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Deep features can exhibit superior retrieval performance than low-level features. However, low-level features (e.g. colour and orientation) can be extracted by generally imitating the human visual perceptual system. Combining human-like low-level and deep features can harmoniously yield more discriminative representations. However, it remains challenging. To address this problem, a new representation method for image retrieval, namely the underlying importance feature histogram (UIFH), is presented in this study. Its main highlights are: (1) This new method extracts low-level features by simulating the human visual perception mechanism, such as opponent colour and orientation selectivity mechanisms. (2) Inspired by the salience evaluation mechanism, the new method can harmoniously evaluate the underlying importance information between deep and low-level features. (3) Assisting the various important information can facilitate the UIFH. It can substantially improve the discriminative power of representation. Comprehensive experiments on seven benchmark datasets demonstrated that the proposed UIFH method outperforms some recent state-of-the-art methods based on pre-trained models. The proposed UIFH method is suitable for the retrieval scenes where images have various colours and prominent orientations. [ABSTRACT FROM AUTHOR]

In computer vision, single object tracking in satellite video is a trending research topic with challenging problems. The high-resolution remote sensing camera captures spatiotemporal information from Earth, where tracking of interested targets is possible. Due to nadir view, single object tracking in satellite videos faces model drift problem due to limited features of tiny objects. Rotation of object, background clutter and long period occlusion of moving objects lead to model drift problem. This study proposes a kernelized correlation filter framework-based tracker using modified ResNet50 with spatial pyramid pooling as feature extractor which solve the short rotation problem and get the shape, textures and patterns features of small size object. To solve long period occlusion problem, this paper proposes an average of average based motion estimation method with Kalman filtering. The proposed method was implemented, tested and quantitative experiments were conducted on satellite video SatSOT dataset. From the obtained results, it is found that the proposed tracker produces better performance in comparison to state-of-the-art tracker methods available in literature in terms of success score, precision score and speed. [ABSTRACT FROM AUTHOR]

This study evaluates the reproducibility of machine learning models that integrate radiomics and deep features (features extracted from a 3D autoencoder neural network) to classify various brain hemorrhages effectively. Using a dataset of 720 patients, we extracted 215 radiomics features (RFs) and 15,680 deep features (DFs) from CT brain images. With rigorous screening based on Intraclass Correlation Coefficient thresholds (>0.75), we identified 135 RFs and 1054 DFs for analysis. Feature selection techniques such as Boruta, Recursive Feature Elimination (RFE), XGBoost, and ExtraTreesClassifier were utilized alongside 11 classifiers, including AdaBoost, CatBoost, Decision Trees, LightGBM, Logistic Regression, Naive Bayes, Neural Networks, Random Forest, Support Vector Machines (SVM), and k-Nearest Neighbors (k-NN). Evaluation metrics included Area Under the Curve (AUC), Accuracy (ACC), Sensitivity (SEN), and F1-score. The model evaluation involved hyperparameter optimization, a 70:30 train–test split, and bootstrapping, further validated with the Wilcoxon signed-rank test and q-values. Notably, DFs showed higher accuracy. In the case of RFs, the Boruta + SVM combination emerged as the optimal model for AUC, ACC, and SEN, while XGBoost + Random Forest excelled in F1-score. Specifically, RFs achieved AUC, ACC, SEN, and F1-scores of 0.89, 0.85, 0.82, and 0.80, respectively. Among DFs, the ExtraTreesClassifier + Naive Bayes combination demonstrated remarkable performance, attaining an AUC of 0.96, ACC of 0.93, SEN of 0.92, and an F1-score of 0.92. Distinguished models in the RF category included SVM with Boruta, Logistic Regression with XGBoost, SVM with ExtraTreesClassifier, CatBoost with XGBoost, and Random Forest with XGBoost, each yielding significant q-values of 42. In the DFs realm, ExtraTreesClassifier + Naive Bayes, ExtraTreesClassifier + Random Forest, and Boruta + k-NN exhibited robustness, with 43, 43, and 41 significant q-values, respectively. This investigation underscores the potential of synergizing DFs with machine learning models to serve as valuable screening tools, thereby enhancing the interpretation of head CT scans for patients with brain hemorrhages. [ABSTRACT FROM AUTHOR]

The cardiovascular disease (CVD) is the dangerous disease in the world. Most of the people around the world are affected by this dangerous CVD. In under-developed countries, the prediction of CVD remains the toughest job and it takes more time and cost. Diagnosing this illness is an intricate task that has to be performed precisely to save the life span of the human. In this research, an advanced deep model-based CVD prediction and risk analysis framework is proposed to minimize the death rate of humans all around the world. The data required for the prediction of CVD is collected from online data sources. Then, the input data is preprocessed using data cleaning, data scaling, and Nan and null value removal techniques. From the preprocessed data, three sets of features are extracted. The three sets of features include deep features, Principal Component Analysis (PCA), and Support Vector Machine (SVM)-based features. A Multi-scale Weighted Feature Fusion-based Deep Structure Network (MWFF-DSN) is developed to predict CVD. This structure is composed of a Multi-scale weighted Feature fusion-based Convolutional Neural Network (CNN) with a Residual Gated Recurrent Unit (GRU). The retrieved features are given as input to MWFF-DSN, and for optimizing weights, a Modernized Plum Tree Algorithm (MPTA) is developed. From the overall analysis, the developed model has attained an accuracy of 96% and it achieves a specificity of 95.95%. The developed model takes minimum time for the CVD and it gives highly accurate detection results. [ABSTRACT FROM AUTHOR]

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Copyright of Food & Machinery is the property of Food & Machinery Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Alzheimer disease is a neurological disorder that affects the elderly, caused by abnormal protein buildup in the brain. It leads to difficulties such as financial mismanagement, disorientation, behavioral changes, and repetitive speech. The existing methods use traditional features to detect the early signs of AD with low detection accuracy. The potential features have to be identified that represent best the patterns associated with alzheimers. Feature selection using ant lion optimization resolves the issue by using complementary information from hybrid features. The proposed HybridOpt pipeling for AD diagnosis combines the high level and low level features for early stage detection The objective of this work is to select efficient features from different deep networks, such as AlexNet, Googlenet, VGG16, ResNet, Efficient, DenseNet, and traditional texture features. Ant Lion Optimization is used to select the best feature among the deep network and traditional texture feature groups. Extensive experimentation on two highly challenging datasets called the Alzheimer’s disease neuroimage dataset and KAGGLE reveals that the proposed HybridOpt pipeline achieves an accuracy of 99% and 98.1% respectively.

