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From Pixels to Prognosis: A Deep Dive into Lung Cancer Subtype Classification using Transfer learning 2

From Pixels to Prognosis: A Deep Dive into Lung Cancer Subty...

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2nd IEEE international conference on Contemporary computing and Communications, InC4 2024

作者： Ravindra, Chaya Nalband, Abdul Haq Kumar, Govind Basheer, Salman Ravindra, Manjunath School of Computer Science Engineering REVA University Bengaluru India

ISBN: (纸本)9798350383652

Current diagnostic procedures, such as imaging tests and biopsies, are time intensive and prone to human error. As a result, we employed deep learning to uncover patterns and identify lung cancer from histology pictures. We hypothesised that combining two separate methods (transfer learning and data augmentation) would increase the efficacy of lung cancer detection. The study's dataset consisted of histology images for Lung Cancer and was quite unbalanced, with very few photos containing tumours. We hypothesised that transfer learning, which employs existing knowledge for classification and detection, would be effective on such an imbalanced dataset. To boost our learning, we employed data augmentation to incorporate changes into the dataset. This can be accomplished by implementing Sequential Keras Model Layers. We then tested the efficacy of various convolutional neural network models on this job. We achieved an accuracy of 87.50% utilising the transfer learning model MobileNetV2 as the basis model and numerous geometric changes, beating the cutting-edge convolutional neural network-based technique. We then analysed the results and published them on a website that people can access at any time and from any location. © 2024 IEEE.

关键词： Lung cancer

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Detection and Classification of Type 2 Diabetes using Machine learning Techniques 2

Detection and Classification of Type 2 Diabetes using Machin...

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2nd international conference on Advancements in Electrical, Electronics, Communication, computing and Automation, ICAECA 2023

作者： Keerthana, N. Sri Ramakrishna Engineering College Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350306811

The Type 2 Diabetes has several consequences, one of which is diabetic retinopathy (DR). It typically comes from excessive glucose content in the blood arteries that supply nutrition to the retina, and it can also have many additional negative effects. In this paper, machine learning algorithms like CNN (Convolutional Neural Network), GLCM (Gray Level Co-occurrence Matrix), and Random Forest( RF) are used to predict diabetic retinopathy. In terms of performance metrices like Accuracy, Precision, Recall, and F1-score, the Convolutional Neural Network (CNN) produced the best results. Results generated by the CNN algorithm had a higher accuracy rate of 92%. The Django framework was used to develop the website, which aims to let people submit photographs and classify them into No DR, Mild DR, Moderate DR, Severe DR, and Proliferative DR categories. © 2023 IEEE.

关键词： Convolutional neural networks

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News Article Topic Classification Using Embeddings 14

News Article Topic Classification Using Embeddings

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14th international conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Sandhiya, B. Santhanalakshmi, S. Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798350335095

The rate at which data is produced has increased dramatically in recent days. Without a suitable category or tag, having a lot of data is merely useless information. The consumer may quickly find the content they're looking for and search for older news stories with ease when news items are properly categorized or classified. The classification of News Article Topics into proper categories aids in many tasks such as finding older data in a faster way, useful organization of data, enabling quick fetching of required data, etc. The proposed system analyses the Antonio Gulli's (AG) News Classification Dataset using different embeddings. The proposed model also experiments with different types of Machine learning and Deep learning Classifiers for classifying the embeddings. © 2023 IEEE.

关键词： Classification (of information)

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Breast Cancer Detection with Machine learning-A Review 2

Breast Cancer Detection with Machine Learning-A Review

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2nd international conference on Sustainable computing and Data Communication Systems, ICSCDS 2023

作者： Meghana, Karnam Nandal, Neha Tanwar, Rohit Goel, Lipika Chhabra, Gunjan Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Computer Science and Engineering Hyderabad India University of Petroleum and Energy Studies Dept. of Computer Science and Engineering Dehradun India Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9781665491990

Malignancy in the breast is a significant public health concern, where timely identification is essential for effective treatment. Machine learning (ML) and Deep learning (DL) algorithms are potential tools for prompt detection og breast malignancy through examination of medical images such as mammograms. Convolutional neural networks (CNNs), transfer learning, and ensemble learning are some of the recent techniques being used in this field. Despite the advantages of ML and DL algorithms for breast cancer detection, there are still several challenges that need to be addressed. The lack of diversity in the datasets used to train algorithms is one major challenge, with many datasets based on specific populations that may not represent others. Highly annotated data is also limited in medical field. The objective of this study is to provide researchers with valuable insights and guidance. © 2023 IEEE.

关键词： Convolutional neural networks

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Intrusion detection system using soft computing techniques in 5G communication systems

Intrusion detection system using soft computing techniques i...

