检索结果-内蒙古大学图书馆

2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Tejas, V. Harsith Harshitha, S. Yogitha, R. Kalaiarasi, G. Selvi, M. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350375237

The integration of advanced security technologies, particularly the widespread deployment of mobile cameras equipped with real-time face recognition capabilities, heralds a new era of surveillance capabilities. This paper presents a comprehensive overview of a cutting-edge surveillance system designed to enhance security measures in public and private spaces. Unlike traditional CCTV setups, this system leverages strategically positioned mobile cameras equipped with sophisticated face recognition algorithms such as YOLO, Haar featurebased cascade classifiers, OpenCV, and LBPH (Local Binary Pattern Histograms).Upon detection of a person of interest, the system swiftly matches the face against a pre-existing database, providing instantaneous identification. Moreover, the system concurrently displays the associated location information in real-time, facilitating swift and targeted response *** features of this system include its ability to send immediate notifications containing the individual's name and precise location details to specified recipients, typically via text messages to predetermined numbers. This functionality ensures that relevant authorities are promptly informed, enabling them to address potential concerns or threats *** amalgamating cutting-edge face recognition technology with mobile surveillance cameras, this system not only strengthens security efforts but also streamlines response mechanisms. © 2024 IEEE.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Model for UAV aided Traffic Analysis and Vehicle Classification 5

Deep Learning Model for UAV aided Traffic Analysis and Vehic...

引用

5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Kumar, Mayank Anwar, Shamama Birla Institute of Technology Department of Computer Science and Engineering Mesra Ranchi India

ISBN: (纸本)9798350389609

Intelligent transportation systems (ITS) depend upon a range of communication that allows for data exchange between vehicles and traffic information data centers to create applications for providing information like traffic alert in view of lane closure, traffic congestion, track unsafe driving behaviour, optimize routes and emergency management, etc. An important component in this setup are video clips of highways or cross-section areas which needs to be monitored. Traditionally, a closed circuit television (CCTV) is the most often used method of monitoring traffic information, but have limitations like fixed positions, small monitoring region leading to numerous CCTV installations which are costly. A UAV, unlike CCTV, is capable of recording an extensive area of roadways by elevating its altitude or altering its location, as well as travel to a specific spot to monitor unforeseen circumstances such as crashes. Additionally, a UAV observes highways perpendicularly, thus the automobiles in the captured footage do not overlap. This paper explores the issue of vehicle identification on highways using UAV images through deep learning models. Deep learning approaches can automatically learn problem-specific features, eliminating the need for standard feature extraction procedures. Three pre-trained deep learning based model VGG16, ResNet-18 and EfficientNetB3 models have been used for the vehicle classification problem, which is essentially a five class classification problem. The highest accuracy of 97% is reported by the EfficientNet model. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

来源：评论

学校读者我要写书评

暂无评论

Echo Guidance: Voice-Activated Application for Blind with Smart Assistive Stick Using Machine Learning and IoT

Echo Guidance: Voice-Activated Application for Blind with Sm...

引用

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

作者： Abishek Kumar, G. Surya, G. Sathyadurga, V. Hindustan Institute Of Technology And Science Department Of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350364828

Navigating the world without sight presents profound challenges for millions of visually impaired individuals. Echo Guidance includes a pioneering application, addresses these obstacles by facilitating independence and mobility through voice-driven interaction and an IoT smart assistive device for obstacle detection by utilizing Ultrasonic, GPS and GSM modules powered by Arduino. This study explores the design and evaluation of Echo Guidance, highlighting its utilization of advanced algorithms like RGB888 conversion, matrix transformation, and Optical Character Recognition (OCR) for real-time object recognition. The application boasts a voice-centric interface tailored to the needs of the visually impaired, seamlessly integrating day-to-day functionalities such as weather forecasting, reminders, and a calculator accessible through voice commands. Central to Echo Guidance is its innovative object detection and recognition approach, employing the MobileNetObjDetector class powered by TensorFlow Lite for swift and accurate object detection. The OverlayView class visually represents detected objects, enhancing spatial awareness. Additionally, the integration of IoT devices, such as the smart stick with ultrasonic sensors for obstacle detection and head-level obstacle detection, coupled with SOS functionality in case of any emergency using GPS and GSM modules, further enhances Echo Guidance's capabilities. This research underscores Echo Guidance's transformative potential in improving the quality of life for visually impaired individuals, representing a significant advancement in assistive technology. Prioritizing accessibility and user experience, this paper empowers individuals worldwide with newfound independence and confidence in navigating their surroundings. © 2024 IEEE.

