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Multiple Cancer Prediction with Image Analysis and Machine Learning 5

Multiple Cancer Prediction with Image Analysis and Machine L...

引用

5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Nithya Sree, N. Ramyadevi, R. Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

Cancer, the second biggest cause of death globally, was recently discovered to pose a risk sickness for humans, despite early discovery not doing much to keep people from dying. Thus, efforts to give an ecologically friendly design with proven cancer-prevention estimations and means during the preliminary stages identification of cancer are critically needed. The growth of learning machine procedures have gotten better the cancer diagnosis sector by beating people in terms of both effectiveness and mistake rate. During the previous period, there certainly has seen a massive shift in the learning from machines supported tactics for separating and identifying separate cancerous tumors. This paper gives an outline of numerous forms of cancer detection processes that utilize learning algorithms alongside deep neural-based computers, in addition to a multitude of strategies, to process diverse data types. for finding illustrates and typical datasets have been employed in recent study. The objective of this endeavor is to put into practice machines instruction and algorithms that use deep learning to assess, rate, and fix the current improvements in cancer diagnosis and recognition for four types of cancer: brain, lung, kidney, and oral cancer. Various new methods were combined, and the results were examined using important performance measures such as accuracy, area under the curve, precision, sensitivity, and Dice score against standard datasets. These results will be discussed in future works. © 2024 IEEE.

关键词： cancer deep learning image processing oncology

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Software Component Update for IoT Systems 11

Software Component Update for IoT Systems

引用

11th International Conference on Internet of Things: Systems, Management and Security, IOTSMS 2024

作者： Nordahl, Mattias Åkesson, Alfred Johnsson, Björn A. Hedin, Görel Magnusson, Boris Lund University Dept. of Computer Science Sweden

ISBN: (纸本)9798350366501

Frequent updates in IoT software are crucial for fixing security vulnerabilities, correcting bugs, and adding new features. However, for systems comprising geographically distributed devices, implementing updates is challenging. Such updates must be coordinated across multiple devices, automated without end-user involvement, adaptable to weak connectivity, and minimally disruptive to end users. In this paper we introduce an update method that addresses these challenges. Our approach utilizes a versioned, component-oriented software model for light-weight microservices, alongside a domain-specific language for specifying configurations, facilitating updates of individual components. We evaluate the effectiveness of this method within a Hospital-at-Home system, including servers, tablets for nurses and patients, and medical equipment located in patients' homes. Our results demonstrate that this method offers substantial flexibility and significantly reduces total update time, switchover time, and network load compared to previous methods. © 2024 IEEE.

关键词： component-oriented software configuration language eHealth IoT microservices software updates

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What Is Needed for Intra-document Disambiguation of Math Identifiers? 30

What Is Needed for Intra-document Disambiguation of Math Ide...

引用

Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Asakura, Takuto Miyao, Yusuke Dept. of Computer Science The University of Tokyo Japan

ISBN: (纸本)9782493814104

In automated scientific document analysis, accurately interpreting math formulae is imperative alongside comprehending natural language. Ambiguity in math identifiers within a single document poses significant challenges to understanding math formulae. While disambiguating math identifiers across documents has seen some progress, resolving ambiguity within a document remains inadequately researched due to complexity and insufficient datasets. The level of difficulty and information required to accomplish this task was uncertain. This study aims to determine which information is necessary for the intra-document disambiguation of math identifiers. Our findings indicate that the position data and local formula structure surrounding the identifiers, including modifiers, are particularly critical. For our study, we expanded a dataset for formula grounding and doubled its size to include annotations for 27,655 math identifier occurrences. We have created a multi-layer perceptron model that performs similarly to humans, with an 85% accuracy and a kappa value of 0.73, outperforming rule-based baselines. We trained and evaluated the model with papers in natural language processing (NLP). Our findings were also confirmed valid in fields other than NLP by applying the trained models to papers from various fields. These results will aid in improving mathematical language processing, such as mathematical information retrieval. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Information retrieval

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Time Series Forecasting of Sales data using Hybrid Analysis 5

Time Series Forecasting of Sales Data using Hybrid Analysis

引用

5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Rajasree, T. Ramyadevi, R. Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

Forecasting of sales data based on time for businesses and organizations is absolutely an efficient thing to be done for the successful market trend analysis, and it will be an added advantage for the manufacturing sector to meet the business needs for an organization. Knowing about the future trends will be a crucial thing for sales and production, hence sales forecasting plays a vital role in achieving higher sales rate and thereby improving the efficiency of the businesses. Using machine learning methods like random forest, linear regression, XG-boosting, K Neighbors regression model, and Keras deep neural network regressor to predict sales for future demands with precision. This Forecasting approach is based on the product attributes, customer information, and various necessary parameters such as data related to product sales, past sales performance, customer demands according to time considering all these factors the analysis were done in the trained data. © 2024 IEEE.

