LRPS Method for Solving Linear Partial Differential Equations and Neutrosophic Differential Equations of Fractional Order with Numerical Solutions

Mohammed Qassim; Mohammed Abed Daim Zoba; Ahmed Hadi Hussain

doi:https://doi.org/10.54216/IJNS.240419

Journal of Cybersecurity and Information Management | 2024

Cyber Security Protection in Roadside Unit Based on Cross-Layer Adaptive Graph Neural Networks (Gnns) in Vanet

The proposed systems can improve cyber security in VANET applications by enabling efficient detection of complex attacks on the RSU component. The subsequent sections discuss the systems that are applied and support the suggestions for improving the VANET trustworthiness. VANETs and show that the utilization of Cross-Layer Adaptive GNNs can improve cyber security and LEARNING in VANET-based RSUs. As a result, the suggested system can provide robust ways for detecting cyber-attacks by: modeling the network architecture using graphs while combining information regarding several protocol layers to detect complicated interactions between the network entities and find the abnormal activities. the nature of the GNN enables it to update in real-time by adapting to the evolving attack patterns and the shifting network conditions, making them sturdy and flexible defense ways for cyber security. The proposed network e systems can efficiently detect multiple cyber threats and focus on reducing the number of false positives while maintaining low computation costs. Therefore, chances are that incorporating the Cross-layer adaptive GNNs into the RSUs can improve cyber security in VANETs, enhancing the robustness and reliability of prospective smart transportation systems.

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Raj Kumar mail -

Sakshi Pandey mail -

Asha KS mail -

Rakesh Kumar Yadav mail -

Abhinav Mishra mail -

Sunil Sharma mail

link https://doi.org/10.54216/JCIM.140112

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2024

Optimizing Brain Tumor Classification Accuracy Through Transfer Learning and Internet of Things Integration

Brain tumor classification using medical images is crucial for identification and therapy. However, brain tumors are complex and vary, making grouping them difficult. This work demonstrates a novel transfer learning method for brain tumor classification. We employ trained Convolutional Neural Networks (CNNs) models and data enrichment approaches to extract meaningful information from medical images. We want to fine-tune the models built on our dataset to uncover hierarchical patterns that distinguish tumor types. Through data enrichment, the training sample becomes more diverse and richer, making the model more generic and robust. Our team's extensive testing and research have shown that the suggested procedure can identify brain tumors. Our machine-learning approach performs better than others in terms of accuracy, sensitivity, specificity, and precision. Our technique improves brain tumor categorization and assures accurate clinical diagnosis. Automated testing systems are one way for physicians to assist patients in selecting the best course of treatment. Researchers may improve classification performance by incorporating modern imaging technology or topic-specific data. The Internet of Things, or IoT, is helping to drive the development of complex real-time data collection, processing, and sharing systems. These technological advancements have transformed medical imaging. This graphic depicts a cutting-edge transfer learning system that may be able to identify brain cancer from medical photos. This technology has the potential to enhance data collection and processing via the Internet of Things. Data augmentation and pre-trained convolutional neural networks may help to extract interpretable medical images. The Internet of Things improved the model's flexibility, resilience, and utility. We achieved this by expanding the training data set. Rapid categorization advancements have made clinical diagnosis more efficient. Classification, deep learning, medical imaging, machine learning, transfer learning, tumor detection, and image analysis all relate to this topic.

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Bhanu Bhushan Parashar mail -

Munesh Chandra mail -

Sachin Malhotra mail

link https://doi.org/10.54216/JISIoT.130112

Volume & Issue

Vol. Volume 13 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2024

An Advance Study of an Efficient CNN-Grounded Deep Learning Classification Technique for the Diagnosis of IoT based Cardiac Arrhythmias

Deep Learning, or DL for short, is an emerging subfield within the larger discipline of machine learning in today's world. The study being conducted in this area is progressing at an immediate stride, and the discoveries are contributing to the progression of technology. Deep learning (DL) methods were developed with the intention of developing a general-purpose learning method that would enable the gradual learning of characteristics at multiple levels without relying on human-engineered features. This was the goal of deep learning. Because of this, the system is able to acquire intricate purposes and directly map input to output by making use of the data that it has acquired which is based on Internet of things (IoTs). This study places an emphasis on the application of CNN (Convolutional Neural Networks), which are a subcategory of DNN (Deep Neural Networks), and it develops an efficient layered CNN for the classification of ECG arrhythmias. Even while FC-ANNs (Fully Connected Artificial Neural Networks), which are sometimes referred to as Multilayer-Perceptron networks, are effective in categorising ECG arrhythmias, the optimization process for many classification networks takes a significant amount of time in terms of computation. In addition, the features extracted by engineers are what define the accuracy of the categorization of ECG arrhythmias. An improved CNN based filtering, feature abstraction, and classification prototypical is established in order to conduct an accurate analysis of an electrocardiogram (ECG). When measured against ANN, the performance was found to have an accuracy rating of 99.6%. Consequently, the CNN model that was suggested is useful to doctors in arriving at the definitive diagnosis of AFL (atrial flutter), AFIB (atrial fibrillation), VFL (ventricular flutter), and VT (ventricular tachycardia). It includes denoising, feature extraction, and categorization as part of its functionality.

