检索结果-内蒙古大学图书馆

5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Devi, M. Shyamala Harivishnu, K.C. Suriya, Jaya V. Jayasuriya, K. Jason, Infant G.A. Farih, M.D. Department of Computer Science and Engineering Panimalar Engineering College Tamilnadu Chennai India Department of Artificial Intelligence and Data Science Panimalar Engineering College Tamilnadu Chennai India

ISBN: (纸本)9798350361155

Among the edible varieties of fungi, mushrooms possess the strongest nutrients found in the plant. Nevertheless, because of their great demand for food and significant benefits to medical research, it is imperative to identify the different varieties of mushrooms within the current species. The deep learning technology has recently rendering the CNN models towards classification of various object in interdisciplinary fields. This paper proposes Regularized Dense Layered LeNet (RDL-LeNet) that categorizes the mushroom types with high accuracy. The Mushrooms Classification dataset, which includes 3150 mushroom pictures was used for this execution. The Mushrooms Classification dataset was fitted with the existing CNN models to select the best CNN model. To determine the most efficient CNN framework, the Mushrooms Classification dataset was fitted using the available CNN models. When compared to the other CNN model, the LeNet performs better, with an accuracy rate of 90.67%. LeNet has now been chosen to adjust the quantity of dense layers. LeNet typically consist of three convolutions, two pooling layers, and two dense layers. In order to suggest the recommended framework RDL-LeNet, the LeNet is now changed by adding an additional dense layer that is made up of a single block of batch normalization, relu optimization, and drop out at the end that further compresses the output into vector size of 84. The RDL-LeNet is made up of three convolutional layers, two pooling layers, and three dense layers. The third dense layer consists of a single block that is made up of batch normalization, relu optimization, and dropout, ultimately leading to the final output layer. The suggested RDL-LeNet and the current CNN have been implemented with the Mushroom Classification dataset. The experiment demonstrates that the suggested RDL-LeNet model works better in the categorization of mushroom types, with a high accuracy of 98.68%. © 2024 IEEE.

关键词： Fungi

来源：评论

学校读者我要写书评

暂无评论

ResNet Blended Five Dense Layered Deep Fully Connected Neural Network based Road Damage Classification 2

ResNet Blended Five Dense Layered Deep Fully Connected Neura...

引用

2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Shyamala Devi, M. Keerthivasan, R. Jai Harshan, H. Kamalesh, P. Harishraj, B. Hibban Rashid, M.D. Panimalar Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Chennai India

ISBN: (纸本)9798350371314

Operating a vehicle on a deteriorated roadway causes an enormous threat to safety, particularly for self-driving automobiles. Deep learning-based image processing technology has made it possible to study models that assess roadway conditions. Disparity of roadway damage and complicated backgrounds in roadway shots makes it an extremely difficult challenge classifying the road damage. To address this problem, this paper recommends ResNet Blended Five Dense Layered Deep Fully Connected Neural Network (RB-5DFCNN) that classifies the road damage with high efficiency. The KAGGLE Road Damage dataset, which consists of 2074 road snapshots, is used by the proposed RB-5DFCNN model. In order to classify road damage, the RB-5DFCNN model first preprocesses the images before fitting ResNet CNN, which consists of five steps of convolutional block and identity block processing. After the ResNet model completed its task, five dense layers were applied to the DFCNN in order to classify the state of road damage. The classification of road damage condition and the determination of the necessary number of dense layers to identify road damage with high accuracy are the innovative aspects of the proposed RB-5DFCNN. To test the effectiveness of the road damage categorization, the Road Damage dataset was applied to both the proposed RB-5DFCNN model and the current CNN approaches. Python execution on a GTX Geforce Tesla V100 Nvidia graphics card server over 64 and 150 training intervals shows that the suggested RB-5DFCNN model projects an accuracy of 98.77% in comparison to the state-of-the-art CNN models. © 2024 IEEE.

