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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Reshma Pise Abhishek Singh Chauhan Bharati Ainapure Aditya Shenolkar Leena Deshpande Shivraj Jadhav Computer Engineering Dept. Vishwakarma University Pune India CE (Software Engineering) Dept. Vishwakarma Institute of Information Technology Pune India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Mosquitoes, as vectors of life-threatening diseases such as dengue, Zika, and West Nile virus, present a significant public health challenge. Precise and rapid identification of mosquito species is crucial for early detection and prevention of these diseases. Deep learning techniques have proven to be highly effective for automatic visual insect classification tasks. However, the imbalanced distribution of mosquito species within training datasets severely impacts the algorithm’s classification performance. This study investigates data sampling solutions for learning from imbalanced mosquito images dataset. We transfer-learn InceptionV3, a pre-trained convolutional neural network to classify images of two vector mosquito species Aedes Aegypti and Anopheles stephensi. The effectiveness of these data sampling solutions is evaluated and compared in terms of their impact on the performance of CNN classifier.

关键词： Training Visualization Technological innovation Accuracy Sensitivity Prevention and mitigation Vectors Convolutional neural networks Viruses (medical) Diseases

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Using a Multi-Agent System to Better Simultaneous Collaboration software Systems 2

Using a Multi-Agent System to Better Simultaneous Collaborat...

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2nd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2022

作者： Abd Alati Elfargane, Rabeia N. Yousef, Khamisa. A. Saleh, Eiman M. Abd Elati, Naeima N Benghazi University Dept. Computer Science Benghazi Libya University of Tobruk Dept. Computer Science Tobruk Libya Benghazi University Dept. Software Engineering Benghazi Libya Benghazi University Dept. Statistics Benghazi Libya

ISBN: (数字)9781665479189

ISBN: (纸本)9781665479189

In each field, there is a need for better collaborative efforts among team members. The software development community is currently concentrating its efforts on developing tools that will make this endeavor easier. As a result, the two major purposes of a collaborative system are to solve the lack of community consciousness (since group members must be aware of one another) and to maintain flooring directive (i.e. provide means to manage access to shared resource in synchronous groupware systems). The goal of this study is to design a dynamic, extendable, and adaptable framework capable of handling challenges in synchronous community consciousness systems in order to solve these concerns. The major purpose of this study is to improve Simultaneous Collaboration software Systems utilizing the structure analysis real-Time approach (SART), which is based on a multiagent system (MAS)). © 2022 IEEE.

关键词： Multi agent systems

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Towards Accurate and Certain Molecular Properties Prediction 13

Towards Accurate and Certain Molecular Properties Prediction

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13th International Conference on Information and Communication Technology Convergence, ICTC 2022

作者： Ham, KyungPyo Yoon, JeongNoh Sael, Lee Ajou University Dept. of Artificial Intelligence Suwon Korea Republic of Ajou University Dept. of Software and Computer Engineering Dept. of Artificial Intelligence Suwon Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

In this review, we survey the fundamental problems and their solvers in molecular property prediction problems: graph neural networks modeling and uncertainty estimation. Molecules are naturally represented with graphs, thus graph neural networks, such as graph convolution networks and several types of message passing networks, are their natural models for accurate predictions. However, graph neural networks and any other deep learning models require a large amount of data. Unfortunately, molecular data are insufficient and skewed. Thus, not just an accurate method but models that return uncertainty estimations, such as Bayesian estimations, deep ensembles, and evidential methods, are required. © 2022 IEEE.

关键词： Graph neural networks

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Automated Romberg Test: Leveraging a CNN and Centre of Mass Analysis for Sensory Ataxia Diagnosis

arXiv

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arXiv 2024年

作者： Haskins, Reilly Green, Richard Dept. of Computer Science and Software Engineering University of Canterbury Christchurch New Zealand

This paper proposes a novel method to diagnose sensory ataxia via an automated Romberg Test - the current de facto medical procedure used to diagnose this condition. It utilizes a convolutional neural network to predict joint locations, used for the calculation of various bio-mechanical markers such as the center of mass of the subject and various joint angles. This information is used in combination with data filtering techniques such as Kalman Filters, and center of mass analysis which helped make accurate inferences about the relative weight distribution in the lateral and anterior-posterior axes, and provide an objective, mathematically based diagnosis of this condition. In order to evaluate the performance of this method, testing was performed using dual weight scales and pre-annotated diagnosis videos taken from medical settings. These two methods both quantified the veritable weight distribution upon the ground surface with a ground truth and provided a real-world estimate of accuracy for the proposed method. A mean absolute error of 0.2912% was found for the calculated relative weight distribution difference, and an accuracy of 83.33% was achieved on diagnoses. © 2024, CC BY.

