Deep Spectral Convolution Neural Network Based Leukemia Cancer Detection Using Invariant Entity Scalar Feature Selection

B.; A.

doi:https://doi.org/10.54216/JCIM.140203

International Journal of Neutrosophic Science | 2025

Energy of Fuzzy, Intuitionistic Fuzzy, and Neutrosophic Graphs in Decision Making-A Literature Review

This review of the literature delves into the complex interplay between energy measures and decision-making processes in the domains of fuzzy graphs, intuitionistic fuzzy graphs, and neutrosophic graphs. In graph theory, energy is a key quantity that is used to measure structural properties and evaluate decision model dynamics. The research methodically examines the theoretical underpinnings, computational techniques, and practical applications of energy measures in contexts involving decision-making, considering the special features brought forth by fuzzy, intuitionistic fuzzy, and neutrosophic graph models. This review attempts to provide a thorough understanding for researchers and practitioners looking to use energy measures for efficient decision support in the setting of uncertainty contained within these specific graph topologies by synthesizing prior research.  

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Sasipriya A. S. mail -

Hemant Kumar mail

link https://doi.org/10.54216/IJNS.250123

Volume & Issue

Vol. Volume 25 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2025

Counterpart of Marshall-Olkin bivariate copula with negative dependence and its neutrosophic application in meteorology

Variables that have revived new interest through computational developments and extensive data analysis. This article contributes to the subject by generalizing the bivariate copula introduced recently in8 and based on the concept of the counter-monotonic shock method. The proposed copula has the feature of covering the full range of negative dependence induced by two dependence parameters, which is not so common in the specialized literature. We examine the main characteristics of this copula. In particular, the absolutely continuous and singular copula components are derived. Analytical expressions of important concordance measures, such as Spearman’s rho and Kendall’s tau, are established, along with expressions of the product moments. A real neutrosophic data set, based on the daily quality of air in the New York Metropolitan Area, is used to illustrate the applicability of the proposed copula, with quite convincing results.  

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Rachid Bentoumi mail -

Farid El Ktaibi mail -

Christophe Chesneau mail

link https://doi.org/10.54216/IJNS.250124

Volume & Issue

Vol. Volume 25 / Iss. Issue 1

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International Journal of Neutrosophic Science | 2025

Leveraging Double-Valued Neutrosophic Set for Real-Time Chronic Kidney Disease Detection and Classification

Chronic kidney disease (CKD) is a non-communicable disease that has made a significant contribution to admission, morbidity, and mortality rates of patients globally. CKD is a common kidney disease that happens when both kidneys fail, and the CKD patient suffers from these conditions for a long time. Machine learning (ML) is becoming more crucial in medical diagnoses as it allows detailed examination, thus reducing human error and optimizing prediction accuracy. Now, ML classifiers and algorithms are highly dependable techniques for the diagnoses of diverse diseases such as diabetes, heart disease, liver disease, and tumor disease predictions. A neutrosophic set (NS) is especially suitable in applications where information is vague, incomplete, or inconsistent, which provides an effective means for analyzing and modeling intricate mechanisms. A NS is a mathematical approach to handle indeterminacy, uncertainty, and imprecision. It expands IF sets, classical sets, and fuzzy sets by introducing three degrees: truth (T), indeterminacy (I), and false (F). This manuscript offers a Double-Valued Neutrosophic Set for Chronic Kidney Disease Detection and Classification (DVNS-CKDDC) technique. In the DVNS-CKDDC technique, three major processes are involved. At the primary phase, the DVNS-CKDDC technique performs a linear scaling normalization (LSN) model. Next, the DVNS-CKDDC technique makes use of the DVNS model for the identification of CKD. Finally, the beluga whale optimization (BWO) algorithm is employed for the parameter tuning of the DVNS method. To ensure the supremacy of the DVNS-CKDDC technique, a widespread simulation analysis is involved. The experimental values stated that the DVNS-CKDDC approach attains improved performance over other models  

