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检索条件"机构=Dept. of Applied Computer Engineering"

共 1547 条记录，以下是1-10 订阅

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AAF-SCM: An Authenticated Framework for Supply Chain Management

AAF-SCM: An Authenticated Framework for Supply Chain Managem...

引用

2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Roy, Prince Kumar, Rajneesh Morshed, Md Neaz Fairleigh Dickinson University Dept. of Applied Computer Science United States Dept. of Computer Science & Engineering India

ISBN: (纸本)9798331539948

The process of a supply chain involves a series of activities that distribute, assemble, and manage products, transferring them from the supplier to the end consumer. It operates as a complex, interconnected network regulated by supply and demand. Security in supply chain management is a crucial aspect of its overall security measures, focusing primarily on maintaining essential end-to-end protection. Without this, several attacks can be used to manipulate data and valuable information from the entire chain of networks. This paper explains how encrypted intermediates can secure the entire SCM by validating each intermediate using public-private key encryption. We also demonstrate and assess how the proposed AAF-SCM model can aid in tracing security breaches or unauthorized data tampering. We also validate this model against nefarious attacks like Fabrication, Assertion, Deletion, and Modification. Finally, we propose opportunities to develop innovative systems that enhance security through intermediary steps. © 2024 IEEE.

关键词： Supply chain management

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Building Cloud Base Cyber Range by Designing an Efficient Container Distribution API Using Docker 16

Building Cloud Base Cyber Range by Designing an Efficient Co...

引用

16th IIAI International Congress on Advanced applied Informatics, IIAI-AAI 2024

作者： Shin, Sanggyu Tokai University Dept. of Applied Computer Engineering Hiratsuka-shi Japan

ISBN: (数字)9798350377903

ISBN: (纸本)9798350377903

This study proposes the Cyber Range environment built in a virtual space on the Cloud via a local web browser. The training contents are developed using Docker, which has excellent portability, and attack and defense exercise scenarios are produced as containers. This study (1) develops a Cloud platform that can be connected to the cloud-based training environment via a web browser. (2) enable users to interactively attack and defend exercises in a virtual space on the Cloud in an internet environment. Finally, (3) The curriculum is structured based on micro-service to be configured flexibly in the open environment. © 2024 IEEE.

关键词： Cloud platforms

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Construction and Examination of an Emotional Speech Synthesis System Using a CNN Image Classification Model 16

Construction and Examination of an Emotional Speech Synthesi...

引用

16th IIAI International Congress on Advanced applied Informatics, IIAI-AAI 2024

作者： Shin, Sanggyu Tokai University Dept. of Applied Computer Engineering Hiratsuka-shi Japan

ISBN: (数字)9798350377903

ISBN: (纸本)9798350377903

In this study, the proposal system investigates the possibility of simplifying the emotional speech synthesis system by embedding styles built with a feature extractor based on a CNN image classification model. To simplify the emotional speech synthesis system, the proposal system attempted to add emotion to Tacotron2 speech synthesis by creating a style embedding using a feature extractor based on the CNN image classification model. © 2024 IEEE.

关键词： Speech analysis

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Review of Teeth Image Segmentation 11

Review of Teeth Image Segmentation

引用

11th International Conference on Electrical, Electronic and Computing engineering, IcETRAN 2024

作者： Grujev, Milan Ilic, Milos Milosavljevic, Aleksandar Ilic, Aleksandra Stoinev Faculty of Electronic engineering Dept. Computer Sciences Nis Serbia Toplica Academy of Applied Studies Dept. Computer Sciences Prokuplje Serbia

ISBN: (纸本)9798350386998

Tooth segmentation is a vital task in medical image analysis aimed at extracting and delineating tooth structures from computed tomography scans. This process plays a crucial role in various clinical applications, including dental diagnostics, treatment planning, and surgical interventions. Through the years, different segmentation approaches are applied on tooth scans that comes from different CT machines. Different approaches archive different segmentation results. In particular most of them provided promising results. The goal of this paper is to present current state of the art in this field. Our research mainly was focused on research results from last couple of years. In this way this paper represents good start for all researches which plan is to contribute in the field of tooth segmentation. © 2024 IEEE.

关键词： Image segmentation

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Computational Web Drug Discovery Application forSARS- Cov-2 Disease Using Predictive and Analytical Approach 10

Computational Web Drug Discovery Application forSARS- Cov-2 ...

