检索结果-内蒙古大学图书馆

26th International Conference on computer and Information technology, ICCIT 2023

作者： Islam Arif, Md. Ariful Islam, Zohirul Sharmee, Sharmin Sultana Akhtaruzzaman, M. Military Institute of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

Securing the nation's airspace demands the integration of an advanced air defense system. Automation technology can be incorporated to deploy anti-missile rockets, acting as a deterrent against various air-missile threats. This research aims to identify potential threats within a protected area and integrate an automatic system into the air defense measures based on detection. In the event of identifying incoming missiles, decisions will be made by gathering and validating information on specific issues through dedicated radars. Special emphasis is placed on utilizing a Fuzzy system to determine the nature of the threat, distinguishing between scrambling or surface-to-air missile launches based on distance, secondary radar input, and the speed of the airborne object. The paper provides a comprehensive comparison of defuzzification methods, demonstrating their effectiveness in modern air defense systems. The chosen method proves suitable, emphasizing its pertinence to contemporary air defense needs. The Fuzzy system offers insights into optimal times, types, and launch locations for interceptors or surface-to-air missiles (SAMs) within an integrated air defense system. The research evaluates the defuzzification process using five different methods, with MoM method yielding notably superior results. The incorporation of technology is crucial for ensuring the safety of protected areas, key installations, and the dynamic decision-making processes inherent in air defense systems, necessitating the use of fuzzy logic-based systems. © 2023 IEEE.

关键词： Air defense fuzzy inference system fuzzy logic integrated system intelligent system

来源：评论

学校读者我要写书评

暂无评论

Enhancing Dietary Monitoring using Deep Learning: Food Recognition and Calorie Estimation 15

Enhancing Dietary Monitoring using Deep Learning: Food Recog...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Suneela, M. Sahithi, Mandadi Vamsi, Nuthalapati Naik, Nunsavathu Balaji Chowdary, Narra Sujitha Vignan's Lara Institute of Technology & Science College Dept. of Computer Science & Engineering Guntur India

ISBN: (纸本)9798350370249

The project 'Enhancing Dietary Monitoring Using Deep Learning:Food Recognition And Calorie Estimation' introduces an innovative method aimed at improving dietary tracking and fostering healthier eating habits. By employing cutting-edge deep learning techniques, its core objective is to precisely identify various food items and rapidly estimate their caloric content, offering users advanced and personalized monitoring capabilities. Utilizing the Python programming language alongside the MobileNet architecture model, the project underwent rigorous training and evaluation using the expansive Food 101 dataset, comprising 37,046 food images distributed across 101 distinct classes. Notably, the model demonstrated exceptional performance, achieving a training accuracy of 97.02% and a validation accuracy of 98.17%, highlighting the efficacy of this approach in accurately categorizing a wide array of food items. This system provides users with several crucial functionalities. Furthermore, it furnishes users with essential insights by estimating the caloric content of recognized foods, facilitating effective monitoring of dietary intake. The intelligent diet monitoring capabilities enabled by Food Recognition and Calorie Estimation empower users to make informed choices regarding their dietary preferences. Through the continuous tracking and analysis of their daily food consumption, users can glean valuable insights into their nutritional habits, set personalized goals, and make necessary adjustments to achieve a well-balanced and healthy diet. Enhancing Dietary Monitoring Using Deep Learning:Food Recognition And Calorie Estimation stands as a prominent illustration of successful implementation of deep learning techniques, particularly with the utilization of the MobileNet architecture, for food recognition and calorie estimation. With its remarkable accuracy, real-time processing capabilities, and intelligent monitoring features, this project has the potential to revolutioni

关键词： Convolutional Neural Network (CNN) algorithm Datasets MobileNet architecture Web application

来源：评论

学校读者我要写书评

暂无评论

Exploring the Performance of Diffusion Models in Weighted Social Networks 2

Exploring the Performance of Diffusion Models in Weighted So...