关键词： Linear transformations

来源：评论

学校读者我要写书评

暂无评论

Air Predictive Modeling for Air Quality: A Comparative Study of Machine Learning and Deep Learning Techniques

Air Predictive Modeling for Air Quality: A Comparative Study...

引用

2024 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2024

作者： Dhongade, Shubhangi Tiwaskar, Shweta Koturwar, Abhijeet Motiramani, Raksha Zarekar, Devyani Vishwakarma Institute of Information Technology Department of Computer Engineering Maharashtra Pune India

ISBN: (纸本)9798350348460

Air quality emerges as a critical global concern affecting millions of individuals. This paper explores into the development and application of data-driven models for air quality prediction, Motivated by air quality's profound impact on both the environment and human well-being, we leverage deep learning and machine learning methodologies to enhance the precision of air quality forecasts. Dataset used comprising over 33,360 entries. This dataset encompasses diverse characteristics, including PM2.5, PM10, SO2, NO2, CO, O3, etc., with pollutant levels documented on an hourly basis. To gauge air quality levels, we employ various models such as the Random Forest model, Long Short-Term Memory (LSTM), Artificial Neural Network (ANN), Support Vector Machine (SVM), and K-Nearest Neighbors (K-NN). A thorough evaluation and performance comparison of these models is conducted using Kaggle's air quality dataset. The study seeks to not only address the immediate challenge of air quality prediction but also holds implications for broader environmental and public health considerations on a global scale. © 2024 IEEE.

关键词： Artificial Neural Network Deep learning K-Nearest Neighbors and Support Vector Machine Long Short-Term Memory Random Forest model

来源：评论

学校读者我要写书评

暂无评论

Advancing Crowd Management through Innovative Surveillance using YOLOv8 and ByteTrack 9

Advancing Crowd Management through Innovative Surveillance u...

引用

9th International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2024

作者： Cruz Antony, J. Leela Sri Chowdary, C.H. Nanda Prabhu, B. Murali, E. Mayan, Albert Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350350845

Crowd management is a cumbersome task that requires broad analysis and grasp of various constraints. The main hurdles include security issues, unexpected crowd dynamics over which there is typically minimal or no control, limited infrastructure to accommodate. This paper suggests a model that is custom trained to enhance people detection in crowd and monitor crowd density as well as tracking people in a video frame. The paper proposes using YOLOv8 which is known for its capabilities as a fast single shot detector and for being extremely customizable combined with ByteTrack, an advanced model that specializes in tracking people in crowded or congested areas. Together, the models help create a powerful system that can accurately locate and follow people in dense groups, therefore revolutionizes traditional approaches to crowd management. The people counting system also helps with crowd control by indicating how many people are in a certain area. In addition to addressing the issues associated with congested areas, this system provides trustworthy means of tracking people in a variety of scenarios, hence enhancing public safety and security. © 2024 IEEE.

关键词： ByteTrack Crowd Management CrowdHuman YOLOv8

来源：评论

学校读者我要写书评

暂无评论

Privacy and Data Usefulness Trade-off for Autonomic IoT Applications: A Framework 2

Privacy and Data Usefulness Trade-off for Autonomic IoT Appl...

