Strategic Improved K-Means Clustering in Mining Blood Donor Data Analysis and IoT-based Allocation

Vibha Tiwari; Chopparapu Gowthami; Bhavani R.; S. Kayalvizhi; S. Selvakanmani; Deepak Chowdary Edara

doi:https://doi.org/10.54216/JISIoT.130110

International Journal of Neutrosophic Science | 2024

Neutrosophic Delphi for evaluating sustainability models of native and non-native digital media.

Technological globalization has brought many changes in different fields, one of which is related to the media. In the case of traditional media, they are forced to find new ways to rethink practice, while digital media emerges in a digital context, albeit with limitations. Experience In both cases, sustainability is one of the factors to be rethought. Building on this, the overall objective is to use the Neutrosophic Delphi method to investigate the extent to which native and non-native digital media have durable patterns that allow them to be successful in their communication activities. To achieve this objective, we work with a mixed methodology, that is, qualitative and quantitative approaches: for qualitative, we use interview methods, for quantitative, we use survey methods. The population studied included both native and non-native digital media. Specifically, the survey and interviews were applied to a group of media owners. The article concludes with a series of Neutrosophic reflections on the conditions of media sustainability.

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Karla Valeria A. Sigcha mail -

Evelyn M. Lema Basantes mail -

Lourdes Y. Cabrera Martinez mail -

Tonguc Cagin mail

link https://doi.org/10.54216/IJNS.240404

Volume & Issue

Vol. Volume 24 / Iss. Issue 4

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International Journal of Neutrosophic Science | 2024

Leveraging Bat Algorithm with Rough Neutrosophic Soft Set for Enhanced Oral Cancer Detection and Classification

Neutrosophic soft sets (NSS) are highly effective in representing neutral uncertain data. NSS model attracts several authors because it has huge range of applications in several areas such as decision-making, data analysis, smoothness of functions, probability theory, measurement theory, predicting, and operations research. Oral squamous cell carcinoma (OSCC) is the most general tumor around the world and its occurrence is on the increase in several populations. Early diagnosis plays vital role in improving diagnosis, treatment outcomes and survival rates. Although the new developments in understanding molecular mechanisms, late analysis and the implementation of precision medicine for OSCC patients continue to present problems. Early diagnosis and detection can support doctors in offering optimum patient care and effectual treatment. In recent years, the execution of several machine-learning (ML) approaches in cancer analysis has provided valuable insights, facilitating more effective and precise treatment decision-making. Oral Cancer screening can progress with the execution of artificial intelligence (AI) approaches. AI offers support to the oncology region by correctly examining a huge database in many imaging modalities. This article develops a Bat Algorithm with Rough Neutrosophic Soft Set for Oral Cancer Diagnosis (BARNSS-OCD) technique. The main intention of the BARNSS-OCD technique is to exploit deep learning (DL) model for enhanced identification of OC. In the BARNSS-OCD technique, median filtering (MF) is used for image pre-processing and the feature extraction takes place using deep convolutional neural network (DCNN) model. In addition, bat algorithm (BA) is used for the hyperparameter selection of the DCNN model. For OC detection process, the BARNSS-OCD technique applies RNSS model. To exhibit the improved performance of the BARNSS-OCD technique, a sequence of experiments is involved. The simulation outcomes indicate that the BARNSS-OCD technique gains better performance compared to other DL models

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Arwa Darwish Alzughaibi mail -

Ebtesam Al-Mansor mail

link https://doi.org/10.54216/IJNS.240405

Volume & Issue

Vol. Volume 24 / Iss. Issue 4

Details open_in_new

International Journal of Neutrosophic Science | 2024

Neutrosophic analysis of avocado oil extraction conditions by varieties

Avocado oil is defined by the composition of the fruit and its nutritional value, which according to previous studies suggests that it provides health benefits, reduces cardiovascular disease, and provides anti-inflammatory and antioxidant effects. However, the nutritional value is determined by the amount of acid. Monounsaturated and polyunsaturated fatty acids make this product useful in cooking. The quality of the oil is affected by the method and conditions of extraction, as these processes affect the preservation of nutrients and beneficial properties of avocado oil. This study aimed to conduct a Neutrosophic analysis of avocado oil extraction conditions depending on the cultivar, dehydration and cold pressing conditions. As a result, the physicochemical properties of the reaction variables were determined and the values of acidity, moisture, density, and impurities were obtained for the oil obtained from the Hass variety by dehydration and pressing.

