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共 36226 条记录，以下是551-560 订阅

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Real-time Event Streaming for Financial Enterprise System with Kafka 3

Real-time Event Streaming for Financial Enterprise System wi...

引用

3rd IEEE Asian Conference on Innovation in Technology, ASIANCON 2023

作者： Sanjana, N. Raj, Sinchana Vishalakshi Prabhu, H. Sandhya, S. Rv College of Engineering Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350302288

Real-time event streaming refers to the continuous and immediate processing and analysis of events as they occur in real time. Presently, Apache Kafka is a very popular and preferred framework for real-time data streaming. Data stream pipelines to serve enormous data for large organizations are built between systems in Kafka for real-time streaming. Kafka provides a distributed, scalable, and fault-tolerant platform for capturing, storing, and processing streams of events. It provisions seamless integration with different systems and applications, enabling real-time data analysis and processing. Thus, event streaming with Kafka is a modern approach to processing and analyzing large volumes of data in real-time. Event streaming with Kafka has a wide range of applications in varied industries such as finance, e-commerce, and more. It has the potential to transform the way businesses operate by enabling faster decision-making, improved operational efficiency, and better customer experiences. With financial enterprise application as our main goal, we focus on how Kafka could be a better-suited alternative to enhance event streaming. © 2023 IEEE.

关键词： Decision making

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Deep Analysis of Machine Learning Techniques for the Effective Retrieval of Images Based on Content 2

Deep Analysis of Machine Learning Techniques for the Effecti...

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Jain, Ashish Varshney, Sudeep Set Sharda University Dept. of Computer Science and Engineering Greater Noida India G L Bajaj Institute of Technology and Management Dept. of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350371055

Our research aims to investigate how machine learning techniques can be applied for efficient image retrieval based on content. It is a very difficult process to obtain accurate images from vast digital image collections. CBIR has been particularly interested in this area. It has been shown that content- or feature-based approaches are more effective than text-based query techniques. Research has shown that the performance of these methods varies depending on the type and size of the data set analyzed, with some algorithms performing well for specific small data sets and others for larger data sets. This article provides a comprehensive and systematic review of effective methods for obtaining the image. The paper's objective is to examine different ML classifiers that are applied via the researchers and compare their results based on accuracy and performance. These algorithms include SVM, KNN, random forest, BoVW, Naive Bayes, and LDA. This review paper can assist researchers in selecting the best classifier for their research, as well as in improving existing classifiers by combining them with other algorithms. © 2024 IEEE.

关键词： CBIR classifier feature extraction Random Forest SVM

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A Lightweight-CNN Model for Efficient Lung Cancer Detection and Grad-CAM Visualization 2

A Lightweight-CNN Model for Efficient Lung Cancer Detection ...

引用

2nd International Conference on Information and Communication Technology for Sustainable Development, ICICT4SD 2023

作者： Tasmim Bakchy, Sagor Chandro Peyal, Hasibul Islam Islam, Md. Injamul Yeamin, Golam Kibria Miraz, Shafiun Abdal, Md. Nazmul Rajshahi University of Engineering & Technology Dept. of Electrical & Computer Engineering Rajshahi Bangladesh Uttara University Dept. of Computer Science & Engineering Dhaka Bangladesh Rajshahi University of Engineering & Technology Dept. of Electrical & Electronic Engineering Rajshahi Bangladesh Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350358667

The lungs' abnormal cell growth leads to the development of lung cancer. Early cancer identification could make treatment easier, potentially saving millions of lives annually. This study's main goal is to more rapidly and effectively classify various types of lung cancer by employing a lightweight, computationally efficient convolutional neural network (CNN) model to categorize three different types of lung cancer. With an outstanding validation accuracy of 99.48%, the suggested model surpasses the achievements of previous works. The 15,000 CT scan images in our dataset include three different forms of lung cancer. The proposed model performs exceptionally well, as evidenced by its astounding precision, recall, and F1-score, all above 99%, and by its flawless Area Under Curve (AUC) score of 100%. The proposed model has fewer parameters than the existing transfer learning models. Gradient Weighted Class Activation Mapping (Grad-CAM) was used to create class activation maps, which were then used to create a heatmap to display the classification zone. © 2023 IEEE.

