检索结果-内蒙古大学图书馆

2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Dhayanithi, A. Dalali, Shivakumar Madhavi, B. Surla, Govindu Marapelli, Bhaskar Arigela, Arun Kumar Sri Sairam Engineering College Department of Information Technology India Pes University Department of Computer Science and Engineering R R Campus India Srkr Engineering College Department of Information Technology Bhimavaram India Department of Cse Andhra Pradesh Mylavaram India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh India Department of Csit Telangana Hyderabad500043 India

ISBN: (纸本)9798350384277

In the dynamic landscape of healthcare, the accurate identification of medications stands as a critical pillar for patient safety and effective medical management. Traditional methodologies for identifying pills often rely on manual processes, placing a considerable burden on healthcare professionals and leaving room for errors, particularly when patients are unable to furnish precise prescription details. Compounding this challenge is the common practice of patients discarding medication containers, further complicating the identification process. At its core, the system harnesses the cutting-edge capabilities of artificial intelligence, specifically delving into advanced deep learning techniques. The overarching goal is to automate the intricate task of pill detection from images while simultaneously providing intelligent recognition of various medicinal *** by the robust Python programming language and underpinned by the sophisticated MobileNet architecture, the system aspires to achieve unprecedented accuracy and efficiency in the identification of pills and recognition of medicinal drugs. The training and testing processes unfold across a diverse dataset, encompassing a myriad of pill *** underscore the system's adaptability and real-world applicability within healthcare scenarios. The underlying ambition of this project is to furnish healthcare professionals with a state-of-the-art tool that not only mitigates the risk of medication errors but also optimizes time and resource utilization. Beyond its clinical applications, the system extends its impact to patients, affording them access to comprehensive information about their prescribed medications, thereby enhancing overall treatment comprehension and adherence. Over the ensuing sections, this project unfurls its narrative, delving into the intricate details of its methodology, implementation strategies, and the meticulous evaluation that underscores its potential to revolutionize healthcare t

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Effective Code Testing Strategies in DevOps: A Comprehensive Study of Techniques and Tools for Ensuring Code Quality and Reliability 4

Effective Code Testing Strategies in DevOps: A Comprehensive...

引用

4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Charan, Kolli Karthikeya, Nadipineni Narasimharao, Pujala Bhogeswara Devi, S Anjali Abhilash, Vaka Srinivas, P.V.V.S. Koneru Lakshmaiah Education Foundation Department of Computer Science and Information Technology Andhra Pradesh Vaddeswaram522502 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522502 India

ISBN: (纸本)9798350300093

This research study focuses on exploring effective code testing strategies in DevOps for ensuring code quality and reliability. DevOps is an approach that emphasizes the collaboration and integration between software development and IT operations teams to automate the software delivery process and ensure fast and continuous delivery of high-quality software. Code testing is an essential part of the DevOps process that ensures that the software code is working as expected and meets the user's requirements. This research aims to provide a comprehensive overview of the different techniques and tools used for code testing in DevOps. The study explores various testing techniques, such as unit testing, integration testing, acceptance testing, and automated testing, along with tools such as Pytest, Robot Framework, and Selenium. Additionally, the research examines how code coverage analysis, mutation testing, and load testing can help improve code quality and reliability. The study presents a systematic analysis of the strengths and weaknesses of each testing technique and tool, along with their applications in different stages of the DevOps process. Furthermore, the research provides insights into the challenges and best practices for implementing code testing in a DevOps environment. The results of this study contribute to the existing knowledge of code testing in DevOps and provide guidance for developers, testers, and IT professionals to improve the quality and reliability of their software code. © 2023 IEEE.

关键词： Software design

来源：评论

学校读者我要写书评

暂无评论

The Challenges and Opportunities of Cloud Security as a Service for Enhancing IoT Security

The Challenges and Opportunities of Cloud Security as a Serv...

引用

2023 IEEE International Conference on Paradigm Shift in information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Mandal, Sanjeev Kumar Hemelatha, S. Sasirekha, R. Chavan, Gurunath T. Kaneria, Ojasvee Jaffar, A Mohamed Department of Computer Science and Information Technology Karnataka Bangalore India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Atlas SkillTech University Department of Isme Maharastra Mumbai India

