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intelligent Computing System for Breast Cancer Identification utilizing U-Net for Digital Image Segmentation 3

Intelligent Computing System for Breast Cancer Identificatio...

引用

3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Goli, Ranadheer Reddy Jadala, Vijaya Chandra Jamalpur, Bhavana Department of Computer Science and Artificial Intelligene School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India

ISBN: (纸本)9798331527549

As per WHO calculation of deaths, globally due to Breast Cancer is around one million. Breast cancer can be cured by early detection, in which abnormal breast cells proliferate uncontrollably, leading to tumor formation. Early detection of breast cancer may reduce deaths, as per World Health Organization records there are about one million deaths are caused by 2024 among them only 0.5 percent are of men. The different methodologies adopted in medical image processing related to breast cancer detection methods include mammography. We concentrated on medical image segmentation which helps to get visual information about breast tissue. This paper presents an intelligent computing system designed to support medical image processing through utilizing U-Net methodology for segmentation as part of deep learning methodologies. As part of Experimental Analysis, a BUSI dataset is used for the implementation of Deep learning methodologies. A graphic analysis based on medical images, followed by an analysis of the results. This intelligent Computing System based on artificial intelligence helps radiologists, medical professionals, and doctors with early and simplified breast cancer diagnosis. © 2025 IEEE.

关键词： Breast Cancer intelligent System Segmentation U-Net

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Smartphone Price Prediction and Category Classification using Machine learning 3

Smartphone Price Prediction and Category Classification usin...

引用

3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Agalayil, Helen Terese Radha, D. Kirthika Devi, V.S. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

The rapid evolution of smartphone technology and the diverse range of available models have made selecting a cost-effective mobile phone a complex decision for consumers. Although brand, internal memory, camera quality, battery life, processor speed are all important determinants of purchase decisions, it is frequently unclear how these factors relate to the price. This work explores the application of machine learning algorithms to predict mobile phone prices and their price category classification based on these characteristics, offering insights to aid consumer decision-making. Five classifiers and five regressors were compared using accuracy and time complexity as evaluation metrics and k-NN was found to be the best classifier for the price class classification and LightGBM was found to be the best regressor for price prediction. © 2025 IEEE.

关键词： Smartphones

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Predicting Digital Payment Platforms Adoption by Retail Vendors Using Machine learning Techniques 3

Predicting Digital Payment Platforms Adoption by Retail Vend...

引用

3rd International conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Manjula Devi, P. Vinoth, S. Chinnasamy, Gopalakrishnan Cms Business School Jain Deemed-to-be University Faculty of Management Bengaluru India Jain Deemed-to-be University Faculty of Management Bengaluru India

ISBN: (纸本)9798331515911

The goal of this study is to use machine learning techniques to anticipate retail vendors' adoption of digital payment platforms. Data was collected from 270 merchants throughout 10 major retail markets in Bangalore, with a focus on small and medium-sized businesses. Using multiple regression, exploratory factor analysis, and support vector machines (SVM), the study identifies significant factors driving adoption, including perceived usefulness, social influence, and compatibility. The data show that, while perceived usefulness and social influence are important drivers, other factors such as perceived ease of use and technical support have limited effect. The SVM model was moderately accurate in predicting platform adoption. The report also evaluates customer satisfaction across various digital payment platforms and predicts future trends using ARIMA models. These findings have significant implications for improving digital payment adoption tactics among retailers. © 2025 IEEE.

关键词： Adversarial machine learning

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A Comprehensive Analysis of Deep learning Techniques for Wheat Yellow Rust Disease Detection and Classification 3

A Comprehensive Analysis of Deep Learning Techniques for Whe...

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3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Mishra, Komal Kaur, Kawaljit University Institute of Computing Chandigarh University Punjab India

ISBN: (纸本)9798331527549

Puccinia striiformis f. sp. tritici, the pathogen responsible for yellow rust in wheat, poses a significant threat to global wheat manufacture, causing substantial economic and agricultural losses. The timely and precise detection and classification of yellow rust are dangerous for implementing effective disease management strategies and minimizing crop damage. This review explores the latest advancements in wheat yellow rust detection, focusing on the integration of conventional approaches with cutting-edge machine learning (ML) and deep learning (DL) approaches. ML techniques, such as support vector machines (SVM) and random forests;have proven effective in analyzing spectral data for disease classification. Meanwhile, advanced DL methods, containing ResNet-50 and GhostNet-V2, demonstrated superior accuracy, with models achieving up to 96% in categorizing disease severity. These advancements highlight the transformative potential of DL methods in enhancing precision and competence in disease detection. This review provides a comprehensive evaluation of current methodologies and their outcomes, offering valuable insights for future research and practical applications in agricultural disease management. © 2025 IEEE.

关键词： Convolutional neural networks

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Integrating Artificial Intelligence and Educational Technology for Inclusive and Ethical Education Practices 3

Integrating Artificial Intelligence and Educational Technolo...

引用

3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Ilayaraja, Lathika Yukthika, S.S. Prabakaran, Subramani Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai600069 India

ISBN: (纸本)9798331527549

AI and EdTech are transforming education by making it more accessible, personalized, and assessable, even in low and middle income countries (LMICs) facing resource constraints. However, challenges come with unequal access, issues in data privacy, and bias in AI model creations. As AI continues to become more popular, the issue of whether the work made by the students will be original. This paper focuses on the impact of artificial intelligence on learning in both its ethical and practical dimensions while highlighting the themes of inclusivity, fairness, and authenticity. Guidelines and policies for responsible AI in integration will be proposed further, alongside teacher training. Results should depict that this moral framework, policies, and teacher education should aid in ensuring that the implications of AI are positive and fruitful for learning. Ideal AI guidelines would foster an educational system that is inclusive, transparent, and considers uniqueness and innovation. © 2025 IEEE.