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Singhal, Megh Saxena, Bhawna Jaypee Institute of Information Technology Department of Computer Science & Engineering and Information Technology Noida India

ISBN: (纸本)9798350371055

In the present time, social networks have become a quintessential part of people's lives, by transforming how people connect and communicate. Social networks have become an important medium for information diffusion and social interactions. The rate of diffusion often depends upon network topology, influencing power of the initial adopters and the underlying node activation criteria. Most real-world social networks are weighted in nature, with edge weights representing the tie strength between the two connected nodes. In our research, we explore and analyze the performance of popular diffusion models in weighted social networks. The performance of 4 prevalent diffusion models, namely - Independent Cascade, Weighted Cascade, Trivalency and Linear Threshold, is compared with regards to the attained spread of information. The study was performed on 2 real-world weighted social networks. © 2024 IEEE.

关键词： Independent Cascade Influence Maximization Information Diffusion Linear Threshold Weighted Social Networks

来源：评论

学校读者我要写书评

暂无评论

Siamese Multiple Attention Temporal Convolution Networks for Human Mobility Signature Identification 27

Siamese Multiple Attention Temporal Convolution Networks for...

引用

27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Zheng, Zhipeng Jiang, Yuchen Zhang, Shiyao Wei, Xuetao Research Institute for Trustworthy Autonomous Systems Southern University of Science and Technology Department of Computer Science and Engineering China

ISBN: (纸本)9798331505929

The Human Mobility Signature Identification (HuMID) problem stands as a fundamental task within the realm of driving style representation, dedicated to discerning latent driving behaviors and preferences from diverse driver trajectories for driver identification. Its solutions hold significant implications across various domains (e.g., ride-hailing, insurance), wherein their application serves to safeguard users and mitigate potential fraudulent activities. Present HuMID solutions often exhibit limitations in adaptability when confronted with lengthy trajectories, consequently incurring substantial computational overhead. Furthermore, their inability to effectively extract crucial local information further impedes their performance. To address this problem, we propose a Siamese Multiple Attention Temporal Convolutional Network (Siamese MA-TCN) to capitalize on the strengths of both TCN architecture and multi-head self-attention, enabling the proficient extraction of both local and long-term dependencies. Additionally, we devise a novel attention mechanism tailored for the efficient aggregation of multi-scale representations derived from our model. Experimental evaluations conducted on two real-world taxi trajectory datasets reveal that our proposed model effectively extracts both local key information and long-term dependencies. These findings highlight the model's outstanding generalization capabilities, demonstrating its robustness and adaptability across datasets of varying sizes. © 2024 IEEE.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Harnessing the Potential of Visual and Audio Data using IoT in Multimedia Analytics 1

Harnessing the Potential of Visual and Audio Data using IoT ...

引用

1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Doni, A. Ronald Nirmalrani, V. Franklin, Ramya G Poornima, D. Sathyabama Institute of Science & Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350359756

In the cutting-edge digital panorama, the amalgamation of multimedia analytics and the Internet of Things (IoT) has ignited a paradigm shift in information usage. This transformation leverages the abundance of visible and audio records, encompassing photos, films, and sound recordings, facilitated via the interconnected IoT community. This convergence gives challenges, which includes information privacy troubles, bandwidth limitations, and the need for superior information processing algorithms. Multimedia analytics, as a subject, revolves throughout the extraction of significant insights from multimedia records. IoT, then again, constitutes a widespread array of interconnected devices and sensors that generate copious portions of visible and audio facts. This mixture permits the actual-time collection, transmission, and evaluation of multimedia information, thereby empowering decision-makers to make extra informed alternatives and benefit profound insights at some point of various domain names. Hence, this paper proposes harnessing the potential of the Internet of Things in Multimedia Analytics (IoT-MA) to enhance the visible and audio facts. The convergence of IoT and multimedia analytics opens up a realm of exceptional possibilities spanning numerous industries and programs. This synergy has a long way-reaching implication in sectors along side healthcare, production, transportation, agriculture, and safety. IoT in multimedia analytics plays a pivotal feature in protection and surveillance, improving public protection, catastrophe response, and danger control. This article sets the stage for a comprehensive exploration of the potential of visible and audio information harnessed through IoT in multimedia analytics. The studies delves into the technologies, applications, and implications of this integration, shedding light on how it's miles reshaping the interactions with the sector and revolutionizing decision-making in an more and more interconnected and informat

