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In recent years, a variety of deep learning networks for cardiac MRI (CMR) segmentation have been developed and analyzed. However, nearly all of them are focused on cine CMR under breathold. In this work, accuracy of deep learning methods is assessed for volumetric analysis (via segmentation) of the left ventricle in real-time free-breathing CMR at rest and under exercise stress. Data from healthy volunteers (n = 15) for cine and real-time free-breathing CMR at rest and under exercise stress were analyzed retrospectively. Exercise stress was performed using an ergometer in the supine position. Segmentations of two deep learning methods, a commercially available technique (comDL) and an openly available network (nnU-Net), were compared to a reference model created via the manual correction of segmentations obtained with comDL. Segmentations of left ventricular endocardium (LV), left ventricular myocardium (MYO), and right ventricle (RV) are compared for both end-systolic and end-diastolic phases and analyzed with Dice's coefficient. The volumetric analysis includes the cardiac function parameters LV end-diastolic volume (EDV), LV end-systolic volume (ESV), and LV ejection fraction (EF), evaluated with respect to both absolute and relative differences. For cine CMR, nnU-Net and comDL achieve Dice's coefficients above 0.95 for LV and 0.9 for MYO, and RV. For real-time CMR, the accuracy of nnU-Net exceeds that of comDL overall. For real-time CMR at rest, nnU-Net achieves Dice's coefficients of 0.94 for LV, 0.89 for MYO, and 0.90 for RV and the mean absolute differences between nnU-Net and the reference are 2.9 mL for EDV, 3.5 mL for ESV, and 2.6% for EF. For real-time CMR under exercise stress, nnU-Net achieves Dice's coefficients of 0.92 for LV, 0.85 for MYO, and 0.83 for RV and the mean absolute differences between nnU-Net and reference are 11.4 mL for EDV, 2.9 mL for ESV, and 3.6% for EF. Deep learning methods designed or trained for cine CMR segmentation can perform well on real-time CMR. For real-time free-breathing CMR at rest, the performance of deep learning methods is comparable to inter-observer variability in cine CMR and is usable for fully automatic segmentation. For real-time CMR under exercise stress, the performance of nnU-Net could promise a higher degree of automation in the future., (© 2024. The Author(s).)

To explore whether the brain contains pattern differences in the rock–paper–scissors (RPS) imagery task, this paper attempts to classify this task using fNIRS and deep learning. In this study, we designed an RPS task with a total duration of 25 min and 40 s, and recruited 22 volunteers for the experiment. We used the fNIRS acquisition device (FOIRE-3000) to record the cerebral neural activities of these participants in the RPS task. The time series classification (TSC) algorithm was introduced into the time-domain fNIRS signal classification. Experiments show that CNN-based TSC methods can achieve 97% accuracy in RPS classification. CNN-based TSC method is suitable for the classification of fNIRS signals in RPS motor imagery tasks, and may find new application directions for the development of brain–computer interfaces (BCI).

These data were used to build an object detection model to locate Relict Charcoal Hearths (RCH) as described in the paper “When Computers Dream of Charcoal: Using Deep Learning, Open Tools and Open Data to Identify Relict Charcoal Hearths in and around State Game Lands in Pennsylvania” [1]. This is the second grouping of data for the paper above. The first grouping is also available in this journal, see “Geospatial and image data from the “When Computers Dream of Charcoal: Using Deep Learning, Open Tools and Open Data to Identify Relict Charcoal Hearths in and around State Game Lands in Pennsylvania” paper” [2]. These files consist of: JPEGs representing tiles of larger Slope TIFF files derived from LiDAR for the State Game Lands (SGL) of Pennsylvania, United States [3456]. A subset of these tiles was used to train the model. A Shapefile of points of known relict charcoal hearths (RCH). XML files representing the pixel points of known RCHs on JPEG files used for training. Jupyter notebooks of programs used to prepare data and train a Mask R-CNN model. The Mask R-CNN model H5 file. Shapefile and GeoJSON of object detection results from the model showing locations of possible RCH in all SGLs. XML files representing the pixel points of predicted RCH on JPEG files used for predictions. GeoJSON of results using cluster analysis. These data are stored onzenodo.org. The programs are stored on Github.com.

