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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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RAN Explainable Anomaly Prediction for 6G Networks 29

RAN Explainable Anomaly Prediction for 6G Networks

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29th IEEE International Workshop on computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2024

作者： Marantis, Panagiotis Ramantas, Kostas Verikoukis, Christos Universitat Politècnica de Catalunya Dept. of Signal Theory and Communications Barcelona Spain Iquadrat Informatica S.L. Barcelona Spain University of Patras Dept. of Computer Engineering and Informatics Patras Greece Athena Research Center Athens Greece Industrial Systems Institute Patras Greece

ISBN: (纸本)9798350377644

Machine Learning (ML) models integrated into future 6G network architectures are anticipated to address the demanding task of Anomaly Detection (AD) at the Radio Access Network (RAN). A challenging but essential aspect of this task is forecasting anomalies ahead of time, aiming to enable automated interventions to proactively prevent or mitigate service degradation. To address this challenge, we propose an Explainable Anomaly Prediction (XAP) framework, that combines a time series forecasting Long Short-Term Memory Encoder- Decoder (LSTM-ED) model, and an Isolation Forest (IF) anomaly detector whose output is interpreted using the SHAP algorithm. The framework is evaluated using a real- world dataset containing RAN Key Performance Indicators (KPIs) collected from a User Equipment (UE) device. The results validate the effectiveness of our framework in predicting RAN anomalies and identifying the specific KPIs linked to each predicted anomaly. © 2024 IEEE.

关键词： Radio access networks

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Designing and Developing a Mobile Application for Monitoring & Visualizing Blood Pressure Data

Designing and Developing a Mobile Application for Monitoring...

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IEEE Sensors Applications Symposium, SAS

作者： Mahsa Sinaei Hamed Laura Reid Alice Olorunnife David Casciano Fateme Rajabiyazdi dept. Systems and Computer Engineering Carleton University Ottawa Canada

High blood pressure is one of the major causes of cardiovascular diseases, renal failure, and even sudden death. To avoid developing these health issues, it is important to consistently monitor blood pressure levels. Monitoring blood pressure levels regularly allows people to observe changes in their blood pressure measurements and contact their healthcare providers for guidance if needed. The aim of this paper is designing and developing a mobile application that can assist individuals to better understand the changes in their blood pressure levels by introducing a novel approach to visualizing blood pressure data and managing missing data. Employing a user-centered design approach, we designed and developed an app. To complete this study, we conducted a literature review to identify key design requirements for such apps and then, designed our low-fidelity prototype based on these design requirements. Next, we consulted with two experts to obtain their feedback on the content, presentation, and usability of our initial low-fidelity prototype. Based on these experts' feedback we designed our mid-fidelity and high-fidelity prototype. Eventually, we conducted a user evaluation study to evaluate our high-fidelity prototype. The results demonstrated this app provides a clear visual representation of blood pressure measurements over time.

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Service Function Chaining Implementation using VNFs and CNFs

Service Function Chaining Implementation using VNFs and CNFs

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IEEE International Conference on Cloud engineering (IC2E)

作者： Abdullah Bittar Ziqiang Wang Changcheng Huang Dept. of Systems and Computer Engineering Carleton University Ottawa Canada

The increasing popularity of cloud-based platforms has led to the emergence of two prominent open-source solutions: OpenStack and Kubernetes. OpenStack facilitates computing and networking resources through virtual machine instances, while Kubernetes excels in container orchestration, managing containerized workloads and services. This paper explores the implementation of Service Function Chaining (SFC) using a combination of virtual machines in OpenStack and containers in Kubernetes. The primary focus of our study is to analyze the performance of containers within the context of chain deployment in Kubernetes. Our experimentation reveals compelling insights into container bootup times, showcasing their efficiency when compared to virtual machines. Additionally, we meticulously evaluate the impact of integrating SFC-related interfaces into pods forming a chain, particularly assessing CPU, memory, and bandwidth utilization. Our findings underline the advantages of utilizing containers in SFC deployment scenarios and shed light on the potential overhead that arises during interface integration.

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CapsiScan: Autonomous Robotic System for Monitoring and Managing Greenhouse-Based Capsicum Farms

CapsiScan: Autonomous Robotic System for Monitoring and Mana...

