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15th International Conference on Communication Software and Networks, ICCSN 2023

作者： Wan, Xia Peng, Yufei Hao, Run Guo, Yingya College of Computer and Data Science Fuzhou University Fuzhou China

ISBN: (纸本)9798350329360

Network traffic prediction is essential and significant to network management and network security. Existing prediction methods cannot well capture the temporal-spatial correlations hidden in the network traffic and suffer from a prediction accuracy degradation for two reasons. First, existing approaches concentrate on capturing the spatial relations and dependence in an explicit network topology graph, which fails to reflect the inherent relations in network traffic. Second, the common recurrent neural network models which are leveraged to learn the temporal relations in network traffic exhibit a poor performance in long-term prediction for its limited receptive field. To tackle these two problems, we propose an Attention-based Graph Convolutional Network model (AGCN) for capturing both the spatial and temporal correlations in network traffic. To catch the hidden spatial dependencies in network traffic, we combine graph attention network with graph convolutional network to mine the spatial relationships of network traffic. To efficiently learn the temporal long-term relations embedded in network traffic, we design a dilated convolution module to enable an exponentially growing receptive field for handling long sequences. Experimental results on three network traffic datasets show that AGCN has excellent performance in terms of prediction accuracy and inference time compared to current mainstream methods. © 2023 IEEE.

关键词： Convolution

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Content Based Email Spam Filtering and Detection using Hybrid Supervised Learning Approach 7

Content Based Email Spam Filtering and Detection using Hybri...

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7th World Conference on Computing and Communication Technologies, WCCCT 2024

作者： Saquib, Najam U Khalid, Waqar Shaheen, Sahibzadi Annum Asim, Muhammad Ahmad, Naveed Abasyn University Department of Computer Science Islamabad Pakistan Institute of Management Sciences Department of Computer Science Peshawar Pakistan Capital University of Science & Technology Department of Computer Science Islamabad Pakistan Prince Sultan University Eias Data Science and BlockChain Lab College of Computer and Information Science Riyadh11586 Saudi Arabia Prince Sultan University College of Computer and Information Science Riyadh11586 Saudi Arabia

ISBN: (纸本)9798350372328

In today's world, email is used widely for communication purposes globally. Email spam are unwanted emails that are sent to many recipients receivers. It is usually used for commercial purposes More likely spam enail is also used for malicious attempts to gain access to receiver computer/device. This article presents a novel hybrid architecture for spam emails filtering using mails content-based approach. For filtering, Word2Vec (W2V) word embedding procedure is used for feature selection with bio-inspired 'Particle Swarm Optimization (PSO)' and 'Support Vector Machine (SVM)' combined with 'Convolutional Neural Network (CNN)'. Moreover, our proposed hybrid architecture is simulated in open-source tool and the performance is evaluated using standard confusion matrix. Afterw ards, the results are compared with various previous algorithms/models. Experimental results show that our proposed hybrid scheme enhances detection accuracy of spam emails. © 2024 IEEE.

关键词： Support vector machines

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Automated soccer event detection and highlight generation for short and long views

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Multimedia Tools and Applications 2024年 1-21页

作者： Afzal, Maira Shah, Jamal Hussain ur Rehman, Saeed Khokhar, Fahad Ahmed Yasmin, Mussarat Kadry, Seifedine Department of Computer Science COMSATS University Islamabad Wah Campus Rawalpindi Pakistan Department of Mathematics and Information University of Florence Florence Italy Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Department of Applied Data Science Noroff University College Kristiansand Norway