关键词： Statistical tests

来源：评论

学校读者我要写书评

暂无评论

BiGRU-ANN based hybrid architecture for intensified classification tasks with explainable AI

引用

International Journal of Information Technology (Singapore) 2023年第8期15卷 4211-4221页

作者： Chakraborty, Sovon Talukder, Muhammad Borhan Uddin Hasan, Mohammad Mehadi Noor, Jannatun Uddin, Jia Department of Computer Science and Engineering University of Liberal Arts Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering European University of Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering BRAC University Dhaka Bangladesh AI and Big Data Department Woosong University Daejeon South Korea

Artificial Intelligence (AI) is increasingly being employed in critical decision-making processes such as medical diagnosis, credit approval, criminal justice, and many more. However, many AI models exploit complex algorithms that are difficult for humans to see through, which can lead to concerns about accountability, bias, and the ability to trust the outcomes. With the increasing demand for AI systems to be transparent, interpretable, and reliable, the field of Explainable AI (XAI) has gained attention of the researchers. This paper presents a robust hybrid architecture that combines Bidirectional Gated Recurrent Units (BiGRU) and Artificial Neural Networks (ANN) for the classification of texts and sentiment analysis. Interpretable Model Agnostic Explanation (LIME) has been employed with our proposed model to enhance confidence in the outcomes. The proposed architecture is found to be effective for sentiment analysis from texts, and classifying images containing handwrit- ten characters. It leverages the BiGRU to model the sequential dependencies in the data, while the ANN is used for the final classification. Evaluations on both Bengali and English datasets show that the proposed architecture outperforms state-of-the-art models in various performance metrics, providing meaningful and interpretable explanations for its predictions. The model can be used in systems that require the architectures to be computationally less demanding, yet a decent accuracy is secured. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： ANN BiGRU Explainable AI LIME Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Emotion Recognition Using Focal Network and Resnet

Emotion Recognition Using Focal Network and Resnet

引用

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

作者： Meena, T. Balasai, Sigireddy Anandh Duggireddy Kumar, P. Phaneendhra Velagapudi Ramakrishna Siddartha Engineering College Department of Computer Science and Engineering India Velagapudi Ramakrishna Siddartha Engineering College Department of Artificial Intelligence and Data Science India

ISBN: (纸本)9798350382693

Image emotion recognition involves finding the emotions from visual data, usually done through convolutional neural networks (CNN) or deep neural networks (DNN). The existing methodologies are often high complex or time complex and it is not giving the good considerable results and computationally expensive due to high number of parameters. In this paper we try to accomplish a better output by using focal network which stood among other transformers architecture due to the unique property of the dynamic attention Recent studies have shown the efficacy of transformer architectures in various deep learning tasks and challenged the convolution neural networks on various tasks. but, It has a tradeoff between reduced parameter complexity and the need for extensive training data. Drawing inspiration from recent publications, the study adopts a focal network for training, demonstrating its effectiveness in enhancing the model's attention and optimizing emotional feature extraction. The proposed methodology aims to do the process of image emotion recognition by using the advantages of transformers while mitigating the computational problem associated with the deep neural networks. The incorporation of a focal network addresses attention-related challenges, and it contributes to the development of more efficient emotion recognition systems. This research offers insights into the evolving landscape of image emotion recognition methodologies and underscores the potential of transformer architectures in advancing this field. © 2024 IEEE.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

ReLU Activated Attention UNet Encipher Decipher Framework based Species Classification of Butterfly

ReLU Activated Attention UNet Encipher Decipher Framework ba...

引用

2024 International Conference on Advancements in Power, Communication and Intelligent Systems, APCI 2024

作者： Devi, M. Shyamala Jeeva, R. Jagadheeswaran, R. Kumar V, Hemand Erwin Nicholas, M. Keerthi Vasan, P. Panimalar Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Chennai India

ISBN: (纸本)9798350363289

The recognition and categorization of butterflies is crucial for the preservation of butterfly species in the fields of entomology, computer vision and deep learning. Environmentalists have long utilized butterflies as model organisms in the research on the effects of habitat degradation, dispersion, and warming temperatures. This paper recommends Relu Activated Attention UNet (RAA-UNet) that categorizes the nine butterfly species with high precision. The Butterfly dataset contains 1664 butterfly images that were used in this investigation. The dataset consists of 832 photos of butterflies and 832 related images that have been segmented. The proposed RAA-UNet starts by creating masked butterfly pictures by masking the original image with a segmented butterfly image. To classify the butterfly species, the masked butterfly pictures are assigned to Attention UNet with Decoder and Encoder combined with the Attention gate and activated using Relu Activation function. The proposed RAA-UNet and conventional CNN models are fitted to the masked butterfly pictures. As demonstrated by the experiments, the RAA-UNet model works better with a high accuracy of 97.75% in the classification of butterfly species. © 2024 IEEE.

