Complex Proportional Assessment Based Neutrosophic Approach for Ransomware Detection in Cybersecurity IoT System

Louai A.

doi:https://doi.org/10.54216/IJNS.250203

Fusion: Practice and Applications | 2025

Advanced Stress Detection and Analysis Framework using Integration of FFT, SVM, and CNN

With the prevalence of stress-related disorders on the rise, there is an increasing demand for advanced methodologies that can effectively detect and analyze stress levels. In response to this need, this research explores the integration of Fast Fourier Transform (FFT), Support Vector Machine (SVM), and Convolutional Neural Network (CNN) techniques for unlocking insights into stress dynamics from Electroencephalogram (EEG) signals. Stress, a multifaceted phenomenon with far-reaching implications for mental health, necessitates innovative approaches for its identification and management. The study begins by elucidating the complexity of stress and its impact on individuals' well-being, highlighting the urgency for accurate and efficient stress detection methodologies. Building upon this foundation, the technical intricacies of FFT, SVM, and CNN integration are explored, elucidating their respective roles in the stress detection framework. The FFT method is employed for spectral analysis of EEG signals, providing a foundation for identifying stress-related patterns in the frequency domain. The application of Artificial Neural Networks (ANNs) for feature extraction and classification is explored, leveraging their capacity to discern intricate relationships within EEG data structures. Complementing ANNs, Support Vector Machines (SVMs) are harnessed for stress level classification, capitalizing on their robustness and efficiency in handling high-dimensional data spaces. Furthermore, Convolutional Neural Networks (CNNs) are integrated into the framework to automatically learn hierarchical features from raw EEG signals, enhancing the accuracy and efficacy of stress detection methodologies. Through comprehensive evaluation and comparison with existing algorithms, the integrated approach demonstrates superior performance across key metrics. Stress detection algorithms, such as SVM, exhibit accuracy levels ranging from 70% to 96.5%, with our proposed approach achieving remarkable results. The integrated model achieves an accuracy of 96.5% and an Area under the Curve (AUC) of 0.98, surpassing existing methods in terms of accuracy, sensitivity, specificity, and AUC.

groups

V. H. Ashwin mail -

R. Jegan mail -

Subha Hency Jose mail -

P. Rajalakshmy mail -

P. Anantha Christu Raj mail

link https://doi.org/10.54216/FPA.170107

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

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Fusion: Practice and Applications | 2025

An Energy-Efficient Cluster-Based Routing Protocol for WBAN in Elk Herd Optimizer

A wireless body area network (WBAN) is a wireless sensor network (WSN) that is essential to monitor patient health. Sensor nodes (SNs) are commonly positioned either inside or outside the patient's body within this network. These nodes have the ability to send data to the sink node if any functional modifications in the patient are observed. Delivering efficient routing and energy management of network nodes is a complex effort in WBAN. The energy efficiency of SNs is a primary challenge to the effective deployment of WBAN. To handle this problem, a new metaheuristic optimization algorithm called Elk Herd Optimizer (EHO) is proposed in this research. This research aims to focus on energy-efficient routing methods in WBAN sensors that are connected to the human body to enhance health monitoring efficiency. The proposed WBAN model includes the deployment of eight biosensor nodes on the human body. The primary objective is to minimize the energy utilization of WBANs by selecting the most appropriate cluster heads (CHs) based on the EHO. The EHO-based routing protocol showed higher performance in WBANs in terms of energy consumption, End-to-End (E2E) delay, packet delivery rate (PDR), network lifetime (NLT), packet loss rate (PLR), and throughput. The research model was validated by comparing its findings with the existing routing protocols. The research model surpassed all the comparable models in terms of energy consumption, latency, NLT, PDR, PLR, and throughput. The routing protocol based on the EHO algorithm improves energy efficiency by effectively selecting CHs and routing paths. The EHO model efficiently reduces the total time delay, which is essential for monitoring health in real time. It achieves a high PDR while maintaining a low packet loss rate. Furthermore, the EHO-based routing extends the longevity of the network. Additionally, it enhances network performance, hence facilitating uninterrupted and dependable monitoring of health data.

