Time Series Forecasting of Energy Consumption Using Advanced Neutrosophic Statistical and Machine Learning Models

Ammar  Kuti Nasser

doi:https://doi.org/10.54216/IJNS.270107

Journal of Intelligent Systems and Internet of Things | 2026

Feature Weight-Based Optimization in Software Development Model Using Meta Heuristic Machine-Learning Algorithms

System users are increasingly interested in software correctness and efficiency checks prior to usage. Programmers in the twenty-first century are therefore making a conscious effort to create software that is more accurate, more efficient, and less prone to bugs. A software development model utilizing metaheuristic machine learning algorithms involves using metaheuristic optimization techniques to enhance various aspects of the software development lifecycle, such as optimizing machine learning models, hyperparameters, and even software architecture. This research propose novel technique in feature weight model based optimization in software development utilizing Meta heuristic ML method. Here the feature weight and feature selection is carried out for software model using support additive regression Laplacian score perceptron neural network. Then the software model parameter optimization is carried out using ant binary swarm component encoder optimization method. Simulation analysis is carried out in terms of training accuracy, MAR (Mean absolute residual), Mean balanced relative error (MBRE), F-measure.

groups

N. Durga Devi mail -

Tirimula Rao Benala mail

link https://doi.org/10.54216/JISIoT.180121

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2026

Machine Learning Model Based Urban Temperature Analysis with Fuzzy Reinforcement Neural Network

  Temperature increases in metropolitan areas are referred to as urban heat island (UHI) effect. In recent decades, urbanization as well as dramatic increase in population of cities have exacerbated the impact of UHI. The uneven development and growth of the metropolis will lead to an uneven rate of temperature growth in the corresponding area. This work proposes a new machine learning approach based on temperature pattern analysis to determine the rate of deforestation, representing the diversity of geographical regions. The proposed model collect temperature pattern based deforestation data as well as processed for noise removal and normalization. Then this data features has been extracted as well as classified utilizing kernel principal fuzzy reinforcement NN with variational Gaussian encoder markov model. Experimental analysis is carried out in terms of random accuracy, mean precision, AUC, normalized co-efficient, F1 score. Proposed method mean precision was 94%, normalized co-efficient was 97%, AUC was 95%, random accuracy 98%, F1-score 93%.  The most important land use categories causing LST increases were determined by analyzing the landscape composition at the class level.

groups

L. Pallavi mail -

Gattu Shravani mail -

J. Sirisha Devi mail -

Bandaru Satya Lakshmi mail -

M. Pushpalatha mail -

S. Gopinath mail -

M. Rajesh mail

link https://doi.org/10.54216/JISIoT.180122

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2026

AI-based System for Transforming Text and Sound to Educational Videos

Technological developments have produced methods that can generate educational videos from input text or sound. Recently, the use of deep learning techniques for image and video generation has been widely explored, particularly in education. However, generating video content from conditional inputs such as text or speech remains a challenging area. In this paper, we introduce a novel method to the educational structure, Generative Adversarial Network (GAN), which develop frame-for-frame frameworks and are able to create full educational videos. The proposed system is structured into three main phases in the first phase; the input (either text or speech) is transcribed using speech recognition. In the second phase, key terms are extracted and relevant images are generated using advanced models such as CLIP and diffusion models to enhance visual quality and semantic alignment. In the final phase, the generated images are synthesized into a video format, integrated with either pre-recorded or synthesized sound, resulting in a fully interactive educational video. The proposed system is compared with other systems such as TGAN, MoCoGAN, and TGANS-C, achieving a Fréchet Inception Distance (FID) score of 28.75%, which indicates improved visual quality and better over existing methods.

groups

M. E. ElAlami mail -

S. M. Khater mail -

M. El. R. Rehan mail

link https://doi.org/10.54216/FPA.210115

Volume & Issue

Vol. Volume 21 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2026

Distributed Ledger Technology-Enhanced 6G Wireless Communication: Overcoming Trust, Privacy, And Scalability Challenges