关键词： Convolutional neural networks

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GreenLight: Green Traffic Signal Control using Attention-based Reinforcement Learning on Fog Computing Network

GreenLight: Green Traffic Signal Control using Attention-bas...

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International Green Computing Conference

作者： Chengyu Tang Sanjeev Baskiyar Dept. of Computer Science and Software Engineering Auburn University Auburn Alabama USA

ISBN: (数字)9798331507862

ISBN: (纸本)9798331507879

Traffic congestion is a pervasive problem causing severe environmental and economic issues. In recent years, traffic signal control using reinforcement learning (RL) has come a long way. Most existing studies focus on using distributed agents with data exchange among neighbors, which, however, increases network complexity and usage and still suffers from the lack of broader coordination. Meanwhile, the attention mechanism has achieved tremendous success, and advances in vehicle-to-infrastructure (V2I) communications have enabled real-time collections of granular data. However, integrating these technologies into traffic signal control remains under-explored. Therefore, we present GreenLight, a forward-thinking and eco-friendly traffic signal control framework that can be applied to V2I-equipped fog computing environments. For a large urban area, traffic signals are divided into clusters, each coordinated by a fog node with an RL agent. Intra-cluster indexed self-attention is applied to extract context-aware features that the fog-residing RL agent utilizes to determine the proper signal control command. Results of simulation experiments using both synthetic and real-world scenarios show that the presented framework yields lower waiting time, emissions, and fuel consumption compared to baseline methods, indicating its potential for next-generation transportation systems.

关键词： Vehicle-to-infrastructure Green products Urban areas Transportation Reinforcement learning Feature extraction Real-time systems Fuels Traffic congestion Edge computing

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Line Graph is the Key: An Exploration of Line Graph on Link Prediction with Social Networks

Line Graph is the Key: An Exploration of Line Graph on Link ...

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International Conference on Behavior, Economic and Social Computing (BESC)

作者： Chen Xing Masoud Makrehchi Dept. of Electrical Computer and Software Engineering Ontario Tech University Oshawa Canada

ISBN: (数字)9798331531904

ISBN: (纸本)9798331531911

Network data plays a crucial role in various real-world applications, as connections between entities can be represented and analyzed through graphs. These include various types, such as social, information, and technical networks. However, the complex topologies of these networks present challenges in converting graph data into machine-readable vector formats. Existing models, such as Graph Neural Networks, Graph Attention Networks, and node2vec, have made strides in graph embeddings. For edge-related tasks, models such as node2vec typically use indirect methods like concatenating node vectors to represent edges. This approach is useful for tasks like link prediction. In this paper, we present LineDi2vec, a novel approach that improves the Node2vec embedding method by utilizing a line graph. The proposed LineDi2vec not only generalizes the original graphs, transforming the relationships between edges and nodes but also maintains the original graphs' topological integrity for effective node embedding by Node2vec. We evaluated LineDi2vec on four real-world datasets, focusing on link prediction. The results demonstrate that LineDi2vec outperforms traditional node concatenation methods.

关键词： Analytical models Social computing Social networking (online) Network topology Navigation Network analyzers Predictive models Vectors Robustness Topology

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A software Framework Based on JUnit for Automated software Testing in computer Science Courses

A Software Framework Based on JUnit for Automated Software T...

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Frontiers in Education (FIE) Conference

作者： Michael A. Zmuda Dept. of Computer Science and Software Engineering Miami University Oxford OH USA

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This innovative practice full paper describes a new software framework based on JU nit to test student work. Automated testing is an important capability when teaching software development at the college level. Ideally, a testing system will allow the instructor to efficiently create a thorough set of tests. Also, the software should facilitate grading tasks and produce informative reports that can be distributed to the students in a timely fashion. For Java development, the well-known JUnit framework enables a test suite to be applied to a student's submission. The mutools library presented here extends the JUnit framework in novel ways to accelerate the instructor's task of creating test suites. This new framework allows the instructor to augment tests with directives to control scoring and reporting. The four main capabilities of the software include: 1) An assert statement that does not terminate the test when it has failed. Instead, statistics are maintained regarding the success or failure of each assert statement. 2) Tests that can be configured to award partial credit. This can be useful in situations where the instructor deems it appropriate to award students some credit even in the presence of incorrect asserts. 3) Tests that can be grouped into categories that match a particular rubric item. Java annotations are placed on the test suite to define these categories. For example, @TestCategory(name= “remove”, points=10.0) specifies that 10 points will be awarded for successful implementation of all tests related to removing an item from the collection class. 4) Testing reports that contain varying levels of information. With minimal details, the testing report shows each testing category with the following information: assert statistics, whether the test timed out or had abnormal termination. This software has been used for many semesters and has been found to increase the speed at which the instructor can develop test suites for grading. The framework is av

