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Deep Learning-Based Parasitic Egg Identification From a Slender-Billed Gull's Nest

IEEE ACCESS 2023年 11卷 37194-37202页

作者： Nhidi, Wiem Ben Aoun, Najib Ejbali, Ridha Univ Gabes Natl Engn Sch Gabes ENIG Res Team Intelligent Machines RTIM Gabes 6029 Tunisia Al Baha Univ Coll Comp Sci & Informat Technol Al Baha 657797738 Saudi Arabia Univ Sfax Natl Sch Engineers Sfax ENIS REGIM Lab Res Grp Intelligent Machines Sfax 3038 Tunisia

Intraspecific nest parasitism is a phenomenon that attracts the attention of biologists since it helps in saving the endangered species such as Slender Billed Gull. The problem comes from the fact that a parasite female lays its eggs in the nest of another female (host) of the same species which causes the abandon of the nest by the host. This behavior causes a significant reduction in future birds number and leads to the expansion of this specie. Thus, there has been an urgent necessity to clean the nest from parasitic eggs. So, our aim is to build an automatic parasitic egg identification system based on egg visual features information. Our system uses deep learning models which have proven their success for image classification. Indeed, our system conduct an egg image's pre-processing phase followed by Fast Beta Wavelet Network (FBWN) to extract the most efficient descriptors (shape, texture, and color). Then, these features will be inputted to the stacked autoencoder for egg classification. Our proposed system, has been evaluated on 91-egg dataset collected from 31 clutches of eggs in Sfax region, Tunisia. Our model has given a parasitic egg identification accuracy of 89.9% which has outperformed the state-of-the-art method and shows the efficiency and the robustness of our system.

关键词： Feature extraction Image color analysis Visualization Birds Deep learning Support vector machines Intraspecific nest parasitism slender-billed gull parasitic egg identification fast beta wavelet network stacked autoencoder deep learning

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Transfer Learning for Molecular Cancer Classification Using Deep Neural Networks

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019年第6期16卷 2089-2100页

作者： Sevakula, Rahul K. Singh, Vikas Verma, Nishchal K. Kumar, Chandan Cui, Yan Indian Inst Technol Kanpur Dept Elect Engn Kanpur 208016 Uttar Pradesh India Indian Inst Technol Guwahati Gauhati 781039 Assam India Univ Tennessee Ctr Hlth Sci Dept Genet Genom & Informat Memphis TN 38163 USA

The emergence of deep learning has impacted numerous machine learning based applications and research. The reason for its success lies in two main advantages: 1) it provides the ability to learn very complex non-linear relationships between features and 2) it allows one to leverage information from unlabeled data that does not belong to the problem being handled. This paper presents a transfer learning procedure for cancer classification, which uses feature selection and normalization techniques in conjunction with s sparse auto-encoders on gene expression data. While classifying any two tumor types, data of other tumor types were used in unsupervised manner to improve the feature representation. The performance of our algorithm was tested on 36 two-class benchmark datasets from the GEMLeR repository. On performing statistical tests, it is clearly ascertained that our algorithm statistically outperforms several generally used cancer classification approaches. The deep learning based molecular disease classification can be used to guide decisions made on the diagnosis and treatment of diseases, and therefore may have important applications in precision medicine.

关键词： Transfer learning cancer classification deep neural network stacked autoencoder feature selection

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Multilayer stacked Evolving Fuzzy System Combined With Compressed Representation Learning

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第4期32卷 2223-2234页

作者： Huang, Hui Rong, Hai-Jun Yang, Zhao-Xu Vong, Chi-Man Xi An Jiao Tong Univ Sch Aerosp State Key Lab Strength & Vibrat Mech Struct Shaanxi Key Lab Environm & Control Flight Vehicle Xian 710049 Peoples R China Univ Macau Dept Comp & Informat Sci Macau 999078 Peoples R China

