IoT and AI for Clinical Decision Support with Hierarchical Attention

Sasikumar  M. S.; Ranganayaki V.; R.; Nalini; T.; T. A.

doi:https://doi.org/10.54216/JISIoT.170213

Fusion: Practice and Applications | 2026

Optimizing Smart-Home Energy Forecasting with Evolutionary Attention-based LSTM and Greylag Goose Optimization

This study addresses the challenge of smart-home energy forecasting across multiple appliances under varying temperature and seasonal regimes, aiming to improve demand planning and household energy efficiency. The analysis leverages a 100,000-row dataset from Kaggle, encompassing appliance type, time of consumption, outdoor temperature, season, and household size. The study benchmarks several recurrent neural network models, including Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), and Bidirectional RNN (BiRNN), as well as a feedforward Artificial Neural Network (ANN). A novel enhancement, the Evolutionary Attention-based LSTM (EALSTM), is introduced, and its hyperparameters are optimized using the Greylag Goose Optimization (GGO) algorithm. The performance of GGO-optimized EALSTM is compared to other metaheuristics, such as Differential Evolution (DE), Genetic Algorithm (GA), Quantum-Inspired Optimization (QIO), JAYA, Bat Algorithm (BA), and Stochastic Fractal Search (SFS). The results indicate that GGO-optimized EALSTM outperforms all other models, achieving superior accuracy across multiple metrics, including MSE, RMSE, MAE, r, R2 , RRMSE, NSE, and WI. Key contributions of the paper include (i) the establishment of an appliance- and season-aware forecasting benchmark, (ii) a comprehensive optimizer comparison for EALSTM using GGO, and (iii) the provision of actionable visual analytics to enhance the understanding of energy demand patterns and model errors.

groups

El-Sayed M. El-Kenawy mail

link https://doi.org/10.54216/FPA.210218

Volume & Issue

Vol. Volume 21 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2026

Greylag Goose Optimization-Driven EALSTM for Accurate HVAC Chiller Energy Prediction

Forecasting the energy consumption of heating, ventilation, and air conditioning (HVAC) chillers is vital for enhancing building efficiency, reducing operating costs, and supporting sustainability goals. However, the task remains challenging due to nonlinear system dynamics, strong dependence on weather conditions, and the scarcity of high-quality real-world datasets. In this work, we employ the Chiller Energy Data from Kaggle, which contains 13,561 cleaned records collected between August 2019 and June 2020, incorporating ten operational and meteorological features. Six baseline models, namely the Evolutionary Attention-based Long Short-Term Memory (EALSTM), Bidirectional LSTM (BILSTM), standard LSTM, Gated Recurrent Unit (GRU), Temporal Convolutional Network (TCN), and Artificial Neural Network (ANN), are first benchmarked to assess their forecasting capability. To further improve predictive accuracy, we integrate EALSTM with ten meta-heuristic optimization algorithms, focusing on the Greylag Goose Optimization Algorithm (GGO) and comparing it with alternatives such as Harris Hawks Optimization (HHO), Artificial Physics Optimization (APO), Simulated Annealing Optimization (SAO), Grey Wolf Optimizer (GWO), and others. The optimized GGO+EALSTM framework achieves state-of-the-art performance with a mean squared error of 6.83×10−6 and an R2 value of 0.98, reflecting a 96% reduction in error relative to simple feedforward models and significant improvements over other recurrent networks and optimizer-enhanced variants. The main contributions of this study include a structured benchmarking of neural architectures for chiller forecasting, the first systematic comparison of ten meta-heuristic optimizers applied to deep learning in this domain, and a visualization-based error analysis that strengthens interpretability and supports practical deployment. These results establish optimization-enhanced EALSTM as a robust and generalizable framework for HVAC energy forecasting, paving the way toward more efficient, reliable, and sustainable building energy management.

groups

Doaa Sami Khafaga mail

link https://doi.org/10.54216/FPA.210219

Volume & Issue

Vol. Volume 21 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2025

An Efficient Wireless Sensor Network Developed Election Protocol for Extendable Lifetime

Wireless sensor networks (WSNs) are made up of thousands of sensor nodes that are distributed in an area where their energy is limited. To overcome the issue of energy consumption. This paper study different deployment configuration as well as evaluating the two different clustering-based routing protocols. This work describes a hybrid distance, energy, and zonal SEP (HDEZ-SEP), which combines the strengths of the Distance and Energy-Aware Stable Election Routing Protocol (DE-SEP) and Zone-Based Stable Election Protocol (Z-SEP) to improve WSN energy efficiency and longevity. The suggested HDEZ-SEP was executed and compared to other protocols, including DE-SEP and Z-SEP. Using the MATLAB R2022b simulator; we assess the suggested protocol and contrast it with the others. According to the simulation results, the overall performance is improved. This study shows how hybrid techniques can effectively optimize data transmission and energy use in WSNs.

