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共 12901 条记录，以下是71-80 订阅

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Predictive Modeling of Earthquake Impact on Population Centers Using Random Forest Algorithm 6

Predictive Modeling of Earthquake Impact on Population Cente...

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6th International Conference on Computational Intelligence and Networks, CINE 2024

作者： Vennam, Tharun Reddy Nallamalli, Saketh Dyavathi, Adarsh Regatti, Rohith Kumar, S. Satheesh Regulwar, Ganesh.B. Vardhaman College of Engineering Shamshabad Dept. Information Technology Hyderabad India

ISBN: (纸本)9798331516796

Natural disasters like earthquakes have a profound and a wide-ranging effect on infrastructure, economic systems, and communities across the world. Accurately estimating an earthquake's 'level of impact' is essential for risk reduction, effective disaster management, and long-term planning in seismically active areas. The analysis of the effects of earthquakes has been facilitated by recent developments in information technology, including increased storage capacity and novel techniques for organizing and gathering data. This study is about using a comprehensive dataset, made up of earthquake characteristics, geographical parameters, and the degree of impact of the earthquake, to construct a model that can effectively predict the level of impact of earthquakes using machine learning regression methods. Pre-processing of the raw data, feature selection, model validation, and optimization are necessary to guarantee the robustness and generalizability of the final product. The models produced will be assessed for accuracy and performance. A variety of machine learning algorithms including linear and polynomial regression, support vector regression, decision trees, and ensemble methods like random forest regression, will be used. Urban planners, emergency response teams, and legislators can all benefit by using these models to develop and carry out leaner, more effective disaster response plans. © 2024 IEEE.

关键词： Risk management

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Agritech Harmony: Real-time ESP32 Automation and Cloud Computing for Socially-Informed Crop Suggestions 5

Agritech Harmony: Real-time ESP32 Automation and Cloud Compu...

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5th IEEE International Conference for Emerging technology, INCET 2024

作者： Bawadkar, Vishal Verma, Aman Pawar, Aditya Phatale, Utkarsh Sadaphule, Prashant Dept. of Computer Engineering AISSMS Institute of Information Technology Pune India

ISBN: (纸本)9798350361155

Accurately predicting long-term crop yield trends remains a crucial challenge in optimizing agricultural practices and ensuring food security. This paper proposes a novel framework that merges real-time data acquired from Internet of Things (IoT) sensors with a Bi-directional LSTM model with an attention mechanism to achieve this objective. This architecture extracts insights from historical and future environmental conditions and prioritizes critical factors influencing yield through attention weighting. The framework incorporates an ESP32-managed sensor network measuring humidity, temperature, moisture, rainfall, and NPK levels, providing a continuous stream of real-time field data. Evaluation of real-world datasets demonstrates the framework's superiority over traditional methods, yielding significantly higher prediction accuracy and exposing key drivers of yield fluctuations through attention analysis. An accessible API facilitates seamless integration with farm management systems, empowering farmers with data-driven decision-making capabilities. This paper highlights the transformative potential of integrating deep learning, real-time IoT data, and APIs for revolutionizing agriculture, paving the way for a more sustainable and resilient agricultural futur. © 2024 IEEE.

关键词： Crops

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Real Time Detection of Learner Fatigue in E-Learning Environments through the Combination of Eye and Mouth Features 11

Real Time Detection of Learner Fatigue in E-Learning Environ...

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11th International and the 17th National Conference on E-Learning and E-Teaching, ICeLeT 2024

作者： Abdolhosseinzadeh, Alireza Montazer, Gholam Ali Tarbiat Modares University Dept. Information Technology Engineering Tehran Iran

ISBN: (纸本)9798350387704

one of the features of intelligent e-learning environments is the presence of a personalized mechanism to assist the effective learning for each learner. Exploiting personalized tutoring models, learners can experience successful and satisfying learning outcomes due to their learning objectives. In this way, the learner (student) feels that there is always an aware and insightful tutor (teacher) alongside, providing private guidance on the learning path. While personalizing the learning environment is highly desirable, the continuous monitoring of the virtual teacher may lead to fatigue (and even disillusionment) for the learner, potentially reducing the efficiency of the e-learning environment. Considering the above points, in this article an intelligent system has been designed to identify fatigue in learners by examining changes in their facial expressions. To achieve this, a combination of eye and mouth features of the learner is utilized, and with the help of a deep neural network, the level of learner fatigue is detected. The proposed system employs the Media‌Pipe algorithm for face and eye-mouth feature identification, and for training the neural network model, the Efficient‌Net neural network has been utilized. In this system, databases NITYMED and Drowsiness‌Detection have been utilized for training the network. Additionally, 80% of dataset has been used for training and the remaining 20% for testing. It is worth mentioning that given the balanced distribution of samples for each class, accuracy is employed as the evaluation metric, considering the objective of minimizing false positives. The experimental results show the accuracy and precision of 99.73% and 99.72% for the eye model, respectively, and the same measures for the mouth model are 99.85% and 99.95%, respectively. The standard deviation value for the eye model is 0.4997, and for the mouth model, it is 0.4998, indicating that most of the data points are close to the mean. Additionally, the mean va

关键词： Intelligent systems

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Enhancing Academic Collaboration with a Student Project-Sharing Platform 6

Enhancing Academic Collaboration with a Student Project-Shar...