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María M. Morales Padilla mail -

Cristian I. Cuchipe Chacha mail -

Vicente A. Guerrón Troya mail -

Kholmuminov Shayzak Rakhmatovich mail

link https://doi.org/10.54216/IJNS.240406

Volume & Issue

Vol. Volume 24 / Iss. Issue 4

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International Journal of Neutrosophic Science | 2024

Enhancing Guinea Pig Farming: A Neutrosophic Approach with Interval-Valued and Bipolar Sets in Decision-Making Methods

The study emphasizes the need of implementing several ways to promote guinea pig farming in small family units. It highlights the relevance of enhanced nutrition, effective health management, genetic enhancement, and acceptable habitat conditions as essential factors for enhancing productivity and profitability. Suggestions encompass the adoption of advanced breeding methods, offering training and technical support, and expanding the range of goods and markets to ensure the long-term economic viability of guinea pig farming. The utilization of neutrosophic sets provided a strong framework for assessing these techniques, enabling a thorough study that considers the inherent uncertainties in decision-making processes. To enhance future study, it is recommended to improve and broaden neutrosophic approaches to comprehend the intricacies of guinea pig farming systems more effectively. It will be beneficial to create more advanced models that include a broader set of factors and extensive data, as well as to undertake longitudinal studies to evaluate the long-term effects. It is essential to work together with local communities to customize tactics that are suitable for specific geographical conditions and socioeconomic contexts. This is necessary to ensure that these interventions are practical and successful.

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Patricia M. Andrade-Aulestia mail -

Luis A. Chicaiza-Sánchez mail -

César R. Delgado-Acurio mail -

Rafael A. Garzón-Jarrín mail -

Tonguc Cagin mail

link https://doi.org/10.54216/IJNS.240407

Volume & Issue

Vol. Volume 24 / Iss. Issue 4

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International Journal of Neutrosophic Science | 2024

Possibility Fermatean Neutrosophic Soft Set

In this paper, we introduce the concept of Possibility Fermatean Neutrosophic Soft Set and define some related concepts such as Possibility Fermatean Neutrosophic Soft subset, Possibility Fermatean Neutrosophic Soft null set, and Possibility Fermatean Neutrosophic Soft universal set. Then, we define set-theoretical operations of Possibility Fermatean Neutrosophic Soft Sets such as union, intersection, and complement, and investigate some properties of these operations. We also introduce AND-product and OR-product operations between two Possibility Fermatean Neutrosophic Soft Sets. We propose a decision-making method called the Possibility Fermatean Neutrosophic Soft decision-making method (PFNS-decision-making method) which can be applied to decision-making problems involving uncertainty based on AND-product operation. We finally give a numerical example to display the application of the method that can be successfully applied to the problems.

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Shawkat Alkhazaleh mail -

Belal Batiha mail -

Areen Al-khateeb mail -

Hamzeh Zureigat mail -

Abedallah Al-shboul mail -

Khaldoun Batiha mail

link https://doi.org/10.54216/IJNS.240408

Volume & Issue

Vol. Volume 24 / Iss. Issue 4

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2024

An Improved Internet of Thing-based Optimized SVM Approach for ECG-founded Cardiac Arrhythmia Classification

