Search

A pre-treatment process was evaluated in this work for wastewater from paper recycling using microwave technology followed by rapid precipitation of contaminants through centrifugation. Artificial neural networks (ANNs) were used to analyze and optimize the turbidity values. Thirty experimental runs were utilized including microwave (MW) power, duration, centrifuge time, and centrifuge speed as input variables, generated by the Central Composite Full Design (CCFD) approach. The experimental turbidity ranged from 8.1 to 19.7 NTU, while predicted values ranged from 8.4 to 19.7 NTU by ANN. The ANN model showed a robust prediction performance with low mean squared error values during training and testing. Moreover, high R² values showed a remarkable agreement between the experimental observations and ANN predictions. The results obtained from the input values (A:150.00, B:60.00, C:15.00, D:30.00) of sample 2, which gave the lowest turbidity value, showed the most removal of pollution. The results obtained from the input values (A:250.00, B:60.00, C:7.00, D:20.00) of sample 30, which gave the highest turbidity value, showed the least removal of pollution. The results showed that increasing RPM and time of the centrifugation process significantly affected the removal of pollution in wastewater. [ABSTRACT FROM AUTHOR]

It is difficult to predict index values or stock prices with a single financial formula. They are affected by many factors, such as political conditions, global economy, unexpected events, market anomalies, and the characteristics of the relevant companies, and many computer science techniques are being used to make more accurate predictions about them. This study aimed to predict the values of the XKAGT index by using the monthly closing values of the Borsa Istanbul (BIST) Forestry, Paper and Printing (XKAGT) index between 2002 and 2023, and the machine learning techniques artificial neural networks (ANN), random forest (RF), k-nearest neighbor (KNN), and gradient boosting machine (GBM). Furthermore, the performances of four machine learning techniques were compared. Factors affecting stock prices are generally classified as macroeconomic and microeconomic factors. As a result of examining the studies on determining the macroeconomic factors affecting the stock markets, 10 macroeconomic factors were determined as input. The macroeconomic variables used were crude oil price, exchange rate of USD/TRY, dollar index, BIST100 index, gold price, money supply (M2), S&P 500 index, US 10-year bond interest, export-import coverage rate in the forest products sector, and deposits interest rate. It was determined that all machine learning techniques used in the study performed successfully in predicting the index value, but the k-nearest neighbor algorithm showed the best performance with R2=0.996, RMSE=71.36, and a MAE of 40.8. Therefore, in line with the current variables, investors can make analyzes using any of the ANN, RF, KNN, and GBM techniques to predict the future index value, which will lead them to accurate results. [ABSTRACT FROM AUTHOR]

Background: In low-middle income countries, healthcare providers primarily use paper health records for capturing data. Paper health records are utilized predominately due to the prohibitive cost of acquisition and maintenance of automated data capture devices and electronic medical records. Data recorded on paper health records is not easily accessible in a digital format to healthcare providers. The lack of real time accessible digital data limits healthcare providers, researchers, and quality improvement champions to leverage data to improve patient outcomes. In this project, we demonstrate the novel use of computer vision software to digitize handwritten intraoperative data elements from smartphone photographs of paper anesthesia charts from the University Teaching Hospital of Kigali. We specifically report our approach to digitize checkbox data, symbol-denoted systolic and diastolic blood pressure, and physiological data. Methods: We implemented approaches for removing perspective distortions from smartphone photographs, removing shadows, and improving image readability through morphological operations. YOLOv8 models were used to deconstruct the anesthesia paper chart into specific data sections. Handwritten blood pressure symbols and physiological data were identified, and values were assigned using deep neural networks. Our work builds upon the contributions of previous research by improving upon their methods, updating the deep learning models to newer architectures, as well as consolidating them into a single piece of software. Results: The model for extracting the sections of the anesthesia paper chart achieved an average box precision of 0.99, an average box recall of 0.99, and an mAP0.5-95 of 0.97. Our software digitizes checkbox data with greater than 99% accuracy and digitizes blood pressure data with a mean average error of 1.0 and 1.36 mmHg for systolic and diastolic blood pressure respectively. Overall accuracy for physiological data which includes oxygen saturation, inspired oxygen concentration and end tidal carbon dioxide concentration was 85.2%. Conclusions: We demonstrate that under normal photography conditions we can digitize checkbox, blood pressure and physiological data to within human accuracy when provided legible handwriting. Our contributions provide improved access to digital data to healthcare practitioners in low-middle income countries. [ABSTRACT FROM AUTHOR]

