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Enhanced Multimodal Conversational AI Using Speech and image Integration 5th

Enhanced Multimodal Conversational AI Using Speech and Image...

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5th International conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Dixit, Deepali Tyagi, Mihul Kandoi, Raunak Singh, Raghuraj School of Engineering and Technology Sharda University Greater Noida201310 India

ISBN: (纸本)9789819773701

The development of conversational artificial intelligence (AI) is examined in this research paper, with a focus on how speech and image recognition technologies can be combined to transform and interact with systems. The emphasis is on cutting-edge developments in multimodal conversational AI, particularly the smooth fusion of sophisticated image and speech processing methods to improve user experiences. The study addresses the applications, difficulties and potential applications of multimodal conversational AI by conducting a thorough review of the existing literature and case studies. By emphasizing the significance of enhancing voice recognition algorithms and utilizing advanced image processing techniques, the study promotes contextual understanding and customization. We have developed an innovative multimodal conversational AI system that integrates speech, text and image processing capabilities for seamless human–computer interactions. This study presents an improved multimodal conversational AI system that integrates several techniques, such as Google Text-to-Speech (gtts), speech recognition, sound playback and Convolutional Neural Networks (CNN). Our model architecture combines RNNs for sequential data analysis, CNNs for image processing and attention mechanisms for context extraction. Trained on diverse datasets, our model is optimized for accuracy and robustness. Evaluation against existing models demonstrates superior performance in terms of accuracy, speed and resource efficiency. Our study highlights how improved multimodal conversational AI can advance the field of human–computer interaction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Accurate identification and Advanced mapping for comprehensive Skin Cancer Staging

Accurate identification and Advanced mapping for comprehensi...

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Control Instrumentation System conference (CISCON) - Guiding Tomorrow - Emerging Trends in Control, Instrumentation, and systems Engineering

作者： Praneethal, Devulapalli Pandala, Madhavi Latha Sai, Gopu Nithin Velagapudi Ramakrishna Siddhartha Engn Coll Dept IT Vijayawada AP India

ISBN: (纸本)9798350375480;9798350375497

The most prevalent and dangerous kind of cancer in humans is skin cancer. Melanoma is a kind of skin cancer that is fatal. One of the deadliest cancers in the world, melanoma will spread to other body parts if it is not detected at an early stage. It is easily curable if detected in its early stages. The goal's highlights the issues of a global increase in skin cancer cases, excessive medical expenses, and an exponential rise in the effect of death from delayed diagnosis due to missed opportunities for early intervention. The biopsy procedure is the official way for melanoma diagnosis and detection. This approach can be extremely painful and time-consuming. In the modern years, deep learning techniques have shown promise in automating the detection process, offering the potential for more accurate and timely diagnoses. This system is a deep learning-based predictive algorithm that uses dermoscopic image analysis to dynamically forecast melanoma skin cancer. The main objectives of this research are to find skin cancer more accurately and quickly in its early stages. This work provides a computer-aided detection technique for melanoma early detection. Our research work suggests using InceptionV3 and Resnet50 algorithms, along with image processing techniques, to create an effective diagnosis system in this study. An image of the impacted skin is captured and subjected to a few pre-processing methods to produce an improved and smoothed image. Next, the picture is subjected to morphological, thresholding, and grey scaling techniques during the data augmentation phase. It categorizes the provided image as either Stage 1 or Stage 2 melanoma. An impressive 84.5% accuracy is attained. Clinical diagnosis of many disorders is increasingly dependent on harmless clinical computer vision or medical image processing. These methods offer an automatic evaluation of images tool for a quick and precise assessment. Overall, the suggested technique is a major improvement in the identif

关键词： Skin Cancer Melanoma image processing InceptionV3 ResNet50 Healthcare technology

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IEEE International conference on Signal processing and Advance Research in Computing, SPARC 2024

IEEE International Conference on Signal Processing and Advan...

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2024 IEEE International conference on Signal processing and Advance Research in Computing, SPARC 2024

ISBN: (纸本)9798350385199

The proceedings contain 118 papers. The topics discussed include: comparative analysis of neural network-based routing algorithms for wireless sensor networks;leveraging ShuffleNet and LLaVA-Phi for state-of-the-art image deblurring and description for mobile devices;spotting deep fakes: exploring detection techniques for image recognition;a transfer learning approach for enhancing road surface classification in intelligent driving systems;advanced U-Net++ architecture for precise brain tumor segmentation in MRI images: a robust solution for medical image analysis;a modeling and control of load frequency for interconnected thermal/hydro/nuclear power system using pi and fuzzy controller;systematic exploration of GAN-generated image recognition techniques;controlling mouse cursor through eye movement;improvement of power quality in distribution network at low voltage side by using DVR;facial emotion recognition using image filtering techniques;and enhancing financial trading strategies: a comprehensive sentiment analysis pipeline.

