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Hyperspectral imaging is a highly advanced and sophisticated method for capturing images in hundreds of narrow, contiguous spectral bands. However, processing and analyzing such large amounts of data are challenging. Deep learning algorithms, especially those based on convolutional neural networks (CNNs), effectively extract rich feature representations from complex datasets, such as hyperspectral images (HSIs). These representations capture high-level patterns and characteristics that can facilitate accurate classification. The success of this approach has led to its widespread use in various remote sensing applications. However, the standard CNN inputs and outputs are scalars, ignoring the relative position relationships between features. In this paper, we propose a hierarchical capsule network for HSI classification. This network incorporates a multi-level convolutional structure for feature extraction and fusion. It utilizes convolutional feature maps of various depths to generate initial capsules, followed by vector computation using capsule neurons and a weight matrix to encode spatial location relationships among features. Furthermore, the shallow convolution of the hierarchical capsule network is pre-trained based on transfer learning to further improve the performance of HSI classification. According to experimental results, the proposed method for hyperspectral image classification has been found to outperform other state-of-the-art deep learning models on four benchmark datasets. [ABSTRACT FROM AUTHOR]

This paper explores the application of a machine learning approach to predict equipment failure rates in power distribution networks, motivated by the significant impact of power outages on citizens' daily lives and the economy. In this research, data on equipment failure rates and maintenance records were collected from power distribution networks in Baghdad, Iraq. The collected data underwent preprocessing, and features were extracted to train Adaptive NeuroFuzzy Inference System (ANFIS) and Periodic Autoregressive Moving Average (PARMA) time series models. To initiate the project, information regarding blackouts that occurred between January 2018 and December 2021 was retrieved from the database. The RMSE index results for the PARMA time series and ANFIS model are 3.518 and 2.264, respectively, demonstrating the superior performance of the ANFIS model in predicting equipment failure rates and its potential for future predictions. This study highlights the ANFIS model's capacity to anticipate equipment failure rates, potentially enhancing maintenance efficiency and reducing power outages in Baghdad. The error mean square was employed to evaluate the proposed models' error rate. [ABSTRACT FROM AUTHOR]

This paper studied music feature recognition and classification. First, the common signal features were analyzed, and the signal pre-processing method was introduced. Then, the Mel–Phon coefficient (MPC) was proposed as a feature for subsequent recognition and classification. The deep belief network (DBN) model was applied and improved by the gray wolf optimization (GWO) algorithm to get the GWO–DBN model. The experiments were conducted on GTZAN and free music archive (FMA) datasets. It was found that the best hidden-layer structure of DBN was 1440-960-480-300. Compared with machine learning methods such as decision trees, the DBN model had better classification performance in recognizing and classifying music types. The classification accuracy of the GWO–DBN model reached 75.67%. The experimental results demonstrate the reliability of the GWO–DBN model. The GWO–DBN model can be further promoted and applied in actual music research. [ABSTRACT FROM AUTHOR]

The advancement of ultra-hyperspectral imaging technology, exemplified by the AisaIBIS sensor, has enabled a leap from hyperspectral data (hundreds of bands) to ultra-hyperspectral data (thousands of bands). It provides immense potential for precise ground object recognition within intricate scenes. However, the complexities inherent to features of the ground objects, coupled with the copious redundant information within the ultra-hyperspectral data, pose substantial challenges for accurate object recognition. Therefore, this paper proposed a comprehensive framework to explore the optimal precise classification strategy of ultra-hyperspectral data in complex scenes (12 vegetation and non-vegetation classes). (a) Our investigation delves into the influence of diverse feature subsets and a range of machine learning classifiers on the precision of ground objects recognition. The proposed strategy is up to an overall accuracy of 88.44%, effectively avoiding the curse of dimension, and significantly enhancing the capability to recognize the complex ground objects. (b) Furthermore, based on the simulation of hyperspectral images with different spectral resolutions, we compared the classification results of ultra-hyperspectral data (0.11 nm) and the hyperspectral datasets (10 nm, 5 nm, and 1 nm) by machine learning methods. Compared with the hyperspectral datasets, ultra-hyperspectral data improved the classification accuracy by 5.30–6.38%. This substantiates the pronounced advantages of ultra-hyperspectral data in precision land cover classification. This study provides a valuable reference for the application of ultra-hyperspectral data in recognition of complex ground objects scenes, and urban accurate monitoring. [ABSTRACT FROM AUTHOR]

In this paper, laser-induced breakdown spectroscopy (LIBS) combined with a probabilistic neural network (PNN) was applied to classify engineering structural metal samples (valve stem, welding material, and base metal). Additionally, utilizing data from the plasma emission spectrum generated by laser ablation of samples with different aging times, an aging time prediction model based on a firefly optimized probabilistic neural network (FA-PNN) was established, which can effectively evaluate the service performance of structural materials. The problem of insufficient features obtained by principal component analysis (PCA) for predicting the aging time of materials is addressed by the proposal of a time-frequency feature extraction method based on short-time Fourier transform (STFT). The classification accuracy (ACC) of time-frequency features and principal component features was compared under PNN. The results indicate that, in comparison to the PCA feature extraction approach, the time-frequency feature extraction method based on STFT demonstrates higher accuracy in predicting the time of aging materials. Then, the relationship between classification accuracy (ACC) and settings of PNN was discussed. The ACC of the PNN model for both the material classification test set and the aging time test set achieved 100% with Firefly (FA) optimization algorithms. This result was also compared with the ACC of ANN, KNN, PLS-DA, and SIMCA for the aging time test set (95%, 87.5%, 85%, and 62.5%, respectively). The experimental results demonstrated that the classification model using LIBS combined with FA-PNN could realize better classification accuracy. [ABSTRACT FROM AUTHOR]

Prediction of conformational B-cell epitopes (CBCE) is an essential phase for vaccine design, drug invention, and accurate disease diagnosis. Many laboratorial and computational approaches have been developed to predict CBCE. However, laboratorial experiments are costly and time consuming, leading to the popularity of Machine Learning (ML)-based computational methods. Although ML methods have succeeded in many domains, achieving higher accuracy in CBCE prediction remains a challenge. To overcome this drawback and consider the limitations of ML methods, this paper proposes a novel DL-based framework for CBCE prediction, leveraging the capabilities of deep learning in the medical domain. The proposed model is named Deep Learning-based Temporal Convolutional Neural Network (DL-TCNN), which hybridizes empirical hyper-tuned 1D-CNN and TCN. TCN is an architecture that employs causal convolutions and dilations, adapting well to sequential input with extensive receptive fields. To train the proposed model, physicochemical features are firstly extracted from antigen sequences. Next, the Synthetic Minority Oversampling Technique (SMOTE) is applied to address the class imbalance problem. Finally, the proposed DL-TCNN is employed for the prediction of CBCE. The model's performance is evaluated and validated on a benchmark antigen–antibody dataset. The DL-TCNN achieves 94.44% accuracy, and 0.989 AUC score for the training dataset, 78.53% accuracy, and 0.661 AUC score for the validation dataset; and 85.10% accuracy, 0.855 AUC score for the testing dataset. The proposed model outperforms all the existing CBCE methods. [ABSTRACT FROM AUTHOR]