To improve the accuracy of user implicit rating prediction, we combine the traditional latent factor model (LFM) and bidirectional gated recurrent unit neural network (BiGRU) model to propose a hybrid model that deeply mines the latent semantics in the unstructured content of the text and generates a more accurate rating matrix. First, we utilize the user’s historical behavior (favorites records) to build a user rating matrix and decompose the matrix to obtain the latent factor vectors of users and literature. We also apply the BERT model for word embedding of the research papers to obtain the sequence of word vectors. Then, we apply the BiGRU with the user attention mechanism to mine the research paper textual content and to generate the new literature latent feature vectors that are used to replace the original literature latent factor vectors decomposed from the rating matrix. Finally, a new rating matrix is generated to obtain users’ ratings of noninteractive research papers and to generate the recommendation list according to the user latent factor vector. We design experiments on the real datasets and verify that the research paper recommendation model is superior to traditional recommendation models in terms of precision, recall, F1-value, coverage, popularity and diversity.

Machine learning (ML), a form of artificial intelligence (AI), is being increasingly employed in neurology. Reported performance metrics often match or exceed the efficiency of average clinicians. The neurologist is easily baffled by the underlying concepts and terminologies associated with ML studies. The superlative performance metrics of ML algorithms often hide the opaque nature of its inner workings. Questions regarding ML model's interpretability and reproducibility of its results in real-world scenarios, need emphasis. Given an abundance of time and information, the expert clinician should be able to deliver comparable predictions to ML models, a useful benchmark while evaluating its performance. Predictive performance metrics of ML models should not be confused with causal inference between its input and output. ML and clinical gestalt should compete in a randomized controlled trial before they can complement each other for screening, triaging, providing second opinions and modifying treatment.

These data were used to build an object detection model to locate Relict Charcoal Hearths (RCH) as described in the paper “When Computers Dream of Charcoal: Using Deep Learning, Open Tools and Open Data to Identify Relict Charcoal Hearths in and around State Game Lands in Pennsylvania” [1]. This is the first grouping of data for the paper above. The second grouping is also available in this journal, see “Object detection model, image data and results from the “When Computers Dream of Charcoal: Using Deep Learning, Open Tools and Open Data to Identify Relict Charcoal Hearths in and around State Game Lands in Pennsylvania” paper”. These files consist of georeferenced Digital Elevation Model, Hillshade and Slope files derived from LiDAR for the State Game Lands (SGL). Included is a Shapefile and GeoJSON of State Game Land borders as well as the program used for downloading the LiDAR files. These data are stored on 'Zenodo.org'.

Medical imaging is currently using artificial intelligence-based technologies to aid evaluate diagnostic information images, particularly in enforcing kidney stones. Artificial intelligence technology continues to develop, many studies show that deep learning is more widely used compared to traditional machine learning, so an Artificial intelligence system is needed to assist the accuracy of health diagnoses, thus helping in the field of radiology health. The aim of the research is to use artificial intelligence with deep learning models to help detect abnormalities in the kidneys. This research method is a literature review of Scopus data related to deep learning in medical imaging in detecting kidney stones. The results of using Artificial Intelligence in medical imaging can be used in diagnosing diseases including detecting Covid-19, musculoskeletal, calcium scores on Cardiac CT, liver tumors, urinary tract lesions, examination of the abdomen and kidney stones. Utilization of Artificial Intelligence in detecting kidney stones can be done with various classification models including XResNet-50, ExDark19, CystoNet, CNN, ANN. Using the right model and having a high accuracy value can help radiologists to accurately detect kidney stones.

