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Journal of Cybersecurity and Information Management | 2025

Smart Energy Management in Green Cloud Computing using Machine Learning Algorithms

Cloud computing has many advantages as well as some disadvantages. An internet connection is required to use Cloud Computing. In other words, it is not possible to access the data in cases without internet. Cloud Computing can provide infrastructure services, platform services and software services to individuals with any device connected to the internet. If the connection speed is low when there is internet, the data transmission is also slower. In this context, it may not be practical for individuals or institutions to benefit from Cloud Computing in places where internet connection is low, limited, or absent. A new technology was obtained in this study; this method depends on deep learning and machine learning techniques applied to detect the attacks in the cloud computing-based systems. The suggested method compared with many traditional machine learning techniques.

groups
Assel Hashim Salman mail -
Abdullahi Abdu Ibrahim mail
link https://doi.org/10.54216/JCIM.150203

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

Details open_in_new
Journal of Cybersecurity and Information Management | 2025

Dual Convolutional Neural Network for Skin Cancer Classification

Skin cancer detection through deep learning is an evolving field, where convolutional neural networks (CNNs) have proven to be very effective in feature extraction. However, this approach still faces some limitations due to the use of data augmentation, It is the generation of artificial images. Which significantly increase the computational load without generate new clinically meaningful data and may introduce shadowed features. Therefore, this study aims to propose a new approach that use CNNs to extract important features from skin cancer medical images using the HAM 10000 dataset. The proposed approach involves training two different CNN architectures, extracting features from convolutional layers, and then use PCA to make the retrieved features less dimensional. In order to categorize skin cancer into seven different categories of skin lesions, the remaining features are then merged and fed into a classifier that uses neural networks. In comparison to earlier studies that employed CNN architectures on the same dataset, the results demonstrated that this method preserves significant information while improving computational efficiency and achieving superior classification performance. The suggested approach achieved 95.66% accuracy for multi-class classification.

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Raya Sattar Shahadha mail -
Belal Al-Khateeb mail
link https://doi.org/10.54216/JCIM.150204

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

Details open_in_new
Journal of Cybersecurity and Information Management | 2025

Quantum Machine Learning for Video Compression: An Optimal Video Frames Compression Model using Qutrits Quantum Genetic Algorithm for Video multicast over the Internet

The transmission of video is greatly aided by video compression. Redundancy (spatial, temporal, statistical, and psycho-visual) within and between video frames is something that video compression approaches aim to get rid of. The degree to which similarity-based redundancy exists between consecutive frames, however, is a function of how often the frames are sampled and how the objects in the scene are moving. Existing neural network-based video compression approaches rely on a static codebook to perform compression, which prevents them from adapting to new video’s data. In order to create an optimal codebook for vector quantization, which is then employed as an activation function inside a neural network's hidden layer, this research offers a modified video compression method based on a Qutrits based Quantum Genetic Algorithm (QQGA). Using quantum parallelization and entanglement of the quantum state, QQGA is capable of solving the same set of problems as a traditional genetic algorithm while considerably accelerating the evolutionary process. The technique is built on the concept of utilizing Qutrits (three-level quantum system) to represent population individuals. The evolution operator, which is responsible for the updates to the quantum system state, has been constructed using a straightforward approach that does not need a lookup table. Compared to qubit, qudit provides a larger state space to store and process information, and thus can enhance the algorithm’s efficiency. To create the context-based initial codebook, the background subtraction algorithm is used to extract moving objects from frames. Moreover, important wavelet coefficients are compressed losslessly using Differential Pulse Code Modulation (DPCM), whereas low energy coefficients are compressed lossy using Learning Vector Quantization neural networks (LVQ). To obtain a high compression ratio, Run-Length Encoding is then used to encode the quantized coefficients. In comparison to the conventional evolutionary algorithm-based video compression method, experiments have shown that the quantum-inspired system may achieve a greater compression ratio with acceptable efficiency as evaluated by PSNR.