groups

Aya A. Ramadan mail -

Marwa M. Eid mail -

El-Sayed S. A. Said mail -

Shereen H. Ali mail -

Mohamed Yasin I. Afifi mail

link https://doi.org/10.54216/JISIoT.170210

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2026

A Note on Multi-Neutro-Topological Space

Multisets have been the subject of extensive research, and their usefulness has been recognized in various areas such as computation, database management, and more. This study aims to explore certain properties of neutro-topological spaces by introducing a multi-neutro-topological space. Many fundamental features of interior, the exterior, the closure, and the boundary in a neutro-topological space are found to be preserved in a multi-neutro-topological space with the incorporation of multisets.

groups

Jeevan Krishna Khaklary mail -

Bhimraj Basumatary mail

link https://doi.org/10.54216/IJNS.270235

Volume & Issue

Vol. Volume 27 / Iss. Issue 2

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Journal of Intelligent Systems and Internet of Things | 2025

Greylag Goose Optimization for Feature Selection and Hyperparameter Tuning in Chronic Kidney Disease Detection

Chronic Kidney Disease (CKD) is a global health concern that necessitates accurate and timely detection to improve patient outcomes and reduce healthcare costs. This study focuses on enhancing CKD classification using machine learning techniques, leveraging 400 instances with 25 clinical features to predict binary outcomes of CKD or non-CKD. The main objective is to improve detection accuracy by applying feature selection and model optimization. Standard machine learning models, including Multilayer Perceptron (MLP), Random Forest (RF), Support Vector Classifier (SVC), and K-Nearest Neighbors (KNN), were employed, with optimization achieved through binary optimization algorithms such as Greylag Goose Optimization (GGO), Particle Swarm Optimization (PSO), Bat Algorithm (BA), and Whale Optimization Algorithm (WAO), along with hyperparameter tuning using genetic algorithms and other metaheuristics. Results indicate significant improvements in classification performance after feature selection and optimization, with the GGO-optimized MLP model achieving an accuracy of 97.06%. The contributions of this paper include (i) benchmarking baseline models for CKD detection, (ii) a comprehensive analysis of feature selection strategies, (iii) optimization of machine learning models for CKD classification, and (iv) visualization of model performance to aid future research in healthcare machine learning applications.

groups

Mohamed Saber mail -

Ebrahim A. Mattar mail -

Marwa M. Eid mail -

El-Sayed M. El-Kenawy mail

link https://doi.org/10.54216/JISIoT.170211

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Forecasting CO2 Emissions from Cement Manufacturing with iHOW-Tuned Machine Learning Models

Cement production is a major contributor to global CO2 emissions, posing a challenge for climate mitigation efforts. Accurate forecasting of these emissions is vital for guiding policy and industrial decarbonization. This study addresses the need for improved predictive frameworks by developing an optimized ensemble-based machine learning model for CO2 emissions forecasting. The model is trained on a corrected global cement emissions dataset and enhanced through hyperparameter tuning using ten metaheuristic algorithms. Among them, the Improved Henry’s Optimization Algorithm (iHOW) achieved superior performance. The iHOW-optimized model attained an MSE of 1.21×10−6 and R2 of 0.9657, improving over the best baseline model (Gradient Boosting: MSE = 0.0164, R2 = 0.8621) by more than 99%. These results confirm the effectiveness of iHOW in producing accurate and reliable forecasts. The proposed framework offers strong potential for integration into carbon tracking systems and policy support tools.

groups

Omnia M. Osama mail -

El-Sayed M. El-Rabaie mail -

Marwa M. Eid mail

link https://doi.org/10.54216/JISIoT.170212

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2026

Optimizing Earthquake Prediction Accuracy using Somersaulting Spider Optimizer for Dynamic Ensemble Weighting

Earthquake prediction is one of the most challenging problems in geophysical science, and conventional approaches have proven arduous in capturing the complexity and non-linearity of seismic measurements. The multidimensional nature of earthquake variability, along with class imbalance and the strong dependence of prediction results on hyperparameters, necessitates the development of more robust and flexible predictive models. In this paper, we introduce a bio-inspired ensemble learning method based on the Somersaulting Spider Optimizer (SSO) for dynamically adjusting classifier weights in earthquake classification. The proposed method addresses limitations of existing weighting strategies, which primarily focus on maximizing classifier contribution based on performance characteristics. Experiments were conducted on an earthquake dataset augmented with features modeled and mapped by time, space, and magnitude to capture patterns of seismic events. We compared the SSO-optimized ensemble with BaggingClassifier, CatBoost, HistGradientBoosting, LightGBM, and DecisionTree, as well as traditional ensemble approaches. Results show that the SSO-boosted ensemble achieved superior performance, with an accuracy of 97.01%, sensitivity of 97.04%, specificity of 99.36%, precision of 97.64%, and an F1-score of 97.33%, outperforming other models and traditional ensembles. These improvements were confirmed statistically using Wilcoxon signed-rank tests, while visual analyses demonstrated enhanced stability and generalization. Overall, the integration of bio-inspired optimization and ensemble learning shows strong potential to overcome challenges in earthquake forecasting and to support reliable early warning and disaster preparedness systems.

