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共 35541 条记录，以下是31-40 订阅

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Prediction and Classification of Brain Tumor using Modified Transfer Learning 18

Prediction and Classification of Brain Tumor using Modified ...

引用

18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Roy, Sukanya Mandal, Bijoy Kumar Khamaru, Koustov Bhattacharya, Arghya University of Engineering and Management Dept. of Computer Science Engineering Kolkata India Nshm Knowledge Campus Dept. of Computer Science Engineering Durgapur India

ISBN: (纸本)9789380544519

This study uses a novel method of segmenting and computing the tissue area in brain MRI images to predict the presence of brain tumors and classify the type of tumor into common types (pituitary, meningioma, and glioma). The tissues that make up the brain have intricate boundaries. In order to more accurately classify the different types of tumors, we have thus divided the brain tissues into three segments and examined for enlarged segments using Convolutional Neural Networks (CNNs) and a Modified CNN that uses transfer learning and VGGNet. The paper's methodology involves using CNNs with Transfer Learning for efficient image segmentation and clustering, followed by VGGNet for noise reduction and image segmentation refining. We are optimizing the internal parameters of VGGNet in order to outperform the competition. The volume and density of malignant clusters are computed through pixel counting. This kind of method works pretty well for figuring out tissue volume in a wide range of illnesses that affect people of all ages. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Brain Tumor Glioma Image-segementation Meningioma Neural Networks. Image classification Pituitary ResNet Transfer Learning VGGNet

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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...

引用

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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Quantum Network Routing with Heterogeneous Nodes 17

Quantum Network Routing with Heterogeneous Nodes

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Pandey, Anoop Kumar Tamma, Bheemarjuna Reddy Rao, M.V. Panduranga IIT Hyderabad Dept. of Computer Science and Engineering India

ISBN: (纸本)9798331531195

Deciding a route for the transfer of quantum information (namely, qubits) on a quantum network is a significant problem that is being studied extensively. In this work, we investigate the problem of routing on a quantum network of heterogeneous nodes both with and without fidelity and timing *** propose a Path Request Packet (PRP) based routing scheme for dynamically selecting a path that maximizes the overall probability of transmission success while maintaining fidelity above a threshold and adhering to time constraints. While the proposed scheme can perform suboptimally under fidelity and timing constraints, we discuss a straightforward proof of optimality in the absence of these constraints. Simulations on random graphs demonstrate that the PRP-based scheme performs better, both in terms of reliability and fidelity, than a non-trivial baseline routing algorithm. © 2025 IEEE.

关键词： Quantum electronics

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Machine Learning Techniques for Predicting Ovarian Cancer in its Early Stages Using Biomarkers 6

Machine Learning Techniques for Predicting Ovarian Cancer in...

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6th International Conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Alam, Mir Jafikul Anawar, Jannat-E Mohi Uddin, Khandaker Mohammad Rahman, Md. Hasibur Rahman, Md. Mahbubur Dhaka International University Dept. of Computer Science and Engineering Dhaka Bangladesh Southeast University Dept. of Computer Science and Engineering Dhaka Bangladesh Bangladesh University of Business and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350385779

The deadliest gynecological cancer affecting women is ovarian cancer, currently incurable with no effective medication treatments. The key focus of this research is to assess insights for early diagnosis using statistical analysis and machine learning techniques on data from clinical trials obtained from 349 patients. Several techniques, including Random Forest, Decision Tree, Gaussian NB, AdaBoost, and Logistic regression, were applied to find the most reliable factor for ovarian cancer prediction. A clinically evaluated raw dataset of benign samples and malignant ovarian tumor patient data set is used to develop early-stage ovarian cancer predictions, and the effectiveness of ML models was examined utilizing metrics including F1-score, Accuracy, Precision, and Recall. The proposed study shows better outcomes, with the Random Forest classifier exhibiting the highest accuracy for validation at 99% based on the test data of ovarian cancer predictions. Even though early-stage ovarian cancer detection is generally unavailable, cancer diagnosis may be greatly aided by machine learning. © 2024 IEEE.

关键词： Machine learning

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Digital Solutions for Crime control: A Comprehensive Criminal Identification and Reporting Framework 11

Digital Solutions for Crime Control: A Comprehensive Crimina...

引用

11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Harinee, S. Anand, M. Vijay Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350350357

This paper presents the design and implementation of a comprehensive Criminal Identification and Reporting System (CIRS) aimed at enhancing law enforcement capabilities and improving public safety. The system enables users, including victims and law enforcement officers, to submit and manage complaints seamlessly through an intuitive online platform. Key features include the storage of user data in a secure database, real-time monitoring of criminal activity using CCTV footage, and advanced facial recognition technology for swift identification of potential offenders. The CIRS facilitates efficient case management by allowing users to track the status of their complaints and providing law enforcement agencies with valuable information to aid in criminal investigations. Moreover, the system assists local government agencies in maintaining records of known criminals, enabling proactive measures to prevent illegal activities. By leveraging cutting-edge technology and providing seamless integration with existing surveillance infrastructure, the CIRS promises to revolutionize criminal identification and reporting processes, ultimately contributing to improved public safety and law enforcement efficiency. © 2024 IEEE.