Drug discovery relies on predicting drug-target interaction (DTI), which is an important challenging task. The purpose of DTI is to identify the interaction between drug chemical compounds and protein targets. Traditional wet lab experiments are time-consuming and expensive, that's why in recent years, the use of computational methods based on machine learning has attracted the attention of many researchers. Actually, a dry lab environment focusing more on computational methods of interaction prediction can be helpful in limiting search space for wet lab experiments. In this paper, a novel multi-stage approach for DTI is proposed that called SRX-DTI. In the first stage, combination of various descriptors from protein sequences, and a FP2 fingerprint that is encoded from drug are extracted as feature vectors. A major challenge in this application is the imbalanced data due to the lack of known interactions, in this regard, in the second stage, the One-SVM-US technique is proposed to deal with this problem. Next, the FFS-RF algorithm, a forward feature selection algorithm, coupled with a random forest (RF) classifier is developed to maximize the predictive performance. This feature selection algorithm removes irrelevant features to obtain optimal features. Finally, balanced dataset with optimal features is given to the XGBoost classifier to identify DTIs. The experimental results demonstrate that our proposed approach SRX-DTI achieves higher performance than other existing methods in predicting DTIs. The datasets and source code are available at: https://github.com/Khojasteh-hb/SRX-DTI. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the detection and localization of radioactive sources by exploiting supervised machine learning. Machine learning is utilized in a wide variety of applications due to its effectiveness in prediction and autonomous decision-making. However, applying machine learning would only be effective when representative features for the application can be acquired, through which learning algorithms can be trained. Hence, first, we present a feature extraction technique for radioactive source localization, and then propose a parameter estimation method via machine learning. A distributed sensor network is employed to assist in estimating the radioactive source's location and intensity. We propose a feature extraction method that evaluates a feature vector using the reading and position of each sensor located in a region where a radiation source is detected. The feature extraction is based on a data fusion process, where a single feature value is provided to represent both the reading value and position coordinates corresponding to a given sensor. After the feature extraction, we apply the decision tree machine learning method for regression to localize a radioactive source. To examine the effectiveness of the proposed work, a performance comparison is carried out with recent existing methods in terms of the estimation accuracy and the execution time. Experimental results show that the proposed algorithm provides accurate source intensity estimation and achieves a good compromise between localization accuracy and execution time. [ABSTRACT FROM AUTHOR]

Quality monitoring is an essential element of defect detection in semiconductor manufacturing processes, but semiconductor companies use virtual metrology (VM) in addition to actual metrology to prevent productivity degradation due to the time and costs required to obtain measurements. Past VM studies aimed to predict average wafer measurement values via equipment sensor data and focused on achieving improved predictive performance by selecting or extracting important variables among high-dimensional variables such as equipment sensor data. However, the management of wafer chip quality requires not only average measurement values but also measurement value predictions for chips located at the edges, which are vulnerable to defects. In this paper, we therefore propose a graph attention (GAT) network-based VM model that predicts the measurement values of chips located at wafer edges by constructing graph data with measurement data (i.e., the measurement location information in wafers and the measurement values). To verify the performance of the proposed model, we conduct a comparative experiment with conventional machine learning methods. The experimental results show that the proposed VM model contributes to a predictive performance improvement in terms of the measurement values of chips located at wafer edges. [ABSTRACT FROM AUTHOR]

Microarray gene expression-based detection and classification of medical conditions have been prominent in research studies over the past few decades. However, extracting relevant data from the high-volume microarray gene expression with inherent nonlinearity and inseparable noise components raises significant challenges during data classification and disease detection. The dataset used for the research is the Lung Harvard 2 Dataset (LH2) which consists of 150 Adenocarcinoma subjects and 31 Mesothelioma subjects. The paper proposes a two-level strategy involving feature extraction and selection methods before the classification step. The feature extraction step utilizes Short Term Fourier Transform (STFT), and the feature selection step employs Particle Swarm Optimization (PSO) and Harmonic Search (HS) metaheuristic methods. The classifiers employed are Nonlinear Regression, Gaussian Mixture Model, Softmax Discriminant, Naive Bayes, SVM (Linear), SVM (Polynomial), and SVM (RBF). The two-level extracted relevant features are compared with raw data classification results, including Convolutional Neural Network (CNN) methodology. Among the methods, STFT with PSO feature selection and SVM (RBF) classifier produced the highest accuracy of 94.47%. [ABSTRACT FROM AUTHOR]

Surface Water Bodies (SWB) are a renewable water source crucial for maintaining ecosystems and the water cycle. The declining rate of SWB increases owing to the overutilization of these resources, especially for agriculture. A timely and accurate Surface Water Body Extraction (SWBE) is necessary for water resource conservation and planning. Recently, Deep Learning (DL), a subset of Machine Learning (ML) algorithm, got remarkable attention in SWBE. It learns inherent features directly from the images at the expense of time and data. But, the ML algorithms such as K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGB) use optimal hand-crafted features to produce better results with fewer data and time. In this paper, SWBE is performed through two steps: (1) Use of spectral indices and Gabor filters for obtaining Pixel Level Feature (PLF) maps from the multispectral image; (2) Prediction of water and non-water pixels based on PLF maps using the KNN, DT, RF, SVM, and XGB classifiers. The proposed framework has experimented with Resoucesat-2 imagery over major reservoirs in Tamil Nadu and India. The results show that the proposed PLF + XGB outperforms in accuracy, recall, F1-score, kappa, False Negative Rate, Mathews Correlation Coefficient, and mean Intersection over Union with the metric value of 0.995, 0.990, 0.983, 0.979, 0.009, 0.979, and 0.969 with other existing and proposed models. Also, the surface water extent of Bhavani Sagar and Sathanur reservoirs is predicted for 4 years (2016–2019) and the causes of surface water dynamics were analyzed. [ABSTRACT FROM AUTHOR]

Pain management is a crucial concern in medicine, particularly in the case of children who may struggle to effectively communicate their pain. Despite the longstanding reliance on various assessment scales by medical professionals, these tools have shown limitations and subjectivity. In this paper, we present a pain assessment scheme based on skin potential signals, aiming to convert subjective pain into objective indicators for pain identification using machine learning methods. We have designed and implemented a portable non-invasive measurement device to measure skin potential signals and conducted experiments involving 623 subjects. From the experimental data, we selected 358 valid records, which were then divided into 218 silent samples and 262 pain samples. A total of 38 features were extracted from each sample, with seven features displaying superior performance in pain identification. Employing three classification algorithms, we found that the random forest algorithm achieved the highest accuracy, reaching 70.63%. While this identification rate shows promise for clinical applications, it is important to note that our results differ from state-of-the-art research, which achieved a recognition rate of 81.5%. This discrepancy arises from the fact that our pain stimuli were induced by clinical operations, making it challenging to precisely control the stimulus intensity when compared to electrical or thermal stimuli. Despite this limitation, our pain assessment scheme demonstrates significant potential in providing objective pain identification in clinical settings. Further research and refinement of the proposed approach may lead to even more accurate and reliable pain management techniques in the future. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy (DR) is a diabetic complication that predisposes patients to visual impairments that could lead to blindness. Lesion segmentation using deep learning algorithms is an effective measure to screen and prevent early DR. However, there are several types of DR with varying sizes and high inter-class similarity, making segmentation difficult. In this paper, we propose a supervised segmentation method (MSLF-Net) based on multi-scale–multi-level feature fusion to achieve accurate end-to-end DR lesion segmentation. MSLF-Net builds a Multi-Scale Feature Extraction (MSFE) module to extract multi-scale information and provide more comprehensive features for segmentation. This paper further introduces the Multi-Level Feature Fusion (MLFF) module to improve feature fusion using a cross-layer structure. This structure only fuses low- and high-level features of the same class based on category supervision, avoiding feature contamination. Moreover, this paper produces additional masked images for the dataset and performs image enhancement operations to ensure that the proposed method is trainable and functional on small datasets. The extensive experiments are conducted on public datasets IDRID and e_ophtha. The results showed that our proposed feature enhancement method can perform feature fusion more effectively. Therefore, In the end-to-end DR segmentation neural network model, MSLF Net is superior to other similar models in segmentation, and can effectively improve the DR lesion segmentation performance. [ABSTRACT FROM AUTHOR]