ISBN: (纸本)9798350329773

The internet of things (Iota) has had a remarkable impact on modern lifestyles, revolutionizing the way we display, communicate, and have interaction with the sector round us. In conjunction with this new generation, but, come new challenges for organizations in terms of safety. Iota devices frequently lack the security features wished for defensive towards cyber attacks and statistics robbery, leaving them prone to malicious actors. Cloud safety as a carrier (Cases) might also provide the solution to those challenges, via providing a more suitable layer of protection for Iota networks. This new sort of protection answer utilizes cloud-based technologies to provide comfortable get admission to, multi-thing authentication, safety analytics, and strong management abilities for Iota networks. With the aid of imparting this comfy environment, Cases can offer organizations with the warranty that their Iota information can remain comfortable, even within the face of ever-evolving cyber threats. At the identical time, however, heavily investing in Cases increases a number of questions. Factoring in implementation fees, compliance requirements, and commercial enterprise goals may be a daunting task for corporations, and Cases might not always offer a greatest solution. It's vital for businesses to correctly determine their safety necessities when thinking about Cases, and to understand the benefits. © 2023 IEEE.

关键词： Cybersecurity

来源：评论

学校读者我要写书评

暂无评论

Investigating the Impact of Containerization on the Deployment Process in DevOps 2

Investigating the Impact of Containerization on the Deployme...

引用

2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Narasimhulu, Modugula Mounika, Darapureddy Veera Varshini, Puttagunta Amarendra, K. Rao, T.K. Rama Krishna Koneru Lakshmaiah Education Foundation Department of Computer Science and Information Technology Andhra Pradesh Vaddeswaram522502 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522502 India

ISBN: (纸本)9798350347579

This research investigates the impact of containerization on the deployment process in DevOps. Containerization is a technology that enables developers to package their applications and dependencies into lightweight, portable containers that can run anywhere. This study aims to explore how containerization affects various aspects of the deployment process, such as speed, consistency, portability, scalability, and security. Through a comprehensive review of existing literature and case studies, the study identifies the benefits and challenges of using containers in DevOps and highlights best practices and security considerations for organizations adopting containerization. The research findings provide insights into how containerization can improve the deployment process in DevOps, enabling organizations to make informed decisions about incorporating containerization into their software development and deployment workflows. © 2023 IEEE.

关键词： Scalability

来源：评论

学校读者我要写书评

暂无评论

Classification of Pavements into Bleeding and Non-Bleeding using Deep Architectures: A Comparative Study 3

Classification of Pavements into Bleeding and Non-Bleeding u...

引用

3rd International Conference on Smart Technologies, Communication and Robotics, STCR 2023

作者： Sowmiyalakshmi, G. Jegadish, Subiksha Kothai, R. Srinivasa Murthy, Y.V. Vellore Institute of Technology School of Computer Science and Engineering Tamilnadu Vellore India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Dept of Information Technology Manipal India

ISBN: (纸本)9798350370867

Bleeding or flushing refers to a reflective, dark layer of asphalt on the road caused by the upward movement of asphalt within the pavement. This issue is often caused by excess asphalt in the asphalt concrete, high temperatures, low air void content, and poor asphalt quality. The presence of bleeding poses safety risks as it creates an excessively smooth surface, lacking the necessary texture to prevent hydroplaning. This study presents a comparative analysis of various image classification models for the detection of bleeding and non-bleeding roads. The objective is to develop an automated system that accurately identifies road conditions related to bleeding, which is crucial for effective road maintenance and safety measures. In this regard, four pre-trained deep learning models such as VGG16, InceptionV3, EfficientNet, and ResNet50, to classify road images. The advantages of this work lie in the exploration of multiple state-of-the-art models, allowing for a comprehensive evaluation of their performance. The features and approach of the proposed work involve training the models on a labeled dataset of bleeding and non-bleeding road images. Each model is fine-tuned using transfer learning techniques, leveraging the pre-trained weights from large-scale image datasets. This study highlights several notable contributions about pre-trained deep learning models. In addition, the study identifies the strengths and weaknesses of each model, helping to guide future research and development efforts. The findings of this comparative study contribute to the field of road condition analysis and maintenance by providing valuable insights into the performance and limitations of different image classification models. The better performance is identified with InceptionV3 with an accuracy of 70.00% and least performance is given by ResNet50 with 31.66%. © 2023 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Finding Concrete Cracks in a Building using Improved Deep Learning Algorithms 2

Finding Concrete Cracks in a Building using Improved Deep Le...

