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检索条件"机构=Artificial Intelligence and Data science Engineering"

共 5443 条记录，以下是21-30 订阅

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Conversational AI Meets Documents Revolutionizing PDF Interaction with GenAI 2

Conversational AI Meets Documents Revolutionizing PDF Intera...

引用

2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Geetha, M.P. Thirukumaran, G. Pradakshana, C. Sudharsana, B. Ashwin, T. Sri Eshwar College of Engineering Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798331534950

Navigating large PDF documents manually or using basic keyword-based tools can be tedious, especially in fields like academic research, legal analysis, and healthcare, where accurate and timely information retrieval is crucial. To address this, a system integrates Generative AI (GenAI) with a Retrieval-Augmented Generation (RAG) framework, allowing users to interact with PDF content through natural language queries, making the search process faster and more intuitive. For example, querying a legal document for payment terms provides clear answers, helping users navigate complex documents. Traditional keyword-based methods lack contextual understanding, a limitation overcome by the system, which transforms document content into vector embeddings using models like BERT or GPT. These embeddings capture semantic details for precise retrieval from a vector database. Researchers can quickly extract relevant sections of academic papers without manually scanning the entire document. The system processes content into vector embeddings, stores them in a vector database, and generates coherent responses using a large language model when a user submits a query. This approach simplifies tasks like analyzing healthcare reports. Additionally, the system features session management, ensuring continuity across multiple queries. This allows users, such as legal professionals, to maintain context when reviewing a contract, enabling coherent responses to various queries about clauses or penalties. Future enhancements could include supporting data formats like charts, tables, or images, further expanding the system's versatility. By addressing inefficiencies in traditional navigation and introducing advanced features like session continuity, this system revolutionizes document workflows in research, legal analysis, and healthcare. It sets a new standard for conversational AI tools, transforming how users interact with PDF documents. © 2024 IEEE.

关键词： Embeddings

来源：评论

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Multi-Purpose Wearable Biofeedback Accessories 4

Multi-Purpose Wearable Biofeedback Accessories

引用

4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Aishwarya, K.P. Anisha Zulekha, A. Sathish, V. Sitarth, A.L. Balaji, Swetha Sri Sairam Engineering College Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331508845

The rapid advancement of wearable technology has opened new avenues for monitoring and improving individual health and well-being. In this context, we propose a multi-purpose biofeedback wearable device in the form of a necklace and bracelet, that integrates a variety of sensors with advanced algorithms to monitor and analyse physiological and mental parameters continuously. Designed for comfort and discretion, this device enables seamless, non-intrusive monitoring during daily activities, making it an ideal tool for promoting overall *** device offers a comprehensive range of functionalities, including stress and anxiety monitoring through heart rate variability and skin conductance measurements, mood regulation, and providing personalised recommendations for songs and yoga, posture correction via motion sensors and posture detection algorithms, and physical performance enhancement with real-time feedback from motion tracking sensors. By delivering real-time insights and personalised interventions, this wearable device is poised to play a pivotal role in managing well-being, regulating mood, enhancing physical performance, and addressing posture-related issues. Incorporating cutting-edge technology with personalised feedback mechanisms, this biofeedback wearable device presents a promising solution for empowering individuals in their pursuit of optimal health and well-being. © 2024 IEEE.

关键词： Biofeedback

来源：评论

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Retinal Disease Classification: Binary and Multiclass Perspectives 4

Retinal Disease Classification: Binary and Multiclass Perspe...

引用

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

作者： Lanjewar, Rutuja Nitin Pacharaney, Utkarsha Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha India

ISBN: (纸本)9798331523923

Retinal diseases, among which diabetic retinopathy, age-related macular degeneration, and glaucoma account for the greatest number of blindness cases worldwide, affect more than 338 million people. It is essential that early detection leads to timely intervention to preserve vision, but it is limited due to the ever-increasing demand for ophthalmic care and a shortage of specialists. Machine learning-driven systems, especially Convolutional Neural Networks (CNNs), have shown great promise in overcoming these challenges, achieving high accuracy in medical imaging tasks. This paper explores a unified framework for both multiclass and binary classification of retinal diseases, addressing issues like class imbalance and inter-class similarity while leveraging CNNs for robust feature extraction. Techniques like Grad-CAM also increase model interpretability, allowing clinicians to understand and trust AI-driven diagnostic processes. © 2025 IEEE.

关键词： Convolutional neural networks

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Smart Barcode Scanning using X-ray Technology with Computer Vision and Machine Learning 4

Smart Barcode Scanning using X-ray Technology with Computer ...

