检索结果-内蒙古大学图书馆

2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Kumar, Bura Vijay Husain, Saif O. Kulkarni, Megha Das, Abhijeet Gurumoorthy, Sasikumar School of Computer Science and Artificial Intelligence Sr University Warangal India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Civil Engineering Bengaluru India Department of Civil Engineering Odisha Bhubaneswar India School of Engineering and Technology Dhanalakshmi Srinivasan University Computer Science and Engineering Tiruchirappalli India

ISBN: (纸本)9798350373110

Monitoring Land Use and Land Cover (LULC) is beneficial to several applications like non- rural development, planning, agricultural, forest departments etc. which enables for global transformation. In recent years, satellite images have become rapid growth in land surveys and environmental planning's. In this research, for LULC classification introduced a framework called Multi-Scale Convolutional Auto Encoder with Artificial Bee Colony (MSCAE). This research is evaluated by images collected from Eurosat dataset. Once dataset is collected preprocessing techniques are employed such as normalization to improve the quality of images. After preprocessing, haralick texture is applied to extract Spatial, texture from preprocessed images. In the next phase, Artificial Bee Colony is implemented to consider optimal features to improve the performance. ABC uses bit vectors to select optimal features and fed to classifier based on fitness function which automatically improves the accuracy. These optimal features are provided to MSCAE classifier to classify LULC classes. The MSCAE incorporates convolutional kernel to understand and classify images effectively. The proposed method outperformed with accuracy of 98.2 % when compared to state-of-art methods such as Densnet-121, CNN and 1D CNN. © 2024 IEEE.

关键词： Environmental management

来源：评论

学校读者我要写书评

暂无评论

A Robust Automated Framework for Classification of CT Covid-19 Images Using MSI-ResNet

引用

computer Systems science & engineering 2023年第6期45卷 3215-3229页

作者： Aghila Rajagopal Sultan Ahmad Sudan Jha Ramachandran Alagarsamy Abdullah Alharbi Bader Alouffi Department of Computer Science and Business Systems Sethu Institute of TechnologyKariapattiVirudhunagar626115TamilnaduIndia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharj11942Saudi Arabia Department of Computer Science and Engineering School of EngineeringKathmandu UniversityBanepaKathmanduNepal Department of CSE University College of EngineeringPanrutiTamilnaduIndia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Nowadays,the COVID-19 virus disease is spreading *** are some testing tools and kits available for diagnosing the virus,but it is in a lim-ited *** diagnose the presence of disease from radiological images,auto-mated COVID-19 diagnosis techniques are *** enhancement of AI(Artificial Intelligence)has been focused in previous research,which uses X-ray images for detecting *** most common symptoms of COVID-19 are fever,dry cough and sore *** symptoms may lead to an increase in the rigorous type of pneumonia with a severe *** medical imaging is not suggested recently in Canada for critical COVID-19 diagnosis,computer-aided systems are implemented for the early identification of COVID-19,which aids in noticing the disease progression and thus decreases the death ***,a deep learning-based automated method for the extraction of features and classi-fication is enhanced for the detection of COVID-19 from the images of computer tomography(CT).The suggested method functions on the basis of three main pro-cesses:data preprocessing,the extraction of features and *** approach integrates the union of deep features with the help of Inception 14 and VGG-16 *** last,a classifier of Multi-scale Improved ResNet(MSI-ResNet)is developed to detect and classify the CT images into unique labels of *** the support of available open-source COVID-CT datasets that consists of 760 CT pictures,the investigational validation of the suggested method is *** experimental results reveal that the proposed approach offers greater performance with high specificity,accuracy and sensitivity.

关键词： Covid-19 CT images multi-scale improved ResNet AI inception 14 and VGG-16 models

来源：评论

学校读者我要写书评

暂无评论

Unpredictable Switching for Cyber-Physical Security Against Worst-case Attackers 63

Unpredictable Switching for Cyber-Physical Security Against ...

