检索结果-内蒙古大学图书馆

5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Goyal, Pranshu Nigam, Vishwas Khan, Shahnaw A.J. Singh, Shashi Kant Galgotias College Of Engineering And Technology Dept. Of Computer Science And Engineering India

ISBN: (纸本)9798350330861

the manual process of recording and managing attendance in educational institutions and organizations is often time-consuming, prone to errors, and inefficient. To address these challenges, there is a growing need for automated attendance management systems. This research paper presents the design and implementation of an Automated Attendance Management System (AAMS) using modern technologies. The AAMS utilizes various technologies such as biometric recognition, RFID (Radio Frequency Identification), and computer vision to accurately and efficiently track attendance. The system aims to simplify the attendance recording process for both students and instructors, reduce administrative burden, and improve overall efficiency. The proposed AAMS incorporates biometric recognition techniques, such as fingerprint scanning or facial recognition, to identify and verify the identity of individuals. © 2023 IEEE.

关键词： Efficiency

来源：评论

学校读者我要写书评

暂无评论

A ROS-based Voice Controlled Robotic Arm for Automatic Segregation of Medical Waste Using YOLOv3 2

A ROS-based Voice Controlled Robotic Arm for Automatic Segre...

引用

2nd International Conference on computer, Control and Robotics, ICCCR 2022

作者： Tasnim, Samiha Hasan, Md. Zahid Ahmed, Touhid Hasan, Md. Tanvir Uddin, Ferdousi Jamal Farah, Tanjila North South University Dept.of Electrical and Computer Engineering Dhaka Bangladesh

ISBN: (纸本)9781665466745

The outbreak of the Covid-19 pandemic has resulted in a surge in the generation of medical waste. Due to the transmissible nature of the Virus and the lack of effort at proper disposal, the safety of the front-line health workers, as well as the disposer, is at risk. Hence, to mitigate the spread of infectious diseases, a system is proposed that uses a robotic arm for segregating medical waste automatically. The robotic arm is operable through voice commands, and the segregating operation could function in automatic and manual mode. The system uses the YOLOv3 (You Only Look Once) algorithm to detect and classify the medical waste and then uses the Robot Operating System (ROS) platform to pick up and drop the waste object into color-coded bins. For this research, the medical waste has been categorized into 4 types, and for each type, a color-coded bin has been used for segregation. Our system has achieved 94% training accuracy for the YOLOv3 model on a custom dataset, whereas the system's overall accuracy in automated mode was 82.1%, derived after 30 trials. In the case of manual mode, an average accuracy of 82.5% has been achieved for the same number of trials. © 2022 IEEE.

关键词： Health risks

来源：评论

学校读者我要写书评

暂无评论

Enhancing Siamese Neural Network Performance with Custom Local Binary Patterns for Face Matching 8

Enhancing Siamese Neural Network Performance with Custom Loc...

引用

8th IEEE International Conference on Information and Communication Technology, CICT 2024

作者： Koranga, Pawan Singh Indian Institute of Technology Kharagpur Dept. of Computer Science and Engineering Kharagpur India School of Computer Sciences Upes Dehradun248001 India

ISBN: (纸本)9798331531539

In this study, we investigate the efficacy of incorporating custom local binary patterns (LBP) as a preprocessing technique to enhance the performance of Siamese neural networks for face matching tasks. Our research focuses on training Siamese models using the DC Face dataset and comparing their performance when trained with and without LBP integration. The custom LBP function is designed to capture fine-grained texture details from facial images, providing robust feature representations that are invariant to variations in lighting conditions and facial expressions. Through rigorous experimentation and evaluation, we analyze the impact of LBP on the threshold point for image differentiation and compare the matching accuracy of Siamese models trained with and without LBP. Our findings reveal significant improvements in matching accuracy and robustness when utilizing the texture features extracted by the custom LBP function. By encoding local pixel structures and capturing subtle facial characteristics, LBP-enhanced Siamese models demonstrate enhanced performance in accurately matching faces across different images. Further-more, our study highlights the importance of using domain-specific preprocessing techniques, such as LBP, to optimize the capabilities of Siamese neural networks for face matching tasks. The results underscore the effectiveness of customizing feature extraction methods to suit the characteristics of the dataset, leading to improved matching accuracy and robustness. Overall, our research contributes valuable insights into the role of LBP in enhancing the performance of Siamese neural networks for image-based similarity tasks, with implications for various real-world applications in face recognition and biometric authentication. © 2024 IEEE.

关键词： Local binary pattern

来源：评论

学校读者我要写书评

暂无评论

Using Classifier with Gated Recurrent Unit-Sigmoid Perceptron, Order to Get the Right Bird Species Detection

Using Classifier with Gated Recurrent Unit-Sigmoid Perceptro...

