检索结果-内蒙古大学图书馆

2025 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Aryan, Adla Rv, Prakash Aluvala, Srinivas Chowdary, Venigalla Shashank Ardhaman College of Engineering Department of Artificial Intelligence & Machine Learning Telangana Hyderabad501218 India Sr University Department of Computer Science & Artificial Intelligence Telangana Warangal506371 India Nandyal518501 India

ISBN: (纸本)9798331529833

This paper introduces a machine-learning-based LULC classifying model developed with a time series data set, trained on a diverse set of agriculture and non-agricultural classes. It used a strong generalization capability employing an LSTM architecture;that is, it achieved accuracy of 90.04% in training and also validation accuracy of 90.55%. The ultimate accuracy was obtained at the minimal loss of 0.1909 at the accuracy of 95.58%, thereby validating its usability for LULC pattern classification. This model has a wide-range of potential application in environmental monitoring and resource management. Future work involves introducing more variables into the dataset, the inclusion of other environmental factors, and fine-tuning of the model towards more general applications in geospatial applications. Thus, this work advances the current state of remote sensing and LULC classification to present an effective tool in sustainable land management. © 2025 IEEE.

关键词： Environmental monitoring

来源：评论

学校读者我要写书评

暂无评论

An Investigation on CNN-based Lung Cancer Prediction Method

An Investigation on CNN-based Lung Cancer Prediction Method

引用

2023 International Conference on Computer Communication and Informatics, ICCCI 2023

作者： Ram Kumar, R.P. Polepaka, Sanjeeva Likithasree, Dukkipati Keerthika, Sabbineni Department of Artificial Intelligence & Machine Learning Engineering Hyderabad India Department of AI & MLE Hyderabad India

ISBN: (纸本)9798350348217

Diagnosis of any disease is done only after detection of the type and intensity of the disease. Chronic pulmonary disease such as Chronic Obstructive Pulmonary Disease (COPD), lung cancer, and cystic fibrosis needs to be detected in the initial stages to get proper treatment. However, early diagnosis of cancer in the case of lungs is not very easy due to the presence of no recognizable symptoms until much more adverse situations. The proposed work aims at detecting the lung cancer using the Convolutional Neural Network (CNN) regarding the dataset compiled from Kaggle Domain. Various parameters are used to determine the performance of the proposed method, namely, precision, recall and F1-score. Experimental results concluded that, the proposed CNN-based lung cancer detection approach resulted in significantly improved performance when compared to few existing methods. The proposed method has 94% of average precision, 93.6% of recall and 94.6% of F1-score, respectively. © 2023 IEEE.

关键词： Pulmonary diseases

来源：评论

学校读者我要写书评

暂无评论

LSTM based Soft-Sensor for Estimating Nitrate Concentration in Aquaponics pond 3

LSTM based Soft-Sensor for Estimating Nitrate Concentration ...

引用

3rd International Conference for Innovation in Technology, INOCON 2024

作者： Dharshan, A. Kumar, Purushottam Ravimaran, S. Srinivasulu Reddy, U. Saranathan College of Engineering Department of Artificial Intelligence and Data Science Trichy India National Institute of Technology Artificial Intelligence Machine Learning & Data Analytics Lab Trichy India National Institute of Technology Department of Computer Applications Machine Learning & Data Analytics Lab Trichy India

ISBN: (纸本)9798350381931

In the field of aquaponics, where fish and plants coexist in a symbiotic environment, closely monitoring nitrate levels in the water is crucial due to their profound impact on aquatic and plant well-being. Traditional nitrate measurement methods are often time-consuming and costly. Various approaches, including first principles, IoT-based sensors, and machine learning-based soft sensors, have been attempted to address this challenge. However, these efforts face challenges such as expensive sensors, infrequent data collection, multistage data processing using limited sensor types, and the need for regular maintenance like cleaning and calibration. Additionally, varied environmental conditions affect sensor suitability for different water environments, and even some machine learning-based soft sensors have proven inaccurate. In response, soft sensors, especially deep learning-based ones, have gained prominence in industrial applications for their adaptability and accuracy. These sensors provide real-time insights into complex processes without requiring expensive hardware. In this study, an innovative solution was introduced using Long Short-Term Memory (LSTM) technology, a neural network architecture in deep learning known for capturing complex temporal patterns. LSTM is well-suited for modeling and predicting nitrate concentration changes in aquaponics, trained with extensive data collected from various aquaponic ponds. Through rigorous evaluation, a remarkable MSE value of 0.00074 and an impressive R-squared score of 0.98 were achieved, holding potential for scaling up to commercial applications, benefiting aquaponics operations, supporting researchers, and enhancing sustainability and productivity in aquaponic systems. © 2024 IEEE.