The computer vision field has wide applications in various areas, including sports. Almost all sports events have been exploiting the best features. Sports videos are structure-based, and due to this characteristic, these videos can be categorized into interesting and non-interesting events. Identifying the view of the video and separating important events from non-interesting events is challenging. However, correct view detection can lead to correct event detection. Various researchers have proposed many strategies for detecting events in sports videos. Significant research shows some gaps while generating highlights due to limited work available in the long or short view;there is still a need for some automated methods. The main purpose of this research work is to detect key events by normalizing the data, extracting features, fusing those features, and classifying them. In this research, events are detected by classification. A new dataset is created for research purposes. The benchmark dataset is divided into two subsets, which can be used separately or as part of a larger dataset. A proposed novel approach for event highlight generation in long and short view is presented using a fusion of AlexNet and VGGNet architectures to explore the model's efficiency in the context of accurate highlight generation deep learning models fused with handcrafted. AlexNet and VGG16 pre-trained deep CNN models are applied to extract deep prior features, which are then combined with HOG to improve the results. The proposed methodology undergoes evaluation on a dataset that we created, resulting in an accuracy of 99.6%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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A Discrete-Time Collaborative Neurodynamic Approach to Distributed Global Optimization 13

A Discrete-Time Collaborative Neurodynamic Approach to Distr...

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13th International Conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Huang, Haoen Zeng, Zhigang Wang, Jun School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Department of Computer Science Department of Data Science City University of HongKong Kowloon Hong Kong

ISBN: (纸本)9798331516147

In this paper, a discrete-time projection neural network with an adaptive step size (DPNN) is proposed for distributed global optimization. The DPNN is proven to be convergent to a Karush-Kuhn-Tucker point. Several DPNNs are utilized in a collaborative neurodynamic framework for solving distributed global optimization problem. The efficacy of the collaborative neurodynamic approach with DPNNs is demonstrated through simulation results. © 2025 IEEE.

关键词： Neural network models

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Learning a Mixture of Conditional Gating Blocks for Visual Question Answering

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Journal of computer science & Technology 2024年第4期39卷 912-928页

作者： Qiang Sun Yan-Wei Fu Xiang-Yang Xue School of Statistics and Information Shanghai University of International Business and EconomicsShanghai 201620China Academy for Engineering and Technology Fudan UniversityShanghai 200433China School of Data Science Fudan UniversityShanghai 200433China School of Computer Science Fudan UniversityShanghai 200433China

As a Turing test in multimedia,visual question answering(VQA)aims to answer the textual question with a given ***,the“dynamic”property of neural networks has been explored as one of the most promising ways of improving the adaptability,interpretability,and capacity of the neural network ***,despite the prevalence of dynamic convolutional neural networks,it is relatively less touched and very nontrivial to exploit dynamics in the transformers of the VQA tasks through all the stages in an end-to-end ***,due to the large computation cost of transformers,researchers are inclined to only apply transformers on the extracted high-level visual features for downstream vision and language *** this end,we introduce a question-guided dynamic layer to the transformer as it can effectively increase the model capacity and require fewer transformer layers for the VQA *** particular,we name the dynamics in the Transformer as Conditional Multi-Head Self-Attention block(cMHSA).Furthermore,our questionguided cMHSA is compatible with conditional ResNeXt block(cResNeXt).Thus a novel model mixture of conditional gating blocks(McG)is proposed for VQA,which keeps the best of the Transformer,convolutional neural network(CNN),and dynamic *** pure conditional gating CNN model and the conditional gating Transformer model can be viewed as special examples of *** quantitatively and qualitatively evaluate McG on the CLEVR and VQA-Abstract *** experiments show that McG has achieved the state-of-the-art performance on these benchmark datasets.

关键词： visual question answering Transformer dynamic network

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MedEx: Enhancing Medical Question-Answering with First-Order Logic based Reasoning and Knowledge Injection 31

MedEx: Enhancing Medical Question-Answering with First-Order...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Zafar, Aizan Mishra, Kshitij Ekbal, Asif Department of Computer Science and Engineering Indian Institute of Technology Patna India School of AI and Data Science Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798891761964

