Visualization Support for Developing a Matrix Calculus Algorithm: A Case Study

Machine Learning (ML) is increasingly being used in intelligent systems that can perform Artificial Intelligence (AI) functions. Analytical model development and solving problems related with it may be automated by machine learning, which explains the ability of computers to learn from problem-specific learning algorithm. Depending on artificial neural networks, "deep learning" is a kind of machine learning. The performance of deep learning techniques is superior to that of superficial machine learning techniques and conventional methods of data analysis in many situations. Deep Machine Learning (DML) algorithms and frameworks that have been implemented to and supported by wireless communication systems have been thoroughly analyzed in this paper. User associations, power latency and allocation; bandwidth assignment and user selections, and; cloud computing technology on the edge have both been suggested as potential DML implementations.

Vi sualization support is a conceptual framework.Based on the graphic way, it can intuitively and structurally present the relationship of different knowledge.At present, the visual teaching tools have been widely used in various fields.It also played an important role in English teaching.Hoping to apply this knowledge visualization teaching tools into daily English teaching can let us produce unexpected results.

The rapid development and progress in deep machine-learning techniques have become a key factor in solving the future challenges of humanity. Vision-based target detection and object classification have been improved due to the development of deep learning algorithms. Data fusion in autonomous driving is a fact and a prerequisite task of data preprocessing from multi-sensors that provide a precise, well-engineered, and complete detection of objects, scene or events. The target of the current study is to develop an in-vehicle information system to prevent or at least mitigate traffic issues related to parking detection and traffic congestion detection. In this study we examined to solve these problems described by (1) extracting region-of-interest in the images (2) vehicle detection based on instance segmentation, and (3) building deep learning model based on the key features obtained from input parking images. We build a deep machine learning algorithm that enables collecting real video-camera feeds from vision sensors and predicting free parking spaces. Image augmentation techniques were performed using edge detection, cropping, refined by rotating, thresholding, resizing, or color augment to predict the region of bounding boxes. A deep convolutional neural network F-MTCNN model is proposed that simultaneously capable for compiling, training, validating and testing on parking video frames through video-camera. The results of proposed model employing on publicly available PK-Lot parking dataset and the optimized model achieved a relatively higher accuracy 97.6% than previous reported methodologies. Moreover, this article presents mathematical and simulation results using state-of-the-art deep learning technologies for smart parking space detection. The results are verified using Python, TensorFlow, OpenCV computer simulation frameworks.

A 360° video combines the potential of conventional video technology with immersive–interactive design opportunities. The multi-perspective viewing option within a 360° video also enables possible applications as visual training support for motor learning. There are already first methodological–didactic approaches to movement learning; nevertheless, research on 360° video technology can largely be classified as exploratory. This article presents the use of 360° video as a visual training aid for the independent movement acquisition of various fascial movement exercises. In an intervention study, two randomized groups (N = 48) were used to evaluate the subjective benefit. Following the technology acceptance model, subjective benefit perception and rating of format were compared after two crossover interventions with 360° videos and/or conventional training videos. No significant differences were found for perceived usefulness (z = −1.014, p = 0.31, r = 0.105) or perceived ease of use (z = −1.278, p = 0.201, r = 0.132). The same applies for intensity of use (z = −0.247, p = 0.805, r = 0.025) and overall subjective rating (z = −1.745, p = 0.081, r = 0.18). Although no significant differences were found in the evaluation of benefits, the participants tended to use 360° videos as visual training support (M = 3.4, SD = 1.581) on a 7-point scale (1 = 360° videos). Although a generalized statement on 360° videos as visual training support is not possible due to low effect strengths, it can be stated that 360° videos are perceived at least as useful as visual training tools.

