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Labeled Multi-Bernoulli Filter for Distributed multi-target Tracking with Detection and Class Measurements 27

Labeled Multi-Bernoulli Filter for Distributed multi-target ...

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27th international conference on Information Fusion (FUSION)

作者： Klupacs, James Gostar, Amirali K. Bab-Hadiashar, Alireza Hoseinnezhad, Reza RMIT Univ Sch Engn Melbourne Vic Australia

ISBN: (纸本)9798350371420;9781737749769

advanced machine vision and deep learning models are now widely used as virtual sensors in various applications to detect and classify objects in image measurements. Typically, these virtual sensors output measurements as a set of class probabilities for each detected object within the sensor's field of view. However, integrating this type of data into multi-target tracking systems, traditionally based on point measurement detections, presents some challenges. This paper proposes a solution by introducing a labeled multi-Bernoulli filter formulated to handle detections with class measurements from virtual sensors for multi-target tracking. Furthermore, we explore the application of this filter for multi-sensor multi-target tracking within a distributed sensor network. Through numerical experiments involving vehicles equipped with cameras and deep learning classification modules navigating complex roads, we demonstrate that incorporating class information into the tracking process improves tracking accuracy. This improvement is further enhanced when vehicles share and fuse their information in a distributed manner. Our findings highlight the benefits of integrating class probability data into multi-target tracking filters, offering substantial improvements in the accuracy of automated monitoring systems in dynamic and complex environments.

关键词： Random Finite Sets intelligent Transport systems Multi-Object Tracking Information Fusion Class-Based systems

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Ensuring privacy of data in Machine Learning 6

Ensuring privacy of data in Machine Learning

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6th ieee international conference on Recent Advances in intelligent Computational systems, RAICS 2024

作者： Reshmi, T. Divakaran, Sumesh College of Engineering Trivandrum Department of Computer Science and Engineering Thiruvananthapuram India

ISBN: (纸本)9798350381689

Machine Learning is one of the most popular advancements in technology which is being widely used in various domains, including healthcare, avionics, automotive, business, education, etc. A Machine learning approach works by learning the required knowledge from the data to be supplied by a client system. In a typical scenario, a client entrusts a third-party agency to develop a machine learning application and shares the data to the developer to enable the development of a Machine learning application. This is badly affecting the privacy of data as a third-party is getting access to sensitive data from a client system. Therefore, an effective data encoding technique is required to ensure the privacy of sensitive data while enabling a third-party agency to develop a Machine learning application on the encoded data. Existing data masking/encoding techniques such as pseudonymization, anonymization and substitution are badly affecting the machine learning process as the modification they do for masking data is preventing a machine learning approach to learn the required knowledge. Another approach known in the literature is Fully Homomorphic Encryption. But, there is no tool available based on this technique which enables a client system to mask sensitive data before giving it to a third-party Machine Learning developer. We failed to obtain the expected outcome from an off-the-shelf Machine Learning classifier when we tried to classify a benchmark dataset masked using an available implementation of Fully Homomorphic Encryption. Since enough details about the implementation is not available, we could not find and fix the issues which is causing this problem. We propose a technique based on which we implemented a tool which provides an easy-to-use Graphical User Interface enabling the client to mask sensitive data before giving it to a third-party agency for developing a Machine Learning Application. Our tool enables a client to mask sensitive data through few mouse click

关键词： Adversarial machine learning

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The Establishment and Management of Mass Appraisal database 11

The Establishment and Management of Mass Appraisal Database

引用

11th international conference on Information systems and computing Technology, ISCTech 2023

作者： Liu, Biao Zhang, Lei Fu, Wen Deng, Yue The Chinese University of HongKong Shenzhen Research Institute Shenzhen China Tongji University Shanghai Autonomous Intelligent Unmanned System Science Center Shanghai China City University of Macau Faculty of Data Science China Wuhan University Institute of Quality Development Strategy Wuhan China

ISBN: (纸本)9798350342406

As a complicate and systemic program, the mass appraisal of real estate relies heavily upon data. The accuracy, integrity and applicability of data greatly affects the effectiveness of appraisal work. Nowadays, because of the diversity of data acquisition ways, real estate data increase explosively, which brings challenges to data collection, storage, management, sharing and usage. To normalize the real estate data, thus guarantee the precise and completeness of appraisal result, it is necessary to construct a real estate database of high-reliability, high-scalability, and high-integrity. Due to the differences in cultures and national conditions, the real estate data are various among countries. As home to more than 17% of the world's population, China is facing to huge volume real estate data of high complexity, which makes it hard to collect and manage data. On this background, by analyzing real estate data, we discuss the technology route of constructing real estate database under GIS, and study data integration technique for real estate mass appraisal. At last, we take Shenzhen city as an example and verify the effeteness of our methods. © 2023 ieee.

关键词： Information management

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data-Driven Cloud Based Soil Nutrient Prediction System for Precision Agriculture

Data-Driven Cloud Based Soil Nutrient Prediction System for ...

