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

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Unveiling Consumer Sentiments: Leveraging the VADER Algorithm for Product Analysis in the Digital Marketplace 4

Unveiling Consumer Sentiments: Leveraging the VADER Algorith...

引用

4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Kumar Sudabathula, Vijay Sai Rama Swamy, G. Bharath Reddy, S. Gagan Deep Arora Vadher, Harshali Hemant Akhil, Eruva Vardhaman College of Engineering Department of Artificial Intelligence & Machine Learning Telangana Hyderabad India Malineni Lakshmaiah Womens Engineering College Department of Computer Science & Engineering Andhra Pradesh Guntur India

ISBN: (纸本)9798350364729

Customer reviews, feedbacks and comments are crucial in digital marketing, shaping product perception and quality. This paper highlights the VADER algorithm's role in product analysis, using sentiment analysis, natural language processing (NLP), keywords, and design patterns to analyze customer reviews. By converting qualitative data into actionable metrics, organizations can measure satisfaction, identify pain points, and recognize trends. The study shows that using cloud computing and big data for product analysis enables rapid capture of extensive feedback, facilitating swift, informed decisions. Results indicate the algorithm's effectiveness in providing actionable insights, supporting innovation, enhancing reputation, and driving growth. Conclusively, this research underscores the significant impact of advanced analytics on understanding consumer sentiments and improving overall customer satisfaction, thereby driving business success. © 2024 IEEE.

关键词： Marketplaces

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A Comparative Study of DenseNet121 and InceptionResNetV2 model for DeepFake Image Detection 3

A Comparative Study of DenseNet121 and InceptionResNetV2 mod...

引用

3rd International Conference on Applied artificial intelligence and Computing, ICAAIC 2024

作者： Manoranjitham, R. Swaroop, Somu Santhi 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)9798350375190

The rise of deep fake technology poses a significant challenge to societal integrity, necessitating robust detection methods. This research study investigates the effectiveness of DenseNet121 and InceptionResNetV2 deep learning models in detecting deep fake images. The study starts with the curation of the dataset and then applies different preprocessing methods to improve the quality of the dataset like scaling, normalization, and data augmentation. The DenseNet121 and InceptionResNetV2 models are trained to differentiate authentic and manipulated images based on distinctive features. DenseNet121's dense connectivity facilitates feature reuse, enabling effective extraction of discriminative features crucial for identifying manipulated content. In contrast, InceptionResNetV2's sophisticated architecture exploits both inception and residual connections, enhancing its ability to capture intricate patterns indicative of deep fake manipulation. The comparative analysis of performance metrics reveals InceptionResNetV2's superiority, achieving an impressive accuracy rate of 99.78% compared to DenseNet121. These findings underscore the efficacy of leveraging existing architectures for identifying deep fake images, thereby contributing to the ongoing efforts to combat misinformation and safeguard digital content integrity. © 2024 IEEE.

关键词： Deep learning

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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...

引用

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

Cancer Medical Image Analysis Across Modalities Using Attent...

引用

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

Pneumonia Disease Detection Using Hyperparameter Tuned Convo...

引用

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

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

引用

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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Design And Fabrication of Brake Fluid Monitoring System

Design And Fabrication of Brake Fluid Monitoring System

引用

2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Vimalesh, A. Nivendra, Y. Sudharshan, S. Thiyagarajan.g Ragavendhiran Ramesh, S. Rajalakshmi Engineering College Department of Mechatronics Engineering Chennai India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Engineering Chennai India Rajalakshmi Engineering College Department of Mechanical Engineering Chennai India

ISBN: (纸本)9798350378092

This paper presents the design and fabrication of Brake Fluid Monitoring System aimed at enhancing vehicle safety. The system monitors critical parameters such as brake fluid level, moisture content, and pressure drop in vehicles equipped with hydraulic braking systems. By continuously monitoring these parameters, the system helps prevent potential issues associated with low fluid levels, moisture contamination, and pressure drops, which can compromise braking performance and vehicle safety. Our prototype utilizes state-of-the-art sensors and Arduino-based control systems to provide real-time monitoring and alerts to drivers, ensuring optimal braking performance and mitigating the risk of accidents. The development of this Brake Fluid Monitoring System represents a significant step forward in automotive safety technology, contributing to the overall safety and reliability of vehicles on the road. © 2024 IEEE.

关键词： Drops

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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 ...

引用

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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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...

引用

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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Factorised Neural Relational Inference for Particle Decay Reconstruction 5

Factorised Neural Relational Inference for Particle Decay Re...

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5th International Seminar on artificial intelligence, Networking and Information Technology, AINIT 2024

作者： Lu, Junjian Liu, Siwei Liang, Shangsong School of Computer Science and Engineering Sun Yat-sen University China Mohamed Bin Zayed University of Artificial Intelligence Department of Machine Learning United Arab Emirates

ISBN: (纸本)9798350385557

Investigating the interactions among particles in high-energy physics is essential for various tasks, such as reconstructing particle decays. The neural relational inference encoder model, capable of capturing inter-entity dependencies and system dynamics, has been recently introduced for this purpose. However, it faces limitations in accurately capturing diverse relations between complex particles. To address this, we propose the factorised neural relational inference (fNRI) encoder model, which factorises the inferred interaction graph into a multigraph, with each layer encoding a distinct relation. Additionally, we present a simplified version of our model that offers improved efficiency without compromising performance. Moreover, we explore three different inference strategies to leverage the learned relation representations effectively. We demonstrate on a simulated particle physics decay dataset that fNRI improves the reconstruction of the decay tree structure. © 2024 IEEE.

关键词： Graph neural networks

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