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A Novel Machine Learning Strategy for Anomaly Identification Scheme in Wireless sensor Networks Using Supervised Training Principles 5

A Novel Machine Learning Strategy for Anomaly Identification...

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5th International conference on Electronics and Sustainable Communication systems, ICESC 2024

作者： Shifani, S. Agnes Mary, A. Antony Metilda, M.I. Mary Rajkumar, G. Vinoth Sutha, M.S. Maheshwari, S. Saveetha University Chennai

ISBN: (纸本)9798350379945

Anomaly detection in Wireless sensor Networks (WSN s) is critical for maintaining the integrity and reliability of various monitoring systems, especially in agricultural settings. The core of this study is the development of a hybrid anomaly detection model, SVM-RFNet, which integrates Support Vector Machines (SVM), Random Forests (RF), and Neural Networks (NN). By applying noise reduction techniques, such as smoothing algorithms, the data quality is significantly enhanced, resulting in cleaner and more stable datasets. Feature engineering, including temporal trend analysis and Principal Component Analysis (PCA), further refines the dataset by extracting and selecting the most informative features. This model combines the strengths of each method, leveraging SVM's robustness in high-dimensional data, RF's ensemble learning capabilities, and NN's ability to learn complex patterns. The SVM-RFNet model achieved an impressive accuracy of 96%, demonstrating its efficacy in real-time anomaly detection. The paper concludes with a discussion on future research directions, including the integration of advanced deep learning techniques and the development of self-healing networks, which promise to further enhance the capabilities and applications of WSN s in various domains. © 2024 IEEE.

关键词： Noise abatement

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Landsat8-9 Satellite Image Classification with ERDAS for Mapping the Urban Spaces of East Sikkim 3

Landsat8-9 Satellite Image Classification with ERDAS for Map...

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3rd International conference on Intelligent systems, advanced Computing, and Communication, ISACC 2025

作者： Khawas, Chunnu Pradhan, Mohan P. Pradhan, Ratika Pradhan, Ashis Sikkim University Dept of Computer Applications Sikkim India Sikkim Manipal University Sikkim Manipal Ins. of Tech. Dept. of CSE Sikkim India

ISBN: (纸本)9798331523893

This research initiative examines the categorization of Landsat 8-9 satellite imagery from the Operational Land Imager sensor(OLI) using remote sensing to quantify land cover and delineate urban areas in East Sikkim for the year 2023. It employs a system for satellite image processing utilizing supervised classification methods. The processing was transitioned to spectrum preparation and enhancement of spatial resolution utilizing the blue, green, red, near-infrared, and panchromatic channels. LandSat 8-9 OLI and the Thermal Infrared sensor (TIRS) satellites image has been used for this study. ERDAS Imagine software has been used for performing image classification. The validation of the findings from the categorization has been performed using multiple reference points, including a previously validated national processing result and a Google Earth image from the same year. Upon multiple iterations of image classification, the supervised method achieved overall accuracy of 91% and Kappa indices of 0.84 for the Maximum Likelihood classification technique where as the unsupervised method achieved overall accuracy of 76% and Kappa indices of 0.63 for ISODATA classification technique. © 2025 IEEE.

关键词： Landsat

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Development and Comparative Study of advanced Distributed Electronic Control System for Robotic applications

Development and Comparative Study of Advanced Distributed El...

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2023 International conference on Integrated Intelligence and Communication systems, ICIICS 2023

作者： Nair, Shashank Babu Patil, Sushma Mathurkar, Piyush Chippalkatti, Srinivas S. Vishwakarma Institute of Information Technology Electronics and Telecommunication Engineering Maharashtra Pune India Sharnbasva University Electrical and Electronics Engg Kalaburagi India Vishwakarma University Pune Engineering Sciences Maharashtra Pune India

ISBN: (纸本)9798350315455

This paper aims to thoroughly analyze and compare a project report on an advanced electronic control system for robotics with two related scholarly works. The primary focus is on identifying the commonalities and divergences, as well as the strengths and weaknesses of these methods. The paper being reviewed proposes a novel architecture for a control system that employs distributed electronics to enhance robot performance and functionality, detailing the design, implementation, and real-world application effectiveness. It covers key aspects like an electronic board designed for stepper motor control in a closed-loop system, communication protocols, sensor integration, and distributed computing. The review delves into the comparative analysis of control system architectures, communication methods, and implementation techniques across the three papers, shedding light on their potential impact in the field of robotics, and suggests areas for future research and improvements. © 2023 IEEE.

