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检索条件"任意字段=7th IASTED International Conference on Advances in Computer Science and Technology"

共 1494 条记录，以下是31-40 订阅

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Data Encryption: A Compiler Based Approach 7

Data Encryption: A Compiler Based Approach

引用

7th IEEE international conference on Computational Systems and Information technology for Sustainable Solutions, CSITSS 2023

作者： Jaswanth, Kunisetty Sruthi, S. Ramachandrula, Pranav Belwal, Meena Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350343144

In today's age, where data security's a growing concern it is crucial to develop strong encoding and decoding models. In our research paper we introduce a model that combines concepts, from compiler design with diverse hashing algorithms. this model aims to offer an efficient solution for transforming and retrieving data. As technology advances rapidly and the need for data security becomes more prominent in real world scenarios our model tackles the challenges of safeguarding information while ensuring integrity and confidentiality. By utilizing hashing algorithms like SHA, BLAKE2 and MD5 we achieve data encryption. Our experiments demonstrate that our model outperforms approaches showcasing its effectiveness across industries where protecting data privacy is of utmost importance. With its user interface our model provides a solution for secure data transmission and storage contributing significantly to advancements in data security and encryption techniques. © 2023 IEEE.

关键词： Data privacy

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Instantaneous Power based Effective Power Balance Operation in All-Electric Ship Microgrid 7

Instantaneous Power based Effective Power Balance Operation ...

引用

7th international conference on computer Applications in Electrical Engineering-Recent advances, CERA 2023

作者： Abinaya, K. Sowmmiya, U. Srm Institute of Science and Technology Dept of Electrical and Electronics Engineering Chengalpattu Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350305005

Shipboard microgrids suffer from frequently varying, bulkier propulsion loads and the existing operation of diesel engines leads to increased emissions and thereby operational cost. Also, the propulsion loads and other fluctuating loads produce unbalance and nonlinearity in the system thereby demanding load compensation to maintain power quality. therefore, renewable sources and batteries are added to a hybrid shipboard microgrid (AC/DC network) to exert an uninterrupted power supply and to improve the power balance in the shipboard microgrid. the prime aim of this work is the usage of an interlinking converter capable of maintaining power balance in the system, bidirectional power transfer, and uninterrupted power supply through simple control methodology involving reference current generation based on instantaneous power theory. the instantaneous power theory involves the AC side source voltages and currents for the control and operates the interlinking converter at unity power during both PWM rectification and PWM inversion. the performance of the proposed control for an all-electric shipboard microgrid is validated in real-time through Software-In-Loop (SIL) approach by Opal 4510 Controller. the dynamic responses of the system through SIL are found to exhibit rectification at unity and preset power factors and also inversion at unity power factor with lesser total harmonic distortion. © 2023 IEEE.

关键词： Pulse width modulation

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the Development of Polycystic Ovary Syndrome Risk Evaluation System using Advanced Machine Learning Technique 7

The Development of Polycystic Ovary Syndrome Risk Evaluation...

引用

7th international conference on Inventive Computation Technologies, ICICT 2024

作者： Velvizhi, R. Arya Kiruthiknivas, R. Sathyabama Institute of Science And Technology Department of Computer Science And Engineering Chennai India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359299

A complex endocrine disorder that affects fertile women, polycystic ovary syndrome, also known as PCOS, is characterized by an extensive spectrum of symptoms. this work extends previous efforts in PCOS detection and tracking with the goal of utilizing state-of-the-art machine learning techniques to overcome existing constraints. the goal of the project is to improve medical outcomes by utilizing predictive analytics to analyze big patient datasets that include genetic and clinical data. In this work, robust prediction models are developed by applying machine learning techniques, including Random Forest, decision trees, and support vector machines. these models aim to accurately identify individuals who are susceptible to PCOS and forecast the course of the disease in those who have already been diagnosed. Crucially, an extensive performance assessment is provided, contrasting the suggested models with current ones to emphasize their advantages and shortcomings. In-depth dataset investigation, rigorous model training, and comprehensive assessment metrics are conducted as part of the experiments. the outcomes highlight the usefulness of the models and indicate how they could assist medical practitioners in making defensible decisions. Predictive analytics integrated into electronic health records (EHRs) allows for ongoing PCOS patient monitoring, enabling timely interventions, individualized treatment plans, and better patient results. this research advances the area by improving the accuracy of PCOS diagnosis and therapy and by addressing the shortcomings of the previous literature. the suggested machine learning methods improve patient satisfaction, health outcomes, and system productivity, in addition to enhancing identification accuracy. this revised strategy guarantees a more impactful and targeted portrayal of the research activity, in line with the input that was given. © 2024 IEEE.

