Applying Neutrosophic Chi-Square Test and Social Structures to Analyze Gender Parity

Ketty M.; Michael R.; Percy T.; Kathy L.; Paul C.

doi:https://doi.org/10.54216/IJNS.240219

Journal of Neutrosophic and Fuzzy Systems | 2024

On Some Novel Results About Weak Fuzzy Complex Integers

This paper is dedicated to studying for the first time the concept of weak fuzzy complex integers division and units, where we present a full classification of weak fuzzy complex integer units with necessary and sufficient conditions for division in the set of weak fuzzy complex integers. On the other hand, we provide an algorithm for solving weak fuzzy complex linear congruencies with many related examples that explain how algorithms work.

groups

Mohammad Abobala mail

link https://doi.org/10.54216/JNFS.080202

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new

Galoitica: Journal of Mathematical Structures and Applications | 2024

Turiyam Based Four Way Unknown Profile Characterization on Social Networks

Recently Turiyam set and its algebra is introduced for dealing with the data containing human quantum cognition. This paper tries to extend the applications of Turiyam set while profile characterization at Social Network. To achieve this goal, a method is proposed for characterization of social network profiles in true regions (t), false region (f), Neutro region (i) and Liberal or Turiyam region (l), independently. The proposed method also provides implementation to understand the concept of Turiyam logic. In addition, many real life examples for data with Turiyam attributes is given with its four way characterization.

groups

Prem Kumar Singh mail -

Naveen Surathu mail -

Ghattamaneni Surya Prakash mail

link https://doi.org/10.54216/GJMSA.0100203

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2024

Fused and Cascaded Squeeze Excitation Network for Pneumonia Detection

Pneumonia is a medical condition affecting 100 million people globally, and rates are predicted to reach epidemic levels within the next several decades. As a result of the air sacs in both or even one lung becoming inflamed, the patient may experience fever, chills, and trouble breathing. Coughs with pus may also occur. Various organisms can cause pneumonia, including bacteria, viruses, and fungi. Early detection of pneumonia can allow the severity of the purulent material to be reduced. The ability of computer-aided detection techniques to reliably diagnose pneumonia has made them popular among scientists. We used a pre-trained Inception V3Net, Squeeze Excitation-based deep Convolutional Neural Network (SE-CNN) that was trained on the Kermany dataset and the RSNA Pneumonia Detection Challenge dataset in this study. In early-stage detection, the suggested technique beat previous state-of-the-art networks, achieving 91% precision in severity rating. Furthermore, our network's accuracy, recall, f1-score, as well as quadratic weighted kappa were reported to be 91.56%, 91%, and 90%, respectively. In terms of processing time and space, our suggested framework is simple, precise, and effective.

groups

Ramitha M. A. mail -

N. Mohanasundaram mail -

R. Santhosh mail

link https://doi.org/10.54216/FPA.160110

Volume & Issue

Vol. Volume 16 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2024

Link-Based Xcorr Normalization and Attention Mechanism for Predicting the Threats over the Network Model

Sensor Networks (SNs) play an essential role in upcoming technologies like the Internet of Things (IoT), where technical services are highly prone to crucial vulnerability due to attacks. This research motivates to provide a mechanism to identify the link reliability of connected sensor nodes. The privacy-preserving keys are distributed among the corresponding network nodes. When the nodes suffer from an attack, it damages the linking nodes' community. It has the nature of healing itself when the attacks are identified over the network. The self-healing nature is not so complex, and it is termed a lightweight process. A novel link-based intrusion prediction mechanism uses attention-based Deep Neural Networks (-DNN) for lightweight linkage identification and labelling. This model helps predict basic network patterns using topological analysis with better generalization. The simulation is done with Python where the proposed -DNN model outperforms the five different conventional approaches with the adoption of a benchmark dataset (network traffic) for extensive analysis. The AUC is improved in an average manner with the adoption of -DNN. This model enhances the linkage connectivity to make different connectivity processes more efficient and reach the target non-convincing. It is sensed that the proposed -DNN outperforms the existing approaches by improving the network resilience by maintaining higher energy efficiency.

groups

V. Jemmy Joyce mail -

K. Rebecca Jebaseeli Edna mail -

P. Sherubha mail -

Arivazhagi mail

link https://doi.org/10.54216/JCIM.130208

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2024

Hybridization of Deep Sequential Network for Emotion Recognition Using Unconstraint Video Analysis

