Learning Technology Research Articles

Random Numbers for Machine Learning: A Comparative Study of Reproducibility and Energy Consumption

Pseudo-Random Number Generators (PRNGs) have become ubiquitous in machine learning (ML) technologies because they are interesting for numerous methods. In the context of ML, multiple stochastic streams, produced in black boxes for methods such as stochastic gradient descent or dropout, can produce a lack of repeatability, impacting the ability to debug and explain results. The field of machine learning holds the potential for substantial advancements across various domains. However, despite the growing interest, persistent concerns include issues related to reproducibility and energy consumption. Reproducibility is crucial for robust scientific inquiry and explainability, while energy efficiency underscores the imperative to conserve finite global resources. This study delves into the investigation of whether the leading Pseudo-Random Number Generators (PRNGs) employed in machine learning languages, libraries, and frameworks uphold statistical quality and numerical reproducibility when compared to the original C implementation of the respective PRNG algorithms. Additionally, we aim to evaluate the time efficiency and energy consumption of various implementations. Our experiments encompass Python, NumPy, TensorFlow, and PyTorch, utilizing the Mersenne Twister, Permuted Congruential Generator (PCG), and Philox algorithms. Remarkably, we verified that the temporal performance of machine learning technologies closely aligns with that of C-based implementations, with instances of achieving even superior performances. On the other hand, it is noteworthy that ML technologies consumed only 10% more energy than their C-implementation counterparts. However, while statistical quality was found to be comparable, achieving numerical reproducibility across different platforms for identical seeds and algorithms was not achieved.

The Lightweight Fracture Segmentation Algorithm for Logging Images Based on Fully 3D Attention Mechanism and Deformable Convolution

The challenge of fracture segmentation remains a significant obstacle in imaging logging interpretation within the current oil and gas exploration and development field. However, existing image segmentation algorithms still encounter issues related to accuracy, speed, and robustness, as well as a tendency to misdetect or overlook small fractures when applied to logging image fracture segmentation tasks. To address these challenges comprehensively, this paper proposes an end-to-end fracture segmentation algorithm named SWSDS-Net. This algorithm is built upon the UNet architecture and incorporates the SimAM with slicing (SWS) attention mechanism along with the deformable strip convolution (DSCN) module. The SWS introduces a fully 3D attention mechanism that effectively learns the weights of each neuron in the feature map, enabling better capture of fracture features while ensuring fair attention and enhancement for both large and small objects. Additionally, the deformable properties of DSCN allow for adaptive sampling based on fracture shapes, effectively tackling challenges posed by varying fracture shapes and enhancing segmentation robustness. Experimental results demonstrate that SWSDS-Net achieves optimal performance across all evaluation metrics in this task, delivering superior visual results in fracture segmentation while successfully overcoming limitations present in existing algorithms such as complex shapes, noise interference, and low-quality images. Moreover, serving as a lightweight network solution enables SWSDS-Net’s deployment on mobile devices at remote sites—an advancement that lays a solid foundation for interpreting logging data and promotes deep learning technology application within traditional industrial scenarios.

Blockchain and deep learning technology for comprehensive improvement of transaction information quality

Blockchain and deep learning technology for comprehensive improvement of transaction information quality

Application and Prospects of Deep Learning Technology in Fracture Diagnosis

Application and Prospects of Deep Learning Technology in Fracture Diagnosis

Exploring the impact of device ownership, usage patterns, and technology proficiency on mobile learning among higher education students: a multi-faceted study

This study explores the impact of device ownership, frequency of use, and technology proficiency on integrating mobile technologies (IMT) in learning by students in higher education institutions (HEIs) in Ghana. The objective is to understand how these factors are influenced by demographic variables such as age, gender, and program of study. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), data were collected from 457 students selected with a disproportionate stratified sampling technique across multiple HEIs offering STEM programs to analyse these relationships. The results demonstrated that technology proficiency significantly enhances the integration of mobile technologies into learning. It was noted that device ownership alone was insufficient to improve technology proficiency; frequent and effective usage was more essential. Additionally, gender and program of study moderated the effects of device ownership and frequency of use on technology proficiency. These findings highlight the importance of promoting effective usage patterns and advancing technology proficiency to integrate mobile technologies in educational settings better. It is recommended that HEIs focus on providing training programs to staff to integrate more learning activities that promote frequent use of mobile technologies in academic activities to enhance students' technology proficiency.

