检索结果-内蒙古大学图书馆

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

作者： Adnan, Myasar Mundher Devi, S. Vaag Deepthi, V.S. Raghunathareddy, M.V. Yamsani, Nagendar The Islamic University Najaf Iraq City Engineering College Department of Artificial Intelligence and Machine Learning Karnataka Bangalore India Cyber Security Dayananda Sagar College of Engineering Department of Computer Science and Engineering Karnataka Bangalore India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India SR University School of Computer Science and Artificial Intelligence Warangal India

ISBN: (纸本)9798350317558

The ransomware exploits the system data and takes off the significant information of the user without any intimation. Moreover, the ransomware furtively directs that information to the servers which are organized by the attackers. In recent years, many researchers and scientists discovered anti-malware products to identify known malware. But these methods are not robust to detect complicated and packed malware. To overcome these problems, the Sine Cosine Reptile Search Algorithm with Grid Search Support Vector machine (SCRSA-GSSVM) is proposed for ransomware detection and classification. The Drebin dataset is employed in this paper and min-max standardization is utilized aimed at preprocessing. The SCRSA is utilized for feature selection and GSSVM is utilized for classification. The SVM is tuned by GS which reduces the noise and false positives to enhance the model performance. Performance of SCRSA-GSSVM is assessed with presentation measure of accuracy, precision, recall and f1-score. The SCRSA-GSSVM attains 99.85% accuracy, 99.83% precision, 99.83% recall and 99.78% f1-score which is better when compared to Random Forest (RF) and artificial Neural Network (ANN). © 2024 IEEE.

关键词： +Support Vector machine Grid Search Min-max Normalization Ransomware Reptile Search Algorithm

来源：评论

学校读者我要写书评

暂无评论

Efficient Prompting for LLM-Based Generative Internet of Things

引用

IEEE Internet of Things Journal 2025年第1期12卷 778-791页

作者： Xiao, Bin Kantarci, Burak Kang, Jiawen Niyato, Dusit Guizani, Mohsen University of Ottawa School of Electrical Engineering and Computer Science OttawaONK1N 6N5 Canada Guangdong University of Technology School of Automation Guangzhou510006 China Nanyang Technological University School of Computer Science and Engineering Jurong West Singapore Singapore Mohamed bin Zayed University of Artificial Intelligence Machine Learning Department Abu Dhabi United Arab Emirates

Large language models (LLMs) have demonstrated remarkable capacities on various tasks, and integrating the capacities of LLMs into the Internet of Things (IoT) applications has drawn much research attention recently. Due to security concerns, many institutions avoid accessing state-of-the-art commercial LLM services, requiring the deployment and utilization of open-source LLMs in a local network setting. However, open-source LLMs usually have more limitations regarding their performance, such as their arithmetic calculation and reasoning capacities, and practical systems of applying LLMs to IoT have yet to be well-explored. Therefore, we propose an LLM-based Generative IoT (GIoT) system deployed in the local network setting in this study. To alleviate the limitations of LLMs and provide service with competitive performance, we apply prompt engineering methods to enhance the capacities of the open-source LLMs, design a Prompt Management Module and a Postprocessing Module to manage the tailored prompts for different tasks and process the results generated by the LLMs. To demonstrate the effectiveness of the proposed system, we discuss a challenging table question answering (Table-QA) task as a case study of the proposed system, as tabular data is usually more challenging than plaintext because of their complex structures, heterogeneous data types and sometimes huge sizes. We conduct comprehensive experiments on the two popular Table-QA data sets, and the results show that our proposal can achieve competitive performance compared with state-of-the-art LLMs, demonstrating that the proposed LLM-based GIoT system can provide competitive performance with tailored prompting methods and is easily extensible to new tasks without training. © 2014 IEEE.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

DBROA based Convolutional Auto Encoder for European Credit Card Fraud Detection 2

DBROA based Convolutional Auto Encoder for European Credit C...

