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5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Kamalraj, R. Nautiyal, Kshitij Taranum, Ayesha Savita Karnataka Bangalore India Department of Electrical Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Using machine gaining knowledge of algorithms with 6G radio resource allocation can assist to enhance network performance by using automating the selection making technique on a cease-to-stop foundation. This paper proposes a more suitable implementation of 6G radio aid allocation the usage of device getting to know algorithms to expand a framework for optimized useful resource utilization. The consequences of the experiments conducted in a real-world testbed validate the effectiveness of the proposed structure in achieving the desired goals consisting of improved community performance and decreased strength consumption. Moreover, the proposed improved implementation might be relevant in a huge range of other resource allocation eventualities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Resource allocation

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>Student Expression Recognition Algorithm Based on TransConv 2

>Student Expression Recognition Algorithm Based on TransConv

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2nd International Conference on Machine Learning, Control, and Robotics, MLCR 2023

作者： Hu, Siquan Zhang, Xiaoxia Shi, Zhiguo University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China

ISBN: (纸本)9798350381245

Human facial expression is one of the expressions of inner emotion, which is an important means of emotional communication between people. With the development of artificial intelligence, human facial expression recognition has been widely studied as an important topic in the field of computer vision, especially in the context of teaching scenarios with important applications. Therefore, adopting more novel facial expression feature extraction methods and improving models with higher accuracy of expression recognition are crucial for the task of human facial expression recognition. In this paper, we carry out the research on student expression recognition algorithms in the context of teaching scenarios, in order to improve the accuracy of facial expression recognition, the experiments are based on the JAFFE dataset and the CK+ dataset to construct a caricatured expression dataset consisting solely of locally populated key points of facial expressions, and improve the expression recognition method based on the traditional convolutional neural network, and put forward an expression recognition method based on the TransConv algorithm. The expression recognition method is trained and compared under the same dataset. The experimental results show that the training accuracy of the method proposed in this paper is higher, with an accuracy of up to 90%, and the processing speed is faster. The method described in this paper is feasible and effective, and can play a good role in assisting teachers to grasp the teaching process. © 2023 IEEE.

关键词： Face recognition

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CARNet: Channel Attention Guided Compression of Residual Network 2

CARNet: Channel Attention Guided Compression of Residual Net...

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2nd International Conference on Image Processing, computer Vision and Machine Learning, ICICML 2023

作者： Wang, Feiyang Macau University of Science and Technology School of Computer Science and Engineering 999078 China

ISBN: (纸本)9798350331417

As deep neural networks continue scaling up, the huge computational costs have become a major bottleneck for many real-world applications. In this paper, a Channel-Attention Residual Network (CARNet) is proposed to significantly reduce the complexity of ResNet models. By designing a lightweight attention module to recalibrate features, CARNet reduces 94.2% parameters (from 11.2M to 0.65M), 95% computations (from 32.6 GFLOPs to 1.63 GFLOPs), and 94.2% model size (from 42.8MB to 2.55MB) compared to original ResNet, while maintaining comparable accuracy on the same dataset. More importantly, the proposed CARNet demonstrates a promising direction to enable attention mechanism for computer vision models on resource constrained platforms. The extreme reduction in model complexity allows attention-based models to be deployed on edge devices for the first time. This work reveals the possibility of combining visual attention with lightweight designs and makes an important step towards bringing human-like visual recognition capability to real-world applications such as augmented reality, surveillance, and mobile robotics. The code and models are open sourced for reproducing the results: https://***/Faiyong/CARNet. © 2023 IEEE.

关键词： attention mechanism deep learning neural network

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New Machine Learning Model to Movie Recommender and Sentiment Analysis

New Machine Learning Model to Movie Recommender and Sentimen...

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2023 International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2023

作者： Marappan, Raja Saraswatikaniga, R. Vellore Institute of Technology School of Computer Science and Engineering Chennai600127 India

ISBN: (纸本)9798350344387

Recently, technology has put the limelight on every industry, which ends in an overload of massive data that might be resolved using efficient recommender systems. The recommendation system is handy to accommodate large amounts of knowledge and separate helpful information. One in each of its applications falls in movie recommendations. Currently, numerous movies are developing, which makes it difficult for movie addicts to settle on one in all of them. The movie recommendation system intends to assist movie enthusiasts in cutting back on human effort by suggesting movies efficiently and effectively without wasting much time in useless browsing. Recommending products that support user interest is one of the principal business tasks achieved using machine learning (ML). This research develops a new supervised ML model to recommend movies with the naïve Bayes' (NB) and support vector machine (SVM) classifier that combines cosine similarity (CS) and sentiment analysis (SA). The CS may be a standard accustomed measure of finding similarity between two items, and the SA of a movie review can classify the type of a review. The proposed model applies the training strategy and the reviews classifications are done accordingly and the predictions are made. This research model performance is tested with benchmark instances, 97% accuracy is obtained by applying SA with the NB approach, and 98% accuracy is obtained using an SVM. The simulation results conclude that the proposed model produced more significant outcomes than the existing approaches in deploying the proposed recommender engine as a website that takes input from users and recommends similar movies with related images in minimal computing time. © 2023 IEEE.