The article focuses on visual supports as a communication technique used for autistic students in physical education class. The physical education environment for autistic students can be chaotic, which causes withdrawal and disruptive or self-stimulatory behaviors. Visual supports give autistic students clear expectations, create predictability, and promote independent transitions. Furthermore, visual information is processed more effectively by autistic students than auditory information. It explores the Picture Exchange Communication System (PECS) as a visual communication tool for educators and their autistic students. The tool is consists of a card with colored or black-and-white drawings that represent an object or skill and descriptions written above it.

Recent progress in large-volume microscopy, tissue-staining, as well as in image processing methods and 3D anatomy reconstruction allow neuroscientists to extract previously inaccessible anatomical data with high precision. For instance, determination of neuron numbers, 3D distributions and 3D axonal and dendritic branching patterns support recently started efforts to reconstruct anatomically realistic network models of many thousand neurons. Such models aid in understanding neural network structure, and, by numerically simulating electro-physiological signaling, also to reveal their function. We illustrate the impact of visual computing on neurobiology at the example of important steps that are required for the reconstruction of large neural networks. In our case, the network to be reconstructed represents a single cortical column in the rat brain, which processes sensory information from its associated facial whisker hair. We demonstrate how analysis and reconstruction tasks, such as neuron somata counting and tracing of neuronal branches, have been incrementally accelerated – finally leading to efficiency gains of orders of magnitude. We also show how steps that are difficult to automatize can now be solved interactively with visual support. Additionally, we illustrate how visualization techniques have aided computer scientists during algorithm development. Finally, we present visual analysis techniques allowing neuroscientists to explore morphology and function of 3D neural networks. Altogether, we demonstrate that visual computing techniques make an essential difference in terms of scientific output, both qualitatively, i.e., whether particularKeywordsInteractive VisualizationMaximum Intensity Projection ImageNeuron SomataCortical ColumnCable EquationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

System modeling (identification) in complex systems like kinesiological and biological in general is extremely difficult due to the high dimensions of parameters and usually non-linear functional dependencies. Data Science and especially Machine Learning (Deep Learning) algorithms seem to be quite a good tool for analysis and problem-solving in sports today. Data Science (Machine or Deep Learning) algorithms rely on basic use of statistical algorithms, but extend those with models such as Decision tree, K-means clustering, Neural networks, and Reinforcement learning, creating new algorithms that handle input data by predicting outputs that describe correlation relations or predict future states at time points (regression). This study is an attempt to analyze and research applications of machine learning in Sport science - Kinanthropometry related problem of determining somatotype by using the Microsoft Azure Machine Learning platform and comparing several supervised classifier algorithms (Multiclass Neural Network, Multiclass Decision Forest, Multiclass Decision Jungle and Multiclass Logistic Regression) which were compared versus classical somatotype categorization algorithms with dataset based on the Heath-Carter method Somatotype determination to gain experience and expertise.

In recent years, data visualization technology has received widespread attention, offering users a way to gain an in-depth understanding and effective exploration of various datasets by visualizing their composition and characteristics. Simultaneously, with the continuous development of hardware and algorithms, artificial intelligence (AI) has permeated every aspect of contemporary social life. Some studies have proposed the use of intelligent visualization tools that leverage AI to achieve significant results in data analysis, decision support, and automated reporting. However, there is a lack of systematic work to summarize and analyze these literature. In this paper, we conduct a comprehensive review of AI-based intelligent visualization tools and their applications in various domains. Firstly, we introduced the foundational concepts of AI and data visualization and explored the relationship between these two. we provided an overview of prevalent AI-based intelligent visualization tools along with their diverse applications. Our discussion encompassed renowned tools like Tableau, Power BI, QlikView, D3.js, IBM Watson Analytics, and FusionCharts, delving into their distinctive features, functionalities, and practical implementations. Furthermore, we have explored the advantages and challenges of AI-integrated visualization. The advantages include enhanced data understanding and insights, as well as automation of analysis and visualization generation. Some challenges coexist in the meantime, including data quality and accuracy, privacy and security concerns, and user experience and usability issues. Based on our findings, we have added a great deal to the corpus of knowledge by giving a thorough rundown of visualization tools with AI integration and their possible uses. We have also identified existing issues and limitations that need to be addressed for further advancements in this field. Lastly, we have proposed future research directions and development trends to guide researchers and practitioners in fully harnessing the potential of AI and visualization.