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Deepa devasenapathy mail -

Rohit Pachlor mail -

Ramesh M. mail -

G. Shanmugaraj mail -

Aby K. Thomas mail -

K. Sridhar mail

link https://doi.org/10.54216/JISIoT.130113

Volume & Issue

Vol. Volume 13 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2024

An Ensemble Machine Learning Method for Analyzing Various Medical Datasets

In recent years, machine learning (ML) has shown a significant impact in tackling various complicated problems in different application domains, including healthcare, economics, ecological, stock market, surveillance, and commercial applications. Machine Learning techniques are good enough to deal with a wide range of data, uncover fascinating links, offer insights, and spot trends. ML can improve disease diagnosis accuracy, predictability, performance, and reliability. This paper reviews various machine learning techniques applied to different medical datasets and proposes an ensemble method for helping in the early diagnosis of different diseases. The study compares existing machine learning techniques with the proposed ensemble method. The ensemble method uses the AdaBoost algorithm to combine the traits of choice trees, random forests, and support vector machines. Three feature selection techniques, Fisher’s score, information gain, and genetic algorithm, are used to select appropriate dataset features. The ensemble method also uses the K-fold cross-validation technique (where k=15) for validating results. SMOTE was employed to balance some of the datasets because they were quite unbalanced. All the methods used in this study are evaluated based on accuracy, AU Curve, Recall, Precision, and F1-score. The paper uses different medical datasets at the University of California Irvine and the Kaggle directory to compare machine-learning models with the proposed ensemble method. The encouraging results show that the ensemble method outperforms the existing machine-learning techniques. The paper thoroughly analyzes how machine learning is used in the medical industry, covering established technologies and their impact on medical diagnosis. An early diagnosis is needed to prevent people from deadly diseases. Hence, this study proposes an ensemble method that may be used to diagnose different diseases early.

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Chhaya Gupta mail -

Nasib Singh Gill mail -

Priti Maheshwary mail -

Shraddha V. Pandit mail -

Preeti Gulia mail -

Piyush Kumar Pareek mail

link https://doi.org/10.54216/JISIoT.130114

Volume & Issue

Vol. Volume 13 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2024

Systematic Analysis based on Conflux of Machine Learning and Internet of Things using Bibliometric analysis

IoT devices produce a gigantic amount of data and it has grown exponentially in previous years. To get insights from this multi-property data, machine learning has proved its worth across the industry. The present paper provides an overview of the variety of data collected through IoT devices. The conflux of machine learning with IoT is also explained using the bibliometric analysis technique. This paper presents a systematic literature review using bibliometric analysis of the data collected from Scopus and WoS. Academic literature for the last six years is used to explore research insights, patterns, and trends in the field of IoT using machine learning. This study analyses and assesses research for the last six years using machine learning in seven IoT domains like Healthcare, Smart City, Energy systems, Industrial IoT, Security, Climate, and Agriculture. The author’s and country-wise citation analysis is also presented in this study. VOSviewer version 1.6.18 is used to provide a graphical representation of author citation analysis. This study may be quite helpful for researchers and practitioners to develop a blueprint of machine learning techniques in various IoT domains.

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Ayushi Chahal mail -

Santosh Reddy Addula mail -

Anurag Jain mail -

Preeti Gulia mail -

Nasib Singh Gill mail -

Bala Dhandayuthapani V. mail

link https://doi.org/10.54216/JISIoT.130115

Volume & Issue

Vol. Volume 13 / Iss. Issue 1

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Journal of Intelligent Systems and Internet of Things | 2024

Cyber Security Based Application-Specific Integrated Circuit for Epileptic Seizure Prediction Using Convolutional Neural Network

In the event of an epileptic attack, the Field-Programmable Gate Array (FPGA)-accelerated Convolutional Neural Network (CNN) model is paired with Electroencephalogram (EEG) acquisition equipment to produce a reliable production system that can be used in clinical medical diagnosis. Additionally, this study includes cybersecurity to protect both the epileptic patient’s data and the prediction system. Epilepsy is a frequent neurological disorder that manifests as recurrent seizures, a sign that indicates rapid intervention is necessary to minimize adverse events and improve patient health. The study provides a new real-time design for predicting epileptic seizures based on the Application-Specific Integrated Circuit (ASIC)-based Very Large-Scale Integration (VLSI) architecture. As a first step, EEG data from epilepsy patients were captured and pre-processed. Afterwards, faults and artefacts in the data were removed. Additionally, data was divided into short-time windows and then classified as either ictal, pre-seizure, or interictal. The CNN model was adapted for EEG signal analysis and then trained with categorized data. This technique is more effective and efficient for predicting epileptic seizures accurately, which is advantageous for patient monitoring and treatment. Additionally, cybersecurity measures were implemented to secure patient data and the prediction system.

