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共 213 条记录，以下是11-20 订阅

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A simple Piezoelectric structural Health Monitoring system for use in concrete applications in Real-Time

A Simple Piezoelectric Structural Health Monitoring System f...

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2024 IEEE International Conference on Modeling, simulation and Intelligent Computing, MosICom 2024

作者： Moorthy, Vijai Meyyappan Rani, Deevi Radha Karthikeyan, M. sai, Challagulla Teja Amrutha Vignan's Foundation for Science Technology and Research Department of Advanced Computer Science and Engineering Andhra Pradesh Guntur522202 India Vignan's Foundation for Science Technology and Research Department of Civil Engineering Andhra Pradesh Guntur522202 India

ISBN: (纸本)9798331533311

This study outlines the creation of a piezoelectric sensor-based system for real-time building monitoring. The sensors convert mechanical vibrations and stresses in the building structure into electrical impulses using the piezoelectric effect. signal processing is used to discover abnormalities and structural defects in these signals. Their noise floor should be almost zero and their sensitivity high. The proposed technology monitors the building structure instantly, allowing early detection of problems and prompt repairs. Laboratory and outdoor studies prove the approach can effectively detect and pinpoint construction faults. This project aims to create a piezoelectric sensor-based structural health monitoring (sHM) system. Empirical testing showed that these sensors had great sensitivity, low noise, and good sensor resolution. Indeed, they outperform commercial equipment. © 2024 IEEE.

关键词： structural health monitoring

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IoT-Driven Precision Crop Recommendation system Using LsTM & TsC-NET for sustainable Agriculture 1

IoT-Driven Precision Crop Recommendation System Using LSTM &...

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1st International Conference on Innovative sustainable Technologies for Energy, Mechatronics and smart systems, IsTEMs 2024

作者： satya sandeep, K. Lakshmi sahithi, K. shabana Azmi, sd. Renuka Devi, J. Nirmala Jyothi, B. Vignan 'S Nirula Institute of Technology and Science for Women Dept. of Computer Science and Engineering Guntur India

ISBN: (数字)9798350384246

ISBN: (纸本)9798350384246

Andhra Pradesh is a well-known agricultural state with a major contribution to the production of rice, cotton, and chilies it is also known as the 'Rice Bowl of India'. Unpredictable weather events have been a concern to the state, it is proving to show a negative impact on the state's farming population, nevertheless the state still contributes materially to the country's cultivation production. on thorough research and historical analysis, it is stated that the food diffidence and conservationist problems arise from the established age-old agricultural methods, these methods are being followed without any systematic scientific pursuit. this study pursues to solve the mentioned problem by transitioning agricultural practices with the use of Internet of Things (IoT) technologies. A preferred multi-class classification model leveraging IoT data. the data primarily includes meteorological parameters such as soil NPK values, temperature, humidity, and more. The hypothesized model, a hybrid of Long short-Term Memory (LsTM) and Time series - Convolutional Neural Networks (TsC-NET), which recommends suitable crops for the cropland location, it is accomplished by integrating Time-space data. the food grain acreage in Andhra Pradesh is seen dwindling by 4.2 % in 2022-2023, underscoring the susceptibility of farmers to monsoon failures. In addition, macroeconomically speaking, the slowdown in agrarian development disrupts economy's advancement as a whole, advocating for agrarian development. The IoT -driven crop classification model provides the novel approach to enhance crop selection and optimize production potential. therefore, improving the precision and efficacy of crop forecasting while enabling farmers to make well-informed decisions accounting for soil conditions and climate data. It intends to tackle both macro-level economic concerns and micro-level farmer vulnerabilities, this research levels to improve the sustainability and efficiency of agriculture in Andhra Pr

关键词： Internet of things

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Enhancing Dietary Monitoring using Deep Learning: Food Recognition and Calorie Estimation 15

Enhancing Dietary Monitoring using Deep Learning: Food Recog...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： suneela, M. sahithi, Mandadi Vamsi, Nuthalapati Naik, Nunsavathu Balaji Chowdary, Narra sujitha Vignan's Lara Institute of Technology & Science College Dept. of Computer Science & Engineering Guntur India

