Classification Module Research Articles

Emotion-Based Music Player

Songs have always been a popular medium for communicating and understanding human emotions. Reliable emotion-based categorization systems can be quite helpful to us in understanding their relevance. However, the outcome of the research on Emotion-based music classification have not been the greatest. Here, we introduce EMP, a cross-platform emotional music player that play songs in accordance with the user’s feelings at the time. EMP provides intelligent mood-based music player by incorporating emotion context reasoning abilities into our adaptive music engine. EMP revolutionizes how users interact with music, fostering deeper connections between emotions and musical experiences. Our music player is composed of three modules: the emotion module, the classification module, and the queue-based module. The Emotion Module analyses a picture of the user’s face and uses the VGG16 algorithm to detect their mood with a precision exceeding 95%. The Music Classification Module gets an outstanding result by utilizing aural criteria while classifying music into 7 different mood groups. The queue module plays the songs directly from the mapped folders in the order they are stored, ensuring alignment with the user’s mood and preferences.

Sign Language Recognition Using Convolutional Neural Network

This project introduces a real-time sign language detection system powered by Convolutional Neural Networks (CNNs), designed to aid individuals with hearing impairments in communication. Leveraging OpenCV for video capture and hand detection, along with custom modules for hand tracking and image classification, the system seamlessly integrates deep learning methodologies. A pre-trained CNN model, trained on a comprehensive dataset of sign language gestures, forms the core of the system, ensuring accurate classification in real-time. By capturing video frames from a webcam, detecting hands, and processing them through the CNN model, the system provides immediate feedback by overlaying predicted gestures onto the video stream. Through its effective implementation, this project underscores the potential of CNNs in facilitating accessibility and inclusivity, while also paving the way for future enhancements and applications in assistive technology and human-computer interaction. Top of Form Keywords— Real-time, Sign Language Detection, Convolutional Neural Networks (CNNs), Communication, Hearing Impairments, Deep Learning.

Mirrored X-Net: Joint classification and contrastive learning for weakly supervised GA segmentation in SD-OCT

Deep learning achieves impressive performance in medical image segmentation, but the training phase requires a large amount of annotated data with precise clinical definitions. Weakly supervised lesion segmentation aims to produce pixel-level masks by learning discriminatory information from weak annotations. In this paper, Mirrored X-Net, a novel segmentation model only supervised by image-level category labels, is proposed to segment the Geographic Atrophy (GA) regions in en-face projection of Spectral-Domain Optical Coherence Tomography (SD-OCT) data. Characterized by the dimension-asymmetric information in SD-OCT images, a novel Anisotropic Downsampling (ADS) is proposed to augment feature shapes. To extract the regions of normal retina for both images with and without lesions, we propose a contrastive learning module to assimilate the deep representation of normal tissue in the SD-OCT images and improve the class-wise difference of deep representation. Based on the contrastive learning module, Anomalous Probability Map (APM) can be obtained to draw the distribution of difference with normal tissue in images. We jointly train the image classification and contrastive learning module, and the final GA segmentation is refined based on the en-face projection of APM. The experimental results on two independent GA datasets demonstrate that the proposed weakly supervised model can produce satisfactory results, and obtain even higher accuracy than fully supervised approaches. The source code is available at https://github.com/maxiao0234/Mirrored-X-Net-pytorch.

Real-Time Traffic Sign Detection and Recognition System using Computer Vision and Machine Learning

The use of computer vision technology for our system has taken a huge leap and has revolutionized the way in which road safety is managed and maintained. This part of the paper represents the key components and functionalities of such a system which helps in enhancing the road safety and also in traffic management. The proposed system is meant to contain computer vision algorithms and some machine learning techniques. These will help in detecting and recognizing the traffic signs in real time video streams. With the help of deep learning models like convolutional neural networks, the proposed system can precisely identify various traffic signs present on roads. The major components of the proposed system include a camera interface which will capture real time video frames from the road, image processing modules for color space conversions, binary thresholding, contour detection and a classification module for the purpose of sign recognition. Deep learning frameworks like TensorFlow and OpenCV libraries are used for image processing and model training In the proposed system. The real time nature of this system allows the spontaneous detection and recognition of traffic signs enabling us to change the road conditions. Hence, the system contributes to prevent accidents and provide timely warnings to drivers. In addition, this system offers potential applications in traffic management and for autonomous driving system. With the help of accurate information, the system can contribute towards more efficient traffic flow and reduce congestion on roads. Overall, the system has very good impacts in road safety and efficiency. It shows advancement in road safety and traffic management.

