检索结果-内蒙古大学图书馆

SMART DRIVING LICENSING SYSTEM AND AUTHORIZATION OF AUTONOMOUS VEHICLE INTEGRATING WIRELESS SENSOR NETWORKS AND IoT

Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2024年第9期83卷 69-85页

作者： Santhakumar, D. Sudhan, M.B. Prasanna, R. Manikandan, S.P. Prathaban, Banu Priya Kanna, Rajesh R. Saravanan, G. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai Ramapuram India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering Sree Sastha Institute of Engineering and Technology Chennai India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering Sri Sairam Institute of Technology Tamil Nadu Chennai India

The advent of autonomous vehicles has revolutionized the automotive industry, offering promising advancements in safety, efficiency, and mobility. To integrate these autonomous vehicles into our society seamlessly, it is imperative to establish a robust licensing system that ensures the safe operation of automated vehicles on public roads. This article presents an effective smart Driving Licensing System (SDLS), a digital framework designed to regulate the existing licensing system and authorize licensing for autonomous vehicles. Through a comprehensive assessment process, the system assesses the drivers and the autonomous vehicle’s ability to navigate various road conditions, respond to dynamic environments, and prioritize safety for passengers and other road users. SDLS integrates high-level security through facial and fingerprint verification for identity verification to preserve licensing data. Hacking and unauthorized access to autonomous vehicles is prevented by robust cybersecurity protocols. The key benefits of SDLS include automated data collection, integration of wireless sensor networks (WSN) and Internet of Things (IoT), smart application processing, real-time license checks, cybersecurity measures, automated testing, and digital certification. The proposed system offers a comprehensive approach to safely integrating autonomous vehicles into our transportation ecosystem. The autonomous vehicles are integrated with antennas at rooftops or into bumpers, depending upon the vehicle design which can be connected with the SDLS for signal communication. Furthermore, the SDLS establishes a framework for continuous monitoring and updates, enabling ongoing assessments to keep pace with evolving technology and safety standards. © 2024 by Begell House, Inc.

关键词： Autonomous vehicles

来源：评论

学校读者我要写书评

暂无评论

Enabling Cursor Control Through Eye Movement Using Hidden Markov Model 2nd

Enabling Cursor Control Through Eye Movement Using Hidden Ma...

引用

2nd International Conference on Data science and Network engineering, ICDSNE 2024

作者： Tanusha, G. Havirbhavi, P. Ashwini, K. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9789819783359

First and foremost, we present a novel scheme for controlling the cursor based on eye movement. Other strategies rely on physical items like touchpads or a mouse and place strict requirements on people with motor handicaps. Our solution connects digital interaction with human visual attention through eye-tracking technology. We examine the nuanced gaze patterns that develop, allowing for cursor control while also developing a sense of interaction context. Gaze data is transformed by our method into the motion of a cursor. It is highly accurate and adjustable. Cursor motions are generated based on the gaze movement pattern, making the interface more dynamically accurate and more tailored to individual behaviors. The context-aware interface maximizes efficiency through its adaptability as it learns and adopts the behaviors. In addition, our research introduces a novel scheme through the use of software tools to offer an eye-controlled mouse system. This system accurately recognizes facial features and interprets eye movements through image processing techniques. The system enables intuitive cursor control by interpreting observable eye movements. Transforming eye motions into a cursor's actions, it enhances user accessibility and hands-free interaction;in other words, its performance is captured in the experimental analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Eye movements

来源：评论

学校读者我要写书评

暂无评论

Advanced Adaptive Traffic Management System for Urban Environments 3

Advanced Adaptive Traffic Management System for Urban Enviro...

引用

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

作者： Sai Srujan Reddy, P. Naveen Raj, B.U. Jose, Nikhil Tom Belwal, Meena Department of Computer Science and Engineering Amrita School of Computing Amrita Viswa Vidhyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Urban traffic congestion is still a major problem that causes delays, collisions, and wasteful use of resources. Conventional fixed-schedule systems, which frequently exacerbate problems at junctions, U-turns, and pedestrian crossings, are insufficient for dynamic traffic situations. The Smart Dynamic Traffic Management System described in this work makes use of servo motors, proximity sensors and ultrasonic sensors, Arduino microcontrollers, and AI-powered cameras. To improve safety and streamline traffic flow, the system prioritizes emergency vehicles, controls U-turns, dynamically modifies traffic signals based on real-time data, and monitors road conditions. Scalability and adaptability for a range of urban scenarios are ensured by the integration of IoT technology and intelligent intercommunication between crossings. A visualization tool based on Pygame was used to simulate and assess the suggested solution. The findings showed that traffic flow had significantly improved, with an average increase in the exit-to-entry ratio of 30.71% for the three roadways. More specifically, traffic on Road A decreased by 15.28%, on Road B decreased by 31.25% and on Road C decreased by 45.61%. The outcomes confirm just how well the system works for reducing traffic and giving priorities to critical traffic situations, such as emergency response and turning. This system uniquely integrates real-time adaptive traffic control algorithms with IoT and machine learning to dynamically prioritize traffic flow and emergency vehicles, overcoming the limitations of existing static systems. © 2025 IEEE.

