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IMU-CNN: implementing remote sensing image restoration framework based on Mask-Upgraded Cascade R-CNN and deep autoencoder

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第27期83卷 69049-69081页

作者： Dewangan, Somesh Kumar Choubey, Siddharth Patra, Jyotiprakash Choubey, Abha Shri Shankaracharya Tech Campus Dept Comp Sci Engn Bhilai 490001 Chhattisgarh India Shri Shankaracharya Inst Profess Management & Tech Dept Comp Sci Engn Raipur 492015 Chhattisgarh India

The effective restoration of degraded remote sensing images is one of the major concerns as it directly affects imaging system performance. In recent years, investigators have developed innumerable systems and methods for remotely sensed hazy or blurred image restoration to enhance the system's performance. However, the existing remotely sensed image restoration system and methods still have untold limitations such as texture degradation, high computational complexity, preparative parametric tunning, more time consumption in restoration procedure, lesser accuracy, and many more. Hence, effective and fast restoration of the degraded remote sensing images with more accuracy is still a challenging problem, which requires enormous attention toward a novel image restoration approach. In this work, the researchers implement a remote sensing image restoration framework namely IMU-CNN, which is based on mask-upgraded cascade R-CNN and deep autoencoder. Our proposed IMU-CNN model performs hazy or blurred remotely sensed image restoration in a faster manner with improved accuracy, as well as saves time in the image restoration process, significantly. The outcome of the proposed image restoration framework is found improved and optimal and measured accuracy, precision, F1 score, and Recall is 99.59%, 97.24%, 97.69%, and 96.49%, respectively. Recently, researchers have conducted much investigation on remotely sensed image restoration systems by using various hybrid approaches. However, still, there is a need for further investigation in the future for building sophisticated and less computationally complex systems for effective remotely sensed image restoration in less time.

关键词： Cascade R-CNN deep autoencoder Image restoration IMU-CNN Remote sensing

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NDAMA: A Novel deep autoencoder and Multivariate Analysis Approach for IoT-Based Methane Gas Leakage Detection

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IEEE ACCESS 2023年 11卷 140740-140751页

作者： Dashdondov, Khongorzul Kim, Mi-Hye Jo, Kyuri Gachon Univ Coll IT Convergence Dept Comp Engn Seongnam 13120 South Korea Chungbuk Natl Univ Sch Elect & Comp Engn Dept Comp Engn Cheongju 28644 Chungcheongbuk South Korea

Natural gas is widely used for domestic and industrial purposes, and whether it is being leaked into the air cannot be directly known. The current problem is that gas leakage is not only economically harmful but also detrimental to health. Therefore, much research has been done on gas damage and leakage risks, but research on predicting gas leakages is just beginning. In this study, we propose a method based on deep learning to predict gas leakage from environmental data. Our proposed method has successfully improved the performance of machine learning classification algorithms by efficiently preparing training data using a deep autoencoder model. The proposed method was evaluated on an open dataset containing natural gas and environmental information and compared with extreme gradient boost (XGBoost), K-nearest neighbors (KNN), decision tree (DT), random forest (RF), and naive Bayes (NB) algorithms. The proposed method is evaluated using accuracy, F1-score, mean square error (MSE), mean intersection over union (mIoU), and area under the ROC curve (AUC). The presented method in this study outperformed all compared methods. Moreover, the deep autoencoder and ordinal encoder-based XGBoost (DA-MA-XGBoost) showed the best performance by giving 99.51% accuracy, an F1-score of 99.53%, an MSE of 0.003, mIoU of 99.40 and an AUC of 99.62%.

关键词： Leak detection Machine learning algorithms Data models Predictive models Pipelines Labeling Surveys Encoding Multivariate regression deep autoencoder multivariate analysis methane gas risk detection XGBoost

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A drug repositioning algorithm based on a deep autoencoder and adaptive fusion

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BMC BIOINFORMATICS 2021年第1期22卷 1-18页

作者： Chen, Peng Bao, Tianjiazhi Yu, Xiaosheng Liu, Zhongtu China Three Gorges Univ Coll Comp & Informat Technol Yichang Hubei Peoples R China