引用

2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Pareek, Piyush Kumar Mohan, B.R. Vidya, E.V. Zanke, Pankaj Navyashree, K.S. Nitte Meenakshi Institute Of Technology Artificial Intelligence And Machine Learning Bengaluru India East West Institute Of Technology Department Of Computer Science And Engineering Bengaluru India Sapiens International Corporation United States

ISBN: (纸本)9798350381665

Numerous studies using bad neuronic nets (DNNs) for the objective of detecting recognition card fraud have concentrated on enhancing point prediction accuracy and limiting unwelcome biases by developing various network designs or learning models. It is crucial to reckon uncertainty together with point estimate of the training data has a significant impact on how well machine learning algorithms function, therefore data imbalance is not a minor problem. In general, the data only shows a tiny portion of false communications. The effectiveness of simple machine acquiring classifiers is significantly impacted by this. This research studies several data augmentation strategies to handle the unbalanced data problem for credit card fraud detection in order to deal with the shortage of false events. The balanced data is then sent to a Convolutional Auto Encoder (CAE) for classification, and a Developed Battle Royale optimisation algorithm (DBROA) is used to determine the model's weight in the best possible way. For the purpose of recognising credit card fraud involving European cardholders, each model includes stratified K-fold cross-validation and a real-world dataset. The results show that the suggested model is more accurate than current models. © 2024 IEEE.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Pneumonia Detection in Chest X-Rays Using Combined Approach of Deep learning 2

Pneumonia Detection in Chest X-Rays Using Combined Approach ...

引用

2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Aditya Pai, H. Pareek, Piyush Kumar Guruprasad, M.S. Chandrappa, S. Bhargava, Krishna Mit Art Design and Technology University Mit School of Computing Pune India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Department of Computer Science and Engineering Dehradun India School of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350372892

A respiratory infection like pneumonia faces a serious health challenge across the globe, especially in the poor and underdeveloped countries. The pneumonia is affected by various factors like bacteria or viruses. The reason for the affection is due to higher level of pollution, poor air quality, excess of population, poor infrastructure etc. To reduce the threat from pneumonia, there is a need for early and accurate detection of the infection by using chest X-ray images as the most effective diagnostic method. The paper proposes an automated chest X-ray analysis system by using VGG16 architecture and a weighted average ensemble method to enhance diagnostic accuracy. By integrating deep transfer learning, the system overcomes data scarcity issues, achieving a remarkable 99% accuracy on a publicly available dataset. The research underscores the potential of automated systems in improving pneumonia diagnosis, highlighting the urgent need for advanced diagnostics in resource-limited settings. © 2024 IEEE.

关键词： Air quality

来源：评论

学校读者我要写书评

暂无评论

Apriori Analysis of Deep learning Models on a Multi-class Data Set 4th

Apriori Analysis of Deep Learning Models on a Multi-class Da...

引用

4th International Conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Kosuru, Sai Sathwik Padmanabha Reddy, Y.C.A. Akanksha, Perala Venkata Chaitanya, Kotagiri Parsi, Snehaja Chunduri, Madhu Babu Department of Computer Science and Engineering B V Raju Institute of Technology Telangana Medak Narsapur502313 India Department of Artificial Intelligence & Machine Learning Chaitanya Bharathi Institute of Technology Ganidept Hyderabad500075 India

ISBN: (纸本)9789819751990

Deep learning techniques have large scopes in terms of applications and resource requirements, hence calling for a deep analysis of their behavior. Many studies focused on the application part of these models and there is a relatively lesser discussion about the unit-wise behavior. A multi-class dataset was taken and three different models were trained where their performances of training processes are described in terms of runtime characteristics like learning rate, times, etc. An apriori analysis was conducted where the runtime characteristics are investigated for their similarity with pre-defined knowledge of their architectures, as opposed to the posterior analysis which particularly focuses on observations. The work also serves as a framework for defining the logistics of the model training processes and fine-tunes the parameters devised in future for the maximum accuracy of the model. The results were further converted into graphical representations for enhanced delivery of insights. The study is also novel where, as opposed to previous works, in which performances are only tested, the model is evaluated in a design-specific manner dealing with only architectures and the parameters defining them. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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

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

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

ISBN: (纸本)9798350398465

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 © 2023 IEEE.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Predicting Soil Types and Crop Suitability through Comprehensive Soil Analysis

Predicting Soil Types and Crop Suitability through Comprehen...