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Advancements in Computing (ICAC), International Conference on

作者： E.R.T. Jayakody W.T.T. Rabel R.D.V. Estrop K.G.L.P. Herath S. M. B. Harshanath Pradeep K. W. Abeygunawardhana Dept. of Computer Systems Engineering Sri Lanka Institute Information Technology Malabe Sri Lanka Dept. of Software Engineering Sri Lanka Institute of Information Technology Colombo Sri Lanka

The automation of processes in greenhouses provides a significant advantage to the agricultural sector. Due to the rapid growth of the population, increasing demand for food and labor shortage make the autonomous monitoring and management of agricultural work more important. Capsicum is a largely consumed vegetable in South Asia, which can be efficiently grown in greenhouses. Although there are many capsicum farms, they lack quality and efficient production to match the increasing demand. This paper proposes a robotic system for use in capsicum greenhouses that can navigate and monitor the plants to identify diseases, track the yield, and take remediation actions. The robot navigates to each plant in the greenhouse autonomously by using the SLAM approach with the use of laser range sensor data. The 4 DoF robotic arm of the robot has a camera and a sprayer attached to its end to investigate plants and spray remedies to diseased plants. The robotic arm moves around the plant to capture the images to detect deceases and count the pods to estimate the yield. The sprayer of the robot sprays remedies precisely to targeted plants to control pests and diseases. The system was successfully implemented and tested with the proposed functions.

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Applying Deep Learning Methods on Spam Review Detection

Applying Deep Learning Methods on Spam Review Detection

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International Conference on Computing Methodologies and Communication (ICCMC)

作者： M. Ramu Chinnakotla Jayanth Raj Apthiri Nithish Chandhu Boggula Ganesh Babu G Srikanth K.I Goud Dept. of computer science and systems engineering SVEC Tirupati andhrapradesh

In today's environment, a reliable and effective technique for identifying spam reviews is essential if you want to purchase things online without being taken advantage of. There are possibilities for publishing reviews in many internet locations, which opens the door for sponsored or deceptive fake reviews. These fabricated evaluations may mislead the general audience and leave them unsure of whether or not to believe them. The issue of spam review finding has been solved by the introduction of prominent deep literacy methods. The focus of recent research has been on supervised literacy practices that contain labelled data, which is inadequate for online review. This initiative aims to expose any dishonest textbook reviews. To do this, we've used both labelled and unlabeled data and suggested deep learning techniques for spam review detection, including Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), and a Long Short-Term Memory (LSTM) variation of Recurrent Neural Networks (RNN). We also used standard machine learning classifiers to identify spam reviews, including Naive Bayes (NB), K Nearest Neighbor (KNN), and Support Vector Machine (SVM). Finally, we compared the effectiveness of traditional and deep literacy classifiers. We'll use deep literacy classifiers to boost the finesse and efficiency.

关键词： Support vector machines Deep learning Recurrent neural networks Publishing Manuals Programming Convolutional neural networks

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CLIP-IT: CLIP-based Pairing for Histology Images Classification

arXiv

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arXiv 2025年

作者： Karimian, Banafsheh Avanzato, Giulia Belharbi, Soufian McCaffrey, Luke Shateri, Mohammadhadi Granger, Eric LIVIA ILLS Dept. of Systems Engineering ETS Montreal Canada Dept. of Computer Engineering University of Cagliari Italy Goodman Cancer Research Centre Dept. of Oncology McGill University Canada

Multimodal learning has shown significant promise for improving medical image analysis by integrating information from complementary data sources. This is widely employed for training vision-language models (VLMs) for cancer detection based on histology images and text reports. However, one of the main limitations in training these VLMs is the requirement for large paired datasets, raising concerns over privacy, and data collection, annotation, and maintenance costs. To address this challenge, we introduce CLIP-IT method to train a vision backbone model to classify histology images by pairing them with privileged textual information from an external source. At first, the modality pairing step relies on a CLIP-based model to match histology images with semantically relevant textual report data from external sources, creating an augmented multimodal dataset without the need for manually paired samples. Then, we propose a multimodal training procedure that distills the knowledge from the paired text modality to the unimodal image classifier for enhanced performance without the need for the textual data during inference. A parameter-efficient fine-tuning method is used to efficiently address the misalignment between the main (image) and paired (text) modalities. During inference, the improved unimodal histology classifier is used, with only minimal additional computational complexity. Our experiments4 on challenging PCAM, CRC, and BACH histology image datasets show that CLIP-IT can provide a cost-effective approach to leverage privileged textual information and outperform unimodal classifiers for histology. © 2025, CC BY.

关键词： Taxonomies

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Deep Learning-Based Energy Efficiency Maximization in Multi-STAR-RIS-Assisted Massive MIMO-NOMA Networks

Deep Learning-Based Energy Efficiency Maximization in Multi-...

