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16th International Workshop on Ontology Matching, OM 2021

作者： Sharma, Abhisek Patel, Archana Jain, Sarika Department Of Computer Applications National Institute Of Technology Kurukshetra India Department Of Software Engineering Eastern International University Viet Nam

This paper presents the Large Scale Ontology Matching System (LSMatch) and its results on OAEI 2021 datasets. LSMatch is an element-level and label-based ontology matching system that uses string similarity and synonyms matcher. The current version of the system is focused on finding similarities between the classes of the two ontologies. This is the first participation of LSMatch in the OAEI campaign on five tracks, namely Anatomy, Conference, Disease and Phenotype, Common Knowledge Graphs, and Knowledge Graph. LSMatch has demonstrated promising results in all five tracks. We also discuss the strengths and weaknesses of the LSMatch system. Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Ontology

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Ensemble Method for User Activity classification in Ambient Assisted Living

Ensemble Method for User Activity classification in Ambient ...

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2022 International Conference on Innovative Trends in Information Technology, ICITIIT 2022

作者： Madhan Kumar, G.S. Shiva Prakash, S.P. Krinkin, Kirill JSS Science and Technology University Mysuru Mysuru India JSS Science and Technology University Mysuru Department of Information Science and Engineering Mysuru India Saint Petersburg Electrotechnical University LETI Department of Software Engineering and Computer Applications Saint Petersburg Russia

ISBN: (纸本)9781665406703

Artificial Intelligence(AI) has become a global plat-form that allows objects in IoT to Interact and perform computations. The wide range of application areas of IoT are Smart Cities, Smart grids, Smart Supply chain and Ambient Assisted Living(AAL). These applications have challenges like tolerance to uncertainty,adaptiveness to the changing environment and improved trust among users. Thus, machine learning algorithms improve the performance of smart objects in various environment. The AAL environment deploys heterogeneous devices and sensors to capture various activities carried out through the daily by the individuals who resides in the smart home. In this work, an ensemble method using k-Nearest Neighbor(KNN), Decision Tree(DT) and Logistic Regression(LR)is proposed by investigating the performance of existing conventional supervised machine learning algorithms and selecting best model by considering the sensors features and improves the performance metrics. The work is evaluated using the benchmark ARAS (Activity Recognition with Ambient Sensing) dataset. The results are analysed using different parameters. The comparative analysis show that the proposed ensemble method gives accuracy of 76.28%. © 2022 IEEE.

关键词： Decision trees

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Product Recommendation System using Deep Learning based Recurrent Neural Network

Product Recommendation System using Deep Learning based Recu...

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International Conference on computer Communication and Informatics (ICCCI)

作者： S. Shanthi K. Nirmaladevi N. Mathiarasi As. Krithika Tharani A. Monisha R. Kuppuchamy N. Krishnamoorthy Department of Computer Science & Engineering Kongu Engineering College Perundurai India Department of Computer Applications PSNA College of Engineering and Technology Dindigul Tamilnadu India Department of Software Systems and Engineering Vellore Campus Vellore Institute of Technology India

Today’s marketing strategies place a high priority on comprehending customer sentiments. It will not only give businesses a better understanding of how their clients view their goods and/or services, but it will also give them suggestions on how to enhance their offerings. We make an effort to comprehend the relationship between various factors in customer reviews on an online store for women’s clothing. The proposed system also classifies each review, according to, whether it recommends the product in question or not, and whether it has a positive, negative, or neutral sentiment. With the exception of review titles and review texts, we used univariate and multivariate analyses on datasets features to accomplish these goals. Additionally, we have implemented a bidirectional recurrent neural network (RNN) with a long-short term memory unit (LSTM) for sentiment and recommendation.

关键词： Deep learning Recurrent neural networks Filtering Clothing Informatics Recommender systems Business

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A Novel Method for Pneumothorax Diagnosis and Segmentation Using Deep Convolution Neural Network

A Novel Method for Pneumothorax Diagnosis and Segmentation U...

