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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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Comprehensive Analysis of Melanoma Detection using CNN methods 5

Comprehensive Analysis of Melanoma Detection using CNN metho...

引用

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

作者： Patinge, Sayali Ponde, Kshitija Pokar, Jaykumar Gadgile, Vaishnavi Raut, Maithili Sonar, Leena JSPM's Rajarshi Shahu College of Engineering Information Technology Tathawade Pune India

ISBN: (纸本)9798350395235

Melanoma is a rare kind of skin cancer that is mostly caused by UV radiation. It appears in the form of pigmented lesions and develops from melanocytes. Much research has been conducted to assist in the classification of melanoma and non-melanoma using machine learning or deep learning models. To get the most accurate outcomes, researchers applied a variety of approaches such as the ABCDE method, SVM, KNN, CNN, Deep CNN, and transfer learning. Despite the fact that such algorithms give great accuracy, there are no medical organizations that rely entirely on them. Another constraint might be the number of melanoma datasets. Because it is such a rarely seen cancer the model will have difficulty categorizing the skin lesions. Previous studies show that researchers have used both machine-learning models and conventional CNN techniques. The accuracy using these was far lower than what is obtained using recent technologies. This study addresses all of these shortcomings and examines previous works/research. © 2024 IEEE.

关键词： Convolutional neural networks

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An Explainable Hybrid learning Model for Indian Food Image Classification 15

An Explainable Hybrid Learning Model for Indian Food Image C...

引用

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

作者： Harishankar, C. Anoop, Neha S. Niranjana, K.S. Biju, Adity Venugopal, Vivek Vekkot, Susmitha Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electronics and Communication Engineering Amritapuri India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350370249

As the prevalence of diet management applications continues to rise for personal health tracking, the need for accurate food image classification becomes paramount. While various food classification models exist, understanding and categorizing Indian cuisine remain challenging. Although, deep learning models provide state of the art performance, they lack interpretability in terms of identifying the significant features that lead to accurate classification. In this paper, we propose an Explainable AI backed classification model specifically tailored for Indian food images. We explore the efficacy of multiple deep features learned through standard CNN architectures in conjunction with machine learning algorithms for Indian food classification. In this regard we observe that the features learned from InceptionNet and MobileNet provided superior performance of around 89% with SVM as the classifier. Further we leverage Explainable AI frameworks such as SHAP and LIME to provide insights into the model's decision making process. Through SHAP summary plots we provide understanding of the important deep features contributing for enhanced classification and LIME provides heatmaps that highlights the significant patches in the input image which contributed to the decision. © 2024 IEEE.

关键词： Explainable AI InceptionNet Indian Food Classification MobileNet ResNet VGG16

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Intelligent Helmet for Coal Miners Monitoring System using LORA Technology and Machine learning 10

Intelligent Helmet for Coal Miners Monitoring System using L...

引用

10th international conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Sri, Morampudi Anu Siva Satya Sreedhar, P. Gupta, Maddhali Rudra Sai Naga Venkata Sanjay Sreyesh, Kunduru Anirudh Ram Naga Sri Chandhini, Kundeti Seshadri Rao Gudlavalleru Engineering College Department of Information Technology Andhra Pradesh Gudlavalleru India

ISBN: (纸本)9798350384369

With its machine learning and LoRa technology integration, the Intelligent Helmet for Coal Miners Monitoring System is a significant advancement in tackling safety issues in the coal mining sector. This novel method ensures real-time monitoring of miners' circumstances by utilizing the dynamic insights from machine learning and the dependability of LoRa connectivity. The system incorporates multiple sensors, including MQ4, MQ7, temperature and humidity, and infrared, to identify abnormalities such as helmet absence or alterations in surrounding conditions. The solution improves emergency response capabilities by sending accurate user positions to the Telegram cloud during emergencies. After a thorough assessment, the system shows increased safety, quick reaction to dangers, and insightful data. This study emphasizes the value of helmet-based monitoring systems, the revolutionary possibilities of LoRa, and machine learning to improve safety protocols in mining. © 2024 IEEE.

关键词： Miners

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Transfer learning with ResNet50 for Enhanced Mammographic Breast Cancer Identification 5

Transfer Learning with ResNet50 for Enhanced Mammographic Br...