Background Drug repositioning has caught the attention of scholars at home and abroad due to its effective reduction of the development cost and time of new drugs. However, existing drug repositioning methods that are based on computational analysis are limited by sparse data and classic fusion methods;thus, we use autoencoders and adaptive fusion methods to calculate drug repositioning. Results In this study, a drug repositioning algorithm based on a deep autoencoder and adaptive fusion was proposed to mitigate the problems of decreased precision and low-efficiency multisource data fusion caused by data sparseness. Specifically, a drug is repositioned by fusing drug-disease associations, drug target proteins, drug chemical structures and drug side effects. First, drug feature data integrated by drug target proteins and chemical structures were processed with dimension reduction via a deep autoencoder to characterize feature representations more densely and abstractly. Then, disease similarity was computed using drug-disease association data, while drug similarity was calculated with drug feature and drug-side effect data. Predictions of drug-disease associations were also calculated using a top-k neighbor method that is commonly used in predictive drug repositioning studies. Finally, a predicted matrix for drug-disease associations was acquired after fusing a wide variety of data via adaptive fusion. Based on experimental results, the proposed algorithm achieves a higher precision and recall rate than the DRCFFS, SLAMS and BADR algorithms with the same dataset. Conclusion The proposed algorithm contributes to investigating the novel uses of drugs, as shown in a case study of Alzheimer's disease. Therefore, the proposed algorithm can provide an auxiliary effect for clinical trials of drug repositioning.

关键词： Drug repositioning Adaptive fusion deep autoencoder

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Network anomaly detection using deep autoencoder and parallel Artificial Bee Colony algorithm-trained neural network

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PEERJ COMPUTER SCIENCE 2024年 10卷 e2333-e2333页

作者： Hacilar, Hilal Dedeturk, Bilge Kagan Bakir-Gungor, Burcu Gungor, Vehbi Cagri Abdullah Gul Univ Dept Comp Engn Kayseri Turkiye Erciyes Univ Dept Software Engn Kayseri Turkiye Turkcell Istanbul Turkiye

Cyberattacks are increasingly becoming more complex, which makes intrusion detection extremely difficult. Several intrusion detection approaches have been developed in the literature and utilized to tackle computer security intrusions. Implementing machine learning and deep learning models for network intrusion detection has been a topic of active research in cybersecurity. In this study, artificial neural networks (ANNs), a type of machine learning algorithm, are employed to determine optimal network weight sets during the training phase. Conventional training algorithms, such as back- propagation, may encounter challenges in optimization due to being entrapped within local minima during the iterative optimization process;global search strategies can be slow at locating global minima, and they may suffer from a low detection rate. In the ANN training, the Artificial Bee Colony (ABC) algorithm enables the avoidance of local minimum solutions by conducting a high-performance search in the solution space but it needs some modifications. To address these challenges, this work suggests a deep autoencoder (DAE)-based, vectorized, and parallelized ABC algorithm for training feed-forward artificial neural networks, which is tested on the UNSW-NB15 and NF-UNSW-NB15-v2 datasets. Our experimental results demonstrate that the proposed DAE-based parallel ABC-ANN outperforms existing metaheuristics, showing notable improvements in network intrusion detection. The experimental results reveal a notable improvement in network intrusion detection through this proposed approach, exhibiting an increase in detection rate (DR) by 0.76 to 0.81 and a reduction in false alarm rate (FAR) by 0.016 to 0.005 compared to the ANN-BP algorithm on the UNSWNB15 dataset. Furthermore, there is a reduction in FAR by 0.006 to 0.0003 compared to the ANN-BP algorithm on the NF-UNSW-NB15-v2 dataset. These findings underscore the effectiveness of our proposed approach in enhancing network security against

关键词： Artificial neural network Artificial bee colony Metaheuristics deep autoencoder Network intrusion detection systems (NIDS) Anomaly detection UNSW-NB15 Swarm intelligence NF-UNSW-NB15-v2

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A composite framework for photovoltaic day-ahead power prediction based on dual clustering of dynamic time warping distance and deep autoencoder

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RENEWABLE ENERGY 2022年第0期194卷 659-673页

作者： Yang, Mao Zhao, Meng Huang, Dawei Su, Xin Northeast Elect Power Univ Key Lab Modern Power Syst Simulat & Control & Rene Minist Educ Jilin 132012 Peoples R China

