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检索条件"机构=Artificial Intelligence and Machine Learning School of Computer and Engineering"

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Innovative Solutions for Cervical Cancer: Deep learning Models for Accurate Stage Prediction 2

Innovative Solutions for Cervical Cancer: Deep Learning Mode...

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Yadav, Roshan Kumar Kumar, Divyam Kadam, Kalyani Devi, Seeta Gangarde, Rupali Department of Artificial Intelligence and Machine Learning Pune India Department of Obstetrics and Gynecological Nursing Pune India Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798350383652

Cervical cancer, caused by the skin infection of the Human papillomavirus (HPV), ranked as the fourth most prevalent cancer globally, has profound and sometimes fatal implications for women. With approximately 500,000 new cases reported annually, the disease significantly impacts women's overall well-being. Ongoing efforts by researchers focus on developing a more cost-effective solution than regular HPV testing to manage and control this widespread health concern. Our research included an innovative framework for identifying and classifying stages of cervical cancer by processing the images of the tumours. To optimize the performance of our model, we developed a unique approach for selecting the best image data points by calculating a score, which is defined as the product of focus, sharpness, and resolution. Additionally, our study distinguishes itself by integrating three diverse feature extraction techniques-Local Binary Pattern (LBP), ResNet, and VGGNet-an inclusive strategy not commonly observed in similar projects. We even added deep convolutional architectures and machine learning techniques, including Random Forest, SVM, Decision Tree, XGBoost, a basic Neural Network Classifier, and a Multi-layered Perceptron (MLP). This hybrid approach allowed for an intrinsic comparison, identifying the most effective strategy among these. In contrast to current state-of-the-art methods, our approach yielded around 75% accuracy and 81% AUC score, which is greater than the existing studies on such dataset. © 2024 IEEE.

关键词： Contrastive learning

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Transformer based Compressed Data Recovery for Efficient Data Collection in Wireless Sensor Networks

Transformer based Compressed Data Recovery for Efficient Dat...

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2024 IEEE Students Conference on engineering and Systems, SCES 2024

作者： Balaji, V.S. Sekar, K. Devendar Rao, B. Dheepa, T. Rajalakshmi Engineering College Dept. of Artificial Intelligence and Machine Learning Chennai India Kongunadu College of Engineering and Technology Dept. of Computer Science and Engineering Tiruchirappalli India

ISBN: (数字)9798350374711

ISBN: (纸本)9798350374711

Data collection is challenging in wireless sensor networks (WSNs) since energy consumption remains a significant constraint. Although energy consumption has increased, most data collection methods incur excessive computation overhead. Compressive sensing is used to minimize the number of sensing samples with a suitable sparse basis. A novel Transformer neural network-based compressed data recovery (TCDR) method has been proposed for extracting temporal correlation structures from compressed signals and reconstructing them with high accuracy. The compressed signal is forwarded to the base station through multi-hop communication. The base station uses an attentionbased Transformer Neural Network (TNN) to recover the compressed signal. This model provides greater energy saving performance than the other baseline models considered for evaluation, which are 5.23 percent, 6.82 percent, 10.26 percent, and 11.47 percent, respectively. © 2024 IEEE.

关键词： Spatio-temporal data

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Early Melanoma Detection and Classification Using CNN and Confusion Matrix Analysis

Early Melanoma Detection and Classification Using CNN and Co...

引用

2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Dinesh Kumar, K. Deepthi, K. Jaya Saravanakumar, S. Balamurugan, S. Govindharaj, I. Reddeppa, Pulikandla Akshay Vel Tech University Department of Computer Science and Engineering Chennai India Learning Mohan Babu University Department of Artificial Intelligence and Machine Tirupati India Paavai Engineering College Department of Information Technology Namakkal India Bengaluru India Learning Vel Tech University Department of Artificial Intelligence and Machine Chennai India

ISBN: (纸本)9798331510022

This article brings up a new method for detecting and classifying skin cancers using Convolutional Neural Networks (CNN) and Confusion Matrix analysis. The main work of this work evolves around detection of Melanoma and Non Melanoma cells. The large dataset on skin cancers is used to teach the CNN model to accurately classify different types of skin lesions into different malignant and non-cancerous groups. The integration of Confusion Matrix, in this case, allows for accurate identification of the classification errors made by the model as well as possible areas for improvement hence making it possible to comprehensively evaluate how well the model performs. Therefore, early recognition is indispensable to successful treatment as skin cancer is a typical yet fatal condition. Results shows that the suggested proposed model achieves 95.5% accuracy and performance in comparison with other methods found in literature. Additionally, Modified DenseNet201 model has a sensitivity of 95.96% as well as a specificity of 98.03%. © 2024 IEEE.

关键词： Lung cancer

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Multilingual Facial Emotion Decoding and Posture Recognition using Random Forest Classifier

Multilingual Facial Emotion Decoding and Posture Recognition...