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Oday Ali Hassen mail -
Huda Lafta Majeed mail -
Mohammed Abdulhasan Hussein mail -
Saad M. Darwish mail -
Omar Al-Boridi mail
link https://doi.org/10.54216/JCIM.150205

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

Details open_in_new
Journal of Intelligent Systems and Internet of Things | 2025

Social Media Platform Based Evaluation of Teaching Style on Online Education System using Heuristic Search with Stacked Sparse Autoencoder Model

As online education has become increasingly prominent, the primary objective of this study is to evaluate students' opinions of online classes taught by teachers with no prior experience in online teaching, focusing on their teaching style, teaching efficiency, and pedagogy in the online classroom. Online teaching is a kind of teaching system that depends on network management technology. It concludes the teaching method by the process of live courses or recorded courses employing software containing special online teaching environments and any APP software employed for teaching. Social media, with its massive pool of user-generated content and instant feedback, offers a great opportunity to calculate teaching styles in online class management. Therefore, this study offers a Social Media Based Evaluation of Teaching Style in Online Education Systems using Heuristic Search (SMBETS-OESHS) Algorithm. The main objective of the SMBETS-OESHS technique for evaluate teaching styles in online education systems using insights derived from social media platforms. At primary stage, the SMBETS-OESHS model takes place linear scaling normalization (LSN) is implemented for scaling the input data. Next, the bayesian optimization algorithm (BOA) based feature selection process can be employed to allow for the detection of the most relevant features from the data. In addition, the SMBETS-OESHS model exploits stacked sparse autoencoder (SSAE) technique for classification process. In order to achieve optimal performance, the SSAE model parameters are fine-tuned using the improved beetle optimization algorithm (IBOA), ensuring robust evaluation accuracy. The experimental validation outcome of the SMBETS-OESHS algorithm undergoes and the performances are examined over various measures. The simulation outcome stated that the enhanced solution of the SMBETS-OESHS system over the recent techniques.

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Walaa Fouda mail -
Sanjar Mirzaliev mail -
Reneh Abokhoza mail
link https://doi.org/10.54216/JISIoT.140210

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new
Journal of Intelligent Systems and Internet of Things | 2025

Artificial Intelligence based Automated Sign Gesture Recognition Solutions for Visually Challenged People

Gesture recognition is employed in human-machine communications, enhancing human life with impairments or who depend on non-verbal instructions. Hand gestures role an important role in the field of assistive technology for persons with visual impairments, whereas an optimum user communication design is of major importance. Many authors with substantial development for gesture recognition modeled several methods by using deep learning (DL) methods. This article introduces a Robust Gesture Sign Language Recognition Using Chicken Earthworm Optimization with Deep Learning (RSLR-CEWODL) approach. The projected RSLR-CEWODL algorithm majorly focuses on the recognition and classification of sign language. To accomplish this, the presented RSLR-CEWODL technique utilizes a residual network (ResNet-101) model for feature extraction. For optimal hyper parameter tuning process, the presented RSLR-CEWODL algorithm exploits the CEWO algorithm. Besides, the RSLR-CEWODL technique uses a whale optimization algorithm (WOA) with deep belief network (DBN) method for the sign language recognition method. The simulation result of the RSLR-CEWODL algorithm is tested using sign language datasets and the outcome was measured under various measures. The simulation values demonstrated the enhancements of the RSLR-CEWODL technique over other methodologies.

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Khalid Hamed Allehaibi mail
link https://doi.org/10.54216/JISIoT.140211

Volume & Issue

Vol. Volume 14 / Iss. Issue 2

Details open_in_new
International Journal of Neutrosophic Science | 2025

Boosting Road Damage Detection via DEMATEL with Bipolar Neutrsophic Dombi for Intelligent Smart City Infrastructure

In decision-making, NS permits the representation of information with three membership functions: indeterminacy (I), false (F), and truth (T). All components in an NS have indeterminacy, non-, and membership degrees that are autonomous and vary from (0-1). This generates NS particularly appropriate in composite decision-making situations where information is incomplete, ambiguous, or contradictory, which allows strong and more complex solutions and analysis. Detecting road damage accurately and quickly enables the capability of road maintenance agencies to generate timely maintenance to road surfaces, retain optimum road conditions, enhance the safety of transportation, and reduce transportation charges. Research on road damage detection using AI models achieved more attention at present, particularly in smart cities. This paper develops a Boosting Road Damage Detection using DEMATEL with Bipolar Neutrosophic Dombi and Siberian Tiger Optimization (BRDD-DBNDSTO) algorithm. The presented BRDD-DBNDSTO technique is mainly intended to improve the accuracy and reliability of road damage classification for intelligent smart city infrastructure. To accomplish this, the BRDD-DBNDSTO technique employs adaptive bilateral filtering (ABF) using image preprocessing to effectively enhance image quality by reducing noise. Then, the SqueezeNet method was used to create a collection of feature vectors. For the classification and detection of road damage, the DEMATEL with bipolar neutrosophic Dombi model is exploited. At last, the Siberian tiger optimization (STO) algorithm is used to adjust the parameters related to the classifier model. To guarantee the improved performance of the BRDD-DBNDSTO method, an extensive experimental study was carried out and the gained outcomes illustrate the improvement of the BRDD-DBNDSTO model across the existing techniques.

