Benchmark Datasets Research Articles

AIUPred: combining energy estimation with deep learning for the enhanced prediction of protein disorder.

Intrinsically disordered proteins and protein regions (IDPs/IDRs) carry out important biological functions without relying on a single well-defined conformation. As these proteins are a challenge to study experimentally, computational methods play important roles in their characterization. One of the commonly used tools is the IUPred web server which provides prediction of disordered regions and their binding sites. IUPred is rooted in a simple biophysical model and uses a limited number of parameters largely derived on globular protein structures only. This enabled an incredibly fast and robust prediction method, however, its limitations have also become apparent in light of recent breakthrough methods using deep learning techniques. Here, we present AIUPred, a novel version of IUPred which incorporates deep learning techniques into the energy estimation framework. It achieves improved performance while keeping the robustness of the original method. Based on the evaluation of recent benchmark datasets, AIUPred scored amongst the top three single sequence based methods. With a new web server we offer fast and reliable visual analysis for users as well as options to analyze whole genomes in mere seconds with the downloadable package. AIUPred is available at https://aiupred.elte.hu.

TransFOL: A Logical Query Model for Complex Relational Reasoning in Drug-Drug Interaction.

Predicting drug-drug interaction (DDI) plays a crucial role in drug recommendation and discovery. However, wet lab methods are prohibitively expensive and time-consuming due to drug interactions. In recent years, deep learning methods have gained widespread use in drug reasoning. Although these methods have demonstrated effectiveness, they can only predict the interaction between a drug pair and do not contain any other information. However, DDI is greatly affected by various other biomedical factors (such as the dose of the drug). As a result, it is challenging to apply them to more complex and meaningful reasoning tasks. Therefore, this study regards DDI as a link prediction problem on knowledge graphs and proposes a DDI prediction model based on Cross-Transformer and Graph Convolutional Networks (GCN) in first-order logical query form, TransFOL. In the model, a biomedical query graph is first built to learn the embedding representation. Subsequently, an enhancement module is designed to aggregate the semantics of entities and relations. Cross-Transformer is used for encoding to obtain semantic information between nodes, and GCN is used to gather neighbour information further and predict inference results. To evaluate the performance of TransFOL on common DDI tasks, we conduct experiments on two benchmark datasets. The experimental results indicate that our model outperforms state-of-the-art methods on traditional DDI tasks. Additionally, we introduce different biomedical information in the other two experiments to make the settings more realistic. Experimental results verify the strong drug reasoning ability and generalization of TransFOL in complex settings. Data and code are available at https://github.com/Cheng0829/TransFOL.

A differential evolution framework based on the fluid model for feature selection

Feature selection in machine learning is a crucial step to effectively address the issue of feature redundancy in classification problems. Numerous feature selection algorithms have been developed to minimize the number of features, reduce computational cost, and improve classification accuracy. Differential evolution algorithms have the advantage of being simple in structure, robust, fast in convergence, and frequently used to solve feature selection problems. However, it is worth noting that differential evolution algorithms are susceptible to local optimum and stagnation issues, particularly when applied to high-dimensional data. To address this issue, in this study, we propose a differential evolution framework based on the fluid model, named DEF-FM, for feature selection. DEF-FM has the capability to speed up the convergence of differential evolution algorithms and alleviate the effects of local optima. The proposed framework is validated and compared against eight popular differential evolution algorithms using 12 publicly available benchmark datasets and experimental results unequivocally demonstrate the superiority of the proposed framework.

Transfer learning enables identification of multiple types of RNA modifications using nanopore direct RNA sequencing

Nanopore direct RNA sequencing (DRS) has emerged as a powerful tool for RNA modification identification. However, concurrently detecting multiple types of modifications in a single DRS sample remains a challenge. Here, we develop TandemMod, a transferable deep learning framework capable of detecting multiple types of RNA modifications in single DRS data. To train high-performance TandemMod models, we generate in vitro epitranscriptome datasets from cDNA libraries, containing thousands of transcripts labeled with various types of RNA modifications. We validate the performance of TandemMod on both in vitro transcripts and in vivo human cell lines, confirming its high accuracy for profiling m6A and m5C modification sites. Furthermore, we perform transfer learning for identifying other modifications such as m7G, Ψ, and inosine, significantly reducing training data size and running time without compromising performance. Finally, we apply TandemMod to identify 3 types of RNA modifications in rice grown in different environments, demonstrating its applicability across species and conditions. In summary, we provide a resource with ground-truth labels that can serve as benchmark datasets for nanopore-based modification identification methods, and TandemMod for identifying diverse RNA modifications using a single DRS sample.

