检索结果-内蒙古大学图书馆

17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Yadav, Dhyan Chandra Pal, Saurabh Yadav, Rakesh Kumar Pant, Hemlata J S University Department of Computer Science Shikohabad India Vbsp University Department of Computer Applications Jaunpur India Maharishi University of Information Technology Department of Computer Science and Engineering Lucknow India Ambalika Institute of Management and Technology Department of Information Technology Lucknow India

ISBN: (纸本)9789380544472

Globally, cardiovascular disease is the leading cause of death. In CVDs, the heart is a vital organ for supplying blood to other body organs. Complex datasets with heart problems are predicted by machine learning methods. Analysts and experts in diagnosis can easily locate reports of data on CVDs at the primary level. In the study, we investigate the performance of neural networks when used on datasets for CVD prediction. The performance of the method is assessed based on the characteristics of the dataset using Random Forest and Extra Tree feature selection strategies with the following basic analyses: accuracy score, precision, recall, f1-score, and roc. Our study highlights the critical properties of CVD datasets in order to determine which accuracy measures work best for predicting and diagnosing CVD. Our main findings were predictive Neural Network performance with an accuracy metric based on Random Forest and Extra Tree, highlighting key features for CVD diagnosis and prediction. Finally, a neural network using the Extra Tree features selection strategy achieved 97% accuracy, while a neural network using Random Forest achieved 98% accuracy. These can be effectively used to improve current CVD diagnosis efforts and help patients all around the world. Especially in places where there is a scarcity of medical professionals. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Uncovering Android Zero-Day Threats: The Zero-Vuln Approach with Deep and Zero-Shot Learning 2

Uncovering Android Zero-Day Threats: The Zero-Vuln Approach ...

引用

2nd International Conference on Computing and Data Analytics, ICCDA 2024

作者： Palarimath, Suresh Maran, Pyingkodi Wilfred Blessing, N.R. Kavitha, S.J. Cibi, A. Sutherlin, Subitha G. College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman Kongu Engineering College Department of Computer Applications Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Department of Computer Science and Engineering Pallavaram Chennai India Virtusa Consulting Services Private Limited Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Kanyakumari India

ISBN: (纸本)9798331542573

The growing occurrence of cybersecurity hazards, such as Android zero-day vulnerabilities, poses substantial difficulties because they are unattended and imperceptible. With the increasing popularity of Android smartphones, hackers are taking advantage of these weaknesses to quickly spread advanced malware. Conventional detection approaches frequently prove inadequate due to the absence of shown criteria for emergent threats. In order to address this crucial problem, this article presents the Zero-Vuln method, an advanced technique specifically developed to identify and categorize previously unidentified malware on Android platforms. Zero-Vuln combines sophisticated supervised deep learning methods with zero-shot learning algorithms to accurately detect zero-day threats. Through the utilization of comprehensive datasets, the method attains an average classification metric score of 84.22%, showcasing exceptional accuracy, precision, and recall. This advanced solution significantly improves the ability to identify and handle Android zero-day threats, representing a major progress in cybersecurity research and implementation. The Zero-Vuln strategy provides a strong and adaptable solution for the constantly changing mobile security risks, representing a notable advancement in the sector. © 2024 IEEE.

关键词： Zero-shot learning

来源：评论

学校读者我要写书评

暂无评论

Intrusion Detection at the Edge Computing: A Deep Learning Approach Using the UNSW-NB15 Dataset

Intrusion Detection at the Edge Computing: A Deep Learning A...

引用

International Conference on Communication Systems and Network Technologies (CSNT)

作者： Vipin Kumar Vivek Kumar Abhay Pratap Singh Bhadauria Jay Dixit Amit Kumar Department of Computer Science Gurukula Kangri (Deemed to be University) Haridwar India Department of Computer Engineering & Applications GLA University Mathura Department of Computer Engineering & Applications GLA University Mathura UP

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Edge computing has transformed technology by enabling seamless connections between IoT devices, but it also introduces significant security challenges. EC is crucial for providing minimal latency processing and reducing the load on centralized servers, ensuring timely and scalable intrusion detection for IoT environments. The findings underscore the potential of combining edge computing, IoT, and deep learning to address evolving security challenges in distributed systems. To mitigate these risks, efficient models for detecting malicious activities are crucial. This study investigates the application of edge computing, Internet of Things (IoT), and deep learning to improve the performance of Intrusion Detection Systems (IDS). By leveraging the UNSW-NB15 dataset, the research assesses the capability of deep learning models to identify and mitigate cyber threats effectively. The results demonstrate better performance, showing their suitability for real-time security applications. These findings highlight the potential of intelligent intrusion detection systems powered by robust datasets and deep learning for addressing evolving security threats in edge computing environments.