Prediction of Salicornia europaea L. biomass using a computer vision system to distinguish different salt-tolerant populations.

Salicornia europaea L. is emerging as a versatile crop halophyte, requiring a low-cost, non-destructive method for salt tolerance classification to aid selective breeding. We propose using a computer vision system (CVS) with multivariate analysis to classify S. europaea based on morphometric and colour traits to predict plant biomass and the salinity in their substrate. A trial and validation set of 96 and 24 plants from 2 populations confirmedthe efficacy. CVS and multivariate analysis evaluated the plants by morphometric traits and CIELab colour variability. Through Pearson analysis, the strongest correlations were between biomass fresh weight (FW) vs. projected area (PA) (0.91) and anatomical cross-section (ACS) vs. shoot diameter (Sd) (0.94). The PA and FW correlation retrieved different equation fits between lower and higher salt-tolerant populations (R2 = 0.93 for linear and 0.90 for 2nd-degree polynomial), respectively. The higher salt-tolerant reached a maximum biomass PA at 400 mM NaCl, while the lower salt-tolerant produced less under 200 and 400 mM. A second Pearson correlation and PCA described sample variability with 80% reliability using only morphometric-colour parameters. Multivariate discriminant analysis (MDA) demonstrated that the method correctly classifies plants (90%) depending on their salinity level and tolerance, which was validated with 100% effectiveness. Through multiple linear regression, a predictive model successfully estimated biomass production by PA, and a second model predicted the salinity substrate (Sal.s.) where the plants thrive. Plants' Sd and height influenced PA prediction, while Sd and colour difference (ΔE1) influenced Sal.s. Models validation of actual vs. predicted values showed a R2 of 0.97 and 0.90 for PA, and 0.95 and 0.97 for Sal.s. for lower and higher salt-tolerant, respectively. This outcome confirms the method as a cost-effective tool for managing S. europaea breeding. The CVS effectively extracted morphological and colour features from S. europaea cultivated at different salinity levels, enabling classification and plant sorting through image and multivariate analysis. Biomass and salinity substrate were accurately predicted by modelling non-destructive parameters. Enhanced by AI, machine learning and smartphone technology, this method shows great potential in ecology, bio-agriculture, and industry.

The Use of Deepfake Technology in China: Problems of Legal Regulation and Ways to Solve Them

The core of the deepfake technology is based on a generative-adversarial network built on a combination of two neural networks: a generative network (network G) creates samples, a discriminative network (network D) tries to distinguish correct («genuine») samples from incorrect ones. Networks G and D compete with each other thousands or even millions of times until network G improves its performance. Thus, the network D will no longer be able to distinguish real data from fake data. With the development of big data and machine learning technologies, the scenario for using deepfake technology has gradually changed from creating sound models and imitating text to deep video forgery. For a long time, images modified using traditional Photoshop and other technologies were easily recognized. Deepfake technology changed this situation, making it more difficult to identify fakes. As an important technological innovation in the field of artificial intelligence, deepfake technology is widely used in various areas of society, creating enormous applied value. However, any technology is a double-edged sword. The use of deepfake technology poses a great threat to personal privacy, property security and even national security. In order to find a balance between technological innovation and risk prevention and control, countries around the world are actively exploring various ways to manage. The paper describes the main risks posed by modern deepfake technology, provides an overview of legal regulation in this area in China and offers an effective way to solve problems.