关键词： Decision trees

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A Comparative Analysis on Various Machine Learning Methods for GAN Based Video Anomaly Detection 7th

A Comparative Analysis on Various Machine Learning Methods f...

引用

7th IFIP TC 12 international conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Chiranjeevi, Rahul Shyam, C. Hirthik Kaushiik, K. Kumar, P. Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9783031699818

In recent years, surveillance has undergone tremendous change and developed into an essential instrument for maintaining security and keeping an eye on sensitive areas. this essay investigates the idea of what defines surveillance. It explores the crucial topic of anomaly detection, which is a vital component of contemporary surveillance systems. the expanding importance of the use of deep learning methods is highlighted in this paper’s discussion of current developments in surveillance technology. through the processing of massive volumes of data, deep learning has transformed surveillance by allowing more precise and effective anomaly detection. Examining several kinds of deep learning techniques, their special qualities and uses are highlighted. this study concludes with an in-depth analysis of the monitoring, highlighting the role of deep learning in improving anomaly detection. It is an invaluable tool for researchers, professionals and decision makers interested in the development of surveillance technology and its use in different contexts. © IFIP international Federation for Information Processing 2024.

关键词： Contrastive Learning

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Text Based Traffic Signboard Detection Using YOLO v7 Architecture 1

引用

Proceedings of the 7th international conference on advances in Computing and Data sciences, ICACDS 2023

作者： Negi, Ananya Kesarwani, Yash Saranya, P. Department of Computing Technologies SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India

ISBN: (数字)9783031379406

ISBN: (纸本)9783031379390

Recent developments in computer vision and deep learning technology have increased the prevalence of advanced driver assistance systems (ADAS). ADAS technologies aim to reduce traffic accidents and make driving safer. the proposed work is an additional ADAS feature or can help the driver navigate better through roads while focusing more on the roads. the system uses a small camera mounted at the front of the car, and images from that are then fed into the YOLOv7 model, which can run on jetson nano or other such computing hardware. In the proposed model, the results we have achieved have an overall accuracy of 86% with the system and speed at which it can perform efficiently, ranging from object detection to reading the data on the sign boards. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Automobile drivers

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Deep Learning Approaches for Solving Challenges in computer Vision Applications and Exploring Evolving Patterns 7

Deep Learning Approaches for Solving Challenges in Computer ...

引用

7th international conference on Contemporary Computing and Informatics, IC3I 2024

作者： Gejji, Sujay Supreeth, B.R. Khan, Pathan Firoze Venkat, S. Pradeepa, K. Kaliappan, S. S. G. Balekundri Institute of Technology Department of Electronics & Communication Engineering Karnataka Belagavi India Oxford Degree and Pg College Department of Computer Science Sir. M. Visveshwaraiah Layout Bangalore India Madanapalle institute of Technology & Science Department of Computer Science & Technology Andhra Pradesh Madanapalle India S. A. Engineering College Department of Electronics and communication Engineering Tamil Nadu Chennai India Kpr College of Arts Science and Research Department of Artificial Intelligence and Data Science Tamil Nadu Coimbatore India Lovely Professional University Division of Research and Development Punjab Phagwara India

ISBN: (纸本)9798350350067

Deep learning has brought substantial advancements to computer vision in recent years by providing answers to challenging visual interpretation problems. It investigates several DL methodologies, such as self-supervised learning strategies, hybrid CNN-RNN models, enhanced CNNs, generative adversarial networks (GANs), and vision transformers (ViTs). Novel CNN architectures are designed, GANs are used for image synthesis, hybrid models are used to integrate spatial and temporal information, self-attention is used in ViTs, and self-supervised learning is used for feature extraction. the results of the study show significant improvements: the accuracy of Enhanced CNNs was 81.6%, the accuracy of GANs was 14.8%, the accuracy of Hybrid CNN-RNN models was 86.2%, the accuracy of ViTs was 79.2%, and the accuracy of transfer for self-supervised learning techniques was 76.8%. these findings demonstrate the improved effectiveness and performance of the suggested techniques, highlighting their potential to resolve challenging problems in feature extraction, video analysis, object recognition, picture enhancement, and image classification. It concludes that advances in computer vision will be facilitated by the ongoing development of DL methods. these advancements will lead to more precise and adaptable applications in a range of fields, including autonomous driving, medical imaging, surveillance, and augmented reality. © 2024 IEEE.

关键词： Generative adversarial networks

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Enhancing ASD Cognitive Assessment with P300 Classification using EMD-based qEEG Wavelet Features 7

Enhancing ASD Cognitive Assessment with P300 Classification ...