ISBN: (纸本)9798350370249

The project 'Enhancing Dietary Monitoring Using Deep Learning:Food Recognition And Calorie Estimation' introduces an innovative method aimed at improving dietary tracking and fostering healthier eating habits. By employing cutting-edge deep learning techniques, its core objective is to precisely identify various food items and rapidly estimate their caloric content, offering users advanced and personalized monitoring capabilities. Utilizing the Python programming language alongside the MobileNet architecture model, the project underwent rigorous training and evaluation using the expansive Food 101 dataset, comprising 37,046 food images distributed across 101 distinct classes. Notably, the model demonstrated exceptional performance, achieving a training accuracy of 97.02% and a validation accuracy of 98.17%, highlighting the efficacy of this approach in accurately categorizing a wide array of food items. This system provides users with several crucial functionalities. Furthermore, it furnishes users with essential insights by estimating the caloric content of recognized foods, facilitating effective monitoring of dietary intake. The intelligent diet monitoring capabilities enabled by Food Recognition and Calorie Estimation empower users to make informed choices regarding their dietary preferences. Through the continuous tracking and analysis of their daily food consumption, users can glean valuable insights into their nutritional habits, set personalized goals, and make necessary adjustments to achieve a well-balanced and healthy diet. Enhancing Dietary Monitoring Using Deep Learning:Food Recognition And Calorie Estimation stands as a prominent illustration of successful implementation of deep learning techniques, particularly with the utilization of the MobileNet architecture, for food recognition and calorie estimation. With its remarkable accuracy, real-time processing capabilities, and intelligent monitoring features, this project has the potential to revolutioni

关键词： Convolutional Neural Network (CNN) algorithm Datasets MobileNet architecture Web application

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Career Prediction Using Machine Learning 8

Career Prediction Using Machine Learning

引用

8th International Conference on Inventive systems and Control, ICIsC 202

作者： Jha, Kundan Likhitha, D. Chandana, M.siri Reddy, M.Ram Prakash Bhargavi, Maridu Vignan's Foundation for Science Technology and Research Dept.Computer Science and Engineering Guntur AP Vadlamudi India

ISBN: (纸本)9798350386578

This study uses advanced machine learning techniques to predict students' career paths more accurately. By analyzing and preprocessing various data, including academic performance, extracurricular activities, internships, and personality traits, the study identifies patterns. Ethical considerations, privacy protection, and bias prevention are part of the model development process. The practical applications include personalized career counseling, improving curricula, and optimizing alumni networks. Despite challenges like model interpret ability and adapting to changing job markets, interdisciplinary collaboration is crucial. Overall, educational institutions can use these techniques to provide customized support, helping students make informed career decisions in today's ever-changing workforce. © 2024 IEEE.

关键词： Adversarial machine learning

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Implementation of an IoT Based Intrusion Detection system for smart City Applications

Implementation of an IoT Based Intrusion Detection System fo...

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2024 International Conference on Computational Intelligence for Green and sustainable Technologies, ICCIGsT 2024

作者： Lova Raju, K. Nishith sai sesha Kumar, K. Reginald, P.J. sobha, D. Vignan's Foundation for Science Technology and Research Vadlamudi Department of Electronics and Communication Engineering Andhra Pradesh Guntur522213 India Vignan's Foundation for Science Technology and Research Vadlamudi Department of Advanced Computer Science and Engineering Andhra Pradesh Guntur522213 India

ISBN: (纸本)9798350381887

The occurrence of intrusions in smart cities has made the development of an inexpensive and effective intrusion detection system essential. Intrusion remains one of the most important aspects of smart city applications like homes, hospitals, transport, etc. Most of the existing systems employ focus only on one particular application like home security, not portable systems, and also use cameras only for surveillance. Therefore, taking into account the aforementioned drawbacks, this paper presents an intrusion system for smart city applications, leveraging Internet of Things (IoT) technology. The proposed system uses hardware components like Raspberry Pi model B+, Pi Camera, and PIR sensor (Passive Infrared) as well as software components like VNC viewer (Virtual Network Computing) and Thonny Python IDE (Integrated Development Environment). Motion detection is facilitated by the utilization of the PIR sensor, then the Pi Camera photographs the image automatically, and the Raspberry Pi device will send an alert via email to end-user devices that are connected with the email such as personal computers or smartphones. The captured data is securely stored in the email of the end-user's devices, ensuring confidentiality and privacy. © 2024 IEEE.

关键词： Network intrusion

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A Deep Learning Approach for YouTube Music Recommendation Based on Facial Emotion 2

A Deep Learning Approach for YouTube Music Recommendation Ba...

