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Enhancing Q&A Systems with Multilingual Text Conversion and Speech Integration: Harnessing the Power of LangChain and Large Language Models 8

Enhancing Q&A Systems with Multilingual Text Conversion and ...

引用

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

作者： Priya, K. Kamath, Akshatha Chandan, K.M. Praveen, C. Omkar, S.N. Aaditya, S.J. Ramaiah Institute of Technology Dept of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331505462

Searching through URLs and PDFs can be tedious and time-consuming because of the unstructured nature of these documents and the challenge of finding accurate and, relevant information. LangChain addressed these issues using advanced natural language processing algorithms to extract pertinent data from URLs and PDFs. With its user-friendly search interface, customizable filters, and efficient indexing and retrieval mechanisms, LangChain significantly enhances the search experience. Users can annotate important sections, store queries, and create bookmarks, making information retrieval from URLs and PDFs more efficient and improving the overall productivity. Traditional text analysis systems often struggle with interactivity, flexibility, and data integration, making it difficult for users to gain meaningful insights from diverse data sources such as websites and PDFs. Our research combines state-of-the-art technologies, including Dash, LangChain, Google Generative AI, and FAISS, to provide a comprehensive solution for extracting, analysing, and interacting with textual data from various sources. This includes handling both PDFs and the data uploaded via URLs. Our research demonstrates significant improvements in the efficiency and accuracy of information retrieval, paving the way for more complex applications such as text summarization and question-answering. Our system is also capable of converting text into speech and translating it into 10 different languages. © 2024 IEEE.

关键词： Semantics

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Voice Analysis for Detecting Artificial Speech and Predicting Gender

Voice Analysis for Detecting Artificial Speech and Predictin...

引用

2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Bharadwaj, Rakhi Dugad, Raj Gile, Akshata Patil, Gyaneshwari Hatyalikar, Shrinivas Vishwakarma Institute of Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350373783

This research paper presents a comprehensive voice analysis system capable of detecting artificial speech and accurately predicting the gender and age of the speaker. Leveraging machine learning, acoustic features, and spectral analysis, the system distinguishes between natural human speech and artificial synthesis with high accuracy. It also excels in predicting the gender and age of the speaker, offering versatile applications in marketing, human-computer interaction, and demographic analysis. The study not only introduces an innovative solution to these challenges but also emphasizes its practical applicability across various domains, underscoring the relevance of voice analysis in today's technology landscape. © 2024 IEEE.

关键词： Prediction models

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eDermaCare: Enhancing Skin Disease Classification with ResNet50 18

eDermaCare: Enhancing Skin Disease Classification with ResNe...

引用

18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Karambele, Aakanksha Tamhane, Mrunal Jayanty, Pranati Patil, Rutuja Shirsat, Kavita Vidyalankar Institute of Technology Dept. of Computer Engineering Mumbai India

ISBN: (纸本)9789380544519

In today's world, skin diseases are caused by various factors and affect a person's mental and physical wellbeing. Particularly in private areas, there is considerable reluctance to disclose skin diseases, especially among female patients, which further intensifies the challenge of early detection and treatment. To tackle this issue, the study proposes the implementation of eDermaCare, a novel approach leveraging the advanced Residual Network-50 (ResNet-50) convolutional neural network architecture. Several convolutional neural network (CNN) architectures were explored, but unsatisfactory results were obtained. ResNet-50 has the ability to capture complex features from skin images. The goal is to improve skin disease classification using ResNet-50's deep learning capabilities. This model uses the rest of the course to explain training and retention issues. Block balances allow training deep networks without suffering from degradation in performance. As a result, the model achieves a classification accuracy of 76.07%. The proposed system wm help improve early detection and treatment of skin diseases, address societal challenges, and support effective healthcare. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： classification CNN Deep Learning Resnet50 Skin disease

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Advancements, Methods, and Obstacles in Emotion Recognition with Multimodal Approaches 3

Advancements, Methods, and Obstacles in Emotion Recognition ...

