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12th International Conference on information and Education technology, ICIET 2024

作者： Kanazawa, Keiko Sun, Yi Graduate School Of Information Technology Kobe Institute Of Computing Kobe Japan

ISBN: (纸本)9798350371772

This paper presents an application, 'Edu MEAL', that aims to facilitate taste education for children and enable them to engage in taste education regularly. Edu MEAL is designed for children who interact with the application after meals to record their impressions of the food they ate. The application uses a sentence-generating AI to provide examples of meal impressions based on the menus, which can help children pay attention to the taste of the food and enhance their vocabulary and expression skills. We conducted a user study with an 8-year-old boy who used Edu MEAL and we reported on the effects of the application on the user the system's interface, and the usage experience. © 2024 IEEE.

关键词： AI dietary education elementary school five senses food education GPT voice input

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Topic Modelling Swahili Using LDA and Contextualized Embeddings 2

Topic Modelling Swahili Using LDA and Contextualized Embeddi...

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2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Kahenya, Dorothy W. Mwangi, Waweru Rimiru, Richard School of Computing and Information Technology Jomo Kenyatta University of Agriculture & Technology Nairobi Kenya

ISBN: (纸本)9798350362633

Technological innovation in today's society has led to a highly interconnected world, the fruit of which has been an unprecedented breakthrough in new technologies and a tremendous growth of information resources. The availability of online platforms with user-generated content has increased the volume of textual data, leading to a great need for better tools and methods to process the data into meaningful information. While there has been a lot of research on topic modelling in high-resource languages, there is limited research in resource-scarce languages like Swahili despite being spoken by millions world-wide. New topic modelling techniques provide an opportunity to advance NLP research in low-resource settings. This paper provides benchmark experiments for Swahili topic modelling using different transformer based pre-trained language models as embeddings and compares the results with the baseline LDA technique. We also conduct experiments to examine the impact of two dimensionality reduction techniques, PCA and UMAP, on the performance of the topic modelling framework in long Swahili texts. We evaluate the resulting topic models using the C-V and Normalized pointwise Mutual information (npmi) coherence scores. The results show that BERTopic using XLM-Roberta Pretrained Language model embeddings with UMAP dimensionality reduction and HDBSCAN clustering was best suited for topic modelling Swahili, achieving a c-v coherence of 0.776 and nmpi of 0.199. This was followed by the model developed using SwahBERT embeddings with UMAP and HDBSCAN clustering, which achieved a c-v Coherence of 0.707 and npmi of 0.171. Our findings provide a benchmark NLP contribution in automatically categorising Swahili news articles using topic modelling techniques. © 2024 IEEE.

关键词： Modeling languages

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A Systematic Study On Cloud Forensic Framework, Challenges And Technologies Used For Evidence Preservation 3

A Systematic Study On Cloud Forensic Framework, Challenges A...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Pirjade, S.T. Dhotre, Prashant MIT-Art Design & Technology University School of Computing Department of Information Technology India

ISBN: (纸本)9798350354348

In this study we have focused on 3 research questions and tried to do analysis of that questions. The term "cloud computing"is wide yet ambiguous, encompassing a range of dynamically allocated services. Cloud computing can be seen as having two benefits and drawbacks for forensic analysis and legal applications. On the one hand, obtaining digital evidence from cloud sources could pose challenging legal and technological issues across jurisdictions. One potential remedy for these issues is the cloud forensic zero trust concept. A diligent and critical attitude to security is required when using the zero-trust model for cloud forensics, since no user or device can be considered completely trustworthy. In this paper we have proposed a zero trust model for security of evidence in cloud forensic. Cloud forensic investigations are crucial for identifying and analysing security incidents within cloud environments. By adopting a least privilege approach, organizations can minimize the attack surface and limit potential exposure to security risks. Additionally, leveraging encryption and strong identity management practices further strengthens the security of cloud forensic processes. Organizations may proactively identify and address security events in their cloud environments by combining threat detection tools and advanced security analytics. This proactive strategy improves the overall security posture and increases the efficacy of forensic investigations. This study outlined a number of debate points that paved the path for technological advancement in the future when it comes to crime scene evidence. © 2024 IEEE.

关键词： Cloud security

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Ens5B-UNet for Improved Microaneurysms Segmentation in Retinal Images

Ens5B-UNet for Improved Microaneurysms Segmentation in Retin...

