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The Correlation Between Architecture Smells, Package Stability and Reusability: A Case Study 4

The Correlation Between Architecture Smells, Package Stabili...

4th IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer Engineering, MI-STA 2024

作者： Benidris, Mrwan Elfallah, Ehab A. Omar Cheema, Marwa Aslam Alsma, Mohammed Salah Addin Kadiki, Tareq Ziad Elbarsha, Abdelfattah University of Benghazi Faculty of Information Technology Dep. of Computer Science Libya Libyan International Medical University Faculty of Information Technology Dept. of Software Engineering Libya University of Benghazi Faculty of Public Health Health Informatics Department Libya

ISBN: (纸本)9798350372632

the design decisions made in the architecture of a software system are essential to its maintainability, and thus its quality is of great importance. Architecture smells (ASs) can be used to identify any quality issues within the system. To further explore the relationships between ASs, stability, and reusability, Spearman correlation coefficients will be calculated. This paper seeks to examine the connection between architecture smells, software stability, and reusability. To do this, we utilized a dataset created by other researchers and conducted an empirical study of the associations between five types of architecture smells and software stability and reusability. This research is necessary as there is a lack of in-dept. research on architecture smells and their relationship to software stability and reusability. We found a relationship between the presence of Feature Concentration in package and the stability and reusability of the package. Also, package's stability is affected by both Cyclic Dependency and Unstable Dependency. A slight positive correlation has been found between God Class and stability, as well as a very weak negative correlation between Unstable Dependency and reusability. Finally, we found a slight negative correlation between package stability and reusability. © 2024 IEEE.

关键词： Reusability

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Using clinical entity recognition for curating an interface terminology to aid fast skimming of EHRs

Using clinical entity recognition for curating an interface ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Kollapally, Navya Martin Dehkordi, Mahshad Koohi H. Perl, Yehoshua Geller, James Deek, Fadi P. Liu, Hao Keloth, Vipina K. Elhanan, Gai Einstein, Andrew J. Zhou, Shuxin Kean University Department of Computer Science Union United States New Jersey Institue of Technology Department of Computer Science Newark United States New Jersey Institue of Technology Department of DataScience Newark United States New Jeresy Institue of Technology Department of Informatics Newark United States Montclair State University School of Computing Montclair United States Yale University Section of Biomedical Informatics and DataScience New Haven United States University of Nevada School of Medicine Nevada United States Columbia Medical Center Dept. of Medicine New York United States

ISBN: (纸本)9798350386226

Highlighting of Electronic Health Records (EHRs) involves marking essential content of EHR notes, corresponding to concepts of a clinical terminology. However, employing the best clinical terminology (SNOMED CT) for highlighting EHRs, captures only a portion of their crucial content. In this paper, we describe the curation of a Cardiology Interface Terminology (CIT) dedicated to the application of highlighting EHRs of cardiology patients. We utilize a Clinical-Named Entity Recognition (Clinical NER) approach for extracting phrases, of higher granularity than SNOMED CT concepts, from EHRs, for enriching CIT. For this purpose, we train a neural network model with BIOE-tagged (Beginning, Inside, End, and Outside) cardiology entities. Transfer Learning can be used to facilitate the curation of an interface terminology for highlighting EHRs for other specialties e.g. Nephrology. Large-scale highlighting enables overworked physicians and other healthcare providers to fast skim the dense volume of EHRs they regularly read. Secondary research and EHRs interoperability are other applications that can be supported by highlighting. © 2024 IEEE.

关键词： Electronic health record

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Automated Detection of Poultry Farms from Aerial Images for Actionable AI System toward Biosecurity Applications

Automated Detection of Poultry Farms from Aerial Images for ...

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2023 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2023

作者： Li, Yu Das, Rana Duong, Cuong Lim, Teng-Teeh Haithcoat, Timothy Ersoy, Ilker Shyu, Chi-Ren University of Missouri Department of Electrical Engineering and Computer Science ColumbiaMO United States University of Missouri Biological Engineering ColumbiaMO United States University of Missouri Plant Science and Technology Extension ColumbiaMO United States University of Missouri Institute for Data Science and Informatics Columbia United States University of Missouri Institute for Data Science and Informatics Electrical Eng. and Computer Science Dept. ColumbiaMO United States