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Arunkumar, M. Athiban, R. Selva Jothi, M. Pavithra, S. Dept of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu India

ISBN: (纸本)9798331527549

Emotion recognition has gained an avid curiosity in the research area due to its broad range of applications in human-computer interaction particularly in real time emotion recognition, sentiment analysis, and affective computing. Deep learning techniques are advancing exponentially, automating feature engineering and transcending traditional machine learning techniques. Appeal of the above models can be highly beneficial but also raise certain technical and ethical concerns that are not often discussed enough in the existing studies. While highlighting the recent advancements, the methods involved and obstacles in emotion recognition, this review also proposes future directions in developing a standard unified framework for multimodal approaches. Categorizing and analysing the recent studies depends upon the model types employed, data modalities used and the type of fusion techniques used, this review was able to gain insights into the effect of recent multimodal approaches addressing technical and ethical concerns. Utilization of fusion techniques in multimodal models is still not reliable but they better convey the connection between different modalities and have a huge potential for development in the future. This review provides insights into the state of art multimodal methods using modalities like facial expressions, body gestures, vocal cues, text, and physiological signals for researchers to understand the trends and future directions in this field. © 2025 IEEE.

关键词： Contrastive Learning

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Real- Time Prediction of Medicinal Herb Plants using Deep Learning 4

Real- Time Prediction of Medicinal Herb Plants using Deep Le...

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

作者： Sumathy, R. Shalom, R Jehitha Ragul, G. Antony, A Nickson Kit - KalaignarKarunanidhi Institute of Technology Dept of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331523923

Medicinal plants have been important in India's traditional healthcare for centuries, but identifying them can be challenging due to the many different species. This project aims to create a real-time system that recognizes medicinal herb plants using Convolutional Neural Networks (CNN), a type of deep learning that works well for image recognition. The system will be trained on a dataset of herb plant images, using techniques like resizing and data augmentation to improve performance. By doing this, the model will learn to spot even small differences between plant species. We will evaluate the system's effectiveness through accuracy, precision, and recall metrics to ensure reliable identification. By making it easier to identify medicinal plants, this project could help preserve traditional Indian herbal knowledge and encourage the use of these herbs in modern healthcare. In the future, the system will also incorporate real-time prediction capabilities, allowing users to identify herb plants instantly in their natural environments. Additionally, the system could be expanded to include more plant species, contributing to the protection and conservation of medicinal plant biodiversity. © 2025 IEEE.

关键词： Medicinal chemistry

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Advancing Pediatric Appendicitis Diagnosis: Harnessing CNN and Hybrid Models with Ultrasound Imaging

Advancing Pediatric Appendicitis Diagnosis: Harnessing CNN a...

引用

2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Kulkarni, Supritha Sharma, Sanchit Manavi, J. Vachana, U. Bhargavi, M.S. Bangalore Institute of Technology Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331508685

Pediatric appendicitis, an acute inflammatory disease, arises from the obstruction of the appendix, often due to inflammation or a fecalith. This common abdominal emergency in children presents diagnostic challenges due to its varied clinical manifestations, which can lead to misdiagnosis or higher perforation rates stemming from atypical symptoms. This research investigates the use of deep learning, more especially Convolutional Neural Networks (CNNs), by utilizing ultrasonic imaging data, which is less expensive, non-invasive, and quicker than the other approaches now in use. Several variants are evaluated including a basic CNN and more advanced hybrid models such as CNN-Support Vector Regression, CNN-Transformer, and Hierarchical Deep CNN, to identify the most optimal model to significantly enhance diagnostic accuracy, for young patients facing this condition. The study's main goal is to develop a highly accurate and dependable diagnostic tool for evaluating the severity of pediatric appendicitis by differentiating between simple and complex cases and identifying whether surgery is required to inform treatment plans. Various challenges that need to be tackled are managing significant dataset imbalances, enhancing interpretability and transparency for real-world clinical settings, and ensuring the robustness and adaptability of all these models across various healthcare environments. © 2025 IEEE.

关键词： Pediatrics

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Serial vs parallel execution of Principal Component Analysis using Singular Value Decomposition 2

Serial vs parallel execution of Principal Component Analysis...

引用

2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Mallegowda, M. Tanupriya, R. Vishnupriya, C. Kanavalli, Anita Ramaiah Institute of Technology Dept of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350306415

Principal component analysis (PCA) is a crucial technique in data science and machine learning for reducing dimensionality and identifying crucial characteristics. But when dealing with big datasets, it uses Singular Value Decomposition (SVD), which is computationally costly. To address this issue, this study compares the effectiveness of parallel and serial PCA implementations. Serial execution uses a step-by-step data processing method, which is simple but falls short when dealing with large datasets. The task is divided across many processing units in parallel execution, which is preferred in high-performance computing environments, and thus results in significant speed increases for large datasets. While parallel execution shines with huge datasets and time-sensitive activities, serial execution improves with smaller datasets and simple scenarios. Depending on variables like dataset size and available computer resources, one of these techniques may be preferred over the other. In conclusion, this paper examines the advantages of utilizing parallel computing for PCA via SVD, offering a quick way to speed up calculations for high-dimensional datasets. It emphasizes how flexible it is to choose serial or parallel execution depending on the particular dataset's properties and processing needs. © 2024 IEEE.