Due to the completely open-source nature of Android, the exploitable vulnerability of malware attacks is increasing. To stay ahead of other similar review work attempting to deal with the serious security problem of the Android environment, this work not only summarizes the approaches in the malware classification phase but also lays emphasis on the Android feature selection algorithm and presents some areas neglected in previous works in the field of Android malware detection, like limitations and commonly applied datasets in machine learning- based models. In this paper, the Android OS environment, feature selection, classification models, and confronted challenges of machine learning detection are described in detail. Based on the brief introduction to Android background knowledge, feature selection methods are elaborated from key perspectives as feature extraction, raw data preprocessing, valid feature subsets selection, and machine learning-based selection models. For the algorithms of the malware classification, machine learning methods are categorized according to different standards to present an all-around view. Furthermore, this paper focuses on the study of deterioration problems and evasion attacks in machine learning detectors. [ABSTRACT FROM AUTHOR]

Gun violence has been on the rise in recent years. To help curb the downward spiral of this negative influence in communities, machine learning strategies on gunshot detection can be developed and deployed. After outlining the procedure by which a typical type of gunshot-like sounds were measured, this paper focuses on the analysis of feature importance pertaining to gunshot and gunshot-like sounds. The random forest mean decrease in impurity and the SHapley Additive exPlanations feature importance analysis were employed for this task. From the feature importance analysis, feature reduction was then carried out. Via the Mel-frequency cepstral coefficients feature extraction process on 1-sec audio clips, these extracted features were then reduced to a more manageable quantity using the above-mentioned feature reduction processes. These reduced features were sent to a random forest classifier. The SHapley Additive exPlanations feature importance output was compared to that of the mean decrease in impurity feature importance. The results show what Mel-frequency cepstral coefficients features are important in discriminating gunshot sounds and various gunshot-like sounds. Together with the feature importance/reduction processes, the recent uniform manifold approximation and projection method was used to compare the closeness of various gunshot-like sounds to gunshot sounds in the feature space. Finally, the approach presented in this paper provides people with a viable means to make gunshot sounds more discernible from other sounds. [ABSTRACT FROM AUTHOR]

Machine learning techniques aim to mimic the human ability to automatically learn how to perform tasks through training examples. They have proven capable of tasks such as prediction, learning and adaptation based on experience and can be used in virtually any scientific application, ranging from biomedical, robotic, to business decision applications, and others. However, the lack of domain knowledge for a particular application can make feature extraction ineffective or even unattainable. Furthermore, even in the presence of pre-processed datasets, the iterative process of optimizing Machine Learning parameters, which do not translate from one domain to another, maybe difficult for inexperienced practitioners. To address these issues, we present in this paper a Vectorized Automated ML Pre-processIng and post-pRocEssing framework, approximately named (VAMPIRE), which implements feature extraction algorithms capable of converting large time-series recordings into datasets. Also, it introduces a new concept, the Activation Engine, which is attached to the output of a Multi Layer Perceptron and extracts the optimal threshold to apply binary classification. Moreover, a tree-based algorithm is used to achieve multi-class classification using the Activation Engine. Furthermore, the internet of things gives rise to new applications such as remote sensing and communications, so consequently applying Machine Learning to improve operation accuracy, latency, and reliability is beneficial in such systems. Therefore, all classifications in this paper were performed on the edge in order to reach high accuracy with limited resources. Moreover, forecasts were applied on three unrelated biomedical datasets, and on two other pre-processed urban and activity detection datasets. Features were extracted when required, and training and testing were performed on the Raspberry Pi remotely, where high accuracy and inference speed were achieved in every experiment. Additionally, the board remained competitive in terms of power consumption when compared with a laptop which was optimized using a Graphical Processing Unit. [ABSTRACT FROM AUTHOR]

With the gradual popularization of autonomous driving technology, how to obtain traffic sign information efficiently and accurately is very important for subsequent decision-making and planning tasks. Traffic sign detection and recognition (TSDR) algorithms include color-based, shape-based, and machine learning based. However, the algorithms mentioned above are insufficient for traffic sign detection tasks in complex environments. In this paper, we propose a traffic sign detection and recognition paradigm based on deep learning algorithms. First, to solve the problem of insufficient spatial information in high-level features of small traffic signs, the parallel deformable convolution module (PDCM) is proposed in this paper. PDCM adaptively acquires the corresponding receptive field preserving the integrity of the abstract information through symmetrical branches thereby improving the feature extraction capability. Simultaneously, we propose sub-pixel convolution attention module (SCAM) based on the attention mechanism to alleviate the influence of scale distribution. Distinguishing itself from other feature fusion, our proposed method can better focus on the information of scale distribution through the attention module. Eventually, we introduce GSConv to further reduce the computational complexity of our proposed algorithm, better satisfying industrial application. Experimental results demonstrate that our proposed methods can effectively improve performance, both in detection accuracy and mAP@0.5. Specifically, when the proposed PDCM, SCAM, and GSConv are applied to the Yolov5, it achieves 89.2% mAP@0.5 in TT100K, which exceeds the benchmark network by 4.9%. [ABSTRACT FROM AUTHOR]

Recommendation algorithms save a lot of valuable time for people to get the information they are interested in. However, the feature calculation and extraction process of each machine learning or deep learning recommendation algorithm are different, so how to obtain various features with different dimensions, i.e., how to integrate the advantages of each model and improve the model inference efficiency, becomes the focus of this paper. In this paper, a better deep learning model is obtained by integrating several cutting-edge deep learning models. Meanwhile, to make the integrated learning model converge better and faster, the parameters of the integrated module are initialized, constraints are imposed, and a new activation function is designed for better integration of the sub-models. Finally, the integrated large model is distilled for knowledge distillation, which greatly reduces the number of model parameters and improves the model inference efficiency. [ABSTRACT FROM AUTHOR]