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications systems, ICMACC 2024

作者： Renugadevi, R. selvamuthukumar, M. Manikanta, K. Jeyaprakash, K. Kalaiarasi, G. Arul Edwin Raj, A. Vignan 's Foundation for Science Technology and Research Computer Science and Engineering Andhrapradesh Guntur India Vignan 's Foundation for Science Technology and Research Mechanical Engineering Andhrapradesh Guntur India Kalasalingam Academy of Research and Education Electronics and Communication Engineering Tamilnadu Virudhunagar India Vignan 's Foundation for Science Technology and Research Advanced Computer Science and Engineering Andhrapradesh Guntur India Universiti Tun Hussein Onn Malaysia Persiaran Tun Dr. Ismail Johor86400 Malaysia

ISBN: (纸本)9798350366570

In the contemporary digital era, the intersection of emotion recognition technology and music recommendation systems has paved the way for a transformative user experience. This research introduces an innovative 'Emotion-Based Music Recommendation system,' designed to enhance the personalization of music consumption. Leveraging real-time facial expression analysis and a curated music dataset, the system dynamically adapts to users' emotional states, providing a sealess and engaging music discovery process. The integration of Facial Recognition (FR) technology enables the system to capture and interpret users' moods instantaneously, eliminating the need for manual classification of songs. This novel approach not only streaDlines the user experience but also holds significant potential for mitigating revenue loss associated with prolonged music search times. The research utilizes Python and PyCharm, employing Haar cascades for face detection and collaborative filtering and content-based filtering methods for music recommendation. The datasets utilized include FER, containing grayscale images of faces classified into seven emotions, and YouTube Music data, incorporating diverse attributes for mood-based classification. Through this synthesis of facial recognition and music datasets, the proposed system stands poised to redefine the landscape of music recommendation, emphasizing real-time adaptability and user-centricity. © 2024 IEEE.

关键词： Collaborative filtering

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A Convolution Neural Network Based Classifier for Discerning AI Generated Images and Real Images 4

A Convolution Neural Network Based Classifier for Discerning...

引用

4th IEEE Asian Conference on Innovation in Technology, AsIANCON 2024

作者： Kumar, Uppala Eswar Basava Vivekreddy, Chintakuntla sujatha, Kamepalli Vignan's Foundation for Science Technology & Research Deemed to be University Dept. Information Technology & Computer Applications Guntur India

ISBN: (纸本)9798350354218

The exponential advancement of artificial intelligence (AI) has led to the creation of synthetic images that closely resemble real-world photographs. However, the widespread distribution of AI-generated content poses significant challenges in distinguishing between genuine and synthetic imagery, particularly in cybersecurity, content moderation, and image forensics. Traditional image classification methods struggle with this task due to the striking visual similarities between the two types of images. Convolutional Neural Networks (CNNs) have emerged as powerful tools for image classification, leveraging their ability to extract complex features from raw pixel data. In this study, we propose an enhanced CNN methodology to differentiate between AI-generated and real images. specifically, we focus on three popular CNN architectures: ResNet-50, RexNet-150, and RexNet-200. By augmenting these models with advanced architectural enhancements and training strategies, we aim to improve their discriminative capabilities. Our experimental findings reveal that RexNet-150 surpasses ResNet-50 and RexNet-200, achieving superior accuracy in distinguishing between AI-generated and real images. RexNet-150 attained an impressive 97.9% accuracy on the test dataset, outperforming ResNet-50 (93.1%) and RexNet-200 (90.3%). Moreover, RexNet-150 demonstrated enhanced training convergence and speed, indicating its effectiveness in learning discriminative features for image classification tasks. The superior performance of RexNet-150 holds significant implications for online safety, content moderation, and image authenticity detection. These advancements in CNN technology foster trust in digital imagery and enhance confidence in AI applications across diverse domains. © 2024 IEEE.

关键词： Convolutional neural networks

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RTPD-YOLO: Reconciling YoLo-V8 Model for Real-Time Potholes Detection

RTPD-YOLO: Reconciling YoLo-V8 Model for Real-Time Potholes ...