关键词： Accuracy prediction Comparison Customer Needs data analysis Machine learning methods Market Trend Analysis Model analysis Sales forecasting

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Construction and Enhancement of an RNA-Based Knowledge Graph for Discovering New RNA Drugs 40

Construction and Enhancement of an RNA-Based Knowledge Graph...

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Cavalleri, Emanuele Mesiti, Marco AnacletoLab Dept. of Computer Science Milan Italy

ISBN: (纸本)9798350317152

Cutting-edge technologies in RNA biology are pushing the study of fundamental biological processes and human diseases and accelerate the development of new drugs tailored to the patient's biomolecular characteristics. Even if many structured and unstructured data sources report the interaction among different RNA molecules and some other biomedical entities (e.g., drugs, diseases, genes), we still lack a comprehensive and well-described RNA-centered Knowledge Graph (KG) that contains such information and sophisticated services that support the user in its creation, maintenance, and enhancement. This PhD project aims to create a biomedical KG (named RNA-KG) to represent, and eventually infer, biological, experimentally validated interactions between different RNA molecules. We also wish to enhance the KG content and develop sophisticated services designed ad-hoc to support the user in predicting uncovered relationships and identifying new RNA-based drugs. Services will rely on deep learning methods that consider the heterogeneity of the graph and the presence of an ontology that describes the possible relationships existing among the involved entities. Moreover, we will consider Large Language Models (LLMs) in combination with RNA-KG for interacting with the user with the ground truth information contained in our KG for extracting relationships from unstructured data sources. © 2024 IEEE.

关键词： RNA

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DecMEN: Scalable Measurement and Impairment Framework for Network Characterization in 5G(+)

DecMEN: Scalable Measurement and Impairment Framework for Ne...

引用

2024 IEEE International Performance, Computing, and Communications Conference, IPCCC 2024

作者： Mondal, Niladri Niggemeyer, Mario Welzel, Simon Klingler, Florian TU Ilmenau Dept. of Computer Science Germany

ISBN: (纸本)9798350367942

The fifth generation (5G) is the current state-of-the-art mobile communication system that has features for ensuring reliability and throughput being key ingredients for many application domains. A major distinction of applications using cellular networks is that they employ different requirements regarding the QoS, e.g., latency and data rates for communication. When adapting network protocol to the underlying communication system for increasing the reliability of applications, reproducible tests and measurements of network characteristics are of utmost importance to streamline a systematic development process of those applications. In this paper, we present a framework called Decentralized Measurement and Emulation of Networks (DecMEN) which is a versatile, decentralized and scalable tool for reproducible measurement and emulation of network characteristics of 5G and beyond networks. Along presenting a novel architecture for scalable measurements and network emulation, we perform a feasibility study on a private 5G campus network to showcase our developed framework in a real world setting. To assess the performance, we empirically evaluate the results of measurements of our 5G network and the emulation of network characteristics to observe differences of important networking metrics consisting of communication latency, and data rate. Our study reveals promising results, where the evaluation shows a good match of network characteristics between the real 5G network under test and the emulated 5G network. © 2024 IEEE.

关键词： 5G mobile communication systems

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A Semi-Supervised Approach For Detecting Agricultural Land Reduction In Bangladesh Using Satellite Images

A Semi-Supervised Approach For Detecting Agricultural Land R...

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

作者： Shafin, Wahid Mahmood Ahsan, Sk. Md. Masudul Dept. of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798331529765

Recently, remote sensing change detection has significantly improved because of greater access to global satellite images and developments in satellite technology. Several CNN-based techniques have demonstrated exceptional precision. Detecting the amount of agricultural area that has been reduced is a type of change detection problem that can easily be solved using deep learning methods on satellite data. But the majority of deep learning methods have been specifically created for high-resolution images. This data lacks global coverage, consists of at most 3 to 4 bands, and is costly. The Sentinel-2 satellite, which was launched in 2015, offers medium-resolution multi-spectral image data consisting of 14 bands and provides free global coverage. Having more bands enhances the capabilities for model analysis. But there is a lack of labeled satellite data. This research proposes a semi-supervised method for generating ground truth by employing a multi-step procedure. This technique began with semi-supervised classification to label the images, followed by a sequence of refinement processes to enhance the accuracy of the ground truth mask. The mask was created using the XGBoost machine learning technique. The dataset was assessed using the output images generated by the trained model. Detecting changes in low-resolution image may be difficult, but proposed method will greatly streamline the procedure. Applying this approach revealed an approximately 8.35% decrease in agricultural land area inside the test image area between 2018 and 2022. In the test area of 237.77 square kilometers, the reduced area measures approximately 19.861 square kilometers. © 2024 IEEE.

