A Deep Learning and Metaheuristic Optimization Framework for Short-Term Electricity Consumption Forecasting Using High-Resolution SCADA Data

Wei Hong; Amel Ali

doi:https://doi.org/10.54216/JAIM.110101

American Journal of Business and Operations Research | 2026

Interactive Teaching Methods in Higher Education: An IMRaD-Based Framework and Empirical Evaluation Protocol

Interactive methods are increasingly used in higher education to improve engagement and learning outcomes; however, universities often lack a reproducible procedure for selecting methods, sequencing them within a class, and evaluating effectiveness with comparable indicators. This research article develops and reports an IMRaD‑aligned framework for integrating interactive methods into university classes and provides an empirical evaluation protocol that can be implemented in practice. The study uses a mixed design: (i) a structured literature synthesis on active learning, cognitive engagement, and instructional design, and (ii) a quasi‑experimental classroom evaluation protocol (recommended for adoption) combining observation, short surveys, and learning analytics from digital tools (polling/quizzes, interactive whiteboard logs, and learning management systems). Results are presented as an operational toolkit: a taxonomy of interactive methods and didactic functions, a method–outcome–motivation mapping, a standardized 90‑minute lesson architecture, implementation checklists, and a monitoring model with defined indicators for achievement, participation, and motivation. The discussion highlights how method coherence across lesson phases supports cognitive activity as a unity of perception, reasoning, and practice, and outlines limitations and future research using experimental designs. The article contributes practical instruments for evidence‑based teaching and offers a pathway for universities to move from descriptive claims about ‘interactivity’ to measurable improvement.

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Nam Tatyana Gennadievna mail

link https://doi.org/10.54216/AJBOR.140105

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

American Journal of Business and Operations Research | 2026

Enhancing the Effectiveness of NGO Project Management through International Best Practices: A Comparative Analysis

Non-governmental non-profit organizations (NGOs) play a critical role in addressing social challenges and supporting sustainable socio-economic development. The effectiveness of their activities largely depends on the quality of project management systems. While developed countries have established advanced institutional and managerial frameworks for NGO project implementation, many developing countries, including Uzbekistan, continue to face limitations related to funding, professional capacity, and monitoring mechanisms. The aim of this study was to assess international best practices in NGO project management and identify applicable directions for improving project effectiveness in Uzbekistan. A comparative analytical approach was applied using secondary data from the United States, Canada, Germany, Norway, Sweden, and Uzbekistan. The analysis focused on public funding volumes, adoption of international project management standards, monitoring and evaluation effectiveness, and the share of certified project managers. The results demonstrate a significant performance gap between Uzbekistan and developed economies. The study concludes that systematic integration of international standards, strengthened monitoring systems, and professional certification are essential for improving NGO project effectiveness.

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Sultonova Dilnoza Dilshodovna mail

link https://doi.org/10.54216/AJBOR.140106

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2026

A Hybrid Deep Learning Model for Enhanced Detection of Zero-Day and Ransomware Attacks

The increasing sophistication of ransomware and zero-day attacks demands advanced intrusion detection systems. This paper proposes a hybrid deep learning model that combines Temporal Convolutional Networks (TCN) and Long Short-Term Memory (LSTM) networks, augmented with Principal Component Analysis (PCA) for feature selection. Evaluated on the UGRansome dataset, our hybrid TCN-LSTM-PCA model achieves superior performance compared to standalone LSTM, TCN-PCA, and LSTM-PCA baselines, attaining 98.82% accuracy (a 4.09 percentage-point improvement over LSTM-PCA) and 0.99 F1-score across all attack classes while maintaining computational efficiency at 13 seconds per epoch. The architecture’s effectiveness stems from its synergistic design: TCN layers capture local temporal patterns in network traffic, while LSTM modules model long-range attack sequences. PCA preprocessing reduces feature dimensionality by 83%, retaining seven critical indicators including Netflow Bytes and Protocol flags that collectively explain 92% of variance. Experimental results demonstrate exceptional robustness, with only 0.18% misclassification between attack categories and consistent performance across ransomware variants. This study sets a new state of the art in real-time threat detection, delivering an efficient hybrid architecture that satisfies practical deployment constraints while achieving 98.82% accuracy and 0.99 precision, thereby striking a strong accuracy–efficiency balance.

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Mohammed Ibrahim Kareem mail -

Aladdin Abdulhassan mail -

Abdullah Yousif Lafta mail -

Hussein Ibrahim Hussein mail -

Ali Z. K. Matloob mail

link https://doi.org/10.54216/JCIM.170217

Volume & Issue

Vol. Volume 17 / Iss. Issue 2

Details open_in_new

International Journal of Neutrosophic Science | 2026

On SG-Fréchet Space and SG-Hausdorff Space in Soft Group Topological Spaces and Neutrosophic Soft Group Sets

In this paper, we introduce some concepts : soft group point, soft group set, soft group topology ,define soft group Fréchet space and soft group Hausdorff space in soft group topological spaces, study a relation between FG - topological space and soft group topological space with examples. Finally we introduce a new generalized definition called NSG- sets study the relations between it and the related sets.