引用

5th international conference on Circuits, Control, Communication and computing, I4C 2024

作者： Deshnande, Sumeet Patel, Rajneesh Kumar Chouhan, Siddharth Singh Vishwakarma, Harshlata School of Computing Science and Engineering Vit Bhopal University Madhya Pradesh Sehore466114 India

ISBN: (纸本)9798331528539

One of the biggest causes of death for women globally is still Breast Cancer, necessitating the development of advanced diagnostic tools for early and accurate detection. This study explores the application of transfer learning using ResNet50 architecture to enhance mammographic breast cancer identification. Leveraging the per-trained ResNet50 model, we fine-tuned it on a comprehensive dataset of mammographic images, aiming to improve the model that can classify between cancerous and non-cancerous cases. The transfer learning approach capitalizes on the model's pre-existing knowledge, significantly reducing the computational burden and training time while enhancing performance. Our experimental results demonstrate a marked improvement in classification accuracy and sensitivity compared to traditional methods. The findings underscore the potential of transfer learning with ResNet50 as a robust model for supporting radiologists in the early detection and diagnosis of breast cancer, ultimately contributing to better patient outcomes. A deep learning model based on ResNet50v2 with 93.4% classification accuracy was developed. The results showed a significant improvement over traditional methods, highlighting the potential of deep learning to increase the accuracy of cancer diagnosis and facilitate personalized treatment strategies. Future advances, such as the integration of multi-omics data and the support of cognitive-driven decision-making, are expected to improve the diagnosis and management of cancer further. © 2024 IEEE.

关键词： Lung cancer

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Gender-Specific Speech Enhancement Architecture for Improving Deep Neural Networks learning

Gender-Specific Speech Enhancement Architecture for Improvin...

引用

2024 international conference on Innovation and Intelligence for Informatics, computing, and Technologies, 3ICT 2024

作者： Nossier, Soha A. Sharif, Mhd Saeed Biomedical Engineering Department Medical Research Institute Alexandria University Alexandria Egypt Intelligent Technologies Research Group School of Architecture Computing and Engineering UEL London United Kingdom

ISBN: (纸本)9798331533137

Deep learning techniques for speech enhancement rely on training a deep neural network to process noisy speech, regardless the gender of the speaker. However, research shows that the speech of male and female stimulates different parts in human brain, and that female speech requires more complex analysis. This implies that different processing is applied on the speech, based on the speaker gender. In this work, we argue that male and female speeches have different features that can help in the learning process of speech enhancement deep neural networks if the training is performed on male and female speech data, independently, and using two different deep neural networks, specifically implemented for enhancing the noisy speech signal of the target gender. This work presents a gender-specific speech enhancement architecture, which consists of a front-end binary classifier to detect the speaker gender. Based on the classifier decision, the noisy speech is enhanced using either a male or female speech enhancement model. One-stage and two-stage speech enhancement approaches are used to process male and female speeches, respectively. The results reveal that gender-specific speech enhancement has positive impact on the enhanced speech by deep neural networks. Additionally, the developed architecture achieved classifier accuracy 96.9% and 0.11 increase in Covl speech quality metric for the test data, in comparison to other best-performing networks. ©2024 IEEE.

关键词： Deep neural networks

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Secure Face Track: A Deep learning-Powered Attendance Automation 1

Secure Face Track: A Deep Learning-Powered Attendance Automa...

引用

1st IEEE international conference on computing and Data Science, ICCDS 2024

作者： Umesh, R. Sriram, K.K. Sathieshkumar, P. Velammal College of Engineering and Technology Department of Information Technology Madurai India

ISBN: (数字)9798350365337

ISBN: (纸本)9798350365337

A Deep learning-Powered Attendance Automation addresses a pressing challenge in the realm of smart attendance systems: spoofing or deception attempts by individuals seeking to manipulate the system with fake or altered facial images or videos. This project presents an intelligent anti-spoofing system, named Deep learning-Powered Attendance Automation, designed to accurately distinguish between genuine and fraudulent faces within a smart attendance context. The system's development encompasses the creation of a comprehensive dataset comprising real and fake facial images and videos. Leveraging deep learning, the project employs a robust feature extraction and classification pipeline, including the utilization of the Facenet model for facial recognition. Facenet operates across three phases: registration, training, and testing, integral to the functioning of the face recognition model. Furthermore, various data augmentation techniques, including rotation, scaling, and translation, are incorporated to enhance the system's resilience against diverse spoofing attacks. Performance metrics such as accuracy, precision, and recall are used to evaluate the system's effectiveness in detecting and preventing spoofing attempts. © 2024 IEEE.

关键词： Automation

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A Comprehensive Survey on Deep learning for Enhanced Node Position Prediction in Vehicular Ad-Hoc Networks 4

A Comprehensive Survey on Deep Learning for Enhanced Node Po...