Enhancing speech emotion recognition through deep learning and handcrafted feature fusion

In this paper, we introduce an innovative investigation in speech emotion recognition (SER). The proposed model combines deep learning-based and handcrafted audio features to achieve optimal accuracy. The proposed model employs an iterative feature selection and majority voting pipeline to obtain better results by fusing deep learning-based and handcrafted features. The wav2vec2 model and the openSmile audio processing library are used in order to extract audio features from audio data. Then the feature selection and majority voting techniques are used to identify the optimal feature selection methods for diverse feature sets and to combine their strengths. The experiments are performed using a diverse and extensive corpus to ensure the robustness of the proposed method. In the construction of this multi-corpus dataset, we used four well-known benchmark datasets, namely Ravdess, Savee, Crema-D, and Tess. All of these datasets are publicly available. These datasets are combined on six common emotions: sadness, happiness, fear, anger, surprise, and disgust. The resultant dataset comprises 11,511 samples across these categories. The proposed method has been shown to achieve results comparable to those reported in the existing literature. The experimental results indicate that the proposed pipeline leads to a 3 % improvement in classification accuracy. The highest achieved accuracy on the multi-corpus dataset is 92.55 %.

DeepPepPI: A deep cross-dependent framework with information sharing mechanism for predicting plant peptide-protein interactions

Peptide-protein interactions (PepPIs) play a crucial role in various fundamental biological activities in plants. As traditional experimental technologies are usually time-consuming and labor-intensive, it is indispensable to develop computational methods for PepPI identification, which has potential applications across diverse fields including food safety and peptide drug discovery. However, existing methods have received relatively little attention in adequately representing the interaction information between peptides and proteins, consequently limiting the prediction performance. To this end, we present DeepPepPI, a novel deep cross-dependent framework for accurate prediction of plant PepPIs. Concretely, a data-driven context-embedded representation (DCR) module is developed as the peptide feature extractor that can capture rich contextual semantic information, even from short sequences. To fully characterize inherent properties of proteins, we propose a bi-level self-correlation search (BSS) module, which integrates the primary sequence and secondary structure into a unified space to learn their potential relationships. In addition, a cross-dependent feature integration (CFI) module with information sharing mechanism is introduced, aimed at providing a comprehensive feature representation to portray the intricate interaction patterns between peptides and proteins from a global perspective. Extensive experimental results conducted on the benchmark datasets show that DeepPepPI achieves superior prediction performance compared to other state-of-the-art methods.

Fine-resolution estimation for urban surface water pollution susceptibility with multi-modal earth observation data

Abstract The sustainability and suitability of water resources are of great importance for maintaining urban populations. The landscapes and environment around urban waters have always been the main focus of maintaining water quality for sustainable water supplies. Early-stage field investigations recognized the influence of land use/land cover (LULC) on water quality. To extend the research scope in spatial and temporal dimensions, remote sensing techniques have been utilized to discover the relationships between LULC and water quality. However, these remote sensing datasets generally had a medium spatial resolution, making them unable to support the fine-detailed land classifications that are critical to explore the water quality in an urban area. Moreover, although more details regarding the land surface are available from the currently-generated high-resolution and very-high-resolution remote sensing images, this land surface information is too complex for the state-of-the-art deep learning approaches and benchmark datasets. This manuscript reports our efforts on developing a framework to explore the fine-resolution relationship between surface water pollution and LULC. To address the cost of computing time and limitations of well-labelled datasets, we employ a foundation model-enhanced approach for water extraction and water-surrounded LULC classification. We propose an estimator of surface water pollution susceptibility to main pollutants based on the surrounding LULCs. Selecting the Future City of Beijing as the study area, based on very-high-resolution remote sensing images, the experiment proved that our proposed approach could effectively map the susceptibility of surface water pollution caused by its surrounding land use and land cover. To our knowledge, the relationship of LULCs and water quality have not been investigated using 0.5m spatial resolution data. We hope our work can provide a prospective fine-detailed water quality analysis in the community of water environment of remote sensing.

