检索结果-内蒙古大学图书馆

2024 IEEE International Conference on Intelligent systems and Advanced Applications, ICISAA 2024

作者： Simonthomas, S. Rohith, K. Shalini, K.Shantha Aarupadai Veedu Institute of Technology Department of Computer Science and Engineering Chennai India Aarupadai Veedu Institute of Technology Department of Intelligence and Data Science Chennai India

ISBN: (纸本)9798331539948

The increasing incidence of vehicle-animal collisions poses significant risks to both human and wildlife safety. To address this challenge, the implementation of IoT (Internet of Things) sensor networks for wild-animal detection near roads offers a promising solution. This paper explores the design and deployment of an IoT-based system aimed at detecting the presence of wild animals near roadways, thereby enhancing road safety and wildlife conservation. The proposed system utilizes a network of infrared (IR) sensors, motion detectors, and cameras strategically placed along road sections prone to wildlife crossings. These sensors continuously monitor the area, and upon detecting an animal, they transmit real-time data to a central processing unit via wireless communication protocols. The system then processes this data to generate immediate alerts for drivers through connected road signs or mobile applications, warning them of potential hazards ahead. Additionally, the collected data is stored and analyzed to understand animal movement patterns, which aids in making informed decisions about further preventive measures and the placement of wildlife crossings. The incorporation of machine learning algorithms improves the system's precision in distinguishing between different types of animals and minimizing false alarms. This IoT sensor network not only mitigates the risk of accidents but also contributes to the conservation of wildlife by preventing unnecessary fatalities. This project explores the use of Genetic Algorithms (GA) to optimize a detection system that can identify wild animals near roadsides. The paper summarize with a discussion on the effectiveness of the system, potential challenges in implementation, and outlines future pathways for research and innovation in this domain. © 2024 IEEE.

关键词： Traffic signs

来源：评论

学校读者我要写书评

暂无评论

Drowsy Driver Detection System using computer Vision and Transfer Learning for Preventing Road Accidents 3

Drowsy Driver Detection System using Computer Vision and Tra...

引用

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

作者： Bagal, Naisargi Dingreja, Kashish Atterkar, Atharva Bachhav, Prasad Sable, Nilesh P. Yenkikar, Anuradha Alkunte, Ritesh Vishwakarma Institute of Information Technology Department of Computer Science and Engineering - Artificial Intelligence Pune India

ISBN: (纸本)9798350354348

Major traffic accidents are attributed to driver fatigue, according to study on the topic. Driver drowsiness is a state in which the driver of a car is on the verge of falling asleep or losing consciousness. It can be brought on by a number of variables, including biological, physical, psychological, and other factors, all of which can impede and inhibit safe driving. The several methods that can be applied to the implementation of a system for the detection of driver sleepiness will be reviewed in this paper. Improving the ability to identify drowsiness in real time through computer vision is the main objective of all technologies. Thus, our work also pertains to the identification of driver drowsiness, whereby a motorist's tiredness can be ascertained by first recognizing their face, followed by eye tracking. The system compares the extracted eye image with the dataset. The system used the dataset and predicts using transfer learning with our base model as InceptonV3 to identify that it could alert the driver with an alarm if the driver's eyes were closed for a predetermined amount of time, and it could resume monitoring if the driver's eyes were open following the alarm alert. We established a score that grew if the eyes were closed and dropped if they were open. This study aims to reduce traffic accidents by solving the sleepiness detection problem with InceptionV3 with an accuracy of 95.46% compare to 92.11% of ***, driving drowsiness detection - which assesses a driver's degree of fatigue using facial recognition and eye tracking becomes the main focus of our research. © 2024 IEEE.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Impact of Cluster Attack on Wireless Sensor Network

Impact of Cluster Attack on Wireless Sensor Network

引用

2024 International Conference on Recent Innovation in Smart and Sustainable technology, ICRISST 2024

作者： Brindha Shree, A. Harshinee, N. Ramya, M. St. Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798350309140

ISBN: (纸本)9798350309140

Wireless Sensor Networks (WSNs) are widely used for various applications, including environmental monitoring, healthcare, and industrial automation, due to their scalability, cost-effectiveness, and ease of deployment. However, the security of these networks remains a critical concern, as they are vulnerable to a variety of attacks. Cluster attacks, in particular, pose a significant threat to the integrity and availability of WSNs. The impact of cluster attacks on WSNs focuses on their detection, prevention, and *** attacks target the hierarchical structure of WSNs, which typically consist of sensor nodes organized into clusters with a centralized cluster head. Such attacks can disrupt communication, compromise data confidentiality, and drain the energy resources of cluster heads, leading to network degradation or even failure. Understanding the mechanisms and implications of cluster attacks is essential for developing robust security *** reviews of existing cluster attack models, categorize their impact on WSNs, and survey the state-of-the-art detection and prevention techniques. It also describes the trade-offs between security and resource constraints in WSNs, as these networks often operate in resource-constrained environments. This concludes by highlighting the need for a holistic approach to secure WSNs against cluster attacks, combining intrusion detection, encryption, and energy-efficient protocols. By shedding light on the challenges and potential solutions related to cluster attacks in WSNs, this research aims to contribute to the development of more resilient and secure WSNs, ensuring their continued effectiveness in various applications. © 2024 IEEE.