引用

2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Devisurya, V. Paulson, J. Seshadhri, M. Varshini, P. Nagaraj, P. Muneeswaran, V. Kongu Engineering College Department of Information Technology Erode India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Virudhunagar India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Virudhunagar India

ISBN: (纸本)9798350325799

To ensure functionality and durability, engineering constructions must have fractures identified and repaired. Since cracks are typically discovered through visual inspection, the examiner's personal judgment plays a vital part in both the detection of concrete cracks and the estimation of their characteristics. In the past, this has helped with both the evaluation of the crack phenomenon and the repair of various structures. The number of engineering structures has expanded as a result of industrial development, but relative to that, the level of skill in the crack-detecting field has not improved. Therefore, a quicker yet more precise method is required. The image processing method detects the cracks and their characteristics simultaneously. The formulation of the algorithm and the implementation is simple. However, other flaws, such as mistaking noises for fractures and a lack of accuracy in spotting micro-cracks, have turned into major obstacles for this technology. Finding transverse cracks in concrete structures is another critical problem. Therefore, a thorough analysis of the most recent articles is required to establish an accurate technique. In order to comprehend the potential of this technology, a critical study of the image-processing phenomenon and a thorough literature review has been done in this paper. A universal DenseNet121 classification-based algorithm with camera pictures for crack identification could be an effective strategy with greater accuracy. © 2023 IEEE.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Cervical Cancer Classification using Multi-Scale, Multiple-Attention-based Deep Learning Approach

Cervical Cancer Classification using Multi-Scale, Multiple-A...

引用

2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Maurya, Ritesh Deshwal, Suvidha Rajput, Lucky Gopalakrishnan, T. Krishna, Raguru Jaya Amity Centre for Artificial Intelligence Amity University Noida India Amity School of Engineering & Technology Amity University Noida India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9798350374933

Cervical cancer is a widely acknowledged health concern, especially in the areas lacking appropriate medical treatment. The early diagnosis and the explicit categorization of cervical lesions may bring about effective therapy and restrict the expansion of the disease. The novel approach proposed in this work employs a multi-scale, multi-attention based MobileNetV2 network for the categorisation of cervical cancer. The proposed model improves the efficiency of MobileNetV2, a lightweight convolutional neural network (CNN), by incorporating multi-scale and multi-attention techniques to improve feature extraction capabilities and categorisation accuracy. By incorporating multi-scale component, the suggested model can effectively detect features at different resolutions. Moreover, the integration of multi-attention mechanism allows the model to dynamically highlight salient features over multiple scales. The proposed method has been evaluated using single cell conventional Pap Smear images, exhibiting improved performance compared to existing approaches in terms of accuracy. The results suggest that the Multi-Scale, Multiple-Attention MobileNetV2-based network proves to be a useful tool for cervical cancer categorisation, offering potential interests for clinical diagnosis with an accuracy of 92.4. © 2024 IEEE.

关键词： cervical cancer categorisation deep learning MobileNetV2 multi-attention multi-scale transfer learning

来源：评论

学校读者我要写书评

暂无评论

The Role of Ensemble Learning in Advancing Child Fetal Health: A Focus on Voting and Bagging Techniques 1

The Role of Ensemble Learning in Advancing Child Fetal Healt...

引用

1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Swain, Satyaprakash Patra, Kumar Janardan Jayasingh, Suvendra Kumar Mohanty, Mihir Narayan Pattanayak, Binod Kumar Institute of Technical Education and Research Siksha 'O' Anusandhan Deemed to be University Department of Computer Science and Engineering Odisha Bhubaneswar India Odisha University of Technology and Research Department of Computer Science and Engineering Odisha Bhubaneswar India Institute of Management and Information Technology BPUT Department of Computer Science and Engineering Odisha Cuttack India Institute of Technical Education and Research Siksha 'O' Anusandhan Deemed to be University Department of Electronics and Communication Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350361780

The field of healthcare has witnessed significant advancements in leveraging technology for in-depth analysis of fetal health data, offering profound insights into child development. We meticulously gathered an extensive dataset comprising 1500 instances of child fetal health data sourced from New Born and Pediatric Health Centre, Cuttack, Odisha. We proposed a robust analytical pipeline for effectively classifying and predicting fetal health outcomes. The process initiated with meticulous data preprocessing and refinement, ensuring data integrity and quality. We use clustering techniques unveiled inherent patterns and groupings within the dataset, setting the stage for in-depth analysis and feature extraction. Further enriching the analysis, Principal Component Analysis (PCA) is deployed to streamline data dimensionality while retaining essential information. For prediction we use ensemble learning techniques. To predict the condition, we used voting ensemble method encompassing Decision Trees (DT), Support Vector Machines (SVM) and Naïve Bayes (NB), after predictive models are successfully constructed, showcasing notable accuracies of 92.28%, 83.89% and 82.22%. These results underscore the effectiveness of these models in predicting fetal health outcomes based on the comprehensive dataset. To further refine model performance, a meticulous GridSearchCV approach exhaustively explored optimal parameter values within predefined parameter grids, significantly enhancing predictive capabilities. Additionally, leveraging a bagging method, particularly Random Forest (RF), Logistic Regression (LR), and Gradient Boosting, further bolstered predictive accuracies. These bagging models demonstrated exceptional achieving accuracy of 94.02%, 93.22% and 92.28%. The systematic approach involving clustering, dimensionality reduction, ensemble learning, and parameter optimization resulted in highly accurate predictive models for fetal health assessment. Our research not only highligh