Avoiding financial losses requires preventing catastrophic oil‐filled power transformer breakdowns. Continuous online transformer monitoring is needed. The authors use paper insulation to evaluate transformer health for continuous online transformer monitoring. The study suggests a new artificial intelligence method for estimating paper insulation residual life in oil‐immersed power transformers. The four artificial intelligence models use backpropagation‐based neural networks to predict paper insulation lifespan. Four primary transformer insulating paper failure indices—degree of polymerisation, 2‐furfuraldehyde, carbon monoxide, and carbon dioxide—form the basis of these models. Each model, including the backpropagation‐based neural networks, estimates paper insulation life using one failure index, along with moisture and temperature data. Optimisation techniques enhance hidden layer neurons and epoch count for improved performance. Results are validated against literature‐based life models, establishing a precise input–output correlation. This method accurately predicts the remaining useable life of power transformer paper insulation, enabling utilities to take proactive measures for safe and efficient transformer operation. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue of the journal Sensors, titled "Emerging AI+X-Based Sensor and Networking Technologies including Selected Papers from ICGHIT 2022–2023." The special issue features selected papers from the 10th and 11th International Conferences on Green and Human Information Technology (ICGHITs), which were held in Korea and Thailand. The conferences focused on the theme of "Emerging Artificial Intelligent (AI)+X technology" and "Hyper Automation + Human AI" respectively. The selected papers cover various topics such as network security, routing protocols, signal detection, and clustering mechanisms, all incorporating AI-based methods. The issue also includes papers on topics like secure authentication, distance estimation in RFID systems, energy optimization in smart homes, blockchain technology, and radar signal detection. The authors emphasize the importance of both technology and humanity in advancing green and information technologies. [Extracted from the article]

Geotechnical designs and analyses of earth structures and foundations exclusively involve the assessment and consideration of unsaturated soil shear strength. The laboratory testing equipment and methods for predicting the unsaturated soil shear strength are complicated and more expensive. The experimental program attempted to involve undrained triaxial and filter paper for evaluating the unsaturated soil shear strength of identically compacted clayey soil. This study undertakes a comparison of shear strength in clayey soil under undrained loads, examining both saturated and unsaturated conditions. A 60 kPa air entry suction value is the key point at which linearity between the unsaturated shear strength parameter Øb and effective friction Ø′ with 15° linear slopes turns to non-linearity. Unsaturated shear strength increased by 22.76% in optimally wet conditions, 52.68% in optimum conditions, and 77.81% in optimally dry conditions as compared to saturated shear strength. This study utilizes an artificial neural network (ANN) to predict clayey soil's unsaturated shear strength, finding that the optimal ANN configuration (2-5-1 topology, Levenberg–Marquard optimization, and logsig transfer function) achieved high reliability with a correlation coefficient (R) of 0.9289 and mean square error values of 2.22, 7.12, and 3.012 for training, testing, and validation, respectively. This experimental investigation improves our understanding of clayey soil shear strength and emphasizes the importance of saturation and moisture content in geotechnical assessments under undrained loading conditions. [ABSTRACT FROM AUTHOR]