ISBN: (纸本)9798350359527

Poultry productions have shifted towards larger farms and often cluster in certain regions. However, many of the smaller farms with a considerable amount of production are not considered concentrated animal feeding operation under regulation in many regions, and therefore are not required to obtain a permit. As a result, information of these operations is not being recorded and monitored, contributing to an increased risk for biosecurity issues. The development in computer vision and availability of high-resolution remote sensing imagery allows the possibility for automatic localization and operation estimation of the unregistered farms, filling in the missing information needed for biosecurity risk management. The farm information extracted from images can be incorporated with other available geospatial data, such as road networks, watersheds, etc., to build a comprehensive knowledge base for actionable plans. In the current study, we present a framework for automated localization, analysis, and visualization of poultry farms from remote sensing images. A robust machine learning method was developed to computationally link the visual features from remote sensing imagery to Missouri Department of Natural Resources Concentrated Animal Feeding Operation data, to identify the animal farm type, location, and potential capacity in the region. The developed process can be performed automatically on images of different timestamps to analyze temporal and spatial development of poultry production facilities of a region. This research contributes to the effective management and regulation of poultry and potentially other animal farms, providing insights into farm characteristics and capacities while facilitating informed decision-making. © 2023 IEEE.

关键词： Object detection

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Interactive Graphical Access Control Tools

Interactive Graphical Access Control Tools

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IEEE Symposium on Visual Languages and Human Centric Computing (VL/HCC)

作者： Rachael Fernandez Peter Cheng Ben Smith Tania Fenton Yehia Boraey Armstrong Nhlabatsi Khaled Khan Noora Fetais Dept. of Computer Science Qatar University Doha Qatar Dept. of Informatics University of Sussex Brighton UK

Access control (AC) policy creation and implementation is challenging because of its complex structure and specifications. This paper explores the potential of two novel interactive graphical displays whose design draws upon alternative principles of effective interface design. One tool (CDAC) exploits the power of Euler diagrams to visualize information and interrelate an organization's management structure and details of the file structure of digital resources. The other tool (DMAC) deploys a diagrammatic matrix to coordinate permissions allocations into a context of structural information. The third tool (SLAC) utilized a conventional visualization and served as the control group for comparison against the other two tools. An experiment with 91 relatively novice participants demonstrates that both tools, even with minimal training, support users' comprehension of AC policies and their implementation, as well as a more familiar traditional design of AC tools.

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A Study on Neuro-Symbolic Artificial Intelligence: Healthcare Perspectives

arXiv

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arXiv 2025年

作者： Hossain, Delower Chen, Jake Y. Dept. of Computer Science The University of Alabama Birmingham United States Dept. of Genetic Biomedical Engineering Informatics and Data Science Heersink School of Medicine The University of Alabama Birmingham United States Systems Pharmacology AI Research Center The University of Alabama Birmingham United States

Over the last few decades, Artificial Intelligence (AI) scientists have been conducting investigations to attain human-level performance by a machine in accomplishing a cognitive task. Within machine learning, the ultimate aspiration is to attain Artificial General Intelligence (AGI) through a machine. This pursuit has led to the exploration of two distinct AI paradigms. Symbolic AI, also known as classical or GOFAI (Good Old-Fashioned AI) and Connectionist (Sub-symbolic) AI, represented by Neural Systems, are two mutually exclusive paradigms. Symbolic AI excels in reasoning, explainability, and knowledge representation but faces challenges in processing complex real-world data with noise. Conversely, deep learning (Black-Box systems) research breakthroughs in neural networks are notable, yet they lack reasoning and interpretability. Neuro-symbolic AI (NeSy), an emerging area of AI research, attempts to bridge this gap by integrating logical reasoning into neural networks, enabling them to learn and reason with symbolic representations. While a long path, this strategy has made significant progress towards achieving common sense reasoning by systems. This article conducts an extensive review of over 977 studies from prominent scientific databases (DBLP, ACL, IEEExplore, Scopus, PubMed, ICML, ICLR), thoroughly examining the multifaceted capabilities of Neuro-Symbolic AI, with a particular focus on its healthcare applications, particularly in drug discovery, and Protein engineering research. The survey addresses vital themes, including reasoning, explainability, integration strategies, 41 healthcare-related use cases, benchmarking, datasets, current approach limitations from both healthcare and broader perspectives, and proposed novel approaches for future experiments. Moreover, it identifies critical challenges in applying Neuro-Symbolic AI to medicine and represents a comprehensive exploration of its transformative potential in the biomedical field. © 2025, CC BY-NC

