Communications on Applied Nonlinear Analysis最新文献_第10页

Deep Learning for Skin Cancer Classification: A Comparative Study of CNN and Vgg16 on HAM10000 Dataset 深度学习用于皮肤癌分类：HAM10000 数据集上的 CNN 和 Vgg16 比较研究

Communications on Applied Nonlinear Analysis Pub Date : 2024-07-05 DOI: 10.52783/cana.v31.944

Yashwant S. Ingle, Dr. Nuzhat Faiz

{"title":"Deep Learning for Skin Cancer Classification: A Comparative Study of CNN and Vgg16 on HAM10000 Dataset","authors":"Yashwant S. Ingle, Dr. Nuzhat Faiz","doi":"10.52783/cana.v31.944","DOIUrl":"https://doi.org/10.52783/cana.v31.944","url":null,"abstract":"Skin cancer is one of the most dangerous types of cancer among the cancers. The early detection of skin cancer helps resolve it. Hence, it is necessary to diagnose the disease as early as possible. This paper presents Convolutional Neural Networks and the Vgg16 algorithm to recognize skin cancer types. The HAM10000 dataset, which comprises seven distinct forms of skin cancer, melanocytic nevi (nv), Melanoma (mel), basal cell carcinoma (bcc), actinic keratoses (akiec), vascular lesions (vasc), and dermatofibroma (df). This system aims to improve classification accuracy; the methodology necessitates extensive dataset preparation, including scaling, normalization, and augmentation. The Vgg16 algorithm, when combined with the CNN architecture, offers a robust basis for the classification of skin cancer. Comprehensive details on regularization techniques, optimization strategies, and training parameters are included to ensure openness and reproducibility. The system's performance is evaluated using accuracy, precision, recall, and F1-score for every type of skin cancer. This paper highlights the usefulness of the proposed method in skin cancer diagnosis and looks at challenges, constraints, and prospects for further research. New methods for identifying skin cancer are being developed with the help of this research, which can improve patient outcomes and clinical decision-making.","PeriodicalId":40036,"journal":{"name":"Communications on Applied Nonlinear Analysis","volume":" 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141673321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On Mean Convergence of Random Fourier - Hermite Series 论随机傅立叶-赫米特数列的平均收敛性

Communications on Applied Nonlinear Analysis Pub Date : 2024-06-08 DOI: 10.52783/cana.v31.708

B. Mangaraj, Sabita Sahoo, Phd Scholar

引用次数: 0

On Fixed Point for Nonexpansive Mappings in Partial Metric Spaces 论部分公设空间中无穷映射的定点

Communications on Applied Nonlinear Analysis Pub Date : 2024-06-07 DOI: 10.52783/cana.v31.697

Arta Ekayanti, Erika Eka Santi

引用次数: 0

Multiple Integrals Involving Pragathi-Satyanarayana’s I-Function, Generalized Gamma and Generalized Hypergeometric Functions 涉及 Pragathi-Satyanarayana I 函数、广义伽马函数和广义超几何函数的多重积分

Communications on Applied Nonlinear Analysis Pub Date : 2024-06-07 DOI: 10.52783/cana.v31.698

B. Satyanarayana

引用次数: 0

An Analysis of Crop Insurance as an Adaptation Tool of Climate Vulnerability in Cauvery Delta Zone 将农作物保险作为考弗里三角洲地区气候脆弱性适应工具的分析

Communications on Applied Nonlinear Analysis Pub Date : 2024-06-07 DOI: 10.52783/cana.v31.694

D. H. Beula, Sindhu J. Kumaar

{"title":"An Analysis of Crop Insurance as an Adaptation Tool of Climate Vulnerability in Cauvery Delta Zone","authors":"D. H. Beula, Sindhu J. Kumaar","doi":"10.52783/cana.v31.694","DOIUrl":"https://doi.org/10.52783/cana.v31.694","url":null,"abstract":"Using the Climate Vulnerability Index (CVI), this study investigates the efficacy of crop insurance as an adaptation strategy for reducing climate vulnerability in the Cauvery Delta Zone. The effects of climate change, such as increased temperatures, changed precipitation patterns, and extreme weather events, are particularly vulnerable to the region's agriculture. In order to create a composite Climate Vulnerability Index (CVI) score, indicators such as Rainfall, Paddy Production, Cultivated Land, and Insured Land are normalised. Our assessment of crop insurance concentrated on how it can improve adaptive capacity by offering monetary protection against crop failures are induced by climate change. The findings demonstrate crop insurance's important role in boosting resilience by showing a considerable reduction in the CVI. In order to reduce climate risks and promote sustainable agriculture in the Cauvery Delta Zone (CDZ), this study emphasises the significance of incorporating crop insurance into more comprehensive adaption measures. A five-year data collection covering the fiscal years 2018–2019 through 2022–2023 was considered for this study from the Government of India's Directorate of Economics & Statistics.","PeriodicalId":40036,"journal":{"name":"Communications on Applied Nonlinear Analysis","volume":" 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141374157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient Facial Emotion Detection through Deep Learning Techniques 通过深度学习技术进行高效面部情绪检测

Communications on Applied Nonlinear Analysis Pub Date : 2024-06-06 DOI: 10.52783/cana.v31.690