Predicting disease status for a complex human disease using genomic data is an important, yet challenging, step in personalized medicine. Among many challenges, the so-called curse of dimensionality problem results in unsatisfied performances of many state-of-art machine learning algorithms. A major recent advance in machine learning is the rapid development of deep learning algorithms that can efficiently extract meaningful features from high-dimensional and complex datasets through a stacked and hierarchical learning process. Deep learning has shown breakthrough performance in several areas including image recognition, natural language processing, and speech recognition. However, the performance of deep learning in predicting disease status using genomic datasets is still not well studied. In this article, we performed a review on the four relevant articles that we found through our thorough literature review. All four articles used auto-encoders to project high-dimensional genomic data to a low dimensional space and then applied the state-of-the-art machine learning algorithms to predict disease status based on the low-dimensional representations. This deep learning approach outperformed existing prediction approaches, such as prediction based on probe-wise screening and prediction based on principal component analysis. The limitations of the current deep learning approach and possible improvements were also discussed.

Traditional bulk sequencing methods are limited to measuring the average signal in a group of cells, potentially masking heterogeneity, and rare populations. The single-cell resolution, however, enhances our understanding of complex biological systems and diseases, such as cancer, the immune system, and chronic diseases. However, the single-cell technologies generate massive amounts of data that are often high-dimensional, sparse, and complex, thus making analysis with traditional computational approaches difficult and unfeasible. To tackle these challenges, many are turning to deep learning (DL) methods as potential alternatives to the conventional machine learning (ML) algorithms for single-cell studies. DL is a branch of ML capable of extracting high-level features from raw inputs in multiple stages. Compared to traditional ML, DL models have provided significant improvements across many domains and applications. In this work, we examine DL applications in genomics, transcriptomics, spatial transcriptomics, and multi-omics integration, and address whether DL techniques will prove to be advantageous or if the single-cell omics domain poses unique challenges. Through a systematic literature review, we have found that DL has not yet revolutionized the most pressing challenges of the single-cell omics field. However, using DL models for single-cell omics has shown promising results (in many cases outperforming the previous state-of-the-art models) in data preprocessing and downstream analysis. Although developments of DL algorithms for single-cell omics have generally been gradual, recent advances reveal that DL can offer valuable resources in fast-tracking and advancing research in single-cell.

In contemporary architecture, effective communication between architects, stakeholders, and end users is critical, especially at early stages where decisions shape the project’s direction and long-term impact. Traditional representational tools, such as 2D drawings, static renderings, and even physical models, often fail to fully convey the spatial, functional, and emotional qualities of design. These limitations can lead to misunderstandings, delays, and compromised outcomes. The article explores the potential of augmented reality (AR) as an innovative visual and communicative tool during the conceptual phase of architectural design, particularly in the context of educational institutions. Through a detailed case study of a safe learning environment designed for a public university and supported by philanthropic funding from the private sector, this study analyzes how the integration of AR influenced the perception of an architectural project, communication dynamics, and decision-making processes. The project brought together architects, university administration, students, and donors in a collaborative design process that emphasized transparency, trust, and inclusivity. The research methodology combines descriptive analysis of the design workflow, qualitative changes in the nature of feedback and the effectiveness of decision-making before and after AR implementation. The findings demonstrate that AR offers substantial advantages over conventional methods by enabling to interact with virtual spaces in real time. This immersive experience enhances emotional engagement and cognitive comprehension, allowing users to assess spatial qualities, test scenarios, and provide immediate, grounded feedback. As a result, AR fosters more informed, participatory dialogue, reduces ambiguity, and accelerates consensus-building. Moreover, it shifts the communicative paradigm from expert-led presentations to shared spatial experiences, giving voice to users who are typically underrepresented in technical design discussions. In addition to its communicative benefits, AR supports a more democratic and inclusive design process by empowering stakeholders, particularly students, educators, and administrative staff, to co-create environments tailored to their real needs and everyday practices. This democratization aligns with broader trends in participatory and human-centered design, reinforcing the social responsibility of architecture in shaping equitable, responsive, and sustainable spaces. Ultimately, the article positions augmented reality not merely as a visualization tool, but as a transformative medium that bridges the gap between design intent and user experience. As digital technologies continue to redefine professional workflows, AR stands out as a critical enabler of more collaborative, transparent, and impactful architectural practices.

