检索结果-内蒙古大学图书馆

IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Bandyopadhyay, Biswadip Kuila, Pratyay Bey, Marlom Govil, Mahesh Chandra Department of Computer Science and Engineering National Institute of Technology Sikkim Ravangla India

With the development of the sixth-generation network, Digital Twin (DT) is driving the explosive growth of Internet-of-Vehicles (IoVs). The rapid proliferation of highly mobile IoVs, coupled with advanced applications, resulted in rigorous demands for quality of experience (QoE) and intricate task caching. The diverse requirements of on-vehicle applications, as well as the freshness of dynamic cached information, provide significant challenges for edge servers in efficiently fulfilling energy and latency demands. This work studies a freshness-aware caching-aided offloading-based task allocation problem (FCAOP) in DT-enabled IoV (DTIoV) with Intelligent Reflective Surfaces (IRS) and edge computing. DT is used to accumulate real-time data and digitally depict the physical objects of the IoV to enhance decision-making. A quantum-inspired differential evolution (QDE) algorithm is proposed to reduce the overall delay and energy consumption in DTIoV (QDE-DTIoV). The quantum vector (QV) is encoded to represent a complete solution to the FCAOP. The decoding of the QVs is done using a one-time hashing algorithm. The fitness function is derived by considering delay, energy consumption, and freshness of the tasks. Extensive simulations demonstrate the superiority of QDE-DTIoV over other benchmark algorithms, showing an average latency improvement of 23%-26% and a reduction in energy consumption ranging from 22% to 33%. IEEE

关键词： Energy utilization

来源：评论

学校读者我要写书评

暂无评论

Hierarchical Patch Selection: An Improved Patch Sampling for No Reference Image Quality Assessment

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第2期5卷 541-555页

作者： Nandhini, C. Brindha, M. National Institute of Technology Department of Computer Science and Engineering Tiruchirappalli620015 India

Quality degradation due to the compression and the transmission of images is a significant threat to multimedia applications. Blind image quality assessment (BIQA) is a principal technique to measure the distortion and dynamically set the optimal parameters for developing image compression standards, image restoration algorithms, etc. Insufficient training data with quality scores are a challenge for image quality assessment (IQA) tasks. The existing solutions to deep-convolution-neural-network-based BIQA, which rely on patchwise training, struggle to find an ideal set of patches consistent with the human visual system. To address these issues, HIerarchical paTch Selection (HITS) is proposed. HITS keeps the patches with considerable details in each quadrant according to their intensity variance scatter ratio (IVSR). IVSR identifies the best nonhomogeneous patches by computing patch intensity variance. Extensive trials are conducted, and the performance reveals that the proposed approach achieves excellent performance on synthetic and authentic distortion datasets with less memory and processing power. Moreover, the proposed approach outperforms other BIQA methods that adapt patchwise training strategies. © 2023 IEEE.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Brain haemorrhage classification from CT scan images using fine-Tuned transfer learning deep features

引用

International Journal of Business Intelligence and Data Mining 2024年第2期24卷 111-130页

作者： Ghosh, Arpita Soni, Badal Baruah, Ujwala Department of Computer Science and Engineering National Institute of Technology Assam Silchar India

Classification of brain haemorrhage is a challenging task that needs to be solved to help advance medical treatment. Recently, it has been observed that efficient deep learning architectures have been developed to detect such bleeding accurately. The proposed system includes two different transfer learning strategies to train and fine-Tune ImageNet pre-Trained state-of-The-Art architecture such as that VGG 16, Inception V3 and DenseNet121. The evaluation metrics have been calculated based on the performance analysis of the employed networks. Experimental results show that the modified fine-Tuned Inception V3 performed well and achieved the highest test accuracy. © 2024 Inderscience Enterprises Ltd.. All rights reserved.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

Multi-scale Vision Transformer toward improved non-invasive anaemia detection using palm video

引用

Multimedia Tools and Applications 2024年第38期83卷 85825-85848页

作者： Kesarwani, Abhishek Das, Sunanda Kisku, Dakshina Ranjan Dalui, Mamata Department of Computer Science and Engineering National Institute of Technology Durgapur Durgapur India

Anaemia, a condition characterised by reduced haemoglobin levels, exerts a significant global impact, affecting billions of individuals worldwide. According to data from the World Health Organisation (WHO), India exhibits notably high anaemia prevalence in comparison to other developing nations. The urgency arises for the development of non-invasive, cost-effective, and accurate anaemia screening technologies, primarily due to the substantial costs and logistical complexities associated with invasive screening methods, which impede their broad adoption on a global scale. The current endeavor aims to establish a robust anaemia detection system through the integration of cutting-edge computational methodologies with the traditional yet less precise approach of assessing haemoglobin levels based on observation of palm pallor. The proposed method employs time-domain analysis to establish a correlation between blood haemoglobin concentration and the observed changes in palm colour resulting from the application and release of pressure. This involves employing a custom device to capture and record the complete sequence of palm colour changes using a smartphone camera sensor, followed by data processing and analysis. The Multi-scale Vision Transformer is then employed to extract features from dominant frames. The approach further incorporates the application of a multi-layer perceptron (MLP) network as a regression model based on the reduced feature outcomes. The proposed system accurately estimates blood haemoglobin levels with RMSE, sensitivity, specificity, and accuracy of 0.509, 96.88%, 86.39%, and 92.59%, respectively based on 531 video samples of palm evidence. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diagnosis

