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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Weng, Shudi Li, Chengxi Xiao, Ming Skoglund, Mikael Kth Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm Sweden

ISBN: (纸本)9798350351255

Stragglers' effects are known to degrade FL performance. In this paper, we investigate federated learning (FL) over wireless networks in the presence of communication stragglers, where the power-constrained clients collaboratively train a global model by iteratively optimizing a local objective function with their local datasets and transmitting local model updates to the central parameter server (PS) through fading channels. To tackle communication stragglers without dataset sharing or prior information about the network at PS, we propose cooperative gradient coding (CoGC) for semi-decentralized FL to enable the exact global model recovery at PS. Furthermore, we conduct a thorough theoretical analysis of the proposed approach. Namely, an outage analysis of the proposed approach is provided, followed by a convergence analysis based on the failure probability of the global model recovery at PS. Nevertheless, simulation results reveal the superiority of the proposed approach in the presence of stragglers under imbalanced data distribution. © 2024 IEEE.

关键词： Fading channels

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A Unified Framework for Dangerous Situation Detection by Integrating Weapon Detection and Distressed Face Analysis

A Unified Framework for Dangerous Situation Detection by Int...

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2024 IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024

作者： Narayanan, Swathi Jamjala Perumal, Boominathan Mandal, Siddarth Mohandas, Gayathri Jain, Naman Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Vellore India

ISBN: (纸本)9798331533311

The escalating prevalence of violent threats necessitates proactive security measures to safeguard human life. Within the realm of artificial intelligence (AI), computer vision has emerged as a pivotal tool, leveraging sophisticated algorithms to substantially alleviate the burden on human resources. In this research work, we propose a novel approach for dangerous situation detection that combines object detection and facial emotion recognition. This work includes detecting guns, knives, blood, alcohol, insulting gestures, and cigarettes as these are considered as items that are commonly associated with potential threats or dangerous situations in various contexts, such as crime scenes, conflict zones, or public safety scenarios. To achieve this, the harmful object detection model employs YOLOv8, which has been successful based on the mAP metric. The proposed work also incorporates a CNN model that relies on facial emotions for indicators of distress, fear, or discomfort, enabling the system to identify potential threats even in scenarios devoid of harmful objects. The results obtained demonstrate YOLOv8's superiority over a two-stage detector, and the Facial Expression Recognition (FER) CNN achieves 7 9% test accuracy and 8 4% validation accuracy. The unified framework enhances environmental threat detection, contributing to effective risk mitigation. © 2024 IEEE.

关键词： Emotion Recognition

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Coarser to Finer Level Document Classification through Recurrent Attention Mechanism using RL Agent 14

Coarser to Finer Level Document Classification through Recur...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Mariyam, Ayesha Basha, Althaf Hussain S.K. Raju, Viswanadha S. Computer Science and Engineering JNTUH Hyderabad India Computer Science and Engineering Krishna Chaitanya Institute of Technology and Sciences Markapur India Computer Science and Engineering JNTUH College of Engineering Nachupally India

Document Classification is a Natural Language Processing task, which generally uses deep neural networks to extract features from full textual information. The extracted features may or may not be relevant for classification of a document. We propose a framework to address the classification of long documents from coarse level to finer level by combining recurrent attention mechanism. It constructs the discriminative features with fewer words. The main idea is to train the recurrent neural network which focuses its attention on distinct parts of the document. It includes reinforcement learning agent at the word level for emitting the next block location to be glimpsed. Convolutional neural network (CNN) is used to extract glimpsed features from focused words. Both sentence and document representation can be obtained from word level and sentence level respectively. Experiments conducted on our collected 5-class arXiv papers dataset, the proposed method surpass the existing methods with less observed words. © Grenze Scientific Society, 2023.

关键词： Reinforcement learning

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An Enhanced Ensemble-based Approach for Network Anomaly Detection using Voting Classifier 8

An Enhanced Ensemble-based Approach for Network Anomaly Dete...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Rafi, Rayees Anusha Bamini, A.M. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Tamil Nadu India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376425

Anomaly detection systems are vital for identifying malicious activities within network traffic, but they often face challenges such as class imbalance, high dimensionality, and feature correlation in datasets like NSL-KDD. This work tackles these issues by creating a robust anomaly detection system using a hybrid approach that combines Synthetic Minority Over-sampling Technique, feature engineering, and ensemble learning. SMOTE is used to balance the dataset, while feature engineering reduces dimensionality and eliminates redundant features. The ensemble learning model, which integrates Support Vector Machines, Logistic Regression, and Decision Trees, achieved a high accuracy of 98.71% using a Voting Classifier. Additionally, the model shows a macro average precision of 0.89, a recall of 0.82, an F1-score of 0.85, and an overall robust ROC AUC score of 0.9871, indicating its effectiveness in distinguishing between normal and anomalous network behavior. © 2024 IEEE.