In the context of large-scale video surveillance systems, the person re-identification from UAVs is challenging due to the myriad appearance variation of a person across different UAVs. Actually, it is challenging issue to correctly match the same person in different UAV views. This paper introduced a new person re-identification method from UAVs that involves an offline phase and an inference phase. The first allows generating the person re-identification model, whereas the second aims to re-identify a person. Our method contributions are (1) the enhancement of the person re-identification robustness, using three complementary modules, namely the amplified occlusion, the hybrid discriminative features representation, and the bi-model deep metric learning; (2) the improvement of the proposed method effectiveness through relying on a new multi-shot-person retrieval module. Referring to the experimental assessment of the proposed method, the effectiveness and the efficiency of our method were evinced through a comparison with the state-of-the-art person re-identification methods. [ABSTRACT FROM AUTHOR]

COVID-19 disease, an outbreak in the spring of 2020, reached very alarming dimensions for humankind due to many infected patients during the pandemic and the heavy workload of healthcare workers. Even though we have been saved from the darkness of COVID-19 after about three years, the importance of computer-aided automated systems that support field experts in the fight against with global threat has emerged once again. This study proposes a two-stage voting framework called ETSVF-COVID19 that includes transformer-based deep features and a machine learning approach for detecting COVID-19 disease. ETSVF-COVID19, which offers 99.2% and 98.56% accuracies on computed tomography scan and X-radiation images, respectively, could compete with the related works in the literature. The findings demonstrate that this framework could assist field experts in making informed decisions while diagnosing COVID-19 with its fast and accurate classification role. Moreover, ETSVF-COVID19 could screen for chest infections and help physicians, particularly in areas where test kits and specialist doctors are inadequate. [ABSTRACT FROM AUTHOR]

On the basis of remote sensing images, land cover change detection (LCCD) has gained popularity. Even though several techniques have been promoted lately, their effectiveness and usability still need to be improved. To assist urban sustainable development, evaluate natural disasters, and analyze the environment, very high-resolution (VHR) remote sensing images can be used to detect land cover change. Planning professionals can evaluate changes in land cover using remote sensing and geographic information systems, which have a reputation for excellence. A fully convolutional two-stream architecture is used in this paper to first extract highly representative deep features from bi-temporal images. After that, a Lenet5 is given the deep features to segment the land changes and detect changes. With the help of Faster-RCNN the land change classes are classified. We implement the proposed model in Python and conduct experiments on the land cover dataset. The outcomes of the experiments demonstrate that the proposed method outperformed previous deep learning algorithms, achieving the best accuracy, precision, recall, and F1-score. [ABSTRACT FROM AUTHOR]

Human action recognition is a challenging task in the field of computer vision, where deep learning-based methods have made significant progress. Existing methods often use uniform or random sampling, which results in behaviorally irrelevant frames that contain redundant irrelevant features, which in turn leads to misclassification and high computational cost. To solve the problem of misclassification caused by unsuitable sampling methods and to reduce the computational cost, we propose a novel framework named Multi-Stream network based on Key Frame Sampling for human action recognition (MS-KFS). Specifically, we first introduce self-attention to associate deep information between different regions. On this basis, a key frame sampling module will be trained based on rewards and pseudo-labels to extract the moments where key actions are performed. Finally, the novel difference feature as well as the appearance and motion feature is developed to enhance feature characteristic in terms of both depth and timing, and thus, the features will be put into classifier and accomplish the task. A series experiment results validate that the MS-KFS outperforms the state-of-art methods. [ABSTRACT FROM AUTHOR]

Cancer remains a severe illness, and current research indicates that tumor homing peptides (THPs) play an important part in cancer therapy. The identification of THPs can provide crucial insights for drug-discovery and pharmaceutical industries as they allow for tailored medication delivery towards cancer cells. These peptides have a high affinity enabling particular receptors present upon tumor surfaces, allowing for the creation of precision medications that reduce off-target consequences and enhance cancer patient treatment results. Wet-lab techniques are considered essential tools for studying THPs; however, they're labor-extensive and time-consuming, therefore making prediction of THPs a challenging task for the researchers. Computational-techniques, on the other hand, are considered significant tools in identifying THPs according to the sequence data. Despite many strategies have been presented to predict new THP, there is still a need to develop a robust method with higher rates of success. In this paper, we developed a novel framework, THP-DF, for accurately identifying THPs on a large-scale. Firstly, the peptide sequences are encoded through various sequential features. Secondly, each feature is passed to BiLSTM and attention layers to extract simplified deep features. Finally, an ensemble-framework is formed via integrating sequential- and deep features which are fed to a support vector machine which with 10-fold cross-validation to carry to validate the efficiency. The experimental results showed that THP-DF worked better on both and datasets by achieving accuracy of > 95% which are higher than existing predictors both datasets. This indicates that the proposed predictor could be a beneficial tool to precisely and rapidly identify THPs and will contribute to the cutting-edge cancer treatment strategies and pharmaceuticals. [ABSTRACT FROM AUTHOR]