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Exploring Social Virtual Worlds for Virtual Communication 2

Exploring Social Virtual Worlds for Virtual Communication

引用

2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Levin McLeish, M. Lakshmanan, M. Judgi, T. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350347982

Social Virtual Worlds have begun to offer great potential for communication in recent years. The recent development of SVWs, VR and blockchain has led to the metaverse. SVW is all about creating an immersive virtual social environment that simulates a three dimensional social environment for communication, which is primarily based on personal presence and social presence. In this research we connect users in a surreal virtual environment and establish voice communication in order to make them feel each other's presence. Users can interact and hangout in the virtual world. © 2023 IEEE.

关键词： Virtual reality

来源：评论

学校读者我要写书评

暂无评论

Securing Data storage in Cloud after Migration using Immutable Data Dispersion 4

Securing Data storage in Cloud after Migration using Immutab...

引用

4th International Conference for Emerging technology, INCET 2023

作者： Rajesh Kumar, C. Aroul Canessane, R. Sathyabama Institute of Science &technology Chennai India Sathyabama Institute of Science & Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350335750

Cloud computing has emerged as a technology behemoth with applications in a wide range of fields. When data is being migrated from offline data centres and stored in multiple cloud environments part of the control is always with the Cloud Service Providers(CSPs) leads to security concerns. The data stored in the cloud may sometimes be compromised even though the CSPs may take precautions to avoid such situations. In this paper, we discuss securely storing the data using the data dispersion technique by breaking the data into multiple segments and combining it with encryption along with replication. The division of data and storing it in the cloud helps in protecting the complete data even if an attacker tries to access the data it will not be easy for him to make sense of the retrieved data because the data is already being encrypted and combined with dispersion and replication adds to the complexity of retrieval. Security is achieved as the dispersed data is spread across multiple locations which makes it difficult for an attacker to get all the segments. In most scenarios be able it depends on traditional encryption techniques alone to protect the data. Here, We propose focusing more on how data is stored in the cloud to relieve the system of costly computational methodologies. In this strategy, the trade-off between security and the data retrieval time must also be considered. © 2023 IEEE.

关键词： Cloud storage

来源：评论

学校读者我要写书评

暂无评论

Two-view attention-guided convolutional neural network for mammographic image classification

引用

CAAI Transactions on Intelligence technology 2023年第2期8卷 453-467页

作者： Lilei Sun Jie Wen Junqian Wang Yong Zhao Bob Zhang Jian Wu Yong Xu College of Computer Science and Technology Guizhou UniversityGuiyangChina Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of TechnologyShenzhenChina Harbin Institute of Technology ShenzhenChina School of Electronic and Computer Engineering Shenzhen Graduate School of Peking UniversityShenzhenChina Department of Computer and Information Science University of MacaoTaipaChina Science for Life Laboratory KTH Royal Institute of TechnologyStockholmSweden

Deep learning has been widely used in the field of mammographic image classification owing to its superiority in automatic feature ***,general deep learning models cannot achieve very satisfactory classification results on mammographic images because these models are not specifically designed for mammographic images and do not take the specific traits of these images into *** exploit the essential discriminant information of mammographic images,we propose a novel classification method based on a convolutional neural ***,the proposed method designs two branches to extract the discriminative features from mammographic images from the mediolateral oblique and craniocaudal(CC)mammographic *** features extracted from the two-view mammographic images contain complementary information that enables breast cancer to be more easily ***,the attention block is introduced to capture the channel-wise information by adjusting the weight of each feature map,which is beneficial to emphasising the important features of mammographic ***,we add a penalty term based on the fuzzy cluster algorithm to the cross-entropy function,which improves the generalisation ability of the classification model by maximising the interclass distance and minimising the intraclass distance of the *** experimental results on The Digital database for Screening Mammography INbreast and MIAS mammography databases illustrate that the proposed method achieves the best classification performance and is more robust than the compared state-ofthe-art classification methods.