引用

10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Hemkiran, S. Kartik, R. Aswath, S. Rathan Kabilan, K. Sudha Sadasivam, G. Psg Institute of Technology and Applied Research Dept. of Computer Science and Engineering Coimbatore India Psg College of Technology Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350384369

In response to the urgent need for coronavirus treatments, this research focuses on leveraging bioactivity data collection and processing for efficient drug discovery, employing computational methods to predict potential antiviral compounds. Exploratory data analysis was performed to identify patterns and trends, and various molecular descriptors were computed for the Mpro inhibitors in the descriptor dataset preparation step. A regression model was trained using the developed random forest algorithm to predict the bioactivity of new compounds against the standard value and compared with other regression models. Additionally, a web-based application was created using the random forest model to allow users to obtain predicted bioactivity values against Mpro by providing information about molecular structures. Machine learning-based bioactivity prediction offers an intriguing plan for drug discovery, and the proposed work provides a comprehensive workflow for COVID- 19 drug discovery. The web application provides a user-friendly interface for drug discovery researchers to evaluate the potential of compounds against the Mpro target quickly. © 2024 IEEE.

关键词： COVID-19

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Content Based Classification of Short Messages using Recurrent Neural Networks in NLP

Content Based Classification of Short Messages using Recurre...

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2024 International Conference on Artificial Intelligence, computer, Data Sciences, and Applications, ACDSA 2024

作者： Bennet, Deepthi Tabitha Bennet, Preethi Samantha Thiagarajan, Priya Sundarakantham, K. Thiagarajar College of Engineering Dept. of Applied Mathematics and Computational Science Madurai India Thiagarajar College of Engineering Dept. of Computer Science and Business Systems Madurai India Thiagarajar College of Engineering Dept. of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350394528

Short message spam poses a significant threat for all mobile phone users, as it can act as an efficient tool for cyberattacks including spreading malware and phishing. Traditional anti-spam measures are only minimally effective against modern spammers. Intelligent analysis of the content to categorise the messages as Spam (unwanted and unsolicited) or Ham (useful messages) is therefore essential to safeguard the user from such attacks. Various artificial intelligence (AI) techniques are proving to be useful in the analysis of the content of such short messages to classify and filter spam. We have trained, validated and tested seven such AI techniques on the SMS spam collection dataset to identify the best model for designing and developing a content based classification system. Recurrent Neural Networks (RNN) have shown the highest performance metrics (Test Accuracy: 99.28%) and hence our proposed system includes a RNN model for classification. A web app of this system has also been deployed where a single SMS can be input and the designed system can classify it as Spam or Ham. The designed system is compared against existing systems and is found to be significantly better. © 2024 IEEE.

关键词： Recurrent neural networks

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Classification of Depression, Anxiety and Stress from Handwriting and Drawing with Stacking Models 8

Classification of Depression, Anxiety and Stress from Handwr...

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8th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2024

作者： Bayrak, Semra Golgiyaz, Sedat Aykut, Murat Karadeniz Technical University Graduate School of Natural and Applied Science Dept. of Computer Engineering Trabzon61080 Turkey Bingöl University Dept. of Computer Engineering Bingöl Turkey Karadeniz Technical University Dept. of Computer Engineering Trabzon Turkey

ISBN: (纸本)9798331540104

Recently, emotion analysis from online handwriting and drawings has become an important research topic. This study aims to determine the optimal model combination using dynamic and statistical features obtained from handwriting and drawings for detecting emotional states. The feature vectors are derived from physical (kinematic), statistical and signal processing (spectral, cepstral and frequency domain) analyses. To address the class imbalance problem, ADASYN (Adaptive Synthetic Sampling) and Tomek Links methods were applied together. In this way, the number of samples of the minority class has been increased, while over-sampled classes have been reduced. Dimensionality reduction was realized using PCA, and Gradient Boosting Classifier (GBC) feature selection. After determining the fewest and most effective features, the samples were classified using the two-level stacking ensemble method. eXtreme Gradient Boosting (XGBoost), Light Gradient-Boosting Machine (LightGBM), GBC, Random Forest (RF) methods are selected for base and meta models. Model and hyperparameter optimization was carried out by Optuna frameork. The performance of the models is evaluated through experiments in publicly available EMOTion recognition from HAndWriting and draWing (EMOTHAW) database. The aim was to determine the emotional states of depression, anxiety and stress with the data obtained from 7 different drawing/writing tasks of 129 people. The results obtained from accuracy, f1-score, precision and recall metrics show that the model provides remarkable performance. © 2024 IEEE.

关键词： Dimensionality reduction

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Unleashing Machine Learning for Hepatitis C Prediction: A Holistic Exploration of Clinical Insights

Unleashing Machine Learning for Hepatitis C Prediction: A Ho...