关键词： Face recognition

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AAF-SCM: An Authenticated Framework for Supply Chain Management

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

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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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A Blockchain-Based Approach for Detecting Counterfeit Pharmaceuticals 15

A Blockchain-Based Approach for Detecting Counterfeit Pharma...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Afrin, Farhina Laboni, Marfi Akter Rimi, Iffat Firozy Rahman, Md Sadekur Habib, Md. Tarek Dept. of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Dept. of Computer Science and Engineering Independent University Dhaka Bangladesh

ISBN: (纸本)9798350370249

Nowadays, the contemplations for improving the item circulation supply structure have been expanded around the world. Simultaneously, it acquires different complexities in the inventory network and things get a lot defiled inside the production network at the hour of cataclysms. For example, alongside the false quality of items, costs to get a colossal illicit raise and unlawful cruel amassing happen to exacerbate things. Henceforth, conveyance costs are present to be uncovered and item data is to be imparted to purchasers. As it is now referenced, dispersion channels are muddled and supply chain management (SCM) is self-sufficiently accomplished in a freeway. For common sense reasons, it has been consistently called attention to that the circulation system isn't straightforward and the dissemination edge is quite high. The motivation behind this examination is to structure a framework that vows the straightforwardness of the item dispersion structure by blockchain on inventory network the board frameworks. This methodology permits organizations just like any purchasers to follow their exchanges, items by upgrading straightforwardness in the Supply Chain Management, accordingly cursing down organizations and gobetweens from selling counterfeit drugs. ©2024 IEEE.

关键词： Blockchain

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Using Ant Colony Optimised Algorithms for Shortest Path Exploration by Robots 2

Using Ant Colony Optimised Algorithms for Shortest Path Expl...

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

作者： Singh, Aashdeep Dhawan, Sakshi Verma, Jyoti Singh, Gurpreet Kaur, Amanpreet Dept. of Computer Science & Engineering Punjab Rajpura India Dept. of Computer Application Punjab Rajpura India Chitkara University Institute of Engineering & Technology Dept. of Computer Science & Engineering Punjab Rajpura India

ISBN: (纸本)9798350360523

In the realm of robotics and autonomous systems, efficient path planning is a critical aspect for optimizing resource utilization and achieving mission objectives. This study explores the application of Ant Colony Optimized (ACO) algorithms to enhance the efficiency of robots in navigating and exploring unknown environments. Inspired by the foraging behavior of ants, ACO algorithms are employed to find the shortest paths by leveraging pheromone communication and decentralized decision-making. Ant Colony Optimization is then introduced as a bio-inspired solution to address these challenges. The experimental results demonstrate the capability of the ACO algorithm to efficiently explore complex environments, adapt to changes in the surroundings, and converge to optimal paths. Insights gained from these analyses contribute to the refinement of the ACO algorithm for diverse robotic applications. The findings of this research have practical implications for the development of autonomous robots operating in real-world scenarios, such as search and rescue missions, environmental monitoring, and surveillance. This study provides a valuable foundation for the integration of bio-inspired algorithms in robotics, advancing the state-of-the-art in autonomous exploration and path planning. © 2024 IEEE.

关键词： Motion planning

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Breaking Barriers: Investigating the Sense of Belonging Among Women and Non-Binary Students in Software engineering 46

Breaking Barriers: Investigating the Sense of Belonging Amon...

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46th International Conference on Software engineering: Software engineering Education and Training, ICSE-SEET 2024

作者： Boman, Lina Andersson, Jonatan de Oliveira Neto, Francisco Gomes University of Gothenburg Dept. of Computer Science and Engineering Gothenburg Sweden Chalmers and the University of Gothenburg Dept. of Computer Science and Engineering Gothenburg Sweden

ISBN: (纸本)9798400704987

Women in computing were among the first programmers in the early 20th century and were substantial contributors to the industry. Today, men dominate the software engineering industry. Research and data show that women are far less likely to pursue a career in this industry, and those that do are less likely than men to stay in it. Reasons for women and other underrepresented minorities to leave the industry are a lack of opportunities for growth and advancement, unfair treatment and workplace culture. This research explores how the potential to cultivate or uphold an industry unfavourable to women and non-binary individuals manifests in software engineering education at the university level. For this purpose, the study includes surveys and interviews. We use gender name perception as a survey instrument, and the results show small differences in perceptions of software engineering students based on their gender. Particularly, the survey respondents anchor the values of the male software engineer (Hans) to a variety of technical and non-technical skills, while the same description for a female software engineer (Hanna) is anchored mainly by her managerial skills. With interviews with women and non-binary students, we gain insight on the main barriers to their sense of ambient belonging. The collected data shows that some known barriers from the literature such as tokenism, and stereotype threat, do still exist. However, we find positive factors such as role models and encouragement that strengthen the sense of belonging among these students. © 2024 Copyright held by the owner/author(s).

关键词： Students

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