关键词： Federated learning

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Attribute Extraction of Geographic Entities Based on the LERT Pre-trained Model 24

Attribute Extraction of Geographic Entities Based on the LER...

引用

3rd International conference on Artificial Intelligence and intelligent Information Processing, AIIIP 2024

作者： Li, Zeyu North China Institute of Computing Technology Beijing China

ISBN: (纸本)9798400707308

Geographic information systems(GIS) are now changing from two-dimensional expression to three-dimensional expression, and the geographic data gradually transforms into a more intuitive and more applied method of expression. In order to support the data expression in GIS, attributes extraction from unstructured Chinese geographic texts is needed. Manual extraction or pattern-matching methods have been insufficient to get geographic attributes, therefore deep learning methods for attribute extraction are required to meet the needs of the supplementary for entity properties. This paper took LERT for pre-training in the word-embedding layer and the BiLSTM module combined with attention mechanism for the feature extraction layer, finally obtained the attribute-value pairs as the output. The experimental results show that the accuracy, recall rate and F1 value of the proposed model on the test set improved compared with the existing model, proving the effectiveness of the proposed model. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Voice AI-Intelligence Based Voice Assistant 3

Voice AI-Intelligence Based Voice Assistant

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3rd International conference on intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Garai, Pooja Nikam, Aditya Kerkar, Suyash Deshmukh, Samruddhi Mane, Atharva Bhise, Suvarna Vasantdada Patil College Of Engineering & Visual Arts Dept. of Artificial Intelligence & Data Science Mumbai India

ISBN: (纸本)9798331515911

Voice assistant applications have become integral parts of modern technology ecosystems, offering users convenient and efficient ways to interact with their devices. This paper introduces Voice Ai, a versatile voice assistant application developed using Android Studio and Google's Machine learning Kit Machine learning(MLKit) for Optical Character Recognition (OCR) functionalities. The app includes features like setting alarms, making calls, opening applications, and conducting question-answer sessions powered by G4F. A comparative analysis between G4F and Google's backend is performed, assessing their performance and user satisfaction during question-answer interactions. Voice Ai simplifies daily tasks and boosts productivity, providing insights for developers, researchers, and end-users interested in AI-based voice assistant applications. © 2025 IEEE.

关键词： Adversarial machine learning

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Soil Analysis for Crop Recommendation using Machine learning Algorithms 3

Soil Analysis for Crop Recommendation using Machine Learning...

引用

3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Sathya, J. Asha, V. Kalaivani, A. Hanchinal, Sanjeev Sangappa Fathima, Sarana Sachin, K.S. Department of MCA New Horizon College of Engineering Bengaluru India

ISBN: (纸本)9798331527549

In the area of farming, parts of the soil are of special note as it has to do with growing crops and provision of food for consumption. Soil analysis is also important in crop rating since it reflects some important Indices of the soils such as nutrients, pH, and texture which all affect plants’ growing condition. Farmers are able to utilize this strategy in order to plant the right sorts of crops on their area of land, hence gaining scope in the yield and resources consumed. This paper showcases the application of advanced machine learning algorithms to optimize crop recommendations based on essential climatic, geographic, and eco-spatial parameters. In such techniques, depending on humidity, temperature and rainfall, certain patterns and relations are established and the best crops are recommended. For the purpose of generating predictive models that recommend the growth of specific crops in specific regions with the aim of increasing agricultural productivity, the system deploys the algorithms of Gradient Boosting, XGBoost, and LightGBM. © 2025 IEEE.

关键词： Adversarial machine learning

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Deep learning based Brain Tumor Detection using YOLO11n and Segmentation using SAM2 Model 3

Deep Learning based Brain Tumor Detection using YOLO11n and ...

引用

3rd International conference on intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Pasunoori, Krupa Chary Rajendra Prasad, Ch. Raj Kumar, K. Dept. of ECE SR University Warangal506371 India

ISBN: (纸本)9798331527549

The central nervous system (CNS) contains the brain, spinal cord, and it governs all essential functions. These functions encompass cognition, verbal communication, and locomotion. When a tumor develops in the CNS, it can disrupt an individual's cognitive functions, speech, and motor skills. A brain tumor is a condition resulting from the proliferation of atypical cells inside the brain. Any disorder in the brain might be life-threatening. This paper presents deep learning based YOLO11n algorithm for tumour prediction. Then the Segment Any Model2 is employed for tumor segmentation. The proposed model significantly improve that segmentation Dice Score (96.3%) and IOU Score(93.03%) compared with previous models. © 2025 IEEE.

关键词： Deep learning

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The Application of Emotion Recognition Based on Deep learning in Photography Works 24

The Application of Emotion Recognition Based on Deep Learnin...

引用

3rd International conference on Artificial Intelligence and intelligent Information Processing, AIIIP 2024

作者： Ismail, Adzrool Idzwan Bin Wang, Shihui University Utara Malaysia Changloon Kedah Malaysia Qingdao Preschool Education College Shandong Qingdao China

ISBN: (纸本)9798400707308

In order to understand the application of emotion recognition in photography, research on the application of emotion recognition based on deep learning in photography works has been proposed. This article first proposes an improved Word2Vec method for constructing an emotional dictionary for photography works. Secondly, the importance of the Word2Vec training result set is modified using the TF-IDF training result word set and the artificial photography emotion basic word set to generate the photography work emotion word set. Seed words and emotion word sets are selected from the university emotion vocabulary ontology library to calculate the polarity and strength of emotion words, and combined with the HowNet emotion dictionary to form a photography work emotion dictionary. Finally, the experimental results indicate that this method has achieved good results on actual photography datasets. © 2024 Copyright held by the owner/author(s).

关键词： Emotion Recognition

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