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检索条件"任意字段=International Conference on Teaching and Learning in Computing and Engineering"

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Robustness Analysis of Neuro-Inspired Architectures against Common Perturbations

Robustness Analysis of Neuro-Inspired Architectures against ...

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2024 OPJU international Technology conference on Smart computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Alekhya, V. Tamrakar, Deepesh Swathi, B. Nagpal, Amandeep Kumar, Ashwani Al-Abadi, Noor K. Abed Institute of Aeronautical Engineering Hyderabad Dundigal India Mangalayatan University Faculty of Engineering Cse Department Jabalpur India New Horizon College of Engineering Department of Information Science Engineering Bangalore India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India National University of Science and Technology College of Health and Medical Technology Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

Making neuro-inspired structures more resistant to common difficulties is the focus of this study. AI and deep learning are crucial in many fields;therefore, neural networks must be reliable and durable. This post proposes a robust framework using 10 advanced tactics for a new and complete robustness approach. Our technique uses dropout, gradient masking, transfer learning, adversarial training, randomized smoothing, input preparation, strong activation functions, ensemble learning, Bayesian neural networks, and gradient masking. These strategies are aimed at addressing hostile assaults, noise, and environmental changes. Combining these approaches makes the system more versatile, making it ideal for self-driving vehicles, medical diagnostics, and hacking. We conducted comprehensive research to evaluate the proposed method's accuracy, durability, resistance, generalization, computational cost, and real-world usage. Line charts illustrate the method's strengths and weaknesses. These numbers demonstrate how the proposed approach differs from previous ones. They demonstrate its versatility and efficacy. Finally, our results demonstrate a reliable, versatile, and proven method for neural network resilience. Our method handles common modifications using sophisticated methods. This yields a forward-thinking, effective solution to AI environmental changes. The reliability of neuro-inspired systems has improved, which will affect artificial intelligence. © 2024 IEEE.

关键词： Adversarial machine learning

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Real Time Object Detection & Recognition: A ComparativeStudy of YOLOv3 and YOLOv7 in OpenCV 15

Real Time Object Detection & Recognition: A ComparativeStudy...

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

作者： Geethanjali, T.M. Prithviraj, B. Prajwal, K.M. Gowda, Prajwal C.M. Priyanka Sujithra, M. Chaba, Yogesh PES College of Engineering Department of ISE Mandya India Department of Computing-Data Science Coimbatore Institute of Technology Coimbatore India Department of Computer Science and Engineering Guru Jambeshwar University of Science and Technology Haryana Hisar India

ISBN: (纸本)9798331300579

Real-time object detection is a fundamental task in computer vision, finding applications in various domains such as autonomous vehicles, surveillance systems, robotics, and *** proposed work presents the design and implementation of a real-time object detection system using OpenCV (Open-Source Computer Vision Library).The system aims to accurately and efficiently detect and localize objects in video streams or captured *** proposed work begins with dataset collection and annotation, acquiring a diverse dataset of images with annotated bounding boxes representing objects of *** annotated dataset is used for model training and *** deep learning algorithms are considered for object detection, including Single Shot MultiBox Detector (SSD), You Only Look Once (YOLO), and Faster R-CNN, and their performance is compared to identify the most suitable *** techniques like resizing, normalization, and noise reduction are applied to enhance the quality of the input *** extraction is performed using deep learning models VGG16, which is fine-tuned on the annotated *** selected deep learning model is integrated into the real-time system using OpenCV's *** system is evaluated using standard metrics like precision, f1 score, recall, and mean average precision (mAP) to assess its detection *** evaluation is carried out on benchmark datasets and real-world scenarios to gauge the system's robustness and generalization capabilities using two different YOLO models i.e., YOLOv3 and YOLO v7. © Grenze Scientific Society, 2024.