IMPLEMENTASI SISTEM PEMBELAJARAN EFEKTIF SEBAGAI STRATEGI PENGUATAN PROFESIONALISME GURU DALAM ERA DIGITAL

This research aims to analyze teacher professionalism strengthening through the implementation of effective learning systems. Using a mixed-method approach, the study was conducted on 100 teachers selected through purposive sampling. Data collection included questionnaires, in-depth interviews with 20 teachers, learning observations, and focus group discussions. The results showed significant improvements in teacher competencies: pedagogical (25%), professional (28%), learning technology utilization (35%), and evaluation skills (30%). The implementation of effective learning systems positively impacted student learning motivation (75%) and learning outcomes (68%). Key supporting factors included policy support and availability of continuous professional development programs, while inhibiting factors included administrative burden and limited technology access. The teacher professionalism strengthening program proved effective in improving learning quality, although it still requires strengthening of the mentoring system and development of digital learning platforms..

Early detection of verticillium wilt in eggplant leaves by fusing five image channels: a deep learning approach.

As one of the world's most important vegetable crops, eggplant production is often severely affected by verticillium wilt, leading to significant declines in yield and quality. Traditional multispectral disease-imaging equipment is expensive and complicated to operate. Low-cost multispectral devices cannot capture images and cover less information. The traditional approach to early disease diagnosis involves using multispectral disease-imaging equipment in conjunction with machine learning technology. However, this approach has significant limitations in early disease diagnosis, including challenges such as high costs, complex operation, and low model performance. The aim of this study was to combine low-cost multispectral cameras with deep learning technology to detect early stage Verticillium wilt in eggplant effectively. Using the Manual FS-3200T-10GE-NNC multispectral camera to perform multispectral imaging of the leaves of eggplant seedlings at the early infection stage, information fusion was performed on the collected multispectral images, and a five-channel image information fusion model was established. Image information fusion technology was combined with deep learning technology, among which the VGG16-triplet attention model performed the best, achieving a precision of 86.73% on the test set. Model validation on 48- and 72-hour data reached a precision of 75% and 82%, respectively, achieving an early diagnosis of Verticillium wilt. This highlighted the potential of multispectral cameras for early disease detection. In this study, we successfully developed a method for the non-destructive detection of the early stages of eggplant wilt disease by combining multispectral imaging technology with deep learning algorithms. While ensuring high accuracy, this method significantly reduces the cost of experimental equipment. The application of this method can reduce the cost of agricultural equipment and provide a scientific basis for agricultural production practices, helping to reduce losses caused by diseases.

The Evaluation and Learning of Web Crawler Technology in the Field of Digital Media Art

With the rapid advancement of digital technology, digital media art has progressively emerged as a novel darling in the contemporary art market, primarily due to its distinctive interactivity, generativity, and digital distribution methods.The appraisal of market value is crucial in the transaction of digital art, and web crawling technology presents an innovative approach for data acquisition and processing. This study is dedicated to exploring the convergence of digital media art and web crawling technology, conducting an in-depth investigation on the application of web crawlers in the determination of market value for digital art.In the context of developing a theoretical framework, a novel value assessment model is proposed by integrating the features of the digital art market with established value assessment theories. On this basis, a value assessment tool targeted at the digital media art market was developed.This tool integrates multiple factors such as historical transaction data of artworks, artist backgrounds, work style, and media exposure, and employs machine learning algorithms to conduct market value assessment of artworks.Case analyses have also confirmed the accuracy of the assessment results and the practicality of the assessment model.Results show that this assessment tool not only provides powerful market guidance for art investors and collectors but also offers a pricing decision basis for digital art trading platforms.This research not only provides new perspectives and practical tools for the assessment of digital art but also makes a valuable exploration into the application of web crawling technology in the acquisition of art data information.It is of positive significance for the value discovery of digital media art and the long-term healthy development of the art market.

