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Improved End-to-End Wireless Transmission Integrating NOMA and DL-Based autoencoder

IETE JOURNAL OF RESEARCH 2024年第8期70卷 6819-6827页

作者： Choubey, Namrata Trivedi, Aditya Kushwah, Vivek Singh Amity Univ Madhya Pradesh Dept ECE ASET Gwalior 474005 MP India ABV IIITM Dept IT Gwalior MP India

The field of wireless communication systems has experienced significant advancements in recent years, leading to the emergence of two promising technologies: non-orthogonal multiple access (NOMA) and deep learning (DL)-based autoencoders (AE). Through power allocation, NOMA enables multiple users to share a single frequency band, while AE can compress and decompress data with high precision. Integrating NOMA and AE enables end-to-end (E2E) transmission with a superior signal-to-noise (SNR) ratio. To further enhance the wireless network's block error rate (BLER) performance, the multiple-input, multiple-output (MIMO) technique is also incorporated into the newly proposed system. With the incorporation of the MIMO signal, the system is abbreviated as the MIMO-NOMA-AE system. The suggested technique for detecting MIMO-NOMA-AE signals has demonstrated a remarkable performance gain in SNR surpassing the traditional successive interference cancellation (SIC)-based NOMA system. The proposed system also performs better than previously utilized deep neural network (DNN)-based SISO-NOMA-AE communication systems. While this method shows potential for future wireless communication systems, further research and testing are necessary to evaluate its practical feasibility and effectiveness.

关键词： autoencoder DL DNN E2E communication system MIMO NOMA

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RISE : Privacy preserved data analytics using Regularized Inference Specific autoencoder

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 133卷

作者： Mallikarjunan, Narasima Rajashree, R. Harine Sundarakantham, K. Shalinie, S. Mercy Thiagarajar Coll Engn Dept CSE Madurai 625015 Tamil Nadu India

Edge computing enables real -time processing and response by storing, analyzing, and processing data near the source. The surge in the amount of data generated as a result of the Internet of Things (IoT) civilization has further expanded the use of edge computing. Industries, including healthcare, manufacturing, and transportation, benefit from analytics on the generated data. However, analytics on the generated data pose privacy threats. Existing privacy preserving approaches suffer from limitations such as high cost and poor utility of data. To overcome the limitation, we propose a privacy-aware image classification framework deployable at the edge. The framework uses a novel autoencoder training approach that learns to transform raw data into task-specific, insensitive features. This framework reduces the computation overhead and increases the privacy of the end user. The following milestones can be realized if our proposed framework is utilized: (i) Service Providers can retain proprietary models as there will be no need to share the moderator as in federated learning techniques (ii) End users can utilize inexpensive, resource-constrained devices to gain data privacy at a low cost. We tested our model on the benchmark datasets, the CIFAR10 and MNIST datasets, and reported the results. The framework achieves a classification accuracy of more than 89%, which denotes that the utility of the data is maintained. Furthermore, only 21% data leakage is recorded in the proposed framework, thereby making it feasible for practical applications.

关键词： Internet of Things Privacy preservation Data analytics autoencoder Privacy-utility trade-off

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Recursive autoencoder network for prediction of CAD model parameters from STEP files

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Procedia Computer Science 2024年 232卷 3239-3246页

作者： Victoria Miles Stefano Giani Oliver Vogt Raheleh Kafieh Department of Engineering Durham University Stockton Road Durham DH1 3LE United Kingdom

Databases of 3D CAD (computer aided design) models are often large and lacking in meaningful organisation. Effective tools for automatically searching for, categorising and comparing CAD models, therefore, have many potential applications in improving efficiency within design processes. This paper presents a novel asymmetric autoencoder model, consisting of a recursive encoder network and fully-connected decoder network, for the reproduction of CAD models through prediction of the parameters necessary to generate a 3D part design. Inputs to the autoencoder are STEP (standard for the exchange of product data) files, an ISO standard CAD model format, compatible with all major CAD software. A complete 3D model can be accurately reproduced using a STEP file, meaning that all geometric information can be used to contribute to the final encoded vector, with no loss of small detail. In a CAD model of overall size 10 × 10 × 10 units, for 90% of models, the class of an added feature is estimated with maximum error of 0.6 units, feature size with maximum error of 0.4 units and coordinate values representing position with maximum error of 0.3 units. These results demonstrate the successful encoding of complex geometric information, beyond merely the shape of the 3D object, with potential application in the design of search engine functionality.