Precision in the diagnosis of diverse central nervous system (CNS) tumor types is crucial for optimal treatment. DNA methylation profiles, which capture the methylation status of thousands of individual CpG sites, are state-of-the-art data-driven means to enhance diagnostic accuracy but are also time consuming and not widely available. Here, to address these limitations, we developed Deep lEarning from histoPathoLOgy and methYlation (DEPLOY), a deep learning model that classifies CNS tumors to ten major categories from histopathology. DEPLOY integrates three distinct components: the first classifies CNS tumors directly from slide images ('direct model'), the second initially generates predictions for DNA methylation beta values, which are subsequently used for tumor classification ('indirect model'), and the third classifies tumor types directly from routinely available patient demographics. First, we find that DEPLOY accurately predicts beta values from histopathology images. Second, using a ten-class model trained on an internal dataset of 1,796 patients, we predict the tumor categories in three independent external test datasets including 2,156 patients, achieving an overall accuracy of 95% and balanced accuracy of 91% on samples that are predicted with high confidence. These results showcase the potential future use of DEPLOY to assist pathologists in diagnosing CNS tumors within a clinically relevant short time frame., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Background: Recommendations for cosmetics are gaining popularity, but they are not being made with consideration of the analysis of cosmetic ingredients, which customers consider important when selecting cosmetics., Aims: This article aims to propose a method for estimating the efficacy of cosmetics based on their ingredients and introduces a system that recommends personalized products for consumers, combined with AI skin analysis., Methods: We constructed a deep neural network architecture to analyze sequentially arranged cosmetic ingredients in the product and incorporated skin analysis models to get the precise skin status of users from frontal face images. Our recommendation system makes decisions based on the results optimized for the individual., Results: Our cosmetic recommendation system has shown its effectiveness through reliable evaluation metrics, and numerous examples have demonstrated its ability to make reasonable recommendations for various skin problems., Conclusion: The result shows that deep learning methods can be used to predict the effects of products based on their cosmetic ingredients and are available for use in personalized cosmetic recommendations., (© 2024 The Authors. Journal of Cosmetic Dermatology published by Wiley Periodicals LLC.)

This paper provides a bibliometric analysis of current research trends in the field of artificial intelligence (AI), focusing on key topics such as deep learning, machine learning, and security in AI. Through the lens of bibliometric analysis, we explore publications published from 2020 to 2024, using primary data from the Clarivate Analytics Web of Science Core Collection. The analysis includes the distribution of studies by year, the number of studies and citation rankings in journals, and the identification of leading countries, institutions, and authors in the field of AI research. Additionally, we investigate the distribution of studies by Web of Science categories, authors, affiliations, publication years, countries/regions, publishers, research areas, and citations per year. Key findings indicate a continued growth of interest in topics such as deep learning, machine learning, and security in AI over the past few years. We also identify leading countries and institutions active in researching this area. Awareness of data security is essential for the responsible application of AI technologies. Robust security frameworks are important to mitigate risks associated with AI integration into critical infrastructure such as healthcare and finance. Ensuring the integrity and confidentiality of data managed by AI systems is not only a technical challenge but also a societal necessity, demanding interdisciplinary collaboration and policy development. This analysis provides a deeper understanding of the current state of research in the field of AI and identifies key areas for further research and innovation. Furthermore, these findings may be valuable to practitioners and decision-makers seeking to understand current trends and innovations in AI to enhance their business processes and practices. [ABSTRACT FROM AUTHOR]

This research aimed to compare two strategies for vocabulary learning (digital-game-based learning and paper-based learning). The research was conducted during the first semester of the academic year 2022/2023. A total of 40 EFL undergraduate students within the Applied English Program of a private university located in the middle part of Taiwan were selected and divided into two groups: digital-based (n = 20) and paper-based (n = 20). The instrument developed by the researcher was pre- and post-vocabulary tests for both groups. The pre-vocabulary test was implemented to identify the level of students' prior knowledge of vocabulary mastery. For the intervention, Kahoot! quiz exercises were conducted weekly for the digital-game-based group, while the paper-based group received the same quiz on paper every week. The post-vocabulary tests showed no significant difference between the students using digital-game-based quizzes and paper-based quizzes during the six-week intervention. [ABSTRACT FROM AUTHOR]