引用

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

作者： Vijayaraj, J. Hari Yogith, S. Rithikravivarma, V. Siddharth, S. Easwari Engineering College Dept. of Artificial Intelligence and Data Science Chennai India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523923

Barcode scanning errors in retail environments contribute to inventory discrepancies, financial losses, and inefficiencies in stock management. Traditional optical barcode scanning methods are prone to failures due to occlusion, poor lighting conditions, and barcode damage. While recent advancements such as computer vision, deep learning-based error detection, and RFID integration have improved scanning accuracy, these approaches still face challenges related to high computational requirements, environmental dependencies, and implementation costs. This paper proposes a novel method utilizing X-ray imaging technology and advanced machine learning algorithms to detect improperly scanned or missing barcodes in real time. The system captures detailed X-ray images of items as they enter retail stores or warehouses and applies deep learning models to analyze barcode visibility. Real-time alerts are generated when scanning anomalies are detected, enabling prompt corrective action. Experimental evaluations demonstrate that the proposed approach significantly improves barcode detection accuracy, enhances inventory management, and reduces operational costs. The findings highlight the potential for widespread deployment in retail and logistics environments, offering a robust solution to barcode scanning challenges. © 2025 IEEE.

关键词： Sales

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Dynamic Algorithmic Configuration for Enhanced Malware Detection 3

Dynamic Algorithmic Configuration for Enhanced Malware Detec...

引用

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

作者： Ramya, S. Periyasamy, M. Ramprasanth, P.S. Srinivasa Aravindh, S. Mohan kumar, B. Department of Artificial Intelligence And Data Science M Kumarasamy College of Engineering Karur India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

As edge-based decisions continue to grow in demand, organizations are increasingly seeking user data, raising privacy concerns. Federated Learning (FL) addresses this problem but is typically dependent on computationally intensive Neural Network (NN) models. This work introduces a novel lightweight approach by integrating Support Vector Machines (SVMs) into FL frameworks. Optimized methodology SVM with the compatibility of SGD is used where the meta-trained controller dynamically adapts the learning rate to the improvement of models. The proposed method shows equal performance to those based on the NN-based FL methods but drastically reduces the cost of computation for being applicable to resource-constrained environments. © 2025 IEEE.

关键词： Federated learning

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Analysis of GAN for Melanoma Skin CancerClassification with Dermatologist Recommendation

Analysis of GAN for Melanoma Skin CancerClassification with ...

引用

2024 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational intelligence, RAEEUCCI 2024

作者： Saranya, N. Jowin Alfred, C. Rr, Rishikesh Gilbert Idayan, I. KPR Institute of Engineering and Technology Department of Artificial Intelligence and Data Science India

ISBN: (数字)9798350354539

ISBN: (纸本)9798350354539

Melanoma is a type of skin cancer that poses a significant health threat globally, necessitating effective early detection methods. Melanoma arises from the pigment-producing cells(melanocytes) and is known for its higher metastatic potential. Designing a reliable Computer-Aided Diagnosis (CAD) system for accurate melanoma detection is challenging due to the appearance of the nevus. This study investigates the application of a GAN for classification of melanoma. The GAN architecture is employed to learn and generate synthetic melanoma images, enhancing the diversity and richness of the training dataset. We mainly focus on evaluating performance of GAN-enhanced model through comprehensive metrics like specificity, accuracy, the receiver operating characteristic curve (ROC) and area under the curve (AUC). This study sheds light on utility of synthetic data in clinical image analysis and provides insights into the strengths and limitations of utilizing GANs for melanoma skin cancer classification. We also added a dermatologist recommendation feature to our model. It uses computer programs to understand what youneed and then matches you with the best doctor available nearby. This system helps you get the care you need faster and easier, making sure you see a doctor who's a good fit for you and your health needs. This system matches patients with suitable doctors. This matching involves considering factors like doctor specialty and location. © 2024 IEEE.

关键词： Dermatology

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An Intelligent Framework of Microcalcifications Breast Cancer Detection using Heuristic Approach aided Residual Attention Network 2

An Intelligent Framework of Microcalcifications Breast Cance...

引用

2nd IEEE International Conference on Integrated intelligence and Communication Systems, ICIICS 2024

作者： Anitha, J. Malathi, S. Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai India

ISBN: (纸本)9798331504960

Background: Breast cancer detection has advanced in recent years, and one such recent technique is detecting the presence of Microcalcifications in the breast tissue from Mammogram images. Also, it is crucial to examine the favorable outcomes to examine whether the microcalcification samples to benign or tumor. Methods: A deep learning-based breast cancer microcalcifications detection model is developed in this work. The initial step involves in gathering images from reputable sources to ensure a diverse and comprehensive dataset. Following collection, these images are given to the detection phase, where the detection process is done using Adaptive Residual Attention Network (ARAN). During this phase, the parameters of the ARAN are optimally tuned using the Pine Cone Optimization Algorithm (PCOA) to ensure better performance. Result: Overall 92% of accuracy attained in the developed ***: Experimental analysis is conducted on this deep learning-based model to validate its effectiveness in microcalcifications detection. © 2024 IEEE.

关键词： Lung cancer

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Effective Farming for Zero Wastage and Enhancement of Farmer's Income 4

Effective Farming for Zero Wastage and Enhancement of Farmer...