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international conference on Artificial Intelligence, Blockchain, computing and Security, ICABCS 2023

作者： Dhanya, D. Shankari Kathir, I. Kuchipudi, Ramu Thamarai, I. Kumar, E. Rajesh Department of Computer Science and Engineering Mar Ephraem College of Engineering and Technology Tamil Nadu Kanyakumari India Department of Computer Science and Engineering RMK College of Engineering and Technology Tamil Nadu Thiruvallur India Department of Electrical and Electronics Engineering V.S.B. Engineering College Tamil Nadu Karur India Department of Information Technology Chaitanya Bharathi Institute of Technology Telangana Hyderabad India Department of Computer Science and Engineering Panimalar Engineering College Chennai City Campus Tamil Nadu Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9781032669663

The research deals with a thorough survey and starts by reviewing the fundamental knowledge of fuzzy systems over 5G communication. Future directions and scope can be used to demonstrate the desire for 5G communication networks to provide access to new applications and widespread connection. Therefore, the difficulties with the underlying technologies and hardware supporting them should allow for a greater level of service quality. The goal of this study is to create an intrusion detection system that employs fuzzy logic. This study proposes a model for a real-time intrusion detection expert system that seeks to find a variety of security breaches, from outsiders trying to break in to insiders breaking in and abusing the system. Analysis has also been done on the impact of changing the learning algorithms and membership function. The structure can manage very sizeable amount of data with the aid of data collection, storage, retrieval, and analysis, and it integrates the big data stream inside the network to enhance performance of the network. © 2024 The Author(s).

关键词： Intrusion detection

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Advancements in Language Processing Algorithms Transforming Linguistic computing

Advancements in Language Processing Algorithms Transforming ...

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2024 international conference on Advances in computing Research on Science engineering and Technology, ACROSET 2024

作者： Mishra, Poonam Pandey, Tushar Kumar Roy, Vandana Kashyap, Ramgopal Chhaya Ahluwalia, Ritu Amity University Raipur India Krishi Vigyan Kendra-Birauli Dr. Rajendra Prasad Central Agricultural University Pusa India DoEC Ggits Jabalpur India Mangalayatan University Jabalpur India Ggits Dep of Cse Jabalpur India

ISBN: (纸本)9798350388800

New approaches that alter linguistic computing have resulted in breakthroughs in natural language processing. New technologies are to blame for these advancements. In this paper, we look at five cutting-edge methods: attention mechanisms, transformer models (like BERT and GPT), transfer learning (like ULMFiT), neural machine translation (NMT), and BERT-based embedded systems for tasks further down the line. All of these methodologies are considered to be at the forefront of their respective fields. When it comes to creating language, understanding language, and comprehending language in context, the integrated methodology is a way that is successful. To study the minute benefits that each technique provides in terms of sentiment analysis, language growth, and interaction between languages, the objective of this in-depth research is to investigate the implications of these advantages. A solution that is superior to others that came before it is produced because of the integration of cutting-edge models. As the field of linguistic computing continues to expand, this guide provides professionals and academics with assistance in making the most of new advancements to improve language processing platforms. © 2024 IEEE.

关键词： Neural machine translation

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Evaluation of learning effect based on data analysis and BOPPPS teaching model 5

Evaluation of learning effect based on data analysis and BOP...

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5th international conference on E-Business, Information Management and Computer Science, EBIMCS 2022

作者： Li, Zhaomin Zhou, Shangheng College of Electronic Information Engineering Wuhan Business University Hubei Wuhan China

ISBN: (纸本)9781450397834

This paper takes the content of "Collaborative Filtering"in "Data Mining"as an example, discusses the method of course teaching design based on the BOPPPS model, puts forward a student-centered teaching design concept, encourages students to learn independently, and fully taps students' abilities. Interactive teaching practice that realizes the needs of learners' personalized learning and realizes the natural transition from the known to the unknown. The results show that the six-link teaching mode of BOPPPS can effectively stimulate students' interest in learning and strengthen their awareness of autonomous learning. Improve the practical ability, thereby greatly improving the teaching effect. CCS CONCEPTS •Applied computing ∼Education ∼Computer-assisted instruction © 2022 ACM.