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Fog Computing in the Internet of Things: Challenges and Opportunities 6th

Fog Computing in the Internet of Things: Challenges and Oppo...

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6th IFIP International Conference on Internet of Things, IFIP IoT 2023

作者： Shah, Iqra Amin Chishti, Mohammad Ahsan Baba, Asif I. Department of Information Technology School of Engineering and Technology Central University of Kashmir Kashmir India Department of Computer Science and Engineering National Institute of Technology Srinagar Kashmir India Department of Computer Science and Engineering University of North Texas DentonTX76207 United States

ISBN: (纸本)9783031458811

The expansion of the Internet of Things (IoT) has made it possible for numerous widespread objects to connect to one another and communicate with one another, leading to unprecedented data releases. However, regardless of the fact that cloud computing has been a useful tool for processing and storing these data, problems like the growing demand for real-time applications and the constrained availability of network bandwidth cannot yet be resolved solely through the use of cloud computing. As a supplement to the cloud solution, a new approach to computing called the fog computing paradigm has been developed. By moving processing, connectivity, and capacity nearer to edge gadgets and end clients and taking cloud computing to the network’s edge, fog computing aims to increase low latency, activity, data traffic, reliability, and privacy. The present paper discusses the framework of the fog computing model and presents a literature survey of various works that is carried out on fog computing. It also covers the current problems and difficulties in fog computing and opportunities for research in the area of Fog computing for the Internet of Things. © 2024, IFIP International Federation for Information Processing.

关键词： Fog computing

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Estimating the cost of dining for two: An Integration of Sentiment Analysis and Feature Based Approach 5

Estimating the cost of dining for two: An Integration of Sen...

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Shashank, Salabh Behera, Rajat Kumar Amity University Auup Amity School of Engineering and Technology Uttar Pradesh Noida201301 India KIIT Deemed to be University School of Computer Engineering Odisha Bhubaneswar751024 India

ISBN: (纸本)9798331522100

In rapidly growing food industry, where more than 1200 orders are processed in a minute, it is essential for owners to optimize their services. This study focuses into the food industry and analyzes the approximate cost for two people along with the sentimental analysis using the data taken from Zomato. The approximate cost for two is based on various factors like restaurants location, availability of online ordering, table booking facilities, restaurant ratings and, cuisines offered. Since there is huge competition among restaurants, so some fake reviews can also be posted. Sentimental analysis is applied to check if the review posted is genuine or fake. Results found that there are around 10k fake reviews out of 51k total reviews. Various regression algorithms like Decision Tree, Random Forest, and others are used to calculate approximate cost for two. Also, various classification algorithms like Naïve Bayes, Support Vector, and others are used to verify sentiment analysis. The regression problem is analyzed using two different approaches: first by considering all the features and second by reducing the features by using Principal Component Analysis. Various computation metrics for regression as well as classification are computed, and the results are incorporated. © 2025 IEEE.

关键词： Food industry

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A Comprehensive Feature Aggregation Network for Sleep Apnea Detection using Respiratory Signals

A Comprehensive Feature Aggregation Network for Sleep Apnea ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Shu, Shiwen He, Kejing South China University of Technology School of Computer Science and Engineering Guangzhou China

ISBN: (纸本)9798350337488

Sleep apnea is a prevalent sleep disorder that poses a significant public health concern. Polysomnography, the gold standard for detecting sleep apnea, is costly and time-consuming, making widespread adoption challenging. To address this issue, in this paper, we propose a novel Comprehensive Feature Aggregation Network(CFAN) model comprising an embedding layer, an attention module, and a temporal module. The proposed CFAN leverages one of the respiratory signals as its input. Grouped dilated convolutional neural networks with various dilation rates, multi-head self-attention mechanism, and bidirectional Gated Recurrent Unit effectively integrate global and local spatial-temporal information from the input signal, enabling the characterization of intricate internal details and facilitate the detection of sleep apnea syndrome. Experimental results unequivocally demonstrate the superior performance of our proposed model, surpassing both benchmark models and the current state-of-the-art in terms of its comprehensive ability and robustness. Furthermore, ablation experiments confirm the indispensability of each component within the model. Our proposed method offers a convenient and innovative approach to automatically detect sleep apnea syndrome using respiratory signals, presenting a promising avenue for further application. © 2023 IEEE.