Anomaly classification based on self-supervised learning and its application

BackgroundAnomaly classification remains a challenging task in computer vision applications across diverse practical fields such as industrial detection and security check. The purpose of this study is to develop a new anomaly classification method based on self-supervised learning (Self-ACM), which is expected to enhance anomaly classification accuracy significantly. MethodsThe feature maps of images were abstracted using VGG16 model pre-trained on ImageNet, which were then fed into the innovative model to capture the normality distribution within the dataset domain. Then, a self-supervised adversarial anomaly classification learning framework was proposed to facilitate the acquisition of a higher-level semantic representations for improved anomaly detection. Thirdly, we collected and constructed a novel terahertz (THZ) dataset, which serves as a pioneering resource for benchmarking anomaly classification tasks in the field. ResultsThrough a series of rigorous experiments, our findings unequivocally demonstrate the following key insights: Firstly, harnessing feature maps as input data yielded a significant enhancement in anomaly detection performance, underscoring the effectiveness of this approach. Secondly, the integration of self-supervision enriched the dataset with invaluable information, empowering both the discriminator and generator to acquire superior feature representations. The culmination of these advancements is our novel method achieving unparalleled state-of-the-art performance across multiple benchmark datasets. This breakthrough underscores the transformative impact of our approach on anomaly detection methodologies, solidifying its position as a pioneering solution in the field. ConclusionThe Self-ACM strategy not only advances anomaly detection methodologies but also offers a remarkable contribution to dataset creation, setting a new standard for anomaly classification research.

Novel CAD Diagnosis Method Based on Search, PCA, and AdaBoostM1 Techniques

Background: Cardiovascular diseases (CVDs) are the primary cause of mortality worldwide, resulting in a growing number of annual fatalities. Coronary artery disease (CAD) is one of the basic types of CVDs, and early diagnosis of CAD is crucial for convenient treatment and decreasing mortality rates. In the literature, several studies use many features for CAD diagnosis. However, due to the large number of features used in these studies, the possibility of early diagnosis is reduced. Methods: For this reason, in this study, a new method that uses only five features—age, hypertension, typical chest pain, t-wave inversion, and region with regional wall motion abnormality—and is a combination of eight different search techniques, principal component analysis (PCA), and the AdaBoostM1 algorithm has been proposed for early and accurate CAD diagnosis. Results: The proposed method is devised and tested on a benchmark dataset called Z-Alizadeh Sani. The performance of the proposed method is tested with a variety of metrics and compared with basic machine-learning techniques and the existing studies in the literature. The experimental results have shown that the proposed method is efficient and achieves the best classification performance, with an accuracy of 91.8%, ever reported on the Z-Alizadeh Sani dataset with so few features. Conclusions: As a result, medical practitioners can utilize the proposed approach for diagnosing CAD early and accurately.

G2D-Net: Efficient Dehazing with Second-Order Gated Units

Image dehazing aims to reconstruct potentially clear images from corresponding images corrupted by haze. With the rapid development of deep learning-related technologies, dehazing methods based on deep convolutional neural networks have gradually become mainstream. We note that existing dehazing methods often accompany an increase in computational overhead while improving the performance of dehazing. We propose a novel lightweight dehazing neural network to balance performance and efficiency: the g2D-Net. The g2D-Net borrows the design ideas of input-adaptive and long-range information interaction from Vision Transformers and introduces two kinds of convolutional blocks, i.e., the g2D Block and the FFT-g2D Block. Specifically, the g2D Block is a residual block with second-order gated units, which inherit the input-adaptive property of a gated unit and can realize the second-order interaction of spatial information. The FFT-g2D Block is a variant of the g2D Block, which efficiently extracts the global features of the feature maps through fast Fourier convolution and fuses them with local features. In addition, we employ the SK Fusion layer to improve the cascade fusion layer in a traditional U-Net, thus introducing the channel attention mechanism and dynamically fusing information from different paths. We conducted comparative experiments on five benchmark datasets, and the results demonstrate that the g2D-Net achieves impressive dehazing performance with relatively low complexity.