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Lung Cancer Detection using CT Scans: image processing through Deep Learning - A Review 8

Lung Cancer Detection using CT Scans: Image Processing throu...

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8th International conference on Communication and Electronics systems, ICCES 2023

作者： Asha, V. Bhavanishankar, K. Rns Institute of Technology Dept of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350396638

Lung cancer being one of the catastrophic diseases is haunting mankind from past seven decades. Unfortunately, early detection of lung cancer is unlikely, hence leading to highest mortality rates. However, various imaging modalities including Computed Tomography (CT) helps in detecting the lung cancer possible at the earliest. processing such huge data of CT scans is highly time demanding and Computed Aided Diagnosis system (CAD)does a great job from image acquisition till the detection/classification of lung nodules through series of processing stages. This research study covers all the processing stages and major contributions in those stages. This study also summarizes various methods used in basic image processing through deep learning algorithms. A tabulation of various datasets and metrics descriptions is also discussed. © 2023 IEEE.

关键词： Diseases

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Calculating Leaf Area Index Using Neural Network and WorldView 3 Multispectral imagery 59

Calculating Leaf Area Index Using Neural Network and WorldVi...

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59th International Scientific conference on Information, Communication and Energy systems and Technologies (ICEST)

作者： Polimenov, Ventsislav ivanova, Krassimira Tsvetkova, Mihaela Anastasova, Elena Dimitrova, Katya Bulgarian Acad Sci Inst Math & Informat Sofia Bulgaria Bulgarian Acad Sci Risk Space Transfer Technol Transfer Off RST TTO Sofia Bulgaria

ISBN: (纸本)9798350386202;9798350386196

Leaf Area Index (LAI) holds significant importance as a specific characteristic of Leaf Areas in the field of smart agriculture. This study explores the estimation of LAI using a multi-spectral image from WorldView 3 satellite. The image combines 8 VNIR bands and has a spatial resolution of 1.24m. To overcome the limited amount of available data, the image was split into smaller subsets called paxels, resulting in 500 paxels for training and testing. For enhancing machine learning models. performance, the standardisation of a dataset is made, after that, a Multilayer Perceptron with a specific architecture aimed to predict LAI from the multiple bands is trained. The achieved results showed promising performance in LAI prediction. Overall, the study demonstrates the potential of using satellite imagery and machine learning algorithms to improve our understanding of crop health and productivity.

关键词： remote sensing machine learning image processing smart agriculture

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Optimization of deep learning algorithms in image recognition

Optimization of deep learning algorithms in image recognitio...

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2024 International conference on Computer Graphics, Artificial Intelligence, and Data processing, ICCAID 2024

作者： Liu, Yi Shaanxi University of Science and Technology Xi’an China

ISBN: (纸本)9781510689275

With the widespread application of deep learning technology in the field of image recognition, the main challenges it faces include insufficient recognition accuracy and high consumption of computing resources. This paper aims to improve the performance and efficiency of image recognition by analyzing and optimizing deep learning algorithms. The article first outlines the basic theory of deep learning and the commonly used models in image recognition, and then discusses in detail the optimization strategies such as data enhancement, network architecture innovation, transfer learning and hyperparameter adjustment. Through case analysis, the actual effect of these strategies in improving the accuracy and processing speed of facial recognition systems is demonstrated. The results show that the comprehensive application of these optimization methods can significantly improve the recognition performance and environmental adaptability of the model. The research conclusions support the future development of deep learning in image recognition technology, and will continue to optimize algorithms and improve application efficiency. © 2025 SPIE.

关键词： data enhancement deep learning image recognition network architecture

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Fuzzy Morphological processing Algorithm of Blood image 25

Fuzzy Morphological Processing Algorithm of Blood Image

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25th IEEE International conference of Young Professionals in Electron Devices and Materials, EDM 2024

作者： Turgunov, Abrorjon M. Narzullaev, Temur N. Matyakubov, Marks Ya. Payzieva, Munira T. Kamolova, Sevara J. Rakhmonova, Nilufar N. Department of Algorithms and Mathematical Modeling Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan

ISBN: (纸本)9798350389234

This article, a morphological image processing algorithm is developed for early detection of viral hepatitis from microscopic blood images. It is mentioned that it can be used to accurately determine each boundary detail in the fuzzy morphological processing of the gray image of the blood image. Boundary detection leads to higher recognition results in neural network training. Compared to the work of a number of scholars, our method offers such performance, but it offers improved dynamics and flexibility in the formulation of linguistic boundary criteria, which can be a leading factor in the design of image processing systems with dynamic and flexible rules, such as Type 2 allows fuzzy rules, which offers an interesting alternative to currently common deep learning applications. The validated experimental results show the reliability of the modified method compared to the Chaira method. Mean, entropy and standard deviation were calculated. It turned out to be higher than the values of the Chaira method. © 2024 IEEE.