关键词： Sensor nodes

来源：评论

学校读者我要写书评

暂无评论

Cross Border Secure Messaging with Blockchain A Global Perspective

Cross Border Secure Messaging with Blockchain A Global Persp...

引用

2024 International Conference on Advances in Data engineering and Intelligent Computing systems, ADICS 2024

作者： Lokesh Kumar, V. Madhubalan, M. Sheeba, Adlin Subasini, C.A. St. Joseph's Institute Of Technology Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

Blockchain technology is widely recognized for its ability to secure data integrity and confidentiality in a trustless environment, safeguarding against cyber threats. Organizations face challenges in distinguishing personal and business communications, particularly with insecure workplace applications, heightening security risks. Lack of awareness about employee applications further complicates safeguarding crucial business communications. Public communication platforms often lack essential regulatory features for organizational use, falling short of data protection compliance and exposing industries to systemic risks. This research addresses these challenges by developing a secure, enterprise-level chat application, utilizing innovative technologies like blockchain for enhanced data security, privacy, and advanced auditing features. © 2024 IEEE.

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

Taming Breast Cancer Diagnosis Through Transfer Learning: A Binary Classification Framework for Enhanced Disease Detection 4

Taming Breast Cancer Diagnosis Through Transfer Learning: A ...

引用

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

作者： Shandilya, Gunjan Anand, Vatsala Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science Punjab India

ISBN: (纸本)9798350387490

Among the deadliest types of disease is cancer. It is also the most prevalent cancer in women, and if it is not correctly identified, it can even be fatal. Although medical technology has advanced significantly over the years, breast cancer detection remains a challenge that can only be solved through biopsy. By examining histological pictures seen under a microscope, pathologists can identify cancer. Visual inspection is an important part of cancer inspection;it takes time, competence, and a great deal of attention to detail. A quicker and more effective method of identifying breast cancer is therefore required. Numerous studies are being conducted to develop an effective partially or computer-monitored diagnostic system due to developments in deep learning and image analysis. To differentiate between benign and malignant forms of breast cancer, this study uses histopathological scans and convolutional neural networks in combination with transfer learning, a sophisticated kind of image-based machine learning. A large-scale multi-cancer dataset is used in the study's analysis. A refined VGG16 model that has been pre-trained for the categorization of breast cell images is presented in the article, exhibiting outstanding outcomes with an overall accuracy of 97.89 %, precision of 97 % and recall of 98 %. The incorporation of this model into healthcare systems has the potential to enable timely and precise diagnosis of breast cancer. © 2024 IEEE.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

A Framework for Quantum based Software Development Process

A Framework for Quantum based Software Development Process

引用

2024 International Conference on Advances in Data engineering and Intelligent Computing systems, ADICS 2024

作者： Nagarajan, Sathishkumar Malarvel, Muthukumaran Thangakumar, J. Hindustan Institute Of Technology And Sciences Department Of Computer Science And Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350364828

Quantum software is essential for fully harnessing the capabilities of quantum computing systems. Consequently, it has been garnering growing interest in recent times. This document provides a precise definition of the concept of 'quantum software engineering' and presents a comprehensive framework for the life cycle of quantum software. Given the interdisciplinary nature of quantum computing, it is essential to establish a shared understanding of the process for developing and implementing a quantum software application. At present, there isn't a complete methodology or lifecycle that covers every significant stage that could come up along the process of development and execution. That's why we provide the quantum software lifecycle in this document. With this paradigm, practitioners, developers, and scholars have a clear path forward for quantum computing. The provision of a methodical development roadmap for quantum- principles-based software encourages creativity in this quickly expanding subject. © 2024 IEEE.

关键词： Error correction

来源：评论

学校读者我要写书评

暂无评论

PythonSaga: Redefining the Benchmark to Evaluate Code Generating LLMs

PythonSaga: Redefining the Benchmark to Evaluate Code Genera...

