In today’s information-rich digital age, the volume of web content available to users has become overwhelming, making it challenging for individuals to find relevant and personalized content. Recommendation systems h...
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ISBN:
(纸本)9789819747108
In today’s information-rich digital age, the volume of web content available to users has become overwhelming, making it challenging for individuals to find relevant and personalized content. Recommendation systems have emerged as a transformative solution, catering to individual users by offering customized suggestions aligned with their unique interests. This research explores a novel approach that utilizes topic modeling techniques on web content titles for recommendation purposes. Topic modeling, a subfield of natural language processing (NLP) is utilized to automatically identify latent topics within a large corpus of text. The proposed work begins by collecting a diverse dataset of web content titles across the domains. It employs a combination of other state-of-the-art topic modeling algorithms like BERTopic modeling and statistical model to uncover the underlying topics in the titles. By leveraging this approach on web content titles, aim to extract meaningful themes and categorize the content efficiently. Then preprocess the data to remove irrelevant information, ensuring that the subsequent topic modeling process yields accurate and meaningful results. This approach not only expedites the recommendation process but also conserves computational resource. Once the topics are identified, associate them with appropriate metadata, such as user preferences, and content types. This step forms the foundation of our content-based recommendation approach. Then maps the user’s interests to the most relevant topics, enabling us to present a tailored list of web content titles. By recommending content based on underlying themes rather than just keywords, this approach surpasses traditional methods, ensuring more accurate and diverse suggestions for users. The results demonstrate the system’s ability to provide highly personalized recommendations, enhancing user satisfaction and engagement. By delving into the semantic structure of content rather than relying solely on
The proceedings contain 32 papers. The special focus in this conference is on Futuristic Advancements in Materials, Manufacturing, and Thermal Sciences,. The topics include: Design and Development of Welding Fixture o...
ISBN:
(纸本)9789819756209
The proceedings contain 32 papers. The special focus in this conference is on Futuristic Advancements in Materials, Manufacturing, and Thermal Sciences,. The topics include: Design and Development of Welding Fixture on Release Welding Machine;advancements in Design of Semi-Active Suspension Control system During Pre- and Post-Covid-19—A Review of Research;Design of Flexural Bearings in Experimental Analysis and PID Control of a Voice Coil Actuator;design and Optimization of Railway Power Axle system for Structural Safety;design and Analysis of Rotary Slag Skimmer Machine;developing and Implementing Vision-Based Production Lines for Detecting and Removing Defective Components;designing a Piezo-Actuated Four-Bar Motion Amplification Mechanism for Enhanced Compliance;geometrical Aspects of Snow Sinkage for Robotic application;finite Element Analysis of a Cable-Driven Robotic Hand Exoskeleton;envisioning the Future of Robotics Sensors: Innovations and Prospects;Home Automation system with Multiple Control Access Using IoT and RTC Module;dynamic Analysis of Underactuated Soft Robotic Gripper for Space applications;EMG-Controlled Upper Arm Exoskeleton Powered by Pneumatic Artificial Muscle;self-Operated Optimized Design of an Automated Seed-Sowing Robot;Fault Diagnosis in a Centrifugal Pump Using MODWPT and SVMA;methodology for Wall Thickness Validation with Stress Analysis of ClO2 Generator Piping system;A Comparative Study of Live Load for Bridge Deck with Custom Fighter Aircraft Loading and IRC Standard Loading for Design of Elevated Taxiway;modeling and Simulation of Self-stabilizing Platform for Industrial application;enhancing the Thermal Performance of a Solar Air Heater by Incorporating Artificial Roughness to the Absorber Plate;topology Optimization of Wind Turbine Structural Components.
Clever system that can look at pictures of fruits and figure out what kind of fruit each picture shows. AI algorithms like deep learning, which is like giving the Machine learning model a crash course in fruit recogni...
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The "VTU Question Classification system Based on BERT and Bloom's Taxonomy"integrates advanced Natural Language Processing (NLP) techniques to automate the classification of questions related to educatio...
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With the rapid advancement of internet technology, e-commerce has gradually become the preferred shopping method for modern consumers. E-commerce not only provides convenient shopping channels, but also brings consume...
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Coupled transmission lines are essential components of modern electronic systems, which facilitate a reliable and an efficient transmission of high-frequency signals from source to destination and are w...
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With the number of low Earth orbit satellites and the expansion of application demands, satellite trajectory prediction is crucial for efficiently operating satellite systems. Traditional trajectory prediction methods...