Machine learning has been leveraged for image analysis applications throughout a multitude of subspecialties. This position paper provides a perspective on the evolutionary trajectory of practical deep learning tools for genitourinary pathology through evaluating the most recent iterations of such algorithmic devices. Deep learning tools for genitourinary pathology demonstrate potential to enhance prognostic and predictive capacity for tumor assessment including grading, staging, and subtype identification, yet limitations in data availability, regulation, and standardization have stymied their implementation. [ABSTRACT FROM AUTHOR]

In the production of paper cups in industrial factories, it is tried to print high quality cups with less waste loss with the help of sensors and heating resistances mounted on the paper cup machine. In this study, a system that detects faulty products based on image processing and removes it by controlling the machine with servo motors, asynchronous motors and programmable logic controller (PLC) is designed. For fault product detection, classification has been performed using real-time Haarcascade algorithm and You Only Look Once (YOLO) algorithm which is a deep learning methods, and real-time object detection has been carried out using the OpenCv library. With this study, an effective faulty product detection and removing hardware system was realized by adapting artificial intelligence algorithms to a machine used in industry. Based on the results, a whole system can be applied to systems that involve removing a faulty product from a band in any production, packaging etc. facility is proposed. A hardware consisting of servo motors, asynchronous motors and PLC was designed to separate faulty cups from the existing paper cup production machine in this study. Then, a data set composed of 1068 images was created with images taken from the camera for faulty and faultless paper cups. Using this dataset, the effect of different deep learning methods on performance in the real-time system has been examined and successful results have been obtained. The optimal outcome was achieved, yielding a real-time application accuracy rate of 90.8% through the utilization of the Yolov5x architecture. [ABSTRACT FROM AUTHOR]

Thyroid nodules are a common occurrence, and although most are non-cancerous, some can be malignant. The American College of Radiology has developed the Thyroid Imaging Reporting and Data System (TI-RADS) to standardize the interpretation and reporting of thyroid ultrasound results. Within TI-RADS, a category 4 designation signifies a thyroid nodule with an intermediate level of suspicion for malignancy. Accurate classification of these nodules is crucial for proper management, as it can potentially reduce unnecessary surgeries and improve patient outcomes. This study utilized deep learning techniques to effectively classify TI-RADS category 4 thyroid nodules as either benign or malignant. A total of 500 patients were included in the study and randomly divided into a training group (350 patients) and a test group (150 patients). The YOLOv3 model was constructed and evaluated using various metrics, achieving an 84% accuracy in the classification of TI-RADS category 4 thyroid nodules. Based on the predictions of the model, along with clinical and ultrasound data, a nomogram was developed. The performance of the nomogram was superior in both the training and testing groups. Furthermore, the calibration curve demonstrated good agreement between predicted probabilities and actual outcomes. Decision curve analysis further confirmed that the nomogram provided greater net benefits. Ultimately, the YOLOv3 model and nomogram successfully improved the accuracy of distinguishing between benign and malignant TI-RADS category 4 thyroid nodules, which is crucial for proper management and improved patient outcomes., (© 2024. The Author(s).)

Accurately predicting population mortality rates is crucial for effective retirement insurance and economic policy formulation. Recent advancements in deep learning time series forecasting (DLTSF) have led to improved mortality rate predictions compared to traditional models like Lee-Carter (LC). This study focuses on mortality rate prediction in large clusters across Europe. By utilizing PCA dimensionality reduction and statistical clustering techniques, we integrate age features from high-dimensional mortality data of multiple countries, analyzing their similarities and differences. To capture the heterogeneous characteristics, an adaptive adjustment matrix is generated, incorporating sequential variation and spatial geographical information. Additionally, a combination of graph neural networks and a transformer network with an adaptive adjustment matrix is employed to capture the spatiotemporal features between different clusters. Extensive numerical experiments using data from the Human Mortality Database validate the superiority of the proposed GT-A model over traditional LC models and other classic neural networks in terms of prediction accuracy. Consequently, the GT-A model serves as a powerful forecasting tool for global population studies and the international life insurance field., (© 2024. The Author(s).)