关键词： software testing computer science Java Annotations Education Life estimation software Libraries software development management

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PRAGyan - Connecting the Dots in Tweets

arXiv

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arXiv 2024年

作者： Ravi, Rahul Ginde, Gouri Rokne, Jon Dept. of Computer Science University of Calgary Canada Dept. of Electrical and Software Engineering University of Calgary Canada

As social media platforms grow, understanding the underlying reasons behind events and statements becomes crucial for businesses, policymakers, and researchers. This research explores the integration of Knowledge Graphs (KGs) with Large Language Models (LLMs) to perform causal analysis of tweets dataset. The LLM aided analysis techniques often lack dept. in uncovering the causes driving observed effects. By leveraging KGs and LLMs, which encode rich semantic relationships and temporal information, this study aims to uncover the complex interplay of factors influencing causal dynamics and compare the results obtained using GPT-3.5 Turbo. We employ a Retrieval-Augmented Generation (RAG) model, utilizing a KG stored in a Neo4j (a.k.a PRAGyan) data format, to retrieve relevant context for causal reasoning. Our approach demonstrates that the KG-enhanced LLM RAG can provide improved results when compared to the baseline LLM (GPT-3.5 Turbo) model as the source corpus increases in size. Our qualitative analysis highlights the advantages of combining KGs with LLMs for improved interpretability and actionable insights, facilitating informed decision-making across various domains. Whereas, quantitative analysis using metrics such as BLEU and cosine similarity show that our approach outperforms the baseline by 10%. © 2024, CC BY.

关键词： Knowledge graph

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Enhancing Brain Tumor Classification Using Data-Efficient Image Transformer

Enhancing Brain Tumor Classification Using Data-Efficient Im...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Jiafeng Yang Alena Rusak Alexander Belozubov Technology Dept. Software Engineering and Computer ITMO University Saint-Petersburg Russia

Medical image analysis is essential in the diagnosis and treatment of many diseases, including brain tumors. This work investigates the effectiveness of transformer models in the multiclass classification of brain tumor MRI images. We use a combination of three publicly available brain tumor datasets classified into four categories and appropriately modify existing transformer variants, including adjustments to patch size, number of categories, dropout, etc. The results indicate that, due to the condition of insufficient data volume, most transformer variants are still inferior to the benchmark model VGG-16. The DeiT model with knowledge distillation has significantly improved various indices on the test set, verifying its effectiveness in the classification of brain tumor MRI images under limited data. The article supports the application of transformer-based models to medical image analysis. Follow-up work will verify the generalization performance of the model on other datasets and explore methods to reduce the dependence of the transformer on the data, making it more applicable to tasks such as medical images classification.

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XAI for Understanding the Success in Curbing COVID-19 6

XAI for Understanding the Success in Curbing COVID-19

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6th International Conference on Intelligent Systems and computer Vision, ISCV 2024

作者： Bouktif, Salah Kunnath, Nimmi Ouni, Ali CIT United Arab Emirates University Dept. of Computer Science and Software Engineering Al Ain United Arab Emirates University of Quebec École de Technologie Supérieure MontrealQC Canada

ISBN: (纸本)9798350350180

The Covid-19 pandemic has prompted governments worldwide to implement various non-pharmaceutical interventions (NPIs) in an effort to curb the pandemic to attenuate the harmness of the pandemic. However, there is a debate about how to assess the effectiveness of these interventions. A set of indicators has been used to monitor the outbreak such as case counts, infection rates, death rate, hospitalization. In this study, we contribute to the debate by firstly proposing a definition of the success in managing the pandemic in terms of mortality rate, socio-demographic and epidemiological factors. Secondly, we propose a modeling framework based on (1) analysing the time series pandemic data and (2) employing eXplainable Artificial Intelligence (XAI) to provide interpretability of the successfulness of countries in responding to the pandemic. By using a rich dataset collected by Oxford COVID-19 Government Response Tracker (OxCGRT), we built a success model based on Random Forest Regressor for time series analysis and we identified the major factors/practices/measures/protocols. Our findings indicate a significant relationship between policy compliance levels and death counts, highlighting the importance of considering mortality outcomes in evaluating the efficacy of interventions. Additionally, we discovered that socio-demographic and epidemiological factors such as elderly populations aged (65+), prevalent cardiovascular disease, high diabetes prevalence, increased smoking rates, and reduced life expectancy based on the person correlation coefficient are key determinants of success in managing the pandemic. © 2024 IEEE.

关键词： COVID-19

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