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Majd Hamid Mahmood mail

link https://doi.org/10.54216/IJNS.270239

Volume & Issue

Vol. Volume 27 / Iss. Issue 2

Details open_in_new

International Journal of BIM and Engineering Science | 2026

Quantifying the ISO 19650 Dividend: Developing Practical KPIs for BIM Implementation ROI

Purpose – The current global transformation in the construction industry, through the use of Building Information Modeling (BIM) and ISO 19650 information management, is hindered by a missing financial Return on Investment (ROI) on the ISO 19650 information management. This is a hindrance for investment and decision-making. The research seeks to solve the problem through the establishment of a Key Performance Indicator (KPI) for the realization of the “ISO 19650 Dividend.” Design/methodology/approach – A sequential explanatory mixed-methods approach was adopted, integrating a systematic literature review, the analysis of existing data (n = 104), a cross-sectional study involving a primary survey of a targeted cohort in the UK and Saudi Arabia (n = 187), and in-depth expert interviews (n = 15). Quantitative data were analysed using weighted mean, gap, and path analyses, while qualitative data were examined through thematic analysis. Findings – The paper pinpoints the attainment of operational efficiency and cost competitiveness as the key priority level for the value drivers, while pointing out the substantial gap in measuring the intangible value, such as organization capital and sustainability. Commitment to the organization by the leaders stands as the key critical success factor. The key outcome of this paper includes the development of the four-leveled KPI Framework and the conceptual model focusing on the adoption and successful measurement, resulting in the ROIa. Practical implications – The framework offers a structured roadmap or a step-by-step process change that enables organizations to move from basic process compliance measurement metrics to financial metrics measurement in their digital projects. This framework provides professionals in this domain a way in which benefits realized from collaboration are converted into a proxy measures. Originality/value – Instead, this research breaks the mold of general sets of BIM benefits in offering the first-ever integrated measurement framework that specifically sets out to quantify the ROI of implementing ISO 19650, by synthesizing performance metrics with qualitative knowledge of leadership and change management in a holistic approach for the realization of digital promise and profit.

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Ashraf Elhendawi mail -

Muhaideb AlMuhaideb mail -

Abdul Salam Darwish mail

link https://doi.org/10.54216/IJBES.120102

Volume & Issue

Vol. Volume 12 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Robust Forgery Detection in Digital Images Utilizing the Multiple Image Splicing Data Set (MISD)

In the area of digital information, establishing the authenticity of an image has grown to have greater significance as more and more persons have access to sophisticated image editing technologies. There is however a challenge in detecting such a forgery since it is usually very realistic and it is hard to know the difference between the real images and the fake ones. This paper aims at creation of a mechanism of identifying forged images based on Multiple Image Splicing Dataset (MISD) as a reference point. The suggested system will help to improve the results of the forgery detection, paying particular attention to the images processing during some of the pre-processing steps Firstly, converting colors into the hue-based histograms and RGB histograms, and hue-based histograms in an HSV, in comparison between the original and forged image, its HSV histogram, and its grayscale histogram, etc. Lastly, compute MSE and SSIM original and forged image. The implementation results showed that average value of MSE and SSIM metrics on Multiple Image Splicing Dataset (MISD) equal to 184.82 and 0.65 respectively that means the suggested method proved the efficiency of the technique to identify forged images as quickly as possible but still retain accuracy.

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Heba Adnan Raheem mail

link https://doi.org/10.54216/JISIoT.170129

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new

Galoitica: Journal of Mathematical Structures and Applications | 2026

A Comparison between Elastic Net Logistic and a Set of Machine Learning Algorithms in Predicting Breast Cancer

Breast cancer is a common type of cancers and the main reason of increased death of women universally. Recently, ML methods have become important in varying fields, such as Logistic Regression, Elastic Net Logistic, Decision Tree, Random Forest, Boosting, Naive Bayes and K Nearest Neighbor. The aim of the current study is to know and predict the type of cancerous tumor whether it is benign or malignant. These above techniques are expected to be helpful. Breast tumor type diagnosis using numerous performance metrics i.e. accuracy, classification error, sensitivity and specificity, both certified and trained models were assessed. The models were developed to determine which model would provide the best performance and comparisons were done. A separate data set from the one used to create the models was utilized to confirm every model. According to the analysis, the findings showed that elastic net logistic model had the highest performance in accurate classification rate (accuracy), classification error and sensitivity. Making it the best classifier for predicting the kind of breast cancer among all other models, privacy and it was also distinguished by reduce the high dimensionality and multicollinearity problems.

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Hadeel Imad Naser mail -

Wakaa Ali Hadba mail

link https://doi.org/10.54216/GJMSA.130101

Volume & Issue

Vol. Volume 13 / Iss. Issue 1

Details open_in_new

Galoitica: Journal of Mathematical Structures and Applications | 2025

A Short Note on Interval-Valued Bipolar Fuzzy SuperHyperGraphs

Hypergraphs extend classical graphs by allowing hyperedges to connect arbitrary nonempty subsets of vertices, thereby capturing higher-order, group-level interactions. Superhypergraphs further broaden this setting by iterating the powerset construction, which yields layered supervertices and supports multi-level relational structure. An interval-valued bipolar fuzzy graph assigns positive and negative membership intervals to vertices and edges while satisfying bipolar consistency constraints. In this paper, we extend interval-valued bipolar fuzzy graphs to the settings of hypergraphs and superhypergraphs.