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International Conference on computer Communication and Informatics (ICCCI)

A pneumothorax is the accumulation of air in the pleural space between the chest and lungs. Air that is in the lungs during breathing can leak out of the lungs and become trapped between the chest and lungs in the thoracic cavity, causing chest pain, difficulty breathing, and ultimately the complete collapse of the taut lungs. pneumothorax. Therefore, early diagnosis and treatment should be started. Neural networks have proven effective in image classification tasks, and many researchers believe that training neural network models can detect radiologist-diagnosed chest abnormalities such as pneumonia and pneumothorax. is proving. Moreover, we utilize various techniques, including Stochastic Weight Averaging (SWA), aggressive data augmentation, and learning rate schedule. We use the chest x-ray dataset provided by the 2019 SIIM-ACR In this study, a ResNet-34 neural network is trained on a data set of chest x-rays containing over 10,675 images to create a model that predicts the presence of pneumothorax from chest *** suggested system receives a Dice Similarity Coefficient (DSC) of 0.8410

关键词： Training Image segmentation Schedules Pulmonary diseases Neural networks Lung Stochastic processes

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Benchmarking Deep Learning Models on NVIDIA Jetson Nano for Real-Time Systems: An Empirical Investigation

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Procedia computer Science 2025年 260卷 906-913页

作者： Tushar Prasanna Swaminathan Christopher Silver Thangarajah Akilan Jitendra Kumar Department of Computer and Electrical Engineering Lakehead University Thunder Bay ON P7B5E1 Department of Software Engineering Lakehead University Thunder Bay ON P7B5E1 Department of Mathematics Bioinformatics and Computer Applications Maulana Azad National Institute of Technology Bhopal 462003 M.P. India

The proliferation of complex deep learning (DL) models has revolutionized various applications, including computer vision-based solutions, prompting their integration into real-time systems. However, the resource-intensive nature of these models poses challenges for deployment on low-computational power and low-memory devices, like embedded and edge devices. This work empirically investigates the optimization of such complex DL models to analyze their functionality on an embedded device, particularly on the NVIDIA Jetson Nano. It evaluates the effectiveness of the optimized models in terms of their inference speed for image Classification and video action detection. The experimental results reveal that, on average, optimized models exhibit a 16.11% speed improvement over their non-optimized counterparts. This not only emphasizes the critical need to consider hardware constraints and environmental sustainability in model development and deployment but also underscores the pivotal role of model optimization in enabling the widespread deployment of AI-assisted technologies on resource-constrained computational systems. It also serves as proof that prioritizing hardware-specific model optimization leads to efficient and scalable solutions that substantially decrease energy consumption and carbon footprint.

关键词： Deep learning edge devices optimization NVIDIA Jetson Nano TensorRT

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Double Encryption Technique for Sharing and Storing the Images in the Cloud Environment

Double Encryption Technique for Sharing and Storing the Imag...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： P. Anusha R. Maruthi D. Maghesh Kumar H. Parveen Begum Department of Software Engineering Periyar Maniammai Institute of Science and Technology Thanjavur Tamil Nadu India Department of Computer Applications Hindustan Institute of Technology and Science Chennai Tamil Nadu India

Cryptography is used by all organizations to protect the data files and ensures confidentiality mainly at the time of sharing and storing in the cloud data storage. The cloud service providers use a wide range of tools and techniques for encryption and storage technologies. The intruders are finding number of ways to break or attack the cryptographic systems, so that new ways are constantly being explored to safeguard our files from the attackers This paper explores double encryption cryptographic technique based on Elliptic curve cryptography (ECC) and Advanced Encryption Standard (AES) to perform end–to end encryption for sharing and storing files in the cloud environment.

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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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Application of Neural Network Keyword Extraction Methods for Student’s CV Compilation from Discipline Work Programs

Application of Neural Network Keyword Extraction Methods for...

