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检索条件"机构=Computer Science Engineering with Specialization in Artificial Intelligence & Machine Learning"

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Ensemble of Vision Transformers and CNNs for Accurate Diabetic Foot Ulcer Classification 1

Ensemble of Vision Transformers and CNNs for Accurate Diabet...

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Pagadala, Arnold Anand Silas, Salaja Joy, Emmanuel Karunya Institute of Technology and Sciences Division of Computer Science Engineering Coimbatore India Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Coimbatore India

ISBN: (纸本)9798350372748

Diabetic Foot Ulcers (DFU) are a significant diabetes complication that can lead to lower limb amputation. Currently, 537 million suffer from diabetes worldwide and it is anticipated to rise to 783 million by 2045. Given the speed at which DFU is developing, prompt action is necessary to avoid the serious consequences of amputation and associated various medical conditions. With the evolution of image-based ML algorithms, automated methods for identifying and assessing DFUs are becoming more common. Existing research works on visual computing techniques concerns tissue classification and detects the DFU's visual appearance. This research study combines ResNet50 with Vision Transformers (ViT) and MobileNet with Vision Transformers (ViT) to develop an ensemble model to classify the existence or absence of a Diabetic Foot Ulcer (DFU). Experimental results of the proposed model when tested on challenging datasets achieved a validation accuracy of 98.6%. © 2024 IEEE.

关键词： Neural networks

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Peli-Remo Optimization Algorithm for Reliable routing in MANET: K-means node clustering process and CNN-based Energy Prediction 2

Peli-Remo Optimization Algorithm for Reliable routing in MAN...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Biradar, Manjula A. Mallapure, Sujata Sharnbasava University Department of Computer Science and Engineering Sharan Nagar Karnataka Kalaburagi India Department of Artificial Intelligence and Machine Learning Sharan Nagar Karnataka Kalaburagi India

ISBN: (纸本)9798350317558

MANET, an ad hoc wireless network is made up of several nodes connected, governed by certain rules for packet movement from one node point to another. The structure of communication may be designed using a variety of protocols. For an ad hoc network, routing protocols are used to manage the connections. To improve communication, this article suggests an effective MANET cluster-based routing paradigm for multimedia communication. The clustering process is started using the K-means clustering method. The Deep Convolutional Neural Network (DCNN) model will first predict the node energy. The cluster head will be chosen using the Hybrid Remora with Pelicon Optimization technique (HRPOA), which integrates the Remora optimization technique and the Pelican optimization algorithm. The HRPOA model's channel quality for round 500 is around 1.56, whereas the conventional techniques obtained the lowest channel capacity, with BWO=~0.5, GWO=~0.78, BA=~0.98, POA=~0.57, and ROA=~0.78, in that order. © 2024 IEEE.

关键词： DCNN HRPOA Model K-Means Clustering Mobile Adhoc Wireless Network Reliable Retransmission

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A Review of Vehicular Adhoc Network Routing and Congestion Control

A Review of Vehicular Adhoc Network Routing and Congestion C...

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2024 International Conference on Advancements in Power, Communication and Intelligent Systems, APCI 2024

作者： Kori, Kaveri Sridevi, H. Sharnbasva University Department of Computer Science and Engineering Karnataka Kalaburagi585103 India Sharnbasva University Department of Artificial Intelligence and Machine Learning Karnataka Kalaburagi585103 India

ISBN: (数字)9798350363289

ISBN: (纸本)9798350363289

In VANET, the efficiency of the RSUs positioned beside the roadways is crucial for both transportation and service provision. Direct communication between vehicles and infrastructure is supported by the network (V2V and V2I, correspondingly). The architecture of a VANET should satisfy several requirements to function properly and enable dependable real-time communication between vehicles while considering their low-power radio transmission of units, dynamic topology, and high mobility. Numerous routing protocols are suggested and assessed for VANETs. There are very few testbeds worldwide since they need a large count of wireless devices to test routing protocols with genuine VANETs and MANETs prototypes. By examining 25 publications, this survey aims to create a brief summary of VANET routing. In the current work, it offers an analysis of reliable routing models based on optimization algorithms. The best outcomes will be displayed once the performance metrics of the works that were examined are examined. © 2024 IEEE.

