检索结果-内蒙古大学图书馆

2024 International Conference on Smart devices, ICSd 2024

作者： Agrawal, Navin Kumar Sharma, Viboo Singh, Pranita Sachi, Savya Jain, Arpit Alam, Md Mohtab GLA University Department of Computer Engineering and Applications U.P. Mathura India Hindustan College of Science and Technology Department of IT U.P. Mathura India Roorkee Institute of Technology Department of Computer Science and Engineering U.K. Roorkee India L N Mishra College of Business Management Department of Information Technology Bihar Muzaffarpur India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering AP Vaddeswaram India Eklavya University Department of Computer Science & Engineering M.P. Damoh India

ISBN: (纸本)9798350394412

Issues like low vision and the distortion of spectral and spatial information in photographs can be brought on by unfavorable atmospheric circumstances including smog, rain, overcast sky, fog, and smoke. This piece looks at images taken in a hazy environment. While de-smogging requires veil and transmission map data, optical imaging systems are limited to capturing smoggy images. To restore blurry photographs, effective optical information prediction is necessary. Research has shown that optical information anticipated by the dark channel prior technique frequently produces subpar images in photographs with areas of higher brightness, noticeable pollution gradients, or textured information. In order to estimate optical information, this study makes use of deep Transfer learning (dTl) and Optimized Gradient Prior Prediction (OGPP). We gathered benchmark images of both smoggy and smog-free environments in order to train the dTl model. The OGPP technique uses the smog gradient, which the dTl model is taught to anticipate, to recover pictures free of pollution. A comparison analysis shows that when it comes to recovering hazy photos, the OGPP restoration model performs better than other approaches. © 2024 IEEE.

关键词： Restoration

来源：评论

学校读者我要写书评

暂无评论

Temporal-Spatial Feature Extraction in IoT-Based SCAdA System Security: Hybrid CNN-lSTM and Attention-Based Architectures for Malware Classification and Attack detection

引用

IEEE Access 2025年 13卷 102109-102132页

作者： Onur Polat Ali Ayid Ahmad Saadin Oyucu Enes Algül Ferdi doäan Ahmet Aksöz Department of Computer Engineering Bingöl University Bingöl Türkiye Department of Electrical Engineering College of Engineering University of Kirkuk Kirkuk Iraq Department of Computer Engineering Faculty of Technology Gazi University Ankara Türkiye Department of Computer Engineering Adıyaman Üniversitesi Adiyaman Türkiye Department of Electrical and Electronics Engineering Kayseri University Kayseri Türkiye

This research presents a Convolutional Neural Network (CNN) and long Short-Term Memory (lSTM) model developed for malware classification from IoT devices in the SCAdA system and for detecting anomalies in the network. The developed model identifies complex attacks in the network by taking advantage of the strengths of CNNs that reveal spatial features and lSTMs that detect temporal dependency. CICIoT 2023 is used as the dataset. AdAM optimization algorithm with cross-entropy loss is used to eliminate overfitting and training is performed. Within the scope of the study, the proposed model is compared by applying it with six deep learning architectures (hybrid CNN-lSTM, Non-local Neural Network (NlNN), Residual Attention Network (RAN), dual Attention Network (dANet), Transformer-CNN and Attentional CNN). The obtained results show that the proposed CNN-lSTM model outperforms other complex architectures and achieves a high test accuracy of 99.23%. It has demonstrated remarkable performance according to precision, recall and F1 evaluation metrics in detecting distributed denial of service (ddoS) and denial of service (doS) attacks. The proposed model successfully identifies Mirai botnet variants and fragmentation-based attacks. Although other models, Transformer-CNN (98.81%) and dANet (98.07%), provide high performance, they fall behind the superior temporal modeling capabilities of CNN-lSTM. When the obtained findings are examined, they highlight the relative strengths of various deep learning approaches for IoT security applications. The performances of the implemented deep learning models reached accuracies exceeding 96%, demonstrating the importance of IoT-based SCAdA systems against evolving cyber threats. The study revealed the superior successes of deep learning-based approaches for IoT security.

关键词： Security SCAdA systems Internet of Things Malware Convolutional neural networks Protocols long short term memory Feature extraction Access control Real-time systems

来源：评论

学校读者我要写书评

暂无评论

An Automated Sleep Stage Classification for Healthcare Monitoring by using Single Channel EEG Signal

An Automated Sleep Stage Classification for Healthcare Monit...