引用

63rd IEEE Conference on Decision and Control, CDC 2024

作者： Kanellopoulos, Aris Fotiadis, Filippos Vamvoudakis, Kyriakos G. Sandberg, Henrik Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm10044 Sweden Georgia Institute of Technology Daniel Guggenheim School of Aerospace Engineering AtlantaGA30332 United States

ISBN: (纸本)9798350316339

In this paper, we investigate the problem of securing a system against actuator attacks. Specifically, we employ an unpredictability-based defense algorithm according to the principles of Moving Target Defense, while explicitly considering the worst-case attack that can be launched into the system. Consequently, we allow the system to alternate between different actuating modes, but also between optimal and robust control schemes. Via game-theoretic methods, we derive mixed strategies dictating the probabilities of utilizing different controllers for a given mode of operation, whereas an entropy-augmented optimization problem gives the probabilities of using these different modes. Finally, the overall stability of the system is analyzed, and simulation results showcase the efficacy of our proposed method. © 2024 IEEE.

关键词： Cyber attacks

来源：评论

学校读者我要写书评

暂无评论

Image Emotion Classification Network Based on Multilayer Attentional Interaction,Adaptive Feature Aggregation

引用

computers, Materials & Continua 2023年第5期75卷 4273-4291页

作者： Xiaorui Zhang Chunlin Yuan Wei Sun Sunil Kumar Jha Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China Adani University AhmedabadGujaratIndia

The image emotion classification task aims to use the model to automatically predict the emotional response of people when they see the *** have shown that certain local regions are more likely to inspire an emotional response than the whole ***,existing methods perform poorly in predicting the details of emotional regions and are prone to overfitting during training due to the small size of the ***,this study proposes an image emotion classification network based on multilayer attentional interaction and adaptive feature *** perform more accurate emotional region prediction,this study designs a multilayer attentional interaction *** module calculates spatial attention maps for higher-layer semantic features and fusion features through amultilayer shuffle attention *** layer-by-layer up-sampling and gating operations,the higher-layer features guide the lower-layer features to learn,eventually achieving sentiment region prediction at the optimal *** complement the important information lost by layer-by-layer fusion,this study not only adds an intra-layer fusion to the multilayer attention interaction module but also designs an adaptive feature aggregation *** module uses global average pooling to compress spatial information and connect channel information from all ***,the module adaptively generates a set of aggregated weights through two fully connected layers to augment the original features of each ***,the semantics and details of the different layers are aggregated through gating operations and residual connectivity to complement the lost *** reduce overfitting on small datasets,the network is pre-trained on the FI dataset,and further weight fine-tuning is performed on the small *** experimental results on the FI,Twitter I and Emotion ROI(Region of Interest)datasets show that the proposed network exceeds existing image emotion classification methods

关键词： Attentionmechanism emotional region prediction image emotion classification transfer learning

来源：评论

学校读者我要写书评

暂无评论

Real-time Audio Recognition for Hearing Impaired 3

Real-time Audio Recognition for Hearing Impaired

引用

3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Peddi, Muralidhar Vardhan, S H V Harsha Peddi, Giridhar Ponnam, Satwik Rani, C. Rajesh Kumar, M. Vellore Institute of Technology School of Electronics Engineering Vellore India University of California Riverside Electrical Engineering Riverside United States Arizona State University Computer Science Tempe United States Vellore Institute of Technology School of Electrical Engineering Vellore India

ISBN: (纸本)9798350372120

Audio recognition technology has seen significant advancements in recent years, offering promising solutions to improve communication and accessibility for deaf individuals. This paper explores the landscape of audio recognition technologies tailored specifically for the deaf community. It delves into the challenges faced by deaf individuals in everyday communication scenarios and how innovative audio recognition systems can mitigate these challenges. The abstract discusses various approaches, including speech-to-text systems, sound recognition algorithms, and real-time captioning tools, highlighting their effectiveness and limitations in facilitating seamless communication for deaf individuals. Furthermore, it examines the integration of machine learning and artificial intelligence techniques in enhancing the accuracy and adaptability of audio recognition systems for diverse environments and user preferences. Additionally, the abstract explores the potential socio-economic impacts of the widespread adoption of audio recognition technologies in empowering deaf individuals, fostering inclusion, and promoting equal opportunities in education, employment, and social interactions. © 2024 IEEE.

关键词： Audio signal processing

来源：评论

学校读者我要写书评

暂无评论

FQCSpark: Efficient Spark-based Parallel Compression Algorithm for FASTQ Genome Sequences 25

FQCSpark: Efficient Spark-based Parallel Compression Algorit...

