 2023

作者： Kumar, Kunal Kumar, Prince Kar, Susmita Bohre, Aashish Kumar Department of Electrical Engineering NIT Rourkela Odisha Rourkela India Department of Electrical Engineering NIT Silchar Assam Silchar India Department of Electrical Engineering NIT Durgapur West Bengal Durgapur India

ISBN: (纸本)9789819604753

Electricity theft remains a persistent challenge in many regions, causing significant financial losses for utility companies and undermining the integrity of energy distribution systems. This paper presents a comprehensive study on the simulation and development of a GSM-based smart energy meter equipped with Arduino as the central processing unit and GSM modules for data communication, aimed at detecting, and preventing electric theft in real-time. Innovative algorithms and data analysis techniques are employed to recognize anomalies in energy consumption, voltage fluctuations, and other indicators indicative of theft. Simulations and real-world testing are conducted to assess the system's effectiveness in detecting and preventing theft instances. Moreover, the suggested detection technique is replicated in Proteus to assess its performance under typical conditions and diverse theft scenarios. Ultimately, the system's capabilities are evaluated by applying various theft techniques, and the figures within the research paper illustrate the warning messages corresponding to each specific theft scenario. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Information management

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