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International Conference on Soft Computing and Measurements (SCM)

作者： Ivan I. Kholod Pavel V. Korytov Maria V. Sorochina Department of Computer Science and Engineering Saint-Petersburg Electrotechnical University “LETI” Saint-Petersburg Russia Department of Software Engineering and Computer Applications Saint-Petersburg Electrotechnical University “LETI” Saint-Petersburg Russia

The paper considers application of natural language processing methods (NLP) to solve the task of identifying the core skills acquired by students in the process of education. An ensemble solution, consisting of models WikiNEuRAL, KBIR-Inspec, ruGTP3Large, word2vec, and other NLP methods, is used to extract and process Russian language keywords. The described method was developed to automate the compilation of a student’s CV in the information system called “Partner’s Account” of the LETI” university.

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Semantic Rules for Service Discovery in Social Internet of Things 4

Semantic Rules for Service Discovery in Social Internet of T...

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4th International Conference on Smart Systems and Inventive Technology, ICSSIT 2022

作者： Mohana, S.D. Prakash, S.P. Shiva Krinkin, Kirill Department of IS&E JSS Science and Technology University Karnataka Mysuru India Department of Information Science and Engineering JSS Science and Technology University Karnataka Mysuru India Department of Software Engineering and Computer Applications Saint Petersburg Electrotechnical University"LETI" Saint Petersburg Russia

ISBN: (纸本)9781665401180

World is moving towards preceding generation Alpha that believes in socializing the objects or devices within the network known as Social Internet of Things(SIoT). In SIoT the services are discovered based on the user preferences by establishing relationship between the objects and user. Thus object intelligent model is necessary to efficiently discover the services based on the context and semantic relationships between the users and objects. SIoT posses certain challenges like service discovery, compositions, etc. To address these challenges, in this work a semantic rules are proposed to discover services and it is evaluated using various machine learning algorithms on SIoT data. Hence, this work focusses on implementing and evaluating the various machine learning algorithms on SIoT device data considering health as an application. The performance is evaluated with dataset ratio of 60:40, 70:30, 80:20 and 90:10. The machine learning classifier techniques like Decision Tree, Naive Bayes, K-nearest neighbour and Artificial Neural Network classifiers gives the average accuracy 100%, 99%, 100%and 100% respectively. The results show that ANN gives best results compared to other algorithms. © 2022 IEEE

关键词： Internet of things

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Assessing Cybersecurity Awareness among the Hashemite University Students in Terms of computer Usage 16

Assessing Cybersecurity Awareness among the Hashemite Univer...

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16th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2024

作者： Aljammal, Ashraf H. Qawasmeh, Ahmad Taamneh, Salah Wedyan, Fadi Obiedat, Mamoon Salameh, Hani Bani The Hashemite University Prince Al Hussein Bin Abdullah Ii Faculty of Information Technology Department of Computer Science and Applications Zarqa Jordan Lewis University Department of Engineering Computing and Mathematical Sciences RomeovilleIL United States The Hashemite University Prince Al Hussein Bin Abdullah Ii Faculty of Information Technology Department of Information Technology Zarqa Jordan The Hashemite University Prince Al Hussein Bin Abdullah Ii Faculty of Information Technology Department of Software Engineering Zarqa Jordan

ISBN: (纸本)9798350371154

Internet usage among information technology users has expanded substantially, as have cybercrimes with potentially disastrous repercussions. Therefore, internet users have to employ the available security measures and policies during internet usage. Nowadays, University students make up a sizable portion of internet users. This study assesses the cybersecurity awareness among The Hashemite University students In terms of computer Usage. A questionnaire survey is used to assess students' cybersecurity knowledge while using their PCs and connecting to the internet, concentrating on their behaviors and use of security measures. © 2024 IEEE.

关键词： Cybersecurity

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