引用

7th international conference on computer Applications in Electrical Engineering-Recent advances, CERA 2023

作者： Das, Madhuparna Mahadevappa, Manjunatha Indian Institute of Technology School of Medical Science and Technology Kharagpur India

ISBN: (纸本)9798350305005

Individuals with Autism Spectrum Disorder (ASD) often face challenges in effective communication due to deficiencies in their joint attention skills. Brain-computer Interface (BCI) systems based on the P300 Electroencephalogram (EEG) signal has shown promising results in neuro-rehabilitation training for individuals with Autism spectrum disorder(ASD), helping them overcome these communication barriers. However, detecting the P300 signal in ASD is particularly difficult due to its noisy nature, lower amplitude, and higher latency compared to other individuals. In this paper, we propose a novel application of the Empirical mode decomposition (EMD) using the Hilbert Huang transform to identify the P300 signal in a BCI system involving ASD subjects. Further, the optimized mode was selected and utilized and the qEEG fused time-frequency domain wavelet features were extracted for each selected mode of each individual channel. To address the class imbalance issue in the dataset, To handle imbalanced data, we applied SMOTE for data augmentation, and for optimized feature selection, we used PCA. We conducted a comparative classification analysis using five popular machine-learning classifiers. Among the classifiers, Random Forest outperformed the others, achieving 93% accuracy. these results demonstrate the effectiveness of our approach in enhancing P300 signal identification for joint attention-based cognitive task classification for individuals with ASD, surpassing state-of-the-art methods. © 2023 IEEE.

关键词： Electroencephalography

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Empowering Medical Image Analysis: Unveiling Anomalies through GANs and BiGAN’s Models 7th

Empowering Medical Image Analysis: Unveiling Anomalies Throu...

引用

7th IFIP TC 12 international conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Sharma, Vatsal Kumar Jakhar, Aryan Vats, Aaroh Singh, Gurwinder Computer Science Engineering with AIML Chandigarh University Mohali India

ISBN: (纸本)9783031699818

In many researches, the field of medical image analysis has made noteworthy strides, thanks to the implementation of sophisticated machine learning methods. this project, titled "Enhancing Medical Image Analysis: Uncovering Abnormalities through Innovative Models", stands as a modern game powerup to utilize rapidly progressing computer algorithms known as Generative Adversarial Networks (GANs) and Bidirectional GANs (BiGANs) for the automated identification and categorization of anomalies within medical images. Medical image analysis plays a crucial role in diagnosing and treating a range of health conditions, in this case particularly of 2 types, Bacterial and Viral. However, the precision and accuracy of image analysis often depend upon the expertise and execution of radiologists, which can introduce the potential for human errors and result in time-consuming processes. To tackle these challenges, this project introduces a fresh approach that makes use of GANs and BiGANs to improve the identification and categorization of abnormalities in medical images, thereby providing valuable insights to healthcare professionals and ultimately enhancing patient diagnosis and treatment outcomes. © IFIP international Federation for Information Processing 2024.

关键词： Generative adversarial networks

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Semantic Topic Extraction from Research Artifacts 7

Semantic Topic Extraction from Research Artifacts

引用

7th international conference on computer science and Artificial Intelligence, CSAI 2023

作者： Sai P, Daiveek Rajesh, Anouksha Indian Institute of Technology Kharagpur India Srm University India

ISBN: (纸本)9798400708688

the GENESIS project introduces a novel framework for Research Artifact Data Semantics to enhance semantic web technologies within academic institutions. this paper introduces a system developed as part of the GENESIS challenge, focusing on extracting and labelling topics from diverse research artifacts. the approach employs knowledge graphs, topic models, and named entity recognition to infer meaningful topics. Evaluation encompasses measures such as topic coherence and semantic similarity. Future work includes refining entity linking techniques, optimizing system performance, and implementing advanced graph centrality algorithms. this contribution advances semantic understanding in various research contexts. © 2023 ACM.

关键词： Linked data

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Neuro-Evolution-Based Language Model for Text Generation 7th

Neuro-Evolution-Based Language Model for Text Generation

引用

7th IFIP TC 12 international conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Bagavathi, C. Prakash, Abhijith C. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore Coimbatore India

ISBN: (纸本)9783031699818

In the dynamic field of natural language processing, the enhancement of text generation models presents a complex challenge, compounded by the intricate architectures and substantial parameters of contemporary neural networks. this study introduces a groundbreaking method that applies Genetic Algorithms to evolve the architecture of Long Short-Term Memory networks (LSTM), specifically tailored for text generation tasks. Our approach employs a sophisticated gene encoding mechanism that captures the diverse LSTM network configurations and the optimal depth of the network required for generating coherent and contextually relevant text. Our method systematically refines and evolves network architectures through iterative selection, crossover, and mutation processes, uncovering the most effective configurations for text generation. this evolutionary process is designed to yield an LSTM network architecture with enhanced performance in text generation. © IFIP international Federation for Information Processing 2024.

关键词： Genetic algorithms

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