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

共 5571 条记录，以下是111-120 订阅

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Graph-based Named Entity Information Retrieval from News Articles using Neo4j 18

Graph-based Named Entity Information Retrieval from News Art...

引用

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

作者： Chaudhary, Shikha Vyas, Hirenkumar Arora, Naveen D'Mello, Sejal Vidyavardhini's College of Engineering and Technology Dept. of Artificial Intelligence and Data Science Mumbai India

ISBN: (纸本)9789380544519

The internet, social media, and other cutting-edge technologies are all contributing to the exponential growth of digital information. This makes interpreting and applying this enormous amount of data both possible and challenging. Our research proposes a smart system that can convert unprocessed text into a knowledge graph in order to address this. This system makes use of the graph database Neo4j. Although there are currently available tools for linking entities and models to extract relationships, such as Spacy, NLTK, and Flair, their combined use is inefficient. Our suggested method seeks to efficiently blend entity linkage and relation extraction, allowing us to convert raw data into a knowledge graph. There are many real-world uses for this strategy, particularly in data analysis and decision-making. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Cypher Queries Graph database Information Retrieval Named Entity Recognition Neo4j News Articles Relation extraction

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Disaster Management Ecosystem Using Crowdsourced data, Deep Learning and NLP 5

Disaster Management Ecosystem Using Crowdsourced Data, Deep ...

引用

5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Savale, Vaishali Patil, Siddhi Raut, Atharva Phadke, Mrunmayee Sundkar, Rahul Dongre, Rajkumar Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science Engineering Maharashtra Pune411037 India

ISBN: (纸本)9798331530013

In recent years, the frequency and intensity of natural disasters have increased, necessitating more efficient and responsive disaster management systems. This paper presents a novel disaster management solution leveraging crowdsourced data to enhance disaster response and coordination. The system bridges the gap between citizens and emergency management officials by composing a mobile application for citizens to send real-time disaster alerts and a web application for administrators to monitor and manage these alerts. A customized DBSCAN algorithm, adapted to use haversine distance, is deployed to detect clusters of disaster alerts in real time, indicating potential disaster events. Upon cluster detection, administrators can delineate affected areas on a map, triggering notifications to users within these regions and enabling them to send help requests with images and messages. These help requests are processed using the BLIP model for image captioning and the ChatGPT API for categorization, assigning relevant tags to each request. The system provides administrators with real-time data analysis features, including map filtering and citizen help request category visualization, enabling more informed decision-making. Utilizing a cloud-based architecture ensures scalability, reliability, and efficient processing of large volumes of data. This research demonstrates the system's capability to provide timely alerts and comprehensive situational awareness, ultimately contributing to more efficient disaster management. © 2024 IEEE.

关键词： Information management

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Modelling a Multi-Perceptron Q Network Layer for Analyzing Patient Condition in Healthcare Sector

Modelling a Multi-Perceptron Q Network Layer for Analyzing P...

引用

2025 International Conference on Multi-Agent Systems for Collaborative intelligence, ICMSCI 2025

作者： Karthi, M. Manirajan, S.P. Prince, A. Santhosh, R. Department of Artificial Intelligence and Data Science India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331509828

Chronic diseases like chronic respiratory diseases, diabetes, heart disease (HD) and cancer are the important causes of mortality globally. The diagnosis of Heart-related diseases with a variety of symptoms or characteristics is incredibly complex. Internet of Things (loT) embedded solution has improved with the increase in smart wearable devices. However, people who undergo heart attacks unexpectedly have poor survival rates. As a result, an Internet of Things based Multi-Perceptron Q-Network (MQN)-based monitoring program is suggested for cardiac patients to assist in identifying heart disease and administering medication appropriately. The technology based on MQN is used to securely encrypt this sensor data before sending it to the cloud. The encrypted and decrypted data is eventually done and MQN classifier is used to complete the classification process. The two categories of data that make up the classified outcomes are i) regular and ii) irregular. It indicates the individual's cardiac status, and if the results are peculiar, the doctor is notified via text so that the patient can receive treatment. When compared to current algorithms, the MQN for HD diagnosis exhibits better enhancement in the estimated investigational results. Furthermore, when contrasted against the current MQN, the introduced Multi-layered Perceptron (MLP) system facilitates safe data transmission yields the maximum security level of 96%, and is accomplished in the shortest amount of time for both decryption and encryption. © 2025 IEEE.