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6th International Conference on Computational Intelligence and Networks, CINE 2024

作者： Sallakunta, Ramya Salahuddin, Mohd Sara, Sharanya Munugala, Vaishnavi Vennelakanti, Aasritha Varshini Devulapalli, Indusree Vardhaman College of Engineering Dept. of Information Technology Telangana Hyderabad India

ISBN: (纸本)9798331516796

Technological advancements have resulted in a deluge of innovative activities, each requiring rigorous investigation and appraisal. The report recognizes the need for a centralized forum to address these limits by allowing innovators to propose their ideas to domain experts and peers. An online platform where students may submit projects, have them reviewed by mentors, and examine other students' projects. An assessment of existing systems identified a variety of restrictions for students. Such a platform aims to inspire creative thinking and the execution of unique ideas, hence helping students by boosting cooperation, networking, and project-based learning. This platform serves as a dynamic social hub, allowing students to communicate, present work, and exchange ideas in a lively digital setting. The major project is at the center of a student's academic journey, a necessary component of their graduation process that challenges them to apply their gathered knowledge to create something unique. Students are currently unaware of their peers accomplishment due to a lack of engagement. This website seeks to address this issue by allowing students to discover their skills and find inspiration for their projects. The platform is intended to streamline the tasks of project mentors and teams. Furthermore, developing a dedicated web portal for our college community enhances access to completed projects, making it a great resource for students of all levels. Centralizing project information aims to encourage knowledge sharing and collaboration, ultimately improving the learning experience and developing a culture of student-driven innovation. © 2024 IEEE.

关键词： Students

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Legal lens: Exploring NLP for Document Analysis in Law 3

Legal lens: Exploring NLP for Document Analysis in Law

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Tekade, Pallavi Jadhav, Narendra Chawda, Sumit Bhosale, Vikram Akashe, Yash Rajarshi Shahu College of Engineering Dept. of Information Technology Pune India

ISBN: (纸本)9798350354348

This study explores the utilization of Natural Language Processing (NLP) methods for analyzing documents in the legal field. More specifically, the study concentrates on summarizing legal documents and comparing their content. By employing advanced NLP techniques, such as transformer-based models like BERT, GPT-2, the study aims to extract significant information from legal texts and evaluate the similarity between various documents. Through utilizing pre-training on specialized corpora and fine-tuning for legal contexts, the study seeks to enhance the performance of NLP models for analyzing legal documents. The results of this research effort show potential for increasing efficiency and accuracy while processing legal papers, thereby aiding in better decision-making and understanding of legal matters. © 2024 IEEE.

关键词： Natural language processing systems

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Evolve Aggregation Management of Data for Heterogeneity 3

Evolve Aggregation Management of Data for Heterogeneity

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Kolekar, Vikas Patil, Medini Shete, Shreyash Vishwakarma Institute of Information Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350354348

Cloud computing is the most emerging technology. It has the capability to provide various services like infrastructure, security, data management, databases, and network over the internet. Several cloud service providers in the market provide computing resources. Cloud brokering in cloud computing is a crucial part as it acts as a middleware between cloud users and cloud services providers. In this paper we aim to provide an overview on adaptive data management middleware for cloud brokers. To manage the heterogeneous data and other factors, policy-based task scheduling can be integrated with the cloud broker, which supports dynamic data placement and takes decisions and actions at the runtime considering the SLA (service level agreements) and QoS (quality of service) requirements without any manual involvement. By aggregation of data, the next task of scheduling and assigning priority will be taken carefully. At the end service requests should be fulfilled by utilizing minimum resources. © 2024 IEEE.

关键词： Cloud platforms

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Designing Real-Time Hand Gesture Recognition Systems for Hearing Impaired: Combining CNNs with Human-Centric HCI Approaches 5

Designing Real-Time Hand Gesture Recognition Systems for Hea...