关键词： Deep learning

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Enhancing Detection of Nutritional Deficiencies in Coconut Trees Using Transformer-Based Convolutional Neural Networks 15

Enhancing Detection of Nutritional Deficiencies in Coconut T...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Ramesh, M. Kodeeswari, K. Anitharani, A. Maheswaran, S. Keerthi, P. Suganth, K.M. Kongu Engineering College Department of Electronics and Communication Engineering Erode India Excel Engineering College Department of Electronics and Communication Engineering Namakkal India

ISBN: (纸本)9798350370249

The Coconut trees are integral to tropical ecosystems providing sustenance, economic stability, and environmental benefits. The nutrient deficiencies in coconut palms can severely hamper their productivity, resulting in economic losses andend-angering food security. The leaf images displaying symptoms of various deficiencies were amassed from diverse geographic regions. Traditional methods for diagnosing these deficiencies are laborious and subjective. This research explores a deep learning approach to automatically identify nutrient deficiencies in coconut trees. The model achieved impressive accuracy in classifying various deficiencies, including nitrogen, iron, potassium, and calcium. By analyzing leaf images, the system effectively detects these issues, demonstrating its ability to function well under different environmental conditions. A dataset of real-time images showcasing the four main deficiency categories was created for training and validating the model. Dataset consists of 8990 nutrition deficiency images namely calcium, iron, nitrogen and potassium. There are 2390 images under calcium, 2200 images under iron 2200 images under nitrogen and 2200 potassium The efficacy of the model using a variety of metrics, including accuracy, F1 score and the confusion matrix. The proposed method using Transformer Convolutional Neural Network (TCNN) achieved training and validation accuracy of 99.97% and 98.61% respectively. © 2024 IEEE.

关键词： Deep learning Image Classification Nutrition Deficiency Transfer learning Transformer Convolution Neural Network

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Machine learning Technique Enabled learning Methodology for Human Depression Prediction 2

Machine Learning Technique Enabled Learning Methodology for ...

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2nd IEEE international conference on Advances in Information Technology, ICAIT 2024

作者： Shanthi, J. Maria Sharma, Himanshu Rao, P. Srinivasa Thomas, G. Arun Sampaul Gupta, Amit Department of AI and ML J. B. Institute of Engineering and Technology Telangana Hyderabad India Department of ECE J. B. Institute of Engineering & Technology Telangana Hyderabad India Department of CSE J. B. Institute of Engineering &Technology Telangana Hyderabad India Department of AI and ML J. B. Institute of Engineering &Technology Telangana Hyderabad India Department of AI and ML J. B. Institute of Engineering & Technology Telangana Hyderabad India

ISBN: (纸本)9798350383867

Human Depression Prediction is important for a number of reasons, the most important of which are that it can help with faster interventions and better mental health outcomes. First and foremost, early identification of depression facilitates timely and focused interventions, allowing patients to obtain the right care and support prior to the worsening of their illness. By combining the deep learning powers of the LeNet architecture with the optimization skills of the Grey Wolf Optimization (GWO) algorithm, the Human Depression Prediction Scheme (HDPS) presents a novel approach to depression prediction. The proposed technique shows promise in identifying depression tendencies with a noteworthy 95% accuracy ***, Google Colab uses the HDPS, highlighting its usability and accessibility. By combining cutting-edge technologies and optimization strategies, HDPS is positioned as a potentially useful tool for depression early diagnosis, providing high accuracy and useful application in a computing environment that is widely available. ©2024 IEEE.

关键词： Adversarial machine learning

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A Survey on Predicting Depression Among Teachers and Students at Metropolitan University (Sylhet, Bangladesh) Using Machine learning 3

A Survey on Predicting Depression Among Teachers and Student...

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3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Tabassum, Anika Ema, Yasmin Firuza Rafee, Md. Mushtaq Shahriyar Limon, Md Shamihul Islam Khan Metropolitan University Department of Computer Science and Engineering Sylhet Bangladesh

ISBN: (纸本)9798331515911

Depression, a psychiatric condition marked by profound sadness and apathy, influences cognitive processes, emotions, and behaviors, causing psychological and physiological challenges. Everyday tasks become arduous, life loses vibrancy, and in extreme cases, may lead to suicidal tendencies. University students face unique stressors due to academic, social, and personal pressures, underscoring the need for mental health support. This thesis investigates depression diagnosis among Metropolitan University teachers and students using machine learning techniques. Survey data, comprising 516 anonymized responses covering mental well-being, mood, and behavior, form the basis. The researchers preprocess data, visualize it, and train models to detect depression indicators, evaluating techniques like Logistic Regression, Decision Trees, and Gradient Boosting for effectiveness. The research highlights that the reasons for depression differ between females and males. The aim is to enhance early detection and support mechanisms, fostering proactive intervention within the university community. © 2025 IEEE.

