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检索条件"任意字段=International Conference on Advances in Data Science and Computing Technologies, ASDC 2022"

共 6586 条记录，以下是101-110 订阅

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Isolating Brain Tissue from Abnormal Tissue Using MRI-Based U-Net Convolutional Neural Network

Isolating Brain Tissue from Abnormal Tissue Using MRI-Based ...

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international conference on advances in data science and computing technologies, asdc 2022

作者： Bhattacharjee, Brijit Debnath, Bikash Chandra Das, Jadav De, Debashish Department of Computer Science and Engineering Swami Vivekananda Institute of Science and Technology West Bengal Kolkata700145 India Amity Institute of Information Technology Amity University West Bengal Kolkata700135 India Department of Information Technology Maulana Abul Kalam Azad University of Technology West Bengal West Bengal Haringhata741249 India Department of Computer Science and Engineering Maulana Abul Kalam Azad University of Technology West Bengal West Bengal Haringhata741249 India

ISBN: (纸本)9789819936557

There is widespread agreement that efficient deep network training necessitates thousands of annotated training examples. The proposed method offers a network and training technique that heavily depends on the amplification of data to make better use of the available annotated samples. Deep Neural Networks have been shown to be extremely effective in the fields of visual recognition, discovery, and cleaving in computer vision. These operating procedures have been used in the context of biomedical record analysis. Convolutional neural networks (CNNs), a crucial aspect of deep learning practice for visual analysis, have received a lot of exposure in recent times due to their advanced capabilities in computer vision applications. The U—Nets are a type of CNN with accuracy comparable to that of human brain images. The goal of the proposed research is to focus on preliminary processing images on isolating aberrant tissue utilizing U-Net and semantic analysis. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolution

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A Comprehensive Review on Varicose Veins and its Implications using AI Methods 15

A Comprehensive Review on Varicose Veins and its Implication...

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15th international conference on advances in computing, Control, and Telecommunication technologies, ACT 2024

作者： Kumar, Prasanna M.J. Prakash, H.N. Department of Computer Science & Engineering Rajeev Institute of Technology Visvesvaraya Technological University Hassan Belagavi India Rajeev Institute of Technology Visvesvaraya Technological University Hassan Belagavi India

ISBN: (纸本)9798331300579

This study investigates the emerging role of artificial intelligence (AI) in the diagnosis of varicose veins, a disorder characterised by swollen, convoluted veins that primarily affect the lower limbs and are accompanied by symptoms such as discomfort and *** the widespread usage of traditional imaging techniques such as ultrasonography, the risk of human error and the inherent constraints in processing complex visual data demand more robust *** evaluate cutting-edge AI models, particularly those that use deep learning, for their capacity to autonomously analyse imaging data, highlight problematic abnormalities, and aid in clinical *** study recognises significant advances in algorithmic approaches, evaluates their diagnostic accuracy in comparison to older methods, and analyses the scalability of these solutions for routine clinical ***, we discuss the technological and ethical hurdles of integrating AI into medical practice, such as data security, algorithm openness, and regulatory *** review intends to highlight AI's potential to transform the diagnostic environment for varicose veins, as well as provide insights into practical implementation and future research directions. © Grenze Scientific Society, 2024.

关键词： Deep learning

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Hybrid Image Encryption for WISN in Privacy Protection and Security of Public Big data Using Chaotic Maps

Hybrid Image Encryption for WISN in Privacy Protection and S...

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2024 international conference on advances in Modern Age technologies for Health and Engineering science, AMATHE 2024

作者： Geddada, Nikhitha Sahu, Aditi Suseela, G. Jayshnav, S. Arora, Suchi Srm Institute Of Science And Technology Department Of Networking And Communications Chennai India Srm Institute Of Science And Technology Department Of Computing Technologies Chennai India

ISBN: (纸本)9798350371567

Image big data has become pervasive in today's world. Every day huge volumes of data are being transferred across systems. Significant advancements have been made in networking and file transfer technologies. Due to the remarkable expansion of network infrastructures, multimedia transmission has become inevitable. Image data is being produced at a faster rate and the variety of data types is expanding rapidly. A number of big data-related surveillance applications use wireless image sensor networks (WISN). Important and sensitive data must be hidden from different hackers trying to break into the system. So, encryption is used to hide sensitive information from various hackers. Lorenz attractor along with the chaotic maps are used to withstand statistical attacks. Dual confusion and dual diffusion are implemented so as to ensure maximum chaos and makes the encryption system strong against attacks. The pixels of the image are scrambled using logistic maps so that only authorized users can decipher it. Through the integration of tent map and logistic map, this study approaches an efficient image encryption algorithm. © 2024 IEEE.

关键词： Sensitive data

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Evaluation of Teachers’ Performance Using Fuzzy Inferences System

Evaluation of Teachers’ Performance Using Fuzzy Inferences ...