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2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Usman, Tiwalade Modupe Saheed, Yakub Kayode Ajibesin, Adeyemi Abel Nsang, Augustine Shey American University of Nigeria School of Information Technology & Computing Yola Nigeria

ISBN: (纸本)9798350358155

The appearance of Microaneurysms (MAs) in the retina is one of the earliest clinical signs for the diagnosis of Diabetic Retinopathy (DR) and other sight threatening diseases However, manual detection and segmentation of MAs for diagnosis is tedious, time consuming and prone to misclassification. Hence, accurate automated methods may significantly reduce diagnostic workload of clinician and prevent misdiagnosis. Even though, several single network semantic segmentation models have been proposed in literature, ensemble learning remains insufficiently explored despite its established benefits. We propose a novel ensemble learning architecture, Ens5B-UNet that combines the weights of 5 individual modified U-Net models to create a unified approach for improved microaneurysms segmentation. The model was trained on the IDRiD dataset and evaluated on EOphta-MA dataset. Ens5B-UNet performance is 3.17% (AUPR) higher than the 1st position in the IEEE ISBI - 2018 (International Symposium on Biomedical Imaging) competition. The proposed model was further compared with state-of-the-art models and it outperformed all in IOU, Dice, AUPR and specificity evaluation metrics with scores of 67.76%, 49.07%, 52.74%, and 99.93% respectively. These results are over the best state-of-the-art models by 19.67%, 10.57%, 3.43%, and 6.65% respectively in IOU. Evaluation on E-Ophta_MA shows remarkable improvements over the basic U-Net architecture with 26.53% in IOU. These results demonstrate that Ens5B-UNet improved the accuracy of MA segmentation and can be integrated into clinical practice. This study introduces a paradigm shift into the application of AI, especially computer vision in eye care for the segmentation of MA. © 2024 IEEE.

关键词： Semantic Segmentation

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A Contextualized Deep Neural Network Model for Classifying IT Incidents Based on Severity

A Contextualized Deep Neural Network Model for Classifying I...

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2024 International Conference on Computer and Applications, ICCA 2024

作者： Mutuku, Andrew Kyalo Nderu, Lawrence Mwalili, Tobias School of Computing and Information Technology Jomo Kenyatta University of of Agriculture and Technology Nairobi Kenya

ISBN: (纸本)9798350367560

IT service management operational efficiency is significantly dependent on the accuracy and cost of IT tickets classification. Whilst several machine learning techniques have made significant progress in the task of IT tickets classification, Classifying tickets by severity remains inherently complex, costly and multifaceted. It requires a deep understanding and interpretation of contextual information in words, phrases, and sentences. Additionally, the correlation between the classification process and operational costs impacts the efficacy and effectiveness of IT operations. This paper introduces the Severity Misclassification Impact (SMI) as a novel metric of quantifying the operational costs of misclassification errors by accounting for the varying costs associated with the different types of errors due to misclassifications. To highlight the need for SMI in contextual classification, this paper proposes a novel blended model approach comprising Bidirectional Encoder Representations from Transformers (BERT), Term Frequency-inverse Document Frequency (TF-IDF), Long Short-Term Memory (LSTM), and Light Gradient-Boosting Machine (LightGBM). By conducting extensive experiments on real-world IT Tickets, it demonstrates that incorporating SMI as an evaluation metric enables a robust way to assess a classification model's potential. This approach offers a nuanced understanding of its effectiveness in minimizing operational costs of misclassification, hence significantly improving IT tickets classification. © 2024 IEEE.

关键词： Long short-term memory

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DiffMat:Latent diffusion models for image-guided material generation

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Visual Informatics 2024年第1期8卷 6-14页

作者： Liang Yuan Dingkun Yan Suguru Saito Issei Fujishiro Graduate School of Science and Technology Keio UniversityKanagawaJapan School of Computing Tokyo Institute of TechnologyTokyoJapan Department of Information and Computer Science Keio UniversityKanagawaJapan

Creating realistic materials is essential in the construction of immersive virtual *** existing techniques for material capture and conditional generation rely on flash-lit photos,they often produce artifacts when the illumination mismatches the training *** this study,we introduce DiffMat,a novel diffusion model that integrates the CLIP image encoder and a multi-layer,crossattention denoising backbone to generate latent materials from images under various *** a pre-trained StyleGAN-based material generator,our method converts these latent materials into high-resolution SVBRDF textures,a process that enables a seamless fit into the standard physically based rendering pipeline,reducing the requirements for vast computational resources and expansive *** surpasses existing generative methods in terms of material quality and variety,and shows adaptability to a broader spectrum of lighting conditions in reference images.

关键词： SVBRDF Diffusion model Generative model Appearance modeling

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Identifying important nodes of hypergraph:An improved PageRank algorithm

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Chinese Physics B 2025年第4期34卷 162-171页

作者： Yu-Hao Piao Jun-Yi Wang Ke-Zan Li School of Mathematics and Computing Science Guilin University of Electronic TechnologyGuilin 541004China School of Information and Communication Guilin University of Electronic TechnologyGuilin 541004China Guangxi Wireless Broadband Communication and Signal Processing Key Laboratory Guilin University of Electronic TechnologyGuilin 541004China

Hypergraphs can accurately capture complex higher-order relationships,but it is challenging to identify their important *** this paper,an improved PageRank(ImPageRank)algorithm is designed to identify important nodes in a directed *** algorithm introduces the Jaccard similarity of directed *** comparing the numbers of common neighbors between nodes with the total number of their neighbors,the Jaccard similarity measure takes into account the similarity between nodes that are not directly connected,and can reflect the potential correlation between *** improved susceptible–infected(SI)model in directed hypergraph is proposed,which considers nonlinear propagation mode and more realistic propagation *** addition,some important node evaluation methods are transferred from undirected hypergraphs and applied to directed ***,the ImPageRank algorithm is used to evaluate the performance of the SI model,network robustness and *** of real networks demonstrate the excellent performance of the proposed algorithm and provide a powerful framework for identifying important nodes in directed hypergraphs.