关键词： Traffic congestion

来源：评论

学校读者我要写书评

暂无评论

Development of an Adaptive Smart Toy with Emotional Feedback 3

Development of an Adaptive Smart Toy with Emotional Feedback

引用

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

作者： Mohandas, Gayathri Sanay Santh, V. Padmakumar, Keshav Thangam, S. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Traditional toys often do not adapt to the emotional and physical needs of children, particularly those who may have limited verbal communication abilities. The project’s goal is to create a smart toy that is adaptive and can communicate with a child by using nonverbal signs to express his or her physical and emotional states. There are sensors like accelerometers to detect movement, a gesture sensor indicating light intensity, a buzzer sensor to produce sound, and an IR sensor to detect the child’s hand when the child is near the toy. All these components will help the user to get in-time feedback with a set of visual indicators (LEDs) and tactile feedback in the form of vibration motors. This smart toy seeks to improve the experience of play and developmental support for children by being more responsive and engaging with individual needs. © 2025 IEEE.

关键词： Intelligent robots

来源：评论

学校读者我要写书评

暂无评论

Evolving GAN-BERT Architecture for Efficient Text Categorization with Minimal Labeled Data 8th

Evolving GAN-BERT Architecture for Efficient Text Categoriza...

引用

8th IFIP TC 12 International Conference on computer, Communication and Signal Processing, ICCCSP 2024

作者： Bagavathi, C. Raakesh, M. Bhanu, Chanamala Vipasyana Reddy, Katheram Venkata Sai Ram Sathwik, Kancharla Rupesh Sai Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9783031736162

Categorical text generation is an upcoming field within natural language processing focusing on producing text tailored to specific categories or domains. The goal of categorical text production is to produce text that complies with the requirements and characteristics of the chosen category while also adhering to the grammatical and structural conventions of the language. Text classification works across multiple data categories, and the synthetic data generated usually adheres to a preset pattern and ignores the text’s nuanced emotional content. The proposed approach begins with using BERT to get the text’s deep contextual embeddings. Using these text embeddings, the underlying semantics of the target category can be better understood by adding a task-specific layer that places the sentences in the closest corresponding class. After that, the architecture and settings of the GAN are optimized through evolutionary computing techniques to guarantee that the generated text conforms to the constraints and characteristics specific to each category. © IFIP International Federation for Information Processing 2025.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Effective Complaint Detection in Financial Services through Complaint, Severity, Emotion and Sentiment Analysis 3

Effective Complaint Detection in Financial Services through ...

引用

3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Athira Adith, M. Gupta, Deepa Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

Sentiment analysis plays a crucial role in understanding customer feedback and opinions within the financial domain. This research addresses the problem of sentiment-aware complaint identification using the FINancial Complaint CORpus (FINCORP) dataset, which provides annotated complaints along with sentiment, emotion and severity labels. The dataset encompasses various sentiment classes including Negative, Neutral, and Positive, enabling a wide understanding of customer sentiment towards financial products and services. Leveraging natural language processing (NLP) techniques such as text classification and sentiment analysis, this study aims to develop models capable of accurately detecting and categorizing sentiment, emotion of customers and the severity level of financial complaints. Effectively analyzing these factors allows financial institutions to gain valuable insights into customer satisfaction levels, pinpointing areas for improvement and enhance both customer experience and service quality. This research contributes to the advancement of sentiment analysis methodologies in the financial domain, facilitating better decision-making and customer relationship management strategies. DistilBERT, Glove and RoBERTa has outperformed in classifying the sentiment, emotion and severity of complaints, achieving F1-scores of 0.71, 0.74, and 0.86, respectively using logistic regression and Bi-LSTM classifiers. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Federated Learning based Gender Classification in Heterogeneous and Distributed Data having Concept Drift 4

Federated Learning based Gender Classification in Heterogene...

引用

4th International Conference on Evolutionary computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Sharma, Vishwash Reddy Challa, Venkatahemant Kumar Pranavi, Pasupuleti Singh, Rimjhim Padam Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

Federated Learning is gaining significant traction in recent times, due to its ability to protect privacy while giving comparable results across various use cases. However, it often falls short in delivering good results in scenarios where the client devices at different places store huge and heterogeneous data. The studies explore the effectiveness of federated learning, when utilized for model training on heterogeneous data having concept drift nature and being placed on different client devices. The paper proposes a novel combination of EfficientNet-B0 model fine-tuned with adaptive and weighted normalization layers in a federated learning setup for gender classification in heterogeneous Fairface dataset having human images from different races. Several other Convolutional Neural Network (CNN) models namely, ResNet50, DenseNet121, GoogLeNet, AlexNet etc. have also been trained in different traditional and federated setups. Experiments revealed that EfficientNet-B0 outperformed the other models trained in all the scenarios with an outperforming accuracy of 88.18% in the traditional federated setup and 90.56% when fine-tuned using normalization methods and evaluated in a federated setup with four client devices. These findings highlight the potential of normalization techniques to improve the effectiveness of federated learning, especially when dealing with concept drift data. © 2025 The Authors. Published by Elsevier B.V.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

EquiMood: Elevating Emotional Wellness for College Students Through the Power of Machine Learning

EquiMood: Elevating Emotional Wellness for College Students ...