The reliable way to discern human emotions in various circumstances has been proven to be through facial expressions. Facial expression recognition (FER) has emerged as a research topic to identify various essential emotions in the present exponential rise in research for emotion detection. Happiness is one of these basic emotions everyone may experience, and facial expressions are better at detecting it than other emotion-measuring methods. Most techniques have been designed to recognize various emotions to achieve the highest level of general precision. Maximizing the recognition accuracy for a particular emotion is challenging for researchers. Some techniques exist to identify a single happy mood recorded in unrestricted video. Still, they are all limited by the processing of extreme head posture fluctuations that they need to consider, and their accuracy still needs to be improved. This research proposes a novel hybrid facial emotion recognition using unconstraint video to improve accuracy. Here, a Deep Belief Network (DBN) with long short-term memory (LSTM) is employed to extract dynamic data from the video frames. The experiments conducted uses decision-level and feature-level fusion techniques are applied unconstrained video dataset. The outcomes show that the proposed hybrid approach may be more precise than some existing facial expression models.

groups

P. Naga Bhushanam mail -

Selva Kumar S. mail

link https://doi.org/10.54216/JCIM.130209

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2024

Modelling an Improved Swarm Optimizer and Boosted Quantile Estimator For Malicious Flow Monitoring And Prediction In Network

For a long time, malware has posed a significant risk to computer system security. The effectiveness of conventional detection techniques based on static and dynamic analysis is restricted due to the quick advancement of anti-detection technologies. In recent years, AI-based malware detection has increasingly been employed to combat malware due to its improved predictive ability. Unfortunately, because malware may be so diverse, it can be challenging to extract features from it, which makes using AI for malware detection ineffective. A malware classifier based on an Improved Salp Swarm optimization for feature selection and a Boosted tree with Conditional Quantile Estimation (ISSO-BCQE) is developed to adapt different malware properties to solve the problem. Specifically, the malware code is extracted, and the feature sequence is generated into a boosting tree where the feature map of the node is extracted using BCQE, where a boosting network is used to design a classifier and the method's performance is finally analyzed and compared. The results show that our model works better than other approaches regarding FPR and accuracy. It also shows that the method beats current methods with the highest accuracy of 99.6% in most detecting circumstances. It is also stable in handling malware growth and evolution.

groups

U. Harita mail -

Moulana Mohammed mail

link https://doi.org/10.54216/JCIM.130210

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2024

Heterogeneous Wireless Sensor Network Design with Optimal Energy Conservation and Security through Efficient Routing Algorithm

A heterogeneous wireless sensor network (H-WSN) comprises multiple sensor nodes having varied abilities, like diverse processing power and sensing range. H-WSN deployment and topology control seem to be more difficult than homogeneous WSNs. Research on H-WSNs has increased in the last few years to improve real-time sensor networks' reliability and deliver better networking services than a homogenous WSN does. When it comes to H-WSN's energy consumption and security, the major problem remains the efficient routing process.  To that end, this research aims at demonstrating how an efficient routing algorithm of hierarchical H-WSN can greatly enhance the network's performance. It is important to note that the nodes' capabilities mostly determine the suitability of a given routing algorithm. Hence, the H-WSN design issues for routing in a heterogeneous environment are discussed in this paper. This research designs an Optimal Energy Conservation and Security-aware Routing Algorithm (OECS-RA) for H-WSN using clustering and a secure-hop selection scheme. In this proposed model, the optimal cluster head selection and routing have been found through various computational stages based on the energy conservation of each sensor node. It further secures the transmission by selecting the secured node with credential factor computation and comparing each hop of the optimal route. The MATLAB simulation scenario finds the significant performance of the routing mechanism with security compared to existing models. The proposed OECS-RA gives highly recognizable throughput, lifetime, energy efficiency, and reliability. With these results, this proposed algorithm is suggested for real-time implementation in the medical industry, transportation, education, business, etc.

groups

D. Bhanu mail -

R. Santhosh mail

link https://doi.org/10.54216/JCIM.130211

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2024

Prediction of Skin Lesions Using Integrated Multi-Layered Network Model with Baseline Learning Approaches