来源：评论

学校读者我要写书评

暂无评论

Prediction of strength properties of concrete under the influence of recycled aggregate using machine learning models

引用

Interactions 2024年第1期245卷 1-21页

作者： Ashwathi, R. Soundariya, R.S. Tharsanee, R.M. Yuvaraj, S. Ramya, R. Department of Civil Engineering Bannari Amman Institute of Technology Erode India Department of Computer Technology Bannari Amman Institute of Technology Erode India Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Civil Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Information Technology Dr NGP Institute of Technology Coimbatore India

The upsurge in urbanization is depleting natural resources due to the wide usage of aggregates in construction, posing a menace to further progress. If the contemporary state persists, it becomes a threat for further progress. As an initiative to preserve the natural resources, recycled aggregate is employed as a partial additive of fine aggregate, this practice endorses sustainability but demands a cautious investigation of its effect on concrete strength. Predicting the strength characteristics of concrete becomes crucial in this situation. Machine Learning (ML) algorithms estimate these properties, enabling engineers to evaluate and enhance the concrete mix with recycled materials, confirming durability and performance without solely depending on outdated testing methods The proposed work attempts how ML algorithms have been utilized to forecast the strength properties of concrete. Input samples were collected from the experimental setup for three grades of concrete (M15, M20, M25) with four various proportions (5%, 10%, 15% and 20%) at 7, 14, 21 and 28 ages of curing excluding the conventional mix. The ML models are developed by training the collected laboratory samples with respect to four parameters– cement, fine aggregate (FA), coarse aggregate (CA) and recycled aggregate (RA). The trained models predict the three properties of concrete– compressive strength, split tensile strength and flexural strength. The test outcomes are further validated using statistical measures such as correlation coefficient– R2, Mean Square Error (MSE) and Mean Absolute Error (MAE) to assess the performance of the model. The performance evaluation of the proposed approach shows that The Support Vector Regressor outperforms the other ML models, achieving an R² value of about 90% and showing a reduced error rate in terms of MSE and MAE. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Tensile testing

来源：评论

学校读者我要写书评

暂无评论

Online Convex Optimization with Switching Cost and Delayed Gradients

引用

Performance Evaluation Review 2024年第4期51卷 22-23页

作者： Senapati, Spandan Vaze, Rahul Department of Computer Science & Engineering Indian Institute of Technology Kanpur India School of Technology and Computer Science Tata Institute of Fundamental Research India

We consider the online convex optimization (OCO) problem with quadratic and linear switching cost when at time t only gradient information for functions fτ, τ 16(Lµ+5) for the quadratic switching cost, and also show the bound to be order-wise tight in terms of L, µ. In addition, we show that the competitive ratio of any online algorithm is at least max{Ω(L), Ω(pLµ )} when the switching cost is quadratic. For the linear switching cost, the competitive ratio of the OMGD algorithm is shown to depend on both the path length and the squared path length of the problem instance, in addition to L, µ, and is shown to be order-wise, the best competitive ratio any online algorithm can achieve. Copyright is held by author/owner(s).

关键词： Convex optimization

来源：评论

学校读者我要写书评

暂无评论

Cellular Automaton–Based Sentiment Analysis for Bipolar Classification of Reviews

引用

Complex Systems 2025年第1期34卷 29-57页

作者： Elizabeth, M.J. Chaudhuri, Parimal Pal Hazari, Raju Department of Computer Science and Engineering National Institute of Technology Kerala Calicut India Indian Institute of Technology Kharagpur India

One of the main goals of sentiment analysis is to analyze human perception to continuously adapt to each person’s demands. The information gathered is structured to understand the mood or emotional tone of the reviews and comments. In every aspect of society, there is always an opportunity for opinions and suggestions from the public. So, the quality of the services provided by various resources can considerably increase if we can determine whether those thoughts are positive, negative or neutral. In this paper, we employ cellular automata (CAs) to categorize the reviews as positive or negative. The fundamental advantage of the suggested technique is that there is less need to be concerned about the linguistic characteristics of human languages. The proposed method performs better by leveraging a variety of parameters that are extracted by analyzing cycle length (CL) graphs generated using CL signals produced by elementary CAs (ECAs) without employing any form of pre-trained language model. We evaluated the effectiveness of the proposed cellular automaton–based machine learning (CAML) classifier by assessing certain parameters such as recall, precision, F1 score and accuracy. Our method achieves a maximum accuracy of 92.44% for the sentiment tweet dataset. We compared the proposed technique with all existing machine learning models, as well as recent methodologies employed in the field. The suggested method performs well in predicting the positive and negative classes of sentiments using CAs. © 2025, Complex Systems Publications, Inc. All rights reserved.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Global-local term fusion for optimised community Q&A topic modelling