引用

2nd International Conference on computer, Communication and Control, IC4 2024

作者： Ataullah, Md. Chauhan, Naveen National Institute of Technology Hamirpur Department of Computer Science and Engineering Hamirpur India

ISBN: (纸本)9798350387933

IoT is an acronym for the Internet of Things, and it refers to the interconnectivity of everyday objects, including home appliances, wearable devices, vehicles, and industrial equipment, through the Internet. IoT enables devices to exchange data and connect. Autonomic IoT applications, on the other hand, are IoT systems that have self-managing capabilities. These systems can monitor and control themselves, make decisions, and adapt to changing environments without the need for human intervention. Autonomic IoT applications aim to improve the efficiency, reliability, and security of IoT systems. IoT applications offer numerous benefits to users by collecting and analyzing data, which can be used to make informed decisions. However, this collection of personal data raises concerns about privacy and security. There is a trade-off between the usefulness of the data collected and the privacy of individuals involved. To ensure the usefulness of the data while protecting the privacy of individuals, it is essential to implement appropriate data privacy and security measures. In this paper, we propose a framework to implement privacy and data usefulness trade-offs for autonomic IoT applications. By adopting this framework, users can benefit from IoT applications while safeguarding their data. © 2024 IEEE.

关键词： Domestic appliances

来源：评论

学校读者我要写书评

暂无评论

An Insight Survey on Sensor Errors and Fault Detection Techniques in Smart Spaces

引用

computer Modeling in engineering & Sciences 2024年第3期138卷 2029-2059页

作者： Sheetal Sharma Kamali Gupta DeepaliGupta Shalli Rani Gaurav Dhiman Chitkara University Institute of Engineering and Technology Chitkara UniversityPunjab140401India PG Department of Information Technology Goswami Ganesh Dutta Sanatan Dharma CollegeChandigarh160030India Department of Computer Science Government Bikram College of CommercePatialaIndia Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon University Centre for Research and Development Department of Computer Science and EngineeringChandigarh UniversityMohali140413India Department of Computer Science and Engineering Graphic Era Deemed to be UniversityDehradun248002India Division of Research and Development Lovely Professional UniversityPunjab144001India

The widespread adoption of the Internet of Things (IoT) has transformed various sectors globally, making themmore intelligent and connected. However, this advancement comes with challenges related to the effectiveness ofIoT devices. These devices, present in offices, homes, industries, and more, need constant monitoring to ensuretheir proper functionality. The success of smart systems relies on their seamless operation and ability to handlefaults. Sensors, crucial components of these systems, gather data and contribute to their functionality. Therefore,sensor faults can compromise the system’s reliability and undermine the trustworthiness of smart *** address these concerns, various techniques and algorithms can be employed to enhance the performance ofIoT devices through effective fault detection. This paper conducted a thorough review of the existing literature andconducted a detailed *** analysis effectively links sensor errors with a prominent fault detection techniquecapable of addressing them. This study is innovative because it paves theway for future researchers to explore errorsthat have not yet been tackled by existing fault detection methods. Significant, the paper, also highlights essentialfactors for selecting and adopting fault detection techniques, as well as the characteristics of datasets and theircorresponding recommended techniques. Additionally, the paper presents amethodical overview of fault detectiontechniques employed in smart devices, including themetrics used for evaluation. Furthermore, the paper examinesthe body of academic work related to sensor faults and fault detection techniques within the domain. This reflectsthe growing inclination and scholarly attention of researchers and academicians toward strategies for fault detectionwithin the realm of the Internet of Things.

关键词： Error fault detection techniques sensor faults outliers Internet of Things

来源：评论

学校读者我要写书评

暂无评论

Integrating deep and handcrafted features for enhanced decision-making assistance in breast cancer diagnosis on ultrasound images

引用

Multimedia Tools and Applications 2025年 1-23页

作者： Abhisheka, Barsha Biswas, Saroj Kumar Purkayastha, Biswajit Das, Soumen School of Computer Science UPES Bidholi Uttrakhand Dehradun248007 India Computer Science Engineering National Institute of Technology Silchar NIT Road Assam Silchar788010 India

