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Journal of Intelligent Systems and Internet of Things | 2025

A Novel Approach to Face Recognition in Videos Based on a Single Reference Image

This paper introduces an advanced method for face recognition in video surveillance systems, leveraging only a single reference image per individual. The challenge of recognizing faces in video is addressed, considering issues like pose variations, occlusions, and lighting changes. The proposed approach utilizes 3D Morphable Models (3DMM) to generate a 3D face mesh from the reference image, facilitating robust face alignment and recognition across video frames. A Convolutional Neural Network based pipeline is employed for face detection, pose estimation, and extraction of invariant features, while an optimization framework refines landmark positions and depth maps for accurate 3D reconstruction. The system performs exceptionally well on the CASIA-WebFace Dataset, with 97.00% pAUC (20%) in surveillance mode and 98.69% in identification mode for frontal views. With an efficiency of 16.72 FPS on modest hardware, the system proves its practicality for real-world deployment. The method incorporates synthetic data augmentation and Random Subspace Methods to enhance adaptability to domain-specific conditions. Compared to existing methods like Eoe-SVM and CCM-CNN, the proposed system demonstrates a superior balance between accuracy and computational efficiency, particularly in Single Sample Per Person (SSPP) scenarios. By focusing on single-reference image recognition, the system offers a promising solution for large-scale surveillance applications, where video footage typically contains multiple poses, expressions, and lighting variations. The results highlight the system's effectiveness and efficiency, making it an excellent alternative for real-time face recognition in complex and dynamic surveillance environments.

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Mohammed Ahmed Talab mail -
Mustafa A. Feath mail -
Ahmed Hadi Ali AL-Jumaili mail -
Mohammed A. Al-shibl mail -
Ravie Chandren Muniyandi mail
link https://doi.org/10.54216/JISIoT.170123

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

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

An Effective Mechanism for Early Pandemic Detecting COVID-19 Rediction based on Time Series Data and WPRO-Based Deep Learning RNN

Rapid spread of Corona virus 2019 (COVID-19) is predictable to create high contact on healthcare organization. Early detection of this disease is required to make precise treatment that further helps to increase the survival rate of humans. However, detecting the COVID-19 at beginning stage is one of a major challenge in the world because of rapid disease spread. Various existing methods are developed to detect the disease, but generating accurate result at the beginning stage still poses a complex task in the medical research community. Hence, an effective mechanism is modeled in this research to predict the pandemic at early with the time-series data using proposed Water Poor and Rich optimization-based Deep Recurrent Neural network (WPRO-based Deep RNN). Accordingly, proposed method is highly effective in generating the most appropriate results through deep learning classifier based on the high dimension features. However, the high dimensional data is generated through the data augmentation process by employing oversampling technique. The proposed method is more robust and increases the efficiency of the optimization algorithm by attaining global convergence results based on the fitness measure. Accordingly, the technical features of time series data to improve effectiveness of developed model. However, the proposed WPRO-based Deep RNN produced minimum Root Mean Square Error (RMSE) as well as MSE values of 0.4 and 0.1714 for confirmed cases, and obtained lesser MSE and RMSE values of 0.1887 and 0.433 for the cases of death. Moreover, proposed model achieved minimal RMSE and MSE of 0.447 and 0.1901 for the recovered cases.

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Zaid Derea mail -
Ammar Kazm mail -
Manar Bashar Mortatha mail -
Oday Ali Hassen mail -
Esraa Saleh Alomari mail
link https://doi.org/10.54216/JISIoT.170124

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new
International Journal of Neutrosophic Science | 2026

Neutrosophic Signed Domination Function of Graphs

This paper introduces the novel concept of a Neutrosophic Signed Domination Function (NSDF) of graphs, generalizing classical domination by assigning each vertex a triple-valued influences (truth, indeterminacy, falsity) from {−1, 0, 1}. We define the Neutrosophic Signed Domination Number γns(G) as the optimal weighted sum under neighborhood constraints ensuring net positive influence. Fundamental properties and sharp bounds for general graphs are established. Exact values for γns(G) are determined for paths and cycles. This work bridges neutrosophic logic with domination theory, enabling sophisticated modeling of complex networks with uncertainty.

groups
Duraisamy Kumar mail -
Florentin Smarandache mail
link https://doi.org/10.54216/IJNS.270126

Volume & Issue

Vol. Volume 27 / Iss. Issue 1

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Fusion: Practice and Applications | 2026

Asymptotic Solution to the Scalar Version of the Two Body Problem When the Two Bodies Collide - A Case Study

The main goal of this paper is to obtain a special form of asymptotic solutions to the scalar version of the two body problem whenever the two bodies collide on the real line at the collision time. It has been shown that the desired asymptotic solution maintains certain properties when t approaches the collision time. However, it is not easy to Handel such a mission without the employment of successive approximations technique. The successive approximations technique has been modified and adjusted to serve as the main tool in the process of obtaining such solution. Moreover, it has been shown that the series of successive approximations converges absolutely and uniformly to a continuous function that approaches to 0 when t attains the collision time in a certain interval. The problem of one dimensional collision between the two bodies has been solved asymptotically at the collision time.