Stress Detection System using Machine Learning and Sensors

This project presents a novel stress detection system that leverages machine learning and sensor technology to monitor and assess stress levels in real-time. As stress has become a widespread concern affecting mental health, our system integrates wearable sensors—such as heart rate monitors, galvanic skin response sensors, and accelerometers—to capture physiological data indicative of stress. The project involves several key components: data collection and preprocessing, feature extraction, and the development of machine learning models capable of accurately classifying stress levels based on the sensor data. Various algorithms, including Support Vector Machines and Neural Networks, will be employed and optimized for performance. The system is designed to provide real-time feedback to users, offering insights and personalized recommendations for stress management based on detected levels. A user-friendly interface will enable individuals to track their stress over time, fostering greater awareness and encouraging proactive coping strategies

Identification of Exploited Unreliable Account Passwords in the Information Infrastructure Using Machine Learning Methods

Accounts are an integral part of most modern information systems and provide their owners with the ability to authenticate within the system. This paper presents an analysis of existing methods for detecting simple account passwords in automated systems. Their advantages and disadvantages are listed. A method was developed to detect simple exploitable passwords that administrators can use to supplement other existing methods to increase the overall security of automated systems against threats from accounts potentially compromised by attackers. The method was based on the analysis of commands executed in automated or manual modes with the indication of credentials in plain text. Minimum password strength requirements are provided based on the security level. A special case was considered in which all passwords analyzed in this way were found explicitly in the system logs. We developed a unified definition of the classification of passwords into simple and strong, and also developed machine learning technology for their classification. The method offers a flexible adaptation to a specific system, taking into account the level of significance of the information being processed and the password policy adopted, expressed in the possibility of retraining the machine learning model. The experimental method using machine learning algorithms, namely the ensemble of decision trees, for classifying passwords into strong and potentially compromised by attackers based on flexible password strength criteria, showed high results. The performance of the method is also compared against other machine learning algorithms, specifically XGBoost, Random Forest, and Naive Bayes. The presented approach also solves the problem of detecting events related to the use and storage of credentials in plain text. We used the dataset of approximately 770,000 passwords, allowing the machine learning model to accurately classify 98% of the passwords by their significance levels.

Cutting-Edge Deep Learning Strategies for Precise Ovarian Disease Detection: A Comprehensive Review

Accurate and timely diagnosis of ovary-related diseases is of paramount importance in the realm of medical imaging. Utilizing deep learning technology has emerged as a viable method to improve the precision and effectiveness of medical image segmentation, which directly impacts the detection of ovarian diseases. Ovarian disease detection through medical image segmentation is critical for early diagnosis and effective treatment. This study evaluates cutting-edge deep learning strategies using the MMOTU ovarian tumour ultrasound dataset, which includes OTU 2D and OTU CEUS subsets. We implemented various models, including U-Net, its variants with ResNet and DenseNet backbones, CR-Unet, and Ocys-Net, along with ensemble learning and transfer learning techniques. Results show that while the baseline U-Net is effective, advanced models, particularly CR-Unet and DenseNet, significantly improve segmentation accuracy. The best performance was achieved using an ensemble model and a fine-tuned pre-trained network, highlighting the potential of these approaches in enhancing the precision and reliability of ovarian disease detection.

Evaluating Machine Learning-Based Elephant Recognition in Complex African Landscapes Using Drone Imagery

