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.
皮肤癌是癌症中最危险的一种。早期发现皮肤癌有助于解决这一问题。因此，有必要尽早诊断这种疾病。本文介绍了卷积神经网络和 Vgg16 算法来识别皮肤癌类型。HAM10000 数据集包括七种不同形式的皮肤癌：黑素细胞痣（nv）、黑色素瘤（mel）、基底细胞癌（bcc）、光化性角化病（akiec）、血管病变（vasc）和皮肤纤维瘤（df）。该系统旨在提高分类准确性；该方法需要大量的数据集准备工作，包括缩放、归一化和增强。Vgg16 算法与 CNN 架构相结合，为皮肤癌分类奠定了坚实的基础。该系统包含正则化技术、优化策略和训练参数的全面细节，以确保开放性和可重复性。该系统的性能使用准确度、精确度、召回率和 F1 分数进行评估，适用于各种类型的皮肤癌。本文强调了所提方法在皮肤癌诊断中的实用性，并探讨了面临的挑战、制约因素和进一步研究的前景。在这项研究的帮助下，识别皮肤癌的新方法正在被开发出来，这将改善患者的治疗效果和临床决策。
{"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}
The work in this article is an initiative to explore random Fourier - Hermite series in orthogonal Hermite polynomials. We choose the random coefficients in the series to be the Fourier-Hermite coefficients of a symmetric stable process with weight function , where . The existence of these random coefficients, which we find to be dependent random variables, is established. The random Fourier-Hermite series is proven to be convergent in the sense of mean if the scalars in the series are the Fourier-Hermite coefficients of a function in the weighted space , where the weights are given by with such that . The sum functions of the series is obtained to the stochastic integral .
{"title":"On Mean Convergence of Random Fourier - Hermite Series","authors":"B. Mangaraj, Sabita Sahoo, Phd Scholar","doi":"10.52783/cana.v31.708","DOIUrl":"https://doi.org/10.52783/cana.v31.708","url":null,"abstract":"The work in this article is an initiative to explore random Fourier - Hermite series in orthogonal Hermite polynomials. We choose the random coefficients in the series to be the Fourier-Hermite coefficients of a symmetric stable process with weight function , where . The existence of these random coefficients, which we find to be dependent random variables, is established. The random Fourier-Hermite series is proven to be convergent in the sense of mean if the scalars in the series are the Fourier-Hermite coefficients of a function in the weighted space , where the weights are given by with such that . The sum functions of the series is obtained to the stochastic integral .","PeriodicalId":40036,"journal":{"name":"Communications on Applied Nonlinear Analysis","volume":" 37","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141368436","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}
In this paper, we establish some fixed points theorem for nonexpansive mappings in partial metric spaces. Our result generalizes Vetro’s results (2015) in the setting of partial metric spaces. This work proves and generalizes some results of Aydi (2017). Suitable example is provided to illustrate the usability of our results.
{"title":"On Fixed Point for Nonexpansive Mappings in Partial Metric Spaces","authors":"Arta Ekayanti, Erika Eka Santi","doi":"10.52783/cana.v31.697","DOIUrl":"https://doi.org/10.52783/cana.v31.697","url":null,"abstract":"In this paper, we establish some fixed points theorem for nonexpansive mappings in partial metric spaces. Our result generalizes Vetro’s results (2015) in the setting of partial metric spaces. This work proves and generalizes some results of Aydi (2017). Suitable example is provided to illustrate the usability of our results.","PeriodicalId":40036,"journal":{"name":"Communications on Applied Nonlinear Analysis","volume":" 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141372808","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}
This paper evaluates the general multiple integrals involving Pragathi-Satyanarayana’s I-function, generalized gamma function and generalized hypergeometric function. The result is perceived as innovative and possesses the ability to generate the previous findings. Furthermore, a collection of corollaries will be revealed at the end.