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international conference on advances in data science and computing technologies, asdc 2022

作者： Haque, Ashadul Ali, Md Firoj Department of Computer Science Vivekananda Mission Mahavidyalaya Bajitpur India Department of Computer Science Ram Krishna Mission Residential College Periyanaickenpalayam India

ISBN: (纸本)9789819936557

Fuzzy means vague, not clear or indistinct. Fuzzy logic helps to build decision making system, Artificial Intelligent System, forecasting or Prediction. In this paper, we discuss about various application of Fuzzy System like Stock Market Prediction, Patient Care, Washing Machine, Expert System in Power Plant, Photovoltaic System, Fraud Detection, Loan Application Evaluation, Writer Identification, extraction of Knowledge from huge database etc. Student Performance can be measured in various ways like taking written examination, internal evaluation, viva or assignment etc. But sometimes we need to evaluate teachers’ performance for various reasons like Promotion, Research Grant, Recognition and Award etc. In this paper, we develop an expert system to evaluate teachers’ performance based on the number of classes, hour spent in evaluation, examination or checking answer script, paper setting, hour spent on ICT based classes, administrative duties like NSS, cultural activities, TIC, convenor, professional development activities, number of journals, working on some projects or guiding ***/Ph.D scholars. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Advanced Encryption Techniques for Protecting Sensitive data in Multi Cloud Architecture: A Security First Approach 1

Advanced Encryption Techniques for Protecting Sensitive Data...

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1st international conference on advances in Computer science, Electrical, Electronics, and Communication technologies, CE2CT 2025

作者： Anand, V. Saravanan, N. Mahendiran, A. School of Computing SASTRA Deemed University Tamil Nadu Thanjavur India Srinivasa Ramanujam Centre SASTRA Deemed University Tamil Nadu Kumbakonam India Thanthai Hans Roever College Tamil Nadu Perambalur India

ISBN: (纸本)9798331518578

'Cloud computing' refers to a broad range of computer ideas that include several PCs linked by a real-time transmission network, such the internet. Cloud computing is a new paradigm that has drawn a lot of attention from researchers recently due to its potential to lower computing expenses. This study compares RSA, AES, and a technique that combines both of them. The effectiveness of these methods is examined in relation to encryption and decryption times for various data set sizes. The experiment was designed to be replicable and was conducted on standardised devices using the Mathematical writing and analysis tool, which is based on the Wolfram Language. In this experiment, encryption and decryption speeds were tested and compared for data sizes ranging from 64 to 176 bytes. As a second indicator of the algorithms' performance, the number of iterations each algorithm completed was also counted. The results demonstrate that AES had the fastest encryption and decryption times, and that the hybrid model had the greatest features of both RSA and AES in terms of speed and security. With encryption times of 0.10 ms and 0.20 ms for 64-byte and 176-byte data, respectively, the hybrid approach provided a balanced performance. AES outperformed RSA in throughput by up to 4 times and the hybrid technique by up to 2 times. According to these results, the hybrid technique successfully strikes a balance between speed and security, even though AES is best for speed. © 2025 IEEE.

关键词： Cloud computing Decryption Encryption Hybrid RSA

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AI-Based Smart Education System to Enhanced the Learning of Students

AI-Based Smart Education System to Enhanced the Learning of ...

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2024 international conference on advances in computing Research on science Engineering and Technology, ACROSET 2024

作者： Barde, Aaryan Thakur, Raj Patel, Shreyansh Sinah, Neelesh Barde, Snehlata Lnct Group of College Dept.of CSE-AIML Bhopal India Sage University Dept. of Aids Indore India Sage University Inst. of Advanced Computing Indore India

ISBN: (纸本)9798350388800

The education sector is poised for a significant shift propelled by Artificial Intelligence (AI) technologies. This paper explores the various uses of AI in education, such as data-driven decision assistance for instructors, adaptive assessment tools, intelligent tutoring, and personalized learning systems. It looks at how chatbots and virtual assistants driven by AI can improve learning by giving students instant, tailored support and direction. This paper also emphasizes how AI may improve student engagement and gamification of course materials, changing the educational environment into one that is dynamic, engaging, and pleasant. © 2024 IEEE.

关键词： Adversarial machine learning

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Analysis of Modified 16-Channel WDM-RoF System upto 340 km Transmission Distance 15

Analysis of Modified 16-Channel WDM-RoF System upto 340 km T...

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15th international conference on advances in computing, Control, and Telecommunication technologies, ACT 2024

作者： Kumari, Pravesh Garg, Deepak Kumar, Vinod Arya, Sandeep Guru Jambheshwar University of Science and Technology E.E.E. Department Hisar India

ISBN: (纸本)9798331300579

This paper introduces a model employing Wavelength Division Multiplexed-Radio - over-Fiber (WDM-RoF) and Fiber Bragg Grating (FBG) technologies for high-speed data transmission over 340 km, meeting modern user needs. The setup includes an efficient RoF network with an Optical Phase Conjugator (OPC) and FBG combination to enhance extended, accurate transmission with a wide bandwidth. Operating at a 50 GHz carrier frequency and 10 Gbps data rate, the proposed 16-channel WDM-RoF system aims to optimize Optical Signal to Noise Ratio (OSNR) and Signal to Noise Ratio (SNR) for efficient fiber optic cable data transmission. This model achieves a minimum OSNR of 21.01 dB and an SNR of 18.88 dB when channel spacing is kept at 0.5 nm. A comparative exploration highlights that the proposed model outperforms existing systems, demonstrating higher transmission efficiency up to 340 km, making it a versatile solution for a wide range of communication applications. © Grenze Scientific Society, 2024.