The terrain-relative navigation (TRN) method is often used in entry, descent and landing (EDL) systems for position estimation and navigation of spacecraft. In contrast to the crater detection method, the image patch matching method does not depend on the integrity of the database and the saliency of the crater features. However, there are four difficulties associated with lunar images: illumination transformation, perspective transformation, resolution mismatch, and the lack of texture. Deep learning offers possible solutions. In this paper, an L2-normed attention and multi-scale fusion network (L2AMF-Net) was proposed for patch descriptor learning to effectively overcome the above four difficulties and achieve lunar image patch matching accurately and robustly. On the one hand, an L2-Attention unit (LAU) was proposed to generate attention score maps in spatial and channel dimensions and enhance feature extraction. On the other hand, a multi-scale feature self and fusion enhance structure (SFES) was proposed to fuse multi-scale features and enhance the feature representations. L2AMF-Net achieved a 95.57% matching accuracy and excellent performance compared with several other methods in lunar image patch dataset generated in this paper. Experiments verified the illumination, perspective and texture robustness of L2AMF-Net and the validity of the attention module and feature fusion structure. [ABSTRACT FROM AUTHOR]

Nowadays, the verification of handwritten signatures has become an effective research field in computer vision as well as machine learning. Signature verification is naturally formulated as a machine-learning task. This task is performed by determining if the signature is genuine or forged. Therefore, it is considered a two‐class classification issue. Since handwritten signatures are widely used in legal documents and financial transactions, it is important for researchers to select an efficient machine-learning technique for verifying these signatures and to avoid forgeries that may cause many losses to customers. So far, great outcomes have been obtained when using machine learning techniques in terms of equal error rates and calculations. This paper presents a comprehensive review of the latest studies and results in the last 10 years in the field of online and offline handwritten signature verification. More than 20 research papers were used to make a comparison between datasets, feature extraction, and classification techniques used in each system, taking into consideration the problems that occur in each. In addition, the general limitations and advantages of machine-learning techniques that are used to classify or extract signature features were summarized in the form of a table. We also present the general steps of the verification system and a list of the most considerable datasets available in online and offline fields. [ABSTRACT FROM AUTHOR]

Purpose: During the current pandemic scientists, researchers, and health professionals across the globe are in search of new technological methods for tackling COVID-19. The magnificent performance reported by machine learning and deep learning methods in the previous epidemic has encouraged researchers to develop systems with these methods to diagnose COVID-19. Methods: In this paper, an ensemble-based multi-level voting model is proposed to diagnose COVID-19 from chest x-rays. The multi-level voting model proposed in this paper is built using four machine learning algorithms namely Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM) with a linear kernel, and K-Nearest Neighbor (KNN). These algorithms are trained with features extracted using the ResNet50 deep learning model before merging them to form the voting model. In this work, voting is performed at two levels, at level 1 these four algorithms are grouped into 2 sets consisting of two algorithms each (set 1 — SVM with linear kernel and LR and set 2 — RF and KNN) and intra set hard voting is performed. At level 2 these two sets are merged using hard voting to form the proposed model. Results: The proposed multilevel voting model outperformed all the machine learning algorithms, pre-trained models, and other proposed works with an accuracy of 100% and specificity of 100%. Conclusion: The proposed model helps for the faster diagnosis of COVID-19 across the globe. [ABSTRACT FROM AUTHOR]

With the continuous development of digital technology, music, as an important form of media, and its digital audio technology is also constantly developing, forcing the traditional music industry to start the road of digital transformation. What kind of method can be used to automatically retrieve music information effectively and quickly in vocal singing has become one of the current research topics that has attracted much attention. Aiming at this problem, it is of great research significance for the field of timbre feature recognition. With the in-depth research on timbre feature recognition, the research on timbre feature extraction by machine learning in vocal singing has also been gradually carried out, and its performance advantages are of great significance to solve the problem of automatic retrieval of music information. This paper aims to study the application of feature extraction algorithm based on machine learning in timbre feature extraction in vocal singing. Through the analysis and research of machine learning and feature extraction methods, it can be applied to the construction of timbre feature extraction algorithms to solve the problem of automatic retrieval of music information. This paper analyzed vocal singing, machine learning, and feature extraction, experimentally analyzed the performance of the method, and used related theoretical formulas to explain. The results have showed that the method for timbre feature extraction in the vocal singing environment was more accurate than the traditional method, the difference between the two was 24.27%, and the proportion of satisfied users was increased by 33%. It can be seen that this method can meet the needs of users for timbre feature extraction in the use of music software, and the work efficiency and user satisfaction are greatly improved. [ABSTRACT FROM AUTHOR]

The synthesis between face sketches and face photos has important application values in law enforcement and digital entertainment. In cases of a lack of paired sketch-photo data, this paper proposes an unsupervised model to solve the problems of missing key facial details and a lack of realism in the synthesized images of existing methods. The model is built on the CycleGAN architecture. To retain more semantic information in the target domain, a multi-scale feature extraction module is inserted before the generator. In addition, the convolutional block attention module is introduced into the generator to enhance the ability of the model to extract important feature information. Via CBAM, the model improves the quality of the converted image and reduces the artifacts caused by image background interference. Next, in order to preserve more identity information in the generated photo, this paper constructs the multi-level cycle consistency loss function. Qualitative experiments on CUFS and CUFSF public datasets show that the facial details and edge structures synthesized by our model are clearer and more realistic. Meanwhile the performance indexes of structural similarity and peak signal-to-noise ratio in quantitative experiments are also significantly improved compared with other methods. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs), commonly known as drones, have been progressively prevalent due to their capability to operate quickly and their vast range of applications in a variety of real-world circumstances. The utilization of UAVs in precision farming has lately gained a lot of attention from the scientific community. This study addresses with the assistance of drones in the precision agricultural area. This paper makes significant contributions by analyzing communication protocols and applying them to the challenge of commanding a fleet of drones to protect crops from parasite infestations. In this research, the effectiveness of nine powerful deep neural network models is measured for the detection of plant diseases using diverse methodologies. These deep neural networks are adapted to the immediate situation using transfer learning and deep extraction of features approaches. The presented study takes into account the used pretrained deep learning model for extracting features and fine-tuning. The deep feature extraction characteristics are subsequently categorized using support vector machines (SVMs) and extreme learning machines (ELMs). For measuring performance, the precision, sensitivities, specific, and F1-score are all evaluated. Deep feature extraction and SVM/ELM classification generated better outcomes than transfer learning, according to the analysis result. Furthermore, the analysis of the various methodologies tries to assess their effectiveness and costs. The different approaches, for example, confront difficulties such as investigating the region in the shortest possible time feasible, while eliminating the same region being searched by more drones, detecting parasites, and stopping their spread by applying the appropriate number of pesticides. Simulation models are a significant aid to researchers in conducting to evaluate these technologies and creating specific tactics and coordinating procedures capable of effectively supporting farms and achieving the aim. The main objective of this paper is to compare the search techniques of two distinct methods of parasitic to identify performance. [ABSTRACT FROM AUTHOR]