关键词： Deep learning Image edge detection Computational modeling Intrusion detection Real-time systems Internet of Things Security Servers Long short term memory Edge computing

来源：评论

学校读者我要写书评

暂无评论

Detection of Retinal Diseases in Retinal Optical Coherence Tomography Images 2

Detection of Retinal Diseases in Retinal Optical Coherence T...

引用

2nd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Anand, M. Sheeba, T.M. Princella, A.S.Anshy SRM Institute of Science and Technology Department of Networking and Communications Chennai India SRM Institute of Science and Technology Department of Computer Applications Chennai India SRM Valliammai Engineering College Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350397635

In biomedicine, disease image classification is a laborious, manual process. However, recent developments in computer vision have made applying artificial intelligence for the detection and categorization of medical images a more practical option. It is essential to analyse retinal illnesses utilising Optical Coherence Tomography (OCT) scans. For patients to survive, these disorders must be accurately detected and classified. The analysis of retinal illnesses is currently done by doctors on a regular basis by looking at the OCT pictures. The manual diagnosing process is laborious, though. In order to aid clinicians in their diagnosis, an automatic detection and classification technique for retinal illnesses has been developed in this work. Utilizing OCT scans, a deep multilayered Artificial Neural Network (ANN) has been employed to identify and categorise the retinal anomalies. On a 150 image open source retinal OCT dataset, the suggested technique has been used. © 2023 IEEE.

关键词： Optical tomography

来源：评论

学校读者我要写书评

暂无评论

Autonomous Vehicle Communication with Parrot-Optimized Orientation-Aware Graph Neural Network for Next-Generation IoT Connectivity 4

Autonomous Vehicle Communication with Parrot-Optimized Orien...

引用

4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Mercy, Immaculate Nainwal, Ankita Balasubramani, R. Nandankar, Praful V. Parikh, Swapnil Taqui, Syed Noeman Department of Computer Applications Vallam Tamil Nadu Thanjavur613403 India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Department of Information Science and Engineering Karnataka Nitte574110 India Government College of Nagpur Department of Electrical Engineering Maharashtra 441108 India Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Gujarat Waghodia India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Vlsi Microelectronics Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331540364

Effective communication between autonomous vehicles is essential for ensuring their safe and efficient operation. This research introduces Parr-OrAGNet, a novel approach that addresses the limitations of traditional communication methods in IoT automotive networks. Parr-OrAGNet integrates Macro-Micro Depth Normalization (MaMiDN) for accurate data normalization, Orientation-Aware Graph Neural Network (OrAGNet) for enhanced neural network modeling, and Parrot Optimizer (ParrOpt) for optimization. This combined approach effectively reduces packet loss, communication overhead, and latency. The evaluation results demonstrate significant improvements in communication efficiency, with a 97% reduction in packet loss and a 95% decrease in communication overhead. Additionally, Parr-OrAGNet achieves exceptional performance metrics in terms of accuracy, precision, recall, and computational efficiency. This research contributes to the advancement of autonomous vehicle communication by establishing a new benchmark for performance and scalability. Parr-OrAGNet offers a promising solution for addressing the challenges of IoT automotive networks and paving the way for the realization of fully autonomous driving. © 2024 IEEE.