Distance plus attention for binding affinity prediction

Protein-ligand binding affinity plays a pivotal role in drug development, particularly in identifying potential ligands for target disease-related proteins. Accurate affinity predictions can significantly reduce both the time and cost involved in drug development. However, highly precise affinity prediction remains a research challenge. A key to improve affinity prediction is to capture interactions between proteins and ligands effectively. Existing deep-learning-based computational approaches use 3D grids, 4D tensors, molecular graphs, or proximity-based adjacency matrices, which are either resource-intensive or do not directly represent potential interactions. In this paper, we propose atomic-level distance features and attention mechanisms to capture better specific protein-ligand interactions based on donor-acceptor relations, hydrophobicity, and π\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pi $$\\end{document}-stacking atoms. We argue that distances encompass both short-range direct and long-range indirect interaction effects while attention mechanisms capture levels of interaction effects. On the very well-known CASF-2016 dataset, our proposed method, named Distance plus Attention for Affinity Prediction (DAAP), significantly outperforms existing methods by achieving Correlation Coefficient (R) 0.909, Root Mean Squared Error (RMSE) 0.987, Mean Absolute Error (MAE) 0.745, Standard Deviation (SD) 0.988, and Concordance Index (CI) 0.876. The proposed method also shows substantial improvement, around 2% to 37%, on five other benchmark datasets. The program and data are publicly available on the website https://gitlab.com/mahnewton/daap.Scientific Contribution StatementThis study innovatively introducesdistance-based features to predict protein-ligand binding affinity, capitalizing onunique molecular interactions. Furthermore, the incorporation of protein sequencefeatures of specific residues enhances the model’s proficiency in capturing intricatebinding patterns. The predictive capabilities are further strengthened through theuse of a deep learning architecture with attention mechanisms, and an ensembleapproach, averaging the outputs of five models, is implemented to ensure robustand reliable predictions.

ADVANCED SHOPLIFTING PREVENTION AND ALERT STSYEM

Detecting human beings accurately in a visual surveillance system is crucial for diverse application areas including abnormal event detection, human gait characterization, congestion analysis, person identification, gender classification and fall detection for elderly people. The first step of the detection process is to detect an object which is in motion. Object detection could be performed using YOLOv7, optical flow and spatio-temporal filtering techniques. Once detected, a moving object could be classified as a human being using shape-based, texture-based or motion-based features. A comprehensive review with comparisons on available techniques for detecting human beings in surveillance videos is presented in this paper. The characteristics of few benchmark datasets as well as the future research directions on human detection have also been discussed. We can use camera for Human Motion Detection. The Camera is used to catch the live images of the area in which it is being implemented, if any object is moving. The captured images are stored for further work. If motion is found in this video, the computer will start recording, buzz an alarm and send SMS to people listed in its database. In this way the system will provide the security against any misdeed. Keywords: YOLOv7, SMS, E-mail, CCTV, SMTP, RFID

Music Information Retrieval using Deep Learning Techniques

Music Information Retrieval (MIR) is gaining attention due to the surge in digital music and the need for efficient search and recommendation systems. Traditional MIR methods rely on hand-crafted features and rule-based systems, limiting their adaptability. Deep Learning (DL) shows promise in automatically extracting complex patterns from raw data. This paper offers an extensive overview of MIR tasks like classification, genre recognition, similarity search, and recommendation, along with DL models like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), including LSTM and Transformer architectures, tailored for MIR. Challenges such as data scarcity, computational complexity, and interpretability persist, with proposed solutions like data augmentation, transfer learning, and attention mechanisms. Experimental results on benchmark datasets demonstrate DL's superiority in accuracy, scalability, and robustness over traditional methods. Practical examples highlight DL's potential to revolutionize music search, recommendation, and analysis. Emphasizing the importance of large annotated datasets for training high-quality DL models, strategies for data collection, labeling, and preprocessing are outlined. DL offers promising prospects for advancing MIR by addressing its inherent challenges and establishing new performance benchmarks. Further DL development is expected to drive innovation and enhance digital music consumption experiences through MIR systems. Keywords: Deep Learning, Convolution Neural Network (CNNs), Recurrent Neural Network (RNNs), Digital Music Consumption, Genre recognition.