Priti Singh, Hari Om, C. S. Raghuvanshi

{"title":"Efficient Facial Emotion Detection through Deep Learning Techniques","authors":"Priti Singh, Hari Om, C. S. Raghuvanshi","doi":"10.52783/cana.v31.690","DOIUrl":"https://doi.org/10.52783/cana.v31.690","url":null,"abstract":"Smart facial emotion detection represents a captivating realm of inquiry that has found applications across diverse sectors such as defense, healthcare, and human-machine interfaces. Researchers are diligently exploring methods to encode, decode, and even obfuscate facial cues to refine algorithmic predictions. Leveraging a combination of deep learning algorithms and Cognitive Internet of Things (CIoT), efforts are underway to bolster efficiency in response to the rapid evolution of this technology. This study aims to distill recent advancements in smart facial expression recognition utilizing deep learning algorithms while pioneering novel approaches to emotion detection. The burgeoning Internet of Things landscape has underscored a deficiency in technological infrastructure within current automated intelligent services, rendering them ill-equipped to cater to industrial demands. The gradual augmentation of Internet of Things technologies tailored for intelligent environments has inadvertently led to delays and diminished market efficacy. Deep learning stands out as a cornerstone in myriad applications and experimental setups. Addressing this challenge necessitates the formulation of emotionally intelligent methodologies within the framework of deep learning, thereby invigorating Internet of Things initiatives, as elucidated by recent strides in facial emotion detection applications.","PeriodicalId":40036,"journal":{"name":"Communications on Applied Nonlinear Analysis","volume":"18 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141380025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Study on the Efficacy of Machine Learning Models in Intrusion Detection Systems 机器学习模型在入侵检测系统中的功效研究

Communications on Applied Nonlinear Analysis Pub Date : 2024-06-06 DOI: 10.52783/cana.v31.691

Praveen Kumar, Dr Hari Om

{"title":"A Study on the Efficacy of Machine Learning Models in Intrusion Detection Systems","authors":"Praveen Kumar, Dr Hari Om","doi":"10.52783/cana.v31.691","DOIUrl":"https://doi.org/10.52783/cana.v31.691","url":null,"abstract":"The electronics industry has seen a rise in demand for faster and more affordable delivery due to developments in information technology. Technology is developing quickly, which simplifies living but also presents a number of security issues. As the Internet has grown over time, so too have the amount of online attacks. The intrusion detection system (IDS) is one of the supporting layers that can be utilized for information security. IDS avoids questionable network activity and provides a pristine environment for conducting business. In the process of building an e-commerce system, the most challenging aspect is ensuring user security during online transactions. Security methods for intrusion detection were investigated in this study. The need for ongoing intrusion detection monitoring stems from the need for continued technological adaptation, which leads to a comparison of adaptive artificial intelligence-based intrusion detection systems. This paper demonstrates the use of reinforcement learning (RL) and regression learning-based intrusion detection systems (IDS) to very challenging problems, including resource allocation and input feature selection.","PeriodicalId":40036,"journal":{"name":"Communications on Applied Nonlinear Analysis","volume":"10 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141380371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Data Security in SPARK Cluster: A Novel Symbol-based Authentication Approach 提高 SPARK 集群的数据安全性：基于符号的新型认证方法

Communications on Applied Nonlinear Analysis Pub Date : 2024-06-06 DOI: 10.52783/cana.v31.692

J.Balaraju, C.Dastagiraiah, P.Ravinder Rao, T.Srikanth, K.Jyothi Goud, V.Subramanyam

引用次数: 0

Advanced Nonlinear Channel Estimation Techniques for 5G Wireless Communication Systems 面向 5G 无线通信系统的先进非线性信道估计技术

Communications on Applied Nonlinear Analysis Pub Date : 2024-06-04 DOI: 10.52783/cana.v31.678

G. Surendher, Tipparthi Anil Kumar, Dhiraj Sunehra

{"title":"Advanced Nonlinear Channel Estimation Techniques for 5G Wireless Communication Systems","authors":"G. Surendher, Tipparthi Anil Kumar, Dhiraj Sunehra","doi":"10.52783/cana.v31.678","DOIUrl":"https://doi.org/10.52783/cana.v31.678","url":null,"abstract":"In this paper, 5G wireless communication systems requirement, standards and practical challenges are studied thoroughly. Multicarrier modulation schemes for 4G wireless communication systems were applied to 5G wireless communication systems for better suitability. Traditional channel estimation techniques for 4G were applied to 5G wireless communication systems and observed better applicability. An M-estimator method waveforms for 5G in Gaussian and non-Gaussian environments is proposed, studied and analyzed. 5G networks transceiver model both Gaussian and non-Gaussian environments for various multicarrier modulation schemes was studied and analyzed. Comparison of 5G candidate waveforms in terms of excess emissions, peak to average power ratio, Flexibility, complicated and spectral efficiency was done and suitability for 5G systems with Gaussian and non-Gaussian environments was observed. Bit error rate comparisons were performed on all candidate waveforms with respect to SNR. MSE versus SNR simulations for proposed estimator-based channel estimation method in various non-Gaussian channel environments was carried out and studied. In simulations, proposed channel estimation technique was compared with other techniques and the proposed method outperforms in 5G networks in non-Gaussian environments with various candidate multicarrier waveforms.","PeriodicalId":40036,"journal":{"name":"Communications on Applied Nonlinear Analysis","volume":"165 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141387105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Eight Error Correction for (57,29,17) Quadratic Residue Code Over Binary Field 二进制字段上（57,29,17）二次残差码的八次纠错

Communications on Applied Nonlinear Analysis Pub Date : 2024-06-04 DOI: 10.52783/cana.v31.681

P. Shakila Banu

引用次数: 0