关键词： OpenMP parallel computation Principal Component Analysis(PCA) serial computation Singular Value Decomposition(SVD)

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DDoS Attack Detection in Blockchain Networks 5

DDoS Attack Detection in Blockchain Networks

引用

5th International Conference on Data Intelligence and Cognitive informatics, ICDICI 2024

作者： Jadhav, Swati Bhosale, Vaibhavi Choudhari, Gauri Bura, Rishita Bhavik, Manasi Vishwakarma Institute of Technology Dept. Of Computer Engineering Pune India

ISBN: (纸本)9798350389609

DDoS attacks pose a significant threat to the security and stability of blockchain networks. Low generalization, high misreporting rates, and ineffective detection capabilities are just a few of the flaws in the detection methods currently in use. DDoS attacks threaten blockchain network security and stability. Current detection methods suffer from low generalization, high misreporting rates, and inefficacy. This project proposes a solution to detect and mitigate the DDOS attack happening on Blockchain. Scapy and Wireshark's real-time data analysis helps identify attack patterns and adapt defenses to evolving threats. This proactive approach ensures the functionality and integrity of blockchain networks. Through thorough experimentation, the solution provides a robust defense against DDoS attacks, enhancing the security of the blockchain ecosystem. © 2024 IEEE.

关键词： Blockchain

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Enhancing Doodling Experience Using Generative Adversarial Networks 2

Enhancing Doodling Experience Using Generative Adversarial N...

引用

2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Ashtagi, Rashmi Rathi, Devanshu Handoo, Ripul Purohit, Rishi Madhavaswala, Abbas Nogamawala, Mohammad Vishwakarma Institute of Technology Dept. Of Computer Engineering Pune India

ISBN: (纸本)9798350388916

Interacting with AI systems solely through textual prompts often encounters limitations due to the challenges of expressing complex visual ideas in text. This paper introduces an advanced AI system that interprets and visualizes users' sketches by employing state-of-the-art Generative Adversarial Networks (GANs), such as pix2pix, SketchGAN, DCGAN, and ESRGAN. This system not only significantly enhances real-time interactions but also shows exceptional proficiency in image editing tasks, thus facilitating more intuitive communication between humans and AI. By enabling rapid and accurate visualizations, this technology substantially streamlines and improves design workflows in the architecture and fashion industries. The advancements fostered by this system contribute significantly towards achieving greater capabilities akin to Artificial General Intelligence, enhancing creative processes and production efficiencies. © 2024 IEEE.

关键词： Generative adversarial networks

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A Hybrid approach for Deepfake Detection using CNN-RNN

A Hybrid approach for Deepfake Detection using CNN-RNN

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2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Antad, Sonali Arthamwar, Vaishnavi Vijay Deshmukh, Rohan Kishor Chame, Akshay Uttam Chhangani, Harshita Pawan Vishwakarma Institute of Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350373783

Reliable deepfake detection of falsified videos generated by deep learning is still a pressing challenge with its growing popularity. Deep adversarial neural networks, which educate on film and target faces to ahead facial resources and facial expressions to targets, underneath DF can without difficulty idiot facial recognition structures the usage of revealed, 3-D photos mask, or video recordings from the valid consumer's face to sensors. We make significant contributions, including building adaptive datasets, comparing primitive and deep models for training, and tunable CNN-RNN tools. The overall evaluation model shows that the results are better than the findings of the most advanced methods. It was acknowledged that Deep faking today greatly impacts our environment;It is done by placing faces on original images/videos using deep neural networks. Together with the sharing of other misinformation via digital social networks, deepfake has formed digital fakery, which has become a real problem of negative social impact and as such there is a great need for successful measures to be taken to examine Deep Fakes [1]. The approach in this research generalizes by capturing physical cues in body shape and size. Our method combines CNN for spatial analysis, capturing facial features and skin texture, and LSTM and RNN for temporal analysis, monitoring expression progress. This method provides stable performance on complex deepfake videos. The modular design allows for future expansion, such as the integration of audio analysis. © 2024 IEEE.

关键词： Convolutional neural networks

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