关键词： Students

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Design and Development of a Novel Algorithm to Predict Fake Logo using learning based Digital Image Analysis Methodology 5

Design and Development of a Novel Algorithm to Predict Fake ...

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5th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2024

作者： Nagajothi, N. Memon, Shabana Mohan, G. Vadar, Parashuram Shankar Soni, Anita Sahaya, S. Prince AP/ Mathematics Nandha Engineering College Erode52 India Institute of Management Kolhapur India K.S.Rangasamy College of Technology Department of Mathematics TamilNadu Tiruchengode637215 India Shivaji University Yashwantrao Chavan School of Rural Development Maharashtra Kolhapur India IES University Department of Computer Science and Engineering Bhopal India Sri Ramkrishna Engineering College Department of Computer Science Engineering Coimbatore India

ISBN: (纸本)9798350395235

In the realm of fake logo detection, the continuous surge in digital content manipulation necessitates advanced and efficient methodologies for safeguarding brand integrity. This study explores the application of diverse machine learning models, ranging from traditional algorithms like K-Nearest Neighbors (KNN) and Support Vector Machines (SVM) to sophisticated deep learning (DL) architectures such as VGG19, Inception, and Convolutional Neural Networks (CNN). The proposed methodology combines feature extraction using VGG19 with classification using an Inception model, presenting a novel approach to enhance detection accuracy. Evaluation metrics, including accuracy, precision, specificity, and sensitivity, which enhance the performance of each model. Computational efficiency is also analyzed, providing a comprehensive understanding of the trade-offs between accuracy and processing speed. The proposed methodology emerges as a promising solution, achieving a remarkable accuracy of 97% while maintaining efficiency. This study contributes to the evolving landscape of fake logo detection, offering valuable insights for practical implementation in scenarios demanding precision, speed, and reliability. © 2024 IEEE.

关键词： Support vector machines

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Detection of Pulp Stones in Radiographic Images: Survey Using Artificial Intelligence

Detection of Pulp Stones in Radiographic Images: Survey Usin...

引用

2024 IEEE international conference on computing, Semiconductor, Mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Manasa, G.R. Venugopala, P.S. Santhosh, G. Umesh, G. NMAM Institute of Technology NITTE Deemed to Be University Computer Science and Engineering Nitte karkala India NMAM Institute of Technology NITTE Deemed to Be University Artificial Intelligence and Data Science Nitte Karkala India NMAM Institute of Technology NITTE Deemed to Be University Mechanical Engineering Nitte Karkala India Conservative Dentistry and Endodontics Bapuji Dental College and Hospital Davangere India

ISBN: (纸本)9798331517892

Pulp stones are calcifications within the dental pulp chamber that can complicate endodontic procedures and negatively impact dental health. Detecting pulp stones accurately is essential for effective diagnosis and treatment planning. Traditionally, manual inspection of radiographs by clinicians is employed for this task, but it is time-consuming and prone to variability. With advancements in artificial intelligence (AI), automated detection systems using deep learning techniques have emerged as promising alternatives. This literature survey reviews state-of-the-art AI-based methods for pulp stone detection, focusing on the application of machine learning and deep learning models. The review covers commonly used techniques, available datasets, evaluation metrics, and challenges faced in model development. A comprehensive analysis of the strengths and limitations of these approaches is provided, alongside a discussion on the potential for integrating AI into clinical practice. Future directions are suggested to address existing challenges, including the need for larger and more diverse datasets, improved model generalization, and better interpretability. This survey aims to guide researchers and practitioners in understanding the current landscape of AI applications in dental imaging, with a specific focus on the detection of pulp stones. © 2024 IEEE.