Abstract This paper evaluates a machine learning-based approach for identifying and analyzing African bush elephants within complex terrains using high-resolution drone imagery. With human-wildlife conflict posing a significant threat to elephants worldwide, accurate and efficient monitoring techniques are crucial, yet challenging in diverse landscapes. Our study utilizes approximately 3,180 drone-captured images from Kasungu National Park in Malawi, encompassing various terrains including dense forests and open bushlands. These images were systematically preprocessed and analyzed using three distinct ML algorithms: Faster R-CNN, RetinaNet, and Mask R-CNN, each fine-tuned for identification of elephants across different age groups. Comparative performance metrics revealed nuanced strengths and limitations: Faster R-CNN showed notable proficiency in detecting adult elephants, particularly in dense foliage. Mask R-CNN, while less precise overall, demonstrated increased effectiveness in identifying juveniles and infants. RetinaNet, optimized for larger images, showed particular adeptness with adult elephants but less so with younger ones. Despite these promising results, overall recognition rates were lower than ideal, highlighting the complexities of wildlife identification in natural settings. This study not only facilitates the identification and counting of individual elephants but also provides insights into the challenges of applying ML in complex ecological contexts. The derived insights can assist conservationists and park officials in making informed decisions related to wildlife protection and habitat preservation. Furthermore, the study offers a valuable blueprint for integrating AI and machine learning technology into wildlife conservation strategies, presenting a scalable model with potential applications for different species and geographic regions, while acknowledging the need for further refinement to enhance accuracy and reliability in diverse ecological settings.

CNN-Based Cancer Image Diagnosis: Current Progress and Future Directions

Abstract. With the development of deep learning technology, CNN (Convolutional Neural Network) models have shown great value in medical image analysis, especially in diagnosing early lung cancer, breast cancer, and brain tumors. In this study, we recall and organize the application and progress of CNN models in the field of cancer and tumor diagnosis in the past five years to provide a theoretical basis and reference for related researchers. This article introduces the principles of different CNN cancer diagnostic models and compares and analyzes their results, and ultimately finds that these models have significant advantages in improving the accuracy and efficiency of cancer diagnosis, but at the same time, there are also problems such as too much reliance on large datasets, high model complexity, and poor generalization ability. In the future, we can consider optimizing the performance of CNN network models, enhancing the generalization ability of the models, and developing data enhancement techniques on this basis, so that CNN models can be better applied in the field of cancer diagnosis.

Using an improved U-Net++ with a T-Max-Avg-Pooling layer as a rapid approach for concrete crack detection

The monitoring of concrete structures has advanced remarkably with the aid of deep learning technologies. Since concrete is multi-purpose and low-cost, it is extensively used for construction purposes. Concrete is very enduring. Nevertheless, it tends to crack which endangers the integrity of the structure and results in complications. The current study offers a new image segmentation approach for detecting cracks in concrete by making use of an optimized U-Net++ architecture. The proposed model gives the features of the T-Max-Avg Pooling layer which effectively combines the advantages of traditional max and average pooling using a learnable parameter to balance feature extraction dynamically. This innovation both improves the output accuracy and processing speed and captures the fine details. In addition, it mitigates noise and transcends the limitations of conventional pooling methods. Moreover, using learnable pruning and shortening skip connections in U-Net++ reduce redundant computations, making the model faster without compromising accuracy. In comparison with other models like Mask R-CNN and VGG-U-Net, the proposed model had considerably faster inference times (21.01 ms per image) and fewer computational requirements (40G FLOPs), making it very suitable for real-time monitoring applications. The DeepCrack and Concrete Pavement Crack datasets were employed to assess the model thoroughly which yielded an MIoU score of 82.1%, an F1 score of 90.12%, a Dice loss score of 93.7%, and an overall accuracy of 97.65%. According to the results, the enhanced U-Net++ with T-Max-Avg Pooling provided a balanced trade-off between segmentation accuracy and computational efficiency. This indicates its considerable potential for automated real-time crack detection in concrete structures by employing resource-constrained environments including drones and mobile platforms.