In order to process the high-dimensionally complicated problems, the intelligence systems need to go deeper to learn high-level data representation. In this article, based on the stacked generalization principle, a multilayered stacked learning system is proposed. Like the deep networks, the proposed system is organized in a layer-by-layer way with evolving fuzzy systems (EFSs) as its base-building units. Each EFS is designed as an autoencoder (AE) to learn the simpler data representation, then multiple EFS-based AEs are stacked in a feedforward manner for learning more complex data representation. Since there exists the redundant or irrelevant information in the new data representation, which may limit high generalization, a novel feature compressing layer is followed by each EFS-based AE to refine the new data representation and reduce the feature dimension via very sparse random projection (VSRP). The proposed system is featured in the following merits: 1) more expressive and complex data representation can be learned in a stacked multilayer architecture;2) manually tuning the structure of each AE is avoided in every layer since the EFS can self-adapt both its structure and parameters online;3) the resulting system can avoid overfitting and obtain higher generalization by removing redundant or irrelevant information via VSRP;4) the steady-state error analysis of the proposed system is studied based on the separable approximation property, which guarantees the learning convergence of the resulting system. Our experimental results on various benchmark datasets indicate the efficacy of the proposed system.

关键词： Evolving fuzzy system (EFS) representation learning stacked autoencoder very sparse random projection

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Identification of Human Breathing-States Using Cardiac-Vibrational Signal for m-Health Applications

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IEEE SENSORS JOURNAL 2021年第3期21卷 3463-3470页

作者： Choudhary, Tilendra Sharma, L. N. Bhuyan, M. K. Bora, Kangkana IIT Guwahati Dept Elect & Elect Engn Gauhati 781039 India Cotton Univ Dept Comp Sci & Informat Technol Gauhati 7810001 India

In this work, a seismocardiogram (SCG) based breathing-state measuring method is proposed for m-health applications. The aim of the proposed framework is to assess the human respiratory system by identifying degree-of-breathings, such as breathlessness, normal breathing, and long and labored breathing. For this, it is needed to measure cardiac-induced chest-wall vibrations, reflected in the SCG signal. Orthogonal subspaceprojection is employed to extract the SCG cycles with the help of a concurrent ECG signal. Subsequently, fifteen statistically significant morphological-features are extracted from each of the SCG cycles. These features can efficiently characterize physiological changes due to varying respiratory-rates. stacked autoencoder (SAE) based architecture is employed for the identification of different respiratory-effort levels. The performance of the proposed method is evaluated and compared with other standard classifiers for 1147 analyzed SCG- beats. The proposed method gives an overall average accuracy of 91.45% in recognizing three different breathing states. The quantitative analysis of the performance results clearly shows the effectiveness of the proposed framework. It may be employed in various healthcare applications, such as pre-screening medical sensors and IoT based remote health-monitoring systems.

关键词： Seismocardiogram ECG heart cycle neural networks stacked autoencoder respiratory efforts

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SALLoc: An Accurate Target Localization in WiFi-Enabled Indoor Environments via SAE-ALSTM

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IEEE ACCESS 2024年 12卷 19694-19710页

作者： Ayinla, Shehu Lukman Abd Aziz, Azrina Drieberg, Micheal Univ Teknol PETRONAS Dept Elect & Elect Engn Seri Iskandar 32610 Perak Malaysia Univ Ilorin Dept Comp Engn Ilorin 240003 Nigeria

Developing a reliable and accurate indoor localization system is a crucial step for creating a seamless and interactive user-device experience in nearly all intelligent internet of things (IIoTs) and smart applications. Indoor localization systems based on WiFi fingerprinting have been considered as a promising alternative to model-based approaches owing to their accuracy, low cost, availability, and ease of configuration. However, recent studies have revealed that in complex environments, WiFi fingerprinting techniques are faced with a lot of challenges as the coverage area increases. These challenges include fingerprint spatial uncertainty, instability in the received signal strength indicator (RSSI) and discrepancy in fingerprint distribution. Furthermore, there is frequent need for database upgrades or even recreation whenever there is a change in the architecture of the location. These challenges have questioned the robustness and efficiency of most of the existing schemes. In this paper, we present an indoor localization architecture for complex multi-building multi-floor location prediction and subsequently propose SALLoc (SAE-ALSTM Localization), a WiFi fingerprinting indoor localization scheme based on stacked autoencoder (SAE) and Attention-based Long Short-Time Memory (ALSTM) framework. Firstly, stratified sampling technique is used to separate validation set from the entire uneven RSSI training set which ensures that the same proportion of RSSI samples are present in both sets. Secondly, SAE is utilized to select core features and decrease the dimensions of the RSSI samples. Finally, ALSTM is trained to focus on these features to achieve robust location prediction. Extensive investigations were conducted using UJIIndoorLoc, Tampere and UTSIndoorLoc datasets, and the results obtained demonstrated the superiority of the proposed scheme in terms of prediction accuracy, robustness, and generalizations when compared to state-of-the-art methods. The mean local

