检索结果-内蒙古大学图书馆

2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Gupta, Shubhi Bansla, Vandna Kumar, Sandeep Singh, Gurwinder Srivastav, Akanksha Jain, Arpit Amity University Department Of Computer Science And Engineering Uttar Pradesh Greater Noida India Shivalik Engineering College Department Of Computer Science And Engineering Uttarakhand Dehradun India Tula's Institute Department Of Computer Science And Engineering Uttarakhand Dehradun India Chandigarh University Department Of Aitcse Punjab Mohali India Koneru Lakshmaiah Education Foundation Department Of Computer Science And Engineering AP Vaddeswaram India

ISBN: (纸本)9798350366846

Tumors of the brain are among the deadliest diseases of the century. Artificial intelligence (AI) and neuroscience are used to identify, identify, and categorize brain tumors. A large amount of MRI scans must be manually segmented and categorized. An effective computer-aided diagnosis (CAD) system is essential for the timely analysis of brain tumors. Magnetic resonance imaging (MRI) is used to segment, extract, and classify brain tumors. All systems have achieved a superior level of performance. We used deep learning-based models to predict brain tumor disease with 99.10% accuracy. By analyzing huge amounts of data and extracting significant features accurately and efficiently, these models can offer improved patient outcomes and timely treatment in the healthcare sector in a short timeframe compared to existing approaches. © 2024 IEEE.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Software Defined Network Framework & Routing Protocol Based on VANET Technology

Software Defined Network Framework & Routing Protocol Based ...

引用

2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Lahari, Pamidimukkala Srilatha, Ravella Chejarla, Omkar Eswar Yogesh, Ravuri Shankar, R. Kumar, P M Ashok Koneru Lakshmiah Education Foundation Department of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798350348217

In recent years, different types of researchers have focused on the topic of vehicular ad hoc networks (VANETs) because of the different services they provide. VANET is a subset of Mobile Ad-hoc Network (MANET), which refers to a group of smart nodes. In present days, communication within the moving vehicles takes place because of the improvement in technology. These smart vehicles communicate within them or with a roadside unit to provide road safety, a more protected and effective driving experience, and protection against intruders. Messages in VANET are sent using open wireless channels. In recent years, several Vanet technologies are being developed to provide the better communication in between the vehicles, to detect accidents and transforming that information to other vehicles through packets. Even though vanet provides several services, there is an issue with this technology which can cause low latency in communication or delay in transferring the message. To overcome all these issues serval technologies have been emerging to provide the efficient communication between the moving vehicles. In this paper, we have discussed about the architecture of the Vanet, present emerging technologies and also about future challenges in Vanet. SDN (Software defined Network) framework is used to provide the effective communication interms of utilizing less delay. Fog Computing is one of the emerging technologies in vanet, where it performs storage and processing functions without any interruption of the third parties. © 2023 IEEE.

关键词： Vehicular ad hoc networks

来源：评论

学校读者我要写书评

暂无评论

Remote Sensing Data-Based Satellite Image Analysis in Water Quality Detection for Public Health Data Modelling

引用

Remote Sensing in Earth Systems sciences 2024年第4期7卷 532-541页

作者： Balakrishnan, S. Preetam Raj, P Michael Somasekar, J. Kumar, Kambala Vijaya Amutha, S. Sangeetha, A. Department of Computer Science and Engineering Aarupadai Veedu Institute of Technology (AVIT) Vinayaka Mission’s Research Foundation (Deemed to be University) Chennai India Department of Computer Science and Engineering MLR Institute of Technology Telangana Dundigal Hyderabad 500043 India Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be-University) Karnataka Bengaluru 562112 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Tamilnadu Chennai India Department of Artificial Intelligence and Data Science Kongunadu College of Engineering and Technology TamilNadu Tholurpatti India

