检索结果-内蒙古大学图书馆

North Karnataka Subsection Flagship International Conference (NKCon), IEEE

作者： Devesh Mirani Harsh Kumar Harshil Dhingra Harshitha SA Vijaya Shetty S SarojaDevi H Amitabh Srivastav Deepak Honahalli Computer Science and Engineering Department Nitte Meenakshi Institute of Technology Bengaluru India Unisys Bengaluru India

From ancient times cryptography has been used for encoding and delivering messages safely with advancements in technologies cryptography is used now all over the internet and over all types of files for encryption. With the advent of Quantum computing, present-day cryptographic methods are insufficient as quantum computers possess exponential computation and can crack present cryptographic methods. But right now quantum computing and its related resources are still under development so its required to develop post-quantum cryptography in advance. This paper deals with the implementation of NIST(National Institute of Standards and technology) shortlisted algorithms till the third round which includes Classic McEliece, Kyber, NTRU, Saber, Dilithium, Falcon, and Rainbow for making quantum-safe IPsec protocols using socket programming and strong swan between two virtual machines.

关键词： Encapsulation Quantum computing Protocols Sockets Programming NIST Virtual machining

来源：评论

学校读者我要写书评

暂无评论

Sliced Bidirectional Gated Recurrent Unit with Sparrow Search Optimizer for Detecting the Attacks in IoT Environment

Sliced Bidirectional Gated Recurrent Unit with Sparrow Searc...

引用

Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Sureka R. Gondkar Siva Balan R V Kavitha S Raju Ramakrishna Gondkar Department of Electronic and Communication Engineering BMS Institute of Technology and Management Bangalore India Department of Computer Science CHRIST (Deemed-to-be-University) Bangalore India Department of MCA Nitte Meenakshi Institute of Technology Bangalore India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

In an era characterized by pervasive interconnectivity facilitated by the widespread adoption of Internet of Things (IoT) devices across diverse domains, novel cybersecurity challenges have emerged, underscoring the imperative for robust intrusion detection systems. Conventional security frameworks, constrained by their closed-system architecture, struggle to adapt to the dynamic threat landscape marked by the continual emergence of unprecedented attacks. This paper presents a methodology aimed at mitigating the open set recognition (OSR) challenge within IoT-specific Network Intrusion Detection Schemes (NIDS). Leveraging image-based representations of data, our approach focuses on extracting geographical traffic patterns. We observe that the Recurrent Neural Network exhibits suboptimal classification accuracy and lacks parallelizability for attack analysis tasks. Our investigation concludes that the Sparrow Search Optimization Algorithm (SSOA) serves as a foundation for constructing an effective assault classification model. This research contributes significantly to the field of network security by emphasizing the importance and ramifications of meticulous hyperparameter tuning. It represents a critical stride toward developing IDSs capable of effectively navigating the evolving cyber threat landscape. In the experimental analysis of proposed model reached the accuracy and 0.963% respectively.

关键词： Training Refining Logic gates Traffic control Search problems Internet of Things Task analysis

来源：评论

学校读者我要写书评

暂无评论

Smart Agriculture in the Era of Big Data: IoT-Assisted Pest Forecasting and Resource Optimization for Sustainable Farming

Smart Agriculture in the Era of Big Data: IoT-Assisted Pest ...

引用

Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Deepak Kumar Priyanka Pramod Pawar Mohan Kumar Meesala Piyush Kumar Pareek Santosh Reddy Addula Shwetha K S Department of Information Technology University of the Cumberlands Williamsburg USA Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering Dayananda Sagar College of Engineering Bangalore India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

The goal was to facilitate quick development and revitalisation of rural areas by utilising IoT knowledge to accomplish smart agriculture against the backdrop of big data. Depleted soil fertility, increased pest attacks, and diseases are some of the climate-related risks that the agricultural sector is battling. Predicting Utilising directly sensed environmental conditions as input, a machine learning model incorporating Random Forest and the Catboost algorithm determines, based on the current environmental conditions, whether pest populations are present or not. Next, Hybrid Weighted Ant Colony Optimisation (HW ACO) is used to fine-tune the Catboost algorithm. This method evaluates the machine learning model's accuracy and precision in making predictions using test data. Predictions have been made using data from 2028 to 2022, a span of five years. Weekly forecasts are produced by the pest prediction model. With high F-measure, Precision, and Recall, the weekly predictions have an overall accuracy of 93%. Over time, the precision of the pest forecast steadily rises. Predictions for the next week are made by averaging all weather data from the past seven days. For immediate defences against pests, the weekly forecasts are crucial. The findings support sustainable farming methods and food security by showing that the device can maximise crop yields while decreasing resource consumption.

关键词： Smart agriculture Adaptation models Accuracy Biological system modeling Crops Pesticides Predictive models Prediction algorithms Cotton Random forests

来源：评论

学校读者我要写书评

暂无评论

A Patient-Centric Blockchain Framework for Transparent and Secure Medical Data Sharing Using Modified AES

A Patient-Centric Blockchain Framework for Transparent and S...