关键词： Congestion control (communication)

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Zenvgg1X: An Advance Hybrid Framework based on Gene Sequencing for Analysis and Prediction of Lung Cancers using KNN-GNN and Custom VGG19

Zenvgg1X: An Advance Hybrid Framework based on Gene Sequenci...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Pandit, Kalpa Chatterjee, Ahan Chowdhury, Debtan Chatterjee, Debraj Techno International New Town Dept of Artificial Intelligence and Machine Learning Kolkata India Techno International New Town Dept of Computer Science Engineering Kolkata India

ISBN: (纸本)9798350390759

Lung cancer affects about 238,340 people annually, of which men account for 117,550 cases whereas women have 120,790. Lung cancer causes roughly 127,070 deaths every year. The survival rate for lung cancer is only 56 percent if it can be detected within the lungs;however, it drops to just 5 percentage where there are distant tumors. As few as 16 percentage of cases can be diagnosed if the cancer is identified early. The study created a new lung cancer detection framework, Zenvgg1X a diagnosis and predictive framework combining VGG19, which is a deep convolutional neural network (CNN), and K-Nearest Neighbors - Graph Neural Network (KNN-GNN) hybrid. The combination of these two allows analyzing both images and texts, thus increasing the success rate of lung cancer detection. By working with medical images for identification purposes only the VGG19 model that is well recognized in terms of image classification analysis for lung cancer while KNN-GNN determine patterns from text related to diseases-which if you ask me, the combinations sometimes provide statistical outputs that are mindbogglingly high in accuracy such as 87.5 percent using only 30 percent of the data or more impressively reaching up to an astonishing figure around 98 percent with larger dataset. Further inclusion of Tp53 gene mutation data can enable early-stage prediction of lung cancer which may lead to improved survival rates. It goes without saying that this hybrid model represents a giant stride in medical diagnostics because it offers hope for what could eventually become a simple routine test for early diagnosis and treatment of lung cancer. © 2024 IEEE.

关键词： Lung cancer

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Enhancing Nutrition Tracking: Webcam-based Fruit and Vegetable Recognition and Calorie Measurement using Image Processing and Convolutional Neural Networks 5

Enhancing Nutrition Tracking: Webcam-based Fruit and Vegetab...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Leo, Sam S. Benjamin, Ruth Moly Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798350361155

The "Enhancing Nutrition Tracking" wander epitomizes a groundbreaking agreeable vitality between fake bits of knowledge (AI) and food science. Through the utilization of Convenient Net convolutional neural frameworks, this creative system passes on real-time normal item affirmation and correct calorie estimations, in this way empowering clients to make taught dietary choices. Central to its value is the visual emphasis on carbohydrate substance, ensuring clients have a comprehensive understanding of their food choices and dietary intake. This project's trademark lies in its user-friendly interface, arranged to streamline the strategy of capturing characteristic item and vegetable pictures for incite dietary bits of information. By reliably joining AI advancement into conventional assignments, individuals can effortlessly get to basic wholesome information, empowering more advantageous eating affinities and way of life choices. Besides, continuous ask almost endeavors search for to refine the system's real-time characteristic item recognizable confirmation capabilities and examine the immersive potential of virtual reality for in-depth wholesome assessments. By initiating the integration of AI and virtual reality advancements, the "Enhancing Nutrition Tracking" amplify focuses to catalyze a worldview move in food watching. © 2024 IEEE.

关键词： Fruits

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Bread Quality Assessment Using Deep learning with shape and Volume Metrics 10

Bread Quality Assessment Using Deep Learning with shape and ...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Rohan Joel, J. Benjamin, Ruth Moly Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Coimbatore India

ISBN: (纸本)9798350384369

With the goal to guarantee customer satisfaction and optimize processes for manufacturing in the baking sector, bread quality evaluation is essential. Conventional techniques for evaluating bread quality are frequently time-consuming, subjective, and labor-intensive. In this study, we suggest a novel method for evaluating bread quality that combines shape and volume metrics with deep learning techniques, namely the YOLOv5 algorithm. We enable the YOLOv5 model to properly recognize and categorize bread quality attributes by training it on a dataset that consists of photos of various types of bread with corresponding quality labels. To provide a more thorough assessment of bread quality, we now include shape and volume metrics in addition to the conventional image-based quality assessment. Bakeries may increase productivity, save expenses, and guarantee a constant level of product quality by automating the quality assessment process. Furthermore, the suggested method may be modified for use in tasks other than evaluating the quality of bread, which would promote automation and quality control in the food sector. © 2024 IEEE.