The classification of breast cancer has emerged as a significant concern in the healthcare sector in recent times. This is primarily due to its status as the second leading cause of cancer-related fatalities among women. Prior research in the field of breast cancer classification has mainly focused on either traditional Machine Learning (ML) based models or employing Deep Learning (DL) models. However, both approaches solely fail to capture the complete spectrum of features necessary for accurate classification. Therefore, this paper introduces a classification model named Hybrid Breast Cancer Prediction System (HBCPS), which utilizes a combination of features derived from deep Convolutional Neural Network (CNN) model and handcrafted features, to improve classification performance. The proposed HBCPS uses the pre-trained ResNet50 network to extract the deep features, while the handcrafted features are obtained using Histogram Orientation Gradient (HOG). For classification the proposed approach uses Support Vector Machine (SVM). In addition, the proposed method integrates the Block Matching 3D(BM3D) denoising filter. This filter efficiently reduces multiplicative noise such as speckle noise from Breast Ultrasound (BUS) images, resulting in improved image quality. This enhancement technique has significantly contributed to improve the overall performance of the system. The proposed framework is evaluated using a widely used Breast Ultrasound Image (BUSI) dataset. The proposed HBCPS exhibited satisfactory performance with an accuracy, precision, recall, F1-score, specificity, and AUC values of 89.02%, 87.67%, 87.17%, 87.36%, 83.87%, and 0.8717, respectively. These metrics indicate the robustness and reliability of the proposed method and offer a comprehensive framework for breast cancer classification using BUS images. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Optimal Hyperparameters for Collaborative Filtering: A Comparative Study 2

Optimal Hyperparameters for Collaborative Filtering: A Compa...

引用

2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Jose, Basil Bhatkande, Aditya Phadte, Siddharth Kerkar, Richa Muzavor, Adnan Agnel Institute of Technology and Design Department of Computer Engineering Goa Assagao India

ISBN: (纸本)9798350371314

In recommendation systems, collaborative filtering algorithms play a vital role in offering personalized recommendations based on user-item interactions. This paper conducts a detailed comparative study of three collaborative filtering algorithms, including Alternating Least Squares (ALS), Singular Value Decomposition (SVD), and Neural Collaborative Filtering (NCF). The paper's objective is to provide the optimal hyperparameter configurations for the three algorithms, which would boost their predictive power, using the Movie Lens 25M dataset. Initially the algorithms are evaluated with default parameters whose results shows that SVD performs better in Mean Absolute Error (MAE) while ALS performs better in RMSE. However, after the hyperparameter tuning with multiple parameters, such as epoch, rank, maxIter, learning rate, and batch size, the most optimal hyperparameter configuration for the algorithms was defined. The results tells that each of the algorithm have the capacity to handle sparsity. According to the RMSE and MAE, ALS and SVD have a slight advantage over NCF. These results are essential for choosing the appropriate hyperparameters enhancing the accuracy of the algorithms and the recommendation system as a whole. © 2024 IEEE.

关键词： Collaborative filtering

来源：评论

学校读者我要写书评

暂无评论

Energy-Efficient and Mobility-Aware Device Selection for Task Offloading in Blockchain-Enabled IoT

Energy-Efficient and Mobility-Aware Device Selection for Tas...

引用

2024 IEEE Middle East Conference on Communications and Networking, MECOM 2024

作者： Sanatan, Kumar Ajay, Shubham Kant Kumar, Satendra Indian Institute of Technology Department of Computer Science and Engineering Patna801106 India

ISBN: (纸本)9798350376715

Blockchain is a decentralized ledger system that securely records transactions across multiple nodes. A key challenge in blockchain networks is forking, where the transaction history diverges due to protocol changes, leading to distinct chains. In Blockchain-enabled IoT networks, forking disrupts consistency and increases latency and energy consumption. To address this issue, we propose a mobility-aware federated learning framework that optimizes the selection of IoT devices and miners to minimize forking events. Specifically, we first employed Markov Predictor to predict the future location of the devices. Further, we select the subset of devices based on their predicted locations and eliminate the devices likely to be involved in the forking event. Finally, the task of eliminating devices is reassigned to underutilized devices. Experimental results show that our Markov-based predictor achieves a 63% prediction accuracy for four to 25 devices. © 2024 IEEE.

关键词： Miners

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：