关键词： Distributed architecture Electronic control system Robotic applications

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sensor-Based Assistive Technology as a Support to Reduce Intra-hospital Infection and Mortality Due to Epidemic Respiratory Diseases: A Proposal 15th

Sensor-Based Assistive Technology as a Support to Reduce Int...

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15th International conference on Digital Human Modeling and applications in Health, Safety, Ergonomics and Risk Management (DHM)

作者： Lizarazo-Gutierrez, Javier Ortiz-Barrios, Miguel de Souza, Helder Celani Isabel Jimenez-Delgado, Genett Ariza-Ortiz, Andres Estela Fernandez-Mendoza, Zaury Johanna Alfaro-Parada, Natali Univ Costa CUC Dept Comp Sci & Elect Barranquilla 080002 Colombia Univ Politecn Valencia Ctr Invest Gest & Ingn Prod CIGIP Camino Vera S-N Valencia 46022 Valencia Spain Univ Costa CUC Dept Product & Innovat Barranquilla 080002 Colombia Univ Estadual Paulista Dept Prod Engn Guaratingueta SP Brazil Inst Univ Barranquilla IUB Dept Ind Engn Barranquilla Colombia Univ Magdalena Santa Marta Colombia

ISBN: (纸本)9783031610592;9783031610608

The rapid spread of Seasonal Respiratory Diseases (SRDs) has put hospitals in need of improving their preventive measures. Contact and proximity are two events that cause the transmission of most respiratory infections. For this reason, the infected patients are generally placed in isolation. Still, even so, they must receive care in hospitals where contact and proximity are almost inevitable i) between patients, ii) between health professionals and patients, and iii) between health professionals, causing intra-hospital transmission of SRDs. This paper presents the design of a sensor-based Assistive Technology (AT) as a support to reduce intra-hospital infection and mortality due to epidemic respiratory diseases. Initially, we characterized the main SRDs. Second, we defined the leading clinical indicators to be monitored in patients with an SRD. Then, we designed the healthcare protocols responding to health deviations in patients with SRD. Fourth, we characterized the targeted population in terms of sociodemographic indicators that may affect the operationalization of the AT in the wild. Finally, we laid the groundwork for the sensor-based AT design. This proposal is an effort to ensure the continuing care of patients with SRD, reducing overcrowding and waiting times in healthcare systems during these periods and consequently tackling high mortality rates.

关键词： sensor-based systems Seasonal Respiratory Diseases (SRDs) Intra-hospital Infection Assistive Technologies (ATs) Healthcare

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A Systematic Analysis of Research Trends on Network Control System in Wireless sensor Network 8

A Systematic Analysis of Research Trends on Network Control ...

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8th International conference on advanced Computing and Communication systems, ICACCS 2022

作者： Paul, Subhra Prosun Aggarwal, Shruti Chandigarh University Department of Computer Science and Engineering Punjab Gharuan India

ISBN: (纸本)9781665408165

In the present world, wireless sensor network-enabled network control system is performing a significant role in science, technology, and our day-to-day life. The Network control system in wireless sensor network has a great contribution in modern technology-oriented lifestyle and is bringing control to engineers in communicating network system through various applications. Though network control system is facing different challenges like latency, security, and multiple accesses, it is widely accepted for the successful communication and effective data transmission process. In this research paper, we are trying to explain generally the research aspect, trends, and variation of research directions through comparative analysis of existing research work and scientometric analysis on network control systems in the wireless sensor networks. With the help of the Scopus database and Web of Science database separately, some assessments were performed to analyze research data. From different experiments, it is noticed that network control systems can be applied in different fields like environment, agriculture, home applications, traffic control, health, aircraft, and motor control system using a wireless sensor networks. Observing in the last twenty years of research works in this field, we have found that network control system in wireless sensor networks is increasingly become a talented topic in the communication and networking field. © 2022 IEEE.

关键词： Database systems

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Characterization of a PVDF sensor embedded in a metal structure using ultrasonic additive manufacturing

Characterization of a PVDF sensor embedded in a metal struct...