关键词： Invertebrates

来源：评论

学校读者我要写书评

暂无评论

A Real-Time Automatic Translation of Text to Sign Language

引用

computers, Materials & Continua 2022年第2期70卷 2471-2488页

作者： Muhammad Sanaullah Babar Ahmad Muhammad Kashif Tauqeer Safdar Mehdi Hassan Mohd Hilmi Hasan Norshakirah Aziz Department of Computer Science Bahauddin Zakariya UniversityMultan60000Pakistan Department of Computer Science Air UniversityMultan60000Pakistan Department of Computer Science Air UniversityIslamabad44000Pakistan Centre for Research in Data Science Department of Computer and Information SciencesUniversitiTeknologi PETRONASSeri Iskandar32610PerakMalaysia

Communication is a basic need of every human being;by this,they can learn,express their feelings and exchange their ideas,but deaf people cannot listen and *** communication,they use various hands gestures,also known as Sign Language(SL),which they learn from special *** normal people have not taken SL classes;therefore,they are unable to perform signs of daily routine sentences(e.g.,what are the specifications of this mobile phone?).A technological solution can facilitate in overcoming this communication gap by which normal people can communicate with deaf *** paper presents an architecture for an application named Sign4PSL that translates the sentences to Pakistan Sign Language(PSL)for deaf people with visual representation using virtual signing *** research aims to develop a generic independent application that is lightweight and reusable on any platform,including web and mobile,with an ability to perform offline text *** Sign4PSL relies on a knowledge base that stores both corpus of PSL Words and their coded form in the notation ***4PSL takes English language text as an input,performs the translation to PSL through sign language notation and displays gestures to the user using virtual *** system is tested on deaf students at a special *** results have shown that the students were able to understand the story presented to them appropriately.

关键词： Sign language sign markup language deaf communication hamburg notations machine translation

来源：评论

学校读者我要写书评

暂无评论

Leveraging 3D Faster R-CNN for 3D Dental X-ray Restoration and Treatment Identification

Leveraging 3D Faster R-CNN for 3D Dental X-ray Restoration a...

引用

International Conference on Computing and Machine Learning, CML 2024

作者： Rai, Bipin Kumar Bisht, Deepanshu Kumar, Ekansh Chaudhary, Aashish Department of Computer Science & Engineering Dayananda Sagar University Bengaluru India Department of CSE-Data Science ABES Institute of Technology Uttar Pradesh Ghaziabad201009 India

ISBN: (纸本)9789819765874

This research aimed to develop a 3D Faster-R-CNN model for detecting dental restorations and treatments in panoramic view radiographs and dental intra-oral X-rays. Trained on a comprehensive collection of 2D and 3D dental X-ray images, it marked a notable advancement. Utilizing the 3D TeethSeg 2022 MICCA dataset, the model demonstrated its ability to automatically detect and classify dental features, including challenging traits like cavity spots and tooth cracks. The study addressed the limitations of current methods for analyzing dental X-rays, especially in 3D format. While existing 2D models have some recognition capabilities, they struggle with 3D data intricacies. Moreover, dedicated 3D models for dental applications are lacking. This proposed model introduced a novel approach tailored for 3D dental image analysis, aiming to fill this gap. The model played a vital role in assisting dentists by identifying restorative and treatment needs for each tooth or patient, significantly enhancing dental care quality and efficiency. It showcased notable performance metrics: an accuracy of 89.14%, a precision of 95.84%, and a recall of 91.93%. Through advanced data augmentation and meticulous model training, it represented a significant advancement in detecting and classifying dental interventions, offering a comprehensive approach to dental image analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： X ray radiography

来源：评论

学校读者我要写书评

暂无评论

AI Sovereignty in Autonomous Driving: Exploring Needs and Possibilities for Overcoming Challenges

AI Sovereignty in Autonomous Driving: Exploring Needs and Po...