关键词： Deep learning Dilated convolution Multi-head self-attention Respiratory signal Sleep apnea

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Ensemble Deep Learning Framework for Situational Aspects-Based Annotation and Classification of International Student’s Tweets during COVID-19

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computers, Materials & Continua 2023年第6期75卷 5355-5377页

作者： Shabir Hussain Muhammad Ayoub Yang Yu Junaid Abdul Wahid Akmal Khan Dietmar P.F.Moller Hou Weiyan School of Information Engineering Zhengzhou UniversityZhengzhouChina School of Computer Science and Engineering Central South UniversityChangshaChina Department of Data Science The Islamia University of BahawalpurBahawalpurPakistan Clausthal University of Technology Clausthal-ZellerfeldGermany

As the COVID-19 pandemic swept the globe,social media plat-forms became an essential source of information and communication for *** students,particularly,turned to Twitter to express their struggles and hardships during this difficult *** better understand the sentiments and experiences of these international students,we developed the Situational Aspect-Based Annotation and Classification(SABAC)text mining *** framework uses a three-layer approach,combining baseline Deep Learning(DL)models with Machine Learning(ML)models as meta-classifiers to accurately predict the sentiments and aspects expressed in tweets from our collected Student-COVID-19 *** the pro-posed aspect2class annotation algorithm,we labeled bulk unlabeled tweets according to their contained aspect ***,we also recognized the challenges of reducing data’s high dimensionality and sparsity to improve performance and annotation on unlabeled *** address this issue,we proposed the Volatile Stopwords Filtering(VSF)technique to reduce sparsity and enhance classifier *** resulting Student-COVID Twitter dataset achieved a sophisticated accuracy of 93.21%when using the random forest as a *** testing on three benchmark datasets,we found that the SABAC ensemble framework performed exceptionally *** findings showed that international students during the pandemic faced various issues,including stress,uncertainty,health concerns,financial stress,and difficulties with online classes and returning to *** analyzing and summarizing these annotated tweets,decision-makers can better understand and address the real-time problems international students face during the ongoing pandemic.

关键词： COVID-19 pandemic situational awareness ensemble learning aspect-based text classification deep learning models international students topic modeling

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Comparing Tree-Based Ensemble Machine Learning Models and Convolutional Neural Network for Automatic Classification of Heart Abnormalities from ECG Images 14

Comparing Tree-Based Ensemble Machine Learning Models and Co...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Ashokan, Pillai Lekshmi Sathya, S. Siva Pondicherry University School of Engineering & Technology Department of Computer Science Puducherry India

ISBN: (纸本)9798350335095

Electrocardiogram (ECG) is a diagnostic technique which records electrical activity of the heart. Since interpretation of ECG requires expertise, development of automatic ECG analysis systems can aid clinicians. In this study, performance of Tree-based Ensemble Machine Learning models and Convolutional Neural Network (CNN) were compared to classify four heart pathologies from ECG signals. ECG images were transformed into time-series signals and classifiers were evaluated based on accuracy, precision, F1 score, and recall. The tree-based ensemble methods, Extreme Gradient Boosting (XGBoost), and Random Forest (RF) demonstrated outcomes outperforming CNN by a small margin. This observation suggests Deep Learning (DL) models can perform well on tabular data. However, it is notable that these frameworks are complex, require intensive hyperparameter tuning, may fail to generalize new data and learn irregular patterns. The findings provide insights for developing more accurate and interpretable ECG classification systems. © 2023 IEEE.

关键词： Electrocardiograms

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Breast Cancer Diagnosis Using Elephant Herding Optimization and Sparse Autoencoder Through Gene Expression Analysis 15th

Breast Cancer Diagnosis Using Elephant Herding Optimization ...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Bharanidharan, N. Chakravarthy, S. R. Sannasi Kumar, V. Vinoth Kavya, B. Meghana, G. Rajaguru, Harikumar School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Computer Science and Engineering Dayananda Sagar University Bangalore India Department of Computer Science and Engineering PES University RR Campus Bangalore India

ISBN: (纸本)9783031810794

Gene expression analysis is an efficient modality to detect breast cancer. But analysis of the gene expression data is a complicated task due to the involvement of large number of numerical data for each subject. This research work focuses on the development of computerized diagnosis systems for analyzing the gene expression data and classifying the subjects as either normal or breast adenocarcinoma. Sparse autoencoder is used for dimensionality reduction and Elephant Herding Optimization is used for transforming the features. Four different supervised classifiers namely support vector machine, random forest classifier, decision trees, and naive bayes classifier are examined for classification task. Among them, random forest provides the superior balanced accuracy of 86% while it was further enhanced to 92% with the help of sparse autoencoders and elephant herding optimization algorithm. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Transcription

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