关键词： Nitrates

来源：评论

学校读者我要写书评

暂无评论

Diagnosis of Advanced Diabetic Retinopathy from Fundus Image Using machine learning

Diagnosis of Advanced Diabetic Retinopathy from Fundus Image...

引用

2024 International Conference on artificial intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Mankar, Bhagyashri Bawankar, Bhushan Gabhane, Diksha Wanjari, Mohini Department of Information Technology Nagpur India J.D. College of Engineering Department of Artificial Intelligence and Machine Learning Nagpur India

ISBN: (纸本)9798331517953

Diabetic Retinopathy is an eye disease which mainly caused to the diabetic patients. The patients who have been suffering from diabetes since long time have major chances to suffer from Diabetic Retinopathy (DR) as well. If the DR is not detected early, the patient may experience vision loss, so early detection of this disease is very important. Micro-aneurysm and exudates are the features from which we can identify whether patients suffer from DR or not. Identifying retinal micro-aneurysms and exudates from the retinal fundus image is the primary goal of the proposed work to test for diabetic retinopathy. Firstly, the pre-processing is performed on Fundus image. Preprocessing techniques include adaptive median filtering, adaptive histogram equalization, and grey scale conversion. Feature extraction is utilized to extract the features from the fundus image after pre-processing. The extracted features are act as an input to the SVM classifier. The fundus image is classified as normal or impacted using a vector machine. © 2024 IEEE.

关键词： Median filters

来源：评论

学校读者我要写书评

暂无评论

Optimizing AUV Positioning and Data Transmission with Actor Critic (A3C) Algorithm in Underwater Wireless Sensor Networks 4

Optimizing AUV Positioning and Data Transmission with Actor ...

引用

4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Kodukulla, Aruna Gayatri Amanullah, M. Kalaiselvan, S.A. Saveetha School of Engineering Simats Department of Computer Science & Engineering Chennai India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331529635

In recent years, UWSNs have emerged as a game-changer in many aquatic applications, including exploration, environmental monitoring, target tracking for underwater navigation, terrain-assisted navigation, and many more. The intricacy of the underwater environment presents several obstacles for the deployment of sensor nodes, and UWSNs are dynamic networks, unlike terrestrial wireless sensor networks. Instead of transferring data from every node to a destination node, autonomous underwater vehicles (AUVs) provide a more reliable option for data collecting in UWSNs. Nevertheless, a major challenge in data collection for UWSNs is the development of AUV positioning algorithms that are both delay-aware and energy-efficient. A trustworthy AUV-based data-collection routing strategy employed for UWSNs with the goal of decreasing network latency and increasing network lifespan. A positioning mechanism is used by the proposed protocol to gather data utilizing AUVs. With the goal of lowering power usage, the sink node coordinates AUV data collecting from the sensor nodes. To improve scalability, sensor nodes are first grouped into clusters, and then an AUV is assigned to each cluster. Second, in order to ensure the trustworthy gathering of data, the Actor 3 Critic (A3C) for every AUV is designed using the Markov decision process (MDP). In regards to energy consumption, delay, coverage percentage, and dependability, the simulation results validate the efficacy and efficiency of the proposed method. © 2024 IEEE.