引用

2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Mehra, Ritika Ragahvendra, R. Dev Bhoomi Uttarakhand University Dept of Computer Science and Engineering Uttarakhand Dehradun India Department of Computer Science and It Bangalore India

ISBN: (纸本)9798350322309

The method for classifying a picture using the CNN algorithm and various activation functions is the subject of this research study. From a human perspective, there are several sorts of situations found in birds, such as various sizes, altitudes, colours, and forms. There is a more substantial variety in photos to recognize the species of bird than there is in the accepted audio categorization of birds. Additionally, people have a really tough time identifying birds from photos. The system is suggested to support the concepts of identifying a portion of the birds and extracting the CNN characteristics using several convolutional layers in order to discriminate and categorise the birds. These characteristics are then gathered and presented to the decoder at that time for classification. Various bird photos and activation functions are used to test the CNN method. The nodes of the picture are checked to the different testing data sets, such as cifar-10 and Berkeley Birds 200, with order to determine their correctness. Python is used to create the suggested system in either Omnibus 3 or Dragonfly 3 programs. According to the experiment results and analyses, CNN with activated Re LUSigmoid outperforms CNN with the other activation functions in terms of accuracy. © 2023 IEEE.

关键词： Chemical activation

来源：评论

学校读者我要写书评

暂无评论

LightNN: An Optimized Deep Learning Framework for Enhanced Fruit Classification

LightNN: An Optimized Deep Learning Framework for Enhanced F...

引用

2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Afroj, Maharin Jahan, Israt Islam, Md Polash Saifur Rahman, Md. Bangladesh University of Business and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh Stockholm University Dept. of Computer and Systems Sciences Stockholm Sweden

ISBN: (纸本)9798331529765

The crucial field of fruit classification in computer vision and deep learning brings about breakthroughs that have broad applications in the retail and agricultural industries. By automating the identification and sorting of various fruit types, these technologies enhance efficiency, accuracy, and scalability, benefiting processes from farm production to market distribution. The integration of sophisticated algorithms and neural networks not only improves the speed and reliability of fruit classification but also offers the potential for real-time quality control and inventory management, revolutionizing traditional methods and paving the way for more innovative, data-driven approaches in these sectors. This study introduces LightNN, a novel and effective paradigm with a lightweight architecture that tackles the complex problems related to computational efficiency. Combining a small classifier with a simplified feature extractor, LightNN achieves the ideal balance between simplicity and accuracy. The feature extractor is capable of extracting hierarchical features from RGB images by using convolutional layers with ReLU activations and max-pooling procedures. The flattened output is then processed by the classifier, which consists of fully connected layers with ReLU activations and dropout regularisation. Remarkably, LightNN, with just 298,835 parameters, manages to get a respectable 96.37% test accuracy in fruit categorization tasks. The results highlight LightNN's promise as an effective and efficient fruit classifying method, particularly in situations with constrained computational resources. © 2024 IEEE.

关键词： Fruits

来源：评论

学校读者我要写书评

暂无评论

SMART HOME ENVIRONMENT MODELLED WITH A MULTI-AGENT SYSTEM

UPB Scientific Bulletin, Series C: Electrical Engineering an...

引用

UPB Scientific Bulletin, Series C: Electrical engineering and computer science 2023年第1期85卷 365-376页

作者： Rasras, Mohammad Marin, Iuliana Radu, Șerban Dept. of Engineering in Foreign Languages University POLITEHNICA of Bucharest Romania Dept. of Computer Science University POLITEHNICA of Bucharest Romania

A smart home can be considered a place of residence that enables the management of appliances and systems to help with day-to-day life by automated technology. In the current paper is described a prototype that simulates a context-aware environment, developed in a designed smart home. The smart home environment has been simulated using three agents and five locations in a house. The context-aware agents behave based on predefined rules designed for daily activities. Our proposal aims to reduce operational cost of running devices. In the future, monitors of health aspects belonging to home residents will sustain their healthy life daily. © 2023, Politechnica University of Bucharest. All rights reserved.