Background: Drug discovery processes, such as new drug development, drug synergy, and drug repurposing, consume significant yearly resources. Computer-aided drug discovery can effectively improve the efficiency of drug discovery. Traditional computer methods such as virtual screening and molecular docking have achieved many gratifying results in drug development. However, with the rapid growth of computer science, data structures have changed considerably; with more extensive and dimensional data and more significant amounts of data, traditional computer methods can no longer be applied well. Deep learning methods are based on deep neural network structures that can handle high-dimensional data very well, so they are used in current drug development., Results: This review summarized the applications of deep learning methods in drug discovery, such as drug target discovery, drug de novo design, drug recommendation, drug synergy, and drug response prediction. While applying deep learning methods to drug discovery suffers from a lack of data, transfer learning is an excellent solution to this problem. Furthermore, deep learning methods can extract deeper features and have higher predictive power than other machine learning methods. Deep learning methods have great potential in drug discovery and are expected to facilitate drug discovery development., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

As we all know day by day the technology is getting better and better, the production of counterfeit currency has been rapidly increasing. The counterfeit currency problem is faced by almost all countries. Since the real economy is affected, it has affected the economy of the country. Even when the drastic step of demonetization was taken in 2016 to overcome counterfeit currency, this problem did not end. The only one solution for this problem for a common man is to detect the fake currency, by using the fake currency detector machine. These machines are used in banks and large scale business, but for small scale businesses or for a common man these machines are not affordable. There are lot of researches taking place on this matter by using deep learning, image processing and machine learning techniques. This paper gives the complete methodology of fake note detector machine, which is affordable even for a common man. By implementing the applications of image processing techniques we can find out whether the currency notes are fake or not. Image processing technique consists of a number of operations that can be performed on an image, some of which include image segmentation, edge detection, gray scale conversion, preprocessing etc. The proposed system will detect the counterfeit currency of new denominations by distinguishing each denomination based on its size and depending on the features of each currency the comparison takes place. Based on the features matched, it detects whether the currency is counterfeit or not. The system have advantages like simplicity, reliability and cost effective. Which is affordable by a common man since the common man is the one who will be effected most, when the counterfeit currency are circulated in the market because he has to pay the real value of that currency. [ABSTRACT FROM AUTHOR]

We develop a vision system for batch inspection by oven white paper model color by manufacturing a machine vision system for the oven manufacturing automation process. In the vision system, white paper object detection (spring), color clustering, and histogram extraction are performed. In addition, for the automated process of home appliances, we intend to develop an automatic mold combination detection algorithm that inspects the label position and direction (angle/coordinate) using deep learning. [ABSTRACT FROM AUTHOR]

To uncover the evaluation information on the academic contribution of research papers cited by peers based on the content cited by citing papers, and to provide an evidence-based tool for evaluating the academic value of cited papers. CiteOpinion uses a deep learning model to automatically extract citing sentences from representative citing papers; it starts with an analysis on the citing sentences, then it identifies major academic contribution points of the cited paper, positive/negative evaluations from citing authors and the changes in the subjects of subsequent citing authors by means of Recognizing Categories of Moves (problems, methods, conclusions, etc.), and sentiment analysis and topic clustering. Citing sentences in a citing paper contain substantial evidences useful for academic evaluation. They can also be used to objectively and authentically reveal the nature and degree of contribution of the cited paper reflected by citation, beyond simple citation statistics. The evidence-based evaluation tool CiteOpinion can provide an objective and in-depth academic value evaluation basis for the representative papers of scientific researchers, research teams, and institutions. No other similar practical tool is found in papers retrieved. There are difficulties in acquiring full text of citing papers. There is a need to refine the calculation based on the sentiment scores of citing sentences. Currently, the tool is only used for academic contribution evaluation, while its value in policy studies, technical application, and promotion of science is not yet tested. [ABSTRACT FROM AUTHOR]