In the field of mechanical and electrical equipment, the motor rolling bearing is a workpiece that is extremely prone to damage and failure. However, the traditional fault diagnosis methods cannot keep up with the development pace of the times because they need complex manual pretreatment or the support of specific expert experience and knowledge. As a rising star, the data-driven fault diagnosis methods are increasingly favored by scholars and experts at home and abroad. The convolutional neural network has been widely used because of its powerful feature extraction ability for all kinds of complex information and its outstanding research results in image processing, target tracking, target diagnosis, time-frequency analysis, and other scenes. Therefore, this paper introduces a convolutional neural network and applies it to motor-bearing fault diagnosis. Aiming at the shortcomings of fault signal and convolutional neural network, a large-scale maximum pooling strategy is proposed and optimized by wavelet transform to improve the fault diagnosis efficiency of motor bearing under high-voltage operation. Compared with other machine learning algorithms, the convolution neural network fault diagnosis model constructed in this paper not only has high accuracy (up to 0.9871) and low error (only 0.032) but also is simple to use. It provides a new way for motor bearing fault diagnosis and has very important economic and social value. [ABSTRACT FROM AUTHOR]

Facial expression recognition based on residual networks is important for technologies related to space human-robot interaction and collaboration but suffers from low accuracy and slow computation in complex network structures. To solve these problems, this paper proposes a multiscale feature fusion attention lightweight wide residual network. The network first uses an improved random erasing method to preprocess facial expression images, which improves the generalizability of the model. The use of a modified depthwise separable convolution in the feature extraction network reduces the computational effort associated with the network parameters and enhances the characterization of the extracted features through a channel shuffle operation. Then, an improved bottleneck block is used to reduce the dimensionality of the upper layer network feature map to further reduce the number of network parameters while enhancing the network feature extraction capability. Finally, an optimized multiscale feature lightweight attention mechanism module is embedded to further improve the feature extractability of the network for human facial expressions. The experimental results show that the accuracy of the model is 73.21%, 98.72%, and 95.21% on FER2013, CK+ and JAFFE, respectively, with a covariance of 10.14 M. Compared with other networks, the model proposed in this paper has faster computing speed and better accuracy at the same time. [ABSTRACT FROM AUTHOR]

This paper proposed a hybrid approach for the identification of encrypted traffic based on advanced mathematical modeling and computational intelligence. Network traffic identification is the premise and foundation of improving network management, service quality, and application security. It is also the focus of network behavior analysis, network planning and construction, network anomaly detection, and network traffic model research. With the increase in user and service requirements, many applications use encryption algorithms to encrypt traffic during data transmission. As a result, traditional traffic classification methods classify encrypted traffic on the network, which brings great difficulties and challenges to network monitoring and data mining. In our article, a nonlinear modified DBN method is proposed and applied to encrypted traffic identification. Firstly, based on Deep Belief Networks (DBN), this paper introduces the proposed Eodified Elliott (ME)-DBN model, analyzes the function image, and presents the ME-DBN learning algorithm. Secondly, this article designs an encrypted traffic recognition model based on the ME-DBN model. Feature extraction is carried out by training the ME-DBN model, and finally, classification and recognition are carried out by the classifier. The experimental results on the ISCX VPN-non-VPN database show that the MEDBN method proposed in this article can enhance the classification and recognition rate and has better robustness to encrypt traffic recognition from different software. [ABSTRACT FROM AUTHOR]

This paper attacks the challenging problem of video retrieval by text. In such a retrieval paradigm, an end user searches for unlabeled videos by ad-hoc queries described exclusively in the form of a natural-language sentence, with no visual example provided. Given videos as sequences of frames and queries as sequences of words, an effective sequence-to-sequence cross-modal matching is crucial. To that end, the two modalities need to be first encoded into real-valued vectors and then projected into a common space. In this paper we achieve this by proposing a dual deep encoding network that encodes videos and queries into powerful dense representations of their own. Our novelty is two-fold. First, different from prior art that resorts to a specific single-level encoder, the proposed network performs multi-level encoding that represents the rich content of both modalities in a coarse-to-fine fashion. Second, different from a conventional common space learning algorithm which is either concept based or latent space based, we introduce hybrid space learning which combines the high performance of the latent space and the good interpretability of the concept space. Dual encoding is conceptually simple, practically effective and end-to-end trained with hybrid space learning. Extensive experiments on four challenging video datasets show the viability of the new method. Code and data are available at https://github.com/danieljf24/hybrid_space. [ABSTRACT FROM AUTHOR]

To solve the information explosion problem and enhance user experience in various online applications, recommender systems have been developed to model users’ preferences. Although numerous efforts have been made toward more personalized recommendations, recommender systems still suffer from several challenges, such as data sparsity and cold-start problems. In recent years, generating recommendations with the knowledge graph as side information has attracted considerable interest. Such an approach can not only alleviate the above mentioned issues for a more accurate recommendation, but also provide explanations for recommended items. In this paper, we conduct a systematical survey of knowledge graph-based recommender systems. We collect recently published papers in this field, and group them into three categories, i.e., embedding-based methods, connection-based methods, and propagation-based methods. Also, we further subdivide each category according to the characteristics of these approaches. Moreover, we investigate the proposed algorithms by focusing on how the papers utilize the knowledge graph for accurate and explainable recommendation. Finally, we propose several potential research directions in this field. [ABSTRACT FROM AUTHOR]

To improve the effect of landslide feature extraction, this paper improves the remote sensing image recognition algorithm with the support of a machine learning algorithm. Moreover, this paper combines UAV remote sensing images to extract landslide features, classifies and introduces the evaluation criteria for target detection and several representative target detectors. This paper also constructs the functional structure of the system according to the landslide feature extraction requirements and designs a set of optimization schemes for landslide feature data collection and control measurement suitable for field operations. In addition, this paper analyses the system kernel algorithm process and analyses the system function realization through simulation research. Finally, this paper designs an experiment to evaluate the practicability of the system constructed in this paper. From the results of experimental statistics, we can see that the system constructed in this paper has good practicability. [ABSTRACT FROM AUTHOR]

In order to eliminate the influence of scene illumination on the evaluation of the color difference of dyed fabrics, this paper proposes a dyed fabric illumination correction algorithm based on the extreme learning machine (ELM) with grey wolf optimizer (GWO)‐optimized ant lion optimizer (ALO). Firstly, the Grey Edge framework is used to extract the features of the dyed fabric image as the input vector. Then, to improve the optimization ability of the ALO algorithm, the GWO algorithm is used to provide a set of optimized initial populations to the ALO algorithm, and then the improved ALO algorithm is used to optimize the parameters of the ELM. Finally, the proposed GWO‐ALO‐ELM algorithm is used to correct the illumination of the dyed fabric, and restore the graphics to the effect display under standard illumination through the diagonal reduction model. Compared with the experimental results of GWO‐ELM, ALO‐ELM, backpropagation (BP), ELM, random vector function link (RVFL), and other algorithms, it can be seen that the GWO‐ALO‐ELM algorithm proposed in this paper has good predictive value and quasi‐bias effect, and good stability. [ABSTRACT FROM AUTHOR]