Visual SLAM technology is one of the important technologies for mobile robots. Existing feature-based visual SLAM techniques suffer from tracking and loop closure performance degradation in complex environments. We propose the DFD-SLAM system to ensure outstanding accuracy and robustness across diverse environments. Initially, building on the ORB-SLAM3 system, we replace the original feature extraction component with the HFNet network and introduce a frame rotation estimation method. This method determines the rotation angles between consecutive frames to select superior local descriptors. Furthermore, we utilize CNN-extracted global descriptors to replace the bag-of-words approach. Subsequently, we develop a precise removal strategy, combining semantic information from YOLOv8 to accurately eliminate dynamic feature points. In the TUM-VI dataset, DFD-SLAM shows an improvement over ORB-SLAM3 of 29.24% in the corridor sequences, 40.07% in the magistrale sequences, 28.75% in the room sequences, and 35.26% in the slides sequences. In the TUM-RGBD dataset, DFD-SLAM demonstrates a 91.57% improvement over ORB-SLAM3 in highly dynamic scenarios. This demonstrates the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Scene recognition is an important computer vision task that has evolved from the study of the biological visual system. Its applications range from video surveillance, autopilot systems, to robotics. The early works were based on feature engineering that involved the computation and aggregation of global and local image descriptors. Several popular image features such as SIFT, SURF, HOG, ORB, LBP, KAZE, etc. have been proposed and applied to the task with successful results.Features can be either computed from the entire image on a global scale, or extracted from local sub-regions and aggregated across the image. Suitable classifier models are deployed that learn to classify these features. This review paper analyzes several of these handcrafted features that have been applied to the scene recognition task over the past decades, and tracks the transition from the traditional feature engineering to deep learning which forms the current state of the art in computer vision. Deep learning is now deemed to have overtaken feature engineering in several computer vision applications. Deep convolutional neural networks and vision transformers are the current state of the art for object recognition. However, scenes from urban landscapes are bound to contain similar objects posing a challenge to deep learning solutions for scene recognition. In our study, a critical analysis of feature engineering and deep learning methodologies for scene recognition is provided, and results on benchmark scene datasets are presented, concluding with a discussion on challenges and possible solutions that may facilitate more accurate scene recognition. [ABSTRACT FROM AUTHOR]

Forest fires cause great harm to people, environment, and nature. Fire detection using forest landscape images can play a critical role in the design of expert systems required to solve the forest fire problem. The main aim of this study is to evaluate the classification accuracy of different classifier models for efficiently detecting forest fires and to present an effective and successful model. At this point, classification performances of traditional and deep neural networks (DNN) based classifiers were compared on landscape images dataset taken from the Mendeley repository within the frame of well-known metrics such as accuracy, sensitivity, specificity, precision and false negative rate. The DNN-3 classifier performed very well on the ResNet50 deep features extracted from images with 97.11% accuracy, 96.84% sensitivity, 3.16% false negative rate, 97.37% specificity, and 97.35% precision. This model (ResNet50+DNN-3) offered the most area under the curve with 0.971. In this context, it is thought that the proposed model could play an active role in the design of expert systems that will support the forest protection and monitoring units by easily integrating with real-time internet of things and embedded system applications. [ABSTRACT FROM AUTHOR]

• Design and train 55-layer CNN model on the CIFAR-100 dataset. • Build two-stream network to extract spatial and temporal features from the images. • Feature optimization is performed using a Genetic algorithm. Human Gait Recognition (HGR) is referred to as a biometric tactic that is broadly used for the recognition of an individual by using the pattern of walking. There are some key factors such as angle variation, clothing variation, foot shadows, and carrying conditions that affect the human gait. In this work, a new approach is proposed for the HGR that contains five major steps. In the first step, the video data is converted into image frames. In the second step, RGB to GRAY conversion is carried out. After that, a two-stream network is designed by using a 55-layer CNN model called CNN-55 trained on CIFAR-100. The CNN-55 is designed from scratch and trained on the CIFAR-100 dataset by selecting hyperparameters. This pre-trained CNN-55 is used to build a two-stream network. In Stream-1 the optical flow frames are obtained by Horn and Schunk algorithm. These frames are fed into a CNN-55 to extract temporal features. In Stream-2 the GRAY frames are fed to the CNN-55 model for extraction of spatial features. After that, both vectors are serially fused. In the fourth step, the fused feature vector is fed into the Genetic Algorithm for optimization. Finally, the feature vector is fed into the One-Versus-All SVM classifier for recognition. The system is tested on all CASIA-B angles such as 000, 180, 360, 540, 720, 900, 1080, 1260, 1440, 1620, and 1800 which provides accuracy of 97.10%, 96.80%, 94.60%, 98.0%, 98.30%, 96.80%, 97.60, 96.90%, 99.60%, 96.80%, and 97.60%, respectively. The proposed method produces better outcomes compared to recent techniques. [ABSTRACT FROM AUTHOR]

This study investigates techniques for medical image classification, specifically focusing on COVID-19 scans obtained through computer tomography (CT). Firstly, handcrafted methods based on feature engineering are explored due to their suitability for training traditional machine learning (TML) classifiers (e.g., Support Vector Machine (SVM)) when faced with limited medical image datasets. In this context, I comprehensively evaluate and compare 27 descriptor sets. More recently, deep learning (DL) models have successfully analyzed and classified natural and medical images. However, the scarcity of well-annotated medical images, particularly those related to COVID-19, presents challenges for training DL models from scratch. Consequently, I leverage deep features extracted from 12 pre-trained DL models for classification tasks. This work presents a comprehensive comparative analysis between TML and DL approaches in COVID-19 image classification. [ABSTRACT FROM AUTHOR]

Identifying similar images within databases is a significant challenge in the field of image retrieval. This challenge is further compounded by escalating demands for heightened precision and speed, propelled by advancements in information technology. This study introduces a distinctive network design to address common problems associated with high-probability image retrieval, such as sluggish retrieval speeds and inadequate discrimination. The network architecture primarily employs CNNs for feature extraction. To maximize the advantages of deep learning in feature extraction, the design includes a second-order attention module and a second-order similarity loss. The incorporation of second-order information enhances the correlation among local spatial locations in the image, adjusting each local contribution to the global context. This facilitates a more comprehensive understanding of the image's structure and content, thereby improving feature extraction. Furthermore, dimension reduction techniques are applied to effectively capture the profound information within images. This aids in the elimination of superfluous features while preserving essential information necessary for the retrieval task. This method improves the model's retrieval accuracy by concentrating on the image's deep features, allowing it to disregard unnecessary information. Experimental results demonstrate that our image retrieval technique not only improves accuracy but also significantly accelerates retrieval speed across various datasets, exceeding previous benchmarks in the field. [ABSTRACT FROM AUTHOR]

The proliferation of medical imaging in clinical diagnostics has led to an overwhelming volume of image data, presenting a challenge for efficient storage, management, and retrieval. Specifically, the rapid growth in the use of imaging modalities such as Computed Tomography (CT) and X-rays has outpaced the capabilities of conventional retrieval systems, necessitating more sophisticated approaches to assist in clinical decision-making and research. Our study introduces a novel deep hash coding-based Content-Based Medical Image Retrieval (CBMIR) framework that uses a convolutional neural network (CNN) combined with hash coding for efficient and accurate retrieval. The model integrates a Dense block-based feature learning network, a hash learning block, and a spatial attention block to enhance feature extraction specific to medical imaging. We reduce dimensionality by applying the Reconstruction Independent Component Analysis (RICA) algorithm while preserving diagnostic information. The framework achieves a mean average precision (mAP) of 0.85 on ChestX-ray8, 0.82 on TCIA-CT, 0.84 on MIMIC-CXR, and 0.82 on LIDC-IDRI datasets, with retrieval times of 675 ms, 663 ms, 735 ms, and 748 ms, respectively. Comparisons with ResNet and DenseNet confirm the effectiveness of our model, enhancing medical image retrieval significantly for clinical decision-making and research.