Due to the increasing size of integrated circuits (ICs), their design and optimization phases (i.e., computer-aided design, CAD) grow increasingly complex. At design time, a large design space needs to be explored to find an implementation that fulfills all specifications and then optimizes metrics like energy, area, delay, reliability, etc. At run time, a large configuration space needs to be searched to find the best set of parameters (e.g., voltage/frequency) to further optimize the system. Both spaces are infeasible for exhaustive search typically leading to heuristic optimization algorithms that find some tradeoff between design quality and computational overhead. Machine learning (ML) can build powerful models that have successfully been employed in related domains. In this survey, we categorize how ML may be used and is used for design-time and run-time optimization and exploration strategies of ICs. A metastudy of published techniques unveils areas in CAD that are well explored and underexplored with ML, as well as trends in the employed ML algorithms. We present a comprehensive categorization and summary of the state of the art on ML for CAD. Finally, we summarize the remaining challenges and promising open research directions. [ABSTRACT FROM AUTHOR]

Remote sensing is one of the most important methods for analysing the multitemporal changes over a certain period. As a cost-effective way, remote sensing allows the long-term analysis of agricultural land by collecting satellite imagery from different satellite missions. Landsat is one of the longest-running world missions which offers a moderate-resolution earth observation dataset. Land surface mapping and monitoring are generally performed by incorporating classification and change detection models. In this work, a deep learning-based change detection (DCD) algorithm has been proposed to detect long-term agricultural changes using the Landsat series datasets (i.e., Landsat-7, Landsat-8, and Landsat-9) during the period 2012 to 2023. The proposed algorithm extracts the features from satellite data according to their spectral and geographic characteristics and identifies seasonal variability. The DCD integrates the deep learning-based (Environment for visualizing images) ENVI Net-5 classification model and posterior probability-based post-classification comparison-based change detection model (PCD). The DCD is capable of providing seasonal variations accurately with distinct Landsat series dataset and promises to use higher resolution dataset with accurate results. The experimental result concludes that vegetation has decreased from 2012 to 2023, while build-up land has increased up to 88.22% (2012-2023) for Landsat-7 and Landsat-8 datasets. On the other side, degraded area includes water (3.20-0.05%) and fallow land (1-0.59%). This study allows the identification of crop growth, crop yield prediction, precision farming, and crop mapping., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The honey bee is the most essential pollinator and a key contributor to the natural ecosystem. There are numerous ways for thousands of bees in a hive to communicate with one another. Individual trajectories and social interactions are thus complex behavioral features that can provide valuable information for an ecological study. To study honey bee behavior, the key challenges that have resulted from unreliable studies include complexity (high density of similar objects, small objects, and occlusion), the variety of background scenes, the dynamism of individual bee movements, and the similarity between the bee body and the background in the beehive. This study investigated the tracking of individual bees in a beehive environment using a deep learning approach and a Kalman filter. Detection of multiple bees and individual object segmentation were performed using Mask R-CNN with a ResNet-101 backbone network. Subsequently, the Kalman filter was employed for tracking multiple bees by tracking the body of each bee across a sequence of image frames. Three metrics were used to assess the proposed framework: mean average precision (mAP) for multiple-object detection and segmentation tasks, CLEAR MOT for multiple object tracking tasks, and MOTS for multiple object tracking and segmentation tasks. For CLEAR MOT and MOTS metrics, accuracy (MOTA and MOTSA) and precision (MOTP and MOTSP) are considered. By employing videos from a custom-designed observation beehive, recorded at a frame rate of 30 frames per second (fps) and utilizing a continuous frame rate of 10 fps as input data, our system displayed impressive performance. It yielded satisfactory outcomes for tasks involving segmentation and tracking of multiple instances of bee behavior. For the multiple-object segmentation task based on Mask R-CNN, we achieved a 0.85 mAP. For the multiple-object-tracking task with the Kalman filter, we achieved 77.48% MOTA, 79.79% MOTSP, and 79.56% recall. For the overall system for multiple-object tracking and segmentation tasks, we achieved 77.00% MOTSA, 75.60% MOTSP, and 80.30% recall., (© 2024. The Author(s).)