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D. Abdul Kareem mail -

D. Rajesh mail

link https://doi.org/10.54216/FPA.170108

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

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Fusion: Practice and Applications | 2025

Skin Lesion Classification using Deep Learning Methods

The incidence of cancer cases has been rising rapidly over the last few decades. Skin cancer is one of the widely found types of cancer, is further classified into two main types, Melanoma and Non-Melanoma. Though Melanoma is less common than other types of skin cancer, it can be lethal if not treated promptly. But it is not the only type of skin lesion that needs attention. It becomes necessary to promptly identify and classify the skin lesions for the recovery of the patient. The machine learning models of Deep Learning prove to be very efficient in this regard. Hence, we developed a deep learning model which is an ensemble of InceptionV3, Xception and ResNet152 models. It can classify the skin lesions into seven main types -Melanoma, Melanocytic Nevi, Benign Keratosis-like lesions, Basal cell carcinoma, actinic keratosis, vascular lesions, Dermatofibroma. The method was applied to dermoscopic images from the HAM10000 dataset. The presence of noise and artifacts in the images makes it difficult to classify. So, as a preprocessing step, we performed hair removal on the dermoscopic images which is a series of methods that starts with blackhat filtering, subsequently creating a mask for inpainting and then applying the inpainting algorithm. Further Contrast enhancement was performed by applying the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm on the luminance channel of HSV image to improve the contrast of the image and also makes sure that it is not over-amplified. It is then followed by Skin Lesion Segmentation where a grabcut algorithm is applied on the enhanced image which segments the image. Thus, the segmented images are produced which are fed to the Model for training and testing. To cope up with the unbalanced dermoscopy image dataset available, we performed Image augmentation on the images generated in the previous step which alters the existing images to create some more images for the model training process, thus solving the problem of paucity of dataset and substantially increases the performance of the model. The final dataset generated is fed to the three deep learning models InceptionV3, Xception and Resnet152 which achieved an accuracy of 84.6%, 86.5% and 86.7% respectively. These were later given to two different ensemble models - Stacking and Random Forest. The Stacking model achieved an accuracy of 88.6% and Random Forest achieved an accuracy of 92.59%. The proposed system includes a GUI for a good user experience.

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Nyemeesha .V mail -

M. Kavitha mail

link https://doi.org/10.54216/FPA.170109

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Real Time Sign Recognition using YOLOv8 Object Detection Algorithm for Malayalam Sign Language

Sign language recognition is important for enhancing message and user-friendliness for the community of deaf and hearing-impaired people. This paper proposes a Malayalam Sign Language (MSL) method using sign language that emerged from the state of Kerala. The main factor contributing to this emergence of such regional sign language is the absence of a standardized and consistent approach to the use of Indian Sign Language (ISL) in various states. This is due to the variations in signs, grammar, and syntax used in different regions. The system uses the You Only Look Once v8 (YOLOv8) algorithm-based object detection method which is based on Convolution Neural Network (CNN), a widely accepted deep learning neural network design employed mainly in computer vision. As the dataset for MSL is not publicly available, we used an MSL video from YouTube provided by the National Institute of Speech and Hearing for training a custom model. We pre-processed the video to extract the frames and annotate them with sign labels. Then, we trained the YOLOv8 algorithm on the annotated frames to detect the hand region and recognize signs in real time. The proposed approach achieved an accuracy of 97.21% calculated from the mean Average Precision value on the MSL dataset. The result achieved outperformed other existing approaches even while using less dataset count compared to others.

groups

Esther Daniel mail -

V. Kathiresan mail -

Priyadarshini .C mail -

Golden Nancy .R mail -

P. Sindhu mail

link https://doi.org/10.54216/FPA.170110

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Wielding Neural Networks to Interpret Facial Emotions in Photographs with Fragmentary Occlusions

For many years, scientists have studied the way people express their emotions through body language and facial expressions. However, it is extremely difficult to accurately interpret the emotions of a person from just a single image. Interpreting facial emotions in photographs is a complex task. It is challenging to accurately detect facial emotions with the help of neural networks when the face is occluded with fragmentary blocks. With the advent of technology, emotion detection has become more accurate and reliable. It is now possible to use facial expression recognition in images to detect emotions such as happiness, sadness, anger, fear, surprise, and more. This research discusses the effectiveness of using neural networks to identify facial emotions in photographs with occlusions present. The datasets like Fer2013 dataset, CREMA-D and RAVDESS were used to train the model and the datasets were altered by implanting occlusions randomly in the images. The altered datasets were also used to evaluate the model. The challenges and opportunities that arise when neural networks are used in this context are explored. Additionally, insight is also provided into the best approach to accomplish the task.

