A Study on Decision Making and Teaching Competency: Processing Self Perception and Cognitive Schema through Neutrosophic Science

Saziye

doi:https://doi.org/10.54216/IJNS.240413

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.

groups

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

Details open_in_new

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.

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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

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International Journal of Neutrosophic Science | 2024

Convergence of Filters on Bornological Vector Spaces and Neutrosophic Filters

In this research, we construct new type of convergence of bornological vector spaces called convergence of filters through using conception bounded sets. As well, we have considered several characteristics of these concepts like Fréchet filter associated with sequence, filter that has a unique limit and ultra-filter which is very useful in the study of neutrosophic topological spaces and neutrosophic filters.

groups

Fatma Al-Basri mail -

Asawer Khdeidan mail

link https://doi.org/10.54216/IJNS.240409

Volume & Issue

Vol. Volume 24 / Iss. Issue 4

Details open_in_new

International Journal of Neutrosophic Science | 2024

A Study on First and Second Order Bipolar Fuzzy Topological Spaces and Crisp Topological Spaces and Analyzing the Connections Between Them

In our previous paper we discussed about the concept of SOBPFS, SOBPFT and its mathematical modelling in medical diagnosis. In this paper, the detailed study about SOBPFT accordance with FOBPFT and crisp topological spaces are analysed and also some natural examples of SOBPFT are provided. In third section, the connections between FOBPFT and SOBPFT under five different cases are discussed. And last section tells that, from a crisp topology τ on X there exists three different SOBPFT denoted by (ω(τ)) ̂, (ω_* (τ)) ̂ and (ω_ε (τ)) ̂ and from a SOBPFT on X there exists three crisp topologies denoted by i(τ ̂_B ), i^* (τ ̂_B ) and i_ε (τ ̂_B ).  

groups

Muthamizhselvi S. mail -

V. M. Vijayalakshmi mail

link https://doi.org/10.54216/IJNS.240410

Volume & Issue

Vol. Volume 24 / Iss. Issue 4

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International Journal of Neutrosophic Science | 2024

Type-I extension Diophantine neutrosophic interval valued soft set in real life applications for a decision making

We describe certain operations and present the theory of the Type-I extension Diophantine neutrosophic interval valued soft set. Additionally, we go over an algorithm that uses the Type-I soft set model to address the decision-making problem. We present a similarity measure between two Type-I extension Diophantine neutrosophic interval valued soft sets and talk about how it might be used in practical applications. A few exemplary cases are provided to demonstrate their practical application in solving uncertain problems.

groups

Lejo J. Manavalan mail -

Sadeq Damrah mail -

Mutaz M. Abbas Ali mail -

Abdallah Al-Husban mail -

M. Palanikumar mail

link https://doi.org/10.54216/IJNS.240411

Volume & Issue

Vol. Volume 24 / Iss. Issue 4

Details open_in_new

International Journal of Neutrosophic Science | 2024

Enhancing Predictive Accuracy of Insurance Stock Market in Jordan using Hyprid GFS.Thrift Model: A Genetic Fuzzy System-based Fintech Approach

This study focuses on improving the predicting accuracy of the daily ASE's weighted price index of the insurance sector (ICI) using a nonlinear spectral model called maximum overlapping discrete wavelet transform (MODWT) with five mathematical functions, namely, Haar, Daubechies (d4), least square (la8), best localization (bl14), and Coiflet (c6). Using a nonlinear spectral model called maximum overlapping discrete wavelet transform (MODWT) with five mathematical functions—Haar, Daubechies (d4), least square (la8), best localization (bl14), and Coiflet (c6)—this study aims to increase the daily ASE's weighted price index of the insurance sector's (ICI) prediction accuracy. The model utilizes a genetic fuzzy system based on Thrift's methodology (GFS.Thrift). The Amman Stock Exchange (ASE) supplied a dataset with 4,478 observations for the purpose of the study. The dataset represented daily data from January 2, 2006, to March 24, 2024.  The adaptive GFS.THRIFT model was trained with 90% of the dataset, while the remaining 10% was used to test its prediction performance. Multiple egressions and multicollinearity tests were used to select input variables such as standardized foreign direct investment (FDI), standardized value traded (VT) and consumer price index (CPI). Insights from this study indicate that all input variables are positively related to the output variable. Secondly, the proposed model (MODWT-Haar-GFS. Thrift) significantly outperforms other existing models including the GFS. Thrift model.