The improvement of photovoltaic (PV) power prediction precision plays a crucial role in the new energy consumption. This paper proposes a composite prediction framework (DC (DWT-DAE)-CNN) consisting of dual clustering and convolutional neural network to achieve day-ahead prediction of PV power. To avoid the temporal uncertainty of PV power and the high-dimensional complexity of numerical weather prediction, the raw data are processed by Discrete Wavelet Transform (DWT) and deep autoencoder respectively (DAE) to reduce the data redundancy. Secondly, a dual clustering pattern based on dynamic time warping distance clustering and Fuzzy C-Mean (FCM) clustering is proposed to progressively realize the dynamic characteristics of the power curve and numerical clustering of the weather information data. Finally, the data from PV plants in northeast China are used for validation. The results show that the annual average day-ahead prediction AR of the DC (DWT-DAE)-CNN model can reach 90.17%, which is better than other competing models. In addition, the dual clustering pattern performs better than other traditional clustering patterns with the same predictor. Using this method to predict the PV output power can provide better theoretical guidance for the stable and safe operation of grid-connected PV. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： PV power Day-ahead prediction Dual clustering Discrete wavelet transform deep autoencoder Convolutional neural network

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DAESTB: inferring associations of small molecule-miRNA via a scalable tree boosting model based on deep autoencoder

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BRIEFINGS IN BIOINFORMATICS 2022年第6期23卷 bbac478-bbac478页

作者： Peng, Li Tu, Yuan Huang, Li Li, Yang Fu, Xiangzheng Chen, Xiang Hunan Univ Sci & Technol Xiangtan Hunan Peoples R China Hunan Key Lab Serv Comp & Novel Software Xiangtan Hunan Peoples R China Tsinghua Univ Beijing Peoples R China Xiangtan Univ Xiangtan Peoples R China Hunan Univ Changsha Hunan Peoples R China

MicroRNAs (miRNAs) are closely related to a variety of human diseases, not only regulating gene expression, but also having an important role in human life activities and being viable targets of small molecule drugs for disease treatment. Current computational techniques to predict the potential associations between small molecule and miRNA are not that accurate. Here, we proposed a new computational method based on a deep autoencoder and a scalable tree boosting model (DAESTB), to predict associations between small molecule and miRNA. First, we constructed a high-dimensional feature matrix by integrating small molecule-small molecule similarity, miRNA-miRNA similarity and known small molecule-miRNA associations. Second, we reduced feature dimensionality on the integrated matrix using a deep autoencoder to obtain the potential feature representation of each small molecule-miRNA pair. Finally, a scalable tree boosting model is used to predict small molecule and miRNA potential associations. The experiments on two datasets demonstrated the superiority of DAESTB over various state-of-the-art methods. DAESTB achieved the best AUC value. Furthermore, in three case studies, a large number of predicted associations by DAESTB are confirmed with the public accessed literature. We envision that DAESTB could serve as a useful biological model for predicting potential small molecule-miRNA associations.

关键词： microRNA small molecule association prediction deep autoencoder scalable tree boosting

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Trust Management for deep autoencoder based Anomaly Detection in Social IoT

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第1期14卷 981-989页

作者： Rashmi, M. R. Raj, C. Vidya VTU Belagavi India NIE Dept Comp Sci & Engn Mysore Karnataka India

Social IoT has gained huge traction with the advent of 5G and beyond communication. In this connected world of devices, the trust management is crucial for protecting the data. There are many attacks, while DDOS is the most prevalent BotNet attack. The infected devices earnestly require anomaly detection to learn and curb the malwares soon. This paper considers 9 IoT devices deployed in a Social IoT *** introduce a couple of attacks like Bash lite and Mirai by compromising a network node. We then look for traces of malicious behavior using AI algorithms. The investigation starts from a simple network approach -Multi-Layer Perceptron (MLP) then proceeds to ML -Random Forest (RF). While MLP detected the malicious node with an accuracy of 89.39%, RF proved 90.0% accurate. Motivated by the results, the deep learning approach -deep autoencoder was employed and found to be more accurate than MLP and RF. The results are encouraging and verified for scalability, efficiency, and reliability.