关键词： Macroinvertebrates

来源：评论

学校读者我要写书评

暂无评论

Generative AI-Driven Incentive Mechanism for Semantic Communications in RSMA Networks

引用

IEEE Transactions on Cognitive Communications and Networking 2025年第3期11卷 1708-1722页

作者： Liu, Dongqing Liu, Yongwen Zhang, Lei Hafid, Abdelhakim Khoukhi, Lyes Li, Mingyuan Liu, Yutao Dong, Yadong The First Affiliated Hospital of Zhengzhou University National Engineering Laboratory for Internet Medical Systems and Applications Zhengzhou China Zhengzhou University of Light Industry Software Engineering College Zhengzhou China Henan University of Technology College of Electrical Engineering Zhengzhou China University of Montreal Department of Computer Science and Operational Research Canada GREYC Laboratory ENSICAEN Normandie University France

This paper proposes a framework integrating Rate Splitting Multiple Access (RSMA), semantic communications, and generative AI for optimizing next-generation wireless networks. We present a unified model that combines RSMA with semantic communications to enhance both spectral efficiency and semantic fidelity. Our system model focuses on a downlink semantic communication system with a multi-antenna base station serving multiple single-antenna users. A dynamic contract-based incentive mechanism is developed to address user heterogeneity and information asymmetry in semantic RSMA scenarios. We introduce a diffusion model-based approach for joint optimization of resource allocation and contract design in RSMA systems. The semantic encoding process extracts key information, i.e., free-space detection, interest points, object attributes, and spatial relationships, from image data. A loss function is designed to train the semantic encoder and RSMA scheme, incorporating semantic extraction, partitioning, reconstruction, and task specific components. Our framework includes a multi-objective performance evaluation that considers both conventional metrics and semantic accuracy in a multi-user RSMA environment. We also define a multi-component semantic accuracy metric to assess the quality and utility of the extracted semantic information. Extensive simulation results demonstrate the superiority of our proposed framework over existing approaches in terms of system throughput, energy efficiency, and semantic fidelity across various network scenarios and user distributions. © 2015 IEEE.

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

Secured Reversible Data Hiding Scheme with NMI Interpolation and Arnold Transformation

Secured Reversible Data Hiding Scheme with NMI Interpolatio...

引用

International Conference on Network Security and Blockchain Technology, ICNSBT 2023

作者： Jana, Manasi Jana, Biswapati Joardar, Shubhankar Jana, Sharmistha Lu, Tzu Chuen Department of Computer Applications Haldia Institute of Technology West Bengal Haldia India Department of Computer Science Vidyasagar University West Bengal West Midnapore India Department of Computer Science and Engineering Haldia Institute of Technology West Bengal Haldia India West Bengal Midnapore India Department of Information Management Chaoyang University of Technology Taichung Taiwan

ISBN: (纸本)9789819944323

A novel and efficient 2-level secure reversible data hiding (RDH) method has been proposed based on image interpolation and Arnold transformation. An input image is scaled-down to original image and then scaled up to cover image using NMI interpolation method. The gray values of pixels are classified into a set of ranges. A difference value is calculated from adjacent pixels and it is used to find out the lower bound and upper bound of the range interval to which it belongs to. Before embedding, the secret data is scrambled using Arnold transformation implementing the 1st level security of proposed method. The scrambled secret data again updated to modified secret message to implement 2nd level security of proposed scheme. The input image is taken as reference during data embedding phase to raise the quality of the stego image. The experimental analysis shows that the proposed method has relatively higher imperceptibility and data embedding capacity than other schemes. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Interpolation

来源：评论

学校读者我要写书评

暂无评论

Automated Detection of Pain Across Varied Intensity Levels Through the Fusion of CNN and Random Forest 3

Automated Detection of Pain Across Varied Intensity Levels T...

引用

3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Suryavanshi, Ankita Kukreja, Vinay Dogra, Ayush Bhattacherjee, Abhishek Singh Brar, Tejinder Pal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Lovely Professional University Computer Science and Engineering Punjab Phagwara India Cgc Landran Department of Computer Applications Mohali India