引用

4th international conference on Ubiquitous computing and Intelligent Information Systems, ICUIS 2024

作者： Nayomi, B.Deena Divya Jyothsna, V. Department of Computer Science and Engineering Andhra Pradesh Tirupati India Department of Data Science Andhra Pradesh Tirupati India

ISBN: (纸本)9798331529635

This research study presents a thorough examination of the application of deep learning techniques in enhancing node position prediction within Vehicular Ad-Hoc Networks (VANETs). As VANETs become increasingly integral to the development of intelligent transportation systems, accurate and efficient node position prediction emerges as a critical component for ensuring optimal network functionality, safety, and traffic management. This paper explores the performance and adaptability of multiple deep learning models in the dynamic and complicated VANET environment, such as Graph Neural Networks (GNNs), Recurrent Neural Networks (RNNs), and Convolutional Neural Networks (CNNs). We provide a detailed analysis of the existing literature, highlighting the strengths and limitations of different deep learning approaches in handling the high mobility, variable density, and recurrently changing topology of vehicular networks. Furthermore, the survey discusses the integration of contextual information, such as environmental factors and road conditions, into the prediction models, enhancing their accuracy and reliability. We also explore the implications of advanced methodologies like Transfer learning and Federated learning in this domain. In addition to providing insights into future research paths, the paper finishes with an assessment of the difficulties encountered in the practical application of these models, including data privacy and computing limits. The goal of this thorough investigation is to provide a roadmap for future developments in node location prediction in VANETs, making it an invaluable tool for scholars and professionals working in the subject of intelligent transportation systems. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Footprint-Based Animal Identification: A Hybrid Model Combining CNN, SVM, and Transfer learning for Wildlife Encroachment 3

Footprint-Based Animal Identification: A Hybrid Model Combin...

引用

3rd international conference on Intelligent Systems, Advanced computing, and Communication, ISACC 2025

作者： Jerlin Joy, C.G. Subhashree, S. Jasmine Mystica, K. St. Joseph's College of Engineering Electronics and Communication Engineering Chennai India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523893

Animals can be identified by using various machine learning techniques among which a hybrid model combines multiple techniques together. In this approach a hybrid model with Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Transfer learning is used to recognize and classify animal footprints. The goal is to prevent animal intrusion into human dwellings and protect endangered species by enabling timely detection. Using a pre-trained CNN (e.g., VGG16) for feature extraction, the system identifies distinct footprint patterns from images. These features are then passed to an SVM for accurate classification. The model benefits from Transfer learning, leveraging knowledge from large-scale datasets to enhance detection accuracy, even with limited footprint data. This approach enables efficient monitoring of wildlife near human settlements, allowing for proactive measures to prevent conflicts and supports biodiversity protection. © 2025 IEEE.

关键词： Convolutional neural networks

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Password Strength Checker Using Machine learning 15

Password Strength Checker Using Machine Learning

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

作者： Kayalvili, S. Devadharshini, T. Dharaneesh, N. Dhanush, P. Kongu Engineering College Department of Ai Tamilnadu Erode India

ISBN: (纸本)9798350370249

The security of sensitive data heavily depends on the strength of user passwords. Traditional password regulations often fail to provide strong security against modern threats due to easily guessed or reused passwords. This paper presents a novel machine learning-based method for assessing password strength using algorithms that analyze various factors such as length, complexity, entropy, and patterns. Our methodology was tested on a diverse dataset, demonstrating a 20% improvement in password strength estimation accuracy compared to traditional heuristic-based methods. The proposed system, implemented on a web application, utilizes machine learning techniques like Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Random Forest (RF) to categorize passwords into Weak, Medium, and Strong. This approach ensures real-time feedback, aiding users in creating secure passwords. © 2024 IEEE.

关键词： Decision Tree K-Nearest Neighbors machine learning Password security password strength estimation Random Forest real-time feedback Support Vector Machines web security

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An Enhanced Optimization of Automated Detection of Cardiac Abnormalities Using Deep learning

An Enhanced Optimization of Automated Detection of Cardiac A...

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2024 international conference on Optimization computing and Wireless Communication, ICOCWC 2024

作者： Ojha, Ananta Yadav, Dhyan Chandra Goyal, Manish Kumar Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350383348

Deep gaining knowledge of is a place of artificial intelligence that is becoming increasingly famous inside the scientific area. This paper provides an improved optimization of computerized detection of cardiac abnormalities the use of deep learning. particularly, the authors recommend using a convolutional neural community (CNN) to stumble on abnormalities from ECG records. They use an ensemble of models to further improve accuracy and reduce false superb quotes. moreover, they apply transfer learning techniques to higher generalize the mastering from the EEG facts. The authors take a look at their optimized set of rules on two datasets of ECG recordings and file an normal accuracy of 88.9%. This demonstrates the potential for deep getting to know techniques to end up an increasing number of reliable and sturdy for detecting cardiac abnormalities. The authors also talk the feasible directions of future studies and the potentials of deep learning for clinical safety and diagnostics in phrases of fee and efficiency. © 2024 IEEE.

关键词： abnormalities accuracy diagnostics specifically techniques

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