引用

25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Ji, Yimu Fa, Hu Yao, Haichang Shao, Sisi Wu, Mengxue Fang, Houzhi Liu, Qiang Liu, Shangdong Nanjing University of Posts and Telecommunications School of Computer Science Nanjing China Nanjing Institute of Industry Technology School of Computer and Software Nanjing China

ISBN: (纸本)9781665405270

The rapid development of Next-Generation Sequencing (NGS) technologies has posed serious challenges to the storage and transmission of genomic data, and the bioinformatics community urgently needs efficient genome compression algorithms to support genome analysis. The existing acceleration approaches for genome compression algorithms are mostly multithreading and limited to a single machine, which cannot adapt to the demand of large-scale genome compression in the distributed environment of cloud computing. In this paper, we propose a Spark-based efficient parallel compression algorithm for FASTQ genome sequences - FQCSpark. Experimental results show that FQCSpark outperforms existing algorithms with good compression ratios by several times in speed. This is due to the fine-grained degree of parallelism design and well-designed parallel operator flow. More importantly, the degree of parallelism design in this paper is also applicable to other algorithms which compress blocks independently. Meanwhile, FQCSpark provides good compression ratios, especially on the *** dataset, which is 15.4% better than the latest open-source genome compression tool - Genozip. FQCSpark is the first known Spark-based parallel compression algorithm for FASTQ genome sequences. © 2022 IEEE.

关键词： Genes

来源：评论

学校读者我要写书评

暂无评论

Enhancing the Accuracy of Emoji Conversion from Text using Novel Lagrangian Support Vector Machine Against Long Short-Term Memory Algorithm

Enhancing the Accuracy of Emoji Conversion from Text using N...

引用

2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Saravanan, M.S. Teja, Sai Surya Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350308167

This research study aims to employ a unique method that integrates Lagrangian Support Vector Machine and Long Short-Term Memory techniques to forecast emoji conversions from text on social media platforms. The primary focus of evaluation will be the prediction model's accuracy. This research study involves two distinct groups: the novel Lagrangian Support Vector Machine and Long Short-Term Memory models. The dataset utilized in this study focuses on predicting emojis using text and was obtained from Kaggle. The dataset is openly accessible and comprises 7,476 rows of textual data. In order to facilitate thorough evaluation, the dataset has been split into a training set, comprising 5,980 rows, and a testing set, consisting of 1,496 rows. The accuracy of the prediction models was calculated with a statistical power (G power) of 80%. The experimental study involved conducting twenty iterations using the aforementioned models. This study revealed that the Novel Lagrangian Support Vector Machine achieved an accuracy of 57.68% in predicting emojis from text, while the Long Short Term Memory model achieved an accuracy of 33.33%. Conducting a statistical analysis employing an Independent Sample T-Test with a p-value of 0.001 (significantly lower than the conventional significance level of 0.05), the study conclusively established that the Novel Lagrangian Support Vector Machine outperformed the Long Short Term Memory model by a significant margin in terms of performance. The findings suggest that the Novel Lagrangian Support Vector Machine approach is successful in accurately predicting emojis from text. © 2023 IEEE.

关键词： Emoji Long Short-Term Memory Machine Learning Novel Lagrangian Support Vector Machine Social Media Text

来源：评论

学校读者我要写书评

暂无评论

Optimizing Dynamic Source Routing Protocol Using Computational Intelligent Approach 4

Optimizing Dynamic Source Routing Protocol Using Computation...

引用

4th International Conference on computer, Communication, Control and Information Technology, C3IT 2024

作者： Kathane, Kavita A. Shete, Rushikesh M. Nawkhare, Rahul Damahe, Lalit B. Jadhav, Nitin N. Dehankar, Jiwan N. Yashwantrao Chauhan College of Engineering Department of Computer Science Engineering Maharashtra Nagpur India St. Vt. Palloti College of Engineering Department of Computer Science Engineering Maharashtra Nagpur India School of Electronics and Electrical Engineering Lovely Professional University Punjab Phagwara India Yashwantrao Chauhan College of Engineering Department of Computer Science Engineering India Krishna Institute of Medical Science Taluka-Karad Department of Medicines Dist-Satara Maharashtra India