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Introducing an advanced artificial intelligence (AI) system leveraging computer vision technology to automate rig operations in the oil and gas industry. Our approach uniquely addresses the challenges of deploying com...
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ISBN:
(纸本)9781959025641
Introducing an advanced artificial intelligence (AI) system leveraging computer vision technology to automate rig operations in the oil and gas industry. Our approach uniquely addresses the challenges of deploying computer vision technology in harsh rig environments, ensuring consistent operation under variable conditions. The system reduces manual labor, enhances safety, and improves operational efficiency with a 95% accuracy rate. Tested over two years across diverse geographical locations, it demonstrates robust performance. We detail the process of data preprocessing and constructing a training dataset with images of diverse pipes and environmental conditions to enhance model robustness. We evaluate computer vision models such as YOLOs (You Only Look Once) [Redmon 2016] and RetinaNet [Ross 2017], employing heuristics and statistical smoothing to stabilize results. Additionally, we review camera selection, calibration methods, and settings adaptation for various rig conditions and high-speed operations. The paper discusses the server setup and inference methodology needed at the edge to achieve high-speed inference, high accuracy, and continuous model improvement. One specific application of our approach is automating iron roughneck positioning during the tripping phase—a sector previously limited to trip-in operations due to challenges related to trip-out operations such as mud-covered pipes, thin joints, and variable lighting conditions. The proposed methodology achieves approximately 95% accuracy, with 95% of connections automated without human input, underscoring its efficiency and reducing the workload on drillers. This automation enhances safety, accelerates operations, and maintains high precision across diverse environmental conditions, including adverse weather and day-night cycles. Trained and tested across various rig environments, the system overcomes traditional challenges such as mud interference with camera operations and the detection of mud-cover
Large Language Models (LLMs) and low-code development platforms (LCDPs) have shown potential to fundamentally change the way how software products and applications are developed and to both combat the shortage of skil...
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ISBN:
(纸本)9783031783852;9783031783869
Large Language Models (LLMs) and low-code development platforms (LCDPs) have shown potential to fundamentally change the way how software products and applications are developed and to both combat the shortage of skilled software developers by increasing developer efficiency and lower the entry barrier for citizen developers to develop and maintain software on their own. This paper investigates whether these technologies can be combined to be even more powerful. To do this, a solution concept and prototype implementation were developed. Using the prototype, one can describe desired changes in an Oracle application Express (APEX) low-code application to the GPT4 Turbo LLM in a chat. The LLM then performs the changes for the user by calling specific edit functions that the system offers and generates replies to the user. This solution enables citizen developers to edit a fully functional Web application through natural language without help from a professional software developer. We also present a qualitative user study that we performed with ten Oracle APEX customers. It showed that participants have a rather positive opinion of both the fundamental concept and the prototype, liking aspects like its time-savings and ease of use. The study also uncovered some problems like a lack of a common vocabulary or technical understanding between the LLM and some users. However, participants already suggested ways to remedy such problems like integrating an element inspector into the prototype. Overall, the study highlights the feasibility and potential of the system and outlines multiple directions for its further development.
Electroencephalography (EEG) signals provide us with direct insights into brain function and play a vital role in fields such as neuroscience and medicine. The integration with wearable EEG devices has enabled long-te...
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ISBN:
(纸本)9789819607945;9789819607952
Electroencephalography (EEG) signals provide us with direct insights into brain function and play a vital role in fields such as neuroscience and medicine. The integration with wearable EEG devices has enabled long-term monitoring of specific EEG indicators, offering new methods for human-computer interaction. This not only holds significant potential for improving the quality of human life but also promotes the advancement of scientific research and has the potential to transform the landscape of medical and health services. This paper proposes and develops a wearable EEG signal acquisition system for brain-computer interfaces, with the following specific details: The system uses a main controller STM32F411CEU6, an EEG chip KS1092, a power supply chip IP5306, and protective circuits to build the peripheral circuits. The device dimensions are 7.5 x 5.0 x 3.5 mm, and the total weight is 132.2 g. The embedded software can collect EEG signals in real-time at a high sampling rate of 1 kHz and achieve information transmission through Bluetooth. In practical applications such as physiological response testing and focused task assessment, the system can accurately determine the state of the test subjects, demonstrating its potential for application in areas such as educational assessment, medical health, human-computer interaction, and cognitive research.
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