In medical question-answering, traditional knowledge triples often fail due to superfluous data and their inability to capture complex relationships between symptoms and treatments across diseases. This limits models' ability to provide accurate, contextually relevant responses. To overcome this, we introduce MedEx, which employs First-Order Logic (FOL)-based reasoning to model intricate relationships between diseases and treatments. We construct FOL-based triplets that encode the interplay of symptoms, diseases, and treatments, capturing not only surface-level data but also the logical constraints of the medical domain. MedEx encodes the discourse (questions and context) using a transformer-based unit, enhancing context comprehension. These encodings are processed by a Knowledge Injection Cell that integrates knowledge graph triples via a Graph Attention Network. The Logic Fusion Cell then combines medical-specific logical rule triples (e.g., co-occurrence, causation, diagnosis) with knowledge triples and extracts answers through a feed-forward layer. Our analysis demonstrates MedEx's effectiveness and generalization across medical question-answering tasks. By merging logical reasoning with knowledge, MedEx provides precise medical answers and adapts its logical rules based on training data nuances. © 2025 Association for Computational Linguistics.

关键词： Question answering

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A Comparative Analysis of the Imputing of Missing data on Air Pollution 2

A Comparative Analysis of the Imputing of Missing Data on Ai...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Gayathri, M. Kavitha, V. SRM Institute of Science and Technology Department of Computer Science and Engineering Kattankulathur Chennai India SRM Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India

ISBN: (纸本)9798350365269

Air pollution is a significant environmental hazard in modern society because of its serious impact on human health and the environment. In point of fact, there has been a substantial rise in the levels of pollution in the environment over the course of the past few years. data analyst is commonly encountering the issue of data missing. It is crucial to handle them efficiently to get more accurate and dependable data analysis findings. This study aims to improve understanding of data missing mechanisms, data imputation techniques, and examine the efficacy of frequently utilized data imputation techniques on quantitative datasets. Analysts and data scientists frequently face the challenge of missing data in their investigations. Therefore, it is essential to handle them in an efficient manner so as to gain improved and more trustworthy results through the analysis of the data. The objective of this research is to enhance comprehension of data missing processes and data imputed methods, while also evaluating the effectiveness of data imputed approaches often employed in quantitative datasets. A complete comparison of 5 different data imputation methods is presented in this article. These approaches include mean imputation, median imputation, mode imputation, kNN imputation and Linear Regression. For the purpose of analysing and contrasting the efficacy of the various data imputation approaches, we have utilized five distinct numeric datasets that were gathered from several air pollution repositories. Standardized Root Mean Square Error (RMSE) is the technique for assessing the efficacy of data imputation techniques. According on the findings of the analysis, the kNN imputation approach offers superior performance than the other methods. There is no correlation between the length of the dataset or the number of values that are missing in the dataset and the accuracy of the data imputed approach. © 2024 IEEE.

关键词： Air pollution

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Optimizing Potato Disease Detection in Pakistan with Machine Learning: A Comparative Analysis 3

Optimizing Potato Disease Detection in Pakistan with Machine...

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3rd International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering, ETECTE 2024

作者： Ahmed, Irfan Khalil, Manahal Ghulam, Safoora Mahmood, Jabar Batool, Khadija Khalid, Nibtal Arshad, Pakiza Dept of Computer Science University of Sialkot Sialkot Pakistan State Key Laboratory of Blockchain and Data Security School of Cyber Science and Technology College of Computer Science & Technology Zhejiang University Hangzhou China Dept of Computer Science Int. Institute of Science Art & Technology Gujranwala Pakistan