本文评估了涉及 Pragathi-Satyanarayana 的 I 函数、广义伽马函数和广义超几何函数的一般多重积分。该结果被认为是创新性的，并具有产生先前发现的能力。此外，最后还将揭示一系列推论。
{"title":"Multiple Integrals Involving Pragathi-Satyanarayana’s I-Function, Generalized Gamma and Generalized Hypergeometric Functions","authors":"B. Satyanarayana","doi":"10.52783/cana.v31.698","DOIUrl":"https://doi.org/10.52783/cana.v31.698","url":null,"abstract":"This paper evaluates the general multiple integrals involving Pragathi-Satyanarayana’s I-function, generalized gamma function and generalized hypergeometric function. The result is perceived as innovative and possesses the ability to generate the previous findings. Furthermore, a collection of corollaries will be revealed at the end.","PeriodicalId":40036,"journal":{"name":"Communications on Applied Nonlinear Analysis","volume":" 33","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141371178","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}
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.
{"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}
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.
{"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}
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.
{"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}
User authentication is the process of confirming an individual's identity prior to granting them access to a connected device, an online service, or any other valuable resource. Its importance lies in its capability to protect data, applications, and networks for organizations by restricting access to authorized individuals or approved processes. In this study, the widely used Apache Spark technology was employed for storing and analyzing vast amounts of data, and a unique authentication framework was introduced. A dynamic symbol selection authentication offers a promising alternative to traditional alphanumeric passwords, as well as biometric and facial authentications. This authentication method has been thoroughly tested in the highly distributed Apache Spark cluster. The implementation utilizes SHA512 cryptography in various ways and compares the results with existing authentication and machine learning algorithms. The authentication scheme, combined with the powerful Apache Spark distributed system consisting of 10 nodes, yielded exceptional outcomes.
{"title":"Enhancing Data Security in SPARK Cluster: A Novel Symbol-based Authentication Approach","authors":"J.Balaraju, C.Dastagiraiah, P.Ravinder Rao, T.Srikanth, K.Jyothi Goud, V.Subramanyam","doi":"10.52783/cana.v31.692","DOIUrl":"https://doi.org/10.52783/cana.v31.692","url":null,"abstract":"User authentication is the process of confirming an individual's identity prior to granting them access to a connected device, an online service, or any other valuable resource. Its importance lies in its capability to protect data, applications, and networks for organizations by restricting access to authorized individuals or approved processes. In this study, the widely used Apache Spark technology was employed for storing and analyzing vast amounts of data, and a unique authentication framework was introduced. A dynamic symbol selection authentication offers a promising alternative to traditional alphanumeric passwords, as well as biometric and facial authentications. This authentication method has been thoroughly tested in the highly distributed Apache Spark cluster. The implementation utilizes SHA512 cryptography in various ways and compares the results with existing authentication and machine learning algorithms. The authentication scheme, combined with the powerful Apache Spark distributed system consisting of 10 nodes, yielded exceptional outcomes.","PeriodicalId":40036,"journal":{"name":"Communications on Applied Nonlinear Analysis","volume":"82 s1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141376531","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}
G. Surendher, Tipparthi Anil Kumar, Dhiraj Sunehra
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.
{"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}
This paper introduces novel parameters and presents a method- ology for identifying the necessary syndrome indices required to compute the unknown syndromes within the context of the (57, 29, 17) quadratic residue code. By determining the resulting index sets, the unknown syndromes can be computed, subsequently leading to the derivation of the corresponding error-locator polynomial through the application of a de- coding algorithm.
{"title":"Eight Error Correction for (57,29,17) Quadratic Residue Code Over Binary Field","authors":"P. Shakila Banu","doi":"10.52783/cana.v31.681","DOIUrl":"https://doi.org/10.52783/cana.v31.681","url":null,"abstract":"This paper introduces novel parameters and presents a method- ology for identifying the necessary syndrome indices required to compute the unknown syndromes within the context of the (57, 29, 17) quadratic residue code. By determining the resulting index sets, the unknown syndromes can be computed, subsequently leading to the derivation of the corresponding error-locator polynomial through the application of a de- coding algorithm.","PeriodicalId":40036,"journal":{"name":"Communications on Applied Nonlinear Analysis","volume":"39 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141387674","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}
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