Integrated CNN and Waterwheel Plant Algorithm for Enhanced Global Traffic Detection

Faris H.; Sofia; Ahmed Mohamed; Mohamed Ahmed; S. K.

doi:https://doi.org/10.54216/JAIM.060204

Galoitica: Journal of Mathematical Structures and Applications | 2023

Designing a Computer Simulation Model to Study the Failure Mechanism in Rotating Shaft

The phenomenon of the failure of rotating mechanical structures is considered one of the dangerous phenomena that researchers are trying to study to find effective ways to avoid it. Many mathematical models describing the mechanism of crack formation in structures and the occurrence of failure in them have been adopted. In this paper, a computer simulation model of the failure mechanism in the intermediate conduction shaft of the ship's propeller (oil shuttle tanker) was designed based on the mathematical model that describes the stages of failure according to the number of revolutions that the shaft bears and the dimensions of fatigue cracks in it. In the beginning, a detailed mathematical model of the crack propagation mechanism was deduced as an initial stage, through which the differential equations that express the failure of the shaft were described. In the second stage, numerical simulations were implemented using the Euler and Rung-Kutta numerical methods used in solving ordinary differential equations. Also, for both methods using the Matlab language to reach the results and accurate graphs and analyze them. In this way it is possible to predict the number of cycles that the column will bear before the collapse, and this will reduce the occurrence of sudden collapses and save time, effort, and costs.

groups

Warshine Barry mail

link https://doi.org/10.54216/GJMSA.090201

Volume & Issue

Vol. Volume 9 / Iss. Issue 2

Details open_in_new

Galoitica: Journal of Mathematical Structures and Applications | 2023

Neutrosophic Soft n-Topological Spaces: A Framework for Decision-Making Problems

This article introduces the concept of neutrosophic soft n-topological spaces and their application in decision-making problems. Neutrosophic soft sets are used to define the open sets in these spaces, which allow for greater flexibility and uncertainty in the decision-making process. The concept of neutrosophic soft n-homeomorphism is also introduced, which describes the topological equivalence between two neutrosophic soft n-topological spaces. The article provides examples of how neutrosophic soft n-topological spaces can be used in decision-making problems, such as medical diagnosis and stock market analysis. The theory and applications presented in this article provide a valuable tool for dealing with uncertainty in decision-making problems.

groups

Hasan Dadas mail -

Necati Olgun mail

link https://doi.org/10.54216/GJMSA.090202

Volume & Issue

Vol. Volume 9 / Iss. Issue 2

Details open_in_new

Prospects for Applied Mathematics and Data Analysis | 2023

A New Proof of Feuerbach’s Theorem

Feuerbach’s theorem on the tangent of the circle of the nine points and the inscribed and exinscribed circle is considered one of the most beautiful theorems in geometry. In this paper, we offer a basic proof of this theorem starting from one of Gh. Buicliu’s ideas [1].

groups

Ion Patrascu mail -

Florentin Smarandache mail

link https://doi.org/10.54216/PAMDA.020205

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2026

Algorithmic Hallucination in LLM-Generated Metaheuristics: A Reproducibility, Constraint-Violation, and Failure-Mode Audit

Large language models now routinely produce metaheuristic optimisation code in response to natural-language prompts, yet the structural integrity of such generated implementations has received little systematic scrutiny. Syntactically valid code that misrepresents algorithmic logic or parameter semantics constitutes a form of hallucination specific to optimisation software — one that is difficult to detect without execution-level testing and that can produce catastrophic performance degradation without any visible error signal. This work introduces a five-category taxonomy of algorithmic hallucination covering parameter value hallucination, update-formula omission, boundaryhandling failure, reproducibility failure, and termination-logic error. A controlled audit is conducted by injecting each hallucination type into verified reference implementations of three widely used metaheuristics — particle swarm optimisation, differential evolution, and a genetic algorithm — and evaluating their behaviour across five standard continuous benchmark functions. Across twenty independent trials per condition, parameter hallucination and formula-omission errors produce best-fitness degradation exceeding 108-fold relative to the reference, while boundary-handling failures generate more than 4 000 out-of-bounds constraint violations per trial. Reproducibility failure inflates inter-trial variance by three to four orders of magnitude, and premature-termination errors induce a 100% trial failure rate. The results demonstrate that current LLM-generated metaheuristic code requires structured execution-level validation before deployment, and a practical audit protocol is proposed to support that process. Experimental artefacts, including all source code, generated data, and benchmark results, are released to support reproducible follow-on research.