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G. Nalinipriya mail -

M. Suneetha mail -

Maria Mikhailova mail -

Sripada NSVSC Ramesh mail -

Kollati Vijaya Kumar mail

link https://doi.org/10.54216/IJNS.250125

Volume & Issue

Vol. Volume 25 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2025

Role of Rough Neutrosophic Attribute Reduction with Deep Learning-Based Enhanced Kidney Disease Diagnosis

The kidneys have an important role in keeping blood pressure, electrolyte sense, and acid-base sense of body balance to remove toxins from our body. Malfunction is responsible for irrelevant to life-threatening diseases, along with malfunction in the other functional organs. As a result, scholars worldwide have committed to finding methods for effectively diagnosing and accurately treating chronic kidney disease. As machine learning (ML) classifier is widely deployed in the healthcare field for diagnoses, also CKD is now involved in the collection of disorders that could be predicted through the ML classifier. Neutrosophic logic (NL) can be employed as a form of logic that expands classical, fuzzy, and intuitionistic fuzzy logic (IF) by integrating a third constituent: indeterminacy. It enables data handling and representation with three dissimilar membership functions: truth (T), indeterminacy (I), and false (F). The complete set is independent and may differ in the interval [0, 1], providing a convoluted strategy to handle, data incompleteness, vagueness and uncertainty. This makes NL especially relevant in complicated systems where data might be partially unknown, ambiguous, or inconsistent. This article employs a Rough Neutrosophic Attribute Reduction with Deep Learning based Enhanced Kidney Disease Diagnosis (RNSAR-DLKDD) technique. Initially, the RNSAR-DLKDD technique reduces the attributes via the RNSAR technique. Followed by, the detection and classification of kidney disease take place using long short-term memory (LSTM) model. Finally, the hyperparameter selection process is carried out via crow search algorithm (CSA). To highlight the performance of the RNSAR-DLKDD technique, a series of experiments were involved. The extensive results inferred the betterment of the RNSAR-DLKDD technique over other models

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Alexey Yumashev mail -

P. Udayakumar mail -

Sripada NSVSC Ramesh mail -

E. Laxmi Lydia mail -

Kollati Vijaya Kumar mail

link https://doi.org/10.54216/IJNS.250126

Volume & Issue

Vol. Volume 25 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2025

Socioeconomic and environmental impacts of dehydrated whey protein extraction: an analysis using the neutrosophic PEST-SWOT approach.

The extraction of dehydrated proteins from whey is not only a technological innovation in the field of biotechnology, but also a complex intersection of socioeconomic and environmental factors that deserve detailed evaluation. This article delves into the analysis using the neutrosophic PEST-SWOT approach, revealing how the political, economic, social, and technological dimensions interact with the strengths, opportunities, weaknesses, and threats of this emerging practice. The neutrosophic methodology allows us to unravel nuances that other approaches might overlook, highlighting both the potential benefits and the possible negative repercussions that may arise in different contexts. Whey, traditionally considered waste, is revalued by being transformed into a source of protein, which has profound implications for sustainability and the circular economy. However, neutrosophic analysis also exposes the complexities and ambiguities inherent to this activity. From an environmental perspective, whey extraction and processing pose significant challenges, such as energy consumption and waste generated, that must be carefully managed. In the socioeconomic sphere, the creation of new value chains can generate employment and foster innovation, but it can also destabilize existing markets and generate inequalities. Adopting a neutrosophic approach allows for a more holistic evaluation, recognizing the coexistence of multiple truths and the need for a balance between the various interests involved. Thus, this article invites deep reflection on the implications of technology, proposing an informed and multifaceted debate on its future development and application.

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Edwin Fabián Cerda Andino mail -

Jaime Orlando Rojas Molina mail -

Nuria Danae Toapanta Naranjo mail -

Dina Mariel Yánez Sánchez mail

link https://doi.org/10.54216/IJNS.250127

Volume & Issue

Vol. Volume 25 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2025

A Study Using Treesoft Set and Neutrosophic Sets on Possible Soil Organic Transformations in Urban Agriculture Systems.