As a tool for near-earth remote sensing, unmanned aerial vehicle (UAV) can be used to acquire images and data of the earth's surface. This provides a powerful support for Earth observation and resource management. Object tracking in UAV videos has been a topic of much interest in recent years. A large number of algorithms have been proposed. Among these algorithms, deep learning has achieved a high accuracy rate. However, it is difficult to carry hardware devices for UAV, which makes it difficult to be practically applied. The correlation filter does not require a graphic processing unit to accelerate the computation, but it uses only manual features, which makes it difficult to achieve satisfactory performance. In order to solve the above problems, we proposed adaptive spatial regularization correlation filters, called DTSRT. Specifically, we first introduce deep features in the correlation filter instead of the original manual features, which can greatly improve the discriminative ability of the model. At the same time, in order to prevent affecting the real-time performance of the algorithm, we use histogram of oriented gradients features to determine the target scale and deep features to determine the target location. In addition, considering the large inter-frame distance between targets in UAV videos, we use a saliency detection method to dynamically generate spatially constrained templates. The proposed DTSRT outperforms other state-of-the-art algorithms with area under curve of 0.481, 0.431, and 0.474 on UAV123@10FPS, UAVDT, and DTB70 datasets, respectively.

Nitrogen is one of the critical factors in water pollution and eutrophication, so applying the deep learning method in remote sensing inversion of nitrogen can provide basic information for environmental management. This paper proposes a two-step feature extraction method to solve the problem that the number of bands in water quality inversion is insufficient and the deep learning method cannot be fully exploited. Firstly, manual feature extraction is completed through the fusion between bands to obtain a set of high-latitude shallow factors, which make the features rich and diverse. Then, a one-dimensional convolutional residual network (ResNet-1D) is constructed, and the deep features are automatically extracted through convolution operations of the model, where the residual learning is used to reduce the training difficulty. The full connection is established through depth features. The comparison of models shows that the Mean Relative Error (MRE) is decreased by at least 10% in both test and validation datasets. Finally, the spatiotemporal distribution of total nitrogen concentration (TNC) in the coastal waters of Shandong is explored. In general, the spatial distribution is that the concentration near the coast is higher than the far. The temporal variation is that the monthly mean of the TNC is low in March, moderate in May and August, and high in October; the annual average value of TNC is 0.3mg/L, which has decreased slightly year by year since 2014. [ABSTRACT FROM AUTHOR]

The use of a convolutional neural network with transfer learning is a strategy that defines high-level features, commonly explored to study patterns in medical images. These features can be analyzed via different methods in order to design hybrid models with more useful and accurate solutions for clinical practice. In this paper, a computational scheme is presented to define hybrid models through deep features by transfer learning, selection by ranking and a robust ensemble classifier with five algorithms. The obtained models were applied to classify histological images from breast, colorectal and liver tissue. The strategy developed here allows knowing important results and conditions to improve models of computer-aided diagnosis, even exploring classic CNN models. The features were defined using layers from the AlexNet and ResNet-50 architectures. The attributes were organized into subsets of the most relevant features and submitted to a k-fold cross-validation process. The best hybrid models were obtained with deep features from the ResNet-50 network, using distinct layers (activation_48_relu and avg_pool) and a maximum of 35 descriptors. These hybrid models provided 98.00% and 99.32% of accuracy values, with emphasis on histological images of breast cancer, indicating the best solution among those available in the specialized Literature. Also, these models provided more relevant results for classifying UCSB and LG datasets than regularized techniques and CNN architectures, exploring data augmentation or not. The computational scheme with detailed information regarding the main hybrid models is a relevant contribution to the community interested in the study of machine learning techniques for pattern recognition. [ABSTRACT FROM AUTHOR]

Learning discriminate representations from images plays crucial role in medical image analysis. The attention mechanism, on the other hand, leads to breakthrough results in the computer vision field by allowing models to provide varying levels of focus across image regions. In this work, we present Tumor Aware Net, an end-to-end trainable attention based Convolutional Neural Network that learns effective representations from Magnetic Resonance (MR) images suitable for effective tumor recognition. The proposed model employs a Sparse Convolutional Denoising Autoencoder (SCDA) to project the higher dimensional MR image representations to a lower dimensional space with improved discrimination. These lower dimensional descriptors are passed through an attention module, which prioritizes tumor descriptors over the rest. Furthermore, the proposed SCDA is trained jointly with the Neural induced Support Vector Classifier (NSVC) to achieve maximum margin separation. The proposed model has been validated on several publicly available benchmark datasets for tumor recognition. Based on the outcomes of the experimental studies, we claim that the proposed model favours stability and complements the learned representations when combined with attention. Despite its simplicity in terms of model parameters, the proposed model outperforms existing models for tumor type categorization. [ABSTRACT FROM AUTHOR]

To obtain better productivity in soybean crops, it is essential to use high-quality seeds. The quality impacts the growing process of the plant. Currently, human specialists perform this quality control process through seed vigor analysis.It is not only a labor-intensive approach highly susceptible to failure, but also the analysts must understand seed anatomy. This paper proposes a learning-based expert approach and pipeline capable of automatically defining the seeds' vigor. Our proposed approach simplifies, in an intuitive manner, the entire process of the seed vigor analysis. To corroborate our learning-based approach, we applied it in a real environment to capture, process, and use two real-world datasets composed of hundreds of soybean images with different damages from a seed classification laboratory. From the experiments, we can testify to a strong efficacy and efficiency in these real-world cases, reaching up to 80.17%±2.37 of accuracy. Our approach provides technological enhancements and opens new ways to solve the seed vigor definition regarding in-place image analysis, aggregating an entire on-the-fly seed analysis pipeline. [ABSTRACT FROM AUTHOR]