• Five Machine Learning (ML) models compared for wave height time series prediction. • Complex ML models do not outperform simple AR in wave height time series prediction. • Comment to related papers: signal decomposition in test set series is WRONG. Significant Wave Height (SWH) is crucial in many aspect of ocean engineering. The accurate prediction of SWH has therefore been of immense practical value. Recently, Artificial Intelligence (AI) time series prediction methods have been widely used for single-point short-term SWH time-series forecasting, resulting in many AI-based models claiming to achieve good results. However, the extent to which these complex AI models can outperform traditional methods has largely been overlooked. This study compared five different models - AutoRegressive (AR), eXtreme Gradient Boosting (XGB), Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and WaveNet - for their performance on SWH time series prediction at 16 buoy locations. Surprisingly, the results suggest that the differences of performance among different models are negligible, indicating that all these AI models have only "learned" the linear auto-regression from the data. Additionally, we noticed that many recent studies used signal decomposition method for such time series prediction, and most of them decomposed the test sets, which is WRONG. [ABSTRACT FROM AUTHOR]

This article discusses a special issue of the journal Applied Sciences that focuses on the International Conference Wood Science and Engineering (ICWSE) held in November 2023. The conference covered various topics related to wood, including wood structure and properties, wood constructions, wood drying and heat treatments, conservation and restoration of wooden objects, wood-based materials, mechanical wood processing, and surface quality. The articles in the special issue explore different aspects of these topics, such as the characteristics and potential uses of different wood species, innovative wood construction techniques, optimal drying conditions for specific wood species, and the production and properties of wood-based materials. The research presented aims to promote the production of high-value wood products and contribute to the preservation of cultural heritage. The text provides a summary of various research papers related to wood science and engineering, covering topics such as the utilization potential of different tree species, wood material properties, wood preservation and coating techniques, and furniture design. The research findings highlight the importance of factors such as printing parameters, surface quality, adhesion of varnish coatings, and light-induced color changes in wood. The authors hope that these studies contribute to a better understanding of the scientific potential of wood and will be a starting point for further research in the field. [Extracted from the article]

Many stray animals throughout the world unfortunately do not get the opportunity to find the loving home that they deserve. In this classification task, we look to develop an algorithm to predict the speed of pet adoptions. The input to our algorithm includes animal type (dog or cat), breed, gender, colour, profile picture online and the descriptions about the pet, etc. We then use both traditional machine learning techniques (Logistic Regression, Naive Bayes, SVM, Decision Tree, Random Forest and Gradient Boosting) and neural networks (fully connected neural networks and long short-term memory Model) to predict the adoption rate. In particular, we build feature vectors using information extracted from description scripts and feed them into neural network models. We are hoping to examine the results to develop strategies to help improve the overall adoption rate (i.e. what features lead to faster adoption). Every year, 3.3 million canines visit animal shelters, out of a total population of 200 million. Only 2% to 17% of pets are returned to their owners. The remaining animals are euthanized due to a shortage of room in shelters (killed). The present procedures for adapting or finding a pet are inefficient and haphazard. People disseminated leaflets to the general public and spread the word to others in the vicinity of the pet's disappearance. When individuals print fliers, they waste paper and money since there is no good impact. People also share their tales on social media sites like Instagram and Facebook. There are also several fraudsters who attempt to fraudulently claim the incentive for returning the pet to its legitimate owner. We want to provide an analysis on how fast pets(cats and dogs) can be adopted based on various factors like their health conditions, age, colour, breed etc. [ABSTRACT FROM AUTHOR]