引用

2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Yadav, Ankit Beniwal, Himanshu Singh, Mayank Department of Computer Science and Engineering Lingo Research Group Indian Institute of Technology Gandhinagar India

ISBN: (纸本)9798891761681

Driven by the surge in code generation using large language models (LLMs), numerous benchmarks have emerged to evaluate these LLMs capabilities. We conducted a large-scale human evaluation of HumanEval and MBPP, two popular benchmarks for Python code generation, analyzing their diversity and difficulty. Our findings unveil a critical bias towards a limited set of programming concepts, neglecting most of the other concepts entirely. Furthermore, we uncover a worrying prevalence of easy tasks that can inflate model performance estimations. To address these limitations, we propose a novel benchmark, PythonSaga, featuring 185 hand-crafted prompts in a balanced representation of 38 programming concepts across diverse difficulty levels. The robustness of our benchmark is demonstrated by the poor performance of existing Code-LLMs. The code and data set are openly available to the NLP community at https://***/PythonSaga/PythonSaga. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

Data Analysis on Determining False Movie Ratings

Data Analysis on Determining False Movie Ratings

引用

Congress on Control, Robotics, and Mechatronics, CRM 2023

作者： Sahni, Ridhika Arockiasamy, Karmel School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India

ISBN: (纸本)9789819955206

Online movie ratings have evolved into a serious business. Hollywood generates around $10 billion in box office revenue in the United States each year, and online ratings aggregators may have an increasing influence over where that money goes. A single film critic can no longer make or break a film, but perhaps thousands of critics, both professional and amateur, can how well can these platforms be trusted especially if they are showing the ratings and making money by selling the tickets? Do they have a bias by rating movies higher than it should be? The objective of the analysis is to provide brief research of one such online movie-ticket selling company, Fandango. The data collected from Fandango are analyzed and compared with the data collected from sites like Rotten Tomatoes, IMDB, and Metacritic. With the help of visualization, fraud and risk can be reduced offered by such online sites. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Visualization

来源：评论

学校读者我要写书评

暂无评论

Experimental Thermal Model-Based SOC Estimation Algorithm for Lithium-Ion Phosphate Batteries Considering Internal Temperature

引用

Transactions of the Korean institute of Electrical Engineers 2024年第12期73卷 2289-2296页

作者： Kim, Dong Hwan Ko, Jeong Soo Kim, Do Hyeon Lim, Jong-Hun Lim, Je Yeong Lee, Jaein Lee, Byoung Kuk Dept. of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of Advanced Defense Science & Technology Research Institute Agency for Defense Development Korea Republic of

This paper proposes a method for estimating the State of Charge (SOC) of lithium iron phosphate (LFP) batteries based on an experimental thermal model that considers internal battery temperatures. The proposed algorithm involves selecting a thermal model and suggesting methods for extracting parameters from the chosen model. Using the proposed thermal model, the algorithm compensates for temperature differences in cold and hot environments, thereby correcting the internal temperature to estimate SOC accurately. The effectiveness of the proposed algorithm is verified by estimating the SOC of electric vehicle LFP batteries and comparing the accuracy before and after correction. Copyright © The Korean institute of Electrical Engineers.

关键词： State of charge

来源：评论

学校读者我要写书评

暂无评论

XGBoost for Novel Predictive Model to Classify Student Performance Based on Academic Metrics

XGBoost for Novel Predictive Model to Classify Student Perfo...

引用

2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Sinha, Sweta Singh, Om Prakash Baruah, Hemanta Biswas, Ujjwal Indian Institute of Information Technology Bhagalpur Department of Computer Science and Engineering Bhagalpur India Indian Institute of Technology Guwahati Department of Centre for Linguistic Science and Technology Guwahati India

ISBN: (纸本)9798331540821

Understanding and predicting student performance with academic metrics is essential for better education and learning. Making precise estimates, selecting appropriate measures, and using suitable predictive algorithms may be tricky. In this research, we investigate the significance of these measurements and predictive models. We built XGBoost predictive model to predict student performance. We deployed a novel educational dataset structured from a macro perspective, which has rarely been used in previous research, in place of using specific students as case studies. We employed data prepossessing, feature selection, and feature building to increase the model's generalizability and prediction accuracy. The XGBoost model outperformed the five traditional machine learning models (Decision Tree, Support Vector Machine, Random Forest, Naive Bayes, Neural Network, and K-nearest Neighbor). These models will predict student performance and be classified according to their function and importance. This research shows how these novel measures can be used to predict and assess student performance. Our goal is to recommend machine learning models for predicting academic performance. The encouraging results show how accurately the proposed model predicted students' academic progress. © 2024 IEEE.

关键词： Contrastive Learning

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

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

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

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

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：