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Smart Soil Monitoring System With Crop and Fertilizer Recommendation Features

Smart Soil Monitoring System With Crop and Fertilizer Recomm...

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International conference on Communication systems and Network Technologies (CSNT)

作者： Gourab Saha Shabbir Hoshen Suvo Farhan Tanjim Tonmoy Jiad Bin Asad Mohammed thushar Imran AKM Abdul Malek Azad Department of EEE Brac University Dhaka Bangladesh Control & Applications Research Center Department of EEE Brac University Dhaka Bangladesh

ISBN: (数字)9798350305463

ISBN: (纸本)9798350305470

this paper represents the design of a portable and IoT based device for monitoring nitrogen, phosphorus, potassium, pH, and moisture of soil, along with surrounding temperature and humidity, for crops and fertilizer recommendation. the idea and execution of a wireless, portable system for assessing soil quality, recommending the optimal amount of fertilizer, and suggesting appropriate crops depending on the soil conditions are the main goals of this research project. the system is designed to have a user-friendly interface. It uses a cloud-based platform for analyzing data and implementation of machine learning models. Multiple classification-based machine learning algorithms are compared to gain better accuracy of the system. the results of the system are validated using different samples of both indoor and outdoor environments. this cost-effective design, which includes monitoring of seven parameters necessary for crop cultivation along with crop recommendation feature and fertilizer suggestion, makes it desirable for agroindustry.

关键词： Wireless communication Temperature sensors Temperature measurement Crops Soil Phosphorus Nitrogen

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Exploration on the Application of Multimodal Biometric Feature Recognition in System Access control

Exploration on the Application of Multimodal Biometric Featu...

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International conference on Communication systems and Network Technologies (CSNT)

作者： Fan Xia Xiaoyan Wei Qingyao Zhao Bo Jin Zheng Yu Zhiyong Zha Dongling Xiao Fei Long Geng Wu Junguo Hu State Grid Information & Communication Branch of Hubei Electric Power Co. Ltd Wuhan China

ISBN: (数字)9798350305463

ISBN: (纸本)9798350305470

Multimodal biometric recognition is a technology that combines multiple biometric features for identity verification and access control. It can be applied to system access control to improve security and convenience. the basic principles and application fields of multimodal biometric recognition technology have been widely studied and applied. By integrating multiple biological features such as fingerprints, iris, voice, facial features, etc., higher security can be provided because these biological features have high uniqueness and unforgeability. Compared with traditional identity verification methods, multimodal biometric recognition technology does not require memorizing complex passwords or carrying identification documents, thus providing a more convenient user experience. However, multimodal biometric recognition technology also faces some challenges and privacy issues in its application process. For example, the storage and protection of biometric data require careful handling to prevent the risk of data leakage and abuse According to the data, the usage rate of multimodal biometric recognition in system access control increased sharply from 6.27% in 2015 to 67.92% in 2021. this shows the role and prospects of multimodal biometric recognition in system access control systems. While promoting and applying multimodal biometric recognition technology, it is also necessary to pay attention to solving privacy and security issues to ensure its effective application in system access control.

关键词： Privacy Technological innovation Biometrics (access control) Passwords Machine learning Biology User experience

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Profiling consumers in a water distribution network using K-Means clustering and multiple pre-processing methods 13

Profiling consumers in a water distribution network using K-...

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13th International conference on Electronics, Computers and Artificial Intelligence, ECAI 2021

作者： Arsene, Diana Predescu, Alexandru Truica, Ciprian-Octavian Apostol, Elena-Simona Mocanu, Mariana Chiru, Costin Automatic Control and Computers University Politehnica of Bucharest Bucharest Romania

ISBN: (纸本)9781665425346

Profiling consumers in a water distribution system is essential for achieving sustainability in terms of resource management and urban development. Unsupervised learning can provide data-driven decision support for evaluating the water demand patterns in a large network, while various pre-processing methods can be added to expand the level of detail in terms of consumer behavior. the K-Means clustering method is used on a dataset based on publicly available data collected from multiple households with an emphasis on the data processing pipeline and its influence on the resulting clusters. Seasonal decomposition is used to evaluate the weekly trends in the dataset, while data normalization provides an in-depth analysis of the patterns and relative variation in terms of consumer demand. the results show different perspectives on the consumer demand patterns which can provide additional details in terms of consumption (volume, pattern, variation). © 2021 ieee.

