检索结果-内蒙古大学图书馆

K-Banhatti Invariants Empowered Topological Investigation of Bridge Networks

Computers, Materials & Continua 2022年第12期73卷 5423-5440页

作者： Khalid Hamid Muhammad Waseem Iqbal Erssa Arif Yasir Mahmood Ahmad Salman Khan Nazri Kama Azri Azmi Atif Ikram Department of Computer Science Superior UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of Lahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi MalaysiaKuala Lumpur54100Malaysia Faculty of Informatics and Applied Mathematics University Malaysia TerengganuMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of Lahore54000Pakistan

Any number that can be uniquely determined by a graph is called graph *** the most recent twenty years’innumerable numerical graph invariants have been described and used for correlation *** the fast and advanced environment of manufacturing of networks and other products which used different networks,no dependable assessment has been embraced to choose,how much these invariants are connected with a network graph or molecular *** this paper,it will talk about three distinct variations of bridge networks with great capability of expectation in the field of computer science,chemistry,physics,drug industry,informatics,and mathematics in setting with physical and synthetic constructions and networks,since K-Banhatti invariants are newly introduced and have various forecast characteristics for various variations of bridge graphs or *** review settled the topology of bridge graph/networks of three unique sorts with three types of K-Banhatti *** concluded outcomes can be utilized for the modeling of interconnection networks of Personal computers(PC),networks like Local area network(LAN),Metropolitan area network(MAN)and Wide area network(WAN),the spine of internet and different networks/designs of PCs,power generation interconnection,bio-informatics and chemical structures.

关键词： Bridge networks invariants k-banhatti indices maple network graph molecular graph

来源：评论

学校读者我要写书评

暂无评论

Cross-Layer Distributed Attack Detection Model for the IoT

引用

International Journal of Ambient Computing and Intelligence 2022年第1期13卷 1-17页

作者： Ahmed, Hassan I. Nasr, Abdurrahman A. Abdel-Mageid, Salah M. Aslan, Heba K. Informatics Department Electronics Research Institute Cairo Egypt Computer Engineering Faculty of Engineering Al-Azhar University Cairo Egypt Computer Engineering Department College of Computer Science and Engineering Taibah University Saudi Arabia Center of Informatics Science Faculty of Information Technology and Computer Science Nile University Egypt

The security of IoT that is based on layered approaches has shortcomings such as the redundancy, inflexibility, and inefficiently of security solutions. There are many harmful attacks in IoT networks such as DoS and DDoS attacks, which can compromise the IoT architecture in all layers. Consequently, cross layer approach is proposed as an effective and practical security defending mechanism. Cross-layer distributed attack detection model (CLDAD) is proposed to enhance security solutions for IoT environments. CLDAD presents a general detection method of DDoS in sensing layer, network layer, and application layer. CLDAD is based on big data analytics techniques, which enable the detection process to be performed in a distributed way, so the model can detect DDoS attacks in any layer on-the-fly and the model support the scalability of the IoT environment. CLDAD is tested based on three datasets, namely artificial jamming attack dataset, BoT-IoT dataset, and BoT-IoT-based HTTP. The results showed that the proposed model is efficient in detecting attacks in the three layers of the IoT. Copyright © 2022, IGI Global.

关键词： Big data

来源：评论

学校读者我要写书评

暂无评论

Designing a Code Obfuscation Scheme for Software Protection

Designing a Code Obfuscation Scheme for Software Protection

引用

2024 International Conference on Electrical and Computer engineering Researches, ICECER 2024

作者： Azeta, Ambrose A. Awal, Abdulganiyu Azeta, Joy I. Hamunyela, Suama L. Santos, Albano dos Namibia University of Science and Technology Department of Software Engineering Windhoek Namibia Covenant University Department of Computer Science and Information Sciences Ota Nigeria Covenant University Department of Biological Sciences Ota Nigeria Namibia University of Science and Technology Department of Informatics Windhoek Namibia

ISBN: (纸本)9798331539733

Software piracy and intellectual property theft has brought about a financial decrease in revenue for the producers of software systems. Moreso, malicious end-users act as Man-At-The-End (MATE) attackers and dangerously inspect software codes either statically or dynamically to steal them. Developers need to create client applications that contain confidential information and data. As a result, large scale software firms constantly strive to secure hidden algorithms and vital programming components contained in their applications in order to have competitive advantage. The focus of this study is to design a code obfuscation scheme for software and intellectual property protection. The methodology employed includes use of programming tool and algorithms that described the key processes of the scheme. To demonstrate the strength and capability of our approach, user evaluation was conducted to arrive at the cost, potency and resilience of the scheme. Results indicate that majority of the respondents believe that the developed obfuscator is inexpensive and that it does not adversely affect memory and execution time. The current study would provide software developers the tool to obscure their intellectual property against theft. © 2024 IEEE.