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2024 ieee international conference on Modeling, Simulation and intelligent computing, MoSICom 2024

作者： Mishra, Amit Dass, Jeevesh Saxena, Siddhant Prakash, Ishan Satya Sargun Garg, Arshia Verma, Karun Bhattacharya, Jhilik Thapar Institute of Engineering & Technology Department of Electronics and Communication Engineering Punjab Patiala India Thapar Institute of Engineering & Technology Department of Computer Science Engineering Punjab Patiala India Thapar Institute of Engineering & Technology Department of Electronics and Computer Engineering Punjab Patiala India

ISBN: (纸本)9798331533311

Decreasing arable acreage and a growing world population are pushing for new farming systems. Achieving high crop production requires maintaining a balanced nutrient level in the soil. This study offers a machine learning-based model that will predict the NPK fertilizer to achieve the higher crop yields. Field sensor data along with meteorological data and remote sensing data from various test fields are integrated. A random forest and decision tree machine learning model are applied to the integrated data to develop a software tool for precision agriculture, enabling accurate estimation of nutrient requirements. A mobile application is used to share the nutrient prediction with farmers, enabling them to make well-informed choices regarding resource utilization efficiency and environmental friendliness. This research can transform agricultural practices globally and contribute towards food security on earth. © 2024 ieee.

关键词： Smart agriculture

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intelligent Lab view based Fuzzy Logic Controlled Irrigation System using IoT and LoRa Technology 15

Intelligent Lab view based Fuzzy Logic Controlled Irrigation...

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15th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Behera, Abhisekh Pahuja, Roop Dr. B. R. Ambedkar National Institute of Technology Instrumentation and Control Department Jalandhar India

ISBN: (纸本)9798331300579

This paper investigates and compares experimental techniques in irrigation systems, focusing on crucial factors such as soil fertility, moisture level, pH level, water availability, and temperature for efficient agriculture and increased food productivity. The proposed method involves transmitting data from sensors (e.g., soil-moisture, pH, temperature, and air moisture) to an IoT platform utilizing LoRa communication protocol, processing it, and utilizing it for developing a fuzzy-control action to determine the optimal irrigation valve opening while also notifying the irrigation water pH. LoRa is a low-power wide area network solution enabling long-range communication with minimal energy consumption. The objective is to create a smart irrigation system for efficient water management in agriculture. The system monitors parameters like soil moisture, ambient temperature, and water pH, utilizing a fuzzy logic controller to regulate irrigation and minimize water wastage. LabVIEW facilitates data processing, valve control, and sensor data acquisition. LoRa is used for data transmission to a data concentrator and then broadcasted to the TTN platform for visualization and analysis. This setup demonstrates the effectiveness of LoRa and the fuzzy logic controller in achieving resource-efficient irrigation practices for sustainable agriculture. © Grenze Scientific Society, 2024.

关键词： Soil moisture

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Adaptive Real-Time Malware Detection for IoT Traffic Streams: A Comparative Study of Concept Drift Detection Techniques 5

Adaptive Real-Time Malware Detection for IoT Traffic Streams...

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5th international conference on IoT Based Control Networks and intelligent systems, ICICNIS 2024

作者： Bharani, D. Lakshmi Priya, V. Saravanan, S. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331518097

As IoT expands dynamically, cybersecurity threats are evolving with time;thus, classical malware detection systems are no more appropriate, due to the phenomenon of concept drift and the necessity of handling large amount of data while detecting malware. Concept drift is used to refer to the statistical properties of malware that change over time as the malware behavior changes. We henceforth propose a real-time system to detect malware within a large-scale network traffic streams in the presence of concept drift. The system is capable of processing continuous large volumes of IoT traffic in real-time as it is implemented using Apache Spark and Apache Kafka. The proposed system also compares the performance of six different concept drift detection techniques: KS test, CUSUM, ADWIN, KSWIN, DDM, Page Hinkley on NSL-KDD and IoT -23 datasets. The comparison results show that KS test correctly identified concept drift in both the datasets. The proposed system makes an IoT environment with better malware detection capabilities by integrating the adaptive mechanism in real-time data processing tools apart from achieving high performance and scalability. © 2024 ieee.

关键词： data integration

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2024 international conference on Automation and Computation, AUTOCOM 2024

2024 International Conference on Automation and Computation,...

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2024 ieee international conference on Automation and Computation, AUTOCOM 2024

ISBN: (纸本)9798350382723

The proceedings contain 124 papers. The topics discussed include: system Verilog real number modelling for 8-bit flash ADC and R2R DAC;password systems that are mind controlled;multiclassification of tomato diseases: CNN and random forest hybrid approach;agricultural resource data acquisition and integration: enhancing efficiency and collaboration through information sharing;maximize yield of crop using recommendation system based on machine learning;an integrated approach to energy harvesting and power management systems: a comprehensive analysis;deep learning algorithm for digital image forensics;over-crowd avoiding in transportation with face detection using raspberry pi;and an efficient swarm intelligence algorithm for multi-objective task scheduling optimization in the context of cloud computing.