groups

Jamil J. Jaber mail -

Anwar Al-Gasaymeh mail -

Maha Shehadeh mail -

Asma S. Alzwi mail

link https://doi.org/10.54216/IJNS.240412

Volume & Issue

Vol. Volume 24 / Iss. Issue 4

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2024

Fractal Dimension on CBCT Images and Modular Neural Networks to Identify Reduced Bone Mineral Density in Women

This paper provides two different methods to diagnose osteoporosis in women; the first method is the fractal analysis evaluated by CBCT at two bone locations (the mandible and the second cervical vertebrae) to see if there is any correlation between the two. At the same time, the second method is deep convolutional neural networks (DCNNs). One hundred eighty-eight patients' mandibular CBCT images were used, and DCNN models based on the ResNet-101 framework were employed. Dual X-ray absorptiometry of the hip and lumbar spine revealed that 139 of the 188 postmenopausal women tested had osteoporosis, whereas 49 had average bone mineral density. The second cervical vertebra and the mandible were selected as locations of interest for FD analysis on the CBCT images. Measurement accuracy, both within and between observers' agreements, and correlations between two data sets were all calculated. To evaluate osteoporosis, we used a segmented, three-phase approach. Stage 1 was devoted to the identification of mandibular bone slices. In Stage 2, the coordinates for the mandible's cross-sectional views were established, and Stage 3 calculated the thickness of the mandible bone, emphasizing osteoporotic variations.  The average FD values within the interest area of the mandible were significantly lower in people with osteoporosis than in those with average bone mineral density. At the same time, the two groups had no significant difference in FD values at the second cervical vertebra. For the mandibular site, areas beneath the curve were 0.644 (P = 0.008), while the area under the curve for the vertebral site was 0.531 (P = 0.720). DCNN training in the first stage yielded an astounding 98.85% training accuracy, the second stage decreased L1 loss to a meager 1.02 pixels, and the bone thickness computation method used in the last stage had a mean squared error of 0. 8377. We concluded that FD was underutilized even though it distinguished between women with normal BMD and those with osteoporosis in the mandibular area. Additionally, even with small mandibular CBCT datasets, the results show the value of a modular transfer learning approach for osteoporosis detection.

groups

Eman Shawky Mira mail -

Ahmed Mohamed Saaduddin Sapri mail -

Taseer Bashir mail -

Khalid Hassan mail -

Abdulhameed Saeed Alghamdi mail -

Yousef Almasaabi mail -

Nagham Talal Maddah mail -

Hind F. Kayal mail -

El-Sayed M. El-Kenawy mail -

Mohamed Saber mail

link https://doi.org/10.54216/JISIoT.130111

Volume & Issue

Vol. Volume 13 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2024

A Study on Decision Making and Teaching Competency: Processing Self Perception and Cognitive Schema through Neutrosophic Science

The objective of this research is to examine the decision-making processes of teachers and explore their self-assessments of teaching competency levels based on the competency indicators proposed by the Ministry of National Education (MoNE) in Turkey. The study adopts a constructivist perspective, offering a fresh look at the cognitive levels of teachers and their decision-making mechanisms. Additionally, it integrates neutrosophic science principles to address the uncertainties and indeterminacies present in teachers' self-evaluation and decision-making processes. Data were gathered using the "General Competencies for Teaching Profession (GCTP)" scale, which was developed according to the competencies defined by the MoNE. This new scale, featuring 15 Likert-type items, was validated and tested for reliability before being administered to a sample of 320 volunteering teachers from various disciplines in Turkey. The scale measures data within the "Professional skills" domain and captures teachers' self-perceived competency beliefs related to their professional skills, considering factors such as years of teaching experience, gender, subjects taught, and the type of school (primary or secondary) where they are employed. SPSS 16.0 was used for data analysis and to obtain descriptive statistics for the item results. The analysis revealed that primary school teachers scored higher on the GCTP scale compared to high school teachers. By incorporating neutrosophic science, the study effectively navigates the uncertainties in assessing teaching competencies, offering a more nuanced understanding of the factors that influence teachers' decision-making processes.

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Saziye Yaman mail

link https://doi.org/10.54216/IJNS.240413

Volume & Issue

Vol. Volume 24 / Iss. Issue 4

Details open_in_new