关键词： Neural networks

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A WEB APPLICATION FOR ULTRASONIC NONDESTRUCTivE TESTING ANALYSIS 51

A WEB APPLICATION FOR ULTRASONIC NONDESTRUCTIVE TESTING ANAL...

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51st Annual Review of Progress in Quantitative Nondestructive Evaluation (QNDE)

作者： Guarneri, Giovanni A. Pegorini, Vinicius Pipa, Daniel R. Pires, Gustavo P. Nunes, Maria Isabela S. Forlini, Felipe Passarin, Thiago A. R. Univ Tecnol Fed Parana Pato Branco Brazil Univ Tecnol Fed Parana Curitiba Parana Brazil Petrobras SA Rio De Janeiro Brazil

ISBN: (纸本)9780791888162

Identifying and mapping corrosion represents a significant challenge in maintaining oil production systems. Mechanical and chemical phenomena, such as abrasion and hydrogen attack, cause corrosion on the internal surface of pipes. Knowing the dimensions of corrosion enables technicians to make decisions about the structural stability of pipelines under operating conditions. Using ultrasound imaging is a non-destructive approach to assessing the state of internal surfaces. We present a web application for processing ultrasound signals and image reconstruction. However, it can be used in any non-destructive ultrasound inspection application. The software is developed mainly in Python and Typescript, using a different approach than conventional web applications. Instead of using a standard structure, with the front-end running in the browser and the back-end running the data processing on a remote server, our application runs entirely on the browser through Pyodide, a Python distribution for the browser and *** based on WebAssembly whilst the server side only hosts the web application files. The application is divided into modules for (i) graphical interface, (ii) reading inspection files, (iii) signal preprocessing, (iv) estimation of inspection parameters, (v) external surface detection (for inspection by immersion), and (vi) image reconstruction. The web application allows Python script execution and offers a user-friendly interface for running image reconstruction algorithms. This paper describes the development of critical features and indicates the chosen implementation of algorithms. The current state of development is presented, and the next steps toward the ultimate goal of corrosion mapping are defined.

关键词： Ultrasonic NDT image reconstruction Web application

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2024 10th International conference on Signal processing and Intelligent systems, ICSPIS 2024

2024 10th International Conference on Signal Processing and ...

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10th International conference on Signal processing and Intelligent systems, ICSPIS 2024

ISBN: (纸本)9798331532543

The proceedings contain 46 papers. The topics discussed include: continuous and non-contact extraction of respiratory signal and rate from thermal video;phonocardiogram signal denoising using dictionary learning;presenting a method for gluing error detection using image processing;alleviating undesired distance effect in spatio-temporal based video anomaly detection;real-time audio analysis for detection of apnea intervals in health-care system;classification of electric motors faults using Fourier-based features and self-organizing maps;validation of CMA simulation in analyzing the impact of electromagnetic waves on drone electronic boards;a hybrid approach for sentiment analysis of Arabic tweets;and line segmentation in Persian texts in double columns using hierarchical clustering algorithms.

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A Review on Satellite image Segmentation Using Metaheuristic Optimization Techniques 1

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5th International conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

作者： Bandikolla, Lakshmi Md Khairuzzaman, Abdul Kayom School of Electronics Engineering VIT-AP University Amaravati Andhra Pradesh Vijayawada522237 India

ISBN: (数字)9789819718412

ISBN: (纸本)9789819718405

image segmentation is essential in digital image processing applications. Multilevel thresholding is a popular technique for image segmentation. An image can be divide into multiple classes. In this paper, the image quality metrics such as peak signal-to-noise ratio (PSNR), mean squared error (MSE), mean structural similarity (SSIM), feature similarity (FSIM) are compared using metaheuristic algorithms. The CPU time of different metaheuristics is also compared to determine the time complexity of the techniques. The Otsu’s method, Kapur’s entropy, Tsallis entropy, and Masi entropy are used as objective functions. Some of the metaheuristic algorithms, adaptive Cuckoo search algorithm (ACS), chaotic coronavirus optimization algorithm (COVIDOA), dynamic quantum-inspired genetic algorithm (DQGA), a modified artificial bee colony algorithm (MABC), dynamic Harris hawks with mutation mechanism (DHHO/M), and particle swarm optimization algorithms (PSO) are compared. As per the comparison of the quality metrics, DHHO/M with Otsu was more efficient in average PSNR. CS with Tsallis provides best value in average MSE, ACS with Otsu gives best results in average SSIM, ACS with Tsallis is more efficient in average FSIM, and MABC with Kapur’s combination was more efficient in average CPU time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image segmentation

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