引用

2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Kumar Gurumoorthy, Sasi Chethana, d.T. Ramesh, M. Upendra Roy, B.P. Sapna Kumari, C. J.J.College of Engineering and Technology Department of Computer Science and Engineering Trichy India Gitam School of Technology Gitam University Department of Computer Science & Engineering India K L University Department of Computer Science & Engineering Vadeeshwaram Vijayawada India Channabasaveshwara Institute of Technology Department of Electronics and Communication Engineering Tumkur India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350398465

It is well-established that biomedical signals convey crucial data regarding the functioning of living systems. The physiological and clinical information included in these signals can be improved with adequate processing. Modern qualitative and quantitative analyses of physiological systems and events rely on digital signal processing and pattern recognition methods. Analysis and interpretation of a medical practitioner's signal carry the weight of the analyst's knowledge and expertise, yet such analysis is inherently subjective. If done logically, computer analysis of biomedical information might provide credibility to the expert's interpretation by providing an objective second opinion. Furthermore, it allows for enhanced diagnosis and online monitoring of critically ill patients. The current research intends to develop effective methods for utilizing sleep-monitoring health gadgets. When it comes to handling complex classification or pattern recognition issues, the Support Vector Machine (SVM) is the instrument of choice. In this article, we focus on using support vector machines (SVMs) to identify and categorize apnea. When compared to other methods of categorization, such as sophisticated statistical approaches, SVM performed better. Both an adaptive classification model and a novel approach to merging the decisions of ensemble-based classification models are proposed in the work. The current method relies on an ensemble classifier system and a huge number of features, making it both effective and trustworthy. © 2023 IEEE.

关键词： Support vector machines

来源：评论

学校读者我要写书评

暂无评论

Enhancing Citywide Flow Forecasting through deep learning and Spatial Temporal learning 2

Enhancing Citywide Flow Forecasting through Deep Learning an...

引用

2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Parvathi, R. Sarkar, Swagata Supraja, M. Swagatha, J.P. Kanagajothi, d. Srimathi, S. SRM Arts and Science College Computer Science Kattankulathur India Sri Sairam Engineering College Department of Artificial Intelligence and Data Science Chennai India Rajarajeshwari College of Engineering Electronics and Communication Engineering Bangalore India Panimalar Engineering College Department of IT Poonamallee India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Mathematics India SIMATS Technology Saveetha School of Engineering Department of Bio Chennai India

ISBN: (纸本)9798350317558

Safe traffic management requires citywide flow projections. Transportation, public safety, and municipal planning are substantially affected. It forecasts city intake and outflow using flow data. Traditional methods are inaccurate and less scalable in complex, dynamic metropolitan settings. This study uses deep learning to predict crow traffic. deep learning can handle complicated nonlinear circumstances, making neural network crowd flow prediction more popular. However, deep learning has solved this problem, but it requires plenty of data. data shortages may develop when a city's service or infrastructure is installed. This research introduces a deep spatial temporal transfer learning system that predicts crowd movement in a data-sparse target city using data from a data-rich source city. Geographic and temporal patterns in crow traffic data may be captured by trained algorithms, enhancing prediction accuracy. We found that our deep learning-based solution is more accurate and robust than prior methods. Crow migration patterns are properly predicted by this research, improving urban planning and management. © 2024 IEEE.

关键词： learning systems

来源：评论

学校读者我要写书评

暂无评论

deep learning for Sentiment Analysis: Exploring the Power of deep learning Techniques in Opinion Mining

Deep Learning for Sentiment Analysis: Exploring the Power of...