Background: Nuclear pleomorphism and tumor microenvironment (TME) play a critical role in cancer development and progression. Identifying most predictive nuclei and TME features of basal cell carcinoma (BCC) may provide insights into which characteristics pathologists can use to distinguish and stratify this entity., Objectives: To develop an automated workflow based on nuclei and TME features from basaloid cell tumor regions to differentiate BCC from trichoepithelioma (TE) and stratify BCC into high-risk (HR) and low-risk (LR) subtypes, and to identify the nuclear and TME characteristics profile of different basaloid cell tumors., Methods: The deep learning systems were trained on 161 H&E -stained sections which contained 51 sections of HR-BCC, 50 sections of LR-BCC and 60 sections of TE from one institution (D1), and externally and independently validated on D2 (46 sections) and D3 (76 sections), from 2015 to 2022. 60%, 20% and 20% of D1 data were randomly splitted for training, validation and testing, respectively. The framework comprised four stages: tumor regions identification by multi-head self-attention (MSA) U-Net, nuclei segmentation by HoVer-Net, quantitative feature by handcrafted extraction, and differentiation and risk stratification classifier construction. Pixel accuracy, precision, recall, dice score, intersection over union (IoU) and area under the curve (AUC) were used to evaluate the performance of tumor segmentation model and classifiers., Results: MSA-U-Net model detected tumor regions with 0.910 precision, 0.869 recall, 0.889 dice score and 0.800 IoU. The differentiation classifier achieved 0.977 ± 0.0159, 0.955 ± 0.0181, 0.885 ± 0.0237 AUC in D1, D2 and D3, respectively. The most discriminative features between BCC and TE contained Homogeneity, Elongation, T-T_meanEdgeLength, T-T_Nsubgraph, S-T_HarmonicCentrality, S-S_Degrees. The risk stratification model can well predict HR-BCC and LR-BCC with 0.920 ± 0.0579, 0.839 ± 0.0176, 0.825 ± 0.0153 AUC in D1, D2 and D3, respectively. The most discriminative features between HR-BCC and LR-BCC comprised IntensityMin, Solidity, T-T_minEdgeLength, T-T_Coreness, T-T_Degrees, T-T_Betweenness, S-T_Degrees., Conclusions: This framework hold potential for future use as a second opinion helping inform diagnosis of BCC, and identify nuclei and TME features related with malignancy and tumor risk stratification., (© 2024 The Authors. Skin Research and Technology published by John Wiley & Sons Ltd.)

Accurately classifying brain tumor types is critical for timely diagnosis and potentially saving lives. Magnetic Resonance Imaging (MRI) is a widely used non-invasive method for obtaining high-contrast grayscale brain images, primarily for tumor diagnosis. The application of Convolutional Neural Networks (CNNs) in deep learning has revolutionized diagnostic systems, leading to significant advancements in medical imaging interpretation. In this study, we employ a transfer learning-based fine-tuning approach using EfficientNets to classify brain tumors into three categories: glioma, meningioma, and pituitary tumors. We utilize the publicly accessible CE-MRI Figshare dataset to fine-tune five pre-trained models from the EfficientNets family, ranging from EfficientNetB0 to EfficientNetB4. Our approach involves a two-step process to refine the pre-trained EfficientNet model. First, we initialize the model with weights from the ImageNet dataset. Then, we add additional layers, including top layers and a fully connected layer, to enable tumor classification. We conduct various tests to assess the robustness of our fine-tuned EfficientNets in comparison to other pre-trained models. Additionally, we analyze the impact of data augmentation on the model's test accuracy. To gain insights into the model's decision-making, we employ Grad-CAM visualization to examine the attention maps generated by the most optimal model, effectively highlighting tumor locations within brain images. Our results reveal that using EfficientNetB2 as the underlying framework yields significant performance improvements. Specifically, the overall test accuracy, precision, recall, and F1-score were found to be 99.06%, 98.73%, 99.13%, and 98.79%, respectively., (© 2023. The Author(s).)

