Neutrosophic Average Edge Connectivity with Applications to Communication Networks

Aparna; Amaresh Chandra; Siva Prasad; Prasanta Kumar; Mana; Said

doi:https://doi.org/10.54216/IJNS.270214

Journal of Intelligent Systems and Internet of Things | 2026

Criminal Activity Classification in Surveillance Videos Using Deep Learning Models

Detecting and identifying crimes in real time represents a very necessary aspect of public safety. Traditional systems are human based monitoring cameras, video surveillance systems are ineffective, time consuming and prone to mistakes. Automated solutions are much needed. Using convolutional neural networks (CNNs) to efficiently examine surveillance video footage is the main goal. This work presents a crime detection system based on deep learning. the study utilize UCF Crime dataset and four deep learning models: ResNet50, EfficientNetB2, Xception, and custom (CNN) were up-graded, trained, and tested. To guarantee best model performance, the suggested approaches required careful dataset preparation, pre-processing, and strategic data separation. By means of fine-tuning, each model addressed the constraints of conventional techniques and enhanced feature extraction and classification accuracy. With extraordinary performance measures of (99.53%) accuracy, (99.07%) precision, (98.43%) recall, and a (98.69%) F1 score, experimental findings show the superiority of the suggested system. These findings reveal the system’s high dependability in detecting and classifying criminal events, thereby far surpassing other CNN-based approaches. The model runs at an average inference speed of (30 ms per frame on CPU), with a lightweight model size of around (20 MB), These results demonstrate the system’s scalability, efficiency, and strong potential for intelligent surveillance applications. This study shows how scalable and effective deep learning models transform crime detection in surveillance systems to support public safety.

groups

Raed Majeed mail -

Hiyam Hatem mail

link https://doi.org/10.54216/JISIoT.180208

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2026

Fault Monitoring in Transmission Lines Using Modular Neural Networks in Simulated Smart Grids

The transmission of energy is one of the main tasks of Electrical Engineering. Transmission lines are used for this purpose, which are susceptible to various problems such as short-circuit, overload, open circuit, and complex faults. From the perspective of smart grids, one of the open challenges is to have autonomous systems that allow the detection, classification, and location of faults in transmission lines. On the other hand, Artificial Neural Networks are computational tools used in classification and control tasks to be applied to different plants and systems. There are several ways to solve problems using ANNs; one is modularity. This strategy consists of dividing the problem into components that are easier to classify. In this way, a modular system is proposed that is composed of three ANNs: One for detection, one for classification, and one more for the location of faults in transmission lines. A simulation model of a three-phase electrical power system was built using Simulink MATLAB, employing a data transmission approach typical of smart grids. Supervised learning and WEKA software were used for network training. Databases were created using the potential difference and line current, as well as the ground fault impedance. The database was developed through cases and mathematical models, and the performance of the networks was evaluated in the simulated model. The results show that the proposed model allows the identification of all cases presented in the test stage (100%), which is a better performance than a single neural network (81.25%) that is responsible for detecting, classifying, and locating faults.

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Sánchez-Juárez J. R. mail -

Aldana-Franco R. mail -

Leyva-Retureta, J. G. mail -

Álvarez-.Sánchez E. J. mail -

López-Velázquez A. mail -

Aldana-Franco F. mail

link https://doi.org/10.54216/JISIoT.180209

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2026

Features Extraction Improvement for Facial Expression Recognition Using HOG and Machine Learning Techniques