关键词： Neural network models

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Design and Implementation of Smart Classroom Using Cisco Packet Tracer 1

Design and Implementation of Smart Classroom Using Cisco Pac...

引用

1st IEEE International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Duvvuri, Kavya Kanisettypalli, Harshitha Kunisetty, Jaswanth Khanna, Manju Amrita School of Engineering Dept. of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798350348057

Schools and colleges play a very crucial role in everyone's life. It is very important to impart good morals and safe education to future generations. As days go by, the necessity for faster job completion and smarter devices is increasing. The safety of students becomes important as the number of students enrolling in schools is gradually increasing. Discipline and time management skills must be imparted to students from a tender age in the school environment. Both safety and discipline can be ensured by making use of devices that are smarter and remotely controllable. Through this work, we have introduced the design of a smart classroom using a network simulation called Cisco Packet Tracer. The work is based on the concept of the Internet of Things. It has multiple subsystems that together make the class a wonderful place for students to build their future. © 2023 IEEE.

关键词： Students

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JARVIS - Smart AI Support System 3

JARVIS - Smart AI Support System

引用

3rd Odisha International Conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Ramya Manaswi, V. Saketh, Meduri Ravi Tejha, Mandumula Sai Karthikeya, Atluri Sai Prakash, Dharani Chandra Alkhayyat, Ahmad Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Computer Science Engineering Hyderabad India Gitam University Dept. of Computer Science Engineering with Specialisation of Cybersecurity Hyderabad India K.G Reddy College of Engineering and Technology Dept. of Electronics & Communication Engineering Telangana Hyderabad India College of Technical Engineering The Islamic University Dept. of Computers Techniques Engineering Najaf Iraq

ISBN: (纸本)9798350354379

The JARVIS AI Support System represents a remarkable fusion of modern technology, blending a sophisticated GUI design, seamless voice control, and inventive features like the captivating 'Air Canvas' facilitated by OpenCV. This AI-driven virtual assistant offers users a natural and intuitive experience, allowing them to effortlessly perform tasks such as browsing the web, interacting with a chatbot, and executing dynamic voice- controlled actions. Moreover, the system showcases advanced capabilities including motion detection and facial recognition with an accuracy of 95% in multiple runs. Leveraging the power of computer vision, the Air Canvas feature empowers users to express creativity through fluid hand gestures, while voice commands effortlessly manage diverse tasks. This innovative project presents an approachable way to interact with technology in the world of AI. © 2024 IEEE.

关键词： Face recognition

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File Encryption using Noise Images as Key 3

File Encryption using Noise Images as Key

引用

3rd International Conference on Smart Data Intelligence, ICSMDI 2023

作者： Bhagya Lakshmi, A. Pradeep, R. Seshadri, K.S. Shriraam, B. Easwari Engineering College Dept. of Computer Science & Engineering TN India

ISBN: (纸本)9781665464871

Everything in today's highly connected digital world is increasingly dependent on instantaneous global data transfers. Internet efficiency facilitates our daily lives. Sharing information online presents significant security dangers and difficulties in the modern day. The use of cryptography is the means through which sensitive information can be protected from various threats. Improved cryptosystems technology is a no-brainer for securing communication networks. An improved cryptosystem is the focus of our research, and to that end we have presented a novel method of data encryption and decryption that guarantees increased efficiency over current best practises. This research work has proposed a noise image encryption and decryption scheme, wherein the noise signal is randomly selected to set the initial values for a chaotic system which also enhances the security of the system. for understanding the effectiveness of the proposed system. Experimental results confirm that the proposed chaos-based cryptosystem is efficient and suitable for information (image) transmission in a highly secured way. This study has developed a file encryption system, where encrypted file is saved to decrypt and download later. The main difference between the existing and proposed system is the implementation of file encryption system using noise images that store key values inside it. © 2023 IEEE.