In the current context of accelerated urbanization and the urgent need for sustainability, urban agriculture has become a vital alternative to guarantee food security and ecological management of cities. This study addresses possible soil organic transformations in these systems using Treesoft Set and neutrosophic sets. Treesoft Set, an advanced tool for complex data analysis, is complemented by neutrosophic set theory, which allows you to manage the uncertainty inherent in natural and human systems. Together, these methodologies provide a more complete and detailed view of how urban land can adapt and improve under sustainable agricultural practices, highlighting the importance of integrating technology and ecology in the design of green cities. The analysis carried out not only unravels the dynamics of soil organic transformations, but also highlights the variability and complex interactions that occur in urban environments. Research shows that, through the application of Treesoft Set and neutrosophic sets, it is possible to identify patterns and trends that would otherwise go unnoticed. Additionally, it highlights how these tools can influence decision-making to optimize land use and encourage agricultural practices that improve the health of the urban ecosystem. This innovative approach opens new avenues for research and development of urban agriculture, promoting more resilient and efficient systems in the management of natural resources in an increasingly urbanized world.  

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Paolo Chasi Vizuete mail

link https://doi.org/10.54216/IJNS.250128

Volume & Issue

Vol. Volume 25 / Iss. Issue 1

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Journal of Cybersecurity and Information Management | 2024

Enhancing Agricultural Productivity in Uzbekistan: Arduino-Based IoT Framework for Sunflower Seed Yield Improvement through Air and Soil Moisture Monitoring

The core theme of the current investigation is to explore the application of an IoT framework protocol based on an Arduino platform designed to optimize sunflower seed production in Uzbekistan based on the levels of air quality and soil moisture. In essence, the need is to give best actionable intelligence to farmers and the stakeholders in the agricultural sector on crop growing opportunities. The above proposed system involves the use of air quality sensors MQ-135 for instance, and soil moisture sensors. The sensors are connected to Arduino boards to collect necessary data and measurements are recorded every 30 minutes using available WiFi and Bluetooth modules for continuous monitoring. The simulation reveals air quality data of the sunflower fields of the present scenario to be an average at PM2.5 is of 75 µg/m³, which poses danger to the wellbeing of plants. It is further expected that the use of MQ-135 air quality sensors will decrease the overall average of PM2.5 to 45 µg/m³, the local authorities managed to cut emissions by 40% as part of the EU plan. At the present time, the content of the field moistures is 15 % VWC, which is not favorable for sunflower development. Soil moisture sensors for accurate irrigation control is another advance that requires soil moisture levels to rise to 25% vadium weight (VWC), up from 66. 7% improvement. Therefore, it means that the yields from the sunflower seeds are expected to rise from the current average of 1, 500Kg/ha to 1, 875 Kg/ha, which is a 25% enhancement. These results imply that IoT systems developed on the Arduino platform may be used to oversee environmental alteration and increase the agricultural crop yield by a wide margin. The possibility was identified to achieve significant outcomes in increasing sunflower seed production based on this framework when implemented on a larger scale for the benefit of Uzbek farmers.  

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Danish Ather mail -

Abu B. Bin Abdul Hamid mail -

Noor I. Binti Ya’akub mail -

Rajneesh Kler mail

link https://doi.org/10.54216/JCIM.130217

Volume & Issue

Vol. Volume 13 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2024

NCBI Medical Data Encryption with Lossless DNA Compression

The health information data includes reports on the patient’s condition, including addresses, names, tests, treatments, diagnoses, and medical history. It is sensitive information for patients, and all means of protection must be provided to prevent third parties from manipulation or fraudulent use. It has been discovered that DNA is now a reliable and efficient biological media for securing data. Data encryption is made possible by DNA's bimolecular computing powers. In this paper proposed a new strategy of safeguard the transfer of sensitive data over an unsecured network using cryptography with non-liner function, and DNA lossless compression to enhance security. The work gains best results in compression processes, as percentages range 75%. for character compression, the different rate ranges between 91% to 94%, and the compression rate ranges from 35% to 37%. the retrieving data with an accuracy rate up to 100% without any data loss, as well as excellent percentages within the, Compression Ratio, Compression Factor, Error Rate, Accuracy measures.

