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Using AI and Blockchain in Case of Algorithm Regulation 2

Using AI and Blockchain in Case of Algorithm Regulation

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2nd International Conference for Innovation in Technology, INOCON 2023

作者： Tripathi, Neha Mishra, Amit Kumar Dept. of Computer Science & Engineering Uttarakhand Dehradun India Graphic Era Hill University Dept. of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350320923

The implication of AI is determined by some basic applications where the important analysis is properly modulated as much as possible. All of the basic applications are determined by the growth of AI technologies for finding a better perspective appropriately. Fundamentally, there are different aspects based on the advantages of AI developed by the basic application on the organized basic application of the various variable perspective. In addition, all of the importance regarding the basic development basis is properly incremented on the functional approach. Examination of some basic applications to determine all of the appropriate resolutions has to be evaluated in this research article. © 2023 IEEE.

关键词： Machine learning

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Visual Analytics of Streaming Data in Concept Drift Detection 11

Visual Analytics of Streaming Data in Concept Drift Detectio...

引用

11th International Conference on Electrical, Electronic and Computing engineering, IcETRAN 2024

作者： Ilic, Aleksandra Stojnev Stojanovic, Dragan University of Nis Faculty of Electronic engineering Dept. Computer Science Nis Serbia

ISBN: (纸本)9798350386998

Recent developments in sensor technology have enabled real-time data acquisition, high-frequency and multimodal data capturing thus underlying the need for monitoring physical or operational conditions in various aspects of the data. High dimensionality and volume of the data poses significant statistical challenges creating a need for accurate concept drift detection and prompt responses, a task that can be performed either by an algorithm or by human expert. Visual analytics plays a crucial role in concept drift detection by enabling analysts to visually explore, analyze, and interpret complex data streams, facilitating real-time monitoring, decision-making, and communication with stakeholders. This paper examines the usability of radar graphs for concept drift exploration, as well as their usage in other steps of analysis. For the purpose of demonstration, a pipeline for real-time health data analysis is presented and steps are visualized using radar graphs. © 2024 IEEE.

关键词： Visual analytics

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Challenges in Certificate Management Over Open RAN Fronthaul Interface 15

Challenges in Certificate Management Over Open RAN Fronthaul...

引用

15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Lee, Jungbum Kim, Hyunsoo Lee, Chang-Gun Kwon, Ted Taekyoung Seoul National University Dept. of Computer Science and Engineering Seoul Korea Republic of

ISBN: (纸本)9798350364637

Open RAN is transforming mobile networks into open and flexible environments through the integration of modern software and artificial intelligence. However, the existing standardization documents provide insufficient guidelines on the issuance and revocation of certificates, posing potential risks for the system. As an initial effort to overcome this limitation, we study the challenges related to the certificate management framework over the Open RAN fronthaul interface, with a particular focus on the mutual authentication and the coordination of security policies across different vendors. © 2024 IEEE.

关键词： Authentication Protocol

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LiCT-Net: Lightweight Convolutional Transformer Network for Multiclass Breast Cancer Classification

LiCT-Net: Lightweight Convolutional Transformer Network for ...

引用

2024 IEEE Region 10 Conference, TENCON 2024

作者： Babita Nayak, Deepak Ranjan Malaviya National Institute of Technology Dept. of Computer Science and Engineering Jaipur India

ISBN: (纸本)9798350350821

Classification of multiclass breast cancer through histopathological images is indispensable and poses daunting challenges due to color inconsistencies, high appearance variations, and large inter-class similarities. Regardless of the success of traditional convolutional neural networks (CNNs) and vision transformers (ViTs), they often fail to capture intricate patterns and local contextual information, respectively, while demanding high computational resources and data requirements. To mitigate these issues, this paper proposes a lightweight convo-lutional transformer network named LiCT-Net for multiclass breast cancer classification. The LiCT-Net introduces a local-global spatially-aware transformer layer and integrates it with a pre-trained FastViT model to effectively capture global and local contextual features, thereby facilitating learning fine-grained and intricate lesion patterns from histopathological images. The LiCT-Net is validated on a benchmark dataset and the experimental results and comparative analysis demonstrate its effectiveness. In specific, it achieves a higher accuracy of 96.16 %, 95.62 %, 95.25 %, and 94.21 % on 40×, 100×, 200×, and 400× magniflcations respectively, © 2024 IEEE.

关键词： Convolution

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An Intelligent Approach to Grape Leaf Disease Diagnosis Through Machine Learning 2

An Intelligent Approach to Grape Leaf Disease Diagnosis Thro...

