检索结果-内蒙古大学图书馆

2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Sasikumar, S. Kumar, M. Guru Vimal Paramesh, J. Sriram, K.P. Anbalagan, E. Saveetha Engineering College Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai600062 India St. Joseph's Institute of Technology Information Technology OMR Chennai600119 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331542344

Supervised deep learning has brought changes to data analysis and prediction function by leveraging its capacity to make many number of relationships with heterogeneous forms of data. This paper compares deep learning techniques like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and long Short-Term Memory (lSTM) networks in three tasks: There are three types of tasks, including image classification, time-series prediction, and text classification. In the picture simulation of the CIFAR-10 set for training, the accuracy of the ResNet model was 95% while the accuracy of VGG16 w 93%. When applied to the M4 competition dataset in time-series prediction, the lSTM model was superior to other benchmarks with MAE of 0.40 and RMSE of 0.60, with the RNN model giving MAE of 0.50 and RMSE of 0.65. It is 0.92 of accuracy, 0.91 of F1 score, and 0.921 for precision using the IMdB reviews dataset in text classification of movies using the CNN-lSTM hybrid model while the Bi-lSTM only had an accuracy of 0.89 and an F1 score of 0.889. Even though models, such as ResNet and Bi-lSTM, took slightly more time to train-Approximately 5.0 hours-it was because the epoch models were significantly superior to the baseline model. The present work focuses on the benefits of deep learning architectures in providing interpretable patterns in numerous applications. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Effective Classification of Synovial Sarcoma Cancer Using Structure Features and Support Vectors

引用

computers, Materials & Continua 2022年第8期72卷 2521-2543页

作者： P.Arunachalam N.Janakiraman Junaid Rashid Jungeun Kim Sovan Samanta Usman Naseem Arun Kumar Sivaraman A.Balasundaram Department of Electronics and Communication Engineering K.L.N.College of EngineeringPottapalayam630612Tamil NaduIndia Department of Computer Science and Engineering Kongju National UniversityCheonan 31080Korea Department of Mathematics Tamralipta MahavidyalayaTamluk721636West Bengal School of Computer Science University of SydneyNSW 2006Australia School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India School of Computer Science and Engineering Centre for Cyber Physical SystemsVellore Institute of TechnologyChennai600127India

In this research work,we proposed a medical image analysis framework with two separate releases whether or not Synovial Sarcoma(SS)is the cell structure for *** this framework the histopathology images are decomposed into a third-level sub-band using a two-dimensional discrete Wavelet ***,the structure features(SFs)such as PrincipalComponentsAnalysis(PCA),Independent ComponentsAnalysis(ICA)and linear discriminant Analysis(ldA)were extracted from this subband image representation with the distribution of wavelet *** SFs are used as inputs of the Support Vector Machine(SVM)***,classification of PCA+SVM,ICA+SVM,and ldA+SVM with Radial Basis Function(RBF)kernel the efficiency of the process is differentiated and compared with the best classification ***,data collected on the internet from various histopathological centres via the Internet of Things(IoT)are stored and shared on blockchain technology across a wide range of image distribution across secure data IoT *** to this,the minimum and maximum values of the kernel parameter are adjusted and updated periodically for the purpose of industrial application in device ***,these resolutions are presented with an excellent example of a technique for training and testing the cancer cell structure prognosis methods in spindle shaped cell(SSC)histopathological imaging *** performance characteristics of cross-validation are evaluated with the help of the receiver operating characteristics(ROC)curve,and significant differences in classification performance between the techniques are *** combination of ldA+SVM technique has been proven to be essential for intelligent SS cancer detection in the future,and it offers excellent classification accuracy,sensitivity,specificity.

关键词： Principal components analysis independent components analysis linear discriminant analysis support vector machine blockchain technology IoT application industry application

来源：评论

学校读者我要写书评

暂无评论

Adversarial Machine learning in the Context of Cybersecurity 12th

Adversarial Machine Learning in the Context of Cybersecurity

引用

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

作者： Tambakhe, Manoj d. Ashok, Wankhede Vishal Pareek, Vaidehi lamba, Monica Wasatkar, N.N. Pawade, Sneha Department of Information Technology Sipna College of Engineering and Technology Maharashtra Amravati India Polytechnic Chandwad Maharashtra Nashik India Nagpur Campus Pune India Electronics and Communication Engineering Arya College of Engineering Rajasthan Jaipur India Computer Engineering Vishwakarma Institute of Information Technology Maharashtra Pune India Department of Mechanical Engineering Dr. D. Y. Patil Institute of Technology Maharashtra Pimpri Pune India