关键词： Knowledge representation

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Fine-Tuning Vision Transformer for Arabic Sign Language Video Recognition on Augmented Small-Scale Dataset

Fine-Tuning Vision Transformer for Arabic Sign Language Vide...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Gochoo, Munkhjargal Batnasan, Ganzorig Ahmed, Ahmed Abdelhadi Otgonbold, Munkh-Erdene Alnajjar, Fady Shih, Timothy K. Tan, Tan-Hsu Wee, Lai Khin United Arab Emirates University Dept. of Computer Science and Software Engineering Al Ain United Arab Emirates College of EECS National Central University Taoyuan Taiwan National Taipei University of Technology Dept. of Electrical Engineering Taipei Taiwan University of Malaya Dept. of Biomedical Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350337020

With the rise of AI, the recognition of Sign Language (SL) through sign-to-text has gained significance in the field of computer vision and deep machine learning. However, there are only a few medium to large open datasets available for this task, as it requires a vast dataset of thousands of signs for words/phrases in different environments, which is a time-consuming and tedious process. Furthermore, there has been very little effort towards Arabic Sign Language Recognition (ArSLR). This research paper presents the results of fine-tuning the Vision Transformer (ViT) model on a small-scale in-house dataset of ArSL. The main goal is to attain satisfactory results by utilizing minimal computing power and a small dataset involving less than 10 individuals, with only one recording made for each sign in every environment. The dataset comprises 49 classes/signs, all of which were made with two hands and belong to the Level I category in terms of popularity. To enhance the dataset, three types of augmentations - translation, shear, and rotation were employed. The ViT model, pre-trained on the Kinetics dataset, was trained on the variation of augmented datasets with 2 to 40 times samples for each original video, where the training set includes original and augmented videos of 8 volunteers and the test set includes only original videos of one particular volunteer. Experimental results reveal that the combination of rotation and shear outperformed the others, achieving an accuracy of 93% on the 20 times augmented samples per class per signer dataset. We believe this study sheds light on small-scale dataset-based SLR tasks and video/action recognition in general. © 2023 IEEE.

关键词： Arabic Sign Language Augmentation Deep Learning Smale-scale dataset Vision Transformer ViT

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Reflector-Integrated Ultrasound Image-Guided Access: Towards Clinical Applications

Reflector-Integrated Ultrasound Image-Guided Access: Towards...

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Medical Imaging 2025: Image-Guided Procedures, Robotic Interventions, and Modeling

作者： Tang, Yichuan Chheda, Dhruv Hersum, Mikkel Taubert, Jena Murakami, Ryo Zhang, Haichong Dept. of Robotics Engineering Worcester Polytechnic Institute Worcester MA United States Dept. of Mechanical Engineering Worcester Polytechnic Institute Worcester MA United States Dept. of Biomedical Engineering Worcester Polytechnic Institute Worcester MA United States Dept. of Computer Science Worcester Polytechnic Institute Worcester MA United States

ISBN: (纸本)9781510685949

Ultrasound (US) image-guided access is widely used in surgical operations such as percutaneous nephrolithotomy (PCNL), percutaneous coronary intervention (PCI) and lumbar puncture (LP). One major challenge conventional US image-guided access faces is sustaining proper alignment between the needle path and the US image plane to maximize needle visibility, since the needle and the US transducer are controlled by each individual hand. Reflector-integrated ultrasound (ref-US) image-guided access was originally proposed to provide by-default alignment of the needle path and the image plane, however issues like bulky size, unconventional handling style and encapsulation of acoustic medium pose difficulties to clinical application of ref-US image-guided access. In this work we focused on improving clinical applicability of ref-US image-guided needle access mechanism. First, the size of the ref-US image guided access mechanism is minimized, and handling style of the mechanism is modified to follow the handling style of a normal ultrasound transducer by using double acoustic reflectors. Second, we use gel-like medium to encapsulate ref-US image-guided access mechanism to reduce imaging artifacts and to prevent medium leakage, while both are common issues in encapsulation using liquid medium. We conducted imaging validation using gelatin phantom with embedded point targets to show the mechanism's sensitivity to the needle tip and small targets. We also conducted insertion validation where needle is inserted into the kidney inside a pig cadaver using proposed ref-US image-guided access mechanism. Validation results indicate that the gap between the concept of ref-US image-guided access towards clinical applications has been bridged. © 2025 SPIE