引用

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

作者： Baniya, Achal George, Angelina Jose, Alphonsa Supriya, M. Amrita School of Computing Department of Computer Science & Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331509828

Depression is a common mental disorder that affects millions of people in the world, and thus correct identification cannot be overemphasized. Focusing on two approaches - Streamlit and Ngrok vs Botpress - this work aims to explore the application of chatbots in offering emotional support to college students. To this end, the chatbots use rule-based systems and machine learning to try to provide support to kids who are struggling emotionally. Other measures such as throughput, latency and dependability are among the measures employed in the study to evaluate the effectiveness and efficiency of these two approaches. It also aims at the feasibility of deploying (or not) chatbots for offering mental support to the users by considering the user perspective, outcomes, and accessibility. Consequently, the present research contributes to elucidate the potential advantages and disadvantages of using chatbots for moderating emotional demands of college students and inform the future innovation and adoption of such technologies in educational context. © 2025 IEEE.

关键词： Bot (Internet)

来源：评论

学校读者我要写书评

暂无评论

EchoVision: RealTime Object Detection and Voice Assistance for the Visually Impaired 3

EchoVision: RealTime Object Detection and Voice Assistance f...

引用

3rd International Conference on Intelligent Systems, Advanced computing, and Communication, ISACC 2025

作者： Varshini, Penumarthi Hima Swati, V. Navya, D. Aiswariya Milan, K. Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering India

ISBN: (纸本)9798331523893

Visual impairment is one of the greatest challenges that individuals must overcome to access and navigate their environment. To address this crucial issue, the paper discusses an intelligent system that aims to improve the accessibility and independence of visually impaired people in their local communities. The proposed system uses an object detection model that interfaces with a text-to-speech system. It enables the user to express orally the names of objects which have been detected in their selected language and provides relevant information in the user's native language. Through a comprehensive evaluation, the efficiency of this system has been carefully examined. The results show important improvements on the object detection score which will enable users to interact with their environments with greater confidence and more independence. This paper takes an important step in narrowing the access gap and bringing independence to visually impaired individuals. © 2025 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Data-Filtered Prediction With Decomposition and Amplitude-Aware Permutation Entropy for Workload and Resource Utilization in Cloud Data Centers

引用

IEEE Internet of Things Journal 2025年第12期12卷 19189-19201页

作者： Yuan, Haitao Hu, Qinglong Wang, Meijia Wang, Shen Bi, Jing Buyya, Rajkumar Shi, Shuyuan Yang, Jinhong Zhang, Jia Zhou, Mengchu Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Computer Science Kowloon Hong Kong Beijing University of Technology School of Software Engineering Faculty of Information Technology Beijing100124 China University of Melbourne Cloud Computing and Distributed Systems Lab School of Computing and Information Systems MelbourneVIC3010 Australia Beihang University School of Integrated Circuit Science and Engineering Beijing100191 China CSSC Systems Engineering Research Institute Beijing100036 China Southern Methodist University Department of Computer Science Lyle School of Engineering DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

In recent years, cloud computing has witnessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in Quality of Service and cost efficiency. However, these data often exhibit nonlinearity, high volatility, and interdependencies across different categories, presenting challenges for accurate forecasting. Consequently, there is a critical need to develop a method that thoroughly and comprehensively analyzes all available data to forecast future trends effectively. This work proposes a novel integrated data-enhanced prediction model named SVAPI for achieving high-accuracy workload prediction in cloud computing systems. SVAPI employs the Savitzky-Golay filter, Variational mode decomposition, and the mode selection based on Amplitude-aware Permutation entropy for feature processing, whose features are subsequently utilized by Informer for multivariate joint analysis of the enhanced data, achieving high-precision prediction. Ablation and comparative experiments with advanced prediction models are conducted on the Google cluster trace and other typical datasets. Realistic data-driven results indicate that SVAPI improves the prediction accuracy by 37.7% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVAPI enhances the prediction accuracy of workload, CPU, and memory by 65.6%, 66.9%, and 70.8%, respectively, demonstrating that SVAPI owns strong abilities in noise filtering, feature processing, and multivariate joint analysis for achieving higher prediction accuracy. © 2014 IEEE.

关键词： Quality of service

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：