Industry 4.0 brings about a convergence between traditional industrial practices and modern computational techniques. The manufacturing sector is expected to make a greater number of decisions and actions based on the advanced computational analysis of the measurable machinery data. Predictive maintenance of the CNC lathe spindle unit helps the machine avoid unexpected downtimes and meet closer machining tolerances. In this paper, the vibration signals acquired from an experimental run-to-failure test rig are first processed to extract health degradation features, which are then subjected to a neighborhood component analysis-based regression feature selection criteria. Finally, the selected spindle health degradation features are used to train a support-vector machine (SVM) algorithm to evolve a remaining useful life (RUL) estimation model. The SVM hyperparameters that strongly affect the prediction performance are tuned using the Bayesian optimization approach. The evolved predictive model is tested using an independent lathe spindle health degradation data set to obtain the root-mean-square error for predicted and actual RUL. The overall predicted RUL is having an acceptable agreement with the actual RUL. An RMSE equal to 206.23 is obtained as a quantitative measure of prediction accuracy for the Bayesian optimized SVM model for the given dataset. In industrial practice, the evolved SVM predictive model can be employed for the real-time RUL estimation of a similar mechanical system. The proposed predictive model with the integrated feature extraction, selection, and prognosis algorithm can be employed on a real-time spindle health monitoring and predictive maintenance platform for maintenance decision-making. [ABSTRACT FROM AUTHOR]

This main intention of this paper is to adopt a new disease detection model for plant leaves. The proposed model involves several steps such as pre‐processing, leaf segmentation, abnormality segmentation, feature extraction and detection. Image scaling and contrast enhancement are performed during the pre‐processing phase. Once the pre‐processing is done, the segmentation phase starts with leaf segmentation by binary thresholding method and abnormality segmentation by K‐means clustering. Further, the local binary pattern and grey‐level co‐occurrence matrix features are extracted and a dimensionality reduced technique called principle component analysis is determined. As the main novelty, the weighted feature extraction is performed, in which the weight functions are optimized by the self‐adaptive deer hunting optimization (SA‐DHOA). Another contribution of this paper is to implement a hybrid classifier for disease detection. Here, the extracted weighted features are subjected to a support vector machine, and the abnormality segmented image is subjected to convolutional neural network (CNN), which is a deep learning algorithm that can learn the features automatically. Here, the same SA‐DHOA is used to improve the performance of CNN, which is termed as SA‐DHOA‐SCNN. Finally, the performance analysis confirms the maximum success rate of the proposed model over other conventional methods. [ABSTRACT FROM AUTHOR]

• Newbie study using standard ML techniques with HAR Application and Discussions. • Activities found in Literature with the corresponding reference. • Co-occurrences between activities and sensors with the corresponding reference. • Summary and comparison among the different datasets found in Literature. • Summary of the experimentation settings with performance scores found in Literature. Human Activity Recognition (HAR) is an essential area of research related to the ability of smartphones to retrieve information through embedded sensors and recognize the activity that humans are performing. Researchers have recognized people's activities by processing the data received from the sensors with Machine Learning Models. This work is intended to be a hands-on survey with practical's tables capable of guiding the reader through the sensors used in modern smartphones and highly cited developed machine learning models that perform human activity recognition. Several papers in the literature have been studied, paying attention to the preprocessing, feature extraction, feature selection, and classification techniques of the HAR system. In addition, several summary tables illustrating HAR approaches have been provided: most popular human activities in the literature with paper references, the most popular datasets available for download (Analyzing their characteristics, such as the number of subjects involved, the activities recorded, and the sensors with online-availability), co-occurrences between activities and sensors, and a summary table showing the performance obtained by researchers. =The paper's goal is to recommend, through the discussion phase and thanks to the tables, the current state of the art on this topic. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) is a crucial detection technique extensively employed in various contexts demanding accurate identification of human actions. Currently, mainstream HAR approaches frequently rely on sequential data generated by wearable sensors, with a primary focus on extracting feature representations that align with various human activities. Nevertheless, complex and diverse human activities encompass various kinds of features and substantial variations in the features emphasized across different activities, making it challenging for current artificial intelligence methods to achieve accurate recognition. To address this challenge, this paper introduces a network framework called the Multi-Feature Combining Attention Neural Network (MFCANN). This framework incorporates diverse feature extraction, along with both local and global feature attention mechanisms. It is composed of stacked Multi-Feature Combining Attention Blocks (MFCAB) designed by us. In contrast to traditional convolutional methods, MFCAB parallelly stacks various convolutional components, enabling the extraction of a broader range of features from human activity data. Additionally, we propose the Intra-Module Attention Block (Intra-MAB) and the Inter-Module Attention Block (Inter-MAB), which simultaneously focus on local fine-grained features within module feature maps and global distinguishing features across module feature maps. This aims to achieve more targeted feature learning, reinforcing the network's ability to distinguish between different human activities. Abundant experimental results demonstrate that the proposed MFCANN in this paper outperforms current mainstream deep learning algorithms in HAR tasks, achieving recognition accuracies of 0.9813, 0.9324, and 0.9930 on the UCI-HAR, USC-HAD, and RealWorld datasets respectively. We release our code at https://github.com/RangerHeart/MFCANN. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) is a crucial detection technique extensively employed in various contexts demanding accurate identification of human actions. Currently, mainstream HAR approaches frequently rely on sequential data generated by wearable sensors, with a primary focus on extracting feature representations that align with various human activities. Nevertheless, complex and diverse human activities encompass various kinds of features and substantial variations in the features emphasized across different activities, making it challenging for current artificial intelligence methods to achieve accurate recognition. To address this challenge, this paper introduces a network framework called the Multi-Feature Combining Attention Neural Network (MFCANN). This framework incorporates diverse feature extraction, along with both local and global feature attention mechanisms. It is composed of stacked Multi-Feature Combining Attention Blocks (MFCAB) designed by us. In contrast to traditional convolutional methods, MFCAB parallelly stacks various convolutional components, enabling the extraction of a broader range of features from human activity data. Additionally, we propose the Intra-Module Attention Block (Intra-MAB) and the Inter-Module Attention Block (Inter-MAB), which simultaneously focus on local fine-grained features within module feature maps and global distinguishing features across module feature maps. This aims to achieve more targeted feature learning, reinforcing the network's ability to distinguish between different human activities. Abundant experimental results demonstrate that the proposed MFCANN in this paper outperforms current mainstream deep learning algorithms in HAR tasks, achieving recognition accuracies of 0.9813, 0.9324, and 0.9930 on the UCI-HAR, USC-HAD, and RealWorld datasets respectively. We release our code at https://github.com/RangerHeart/MFCANN. [ABSTRACT FROM AUTHOR]

Diagnosing Parkinson's disease (PD) in its early stages is a significant challenge in medicine. Hand tremors and dysgraphia, which are typical early motor symptoms of PD, can manifest for decades before a formal diagnosis is made. Therefore, handwriting analysis has become an important tool for detecting PD. While many machine learning algorithms have been applied in this area, they struggle to capture the subtle changes in handwriting and must describe features from various perspectives. To address these issues, this paper proposes a Coordinate Attention Enhanced Swin Transformer (CAS Transformer) model for PD handwriting recognition. It establishes the long‐term dependence of features on the joint coordinate attention application, which enables the model to more accurately localize the important features of handwriting data and also extract the fuzzy edge features of handwriting images.These characteristics of the CAS Transformer enable it to outperform current advanced deep learning methods in classification, with an accuracy of 92.68% in experiments conducted on two handwritten datasets. [ABSTRACT FROM AUTHOR]