Skin cancer has become more common in recent decades, raising severe concerns about world health. Creating an automated system to distinguish between benign and malignant images is challenging because of the subtle variations in how skin lesions appear. This study introduces Computer-Aided Diagnosis (CAD) system that offers high classification accuracy while maintaining low computing complexity for categorizing skin lesions. The system incorporates a pre-processing stage that uses morphological filtering to remove hair and artefacts. With the least minimum of human interaction, deep learning techniques are employed to separate skin lesions automatically. Image processing methods are currently being utilized to investigate the automated implementation of the prediction criteria for distinguishing between benign and malignant melanoma lesions. Various pre-trained convolutional neural networks (CNNs) with multi-layered (ML-CNN) are under examination for the classification of skin lesions as either benign or malignant. The best performance is achieved when RF, k-NN and XGBoost are combined, according to average 5-fold cross-validation findings. The outcomes also demonstrate that data augmentation works better than acquiring novel images for training and testing purposes. The experiment results show that the suggested diagnostic framework performs better than existing methods when used on actual clinical skin lesions, with accuracy at 97.5%, F1-score at 91.3%, precision at 96.5%, sensitivity at 89.2% and specificity at 96.7%. It also takes 2.6 seconds to complete with the MNIST dataset and accuracy at 98.2%, F1-score at 92.5%, precision at 98.4%, sensitivity at 92.3% and specificity of 97.2% with the ISIC dataset. This indicates that medical professionals can benefit from using the suggested framework to classify various skin lesions.

groups

Arpita Roy mail -

Shaik Razia mail

link https://doi.org/10.54216/JCIM.130212

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2024

Neutrosophic ANFIS Machine Learning Model and Explainable AI Interpretation in Identification of Oral Cancer from Clinical Images

This paper introduces a new Neutrosophic Adaptive Neuro-Fuzzy Inference System paired with Explainable Artificial Intelligence to classify oral cancer from clinical photos. The ANFIS model’s interpretability and accuracy have been enhanced in resolving challenging medical images by deploying Neutrosophic logic on a 1000-image dataset to solve the word indeterminacy. A combination of Neutrosophic sets addresses ambiguity, enabling an adaptive neuro-fuzzy network to learn from data to accurately classify oral cancer. This exhibits the benefits of fuzzy logic and neural networks in action. The parameters of this model have been changed meticulously to increase sensitivity, specificity, and accuracy toward diagnostic readiness. These results reflect a substantive enhancement in the model’s ability to distinguish between benign and malignant lesions by delivering accurate and understandable diagnostic decisions existence for clinical adoption. AI medical diagnostic confidence increases the understanding of how the model makes decisions. The ideal objective is to develop a strong, dependable, and easy-to-understand tool to diagnose cancer early. The experimentation on this model can be improved as it may lead to real-time testing, more data for the testing dataset, and using how many types of cancer the model can be applied.

groups

Sakshi Taaresh Khanna mail -

Sunil Kumar Khatri mail -

Neeraj Kumar Sharma mail

link https://doi.org/10.54216/IJNS.240218

Volume & Issue

Vol. Volume 24 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2024

Applying Neutrosophic Chi-Square Test and Social Structures to Analyze Gender Parity

This paper examines the disparities in job opportunities and social prosperity based on gender within Peruvian universities, particularly focusing on the Universidad Peruana Los Andes during 2021-2022. Utilizing Neutrosophic Social Structures and the Neutrosophic 2-tuples Technique, we statistically analyze the entrenched biases that categorize careers by gender, contributing to power imbalances and unequal employment rates between men and women. By modeling student data through intervals or neutrosophic numbers as per Smarandache's theory, we address the unique engagement of each student with their academic environment. Neutrosophic contingency tables are employed to present this data, and a neutrosophic chi-square test is applied to examine the correlation between students' gender and their major fields of study, which include Administrative and Accounting Sciences, Health Sciences, Law and Political Sciences, Engineering Sciences, and Pedagogical Sciences. This neutrosophic approach allows for a nuanced understanding of the indeterminate and complex nature of social phenomena, providing a clearer insight into gender parity in academic professional development.

groups

Ketty M. Moscoso-Paucarchuco mail -

Michael R. Vásquez-Ramírez mail -

Percy T. Avila-Zanabria mail -

Kathy L. Javier-Palacios mail -

Paul C. Calderon-Fernandez mail

link https://doi.org/10.54216/IJNS.240219

Volume & Issue

Vol. Volume 24 / Iss. Issue 2

Details open_in_new