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Application of Data Mining Methods and Techniques for Diabetes Prediction Using Pima Indian Dataset 3rd

Application of Data Mining Methods and Techniques for Diabet...

3rd International Conference on Smart Computing and Cyber Security—Strategic Foresight, Security Challenges and Innovation, SMARTCYBER 2023

作者： Arora, Aditi Sehgal, Chirag Sharma, Shilpi Nayak, Soumya Ranjan Kumar, Raghvendra Amity School of Engineering and Technology Amity University Uttar Pradesh India School of Computer Engineering KIIT Deemed to Be University Odisha Bhubaneswar751024 India Department of Computer Science and Engineering GIET University Gunupur India

ISBN: (纸本)9789819705726

Diabetes is a health condition that only occurs when the body either does not use insulin effectively or produces an insufficient amount of insulin from the pancreas. Insulin, a hormone that regulates blood sugar levels, is crucial. Untreated diabetes can lead to hyperglycaemia, a condition characterized by elevated levels of glucose in the bloodstream. This can have a profound impact on several physiological systems such as blood vessels and neurons. computer vision is a crucial component in the realm of human health as it offers precise outcomes and eliminates any human biases. This research aims to improve diabetes classification. The study focused on analysing how data mining and ML techniques compare to traditional algorithms in predicting diabetes accurately. The "Pima Indians Diabetes Dataset" Standard was used, and feature selection was conducted to improve its potential. To assess the performance of the models, Naive Bayes, SVM, and KNN techniques were utilized, and evaluation criteria such as precision, recall, specificity, and mean absolute error were taken into account. Cross-validation was used to enhance accuracy across all models. The results indicated that the SVM method had an accuracy rate of 78%, making it the best classifier for the sample dataset. Thus, this analysis could help predict diabetes more effectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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Medi-Connect: An End-to-End Intelligent Framework to Facilitate New Era Online Consultations Toward Sustainable Healthcare 5th

Medi-Connect: An End-to-End Intelligent Framework to Facilit...

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5th International Conference on Entrepreneurship, Innovation, and Leadership, ICEIL 2023

作者： Yadav, Jayash Raj Singh Jain, Paras Sharma, Shanu Dubey, Ashwani Kumar Department of Computer Science and Engineering ABES Engineering College Ghaziabad India Department of Electronics and Communication Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India

ISBN: (纸本)9789819716814

The healthcare industry has undergone a significant transformation in recent years, with the integration of information technology and online platforms revolutionizing the way medical services are delivered. One such development is the emergence of online consultations, enabling patients to seek medical advice from doctors without the need for in-person visits. The increasing popularity of online consultations in the healthcare industry has highlighted the need for efficient systems to support remote medical services. In this paper, an intelligent framework is proposed to assist patients and doctors during online consultations. The framework offers end-to-end services starting from patient registration, doctors’ availability, the online appointment of doctors, etc., required during an online consultation. The proposed framework is also capable of assisting the doctors during diagnosis of diseases and drug recommendation through intelligent decision-making capability based on the symptoms provided by the patient. The use of the Naïve Bayes algorithm is explored here to perform classification on patient data and the determination of the probability of specific diseases. In addition to accurate diagnoses, the system recommends appropriate medicinal treatments and provides a database of specialist doctors for further consultation. This integration of technology and health expertise seeks to streamline the healthcare process, saving time and reducing costs for both patients and doctors. By presenting a streamlined framework for online consultations, the main aim of this work is to contribute to the advancement of virtual healthcare services and to bridge the gap between traditional healthcare practices and the evolving digital landscape, ultimately improving healthcare accessibility and outcomes for patients worldwide. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Diagnosis

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Code context-based reviewer recommendation

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Frontiers of computer science 2025年第1期19卷 97-108页

作者： Dawei YUAN Xiao PENG Zijie CHEN Tao ZHANG Ruijia LEI School of Computer Science and Engineering Macao University of Science and TechnologyMacao 999078China Faculty of Science University of AmsterdamAmsterdam WB 1018Netherlands

Code review is a critical process in software development, contributing to the overall quality of the product by identifying errors early. A key aspect of this process is the selection of appropriate reviewers to scrutinize changes made to source code. However, in large-scale open-source projects, selecting the most suitable reviewers for a specific change can be a challenging task. To address this, we introduce the Code Context Based Reviewer Recommendation (CCB-RR), a model that leverages information from changesets to recommend the most suitable reviewers. The model takes into consideration the paths of modified files and the context derived from the changesets, including their titles and descriptions. Additionally, CCB-RR employs KeyBERT to extract the most relevant keywords and compare the semantic similarity across changesets. The model integrates the paths of modified files, keyword information, and the context of code changes to form a comprehensive picture of the changeset. We conducted extensive experiments on four open-source projects, demonstrating the effectiveness of CCB-RR. The model achieved a Top-1 accuracy of 60%, 55%, 51%, and 45% on the Android, OpenStack, QT, and LibreOffice projects respectively. For Mean Reciprocal Rank (MRR), CCB achieved 71%, 62%, 52%, and 68% on the same projects respectively, thereby highlighting its potential for practical application in code reviewer recommendation.