关键词： Self-supervised learning

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NLP based Text Summarization Techniques for News Articles 2

NLP based Text Summarization Techniques for News Articles

引用

2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Kumaran, B. Logesh Ravindran, N.V. Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350360523

This defines a news summarization web application using Streamlit, allowing users to access and read news articles from various sources. Here's an abstract of what the project does the necessary Python libraries are imported, including Streamlit for the web application, BeautifulSoup for web scraping, PIL for image processing, and Newspaper for article extraction. Three functions are defined for fetching news based on different criteria: fetch-news-search-topic for searching news based on a topic, fetch-top-news for fetching the top news, and fetch-category-news for fetching news based on a specific category. A function, fetch-news-poster, is defined to fetch and display the image associated with each news article. The display-news function is defined to present the news articles on the Streamlit web application, along with their associated images, summaries, and publication dates. The run function configures the Streamlit application layout and functionality, including options for selecting news categories, displaying trending news, choosing favorite topics, and searching for specific topics. The application displays a title, logo, and various news categories, allowing users to select their preferred category and view the corresponding news articles. Overall, this application provides a convenient way for users to access and read summarized news articles based on their preferences. © 2024 IEEE.

关键词： Image processing

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Evaluating the Impact of ChatGPT on Exercises of a Software Security Course 17

Evaluating the Impact of ChatGPT on Exercises of a Software ...

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17th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, ESEM 2023

作者： Li, Jingyue Meland, Per Hakon Notland, Jakob Svennevik Storhaug, Andre Tysse, Jostein Hjortland Dept. of Computer Science Trondheim Norway Ntnu Dept. of Computer Science Trondheim Norway

ISBN: (纸本)9781665452236

Along with the development of large language models (LLMs), e.g., ChatGPT, many existing approaches and tools for software security are changing. It is, therefore, essential to understand how security-aware these models are and how these models impact software security practices and education. In exercises of a software security course at our university, we ask students to identify and fix vulnerabilities we insert in a web application using state-of-the-art tools. After ChatGPT, especially the GPT-4 version of the model, we want to know how the students can possibly use ChatGPT to complete the exercise tasks. We input the vulnerable code to ChatGPT and measure its accuracy in vulnerability identification and fixing. In addition, we investigated whether ChatGPT can provide a proper source of information to support its outputs. Results show that ChatGPT can identify 20 of the 28 vulnerabilities we inserted in the web application in a white-box setting, reported three false positives, and found four extra vulnerabilities beyond the ones we inserted. ChatGPT makes nine satisfactory penetration testing and fixing recommendations for the ten vulnerabilities we want students to fix and can often point to related sources of information. © 2023 IEEE.

关键词： Students

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Enhancing Academic Feedback Systems with Deep Learning and Ensemble Methods 1

Enhancing Academic Feedback Systems with Deep Learning and E...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Sharma, Hemant Joshi, Hiren Gujarat University Dept. Computer Science Ahmedabad India

ISBN: (纸本)9798331518578

This paper attempts to improve the academic feedback systems via complex deep learning and ensemble methods. Leveraging the NIRF Rankings 2022 dataset that provides radical performance metrics of Indian academic institutions, the project would attempt to develop trustworthy models to monitor and facilitate improvement in the academic feedback system. The techniques used in data preparation prevent missing values, outliers, and categorical data in using advanced machine learning models. In order to construct the model and capture fine-grained patterns linked to academic achievement, 1D Convolutional Neural Networks (CNNs) along with Multi-Layer Perceptrons (MLPs), both individually and in combination, are employed extrapolated from the National Institutional Ranking Framework to obtain data. Precision, accuracy, recall, along with the F1-score will be used to evaluate these models' performance. By the time these findings are finished, they should be able to provide practical advice on how to improve academic evaluations' effectiveness and advance educational institutions' more flexible feedback systems. © 2025 IEEE.

关键词： Contrastive Learning

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