引用

2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Al-Amain Janin, Fatema Tuj Ahmed Robin, Faizul Ahmed, Shifat Mohi Uddin, Khandaker Mohammad Southeast University Dept. of Computer Science and Engineering Dhaka Bangladesh Hamburg University of Applied Sciences Dept. of Information and Electrical Engineering Hamburg Germany

ISBN: (纸本)9798331529765

Chronic diseases, characterized by long-term effects and persistent symptoms, pose significant challenges to human health and well-being. The hepatitis C virus (HCV) is the cause of chronic hepatitis C, which affects 58 million people globally. It also causes 290,000 deaths annually due to liver cancer and cirrhosis, two conditions connected to HCV. Taking advantage of machine learning (ML) algorithms' ability to analyze large datasets and identify patterns and relationships that aid in diagnosis, this effort focuses on employing algorithms for machine learning to identify and forecast hepatitis C precisely. Various techniques were utilized, including Random Forest, Cat Boost, SVM, and others to create predictive models using blood values from 615 patients, including both hepatitis patients and healthy blood donors. The dataset underwent comprehensive preprocessing, including handling missing values, scaling, and dimensionality reduction via Principal Component Analysis (PCA). Various ML algorithms were evaluated, and the Cat Boost classifier achieved the highest accuracy of 99.17%, recall of 92.00%, precision of 100.00% and F1-score is 96.00%. This study demonstrates the potential of ML in enhancing HCV diagnosis, offering less invasive and cost-effective alternatives to traditional methods, and providing predictive insights for personalized treatment plans. The findings underscore the importance of advanced data preprocessing and feature selection in improving model efficiency. This study adds to the ongoing efforts to lessen the prevalence of hepatitis C worldwide, aligning with the World Health Organization's goal to significantly decrease new HCV cases and related deaths by 2030. © 2024 IEEE.

关键词： Adversarial machine learning

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Advances and Adaptation in Artificial General Intelligence Based on Novel Q∗ Framework 27

Advances and Adaptation in Artificial General Intelligence B...

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27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024

作者： Upreti, Meenakshi Bhatnagar, Abhinav Birla Institute of Applied Sciences Dept. of Master of Computer Applications Bhimtal India Birla Institute of Applied Sciences Dept. of Electronics and Communication Engineering Bhimtal India

ISBN: (纸本)9798350392319

A priority in the researches on artificial intelligence is the creation of Artificial General Intelligence. Even though the regular artificial intelligence was making some progress in certain areas, there were challenges and untried domains. This study introduces a novel Q∗ framework which is intended for improving AGI. Understanding, obtaining and using machine-generated knowledge across multiple domains are some of the aspects of AGI. It integrates concepts form cognitive psychology and reinforcement learning to produces a cohesive approach that includes learning as well as introspection and flexibility. By leveraging the advantages of Q-learning and refining existing methods, achieving AGI through Q∗ is feasible. Q* prioritizes metalearning and hierarchical representations over traditional AI systems that perform well within narrow contexts yet fail when asked to generalize more broadly. This makes it possible to be adaptable and learn well in unfamiliar settings. The paper gives a detailed explanation of the theoretical underpinnings and possible applications of AGI, emphasizing Q∗′ 's commitmentto advancing its pursuit. We discuss the conceptual framework proposed by Q∗ and speculate on its implications for future AI research. © 2024 IEEE.

关键词： Adversarial machine learning

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Multilingual Sentiment Analysis using DistilBert

Multilingual Sentiment Analysis using DistilBert

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Gupta, Nishi Bhattarai, Mandip Chavan, Abhishek Pimpri Chinchwad College of Engineering Dept. of Applied Science and Humanities Maharastra Pune India Pimpri Chinchwad College of Engineering Dept. of Computer Engineering Maharastra Pune India

ISBN: (纸本)9798331517953

In today's virtual age, studying feelings and sentiments through textual content and emojis is essential. This research article explores a way to use natural language processing (NLP) techniques to interpret feelings from textual content and emoji content. We used the distilbert-base-uncased-finetuned-sst2english version, a small variant model of BERT, to categorize feelings as positive or negative. Our experiments confirmed that the version executed extraordinarily properly throughout more than one language, with especially great effects on Indonesian textual content. Our assessment confirmed that the version has an excellent accuracy of91.50% and an F1 rating of 91.44% for Indonesian, highlighting its wonderful overall performance in this language. It additionally plays properly in English, French, German, Arabic, and Italian, even though overall performance may also range through language. These effects reveal the version's effectiveness for duties, social media monitoring, client comments evaluation, and inventory marketplace fashion assessment. We finish with a conclusion about the results of those findings. © 2024 IEEE.

关键词： Natural language processing systems

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