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7th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2025

作者： Perdana, Ridho Hendra Yoga Nguyen, Toan-Van Pramitarini, Yushintia Nguyen, Duy H. N. An, Beongku Hongik University Dept. of Software and Communications Engineering in Graduate School Korea Republic of San Diego State University Department of Electrical and Computer Engineering San DiegoCA United States Hongik University Dept. of Software and Communications Engineering Korea Republic of

ISBN: (纸本)9798331507022

This paper studies deep learning (DL)-based energy efficiency maximization (EEM) in multi-simultaneous transmission and reflection-reconfigurable intelligent surfaces (STAR-RISs) assisted massive multiple-input multiple-output (mMIMO)-non-orthogonal multiple access (NOMA) networks. We formulate the EEM problem to jointly optimize the precoding matrix and STAR-RIS phase shifts subject to the power budget at the base station, STAR-RIS phase shift constraints, and minimum quality-of-service (QoS) requirements. The formulated EEM problem belongs to the mixed-integer programming class, which is difficult to solve optimally. Thus, we develop an alternative optimization approach by dividing the original EEM problem into two sub-problems such as phase shift and beamforming optimization, and solve them alternatively. A bisection search algorithm is proposed to solve the phase shift optimization, while the inner approximation method is employed to address the non-convex beamforming problem through our newly tractable transformations. To enable real-time optimization, we design a DL framework that predicts optimal phase shifts and precoding matrices under various parameter settings. Simulation results demonstrate that the DL-based approach accurately predicts the optimal solutions and is significantly faster than conventional methods. We also evaluate the impact of the essential parameters on the system's performance. © 2025 IEEE.

关键词： Convex optimization

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A Tangible E-Learning Solution for Early Childhood Development 13

A Tangible E-Learning Solution for Early Childhood Developme...

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13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022

作者： Cholitha Hettiarachchi, Thiwanka Sathyajith Semasinghe, Lakisuru De Silva, Rajitha Lokuliyana, Shashika Gamage, Narmada Ratnasuriya, Manisha Sri Lanka Institute of Information Technology Dept. of Computer Systems Engineering Sri Lanka University of Lincoln United Kingdom

ISBN: (数字)9781665452625

ISBN: (纸本)9781665452625

Exploration and manipulation of physical objects are essential for early childhood learning. TangiGuru is an e-Learning platform that allows children to engage with real-world physical objects to provide that essential experience within an e-Learning application. TangiGuru consists of 12 tangible, manipulative objects known as TangiCubes, which are used as a tangible user interface between children and the e-Learning application. It can carry out cognitive learning activities related to colors, languages, shapes and basic math by dynamically varying assigned values by changing the external appearance of TangiCubes. This dynamic nature of the TangiCubes makes it possible to use the same tangibles with endless possibilities compared to traditional tangible learning solutions with static value for each tangible. With the initial observations of how children interact with computers, laptops, and mobile devices, the goal is to provide a playful interface for children by taking the approach to embedding and integrating technology into the children's daily activities. After the prototyping phase, children were evaluated with the traditional tangible learning solutions compared to TangiGuru. They concluded that the more interactive tangible interfaces could make the children perform activities more engagingly. © 2022 IEEE.

关键词： E-learning

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Genetic Algorithm-Based Feature Selection for Accurate Breast Cancer Classification

Genetic Algorithm-Based Feature Selection for Accurate Breas...

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2023 International Conference on IT and Industrial Technologies, ICIT 2023

作者： Shabbir, Attia Ali, Raja Hashim Shabbir, Muhammad Zeeshan Abideen, Zain Ul Khan, Talha Ali Ijaz, Ali Zeeshan Ali, Nisar Imad, Muhammad Bakar, Muhammad Abu Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Department of Electrical Engineering Lahore Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350315974

Breast cancer is a significant global healthcare challenge, particularly in developing and underdeveloped countries, with profound physical, emotional, and psychological consequences, including mortality. Timely diagnosis and accurate treatment are crucial in addressing this issue. We propose the utilization of a feature selection technique to identify the most relevant features from among all features for breast cancer diagnosis, and show that Genetic Algorithms are impressive for this task. The study compares the results of GA with no selection and an alternative method, Principle Component Analysis (PCA). Three machine learning models, all based on supervised learning with data split into training and test data, are employed for binary classification using the selected feature subset. The evaluation metrics employed encompass accuracy, precision, recall, and F1-score. Among the selected models, Random Forest demonstrates the most favorable outcomes, achieving an accuracy score of 0.96, precision score of 0.96, recall value of 0.98, and an F1-score of 0.97. These results underscore the effectiveness of GA in feature selection for breast cancer diagnosis. Consequently, the integration of Genetic Algorithms (GA) with Random Forest showcases the superior performance among the evaluated models. © 2023 IEEE.

关键词： Genetic algorithms

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