Dual-path multi-branch feature residual network for salient object detection

Salient object detection involves identifying visually distinctive objects within an image. However, the effective integration of long-range dependencies and local feature information in search for detailed segmentation remains a challenging task for established methods. In this paper, a novel dual-path multi-branch feature residual network (DMFRN) is proposed for salient object detection. First, we design a dual-path encoder consisting of a convolutional network and a lightweight vision transformer, which ensures the capture of local features and long-range dependencies while balancing efficiency. Secondly, a multi-scale feature interaction module (MFIM) is designed to further extract global feature information. It utilizes skip connections to facilitate interaction between multi-scale information, thereby enhancing feature representation. Finally, a multi-branch feature residual module (MFRM) is designed in the decoder to more effectively fuse the features provided by skip connections with the features in the encoder path. The residual structure is employed to refine the features. Our method is compared with competitors on five benchmark datasets, with average MAE and Fmax of 0.042 and 0.893 respectively, both outperforming other competing methods.

A Robust DDoS Intrusion Detection System Using Convolutional Neural Network

In today’s digital age, the proliferation of network-connected devices has triggered a surge in cyberattacks. Distributed Denial-of-Service (DDoS) attacks pose a particularly formidable challenge to network security by disrupting access to vital services. While numerous researchers have proposed DDoS detection methods utilizing machine learning and deep learning techniques, developing a robust and reliable DDoS intrusion detection system remains challenging. This challenge is exacerbated by issues such as highly imbalanced data, multi-classification, and computational complexity. This paper proposes an innovative feature selection approach to create a robust intrusion detection system capable of detecting and classifying recent common DDoS attack types. We evaluate the performance of our model on the CICDDoS2019 benchmark dataset. Our experimental results demonstrate that our proposed model outperforms existing methods, achieving a detection accuracy of 96.82%, a recall of 96.82%, a precision of 96.76%, and an F1 score of 96.50%. Additionally, our model exhibits faster prediction times, with the ability to predict an attack in just 0.189 ms. Notably, our approach, combined with preprocessing and feature selection techniques, outperforms previous works and baseline models in DDoS attack classification.

Multi-Task Learning in Natural Language Processing: An Overview

Deep learning approaches have achieved great success in the field of Natural Language Processing (NLP). However, directly training deep neural models often suffer from overfitting and data scarcity problems that are pervasive in NLP tasks. In recent years, Multi-Task Learning (MTL), which can leverage useful information of related tasks to achieve simultaneous performance improvement on these tasks, has been used to handle these problems. In this paper, we give an overview of the use of MTL in NLP tasks. We first review MTL architectures used in NLP tasks and categorize them into four classes, including parallel architecture, hierarchical architecture, modular architecture, and generative adversarial architecture. Then we present optimization techniques on loss construction, gradient regularization, data sampling, and task scheduling to properly train a multi-task model. After presenting applications of MTL in a variety of NLP tasks, we introduce some benchmark datasets. Finally, we make a conclusion and discuss several possible research directions in this field.

PointGAT: A Quantum Chemical Property Prediction Model Integrating Graph Attention and 3D Geometry.