关键词： Attackers intellectual property obfuscation scheme software protection

来源：评论

学校读者我要写书评

暂无评论

Early fire danger monitoring system in smart cities using optimization-based deep learning techniques with artificial intelligence

引用

Journal of Reliable Intelligent Environments 2024年第2期10卷 197-210页

作者： Reddy, P. Dileep Kumar Margala, Martin Shankar, S. Siva Chakrabarti, Prasun Telangana Secunderabad India School of Computing and Informatics University of Louisiana at Lafayette Lafayette United States Department of CSE KG Reddy College of Engineering and Technology Telangana Hyderabad501504 India Department of Computer Science and Engineering Sir Padampat Singhania University Rajasthan Udaipur313601 India

One primary safety concern for smart cities is fire. Traditional techniques are not appropriate because of their high false alarm rates, delayed characteristics, and susceptibility in situations with heritage buildings. Smart cities must develop sophisticated methods to mitigate the severe effects of fires and achieve early fire detection in real time. An artificial intelligence-based recurrent neural network with a whale optimization framework (AI-RNN-WO) was introduced to estimate the risk of fire hazards early on. IoT sensor devices are first deployed in smart cities to continuously monitor environmental parameters such as temperature, smoke, flame, relative humidity, fuel moisture, and duff moisture code. These sensed data are then saved in the cloud storage system Firebase. Then, the sensed dataset is updated to the designed model, which pre-processes the data and extracts relevant features from the dataset. The RNN parameters are tuned using whale optimization, which improves the prediction results and attains better accuracy. The performance of the proposed AI-RNN-WO model is validated using a MATLAB tool, and the performance is compared with existing models. The produced model has demonstrated its effectiveness by attaining the highest accuracy (99.5%) and lowest error rate (0.1%). © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

Analysis of Virtual Reality Based on the Internet of Things on Human Psychology ‘Internet of Thoughts’ (IoThs) for Rich Content Extraction Applied Natural Language Processing and Deep Learning 20th

Analysis of Virtual Reality Based on the Internet of Things ...

引用

20th International Conference on Smart Living and Public Health, ICOST 2023

作者： Mah, Pascal Muam Skalna, Iwona Pelech-Pilichowski, Tomasz Derlecki, Tomasz Nasr, Mahmoud Munyeshuri, Eric Amuzang, Gilly Njoh Itoe, Micheal Blake Somaah Tah, Ning Frida Department of Technical Informatics and Telecommunications AGH University of Science and Technology Krakow Poland Department of Business Informatics and Management Engineering AGH University of Science and Technology Krakow Poland Institute of Computer Science AGH University of Science and Technology Krakow Poland Department of Economics and Management Industry AGH University of Science and Technology Krakow Poland Department of Biomedical Engineering AGH University of Science and Technology Krakow Poland Department of Engineering Mathematics and Physics Faculty of Engineering and Technology Future University Cairo Egypt Department of Production Engineering and Logistics Enterprise Silesian University of Technology Gliwice Poland Department of Logistics Management Silesian University of Technology Gliwice Poland

ISBN: (纸本)9783031439490

In the past decade, a lot of challenges to access, assess, and to acquire the needed technological opportunities to teach computers what naturally comes from the human brain and to understand how we naturally react when we rely on technology. The ability to document human thoughts, reactions and behavior to computers has led to the coming of NLP, AI, Dl, & ML. Aim to understand the influence of IoT on humans with the use of DL to achieve content correctness and accuracy with virtual technology. Studies show that the way we think, react, and do the things we think "Internet of thoughts" reflect our personality. The way we think determines the way we react and the way we do things are based on how we think. technology advancement has reinforced a lot of changes in humans which makes humans vulnerable to personal content exposure misappropriation due to the continuously changing nature of humanity and language. The study uses NLP, DL and behavior-oriented drive and influential function and results show that IoT based on VR influences human psychology "Internet of Thoughts". © 2023, The Author(s).