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Sangeetha, K. Balaji, V.S. Manju, M. Renuka Devi, S. Velayutham, P. Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering Aarupadai Veedu Institute of Technology Chennai India

ISBN: (纸本)9798350374933

Effective communication is crucial for success in various interactions, including personal and online interviews. The work proposed is to refine the communication effectiveness and extend the understanding of interactions by incorporating new system functions of real-time emotion and body language capture. Facial and body landmarks are analyzed using Google’s Mediapipe to ensure accurate identification of the frequently performed emotions and frequently posed postures. The proposed system uses three classifiers, namely Random Forest, Gradient Boosting, and Ridge Classifier, all integrated in one pipeline;this makes our work distinct. In the testing phase the classifiers are checked to avoid misidentification of emotions or incorrect posture in different situations. One of the major novelties of this work is its ability to address users of different languages, thus it is relevant anywhere in the world. This dynamic and robust system provides accurate emotion analysis achieving the accuracy of 92%, enhancing understanding in various communication contexts and setting a new standard for interview and interaction analysis. © 2024 IEEE.

关键词： Emotion Recognition

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DROPEX: Disaster Rescue Operations and Probing using EXpert drones 8

DROPEX: Disaster Rescue Operations and Probing using EXpert ...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Kannan, Kausthub Awati, Aditya N. Rao, Smruthi S. Malagi, Vindhya P. Dayananda Sagar College of Engineering Department of Computer Science And Design Bangalore India Dayananda Sagar College of Engineering Department of Artificial Intelligence and Machine Learning Bangalore India

ISBN: (纸本)9798331505462

Disasters, both natural and man-made, pose significant risks to human life and infrastructure, necessitating swift and efficient search and rescue (SAR) operations. Traditional SAR methods often struggle to access hazardous areas, resulting in delayed responses and increased risk to rescuers. These methods are unable to run automated simultaneous rescues which lead the operations to face challenges such as difficulty in quickly assessing damage, locating survivors, and delivering aid. This paper address the problem by proposing a autonomous swarm of drones framework which improves the response time as well as increases accessibility zone of the rescue operation. The proposed framework DROPEX, is an autonomous UAV (Unmanned Aerial Vehicle) which employs a dual-dome architecture with surveillance drones to detect individuals in distress and payload drones to deliver aid. This framework ensures rapid deployment, accurate navigation, and efficient data transmission in disaster-stricken areas while minimizing the need for manual intervention. The drones are able to recognise the victims in need using object detection models such as YOLO and Detection Transformer (DETR) with thermal vision. By using Long Range Wide Area Network (LoRaWAN) and object detection models, the drones are able to reduce the response time and increase accessibility. The main focus is on creating a robust, scalable, and economical system to enhance the speed, efficiency, and effectiveness of disaster rescue operations. © 2024 IEEE.

关键词： Drones

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Survey on Classification of Skin Diseases Using machine learning Techniques 3

Survey on Classification of Skin Diseases Using Machine Lear...

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3rd International Conference on Power Electronics and IoT Applications in Renewable Energy and its Control, PARC 2024

作者： Nirupama Virupakshappa Sharnbasva University KLB Department of Artificial Intelligence and Machine Learning Kalaburagi India Sharnbasva University KLB Department of Computer Science and Engineering Kalaburagi India

ISBN: (纸本)9798350309409

Skin is our body's most important organ;skin disorders are currently a common and serious problem due to patients' sensitive skin, as well as physical and psychological consequences. Skin disease symptoms might differ greatly from person to person and may be permanent or temporary, severe or painless. Skin disease symptoms include clogging, inflammation, rashes, irritation, and other changes in skin appearance. Early detection of skin disorders is ideal because it reduces the patient's actual expenses while additionally preventing illnesses from developing and spreading. In recent years, artificial intelligence (AI) has been used to diagnose skin disorders using machine learning algorithms and a large amount of different skin data stored in database. Recent studies on skin disease identification and classification using predictive algorithms, mechanisms, and specific systems resulted in higher accuracy. machine learning will play an important and promising role in real-time diagnosis of dermatological diseases in the future. © 2024 IEEE.

关键词： machine learning

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An Optimal Real-Time Routing and Clustering Protocol for UAV Networks Based on Security 4

An Optimal Real-Time Routing and Clustering Protocol for UAV...

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4th International Conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Sophia, S. Leelipushpam Paulraj, Getzi Jeba Yuvarajan, D. Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Coimbatore India Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331523923

Unmanned Aerial Vehicle (UAV) networks play an important role in different application areas such as military surveillance, emergency services, and infrastructure. However, these networks face significant challenges such as data leakage, unauthorized access, limited processing capacity, and energy constraints. Available techniques such as Elliptic Curve Cryptography (ECC), hash-based techniques, and dynamic clustering algorithms have been proposed to enhance security and operational performance;however, they often encounter challenges concerning scalability, adaptability to real-time changes within the network, and resource-related constraints. This research presents a streamlined authentication framework with the intent to facilitate secure and effective functionalities in turbulent and fluctuating settings. The proposed protocol uses Dijkstra's algorithm for optimal real-time routing, Adaptive Optimization Clustering (AOC) for dynamic resource distribution, and K-means clustering for noise classification. The proposed methodology achieves substantial improvements in power efficiency, latency, and Packet Delivery Rate (PDR) due to reduced computational overhead as well as risks such as data interception and replay attacks. Simulation results conclude that the system suggested here would be secure, scalable, and energy-efficient to be used for applications in demanding scenarios, such as mission-critical scenarios. © 2025 IEEE.