groups

Safina Shokeen mail -

Vishal Srivastava mail

link https://doi.org/10.54216/IJAACI.080202

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2026

Metaheuristic-Optimized Conditional Diffusion Networks for Industrial Sensor Data Synthesis: An Applied Computational Intelligence Benchmark

Synthetic industrial telemetry can alleviate scarcity and confidentiality constraints, but its value depends on more than distributional similarity. Generated sequences must support downstream engineering analysis, limit disclosure risk, and satisfy operational relations. This paper presents a reproducible three-axis benchmark and a differential-evolution calibration layer for conditional denoising diffusion. Six generators–bootstrap resampling, a shrinkage-Gaussian model, Fourier surrogates, a conditional variational autoencoder, a conditional diffusion model, and its calibrated counterpart–are evaluated on chronologically partitioned manufacturing telemetry. The protocol covers 18,000 generated 80-minute windows and combines train-synthetic/test-real alarm classification, marginal and temporal fidelity measures, record-proximity and membership-inference tests, and five engineering-rule audits. Differential evolution selects four post-generation controls using validation data only. Relative to the uncalibrated diffusion model, calibration reduces the aggregate physical-violation rate by 94.7% and the Wasserstein error by 29.2%, while increasing mean downstream ROC–AUC by 0.009. The improvement is accompanied by a 0.073 increase in membership-inference AUC and a small deterioration in autocorrelation error. Bootstrap resampling provides the strongest mean predictive utility but exactly reproduces 77.9% of its outputs; the conditional variational autoencoder attains the lowest Wasserstein error (0.046) with a 0.020% physical-violation rate. No generator dominates utility, privacy, and plausibility simultaneously. Synthetic industrial data should therefore be selected through deploymentspecific acceptance regions rather than a single realism score.

groups

Khaled Sh. Gaber mail -

Mahmoud Elshabrawy Mohamed mail

link https://doi.org/10.54216/IJAACI.080203

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2026

A Hybrid Computational Intelligence Framework Integrating XGBoost and Residual Neural Networks for Multi-Crop Yield Prediction

Reliable crop-yield prediction is essential for strategic food planning, input allocation, and precision-agriculture analytics, yet a single predictive model must accommodate substantial heterogeneity across crop species, regions, seasons, and management conditions. This paper proposes a hybrid computational-intelligence framework that couples extreme gradient boosting with an ensemble of residual neural networks in order to model both dominant nonlinear structure and systematic residual error. A leakage-controlled benchmark was constructed from a recent public agricultural data release, yielding 2,698 observations spanning eight major crops, 30 Indian states and union territories, six agricultural seasons, and ten complete years from 2010 to 2019. The predictor space combines climatic and agronomic descriptors, including annual rainfall, cultivated area, fertilizer and pesticide intensities, crop identity, state, season, temporal trend, and rainfall–management interaction terms; the production variable was removed to prevent algebraic target leakage. The empirical study compares the proposed framework with nine competitive baselines under a strict chronological design in which 2010–2017 are used for training, 2018 for model selection, and 2019 for independent testing. On the holdout year, the proposed hybrid attains R2 = 0.974, RMSE = 3.146 t ha−1, and MAE = 1.080 t ha−1, outperforming standalone XGBoost by 20.3% in RMSE and a direct deep neural network by 32.5%. Bootstrap analysis places the hybrid RMSE within 2.042–4.112 t ha−1 at the 95% confidence level. Grouped permutation analysis shows that crop identity, geographic location, and cultivated area contribute most strongly to predictive performance. Overall, the results support residual error correction as an effective strategy for strengthening tabular yield prediction while also underscoring the importance of leakage control, temporal validation, and competitive baseline design.

groups

Yasser Elawady mail

link https://doi.org/10.54216/IJAACI.080204

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2026

Metaheuristic Lineage Mining for Electrical and Industrial Engineering Optimization: Detecting Rebranded Algorithms through Equation, Code, and Search-Dynamics Similarity