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5th IEEE Global Conference for Advancement in technology, GCAT 2024

作者： Vidhya Shree, N.S. Rajani, G.N. Shilpa, H.M. Ravishankar, Gowri Siddaganga Institute of Technology Dept. Information Science and Engineering Tumakuru India

ISBN: (纸本)9798350376685

The human-computer interaction (HCI) encompasses a variety of interactions, including gestural ones. In HCI, gesture recognition refers to nonverbal movements that can be utilized for communication. information can be delivered by using a system that recognizes human gestures. This is a major area of HCI that deals with user interfaces and device interfaces. The goal of gesture recognition is to capture certain gestures that are then picked up by a camera or other *** are numerous applications where hand gestures can be utilized as a means of communication. People with a variety of disabilities, such as those suffering from strokes, hearing loss, or speech problems, can use it to communicate and take care of their fundamental needs. The goal of hand gesture recognition is to create more natural and intuitive ways for people to engage with computers while also opening up new avenues for *** gestures have been the subject of numerous studies in the past. Various methods for carrying out hand gesture experiments were suggested by some studies. There are several methods available for extracting characteristics from photos in image processing, and artificial intelligence offers a variety of classifiers for classifying different kinds of data. An efficient method is needed to extract images and identify different small motions and movements from 2D and 3D hand gestures. The goal of this work is to create a hand gesture recognition system based on artificial intelligence that can recognize a hand gesture automatically using the test dataset. Within the class of deep learning neural networks is the convolution neural network. © 2024 IEEE.

关键词： Gesture recognition

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Interactive Coding Platform with Code Summarizer 2

Interactive Coding Platform with Code Summarizer

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Jaiswal, Priyanka Dhomne, Anushree Rawarkar, Sakshi Bulkunde, Sanket Lanjewar, Sarvesh Mundel, Vedant Yeshwantrao Chavan College of Engineering Dept. of Information Technology Nagpur India

ISBN: (纸本)9798331528713

This study presents a Code Summarizer cum Explanation Tool which helps as the student specially those who are beginner in the world of coding to understand other's solution for a given problem statement. Our approach involves the utilization of this state-of- the-art language model, widely acknowledged for its prowess in handling various text- related tasks such as code generation and summarization. In this study we have use an open-sourced state-of-the art large language model which is used for many downstream text related task such as code generation, code summarisation, etc. and consist of 7 Billion parameters (weights & biases) developed by technology Innovation Institute (TIIUAE). We fine-tuned the said model on our custom dataset for code explanation task. We have fine-tuned the model for 1 epoch which took approximate 3 hours on 590 steps. As a result we have obtained a brief summary, when given code as a prompt input. By bridging the gap between theoretical understanding and practical application, our Code Summarizer/Explanation Tool can become an useful tool for beginners on their coding journey. © 2024 IEEE.

关键词： Codes (symbols)

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Real-Time Railway Track Fault Detection and Environmental Monitoring System Using Arduino and GSM 4

Real-Time Railway Track Fault Detection and Environmental Mo...

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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Devulapalli, Indusree Vennelakanti, Aasritha Varshini Sallakunta, Ramya Vardhaman College of Engineering Dept. of Information Technology Telangana Hyderabad India

ISBN: (纸本)9798350387490

In India, railways are a common and affordable mode of transportation, ranking as the fourth largest railway community worldwide. They serve as the primary means of passenger and long-distance travel, accounting for approximately 80% of transportation in the country. However Indian railways have issues like gate crossing, fire accidents and unmonitored track conditions leading to derailment. Most accidents happen at railway track crossings and due to unidentified cracks in the tracks resulting in loss of life and economic impact. These cracks occur due to seasonal changes which cause track contraction and expansion. To overcome these issues we need robust technology to detect cracks in railway tracks. This project uses sensors and a dynamic approach, with a GSM (Global System for Mobile Communications) module to send alert messages. Also, a coolant system is integrated to improve the durability of the tracks by mitigating the effects of extreme temperatures. An Arduino Microcontroller is used to control and coordinate the device's activities. © 2024 IEEE.

关键词： Cracks

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DETReg Incorporating Semi-Supervised Learning for Object Detection in the Advanced Driver-Assistance Systems 1

DETReg Incorporating Semi-Supervised Learning for Object Det...

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1st International Conference on Robotics, engineering, Science, and technology, RESTCON 2024

作者： Nakano, Keita Matsushima, Kousuke National Institute of Technology Kurume College Advanced Engineering School Fukuoka Japan National Institute of Technology Kurume College Dept. of Control and Information Systems Engineering Fukuoka Japan

ISBN: (纸本)9798350348842

In Advanced Driver-Assistance Systems (ADAS) and automatic driving, it is important to accurately recognize objects around the vehicle. DETReg is one of the unsupervised pre-Training methods using Transformer, which is self-supervised by combining localization and categorization. DETReg performs self-supervised learning on unlabeled images. Then, it extracted a wide range of features from rich aspects of the data and gained the flexibility to adapt to many variations. Fine tuning then used the labeled dataset of the target task to fine tune the model to fit the specific dataset. This allowed DETReg to achieve higher accuracy in the object detection task. However, it is difficult to learn DETReg efficiently because of its slow learning time. In this paper, we propose a new pre-Training method for object detection, called Semi-DETReg, that utilizes a few supervised labels during self-supervised learning. We incorporate semi-supervised learning into DETReg by using a portion of the supervised training data in the pre-Training to improve efficiency. We demonstrate the effectiveness of our method by conducting experiments and comparing our method to a similarly trained DETReg. © 2024 IEEE.

关键词： Object detection

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