关键词： code reviewer recommendation code context pull request

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The Security of Using Large Language Models:A Survey With Emphasis on ChatGPT

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 1-26页

作者： Wei Zhou Xiaogang Zhu Qing-Long Han Lin Li Xiao Chen Sheng Wen Yang Xiang the School of Science Computing and Engineering Technologies Swinburne University of Technology IEEE the School of Computer and Mathematical Sciences the University of Adelaide Adelaide the Faculty of Science and Engineering Southern Cross University the School of Information and Physical Sciences the University of Newcastle

ChatGPT is a powerful artificial intelligence(AI)language model that has demonstrated significant improvements in various natural language processing(NLP) tasks. However, like any technology, it presents potential security risks that need to be carefully evaluated and addressed. In this survey, we provide an overview of the current state of research on security of using ChatGPT, with aspects of bias, disinformation, ethics, misuse,attacks and privacy. We review and discuss the literature on these topics and highlight open research questions and future *** this survey, we aim to contribute to the academic discourse on AI security, enriching the understanding of potential risks and mitigations. We anticipate that this survey will be valuable for various stakeholders involved in AI development and usage, including AI researchers, developers, policy makers, and end-users.

关键词： Artificial intelligence (AI) ChatGPT large language models (LLMs) security

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Design and Implementation of Real Time Image Retrieval System for De-Noising Space Images in Chandrayaan 3 Lunar Mission Using Autoencoders 7

Design and Implementation of Real Time Image Retrieval Syste...

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

作者： Shubham, Prince Banerjee, Pallab Thakur, Kanika Kumar, Biresh Mitra, Dipta Gaurav, Kumar Amity University Department of Computer Science Engineering Jharkhand Ranchi India Amity University Amity School of Information Technology Jharkhand Ranchi India

ISBN: (纸本)9798350350067

In the context of the Chandrayaan 3 Lunar Mission, this research paper introduces a real-time image retrieval and denoising system powered by autoencoders, designed to tackle the challenge of noisy space imagery. Leveraging advanced deep learning techniques, our system employs autoencoders to extract essential features from the noisy lunar images and subsequently retrieves and integrates similar, noise-free reference images from a comprehensive database. By doing so, it achieves real-time denoising, ensuring that the mission's acquired lunar images are of high quality, thus facilitating more accurate scientific analysis. The paper details the architecture of the autoencoder-based denoising system, its training process using a meticulously curated lunar image dataset, and its seamless integration into the mission's image processing pipeline. Experimental results underscore the system's remarkable noise reduction capabilities, thereby playing a pivotal role in enhancing the Chandrayaan 3 mission's scientific contributions, ultimately advancing our understanding of the lunar environment and bolstering the success of lunar exploration endeavours. © 2024 IEEE.

关键词： Noise abatement

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Liquid lens based holographic camera for real 3D scene hologram acquisition using end-to-end physical model-driven network

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Light(science & Applications) 2024年第3期13卷 488-497页

作者： Di Wang Zhao-Song Li Yi Zheng You-Ran Zhao Chao Liu Jin-Bo Xu Yi-Wei Zheng Qian Huang Chen-Liang Chang Da-Wei Zhang Song-Lin Zhuang Qiong-Hua Wang School of Instrumentation and Optoelectronic Engineering Beihang UniversityBeijing 100191China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China

With the development of artificial intelligence,neural network provides unique opportunities for holography,such as high fidelity and dynamic *** to obtain real 3D scene and generate high fidelity hologram in real time is an urgent ***,we propose a liquid lens based holographic camera for real 3D scene hologram acquisition using an end-to-end physical model-driven network(EEPMD-Net).As the core component of the liquid camera,the first 10 mm large aperture electrowetting-based liquid lens is proposed by using specially fabricated *** design of the liquid camera ensures that the multi-layers of the real 3D scene can be obtained quickly and with great imaging *** EEPMD-Net takes the information of real 3D scene as the input,and uses two new structures of encoder and decoder networks to realize low-noise phase *** comparing the intensity information between the reconstructed image after depth fusion and the target scene,the composite loss function is constructed for phase optimization,and the high-fidelity training of hologram with true depth of the 3D scene is realized for the first *** holographic camera achieves the high-fidelity and fast generation of the hologram of the real 3D scene,and the reconstructed experiment proves that the holographic image has the advantage of low *** proposed holographic camera is unique and can be used in 3D display,measurement,encryption and other fields.