An instrument-free, user-friendly, and cost-effective PAD colorimetric sensor that can quantitative detection of H 2 O 2 has been realized by using bimetallic nickel–cobalt selenides as highly active peroxidase mimic and smartphone integrated with dark cavity as detector. Integrated with an ANN model and a self-compiled easy-to-use smartphone APP, the intelligent and on-site detection of H 2 O 2 was constructed, which exhibited an ultra-wide dynamic range. [Display omitted] • An effective paper-based colorimetric sensor was prepared based on nanozyme. • The optimized Ni 0.75 Co 0.25 Se exhibited excellent peoxidase-mimetic activities. • The on-site detection of H 2 O 2 was constructed by a self-compiled smartphone APP. • The machine learning-assisted sensor can detect H 2 O 2 over a wide dynamic range. Paper-based analytical devices (PADs) integrated with smartphones have shown great potential in various fields, but they also face challenges such as single signal reading, complex data processing and significant environmental impacting. In this study, a colorimetric PAD platform has been proposed using bimetallic nickel–cobalt selenides as highly active peroxidase mimic, smartphone with 3D-printing dark-cavity as a portable detector and an artificial neural network (ANN) model as multi-signal processing tool. Notably, the optimized nickel–cobalt selenides (Ni 0.75 Co 0.25 Se with Ni to Co ratio of 3/1) exhibit excellent peoxidase-mimetic activities and are capable of catalyzing the oxidation of four chromogenic reagents in the presence of H 2 O 2. Using a smartphone with image capture function as a friendly signal readout tool, the Ni 0.75 Co 0.25 Se based four channel colorimetric sensing paper is used for multi-signal quantitative analysis of H 2 O 2 by determining the Grey, red (R), green (G) and blue (B) channel values of the captured pictures. An intelligent on-site detection method for H 2 O 2 has been constructed by combining an ANN model and a self-programmed easy-to-use smartphone APP with a dynamic range of 5 μM to 2 M. Noteworthy, machine learning-assisted smartphone sensing devices based on nanozyme and 3D printing technology provide new insights and universal strategies for visual ultrasensitive detection in a variety of fields, including environments monitoring, biomedical diagnosis and safety screening. [ABSTRACT FROM AUTHOR]

The text discusses the intersection of natural language understanding (NLU) and reasoning in the context of large language models (LLMs) and traditional logic-based approaches. It highlights the strengths and weaknesses of both approaches and explores the potential for hybrid models that combine symbolic and distributional representations. The text also mentions specific applications of hybrid approaches in natural language inference, question-answering, sentiment analysis, and dialog. The document concludes by introducing a special issue that features selected contributions from the NALOMA workshop series, which focuses on hybrid methods in NLU. [Extracted from the article]

A low‐temperature technology for fabricating thin‐film transistors (TFTs) is essential for the realization of electronic systems on flexible substrates. Presently reviewed are improved techniques for forming the source/drain regions and reducing the population of channel defects in a metal‐oxide TFT. These have been applied to the construction of different TFT structures, including ones with bottom gate, top gate, and dual gates. The utility of the improved 300‐°C technology has been demonstrated by the realization of a variety of electronic systems, such as a gate‐driver on array for active‐matrix displays, an analog front‐end for acquiring biopotential signals, and an artificial neural network for neuromorphic computing. [ABSTRACT FROM AUTHOR]

One key frontier of artificial intelligence (AI) is the ability to comprehend research articles and validate their findings, posing a magnanimous problem for AI systems to compete with human intelligence and intuition. As a benchmark of research validation, the existing peer-review system still stands strong despite being criticized at times by many. However, the paper vetting system has been severely strained due to an influx of research paper submissions and increased conferences/journals. As a result, problems, including having insufficient reviewers, finding the right experts, and maintaining review quality, are steadily and strongly surfacing. To ease the workload of the stakeholders associated with the peer-review process, we probed into what an AI-powered review system would look like. In this work, we leverage the interaction between the paper's full text and the corresponding peer-review text to predict the overall recommendation score and final decision. We do not envisage AI reviewing papers in the near future. Still, we intend to explore the possibility of a human–AI collaboration in the decision-making process to make the current system FAIR. The idea is to have an assistive decision-making tool for the chairs/editors to help them with an additional layer of confidence, especially with borderline and contrastive reviews. We use a deep attention network between the review text and paper to learn the interactions and predict the overall recommendation score and final decision. We also use sentiment information encoded within peer-review texts to guide the outcome further. Our proposed model outperforms the recent state-of-the-art competitive baselines. We release the code of our implementation here: https://github.com/PrabhatkrBharti/PEERRec.git. [ABSTRACT FROM AUTHOR]