APC: Contactless healthy sitting posture monitoring with microphone array

The prevalence of poor sitting posture in daily work has become a growing concern among office workers and students due to the associated health problems. To address this issue, we design an acoustic sitting posture care system (termed, APC) based on a circular microphone array. Compared with classic posture recognition technologies such as visual perception and sensors, acoustic sensing naturally possesses advantages such as privacy protection and contactless capabilities. Concretely, our system leverages a customized and inaudible sound signal sent from a speaker to a user’s body, and an echo signal preprocessing method to sense the body posture. Our system comprises three modules: signal generation and collection, signal preprocessing, and posture classification. The signal generation and collection module is designed to create an appropriate signal waveform for transmitting the sound signal. We also develop a unique alignment method for received signals to implement background interference cancellation. In the signal preprocessing module, we propose a body profile extraction method based on the phase difference between received signals. In the posture classification module, we design an attention mechanism based classification network that can map the output of the previous module to different sitting posture categories. The experimental results show that our proposed method achieves an average accuracy of 98.4% for five common sitting postures. Furthermore, case studies conducted under different practical conditions have validated the robustness of our system.

Enhanced fingerprint pattern classification: Integrating attention modules with lightweight deep learning models

AbstractLarge fingerprint databases can make the automated search process tedious and time‐consuming. Fingerprint pattern classification is a significant step in the identification system's complexity in terms of time and speed. Although several fingerprint algorithms have been developed for classification tasks, further improvements in performance and efficiency are still required. Most of the fingerprint algorithms use deep learning techniques. However, some deep learning techniques can be resource‐intensive and computationally expensive, while others can disregard the spatial relationships between the features used in classifying fingerprint patterns. This study proposes using lightweight deep learning models (i.e., MobileNet and EfficientNet‐B0) integrated with attention modules to classify fingerprint patterns. The two lightweight models are modified, yielding MobileNet+ and EfficientNet‐B0+ models. The lightweight deep learning models can help achieve optimal performance and reduce computational complexity. The attention modules focus on distinctive features for classification. Our proposed approach integrates four attention modules for fingerprint pattern classification into two lightweight deep learning models, that is, MobileNet+ and EfficientNet‐B0+. To evaluate our approach, we use two publicly available fingerprint datasets, that is, the NIST special database 301 dataset and the LivDet dataset. The evaluation results show that the EfficientNet‐B0+ model achieves the highest classification accuracy of 97% with only 854,086 training parameters. As a conclusion, we consider the training parameters small enough for the EfficientNet‐B0+ model to be deployed on low‐resource devices.

Classification of peanut pod rot based on improved YOLOv5s.

Peanut pod rot is one of the major plant diseases affecting peanut production and quality over China, which causes large productivity losses and is challenging to control. To improve the disease resistance of peanuts, breeding is one significant strategy. Crucial preventative and management measures include grading peanut pod rot and screening high-contributed genes that are highly resistant to pod rot should be carried out. A machine vision-based grading approach for individual cases of peanut pod rot was proposed in this study, which avoids time-consuming, labor-intensive, and inaccurate manual categorization and provides dependable technical assistance for breeding studies and peanut pod rot resistance. The Shuffle Attention module has been added to the YOLOv5s (You Only Look Once version 5 small) feature extraction backbone network to overcome occlusion, overlap, and adhesions in complex backgrounds. Additionally, to reduce missing and false identification of peanut pods, the loss function CIoU (Complete Intersection over Union) was replaced with EIoU (Enhanced Intersection over Union). The recognition results can be further improved by introducing grade classification module, which can read the information from the identified RGB images and output data like numbers of non-rotted and rotten peanut pods, the rotten pod rate, and the pod rot grade. The Precision value of the improved YOLOv5s reached 93.8%, which was 7.8%, 8.4%, and 7.3% higher than YOLOv5s, YOLOv8n, and YOLOv8s, respectively; the mAP (mean Average Precision) value was 92.4%, which increased by 6.7%, 7.7%, and 6.5%, respectively. Improved YOLOv5s has an average improvement of 6.26% over YOLOv5s in terms of recognition accuracy: that was 95.7% for non-rotted peanut pods and 90.8% for rotten peanut pods. This article presented a machine vision- based grade classification method for peanut pod rot, which offered technological guidance for selecting high-quality cultivars with high resistance to pod rot in peanut.