关键词： convolutional neural network deep learning medical image processing mammographic image

来源：评论

学校读者我要写书评

暂无评论

Optimized Cognitive Learning Model for Energy Efficient Fog-BAN-IoT Networks

引用

computer Systems science & engineering 2022年第12期43卷 1027-1040页

作者： S.Kalpana C.Annadurai SRM Institute of Science and Technology Chennai600089India Sri Sivasubramaniya Nadar College of Engineering Chennai603110India

In Internet of Things (IoT), large amount of data are processed andcommunicated through different network technologies. Wireless Body Area Networks (WBAN) plays pivotal role in the health care domain with an integration ofIoT and Artificial Intelligence (AI). The amalgamation of above mentioned toolshas taken the new peak in terms of diagnosis and treatment process especially inthe pandemic period. But the real challenges such as low latency, energy consumption high throughput still remains in the dark side of the research. This paperproposes a novel optimized cognitive learning based BAN model based on FogIoT technology as a real-time health monitoring systems with the increased network-life time. Energy and latency aware features of BAN have been extractedand used to train the proposed fog based learning algorithm to achieve low energyconsumption and low-latency scheduling algorithm. To test the proposed network,Fog-IoT-BAN test bed has been developed with the battery driven MICOTTboards interfaced with the health care sensors using Micro Python *** extensive experimentation is carried out using the above test beds and variousparameters such as accuracy, precision, recall, F1score and specificity has beencalculated along with QoS (quality of service) parameters such as latency, energyand throughput. To prove the superiority of the proposed framework, the performance of the proposed learning based framework has been compared with theother state-of-art classical learning frameworks and other existing Fog-BAN networks such as WORN, DARE, L-No-DEAF networks. Results proves the proposed framework has outperformed the other classical learning models in termsof accuracy and high False Alarm Rate (FAR), energy efficiency and latency.

关键词： Fog-IoT-BAN optimized learning model internet of things micott worn dare l-deaf networks quality of service

来源：评论

学校读者我要写书评

暂无评论

Vitamin Deficiency Detection Using Neural Networks 9

Vitamin Deficiency Detection Using Neural Networks

引用

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

作者： Krishna Chaithanya, T. Gupta, Jiniya Roobini, M.S. Department of Computer Science and Engineering Chennai600119 India Sathyabama Institute of Science and Technology Chennai600119 India

ISBN: (纸本)9798350350845

Vitamin deficiency is a pervasive health issue affecting millions globally, often leading to severe health complications if undiagnosed. Various vitamin deficiencies can be identified by identifiable symptoms manifesting in numerous areas across the human body. Blood tests are most commonly used to detect deficiencies. However, blood tests are expensive and have limited availability. The project aims to develop an automated system to detect vitamin deficiencies using Mobilenet, convolutional neural networks (CNN), NasNet-mobile, inception modules, and artificial neural networks (ANN). The program allows users to detect potential vitamin deficiencies without needing blood samples by examining images of the eyes, lips, tongue, and nails. Here we have considered the dataset of abnormalities caused by vitamin deficiencies like glaucoma eyes, bluish nails, and other abnormalities of lips and tongue. We have trained the model on these image datasets, by leveraging image recognition and data analytics, the proposed system offers a quick, efficient, and non-invasive method of detection thereby streamlining the diagnostic process and facilitating early intervention. The aim is to provide an economical, accessible, and efficient clinical screening method. Moreover, the platform furnishes tailored dietary suggestions to tackle pinpointed inadequacies, thus alleviating health hazards linked with insufficient nourishment. By furnishing an easily reachable resource for prompt identification and action, our system aids in tackling a prevalent worldwide health concern. The program functions as a beneficial aid for individuals to combat a widespread global challenge, impacting millions due to inadequate comprehension of nutrition. Serving as a valuable resource, it addresses an international issue, assisting those facing nutritional uncertainties worldwide. © 2024 IEEE.

关键词： Blood

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：