关键词： Collaborative filtering

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Enhanced Detection of Down Syndrome through Convolutional Neural Networks: A Deep learning Approach 6

Enhanced Detection of Down Syndrome through Convolutional Ne...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Saini, Archana Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331522667

Down syndrome is a chromosomal anomaly caused by having an extra copy of chromosome 21 in millions of people worldwide. Early identification of Down syndrome is critical for delivering prompt therapies and assistance that improve the quality of life for affected persons and their families. In this article, a unique technique for detecting Down syndrome using convolutional neural networks (CNNs) is presented. The solution uses deep learning algorithms to analyze children's facial photos and automatically detect probable signs of Down syndrome. The experimental findings illustrate the efficiency of the proposed CNN-based technique in reliably identifying Down syndrome, with high accuracy, sensitivity, and specificity. In this article, three different batch sizes are applied with epoch 100, and the performance of the model is evaluated. At batch size 8 the loss is 0.3560 and the accuracy is 0.8419. Similarly, at batch size 16, the loss is 0.3420 and the accuracy is 0.8474 and lastly, at epoch 32 the minimum loss is measured as 0.3388, and the maximum accuracy of 0.8501 is achieved. The highest AUC, precision, and recall are also calculated at batch size 32 which is 0.9297, 0.8380, and 0.8670 respectively. © 2025 IEEE.

关键词： Convolutional neural networks

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Precision Forecasting of Stock Prices: Leveraging XGBoost and Technical Indicator for Advanced Predictive Modeling 1

Precision Forecasting of Stock Prices: Leveraging XGBoost an...

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1st international conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Yuvaraj, S. Chenni Kumaran, J. Vinston Raja, R. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology School of Computing Department of Computational Intelligence Kattankulathur Chennai India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529246

The investigation aims to evaluate the performance of machine learning techniques, particularly the XGBoost regression method, for stock price prediction with the help of technical indicators. The research targets the adjusted closing price to reduce the autocorrelation problem in time series. The Five Days and Ten Days Exponential Moving Averages (EMA-5 and EMA-10) are involved in refining feature selection. The performance of the model was evaluated using different error metrics, namely, Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R2). The results indicate that the XGBoost model, combined with EMA-5 and EMA-10, achieves high predictive performance, obtaining an r2 value of 0.98. It implies that incorporating short-term EMAs into advanced machine learning techniques drastically improves stock price forecasting. © 2025 IEEE.

关键词： Time series

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Leveraging Internet of Things (IoT) Sensors and Deep learning Techniques for Precision Agriculture 1

Leveraging Internet of Things (IoT) Sensors and Deep Learnin...

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1st international conference on Innovative engineering Sciences and Technological Research, ICIESTR 2024

作者： Wilfred Blessing, N.R. Pyingkodi, M. Wasim Haidar, S.K. Palarimath, Suresh Paravathavarthini, S. Balakumar, C. Sheeja Kumari, V. Sutherlin Subitha, G. Seldev Christopher, S. University of Technology and Applied Sciences-Ibri College of Computing and Information Sciences Ibri Oman Kongu Engineering College Department of Computer Applications Erode India University of Technology and Applied Sciences-Salalah College of Computing and Information Sciences Salalah Oman Kongu Engineering College Department of Computer Technology Erode India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Saveetha Institute of Medical and Technical Sciences Department of Computational Intelligence Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Aruthenganvilai India St. Xavier's Catholic College of Engineering Department of Computer Science and Engineering Nagercoil India

ISBN: (纸本)9798350348637

Precision agriculture, driven by advancements in technology, aims to optimize farming practices by utilizing data and technology to enhance efficiency, productivity, and sustainability This research introduces an innovative pilot investigation centered on the convergence of two revolutionary technologies are the Internet of Things (IoT) and Deep learning. With a specific focus on advancing precision agriculture, the primary objective of this study is to evaluate the combined effects of data collection facilitated by IoT and analytical capabilities of deep learning in enhancing and optimizing various agricultural processes. The integration of IoT in agriculture has revolutionized data acquisition, employing an array of sensors to monitor critical parameters such as soil moisture, temperature, and crop health. Concurrently, Deep learning, a subset of artificial intelligence, exhibits the potential to glean actionable insights from voluminous datasets, offering advanced analytics and predictive capabilities. This study investigates the practical implementation and efficacy of this integration in a controlled agricultural setting. Sensors strategically positioned in the pilot study capture real-time data, while deep learning algorithms process and analyze this information. The primary objectives include evaluating the effectiveness of this combined technology in optimizing irrigation schedules, predicting crop yields, and identifying anomalies in crop health. Preliminary findings underscore the transformative potential of IoT and Deep learning, empowering farmers with real-time data for informed decision-making. Key considerations encompassed in the study include IoT Sensors, Deep learning algorithms, and user adoption. The research not only sheds light on the technical intricacies of the integration but also delves into the challenges and opportunities inherent in merging these technologies within the agricultural landscape. As agriculture transitions towards the next

关键词： Smart agriculture

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