Humans have the ability to quickly identify their own environment, understand, judge and analyze it, which is one of the important reasons why human beings can survive in nature for a long time and gradually develop it into a prosperous society today. The key to human's ability to perceive and understand the environment lies in the ability to accurately find and identify objects, understand and describe visual scenes, and even express emotions on this basis. And if computers can realize automatic and accurate image recognition, and even understand the semantics of images correctly, it will surely improve and facilitate human life. Based on this, the author integrated and optimized the computer image recognition system and applied it in this paper. The core technology of the system is to improve the image algorithm, which can complete the training, testing and classification of target images. The experimental data are available. Choosing this algorithm to improve the learning and training of the data generated by the original image processing is more effective than directly training the original image. [ABSTRACT FROM AUTHOR]

Automatic facial expression recognition (AFER), sometimes referred to as emotional recognition, is important for socializing. Automatic methods in the past two years faced challenges due to Covid-19 and the vital wearing of a mask. Machine learning techniques tremendously increase the amount of data processed and achieved good results in such AFER to detect emotions; however, those techniques are not designed for masked faces and thus achieved poor recognition. This paper introduces a hybrid convolutional neural network aided by a local binary pattern to extract features in an accurate way, especially for masked faces. The basic seven emotions classified into anger, happiness, sadness, surprise, contempt, disgust, and fear are to be recognized. The proposed method is applied on two datasets: the first represents CK and CK +, while the second represents M-LFW-FER. Obtained results show that emotion recognition with a face mask achieved an accuracy of 70.76% on three emotions. Results are compared to existing techniques and show significant improvement. [ABSTRACT FROM AUTHOR]

Improving the reliability and performance of electric and rotating machines is crucial to many industrial applications. This will lead to improved robustness, efficiency, and eco-sustainability, as well as mitigate significant health and safety concerns regarding sudden catastrophic failure modes. Bearing degradation is the most significant cause of machine failure and has been reported to cause up to 75% of low-voltage machine failures. This paper introduces a low complexity machine learning (ML) approach to estimate the remaining useful life (RUL) of rolling element bearings using real vibration signals. This work explores different ML recipes using novel feature engineering coupled with various k-Nearest Neighbour (k-NN), and Support Vector Machines (SVM) kernel and weighting functions in order to optimise this RUL approach. Original non-linear wear state models and feature sets are investigated, the latter are derived from Short-time Fourier Transform (STFT) and Hilbert Marginal Spectrum (HMS). These feature sets incorporate one-third octave band filtering for low complexity multivariate feature subspace compression. Our proposed ML algorithm stage has employed two robust supervised ML approaches: weighted k-NN and SVM. Real vibration data were drawn from the Pronostia platform to test and validate this prognostic monitoring approach. The results clearly demonstrate the effectiveness of this approach, with classification accuracy results of up to 82.8% achieved. This work contributes to the field by introducing a robust and computationally inexpensive method for accurate monitoring of machine health using low-cost vibration-based sensing. [ABSTRACT FROM AUTHOR]

Aerial image data are becoming more widely available, and analysis techniques based on supervised learning are advancing their use in a wide variety of remote sensing contexts. However, supervised learning requires training datasets which are not always available or easy to construct with aerial imagery. In this respect, unsupervised machine learning techniques present important advantages. This work presents a novel pipeline to demonstrate how available aerial imagery can be used to better the provision of services related to the built environment, using the case study of road traffic collisions (RTCs) across three cities in the UK. In this paper, we show how aerial imagery can be leveraged to extract latent features of the built environment from the purely visual representation of top-down images. With these latent image features in hand to represent the urban structure, this work then demonstrates how hazardous road segments can be clustered to provide a data-augmented aid for road safety experts to enhance their nuanced understanding of how and where different types of RTCs occur. [ABSTRACT FROM AUTHOR]

Visual inspection is the typical way for evaluating burns, due to the rising occurrence of burns globally, visual inspection may not be sufficient to detect skin burns because the severity of burns can vary and some burns may not be immediately apparent to the naked eye. Burns can have catastrophic and incapacitating effects and if they are not treated on time can cause scarring, organ failure, and even death. Burns are a prominent cause of considerable morbidity, but for a variety of reasons, traditional clinical approaches may struggle to effectively predict the severity of burn wounds at an early stage. Since computer-aided diagnosis is growing in popularity, our proposed study tackles the gap in artificial intelligence research, where machine learning has received a lot of attention but transfer learning has received less attention. In this paper, we describe a method that makes use of transfer learning to improve the performance of ML models, showcasing its usefulness in diverse applications. The transfer learning approach estimates the severity of skin burn damage using the image data of skin burns and uses the results to improve future methods. The DL technique consists of a basic CNN and seven distinct transfer learning model types. The photos are separated into those displaying first, second, and third-degree burns as well as those showing healthy skin using a fully connected feed-forward neural network. The results demonstrate that the accuracy of 93.87% for the basic CNN model which is significantly lower, with the VGG-16 model achieving the greatest accuracy at 97.43% and being followed by the DenseNet121 model at 96.66%. The proposed approach based on CNN and transfer learning techniques are tested on datasets from Kaggle 2022 and Maharashtra Institute of Technology open-school medical repository datasets that are clubbed together. The suggested CNN-based approach can assist healthcare professionals in promptly and precisely assessing burn damage, resulting in appropriate therapies and greatly minimising the detrimental effects of burn injuries. [ABSTRACT FROM AUTHOR]

Industrial defect detection is a hot topic in the field of computer vision and industry. Industrial defects are diverse and complex, and well‐known machine learning based methods can often not effectively extract features of industrial defects and achieve good detection results. To address the above problems, this paper introduces a deep learning model for industrial defect detection. First, a two‐branch decoupled head, which can facilitate model training through separating the prediction of category and regression is designed. Also, two inverted bottleneck structures are designed to enhance the ability of the model to extract features. Moreover, an attention‐enhanced feature fusion (AEFF) module is designed and integrated into the neck network to achieve effective feature fusion. Extensive experiments are conducted on three public datasets, namely the DeepPCB dataset, NEU‐DET dataset, and NRSD‐MN dataset. The obtained results demonstrate that the proposed model achieves competitive results compared to the state‐of‐the‐art methods. The proposed model achieves mAP@0.5:0.95, 71.78%, 36.04%, and 48.69% on the PCB dataset, NEU‐DET dataset, and the NRSD‐MN dataset, respectively. [ABSTRACT FROM AUTHOR]

Human emotion recognition is a technique for identifying human emotions with respect to various aspects of human life, such as in decision-making, detecting lies, assessing social behaviour, measuring brain-related activity and identifying the personality of a person. In general, this paper aims to propose an efficient EEG-based cognitive personality trait detection based on the human emotion recognition system. Two different algorithms were proposed for this system, namely the normalised window-short time Fourier transform (NW-STFT) algorithm and the cognitive mapping-based Hebbian learning (CM-HL) algorithm. The NW-STFT algorithm dynamically fixes the window size for feature extraction using EEG-based emotion recognition, and the CM-HL algorithm is proposed for mapping the top five big personality traits with the help of recognised primary emotions. The pre-processing method reduces the contaminated artefacts from the benchmark dataset, as well as the brain signal's band power frequency range analysis time. The proposed emotion recognition method has shown a significant improvement in accuracy, leading to greater enhancement in personality-trait mapping. The outcome shows that the proposed two algorithms are effective in finding emotional and personality trait mapping. query Please check the edit made in the article title. [ABSTRACT FROM AUTHOR]