ISBN: (纸本)9789819601462

The constant evolution of adversarial techniques in cybersecurity presents a significant challenge to conventional defense mechanisms. This study explores adversarial machine learning (AMl) to enhance cybersecurity systems by introducing a new hybrid method that combines generative adversarial networks (GANs) and random forests. The proposed model demonstrates an impressive accuracy of 98.89%, highlighting its effectiveness in enhancing cybersecurity defenses. The increasing complexity of cyber threats requires creative solutions in machine learning for detecting and preventing intrusions. Adversarial machine learning is a promising field that emphasizes the resilience of models against adversarial attacks. This study focuses on the critical necessity for improved cybersecurity by combining generative adversarial networks (GANs) and random forests to leverage the advantages of both in a cooperative way. The suggested method starts by using GANs to create artificial data, which helps enhance the training dataset. These artificial data, which closely mimics real-world examples, offer a varied and thorough learning setting for training models. The random forests algorithm is used to uncover the inherent patterns in the data due to its robustness and interpretability. The model is trained using the BOT-IOT dataset, which is a widely recognized dataset in the field of cybersecurity. The hybrid GAN-random forest model demonstrates a remarkable accuracy of 98.89%, indicating its ability to distinguish between normal and adversarial instances with great precision. The accuracy of the hybrid model is confirmed through thorough experimentation, covering different adversarial scenarios and real-world cyber threats. Adversarial training techniques are used repeatedly to improve the model's ability to resist manipulation and remain adaptable to changing threat environments. The research introduces a new approach in Adversarial Machine learning for Cybersecurity by combining the

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

denial of Service Attack detection and Mitigation using Ensemble based Ml in Software defined Network 3

Denial of Service Attack Detection and Mitigation using Ense...

引用

3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Sathish, N. Valarmathi, K. Nagalakshmi, R. Hema Priya, K. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology School of Computing Tamil Nadu Chennai India Panimalar Engineering College India Srm Institute of Science and Technology Computer Science and Engineering. Ramapuram India Easwari Engineering College Department of Cse India

ISBN: (纸本)9798350375190

Software-defined networking (SdN) introduces a novel networking paradigm by decoupling control logic from forwarding operations, resulting in improved network resource management and administration efficiency. However, centralized network design in SdN gives rise to significant security challenges, notably denial-of-service (doS) attacks. Attackers exploit SdN's lack of effective message-verification mechanisms to launch doS attacks by falsifying source address information. In response, this study proposes an innovative approach to Internet of Things (IoT) security leveraging software-based networks and collaborative learning. One key strategy involves partitioning the network domain into smaller subdomains, each with its own controller, facilitating the exchange of security rules among subdomains. However, this topology necessitates routing every packet through the subnet's control node. To continually monitor network traffic data and detect attacks, each controller node employs an integrated learning model. This model combines statistical insights from data streams with artificial neural networks and the Extreme Gradient Boosting Algorithm to anticipate potential attacks. Additionally, the ICOA model selects relevant characteristics for analysis. Through comprehensive performance evaluation, including empirical values, the ensemble model demonstrates superior performance compared to existing approaches. Furthermore, the study compares various models and methodologies, affirming the efficacy of the proposed strategy in accurately identifying distributed denial-of-Service (ddoS) attacks in SdNs, thereby presenting a groundbreaking approach to SdN security. © 2024 IEEE.

关键词： Neural networks

来源：评论

学校读者我要写书评

暂无评论

Firefighter Skill Advancement through IoT-Enabled Virtual Reality and CNN-Based Training 11

Firefighter Skill Advancement through IoT-Enabled Virtual Re...

引用

11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Asha, P. Muthalakshmi, M. Jeslin, J Gnana Nithisha, J. Vijayakumar, S. Srinivasan, C. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Veltech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Biomedical Engineering Tamil Nadu Chennai India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Department of Biomedical Engineering Tamil Nadu Namakkal India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350350357

To maintain the safety and efficacy of firefighters in various circumstances, modern firefighting necessitates constantly improving skills and training techniques. Utilizing the Internet of Things (IoT), virtual reality (VR), and convolutional neural networks (CNN), this paper details a novel method for training firefighters. The proposed system collects real-time data on ambient variables, equipment state, and firefighter biometrics via integrating IoT sensors into firefighting equipment and training settings. Using this information, it can develop lifelike VR training simulations of difficult and potentially dangerous scenarios. To make the training settings more realistic and malleable, CNN-based algorithms are used to assess the data. The capacity to simulate a wide variety of firefighting situations, customize training difficulty depending on individual and team performance, and provide instant feedback and performance metrics to trainees are all major benefits of this method. The method also allows teachers to check in and evaluate their learners remotely, improving instruction quality. An IoT-enabled VR and CNN-based training technique has shown promising preliminary results in pilot trials, suggesting it might greatly enhance firefighter competence, situational awareness, and decision-making ability. Because of this, it has the potential to completely alter the way firefighters are informed and prepared for the ever-changing dangers users may encounter on the job. © 2024 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Experimental Evaluation of Secured Forensic Evidence Handling Scheme using Blockchain Technology 4

Experimental Evaluation of Secured Forensic Evidence Handlin...