关键词： acoustic reflector gel-like medium image-guided intervention

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The journey from 5G towards 6G

The journey from 5G towards 6G

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International Symposium on Electrical and Electronics Engineering (ISEEE)

作者： Tasos Dagiuklas Dept. of Computer Science and Informatics London South Bank University London UK

This paper gives an overview of the journey from 5G towards 6G evolution. The 5G has been built across three main application verticals as defined by ITU, namely: Enhanced Mobile Broadband, Massive Machine Type Communications and Ultra-reliable Low Latency Communications (URRLC). To support these verticals, 5G has defined the following enablers: Massive MIMO, cloudification of network infrastructure, network automation, network slicing and edge cloud computing. It is expected that 5G will provide flexibility in terms of openness, mobility, programmability and agility and robustness in a standardized manner. The journey towards 6G will describe the limitations of 5G technologies and outlines the technology enablers for 6G. These enablers include smooth integration and interworking of Non-Terrestrial Networking technologies (NTN), use of Reconfigurable Intelligent Surfaces (RIS) and use of AI to orchestrate network and cloud resources. Additionally, the paper will give an overview of 6G research initiatives at both regional and international level.

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Robust Exercise-Based Telerehabilitation for Elderly Healthcare Survives 19

Robust Exercise-Based Telerehabilitation for Elderly Healthc...

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19th International Conference on Emerging Technologies, ICET 2024

作者： Awan, Aftab Ahmad Najam, Shaheryar Jalal, Ahmad Riphah International University Dept. of Biomedical Engineering Islamabad Pakistan Riphah International University Dept. of Electrical & Computer Eng Islamabad Pakistan Air University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350379587

Improving motor function post-injury or surgery often relies on physical therapy, but long waiting lists can impede effective monitoring of patient progress. Tele-rehabilitation addresses this issue by providing equitable access to therapy without the need for travel or costly in-home treatments. This paper introduces a machine learning system capable of autonomously detecting exercise correctness during patient performance, thereby enhancing rehabilitation outcomes. Utilizing the IRDS dataset, which includes 29 participants performing nine exercises, extensive feature engineering was conducted, and five machine learning algorithms coupled with various dimensionality reduction techniques were evaluated such as Random Forests (RF), K-Nearest Neighbors (KNN), Logistic Regression (LR), Support Vector Machines (SVM) and Decision Trees (DT). Random Forests combined with correlation-based feature selection emerged as the most effective model with 98% accuracy in exercise recognition, 91% accuracy in exercise correctness detection, and a combined accuracy of 95%. © 2024 IEEE.

关键词： Remote patient monitoring

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DCGAN-based Medical Image Augmentation to Improve ELM Classification Performance 11

DCGAN-based Medical Image Augmentation to Improve ELM Classi...

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11th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2022

作者： Rando Setiawan, Noor Akhmad Permanasari, Adhistya Erna Rulaningtyas, Riries Suksmono, Andriyan Bayu Sitanggang, Imas Sukaesih Universitas Gadjah Mada Dept. of Electrical and Information Engineering Yogyakarta Indonesia Biomedical Engineering Program Dept. of Physics Universitas Airlangga Surabaya Indonesia Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung Indonesia Ipb University Department of Computer Science Bogor Indonesia

ISBN: (纸本)9781665460309

Cervical cancer is one of the deadliest diseases in women. One of the cervical cancer screening methods is pap smear method. However, using a pap smear method to detect cervical cancer takes a long time for a pathologist to diagnose. Hence, a rapid development of medical computerization for early detection to get the results quickly is needed. This paper proposes synthetic data augmentation by using Deep Convolutional Generative Adversarial Network (DCGAN) to increase number of pap smear samples in dataset. Gray Level Co-occurrence Matrix (GLCM) is employed to extract features from dataset. Classification of 3 classes which are Adenocarcinoma, High-Grade Squamous Intraepithelial Lesion (HSIL), and Squamous Cell Carcinoma (SCC) is conducted using Extreme Learning Machine (ELM). The result shows that the addition of synthetic data improves the performance of ELM with the accuracy of 90%. This accuracy is better than the accuracy of ELM using only the original dataset which is 85%. © 2022 IEEE.

关键词： Generative adversarial networks

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