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Takaaki Fujita mail -

Ajoy Kanti Das mail -

Sankar Prasad Mondal mail -

Suman Das mail

link https://doi.org/10.54216/GJMSA.120204

Volume & Issue

Vol. Volume 12 / Iss. Issue 2

Details open_in_new

American Journal of Business and Operations Research | 2026

Enhancing Financial Decision-Making in SMEs: Improving Forecasting Accuracy for Sustainable Growth

The growing complexity of financial decision-making in Small and Medium-Sized Enterprises (SMEs) necessitates advanced predictive models capable of accurately forecasting financial outcomes such as revenue, profit margins, and cash flow. Despite the availability of various machine learning models, there remains a need for optimization techniques that enhance model accuracy, generalization, and efficiency. This paper addresses this gap by applying metaheuristic optimization strategies to improve the performance of baseline financial forecasting models, particularly the Logarithmic Transformation (LogTrans) model. We propose the integration of several state-of-the-art metaheuristic algorithms, including Simulated Simulated Annealing (SSO), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WAO), and others, to optimize hyperparameters and perform feature selection. Our results demonstrate that the optimized SSO + LogTrans configuration outperforms all other models, achieving a remarkable Mean Squared Error (MSE) of 1.95E-07, a Root Mean Squared Error (RMSE) of 4.42E-04, and a high R-squared (R²) value of 0.966. These findings indicate that metaheuristic-driven optimization significantly improves predictive accuracy and generalization capability in SME financial decision-making models. The implications of this study extend beyond SMEs, offering potential applications in industries such as banking, investment, and insurance, where precise financial forecasting is critical. Furthermore, our approach highlights the importance of metaheuristics in the automated optimization of machine learning models, paving the way for further advancements in real-time decision support systems for dynamic financial environments.

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Sayed Elkenawy mail

link https://doi.org/10.54216/AJBOR.140107

Volume & Issue

Vol. Volume 14 / Iss. Issue 1

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2026

A Deep Learning and Metaheuristic Optimization Framework for Short-Term Electricity Consumption Forecasting Using High-Resolution SCADA Data

Accurate prediction of electricity consumption is a critical requirement for improving operational efficiency, enhancing grid reliability, and supporting sustainability objectives in urban power distribution systems, particularly in regions experiencing steady population growth and increasing demand pressure. Motivated by the limitations of conventional statistical and physics-inspired forecasting approaches, as well as the strong sensitivity of deep learning architectures to hyperparameter configuration, t his s tudy p roposes a robust data-driven framework that integrates deep learning with advanced metaheuristic optimization for high-precision short-term electricity consumption forecasting. The main contribution of this work lies in the systematic development and evaluation of hybrid metaheuristic–Bidirectional Long Short-Term Memory (BiLSTM) models, in which multiple state-of-the-art optimization algorithms are employed to tune model hyperparameters. Particular emphasis is placed on the integration of the Ninja Optimization Algorithm with BiLSTM (NijOA + BiLSTM), which is designed to effectively navigate complex, high-dimensional hyperparameter search spaces encountered in deep learning–based load forecasting tasks. Baseline experiments demonstrate that BiLSTM outperforms other deep learning models, including Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU), achieving a baseline Root Mean Squared Error (RMSE) of 0.0964 and a coefficient of determination (R2) of 0.854. These results confirm t he a dvantage o f b idirectional t emporal l earning in capturing the nonlinear and time-dependent characteristics of electricity consumption recorded at high temporal resolution from SCADA systems. Following metaheuristic optimization, the NijOA + BiLSTMmodel delivers a substantial improvement in predictive performance. The optimized configuration reduces RMSE to 0.0038, Mean Squared Error (MSE) to 1.45 × 10−5, and Mean Absolute Error (MAE) to 0.00019, while increasing the correlation strength to r = 0.973 and the explanatory power to R2 = 0.97. Comparative analysis across different optimization strategies further confirms t he s uperiority o f t he NijOA + BiLSTM hybrid model over alternative configurations, including WAO + BiLSTM, BBO + BiLSTM, GA + BiLSTM, SFS + BiLSTM, DE + BiLSTM, and JAYA + BiLSTM. The implications of these findings are significant for real-world urban electricity distribution applications. The proposed framework enables highly accurate and reliable short-term electricity consumption forecasting, making it well suited for deployment within smart grid and distribution management systems. Such predictive capability can support informed operational decision-making, improve demand-side management strategies, reduce uncertainty in short-term planning, and contribute to the long-term sustainability and resilience of urban power distribution networks.

groups

Wei Hong Lim mail -

Amel Ali Alhussan mail

link https://doi.org/10.54216/JAIM.110101

Volume & Issue

Vol. Volume 11 / Iss. Issue 1

Details open_in_new