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Imène Issaoui mail -
Afef Selmi mail
link https://doi.org/10.54216/IJNS.250318

Volume & Issue

Vol. Volume 25 / Iss. Issue 3

Details open_in_new
International Journal of Neutrosophic Science | 2025

Effective Data Classification using Interval Neutrosophic Covering Rough Sets based on Neighborhoods for FinTech Applications

Neutrosophic set (NS) is particularly appropriate in applications where data is incomplete, unclear, or inconsistent, which offers an effectual means for analyzing and exhibiting complex mechanisms. An NS is a mathematical technique to manage uncertainty, indeterminacy, and imprecision. It enlarges classical sets, IF sets, and fuzzy sets by presenting three degrees such as indeterminacy (I), false (F), and truth (T). Financial technology (Fintech) plays an essential part in advancing modern society, technology, economies, and various fields. Financial crisis prediction (FCP) plays a crucial role in shaping economic outcomes. Past research has predominantly focused on using deep learning (DL), machine learning (ML), and statistical methods to forecast the financial stability of business. In this manuscript, we focus on the development of Effective Data Classification using Interval Neutrosophic Covering Rough Sets based on Neighborhoods and Multi-Strategy Improved Butterfly Optimization (EDCINCRS-MSIBO) Algorithm for FinTech Applications. It contains distinct kinds of stages such as data normalization, feature selection, classification, and parameter tuning. In the EDCINCRS-MSIBO technique, a min-max normalization-based data pre-processing model to scale the raw data into a uniform format. For feature subset selection, the whale optimizer algorithm (WOA) is employed to reduce the dimensionality and improve model efficiency by selecting the most relevant features. In addition, the EDCINCRS-MSIBO technique takes place interval neutrosophic covering rough sets (INCRS) classifier is utilized for detection and classification of a financial crisis. Finally, a multi-strategy improved butterfly optimization algorithm (MSIBOA) is exploited for the optimum parameter adjustment of the INCRS model. To confirm the better predictive solution of the EDCINCRS-MSIBO model, a wide range of simulations are executed on the two benchmark databases. The comparative result analysis displays the encouraging outcomes of the EDCINCRS-MSIBO method on the existing techniques

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Maksim Kuznetsov mail -
Irina Kosorukova mail -
Veronika Denisovich mail -
Elena Klochko mail -
Alexey Dengaev mail
link https://doi.org/10.54216/IJNS.250319

Volume & Issue

Vol. Volume 25 / Iss. Issue 3

Details open_in_new
International Journal of Neutrosophic Science | 2025

Optimizing Financial Fraud Detection: Understandings from Variable Selection with Neutrosophic Vague Soft Set