When the faces of individuals are not clearly identifiable in surveillance videos due to variations in poses, camera viewpoints and occlusions, the appearances of people play a vital role in their identification. Appearance based person re-identification (re-id) summarizes appearances of persons to identify them across multiple non-overlapping camera views. Existing person re-id solutions work on the cropped person images to learn the salient features of a person instead of working on the raw surveillance images, hence these solutions need an independent preliminary phase of preparing cropped person datasets for the training and evaluation purposes. In contrast, the proposed solution works on the raw surveillance images instead of prerequisite of the cropped person dataset and the proposed hierarchical association building among various local parts of the images results in rich person representations for person re-id. In the proposed solution of Smart Surveillance with Simultaneous Person Detection and Re-identification (SSPDR), the complete surveillance video scenes are processed to perform simultaneous person detection and re-identification for all of the persons captured by a surveillance network. We use region proposals based localization scheme for person detection with an increased confidence strategy about the estimation of bounding boxes locations and the person re-identification module learns the hierarchical associations among local salient body parts of a person. Firstly, the proposed re-id module establishes associations among local horizontal strips of two persons, and afterwards it builds associations among local salient sub-patches of already associated pairs of horizontal strips. We address two major re-id challenges i.e. background noise and scale differences using the proposed re-id solution. In context of simultaneous person detection and re-identification, the proposed method is evaluated on publicly available re-id benchmark Person Re-identification in Wild (PRW) as well as on a local surveillance dataset, and attains state-of-the performance. [ABSTRACT FROM AUTHOR]

Human Action Recognition (HAR) has become one of the most active research area in the domain of artificial intelligence, due to various applications such as video surveillance. The wide range of variations among human actions in daily life makes the recognition process more difficult. In this article, a new fully automated scheme is proposed for Human action recognition by fusion of deep neural network (DNN) and multiview features. The DNN features are initially extracted by employing a pre-trained CNN model name VGG19. Subsequently, multiview features are computed from horizontal and vertical gradients, along with vertical directional features. Afterwards, all features are combined in order to select the best features. The best features are selected by employing three parameters i.e. relative entropy, mutual information, and strong correlation coefficient (SCC). Furthermore, these parameters are used for selection of best subset of features through a higher probability based threshold function. The final selected features are provided to Naive Bayes classifier for final recognition. The proposed scheme is tested on five datasets name HMDB51, UCF Sports, YouTube, IXMAS, and KTH and the achieved accuracy were 93.7%, 98%, 99.4%, 95.2%, and 97%, respectively. Lastly, the proposed method in this article is compared with existing techniques. The resuls shows that the proposed scheme outperforms the state of the art methods. [ABSTRACT FROM AUTHOR]

Human Gait Recognition (HGR) is a biometric approach, widely used for security purposes from the past few decades. In HGR, the change in an individual walk along with wearing clothes and carrying bag are major covariant controls which impact the performance of a system. Moreover, recognition under various view angles is another key challenge in HGR. In this work, a novel fully automated method is proposed for HGR under various view angles using deep learning. Four primary steps are involved such as: preprocessing of original video frames, exploiting pre-trained Densenet-201 CNN model for features extraction, reduction of additional features from extracted vector based on a hybrid selection method, and finally recognition using supervised learning methods. The extraction of CNN features is a key step in which our target is to extract the most active features. To achieve this goal, we fuse the features of both second last and third last layers in a parallel process. At a later stage, best features are selected by the Firefly algorithm and Skewness based approach. These selected features are serially combined and fed to One against All Multi Support Vector Machine (OAMSVM) for final recognition. Three different angles 180, 360 and 540 of the CASIA B dataset are selected for the evaluation process and accuracy of 94.3%, 93.8% and 94.7% is achieved respectively. Results show significant improvement in accuracy and recall rate as compared to the existing state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

In data science and content-based retrieval, we find many domain-specific techniques that employ a data processing pipeline with two fundamental steps. First, data entities are represented by some visualizations, while in the second step, the visualizations are used with a machine learning model to extract deep features. Deep convolutional neural networks (DCNN) became the standard and reliable choice. The purpose of using DCNN is either a specific classification task or just a deep feature representation of visual data for additional processing (e.g., similarity search). Whereas the deep feature extraction is a domain-agnostic step in the pipeline (inference of an arbitrary visual input), the visualization design itself is domain-dependent and ad hoc for every use case. In this paper, we survey and analyze many instances of data visualizations used with deep learning models (mostly DCNN) for domain-specific tasks. Based on the analysis, we synthesize a taxonomy that provides a systematic overview of visualization techniques suitable for usage with the models. The aim of the taxonomy is to enable the future generalization of the visualization design process to become completely domain-agnostic, leading to the automation of the entire feature extraction pipeline. As the ultimate goal, such an automated pipeline could lead to universal deep feature data representations for content-based retrieval. [ABSTRACT FROM AUTHOR]

Recently, lung cancer prediction based on imaging genomics has attracted great attention. However, such studies often have many challenges, such as small sample size, high-dimensional information redundancy, and the inefficiency of multimodal fusion. Therefore, in this paper, a deep convolution cascade attention fusion network (DCCAFN) based on imaging genomics is proposed for the prediction of lung cancer patients' survival. The network consists of three modules: an image feature extraction module (IFEM), a gene feature extraction module (GFEM), and an attention fusion network (AFN). In the IFEM, a pretrained residual network based on transfer learning is used to extract deep image features to fully capture the computed tomography (CT) image information conducive to prognosis prediction. In the GFEM, the F-test is first used for gene screening to eliminate redundant information, and then, a cascade network with the convolution cascade module (CCM) that contains a convolution operation, a pooling operation, and an ensemble forest classifier is designed to better extract the gene features. In the AFN, a bimodal attention fusion mechanism is proposed to fuse deep image features and gene features to improve the performance of predicting lung cancer survival. The experimental results show that the DCCAFN model achieves good performance, and its accuracy and AUC are 0.831 and 0.816, respectively. It indicates that the model is an effective multimodal data fusion method for predicting the survival prognosis of lung cancer, which can greatly help physicians stratify patients' risks, and achieve personalized treatment for improving the quality of patients' lives. [ABSTRACT FROM AUTHOR]