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

Artificial intelligence based machine learning algorithm for prediction of cancer in female anatomy

引用

Multimedia Tools and Applications 2025年第16期84卷 15735-15761页

作者： Ghantasala, G S Pradeep Hung, Bui Thanh Chakrabarti, Prasun R, Sathiyaraj Pellakuri, Vidyullatha Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Karnataka Bangalore India Data Science Laboratory Faculty of Information Technology Industrial University of Ho Chi Minh city Ho Chi Minh City Viet Nam Deputy Provost ITM SLS Baroda University Gujarat Vadodara India Department of CSE GITAM School of Technology GITAM University Bengaluru India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur India

Breast and cervical cancers account for more than 85 percent of all cancer-related fatalities in developing nations, according to the World Cancer Research Fund. As a result, breast and cervical cancer have become one of the leading causes of mortality among women worldwide. This field is still in its infancy, with only a few studies in gynaecology and computer science looking into the detection of breast and cervical cancer. According to the researchers, medical records and early testing from individuals with breast and cervical cancer will be used in this study to determine the prognosis of those suffering from the diseases. To assess our cervical cancer predictions, we employed machine learning models such as Optimized Hybrid Ensemble Classifier (OHEC), which were trained on patient behavior and variables revealed to be associated with patient behavior. The datasets in this study have a substantial number of missing values, and the distribution of those values has been altered as a function of the missing values. OHEC classifier performance has been shown to improve when the number of features is reduced and the problem of high-class imbalance is resolved, because the accuracy, sensitivity, and specificity of the classifier, as well as the number of false positives, were used to demonstrate the success of feature selection in the suggested model's predictive analysis. This has been demonstrated through the use of numerous tests involving categorization challenges. The study underscores the critical significance of early detection and prognosis in combating breast and cervical cancers, which remain leading causes of mortality worldwide. Through the utilization of machine learning models like the OHEC, the authors have demonstrated the potential for improved predictive accuracy and clinical outcomes. The findings highlight the importance of addressing challenges such as missing data and class imbalance in enhancing the performance of predictive models for effective

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Artificial Humming Bird Optimization with Siamese Convolutional Neural Network Based Fruit Classification Model

引用

computer Systems Science & engineering 2023年第11期47卷 1633-1650页

作者： T.Satyanarayana Murthy Kollati Vijaya Kumar Fayadh Alenezi E.Laxmi Lydia Gi-Cheon Park Hyoung-Kyu Song Gyanendra Prasad Joshi Hyeonjoon Moon Department of Information Technology Chaitanya Bharathi Institute of TechnologyHyderabadTelanganaIndia Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySakakaSaudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of International Affairs and Education Gangseo UniversitySeoul07661Korea Department of Information and Communication Engineering and Convergence Engineering for Intelligent Drone Sejong UniversitySeoul05006Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea

Fruit classification utilizing a deep convolutional neural network(CNN)is the most promising application in personal computer vision(CV).Profound learning-related characterization made it possible to recognize fruits from ***,due to the similarity and complexity,fruit recognition becomes an issue for the stacked fruits on a weighing ***,Machine Learning(ML)methods have been used in fruit farming and agriculture and brought great convenience to human *** automated system related to ML could perform the fruit classifier and sorting tasks previously managed by human ***’s(convolutional neural networks)have attained incredible outcomes in image classifiers in several *** the success of transfer learning and CNNs in other image classifier issues,this study introduces an Artificial Humming Bird Optimization with Siamese Convolutional Neural Network based Fruit Classification(AMO-SCNNFC)*** the presented AMO-SCNNFC technique,image preprocessing is performed to enhance the contrast level of the *** addition,spiral optimization(SPO)with the VGG-16 model is utilized to derive feature *** fruit classification,AHO with end to end SCNN(ESCNN)model is applied to identify different classes of *** performance validation of the AMO-SCNNFC technique is tested using a dataset comprising diverse classes of fruit *** comparison studies reported improving the AMOSCNNFC technique over other approaches with higher accuracy of 99.88%.

关键词： Fruit classification computer vision machine learning deep learning metaheuristics

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：