Compound fault diagnosis of diesel engines by combining generative adversarial networks and transfer learning

In order to solve the problem of compound fault diagnosis of diesel engine fuel injection system under the condition of few samples, a comprehensive diagnosis method based on generative adversarial networks and transfer learning based multi label classification models is proposed in this paper, which is based on convolutional attention networks. The generative adversarial networks are used to enhance the original data, and the enhanced data is used as the source data for transfer learning to ensure its effectiveness. Design multi-dimensional labels for compound fault diagnosis to enable the model to diagnose compound faults and their individual faults. Pre train the convolutional attention network with enhanced data generated from adversarial networks. Subsequently, the original data is used to integrate the feature information in the network and perform secondary training on the classification module. Finally, a compound fault diagnosis is implemented based on the model after secondary training. The effectiveness of the comprehensive diagnostic method proposed in the article is verified using real fault data of the fuel injection system. The diagnostic accuracy on test data reached 80%. The effectiveness of the comprehensive diagnostic method proposed in the article was verified using real fault data of the fuel injection system. The diagnostic accuracy on test data reached 80%. Compared to the method of directly training convolutional attention networks using raw data and the method of directly training convolutional attention networks without transfer learning using GAN enhanced datasets, the accuracy is improved by 16% and 8%, respectively.

Improving Apple Fruit Quality Detection with AI and Machine Vision

The detection of apples using Raspberry Pi is an innovative approach that merges the realms of computer vision, machine learning, and agricultural automation. This abstract provides an extensive overview of the methodologies, implementations, challenges, and future directions pertaining to apple detection using Raspberry Pi, encapsulating the essence of the research conducted in this domain. The quest for automation in agriculture has spurred the development of novel technologies aimed at improving efficiency and reducing manual labor. Fruit detection, particularly the identification of apples, holds significant importance due to the fruit's widespread cultivation and economic value. Traditional methods of fruit detection often involve manual sorting, which is labor>intensive and time>consuming. Hence, there arises a need for automated systems capable of accurately identifying and sorting fruits, thereby streamlining agricultural processes. The implementation section details the practical realization of the apple detection system using Raspberry Pi. Hardware setup involves the integration of Raspberry Pi boards with camera modules and other peripherals necessary for image acquisition and processing. Software development entails the creation of Python>based modules for image preprocessing, feature extraction, and classification. OpenCV and scikit>learn libraries are utilized for implementing image processing and machine learning algorithms, respectively. The system is tested in different environments to evaluate its performance under various conditions, including controlled laboratory settings and outdoor agricultural scenarios.

Enhancing Insect Sound Classification Using Dual-Tower Network: A Fusion of Temporal and Spectral Feature Perception

In the modern field of biological pest control, especially in the realm of insect population monitoring, deep learning methods have made further advancements. However, due to the small size and elusive nature of insects, visual detection is often impractical. In this context, the recognition of insect sound features becomes crucial. In our study, we introduce a classification module called the “dual-frequency and spectral fusion module (DFSM)”, which enhances the performance of transfer learning models in audio classification tasks. Our approach combines the efficiency of EfficientNet with the hierarchical design of the Dual Towers, drawing inspiration from the way the insect neural system processes sound signals. This enables our model to effectively capture spectral features in insect sounds and form multiscale perceptions through inter-tower skip connections. Through detailed qualitative and quantitative evaluations, as well as comparisons with leading traditional insect sound recognition methods, we demonstrate the advantages of our approach in the field of insect sound classification. Our method achieves an accuracy of 80.26% on InsectSet32, surpassing existing state-of-the-art models by 3 percentage points. Additionally, we conducted generalization experiments using three classic audio datasets. The results indicate that DFSM exhibits strong robustness and wide applicability, with minimal performance variations even when handling different input features.