关键词： Deep learning

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Comparative Analysis of Disease Detection among Tomato Plants Using Machine and Deep learning Techniques

Comparative Analysis of Disease Detection among Tomato Plant...

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2024 international conference on Emerging Innovations and Advanced computing, INNOCOMP 2024

作者： Wadmare, Jyoti Joisar, Jai Jadav, Maulik Joshi, Sujal K. J. Somaiya Institute of Technology Computer Engineering Sion India

ISBN: (纸本)9798350376470

Plant diseases pose a major brat to husbandry and cause yearly crop losses. Although there is an all-encompassing hunt on the contactability of plant diseases as well as modern day methods often lacked the accuracy, efficiency and productiveness required for early diagnosis. To this end, this clause introduces new auto learning and deep learning models to improve the truth and speed of plant pyknotic diagnosis. By analyzing 40 existing studies on clear cut plants, including potatoes, tomatoes, corn and bananas, our proposed commercial aims to bridge the gap between engineering and agriculture. Our efficacious model increases contactable truth by a median of 15% and reduces processing time by 30% as well as thus facilitating early pyknotic contactability and increasing the efficiency of crop management. These advances not only benefited farmers by providing them with authorized tools to Saran crop *** also contributed to rural productivity. Our hunt has the ability to improve rural productivity by up to 20% by optimizing yields and reducing losses, thereby solving food credentials issues and promoting permaculture practices. Of the three models that had been trained, Convolutional Neural Network CNN, +-SVM, and YOLO NAS, the CNN model achieves an astonishing truth of 96.7%. In the meantime, the SVM model attains a truth of 90.4%, while the YOLO NAS model displays an astonishing truth rate of 70.34%. © 2024 IEEE.

关键词： Fruits

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Predictive Analytics in Hotel Recommendations Using Machine learning 5

Predictive Analytics in Hotel Recommendations Using Machine ...

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5th international conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Singh, Siddharth Dey, Shayan Sharma, Shreyanshi Prathaban, Banu Priya School of Computing College of Engineering and Technology SRM Institute of Science and Technology Department of Networking and Communications Kattankulathur603203 India School of Computing SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications Kattankulathur603203 India

ISBN: (纸本)9798331518097

In a world where travellers want the perfect stay, a new system emerges as the one that goes beyond simple hotel recommendations. By analysing details like interaction words and prices, it predicts hotel ratings using a powerful Random Forest Regressor which performs better than methods like Linear Regressor. With careful data processing and encoding, it achieves high accuracy, measured through precise metrics like RMSE, MSE and Max Error. It achieves a great RMSE score of 0.424. This system offers a scalable solution, turning every hotel suggestion into a personalized experience, redefining the way people find their ideal stay. © 2024 IEEE.

关键词： Hotels

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Research on Unsupervised Campus Audio Anomaly Detection Based on Generative Adversarial Network 2

Research on Unsupervised Campus Audio Anomaly Detection Base...

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2nd international conference on Signal Processing and Intelligent computing, SPIC 2024

作者： Xin, Yang Chen, Liang Liu, Wenhong Shenyang Ligong University College of Automation and Electrical Engineering Shenyang China Northeastern University College of Information Science and Engineering Shenyang China

ISBN: (纸本)9798350368888

In order to detect inappropriate speech in classrooms, this paper proposes an unsupervised model based on generative adversarial networks (GAN), which integrates an autoencoder structure to detect anomalies in uncalibrated classroom audio, saving a lot of labeling time. Enhance data by adding Latent Space Pitting and weight sharing. Meanwhile, due to the previous model being mostly used for two-dimensional image data, in order to enhance the learning ability of one-dimensional speech information features, a multi head attention mechanism was introduced. Experiments were conducted on a self-made classroom audio dataset, demonstrating that the proposed method can improve the ability of anomaly detection and effectively distinguish anomalies. © 2024 IEEE.

关键词： Generative adversarial networks

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