关键词： Location awareness Fingerprint recognition Wireless fidelity Feature extraction Robustness Target tracking Databases WiFi fingerprinting indoor localization mean localization error stacked autoencoder long short-time memory attention mechanism

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Recurrent-DC: A deep representation clustering model for university profiling based on academic graph

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2021年 116卷 156-167页

作者： Kong, Xiangjie Li, Jiaxing Wang, Luna Shen, Guojiang Sun, Yiming Lee, Ivan Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Peoples R China Dalian Univ Technol Sch Software Dalian 116620 Peoples R China Dalian Univ Technol Inst Sci & Technol Dalian 116024 Peoples R China Univ South Australia Sch Informat Technol & Math Sci Adelaide SA 5001 Australia

Universities play an important role in exploring new concepts and knowledge transfer. University research naturally forms heterogeneous graphs through all real-life academic communication activities. In recent years, there have been many large scholarly graph datasets containing web-scale nodes and edges. However, so far, for these graph data, characterizing research about university output is focusing on counting the volume or evaluating the excellence of research articles and providing a ranking. This paper proposes a novel University Profiling Framework (UPF) from the production and complexity point of view which is different from other straightforward solutions. The framework includes a novel Recurrent Deep Clustering Model (Recurrent-DC) for the learning of deep representations and clusters. In our model, successive operations in a clustering algorithm are expressed as steps in a recurrent process, stacked on top of representations output by a stacked autoencoder (SAE). Our key idea behind this model is that good representations for university clustering task-specific problem can be learned over multiple timesteps. Experimental results illustrate the stability and effectiveness of the proposed model comparing with the other deep clustering and classical clustering methods. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Academic graph University profiling Deep representation learning Deep clustering stacked autoencoder

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A novel hybrid deep learning model for early stage diabetes risk prediction

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JOURNAL OF SUPERCOMPUTING 2024年第13期80卷 19462-19484页

作者： Bulbul, Mehmet Akif Kayseri Univ Dept Software Engn TR-38280 Kayseri Turkiye

Diabetes is a prevalent global disease that significantly diminishes the quality of life and can even lead to fatalities due to its complications. Early detection and treatment of diabetes are crucial for mitigating and averting associated risks. This study aims to facilitate the prompt and straightforward diagnosis of individuals at risk of diabetes. To achieve this objective, a dataset for early stage diabetes risk prediction from the University of California Irvine (UCI) database, widely utilized in the literature, was employed. A hybrid deep learning model comprising genetic algorithm, stacked autoencoder, and Softmax classifier was developed for classification on this dataset. The performance of this model, wherein both the model architecture and all hyperparameters were specifically optimized for the given problem, was compared with commonly used methods in the literature. These methods include K-nearest neighbor, decision tree, support vector machine, and convolutional neural network, utilizing tenfold cross-validation. The results obtained with the proposed method surpassed those obtained with other methods, with higher accuracy rates than previous studies utilizing the same dataset. Furthermore, based on the study's findings, a web-based application was developed for early diabetes diagnosis.