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

SAR Images Change Detection Based on Attention Mechanism-Convolutional Wavelet Neural Network

引用

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12133-12149页

作者： Jiahui, E. Wang, Lu Zhao, Chunhui Mathiopoulos, P. Takis Ohtsuki, Tomoaki Adachi, Fumiyuki Harbin Engineering University College of Information and Communication Engineering Harbin150001 China Ministry of Industry and Information Technology Key Laboratory of Advanced Marine Communication and Information Technology Harbin150001 China National and Kapodistrian University of Athens Department of Informatics and Telecommunications Greece Keio University Department of Information and Computer Science Kanagawa223852 Japan Tohoku University International Research Institute of Disaster Science Sendai Japan

As Synthetic Aperture Radar (SAR) image change detection can continuously acquire target information under all weather conditions, it has been used in the past for various applications. However, it is very challenging to use SAR images for small area change detection due to the presence of noise and the imbalance between classes. To deal with these problems this paper proposes a SAR images change detection scheme which is based upon an Attention Mechanism and Convolutional Wavelet Neural Network (AMCW-NN). Firstly, employing Multi-Scale Superpixel Reconstructed Difference Image (MSRDI) effectively enhances the edge information of the images. Secondly, a twostage Weighted Center Constrained Fuzzy C-Means (WCCFCM) clustering algorithm is proposed. To constrain incorrect migration in the subsequent clustering phase, the algorithm uses a two-stage clustering algorithm. During the first stage weights in the clustering process to improve the accuracy of the resulting clustering centers are introduced while during the second stage, these refined cluster centers can be constrained. Thirdly, a Convolutional Squeeze-and-Excitation (SE) Attention Mechanism Wavelet Neural Network (CSWNN) is proposed which more accurately distinguishes uncertain pixels and thus reduces redundant features. The final Denoising Diffusion Probabilistic Model (DDPM) is introduced into the CSWNN to expand pseudo-labeled samples. Comparing this algorithm with seven sets of contrast experiments on six real SAR datasets shows that the accuracy of detecting changes in small regions is 2%-9% better than the classical algorithm. © 2008-2012 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Design of a Smart Home Door Security System with Face Detection and Smart Bell using ESP32-CAM 6

Design of a Smart Home Door Security System with Face Detect...

引用

6th International Conference on Mobile Computing and Sustainable informatics, ICMCSI 2025

作者： Salsabila, Nurhusna Siswanto, Apri Bayuaji, Luhur Informatics Department Faculty of Engineering Universitas Islam Riau Pekanbaru Indonesia Fac. Data Science and Information Technology INTI International University Malaysia

ISBN: (纸本)9798331522667

This research introduces the design and implementation of an advanced IoT-based home security system aimed at improving both convenience and safety for homeowners. The system integrates a smart doorbell with a mobile application, enabling real-time alerts for guest arrivals. Key features include facial recognition for automated door unlocking and notification of unrecognized visitors. When an unknown face is detected, the system sends an image along with a notification to the homeowner via a Telegram bot. The architecture incorporates the ESP32-CAM microcontroller for face detection, a solenoid lock for automatic door access, and an ultrasonic sensor to detect visitor presence. Extensive testing under various environmental conditions was conducted to assess the system's performance, particularly in terms of face recognition accuracy and notification delivery. The results indicate that the system significantly enhances home security by ensuring timely notifications and automating door access for verified individuals. While the system performs reliably in well-lit environments, challenges remain in low-light conditions. Software testing confirmed the robustness of the underlying logic. This innovative IoT solution overcomes the limitations of traditional security systems, offering a more secure, user-friendly, and efficient alternative for modern home security. © 2025 IEEE.

关键词： Ultrasonic sensors

来源：评论

学校读者我要写书评

暂无评论

Auto-Handover Gateway LoRaWAN based on Autonomous Vehicles 10

Auto-Handover Gateway LoRaWAN based on Autonomous Vehicles

引用

10th IEEE Information technology International Seminar, ITIS 2024

作者： Purnama, Iwan Pane, Rahmadani Sooai, Adri Gabriel Edi, Firman Chamid, Ahmad Abdul Adi, Puput Dani Prasetyo Universitas Labuhanbatu Faculty of Science and Technology Department of Information Technology Labuhanbatu Indonesia Universitas Katolik Widya Mandira Faculty of Computer Science Department of Computer Engineering NTT Kupang Indonesia Batam Institute of Technology Faculty of Industrial Technology Department of Engineering Management Batam City Indonesia Muria Kudus University Department of Informatics Engineering Kudus Indonesia Department of Telecommunication Bandung Indonesia