Facial Expression Recognition (FER) is a vital aspect of human-computer interaction with applications in healthcare, education security, and affective computing. Even with the success of deep learning, generalizability, interpretability, and efficiency of most systems, especially in uncontrolled settings, are still problematic. In this study, we propose an enhanced feature extraction technique based on Histograms of Oriented Gradient (HOG) where the central difference operator, not the conventional forward difference, used for gradient estimation. The modification enhances the accuracy of gradients, reduces truncation error, and leads to more stable facial feature descriptors. The enhanced HOG is tested on five popular datasets, CK+, JAFFE, MMI, ExpW, and AffectNet, using three traditional Machine Learning (ML) classifiers: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF). Experimental results indicate uniform accuracy enhancements across all the classifiers and datasets, with improvements spiking to 7%–10% and recall and F1-score also witnessing marked increases. In this study, RF registered the maximum accuracy, 97.94%, on CK+ and 95.48% on AffectNet, hence solidifying its stability and dependability. This study shows how well mathematical optimization works with classical ML for FER. The approach we suggest provides an easy-to-understand, small, and quick alternative to deep models, making it perfect for real-time and resource-limited applications.

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Dhiaa M. Abed mail -

Awab Qasim Karamanj mail -

Thura J. Mohammed mail -

Saja B. Attallah mail -

Abusnina M. Mukhtar mail

link https://doi.org/10.54216/JISIoT.180210

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2026

A Hybrid Deep Learning and Fuzzy Logic Framework for PM10 Concentration Forecasting in Istanbul

Air pollution, especially&ensp;atmospheric particulate matter with aerodynamic diameters smaller than 10 micrometers (PM10), is one of the constant and serious environmental challenges in urban areas. Its consequences range from negative human health&ensp;effects to broader ecological disruptions. With the increasing necessity of accurate and trustworthy forecasting devices in the sphere of air quality assessment, we propose a new hybrid-modeling platform that merges the sequential&ensp;pattern recognition ability of Long Short Term Memory (LSTM) neural networks with fuzzy logic reasoning. The two approaches implemented in this model complement each other: while approaches taking into account the time dependence of the behavior of air pollutants&ensp;address the complex temporal dynamics present in the problem, methods based on uncertainty propagate inherent uncertainties in the meteorological and environmental data. The model was trained using a well-structured, multi-variable dataset of hourly air quality and meteorological observations for five years (2019–2023) measured in&ensp;Istanbul and further tested of January 2024 data. The hybrid approach outperformed all tested environments in prediction output, reaching an accuracy of 98% at the Aksaray traffic&ensp;station, whereas standalone LSTM (97%) and fuzzy logic (94%) models performed lower. Importantly, it identified minute periodicity and pollution peaks with high fidelity and demonstrated robustness&ensp;across diverse settings such as traffic-dense, industrial, rural and urban zones. These results place&ensp;the hybrid LSTM–Fuzzy Logic model as a trusted and robust forecasting tool for predicting PM10 concentrations, providing valuable assistance to environmental policy-makers, urban planners, and public health authorities in efforts to reduce air pollution and protect the health of the population.

groups

Rusul Al-bayati mail -

Ülkü Alver Şahin mail -

Hüseyin Toros mail

link https://doi.org/10.54216/JISIoT.180211

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2026

Design and Optimization of Energy-Efficient Wireless Sensor Networks for Industrial Automation

To enhance the efficiency of edge-integrated Industrial IoT (IIoT) networks, this paper proposes a deep learning-based resource-scheduling framework for optimized asset booking in Wireless Sensor Networks (WSNs). The novelty of this work lies in the integration of a hybrid Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) model, which enables intelligent allocation of computational resources based on real-time asset demand characteristics. The proposed model is evaluated using the Intel Berkeley WSN dataset and demonstrates superior performance in terms of latency reduction, execution time, and resource utilization compared to conventional approaches such as Genetic Algorithm (GA), Improved Particle Swarm Optimization (IPSO), Long Short-Term Memory (LSTM), and Bidirectional Recurrent Neural Network (BRNN). With a maximum efficiency of 99.48% and the lowest observed average delay, the model proves effective for real-time industrial automation scenarios. This research contributes to the development of scalable, energy-efficient, and responsive WSN architectures by leveraging deep learning for asset booking in edge-IoT environments.