ISBN: (纸本)9798331504861

The agricultural sector of Pakistan depends heavily on the production of potatoes, however diseases like Bacterial Wilt, Late Blight, and Early Blight are posing a growing danger to this industry since they can negatively affect crop productivity and food security. Detecting these illnesses using traditional methods is time-consuming, labor-intensive, and sometimes it requires specialized knowledge which is not easy for farmers. The implementation of a machine learning (ML) framework for illness identification using the methods of K-Nearest Neighbours (KNN), Random Forest (RF), Decision Tree (DT), Naive Bayes (NB), Support Vector Machine (SVM), and Logistic Regression (LR) overcomes these challenges. This study utilizes data mining and preprocessing strategies to improve the accuracy of models and adaptability on a dataset of potato plants’ photos gathered across diverse areas in Pakistan. The models are assessed using performance criteria such computational efficiency, accuracy, precision, recall and F1-score. Random Forest model having 97% accuracy outperforms the other ML models in identifying plant disease. The suggested approach provides a practical, affordable, and effective way to identify diseases in a timely manner, allowing farmers to make informed decisions to lower crop losses. The aim of the study is to highlight how ML has the potential to transform Pakistani agriculture, enhancing food security and promoting long-term economic prosperity. ©2024 IEEE.

关键词： Random forests

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A Scalable Adaptive Approach to Multi-Vehicle Formation Control with Obstacle Avoidance

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IEEE/CAA Journal of Automatica Sinica 2022年第6期9卷 990-1004页

作者： Xiaohua Ge Qing-Long Han Jun Wang Xian-Ming Zhang IEEE School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia Department of Computer Science and School of Data Science City University of Hong KongChina

This paper deals with the problem of distributed formation tracking control and obstacle avoidance of multivehicle systems(MVSs)in complex obstacle-laden *** MVS under consideration consists of a leader vehicle with an unknown control input and a group of follower vehicles,connected via a directed interaction topology,subject to simultaneous unknown heterogeneous nonlinearities and external *** central aim is to achieve effective and collisionfree formation tracking control for the nonlinear and uncertain MVS with obstacles encountered in formation maneuvering,while not demanding global information of the interaction *** this goal,a radial basis function neural network is used to model the unknown nonlinearity of vehicle dynamics in each vehicle and repulsive potentials are employed for obstacle ***,a scalable distributed adaptive formation tracking control protocol with a built-in obstacle avoidance mechanism is *** is proved that,with the proposed protocol,the resulting formation tracking errors are uniformly ultimately bounded and obstacle collision avoidance is *** simulation results are elaborated to substantiate the effectiveness and the promising collision avoidance performance of the proposed scalable adaptive formation control approach.

关键词： Adaptive control collision avoidance distributed formation control multi-vehicle systems neural networks obstacle avoidance repulsive potential

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AI-Driven Pattern Recognition in Medicinal Plants: A Comprehensive Review and Comparative Analysis

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computers, Materials & Continua 2024年第11期81卷 2077-2131页

作者： Mohd Asif Hajam Tasleem Arif Akib Mohi Ud Din Khanday Mudasir Ahmad Wani Muhammad Asim Department of Information Technology Baba Ghulam Shah Badshah UniversityRajouriJammu and Kashmir185234India Department of Computer Science Samarkand International University of TechnologySamarkand141500Uzbekistan EIAS Data Science Lab College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia

The pharmaceutical industry increasingly values medicinal plants due to their perceived safety and costeffectiveness compared to modern *** the extensive history of medicinal plant usage,various plant parts,including flowers,leaves,and roots,have been acknowledged for their healing properties and employed in plant *** images,however,stand out as the preferred and easily accessible source of *** plant identification by plant taxonomists is intricate,time-consuming,and prone to errors,relying heavily on human *** intelligence(AI)techniques offer a solution by automating plant recognition *** study thoroughly examines cutting-edge AI approaches for leaf image-based plant identification,drawing insights from literature across renowned *** paper critically summarizes relevant literature based on AI algorithms,extracted features,and results ***,it analyzes extensively used datasets in automated plant classification *** also offers deep insights into implemented techniques and methods employed for medicinal plant ***,this rigorous review study discusses opportunities and challenges in employing these AI-based ***,in-depth statistical findings and lessons learned from this survey are highlighted with novel research areas with the aim of offering insights to the readers and motivating new research *** review is expected to serve as a foundational resource for future researchers in the field of AI-based identification of medicinal plants.

关键词： Pattern recognition artificial intelligence machine learning deep learning image processing plant leaf identification

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