Neutrosophy is the neutralities study and prolongs the discussion of the truth of opinions. Neutrosophic logic might be used in all sectors, to provide the solution for the indeterminate challenges. Some real-time data experience issues like inconsistency, incompleteness, and indeterminacy. A fuzzy set (FS) offers an uncertain solution, and an intuitionistic fuzzy set (IFS) processes partial data, but both fail to handle uncertain data. Financial fraud, believed as a deceptive strategy to gain financial assistance, has recently become a common threat in organizations and companies. Traditional methods namely manual inspections and verifications are costly, time-consuming, and imprecise to identify such fraudulent actions. With the development of artificial intelligence (AI), machine learning (ML)-based algorithms are applied logically to identify fraud transactions by investigating a larger amount of financial data. Therefore, the study offers an Optimizing Financial Fraud Detection using Bayesian Optimization and Variable Selection with Neutrosophic Vague Soft Set (OFFDBO-VSNVS) Algorithm. The OFFDBO-VSNVS model presents an optimized framework for fraud detection by integrating advanced variable selection techniques and classification models. Initially, the OFFDBO-VSNVS technique applies the Z-score data normalization technique to transform input data into a compatible layout. Next, the grey wolf optimizer (GWO)--based feature selection to effectively reduce dimensionality and highlight the most relevant features. For the classification and detection of financial fraud, the neutrosophic vague soft set (NVS) model can be employed. Eventually, the Bayesian optimization (BO) model adjusts the hyperparameter values of the NVS algorithm optimally and outcomes in greater classification performance. The stimulated outcome study of the OFFDBO-VSNVS model occurs and the outcomes are examined in terms of changing features. The experimental study represented the superiority of the OFFDBO-VSNVS method across the existing state-of-the-art methods

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Z.A. Latipov mail -
K.A. Naminova mail -
I.S. Abdullayev mail -
A.E. Ilyin mail -
R.A. Shichiyakh mail -
E. Laxmi Lydia mail
link https://doi.org/10.54216/IJNS.250320

Volume & Issue

Vol. Volume 25 / Iss. Issue 3

Details open_in_new
International Journal of Neutrosophic Science | 2025

Pythagorean Neutrosophic Bonferroni Mean Based Machine Learning Model for Data Analytics and Sentiment Classification of Product Reviews

To handle incomplete and indeterminate data, neutrosophic logic/set/probability was recognized. The neutrosophic falsehood, truth, and indeterminacy modules show symmetry as the truth and the falsehood appear the similar and perform in a symmetrical method with esteem to the indeterminacy module which aids as a line of the symmetry. Soft set is a general mathematical device to deal with uncertainty. Sentiment analysis (SA) is the foremost task of natural language processing (NLP), where judgments, opinions, thoughts, or attitudes toward an exact subject are removed. Web is a rich foundation of information and unstructured covering numerous text documents with reviews and opinions. The detection of sentiment will be useful for governments, discrete business groups, and decision-makers. With this motivation, this study develops a Data Analytics Framework for Sentiment Classification Using Pythagorean Neutrosophic Bonferroni Mean (DAFSC-PNBM) technique on Product Reviews. The presented DAFSC-PNBM technique mainly aims to determine the nature of sentiments based on product reviews. Primarily, data preprocessing is performed to increase the product review qualities. For the word embedding process, word2vec model is used. Besides, the DAFSC-PNBM model uses the Pythagorean Neutrosophic Bonferroni Mean (PNBM) technique for classification. To enhance the SA performance of the PNBM model, the grey wolf optimizer (GWO) model has been applied as a hyperparameter tune process. The experimentation outcome analysis of the DAFSC-PNBM method occurs and the outcomes are investigated under several features. The experimental study indicated the improvement of the DAFSC-PNBM method across the modern techniques

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Donia Badawood mail
link https://doi.org/10.54216/IJNS.250321

Volume & Issue

Vol. Volume 25 / Iss. Issue 3

Details open_in_new
International Journal of Neutrosophic Science | 2025

A new generalization of interval-valued Q-neutrosophic soft matrix and its applications

Decision-making theory is an effective way to help the decision-maker take the right path to solve a problem. Among the applications of this theory is the medical field, i.e. allowing the decision maker (doctor) to analyze patient data and judge the result of this analysis as to whether the patient is infected or not. In this path and to enrich this theory with more flexible mathematical methods, we present in this work a more flexible expanded method for a previous concept called Interval-valued Q-neutrosophic soft matrix (IV-Q-NSM) as a new generalization of previous mathematical tools. These tools deal with the two-dimensional uncertainty issues that exist in many areas of life. Next, some ordinary algebraic properties and matrix operations have also been studied. After that, we present a new methodology for the decision-making (DM) selection problems in medical diagnoses.

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Yousef Al-Qudah mail -
Abdulqader O. Hamadameen mail -
Nadia Abdalla Kh mail -
Faisal Al-Sharqi mail
link https://doi.org/10.54216/IJNS.250322

Volume & Issue

Vol. Volume 25 / Iss. Issue 3

Details open_in_new