关键词： Multi agent systems

来源：评论

学校读者我要写书评

暂无评论

File Encryption using Noise Images as Key 3

File Encryption using Noise Images as Key

引用

3rd International Conference on Smart Data Intelligence, ICSMDI 2023

作者： Bhagya Lakshmi, A. Pradeep, R. Seshadri, K.S. Shriraam, B. Easwari Engineering College Dept. of Computer Science & Engineering TN India

ISBN: (纸本)9781665464871

Everything in today's highly connected digital world is increasingly dependent on instantaneous global data transfers. Internet efficiency facilitates our daily lives. Sharing information online presents significant security dangers and difficulties in the modern day. The use of cryptography is the means through which sensitive information can be protected from various threats. Improved cryptosystems technology is a no-brainer for securing communication networks. An improved cryptosystem is the focus of our research, and to that end we have presented a novel method of data encryption and decryption that guarantees increased efficiency over current best practises. This research work has proposed a noise image encryption and decryption scheme, wherein the noise signal is randomly selected to set the initial values for a chaotic system which also enhances the security of the system. for understanding the effectiveness of the proposed system. Experimental results confirm that the proposed chaos-based cryptosystem is efficient and suitable for information (image) transmission in a highly secured way. This study has developed a file encryption system, where encrypted file is saved to decrypt and download later. The main difference between the existing and proposed system is the implementation of file encryption system using noise images that store key values inside it. © 2023 IEEE.

关键词： Cryptography

来源：评论

学校读者我要写书评

暂无评论

Design and Implementation of Smart Classroom Using Cisco Packet Tracer 1

Design and Implementation of Smart Classroom Using Cisco Pac...

引用

1st IEEE International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Duvvuri, Kavya Kanisettypalli, Harshitha Kunisetty, Jaswanth Khanna, Manju Amrita School of Engineering Dept. of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798350348057

Schools and colleges play a very crucial role in everyone's life. It is very important to impart good morals and safe education to future generations. As days go by, the necessity for faster job completion and smarter devices is increasing. The safety of students becomes important as the number of students enrolling in schools is gradually increasing. Discipline and time management skills must be imparted to students from a tender age in the school environment. Both safety and discipline can be ensured by making use of devices that are smarter and remotely controllable. Through this work, we have introduced the design of a smart classroom using a network simulation called Cisco Packet Tracer. The work is based on the concept of the Internet of Things. It has multiple subsystems that together make the class a wonderful place for students to build their future. © 2023 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Predicting the Likelihood of a Recurrent Febrile Seizure Using Thermographic Images and Deep Learning with Rules Generation 1

Predicting the Likelihood of a Recurrent Febrile Seizure Usi...

引用

1st International Conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Patil, Pratima Deshpande, Deepa S. MGM University Jawaharlal Nehru Engineering College Dept. of Computer Science and Engineering Chhatrapati Sambhajinagar431003 India

ISBN: (纸本)9798350355611

Recurrent febrile seizures are a common concern in children, often triggered by rapid changes in body temperature during fever episodes. For rapid action and care, early and accurate seizure recurrence prediction is crucial. Limited techniques were developed for predicting the likelihood of a Recurrent Febrile Seizure using a Thermographic image. Several drawbacks were considered for recurrent febrile seizure prediction such as lower performance, high time consumption, and error rate. Hence, the proposed technique is introduced to provide an efficient Deep Learning (DL)-based technique. Initially, the data is gathered from datasets, and the Intra-Attentional Convolutional ResNet (InAtt-CRNet) with the Yolov5-Face model detects the face and reduces the processing time by tacking the face with a bounding box. The patient's forehead or nose temperature is monitored using the Region of Interest (ROI) finding approach following face detection and tracking in each frame. Then, the temperature is predicted using the Enhanced Convolutional Capsule Network (EC2Net). The body temperature, age, duration of fever, and family history are used to construct the clinical decision rule. The Twin-directional Gated Long Short-term memory (TdG-LSTM) is used to predict the likelihood of a subsequent febrile seizure. The proposed technique achieves 98.16% accuracy, 98.45% precision, 0.12 RMSE, and 29 seconds as processing time. © 2025 IEEE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Blinkify - A IoT Based Device for Speech Impaired 14

Blinkify - A IoT Based Device for Speech Impaired

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Harikrishnan, B.A. Pradeep, Karthik Kushal, Kushal Kumar, Sanjay S Mohan, Maya Nss College of Engg Computer Science Dept Palakkad India Nss College of Engg Computer Science and Engineering Palakkad India

ISBN: (纸本)9798350335095

The discovery and popularity that came by, for the Internet of Things have made life more inclusive and productive for people with speech disabilities caused because of motor neuron diseases like ALS. Such people have no means or are sometimes excluded from the usual walk of life. Most invented intelligent detection devices, including portable devices for people with disabilities in speech, are costly and unaffordable for patients in underdeveloped and developing countries. Blinkify is an IoT technology-based wearable device, which uses modified Morse code for communication. Motor neuron disease-affected patients can use this device for communication by means of eye blinking. The device tracks this eye blinks and converts it into Morse code, which is transmitted to a mobile or laptop connected to the system. It also provides a secure pathway for communication. With the added benefits of easy handling, low cost and easy portability, it becomes immensely useful for people who are paralyzed and cannot communicate by mouth or sign. The system also requires less assistance from another person. © 2023 IEEE.

关键词： Internet of things

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：