Developing a Fast Hybrid Metaheuristic Algorithm to Enhance the Efficiency of Resource-Constrained Applications

Alaa Abdalqahar; Ahmed Subhi; Sameeh Abdulghafour

doi:https://doi.org/10.54216/JISIoT.180205

International Journal of Neutrosophic Science | 2026

Modeling Uncertainty in Healthcare Data Using the Neutrosophic Gamma-Lomax Distribution for Optimized Decision-Making

Healthcare data often involve uncertainty, imprecision, and partial information that are hardly handled by classical statistical models. Here, we propose a new generalization of the Gamma Lomax (GL) distribution under the neutrosophic environment, referred to as the neutrosophic Gamma Lomax (NGL) distribution, to overcome this drawback. In addition, the proposed model can be generalized to handle precise as well as uncertain healthcare data by incorporating neutrosophic logic including truth, falsity and indeterminacy. The classical properties of the Gamma-Lomax (GL) distribution are examined alongside their neutrosophic counterparts. Graphical representations, including density plots and associated reliability functions of the proposed model, are presented. The maximum likelihood estimation (MLE) is applied to find unknown parameters. The neutrosophic model is capable of modeling interval-valued results and uncertainties in practical data, and its effectiveness is verified by simulation studies and an illustration with infant mortality rates. The new method is conducive to the interpretability and credibility of statistical inference under uncertainty and is of high utility in health decision-making scenarios.

groups

Mansour F. Yassen mail -

Adnan Amin mail

link https://doi.org/10.54216/IJNS.270207

Volume & Issue

Vol. Volume 27 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2026

Synergising Principal Component Analysis with Pythagorean Neutrosophic Bonferroni Mean Approach for Arrhythmia Detection using Cardiovascular Signals

The neutrosophic set (NS) concept from the philosophical perspective extends and simplifies the principles of fuzzy set (FS) and intuitionistic FS (IFS). A NS is defined by truth, indeterminacy, and falsity membership functions, with each value belonging to the non-standard intervals (−0, 1+). In contrast to IFSs, there is no limitation in the membership function in NS, and the hesitancy degree is incorporated in NS. Arrhythmia is a medical illness wherein the regular pumping mechanism of the human heart becomes abnormal. The arrhythmia detection is one of the most essential steps to identify the disorder that can play a significant role in helping cardiologist with their decision. The initial identification of abnormal heart disease is critical for patients with heart disorders. Computer-aided diagnosis (CAD) has gained popularity in the arrhythmia domain recently, as artificial intelligence (AI) technology has matured. Still, the AI-based deep learning (DL) techniques are applied frequently to classify and detect arrhythmia. This paper presents an Enhanced Diagnostic Model for Cardiac Arrhythmia using Principal Component Analysis and Pythagorean Neutrosophic Bonferroni Mean (DMCA-PCAPNBM) technique in Cardiovascular Signal Processing. The objective is in the automated arrhythmia detection using advanced techniques. Initially, the DMCA-PCAPNBM model applies the min-max scaler-based data pre-processing technique for transforming input data into an appropriate format. In addition, the principal component analysis (PCA) method is applied for the feature subset selection model to pick out the optimal attributes from the dataset. For the procedure of arrhythmia detection, the PNBM model is utilized. Finally, the improved dung beetle optimization (IDBO) approach is applied for parameter tuning, resulting in enhanced classification performance. A comprehensive experimentation is implemented to verify the superior outcome of the DMCA-PCAPNBM model on the ECG arrhythmia classification dataset. The experimental validation of the DMCA-PCAPNBM approach illustrated an improved accuracy value of 99.06% over recent techniques.

groups

Majed Balkheer mail -

Reda Salama mail -

Mahmoud Ragab mail -

Ashis Kumer Biswas mail

link https://doi.org/10.54216/IJNS.270208

Volume & Issue

Vol. Volume 27 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2026

Quantifying Uncertainty in Economic Growth Prediction Using the Neutrosophic Muth Distribution

Uncertainty, imprecision, and incomplete information are commonly found in complex economic and financial systems, and traditional probabilistic models are thus inadequate to accurately model and forecast these systems. In this work, a new extension of the Muth distribution in the neutrosophic environment is presented leading to the neutrosophic Muth distribution (NMD). This new model introduces neutrosophic parameters aiming to quantify vague and uncertain information and provides a flexible and robust approach to modeling right-skewed economic data. Some key characteristics including the density function and cumulative distribution function, moment generating function, and origin moments are obtained in the neutrosophic framework. The study of a model treated under uncertainty is described and an inferential method transforming it into neutrosophic maximum likelihood by interval-valued data is discussed. A real-world financial dataset is considered in order to prove the usefulness of the proposed distribution. The findings emphasize that the proposed distribution has the potential to be a comprehensive, flexible, and potential model for handling uncertainty in economics and finance data.

