A Numerical Study of Neutrosophic Finite Difference Method and Some Applications

Isra Al-Shbeil; Ahmad A. Abubaker; Sara A. Khalil; Maha Alammari; Mohamed Soueycatt; Abdallah Al-Husban

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

Journal of Intelligent Systems and Internet of Things | 2026

Cloud IoT with Remote Sensing Data Segmentation and Classification Using Deep Learning Model for Sustainable Agriculture

  Sustainable Development Goals of United Nations are focused on enhancing agricultural production that has the potential to be transformational at the local as well as the global level. The available technologies in agriculture management that are based on Internet of Things (IoT) encourage sustainable production of more food by farmers, which contributes significantly to the achievement of these SDGs. The aim of this research is to propose novel technique in sustainable agriculture field analysis based on cloud IoT model with remote sensing and deep learning model. Here the cloud IoT model is used in agriculture field based remote sensing data analysis. This image has been segmented using watershed K-means temporal neural network (WKMTNN) and classification is carried out using deep quantile regressive Boltzmann machine (DQRBM). The experimental analysis has been carried out in terms of random accuracy, average precision, sensitivity, specificity for various agriculture field dataset. Proposed model attained average precision 96%, sensitivity 93%, random   accuracy 98%, and Specificity 95%.  These results highlight the superiority of the moisture estimation framework against their regression-based counterparts.

groups

T. Shanmugapriya mail -

RM. Rani mail -

Gaddam Ravindra Babu mail -

T. Srinivasulu mail -

S. Saranya mail -

S. Gopinath mail -

M. Rajesh mail

link https://doi.org/10.54216/JISIoT.180118

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2026

Climate Change Prediction in Urban Environment Using UAV Imaging Based on Cloud IoT and Deep Learning Techniques

Advancements in Unmanned Aerial Vehicles (UAVs), popularly identified as drones, offer unprecedented opportunities to improve various applications of Extensive Internet of Things (IoT). In this framework, Deep Learning (DL) techniques are considered a practical alternative for improving the real-time obstacle detection and avoidance performance of fully autonomous UAVs. This research propose novel technique in urban environment climate change detection utilizing UAV image based on cloud IoT with deep learning model. Here the UAV images has been collected through cloud IoT module and prepared for dataset. This dataset with UAV images has been processed for filtering and contour reduction by normalization. Then processed image features are extracted utilizing graph cut fuzzy convolutional ResNet attention neural network with moath firefly sparrow colony optimization model. The simulation results has been analyzed for various UAV dataset in terms of training accuracy, average precision, recall, QoS, scalability. Proposed technique Average precision of 97%, QOS of 92%, SCALABILITY of 96%, training accuracy of 98%, RECALL of 95%.

groups

M. Prema Kumar mail -

P. Chinnasamy mail -

B. Bala Abirami mail -

Juvvala Sailaja mail -

S. Bhuvana mail -

Sai Krishna Vunnam mail

link https://doi.org/10.54216/JISIoT.180119

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2026

Design and Construction of the Word Embedding Model for Automated Bug Detection Using Deep Learning Techniques

Software quality assurance teams can increase productivity and efficiency by expediting the issue-fixing process through automatic localization of bug files. Although source code and bug reports provide valuable semantic information, current bug localization techniques typically underuse it. Numerous deep learning and word embedding models have been developed over time. The word-embedding model used to represent bug reports and the deep learning model used for categorization determine how effective those methods are. Aim of this research is to construct word-embedding method, which has been automated for bug detection using deep learning techniques. Here the input data has been collected as software design based monitored data and processed. Then this data has been analyzed using Bi-LSTM voting vector word embedding model and the feature classification is carried out using convolutional naïve bays attention perceptron neural network in bug detection model. The experimental analysis is carried out in terms of training accuracy, precision, Mean square error, F-1 score, and recall. Furthermore, cross-training datasets from the same and distinct domains are used to gauge how effective the suggested approach is. For datasets in the same domain, suggested system obtains a good high accuracy rate; for datasets in separate domains, it achieves a poor accuracy rate.

groups

Khasimbee Shaik mail -

K .V. Satyanarayana mail -

Tirimula Rao Benala mail

link https://doi.org/10.54216/JISIoT.180120

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

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.

groups

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.

groups

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.

groups

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