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Ahmed Bakheet mail -
Ali Abdulhussein mail -
Laheeb Muhsen Noman mail
link https://doi.org/10.54216/FPA.210122

Volume & Issue

Vol. Volume 21 / Iss. Issue 1

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International Journal of Neutrosophic Science | 2026

A Mathematical Framework for Indeterminacy in Parabolic PDEs: The Neutrosophic Heat Equation

We develop a neutrosophic framework for the 1-D transient heat equation that treats key thermal parameters as indeterminate rather than fixed or strictly probabilistic. Thermal diffusivity and source strength are represented by neutrosophic intervals; two extreme forward solves yield guaranteed envelopes u_min and u_max , from which we compute a core field u_mean =1/2 (u_min+u_max ), an absolute width W=u_max-u_min, and a relative indeterminacy index I=W/(|u_mean  |+ε). Using an explicit FTCS discretization with stability enforced by α_max , we report decision-oriented diagnostics: spatio-temporal maps of u_mean ,W, and I; band plots along space/time sections; percentile trajectories of I over time; coverage curves quantifying the fraction of space-time with I≤τ; and response surfaces showing sensitivity of u(x^( ^* ),T) to (α,S). Results demonstrate that, even when absolute spreads remain small, localized reliability losses can occur where u_mean  crosses zero, a regime routinely obscured by point-estimate modelling. The framework is transparent (envelopes + core), computationally light (two extreme runs), and compatible with neutrosophic statistics for data-driven interval setting. Beyond thermal diffusion, the method provides a conservative, explainable backbone for transport-driven decisions in materials, interfaces, and infrastructure subject to incomplete or evolving information.

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Ghassan AL-Thabhawee mail -
Hussein Alkattan mail -
El-Sayed M. El-Kenawy mail -
Marwa M. Eid mail
link https://doi.org/10.54216/IJNS.270127

Volume & Issue

Vol. Volume 27 / Iss. Issue 1

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

Path Planning in Mobile Robotics: A Comparative Review of Classical and AI-Driven Techniques

This research presents a comprehensive analysis of path planning and optimization techniques in mobile robotics, focusing on both classical algorithms and modern intelligent approaches. The study systematically reviews fundamental methods such as Dijkstra’s algorithm, the A* search algorithm, and artificial potential fields, together with evolutionary optimization approaches including genetic algorithms and swarm intelligence. It also explores the application of machine learning and deep reinforcement learning models that allow robots to adapt dynamically to complex and changing environments. The comparative evaluation highlights the strengths, weaknesses, and suitable application areas of each approach across scenarios involving obstacle avoidance, energy efficiency, real time adaptability, and multi robot coordination. Particular attention is given to the challenges of uncertain and dynamic environments, computational scalability, and sensor noise, which continue to limit the performance of autonomous navigation systems. By consolidating current advancements and emerging trends, this study provides a structured overview and critical synthesis of existing methodologies, offering a valuable reference for researchers, engineers, and practitioners. It also identifies important research gaps in intelligent hybrid planning, context aware learning and energy constrained mobility, outlining promising directions for the future development of autonomous robotic navigation systems.

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Mohammed KH. Al-Satooree mail -
H. A. El Shenbary mail -
Ashraf A. Gouda mail -
Mohammed Abdel Razek mail
link https://doi.org/10.54216/JISIoT.170125

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new
Fusion: Practice and Applications | 2025

Prediction of Coronary Heart Disease with Multiple Regression Method

Coronary heart disease, a prevalent cardiovascular condition, affects coronary arteries, causing progression over time. Factors include diabetes, hypertension, inactivity, and tobacco use. Treatment includes medications and surgery, while maintaining a balanced diet and regular physical activity can prevent it. This research aimed to develop and validate a predictive model for CHD occurrence, leveraging the power of multiple regression while considering a range of predisposing variables. This study uses a quantitative, retrospective design utilizing multiple regression analysis to predict the likelihood of coronary heart disease (CHD). The study involved 130 participants aged 24-85, with health history data on cardiovascular risk factors, blood pressure, cholesterol, smoking, BMI, and family history of heart disease. Multiple regression analysis was utilized to determine the significant predictors of CHD diagnosis. Significant relationships between responder variables and predictor factors in a multiple linear function are identified using multiple regression analysis. Our model discovered that a higher risk of coronary heart disease (CHD) was closely associated with both total cholesterol and BMI. The model included factors like systolic blood pressure, diabetes, physical activity, and smoking, but they had lower contributions to the prediction equation, despite cholesterol and BMI being the best predictors. This study successfully developed a multiple regression-based prediction model for CHD that can contribute to a more informed and potentially proactive approach to cardiac healthcare. Further work should focus on refining model accuracy and real-world clinical application.