groups

Anas Abdulbast Abbas mail

link https://doi.org/10.54216/IJNS.270209

Volume & Issue

Vol. Volume 27 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2026

Neutrosophic Bounds on Coefficients of Inequality for a Subclass of Holomorphic Functions

This study investigates the second-order Hankel determinant in the context of certain analytic functions to find upper bounds, incorporating neutrosophic logic to handle uncertainty in coefficient estimation. The normalized conditions ג)0)=0 ג′(0) = 1 are analyzed through both classical and neutrosophic frameworks. We derive: • Sharp neutrosophic bounds for |H2,2,ϖ| when ϖ ∈ (1, 3/2] • Optimal bounds for |H2,3| at ϖ = 3/2 in G(ϖ) and Q(ϖ) • Neutrosophic logarithmic coefficient determinants with τ -ι-φ membership degrees The framework demonstrates robustness when coefficients exhibit simultaneous membership/non-membership characteristics.

groups

Isra Al-Shbeil mail -

wael mahmoud mohammad salameh mail -

Saleem Ashhab mail -

Biswajit Rath mail -

Eada Ahmed Al-Zahrani mail

link https://doi.org/10.54216/IJNS.270210

Volume & Issue

Vol. Volume 27 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2026

Modeling Financial Uncertainty Using Neutrosophic Ram Awadh Distribution: An Application to Future Economic Growth

Ram Awadh (RA) distribution is flexible to handle skewedness and heavy tailed observations, which are frequent in financial risk management. With flexible structure, it has potential to be a reliable model in financial data modeling and decision-making process in the scenarios of indeterminacy. The new one parameter lifetime distribution is proposed and called as the neutrosophic RA distribution ( ) in this article. We obtain the raw and central moments of it and investigate some important statistical properties such as the coefficient of variation, skewness, kurtosis and index of dispersion. Moreover, some reliability properties such as the hazard rate function mean residual life function, and stochastic orderings of the distribution are considered. The method of maximum likelihood estimation (MLE) is utilized for parameter estimation. A comprehensive simulation study is carried out to evaluate the behavior of the distribution and its statistical properties.  Finally, a real-world dataset of economic sector is utilized to illustrate its practical importance.

groups

Ahmedia Musa M. Ibrahim mail

link https://doi.org/10.54216/IJNS.270211

Volume & Issue

Vol. Volume 27 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2026

Blockchain-Enabled Beamforming Optimization in 6G-IoT Using ConvMarkov and Laplacian Eigenmaps

In an increasingly fast-paced world of 6G-IoT networks, optimal beamforming techniques will be effective in improving strength, latency, and quality of service delivery in the networks. This work presents a new paradigm in beamforming optimization, especially in tackling dynamic environments and high computational costs in existing approaches. The problems of long training times with traditional methods, along with threats in security make them out rightly less applicable for real time applications. The data is collected from 6G IoT networks then, Laplacian Eigenmaps is used for feature extraction and modelling in time and applied for dimensionality reduction, ConvMarkov is used for model development RC4 encryption secures data exchange, while blockchain supports data logging and promotes transparency. This is a combination of deep learning techniques and advanced encryption methods, which will lead to a wide boost in beamforming efficiency, flexibility, and security. This study achieved the beamforming optimization achieved 97% accuracy with significant gain improvements, as indicated by an ROC curve (AUC = 0.9970) and precision-recall curve. The training loss stabilized below 0.01, while the validation loss fluctuated above 0.1, suggesting minor overfitting. The main achievements converge on proving improvements in optimization under real time conditions in a network, besides integrity and privacy of data. These become great merits into a strong solution for future 6G.

groups

Saleh Ali Alomari mail

link https://doi.org/10.54216/JISIoT.180201

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2026

A New Descriptor for Improving Lightweight Blockchain Environment Using a Hybrid GWO-Levy-GRU Framework for Nonce Discovery

Blockchain technology has recently emerged as a fundamental pillar of decentralized and secure systems. However, many Proof-of-Work (POW) algorithms suffer from some challenges, including their inefficiency in discovering the value of Nonces due to their reliance on random attempts, which consume significant resources, energy, and time, making them difficult to use in lightweight blockchain environments, especially in resource-limited environments such as mobile devices and others. The main goal of this paper is to introduce a smart system that replaces random guessing with a more intelligent and predictive approach using deep learning models like CNN2D, GRU, LSTM, and hybrid models. The intelligent optimization algorithm (GWO) is also used, which has been enhanced with random Lévy jumps, in addition to improved clustering using a genetic algorithm. The results, after applying the system to health data across three difficulty levels (4, 6, and 8), showed that the intelligent neural model was the most stable and accurate, achieving the lowest error values and the highest generalization ability, with a maximum error value of (0.0136) at the highest difficulty level (8). The hybrid GA–KMeans algorithm demonstrated high efficiency in improving clustering accuracy. It achieved the highest similarity index value (0.9980) and the lowest Davis-Bolden index value (0.0000), which plays a significant role in guiding searches efficiently and effectively. The CNN2D model also achieved ideal numerical results, but it is prone to overlearning, while the GRU neural model provided an efficient balance between stability and accuracy. Other hybrid models, such as GRU+CNN, have shown excellent performance, but with varying results. The proposed system proves to be an efficient and intelligent alternative to the low-cost random approach for Nonce discovery in lightweight blockchain environments.