Having the advantages of speed, suitability and high accuracy, computer vision has been effectively utilized as a non-destructive approach to automatically recognize and classify fruits and vegetables, to meet the increased demand for food quality-sensing devices. Primarily, this study focused on classifying apple varieties using machine learning techniques. Firstly, to discern how different convolutional neural network (CNN) architectures handle different apple varieties, transfer learning approaches, using popular seven CNN architectures (VGG16, VGG19, InceptionV3, MobileNet, Xception, ResNet150V2 and DenseNet201), were adopted, taking advantage of the pre-trained models, and it was found that DenseNet201 had the highest (97.48%) classification accuracy. Secondly, using the DenseNet201, deep features were extracted and traditional Machine Learning (ML) models: support vector machine (SVM), multi-layer perceptron (MLP), random forest classifier (RFC) and K-nearest neighbor (KNN) were trained. It was observed that the classification accuracies were significantly improved and the best classification performance of 98.28% was obtained using SVM algorithms. Finally, the effect of dimensionality reduction in classification performance, deep features, principal component analysis (PCA) and ML models was investigated. MLP achieved an accuracy of 99.77%, outperforming SVM (99.08%), RFC (99.54%) and KNN (91.63%). Based on the performance measurement values obtained, our study achieved success in classifying apple varieties. Further investigation is needed to broaden the scope and usability of this technique, for an increased number of varieties, by increasing the size of the training data and the number of apple varieties. [ABSTRACT FROM AUTHOR]

AbstractA heart attack is intended as top prevalent among all ruinous ailments. Day by day, the number of affected people count is increasing globally. The medical field is struggling to detect heart disease in the initial step. Early prediction can help patients to save their life. Thus, this paper implements a novel heart disease prediction model with the help of a hybrid deep learning strategy. The developed framework consists of various steps like (i) Data collection, (ii) Deep feature extraction, and (iii) Disease prediction. Initially, the standard medical data from various patients are acquired from the clinical standard datasets. Here, a One-Dimensional Convolutional Neural Network (1DCNN) is utilized for extracting the deep features from the acquired medical data to minimize the number of redundant data from the gathered large-scale data. The acquired deep features are directly fed to the Hybrid Optimized Deep Classifier (HODC) with the integration of Temporal Convolutional Networks (TCN) with Long Short-Term Memory (LSTM), where the parameters in both classifiers are optimized using the newly suggested Enhanced Forensic-Based Investigation (EFBI) inspired meta-optimization algorithm. Throughout the result analysis, the accuracy and precision rate of the offered approach is 98.67% and 99.48%. The evaluation outcomes show that the recommended system outperforms the extant systems in terms of performance metrics examination. [ABSTRACT FROM AUTHOR]

Currently, deep learning has achieved remarkable success in estimating plant disease from unmanned aerial system (UAS) images. However, two critical challenges remain unexplored: spatiotemporal variations in disease symptoms and the domain shift between source and target datasets. To overcome these challenges, this paper proposes an approach that incorporates temporal aspects of disease progression using time series analysis. Spatiotemporal information is integrated by combining convolutional neural networks and bidirectional long-short term memory (CNN-BiLSTM) to classify the disease into five severity levels. Various feature extraction methods, including both handcrafted and CNN-based feature extractors, are evaluated. Furthermore, to tackle the problem of domain shift, a feature-level domain adaptation method is proposed. This method aims to learn transferable feature representations that remain consistent despite variations between source and target datasets. This approach enhances the spatiotemporal transferability of the CNN-BiLSTM model, enabling the effective utilisation of historical datasets. The study demonstrates that the CNN-BiLSTM model outperforms traditional time-independent machine-learning methods that rely on handcrafted features. Specifically, the Resnet101-BiLSTM model achieves the highest overall classification accuracy of 89.7% among all tested models evaluated on a one-year dataset. Moreover, it shows superior generalisation with 72.7% accuracy for cross-spatiotemporal disease severity classification using domain adaptation, as demonstrated through two-year experiments. By reducing the domain shift of source and target datasets and harnessing time series high-resolution images obtained throughout the crop growing season, this hybrid approach has substantial potential to advance the assessment of crop disease severity in field conditions. • Compared different CNN-based feature extractors for FOV4 symptom detection in UAS images. • Developed a BiLSTM model for temporal analysis in UAS datasets. • Use of domain adaptation enhanced model generalization. • CNN-BiLSTM with CORAL achieved 72.7% accuracy, proving its effectiveness in diverse real-world applications. [ABSTRACT FROM AUTHOR]

Abstract Background Multi-omics research has the potential to holistically capture intra-tumor variability, thereby improving therapeutic decisions by incorporating the key principles of precision medicine. The purpose of this study is to identify a robust method of integrating features from different sources, such as imaging, transcriptomics, and clinical data, to predict the survival and therapy response of non-small cell lung cancer patients. Methods 2996 radiomics, 5268 transcriptomics, and 8 clinical features were extracted from the NSCLC Radiogenomics dataset. Radiomics and deep features were calculated based on the volume of interest in pre-treatment, routine CT examinations, and then combined with RNA-seq and clinical data. Several machine learning classifiers were used to perform survival analysis and assess the patient’s response to adjuvant chemotherapy. The proposed analysis was evaluated on an unseen testing set in a k-fold cross-validation scheme. Score- and concatenation-based multi-omics were used as feature integration techniques. Results Six radiomics (elongation, cluster shade, entropy, variance, gray-level non-uniformity, and maximal correlation coefficient), six deep features (NasNet-based activations), and three transcriptomics (OTUD3, SUCGL2, and RQCD1) were found to be significant for therapy response. The examined score-based multi-omic improved the AUC up to 0.10 on the unseen testing set (0.74 ± 0.06) and the balance between sensitivity and specificity for predicting therapy response for 106 patients, resulting in less biased models and improving upon the either highly sensitive or highly specific single-source models. Six radiomics (kurtosis, GLRLM- and GLSZM-based non-uniformity from images with no filtering, biorthogonal, and daubechies wavelets), seven deep features (ResNet-based activations), and seven transcriptomics (ELP3, ZZZ3, PGRMC2, TRAK1, ATIC, USP7, and PNPLA2) were found to be significant for the survival analysis. Accordingly, the survival analysis for 115 patients was also enhanced up to 0.20 by the proposed score-based multi-omics in terms of the C-index (0.79 ± 0.03). Conclusions Compared to single-source models, multi-omics integration has the potential to improve prediction performance, increase model stability, and reduce bias for both treatment response and survival analysis.