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Elda Maraj mail -
Aida Bendo mail
link https://doi.org/10.54216/FPA.200210

Volume & Issue

Vol. Volume 20 / Iss. Issue 2

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Fusion: Practice and Applications | 2026

Pan-Sharpening Landsat Images through the Component Substitution Methods

Remotely sensed images have played a valuable role in several applications such as image classification, feature extraction, land cover monitoring, and others; thus, the need for high-resolution satellite images has become necessary and essential. In order to produce images with very high spectral and spatial resolution, the pan-sharpening techniques—, which are regarded as a subset of data fusion techniques—combine the color information of the multispectral image from the same scene with the distinct geometric features of the panchromatic image. This work conducts a comparative analysis of four pansharpening methods (Gram, HIS, Brovey, and PC) specifically applied to Landsat 7 images, providing a thorough evaluation across multiple performance metrics. Also we introduce and apply performance metrics that not only measure quantitative accuracy (like RMSE and RASE) but also assess the preservation of spatial details, offering a more holistic evaluation of pansharpening techniques. The qualitative and quantitative results indicate that both GS and IHS techniques have accurate performance.

groups
Asmaa Sadiq mail
link https://doi.org/10.54216/FPA.210123

Volume & Issue

Vol. Volume 21 / Iss. Issue 1

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Journal of Cybersecurity and Information Management | 2026

Cascade Detection Technique for Face Mask ‎Recognition Based on YOLOv9 and CNN

Deep learning showed promise in many real-world applications. Recognition and item ‎identification are the most common. This publication tries to design and describe a system that can ‎classify people from images based on whether they are correctly wearing masks. The proposed ‎system is two-part. The first part is designed for facial detection using the YOLOv9 (You Only Look Once ‎version 9) compact deep learning model, which uses the mean intersection method over union to determine an optimal number of anchoring boxes and the Adam optimizer to improve facial detection efficacy. ‎The second component is a convolutional neural network for face feature extraction. These faces are ‎classified as a mask, without_mask, and incorrect_mask. These two components are integrated into ‎the proposed system for facemask recognition. ‎Empirical evaluations were conducted on the two self-collected datasets to train and evaluate the ‎proposed system's performance. The observed precision value of this system was 94.6% in the last ‎epoch; the recall value is 87.1%, and the mean average precision results are 92.74% as a face ‎detector. The classifier model train accuracy is 98.35%, and validation accuracy is 98.8%. Finally, the ‎comparative results indicated that the proposed framework was an effective model for face ‎detection, attaining a higher mean average precision value and outperforming other networks ‎assessed on the designated dataset for this task. The suggested network effectively detects and ‎classifies several faces in photos, including small faces in congested places.

groups
Amal Sufiuh Ajrash mail -
Wildan Jameel Hadi mail -
Ammar Hussein Jassim mail
link https://doi.org/10.54216/JCIM.170109

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new
Fusion: Practice and Applications | 2026

Black Fungus Disease Identification Using Deep Learning: A Case Study

Black fungus disease (mucormycosis) has emerged as a critical health threat, particularly during the COVID-19 pandemic, where immunosuppressed individuals have shown increased susceptibility to opportunistic fungal infections. The aggressive progression of mucormycosis and its high mortality rate, exacerbated by diagnostic delays, underscore the urgent need for accurate and automated detection systems. In this study, a deep learning-based diagnostic framework is proposed for the early identification of black fungus infection using convolutional neural networks (CNNs). Experimental pipelines were developed and evaluated. Several deep learning models based traditional CNN architectures including VGG16, VGG19, InceptionV3, and MobileNetV2 have been study on a structured dataset comprising high-resolution mucormycosis images. Comparative evaluations across both pipelines revealed that the MobileNetV2 architecture consistently outperformed other models, with accuracy reaching 99.86%, F1-score of 0.98, and minimal overfitting across validation datasets. The proposed system holds strong potential for real-world clinical deployment, particularly in resource-limited healthcare settings, offering rapid, scalable, and explainable AI-driven diagnostics to combat the rising threat of black fungus infections.

groups
Hanan Badri Salman mail -
Matheel Emaduldeen Abdulmunim mail
link https://doi.org/10.54216/FPA.210228

Volume & Issue

Vol. Volume 21 / Iss. Issue 2

Details open_in_new