groups

Ahmed Mohamed Zaki mail -

Hala B. Nafea mail -

Hossam El-Din Moustafa mail -

El-Sayed M. El-Kenawy mail

link https://doi.org/10.54216/JISIoT.180227

Volume & Issue

Vol. Volume 18 / Iss. Issue 2

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International Journal of Neutrosophic Science | 2026

Best Proximity Point Theorems in Neutrosophic Complete Metric Spaces

In this work, we introduce the notion of best proximity point for a non-self map defined in a neutrosophic complete metric space. Moreover, we define the class of neutrosophic proximal contraction of first kind and second kind, and we prove theorems which ensures existence and uniqueness of best proximity point for such mappings in neutrosophic complete metric spaces. Additionally, a technique to identify an optimal approximation solution intended as a best proximity point is demonstrated.

groups

A. Sreelakshmi Unni mail -

V. Pragadeeswarar mail -

Manuel De La Sen mail

link https://doi.org/10.54216/IJNS.270119

Volume & Issue

Vol. Volume 27 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2026

Solving Unconstrained Minimization Problems and Training Neural Networks via Enhanced Conjugate Gradient Algorithms

Artificial neural networks have become a cornerstone of modern artificial intelligence, powering progress in a wide range of fields. Their effective training heavily depends on techniques from unconstrained optimization, with iterative methods based on gradients being especially common. This study presents a new variant of the conjugate gradient method tailored specifically for unconstrained optimization tasks. The method is carefully designed to meet the sufficient descent condition and ensures global convergence. Comprehensive numerical testing highlights its advantages over traditional conjugate gradient techniques, showing improved performance in terms of iteration counts, function evaluations, and overall computational time across a variety of problem sizes. Additionally, this new approach has been successfully used to improve neural network training. Experimental results show faster convergence and better accuracy, with fewer training iterations and reduced mean squared error compared to standard methods. Overall, this work offers a meaningful contribution to optimization strategies in neural network training, displaying the method is potential to tackle the complex optimization problems often encountered in machine learning.

groups

Bassim A. Hassan mail -

Issam A. R. Moghrabi mail -

Talal M. Alharbi mail -

Alaa Luqman Ibrahim mail

link https://doi.org/10.54216/IJNS.270120

Volume & Issue

Vol. Volume 27 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

IoT and AI for Clinical Decision Support with Hierarchical Attention

The integration of Clinical Informatics (NI) and Artificial Intelligence (AI) promises to transform healthcare by improving clinical decisions, optimizing workflows, and personalizing patient care. However, most current systems fail to incorporate contextual reasoning, real-time adaptation, or ethical sensitivity, leading to fragmented support and increased cognitive burden on clinicians. To address these limitations, we propose NI-AIH—a hybrid clinical-AI framework built on a Context-Enriched Hierarchical Attention Network (CE-HAN). This deep architecture employs dual-attention mechanisms to interpret structured and unstructured clinical data—including EHR entries, nursing notes, and real-time IoT sensor feeds—capturing temporal patterns and contextual cues essential to patient status. The NI-AIH framework consists of four core components: a Clinical Context Engine (CCE) that uses CE-HAN for semantic modeling; a Predictive Care Optimizer (PCO) that applies risk-stratified deep ensembles; an Adaptive Interaction Layer (AIL) that enables seamless nurse–AI collaboration; and an Ethical Decision Integrator (EDI) that uses fuzzy logic to ensure real-time ethical alignment. In a trial deployment within a smart geriatric care unit, NI-AIH demonstrated a 23% improvement in early sepsis detection (p<0.01), a 31% reduction in clinician cognitive load (measured via NASA-TLX survey), and a 19% increase in workflow efficiency compared to conventional rule-based systems. By uniting clinical precision with ethical and context-aware intelligence, NI-AIH establishes a new paradigm for compassionate and effective AI-assisted healthcare.

groups

Sasikumar M. S. S. mail -

Ranganayaki V. C. mail -

R. Suganthi mail -

Nalini Subramanian mail -

T. Sethukarasi mail -

T. A. Mohanaprakash mail

link https://doi.org/10.54216/JISIoT.170213

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new