关键词： Sensor nodes

来源：评论

学校读者我要写书评

暂无评论

Spam SMS Classifier Using machine learning Algorithms

Spam SMS Classifier Using Machine Learning Algorithms

引用

2024 International Conference on artificial intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Mankar, Bhagyashri Wanjari, Mohini Gabhane, Diksha Department of Information Technology Nagpur India J.D. College of Engineering Department of Artificial Intelligence and Machine Learning Nagpur India

ISBN: (纸本)9798331517953

Spam via short messaging service (SMS) is becoming more prevalent as spammers use it as a means of reaching their targets. While spam SMS messages can be annoying, they can also be dangerous since they might attempt to steal personal information or contain links to malicious websites. The status of SMS spam detection is surveyed in this research. We go over the various kinds of SMS spam;characteristics that help identify spam, and machine learning techniques that have been employed to address the issue. We also note some of the difficulties and potential paths forward in the identification of SMS spam. Support Vector machine (SVM): SVMs are a class of machine learning algorithms that are applicable to applications involving both regression and classification. SVMs function by locating a hyper plane in the data that has the greatest margin of separation between the various classes. Multinomial Naive Bayes (MNB): A kind of Naive Bayes classifier called MNB is made especially for classification problems involving categorical data. If the data's features are independent of one another, MNB applies the Bayes theorem to determine the likelihood of each class given the data's features. Extra Trees Classifier (ETC): The random forest method serves as the foundation for the ensemble learning algorithm known as ETC. To arrive at a final prediction, ETC builds several decision trees using arbitrary subsets of the data and averages their forecasts. Random Forest Classifier (RFC): Based on the decision tree method, RFC is another kind of ensemble learning algorithm. In order to get at a final prediction, RFC builds several decision trees using arbitrary subsets of the data and averagetheirforecasts. To enhance the overall performance of the model, the predictions made by each individual classifier are combined using the Voting Classifier and Stacking Classifier ensemble models. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Integrating Photonics and Fiber Bragg Grating Sensors with Deep Reinforcement learning for Advanced Robotic Systems 18

Integrating Photonics and Fiber Bragg Grating Sensors with D...

引用

18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Subaranjani, T. Jaideep, R. Manjula, C. Seema, V. The Oxford College of Engineering Dept. of Artificial Intelligence and Machine Learning Bangalore India The Oxford College of Engineering Dept. of Mechatronics Engineering Bangalore India

ISBN: (纸本)9789380544519

This research introduces the Photonics-Enhanced Embedded Robotic intelligence Model (PEERIM), an innovative approach that integrates fiber Bragg grating (FBG) sensors with photonics and deep reinforcement learning (DRL) for advanced robotics applications. Addressing the limitations of current AI models in dynamic and complex environments, PEERIM leverages the rapid data transmission capabilities of photonics to process high-frequency sensor data, enabling real-time decision-making and enhanced automation. The methodology encompasses the development and integration of a variant of the Proximal Policy Optimization (PPO) algorithm, tailored to manage the continuous data stream and execute precise, adaptive control. The proposed model has been empirically evaluated, demonstrating a significant improvement in handling real-time sensor data with an average reward of 99.02 and a low average loss of 0.099, indicating robust performance and learning stability. These findings suggest that PEERIM provides a substantial advantage over existing AI-driven robotic systems, offering a scalable solution for a variety of challenging applications. The study's contributions lay the groundwork for future advancements in autonomous systems, promising a new era of precision and reliability in robotics. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Autonomous Systems Deep Reinforcement learning Fiber Bragg Grating Sensors Photonics-Enhanced Robotics Proximal Policy Optimization Real-Time Decision Making

来源：评论

学校读者我要写书评

暂无评论

Early Melanoma Detection and Classification Using CNN and Confusion Matrix Analysis

Early Melanoma Detection and Classification Using CNN and Co...