ISBN: (纸本)9798350342338

An important part of preserving well-being is addressing pain, a complex human experience. In the medical field, accurate pain assessment is still difficult, especially when verbal communication is restricted. In response, by utilizing state-of-the-art technology, this research offers a strong and automated approach to pain identification. Feature extraction is done using Convolutional Neural Networks (CNNs), face expression analysis is done using deep learning techniques, and classification is done using Random Forest. When image preprocessing techniques are incorporated, data consistency and quality are improved, leading to consistent outcomes for a range of pain intensities. The system's ability to discriminate between various pain intensities, ranging from minor discomfort to excruciating anguish, is demonstrated by a carefully selected dataset. The accuracy and generalization abilities of the model are demonstrated by performance metrics such as precision, recall, and F1-Score. This study adds to the growing body of research on applications of computer vision in healthcare as well as the area of pain assessment. Promising developments in patient care, early intervention, and the comprehension of pain manifestations in many clinical contexts can be attributed to the computerized pain detection system. © 2023 IEEE.

关键词： CNN emotion detection image processing medical technology pain detection random forest

来源：评论

学校读者我要写书评

暂无评论

Deep Learning based Approach for Cassava Leaf Disease Detection

Deep Learning based Approach for Cassava Leaf Disease Detect...

引用

International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： T Kalpana R. Thamilselvan K. Chitra S. Kaviya M. Kiruthiga C. Mahesh Department of Computer Applications Kongu Engineering College Erode Department of Computer Science and Engineering Kongu Engineering College Erode

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

Cassava is a vital staple crop in numerous tropical countries, to enhance plant disease detection using deep learning architecture including ResNet, EfficientNet, Incetion V3 and simple CNN. By levering these advance model significant improve accuracy compared to previous methods. This approach addresses the pressing issue of food insecurity by efficiently identifying diseases in crops. Types of Cassava disease are Cassava Mosaic Disease (CMD), Cassava Green Mottle (CGM), Cassava Brown Streak Disease (CBSD), and Cassava Bacterial Blight (CBB), along with the presence of Whitefly. The proposed work uses Convolution Neural Network (CNN) to identify the leaf diseases to quickly identify the specific disease that is affecting the crop. CNN can automatically detect important features without any human supervision. A vast collection of labelled cassava leaf photos comprising multiple disease classes is utilized for training the CNN model. To advance deep learning architectures, our research integrates techniques for data augmentation and transfer learning to further enchance model performance.

关键词： Training Visualization Plant diseases Accuracy Transfer learning Neural networks Crops Pressing Data models Diseases

来源：评论

学校读者我要写书评

暂无评论

Prediction of water quality parameters by IoT and machine learning

Prediction of water quality parameters by IoT and machine le...

引用

2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Kolli, Srinivas Ranjani, M. Kavitha, P. Daniel, D. Arul Pon Chandramauli, Awadhesh VNR Vignana Jyothi Institute of Engineering & Technology Department of Information Technology Hyderabad India Computer Applications Jawaharlal Nehru College for Women Ulundurpet India Muthayammal Center for Advanced Research Muthayammal College of Arts & Science India Loyola College of Arts & Science Department of Computer Science and Applications India Uttaranchal Institute of Technology Uttaranchal University Department of Civil Engineering Dehrradun India

ISBN: (纸本)9798350348217

Increases in domestical applications and other applications drive the need for better clean Water Management in India. What makes up the 'chemical, physical, and biological' quality of freshwater There are three stages in the conventional method of monitoring water quality: sample, analysis, and inquiry. The scientists do these by hand. This method needs to be foolproof and predict a water supply's quality. They're Using the development of wireless sensor technologies, has also been some investigation into the possibility of monitoring water quality with wireless sensors put in water and communicating with farmers through brief messages regarding water. There has also been a study into the possibility of employing machine learning algorithms to analyze water quality. In light of recent advances in Machine Communication, which allow for devices to communicate with one another and smart ly analyze the data they collect. We have developed an 'smart water quality monitoring system by using wireless sensonrs' for residential areas that rely on water storage tanks. A pH sensor and a total dissolved solids meter are used in this system to provide accurate readings of the water's pH and dissolved solids levels. K-Means clustering, a machine learning technique, has been used to forecast water quality using a training data set consisting of various water samples. This system is now in its prototype stage, used with low-cost embedded devices like the Arduino Uno and Raspberry Pi3. © 2023 IEEE.

关键词： Water quality

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：