关键词： CAD models STEP files recursive neural network autoencoder

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Bilingual Cyber-aggression detection on social media using LSTM autoencoder

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SOFT COMPUTING 2021年第14期25卷 8999-9012页

作者： Kumari, Kirti Singh, Jyoti Prakash Dwivedi, Yogesh Kumar Rana, Nripendra Pratap Siksha O Anusandhan Inst Tech Educ & Res Bhubaneswar India Swansea Univ Sch Management Swansea W Glam Wales Univ Bradford Sch Management Bradford W Yorkshire England

Cyber-aggression is an offensive behaviour attacking people based on race, ethnicity, religion, gender, sexual orientation and other traits. It has become a major issue plaguing the online social media. In this research, we have developed a deep learning-based model to identify different levels of aggression (direct, indirect and no aggression) in a social media post in a bilingual scenario. The model is an autoencoder built using the LSTM network and trained with non-aggressive comments only. Any aggressive comment (direct or indirect) will be regarded as an anomaly to the system and will be marked as Overtly (direct) or Covertly (indirect) aggressive comment depending on the reconstruction loss by the autoencoder. The validation results on the dataset from two popular social media sites: Facebook and Twitter with bilingual (English and Hindi) data outperformed the current state-of-the-art models with improvements of more than 11% on the test sets of the English dataset and more than 6% on the test sets of the Hindi dataset.

关键词： Online social networks Cyber-aggression Cyberbullying autoencoder Long Short-Term Memory

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DDoS Attacks Detection with Half autoencoder-Stacked Deep Neural Network

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INTERNATIONAL JOURNAL OF COOPERATIVE INFORMATION SYSTEMS 2024年第3期33卷

作者： Benmohamed, Emna Thaljaoui, Adel El Khediri, Salim Aladhadh, Suliman Alohali, Mansor Onaizah Coll Coll Engn & Informat Technol Dept Cyber Secur POB 5371 Onaizah Saudi Arabia Univ Sfax Natl Sch Engineers ENIS Res Grp Intelligent Machines BP 1173 Sfax 3038 Tunisia Majmaah Univ Coll Sci Zulfi Dept Comp Sci & Informat POB 66 Al Majmaah 11952 Saudi Arabia Preparatory Inst Engn Studies Gafsa Gafsa Tunisia Qassim Univ Coll Comp Dept Informat Technol Buraydah Saudi Arabia Univ Gafsa Fac Sci Gafsa Dept Comp Sci Gafsa Tunisia

With the growth in services supplied over the internet, network infrastructure has become more exposed to cyber-attacks, particularly Distributed Denial of Service (DDoS) attacks, which can easily cause the disruption of services. The key factor for fighting against these attacks is the earlier separation and detection of the traffic in networks. In this paper, a novel approach, named Half autoencoder-Stacked DNNs (HAE-SDNN) model, is proposed. We suggest using a Stacked Deep Neural Networks (SDNN) model. as a deep learning model, in order to detect DDoS attacks. Our approach allows feature selection from a preprocessed dataset using a Half autoencoder (HAE), resulting in a final set of important features. These features are subsequently used to train the DNNs that are stacked together by applying Softmax layer to combine their outputs. Experiments were performed on a benchmark cybersecurity dataset, named CICD-DoS2017, containing various DDoS attack types. The experimental results demonstrate that the introduced model attained an overall accuracy rate of 99.95%. Moreover, the HAE-SDNN model outperformed existing models, highlighting its superiority in accurately classifying attacks.

关键词： Intrusion detection system DDoS attacks deep learning deep neural network autoencoder

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autoencoder Network for Hyperspectral Unmixing With Adaptive Abundance Smoothing

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2021年第9期18卷 1640-1644页

作者： Hua, Ziqiang Li, Xiaorun Qiu, Qunhui Zhao, Liaoying Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China State Grid Liming Power Supply Co Jiaxing 314100 Peoples R China Hangzhou Dianzi Univ Sch Comp Sci & Technol Hangzhou 310018 Peoples R China

autoencoder is an efficient technique for unsupervised feature learning, which can be applied to hyperspectral unmixing. In this letter, we present an autoencoder network with adaptive abundance smoothing (AAS) to solve the challenges of previous techniques. Specifically, the proposed method uses a multilayer encoder to obtain the abundance and a single-layer decoder to reconstruct the image. The AAS algorithm tackles the outliers by exploiting the spatial-contextual information and can be adaptive for each pixel. Moreover, the softmax function is used as the encoder output function with the help of L-1/2 regularization to produce sparse output. Experimental results of the synthetic and real data reveal the superior performance of the proposed method against other competitors.

关键词： Adaptive abundance smoothing (AAS) autoencoder hyperspectral imagery (HSI) spectral unmixing (SU)

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Enhanced Deep autoencoder Based Feature Representation Learning for Intelligent Intrusion Detection System

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Computers, Materials & Continua 2021年第9期68卷 3271-3288页

作者： Thavavel Vaiyapuri Adel Binbusayyis College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

In the era of Big data,learning discriminant feature representation from network traffic is identified has as an invariably essential task for improving the detection ability of an intrusion detection system(IDS).Owing to the lack of accurately labeled network traffic data,many unsupervised feature representation learning models have been proposed with state-of-theart ***,these models fail to consider the classification error while learning the feature ***,the learnt feature representation may degrade the performance of the classification *** the first time in the field of intrusion detection,this paper proposes an unsupervised IDS model leveraging the benefits of deep autoencoder(DAE)for learning the robust feature representation and one-class support vector machine(OCSVM)for finding the more compact decision hyperplane for intrusion ***,the proposed model defines a new unified objective function to minimize the reconstruction and classification error *** unique contribution not only enables the model to support joint learning for feature representation and classifier training but also guides to learn the robust feature representation which can improve the discrimination ability of the classifier for intrusion *** set of evaluation experiments are conducted to demonstrate the potential of the proposed ***,the ablation evaluation on benchmark dataset,NSL-KDD validates the design decision of the proposed ***,the performance evaluation on recent intrusion dataset,UNSW-NB15 signifies the stable performance of the proposed ***,the comparative evaluation verifies the efficacy of the proposed model against recently published state-of-the-art methods.