关键词： Lung cancer

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Detection of Aircraft from SAR Images Using Deep Neural Networks

Detection of Aircraft from SAR Images Using Deep Neural Netw...

引用

2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Vasavi, S. Prathipati, Krishna Vamsi Bheesetti, Satvika Kattepogu, Varshitha Yelagapudi Ramakrishna Siddhartha Engineering College Department of Artificial Intelligence and Data Science Andhra Pradesh India

ISBN: (纸本)9798350379525

SAR (synthetic aperture radar) images provide valuable data for detection in the field of remote sensing. Detecting aircrafts from SAR images plays a prominent role in military security and surveillance purpose in today's life. Existing methods for object detection such as Convolutional Neural Networks (CNN), Resina-Net had difficulties in detecting small aircrafts. Deep neural networks provide a prominent and efficient solution for this problem. Also, detection of objects from satellite images is a bit difficult when compared to optical images, as the SAR images generally produces only the outline of the object based on the roughness of the surface the radar waves hitting, this decreases the consideration of the features like colour etc. To detect the aircrafts from SAR images an enhanced model of YOLOv8 is proposed in this paper. This model is validated on SADD dataset and produced an accuracy of 95.3% and false alarm rate of 5.9% specifying that the model works well in different weather conditions and contains faster predictions. © 2024 IEEE.

关键词： Synthetic aperture radar

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Deep Learning Enabled Graph database for Complex Queries 4

Deep Learning Enabled Graph Database for Complex Queries

引用

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

作者： Jayachandiran, U. Sujaritha, P. Sahana, A. Surendhar, J. Sri Sairam Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India

ISBN: (纸本)9798331508845

The ability to comprehend and react to intricate logical questions is essential for the development of artificial intelligence. With the aid of an advanced query engine, graph store, and embedded store, this paper presents a novel method for answering complex logical queries by fusing deep learning techniques with intricate query processing and graph traversal patterns. The fundamental aspect of our methodology involves the smooth incorporation of graph traversal algorithms and deep learning models into a resilient query engine. In order to model the relationships and dependencies within the data and capture both the local and the global structures from the knowledge graph, we utilize a neural graph network architecture that combines Graph Neural Networks and Recurrent Neural Network. In order to extract semantic features and break down the query into its fundamental parts, the system encrypts the input first using a transformer-based model. A high-performance graph store knowledge graph is mapped onto these elements. In order to create logical paths required for query resolution, a graph traversal algorithm that has been refined through reinforcement learning investigates pertinent nodes and edges. Utilizing attention mechanisms to prioritize important nodes and edges during traversal, our approach includes a novel query processing pattern that uses attention mechanisms to prioritize important nodes and edges during traversal. © 2024 IEEE.

关键词： Knowledge graph

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Retroflex: Uncovering Visual Equivalences through Reverse Image Recon 18

Retroflex: Uncovering Visual Equivalences through Reverse Im...

引用

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

作者： Yadav, Sneha M. Joshi, Karik Shailendra Mandavia, Divyah Tarun Puri, Parth Mahendra Vidyavardhini's College of Engineering and Technology Dept. Artificial Intelligence and Data Science Mumbai India

ISBN: (纸本)9789380544519

This research introduces a comprehensive method for Reverse Image Search (RIS) this can be also called as CBIR that is Content Based Image Retrieval designed for large datasets with more than 50,000 photos in 25 different classes. The approach uses sophisticated deep learning feature extraction methods, such as Auto-encoders and CNNs, to extract complex visual representations for reliable content characterization. We incorporate advanced indexing techniques to address the issues of scalability and retrieval efficiency. The resultant framework exhibits higher accuracy and speed and provides an optimal and sophisticated solution for picture retrieval problems. This effort is a major step forward in improving the capabilities of RIS systems, offering a flexible tool for real-world applications that need for thorough and effective image retrieval across a range of classes. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Auto-encoders CNN Content Based Image Retrieval (CBIR) datasets Framework Reverse Image Search (RIS) Sophisticated Deep Learning Feature Extraction

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Harmonizing Emotions: A Fusion of Facial Emotion Recognition and Music Recommendation System 14

Harmonizing Emotions: A Fusion of Facial Emotion Recognition...