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Maha A. Hutaihit mail -

Samir I. Badrawi mail -

Haider Makki Alzaki mail -

Riyadh Khlf Ahmed mail -

Marwa Falah Hasan mail

link https://doi.org/10.54216/JISIoT.180212

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2026

Hybrid Neural Networks and Machine Learning for Detection of Diabetic Retinopathy

Diabetic retinopathy (DR) is one of the most common causes of blindness in the world, and early detection plays an important role in therapy. In this paper, we introduce a hybrid framework with the merger of sophisticated image processing techniques and deep learning models for automated DR detection from retinal fundus images. Information starts with an extensive preprocessing pipeline, which includes bilateral filtering for noise reduction, removal of artifacts, adaptive contrast enhancement and a precise segmentation in the U-Net architecture. To increase model robustness, random rotation augmentation was used to mimic different imaging positions. GLCM analysis is used to extract texture features capturing important lesion-related patterns, and deep features are extracted using a fine-tuned EfficientNet-B0 model. The hybrid feature set is then modelled by a Support Vector Machine (SVM) with the radial basis function kernel and optimized with cross-validation and hyperactive parameters. Experiments show our model can well solve the image heterogeneity problem and yields a high level of accuracy in diagnosis and grading corresponding severity requirements of DR stage.

groups

Waleed Khalid Al-zubaidi mail -

Shokhan M. Al-Barzinji mail -

Zaid Sami Mohsen mail -

Omar Muthanna Khudhur mail

link https://doi.org/10.54216/JISIoT.180213

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2026

Residual Graph Convolutional Networks for Improving Rumor Detection from Social Media Texts

Internet and social media have become significant platforms for sharing real-time information, with rumors significantly affecting billions of people's perceptions. Considerably, Rumor recognition is the most challenging task on social media platforms. Numerous Deep Learning (DL) models have been developed to extract linguistic characteristics from short-text tweets for rumor prediction. However, these models struggles to capture the intricate spatiotemporal relationships presenting tweet interactions. To address this issues, Bidirectional Encoder Representation from Transformers with Attention based Balanced Spatial-Temporal Graph Convolutional Networks (BERT-ABSTGCN) was used. This model incorporates Spatial-Temporal Attention Mechanism (STAM) and a Spatial-Temporal Convolution Module (STCM) to effectively model the spatiotemporal dependencies within in tweet interactions to enhance rumor detection.  However, it constitutes to high degradation problem due to convergence issues. A popular solution to these problems is Residual Learning (RL), which introduces identity mappings to speed up training and enhance gradient propagation. However, traditional RL can only be used for layer-wise task refining, which severely restricts its capacity to grasp more generalized dependencies. However, conventional RL is restricted to layer-wise refinement within a single task limiting its ability to capture broader dependencies. To address this, the proposed work is included with a Cross-Residual Learning (CRL) in BERT-ABSTGCN named BERT with Attention-based Balanced Spatial-Temporal Residual Graph Convolutional Networks (BERT-ABSTRGCN) for efficient rumor detection and stance classification. CRL of BERT-ABSTRGCN enable intuitive learning across multiple tasks like rumor detection and stance classification using cross-connections. CRL establishes direct connections between shallow and deep feature representations, mitigating the vanishing gradient issue.   The fitted residual mappings in the CRL will facilitate the BERT- BERT-ABSTRGCN with the provided information by using the short cut connections and lowers the probability of model degradation. BERT-ABSTRGCN effectively identifies rumor with different stances about specific social media posts, thereby preventing the spread of rumors. Experimental evaluations show that BERT-ABSTRGCN achieves 95.62% accuracy on the PHEME dataset and 90.15% on Mendeley’s COVID-19 rumor dataset, significantly surpassing traditional models.

