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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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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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Evaluating 10Gbps Wireless Optical Communication Using NRZ Modulation: Analyzing BER, Signal Quality, and System Performance 7

Evaluating 10Gbps Wireless Optical Communication Using NRZ M...

引用

7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Khant, Shailesh Makwana, Mayur Patel, Atul Faculty of Computer Science and Applications Charusat Changa Anand India Dept. Electronics and Communication Engineering Charusat Changa Anand India

ISBN: (纸本)9798350350067

Free space optical (FSO) communication is a fast and wireless data transfer technique that serves as a viable alternative to traditional fiber-optic systems. This study illustrates how FSO can effectively address the growing demand for rapid data transmission and suggests its potential applicability in various scenarios. FSO can be employed to establish connections between buildings within a corporate campus without the need for physical wiring, or it can provide high-speed internet access in locations where installing cables is challenging. We used Optsim software to assess critical parameters such as Bit Error Rate (BER), eye diagrams, power, and signal quality. The results offer valuable insights into enhancing the efficiency and reliability of FSO systems. FSO can find applications in cellular networks to interconnect network equipment, as well as in high-definition video surveillance. These findings highlight the areas in which FSO can advance communication technology and provide valuable insights for future developments. © 2024 IEEE.

关键词： Bit error rate

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Comparison and characterization of some selected models on the basis of their suitability for various applications 7

Comparison and characterization of some selected models on t...

引用

7th International Conference on Image Information Processing, ICIIP 2023

作者： Gupta, Kirti Kumar, Pardeep Upadhyaya, Shuchita Kurukshetra University Dept. of Computer Science & Applications Kurukshetra India

ISBN: (纸本)9798350371406

One of the most important aspects of data forecasting is selecting the most promising algorithm to model and predict a specific phenomenon. As with other data mining jobs, forecasting (or prediction) selects the optimal model for a given problem based on empirical evidence because there is no one best modelling technique. Our careful examination of the last ten years' worth of literature indicates that, nevertheless, very few scholarly works thoroughly outline the advantages and drawbacks of the most widely used prediction algorithms. However, these models have a poor track record of success when used on intricate and highly nonlinear data. In this article, we summarise a number of lessons learnt and offer suggestions regarding the benefits, limitations, and ideal circumstances under which to apply each model. © 2023 IEEE.

关键词： Forecasting

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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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An Evaluation and Comparison for Phishing Attack Detection using Machine Learning Approaches 3

An Evaluation and Comparison for Phishing Attack Detection u...

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3rd International Conference on Power Electronics and IoT applications in Renewable Energy and its Control, PARC 2024

作者： Sharma, Ajeet Kumar Anushree Rakesh, Nitin Verma, Pawan Kumar Sharda University School of Engineering and Technology Dept. of Computer Science & Engineering Uttar Pradesh Greater Noida India GLA University Dept. of Computer Engineering & Applications Uttar Pradesh Mathura India Nagpur Campus Pune India

ISBN: (纸本)9798350309409

The persistent and evolving threat of phishing attacks demands effective and adaptive detection techniques. This research paper presents a comprehensive evaluation and comparison of various machine learning approaches to detect phishing attacks. We investigated five prominent algorithms: Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), Naive Bayes, and Extreme Gradient Boosting (XGBoost), to determine their efficacy in identifying phishing activities. Our methodology involved a systematic analysis using a large dataset of phishing and legitimate URLs, where each model was trained, tested, and validated to ensure robustness and reliability. The performance of each algorithm was assessed based on accuracy, precision, recall, and F1 score. Among the evaluated models, XGBoost demonstrated superior performance, achieving an exceptional accuracy of 99.75%. This result underscores the potential of XGBoost in phishing attack detection, offering a promising tool for cybersecurity applications. © 2024 IEEE.

关键词： Cybersecurity

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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...

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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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