Research on sports image classification method based on SE-RES-CNN model Scientific Reports

Using this feature allows for more stable and efficient model training, streamlining the process of fine-tuning training parameters without the need to manually adjust the learning rate. The resulting feature map is transformed into a flat vector with a flattening layer. Finally, the output layer has 4 neurons and calculates the probabilities between classes with the softmax activation function. This algorithm dynamically adjusts the learning rate and helps to use gradients more efficiently. Also, categorical cross-entropy is used as the loss function during training, as it is widely used in multiclass classification task.

• In the evolving landscape of image recognition apps, technology has taken significant strides, empowering our smartphones with remarkable capabilities.
• More specifically, three recent studies have reported promising results in the application of deep learning-based models to identify the four molecular subtypes of EC from histopathology images22,23,29.
• In comparison, our model examined up to 13 diagnoses, was built on combinations of datasets and evaluated more datasets when examining external validity.
• We employed a class-discriminative localization method to identify and highlight the relevant histological features on these patches.
• Consequently, it is essential to develop automatic analysis algorithms for infrared images to ensure the reliable diagnosis of thermal faults in electrical equipment and to enhance the intelligence level of the power system.

This combined approach, referred to as CNorm throughout the paper, was utilized to assess the performance of AIDA against other color normalization techniques. The patch-level results obtained from the three aforementioned steps are then aggregated to produce slide-level results. We employ the Vector of Locally Aggregated Descriptors (VLAD)45 approach to generate slide-level features from extracted features of the patches within a slide. This method was selected due to its capability to efficiently aggregate local features into a condensed representation, thereby decreasing computational complexity while retaining discriminative information. VLAD encoding has proven successful in a variety of computer vision tasks, rendering it a fitting choice for our feature aggregation step. Finally, we apply the Support Vector Machine (SVM) classifier to classify the slides (Fig. 1d).

Deep learning approaches for object detection in multimedia

These examples illuminate the expansive realm of image recognition, propelling our smartphones into realms beyond imagination. Targeted at art and photography enthusiasts, Prisma employs sophisticated neural networks to transform photos into visually stunning artworks, emulating the styles of renowned painters. Users can choose from a diverse array of artistic filters, turning mundane snapshots into masterpieces. ai based image recognition This unique intersection of technology and creativity has garnered Prisma a dedicated user base, proving that image recognition can be a canvas for self-expression in the digital age. Combining deep learning and image classification technology, this app scans the content of the dish on your plate, indicating ingredients and computing the total number of calories – all from a single photo!

Artificial intelligence (AI) is a concept that refers to a machine’s ability to perform a task that would’ve previously required human intelligence. It’s been around since the 1950s, and its definition has been modified over decades of research and technological advancements. Table 8 The potato ChatGPT crop diseases with their symptoms based on causative agents (Bacteria, Virus, and Fungus). Table 4 The tomato crop diseases with their symptoms based on causative agents (bacteria, virus, and fungus). Datamation is the leading industry resource for B2B data professionals and technology buyers.

ORIGINAL RESEARCH article

Work accidents remain a huge, cross-industry problem, despite safety regulations and procedures. Visual recognition AI technologies can be used to monitor and enforce safety regulations. For example, PowerAI Vision can alert workers when entering hazardous environments or scan a construction area to alert supervisors when they need to act. Find out how the manufacturing sector is using AI to improve efficiency in its processes. And T.K.K.; Writing—Review & Editing, T.P., T.K.K., K.K., H.K., and H.S.K.; Visualization, T.P., T.K.K., and H.S.K.; Supervision, H.K.

● Remote sensing photos are frequently employed in military and agricultural industries and are detected in real-time. The rapid development of these fields will be aided by automatic model detection and integrated hardware components. All authors contributed to manuscript revision, read, and approved the submitted version. Relevant research points out that sentence length significantly impacts learners’ understanding. Long sentences, especially in language teaching, can pose challenges for students23.

Figure 4 The corresponding improvement scheme of algorithm detection flow (A) Augmentation (B) Deep Learning (C) Results. The SVM classifier is still utilized, which requires a lot of training steps and takes a long time. The test speed is slow, because the CNN structures need to be mined in each test image object proposal, and there is no shared computation. Results of stepwise ChatGPT App multiple regression analysis of the impact of classroom discourse indicators on comprehensive course evaluation. Figure 6 presents the results of stepwise multiple regression analysis examining the impact of classroom discourse indicators on learners’ course evaluation. Correlation analysis between classroom discourse indicators and comprehensive scores of course evaluation.

Artificial intelligence (AI) raises an acute set of challenges with respect to export control. On the one hand, AI opens the door to potentially transformative military technologies. The United States has a strong interest in ensuring that U.S.-developed AI technology is not used by geopolitical rivals in ways that threaten national security. A key framework to further that interest is the Export Control Reform Act of 2018 (ECRA). The ECRA gives the Department of Commerce the authority to promulgate new export control rules regarding AI technologies. We acknowledge the North East Centre for Technology Application and Reach (NECTAR), Department of Science and Technology (DST), Government of India, for providing funds (grant no-NECTAR/404/DST/2021).