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Optimizing AUV Positioning and data Transmission with Actor Critic (A3C) Algorithm in Underwater Wireless Sensor Networks 4

Optimizing AUV Positioning and Data Transmission with Actor ...

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

作者： Kodukulla, Aruna Gayatri Amanullah, M. Kalaiselvan, S.A. Saveetha School of Engineering Simats Department of Computer Science & Engineering Chennai India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331529635

In recent years, UWSNs have emerged as a game-changer in many aquatic applications, including exploration, environmental monitoring, target tracking for underwater navigation, terrain-assisted navigation, and many more. The intricacy of the underwater environment presents several obstacles for the deployment of sensor nodes, and UWSNs are dynamic networks, unlike terrestrial wireless sensor networks. Instead of transferring data from every node to a destination node, autonomous underwater vehicles (AUVs) provide a more reliable option for data collecting in UWSNs. Nevertheless, a major challenge in data collection for UWSNs is the development of AUV positioning algorithms that are both delay-aware and energy-efficient. A trustworthy AUV-based data-collection routing strategy employed for UWSNs with the goal of decreasing network latency and increasing network lifespan. A positioning mechanism is used by the proposed protocol to gather data utilizing AUVs. With the goal of lowering power usage, the sink node coordinates AUV data collecting from the sensor nodes. To improve scalability, sensor nodes are first grouped into clusters, and then an AUV is assigned to each cluster. Second, in order to ensure the trustworthy gathering of data, the Actor 3 Critic (A3C) for every AUV is designed using the Markov decision process (MDP). In regards to energy consumption, delay, coverage percentage, and dependability, the simulation results validate the efficacy and efficiency of the proposed method. © 2024 ieee.

关键词： Sensor nodes

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Fault Prediction Method for 5G Bearer Network Based on Graph Random Walk

Fault Prediction Method for 5G Bearer Network Based on Graph...

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2024 international conference on Cloud and Network computing, ICCNC 2024

作者： Liu, Huiyu Yu, Chen Yao, Quanfeng Xu, Donglei Li, Haitao Huazhong University of Science and Technology Wuhan China Wuhan Fiberhome Technical Services Co. Ltd. Wuhan China

ISBN: (纸本)9798350367805

With the development of 5G networks, the number and complexity of network devices have increased dramatically. Locating and predicting faulty network devices is an important way to ensure the normal operation of 5G networks. Existing methods usually require the use of large amounts of real-Time data, which is unacceptable for widely distributed network devices. We propose a 5G bearer network fault prediction method based on graph random walk. By analyzing the association relationship of network devices' alarm information, we construct an alarm association graph. Use the graph random walk method to learn alarm rules from historical alarm information and use it to predict faulty devices. We verified our method on real data from network service providers and achieved better results than existing methods. © 2024 ieee.

关键词： 5G mobile communication systems

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Hybrid Algorithm for Secure Routing in MANET Using Cryptographic Applications

Hybrid Algorithm for Secure Routing in MANET Using Cryptogra...

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2024 ieee international conference on Electronic systems and intelligent computing, ICESIC 2024

作者： Muruganandam, S. Gnanavel, S. Narayana, K.E. Jaeyalakshmi, M. Antony Kumar, K. Panimalar Engineering College Department of Computer Science and Business Systems Chennai India SRM Institute of Science and Technology Kattankulathur Campus Faculty of Engineering and Technology Department of Computing Technologies Chengalpattu India Rajalakshmi Engineering College Department of Information Technology Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331522988

In Mobile sensor Networks, Each and every wireless node dynamically changes its location due to its mobility nature. The routing information is maintained by the master node to deliver a data packet from source to destination. The attackers easily hack this wireless network. The performance of the complete networks is dropped by packets transmission attacks. Attacker node drops the packet. An enhanced protection technique in response to packets transmission misbehaviour attacks is provided to increase the energy efficiency of mobile nodes. This paper proposed the Cryptographic formed Malicious Node Detection and Secure Routing framework (CMNDSR). This model incorporates three various strategies specially algorithm for control of access, to protects initial illegal node in the network. The suspected identification and prevention techniques based on hop count value. Secure routing method with a private key generated at random like one time password (OTP). The proposed methods are power efficient, by operating a minimum number of nodes in challenging environment. The misbehaviour nodes are promptly identified and isolated using this proposed approach. The Cryptographic based Secure routing algorithm is applied to provide secure transmission in wireless networking environment. The outcomes of the simulation display how effectively the recommended approach can identify malicious nodes in a MANET. In comparison with previous methods, the energy consumption is 10% lower and the suspicious node detection rate is increased by 95%. © 2024 ieee.

关键词： Encryption algorithms

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