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intelligent Transportation systems Make Use of Fog and Edge computing for Navigation 2

Intelligent Transportation Systems Make Use of Fog and Edge ...

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2nd ieee international conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Rathore, Rachna Kaushik, Priyanka Sikarwar, Shailendra Singh Joshi, Hrithik Mishra, Atul Kumar Hudda, Yashika Jiwaji University Dept.of Mathamatics Gwalior India CSE-APEX Chandigarh India Jiwaji University Pgv College Dept. of Computer Science Gwalior India Madhav University Dept. of Management Abu Road India

ISBN: (纸本)9798350360523

The denizens of smart cities now enjoy improved access to navigational information through smartphones and other smart technologies. Addressing the challenges of data management, safeguarding sensitive information in both cloud and vehicular contexts, and elevating the dependability, efficiency, and utility of services become complex when dealing with extensive data volumes. This paper delves into the current state and future trajectory of the Internet of Things (IoT), particularly within intelligent transportation networks. The researchers propose that an automated edge/fog solution serves as a pivotal approach to resolving the dynamic ride-sharing problem. The shift of data processing and storage to the network's periphery is explored. The advent of fully operational next-generation 5G networks is anticipated to trigger a substantial increase in globally connected devices. Favorable conditions for cloud computing encompass low-latency, low-congestion networks with high user and sender density. The proliferation of Edge computing (EC)-enabled networks augments portability, safety, and confidentiality. The term "EC architecture"encapsulates the design scheme of a system in this context. © 2024 ieee.

关键词： Edge computing

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Enabling Self-Service OLAP on NoSQL data Lakes 6

Enabling Self-Service OLAP on NoSQL Data Lakes

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6th international conference on Pattern Analysis and intelligent systems, PAIS 2024

作者： Boukraa, Doulkifli Sellamna, Hamza Chidekh, Yacine University of Jijel LaRIA Faculty of Exact Sc. & Computing Jijel18000 Algeria University of Jijel Jijel18000 Algeria University of Jijel Faculty of Exact Sc. & Computing Jijel18000 Algeria

ISBN: (纸本)9798350350265

Online analytical processing plays a crucial role in data analysis by providing a powerful framework for efficiently exploring and understanding multidimensional data. With the increasing complexity and volume of data in modern organisations, the main challenge is to build OLAP cubes from distributed heterogeneous sources while considering attribute and relationship overlapping across the sources and to let analysts create their cubes on-demand to maximise their usefulness. We propose a self-service, metadata-driven approach for designing and generating OLAP cubes from heterogeneous NoSQL sources composing a data lake. We illustrate our approach through a realistic scenario where the data is scattered and overlaps across different sources. We present a proof-of-concept application that implements our proposed approach. An assessment of our proposal is conducted to highlight the efficacy and usefulness of the generated cubes for analytical purposes. © 2024 ieee.

关键词： Metadata

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AI-Driven intelligent IoT systems for Real-Time Food Quality Monitoring and Analysis 2

AI-Driven Intelligent IoT Systems for Real-Time Food Quality...

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2nd ieee international conference on Trends in Quantum computing and Emerging Business Technologies, TQCEBT 2024

作者： Krishnan, Prabhakar Ebenezar, U. Samson Ranitha, R. Purushotham, N. Balakrishnan, T. Suresh Amrita Vishwa Vidyapeetham Center for Cyber Security Systems and Networks Amritapuri campus India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Mohan Babu University Department of Computer Science and Engineering Mr.palliAP India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350384277

The system will monitor and analyse food quality along the supply chain in real time. The system has high performance due to the CNN algorithms. In terms of accuracy, CNN has a high performance (95.2%), precision (92.8%), recall (94.5%) and F1 score (93.6 %). These features enable the model to predict food quality under good and poor conditions, thus demonstrating its effectiveness in assessing quality. The accuracy of real-time monitoring was evaluated across the supply chain to prove system reliability. Accuracy ratings for transport, storage and processing were 96.3%, 94.7% and 92.1%. These results show the robustness of this system within dynamic supply chain environments. Further testing evaluated environmental resilience capabilities. Under normal conditions, the accuracy of this system was 95.2%, while it reduced to 92.6% and rises again up to a level of 94.3%. The adaptability of the system under harsh conditions demonstrates that it can be applied to ensure food quality in various cases. This study reveals how proposed AI-based intelligent Internet of Things system may be used for real application using an appropriate dataset selected properly. This approach can help improve the quality assurance of food in an industry that is complex by uniting advanced algorithms with real-time data monitoring. © 2024 ieee.

关键词： Artificial Intelligence Food Quality Analysis intelligent IoT systems Real-Time Monitoring

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Fractional-Order PID Controller for MPPT of Variable-Speed Wind Turbines Using Genetic Algorithm 6

Fractional-Order PID Controller for MPPT of Variable-Speed W...