groups

Rasha Hani Salman mail -

Hala Bahjat Abdul Wahab mail

link https://doi.org/10.54216/JISIoT.180202

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2026

A New Strategy for Exploration and Area Coverage Using Swarm Robots by Enhancing the Pelican Optimization Algorithm

Area coverage and exploration of unknown environments by swarm robots autonomously is one of the challenges in the robotics domain. This paper proposes a new strategy for area coverage in two parts; firstly, enhancing a Pelican Optimization Algorithm (POA) using swarm robots to explore an unknown area. Secondly, merges many algorithms with the proposed POA, such as Timed Elastic Band (TEB) as a local planner for obstacle avoidance, SLAM (Simultaneous Localization and Mapping), and a training model which is called You Only Look Once version 8 nano (YOLOv8n) for person detection. The proposed POA algorithm successfully monitored a large area and achieved a high exploration ratio with minimal time. In this work, the new strategy is applied to a robot warehouse environment, utilizing a swarm of robots to explore the area and find targets, which are employees suffocated by the effects of chemical pollution. The simulation and real-world tests for a new strategy were done in the Robot Operating System (ROS) using the TurtleBot3 robot. The total time-consuming for exploration and detection time is less by POA, while the coverage ratio is the largest when compared with the original RRT exploration algorithm for empty, small, and large environments, respectively.

groups

Dena Kadhim Muhsen mail -

Ahmed T. Sadiq mail -

Firas Abdulrazzaq Raheem mail

link https://doi.org/10.54216/JISIoT.180203

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2026

Power Consumption Prediction Using a CNN-LSTM-Attention Hybrid Deep Learning Model

Reducing energy losses and increasing power grid efficiency need accurate prediction of power consumption accurate prediction of future energy consumption requires the use of time series data. To overcome the shortcomings of conventional techniques for forecasting energy consumption in India for the period from 2 January, 2019 to 23 May, 2020, we used an attention mechanism, which is still relatively new and not well known. In this paper, we propose a new approach for predicting energy consumption by combining local feature extraction with convolutional neural networks (CNNs), long short-term memory (LSTM) to capture long-term temporal dependencies, and attention mechanisms to deal with the issue of information loss brought on by extremely lengthy input time series data. After high-dimensional features are extracted from the input data using a one-dimensional CNN layer, temporal correlations within historical sequences are captured using an LSTM layer.  In order to optimize the weighting of the LSTM outputs, strengthen the impact of important information, and enhance the prediction model as a whole, an attention mechanism is finally implemented. This integration improves the model's ability to represent complex spatio-temporal patterns. The mean absolute error (MAE) and root mean square error (RMSE) are used to assess the performance of the proposed model. The results demonstrate that the CNN-LSTM-Attention model outperforms conventional hybrid CNN-LSTM and LSTM models, demonstrating superior performance across a range of prediction scenarios. By supporting more reliable grid management, proactive intervention methods, and predictive maintenance, these developments contribute to reducing load imbalances and energy waste in India. The Future developments could see the proposed model extended to other time series prediction domains.

groups

Nebras Jalel Ibrahim mail -

Samah Faris Kamil mail -

Ghasaq Saad Jameel mail

link https://doi.org/10.54216/JISIoT.180204

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2026

Developing a Fast Hybrid Metaheuristic Algorithm to Enhance the Efficiency of Resource-Constrained Applications

The rapid development of intelligent computing has led to Internet of Things (IoT) applications and embedded devices suffering from severe constraints on energy, processing, and memory. This calls for fast and lightweight algorithms that maintain performance accuracy without draining resources or affecting response time. This paper presents a new hybrid metaheuristic algorithm that combines the advantages of four optimization algorithms to achieve efficient results and reduce computational complexity without compromising output quality. Experiments demonstrate significant improvements in performance and execution time compared to traditional algorithms, in addition to the algorithm's ability to scale and handle diverse workloads. The lowest improvement of the proposed algorithm compared to other algorithms was approximately 25.7%. This algorithm opens up prospects for effective applications in smart systems in urban and industrial areas.

groups

Alaa Abdalqahar Jihad mail -

Ahmed Subhi Abdalkafor mail -

Sameeh Abdulghafour Jassim mail

link https://doi.org/10.54216/JISIoT.180205

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

Details open_in_new