引用

4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Gomathy, G. Esther, C. Manaswini, S. Swedharani, R.V. Hyden, J. Jerry Anand, A. Ajay Sri Sairam Engineering College Department of Artificial Intelligence and Data Science India

ISBN: (纸本)9798331508845

In the field of agriculture, reducing waste while increasing revenue is still a difficult task. However, this initiative tackles this problem head-on by integrating Internet of Things (IoT) Sensors with Machine Learning (ML). By harnessing the power of IoT sensors to gather real-time data on soil moisture, humidity, temperature, and weather conditions, coupled with ML algorithms analyzing past agricultural crop harvested data, market demand, pricing trends, and previous wastage records, the project paves the way for predictive farming. This proactive approach enables farmers to make informed decisions regarding crop selection, planning times, and investment strategies, ultimately leading to minimised wastage and enhanced profitability. Furthermore, the development of a user-friendly mobile application serves as a central hub for disseminating valuable insights to farmers. Through intuitive interfaces and comprehensive data visualization, the application provides farmers with detailed information on investment projections, recommended crops for cultivation, and optimal planting schedules. Moreover, by incorporating Natural Language Processing (NLP) capabilities, the application ensures accessibility for farmers of diverse linguistic backgrounds, facilitating seamless interaction and decision-making. By empowering farmers with actionable information and accessible tools, the project aims to revolutionize agricultural practices, driving towards a future of sustainable farming and increased profitability. © 2024 IEEE.

关键词： Soil moisture

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Compound Screening and Prioritization in Drug Discovery Using Machine Learning

Compound Screening and Prioritization in Drug Discovery Usin...

引用

2024 IEEE International Students' Conference on Electrical, Electronics and Computer science, SCEECS 2024

作者： Mariyam, A. Asiya Varshni, R.R. Samyuktha, S.P. Kumar, V. Anush Narmadha, B. Panimalar Engineering College Department of Artificial Intelligence & Data Science Chennai India

ISBN: (纸本)9798350348460

Drug discovery is a technique to uncover and nurture a new medication for several medical conditions and diseases. It entails a range of scientific and research endeavors designed to identify substances that effectively manipulate biological processes to lessen, control, or cure illnesses. Delivering safe and efficient medications that can enhance patients' quality of lives is the ultimate goal of drug discovery. The first and foremost step in the development of drug is compound inspection. Compound screening which is also referred to as High Throughput Screening (HTS) is a component in the drug discovery which identifies and assesses the potential compounds that can be incorporated to contribute to the target compound. We propose a machine learning model named Quantitative Structure-Property Relationship (QSPR) model that can predict the amount of toxicity, solubility, metabolic stability, bioavailability, and the chemical reactivity level when a variety of compounds are combined with each other. This model can determine data from various sources, like chemical databases and already formed compounds, to predict which compounds are most likely to exhibit the desired biological activity and chemical reactiveness. They can also learn from existent data, the active and inactive compounds and also recognize their patterns and characteristics. They can also predict the effect of one compound binding with a specific target. This machine learning model simplifies the drug discovery process by incorporating advanced technological techniques that predicts the potential compound binding, toxicity and reactive level and also the hit identification and approval. © 2024 IEEE.

关键词： chemical reactivity compound inspection Drug discovery hit identification and approval metabolic stability and bioavailability potential compound binding Quantitative Structure-Property Relationship (QSPR) Model solubility toxicity

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Extended Reality E-Commerce: The Evolution of E-Commerce through XR Based Technologies

Extended Reality E-Commerce: The Evolution of E-Commerce thr...

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

作者： Jawale, Supriya Bondade, Aparna Sontakke, Manish Sohagpure, Aditya Muneshwar, Akash Wankhede, Nisarga Priyadarshini College of Engineering Artificial Intelligence and Data Science Department Nagpur India

ISBN: (纸本)9798350373783

As the usage of apps and e-commerce platforms continues to rise, especially with the integration of virtual tryon features, the importance of being able to compare products and assess their compatibility in 3D becomes increasingly evident. This capability not only allows stores to keep their products available round the clock but also enhances the overall shopping experience for consumers. Moreover, it sets the stage for the integration of artificial intelligence into online retail environments. The very definition of products in this digital landscape is deeply influenced by the advancements in mobile networks and the adoption of Extended Reality (XR) applications. XR, serving as an umbrella term that encompasses a range of immersive technologies such as Augmented Reality (AR), Mixed Reality (XR), and Virtual Reality (VR), is revolutionizing how consumers interact with products and brands online. This article seeks to delve into the intricate factors that shape customer experience and satisfaction when engaging with XR and AI-enabled applications for online shopping. By incorporating dimensions like relative trust, social value, and novelty - parameters commonly explored in similar XR studies - we aim to measure various aspects including intention, perceived utilitarian value, immersion, and hedonic value. Through our research, we have discovered that each of these dimensions significantly influences consumers' willingness to utilize basic XR and AI applications during their shopping journey. When evaluating the collective impact, encompassing both direct and indirect effects, we have identified a sequence of influence, with adequate trust, innovativeness, immersion, social value, perceived health value, and utilitarian value playing crucial roles. Our preliminary findings underscore the significance of social value, assurance of satisfaction, usefulness, and immersion in shaping consumer behavior within the realm of XR and AI applications for online shopping. By u

关键词： Virtual environments

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