Abstract Background The purpose of this study is to investigate the use of radiomics and deep features obtained from multiparametric magnetic resonance imaging (mpMRI) for grading prostate cancer. We propose a novel approach called multi-flavored feature extraction or tensor, which combines four mpMRI images using eight different fusion techniques to create 52 images or datasets for each patient. We evaluate the effectiveness of this approach in grading prostate cancer and compare it to traditional methods. Methods We used the PROSTATEx-2 dataset consisting of 111 patients’ images from T2W-transverse, T2W-sagittal, DWI, and ADC images. We used eight fusion techniques to merge T2W, DWI, and ADC images, namely Laplacian Pyramid, Ratio of the low-pass pyramid, Discrete Wavelet Transform, Dual-Tree Complex Wavelet Transform, Curvelet Transform, Wavelet Fusion, Weighted Fusion, and Principal Component Analysis. Prostate cancer images were manually segmented, and radiomics features were extracted using the Pyradiomics library in Python. We also used an Autoencoder for deep feature extraction. We used five different feature sets to train the classifiers: all radiomics features, all deep features, radiomics features linked with PCA, deep features linked with PCA, and a combination of radiomics and deep features. We processed the data, including balancing, standardization, PCA, correlation, and Least Absolute Shrinkage and Selection Operator (LASSO) regression. Finally, we used nine classifiers to classify different Gleason grades. Results Our results show that the SVM classifier with deep features linked with PCA achieved the most promising results, with an AUC of 0.94 and a balanced accuracy of 0.79. Logistic regression performed best when using only the deep features, with an AUC of 0.93 and balanced accuracy of 0.76. Gaussian Naive Bayes had lower performance compared to other classifiers, while KNN achieved high performance using deep features linked with PCA. Random Forest performed well with the combination of deep features and radiomics features, achieving an AUC of 0.94 and balanced accuracy of 0.76. The Voting classifiers showed higher performance when using only the deep features, with Voting 2 achieving the highest performance, with an AUC of 0.95 and balanced accuracy of 0.78. Conclusion Our study concludes that the proposed multi-flavored feature extraction or tensor approach using radiomics and deep features can be an effective method for grading prostate cancer. Our findings suggest that deep features may be more effective than radiomics features alone in accurately classifying prostate cancer.

Person identification using periocular images has emerged as a challenging scenario in efficient biometric analysis, particularly under less constrained environments. Accurate recognition is significant in rendering effective measures during the COVID-19 pandemic. In this research paper, the person identification process is performed based on a deep learning model. Several effectual methods have already been developed, but certain drawbacks still exist, like deteriorated image quality, high computational cost, increased error, less training ability, a requirement of high storage space and accuracy rate degradation. Hence, the proposed work introduces a Hybrid Optimal Dense Capsule network-based Periocular biometric system (HodCP) to conquer these demerits. The proposed work involves pre-processing, dimensionality reduction, hybrid feature extraction and Image matching. The pre-processing step is undertaken using Parabolic Contrast Enhancement (PCE) to balance the image contrast and enhance the image quality. Then the Two-Dimensional Principal Component Analysis (2D_PCA) is employed to minimize the image dimensionality. Deep features are extracted in the hybrid feature extraction process using Dense Convolutional-121 Capsule Network (DenseCapsNet). The net loss and hyperparameter tuning are performed through African Vultures Optimization (AVO) algorithm. Finally, image matching is performed using Weighted Distance Similarity (WDS), which identifies the similarity between the query image and a set of image samples based on the distance score. The simulation tool used for analyzing better performance is PYTHON. The data required to process the proposed work are collected from four benchmark datasets. The proposed work provides a better accuracy rate of CASIA-Iris-Mobile-V1.0 (99.01%), UBIPr (99.12%), Facemask detection dataset (98.67%) and Glasses versus without glasses dataset (98.83%), which is superior to the existing methods. [ABSTRACT FROM AUTHOR]

Automatic person recognition using finger-vein has been extensively investigated in recent years. Mostly the published research on biometrics are based on the hand-crafted feature descriptors to describe the appearance of a finger-vein image, which is prone to be affected by irregular shading, irrelevant background, varying illumination and non-rigid deformation. To improve the recognition performance of hand-crafted feature-based methods, many researchers have contributed substantially in designing enhancement methods for texture descriptors. However, hand-crafted algorithms are highly targeted with weak generalization ability with respect to the emerging data samples. Nowadays, deep convolutional neural networks (CNN) is emerging as a powerful technology to extract multi-level feature representation instead of traditionally designed features from raw data. These methods are achieving unprecedented performance in the field of computer vision. In context to biometrics modalities, finger-vein recognition using CNN is still in its primary stage. In this paper, we proposed a simple yet effective multi-scale arc-fusion approach to optimize the feature embedding discrimination power. An exhaustive comparative experiment is conducted on four publicly available databases: HKPU, FV-USM, SDUMLA and UTFVP, to demonstrate that the proposed approach is superior to the existing state-of-the-art (SOTA) methods. Also, the experimental result shows that the concept is actually scalable and can be migrated to other deep neural network architectures. [ABSTRACT FROM AUTHOR]

Yellow rust is a devastating disease that causes significant losses in wheat production worldwide and significantly affects wheat quality. It can be controlled by cultivating resistant cultivars, applying fungicides, and appropriate agricultural practices. The degree of precautions depends on the extent of the disease. Therefore, it is critical to detect the disease as early as possible. The disease causes deformations in the wheat leaf texture that reveals the severity of the disease. The gray-level co-occurrence matrix(GLCM) is a conventional texture feature descriptor extracted from gray-level images. However, numerous studies in the literature attempt to incorporate texture color with GLCM features to reveal hidden patterns that exist in color channels. On the other hand, recent advances in image analysis have led to the extraction of data-representative features so-called deep features. In particular, convolutional neural networks (CNNs) have the remarkable capability of recognizing patterns and show promising results for image classification when fed with image texture. Herein, the feasibility of using a combination of textural features and deep features to determine the severity of yellow rust disease in wheat was investigated. Textural features include both gray-level and color-level information. Also, pre-trained DenseNet was employed for deep features. The dataset, so-called Yellow-Rust-19, composed of wheat leaf images, was employed. Different classification models were developed using different color spaces such as RGB, HSV, and L*a*b, and two classification methods such as SVM and KNN. The combined model named CNN-CGLCM_HSV, where HSV and SVM were employed, with an accuracy of 92.4% outperformed the other models. [ABSTRACT FROM AUTHOR]