Tang Chang’an poetry automatic classification: a practical application of deep learning methods

Abstract As the capital of Tang Dynasty, Chang’an was one of the most prosperous cities in the world at that time and had a profound influence on Tang poetry. Poets described Chang’an to illustrate the cultural features of the Tang Dynasty while also invoking emotions in readers. The study of Tang Chang’an poetry has important literary and historical value. In order to understand the interpretation and emotional expression of Tang Chang’an poetry more conveniently and clearly, we conducted a study using deep learning to classify Chang’an poetry into four classes: imperially assigned poetry (应制), emotional poetry (感怀), parting poetry (离别), and other poetry (其他). We suggested a comprehensive framework of text classification based on deep learning, including a text input module, feature encoder module, and classification module. We applied several mainstream deep neural network structures to extract features in different ways, which comprised convolutional neural network (CNN), Fasttext, bi-direction long-short-term memory network, and Attention mechanism. Based on our experimental findings, the CNN-based method achieved the best performance for the task. Our inference was that, in Chinese ancient poetry, the analysis of semantic content is more facilitated by local textual features rather than contextual features. We combined this inference with the theory of image in Chinese ancient poetry to analyze the suitability of the deep learning techniques for the study of Chinese ancient poetry.

A two‐stage substation equipment classification method based on dual‐scale attention

AbstractAccurate classification of substation equipment images remains challenging due to various factors such as unexpected illumination, viewing angles, scale variations, shadows, surface contaminants, and different elements sharing similar appearances. This paper presents a novel two‐stage substation equipment classification method based on dual‐scale attention. Leveraging the region proposal technique from Faster‐regions with CNN features (RCNN), the input images are initially decomposed into multiple scales to capture latent features. A dual‐scale attention module is introduced to enhance the precision of feature extraction. Furthermore, a two‐stage network is proposed to address the challenge of classifying closely similar substation equipment. A multi‐layer perceptron performs a coarse classification to categorize the equipment into broad categories. Then, a lightweight classifier is employed for fine‐grained subclassification, further distinguishing equipment within the same broad category. To mitigate the issue of limited training data, a specialized dataset is collected and annotated for the substation equipment classification. Experimental results demonstrate that the proposed method achieves remarkable accuracy, recall, and F1‐score surpassing 0.91, outperforming mainstream approaches in terms of recall and F1 scores. Ablation experiments further validate the significant contributions of both the dual‐scale attention and the two‐stage classification module in improving the overall performance of the classification network.

Classification-driven intelligent system for automated evaluation of higher education exam paper quality

AbstractEvaluating the quality of university exam papers is crucial for universities seeking institutional and program accreditation. Currently, exam papers are assessed manually, a process that can be tedious, lengthy, and in some cases, inconsistent. This is often due to the focus on assessing only the formal specifications of exam papers. This study develops an intelligent system for the automatic evaluation of university exam papers in terms of form and content, ensuring adherence to quality standards. The system is composed of two subsystems: the first evaluates compliance with formal specifications, and the second analyzes the content. The content analysis involves automatically categorizing exam questions based on Bloom's cognitive levels (BCLs) and determining the representation ratio of these levels in the exam paper. This subsystem comprises four main modules: 1) question collection, 2) text pre-processing using natural language processing (NLP) methods, 3) feature engineering using the CountVectorizer method to convert questions into feature vectors, and 4) a classification module based on the Logistic Regression (LR) algorithm to categorize exam questions into categories like knowledge, comprehension, application, analysis, synthesis, and evaluation. Experimental results indicate that the system achieves an average accuracy of 98.5%.