A novel deep learning-enabled smartphone platform is developed to assist a colorimetric aptamer biosensor for fast and highly sensitive detection of dimethoate. The colorimetric determination of dimethoate is based on the specific binding of dimethoate and aptamer, which leads to the aggregation of AuNPs in high-concentration NaCl solution, resulting in an obvious color change from red to blue. This color change provides sufficient data for self-learning enabled by a convolutional neural network (CNN) model, which is established to predict dimethoate concentration based on images acquired from a smartphone. To enhance user-friendliness for non-experts, the CNN model is then embedded into a smartphone app, enabling offline detection of dimethoate pesticide in real environments within just 15 min using a pre-configured colorimetric probe. The developed platform exhibits superior performance, achieving a regression coefficient of 0.9992 in the concentration range of 0-10 μM. Moreover, the app's performance is found to be consistent with the ELISA kit. These remarkable findings demonstrate the potential of combining colorimetric biosensors with smartphone-based deep learning methods for the development of portable and affordable tools for pesticide detection., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.)

With the proliferation of blockchain technology in decentralized applications like decentralized finance and supply chain and identity management, smart contracts operating on a blockchain frequently encounter security issues such as reentrancy vulnerabilities, timestamp dependency vulnerabilities, tx.origin vulnerabilities, and integer overflow vulnerabilities. These security concerns pose a significant risk of causing substantial losses to user accounts. Consequently, the detection of vulnerabilities in smart contracts has become a prominent area of research. Existing research exhibits limitations, including low detection accuracy in traditional smart contract vulnerability detection approaches and the tendency of deep learning-based solutions to focus on a single type of vulnerability. To address these constraints, this paper introduces a smart contract vulnerability detection method founded on multimodal feature fusion. This method adopts a multimodal perspective to extract three modal features from the lifecycle of smart contracts, leveraging both static and dynamic features comprehensively. Through deep learning models like Graph Convolutional Networks (GCNs) and bidirectional Long Short-Term Memory networks (bi-LSTMs), effective detection of vulnerabilities in smart contracts is achieved. Experimental results demonstrate that the proposed method attains detection accuracies of 85.73% for reentrancy vulnerabilities, 85.41% for timestamp dependency vulnerabilities, 83.58% for tx.origin vulnerabilities, and 90.96% for integer Overflow vulnerabilities. Furthermore, ablation experiments confirm the efficacy of the newly introduced modal features, highlighting the significance of fusing dynamic and static features in enhancing detection accuracy. [ABSTRACT FROM AUTHOR]

Beamforming technique can effectively improve the spectrum utilization in the multi‐antenna systems, while the dirty‐paper coding (DPC) technique can reduce the inter‐user interference. In this letter, it is aimed to maximize the weighted sum‐rate under the total power constraint in the multiple‐input‐single‐output (MISO) system with the DPC technique. However, the existing methods of beamforming optimization mainly rely on customized iterative algorithms, which have high computational complexity. To address this issue, the beamforming neural network (BFNNet) is devised by utilizing the deep learning technique and the uplink‐downlink duality and exploring the optimal solution structure, which includes the deep neural network module and the signal processing module. Simulation results show that the BFNNet can achieve near‐optimal solutions and significantly reduce computational complexity. [ABSTRACT FROM AUTHOR]

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Simple Summary: Automatic segmentation of organs and other regions of interest is a promising approach for reducing the workload of doctors in radiotherapeutic planning, but it can be hard for doctors and researchers to keep up with current developments. This review evaluates 807 papers and reveals trends, commonalities, and gaps in the existing corpus. A set of recommendations for conducting effective segmentation studies is also provided. This review provides a formal overview of current automatic segmentation studies that use deep learning in radiotherapy. It covers 807 published papers and includes multiple cancer sites, image types (CT/MRI/PET), and segmentation methods. We collect key statistics about the papers to uncover commonalities, trends, and methods, and identify areas where more research might be needed. Moreover, we analyzed the corpus by posing explicit questions aimed at providing high-quality and actionable insights, including: "What should researchers think about when starting a segmentation study?", "How can research practices in medical image segmentation be improved?", "What is missing from the current corpus?", and more. This allowed us to provide practical guidelines on how to conduct a good segmentation study in today's competitive environment that will be useful for future research within the field, regardless of the specific radiotherapeutic subfield. To aid in our analysis, we used the large language model ChatGPT to condense information. [ABSTRACT FROM AUTHOR]