引用

2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Dinesh Kumar, K. Deepthi, K. Jaya Saravanakumar, S. Balamurugan, S. Govindharaj, I. Reddeppa, Pulikandla Akshay Vel Tech University Department of Computer Science and Engineering Chennai India Learning Mohan Babu University Department of Artificial Intelligence and Machine Tirupati India Paavai Engineering College Department of Information Technology Namakkal India Bengaluru India Learning Vel Tech University Department of Artificial Intelligence and Machine Chennai India

ISBN: (纸本)9798331510022

This article brings up a new method for detecting and classifying skin cancers using Convolutional Neural Networks (CNN) and Confusion Matrix analysis. The main work of this work evolves around detection of Melanoma and Non Melanoma cells. The large dataset on skin cancers is used to teach the CNN model to accurately classify different types of skin lesions into different malignant and non-cancerous groups. The integration of Confusion Matrix, in this case, allows for accurate identification of the classification errors made by the model as well as possible areas for improvement hence making it possible to comprehensively evaluate how well the model performs. Therefore, early recognition is indispensable to successful treatment as skin cancer is a typical yet fatal condition. Results shows that the suggested proposed model achieves 95.5% accuracy and performance in comparison with other methods found in literature. Additionally, Modified DenseNet201 model has a sensitivity of 95.96% as well as a specificity of 98.03%. © 2024 IEEE.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

A Review on the Role of artificial intelligence in Personalized learning

A Review on the Role of Artificial Intelligence in Personali...

引用

2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Qureshi, Rukaiyya Hajare, Shital Verma, Prateek Sawangi Meghe Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Sawangi Meghe Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India

ISBN: (纸本)9798350361537

This article explores the revolutionary role of artificial intelligence (AI) in personalized learning, focusing on its potential to revolutionize educational processes and improve learning outcomes. This paper provides insights into how AI technologies can be used to tailor instruction, identify learning needs, and provide specific strategies that cater to different students' psychological, cognitive, and motivational preferences. The article examines case studies and examples to demonstrate different uses of AI in personalized learning settings, such as autonomous tutoring systems, personalized education channels, and analysis based on data. However, the use of AI in education raises ethical, technological, and policy concerns that require careful study and proactive actions to assure fair access, preserve learner privacy, and eliminate algorithmic biases. Ultimately, finally, the article emphasizes AI's transformative potential in personalized learning and advocates for coordinated efforts among educators, academics, and policymakers to leverage its benefits while tackling accompanying obstacles. © 2024 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

A Survey on Graph Neural Networks and its Applications in Various Domains

引用

SN Computer Science 2025年第1期6卷 1-12页

作者： Murgod, Tejaswini R. Reddy, P. Srihith Gaddam, Shamitha Sundaram, S. Meenakshi Anitha, C. Department of Artificial Intelligence & Machine Learning BNM Institute of Technology Karnataka Bengaluru India Department of Computer Science and Engineering NITTE Meenakshi Institute of Technology Karnataka Bengaluru India

Graph Neural Networks (GNNs) are neural models that use message transmission between graph nodes to represent the dependency of graphs. Variants of Graph Neural Networks (GNNs), such as graph recurrent networks (GRN), graph attention networks (GAT), and graph convolutional networks (GCN), have shown remarkable results on a variety of deep learning tasks in recent years. In this study, we offer a generic design pipeline for GNN models, go over the variations of each part, classify the applications in an organized manner, and suggest four outstanding research issues. Dealing with graph data, which provides extensive connection information among pieces, is necessary for many learning tasks. A model that learns from graph inputs is required for modelling physics systems, learning molecular fingerprints, predicting protein interfaces, and identifying illnesses. Reasoning on extracted structures (such as the dependency trees of sentences and the scene graphs of photos) is an important research issue that also requires graph reasoning models in other domains, such as learning from non-structural data like texts and images. Graph Neural Networks (GNNs) are primarily designed for dealing with graph-structured data, where relationships between entities are modeled as edges in a graph. While GNNs are not traditionally applied to image classification problems, researchers have explored ways to leverage graph-based structures to enhance the performance of Convolutional Neural Networks (CNNs) in certain scenario. GNN have been increasingly applied to Natural Language Processing (NLP) tasks, leveraging their ability to model structured data and capture relationships between elements in a graph. GNN are also applied for traffic related problems particularly in modeling and optimizing traffic flow, analyzing transportation networks, and addressing congestion issues. GNN can be used for traffic flow prediction, dynamic routing & navigation, Anomaly detection, public transport network

关键词： Computer vision Graph neural networks Intrusion detection Natural language processing Neural networks Traffic control

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：