groups

K. Anji Reddy mail -

K. Sivarama Krishna mail -

Bhanu Prakash Battula mail -

Bajjuri Usha Rani mail -

P. V. V. S. Srinivas mail

link https://doi.org/10.54216/FPA.170111

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new

Prospects for Applied Mathematics and Data Analysis | 2023

The Computation of Particular Roots of Nonlinear Complex Equations of the Form: (an√is K + (x+10y) n√is)n = c

Solving polynomial equations involves finding their roots. In this respect, this idea dominates the minds of many mathematicians about how to find those roots. The Abel Ruffini theorem emphasizes that there is no general formula involving only the coefficients of a polynomial equation of degree five or higher that allows us to compute its solutions using radicals and its associate to the Galois Theory. The mathematical need for solving polynomial equations represents the motivation for the development of systems of numbers from Natural numbers to Complex numbers throughout the history of mathematics. Complex numbers play a central role in this context. The Fundamental Theorem of Algebra tell us that every nonconstant polynomial equation with complex coefficients has at least one complex root. While the Galois group associated with a polynomial captivates its symmetries and determines whether it is solvable by radicals. From a mathematical standpoint, it is customary to visualize polynomials in the form:P_n (x)=a_n x n+a_(n-1) x (n-1)+---+a_1 x 1+a_0, Where the set of coefficients {a_n, a_(n-1),---,a_0}ECand P_n (x)EC[x]. We have reconceptualized the polynomial generated by the formula (ax+y)^n=c in our previous work and computing radicals of more degree 5. In this article, we present a natural procedure formula that will lead us to find a solution for a class of polynomials nonlinear Complex numbers with degree 𝑛 associated with the equation:(ansquris K + (x+10y) nsquris)n = c as a particular class of Complex Polynomials.

groups

Adel Al-odhari mail -

Shaker AL -Assadi mail

link https://doi.org/10.54216/PAMDA.030104

Volume & Issue

Vol. Volume 3 / Iss. Issue 1

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Journal of Cybersecurity and Information Management | 2025

A Novel Authentication Mechanism with Efficient Mathematic Based Technique

The security of any device or data on it is greatly dependent on the authentication and session handling. Using an MFA-based OTP method, the most popular web apps, such as communication mail, social media platforms, and financial transactions, manage spoofing attempts and attempt to keep them to a minimum. There is statistical evidence that indicates that between April 2020 and March 2022, this well-known OTP mechanism lost 1434.75 crore rupees, further weakening its hold on security. This unusual situation is driving research toward authentication methods that rely solely on itself without external aid. In order to improve security, self-dependent authentication methods (passwords, combinations of image clicks, etc.) have not been streamlined or made sufficiently dynamic. By comparing state-of-the-art methods, the suggested work, Mathematic Based Technique (MBT), will enhance the dynamic behaviour of passwords and optimize to give greater security. In the event of an eavesdropping assault, the Mathematic Based Technique (MBT) will make it difficult for hackers to pull the efforts to crack the password with the probability with permutation value is equal to O (7810). Mathematical proof of the result is provided, and it is compared to the six best state-of-the-art mechanisms which are now in use, those are Picasso Pass (PP) which uses layered mechanism, Dynamic Password Protocol (DPP) which uses date and time in it, Dynamic Pattern Image (DPI) which resembles mobile pattern authentication, Dynamic Array Pin (DAP) which uses area based pin or a pre-defined pin, and Bag of Password (BP) which uses image.

groups

Balajee R. M. mail -

Suresh Kallam mail -

M. K. Jayanthi Kannan mail

link https://doi.org/10.54216/JCIM.150107

Volume & Issue

Vol. Volume 15 / Iss. Issue 1

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International Journal of Neutrosophic Science | 2025

On Two Novel Generalized Versions of Diffie-Hellman Key Exchange Algorithm Based on Neutrosophic and Split-Complex Integers and their Complexity Analysis

The objective of this paper is to build the Split-Complex version of Diffie-Hellman key Exchange Algorithm, where we use the mathematical foundations of Split-Complex Number Theory and Integers, such as congruencies, raising a split-complex integer to a power of split-complex integer to build novel algorithms for key Exchange depending of famous Diffie-Hellman algorithm. Additionally, we present the proposed version of the Diffie-Hellman algorithm based on neutrosophic number theory. Also, we analyze the complexity of the novel algorithms with many examples that explain their applied validity.