The prompt and accurate diagnosis of breast lesions, including the distinction between cancer, non-cancer, and suspicious cancer, plays a crucial role in the prognosis of breast cancer. In this paper, we introduce a novel method based on feature extraction and reduction for the detection of breast cancer in mammography images. First, we extract features from multiple pre-trained convolutional neural network (CNN) models, and then concatenate them. The most informative features are selected based on their mutual information with the target variable. Subsequently, the selected features can be classified using a machine learning algorithm. We evaluate our approach using four different machine learning algorithms: neural network (NN), k-nearest neighbor (kNN), random forest (RF), and support vector machine (SVM). Our results demonstrate that the NN-based classifier achieves an impressive accuracy of 92% on the RSNA dataset. This dataset is newly introduced and includes two views as well as additional features like age, which contributed to the improved performance. We compare our proposed algorithm with state-of-the-art methods and demonstrate its superiority, particularly in terms of accuracy and sensitivity. For the MIAS dataset, we achieve an accuracy as high as 94.5%, and for the DDSM dataset, an accuracy of 96% is attained. These results highlight the effectiveness of our method in accurately diagnosing breast lesions and surpassing existing approaches. [ABSTRACT FROM AUTHOR]

Speech separation is a well-known problem, especially when there is only one sound mixture available. Estimating the Ideal Binary Mask (IBM) is one solution to this problem. Recent research has focused on the supervised classification approach. The challenge of extracting features from the sources is critical for this method. Speech separation has been accomplished by using a variety of feature extraction models. The majority of them, however, are concentrated on a single feature. The complementary nature of various features have not been thoroughly investigated. In this paper, we propose a deep neural network (DNN) ensemble architecture to completely explore the complimentary nature of the diverse features obtained from raw acoustic features. We examined the penultimate discriminative representations instead of employing the features acquired from the output layer. The learned representations were also fused to produce a new features vector, which was then classified by using the Extreme Learning Machine (ELM). In addition, a genetic algorithm (GA) was created to optimize the parameters globally. The results of the experiments showed that our proposed system completely considered various features and produced a high-quality IBM under different conditions. [ABSTRACT FROM AUTHOR]

• Automatic handwritten analysis system has been developed to detect the gender of the writer. • Machine learning methods have been used to extract set of gender-related features. • Large Arabic dataset was collected to conduct the experiment of the gender detection system. • Novel set of features were chosen with the consultation of a graphologist and psychologist. • Benchmark dataset was used to compare the proposed detection system with another researcher works. Gender detection from handwritten documents is a crucial research area in many disciplines such as psychology, pyelography, graphology, and forensic analysis. Furthermore, this task is challenging due to the high similarity and overlap between individuals' handwriting. The performance of the document recognition and analysis systems, depends on the extracted features from handwritten documents, which can be a challenging task as this depends on extracting the most relevant information from row text. In this paper, a set of gender-related features suggested by a graphologist, to detect the gender of the writers, have been proposed. These features include margins, space between words, pen-pressure and handwriting irregularity. Both SVM and ANN classifiers have been used to train, validate and test the proposed approach on two different data sets: our data set FSHS and ICDAR2013 dataset. The proposed method has achieved high classification rates of 94.7% and 97.1% using SVM and ANN respectively. Meanwhile, our method outperformed state-of-arts methods when applied to the ICDAR2013 dataset with classification rates of 91.4% and 92.5% using SVM and ANN respectively. [ABSTRACT FROM AUTHOR]

Twitter Spam has turned out to be a significant predicament of these days. Current works concern on exploiting the machine learning models to detect the spams in Twitter by determining the statistic features of the tweets. Even though these models result in better success, it is hard to sustain the performances attained by the supervised approaches. This paper intends to introduce a deep learning-assisted spam classification model on twitter. This classification is based on sentiments and topics modeled in it. The initial step is data collection. Subsequently, the collected data are preprocessed with "stop word removal, stemming and tokenization". The next step is feature extraction, wherein, the post tagging, headwords, rule-based lexicon, word length, and weighted holoentropy features are extracted. Then, the proposed sentiment score extraction is carried out to analyze their variations in nonspam and spam information. At last, the diffusions of spam data on Twitter are classified into spam and nonspams. For this, an Optimized Deep Ensemble technique is introduced that encloses "neural network (NN), support vector machine (SVM), random forest (RF) and convolutional neural network (DNN)". Particularly, the weights of DNN are optimally tuned by an arithmetic crossover-based cat swarm optimization (AC-CS) model. At last, the supremacy of the developed approach is examined via evaluation over extant techniques. Accordingly, the proposed AC-CS + ensemble model attained better accuracy value when the learning percentage is 80, which is 18.1%, 14.89%, 11.7%, 12.77%, 10.64%, 6.38%, 6.38%, and 6.38% higher than SVM, DNN, RNN, DBN, MFO + ensemble model, WOA + ensemble model, EHO + ensemble model and CSO + ensemble model models. [ABSTRACT FROM AUTHOR]

Recently, stereoscopic image quality assessment has attracted a lot attention. However, compared with 2D image quality assessment, it is much more difficult to assess the quality of stereoscopic images due to the lack of understanding of 3D visual perception. This paper proposes a novel no-reference quality assessment metric for stereoscopic images using natural scene statistics with consideration of both the quality of the cyclopean image and 3D visual perceptual information (binocular fusion and binocular rivalry). In the proposed method, not only is the quality of the cyclopean image considered, but binocular rivalry and other 3D visual intrinsic properties are also exploited. Specifically, in order to improve the objective quality of the cyclopean image, features of the cyclopean images in both the spatial domain and transformed domain are extracted based on the natural scene statistics (NSS) model. Furthermore, to better comprehend intrinsic properties of the stereoscopic image, in our method, the binocular rivalry effect and other 3D visual properties are also considered in the process of feature extraction. Following adaptive feature pruning using principle component analysis, improved metric accuracy can be found in our proposed method. The experimental results show that the proposed metric can achieve a good and consistent alignment with subjective assessment of stereoscopic images in comparison with existing methods, with the highest SROCC (0.952) and PLCC (0.962) scores being acquired on the LIVE 3D database Phase I. [ABSTRACT FROM AUTHOR]

Good data feature representation and high precision classifiers are the key steps for pattern recognition. However, when the data distributions between testing samples and training samples do not match, the traditional feature extraction methods and classification models usually degrade. In this paper, we propose a domain adaptation approach to handle this problem. In our method, we first introduce cross-domain mean approximation (CDMA) into semi-supervised discriminative analysis (SDA) and design semi-supervised cross-domain mean discriminative analysis (SCDMDA) to extract shared features across domains. Secondly, a kernel extreme learning machine (KELM) is applied as a subsequent classifier for the classification task. Moreover, we design a cross-domain mean constraint term on the source domain into KELM and construct a kernel transfer extreme learning machine (KTELM) to further promote knowledge transfer. Finally, the experimental results from four real-world cross-domain visual datasets prove that the proposed method is more competitive than many other state-of-the-art methods. [ABSTRACT FROM AUTHOR]