引用

4th International Conference on Intelligent Technologies, CONIT 2024

作者： Anitha, C. Priyadharshini, R. Sivajothi, E. Kumaran, G. Sudha, K. Sireesha, Badepalli Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamilnadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India St.Joseph's College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Mlr Institute of Technology Department of Ece Hyderabad Dundigal India

ISBN: (纸本)9798350349900

Modern criminal investigations rely on forensic evidence management, which necessitates careful handling, safe storage, and precise documenting of the chain of custody. Problems including data manipulation, illegal access, and a lack of transparency plague conventional evidence management systems. In response to these concerns, this study introduces a novel approach to forensic evidence management that makes use of blockchain technology. Nowadays, data plays a crucial role in every stage of the work process. Since data might change, it should be secure. In a diverse organization, we shall handle data capacity and portrayal. data that is vital to a particular organization might be the target of an attack. As the prevalence of cybercrime rises, malicious actors are taking increasingly cunning actions to alter such data. Whatever the case may be, it is significantly impacting the expected scientific confirmation of provenance. Since forensic evidence moves through many stages during scientific examination, it is supposed to be preserved in this manner. With this approach, a report is created and then passes through various levels of delegation, such as a pathology lab, a forensic lab, the police, and so on. To create a clear framework with immutable measurable confirmations, blockchain technology innovation is the best choice. The efficiency of the proposed scheme is evaluated by cross-validation with the conventional security model called Secured Forensic Evidence Handler (SFEH). This paper introduced a novel algorithm called Heterogeneous Key Generation for Forensic data Safety (HKGFdS) to maintain forensic details in a safer manner. By demonstrating how blockchain technology has the ability to revolutionize forensic evidence management methods, this research adds to what is already known. Improved and more trustworthy criminal investigations are possible outcomes of this new line of inquiry and its use in practical forensic contexts. But wider acceptance can't happe

关键词： Information management

来源：评论

学校读者我要写书评

暂无评论

Transforming Homes into Smart Havens: Advanced Control with NodeMCU and Android Technology 4

Transforming Homes into Smart Havens: Advanced Control with ...

引用

4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Selvarathi, C. Santhi, P. deepa, K. Shri, G. Ramya Visali, C. Abirami, T. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Tamilnadu Chennai600 062 India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Tiruchirapalli India M. Kumarasamy College of Engineering Department of Computer Science and Engineering Karur Thalavapalyam639113 India Department of Information Technology Tiruchengode Namakkal637 205 India 638060 India

ISBN: (纸本)9798331508845

Creating groundbreaking solutions that benefit the populace is imperative in the modern era. The Internet of Things (IoT) serves as a trailblazing sector, fostering the development of contrivances automating an array of tasks. The IoT encompasses an interlinked network of cyber-connected contrivances executing functions with minimal human intervention. Capitalizing on IoT principles, this exploration endeavors to contrive a device aiding individuals grappling with mobility impediments. The envisioned gizmo consists of two primary sectors: a hardware module featuring a channel relay, Arduino, and an astute luminescent bulb, and a software element encompassing cloud and IoT platforms alongside a purpose-built app. The chief function of this contraption is to discern specific vocalizations in an individual's utterances, effectuating or deactivating the astute contraption to institute an intelligent habitation. In the initial phase, a luminous bulb will serve as a demonstration, employing for app origination. The distinctive aspect of this program lies in its vocalization-receptive microphone, with Bluetooth and Wi-Fi chips augmenting transmission velocity between the luminescent bulb and the mobile device. Meticulous attention will be accorded to auditory sensitivity and expeditious data transmission, with potential integration of diverse contrivances like ventilators and temperature-regulating apparatus into the envisioned gizmo. Furthermore, contemplation extends to a fortified safety attribute ensuring singular response to a particular vocal identity. © 2024 IEEE.

关键词： luminescent devices

来源：评论

学校读者我要写书评

暂无评论

Breaking Boundaries: Harnessing the Power of deep learning Model design to Predict diabetes with Artificial Intelligence Assisted Prediction Strategy 4

Breaking Boundaries: Harnessing the Power of Deep Learning M...