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conference on NDE 4.0, Predictive Maintenance, Communication, and Energy systems - The Digital Transformation of NDE

作者： Khattak, Mohid M. Headings, Leon M. Dapino, Marcelo J. Ohio State Univ NSF IUCRC Smart Vehicle Concepts Ctr Dept Mech & Aerosp Engn Columbus OH 43210 USA

ISBN: (纸本)9781510660854;9781510660861

This paper investigates the characterization and functional performance of a piezoelectric polyvinylidene fluoride (PVDF) sensor embedded into an aluminum plate using ultrasonic additive manufacturing (UAM). While conventional manufacturing techniques such as non-resin-based powder metallurgy are being used to surface-mount smart materials to metals, they pose their own set of problems. Standard manufacturing approaches can physically damage the sensor or deteriorate electrochemical properties of the active material due to high processing temperatures or long adhesive settling times. In contrast, UAM integrates solid-state metal joining with subtractive processes to enable the fabrication of smart structures by embedding sensors, actuators, and electronics in metal-matrices without thermal loading. In this paper, a commercial PVDF sensor is embedded in aluminum with a pre-compression to provide frictional coupling between the sensor and the metal-matrix, thus eliminating the need for adhesives. Axial impact and bending (shaker) tests are conducted on the specimen to characterize the PVDF sensor's frequency bandwidth and impact detection performance. Metal-matrices with active components have been under investigation to functionalize metals for various applications including aerospace, automotive, and biomedical. UAM embedment of sensors in metals enables functionalization of structures for measurement of stresses and temperature within the structure while also serving to shield smart components from environmental hazards. This technique can serve a wide-range of applications including robotics and tactile sensing, energy harvesting, and structural health monitoring.

关键词： Active metal matrix embedded sensor ultrasonic additive manufacturing piezoelectric PVDF sensor non-destructive testing

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A Comprehensive Survey on Traffic Light Detection sensors for Vehicle Safety and Automatic Emergency Braking systems Using Deep Learning Techniques 3rd

A Comprehensive Survey on Traffic Light Detection Sensors fo...

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3rd International conference on Intelligent systems and Sustainable Computing, ICISSC 2023

作者： Gowri, B. Shyamala Priya, G. Vishnu Anjana, S. Arjun, V. Kodal, Devendraa Department of Computer Science and Engineering Easwari Engineering College Tamil Nadu Chennai India

ISBN: (纸本)9789819783540

The integration of Traffic Light Detection (TLD) systems with advanced Emergency Braking systems (AEBS) marks a critical milestone in enhancing road safety and paving the way for advanced autonomous driving. This survey paper provides a panoramic and extensive overview of the state-of-the-art TLD solutions leveraging sensors and deep learning techniques. With an increasing emphasis on accident prevention and traffic management, the intersection of TLD and AEBS has become a focal point of research and development. This survey begins by elucidating the fundamental challenges associated with TLD, including varying environmental conditions, occlusions, and complex traffic scenarios. We explore the pivotal role of sensors such as cameras, LiDAR, and radar in providing the requisite data for TLD, and delve into the intricacies of sensor fusion techniques for enhanced perception. Deep Learning has emerged as a cornerstone technology in TLD, enabling robust object detection, classification, and real-time decision-making. We meticulously analyze a spectrum of deep learning architectures including Single-Shot Detectors (SSD), Faster R-CNN, YOLO, and custom-designed networks tailored for TLD applications. Furthermore, the survey examines critical components of the TLD pipeline, encompassing data collection, preprocessing, model training, real-time inference, and integration with AEBS. Emphasis is placed on real-time constraints, multi-modal sensor fusion, and adaptability to diverse traffic light configurations. The paper also delves into the significance of accurate traffic light state prediction, going beyond mere detection to anticipate traffic light changes and optimize vehicle control actions. Human-centric interaction and privacy concerns are addressed, encompassing driver warnings, user interfaces, and data anonymization strategies. Moreover, the survey discusses the importance of safety, validation, and collaboration within the TLD and AEBS ecosystem, emphasizing compl

关键词： Road and street markings

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A review on Machine Learning based IDS approaches in Wireless sensor networks 9

A review on Machine Learning based IDS approaches in Wireles...

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9th International conference on advanced Computing and Communication systems, ICACCS 2023

作者： Madhu Midhan, T. Devika, Kalepalli Rajakumar, R. Manikandan, R. Dinesh, K. Sreedevi, M. Madanapalle Institute of Technology & Science Department of Computer Science and Technology Madanapalle India

ISBN: (纸本)9798350397376

In wireless sensor networks (WSNs), sensors are scattered in a particular region to record and evaluate the physical data from the environment. They are widely utilised in various applications, including healthcare, event detection, agriculture, and disaster management. However, the challenges in WSNs include high bandwidth demand, energy consumption, route design, quality of service (QoS) provisioning, unusual activities, etc. However, numerous ways are being considered to solve the aforementioned difficulties. Detecting abnormal network movements is one of the most difficult challenges. An intrusion detection system (IDS) is a productive way to detect infiltration effectively. However, the growth of contemporary technologies necessitates using an effective IDS strategy. As a result, many academicians focus on reducing and eliminating anomalous network security vulnerabilities. According to the findings, machine learning techniques are more critical in addressing these issues. The accuracy of several machine learning algorithms for detecting intrusion in WSNs is investigated in this work. Furthermore, this paper examines the various datasets, performance measurements, and future directions. © 2023 IEEE.

