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共 12901 条记录，以下是61-70 订阅

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Enhancing Multicast Efficiency in Large Cloud Data Center Networks

Enhancing Multicast Efficiency in Large Cloud Data Center Ne...

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2024 International Conference on Science, technology, engineering and Management, ICSTEM 2024

作者： Sri Varsini, S.K. Sai Sanjay Raj, J. Swetha, K. Poonkodi, R. Amitha, S. Dept. Information Technology Sri Eshwar College of Engineering Coimbatore India

ISBN: (数字)9798350376913

ISBN: (纸本)9798350376913

The responsibility of updating setup boundaries and communicating board directives to sensor hubs falls under the purview of an information disclosure and spread convention for remote sensor organizations (WSNs). There are two negative aspects to all of the information disclosure and dissemination conventions in use today. They first rely on a concentrated methodology, where information is only accessed by the base station. It is not realistic to construct multi- proprietor multi-client WSNs using such a methodology. Second, opponents can easily launch sendoff attacks to harm the organization because such conventions were not designed with safety in mind. This work offers the DiDrip convention, which is the first safe and distributed information revelation and propagation protocol. It enables the business owners to directly provide information items to the sensor hubs and approve several business clients with different accolades at the same time. Moreover, as our simulated study illustrates, it tackles some reasonable security defects that we have identified. DiDrip's security may be demonstrated by thorough analysis. Additionally, we use DiDrip in an asset-restricted sensor hub experiment to show its high productivity in practical situations. © 2024 IEEE.

关键词： Wireless sensor networks

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Performance Comparative Analysis of the Accuracy of CNN and RNN in Identifying Photovoltaic Cell Malfunctions

Performance Comparative Analysis of the Accuracy of CNN and ...

引用

2024 International Conference on Science, technology, engineering and Management, ICSTEM 2024

作者： Subhasri, S. Seyad Reesman, T. Dharshini, B. Saranya, D. Priyangababy, D. Dept. of Information Technology Sri Eshwar College of Engineering Coimbatore India

ISBN: (数字)9798350376913

ISBN: (纸本)9798350376913

The goal of this study is to minimize dimensionality in vibration data obtained from rotating machinery through the use of Principal Component Analysis (PCA) for feature extraction, hence enabling prompt failure identification in bearings. After PCA is used to handle the denoised signals, a hybrid deep learning model consisting of CNN and RNN is implemented. This combined method efficiently detects and categorizes different bearing problems by utilizing the temporal dependencies that RNNs learn and the spatial information that CNNs record. This methodology presents a comprehensive framework for proactive bearing health monitoring and maintenance by merging CNN and RNN architectures with PCA-based feature extraction. This might potentially optimize resource allocation and minimize downtime in industrial settings. © 2024 IEEE.

关键词： Principal component analysis

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Predictive Modeling of Student Academic Performance in External Examinations 6

Predictive Modeling of Student Academic Performance in Exter...

引用

6th International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2024

作者： Vennelakanti, Aasritha Varshini Nayakawadi, Rishitha Kodam, Vamshidhar Suryanarayana, G. Sallakunta, Ramya Devulapalli, Indusree Vardhaman College of Engineering Dept. Information Technology Shamshabad Hyderabad India

ISBN: (纸本)9798331505790

In a country like India, where students constitute a major part of the population, there is a need to predict a student's performance. The need for analysing students' academic performance plays an important role in the education sector because the insights gained from this prediction can be used to redesign the curriculum, which helps in meeting the needs of individual students. Educational data mining has been an emerging field to improve educational institutions. Hence, the usage of machine learning techniques in educational data mining has increased rapidly. Accurately predicting students' academic performance is essential for various stakeholders, like instructors, students, and institutions. This study aims to predict a student's performance in external exams using various machine learning techniques. Various metrics, like internal exam scores, previous semester scores, and the attendance of a student, are being considered for predicting GPA in the current semester. Because of the unbalanced nature of the data, it is challenging to develop a high-accuracy model. For this prediction, a variety of machine learning methods can be applied, including Random Forest (RF), K-Nearest Neighbour (kNN), Naïve Bayes (NB), Gradient Boost (GB), Decision Tree (DT), Support Vector Machine (SVM), and Logistic Regression (LR). Ensemble methods can demonstrate high accuracy in model training. By analysing the results produced by our proposed model, valuable insights can be drawn that benefit students', teachers, and educational administrators. © 2024 IEEE.

关键词： Nearest neighbor search

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Poverty Mapping in India using Machine Learning and Deep Learning Techniques

Poverty Mapping in India using Machine Learning and Deep Lea...