ISBN: (纸本)9798331521295

This article presents a simulation study on the use of LoRaWAN technology in autonomous vehicles. The research focuses on developing an Auto-Handover Gateway for Autonomous Vehicles, utilizing LoRaWAN parameters such as bit rates, spreading factor (SF), packet loss, and received signal strength. These parameters are tested in simulations of autonomous vehicle movements, which include both regular and random motion patterns. The aim is to assess the performance of LoRaWAN in different scenarios and its impact on communication in autonomous vehicle systems. The simulation results show several key trends. As the vehicle moves further, the spreading factor (SF) increases, and bit rates tend to decrease, indicating a drop in communication performance over longer distances. The research also identifies that the gateway with the closest proximity to the vehicle is selected, both in random and non-random movement scenarios. Additionally, packet loss increases as the distance between the vehicle and the gateway grows, highlighting the challenges of maintaining reliable communication over long distances. These findings provide insights into optimizing LoRaWAN parameters for real-time communication in autonomous vehicles, contributing to the development of intelligent vehicle networks. © 2024 IEEE.

关键词： Packet loss

来源：评论

学校读者我要写书评

暂无评论

Spectral Generalized Category Discovery by training on combined labels 22

Spectral Generalized Category Discovery by training on combi...

引用

2024 IEEE 22nd World Symposium on Applied Machine Intelligence and informatics, SAMI 2024

作者： Mao, Ruixuan Al-Shouha, Modafar Szucs, Gabor Eötvös Loránd University Faculty of Informatics Department of Data Science BudapestH-1117 Hungary Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Telecommunications and Media Informatics BudapestH-1111 Hungary

ISBN: (纸本)9798350317206

Hyperspectral imaging poses major challenges for supervised classification methods due to the high dimensionality of the data. This classification was solved in previous works with known classes. In this paper we present a different aim where the goal is to discover and distinguish novel categories for hyperspectral images in scenarios where some classes are unknown. This belongs to Generalized Category Discovery (GCD) problem with a further task for automatic clustering of unlabeled data with partial knowledge. Our contribution is an additional supervised learning stage on combined labels coming from two sources. Our approach consists of three stages, (i) dimension reduction, (ii) clusters assignment with the Hungarian method, and (iii) classification, where a special training set is constructed from ground true labels and the filtered test set with (dummy) labels that are the predicted categories from the clustering part. The proposed method (S-GCD) was evaluated on two datasets, and our experiments demonstrate that classification on combined labels greatly improves both classification and clustering scores. © 2024 IEEE.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

A Hand Gesture-Operated System for Rehabilitation Using an End-to-End Detection Framework

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第2期5卷 698-708页

作者： Dutta, H Pallab Jyoti Bhuyan, M.K. Neog, Debanga Raj Macdorman, Karl F. Laskar, R.H. Department of Electronics and Electrical Engineering Indian Institute of Technology Assam Guwahati781039 India Department of Electronics and Electrical Engineering and Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Assam Guwahati781039 India Mehta Family School of Data Science and Artificial Intelligence Indian Institute ofTechnology Assam Guwahati781039 India School of Informatics and Computing Indiana University IN46202 United States Department of Electronics and Communication Engineering National Institute of Technology Assam Silchar788010 India

In this article, we propose a hand gesture-operated system as an AI application to relieve discomfort and restore function in hand and arm movements caused by injuries and nerve and muscle complications. The system trains patients with hand exercises, such as performing hand gestures accurately, traversing within specified bounds, and operating a hand gesture calculator. However, the system requires accurate hand gesture detection, which is impeded by background clutter and variations in illumination and in the hand's size and angle. To address this, we developed a robust hand detection module that uses a single-stage transformer deep network. The transformer network encodes global information and uses bipartite matching to reduce the frequency of spurious detections. It drives a regression head and a classification head to localize the hand gesture in a bounding box and assign it a class label. Hand keypoints are also detected to support drawing, path traversal, and calculator use. The approach is evaluated on two benchmark datasets: 1) OUHANDS and 2) NUS. The method yields 89.6% accuracy for OUHANDS and 100% for NUS. These results indicate that precise hand detection can support a robust system for rehabilitation through hand exercises. Our experiments confirm that the users' hand function progressively improved. © 2023 IEEE.

关键词： Deep neural networks

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：