ISBN: (纸本)9798350389128

Wireless sensor networks have undergone significant advancements in recent decades, with research efforts focusing on areas such as routing, security, localization, deployment, and energy efficiency. One emerging trend in WSN research is the integration of Swarm Intelligence-based techniques, which have shown promise in improving efficiency, performance, robustness, and scalability. The objective of this research paper is to evaluate the performance of various existing routing protocols through simulations including DSR with the analysis of ACO routing protocol's performance in relation to routing overhead, end-to-end delay, throughput, and packet delivery rate particularly, the study focuses on comparing the performance of the DSR protocol with ACODSR. The simulation-based network simulator NS2 results and data analysis reveal that the ACO Based Routing Protocol demonstrates a significant improvement in evaluation of the ACO routing protocol's performance in comparison with existing routing protocols for wireless sensor networks. This research contributes to the ongoing exploration of swarm intelligence-based techniques in WSNs and provides valuable insights into the comparative performance of routing protocols, facilitating informed decision -making in the design and implementation of WSNs.. According to the simulated outcomes,the average performance of ACO-DSR is better than conventional DSR regarding QoS parameters like Packet delivery ratio was increased from 13.406% to 16.728% and the throughput was higher by 0.7638 mbps to 1.0812 mbps. The routing overhead was low from 2.604% to 2.012% and the AE2E delay was found low 9.754 ms. to 8.19 ms. in comparison to DSR. © 2024 IEEE.

关键词： Swarm intelligence

来源：评论

学校读者我要写书评

暂无评论

An Architectural Approach for Enhanced Data Interoperability Across Building Systems 7

An Architectural Approach for Enhanced Data Interoperability...

引用

7th Conference on Cloud and Internet of Things, CIoT 2024

作者： Yefi, Peter Ejaz, Sikandar Menon, Ramanunni Parakkal Eicker, Ursula Guéhéneuc, Yann-Gaël Concordia University Computer Science and Software Engineering Dept. Montreal Canada Concordia University Institute for Information Systems Engineering Montreal Canada Institute for Solar Research German Aerospace Center Jülich Germany Concorida University NextGen Cities Institute Montreal Canada

ISBN: (纸本)9798350356717

Access to building data is crucial for creating portable applications and improving building operation and energy efficiency. Ensuring data transmutability is essential for facilitating research and overcoming the diverse data representation, management, and collection methods across various building management systems (BMSs). Different BMSs use multiple interfaces for data exchange. Some BMSs provide Application Programming Interfaces (APIs) for data exchanges, while others have gateways connecting to cloud services for application subscription and data access. This study introduces a five-layered architecture that describes how client applications, researchers, and other interested stakeholders can exchange data with different BMSs. Our objective is to help researchers and practitioners understand the various ways of accessing transmutable (capable of being transformed into a compatible format) data from BMSs, regardless of the building management system a building uses. Through transmutable access to building systems' data, we aim to enable transferable energy-efficient-related applications for buildings. We evaluate our work with a building model of the Varennes Library in Varennes, QC, Canada. The building model includes information about the building, floors, rooms, electric meter and historical time series data of the meter, weather station and its historical time series data. The model also has carbon dioxide concentration sensors, temperature sensors, humidity sensors, and historical time series data of the sensors. We include two test client programs to show how they access the transmutable data of the building through a model. We conclude that the five-layered architecture facilitates the exchange of transmutable data across diverse BMSs, making it a valuable tool for researchers and practitioners. © 2024 IEEE.

关键词： Middleware

来源：评论

学校读者我要写书评

暂无评论

Deep Acne: Advancing Automated Acne Identification through Deep Neural Networks 1

Deep Acne: Advancing Automated Acne Identification through D...

引用

1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Trivedi, Naresh Kumar Maheshwari, Himani Tiwari, Raj Gaurang Gautam, Vinay Agarwal, Ambuj Kumar Sharma, Manish Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University School of Computing Uttrakhand Dehradun India Sharda University Sharda School of Engineering and Technology Department of Computer Science and Engineering Greater Noida India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttrakhand Dehradun India

ISBN: (纸本)9798350367720

Acne vulgaris is a common dermatological illness that impacts a considerable segment of the population, especially during adolescence. Effective acne medication and management rely on the timely and accurate detection of lesions. The classification of skin lesions is a task in medical image analysis where deep learning methodologies have shown promising outcomes in recent years. This research seeks to assess the efficacy of various advanced object detection models, including Faster R-CNN, R-FCN, SDD, and YOLO, for the automated categorization of acne lesions in dermatological images. YOLO emerges as the best-performing model (with 97.3% accuracy) for acne classification based on these results, closely followed by R-FCN. This study contributes to the development of automated acne classification systems, which can assist dermatologists in clinical practice, telemedicine consultations, and large-scale skin disease screening programs. © 2024 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：