关键词： Decision trees

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LiCT-Net: Lightweight Convolutional Transformer Network for Multiclass Breast Cancer Classification

LiCT-Net: Lightweight Convolutional Transformer Network for ...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Babita Nayak, Deepak Ranjan Malaviya National Institute of Technology Dept. of Computer Science and Engineering Jaipur India

ISBN: (纸本)9798350350821

Classification of multiclass breast cancer through histopathological images is indispensable and poses daunting challenges due to color inconsistencies, high appearance variations, and large inter-class similarities. Regardless of the success of traditional convolutional neural networks (CNNs) and vision transformers (ViTs), they often fail to capture intricate patterns and local contextual information, respectively, while demanding high computational resources and data requirements. To mitigate these issues, this paper proposes a lightweight convo-lutional transformer network named LiCT-Net for multiclass breast cancer classification. The LiCT-Net introduces a local-global spatially-aware transformer layer and integrates it with a pre-trained FastViT model to effectively capture global and local contextual features, thereby facilitating learning fine-grained and intricate lesion patterns from histopathological images. The LiCT-Net is validated on a benchmark dataset and the experimental results and comparative analysis demonstrate its effectiveness. In specific, it achieves a higher accuracy of 96.16 %, 95.62 %, 95.25 %, and 94.21 % on 40×, 100×, 200×, and 400× magniflcations respectively, © 2024 IEEE.

关键词： Convolution

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Cooperative output regulation of heterogeneous directed multi-agent systems:a fully distributed model-free reinforcement learning framework

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science China(Information sciences) 2025年第2期68卷 170-185页

作者： Xiongtao SHI Yanjie LI Chenglong DU Huiping LI Chaoyang CHEN Weihua GUI Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics School of Mechanical Engineering and AutomationHarbin Institute of Technology (Shenzhen) School of Automation Central South University School of Marine Science and Technology Northwestern Polytechnical University School of Information and Electrical Engineering Hunan University of Science and Technology

In this paper, the cooperative output regulation(COR) problem of a class of unknown heterogeneous multi-agent systems(MASs) with directed graphs is studied via a model-free reinforcement learning(RL) based fully distributed eventtriggered control(ETC) strategy. First, we consider the scenario that the exosystem is accessible globally to all agents, an internal model-based augmented algebraic Riccati equation(AARE) is constructed, and its solution is learned by the proposed model-free RL algorithm via online input-output data. Further, for the scenario that the exosystem is accessible only to its adjacent followers, the distributed observers are designed for each agent to get the state of the exosystem, and an internal modelbased fully distributed adaptive ETC protocol is then synthesized to construct the corresponding AARE, and the feedback gain matrix is learned in a model-free fashion. The model-free RL-based control protocol proposed in this paper can not only remove the prior knowledge of agents' dynamics, but also release the dependence on global information by the adaptive event-triggered mechanism(ETM) and the new graph-based Lyapunov function. Finally, simulation results are illustrated to show the feasibility and effectiveness of the proposed control scheme.

关键词： model-free reinforcement learning unknown heterogeneous multi-agent systems fully distributed event-triggered control directed graph

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Revolutionizing Potato Farming: A CNN-Powered Approach for Early Blight and Late Blight Detection to Ensure Global Food Security

Revolutionizing Potato Farming: A CNN-Powered Approach for E...

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2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Ponnuru, Mahesh Datta Sai Amasala, Likhitha Srm Institute of Science and Technology School of Computing Chennai India Vellore Institute of Technology School of Computer Science and Engineering Amaravathi India

ISBN: (纸本)9798350348460

Potato, a crucial global food crop, faces persistent threats from diseases like early blight and late blight, jeopardizing both yields and economic stability. In response, we present an innovative approach using CNN for early disease detection in potato crops. Our objective is to empower farmers with rapid and accurate disease identification, facilitating timely treatments to prevent economic losses and reduce agricultural waste. Our CNN model achieved a remarkable validation accuracy of 99.59%, outperforming conventional methods. With 183,747 trainable parameters, our efficient and accurate model is set to revolutionize agricultural practices. Furthermore, we are deploying our model to Google Cloud and developing a React Native mobile app for potato prediction. This integration aims to provide farmers with real-time disease insights, preventing economic losses and ensuring global food security, thereby enhancing economic stability in the potato farming sector. © 2024 IEEE.