CNN-based transfer learning

Example of an ECG image generated from the signal data of one sample in the dataset. For each individual ECG sample, an image was generated by plotting the signal data. We used the Python ECG plot library (dy1901, 2022), which generates 12-lead ECG images resembling ECG displays and print-outs commonly used in clinical practice. The LEAFIO Shelf Efficiency dashboard now features expanded analytical data and metrics for visual merchandising processes. The newly added Task Report in Visual Analytics provides comprehensive insights into task execution across the retail chain, offering detailed filters by author, executor, brand, supplier, and specific stores. Lookout by Google exemplifies the tech giant’s commitment to accessibility.The app utilizes image recognition to provide spoken notifications about objects, text, and people in the user’s surroundings.

Where A is the input grayscale image; t is the diffusion time; div is the dispersion operator; \(\nabla\) is the partial derivative i.e. gradient operator; Δ is the Laplace operator; c is the diffusion function, which controls the diffusion. Instant access to relevant products reduces decision-making time, fostering higher satisfaction and loyalty among consumers. And even sites that use the updated reCAPTCHA v3 will sometimes use reCAPTCHA v2 as a fallback when the updated system gives a user a low “human” confidence rating. Ben Lutkevich is a writer for WhatIs, where he writes definitions and features. Take the false implication case of 61-year-old Harvey Murphy Jr. as an example of facial recognition gone wrong.

After all the required images have been captured, they are sent to the image preprocessing stage to be adjusted before use. If the collected images do not fulfill the processing requirements, there is a need to employ image-enhancing methods (Basavaiah and Anthony, 2020). Feature engineering sets the stage for effective learning, while model training is where the actual learning happens. In feature engineering, data is analyzed to identify or create new features that are most relevant for classification.

Exploring the role of foundation models in AIDA

As a result, the issue of image classification has garnered significant attention1,2,3. This would seem to be too many competitors for this specialized area but image recognition, and more broadly deep learning-based AI applications have many ways in which to specialize. In the development process of the OrgaExtractor model, which employs supervised learning, binary masks were generated to correspond with the organoid images. These masks served as ground truths for comparison with the predictions of the DL model. The establisher (T.K.K.) of the patient-derived normal colon organoids visually annotated the regions of projected organoids in the original image. Organoids that were neither cut at the edges nor less than 40 μm in size were outlined.

A comprehensive approach is proposed, ranging from preprocessing to recognition, for diagnosing thermal faults in infrared images of electrical equipment. This contributes valuable experience and viable solutions for future automation of electrical equipment inspection. The Path Aggregation Network (PAN) is incorporated subsequent to the FPN module, as indicated in Fig. The PAN structure enables a “bottom-up” feature fusion mechanism by downsampling the shallow feature map with Conv + BN + ReLU and then superimposing it onto the deeper feature map. This approach enriches the target texture and position information conveyed from the shallow to the deeper feature map.

The dataset consisted of 892 images depicting healthy leaves, leaves with early blight, and leaves with late blight. Applying the fuzzy c-mean clustering technique to each image helped identify and distinguish healthy and diseased categories. The simulation showed that the ANN was the most accurate ML technique for detecting diseases.

Disparities across different demographic subgroups have been identified in many areas of medicine11,12, including medical imaging13,14. These disparities span the full care continuum, from access to imaging to patient outcomes and even the image acquisition process itself14. Regarding image acquisition, several studies have shown evidence of bias in image and positioning quality and in access to newer breast imaging technology18,19,20. In our experiments, all methods were executed on an NVIDIA GeForce RTX 3090 GPU, with 24 GB of GDDR6X memory. Given that the Base, Base-FFT, Macenko, HED, and CNorm methods all shared identical architectures, and ADA and AIDA exhibited similar structures, we will focus solely on comparing the time analysis between the Base and AIDA. This comparison will provide insights into their respective training times and utilization of computational resources, as outlined in Supplementary Table 7.

Therefore, this work designates average sentence length as one of the strategic features of classroom discourse in online education for secondary schools. Some educators speak too fast, making it challenging for learners to keep up, while others might talk too slowly, affecting learning interest and garnering negative comments. Therefore, setting a fixed standard for the speaking rate in online courses is challenging.

Against this backdrop, traditional convolutional neural network (CNN)-based UNet models face several issues when processing tunnel face images. This research demonstrates that computer vision AI models can diagnose conditions on ECG with good accuracy. Future research which could bring this technology closer to clinical application could focus on developing models which can generalize to a wide range of ECG image formats from various sources and cover a wider range of relevant clinical diagnoses. You can foun additiona information about ai customer service and artificial intelligence and NLP. Additional diagnoses which would be of interest clinically would include diagnosing STEMI in patients with LBBB or pacemaker, differentiating SVT with aberrancy vs. VT, and specific subtypes of AV block.

Our model demonstrated strong diagnostic performance on unseen ECGs sampled from the same population(s) and dataset(s) as that used for model training. This high level of internal validity has been reflected in the literature from previous computer vision-based models (Mohamed et al., 2015; Jun et al., 2018; Sangha et al., 2022). In comparison, our model examined up to 13 diagnoses, was built on combinations of datasets and evaluated more datasets when examining external validity. Table 3 shows results from this model were comparable to the other recent image-based studies and 12-lead ECG signal based work. Additionally, some of the most accurate single pathology signal-based models e.g., for atrial fibrillation have comparable findings to this study (AUROC 0.997 vs. 1 and Sensitivity of 0.985 to 0.992) (Jo et al., 2021). The key advantage of convolutional neural networks is their ability to automatically learn features from data.