关键词： Decision support systems

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Transforming Modern Medicine with the Role of the Healthcare Internet of things in Enhancing Patient Care and Monitoring

Transforming Modern Medicine with the Role of the Healthcare...

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International conference on Communication systems and Network Technologies (CSNT)

作者： Inzimam Bhuvana J Karishma Desai Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Department of uGDX ATLAS SkillTech University Mumbai Maharashtra India

ISBN: (数字)9798350305463

ISBN: (纸本)9798350305470

the present healthcare sector is experiencing a fundamental upheaval with the emergence of the Healthcare Internet of things (HIoT). this article delves into the transformational potential of HIoT by examining its use in bettering patient care and monitoring. the paper dives into the introduction of HIoT, the methodologies adopted for research and development, the compelling findings gained, and the farreaching consequences in conclusion the opening part sets the scene by describing the fundamental transformation in healthcare facilitated by HIoT. It emphasizes the catalyst for rethinking patient care and monitoring as the combination of internet-connected devices, smart sensors, and data analytics. HIoT equips doctors with up-to-the-minute information, allowing for more precise diagnoses and more individualized plans of care. Furthermore, deep learning and artificial intelligence were incorporated to exploit the richness of HIoTgenerated data, allowing sophisticated analytics and informed decision-making. the compelling findings of this study are clear, with the suggested technique regularly beating existing methods across numerous criteria. A remarkable accuracy score of 0.94 was reached, demonstrating the reliability of the suggested strategy in supporting informed healthcare decisions. Furthermore, in terms of cost efficiency, the suggested approach scored 0.95, suggesting its costeffectiveness and potential for economic viability. the development of HIoT promises a healthcare system that is more patient-centered, data-driven, and efficient in the future. To ensure healthcare continues to grow into an age of unmatched patient care and monitoring, researchers, healthcare providers, and governments must work together to harness this transformational potential while tackling the issues that come with it.

关键词： Deep learning data analysis Telemedicine Government Medical services Internet of things Artificial intelligence

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Innovative Cloud Service Monitoring Techniques for Optimal Performance

Innovative Cloud Service Monitoring Techniques for Optimal P...

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International conference on Communication systems and Network Technologies (CSNT)

作者： Animesh Srivastava Vipin Jaiswal Shivangi Ghildiyal Navin Garg Department of Computer Science & Engineering Graphic Era Hill University Dehradun College of Smart Computing COER University Roorkee Uttarakhand Invertis University Bareilly Uttar Pradesh Computer Science & Engineering Graphic Era (Deemed to be University) Dehradun

ISBN: (数字)9798350305463

ISBN: (纸本)9798350305470

the flexibility of cloud computing has ushered in a new digital era, giving businesses more freedom, prospects for expansion, and lower costs. Cloud computing raises worries about cost, security, and performance. To address these issues and enhance cloud use, we need new cloud monitoring techniques. this study examines three ways to these difficulties. the first application, “Real-time Performance Monitoring,” analyzes key performance metrics to assist businesses in identifying problems. In Algorithm 2, “Predictive Analytics for Anomaly Detection,” machine learning increases the safety of cloud debugging. the third technique, “Cost Optimization and Resource Scaling,” modifies resource use to optimize resource usage. three complete photographs show the results. Diagram 1 depicts shifting metrics and indicates performance concerns. Diagram 2 emphasizes the problems by comparing plans to reality. Dependability and security improve. Diagram 3 depicts resource allocation alternatives. data-driven decisions have the potential to optimize cloud spending. For enterprises, these cutting-edge alternatives enhance cloud cost, speed, and growth. they help cloud-dependent businesses meet their growth objectives and match their cloud operations with their business goals in this digital age.

关键词： Measurement Cloud computing Costs Real-time systems Safety Security Predictive analytics

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data Mining Techniques In Bioinformatics Enhanced by HPC and AI Convergence

Data Mining Techniques In Bioinformatics Enhanced by HPC and...