Predicting quantum chemical properties is a fundamental challenge for computational chemistry. While the development of graph neural networks has advanced molecular representation learning and property prediction, their performance could be further enhanced by incorporating three-dimensional (3D) structural geometry into two-dimensional (2D) molecular graph representation. In this study, we introduce the PointGAT model for quantum molecular property prediction, which integrates 3D molecular coordinates with graph-attention modeling. Comparison with other current models in molecular prediction tasks showed that PointGAT could provide higher predictive accuracy in various benchmark data sets from MoleculeNet, including ESOL, FreeSolv, Lipop, HIV, and 6 out of 12 tasks of the QM9 data set. To further examine PointGAT prediction of quantum mechanical (QM) energies, we constructed a C10 data set comprising 11,841 charged and chiral carbocation intermediates with QM energies calculated at the DM21/6-31G*//B3LYP/6-31G* levels. Notably, PointGAT achieved an R2 value of 0.950 and an MAE of 1.616 kcal/mol, outperforming even the best-performing graph neural network model with a reduction of 0.216 kcal/mol in MAE and an improvement of 0.050 in R2. Additional ablation studies indicated that incorporating molecular geometry into the model resulted in markedly higher predictive accuracy, reducing the MAE value from 1.802 to 1.616 kcal/mol. Moreover, visualization of PointGAT atomic attention weights suggested its predictions were interpretable. Findings in this study support the application of PointGAT as a powerful and versatile tool for quantum chemical property prediction that can facilitate high-accuracy modeling for fundamental exploration of chemical space as well as drug design and molecular engineering.

An Ensemble Learning Framework for Anomaly Detection of Important Geographical Entities

Abstract. Due to the complex landforms and the limited resolution of remote sensing imagery, it is difficult to avoid the problem of incorrectly capturing geographical entities, such as buildings. Therefore, anomaly detection of important geographical entities is of great significance to ensure the authenticity and accuracy of geographical entity data. In this paper, we propose an ensemble learning framework for anomaly detection of geographical entity by aggregating the predicted labels generated by multiple deep learning models. In detail, we explore multiple change detection and semantic segmentation model and fully utilize the advantages of various deep learning neural network architectures. The proposed anomaly detection strategy of buildings has been performed on two benchmark datasets, including WHU Building change detection dataset and LEVIR building change detection dataset, the experimental results prove that the proposed method can achieve a more robust and better performance than using single change detection model in terms of quantitative performance and visual performance.

Semi-supervised PolSAR Image Change Detection using Similarity Matching

Abstract. The lack of precisely labeled data limits the development of supervised polarimetric synthetic aperture radar (PolSAR) image change detection. Therefore, semi-supervised deep learning methods have recently demonstrated their significant capability for PolSAR image change detection. Similarity Matching (SimMatch) improves the performance of semi-supervised learning tasks across different benchmark datasets and different settings. Introducing SimMatch into the field of PolSAR image change detection can improve the performance of semi-supervised PolSAR image change detection under limited labeled data conditions. Usually, semi-supervision solves the problem of insufficient labeled data by generating pseudo-labels. However, when the pseudo-label method is simply applied, the model will fit on the confident but wrong pseudo-labels, resulting in poor performance. SimMatch offers a solution by requiring the strongly augmented view to share the same semantic similarity (i.e. label prediction) and instance characteristics (i.e. similarity between instances) with a weak augmented view for more intrinsic feature matching. Besides, by using a labeled memory buffer, the two similarities can be isomorphically transformed with each other by introducing the aggregating and unfolding techniques. Therefore, the semantic and instance pseudo-labels can be mutually propagated, and then, the detection performance of the PolSAR image change detection is improved. Experimental results on real PolSAR datasets demonstrated that SimMatch is an effective semi-supervised PolSAR change detection method and its performance surpasses some well-known change detection methods. Compared to the fully-supervised algorithm CWNN, the semi-supervised SimMatch algorithm can improve accuracy by up to 14.4%.

AI for clean water: efficient water quality prediction leveraging machine learning

ABSTRACT Water is one of the most critical resources for maintaining life. Although it makes upto 70% of the earth’s surface but only a small amount of it is usable. Since water is used for a variety of functions, its quality must be determined before usage. The rapid increase of the world’s population has also had a significant influence on the environment, particularly on water quality. The quality of water has been deteriorating in recent years due to various pollutants. To control the water pollution, modeling and predicting the water quality has become a crucial need. In this work, we propose a machine learning (ML)-based model to predict and classify the water quality. The results from six different ML models are analyzed for accuracy, precision, recall, and F1 score as performance measures. The proposed approach is validated using benchmark dataset. The results show that Decision Tree ML model has a distinct superiority on other classifiers in terms of performance indicators like accuracy of 97.53%, precision of 87.66%, recall of 74.59%, and F1-score of 80.60%. This will help the aquatic system for better water quality analysis.