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Anfal Emad Lafta mail -

Sahar Adil Kadhum mail

link https://doi.org/10.54216/JCIM.140201

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2024

Deep Layered Network Model to Classify Brain Tumor in MRI Images

Brain tumor is a condition due to the expansion of abnormal cell growth. Tumors are rare and can take many forms; it is challenging to estimate the survival rate of a patient. These tumors are found using Magnetic Resonance (MRI) which is crucial for locating the tumor region. Moreover, manual identification is an extensive and difficult method to produce false positives. The research communities have adopted computer-aided methods to overcome these limitations. With the advancement of artificial intelligence (AI), brain tumor prediction relies on MR images and deep learning (DL) models in medical imaging. The suggested layered configurations, i.e., layered network model, are proposed to classify and detect brain tumors accurately. The modified CNN is proposed to automatically detect the important features without any supervision and the convolution layer present in the network model enhances the training feasibility. To improve the quality of the images, some essential pre-processing is used in conjunction with image-enhancing methods. Data augmentation is adopted to expand the number of data samples for our suggested model's training.  The Dataset is portioned as based on 70% for training and 30% for testing. The findings demonstrate that the proposed model works well than existing models in classification precision, accuracy, recall, and area under the curve. The layered network model beats other CNN models and achieves an overall accuracy of 99% during prediction. In addition, VGG16, hybrid CNN and NADE, CNN, CNN and KELM, deep CNN with data augmentation, CNN-GA, hybrid VGG16-NADE and ResNet+SE approaches are used for comparison.  

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Saran Raj S. mail -

S. V. Sudha mail -

K. Padmanaban mail -

P. Sherubha mail -

S. P. Sasirekha mail

link https://doi.org/10.54216/JCIM.140202

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new

Journal of Cybersecurity and Information Management | 2024

Deep Spectral Convolution Neural Network Based Leukemia Cancer Detection Using Invariant Entity Scalar Feature Selection

Leukemia, a cancer that attacks human white blood cells, is one of the deadliest illnesses.   Detecting affected cells in microscopic images becomes tedious because feature variants are not predicted correctly by a hematologist. Therefore image handling techniques failed to select the importance of the features scaling counts, entities, and precise size and shape of cells presented in the microscopic image. To resolve this problem, Deep Spectral Convolution Neural Network (DSCNN) based on Leukemia cancer detection using Invariant Entity Scalar Feature Selection (IESFS) is proposed to identify the risk factor of cancer for early diagnosis. Initially, preprocessing is carried out using cascade Gabor filters. Based on Structural Cascade Segmentation (SCS), the white blood cell regions are categorized into affected and non-affected margins and verify the edges using canny edge mapping. This estimates the scaling cell size, counts, entities and angular cell projection of weights from each segmented feature region. Then find the entity relation of cell projection equivalence using Color Intensive Histogram Equalization (CIHE). After segmenting the angular vector, projection scaling is applied to correlate the entity's object scaling comparator. Then scaling features were selected using Invariant Entity Scalar Feature Selection (IESFS) by averaging the mean depth values of feature weight and trained with a deep convolution neural network for predicting max equivalence entity weights for finding the affected cells and counts in microscopic images. This improves the prediction of cancer cell accuracy as well high performance in sensitivity 92.7 %, specificity 92.3 %, and f-measure 93.6 % with redundant time complexity.  

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B. Divyapreethi mail -

A. Mohanarathinam mail

link https://doi.org/10.54216/JCIM.140203

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new