关键词： Social IoT trust management anomaly detection DDoS deep autoencoder

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A deep autoencoder-Based Hybrid Recommender System

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INTERNATIONAL JOURNAL OF MOBILE COMPUTING AND MULTIMEDIA COMMUNICATIONS 2022年第1期13卷 1-19页

作者： Bougteb, Yahya Ouhbi, Brahim Frikh, Bouchra Zemmouri, Elmoukhtar Moulay Ismail Univ ENSAM LM2I Lab Meknes Morocco Ecole Natl Sci Appl Fes Fes Morocco Moulay Ismail Univ ENSAM Ensam Morocco

Recommender systems build their suggestions on rating data, given explicitly or implicitly by users on items. These ratings create a huge sparse user-item rating matrix, which opens two challenges for researchers on the field. The first challenge is the sparsity of the rating matrix, and the second one is the scalability of the data. This article proposes a hybrid recommender system based on deep autoencoder to learn the user interests and reconstruct the missing ratings. Then, SVD++ decomposition is employed, in parallel, to hold information of correlation between different features factors. Additionally, the authors gave a deep analysis of the top-N recommender list from the user's perspective to ensure that the proposed model can be used for practical application. Experiments showed that the proposed model performed better with high-dimensional datasets and outperformed various hybrid algorithms in terms of RMSE, MAE, and in terms of the final top-N recommendation list.

关键词： deep autoencoder deep Learning Factorization Machines Learning User's Profile Recommender Systems Sparsity Problem SVD++ Top-N Recommendation List

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Bottleneck Feature-Based Hybrid deep autoencoder Approach for Indian Language Identification

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2020年第4期45卷 3425-3436页

作者： Das, Himanish Shekhar Roy, Pinki Natl Inst Technol Silchar Dept Comp Sci & Engn Silchar 788010 Assam India

Latest and emerging approaches are essential to resolve the communication barrier among different languages in speech processing. The automatic language identification system is developed to identify the spoken language from speech utterances. Feature selection is a very challenging task in language identification. In this paper, bottleneck feature-based hybrid deep autoencoder approach is proposed to identify the given speech signal with corresponding language features. In the proposed approach, initially Mel-frequency cepstral coefficients, linear prediction coefficients, and shifted delta coefficients features are directly extracted from multilingual speech utterances. Further, we extracted bottleneck feature from the bottleneck layer of the bottleneck deep neural network. Initially, recognition rate has been evaluated for each feature set to find out the best feature. Finally, the best feature along with other features is used as the input for deep autoencoder with softmax regression to identify the language based on class labels. The deep autoencoder is fine-tuned to reach the global optimum through Jaya optimization algorithm. To carry out the experiments, the recorded database is used for four Indian languages with special emphasis on northeastern languages. The experimental results demonstrate that the proposed hybrid approach using bottleneck feature with shifted delta coefficients is performing well with 97.10% accuracy. Moreover, experimental results also show that proposed hybrid approach gives superior outcome when compared with the baseline deep neural network-based approaches.

关键词： Speech processing Language identification Bottleneck feature deep autoencoder Softmax regression Jaya optimization

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LDAEXC: LncRNA-Disease Associations Prediction with deep autoencoder and XGBoost Classifier

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INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES 2023年第3期15卷 439-451页

作者： Lu, Cuihong Xie, Minzhu Hunan Normal Univ Coll Informat Sci & Engn Changsha Peoples R China

Numerous scientific evidences have revealed that long non-coding RNAs (lncRNAs) are involved in the progression of human complex diseases and biological life activities. Therefore, identifying novel and potential disease-related lncRNAs is helpful to diagnosis, prognosis and therapy of many human complex diseases. Since traditional laboratory experiments are cost and time-consuming, a great quantity of computer algorithms have been proposed for predicting the relationships between lncRNAs and diseases. However, there are still much room for the improvement. In this paper, we introduce an accurate framework named LDAEXC to infer LncRNA-Disease Associations with deep autoencoder and XGBoost Classifier. LDAEXC utilizes different similarity views of lncRNAs and human diseases to construct features for each data sources. Then, the reduced features are obtained by feeding the constructed feature vectors into a deep autoencoder, and at last an XGBoost classifier is leveraged to calculate the latent lncRNA-disease-associated scores using reduced features. The fivefold cross-validation experiments on four datasets showed that LDAEXC reached AUC scores of 0.9676 +/- 0.0043, 0.9449 +/- 0.022, 0.9375 +/- 0.0331 and 0.9556 +/- 0.0134, respectively, significantly higher than other advanced similar computer methods. Extensive experiment results and case studies of two complex diseases (colon and breast cancers) further indicated the practicability and excellent prediction performance of LDAEXC in inferring unknown lncRNA-disease associations. [GRAPHICS] .

关键词： deep autoencoder XGBoost classifier LncRNA-disease associations prediction

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