Advancements in Fingerprint Recognition Through Deep Learning: A Comprehensive Analysis of Novel Algorithms

Abstract. This paper presents a comprehensive exploration of the application of deep learning technology in fingerprint recognition, focusing on the development and impact of several novel algorithms. These algorithms have been specifically designed to address challenges in key sub-fields such as pose estimation, direction field estimation, minutiae extraction, and minutiae matching. By integrating deep learning techniques, these new approaches significantly enhance the accuracy, stability, and efficiency of fingerprint recognition systems. The study demonstrates that these algorithms surpass traditional methods in several critical areas, offering improved precision in recognizing fingerprints, particularly in high-noise environments. Furthermore, the fully differentiable nature of these models contributes to their robustness, enabling more consistent and reliable performance across diverse scenarios. The results underscore the potential for these deep learning-based algorithms to set new benchmarks in the field, with broad implications for their application in security, law enforcement, and other areas requiring reliable biometric authentication. The use of these cutting-edge methods is anticipated to be vital in influencing the direction of fingerprint recognition technology as it develops further, guaranteeing increased security and precision.

You Only Look Once Version 5 and Deep Simple Online and Real-Time Tracking Algorithms for Real-Time Customer Behavior Tracking and Retail Optimization

The speedy progress of computer vision and machine learning engineering has inaugurated novel means for improving the purchasing experiment in brick-and-mortar stores. This paper examines the utilization of YOLOv (You Only Look Once) and DeepSORT (Deep Simple Online and Real-Time Tracking) algorithms for the real-time detection and analysis of the purchasing penchant in brick-and-mortar market surroundings. By leveraging these algorithms, stores can track customer behavior, identify popular products, and monitor high-traffic areas, enabling businesses to adapt quickly to customer preferences and optimize store layout and inventory management. The methodology involves the integration of YOLOv5 for accurate and rapid object detection combined with DeepSORT for the effective tracking of customer movements and interactions with products. Information collected in in-store cameras and sensors is handled to detect tendencies in customer behavior, like repeatedly inspected products, periods expended in specific intervals, and product handling. The results indicate a modest improvement in customer engagement, with conversion rates increasing by approximately 3 percentage points, and a decline in inventory waste levels, from 88% to 75%, after system implementation. This study provides essential insights into the further integration of algorithm technology in physical retail locations and demonstrates the revolutionary potential of real-time behavior tracking in the retail industry. This research determines the foundation for future developments in functional strategies and customer experience optimization by offering a solid framework for creating intelligent retail systems.

Stochastic Gradient Descent with Pre-Conditioned Polyak Step-Size

Stochastic Gradient Descent (SGD) is one of the many iterative optimization methods that are widely used in solving machine learning problems. These methods display valuable properties and attract researchers and industrial machine learning engineers with their simplicity. However, one of the weaknesses of this type of methods is the necessity to tune learning rate (step-size) for every loss function and dataset combination to solve an optimization problem and get an efficient performance in a given time budget. Stochastic Gradient Descent with Polyak Step-size (SPS) is a method that offers an update rule that alleviates the need of fine-tuning the learning rate of an optimizer. In this paper, we propose an extension of SPS that employs preconditioning techniques, such as Hutchinson’s method, Adam, and AdaGrad, to improve its performance on badly scaled and/or ill-conditioned datasets.

Co-design with children, families, and educators for understanding children’s digital life-worlds for play and learning

ABSTRACT The world is now recognised by many scholars as digitally networked, and it is known that children use technologies for play, learning, and development in their everyday lives. This implies that caregivers and educators need to address these experiences to optimise their response to children’s developmental needs. This has proven challenging as digital technologies are often regarded as hindering more traditional ‘hands-on’ forms of play and as a ‘problem’ that needs regulating. This paper therefore reports on examples from three separate studies of co-design research conducted with children, families, and educators for understanding children’s digital life-worlds for play and learning. By examining these three examples, our aim was to identify common elements of co-design evident in research with children and their adults directed towards addressing the problem of adult hesitancy regarding technology use by children in their play and learning. Five main findings were identified as central to the conduct of co-design research with children and their adults regarding children’s experiences with digital technologies. These were: Mode of action, Collaboration, Creativity, Agency, and Data. The findings are discussed in relation to research with children facilitating a cultural role in informing practice rather than a technical role.