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Uniyal, Sneha Dhoundiyal, Parveen Sharma, Vikrant Vats, Satvik Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350371055

This study underscores the pressing necessity for early and accurate diagnosis of grape leaf diseases, advocating for an automated solution that harnesses the synergistic power of image processing and machine learning. Employing methodologies such as Naïve Bayes, Gradient Boosting, and Random Forest, the primary objective is to proficiently classify grape leaves as either healthy or afflicted by diseases. The model casts its focus on prevalent grape leaf maladies, including Grapes Healthy Leaf, Grape Black Rot,Grape Black Measles, Grape Isariopsis leaf. The process commences with the meticulous preparation of grape leaf images, followed by the extraction of crucial features, which serve as the foundation for the application of machine learning techniques. The viability of the grape crop sector hinges on the efficacy of this innovative approach, which has the potential to significantly curtail the spread of diseases in grape fields while enhancing the precision of disease identification. This study serves as a compelling demonstration of the formidable effectiveness of machine learning algorithms in the detection of grape leaf diseases, thus ushering in the prospects of automation and improved decision-making within the realm of agriculture. © 2024 IEEE.

关键词： Disease Identification Gradient Boosting Grape leaves Machine Learning Naïve Bayes Random Forest

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Vyoma-ADS : An AI-Driven Tool for Adaptive Monitoring and Anomaly Detection for Space Agriculture

Vyoma-ADS : An AI-Driven Tool for Adaptive Monitoring and An...

引用

2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Das, Rajdeep Sharma, Vaishnavi Kumari, Priyanka Raj, Abhishek Das, Sudipta De, Indrajit Institute of Engineering and Management Dept. of Computer Science and Business Systems Kolkata India

ISBN: (纸本)9798350390759

As humanity ventures further into space exploration, securing sustainable resources becomes paramount. The success of space farming depends not only on the ability to cultivate crops but also on the effective management of environmental conditions. Introducing Vyoma-ADS: an innovative system that integrates IoT technology with an adaptive Anomaly Detection System, specifically designed for space agriculture. Our study underscores the crucial role of environmental monitoring within space shuttles, where even minor fluctuations can have a significant impact on plant growth and productivity. To tackle these challenges, we designed a compact IoT-enabled system to monitor the moisture content in the soil, the temperature and humidity of the surrounding environment, and the ppm value of unwanted gases in the atmosphere. Through rigorous testing and analysis, we established optimal thresholds conducive to plant growth in the unique conditions of space. Recognizing the inherent variability and occasional anomalies in environmental data, we investigated three distinct machine learning models for anomaly detection: Isolation Forest, One-Class Support Vector Machines (OCSVM) and Density-Based Spatial Clustering of applications with Noise (DBSCAN). Our findings reveal that DBSCAN outperforms other techniques in identifying anomalies amidst fluctuating data. Additionally, we detail our approach to data visualization using a SaaS cloud platform, which enhances the comprehensibility of the study and facilitates better decision-making processes. This paper demonstrates how the integration of IoT monitoring and advanced anomaly detection methods can significantly improve the reliability and efficiency of space agriculture. By leveraging these technologies, we aim to create a robust system capable of maintaining optimal growing conditions for plants in space. © 2024 IEEE.

关键词： Support vector machines

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An Integrated System for Potato Leaf Disease Recognition 2

An Integrated System for Potato Leaf Disease Recognition

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rathore, Robin Dhoundiyal, Praveen Sharma, Vikrant Vats, Satvik Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350371055

Since potatoes are a staple crop in India, there has been a noticeable growth in their cultivation. Given the significant nutritional and economic value of potatoes, this study addresses the significant issue of leaf diseases. The study enhances farmer lives and supports agricultural success in India by using machine learning techniques to categorize and detect diseases in real time. The major areas of study are image processing, feature extraction, and machine learning models like Random Forest, Naive Bayes, and K-Nearest Neighbors (KNN). The paper proposes a systematic framework that comprises steps for data collecting, picture preprocessing, feature extraction, training, and classification in order to reliably diagnose diseases. It also demonstrates how these procedures can be combined. The datasets used include approximately 5,000 labeled pictures of potato leaves that show early blight, late blight, and healthy leaves. Among the preprocessing methods are data augmentation, contrast enhancement, and image normalization. When it came to classifying diseases, the trained models fared extremely well;Naive Bayes, Random Forest, and KNN were especially accurate. The study demonstrates how integrating image processing and machine learning could aid in addressing potato leaf disease issues in agriculture.. © 2024 IEEE.

关键词： feature extraction image normalization Image processing K-Nearest Neighbors Machine Learning Naive Bayes Random Forest

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An In-dept. Study on Handwritten Signature Authentication Using Unique Writer Traits 1

An In-Depth Study on Handwritten Signature Authentication Us...