关键词： Food industry

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Pneumonia Disease Detection Using Hyperparameter Tuned Convolutional Neural Network from Chest X-Ray Images 10

Pneumonia Disease Detection Using Hyperparameter Tuned Convo...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Jascar Benish, P. Manoranjitham, R. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

Pneumonia is a common and possibly fatal respiratory illness, particularly affecting children, and is now one of the most destructive and fatal respiratory conditions. By obtaining a chest X-ray, the proposed model assists in determining whether or not an individual is suffering from pneumonia. This model seeks to deliver accurate and timely results. The proposed approach uses Convolutional Neural Network with random search algorithm to achieve higher accuracy, reaching a rate of 97%. Compared to current models, this proposed model demonstrates a significant improvement in prediction accuracy. This model has the potential for wider application in the medical domain and could be developed into a web application or program in the future. Its capacity to raise the accuracy of medical treatments, lessen the strain on healthcare systems, and aid in the battle against antibiotic resistance makes it an invaluable tool in the continuous quest for better respiratory care. © 2024 IEEE.

关键词： Convolutional neural networks

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Cancer Medical Image Analysis Across Modalities Using Attention Mechanism and Capsule Network 10

Cancer Medical Image Analysis Across Modalities Using Attent...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Sharon Deborah, A. Princess, P. Joyce Beryl Paulraj, Getzi Jeba Leelipushpam Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Coimbatore India

ISBN: (纸本)9798350384369

Medical image analysis is a critical component of accurate disease diagnosis and treatment planning which relies on traditional methods such as MRI, CT scans, and X-rays. However, challenges arise from these traditional methods due to human errors in image interpretation, lack of standardization in medical image analysis, varying expertise levels among healthcare professionals, and the potential oversight of critical details which leads to variability in diagnosis and treatment outcomes across facilities. To address these issues, advanced computational methods are imperative for medical conditions that are hard to detect visually. This paper proposes an integrated approach using the Deep neural network with Attention mechanism and Capsule Network to deal with diverse imaging modalities, especially histology, microscopy, and MR scans. This innovative approach represents a superior alternative to traditional methods by emphasizing its potential to focus on crucial regions within medical images and to enhance disease prediction which holds a significant progress to contribute to a faster and more accurate diagnostic process, ultimately benefiting both the healthcare industry and patients. © 2024 IEEE.

关键词： computerized tomography

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Optimizing CNNs for Leaf Disease Detection: A Comparative Analysis of Algorithms, Activation functions, and Performance Metrics

Optimizing CNNs for Leaf Disease Detection: A Comparative An...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Nitisha, S. Mohith, N. Vinay, R. Agarwal, Monika Sinha, Aparajita Mokshitha, M. Dayananda Sagar University Dept. Artificial Intelligence and Machine Learning Bangalore India National Institute of Technology Dept. Computer Science Engineering Agartala India

ISBN: (纸本)9798331540821

Agriculture plays an important role in ensuring food security. However, the emergence of plant diseases causes significant economic losses. The routine method of detecting crop diseases is time consuming, requires technical skill and can be inaccurate, especially when new diseases are introduced. To solve these problems, we present a new and effective method for the early identification of leaf diseases using computer vision and machine learning. Extensive testing has been done on the widely used Plant Village dataset to ensure accurate findings. Our plans focus on disease diagnosis and classification. This study demonstrates the pros and drawbacks of this method. The testing process includes data collection, data training, feature extraction, model testing, and classification. The main processing techniques include image acquisition, image preprocessing, extraction and the use of neural networks. Combining computer vision and machine learning, our approach holds great promise for efficient and reliable detection of crop diseases. This has important implications for improving disease control, reducing economic losses and increasing food security. © 2024 IEEE.

关键词： Agriculture Classification CNN computer Vision Plant diseases

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Detection of Sleep Apnea: A Comparative Analysis of Advanced machine learning and Deep learning Approaches 11

Detection of Sleep Apnea: A Comparative Analysis of Advanced...

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11th IEEE International Conference on Advances in Computing and Communications, ICACC 2024

作者： Prajitha, M.V. Naveen Sundar, G. Narmadha, D. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Coimbatore India

ISBN: (纸本)9798350379136

Sleep apnea is a prevalent and serious sleep disorder caused by breathing interruptions during sleep, which is lead to critical health issues like cardiovascular diseases. Traditional diagnostic methods, such as polysomnography, are often very large process, costly, and not easily accessible, highlighting the need for automated and affordable detection solutions. This review paper examines the utilization of machine learning (ML) and deep learning (DL) methods for detecting and diagnosing sleep apnea. We offer a thorough overview of the current research, analyzing various studies that utilize different data types, including electrocardiogram (ECG), electroencephalogram (EEG), and photoplethysmography (PPG) signals. The review assesses the effectiveness of different algorithms, feature extraction techniques, and the integration of multiple signal types to improve detection accuracy. By summarizing key developments and identifying existing challenges, we aim to outline future research directions to enhance the robustness, interpretability, and real-time identification of sleep apnea using machine learning and deep learning techniques. © 2024 IEEE.

关键词： Sleep research

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