引用

International Conference on Communication, Computing and Internet of Things (IC3IoT)

作者： S Sivakumar U Dharani Dineep Hephzibah Department of Computer Science and Engineering SRM Institute of Science and Technology Tiruchirapalli India Department of Computer Science and Engineering Specialization in Artificial Intelligence and Machine Learning SRM Institute of Science and Technology Tiruchirapalli India

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

The analysis of soil nutrients and water levels holds utmost significance in the realms of agriculture. Determining the soil type and identifying the crop suitable for that specific soil are critical factors in optimizing agricultural yield. These considerations play a pivotal role in precision agriculture, ensuring that the chosen crops align with the soil's characteristics. This strategic alignment enhances the efficiency of cultivation practices, promotes sustainable agricultural methods, and ultimately contributes to increased crop yields. In this research paper, we systematically identify key soil properties, including NPK concentrations, temperature, humidity, and soil pH. The collected dataset, encompassing these crucial soil attributes, is utilized to train machine learning models. The primary goal is to forecast the soil type utilizing the gathered data, and subsequently, undertake further analyses, to ascertain the relevant crop types that are most suitable for the identified soil characteristics. This comprehensive approach involves the integration of data-driven insights and machine learning techniques to enhance our understanding of soil composition and facilitate informed decision-making in agriculture.

关键词： Temperature Computational modeling Crops Soil Predictive models Bayes methods Telecommunication computing

来源：评论

学校读者我要写书评

暂无评论

Customer Segmentation Using Clustering Analysis

Customer Segmentation Using Clustering Analysis

引用

2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Jiet, Moses Makuei Kamble, Aahash Puri, Chetan Yesankar, Prajyot Verma, Prateek Rewatkar, Rajendra Faculty of Engineering and Technology Department of Computer Science & Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Data Science Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Machine Learning Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Biomedical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798350382693

This research focuses on the crucial role of the clustering technique in data mining, specifically in market forecasting and planning. The study presents a comprehensive report on utilizing the k-means clustering technique and data visualization tools to develop a real- time system for a supermarket, predicting sales across various annual cycles. An open-source dataset of a mall with n = 200 customers divided into k = 3 groups, demonstrated high accuracy in predicting day-to-day sales statistics when compared with actual results. The research highlights the effectiveness of the clustering technique in real-time sales prediction for supermarkets. © 2024 IEEE.

关键词： Sales

来源：评论

学校读者我要写书评

暂无评论

Clustering Based Segmentation with 1D-CNN Model for Grape Fruit Disease Detection

Clustering Based Segmentation with 1D-CNN Model for Grape Fr...

引用

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

作者： Pareek, Piyush Kumar Ramya, I.M. Jagadeesh, N.B. LeenaShruthi, M.H. Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India East West Institute of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350398465

There has been a lot of focus on agricultural image dispensation research in the previous years. computers employ image processing procedures to do picture analysis. Recent technological advances in the fields of picture imprisonment and data dispensation have greatly facilitated the solution of several agricultural issues. Identification and categorization of crop diseases are essential to the agricultural sector's technical and economic health. The first step in agricultural image processing is acquiring a digital colour photograph of a sick leaf. Grapes have the highest global per capita fruit consumption. It is unacceptable to let the spread of grape leaf contamination in the grapefruit crop to go unchecked, since this would be detrimental to the industry's growth. Farmers in rural regions sometimes lack the skills to spot these illnesses early on, which leads to poor crop yields. In order to detect diseases in grapefruit leaves, the suggested system employs a deep learning-based categorization method. K-means clustering is first used to segregate the backgrounds of the input datasets. The Convolutional Neural Network (CNN) is then fed the segmented regions to get a disease classification. The suggested work utilises the hyperparameter tuning operation with the assistance of Firefly with cyclic randomization to enhance the classification accuracy (FCR). The accuracy, f-measure, recall, and precision of the proposed model are evaluated in light of the experimental data. Finally, compared to the current models, which obtained accuracies of 88% to 90% for grape fruit disease detection, the suggested CNN model achieved 95% accuracy and 92% precision. © 2023 IEEE.

关键词： K-means clustering

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

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

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

ISBN: (纸本)9798350398465

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. © 2023 IEEE.

关键词： Decision making

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：