Artificial intelligence is more present than ever, both in our society in general and in science. At the center of this development has been the concept of deep learning, the use of artificial neural networks that are many layers deep and can often reproduce human‐like behavior much better than other machine‐learning techniques. The articles in this collection are some recent examples of its application for X‐ray photon science and crystallography that have been published in Journal of Applied Crystallography. [ABSTRACT FROM AUTHOR]

This document is a guest editorial from the journal IET Generation, Transmission & Distribution. It discusses the use of artificial intelligence (AI) in reliable forecasting for energy sectors. The editorial highlights the challenges of integrating renewable energy sources and fluctuating electricity demand, and emphasizes the importance of accurate forecasting for system operators. The document also provides summaries of several papers included in a special issue on AI-empowered forecasting in energy sectors, covering topics such as load forecasting, wind power prediction, and control parameter optimization. The editorial concludes by recommending further research and practical implementations of AI approaches in the energy sectors. [Extracted from the article]

This document discusses the degradation and failure mechanisms of dielectric insulation in medium- and high-voltage power equipment. It highlights the importance of controlling insulation material degradation to prevent equipment failures and reduce the risk of environmental pollution. The document includes several research papers that address various topics related to the measurement, monitoring, and improvement of power equipment components. These papers cover issues such as winding breakdown faults in transformers, overvoltages caused by vacuum circuit breaker interruptions, insulation resistance degradation in cables, temperature rise in composite insulators, partial discharges and their effects on insulation, and acoustic inspection for detecting partial discharges. The document also presents studies on the technical condition of on-load tap changers, the behavior of silicone elastomers under electrical and mechanical stress, and the percolation phenomenon in square matrices. [Extracted from the article]

This document is a summary of a special issue on advanced machine learning and deep learning techniques for remote sensing. The issue includes 16 research papers that cover a range of topics, including hyperspectral image classification, moving point target detection, radar echo extrapolation, and remote sensing object detection. Each paper introduces a novel approach or model and provides extensive testing and evaluation to demonstrate its effectiveness. The insights shared in this special issue are expected to contribute to future advancements in artificial intelligence-based remote sensing research. [Extracted from the article]

For the past few years, sweat analysis for health monitoring has attracted increasing attention benefiting from wearable technology. In related research, the sensitive detection of uric acid (UA) in sweat with complex composition based on surface-enhanced Raman spectroscopy (SERS) for the diagnosis of gout is still a significant challenge. Herein, we report a visualized and intelligent wearable sweat platform for SERS detection of UA in sweat. In this wearable platform, the spiral channel consisted of colorimetric paper with Ag nanowires (AgNWs) that could capture sweat for SERS measurement. With the help of photos from a smartphone, the pH value and volume of sweat could be quantified intelligently based on the image recognition technique. To diagnose gout, SERS spectra of human sweat with UA are collected in this wearable intelligent platform and analyzed by artificial intelligence (AI) algorithms. The results indicate that the artificial neural network (ANN) algorithm exhibits good identification of gout with high accuracy at 97%. Our work demonstrates that SERS-AI in a wearable intelligent sweat platform could be a feasible strategy for diagnosis of gout, which expands research on sweat analysis for comfortable and noninvasive health monitoring. [ABSTRACT FROM AUTHOR]

The article focuses on a critical analysis of systematic review methodologies concerning vertical root fractures (VRFs), highlighting concerns about potential biases when including studies on broader categories of dental cracks in VRF-specific reviews. Topics include the definition and characteristics of VRFs, methodological variations in VRF detection studies, and the implications of these variations on the reliability of systematic reviews in dental research.