关键词： Erbium doped fiber amplifiers

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Assessing the Accuracy of Regression and Classification Models in Air Quality Prediction 5

Assessing the Accuracy of Regression and Classification Mode...

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5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Vishwakarma, Harsh Kumar Guru Prasad, M.S. Mittal, Vanshika Mittal, Mudit Naik, PavanKumar Chandrappa, S. Dept. of Computer Science and Engg. Dehradun India School of Computing and Information Technology Reva University Bangalore India Dept. of Computer Science and Engg. Bengaluru India

ISBN: (纸本)9798350356236

This research paper addresses the pressing issue of air quality monitoring and prediction using machine learning algorithms. With a dataset comprising pollutant levels from various states, we employed a comprehensive approach to data analysis, visualization, and preprocessing. The study aimed to predict the Air Quality Index related to key pollutants such as SO2, NO2, RSPM, and SPM. We utilized linear regression, decision tree regression, and random forest regression for prediction tasks and logistic regression, decision tree classification, random forest classification, and k-nearest neighbors for classification tasks. The proposed method was evaluated using different performance matrixes for regression and classification tasks, respectively. The results demonstrated the usefulness of the proposed models in predicting and classifying air quality levels. The proposed model provides a robust framework for air quality management and can be a valuable tool for environmental monitoring and policymaking. © 2025 IEEE.

关键词： Random forests

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Performance Analysis of 5G NR Modulation Techniques 15

Performance Analysis of 5G NR Modulation Techniques

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15th international conference on advances in computing, Control, and Telecommunication technologies, ACT 2024

作者： Noliya, Amandeep Kumar, Dharmender Ankush Kumar, Kulbir Department of Computer Science & Engineering Guru Jambheshwar University of Science & Technology Hisar India

ISBN: (纸本)9798331300579

The rapid proliferation of wireless communication technologies in recent years has driven the need for advanced modulation techniques to meet the increasing demands for higher data rates, lower latency, and enhanced spectral efficiency. In the context of 5G and beyond, several innovative modulation schemes have emerged as promising candidates to address these challenges. This research paper presents a comprehensive performance analysis of five prominent 5G modulation techniques: Weighted Overlap and Add (WOLA), Filter Bank Multi-Carrier (FBMC), Orthogonal Frequency Division Multiplexing (OFDM), Filtered-OFDM (F-OFDM), and Universal Filtered Multi-Carrier (UFMC).This research paper offers a comprehensive and comparative evaluation of WOLA, FBMC, OFDM, F-OFDM, and UFMC modulation techniques, shedding light on their performance characteristics and aiding network designers and operators in making informed decisions for the deployment of 5G systems tailored to specific communication requirements. These findings are expected to be instrumental in shaping the future of wireless communication technologies as we move further into the era of 5G and beyond. © Grenze Scientific Society, 2024.

关键词： Orthogonal frequency division multiplexing

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Transforming High-Frequency Trading Predictions with CNN-LSTM Networks 5

Transforming High-Frequency Trading Predictions with CNN-LST...

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5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Priya, S. Baghavathi Priyanga, S. Kamisetty, Sri Surya Shobith Neelapala, Venkata Sai Vinay Nithila, G. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Chennai India Vellore Institute of Technology Department of Electronics & Communication Engineering Vellore India Vellore Institute of technology Department of Computer Science & Engineering Vellore India

ISBN: (纸本)9798350356236

High-Frequency Trading (HFT) has transformed financial markets by enabling rapid and automated trading. However, the prediction of critical market metrics, such as trading volumes, remains a challenge due to the noisy and complex nature of financial data. This paper presents a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model designed specifically for predicting trading volumes in the NIFTY50 index using high-resolution, minutelevel Yahoo Finance data. By combining CNN's spatial feature extraction capabilities with LSTM's temporal modeling strengths, the proposed approach effectively captures the intricate patterns and sequential dependencies inherent in high-frequency trading datasets. Comprehensive data preprocessing, including noise reduction and temporal segmentation, coupled with advanced cross-validation techniques, ensures robust performance across market conditions. Comprehensive evaluations using metrics such as Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2) demonstrate the model's efficacy in capturing intricate financial dynamics. This work underscores the potential of hybrid deep learning models in enhancing predictive analytics for high-frequency trading applications in emerging markets. © 2025 IEEE.

关键词： Deep learning

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