引用

14th International Conference on Cloud Computing, data science and engineering, Confluence 2024

作者： Adru, Sri Likhita Johnson, Sandra R.M.K. Engineering College Department of Artificial Intelligence and Data Science Kavaraipettai India

ISBN: (纸本)9798350344837

This research tackles the rising significance of Facial Emotion Recognition (FER) for personalized user experiences. Employing transfer learning, the proposed system pinpoints user emotions from. facial expressions with impressive accuracy. To further personalize the experience, a Light Gradient Boosting Machine (LGBMClassifier) recommends music fitting the recognized emotions. This seamless integration. fosters an emotionally responsive music recommendation system, deepening the connection between technology and users. The paper dissects the methodologies, challenges, and results of this combined approach, showcasing its potential to boost user engagement and satisfaction in diverse applications. © 2024 IEEE.

关键词： Emotion Recognition

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Detecting Suspicious Military Tents from Optical Images using Deep Learning Model 3

Detecting Suspicious Military Tents from Optical Images usin...

引用

3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Vasavi, S. Praneeth Polu, Venkata Sai Manikya Venkata Saketh Nandula, Bala Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Artificial Intelligence and Data Science Vijayawada India

ISBN: (纸本)9798350364569

Locating suspicious military tents or encampments is crucial for national security. Recently, deep learning methods have become increasingly popular for detecting military objects at country borders. Traditional methods, which rely on manual checks and morphological algorithms, are often inaccurate and slow at detecting such unauthorized activities. To overcome this issue, a solution that automatically detects suspicious tents from satellite images using deep learning techniques is crucial. An enhanced YOLOv8x-obb model is proposed and trained with a satellite image dataset that is well-prepared with various preprocessing and augmentation techniques. This model was validated and produced an accuracy of 91.9% and false alarm rate of 6.08%, specifying that the model works well in different terrains and situations, and it has the advantage of better border surveillance. © 2024 IEEE.

关键词： Military satellites

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IoT-Based Bin Level Monitoring for Efficient Waste Management 5

IoT-Based Bin Level Monitoring for Efficient Waste Managemen...

引用

5th International Conference on data intelligence and Cognitive Informatics, ICDICI 2024

作者： Anuradha, Ch. Pothuraju, Manoj Manam, Niveditha Chigurupati, Chandana Velagapudi Ramakrishna Siddhartha Engineering College Department of Artificial Intelligence and Data Science Andhra Pradesh India

ISBN: (纸本)9798350389609

Internet of Things is a real-time resource visibility, which enables instant tracking and monitoring of assets and resources. It is easy for a person to monitor a limited or small number of bins, but when it comes to huge areas and municipalities, it becomes a resilient job. In this paper, the responsibility is taken to automate this task to reduce human labor and cost depletion using a distinct tool of the amusing sector IoT. A user-friendly interface has been developed. This interface dynamically displays the shortest possible routes to bins based on their current fullness levels using Dijkstra's algorithm. This helps to improve efficiency, reduce costs, minimize unnecessary trips for garbage collection, help prevent overflows, and keep surroundings clean. It's a smart and effective way to manage waste. © 2024 IEEE.

关键词： Ultrasonic sensors

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Revolutionizing Education for Visually Impaired Students Using Augmented Reality 5

Revolutionizing Education for Visually Impaired Students Usi...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Janapati, Malathi Naveen, Budamcharla Venkata Sanuth Reddy, Mettu Sai Dinesh, Balabomma Sai Velagapudi Ramakrishna Siddhartha Engineering College Dept. of Artificial Intelligence and Data Science Vijayawada India

ISBN: (纸本)9798331530013

This research explores the use of Augmented Reality (AR) to enhance learning experiences for visually impaired students. By utilizing an Android application, the system captures images of classroom boards and processes them using the EasyOCR library for text extraction. The extracted text is then rendered in 3D using Unity, allowing visually impaired students to interact with the content more effectively. The AR experience enables students to zoom in on the text, adjust font sizes, and explore the information at their own pace. This innovative approach integrates the power of AR to overcome the challenges faced by visually impaired students in traditional classroom settings, promoting inclusivity and enhancing their learning experience. © 2024 IEEE.

关键词： Students

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