引用

2023 IEEE Silchar Subsection Conference, SILCON 2023

作者： Sinha, Koesha Misra, Bitan Chakraborty, Sayan Dey, Nilanjan Department of Statistics and Data Science Bangalore India Techno International New Town Department of Computer Science Engineering Kolkata India

ISBN: (纸本)9798350314144

With the development of artificial intelligence, advancements in navigation systems for self-driving cars have become a new direction over the last decade. The inclusion of AI-driven actuators in autonomous vehicles has broken the barriers in terms of real-time high-quality data processing resources, accuracy of decisive actions and generalization of environment-action pairs. Upgradation from a car with no automation to a car with minimal to no human intervention has become a boon of AI, as it resolves most of the transportation problems on roads, including human error, lack of visibility in adverse weather conditions, tiredness of drivers in long journeys, etc. This study focuses on AI-enabled tasks, including object detection and identification, lane detection, notification for lane departure and reinforcement learning from the operational environment. However, there exist serious issues in deploying AI-empowered modules in autonomous cars, as the consumer rights to explain, trustworthiness, and reliability of the machine have not yet met the requirements. Our work explores the needs and prospects of AI sovereignty in autonomous driving by overcoming the aforementioned issues so that the healthy progress of technological society can take care of the future world. © 2023 IEEE.

关键词： Autonomous vehicles

来源：评论

学校读者我要写书评

暂无评论

Auxiliary Dense Layered LeNet Deep Learning based Mango Variety Classification 2

Auxiliary Dense Layered LeNet Deep Learning based Mango Vari...

引用

2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Shyamala Devi, M. Janani, G. Fernandes, Jonita Maria Harshini, T. Nivetha, R. Joshika Rao, O.R. Panimalar Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Chennai India

ISBN: (纸本)9798350371314

In the agriculture sector, physical classification of fruits is a costly process that can produce inconsistent outcomes due to human negligence. Fruit categorization from snapshots is an extremely difficult venture, especially for mango fruits because various species of mango fruits share significant resemblance. This paper proposes Auxiliary Dense Layered LeNet (ADL-LeNet) that classifies the mango type with high accuracy. The Mango Varieties Classification and Grading dataset, which includes 1200 mango pictures was used for this work. The Mango image dataset is applied with the existing CNN model to select the best CNN model. The LeNet outperforms with the accuracy of 92.77% when compared to the other CNN model. Now LeNet is selected for finetuning the number of dense layers. Generally, the LeNet is composed of three convolutions, two subsampling followed by two dense layers. Now, the LeNet is modified by adding one extra dense layer at the end that compress the output further into vector size of 42 to suggest the proposed model ADL-LeNet. The ADL-LeNet composed of three convolutional, two subsampling and three dense layers ending with the final output layer. The Mango Image dataset has been applied with the existing CNN and proposed ADL-LeNet. The implementation shows that the proposed ADL-LeNet model outperforms with the high accuracy of 97.6% in mango type classification. © 2024 IEEE.

关键词： Fruits

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence Strategies for Accurate Segmentation and Categorization of Unveiling Genetic Disorders in Bioinformatics 2

Artificial Intelligence Strategies for Accurate Segmentation...

引用

2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Sarathamani, T. Kavitha, K. Thirumoorthi, C. Vagini, K Jayanthi Manikandaprabhu, P. Sumathi, P. Kolkata India Mohan Babu University School of Computing Department of Ai and Ml Sree Sainath Nagar Tirupati India Psg College of Arts and Science Department of Computer Science Tamilnadu Coimbatore India Kpr College of Arts Science and Research Department of Computer science with Data Analytics Tamilnadu Coimbatore India Amrita Vishwa Vidyapeetham School of Computing Department of Computer Science Karnataka Mysuru India Dr. G R Damodaran College of Science Department of Computer Science Tamilnadu Coimbatore India

ISBN: (纸本)9798350368413

Women all across the world are affected by the potentially fatal condition known as breast cancer. According to clinical experts, early cancer detection helps to save lives. Several machine learning algorithms have been developed to classify cancers in order to diagnose breast cancer, and medical image processing has been found to be a beneficial area for increasing classification accuracy. One of the most crucial signs for detecting breast cancer early is micro classifications. Machine learning methods have been employed in numerous studies to address this problem. Various classification algorithms are used to find the presence of breast cancer whether it is Benign or Malignant. For earlier cancer detection and diagnosis, an image enhancement approach is being developed. A machine learning technique is employed in this work to attempt to categorize breast cancer. The purpose of the present research was to increase the accuracy of machine learning methods for disease diagnosis using ML techniques. © 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

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：