引用

2024 Mid-term Symposium on Spatial information to Empower the Metaverse

作者： Srishti Gulecha, R. Muthu Reshmi, K. Rishitha, N. Vani, K. Dept. of Information Science and Technology College of Engineering Guindy Chennai India

Poverty remains a persistent global challenge, that affects millions worldwide and hinders sustainable development goals. Poverty related data is traditionally collected by an on-the-ground household survey which is conducted once in a few years. Unfortunately, in India, the reach of this conventional data collection method has many limitations, and it is costly, time-consuming, and laborious. The study will assist in identifying areas of poverty and also the levels of poverty which will help policymakers in creating policies that will improve such areas. The research leverages a rich variety of data sources which includes satellite imagery, geospatial data, socio-economic surveys and Point of Interest (POI) data. To extract meaningful patterns and correlations within this diverse dataset, various machine learning and deep learning algorithms such as Decision Tree Regressor, Random Forest Regressor, Convolutional Neural Networks (CNNs) and Multi Layer Perceptron (MLP) are employed. With the help of Random Forest Regressor, the study was able to estimate the poverty at village/town level with a R2-score of 0.778. © Author(s) 2024.

关键词： Adversarial machine learning

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Design of a Blockchain-Based Internet of Healthcare System using Cryptography Network Security

Design of a Blockchain-Based Internet of Healthcare System u...

引用

2024 International Conference on Science, technology, engineering and Management, ICSTEM 2024

作者： Bhoomish, R. Vishnuvaradhan, D. Boobalan, E. Vasanthakumar, C. Sudharsan, D.S. Dept. Information Technology Sri Eshwar College of Engineering Coimbatore India

ISBN: (数字)9798350376913

ISBN: (纸本)9798350376913

In this proposed framework, we advocate the joining of blockchain innovation into Electronic Wellbeing Record (EHR) frameworks to resolve major problems encompassing information security, uprightness, and access control in the medical care area. Our structure offers a versatile, secure, and decentralized arrangement, stressing granular access rules, vigorous validation, and cryptographic hashing for information protection and trustworthiness. With the capacity to help dynamic gatherings and work with consistency with medical care guidelines like HIPAA, our framework plans to alter EHR the board, guaranteeing the highest level of secrecy and dependability in taking care of delicate patient records. © 2024 IEEE.

关键词： Network protocols

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I-Flow: Design and evaluation of a wearable uroflowmeter with non-invasive low power bio-impedance sensing 46

I-Flow: Design and evaluation of a wearable uroflowmeter wit...

引用

46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Dheman, Kanika Glahn, Manuel Magno, Michele Eth Zürich Dept. of Information Technology and Electrical Engineering Switzerland

ISBN: (纸本)9798350371499

Urological problems involving lower urinary tract symptoms (LUTS) such as urinary frequency, urgency, nocturia, and urine volume have a significant impact on patient health and quality of life. At present, clinical uroflowmeter tests and a urination frequency-volume chart (FVC) or bladder diary is recommended for monitoring the symptoms and the efficacy of treatment. However, these solutions are intermittent, needing high user compliance, repeated visits to the clinic and are inconvenient, especially for elderly patients. Such limitations lead to misdiagnosis and an inability to personalise treatments. This work proposes i-Flow, a low-power bio-impedance sensing system enabling wearable uroflowmetry. To demonstrate the effectiveness of the proposed solution, a preliminary dataset has been collected from 6 healthy subjects. Furthermore, the dataset acquired with a developed wearable system has been used to design a low-complexity, lightweight bladder voiding measurement algorithm and evaluates the overall hardware-software performance. In addition, repeatability tests on a single subject with multiple voiding instances (n=5) were conducted. The experimental evaluation demonstrates high-performance bladder voiding estimation with the total end volume (TEV) error for multi-subject voiding instances to be 38.4±18 ml. While the TEV error for repeated measures with a single subject was 21.7±15 ml for five voiding instances. The presented system for recording bladder voiding achieves an unobtrusive, comfortable and reliable method for automated recording of the frequency of voiding, the total voided volume and urine flowrate. Thus providing a means of conducting uroflowmetry tests in at-home settings while also maintaining a digital bladder diary devoid of human lapses. Such a system has the ultimate goal of assisting in diagnosis and personalizing treatment of LUTS. © 2024 IEEE.

关键词： Diagnosis

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Application to Enhance the Programming Knowledge of Children 6

Application to Enhance the Programming Knowledge of Children

引用

6th International Conference on Advancements in Computing, ICAC 2024

作者： Dissanayake, Anuga Lakpriya, Vishwa Sanjeevan, Mahendran Fahmi, Ahamed Siriwardana, Samanthi E.R. Samaraweera, Mihiri Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798331517878

This proposal outlines the development of a comprehensive educational platform aimed at bridging the information technology knowledge gap among Tamil-speaking students in Sri Lanka. The platform is designed to enhance programming skills through personalized quizzes, hands-on projects, and adaptive learning models. The application focuses on two primary components: the first component emphasizes project-based learning and assessments, leveraging advanced Artificial Intelligence models for generating educational content. This model is combined with Retrieval-Augmented Generation, which retrieves relevant external information to refine and tailor the educational content to each student's needs. This component keeps students engaged and challenged at appropriate levels, promoting deeper understanding and practical information technology skills. The second component involves a Level-based IT Fundamental Knowledge Evaluation system, which incorporates machine learning algorithms to predict student performance, recommend customized learning paths, and assess overall technical knowledge. The platform also integrates the OpenAI Application Programming Interface, which provides real-time Tamil translation, making the content more accessible to the target demographic. The platform empowers educators to better address individual student needs with real-time feedback and tailored experiences. Its innovative approach enhances information technology education, equipping Sri Lankan students with essential programming skills to succeed globally. © 2024 IEEE.