This document is a guest editorial from the journal IET Radar, Sonar & Navigation. It discusses the advances in AI-assisted radar sensing applications and the challenges that hinder its adoption in this field. The special issue of the journal features nine papers that address these challenges and offer innovative ideas and experimental results. The papers cover a range of topics, including health monitoring, human activity recognition, voice identification, elderly care health monitoring, track-to-track association, signal pre-processing, traffic congestion alleviation, and target recognition. The authors express their gratitude to the contributors and reviewers and believe that the research presented will inspire further exploration and innovation in this field. [Extracted from the article]

Due to the large intra-class differences between the same categories and the scale imbalance between different categories in the remote sensing image dataset, the semantic segmentation task presents the problem of small-scale object information loss, the imbalance between foreground and background, and simultaneously the background dominates, which seriously affects the performance of the network model. To solve the above problems, this paper proposes an efficient bilateral branch depth neural network model based on the U-Net depth neural network, named BBU-Net. Firstly, one branch of the network learns the distribution characteristics of the original data, and the other focuses on difficult samples. Then the two branches improve the representation and classification ability of the neural network by accumulating learning strategies. Finally, considering the geometric diversity of remote sensing images, this paper adopts test time augmentation and reflection padding strategies and proposes a balanced weighted loss function named CombineLoss to alleviate the imbalance in the training process. The depth neural network proposed in this paper was first tested on the Inria Aerial Image Labeling Dataset, and 87.53% of mean intersection over union and 97.4% of mean pixel accuracy were obtained, respectively. At the same time, to verify the model's complexity, the model proposed in this paper is compared with the neural network based on integrated learning. The comparison results show that the spatial complexity of the network proposed in this paper is much lower than the neural network obtained by integrated learning, and the parameters are also much smaller than the neural network based on integrated learning. Then use the satellite building dataset I in the WHU Building Dataset and mainstream semantic segmentation methods for multiple groups of comparative experiments. The experimental results show that the method proposed in this paper can effectively extract the semantic information of remote sensing images, significantly improve the imbalance of remote sensing image data, improve the performance of the network model, and achieve a good semantic segmentation effect, which fully proves the effectiveness of this method. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue in the journal Applied Sciences titled "Artificial Intelligence Technology in Medical Image Analysis." The special issue explores the applications of artificial intelligence (AI) in medical imaging and its impact on diagnostic and therapeutic processes. The use of AI-powered tools in image interpretation has shown exceptional capabilities in detecting and diagnosing medical conditions from imaging data, particularly in radiology. AI also contributes to improving image quality, automating routine tasks, and streamlining healthcare workflows. However, challenges such as data privacy, ethics, and regulatory frameworks need to be addressed for responsible implementation. The special issue includes several research papers that present advancements in automated medical decision support, age estimation, quality assurance, orthotic insole recommendation, tumor identification, thalamus segmentation, medical image classification, hyperparameter optimization, lung disease classification, and thoracic cavity segmentation. These papers demonstrate the potential of AI in improving accuracy, efficiency, and personalized treatment in medical image analysis. The integration of AI into healthcare requires collaboration between AI researchers, healthcare professionals, and regulatory bodies to ensure responsible and effective deployment. The future of AI in medical image analysis holds promise for improved diagnostic accuracy, early disease detection, and personalized treatment strategies. [Extracted from the article]

This document is a special issue of the journal "Materials" that focuses on the recent progress in the structure transition, processing, characterization, and applications of metallic materials. The issue includes research papers on various topics such as the development of dispersion-strengthened copper alloys, the characterization of carbide precipitation in steel, the effect of magnetic fields on aluminum bronze, and the enhancement of impact toughness in cast iron through heat treatment. The issue aims to provide comprehensive and up-to-date information for researchers, engineers, and industry experts in the field of metallic materials. [Extracted from the article]