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Dima Alrwashdeh mail -

Talat Alkhouli mail -

Ahmed Soiman Rashed Alhawiti mail -

Ali Allouf mail -

Hussein Edduweh mail -

Abdallah Al-Husban mail

link https://doi.org/10.54216/IJNS.250201

Volume & Issue

Vol. Volume 25 / Iss. Issue 2

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International Journal of Neutrosophic Science | 2025

An Efficient Plant Disease Detection: Possibility Neutrosophic Hypersoft Set Approach with Whale Optimization Algorithm

Indian agriculture aims at achieving sustainable development, which increases crop production per square unit without damaging the ecosystem and natural resources. Timely and prompt diagnosis and analysis of plant diseases are very beneficial in increasing food crop productivity and plant health and decreasing plant diseases. Plant disease specialists are not accessible in distant regions therefore there is an urgent need for reliable, automatic low-cost, and approachable solutions to detect plant disease without the expert’s opinion and laboratory inspection. Classical machine learning (ML)-based image classification techniques and Deep learning (DL)-based computer vision (CV) approaches such as Convolutional Neural Networks (CNN) was employed to detect plant disease. Neutrosophic set (NS), a generality of fuzzy set (FS) and intuitionistic FS (IFS), presented to characterize inconsistent, uncertain, imprecise, and incomplete data in realistic conditions. Besides, interval NS (INSs) was exactly proposed to resolve the problems with a collection of numbers in the actual entity. On the other hand, there are high levels of operational reliability for INSs, along with the decision-making method and INS aggregation operators. This study presents an Efficient Plant Disease Detection using the Possibility Neutrosophic Hypersoft Set Approach (EPDD-pNSHSS) method. The suggested EPDD-pNSHSS method uses the DL method for the recognition and classification of plant diseases. Initially, the EPDD-pNSHSS method takes place the Median filtering (MF) through the preprocessing to progress image superiority and eliminate noise. In the meantime, the possibility neutrosophic hypersoft set (pNSHSS) classifier is utilized for the detection of diseased and healthy leaf images. To optimize the detection accuracy of the pNSHSS mechanism, the whale optimization algorithm (WOA) is employed for adjusting the hyperparameter value of the DSAE technique. Wide-ranging experiments are implemented to exhibit the supremacy of the EPDD-pNSHSS method. The empirical findings showcased the development of the EPDD-pNSHSS method over other existing techniques.

groups

Abdalla Ibrahim Abdalla Musa mail -

Mohammed Abdullah Al-Hagery mail

link https://doi.org/10.54216/IJNS.250202

Volume & Issue

Vol. Volume 25 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025

Complex Proportional Assessment Based Neutrosophic Approach for Ransomware Detection in Cybersecurity IoT System

A neutrosophic set (NS) is an advanced computational technique that accesses uncertain information via three membership functions. A soft expert set (SES) is derived from the hypothesis of a “soft set” with computer technology. Currently, this method is utilized in various domains such as intelligent systems, measurement theory, probability theory, cybernetics, game theory, and so on. Internet user faces a myriad of risks with the development of malware worldwide. The most prominent type of malware, Ransomware, encrypts confidential data without releasing the files until the user makes a ransom payment. Internet of Things (IoT) framework is a wide region of Internet-related devices with further computation capacities with storage capabilities that can be damaged by malware creators. Ransomware is a cruel and new malware on Internet with increasing attack levels. Ransomware encrypts the whole information to make users incapable of accessing important information and their files. In this article, we propose a Complex Proportional Assessment Based Neutrosophic Approach for Ransomware Detection in Cybersecurity (CPABNA-RDCS) methodology in IoT environment. The objective of the CPABNA-RDCS approach is to identify and categorize the ransomware to accomplish cybersecurity in the IoT network. Primarily, the CPABNA-RDCS method exploits min-max normalization for scaling the input dataset into relevant format. Meanwhile, the ransomware classification takes place via Complex Proportional Assessment Based Neutrosophic (CPABN) method. Finally, grey wolf optimizer (GWO) is employed for optimum hyperparameter choice of the CPABN system. The experimental results of the CPABNA-RDCS method are inspected on benchmark data. The simulation analysis emphasized the developments of the CPABNA-RDCS method over other existing techniques.

groups

Louai A. Maghrabi mail

link https://doi.org/10.54216/IJNS.250203

Volume & Issue

Vol. Volume 25 / Iss. Issue 2

Details open_in_new