关键词： calculation solution holographic

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Competition for gradient-free tuning of large language models:approaches, results, current challenges and future directions

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National science Review 2023年第6期10卷 16-19页

作者： Tingfeng Cao Liang Chen Dixiang Zhang Tianxiang Sun Zhengfu He Xipeng Qiu Xing Xu Hai Zhang School of Software Engineering South China University of Technology School of Computer Science Fudan University School of Computer Science and Engineering University of Electronic Science and Technology of China Pazhou Laboratory (Huangpu) School of Mathematics Northwest University

PROBLEM Recent years have witnessed the rapid progress of self-supervised language models (LMs)[1],especially large language models (LLMs)[2].LLMs not only achieved state-of-the-art performance on many natural language processing tasks,but also captured widespread attention from the public due to their great potential in a variety of real-world applications (***,search engines,writing assistants,etc.)through providing general-purpose intelligent services.A few of the LLMs are becoming foundation models,an analogy to infrastructure,that empower hundreds of downstream applications.

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On learning the right attention point for feature enhancement

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science China(Information sciences) 2023年第1期66卷 131-143页

作者： Liqiang LIN Pengdi HUANG Chi-Wing FU Kai XU Hao ZHANG Hui HUANG College of Computer Science and Software Engineering Shenzhen University Department of Computer Science and Engineering The Chinese University of Hong Kong School of Computer Science National University of Defense Technology School of Computing Science Simon Fraser University

We present a novel attention-based mechanism to learn enhanced point features for point cloud processing tasks, e.g., classification and segmentation. Unlike prior studies, which were trained to optimize the weights of a pre-selected set of attention points, our approach learns to locate the best attention points to maximize the performance of a specific task, e.g., point cloud classification. Importantly, we advocate the use of single attention point to facilitate semantic understanding in point feature learning. Specifically,we formulate a new and simple convolution, which combines convolutional features from an input point and its corresponding learned attention point(LAP). Our attention mechanism can be easily incorporated into state-of-the-art point cloud classification and segmentation networks. Extensive experiments on common benchmarks, such as Model Net40, Shape Net Part, and S3DIS, all demonstrate that our LAP-enabled networks consistently outperform the respective original networks, as well as other competitive alternatives, which employ multiple attention points, either pre-selected or learned under our LAP framework.

关键词： point convolution feature enhancement attention point deep neural network

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Exploring Advanced Segmentation Strategies for Breast Cancer Detection: A Comparative Study of U-Net Variants and Ensemble Model 1

Exploring Advanced Segmentation Strategies for Breast Cancer...

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1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

作者： Varshney, Tunisha Verma, Karan Kaur, Arshpreet National Institute Of Technology Computer Science and Engineering Delhi India Amity University Computer Science and Engineering Bangluru India

ISBN: (纸本)9798350365016

Ultrasound imaging plays a crucial role in the detection of breast cancer, offering real-time visualization with high resolution and without ionizing radiation. Accurate segmentation of tumors from ultrasound images is essential for precise diagnosis and treatment planning. This study assesses the effectiveness of the basic U-Net architecture and its variants, which involve replacing the encoder with convolutional networks like ResNet50 and VGG16. Additionally, the study explores the ensemble of both models to further enhance breast cancer detection from ultrasound images. The advantage of ensemble learning lies in its ability to harness the strengths of individual models, thereby improving overall performance and robustness by mitigating weaknesses and uncertainties associated with any single model. In this study BUSI, a public dataset is used for experiments. Ensemble model performs better as compare to Resnet50UNet and Vgg16UNet. © 2024 IEEE.

关键词： Ultrasonic imaging

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Comparative Study on Biomass and Solar Energy Driven Hybrid Power Plant 1

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2nd Biennial International Symposium on Fluids and Thermal engineering, FLUTE 2023

作者： Rai, Sushil Kumar Sharma, Meeta Shukla, Anoop Kumar Kumar, Aditya Thermal and Fluid Science Amity School of Engineering and Technology UP Noida India Department of Mechanical Engineering Amity School of Engineering and Technology UP Noida India

ISBN: (数字)9789819724819

ISBN: (纸本)9789819724802

Renewable energy sources, which include sunlight, wind and water, are energy sources that are naturally replenished on a human timescale. These types of energy sources have a number of advantages over fossil fuels, such as reduced greenhouse gas emissions, decreased air pollution and improved energy security. The aim of this study is to examine the different types of renewable energy sources and their potential for generating electricity. It is found that renewable energy sources could play a significant role in the global energy mix, and their implementation could be accelerated through favourable policies and advancements in technology. The supercritical Brayton cycle has the potential to increase the efficiency of solar and biomass-based power plants by utilising higher temperature heat sources. In this study, the exploration of potential of the supercritical Brayton cycle for electricity generation using solar and biomass energy sources are discussed. Also, the clubbing of cycle like, for example, Organic Rankine cycle enhances its energy utilisation and leads to fewer losses is also discussed. The review demonstrates that the supercritical Brayton cycle is a promising technology for generating electricity from renewable energy sources, as it can achieve high efficiencies and cost savings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Hybrid power

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