With the establishment of the "double carbon" goal, various industries are actively exploring ways to reduce carbon emissions. Cloud data centers, represented by cloud computing, often have the problem of mismatch between load requests and resource supply, resulting in excessive carbon emissions. Based on this, this paper proposes a complete method for cloud computing carbon emission prediction. Firstly, the convolutional neural network and bidirectional long-term and short-term memory neural network (CNN-BiLSTM) combined model are used to predict the cloud computing load. The real-time prediction power is obtained by real-time prediction load of cloud computing, and then the carbon emission prediction is obtained by power calculation. Develop a dynamic server carbon emission prediction model, so that the server carbon emission can change with the change of CPU utilization, so as to achieve the purpose of low carbon emission reduction. In this paper, Google cluster data is used to predict the load. The experimental results show that the CNN-BiLSTM combined model has good prediction effect. Compared with the multi-layer feed forward neural network model (BP), long short-term memory network model (LSTM), bidirectional long short-term memory network model (BiLSTM), modal decomposition and convolution long time series neural network model (CEEMDAN-ConvLSTM), the MSE index decreased by 52 % , 50 % , 34 % and 45 % respectively. [ABSTRACT FROM AUTHOR]

Establishing an accurate and robust feature fusion mechanism is key to enhancing the tracking performance of single-object trackers based on a Siamese network. However, the output features of the depth-wise cross-correlation feature fusion module in fully convolutional trackers based on Siamese networks cannot establish global dependencies on the feature maps of a search area. This paper proposes a dynamic cascade feature fusion (DCFF) module by introducing a local feature guidance (LFG) module and dynamic attention modules (DAMs) after the depth-wise cross-correlation module to enhance the global dependency modeling capability during the feature fusion process. In this paper, a set of verification experiments is designed to investigate whether establishing global dependencies for the features output by the depth-wise cross-correlation operation can significantly improve the performance of fully convolutional trackers based on a Siamese network, providing experimental support for rational design of the structure of a dynamic cascade feature fusion module. Secondly, we integrate the dynamic cascade feature fusion module into the tracking framework based on a Siamese network, propose SiamDCFF, and evaluate it using public datasets. Compared with the baseline model, SiamDCFF demonstrated significant improvements. [ABSTRACT FROM AUTHOR]

Due to the diversity and unpredictability of changes in malicious code, studying the traceability of variant families remains challenging. In this paper, we propose a GAN-EfficientNetV2-based method for tracing families of malicious code variants. This method leverages the similarity in layouts and textures between images of malicious code variants from the same source and their original family of malicious code images. The method includes a lightweight classifier and a simulator. The classifier utilizes the enhanced EfficientNetV2 to categorize malicious code images and can be easily deployed on mobile, embedded, and other devices. The simulator utilizes an enhanced generative adversarial network to simulate different variants of malicious code and generates datasets to validate the model's performance. This process helps identify model vulnerabilities and security risks, facilitating model enhancement and development. The classifier achieves 98.61% and 97.59% accuracy on the MMCC dataset and Malevis dataset, respectively. The simulator's generated image of malicious code variants has an FID value of 155.44 and an IS value of 1.72 ± 0.42. The classifier's accuracy for tracing the family of malicious code variants is as high as 90.29%, surpassing that of mainstream neural network models. This meets the current demand for high generalization and anti-obfuscation abilities in malicious code classification models due to the rapid evolution of malicious code. [ABSTRACT FROM AUTHOR]

Biosensors integrated with artificial intelligence (AI) hold immense potential for transforming healthcare through rapid, automated diagnostics and precision therapeutics. This paper reviews the convergence of biosensing and AI towards developing smart biomedical systems. The fundamentals, historical evolution, and classification of biosensors are presented, highlighting key applications across infections, chronic illnesses, and environmental monitoring. Core AI concepts, including machine learning, neural networks, computer vision, and natural language processing, are discussed, along with their implementation to augment biosensor functionality, connectivity, point-of-care adoption, and laboratory automation. Promising research directions and real-world case studies applying AI-integrated biosensors for early diagnosis and drug delivery are discussed. The opportunities and challenges in advancing this synergistic technology are contemplated, underscoring the need for cross-disciplinary collaboration, clinical validation, ethical vigilance and supportive policy environments to successfully translate AI-biosensors into practical healthcare solutions. [ABSTRACT FROM AUTHOR]