Cardiovascular diseases (CVD) stand as the leading cause of global mortality, claiming millions of lives annually. An electrocardiogram (ECG) records the heart's electrical activity based on the Internet of Things (IoT), crucial in detecting cardiac arrhythmias (CA), characterized by irregular heart rates and rhythms. Signals from the MIT-BIH Arrhythmia Physio net database are analyzed. This chapter aims to propose a hybrid approach merging Genetic Algorithm-Support Vector Machine (GSVM) and Particle Swarm Optimization-Support Vector Machine (PSVM) for CA classification. The study introduces an algorithm for categorizing ECG beats into six groups using Independent Component Analysis (ICA)-derived features. Optimal SVM settings are determined using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) on ICA features computed via non-parametric power spectral estimation. The research delves into the origins and methodologies of GA and PSO. Simulation results comparing GSVM and PSVM are presented, emphasizing PSVM's superior performance in accuracy, sensitivity, specificity, and positive predictivity. Detailed performance metrics, including Sensitivity, Specificity, Positive Predictivity, and Accuracy percentages, are scrutinized and compared against the top classifier. The findings endorse PSVM's superiority over GSVM, indicating enhanced performance across multiple evaluation criteria.

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Yogendra Narayan Prajapati mail -

Beemkumar N. mail -

Mary Christeena Thomas mail -

Lovish Dhingra mail -

Rishabh Bhardwaj mail -

Aws Zuhair Sameen mail

link https://doi.org/10.54216/JISIoT.130106

Volume & Issue

Vol. Volume 13 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2024

Intelligent IOT Based Audio Signal Processing for Healthcare Applications

This research introduces a novel approach to intelligent IoT-based audio signal processing for healthcare applications. Leveraging advanced feature extraction techniques such as Mel-Frequency Cepstral Coefficients (MFCC) and Wavelet Transform, combined with sophisticated classification models like Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs), the proposed method demonstrates superior performance in accurately classifying healthcare data. Through extensive experimentation and analysis, the method achieves high accuracy, precision, recall, and F1 score, while exhibiting robustness in discriminating between different classes and maintaining precision in classification, as evidenced by its high AUC-ROC and AUC-PR values. The ablation study provides insights into the significance of key components and parameters, offering guidance for further refinement and optimization of the method. Overall, the proposed method holds promise for revolutionizing healthcare management through proactive monitoring and intervention, leading to improved patient outcomes and healthcare delivery.

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Shagun Agarwal mail -

Lekha Bist mail -

Suresh Kumar Sharma mail -

Sunil Kumar Dular mail -

Rupali Salvi mail

link https://doi.org/10.54216/JISIoT.130107

Volume & Issue

Vol. Volume 13 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2024

An Effective Internet of Things based Assessment of ANN and ANFIS algorithms for Cardiac Arrhythmia

Reducing the influence of significant noise signal components on the obtained raw ECG signal is essential for precise identification of cardiac arrhythmias (CA), which frequently present as irregularities in heart rate or rhythm. Preprocessing is used to remove noise signals and baseline drift from the ECG wave that is recorded using the internet of things (IoT). After that, the denoised signal is subjected to dimensionality reduction and feature extraction. In order to determine whether classification method is more effective in detecting cardiac arrhythmias, this study compares two methods: an adaptive neuro-fuzzy inference system and artificial feed-forward neural networks trained with the back-propagation learning algorithm. An Adaptive Neuro Fuzzy Inference System analyses ICA features obtained by non-parametric power spectral estimates, and an Artificial Neural Network (ANN) classifier uses the ECG signal's morphological and statistical aspects to identify patterns. The creation of artificial feed-forward neural networks provides a rich framework for studying the Back Propagation Algorithm. Sensitivity, specificity, accuracy, and positive predictiveivity are some of the performance characteristics that are thoroughly examined. An overall accuracy of 97.79%, sensitivity of 99.82%, specificity of 99.68%, and positive predictivity of 98.58% were seen in the results of the Artificial Neural Feed Forward Network (ANFFN). The Adaptive Neuro Fuzzy Inference System (ANFIS) outperforms these metrics with an astounding overall accuracy of 99.62%, specificity of 98.63%, and positive predictivity of 99.46%. With a classification accuracy of 99.82%, ANFIS demonstrates to be the most effective classifier for identifying cardiac arrhythmias.