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6th international conference on intelligent computing in data Sciences, ICDS 2024

作者： Zoubaa, Yassamine Chouiekh, Sihame El Bakri, Ayoub Boumhidi, Ismail Ben Abdellah University Fes LISAC Laboratory Faculty of Sciences Sidi Mohammed Dept. of Physics Morocco

ISBN: (纸本)9798350351200

This paper focuses on the development of optimal control strategies under disturbance conditions for a variable-speed wind energy system (VSWES) in the presence of disturbance. The fractional-order PID controller (FOPID), referred to as bmPI\bmλ Dμ , extends the traditional PID controller by offering enhanced flexibility, improved performance, and increased robustness. However, optimizing the parameters of this controller is a challenging task. In this work, genetic algorithms (GA) used to find approximate solutions to various problems, offering efficient optimization techniques. The proposed controller has been implemented as maximum power point trackers (MPPT) for the wind turbine. Simulation results confirm that fractional order PID controller optimized by GA offers the highest performance in terms of transient response and disturbance compensation, especially in terms of overshoot, response time and stabilization. © 2024 ieee.

关键词： Three term control systems

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Robust Heart Disease Prediction: A Hybrid Approach to Feature Selection and Model Building 4

Robust Heart Disease Prediction: A Hybrid Approach to Featur...

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4th international conference on Ubiquitous computing and intelligent Information systems, ICUIS 2024

作者： Kodete, Chandra Shikhi Pasupuleti, Vikram Thuraka, Bharadwaj Sangaraju, Varun Varma Tirumanadham, N. S. Koti Mani Kumar Shariff, Vahiduddin School of Technology Eastern Illinois University CharlestonIL61920 United States School of Computer Science and Information Systems Northwest Missouri State University MaryvilleMO64468 United States University of the Cumberlands WilliamsburgKY4076 United States Sir C R Reddy College of Engineering Dept. of Cse Eluru India

ISBN: (纸本)9798331529635

This research work integrates advanced data preparation methods and advanced modelling approaches to offer a complete approach for creating a predictive model for heart disease. data cleansing starts the procedure in order to fix disparities and manage missing values, thereby guaranteeing the dependability and accuracy of the dataset. By producing synthetic samples for the minority class, the Synthetic Minority Over-sampling Technique (SMOTE) solves class imbalances and improves the accuracy of the model to forecast minority class occurrences. The z-score approach helps to control outliers thereby preventing distortion in the study. Using a hybrid Filter-Recursive Feature Elimination (FRFE) approach-which combines filter and wrapper techniques to find the most pertinent features, therefore lowering multicollinearity and boosting model performance-feature selection is accomplished. Important characteristics found are age, kind of chest discomfort, maximal heart rate attained, ST depression brought on by exercise, and fluoroscopy-induced coloration of several major arteries. Using AdaBoost, an ensemble learning technique combining several weak classifiers to create a strong one, the improved dataset is utilised to develop a predictive model. The resultant model shows great accuracy (93%), precision (93%), recall (94%), and an amazing ROC AUC score (0.98), therefore highlighting its potential use in healthcare environments and heart disease prediction ability. © 2024 ieee.

关键词： Feature Selection

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Design of an intelligent English Writing Assistant System Based on Text Mining Technology 4

Design of an Intelligent English Writing Assistant System Ba...

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4th ieee international conference on Electronic Communications, Internet of Things and Big data, ICEIB 2024

作者： Fangzhou, Zhang Jilin Agricultural Science and Technology University Jilin China

ISBN: (纸本)9798350360721

Proficient English writing skills are indispensable for effective communication and academic or professional success. However, many individuals struggle to refine their writing abilities due to various challenges. To address this need, an intelligent English writing assistant system based on text-mining technology was constructed in this study. This system leverages advanced text mining techniques to analyze, assess, and enhance written content, providing users with personalized guidance and support. Key technologies and principles related to this system include text mining methodologies such as tokenization, TF -IDF analysis, and named entity recognition, as well as web crawling technology for dynamic content acquisition. The system's architecture encompasses requirements analysis, system design, database architecture, and implementation of backend modules. The functionality is secured through the integration of web crawling mechanisms, including spider integration, to retrieve relevant textual resources from online sources. Additionally, an exercise recommendation module enhances user engagement and skill development. Through the seamless integration of these technologies, the intelligent English writing assistant system empowers users to communicate confidently and effectively in English. © 2024 ieee.

关键词： Character recognition

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Overview of UAV-Based Overhead Power Line Defect Detection Based on Deep Learning 6

Overview of UAV-Based Overhead Power Line Defect Detection B...

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6th ieee international conference on Power, intelligent computing and systems, ICPICS 2024

作者： Tian, Xiaoxi Wang, Xin Liu, Zhenpeng Peng, Baoquan Liu, Yuwei School of Electrical and Control Engineering Shenyang Jianzhu University Shenyang China Shenyang Jianzhu University University Government Departments Shenyang China

ISBN: (纸本)9798350374315

Unmanned Aerial Vehicles (UAV) equipped with various sensors can efficiently and safely perform automated detection of overhead power lines. Deep learning-based image recognition technology plays a crucial role in this field, as it can automatically identify and classify power Line defects, enhancing the accuracy and efficiency of the detection process. This paper first outlines the main procedures of UAV-based overhead power line defect detection. It then provides an overview of the key technologies in deep learning-based UAV detection, focusing on algorithm analysis, dataset construction, evaluation metrics, and model lightweight. Finally, it discusses the challenges and prospects of deep learning-based UAV detection for overhead power line defects. © 2024 ieee.