Brain tumors are particularly perilous because they form when cells in the brain multiply uncontrollably within the skull. Therefore, a fast and accurate method of diagnosing tumors is crucial for the patient's health. This study proposes a method for evaluating brain cancer images. The phases of implementation for the proposed work are as follows: In the first phase, we compiled a set of specialized feature vector descriptions for advanced classification tasks by employing both deep learning (DL) and conventional feature extraction techniques. In the second phase, we employ a proposed convolutional neural network (CNN) approach and a traditional subset of features from a genetic algorithm (GA) to select our deep features. The third phase involves using the fusion method to merge the prioritized features. Finally, determine whether the brain image is normal or abnormal. The results showed that the proposed method successfully classified objects accurately and revealed their robustness across different ages and acquisition protocols. According to the results, the classification accuracy of the support vector machines (SVM) classifier has significantly improved by combining conventional features and deep learning features (DLF), achieving an accuracy of up to 86.50% using the T1 weighted brain MR image. [ABSTRACT FROM AUTHOR]

Cherries are spring fruits enriched with nutrients and sweetness, which are widely popular among consumers all over the world. Cherry appearance characteristic is an important indicator for consumers' purchases and robot picking, which is closely related with classifying cherry maturity and monitoring cherry diseases. It is therefore very important to accurately identify the overall appearance quality of cherries. Fruit appearance quality detection has been continuously developed by advanced deep learning techniques. In this paper, an innovative cherry appearance detection method is developed for rapid and accurate classification of different cherry maturity stages and disease status. The different convolutional neural networks are used as a feature extractor to extract the deep features and then the features are imported into nine classifiers to detect the appearance quality of cherries. The fine-tuned method performs quite well with superior accuracy of cherry classification at less computation overhead for training. The experimental results show that: (1) Among the 54 combined models, the accuracy of twenty-two combined models is higher than transfer learning. (2) The training time of all the combined models is tremendously shorter than transfer learning. (3) The best model, Mobilenet_v2 plus support vector machine, have a recognition accuracy of 98.3% and the training time is 73.478 s. The experimental results show that the method of extracting features into classifiers has a good effect on recognizing cherry appearance quality. The successful application of this method provides a new solution for cherry appearance ripeness and disease identification. [ABSTRACT FROM AUTHOR]

Background: The purpose of this study is to investigate the use of radiomics and deep features obtained from multiparametric magnetic resonance imaging (mpMRI) for grading prostate cancer. We propose a novel approach called multi-flavored feature extraction or tensor, which combines four mpMRI images using eight different fusion techniques to create 52 images or datasets for each patient. We evaluate the effectiveness of this approach in grading prostate cancer and compare it to traditional methods. Methods: We used the PROSTATEx-2 dataset consisting of 111 patients' images from T2W-transverse, T2W-sagittal, DWI, and ADC images. We used eight fusion techniques to merge T2W, DWI, and ADC images, namely Laplacian Pyramid, Ratio of the low-pass pyramid, Discrete Wavelet Transform, Dual-Tree Complex Wavelet Transform, Curvelet Transform, Wavelet Fusion, Weighted Fusion, and Principal Component Analysis. Prostate cancer images were manually segmented, and radiomics features were extracted using the Pyradiomics library in Python. We also used an Autoencoder for deep feature extraction. We used five different feature sets to train the classifiers: all radiomics features, all deep features, radiomics features linked with PCA, deep features linked with PCA, and a combination of radiomics and deep features. We processed the data, including balancing, standardization, PCA, correlation, and Least Absolute Shrinkage and Selection Operator (LASSO) regression. Finally, we used nine classifiers to classify different Gleason grades. Results: Our results show that the SVM classifier with deep features linked with PCA achieved the most promising results, with an AUC of 0.94 and a balanced accuracy of 0.79. Logistic regression performed best when using only the deep features, with an AUC of 0.93 and balanced accuracy of 0.76. Gaussian Naive Bayes had lower performance compared to other classifiers, while KNN achieved high performance using deep features linked with PCA. Random Forest performed well with the combination of deep features and radiomics features, achieving an AUC of 0.94 and balanced accuracy of 0.76. The Voting classifiers showed higher performance when using only the deep features, with Voting 2 achieving the highest performance, with an AUC of 0.95 and balanced accuracy of 0.78. Conclusion: Our study concludes that the proposed multi-flavored feature extraction or tensor approach using radiomics and deep features can be an effective method for grading prostate cancer. Our findings suggest that deep features may be more effective than radiomics features alone in accurately classifying prostate cancer. [ABSTRACT FROM AUTHOR]

The online spread of fake news on various platforms has emerged as a significant concern, posing threats to public opinion, political stability, and the dissemination of reliable information. Researchers have turned to advanced technologies, including machine learning (ML) and deep learning (DL) techniques, to detect and classify fake news to address this issue. This research study explores fake news classification using diverse ML and DL approaches. We utilized a well-known "Fake News" dataset sourced from Kaggle, encompassing a labelled news collection. We implemented diverse ML models, including multinomial naïve bayes (MNB), gaussian naïve bayes (GNB), Bernoulli naïve Bayes (BNB), logistic regression (LR), and passive aggressive classifier (PAC). Additionally, we explored DL models, such as long short-term memory (LSTM), convolutional neural networks (CNN), and CNN-LSTM. We compared the performance of these models based on key evaluation metrics, such as accuracy, precision, recall, and the F1 score. Additionally, we conducted cross-validation and hyperparameter tuning to ensure optimal performance. The results provide valuable insights into the strengths and weaknesses of each model in classifying fake news. We observed that DL models, particularly LSTM and CNN-LSTM, showed better performance compared to traditional ML models. These models achieved higher accuracy and demonstrated robustness in classification tasks. These findings emphasize the potential of DL models to tackle the spread of fake news effectively and highlight the importance of utilizing advanced techniques to address this challenging problem. [ABSTRACT FROM AUTHOR]