The transition from 5G to 6G wireless communication systems introduces new challenges, including scalability, privacy, and security. DLT (Distributed Ledger Technology) technology, with its decentralized and secure framework, offers a promising solution to address these issues in a 6G context. In a 6G environment, DLT can facilitate decentralized management, secure authentication, and trusted data exchanges. By leveraging DLT's distributed ledger system, it can support device identity verification, spectrum allocation, and secure data sharing across nodes, creating a trustworthy communication ecosystem. DLT and 6G integration enables efficient spectrum management, where smart contracts automate resource allocation, reducing bottlenecks and improving resource efficiency. Moreover, the decentralized nature of DLT enhances privacy and security by providing an authentication mechanism that works without central authority. This is crucial, as 6G will involve a vast number of connected devices. This research aims to explore the role of DLT in improving the security and scalability of 6G networks, investigate spectrum management techniques, and evaluate decentralized device authentication and trust mechanisms. Additionally, challenges such as latency, scalability, and DLT integration in 6G are examined. DLT's decentralized nature aids in network security and robustness, mitigating vulnerabilities by distributing control across nodes. It also streamlines resource allocation and device authentication, improving privacy. DLT enables users to manage access rights through decentralized mechanisms, fostering trust and compliance with privacy regulations. However, issues like latency due to transaction validation and the need for advanced techniques like sharding are challenges that must be addressed to optimize DLT for 6G applications.

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R. Sivasankari mail -

S. Amsavalli mail -

Kamarunnisha H. mail -

Vetripriya M. mail -

Tamilselvi S. mail

link https://doi.org/10.54216/JISIoT.180123

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2026

Discovering Novel Types of Irresolute and Contra Mappings for m-Polar Neutrosophic Topological Spaces

The present work explores the features of new kinds of neutrosophic continuous mappings, including neutrosophic irresolute β^*−continuous mapping (NIβ^*CM) and neutrosophic continuous mappings, including neutrosophic contra β^*−continuous mapping (NCOβ^*CM) and investigates some properties related them. Moreover, we study the relationships between these two concepts with the concept of irresolute α^* and contra α^*−continuous mapping. Finally, we introduced m-polar neutrosophic irresolute β^*−continuous mapping (MPNIβ^*CM) and neutrosophic continuous mappings, including m-polar neutrosophic contra β^*−continuous mapping (MPNCOβ^*CM) with investigates some properties related them.

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Mohanad Abdulkareem Hasan Hasab mail -

Shadia Majeed noori mail -

Yaseen, S. R. mail -

S. Khalil mail

link https://doi.org/10.54216/IJNS.270101

Volume & Issue

Vol. Volume 27 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2026

A Unified Framework for Solving Abel's and Linear Volterra Integral Equations and Their Neutrosophic Generalizations Using the GALM Transform

Integral equations, including Abel’s integral equation and linear Volterra integral equations of both the first and second kinds and neutrosophic Abel’s integral equation and linear Volterra integral equations of both the first and second kinds, regularly appear in advanced problems across biology, chemistry, physics, and engineering, often modeling systems with memory effects or time-dependent interactions. This study explores the GALM transform as a powerful and unified method for solving these equations. The exact solution of Abel’s integral equation and its neutrosophic version is derived, demonstrating the transform’s simplicity and efficiency through practical applications. Additionally, the GALM transform is employed to solve linear Volterra integral equations of the first and second kinds with their neutrosophic generalizations, with illustrative examples provided to validate its effectiveness. By addressing a wide range of problems, this research establishes the GALM transform as an accurate, reliable, and versatile tool, offering significant advantages over traditional methods in solving complex scientific and engineering equations.

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Maha Alsaoudi mail -

Gharib M. Gharib mail -

Abdallah Al-Husban mail -

Jeireis A. Abudayyeh mail

link https://doi.org/10.54216/IJNS.270103

Volume & Issue

Vol. Volume 27 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2026

A Numerical Study of Neutrosophic Finite Difference Method and Some Applications

In this paper, we present some results about the neutrosophic-generalized version of finite-difference method, where we prove its essential properties, and we apply it to many different examples to clarify the validity of our work. In addition, some numerical tables related to the results will be clarified and presented.