关键词： Cryptography

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Diffusion Models for Automatic Music Mixing

Diffusion Models for Automatic Music Mixing

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Wu, Xinyang Horner, Andrew Hong Kong University of Science and Technology Dept. Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350362480

Music mixing is a process that involves fine-tuning the levels, dynamics, and frequency content of musical elements to ensure clarity and harmony in the final music production. In this paper, we present an automatic mixing system based on diffusion models to correct imbalances in music mixes. We manipulate the well mixed stems' short-time Fourier transform randomly to simulate the frequency and level imbalances commonly encountered in real-world scenarios. The difference between the imbalanced mix and the original mix is treated as noise for the diffusion model to predict, enabling the reverse denoising process to generate an automated mix. We evaluate our model's performance by calculating the signal-to-distortion ratio between the original and predicted mixes. These results are compared with baseline automatic mixing models, demonstrating significant improvements. Test set results in audio: https://***/diffmusicmix/ © 2024 IEEE.

关键词： Signal denoising

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Real-Time Flow Scheduling in Industrial 5G New Radio 44

Real-Time Flow Scheduling in Industrial 5G New Radio

引用

44th IEEE Real-Time Systems Symposium, RTSS 2023

作者： Zhang, Tianyu Wang, Jiachen Hu, Xiaobo Sharon Han, Song University of Connecticut Dept. of Computer Science and Engineering United States University of Notre Dame Dept. of Computer Science and Engineering United States

ISBN: (纸本)9798350328578

Among the many industrial wireless solution candidates, 5G New Radio (NR) has drawn significant attention in recent years due to its capabilities to support ultra-high-speed communication, ultra-low latency, and massive connectivity. Despite its great potential, 5G NR also brings significant complexity in scheduling industrial data flows to meet their hard real-time requirements. In this paper, we first leverage a real-world 5G RAN testbed to benchmark the downlink throughput and explore the impact of modulation and coding scheme (MCS) selection on the network performance. We then formulate a real-time flow scheduling problem in industrial 5G NR, which features per-flow real-time schedulability guarantees through time-frequency-space resource allocation. We propose a novel two-phase scheduling framework, named 5G-TPS, to construct the schedule that meets the deadlines of all the flows. To adapt to dynamic channel conditions, 5G-TPS enables online schedule adjustment for affected flows to meet their timing requirements. To evaluate the performance of 5G-TPS, we present a case study of a motion control panel use case and perform extensive experiments. The results show that 5G-TPS can achieve schedulability ratios comparable to the Satisfiability Modulo Theory (SMT)-based exact solution and outperform many other state-of-the-art scheduling approaches, including the built-in 5G NR schedulers. © 2023 IEEE.

关键词： Benchmarking

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A computer science Perspective on Digital Sustainability: A Thematic Literature Review 22

A Computer Science Perspective on Digital Sustainability: A ...

引用

22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Delnevo, Giovanni Olaiya, Kelvin Salomoni, Paola University of Bologna Dept. of Computer Science and Engineering Bologna Italy

ISBN: (纸本)9798331508050

This paper explores the evolving concept of digital sustainability, emphasizing its growing importance as digital technologies become essential to addressing global sustainability challenges. Through a thematic literature review focused on computer science, we analyze papers from Scopus-indexed journals to identify key themes in the discourse on digital sustainability. Additionally, we map the identified topics to the traditional three-pillar model of sustainable development, noting areas of alignment and divergence. The study offers insights into how digital sustainability is framed within the computer science literature and its implications for future research. © 2025 IEEE.

关键词： Green development

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Screening and Ranking resume's using Stacked Model 1

Screening and Ranking resume's using Stacked Model

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1st IEEE International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Kavitha, D. Padmavathi, B. Sushanth, V. Yashwanth, P. Yuvaraj, R. Easwari Engineering College Dept. of Computer Science & Engineering TN India

ISBN: (纸本)9798350348057

Finding the top candidates for a position is the aim of the resume screening process. The application must make use of machine learning methodologies as well as natural language processing to rate candidates in real time. Natural language processing (NLP) and machine learning methods are used to rate resumes. The output would be the resume of a top candidate, with resumes and job descriptions serving as the input. The output's results are obtained instantly. String matching, Cosine Similarity, and TF-IDF will all be done with Mon. Existing systems are straightforward and efficient, but they lack precision, efficiency, and processing capacity. © 2023 IEEE.

关键词： Natural language processing systems

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