关键词： Adversarial machine learning

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Employing Image Processing to Extract the Sudoku Puzzle from an Image and solving it with a Backtracking Algorithm 15

Employing Image Processing to Extract the Sudoku Puzzle from...

引用

15th International Conference on Advances in Computing, control, and Telecommunication Technologies, ACT 2024

作者： Siraskar, Warsha Prashant Dept. of Information Technology Yeshwantrao Chavan College of Engineering Nagpur India

ISBN: (纸本)9798331300579

Using the Backtracking Algorithm Solve the unsolved Sudoku. The approach for solving Sudoku puzzles comprises extracting the puzzle from a picture using image processing techniques, and then solving the puzzle using a backtracking algorithm. To detect the circles and digits in the image, the initial part of the procedure entails pre-processing the image using a variety of methods, including adaptive thresholding and the Hough Transform. The circles and digits can be separated from the rest of the image by using these techniques to identify their contour. A pre-trained model is used to recognize the numbers and store them in the appropriate places in the puzzle after the circles and digits have been detected. This model can properly identify the digits even when the image quality is poor or the problem has been captured at an angle because it was trained on a big dataset of photographs of Sudoku puzzles. The method uses a backtracking algorithm to solve the puzzle after the recognized digits have been added to it. This method operates by repeatedly attempting various digit combinations in the puzzle's empty cells, and iterating backwards anytime it comes upon a contradiction. Overall, the technique can extract the puzzle from the image and correctly identify the numbers, enabling it to finish the puzzle rapidly and show the solution. © Grenze Scientific Society, 2024.

关键词： Hough transforms

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Methods for Stability Analysis in Converter-Dominated Power Systems: A Comparative Approach in Weak and Strong Grid 3

Methods for Stability Analysis in Converter-Dominated Power ...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Panah, Shahin Fouladi Liu, Haoyang Rwth Aachen University Dept. Electrical Engineering and Information Technology Aachen Germany

ISBN: (纸本)9798331509675

In this project, we focused on the stability analysis of grid when it is connected to wind farm with converter. To assess the dynamic behavior of the system, both frequency domain and time domain analyses were employed. In the frequency domain analysis, Nyquist plots and Bode plots have been analyzed and finally, Nyquist plots were preferred to Bode plots because Nyquist plots are suitable for systems with poles or zeros in the right half-plane and provide a comprehensive view of the system's stability characteristics across the entire frequency range. By observing the Nyquist plots, we evaluated whether the system encircled the (-1,0) point to determine the stability of the closed-loop system. In the time domain analysis, we examined the system's response to initial conditions or external disturbances, and the transient and steady-state behavior of current and voltage waveforms further verified the conclusions of the frequency domain analysis. Bode plots are less effective for non-minimum phase systems and systems with multiple feedback loops, where Nyquist plots provide a clearer, consolidated analysis by combining magnitude and phase information. © 2025 IEEE.

关键词： Power system analysis

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Tackling the Emerging Threat of Image-Based Steganographic Malware with Advanced Machine Learning Techniques 5

Tackling the Emerging Threat of Image-Based Steganographic M...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Adhav, Savita Sarpate, Ashish Gaisamudre, Samrat Tirmanwar, Sakshi Ukey, Smitanshu Rajarshi Shahu College of Engineering Dept. of Information Technology Pune India

ISBN: (纸本)9798350389609

The increasing threat of malware concealed within images through steganography underscores the need for advanced detection methods. Traditional signature-based techniques often fall short as malware evolves rapidly, employing increasingly sophisticated hiding techniques. This paper investigates the application of machine learning (ML) for detecting steganographic malware in images, capitalizing on ML's capability to discern complex patterns and adapt to emerging threats. The study explores various ML-driven approaches for image- based malware detection, including subspace-based methods for image pattern classification, ML classifiers targeting malicious JPEGs, and Attention Capsule Networks for detecting scams. Additionally, the paper reviews advancements in enhancing the robustness of AI-based malware detection through adversarial machine learning and evaluates hybrid deep learning methods for more effective malware classification. Future research directions are identified, including the development of advanced cross-validation techniques to boost model performance and generalizability, hyperparameter-tuning methods for improved optimization, and dynamic detection strategies incorporating behavioral analysis. Real-time detection with online learning is highlighted for its potential in continuous adaptation to new threats. These prospective advancements aim to address challenges related to data, optimization, real-time performance, and model interpretability, ultimately advancing the capabilities of image-based malware detection. © 2024 IEEE.

关键词： Adversarial machine learning

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