关键词： Wireless sensor networks

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Exploiting Context and Attention Using Recurrent Neural Network for sensor Time Series Prediction 8th

Exploiting Context and Attention Using Recurrent Neural Netw...

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8th International Workshop on advanced Analytics and Learning on Temporal Data (AALTD) at European conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Baruah, Rashmi Dutta Munoz-Organero, Mario Univ Carlos III Madrid Dept Telemat Engn Avda Univ 30 Madrid 28911 Spain Indian Inst Technol Guwahati Dept Comp Sci & Engn Gauhati 781039 Assam India

ISBN: (纸本)9783031498954;9783031498961

In the current era of Internet of Things, typically data from multiple sources are captured through various sensors yielding Multivariate Time Series (MTS) data. sensor MTS prediction has several real-life applications in various domains such as healthcare, manufacturing, and agriculture. In this paper, we propose a novel Recurrent Neural Network (RNN) architecture that leverages contextual information and attention mechanism for sensor MTS prediction. We adopt the notion of primary and contextual features to distinguish between the features that are independently useful for learning irrespective of other features, and the features that are not useful in isolation. The contextual information is represented through the contextual features and when used with primary features can potentially improve the performance of the model. The proposed architecture uses the contextual features in two ways. Firstly, to weight the primary input features depending on the context, and secondly to weight the hidden states in the alignment model. The latter is used to compute the dependencies between hidden states (representations) to derive the attention vector. Further, integration of the context and attention allows visualising temporally and spatially the relevant parts of the input sequence which are influencing the prediction. To evaluate the proposed architecture, we used two benchmark datasets as they provide contextual information. The first is NASA Turbofan Engine Degradation Simulation dataset for estimating Remaining Useful Life, and the second is appliances energy prediction dataset. We compared the proposed approach with the state-of-the-art methods and observed improved prediction results, particularly with respect to the first dataset.

关键词： Recurrent Neural Network Gated Recurrent Unit Context Attention Multivariate sensor Time Series Remaining Useful Life Appliance energy prediction

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AI-Powered IoT Framework for Enhancing Building Safety through Stability Detection 8

AI-Powered IoT Framework for Enhancing Building Safety throu...

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8th International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Raja, S.R. Devi, T. Rathi Raj, J. Relin Francis Sankar, V. Krishna Krishnan, R. Santhana Venkatalakshmi, R. Hindustan Institute of Technology and Science Computer Applications Chennai India Christ University Department of Computer Science and Engineering Bangalore India Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Saveetha School of Engineering Chennai India Scad College of Engineering and Technology Department of Civil Engineering Cheranmahadevi India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Psna College of Engineering and Technology Department of Information Technology Dindigul India

ISBN: (纸本)9798350376425

The rapid urbanization and increasing structural complexities of modern buildings have heightened the need for advanced monitoring systems to ensure building safety. The research presents an AI-powered IoT framework that enhances building safety through advanced stability detection mechanisms. The proposed framework employs a novel algorithm, Ensemble Learning with IoT sensor Data Aggregation (EnIoT-SDA), which integrates ensemble learning techniques with aggregated sensor data to provide accurate and real-time stability assessments of building structures. The effectiveness of EnIoT-SDA was evaluated through a comprehensive simulation analysis, comparing its performance against existing algorithms, including Support Vector Machine (SVM), Gradient Boosting Machines (GBM), and Fuzzy Logic systems (FLS). Simulation metrics, such as accuracy, false positive rate, computational time, and detection latency, were used to assess and compare the algorithms' performance. The results demonstrated that EnIoT-SDA outperformed the existing methods in several key areas, offering improved accuracy and reduced detection latency, thus establishing its potential as a robust solution for building safety monitoring. The study underscores the significant advancements brought by integrating ensemble learning with IoT sensor data and highlights areas for future research and development in this domain. © 2024 IEEE.

关键词： Support vector machines

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