One of the most important aspects of environmental sustainability is water quality monitoring. The ecology is impacted by poor water quality in addition to aquatic life. With the volume of data being collected from satellite systems, the issue of effectively and automatically extracting water bodies is growing more pressing. One of the most important things to do for managing and safeguarding water resources is to conduct extensive monitoring of water quality parameters, or WQPs. It is still difficult to monitor optically inactive WQPs in inland waters, such as total nitrogen (TN), ammoniacal nitrogen (AN), and total phosphorus (TP). Water quality (WQ) classification and prediction can be greatly enhanced with the use of artificial intelligence (AI). This research proposes novel method in healthcare detection by water quality analysis based on classification utilising ML model. Input is collected as healthcare dataset based on water quality as well as processed for noise removal and normalisation. The processed data features are extracted and classified utilising reinforcement Gaussian stacked layer network with variational fuzzy Markov basis neural network. In order to suggest best course of action for the water bodies, it is imperative that any study look into the spatiotemporal changes of the dominant water quality parameters (WQPs). WQP concentrations have typically been determined by extensive fieldwork. These findings could help identify and regulate non-point source pollution, offer a large-scale spatial picture of the water quality, and pinpoint vulnerable locations and times of water pollution. Experimental analysis is carried out for various water sample dataset in terms of detection accuracy, mean precision, recall, F-1 score and AUC. The proposed technique obtained 97% of detection accuracy, 95% of recall, 91% of F-1 score, 96% of AUC, and 94% of MAP. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Classification Fuzzy Markov basis Healthcare detection Machine learning model Water quality analysis

来源：评论

学校读者我要写书评

暂无评论

Predicting Chronic Kidney Disease Progression Using Classification and Ensemble Learning 8th

Predicting Chronic Kidney Disease Progression Using Classifi...

引用

8th International Conference on Data Management, Analytics and Innovation, ICDMAI 2024

作者： Valli Mayil, V. Ratha Jeyalakshmi, T. Gupta, Rishu Kumar Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur India Department of MCA Dayananda Sagar College of Engineering Karnataka Bangalore India Department of Computer Science and Applications Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur India

ISBN: (纸本)9789819732418

A serious medical problem that impacts millions of people globally is chronic kidney disease, or CKIDD. Accurate CKIDD progression prediction is essential for disease management and early intervention. Machine learning (ML) has shown great potential in predicting the course of CKIDD by employing a variety of medical, laboratory, and demographic factors. In our research, we suggested an ensemble approach for the increased level of accuracy. We contrast the outcome with well-known machine learning models such as neural networks, Dec-Trees, random forest, and support vector machines. On a sizable dataset of CKIDD patients, where good progression outcomes are identified, our model is trained. Our model is trained on a big dataset of CKIDD patients and proposed to find the result that the patient is affected or not. The chronic kidney disease dataset from Kaggle is utilized to test the model. Standard metrics including accuracy, precision, recall, F1-score, and area under receiver operating characteristic curve (AUC-ROC) are considered for proving the capability of the proposed work. In comparison to the conventional method, our model's experiment results demonstrate 100% accuracy in pattern prediction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Quality aware batch scheduling of containers in cloud computing environment

引用

International Journal of Information Technology (Singapore) 2025年第2期17卷 1155-1163页

作者： Poojitha, S.A. Ravindranath, K. Department of CSE Koneru Lakshmaiah Education Foundation Green Fields Andhra Pradesh Vaddeswaram India Department of Computer Science & Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram 522501 India

The proposed scheduling method for cloud computing called “quality-aware batch scheduling of containers (QABSC)” gives priority to timely completion of tasks that are sensitive to delays. This five-stage approach guarantees task execution with the least amount of delay and the greatest amount of resource efficiency. Throughout Batch Formation, the QABSC framework arranges tasks and containers, and during Batch Correlation, it pairs them effectively. In order to set the stage for scheduling decisions that order task execution, batch priority assignment carefully prioritizes tasks based on resource demand and traffic sensitivity. The scheduling algorithm takes delay-sensitive traffic requirements and energy consumption metrics into account in the last phase, energy consideration and delay sensitivity. The QABSC model performs cloud scheduling tasks more effectively than deep reinforcement learning. The effectiveness of the model is demonstrated by a thorough evaluation, which includes an average resource utilization of 85.9% with a standard deviation of 3.21, an average makespan of 407.4 ms, throughput averaging 133.5 tasks/sec, and an average waiting time of 82.2 ms. QABSC’s strong performance is demonstrated by low SLA violations at 2.3%, energy efficiency at 1156.2 tasks/kWh, scalability at 148.5 tasks/sec, and fault tolerance at 99.45%. The intricacy of batch scheduling and QABSC’s capacity to enhance cloud scheduling operations draw attention to areas in need of ongoing development and innovation. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Agglomerative hierarchical clustering Container failure rate Container resource demands Inter-container dependencies Quality-aware batch scheduling Service level agreements

来源：评论

学校读者我要写书评

暂无评论

Natural Language Processing using Convolutional Neural Network 7

Natural Language Processing using Convolutional Neural Netwo...