Enhancing mass spectrometry data analysis: A novel framework for calibration, outlier detection, and classification

Mass spectrometry (MS) is a powerful analytical technique in metabolomics, enabling the identification and quantification of metabolites. However, analyzing MS data poses challenges such as batch effects, outliers, and high-dimensional data. In this paper, we propose a comprehensive framework for MS data analysis. The framework integrates data calibration, outlier detection, and automatic classification modules. Data calibration is performed using a deep autoencoder to remove batch effects. Outlier detection combines multiple algorithms through ensemble learning to identify and remove outliers. Automatic classification utilizes a transformer model to handle high-dimensional data and capture global feature relationships. Experimental results on myocardial infarction (MI) and coronary heart disease (CHD) datasets demonstrate the effectiveness of the framework. It outperforms traditional classification models and achieves higher accuracy. The proposed framework provides a robust solution for MS data analysis, facilitating more accurate classification and enabling reliable biological insights in metabolomics research.

Hand-held GPU accelerated device for multiclass classification of X-ray images using CNN model

Chest X-ray (CXR) images are the primary investigation aid for many lung diseases and their follow-ups. For diagnosis of SARS-CoV-2, RT–PCR test and chest Computed Tomography (CT) are commonly used but both face false negatives for ruling out the infection. So, there is a demanding need for developing a system combined with Artificial Intelligence (AI) and CXR imaging to detect COVID-19 patients to avoid its spread. Here, a robust and efficient handheld device is proposed. It uses the computational power of the Graphics Processing Unit (GPU) and pre-trained deep learning models for analyzing the CXR images. A Resnet-50 CNN model is deployed on an NVIDIA Jetson Nano GPU module for the real-time classification of COVID-19, Tuberculosis, and Normal using CXR images. The device can perform real-time classification of CXR images from a portable X-ray machine and classify the image into one of the above categories. For the extensive training, a database of 680 COVID-19, 1230 Tuberculosis, and 1050 normal CXR images are extracted by combining several global databases like Kaggle, SIRM, RSNA, and Radiopaedia. The classification accuracy, precision, and loss rate were 0.9879, 0.9758, and 0.0196 respectively and our model would improve with larger data sets. The highly accurate and high-performance GPU device significantly plays a far-reaching role in COVID-19 diagnosis using Chest X-ray, which could be beneficial to triage the health system and to combat the outbreak of COVID-19.

Learning to Synthesize Compatible Fashion Items Using Semantic Alignment and Collocation Classification: An Outfit Generation Framework.

The field of fashion compatibility learning has attracted great attention from both the academic and industrial communities in recent years. Many studies have been carried out for fashion compatibility prediction, collocated outfit recommendation, artificial intelligence (AI)-enabled compatible fashion design, and related topics. In particular, AI-enabled compatible fashion design can be used to synthesize compatible fashion items or outfits to improve the design experience for designers or the efficacy of recommendations for customers. However, previous generative models for collocated fashion synthesis have generally focused on the image-to-image translation between fashion items of upper and lower clothing. In this article, we propose a novel outfit generation framework, i.e., OutfitGAN, with the aim of synthesizing a set of complementary items to compose an entire outfit, given one extant fashion item and reference masks of target synthesized items. OutfitGAN includes a semantic alignment module (SAM), which is responsible for characterizing the mapping correspondence between the existing fashion items and the synthesized ones, to improve the quality of the synthesized images, and a collocation classification module (CCM), which is used to improve the compatibility of a synthesized outfit. To evaluate the performance of our proposed models, we built a large-scale dataset consisting of 20 000 fashion outfits. Extensive experimental results on this dataset show that our OutfitGAN can synthesize photo-realistic outfits and outperform the state-of-the-art methods in terms of similarity, authenticity, and compatibility measurements.

Enhancing temporal action localization in an end-to-end network through estimation error incorporation