引用

Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Priyanka Pramod Pawar Deepak Kumar Mohan Kumar Meesala Piyush Kumar Pareek Santosh Reddy Addula K S Shwetha Department of Information Technology University of the Cumberlands Williamsburg USA Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering Dayananda Sagar College of Engineering Bangalore India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

Due to its decentralised, accessible, and secured structure, blockchain—a framework that has historically been viewed with suspicion—has developed into a groundbreaking breakthrough. Reliable automated scripting and static data records are also a part of this system. The intricacy and expertise required to operate with blockchain technology is one of its disadvantages. Machine Learning (ML) frameworks are the key fixing this problem. It has the potential to make Blockchain more user-friendly. In this research paper, we reviewed the current state of blockchain-based cloud computing, machine learning, and safe data exchange. In this study, we suggest using a blockchain technology to provide a safe space where health entities can share medical information transparently. A modified advanced encryption standard (MAES) based blockchain encryption model is developed by us. This method is based on engineering and AI principles, and its main objective is to design useful artefacts that improve their respective domains.

关键词： Privacy Ecosystems Smart contracts Medical services Machine learning Blockchains Encryption Security Reliability Standards

来源：评论

学校读者我要写书评

暂无评论

Hybrid Optimization Based BERT Model for Drug Detection in NLP

Hybrid Optimization Based BERT Model for Drug Detection in N...

引用

Mysore Sub Section International Conference (MysuruCon), IEEE

作者： C G Kruthika N N Namitha Devi Vijayananda V Madlur K Asha T M Aruna Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering Vidya Vikas Institute of Engineering & Technology Mysuru India Department of Computer Science & Engineering Mangalore Institute of Technology & Engineering Moodabidri Department of Computer Science & Engineering Reva University Bengaluru India

When it comes to natural language processing (NLP), sentence embedding is a hot topic because of the knowledge it transfers to subsequent tasks. Recent studies have shown that sequential neural networks, which can be trained on large quantities of data, can be used to encode info through a one-way analysis of text sequences, making them a powerful tool for drug detection. The purpose of this study is to smear cutting-edge deep learning techniques to publicly available data in order to accurately detect and identify adverse medication reactions that have not been recorded by medical professionals (open data). This study presents a deep learning-based strategy employing Enhanced Encoder on a dataset of 10,000 reviews culled from WebMD and *** and labelled by hand (IBERT). The model is created by integrating the BERT with many parallel blocks of a single-layer deep Convolutional Neural Network (CNN) that use varying kernel sizes and filters. To deal with ambiguity, the biggest obstacle to NLP, such a combination is helpful. Cuckoo Search (CS) and Antlion Optimization (ALO) are coupled to create a hybrid optimization strategy for determining the best values for the proposed model’s hyper-parameters. Results from a number of different classification methods demonstration that the suggested model meaningfully outdoes the state-of-the-art models.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Prediction of Rainfall in Karnataka Region using optimised MVC-LSTM Model

Prediction of Rainfall in Karnataka Region using optimised M...

引用

Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Piyush Kumar Pareek Achyutha Prasad N Chetana Srinivas Jagadeesh B N Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering East West Institute of Technology Bengaluru India

Forecasting precipitation is a prominent topic of study in meteorology. Predictions of precipitation using statistical analysis, learning methods are only a few of the methods that have been offered in the past. Organizations tasked with preventing natural catastrophes might benefit from using meteorological time series data prediction in their decision-making processes. The volume, dimension, and frequency of updates to Time Series data are quite high. The study of period sequence data for forecasting is a crucial part of the practical application. Time series data analysis allows for more precise rainfall forecasts, which is useful for assessing the severity of potential droughts and floods. Precipitation forecasting publications have employed a wide range of approaches. This improved forecast accuracy may be attributed to these methods. In this research, we apply a deep learning technique to examine the precipitation records from the Karnataka Division. This article presents a network consisting of a generator and a predictor for predicting spatial-temporal rainfall data. To imprisonment spatial correlations and build high-resolution data from sparse comments, it uses a multi-layer perceptron (MLP) in its generative module. A Multivariate Convolutional (MVC-LSTM) network is used to create the prediction unit; this network is able to capture the interplay between many variables and their temporal and spatial correlations. The Honey Badger approach finds the appropriate weight for the LSTM, which improves the model's ability to classify data. Additionally, this report suggests several avenues for further study in the fields of rainfall prediction and time series data analysis.

关键词： Deep learning Data analysis Correlation Time series analysis Droughts Predictive models Data models

来源：评论

学校读者我要写书评

暂无评论

Prediction of Floods in Kerala using Hybrid Model of CNN and LSTM

Prediction of Floods in Kerala using Hybrid Model of CNN and...