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2024 IEEE International Conference on Machine Learning and Applied Network Technologies, ICMLANT 2024

作者： Balakrishna, sivadi solanki, Vijender Kumar Department of Advanced Computer Science & Engineering Vignan’s Foundation for Science Technology & Research Guntur A.P Vadlamudi India Department of Computer Science & Engineering Stanley College of Engineering and Technology for Women Telangana Hyderabad India

ISBN: (纸本)9798350389180

Indian road privilege systems employ low-quality building materials, making pothole detection and avoidance difficult. Preventing accidents requires quickly identifying and fixing potholes. Roadside potholes may damage cars and endanger drivers. This article introduces a new method for realtime pothole identification called RTPD-YOLOv8. It can identify several forms of potholes, including cracks, oil stains, patches, and stones. To begin processing, pothole videos are first transformed into picture frames. The Ultratics are applied to these frames before processing to lessen distortions. The next step is to use the pre-processed photos to detect the pothole and identify its region using the sobel edge detector and YOLOv8. Thereafter. The proposed RTPD-YOLO will detect the potholes efficiently. The simulation results show that Accuracy, Precision, Recall, and F1-score were used to measure the performance of the RTPD-YOLO approaches. A total Accuracy of 99.72% is attained by the RTPD-YOLO. Also, the RTPD-YOLO model gets a 98.86% precision, 94.64 percent recall, and 91.35% F1-score. When compared to other models, like Faster RCNN, ssD, and mask R CNN, the RTPD-YOLO network achieves a higher Accuracy range. © 2024 IEEE.

关键词： Edge detection

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smart shopping Cart Using Radio Frequency Identification

Smart Shopping Cart Using Radio Frequency Identification

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2024 IEEE International students' Conference on Electrical, Electronics and computer science, sCEECs 2024

作者： Kesava, Gudipati Rishi Reddy, Vanukuri Karthik Trivikram, sadineni santhi sri, K. Vignan's Foundation for Science Technology & Research Deemed to Be University Dept. Information Technology & Computer Applications Guntur India

ISBN: (纸本)9798350348460

The "smart shopping Cart Using Radio Frequency Identification"project revolutionizes the shopping experience through RFID technology, offering real-time cost updates to shoppers. This innovative system incorporates RFID readers, a microcontroller for instant cost computations, and an LCD screen for displaying the total expense. By tackling common issues associated with traditional shopping carts, like prolonged checkout procedures and the absence of live cost monitoring, our project significantly enhances the efficiency and convenience of shopping. shoppers can now enjoy a seamless experience as the RFID technology effortlessly scans tagged products, allowing the microcontroller to promptly calculate the overall cost, which is then displayed on the LCD screen. This breakthrough not only streamlines the shopping process but also eliminates the need for manual price entries, reducing errors and enhancing overall customer satisfaction. The successful integration of these technologies marks a promising step towards the future of retail, where smart shopping carts pave the way for quicker, more accurate, and enjoyable shopping ventures. © 2024 IEEE.

关键词： Efficiency and Convenience Radio Frequency Identification (RFID) Real-time Cost Updates RFID Technology Integration seamless shopping Experience smart shopping Cart

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stock Market Prediction Using Deep Learning

Stock Market Prediction Using Deep Learning

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2024 IEEE International Conference on Intelligent signal Processing and Effective Communication Technologies, INsPECT 2024

作者： Johnshaida, s.K. Kumar, G. shiva Vignan's Foundation for Science Technology & Research Deemed to be University Dept. Information Technology & Computer Applications Guntur India

ISBN: (纸本)9798350379525

This project focuses on leveraging One kind of deep learning model is the Long short-Term Memory (Ls TM) networks., to forecast stock prices by analyzing historical data and technical indicators. The methodology involves comprehensive steps such as data collection, preprocessing, feature selection, model selection (including LsTM networks), training, and evaluation taking advantage of performance indicators such as Mean squared Error (MsE) and Mean Absolute Error (MAE). It utilizes a dataset comprising historical gold stock prices and trading volume to train and test the LsTM model. Furthermore, Naive Bayes, support Vector Machine (sVM), Linear Regression, and other machine learning algorithms are examined in the study, along with more complex methods like Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN). Results reveal diverse accuracy levels across models, with LsTM demonstrating a tendency to overpredict stock prices. Future endeavors aim to refine model performance, investigate ensemble methods, and integrate additional data sources like economic news and sentiment analysis to bolster prediction accuracy, thereby offering investors and analysts enhanced decision-making tools in the dynamic stock market landscape. © 2024 IEEE.

关键词： Convolutional neural networks

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