引用

1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Vamsikrishna, M. Murari, Haritha Manikandan, M. Mishra, Nilamadhab Raghavendra Gupta, T. Patel, Rohit Aditya University Dept. of Computer Applications Kakinada India Spark Infotech Inc Sr Software Engineer PhoenixAZ United States VeL Tech. R Dr.S Dept. of Computer Science and Engineering Tamilnadu India VIT Bhopal University Dept. of Computer Science and Engineering Sehore India HITM Telenagana Hyderabad India Calorx Teachers' University Dept of CSE Gujrat India

ISBN: (纸本)9798350356816

Handwriting verification, a subset of signature verification, involves using handwritten text to verify or authorize someone's identification. Although simple handwriting can serve as a signature, it is often modified to suit the signer's preferences. The uniqueness of each person's handwritten signature makes it essential for accurate distinction from others. This study presents a handwritten signature verification system using a writer-dependent approach. While signature verification is widely used for identity verification and security, it is susceptible to forgeries. The writer-dependent approach considers the unique characteristics of an individual's signature, training the system to recognize them and making forgery replication more challenging. An extensive review of the literature on signature verification systems, focusing on the writer-dependent approach, was conducted. A system was then developed and implemented based on this approach, utilizing a dataset of signatures from 12 writers. Results indicate that the writer-dependent approach outperforms other methods in terms of accuracy and robustness. The system demonstrated high precision and recall rates, underscoring its effectiveness in detecting both genuine and forged signatures. This study highlights the writer-dependent approach's efficacy in enhancing the accuracy and security of signature verification systems, with implications for developing more reliable and efficient identity verification systems in today's digital age. © 2024 IEEE.

关键词： Authentication

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Active Control for Resonance Mitigation in Electric Drive applications 15

Active Control for Resonance Mitigation in Electric Drive Ap...

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15th International Symposium on Industrial Electronics and applications, INDEL 2024

作者： Berglund, Emelie Borg, Johan Johansson, Jonny Luleå University of Technology Dept. of Computer Science Electrical and Space Engineering Sweden

ISBN: (纸本)9798350352320

The use of inverter-based motor controllers that can generate fast voltage level transitions is increasing in the field of electric drive systems because of their ability to achieve higher efficiency. However, the fast voltage transitions, dv/dt's, are prone to generate bearing currents and cause overvoltages that may harm the electric motor. An LC filter applied between the inverter and the motor terminals can reduce the dv/dt's. However, the LC filter will cause overvoltage due to resonance but this can be overcome by introducing an extra pulse at each pulse-width modulation (PWM) transition. The timing of this extra pulse is crucial for suppressing the resonance, but due to nonlinearities in a real system, the time instants may vary over time and are thus in need of control. In this study, an analytical expression for the optimal pulse location and duration is derived and we propose a control algorithm that continuously calculates the optimal pulse switch time instants based on the measured output voltage. The control algorithm can handle sudden system disturbances and a Monte Carlo simulation is performed to evaluate the performance of the algorithm. This hybrid filter successfully reduces dv/dt's at the motor terminals while simultaneously eliminating overvoltages due to the filter resonance. © 2024 IEEE.

关键词： Control nonlinearities

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'Vulnerabilities in Smart Contracts: A Detailed Survey of Detection and Mitigation Methodologies'

'Vulnerabilities in Smart Contracts: A Detailed Survey of De...

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2024 IEEE International Conference on Emerging Technologies in computer science for Interdisciplinary applications, ICETCS 2024

作者： Kumar, Nayantara K Honnungar, Niranjan V Sharwari Prakash, M. Lohith, J.J. B.M.S. College of Engineering Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350372502

In the ever-changing world of blockchain technology, the emergence of smart contracts has completely transformed the way agreements are executed, offering the potential for automation and trust in decentralized systems. Despite their built-in security features, smart contracts still face persistent vulnerabilities, resulting in significant financial losses. While existing studies often approach smart contract security from specific angles, such as development cycles or vulnerability detection tools, this paper adopts a comprehensive, multidimensional perspective. It delves into the intricacies of smart contract security by examining vulnerability detection mechanisms and defense strategies. The exploration begins by conducting a detailed analysis of the current security challenges and issues surrounding smart contracts. It then delves into established frameworks for classifying vulnerabilities and common security flaws. The paper examines existing methods for detecting, and repairing contract vulnerabilities, evaluating their effectiveness. Additionally, it provides a comprehensive overview of the existing body of knowledge in smart contract security-related research. Through this systematic examination, the paper aims to serve as a valuable reference and provide a comprehensive understanding of the multifaceted landscape of smart contract security. © 2024 IEEE.

关键词： Smart contract

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