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Isra Al-Shbeil mail -

Ahmad A. Abubaker mail -

Sara A. Khalil mail -

Maha Alammari mail -

Mohamed Soueycatt mail -

Abdallah Al-Husban mail

link https://doi.org/10.54216/IJNS.270104

Volume & Issue

Vol. Volume 27 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2026

Some Einstein Operations on Rough Neutrosophic Sets with their Properties

Algebraic operations, which include addition, subtraction, division, scalar multiplication, and exponentiation, are the fundamental mathematical operations utilised in decision-making analysis. When performing on numbers, the algebraic operations are commonly referred to as arithmetic operations. Another alternative for algebraic operations, known as Einstein operations, has gained recognition for its smooth approximation and utilisation of Archimedean norms. However, it is crucial to note that Einstein operations are not designed to effectively address issues of indeterminacy, uncertainty, and lower-upper approximation. Thus, this paper defines some rough neutrosophic-based Einstein operations known as RNS Einstein addition, RNS Einstein multiplication, RNS Einstein scalar multiplication, and RNS Einstein exponentiation. By adopting rough neutrosophic sets (RNS), which incorporate neutrosophic lower and upper approximations, the proposed RNS Einstein operations offer a practical approach for handling uncertain situations. Some examples are provided to demonstrate the applicability of the RNS Einstein operations. Several desirable properties related to the defined RNS Einstein operations are investigated. Finally, the proposed RNS Einstein operations are applied in solving multi-criteria decision-making problems within a rough neutrosophic environment.

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Nur Qasfareeny Abdul Halim mail -

Noor Azzah Awang mail -

Nor Hashimah Sulaiman mail -

Hazwani Hashim mail -

Lazim Abdullah mail

link https://doi.org/10.54216/IJNS.270105

Volume & Issue

Vol. Volume 27 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2026

A New Operator via Regular Open Sets in a New Topological Structure

In this paper, we will use the family of regular open sets in a topological space (Z, τ ) to define an operator ΦR : 2Z → 2Z by ΦR(F) = {s ∈ Z : ∃ D ∈ RO(Z, s) with (D − F )c /∈ P} in frame of primal topological spaces. Then we introduce the notion of topology δ-compatible for a primal in a primal topological space and study some of its properties. Finally, we use the concept of δ-semi-open sets to provide additional properties for the operators (⋄ R) and ΦR(F ), and we add many illustrative examples that help clarify the relationships between the concepts that are presented.

groups

Amani Rawshdeh mail -

Ahmad Al-Omari mail

link https://doi.org/10.54216/IJNS.270106

Volume & Issue

Vol. Volume 27 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2026

Time Series Forecasting of Energy Consumption Using Advanced Neutrosophic Statistical and Machine Learning Models

Predicting future energy consumption plays a vital role in maximizing resource utilization, reducing costs, and enhancing sustainability. Researchers employ advanced statistical and machine learning models to improve the accuracy of time series forecasting. Real-world energy consumption data is analyzed using State-Space Models (SSMs), Vector Auto Regression (VAR), Structural VAR (SVAR), Generalized Additive Models for Location, Scale, and Shape (GAMLSS), and Bayesian Structural Time Series (BSTS). An evaluation of Long Short-Term Memory (LSTM) networks and the Prophet model is conducted alongside a comparison with the aforementioned models. The proposed method integrates neutrosophic statistical models for feature extraction and residual analysis, generating outputs suitable for machine learning processing. The results indicate that incorporating judgment-based neutrosophic statistical approaches with AI-driven neutrosophic prediction models yields superior forecasts of power consumption, contributing to more comprehensive and effective energy usage prediction methodologies.

groups

Ammar Kuti Nasser mail

link https://doi.org/10.54216/IJNS.270107

Volume & Issue

Vol. Volume 27 / Iss. Issue 1

Details open_in_new