关键词： Cybersecurity network intrusion detection deep learning autoencoder stacked autoencoder feature representational learning joint learning one-class classifier OCSVM

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An improved deep learning-based end-to-end autoencoder for UAV-to-ground free space optical communication

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OPTICS COMMUNICATIONS 2023年 549卷

作者： Zhang, Qianwu Chen, Guanwen Liu, Boyang Zhi, Xuzhuang Zhan, Shucheng Zhang, Jing Cao, Bingyao Li, Zhengxuan Shanghai Univ Shanghai Inst Adv Commun & Data Sci Key Lab Specialty Opt & Opt Access Networks Shanghai 200072 Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Sichuan Peoples R China

In this paper, an improved deep learning-based end-to-end autoencoder is proposed for unmanned aerial vehicle (UAV) to ground free space optical communication to mitigate atmospheric turbulence. Deep neural network (DNN) is applied to the intensity modulation/direct detection (IM/DD) autoencoder, including transmitter, receiver as well as channel model. The performance is improved by two-stage deep learning training because the minimum Hamming distance between the codewords is increased through pre-training. Simulation results show that the bit error rate of our proposed scheme can reach the 7% hard-decision forward error correction (HD-FEC) threshold at signal-to-noise ratio of approximately 22 dB and in strong atmospheric turbulence where the maximum Rytov variance is 3.5. Our proposed scheme can outperform the state-of-the-art IM/DD system with PPM transmitter and maximum likelihood receiver by achieving approximately 12 dB improvement and reducing similar to 51.3% of the decoder's running time without the need for accurate channel state information.

关键词： UAV-to-ground Free space optical communication Deep learning autoencoder

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Reduced Biquaternion Stacked Denoising Convolutional autoencoder for RGB-D Image Classification

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IEEE SIGNAL PROCESSING LETTERS 2021年 28卷 1205-1209页

作者： Huang, Xiang Gai, Shan Nanchang Hangkong Univ Sch Informat & Engn Nanchang 330063 Jiangxi Peoples R China

RGB-D image classification based on convolutional neural networks have been extensively explored recently. However, they suffer from problems of effective representation of RGB-D image, intra-class variances and inter-class similarities. To address these problems, this letter proposes a novel RGB-D image classification framework based on reduced biquaternion stacked denoising convolutional autoencoder (RQ-SDCAE). The proposed framework can encode and extract the depth feature effectively by using the reduced biquaternion. The stacked training method is utilized to train the proposed reduced biquaternion convolutional autoencoder. Extensive evaluations for RGB-D image classification demonstrate that RQ-SDCAE outperforms the state-of-the-art methods.

关键词： Training Convolution Feature extraction Tensors Noise reduction Matrix converters Image color analysis Reduced biquaternion RGB-D image classification autoencoder hypercomplex network

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MOCAT: multi-omics integration with auxiliary classifiers enhanced autoencoder

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BIODATA MINING 2024年第1期17卷 1页

作者： Yao, Xiaohui Jiang, Xiaohan Luo, Haoran Liang, Hong Ye, Xiufen Wei, Yanhui Cong, Shan Harbin Engn Univ Qingdao Innovat & Dev Ctr 1777 Sansha Rd Qingdao 266000 Shandong Peoples R China Harbin Engn Univ Coll Intelligent Syst Sci & Engn 145 Nantong St Harbin 150001 Heilongjiang Peoples R China

BackgroundIntegrating multi-omics data is emerging as a critical approach in enhancing our understanding of complex diseases. Innovative computational methods capable of managing high-dimensional and heterogeneous datasets are required to unlock the full potential of such rich and diverse *** propose a Multi-Omics integration framework with auxiliary Classifiers-enhanced autoencoders (MOCAT) to utilize intra- and inter-omics information comprehensively. Additionally, attention mechanisms with confidence learning are incorporated for enhanced feature representation and trustworthy *** experiments were conducted on four benchmark datasets to evaluate the effectiveness of our proposed model, including BRCA, ROSMAP, LGG, and KIPAN. Our model significantly improved most evaluation measurements and consistently surpassed the state-of-the-art methods. Ablation studies showed that the auxiliary classifiers significantly boosted classification accuracy in the ROSMAP and LGG datasets. Moreover, the attention mechanisms and confidence evaluation block contributed to improvements in the predictive accuracy and generalizability of our *** proposed framework exhibits superior performance in disease classification and biomarker discovery, establishing itself as a robust and versatile tool for analyzing multi-layer biological data. This study highlights the significance of elaborated designed deep learning methodologies in dissecting complex disease phenotypes and improving the accuracy of disease predictions.

关键词： Multi-omics integration Auxiliary classifier autoencoder Attention mechanism Trustworthy learning Disease prediction

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