关键词： Algorithm optimization data set construction Deep learning Image defect detection Model lightweight UAV detection

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intelligent Inspection Robot Scene Recognition under Convolutional Neural Network 13

Intelligent Inspection Robot Scene Recognition under Convolu...

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13th ieee international conference on Communication systems and Network Technologies, CSNT 2024

作者： Li, Xue Harbin Cambridge University Faculty of Automotive and Mechatronic Engineering Heilongjiang Harbin China

ISBN: (纸本)9798350305463

With the rapid development of technology, intelligent substation detection robots are becoming a new favorite in the industry. It not only improves the detection efficiency of the substation, but also improves the detection accuracy. At the same time, the integration of multiple advanced technologies endows these robots with unique functions and characteristics. Substation is an important component of the power system, and its working state directly affects the stability of power supply. Traditional, usually manual inspection methods are not only inefficient but also prone to errors. The emergence of intelligent inspection robots has completely changed the inspection method of substations, allowing for large-scale inspections in a short period of time, with much higher accuracy than manual operations. The total number of surveyed data within 24 hours for the intelligent inspection robot scene recognition using convolutional neural networks is 198, with only 2 unmeasured data. However, it can be seen that the intelligent inspection robot scene recognition using convolutional neural networks can quickly and accurately recognize. © 2024 ieee.

关键词： Inspection

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data-Driven Design for IRS-Assisted Massive Multi-Modal Sensing in Industry 5.0 59

Data-Driven Design for IRS-Assisted Massive Multi-Modal Sens...

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59th Annual ieee international conference on Communications (ieee ICC)

作者： Haider, Majumder Ahmed, Imtiaz Lashhab, Fadel O'Shea, Tim Rawat, Danda B. Matin, Mohammad Howard Univ Dept Elect Engn & Comp Sci Washington DC 20059 USA DeepSig Inc Arlington VA USA Univ Denver Dept Elect & Comp Engn Denver CO USA

ISBN: (纸本)9798350304060;9798350304053

The transition into Industry 5.0 underscores the need for advanced communication frameworks capable of overcoming the challenges posed by complex industrial environments. This opens new possibilities for addressing industrial issues related to fading wireless channels and interference, potentially resulting in a significant leap forward in the capacity and reliability of wireless communications. This paper introduces a data-driven approach, utilizing a deep neural network (DNN) model to enhance cascaded channel estimation and mapping in intelligent reflecting surface (IRS)-enabled industrial internet-of-things (IIoT) networks. By employing Remcom's Wireless InSite for realistic Ray tracing simulations, we have generated a robust dataset that accurately represents the spatial characteristics and varying noise conditions of an industrial setting. Our approach strategically divides the channel estimation process, directly estimating a subset of channels for a few users while leveraging the channel mapping model for the remainder. Simulation results demonstrated that the proposed DNN-aided scheme outperforms traditional estimation techniques in terms of reduced complexity and accuracy. Moreover, the proposed approach is particularly effective in both Gaussian and non-Gaussian noise scenarios, significantly contributing to developing reliable, scalable, and intelligent industrial wireless systems.

关键词： IRS Industry 5.0 cascaded channel estimation channel mapping multi-modal sensing deep learning

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Smart Reservoir Navigating Waters with Integrated Automation 10

Smart Reservoir Navigating Waters with Integrated Automation

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10th international conference on advanced computing and Communication systems, ICACCS 2024

作者： Krishna, V. Yesvanth Roshan, B. Kracose Nishanth, J. Anbarasu, P. Mohan Raj, C. Sri Eshwar College of Engineering Department Of Electrical and Electronics Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350384369

The rising need for efficient management of water resources necessitates the inclusion of advanced control systems in essential infrastructure, such as water reservoirs. This study suggests a systematic approach that utilizes SCADA, PLC, and HMI technologies to improve water reservoir control and monitoring. As the reservoir's core control unit, the PLC executes preprogrammed logic to regulate the inflow, outflow, and other vital parameters. While managing the numerous water system components, it ensures responsiveness, dependability, and real-time decision-making. Moreover, complex control algorithms are managed by the PLC by design. With touchscreens and graphical displays, operators can monitor water quality, reservoir levels, and system condition. The SCADA system's integration enables a comprehensive view of the whole network of water reservoirs. It provides a thorough picture of system functioning by gathering and analyzing data from dispersed sensors. Because SCADA makes remote monitoring and control easier, operators can react quickly to changing circumstances and make well-informed decisions. The water reservoir management system's sustainability, efficiency, and dependability are improved by the synergistic integration of PLC, HMI, and SCADA technologies. Water distribution is optimized, energy consumption is reduced, and overall system resilience is increased by the intelligent control system. This strategy helps achieve the dual objectives of smart and resilient infrastructure management in the face of escalating environmental issues and guarantees the sustainable use of water resources. © 2024 ieee.

关键词： SCADA systems

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