引用

Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Piyush Kumar Pareek Chetana Srinivas Nayana S Manasa D S Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering East West Institute of Technology Bengaluru India

Floods are a widespread natural calamity that have far-reaching effects on human society, economics, and culture. Researchers have been hard at work for years on flood prediction models. Predicting the condition of flooding is a difficult task that requires in-depth research of the causes of floods. Changes in LULC are of paramount importance to decision planners and environmentalists because of unsustainable development's impact on ecological systems. Therefore, the purpose of this research is to determine the LULC for the Vembanad Lake System (VLS), Kerala. In this research, we combine the CNN and the LSTM network to create a hybrid CNN-–LSTM classical for flood prognostication, which takes full advantage of the fact that the CNN can efficiently extract features while the LSTM can accurately reflect the longer-term historical procedure of the time series data. Four models are then constructed for flood prediction: a LSTM perfect, a LSTM perfect, a CNN-–LSTM perfect, and a CNN-LSTM model. Finally, these models are evaluated using a variety of metrics, and the suggested CNN-LSTM model is exposed to be the most effective because to its low error and fast training time

关键词： Training Biological system modeling Time series analysis Training data Predictive models Lakes Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Securing Digital Governance: A Deep Learning and Blockchain Framework for Malware Detection in IoT Networks

Securing Digital Governance: A Deep Learning and Blockchain ...

引用

Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Priyanka Pramod Pawar Deepak Kumar Mohan Kumar Meesala Piyush Kumar Pareek Santosh Reddy Addula Shwetha K S Department of Information Technology University of the Cumberlands Williamsburg KY USA Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering Dayananda Sagar College of Engineering Bangalore India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

This research uses deep learning and blockchain frameworks to provide a safe platform that promotes digital governance data exchange and interoperability. Use the bonobo optimization algorithm to start a blockchain-based smart city data authentication approach. This paper presents a Blockchain-based malware detection method and framework that uses AI to account for multiple distributed conditions. An upgraded greedy search algorithm and the XGBoost decision tree construct a two-layer extreme gradient boosting (XGBoost) classification model that detects attacks. Three pre-existing XGBoost significance indices were split and merged based on the model's leaf nodes' tree traversal structural features. The augmented greedy search technique retrieved and imported spectral band variables into the XGBoost model's second layer. Bat method was used to optimize XGBoost modeling parameters. The deployed model increased power consumption per device by 13.5%, while Raspberry Pi devices used 0.2 GB and NVIDIA Jetson devices used 0.42 GB. ML models had 93% f1-scores and 95% detection accuracy on both datasets. Our technology detects malware and attacks in Smart Environments efficiently and accurately, as shown by the models.

关键词： Deep learning Data privacy Power demand Smart cities Malware Blockchains Classification algorithms Internet of Things Security Interoperability

来源：评论

学校读者我要写书评

暂无评论

Retraction Note: Sub-network modeling and integration for low-light enhancement of aerial images

引用

Optical and Quantum Electronics 2024年第10期56卷 1-2页

作者： Uganya, G. Devi, C. H. Sarada Chaturvedi, Abhay Shankar, B. B. Ramesh, Janjhyam Venkata Naga Kiran, Ajmeera Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of CSE Meenakshi College of Engineering Chennai India Department of Electronics and Communication Engineering GLA University Mathura India Department of Electronics and Communication Engineering NITTE NITTE (Deemed to Be University) NMAM Institute of Technology (NMAMIT) Karkala India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur India Department of Computer Science and Engineering MLR Institute of Technology Dundigal Hyderabad India

来源：评论

学校读者我要写书评

暂无评论

An Evolution of Hybrid Bagging Technique in Chemoinformatic and Drug Discovery

An Evolution of Hybrid Bagging Technique in Chemoinformatic ...

引用

Data science and Information System (ICDSIS), IEEE International Conference on

作者： Srinivas Aluvala Jagadevi N Kalshetty Saif O. Husain Jagroop Kaur Harpreet Kaur Thind School of Computer Science and Engineering SR University Warangal Telangana India Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Science and Engineering Punjabi University Patiala India Department of Computer Science and Engineering Dayananda Sagar College of Engineering Bengaluru India

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

The drug deals with every living organism on the earth, for classification of drugs based on their action is an important element in drug development. This research paper presents a machine learning model that predicts the action of a drug, utilizing a large drug network. The model integrates J-48 algorithm and random forest algorithm, and form the hybrid bagging technique with the neural network model demonstrating. The model uses the struck2vec data feature, which derived from the large network. Then the data features and dataset are trained on the hybrid bagging technique for drug classification according to the drug properties. The study’s findings highlight the important of machine learning models in drug classification which offers a valuable tool for researchers and pharmaceutical companies in the drug development process. The output includes the top classes of drugs, the prediction of action and aiding in new drugs discovery with higher accuracy of 95.83% which is superior over other learning models.

关键词： Drugs Machine learning algorithms Predictive models Prediction algorithms Feature extraction Data models Classification algorithms

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：