关键词： Agriculture Convolutional Neural Networks (CNNs) Crop protection Food security Early blight Economic stability Late blight Potato disease detection

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Enhanced Artificial Neural Network Algorithm for Predicting Lung Cancer

Enhanced Artificial Neural Network Algorithm for Predicting ...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Reddy, Narravula Bharathvaz Sai Ganesh Reddy, Settipalle Chidurala, Karthik Krishna Rani Samal, K. Vellore Institute of Technology Department of Computer Science and Engineering Tamilnadu Vellore632014 India

ISBN: (纸本)9798350378092

This study explores the application of deep learning models for the detection of lung cancer subtypes utilizing histopathological images. Leveraging a diverse dataset containing images of adenocarcinoma, squamous cell carcinoma, normal tissue, and large cell carcinoma, our research aims to develop accurate classification models to aid in early diagnosis and treatment planning. The dataset is meticulously preprocessed, ensuring uniform image sizes and balanced class distributions. Two deep learning architectures, VGG16 and MobileNetV2, are employed and trained on the dataset. The VGG16 model is fine-tuned with additional layers for classification, while the MobileNetV2 model is utilized in its pre-trained form. Rigorous training and evaluation procedures are conducted, including stratified train-test splits and performance metric assessments. Experimental results demonstrate the effectiveness of both models in accurately classifying lung cancer subtypes, with the VGG16 model achieving particularly high accuracy. Comparativeanalysis between the two models highlights their respective strengths and performance differences. Overall, this research contributes to the advancement of computer-aided diagnosis systems for lung cancer detection, offering potential solutions for improving patient outcomes and healthcare decision-making. © 2024 IEEE.

关键词： Lung cancer

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Cognitive Condensation Through Adversarial Learning and Ranking in Abstractive Text Summarization 8th

Cognitive Condensation Through Adversarial Learning and Ran...

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8th International Conference on Information and Communication technology for Intelligent Systems, ICTIS 2024

作者： Tank, Meenaxi Thakkar, Priyank Department of Computer Science and Engineering Institute of Technology Nirma University Gujarat Ahmedabad India

ISBN: (纸本)9789819766802

In recent years, there has been a significant increase in the use of neural networks for Abstractive Text Summarization (ATS). Notably, Generative Adversarial Networks (GANs) have demonstrated remarkable efficacy in domains such as computer vision and image generation. Recently, while using GANs for language generation problems, some researchers have observed encouraging results. Existing GANs, however, restrict the discriminator from functioning solely as a binary classifier, thereby impeding its capacity to effectively learn tasks involving outputs with complex structures, such as natural conversational descriptions. To overcome that limitation, we introduce a method incorporating a sentence ranking mechanism into generative adversarial networks to produce high-quality summaries. It is also well known that training GANs for discrete data, such as text, is challenging, so the policy gradient approach is used to optimize the generator. The presented approach yields Rouge-1 as 41.58 and Rouge-L as 38.82 scores on a popular CNN/DM corpora, which are higher than the scores reported by earlier approaches. The proposed approach is also examined qualitatively, demonstrating its superiority over previous studies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Generative adversarial networks

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NVMS-Net: A Novel Constrained Noise-View Multiscale Network for Detecting General Image Processing Based Manipulations

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1233-1247页

作者： Singh, Gurinder Rana, Kapil Goyal, Puneet Kumar, Sathish Cleveland State University Electrical Engineering and Computer Science ClevelandOH44115 United States Indian Institute of Technology Ropar Punjab Rupnagar140001 India Thapar Institute of Engineering and Technology Punjab Patiala147004 India NIMS University Rajasthan Jaipur303121 India

Ensuring the authenticity and integrity of digital images is a major concern in multimedia forensics, driving research on universal schemes for detecting diverse image manipulations (or processing operations). Although some prior works have addressed general-purpose image manipulation detection, they have been evaluated under constrained environments. Developing an approach that effectively identifies multiple manipulations in real-world scenarios remains a challenge. To address this issue, in this article, we have designed a novel constrained noise-view based multiscale network (NVMS-Net) that jointly exploits constrained noise-view and multiscale feature learning for multiple image processing operation detection. Our NVMS-Net includes four stages: constrained noise-view, generalizable feature learning, multiscale feature learning, and classification. First, a noise extraction layer (NEL) is employed to suppress the image content information for the extraction of noise features. The statistical modeling of these noise features is further improved by statistical modeling layer (SML) to achieve a better noise-view of image tampering artifacts. Then, we use multiple convolution-batch normalization-ReLU (CBR) blocks to learn generalizable noise features that are sensitive to image manipulations. Further, a multiscale aggregation module (MSAM) is proposed that leverages image tampering features at multiple scales through the aggregation of low and high-level features. The extensive experimental results show that the NVMS-Net consistently outperforms the existing approaches on different dataset settings. Importantly, NVMS-Net shows better performance in real-world scenarios, particularly against antiforensic techniques and adversarial attacks. The proposed NVMS-Net provides an overall accuracies of 98.21% and 98.60% on BOSSBase and Dresden datasets, respectively. © 2020 IEEE.

关键词： Adversarial machine learning

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