关键词： hypergraph important node PageRank susceptible-infected(SI)model centrality index

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A survey on classification methodologies utilized for classifying the knee joint disorder levels using vibroarthrographic signals

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Materials Today: Proceedings 2023年 80卷 3240-3243页

作者： Balajee, A. Venkatesan, R. School of Computing SASTRA Deemed University TamilNadu Thanjavur India Department of Information Technology School of Computing SASTRA Deemed University TamilNadu Thanjavur India

The largest as well as complicated joint in the human body is knee joint. A minor pathology in human knee can lead to major discomfort to a person. The pathology may be from a simple stress in knee joints to the higher effective levels of pathology that includes osteoarthritis (OA), chondromalacia patella (CMP) and so on. Hence prediction as well as classification of knee joint pathology plays a vital role for computational medical diagnosis. Pathology identification can be categorized in to two levels invasive and non – invasive methodologies. Invasive are purely medical as well as painful process which are less preferred for predictions. Noninvasive are clinical and computational ones that includes X-Rays, Magnetic Resonance Imaging (MRI), Computed Tomography (CT), Vibroarthography (VAG) etc. This paper compares various classification methodologies utilized by Vibroarthographic (VAG) signal based knee joint disorder level identification and performs a binary classification of data samples as a survey. © 2021

关键词： Support vector machines

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Road Rage Detection System Using Deep Learning and Computer Vision 3

Road Rage Detection System Using Deep Learning and Computer ...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Lohare, Shaik Tanveer Maaz, Mohammed Razi, Mahammad Nehal, Mohammed Ahmed, Syed Thouheed Rashmi, C. School of Computing and Information Technology Reva University Bengaluru India

ISBN: (纸本)9798331508456

Road rage detection in India's chaotic traffic conditions demands robust, real-time solutions. This paper proposes a 3D CNN-based system that leverages transfer learning from a violence detection model (98 % accuracy on Kaggle datasets) to a road rage-specific model (94 % accuracy on a custom Indian dataset). By incorporating temporal smoothing with a 5-frame sliding window, the system reduces false positives by 15 % compared to existing methods. Experiments demonstrate its efficacy under varying illumination and traffic densities, validated through precision (0.99 for non-violence) and recall (0.99 for violence). The system's deployment in Bangalore highlights its potential for traffic monitoring, law enforcement, and insurance claim verification. Future work includes edge-device optimization and multimodal data integration. © 2025 IEEE.

关键词： Traffic control

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Enhancing Network Design through Statistical Evaluation of MANET Routing Protocols

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Computers, Materials & Continua 2024年第7期80卷 319-339页

作者： Ibrahim Alameri Tawfik Al-Hadhrami Anjum Nazir Abdulsamad Ebrahim Yahya Atef Gharbi Faculty of Medical Sciences Jabir ibn Hayyan Medical UniversityAlkufa QuNajaf54001Iraq Computer Science Department School of Science and TechnologyNottingham Trent UniversityNottinghamNG118NSUK Department of Computing Faculty of Engineering Sciences&Technology(FEST)Hamdard UniversityKarachi74600Pakistan Department of Information Technology College of Computing and Information TechnologyNorthern Border UniversityArar91431Saudi Arabia Department of Information System College of Computing and Information TechnologyNorthern Border UniversityArar91431Saudi Arabia

This paper contributes a sophisticated statistical method for the assessment of performance in routing protocols salient Mobile Ad Hoc Network(MANET)routing protocols:Destination Sequenced Distance Vector(DSDV),Ad hoc On-Demand Distance Vector(AODV),Dynamic Source Routing(DSR),and Zone Routing Protocol(ZRP).In this paper,the evaluation will be carried out using complete sets of statistical tests such as Kruskal-Wallis,Mann-Whitney,and *** articulates a systematic evaluation of how the performance of the previous protocols varies with the number of nodes and the mobility *** study is premised upon the Quality of Service(QoS)metrics of throughput,packet delivery ratio,and end-to-end delay to gain an adequate understanding of the operational efficiency of each protocol under different network *** findings explained significant differences in the performance of different routing protocols;as a result,decisions for the selection and optimization of routing protocols can be taken effectively according to different network *** paper is a step forward in the general understanding of the routing dynamics of MANETs and contributes significantly to the strategic deployment of robust and efficient network infrastructures.

关键词： Routing protocols statistical approach friedman MANET

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