引用

2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Sindhu, l. Rameshkumar, M. Kumar, Gotte Ranjith Manjunath, B.N. Sapna Kumari, C. Sona College of Technology Department of Information Technology Salem India Paavai Engineering College Department of Computer Science and Engineering India Sr University School of Computer Science & Artificial Intelligence Warangal India R.L. Jalappa Institute of Technology Department of Computer Science & Amp Engineering Bangalore India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350313413

Sentiment analysis (SA) is a popular method for collecting relevant and arbitrary information from text-based data. To locate, examine, extract reactions, and emotions from the data or states, it applies computational linguistics, biometrics, text analysis, and Natural language Processing (NlP). A SA model can be developed and improved with the use of the features analysis method. However, it can be difficult to find the best classification methods for this type of data. When compared to current feature-based methods, deep learning models for the analysis of sentiment, are capable of providing precise representation and improved performance. In this paper, the deep learning method was proposed for improving sentiment classification performance with a sophisticated embedding word method and develop a network of Convolutional Neural Network and long short-term memory (CNN-lSTM). For the purpose of predicting the sentiment of tweets for classification, a large amount of Amazon data is analysed. This study, concentrate on improving sentiment analysis performance by building a CNN-lSTM network and particularized model of deep learning with novel word embedding techniques. The CNN-lSTM model has achieved 98.20% accuracy, 99.86% precision, 99.50% recall, and 98.78% recall in sentiment analysis. © 2023 IEEE.

关键词： Convolutional Neural Network deep learning long-short-term memory (lSTM) Sentiment analysis Word embedding

来源：评论

学校读者我要写书评

暂无评论

Collaborative decision-Making for Adaptive Cybersecurity Policies 12th

Collaborative Decision-Making for Adaptive Cybersecurity Pol...

引用

12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： deshmukh, Anand B. Chopade, Pallavi devidas Yadav, Rajeev Chavhan, Gajanan H. Ingle, Pravin E. Ajani, Samir N. Department of Information Technology Sipna College of Engineering and Technology Amravati MS Amravati India Institute of Management and Entrepreneurship Development Pune411038 India Computer Science and Engineering Arya College of Engineering Jaipur India E&TC VIIT Pune India Department of Mechanical Engineering Dr. D. Y. Patil Institute of Technology Pimpri Maharashtra Pune India Shri Ramdeobaba College of Engineering and Management Maharashtra Nagpur India

ISBN: (纸本)9789819601462

Static cybersecurity policies aren’t working well enough to protect businesses from dynamic and complex attacks in today's fast-changing cyber threat environment. It is becoming clearer that we need cybersecurity policies that can change with new threats and changing business needs in order to deal with this problem. The skills and ideas of many people are needed to make cybersecurity protection more flexible and effective. This is why collaborative decision-making is so important for making and enforcing these kinds of rules. Collaboration for making decisions about changing hacking policies is looked at in great detail in this study. The article looks at how decision-making theories and the creation of cybersecurity policies relate to each other by looking at a lot of current literature, theoretical models, and real-life case studies. What are the ideas behind making decisions together? They are explained using hacking as an example. This shows how important it is for stakeholders to be involved, for people to be able to talk to each other, for trust to exist, and for the organization's culture to shape good policy results. This paper suggests a way to include joint decision-making principles in the creation of flexible cybersecurity policies by looking at examples of successful applications and problems that different organizations have had. To successfully deal with problems that change, this framework stresses the need for flexible decision-making, open lines of communication, and regularly updated partner involvement. This paper also looks at how new tools can help people make decisions together and make it easier to use rules that change over time. Finally, the paper talks about the problems and possible futures in this area, pointing out obstacles to acceptance and suggesting more study and work in this area. Organizations can be more resistant to cyber dangers and better adapt to changes in the security scene if they encourage stakeholders to work together

关键词： Cyber attacks

来源：评论

学校读者我要写书评

暂无评论

A Novel Framework in Scheduling Packets for Energy-Efficient Bandwidth Allocation in Wireless Networks 2

A Novel Framework in Scheduling Packets for Energy-Efficient...

引用

2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Sivajothi, E. Jayaudhaya, J. Santhiya, S. Thapa, Naresh Kumar Kamatchi, S. Ganapathy, N. Bala Sundara Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India R.M. D. Engineering College Department of Electronics and Communication Engineering Tamil Nadu India M. Kumarasamy College of Engineerin Department of Computer Science and Engineering Tamil Nadu India R.M. K. Engineering College Department of Electronics and Communication Engineering Tamil Nadu India Saveetha School of Engineering Simats Department of Electronics and Communication Engineering Tamil Nadu India Panimalar Engineering College Department of Information Technology Tamil Nadu India

ISBN: (纸本)9781665456302

The research work provides a brand-new technique for distributing bandwidth in wireless networks that is also energy-efficient. To begin, look into how channel allocations, which ultimately determine the transmission rate for mobile terminals, affect the fundamental relationship that exists between energy use and transmission rates. The Fatality Selection Algorithm (FSA) and the Receiver Selection Algorithm (RSA) are two methodologies that control connection admittance to reduce the amount of energy used by each individual terminal. Furthermore, provide a corrective strategy for statistically satisfying the quality of service (QoS) criteria throughout the resource allocation process. Throughput, call blockage probability, and the energy consumption rate of each successfully sent bit are used to evaluate the efficiency of the recommended solutions. An extensive investigation into analysis and simulation is carried out in the case of Poisson and self-similar. © 2023 IEEE.