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International conference on Communication systems and Network Technologies (CSNT)

作者： A Kannagi Shashikant Patil Sandeep Kumar Jain Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Department of uGDX ATLAS SkillTech University Mumbai Maharashtra India Department of Electrical Engineering Vivekananda Global University Jaipur India

ISBN: (数字)9798350305463

ISBN: (纸本)9798350305470

High-Performance Computing (HPC) and Artificial Intelligence (AI) have come together to usher in a new age of data mining in the field of bioinformatics. Using high-performance computing (HPC) and artificial intelligence (AI), this research presents a suggested strategy for improving data mining techniques and then evaluates it alongside six more conventional approaches. Discovering latent patterns in high-dimensional biological data is the goal of the proposed technique, which combines Principal Component Analysis (PCA), Convolutional Neural Networks (CNN), and Random Forest (RF). the suggested technique consistently outperforms the state-of-the-art methods in our comparisons, including in terms of accuracy, precision, recall, and F1 score. these performance criteria are essential for effective data mining in bioinformatics, and the system's shown equilibrium between them is rather impressive. the suggested strategy also drastically decreases execution time, which is a crucial aspect in effectively managing large-scale biological information. Our findings highlight the need for AI-driven data mining methods in the field of bioinformatics. the application of PCA offers dimensionality reduction, assisting in feature selection and boosting model performance. When it comes to classifying and ranking the value of features, RF provides ensemble learning capabilities, whereas CNNs excel at extracting complicated patterns from pictures and sequences. the suggested technique is holistic in nature, drawing on the advantages of several algorithms to improve data mining and provide a complete service to the field of biological research. Our results show that the suggested approach has considerable potential to further bioinformatics studies and applications. It helps scientists speed up drug development, improve the accuracy of diagnostics, and unearth new biological insights. In addition, the faster execution time suggests better use of computer resources, which is useful for

关键词： Radio frequency High performance computing Feature extraction Biology data mining Convolutional neural networks Bioinformatics

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Synergistic Fusion of Multi-Contrast Imaging and Computational Image Synthesis for Quadruple Contrast-Enhanced Images and Multi-Map Generation

Synergistic Fusion of Multi-Contrast Imaging and Computation...

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International conference on Communication systems and Network Technologies (CSNT)

作者： Shashi Prakash Dwivedi Kavitha Dasari Asha V Lavish Kansal Laith H. Alzubaidi Anurag Kumar Tiwari Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh Institute of Aeronautical Engineering Dundigal Hyderabad Department of Computer Applications New Horizon College of Engineering Bangalore Lovely Professional University Phagwara The Islamic university Najaf Iraq Department of Electronics & Communication Engineering IES College of Technology Bhopal M. P. India

ISBN: (数字)9798350305463

ISBN: (纸本)9798350305470

Medical imaging has changed a lot since “Synergistic Fusion of Multi-Contrast Imaging and Computational Image Synthesis for Quadruple ContrastEnhanced Images and Multi-Map Generation” came out. this innovative technology might revolutionize diagnostic imaging by providing a complete view of tissue features and enhancing accuracy. this study describes a system that utilizes ultrasound, PET, MRI, and CT data. Better detection is the goal of this technology. this first segment begins a new medical imaging age in which clinicians may view the full patient by combining data from diverse sources. the strategy might increase diagnosis accuracy, treatment focus, and healthcare quality. this research uses cutting-edge deep learning, feature extraction, and fusion techniques. DLESA synthesizes images using convolutional neural networks. Since no one does it by hand, errors are less likely. the Feature Extraction and Fusion Algorithm (FEFA) integrates significant features from several picture approaches to maximize their benefits. CERA and SAEA are essential for picture clarity and line accuracy. Reality reveals that the proposed way performs better than usual. PSNR (13.29%), SSIM (6.98%), CNR (11.61%), and Dice Coefficient (8.54%) have all increased. the technology may be utilized outside of healthcare since it employs science to mix data, learn deep, and extract characteristics. this strategy that integrates multiple disciplines of research might revolutionize how we obtain, analyze, and use information, leading to groundbreaking scientific, technological, and health advances. this novel technology advances diagnostic pictures and data-driven diagnostics, bringing accuracy and comprehension to medical and other disciplines.