引用

4th International Conference on Intelligent Technologies, CONIT 2024

作者： Senthil Kumar, N.K. Kalpana, S. Karthika, K. Anandh, A. Anusha, Badepalli Santhoshini, P. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology School of Computing Department of Cse Tamil Nadu Chennai India Department of Computer Science Dubai United Arab Emirates Prince Shri Venkateshwara Padmavathy Engineering College Department of Civil Engineering Tamil Nadu Chennai India Kamaraj College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Virudhunagar India Mlr Institute of Technology Department of Ece Dundigal Hyderabad India Rmd Engineering College Department of Electronics and Communications Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350349900

Predicting the onset of diabetes at an early stage might greatly mitigate the disease's devastating effects;the condition is currently incurable and results from chronically elevated blood sugar levels. Predicting the onset of diabetes has been the focus of current efforts within the deep learning community, building on decades of prior study. Nowadays, diabetes is a common and unfortunately incurable health problem. People can't give their health the attention it needs because of their busy schedules. The food we eat is broken down into glucose, which is then released into our bloodstream. When blood sugar levels are too high, the pancreas secretes a hormone called insulin. A key function of insulin is to facilitate the uptake of glucose by cells for energy production. Identifying diabetes at an early stage can greatly improve the chances of leading a sustainable life. A fruitful way would be to use machine learning algorithms, which can improve themselves with future predictions after being taught and tested with massive amounts of data. This study presents AI-based diabetes Prediction (AIdP), a model that explores diabetes data to make better predictions about the illness, with the severity of the condition in mind. The suggested rationale is tested against the traditional K-Nearest Neighbor (kNN) learning paradigm to see how well it works. Results from the planned study demonstrate that the model can accurately forecast the onset of diabetes, if not better. When compared to another deep learning method known as kNN, our results reveal that the suggested AIdP attained superior accuracy. © 2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Predictive Modeling with Feature engineering and Outlier detection on Airbnb dataset

Predictive Modeling with Feature Engineering and Outlier Det...

引用

Intelligent Technologies (CONIT), International Conference on

作者： Suraj Gajul Madhuri Potey Department of Computer Engineering D Y Patil College of Engineering Pune India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

In the burgeoning sector of short-term accommodation, Airbnb stands as a beacon of innovation, reshaping the way travelers experience the world. Yet, with its exponential growth, the quest for optimal pricing remains paramount. This study primarily focuses on the optimization of predictive modelling for Airbnb prices through comprehensive feature engineering and preprocessing techniques. leveraging a dataset comprising of data acquired from ten major European cities, an in-depth analysis was conducted to improve the accuracy of regression models. Significant enhancements were achieved in the model performance by implementing outlier detection and removal using Z-score approach, feature selection using Variance Threshold (numerical) and Chi-square test (categorical). linear and Polynomial Regression, Random Forest, decision Tree, Gradient Boosting and Ensemble learning (Stacking) models were used for predictive modelling. These findings emphasize and prove the importance of spatial econometric factors in predicting Airbnb prices.

关键词： Atmospheric modeling Biological system modeling Stacking Pricing Predictive models Feature extraction Numerical models

来源：评论

学校读者我要写书评

暂无评论

Efficient Novel Method for EEG Signal Classification in Epileptic Seizure Identification using Metaheuristic deep learning

Efficient Novel Method for EEG Signal Classification in Epil...

引用

2024 IEEE International Conference on Recent Advances in Science and engineering Technology, ICRASET 2024

作者： Pokkuluri, Kiran Sree Nigam, Nishesh Fuskele, Avita Raja, C. Shukla, Prashant Kumar Chhaya Shri Vishnu Engineering College for Women Department of Computer Science and Engineering Bhimavaram India Ies Institute of Technology and Management Ies University Department of Computer Science and Engineering MP Bhopal462044 India Jabalpur Engineering College Information Technology Department Jabalpur India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Tamil Nadu Chennai India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram Guntur India Mangalayatan University Jabalpur India

ISBN: (纸本)9798350388602

Using a metaheuristic deep learning strategy, the research focuses on the challenge of epileptic seizure detection. A diagnostic tool known as an electroencephalogram (EEG) may detect improperly functioning brain cells, the underlying cause of seizures. To enhance the precision of seizure detection, the offered approach merges deep learning with a metaheuristic optimization technique. This method involves preprocessing raw EEG data in order to extract fundamental characteristics. A classifier that is part of a deep learning system is subsequently fed these characteristics in stages. When it comes to finding a decent solution with high accuracy dimensions, the metaheuristic component finds the optimal values for the deep learning model. due to the sluggish convergence and trapping of local minima in standard clustering methods, this approach seeks to improve detection performance. Using industry standard EEG datasets, we evaluate our hybrid technique's performance to that of other relevant models. These findings demonstrate that the system is a dependable tool for real-time seizure detection, with sensitivity, specificity, and accuracy all surpassing the current standards. This method stands head and shoulders above its predecessors, proving its viability for clinical usage in epilepsy monitoring and diagnosis. © 2024 IEEE.

关键词： Electroencephalography

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：