groups

Vanitha Siddheswaran mail -

Prabahari Raju mail

link https://doi.org/10.54216/JISIoT.180215

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2026

Deep Fake Image Detection Using Ensemble Approach

This paper offers a comprehensive framework for real or fake image classification based on three classifiers: a Standard Convolutional Neural Network (CNN), an EfficientNetV2 model based on transfer learning, and a re-trained GAN discriminator to address the challenges in deepfake detection. The CNN, with four convolutional blocks and dropout regularization, offers computational efficiency (87.2% accuracy, 15 ms/image inference), while EfficientNetV2 utilizes pre-trained ImageNet weights to achieve state-of-the-art performance (94.7% ac-curacy, AUC: 0.98) using hierarchical feature extraction. The fine-tuned and adversarial-pretrained GAN discriminator demonstrates niche strength in the detection of synthetic artifacts (91% recall for GAN-generated fakes). Training used augmented sets (rotation, shifts, and shear) to increase the generalization boost, with loss optimization and early stopping (binary cross-entropy) controlled through validation. Normalized test set validation affirmed EfficientNetV2's capability at balancing recall (94%) with precision (95%), although the GAN discriminator recorded a lead in adversarial resilience. All the models blended, an ensemble model achieved maximum accuracy (96.1%), under complementarities. Computational baselines showed trade-offs EfficientNetV2 accu-racy vs. resource bias (2.5-hour training), the CNN edge-compatibility, and the GAN discriminator arti-fact-sensitive specialization. The work encourages hybrid architectures and ensemble approaches to balance out single-model vulnerabilities, offering a flexible toolkit for deepfake warfare while emphasizing the need for hardware-aware deployment techniques and ongoing adaptation to changing synthetic approaches.

groups

Vijay Madaan mail -

Raghad Tohmas Esfandiyar mail -

Shahad Hussein Jasim mail -

Oday Ali Hassen mail -

Neha Sharma mail -

Ansam A. Abdulhussein mail

link https://doi.org/10.54216/JISIoT.180214

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2025

Neutrosophic Midrange Measure in Bayesian Selection

Many of the problems that we face in our lives and daily work are how to directly and accurately select candidates or categories from multiple sets of candidates (categories). The ranking and selection approach is a modern and direct method for selecting categories easily, which is associated with a probability of correct selection. In this paper, we employ the neutrosophic Bayes procedure for decision to select multinomial population. Select a mid-range category for multiple categories and employ neutrosophic logic to define a modern Bayesian procedure that incorporates parameters with some indeterminacy and has a prior distribution, which we call the neutrosophic prior distribution.

groups

Kawther F. Alhasan mail

link https://doi.org/10.54216/IJNS.260420

Volume & Issue

Vol. Volume 26 / Iss. Issue 4

Details open_in_new

International Journal of Neutrosophic Science | 2026

Neutrosophic Average Edge Connectivity with Applications to Communication Networks

Average edge connectivity is a fundamental concept in graph theory, widely employed to evaluate the robustness of networks through the analysis of local edge cuts. Classical fuzzy extensions allow for graded membership, yet they fail to clearly distinguish between inherent uncertainty and definite absence of edges. To overcome this limitation, we introduce the notion of neutrosophic average edge connectivity, a tri-valued connectivity measure formulated within the framework of single-valued neutrosophic graphs (SVNGs). In this study, we rigorously define neutrosophic local edge cuts, establish key theoretical results including bounds and monotonicity properties, and design efficient algorithms tailored for particular families of graphs. The applicability of the proposed framework is demonstrated through a detailed communication-network case study, which highlights its capacity to capture structural resilience under indeterminate conditions. Overall, the proposed approach generalizes classical robustness indicators and provides a comprehensive tool for analyzing connectivity in networks characterized by vagueness, indeterminacy, and incomplete information.

groups

Aparna Tripathy mail -

Amaresh Chandra Panda mail -

Siva Prasad Behera mail -

Prasanta Kumar Raut mail -

Mana Donganont mail -

Said Broumi mail

link https://doi.org/10.54216/IJNS.270214

Volume & Issue

Vol. Volume 27 / Iss. Issue 2

Details open_in_new