Diagnosing multi-stage diseases typically requires doctors to consider multiple data sources, including clinical symptoms, physical signs, biochemical test results, imaging findings, pathological examination data, and even genetic data. When applying machine learning modeling to predict and diagnose multi-stage diseases, several challenges need to be addressed. Firstly, the model needs to handle multimodal data, as the data used by doctors for diagnosis includes image data, natural language data, and structured data. Secondly, privacy of patients' data needs to be protected, as these data contain the most sensitive and private information. Lastly, considering the practicality of the model, the computational requirements should not be too high. To address these challenges, this paper proposes a privacy-preserving federated deep learning diagnostic method for multi-stage diseases. This method improves the forward and backward propagation processes of deep neural network modeling algorithms and introduces a homomorphic encryption step to design a federated modeling algorithm without the need for an arbiter. It also utilizes dedicated integrated circuits to implement the hardware Paillier algorithm, providing accelerated support for homomorphic encryption in modeling. Finally, this paper designs and conducts experiments to evaluate the proposed solution. The experimental results show that in privacy-preserving federated deep learning diagnostic modeling, the method in this paper achieves the same modeling performance as ordinary modeling without privacy protection, and has higher modeling speed compared to similar algorithms. [ABSTRACT FROM AUTHOR]

The recognition of food images is of great significance for nutrition monitoring, food retrieval and food recommendation. However, the accuracy of recognition had not been high enough due to the complex background of food images and the characteristics of small inter-class differences and large intra-class differences. To solve these problems, this paper proposed a food image recognition method based on transfer learning and ensemble learning. Firstly, generic image features were extracted by using the convolutional neural network models (VGG19, ResNet50, MobileNet V2, AlexNet) pre-trained on the ImageNet dataset. Secondly, the 4 pre-trained models were transferred to the food image dataset for model fine-tuning. Finally, different basic learner combination strategies were adopted to establish the ensemble model and classify feature information. In this paper, several kinds of experiments were performed to compare the results of food image recognition between single models and ensemble models on food-11 dataset. The experimental results demonstrated that the accuracy of the ensemble model was the highest, reaching 96.88%, which was superior to any base learner. Therefore, the convolutional neural network model based on transfer learning and ensemble learning has strong learning ability and generalization ability, and it is feasible and practical to apply the method to food image recognition. [ABSTRACT FROM AUTHOR]

This document is an editorial for a special issue of the journal "Technologies" focused on data science and big data in various fields such as biology, physical science, and engineering. The editorial highlights the importance of analyzing large amounts of data generated by digital technologies and the need for data scientists to use artificial intelligence and machine learning to extract valuable knowledge. The special issue includes 12 papers covering topics such as machine learning techniques for customer churn prediction, agile program management in the U.S. Navy, deep learning for cybersecurity in Industry 5.0, self-directed learning during the COVID-19 era, decision tree-based neural networks for data classification, data-driven governance in technology companies, and more. The papers explore different approaches, models, and tools in the context of data science and big data. [Extracted from the article]

The human brain functions through electrical signals. By measuring these signals, one can monitor brain activity and gain insights into the brain function of the subject. An electroencephalogram (EEG) allows one to monitor brain activity by having the subject wear an array of sensors on their head. This process is frequently used to diagnose medical conditions such as epilepsy. In recent years, there have been efforts to use EEG signals in concert with deep learning to create a brain computer interface (BCI). Such a device would enable the wearer to communicate to a system via brain signals. While such a system would not be so advanced as to enable the translation of complex thoughts, it would enable a user to command a machine to perform a small number of functions. The objective of this paper was to develop and optimize recurrent neural network architectures for use with a brain computer interface. Using EEG data collected from subjects, a variety of neural network models were created to learn from the data. The models that were used were simple recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent units (GRU). This paper proposes a novel approach to EEG signal classification, demonstrating the capabilities of recurrent networks which are seldom explored for this purpose. This study produced promising results for recurrent models, obtaining a 91% accuracy with the 4-layer LSTM architecture. This presents a solid foundation for the argument that LSTM and similar architectures are feasible for BCI applications. [ABSTRACT FROM AUTHOR]