关键词： Quality of service

来源：评论

学校读者我要写书评

暂无评论

Untrustworthy Mobile Node Isolation by Belief Factor in Autonomous Mobile Network 2

Untrustworthy Mobile Node Isolation by Belief Factor in Auto...

引用

2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： dhivya, K. Suganya, J. Premkumar, K. Balasaranya, K. dhanalakshmi, S. R.M.K. College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Mailam Engineering College Department of Electronics and Communication Engineering Tamil Nadu Vilupuram India Srm Institute of Science and Technology Department of Electronics and Communication Engineering Ramapuram Tamil Nadu Chennai India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering Tamil Nadu Salem India

ISBN: (纸本)9798350347579

An Autonomous Mobile Network (AMN) is a wireless network in which no structure is accessible. AMN has significantly assisted in many applications recently. The AMN is often employed in such environments where humans do not exist. Therefore, each mobile node should be understood in case of a malfunction or connection failure between mobile nodes. Ant colony optimization (ACO) techniques have helped to develop AMN routing. An Untrustworthy mobile node Isolation by applying the Belief Factor (UIBF) system to detects the untrustworthy mobile nodes efficiently. The node energy, node cooperativeness, node packet losses, and node delay measures are combined to compute the belief factor. This mechanism uses the ACO algorithm to choose the optimal route from the sender to the recipient. Simulation results evaluate the UIBF approach to provide a better untrustworthy node detection ratio and raise the throughput with minimum network delay. © 2023 IEEE.

关键词： Ant colony optimization

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

Sustainable Energy Evolution Using IoT-Enhanced Carbon Capture and Storage for Oil Recovery 6

Sustainable Energy Evolution Using IoT-Enhanced Carbon Captu...

引用

6th International Conference on Energy, Power and Environment, ICEPE 2024

作者： Valli, K. Vijaya Naga Swathi, G. lumina Julie, R. Saravanan, S. Sujatha, S. Malarvizhi, E. Srkr Engineering College Department of Computer Science and Engineering Andhra Pradesh Bhimavaram India Cmr Technical Campus Department of Computer Science and Engineering Telangana Hyderabad India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Management Studies Tamil Nadu Chennai India Muthayammal Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Namakkal India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India St. Joseph's College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350379297

The global energy environment is shifting toward sustainability and carbon neutrality. Internet of Things (IoT) technology with Carbon Capture and Storage (CCS) in Enhanced Oil Recovery (EOR) may reduce carbon emissions and optimize hydrocarbon recovery. This paper examines IoT-enabled CCS for EOR and carbon-neutral oil and gas operations. Real-time data capture and analysis using IoT technologies optimize CCS operations. Installing IoT sensors and devices across the EOR and CCS infrastructure lets operators monitor CO2 injection rates, reservoir pressure, temperature, and fluid composition. The CCS process is precisely controlled using data, improving carbon capture efficiency and lowering operating costs. Remote monitoring and management of carbon storage sites using IoT in CCS for EOR improves environmental sustainability. The collected CO2 is securely and efficiently contained, reducing leakage and environmental effects. Preventing CCS infrastructure problems with IoT-driven predictive maintenance ensures long-term operational dependability. Securely storing collected CO2 offsets oil and gas emissions and contributes to carbon neutrality. EOR process optimization with IoT technologies may boost hydrocarbon recovery, making the switch to greener energy sources easier. IoT-enabled CCS for EOR has the potential to produce carbon-neutral oil and gas operations. The system improves CCS efficiency and sustainability using real-time data, remote monitoring, and predictive maintenance. Integration of IoT with CCS reduces carbon emissions and promotes the industry's worldwide sustainability objectives. © 2024 IEEE.

关键词： Carbon capture and utilization

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：