引用

International Journal of Innovative Computing and Applications 2024年第1期15卷 50-69页

作者： Prabha, Sneh Sardana, Neetu Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Community question and answer (Q&A) websites have become invaluable information and knowledge-sharing sources. Effective topic modelling on these platforms is crucial for organising and navigating the vast amount of user-generated content. To address these challenges, we propose a novel global-local term fusion with optimised community (GLOCOM) Q&A topic modelling approach that leverages both local and global term importance to enhance topic modelling on community Q&A websites. GLOCOM combines term frequency-inverse document frequency for local importance and entropy for global importance. Further, we employ fuzzy clustering to enhance the representation of multifaceted topics. Furthermore, clustering results are optimised using a genetic algorithm (GA) to refine cluster assignments and centroids. We compared the proposed model with baseline models LDA and FLSA. GLOCOM has performed consistently well for all topic numbers. It has shown an improvement of 8.86% in silhouette score as compared to LDA and excelled for datasets with size > 3 MB. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Inverse problems

来源：评论

学校读者我要写书评

暂无评论

ResdenseNet: a lightweight dense ResNet enhanced with depthwise separable convolutions and its applications for early plant disease classification

引用

Neural Computing and Applications 2025年第8期37卷 6305-6326页

作者： Nagpal, Jyoti Goel, Lavika Department of Computer Science & Engineering The NorthCap University Gurugram122017 India Department of Computer Science and Engineering Malaviya National Institute of Technology Rajasthan Jaipur302017 India

In recent years, artificial intelligence has undergone robust development, leading to the emergence of numerous autonomous AI applications. However, a crucial challenge lies in optimizing computational efficiency and reducing training time while maintaining high accuracy with limited hardware resources. This paper introduces ResdenseNet, a model built upon the MobileNet, DenseNet, and ResNet architectures. ResdenseNet combines dense blocks and residual blocks from the DenseNet and ResNet architectures. In these dense blocks, the standard convolutional units are replaced by depthwise separable convolutional units, a significant part of the MobileNet architecture. The experimental outcomes are contrasted with established models and their iterations, including ResNet-50, ResNet-101, MobileNet-V1, MobileNet-V2, DenseNet-121, and DenseNet-169. The proposed model is tested on benchmark and proposed datasets, showcasing its efficiency in reducing computations and accelerating the training process. Emphasizing hyperparameter importance, ResdenseNet, optimized with a growth rate of 64, 6 layers, and ReLU activation, achieves an accuracy of (98.73%) and a F1-score of (98.20%) on the wheat and barley dataset. The results indicate that ResdenseNet significantly decreases the number of parameters to 0.72M and efficiently shortens training time to 5983.54 s. Particularly noteworthy is ResdenseNet’s superiority over other models in terms of having the fewest parameters, the shortest training time, and the highest accuracy, especially when dealing with wheat, barley, and maize datasets. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Plant diseases

来源：评论

学校读者我要写书评

暂无评论

Face Recognition Using LBPH and CNN

引用

Recent Advances in computer Science and Communications 2024年第5期17卷 48-58页

作者： Shukla, Ratnesh Kumar Tiwari, Arvind Kumar Mishra, Ashish Ranjan Department of Computer Science & Engineering Shambhunath Institute of Engineering & Technology Uttar Pradesh Prayagraj211012 India Department of Computer Science & Engineering Kamla Nehru Institute of Engineering & Technology Uttar Pradesh Sultanpur228118 India Department of Computer Science & Engineering Rajkiya Engineering College Uttar Pradesh Sonbhadra231206 India

Objective: The purpose of this paper was to use Machine Learning (ML) techniques to extract facial features from images. Accurate face detection and recognition has long been a problem in computer vision. According to a recent study, Local Binary Pattern (LBP) is a superior facial descriptor for face recognition. A person's face may make their identity, feelings, and ideas more obvious. In the modern world, everyone wants to feel secure from unauthorized authentication. Face detection and recognition help increase security;however, the most difficult challenge is to accurately recognise faces without creating any false identities. Methods: The proposed method uses a Local Binary Pattern Histogram (LBPH) and Convolution Neural Network (CNN) to preprocess face images with equalized histograms. Results: LBPH in the proposed technique is used to extract and join the histogram values into a single vector. The technique has been found to result in a reduction in training loss and an increase in validation accuracy of over 96.5%. Prior algorithms have been reported with lower accuracy when compared to LBPH using CNN. Conclusion: This study demonstrates how studying characteristics produces more precise results, as the number of epochs increases. By comparing facial similarities, the vector has generated the best result. © 2024 Bentham Science Publishe.

关键词： Face recognition

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：