引用

7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Varshitha, Konda Sai Kumari, Chinni Guna Hasvitha, Muppala Fiza, Shaik Amarendra, K. Rachapudi, Venubabu Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vijayawada India

ISBN: (纸本)9781665464086

Convolutional neural networks (CNN) are multilayer neural networks that are used to learn hierarchical data properties. In recent times, CNN has achieved remarkable advances in the architecture and computation of Natural Language Processing (NLP). The Word2vec technique is considered to introduce Word embeddings, which are used to improve the performance of a variety of Natural Language Processing (NLP) applications. It is a well-known technique for learning word embeddings, which are dense representations of words in a lower-dimensional vector space. Two prominent approaches are used for learning word embeddings, which are dense representations of words in a lower-dimensional vector space, are Continuous Bag-of-Words (CBOW) and Skip-Gram. © 2023 IEEE.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Abnormal Activity Recognition on Surveillance: A Review 3

Abnormal Activity Recognition on Surveillance: A Review

引用

3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Pogadadanda, Vikas Shaik, Shafeullah Neeraj, Gogula Venkata Sai Siralam, Hima Varshini Joseph S, Iwin Thanakumar Rao, K. B. V. Brahma Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9781665462167

Utilizing surveillance cameras to monitor public behaviour has become more important recently for public safety at various sites due to a rise in crimes. Maintaining safety and security has become a survival issue for people due to rising crime rates. With the advancement of security cameras, they now serve as a constant watch on public behavior. Many current surveillance systems require a human operator to continuously monitor them since the amount of video data is increasing daily, making them ineffective. To automatically identify odd behaviour in both public and private places, modern surveillance systems must be intelligent. In current decade, due to industrial 4.0 revolution, machine learning and deep learning based intelligent algorithms plays major role in providing efficient performance in various applications exclusively over automatic detection and identification. This research article mainly focuses on investigation of different intelligent algorithms used for an effective recognition of abnormal activity through surveillance, its major advantages, challenges and contributions in terms of various applications. © 2023 IEEE.

关键词： Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Enhancing Medical Image Recognition through Automated Neural Network Architecture Optimization using Neuroevolvenet

Enhancing Medical Image Recognition through Automated Neural...

引用

2023 International Conference on Recent Advances in science and engineering Technology, ICRASET 2023

作者： Aravinth, S.S. Srithar, S. Sivarambabu, P.V. Naveen Kumar Reddy, B. Babu, A. Anirudh Tata, Suraj Koneru Lakshmaiah Educational Foundation Department of Computer Science and Engineering Guntur India

ISBN: (纸本)9798350306927

Medical image recognition is pivotal for accurate diagnostics and treatment planning. Traditional neural network architecture design methods are often time-consuming and suboptimal. To address this, an automated framework, NeuroEvolveNet is proposed for optimizing neural network architectures in medical image recognition. NeuroEvolveNet effectively investigates alternative architectural configurations for improved recognition accuracy by utilizing evolutionary algorithms. The difficulties of manual architectural design in medical imaging and the evolutionary strategy used by NeuroEvolveNet are outlined here. Our method is tailored for medical imaging data and is evaluated on a diverse medical image dataset. Through comprehensive experiments and various metrics, including accuracy, sensitivity, and specificity, the framework's effectiveness is demonstrated. Results consistently showcase NeuroEvolveNet's superiority over manual designs and existing methods. Evolved architectures exhibit higher accuracy, illustrating the potential of automated optimization in medical image recognition. Our study emphasizes benefits such as customization, efficiency, and adaptability in complex medical imaging *** research contributes a novel method at the intersection of machine learning and healthcare, addressing challenges in medical image recognition. The integration of automated architecture optimization, as demonstrated by NeuroEvolveNet, holds promise for enhancing diagnostic accuracy and patient care in healthcare applications. © 2023 IEEE.

