Prediction of Coronary Heart Disease with Multiple Regression Method

Elda; Aida

doi:https://doi.org/10.54216/FPA.200210

Journal of Neutrosophic and Fuzzy Systems | 2025

Entropy–Remoteness Neutrosophic Fuzzy c-Means for Separating Boundary Ambiguity from Outlierness

A weak fuzzy assignment can mean two geometrically different things. A record may lie between otherwise legitimate clusters, so its membership vector ui = (ui1, . . . ,uiK) is genuinely ambiguous; or it may be remote from every prototype, in which case diffuse membership is a symptom of outlierness rather than a boundary. Treating both cases through one fuzziness scalar makes the prototype update unable to explain why a record should have reduced influence. This paper constructs Entropy–Remoteness Neutrosophic Fuzzy c-Means (ERN-FCM) from two separate quantities. Boundary indeterminacy is Ii = α[−Σk uik loguik]/logK, whereas robust remoteness is Fi = 1−exp(−τρi) with ρi = [(δi−q.90)/(q.90−q.50+ε)]+ and δi = mink ∥xi−vk∥2. Their conjunction gives truth Ti = (1−Ii)(1−Fi) and therefore Ti +Ii +Fi = 1+IiFi. Prototypes are updated by v+k = Σi Tium ikxi/Σi Tium ik, so remote or highly ambiguous records contribute less without being hard-deleted. The numerical study deliberately separates the two geometries. On Wine with 10% gross contamination, mean adjusted Rand index rises from 0.8665 for ordinary FCM to 0.8975 for ERN-FCM; on Breast Cancer the corresponding values are 0.7014 and 0.7074, whereas Iris remains a counterexample where FCM is slightly better (0.6328 versus 0.6179). The remoteness-aware uncertainty 1−Ti identifies gross outliers with AUC 0.9898–0.9956 at 10% contamination, and on a separate boundary benchmark Ii identifies bridge observations with AUC 0.9963. Paired bootstrap contrasts, component ablation, parameter sensitivity, and fixed-point diagnostics support a focused conclusion: ERN-FCM is most useful when cluster recovery and the type of uncertain observation must be distinguished, not as a universal replacement for FCM.

groups

Suman Das mail -

Ajoy Kanti Das mail

link https://doi.org/10.54216/JNFS.100202

Volume & Issue

Vol. Volume 10 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Deep Learning-Based BER Enhancement for 64x64 MIMO-FSO NOMA Systems Under Various Atmospheric Turbulence Senarios

The current exponential growth in the demand for bandwidth is the most urgent challenge for next-generation wireless systems. One of the most appropriate techniques to overcome this situation is Free-space optical (FSO) communication due to the provision of an ample bandwidth. The main disadvantage of FSO communication systems is that the optical beam, propagating through atmospheric turbulence, can be distorted to an unacceptable level. In this work, a 64x64 MIMO-FSO system with Non-Orthogonal Multiple Access (NOMA) and QPSK modulation scheme is assessed. We compare the Bit Error Rate (BER) performance of the system under 4 theoretical turbulence channel models: Log-Normal, Gamma-Gamma, Fisher-Snedecor, Negative Exponential, as well as 4 real seasonal LogCn² datasets. Classical Maximum Likelihood (ML) detection was compared against the deep learning-based ML detection using a Deep Neural Network (DNN) as well as an Autoencoder model. We found that the autoencoder model has outperformed the classical ML detection in terms of BER performance, especially for the weaker user, when NOMA is considered. It was also found that using real datasets that represent real turbulence conditions the proposed system is highly effective and can serve as intelligent fronthaul/backhaul solutions for dense IoT networks such as smart cities, autonomous vehicles, and industrial automation.

groups

Hasan Farooq Radeef mail -

Lwaa F. Abdulameer mail

link https://doi.org/10.54216/JISIoT.170225

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Hybrid Optimization based Clustering with CNN-Based De-Authentication for IoT Enabled Heterogeneous Wireless Sensor Networks

The Internet of Things (IoT) has greatly changed many aspects of human life and is now a vast distributed systems network of interconnected devices that have embedded sensors; however, the battery life of these sensor nodes is limited and requires constant maintenance. Furthermore, IoT networks operating as distributed systems are vulnerable to security threats, like de-authentication and Disassociation Denial-of-Service attacks, which exploit vulnerabilities in Wi-Fi devices. While artificial intelligence, including machine learning, has been integrated into intrusion detection systems to enhance detection of cyberattacks, there is an increasing need for improved accuracy, scalability, efficiency, and IoT-specific security solutions. This paper proposed a novel model, Hybrid Optimization-based Clustering with CNN-Based De-Authentication (HOCCNN), designed to concurrently address both energy conservation and security issues in IoT-enabled heterogeneous wireless sensor networks (WSNs). The HOCCNN adopts a hierarchical clustering technique optimized using the bio-inspired Osprey Optimization Algorithm (OOA) for dynamic and energy-efficient Cluster Head (CH) selection. Additionally, we introduce a CNN model to detect and mitigate De-authentication attacks in HOCCNN by utilizing deep learning techniques and provide a more accurate threat detection solution even in the resource-constrained environment. The performance of HOCCNN was evaluated using MATLAB against existing baseline methods in terms of parameters like packet delivery ratio, network throughput, network lifetime, end-to-end delay, average energy consumption, data accuracy, and data overhead. The model demonstrates superiority over state-of-the-art baselines. Results show significant improvements. 99.1% accuracy in attack detection, 54.18% energy consumption, 6.76 s network lifetime, 0.985 packet delivery ratio, and 53.198 Mb/s throughput. These results prove that HOCCNN is a complete design to achieve scalable, secure, and energy-sustainable HWSNs in IoT.

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Foad Salem Mubarek mail -

Akeel A.Thulnoon mail -

Ahmed Mahdi Jubair mail

link https://doi.org/10.54216/JISIoT.170122

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new

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

Details open_in_new

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.

groups

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

Details open_in_new

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.

groups

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.

groups

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.

groups

Elda Maraj mail -

Aida Bendo mail

link https://doi.org/10.54216/FPA.200210

Volume & Issue

Vol. Volume 20 / Iss. Issue 2

Details open_in_new