关键词： Genetic algorithm stacked autoencoder Softmax classification Hyperparameter optimization Hybrid deep learning model

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Deep learning for predicting the monsoon over the homogeneous regions of India

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JOURNAL OF EARTH SYSTEM SCIENCE 2017年第4期126卷 54页

作者： Saha, Moumita Mitra, Pabitra Nanjundiah, Ravi S. Indian Inst Technol Dept Comp Sci & Engn Kharagpur 721302 W Bengal India Indian Inst Sci Divecha Ctr Climate Change Bangalore 560012 Karnataka India Indian Inst Trop Meteorol Pune 411008 Maharashtra India Indian Inst Sci Ctr Atmospher & Ocean Sci Bangalore 560012 Karnataka India

Indian monsoon varies in its nature over the geographical regions. Predicting the rainfall not just at the national level, but at the regional level is an important task. In this article, we used a deep neural network, namely, the stacked autoencoder to automatically identify climatic factors that are capable of predicting the rainfall over the homogeneous regions of India. An ensemble regression tree model is used for monsoon prediction using the identified climatic predictors. The proposed model provides forecast of the monsoon at a long lead time which supports the government to implement appropriate policies for the economic growth of the country. The monsoon of the central, north-east, north-west, and south-peninsular India regions are predicted with errors of 4.1%, 5.1%, 5.5%, and 6.4%, respectively. The identified predictors show high skill in predicting the regional monsoon having high variability. The proposed model is observed to be competitive with the state-of-the-art prediction models.

关键词： Feature learning stacked autoencoder monsoon predictor ensemble of regression trees regional Indian summer monsoon

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Face recognition algorithm based on stack denoising and self-encoding LBP

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JOURNAL OF INTELLIGENT SYSTEMS 2022年第1期31卷 501-510页

作者： Lu, Yanjing Khan, Mudassir Ansari, Mohd Dilshad Zhengzhou Tech Coll Zhengzhou 450121 Henan Peoples R China King Khalid Univ Coll Sci & Arts Tanumah Dept Comp Sci Abha Saudi Arabia CMR Coll Engn & Technol Hyderabad India

To optimize the weak robustness of traditional face recognition algorithms, the classification accuracy rate is not high, the operation speed is slower, so a face recognition algorithm based on local binary pattern (LBP) and stacked autoencoder (AE) is proposed. The advantage of LBP texture structure feature of the face image as the initial feature of sparse autoencoder (SAE) learning, use the unified mode LBP operator to extract the histogram of the blocked face image, connect to form the LBP features of the entire image. It is used as input of the stacked AE, feature extraction is done, realize the recognition and classification of face images. Experimental results show that the recognition rate of the algorithm LBP-SAE on the Yale database has achieved 99.05%, and it further shows that the algorithm has a higher recognition rate than the classic face recognition algorithm;it has strong robustness to light changes. Experimental results on the Olivetti Research Laboratory library shows that the developed method is more robust to light changes and has better recognition effects compared to traditional face recognition algorithms and standard stack AEs.

关键词： face recognition stacked autoencoder LBP operator denoising

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Index tracking through deep latent representation learning

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QUANTITATIVE FINANCE 2020年第4期20卷 639-652页

作者： Kim, Saejoon Kim, Soong Sogang Univ Dept Comp Sci & Engn 35 Baekbeom Ro Seoul 04107 South Korea

We consider the problem of index tracking whose goal is to construct a portfolio that minimizes the tracking error between the returns of a benchmark index and the tracking portfolio. This problem carries significant importance in financial economics as the tracking portfolio represents a parsimonious index that facilitates a practical means to trade the benchmark index. For this reason, extensive studies from various optimization and machine learning-based approaches have ensued. In this paper, we solve this problem through the latest developments from deep learning. Specifically, we associate a deep latent representation of asset returns, obtained through a stacked autoencoder, with the benchmark index's return to identify the assets for inclusion in the tracking portfolio. Empirical results indicate that to improve the performance of previously proposed deep learning-based index tracking, the deep latent representation needs to be learned in a strictly hierarchical manner and the relationship between the returns of the index and the assets should be quantified by statistical measures. Various deep learning-based strategies have been tested for the stock market indices of the S&P 500, FTSE 100 and HSI, and it is shown that our proposed methodology generates the best index tracking performance.

关键词： Index tracking Index replication Deep learning Deep autoencoder stacked autoencoder

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