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Bala Dhandayuthapani V. mail -

Deepak Dudeja mail -

Sonia Duggal mail -

Sachin Sharma mail -

Anupriya Jain mail -

Piyush Kumar Pareek mail

link https://doi.org/10.54216/JISIoT.130117

Volume & Issue

Vol. Volume 13 / Iss. Issue 1

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International Journal of Neutrosophic Science | 2024

Neutrosophic Delphi method to analyze the impact of Internships on the comprehensive development of university students

Internships play a crucial role in the comprehensive education of university students as they provide practical experience and promote the development of technical and soft skills. These practices not only promote personal development but also ease the transition into the world of work. The study aims to use a Neutrosophic Delphi method to analyze the extent to which work practices influence the comprehensive education of university students in Ecuador in 2023. A descriptive study was conducted with a sample of 410 students from academies and universities in Ecuador. Country Ecuador. Center of the country This method uses structured surveys to collect qualitative and quantitative data about the experiences, advantages, and skills acquired during internships. The results are presented in the form of data tables and statistical graphics that illustrate the close connection between professional experience and the overall educational level of students. Emphasis was placed on acquiring skills such as teamwork, leadership, and problem-solving. In summary, internships are a valuable learning tool for university students as they provide the opportunity to apply knowledge, develop skills, and improve their employability.

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Nery Elisabeth G. Paredes mail -

Anderson I. Chiliquinga García mail -

Isaac E. Cajas Cayo mail -

Mirian N. Carranza Guerrero mail -

Christian Kümmel mail

link https://doi.org/10.54216/IJNS.240416

Volume & Issue

Vol. Volume 24 / Iss. Issue 4

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International Journal of Neutrosophic Science | 2023

A Neutrosophic Multi-Criteria Methodology to Evaluate Different Competitiveness Indicators of Food and Beverage Companies

Neutrosophic multicriteria analysis of the competitiveness and sustainability of companies in the agri-food sector, with suggestions for improvement strategies. Competitiveness is measured using a tool developed by the IDB (Inter-American Development Bank) that includes 103 indicators and 9 operational areas (strategic planning, value chain, quality assurance, accounting and finance, environmental management, sales, and human resources). Talents and information systems). Sustainability is assessed using the tool proposed by the " InnovaRSE " methodology (from Navarra), which includes 30 indicators divided into three aspects: social, economic, and environmental. The study population was 100 catering establishments officially established according to the Tourism Registration Body. To obtain the sample size, the finite population formula was applied, and the results were obtained for the 20 companies studied. Sampling was done using the "simple random probability" method. In the Spearman correlation test, the P value is "0.01" (there is a connection between the company's competitiveness and sustainability). 16 improvement strategies were developed using diagnostic tools.

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Manuel Antonio A. Z. David mail -

David Santiago C. Molina mail -

Rodolfo M. M. Poma mail -

Diana K. Vinueza Morales mail -

Antonella A. García Camacho mail -

Maha Ibrahim mail

link https://doi.org/10.54216/IJNS.240417

Volume & Issue

Vol. Volume 24 / Iss. Issue 4

Details open_in_new

International Journal of Neutrosophic Science | 2024

A Neutrosophic multi-criteria approach for implementing technology in education

The COVID-19 epidemic has greatly expedited the utilization of technology in the realm of education, resulting in the extensive implementation of totally online teaching approaches. These approaches have undergone thorough analysis in various scholarly articles in recent years. This study applies theories of technology acceptance and use in the educational process, employing Neutrosophic analysis to assess criteria for technology utilization in education. The study commenced by formulating an equation to investigate the patterns of technology uptake and use between 2010 and 2024. Additionally, a comprehensive evaluation of the latest literature since 2000 was conducted to identify prevailing trends. The findings suggest that usage plays a vital role in the Technology Acceptance Model (TAM), and structural equations are used as a method to measure it. Neutrosophic analysis provides a thorough and sophisticated viewpoint on the integration of technology in education, emphasizing both the accomplishments made and the obstacles that still exist in this developing area.

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Darío Díaz Muñoz mail -

Patricia Hernández Medina mail -

Saziye Yaman mail

link https://doi.org/10.54216/IJNS.240418

Volume & Issue

Vol. Volume 24 / Iss. Issue 4

Details open_in_new

International Journal of Neutrosophic Science | 2024

LRPS Method for Solving Linear Partial Differential Equations and Neutrosophic Differential Equations of Fractional Order with Numerical Solutions

In this work, fractional partial equations' and neutrosophic fractional partial equations analytical series solutions are presented, we consider the fractional derivative in the meaning of Caputo in these formulas. We offer a novel objective method the LRPS which is a strong instrument for precise analytically and numerical solutions to these problems by setting an excellent example, we stress precision, effectiveness, and application style, also we can find exact answers when there is a pattern between the series' parts; alternatively, we can only offer approximations. The Mathematica application is used to assess the numerical and graphical findings to make sure the solutions generated are accurate and that the approach can be modified to solve this kind of this problem. The findings obtained demonstrated that our current procedure is appropriate and efficient for resolving PDEs.

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Mohammed Qassim mail -

Mohammed Abed Daim Zoba mail -

Ahmed Hadi Hussain mail

link https://doi.org/10.54216/IJNS.240419

Volume & Issue

Vol. Volume 24 / Iss. Issue 4

Details open_in_new