Predicting disease status for a complex human disease using genomic data is an important, yet challenging, step in personalized medicine. Among many challenges, the so-called curse of dimensionality problem results in unsatisfied performances of many state-of-art machine learning algorithms. A major recent advance in machine learning is the rapid development of deep learning algorithms that can efficiently extract meaningful features from high-dimensional and complex datasets through a stacked and hierarchical learning process. Deep learning has shown breakthrough performance in several areas including image recognition, natural language processing, and speech recognition. However, the performance of deep learning in predicting disease status using genomic datasets is still not well studied. In this article, we performed a review on the four relevant articles that we found through our thorough literature search. All four articles first used auto-encoders to project high-dimensional genomic data to a low dimensional space and then applied the state-of-the-art machine learning algorithms to predict disease status based on the low-dimensional representations. These deep learning approaches outperformed existing prediction methods, such as prediction based on transcript-wise screening and prediction based on principal component analysis. The limitations of the current deep learning approach and possible improvements were also discussed.

With more and more organizations working on the cloud over unsecure internet, sharing files and emails and saving them on cloud storage imperative. Securing the end user sensitive data in transit has thus started to get maximum priority to protect it from Cloud company staff, hackers and data thieves. In this study, an attempt is made to review the research of end user data security. There is an urgent need for solutions for end users' data protection, privacy and during the times when migrating from one Cloud service provider to other. This chapter identifies end user data challenges and issues on cloud and presents use of Public Key Cryptography, Multi Factor Authentication and use of Cloud Aware applications as possible solutions.

With more and more organizations working on the cloud over unsecure internet, sharing files and emails and saving them on cloud storage imperative. Securing the end user sensitive data in transit has thus started to get maximum priority to protect it from Cloud company staff, hackers and data thieves. In this study, an attempt is made to review the research of end user data security. There is an urgent need for solutions for end users' data protection, privacy and during the times when migrating from one Cloud service provider to other. This chapter identifies end user data challenges and issues on cloud and presents use of Public Key Cryptography, Multi Factor Authentication and use of Cloud Aware applications as possible solutions.

Forecasting the behavior of the stock market is a classic but difficult topic, one that has attracted the interest of both economists and computer scientists. Over the course of the last couple of decades, researchers have investigated linear models as well as models that are based on machine learning (ML), deep learning (DL), reinforcement learning (RL), and deep reinforcement learning (DRL) in order to create an accurate predictive model. Machine learning algorithms can now extract high-level financial market data patterns. Investors are using deep learning models to anticipate and evaluate stock and foreign exchange markets due to the advantage of artificial intelligence. Recent years have seen a proliferation of the deep reinforcement learning algorithm’s application in algorithmic trading. DRL agents, which combine price prediction and trading signal production, have been used to construct several completely automated trading systems or strategies. Our objective is to enable interested researchers to stay current and easily imitate earlier findings. In this paper, we have worked to explain the utility of Machine Learning, Deep Learning, Reinforcement Learning, and Deep Reinforcement Learning in Quantitative Finance (QF) and the Stock Market. We also outline potential future study paths in this area based on the overview that was presented before.