Temporal action localization presents a significant challenge in computer vision, as the development of an efficient method for this task remains elusive. The objective is to identify human activities within untrimmed videos, determining when and which actions occur in each video. While using trimmed videos could potentially resolve the localization problem and enhance classification accuracy, it is impractical for real-world applications as the trimming process itself requires human intervention. This highlights the importance of temporal localization. Due to the availability of several successful approaches for action recognition in trimmed video, conventional multi-stage methods for untrimmed video, commonly employ a network to generate activity proposals, followed by a separate network for classification. These disjoint networks are optimized individually and thus usually vary from the global optimum, leading to less precise candidate action proposals. To address this challenge, we propose a novel end-to-end neural network that utilizes error estimation for precise action localization and recognition in untrimmed videos. The proposed method performs the localization and classification of action instances simultaneously, thereby optimizing the corresponding networks concurrently. To increase the precision of the action proposal boundaries, the Regression module is innovatively utilized as part of the proposed end-to-end network, along with the Evaluation and Classification modules. This module estimates the potential error in proposal time boundaries and enhances the result accuracy. We have conducted experiments on THUMOS 14 and ActivityNet-1.3, which are considered the most challenging datasets for temporal action localization. The novel, yet fairly simple, proposed network achieves remarkable performance improvement compared to the other state-of-the-art methods. This improvement, which is more pronounced in the cases of high temporal intersection with ground truth, is accomplished without requiring extra data or complicated architecture. By incorporating error estimation, we achieved improvement in mean Average Precision (mAP). The proposed approach particularly shines for the localization of challenging activities in the complex and diverse dataset ActivityNet-1.3. For instance, for the “drinking coffee” activity, the mean Average Precision (mAP) was enhanced fivefold compared to the best-reported results.

TransPTM: a transformer-based model for non-histone acetylation site prediction.

Protein acetylation is one of the extensively studied post-translational modifications (PTMs) due to its significant roles across a myriad of biological processes. Although many computational tools for acetylation site identification have been developed, there is a lack of benchmark dataset and bespoke predictors for non-histone acetylation site prediction. To address these problems, we have contributed to both dataset creation and predictor benchmark in this study. First, we construct a non-histone acetylation site benchmark dataset, namely NHAC, which includes 11 subsets according to the sequence length ranging from 11 to 61 amino acids. There are totally 886 positive samples and 4707 negative samples for each sequence length. Secondly, we propose TransPTM, a transformer-based neural network model for non-histone acetylation site predication. During the data representation phase, per-residue contextualized embeddings are extracted using ProtT5 (an existing pre-trained protein language model). This is followed by the implementation of a graph neural network framework, which consists of three TransformerConv layers for feature extraction and a multilayer perceptron module for classification. The benchmark results reflect that TransPTM has the competitive performance for non-histone acetylation site prediction over three state-of-the-art tools. It improves our comprehension on the PTM mechanism and provides a theoretical basis for developing drug targets for diseases. Moreover, the created PTM datasets fills the gap in non-histone acetylation site datasets and is beneficial to the related communities. The related source code and data utilized by TransPTM are accessible at https://www.github.com/TransPTM/TransPTM.

Advancements in Healthcare: Development of a Comprehensive Medical Information System with Automated Classification for Ocular and Skin Pathologies—Structure, Functionalities, and Innovative Development Methods

This article introduces a groundbreaking medical information system developed in Salesforce, featuring an automated classification module for ocular and skin pathologies using Google Teachable Machine. Integrating cutting-edge technology with Salesforce’s robust capabilities, the system provides a comprehensive solution for medical practitioners. The article explores the system’s structure, emphasizing innovative functionalities that enhance diagnostic precision and streamline medical workflows. Methods used in development are discussed, offering insights into the integration of Google Teachable Machine into the Salesforce framework. This collaborative approach is a significant stride in intelligent pathology classification, advancing the field of medical information systems and fostering efficient healthcare practices.

FIRCNet: Feature-based image reconstruction with classification learning for person re-identification

As an important task in computer vision, the person re-identification problem has two main approaches, identification and verification, in solving this problem. This paper proposed a network associating these two separate methods in training, including a reconstructive model. This design encompasses three core modules: a global feature extractor, a reconstructive decoder, and a classification network. Thus, the total loss function seamlessly integrates triplet loss, cross-entropy loss, and a reconstruction loss, optimizing the network for feature discriminability, accurate identity classification, and faithful image reconstruction concurrently. The global feature plays a pivotal role in both the training and testing phases, aiding in metric learning and ensuring a distinctive representation of identities. To train the decoder and the backbone network, which collectively forms the extractor, simultaneously, the reconstructive model as a part of a wide-range autoencoder can recover the original image from extracted features. Subsequently, the classification module classifies these features, assigning them to distinct person IDs, thereby aiding in precise identity recognition. Experimental results on three major re-id datasets give a demonstration of the notable improvement in the backbone network and the advantages of this approach compared to the state-of-the-art methods.