关键词： Emergency services

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AI-Driven Transcription and Analysis for Medical Conversations 2

AI-Driven Transcription and Analysis for Medical Conversatio...

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2nd International Conference on machine learning and Autonomous Systems, ICMLAS 2025

作者： Pisal, Tejas Hajare, Rasika Verma, Prateek Gote, Pradnyawant M. Jiet, Mosses Chaudhri, Shiv Nath Faculty of Engineering and Technology Department of Computer Science & Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Machine Learning Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

This paper presents an advanced system that integrates Google's Speech Recognition API with the Gemini-l.5-flash model to improve audio processing and response generation, particularly in the context of healthcare communication. The system is designed to transcribe audio files of doctor-patient conversations accurately and generate contextually relevant responses using AI. A user-friendly interface, developed with Tkinter, allows seamless uploading and processing of these audio files. The integration of these technologies aims to enhance the efficiency and effectiveness of automated response systems, thereby improving patient-provider communication. The system's capabilities were evaluated using real-world healthcare datasets, demonstrating its potential to streamline the workflow from audio input to meaningful textual output. This work not only showcases the practical application of AI in healthcare but also lays the groundwork for future innovations in automated medical communication systems. © 2025 IEEE.

关键词： Natural language processing systems

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Hybrid Approach of CNN & RNN for Breast Cancer Detection and Segmentation 3

Hybrid Approach of CNN & RNN for Breast Cancer Detection and...

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3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Manju Bala, P. Priyadharshini, S. Usharani, S. Balachandar, A. IFET College of Engineering An Autonomous Institution Department of Artificial Intelligence and Data Science India IFET College of Engineering An Autonomous Institution Department of Computer Science and Engineering India IFET College of Engineering An Autonomous Institution Department of Artificial Intelligence and Machine Learning India

ISBN: (纸本)9798350391565

Breast cancer is a serious worldwide health concern, and advanced diagnostic tools are needed for an accurate and timely identification of the illness. In order to classify breast cancer through ultrasound pictures, this method suggests a hybrid strategy that combines Convolutional Neural Networks (CNNs) with Recurrent Neural Networks (RNNs). CNN integration makes it possible to extract spatial data from ultrasound pictures and identify complex patterns that may be signs of cancer. Concurrently, RNNs are used to record temporal relationships between successive ultrasound frames, which enables the model to identify changing features of possible anomalies. By combining the advantages of both CNNs & RNNs, the suggested hybrid architecture improves the model's capacity to identify minute, dynamic patterns that are essential for precise diagnosis. In comparison to standalone CNN or RNN models, the hybrid model performs better in classification, as evidenced by experimental assessments carried out on a variety of ultrasound picture datasets. The findings highlight the hybrid approach's potential to improve breast cancer diagnostic skills and present a viable path for raising the precision and dependability of ultrasound-based tumour classification techniques. © 2024 IEEE.

关键词： Convolutional neural networks

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A Secure and Efficient Framework for Storing Medical Data using Blockchain and Cloud Servers 8

A Secure and Efficient Framework for Storing Medical Data us...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Lakshmanan, M. Mala, G.S. Anandha Sivakumar, G. Divya Sundar, V.S. Kesavan, Rupa Ajoe Sweetlin Jeena, A. Dayananda Sagar University Department of Artificial Intelligence and Machine Learning Bangalore India Easwari Engineering College Anna Univeristy Department of Computer Science and Engineering Chennai India Gnanamani College of Technology Anna University Department of Artificial Intelligence and Machine Learning India Anna Univeristy Saveetha Engineering College Chennai India Sri Venkateswara College of Engineering Computer Science and Engineering Sriperumbudur India

ISBN: (纸本)9798350367904

The digitization of Electronic Health Records (EHRs) has brought a revolution in health care delivery and even practices in reporting key organizational HR processes. However, these advancements have their merits and demerits particularly in regard to data security, the speed of data retrieval, and how data transactions are performed. This paper presents a solution to this problem in the medical world. It presents a unique architecture that connects cloud servers with Blockchain technology for better and secure medical databases. Using Merkle trees to protect health information and advanced heuristic algorithms to optimize operations, the system deals with main issues of transaction time, retrieval rate, and data privacy. The method is tested on a realized medical database and shows that the performance indicators increased substantially. It would seem that Blockchain assures that data cannot be altered once uploaded, while cloud servers provide flexibility to users and allow faster data retrieval. Proposed system improves the transaction time by 30 percent and the retrieval rate by 40 percent compared with traditional methods and supports strategic management of EHR systems. © 2024 IEEE.

关键词： Electronic health record

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