关键词： Medical imaging

来源：评论

学校读者我要写书评

暂无评论

An Exploration of Evaluating the Performance of Malware Detection in the Cloud Environment

An Exploration of Evaluating the Performance of Malware Dete...

引用

2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Kaipu, Charan Nivas Reddy Karthik, Gopu Umadevi, Konala Sree Koushik, Koneru Kamal Pavan Kumar, T. Kavitha, S. Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Guntur India

ISBN: (纸本)9798350348217

Numerous businesses now adopt cloud computing, putting their server, storage, and application resources on a network in the cloud somewhere online. Data from the user is stored on cloud at a low price, making cloud computing the most rapidly developing technologies. In a nutshell, cloud technology provides users with the access to massive infrastructures for low latency via a distinct middleware that is comparable to Grid and HPC computing, both of which are currently in use. Over the years, these kinds of systems have become more and more popular since they offer a setting in which to host scalable applications. But rather than offering customers a common platform, the cloud network is vulnerable to several security issues, including malware infections, DDoS assaults, SQL injection, and even more complex attacks like zombie botnets. We describe our research on security challenges and virus spread in cloud networks in this paper. However, the detection capabilities of conventional host-based antivirus software are constrained, and theseprograms frequently miss threats;recent assaults are becoming more complicated and evasive of conventional security measures. To understand cloud security and use cutting-edge malware detection techniques to safeguard cloud, malware detection and research on these approaches are described in this article. Because the resources in a cloud are delivered to the user in the form of VM's, they are susceptible to distributed denial of service attacks, malware exploits,and VM Escape-based attacks. This paper discusses the various detection techniques and the most effectiveones. The discussion of each method will be covered from a broad perspective that also focuses on reliable solutions, future directions, etc. The details are outlined in our article © 2023 IEEE.

关键词： Virtual machine

来源：评论

学校读者我要写书评

暂无评论

Federated learning-based hybrid convolutional recurrent neural network for multi-class intrusion detection in IoT networks

引用

Discover Internet of Things 2025年第1期5卷 1-22页

作者： Selvam, Prabu Karthikeyan, P. Manochitra, S. Sujith, A.V.L.N. Ganesan, T. Ayyasamy, Rajaram Shuaib, Mohammed Alam, Shadab Rajendran, A. School of Computing SRM Institute of Science and Technology Tiruchirappalli621105 India Department of Electronics and Communication Engineering Velammal College of Engineering and Technology Tamil Nadu Madurai625009 India Department of Computer Science Engineering St Joseph’s College of Engineering Tamil Nadu Chennai600119 India School of Engineering Malla Reddy University Telangana Hyderabad500043 India Department of Computer Science Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Department of Electronics and Communication Engineering E.G.S Pillay Engineering College Tamil Nadu Nagapattinam611002 India Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department of ECE Karpagam College of Engineering Tamil Nadu Othakkal Mandapam India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

As Internet of Things (IoT) devices leads an significant challenges in securing the systems from cyber-attacks in large-scale IoT networks. Traditional methods faces struggle to precisely detecting the complex intrusion patterns due to the heterogeneity and distributed IoT devices. There is a pressing need for scalable, real-time, and privacy-preserving solutions to secure IoT and IIoT systems. For multi-class intrusion detection in IoT networks, this study proposed an novel hybrid convolutional recurrent neural network (CRNN) model based on federated learning. By integrating federated learning, the proposed approach ensures privacy by enabling decentralized model training among IoT devices without exchanging sensitive data. The hybrid CRNN model improves detection accuracy for a variety of attack types in IoT networks by combining Recurrent Neural Networks for sequential pattern recognition and Convolutional Neural Networks for feature extraction from raw data. The proposed model classifies incoming network traffic into different attack classes. The experimental results conducted on the Edge-IIoT dataset reveal that the proposed model has attained the detection accuracy of 98.93% with balanced precision and recall for all the attack types and low-support classes such as SQL Injection and Man-in-the-Middle. This balance improves model’s ability to reduce both false negatives and false positives compared with conventional approaches. Together with real-time detection, our approach excels in a privacy-preserving manner, providing a practical, scalable solution for securing complex IoT networks, and addressing critical gaps in current IDS frameworks. © The Author(s) 2025.

关键词： Federated learning

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：