关键词： Deep learning Image segmentation Ultrasonic imaging Image synthesis Magnetic resonance imaging Computed tomography Transforms

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Compound Disturbance Rejection control for Nanopositioning Using a Phase-Locking Loop Observer

Compound Disturbance Rejection Control for Nanopositioning U...

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data driven control and learning systems (ddcls)

作者： Wei Wei Pengfei Xia Zaiwen Liu Min Zuo Beijing Technology and Business University Beijing P. R. China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

In nano-positioning, accuracy and speed are important issues to guarantee the system performance. Integral resonant control (IRC) is able to improve the bandwidth, and phase-locking loop observer (PLLO) based active disturbance rejection control (ADRC) is capable of achieving better closed-loop accuracy. By combining the advantages of PLLO based ADRC and IRC, a compound control technique is proposed. the compound control can deal with hysteresis and vibration, which are main factors affecting the accuracy and speed of a nano-positioning stage driven by a piezoelectric actuator. An identified model of a nano-positioning stage is utilized, and simulations have been performed. Presented numerical results confirm the proposed compound control technology.

关键词： Hysteresis Observers Compounds Nanopositioning Resonant frequency Mathematical model Damping

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Power-Grid controller Anomaly Detection with Enhanced Temporal Deep learning 18

Power-Grid Controller Anomaly Detection with Enhanced Tempor...

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18th ieee International conference on Trust, Security and Privacy in Computing and Communications (ieee TrustCom) / 13th ieee International conference on Big data Science and Engineering (ieee BigdataSE)

作者： He, Zecheng Raghavan, Aswin Hu, Guangyuan Chai, Sek Lee, Ruby Princeton Univ Princeton NJ 08544 USA SRI Int Princeton NJ USA

ISBN: (纸本)9781728127767

controllers of security-critical cyber-physical systems, like the power grid, are a very important class of computer systems. Attacks against the control code of a power-grid system, especially zero-day attacks, can be catastrophic. Earlier detection of the anomalies can prevent further damage. However, detecting zero-day attacks is extremely challenging because they have no known code and have unknown behavior. Furthermore, if data collected from the controller is transferred to a server through networks for analysis and detection of anomalous behavior, this creates a very large attack surface and also delays detection. In order to address this problem, we propose Reconstruction Error Distribution (RED) of Hardware Performance Counters (HPCs), and a data-driven defense system based on it. Specifically, we first train a temporal deep learning model, using only normal HPC readings from legitimate processes that run daily in these power-grid systems, to model the normal behavior of the power-grid controller. then, we run this model using real-time data from commonly available HPCs. We use the proposed RED to enhance the temporal deep learning detection of anomalous behavior, by estimating distribution deviations from the normal behavior with an effective statistical test. Experimental results on a real power-grid controller show that we can detect anomalous behavior with high accuracy (>99.9%), nearly zero false positives and short (<360ms) latency.

关键词： Hardware Sensors Process control Substations Actuators Deep learning Malware

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Application of ADRC in Stability control of Tank Gun System 7

Application of ADRC in Stability Control of Tank Gun System

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ieee 7th data driven control and learning systems conference (ddcls)

作者： Lei, Yang Xu, Jing Hao, Qiang China North Vehicle Res Inst Beijing 100072 Peoples R China

ISBN: (纸本)9781538626184

Overcoming the disturbance of vehicle body is an important function of the tank gun control system. therefore, the tank artillery can be stabilized to a given angle in space. However, due to the change of load factors such as inertia, friction and clearance, the stability precision of the gun control system cannot be further improved, and hitting probability will drop under the condition of tank high speed maneuverability. In this paper, the tank gun control system and the disturbance model were analyzed, and the active disturbance rejection controller was designed. the control performance of this active disturbance rejection controller was verified by MATLAB simulation and the experiments of analog prototype. the results showed that the application of the ADRC is more effective than the traditional PID controller in improving the stability precision of gun control system.

关键词： active disturbance rejection control (ADRC) tank gun control system stable control stability precision

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