groups

Madhura K. mail -

Asha KS mail -

Mary Christeena Thomas mail -

Anubhav Bhalla mail -

Rajat Saini mail -

Aws Zuhair Sameen mail

link https://doi.org/10.54216/JISIoT.130108

Volume & Issue

Vol. Volume 13 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2024

An efficient Analysis based on the Internet of Things, SVM and KNN for Operative Diabetic Retinopathy Classification

These days, diabetes is an incurable disease, with millions of people suffering from it worldwide. Several variables namely lack of education, crowded living conditions, obesity and improper diet are among the causes of this recent upsurge in diabetes cases. They are identified by the name of infections induced by bacteria or viruses, harmful compounds in food, autoimmune reactions, obesity, unhealthy lifestyles, and pollution in the environment. Excessive and sight-threatening diabetic retinopathy (DR) is the most common retinal micro-vascular dysfunction that is characterized by the occurrence of a disorder of retinal blood vessels resulting in impaired vision. The IoT-based work is conducted in this work on the machine learning (ML) techniques, K-Nearest Neighbor (KNN) and Support Vector Machine (SVM). The classification of diabetic retinopathy is a topic that is under research. The range of activities of the processes of downsampling, labelling, image flattening, and format conversion is all within the dataset preparation process. An advanced prognosis model is designed which follows a combination of two machine learning techniques such as SVM and KNN. This approach classifies the images of diabetic retinopathy into five segments (subclasses), thus facilitating in-depth analysis. Our solution proposal in this case is a superior one because of its higher classification accuracy and faster processing speed as the findings showed. The robustness and accuracy that the SVM is known for are ensured by the convergence of the KNN to the SVM. The paper also proves a close linkage of clinical symptoms and blood sugar readings to an algorithmic DM prediction system that is based on IoT and ML approaches. This is another advantage of this method that it outperforms the existing classification methods. Amongst all the classifiers that we used in this project, the KNN ML classifier turned out to be the most accurate one with an accuracy rate of 93%. It was found that the algorithm performed with a 79% accuracy rate after tough testing and training and it was consistently providing number one quality DM predictions.

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Vishakha D Bhandarkar mail -

Arun Khatri mail -

Abhiraj Malhotra mail -

Mahesh TR mail -

Jagmeet Sohal mail -

Raenu Kolandaisamy mail

link https://doi.org/10.54216/JISIoT.130109

Volume & Issue

Vol. Volume 13 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2024

Strategic Improved K-Means Clustering in Mining Blood Donor Data Analysis and IoT-based Allocation

This manuscript proposes Strategic Improved K-Means Clustering to simplify blood donor data analysis and distribution. The technique optimizes blood donor system resources via K-Means++ initialization, hierarchical clustering, and smart data dissemination. The paper begins with a comprehensive overview of clustering techniques and their healthcare applications. It illustrates the need for contemporary blood donor data analysis methods for cluster quality and resource allocation. Cluster purity, silhouette coefficient, Davies-Bould in the index, and other performance indicators are used to rigorously compare the recommended technique to 10 established clustering methods. The approach routinely fulfils these conditions, proving that it creates accurate, well-fitting groupings. Ablation tests how much-enhanced initialization, hierarchical clustering, and strategic data placement improve the entire. The study found that these make the procedure dependable and successful for numerous sorts of data. The study shows that the approach may be applied to other data besides blood donor data. Hierarchical clustering provides important information about the dataset's hierarchical patterns, making clustering findings easier to grasp. Resources are better distributed with strategic data dissemination. The recommended strategy is effective in emergencies and areas with changing blood needs. To conclude, Strategic Improved K-Means Clustering evaluates and distributes blood donor data comprehensively. Its flexibility, adaptability, and speed make it excellent for managing healthcare resources and making collective choices.

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Vibha Tiwari mail -

Chopparapu Gowthami mail -

Bhavani R. mail -

S. Kayalvizhi mail -

S. Selvakanmani mail -

Deepak Chowdary Edara mail

link https://doi.org/10.54216/JISIoT.130110

Volume & Issue

Vol. Volume 13 / Iss. Issue 1

Details open_in_new