Metaheuristic optimization is extensively used in electrical and industrial engineering, where nonlinear, nonconvex, mixed-variable, and simulation-based models arise in power-system operation, energy management, production planning, scheduling, and manufacturing-system design. The rapid proliferation of newly named optimizers, however, makes it difficult to determine whether an apparent contribution introduces a distinct search mechanism or repackages an established algorithmic lineage. This article presents Metaheuristic Lineage Mining (MLM), a multimodal screening framework that integrates executable equation structure, normalized source-code structure, and empirical search dynamics. Identifier-invariant abstract-syntax motifs represent update equations, normalized token n-grams characterize implementation structure, and trajectory fingerprints summarize convergence, diversity, exploration, and improvement behavior. The evaluation includes 57 implementations grouped into 16 documented variant families and 1,710 controlled runs over six continuous benchmark functions and five random seeds. Fusion weights are selected under lineage-held-out validation, preventing algorithms from the evaluated family from influencing model selection. Code similarity is the strongest individual channel, with a mean ROC–AUC of 0.955 and top-1 lineage retrieval of 0.930; equation similarity reaches a ROC–AUC of 0.929. Search-dynamics similarity is weaker alone but supplies behaviorally independent corroboration. Constrained multimodal fusion yields the highest average precision of 0.769, a mean reciprocal rank of 0.935, and clustering normalized mutual information of 0.885. Interpretable cross-family associations, including WhaleFOA–WOA and JADE–SHADE, demonstrate the ability to expose hybrid or ancestral relations that categorical labels omit. The framework is not a detector of misconduct; it is an auditable engineering due-diligence tool for evaluating optimizer originality before costly application studies in power, energy, manufacturing, logistics, and production systems.

groups

Amer Ramadan mail -

Ana Jurasovi´c mail

link https://doi.org/10.54216/IJAACI.080201

Volume & Issue

Vol. Volume 8 / Iss. Issue 2

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2023

Advancing Parking Space Surveillance using A Neural Network Approach with Feature Extraction and Dipper Throated Optimization Integration

This research endeavors to advance the realm of parking space surveillance through a meticulously designed methodology situated within the critical context of urban planning and the dynamic landscape of smart city development. Focused on addressing the challenges posed by escalating urbanization and burgeoning vehicular density, our study introduces a carefully curated dataset comprising images of parking spaces annotated with bounding box masks and occupancy labels. The methodology unfolds across distinct phases, commencing with a comprehensive dataset description that unveils its diversity and intricacies. Feature extraction techniques, harnessing the capabilities of cutting-edge architectures such as AlexNet and ResNet-50, play a pivotal role in enhancing pattern discernment, which is essential for accurate detection. The crux of our approach lies in the integration of Neural Networks with optimization algorithms, including Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and the innovative Dipper Throated Optimization (DTO). Results are presented without explicit mention of tables and figures, strategically emphasizing the methodology's effectiveness in enhancing parking space detection accuracy. Notably, Dipper Throated Optimization (DTO) emerges as a key contributor to optimized Neural Network performance, achieving an impressive accuracy of 0.9908. This research contributes significantly to the ongoing discourse on intelligent urban planning and sets a promising trajectory for the future of efficient parking space utilization in modern cities.

groups

Ahmed Mohamed Zaki mail -

S. K. Towfek mail -

Weiguo Gee mail -

Wang Zhang mail -

Marwa Adel Soliman mail

link https://doi.org/10.54216/JAIM.060202

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2023

Evaluating the Efficacy of Deep Learning Architectures in Predicting Traffic Patterns for Smart City Development

Smart city development necessitates the implementation of effective traffic management strategies. In this vein, various deep learning architectures, including VGG16Net, VGG19Net, GoogLeNet, ResNet-50, and AlexNet, are employed to predict diverse traffic patterns extracted from a comprehensive dataset. Evaluating performance metrics such as accuracy, sensitivity, and specificity reveals discernible variations among models, with ResNet-50 and AlexNet demonstrating superior predictive capabilities. Descriptive statistics and statistical analyses, including ANOVA and the Wilcoxon Signed Rank Test, provide nuanced insights into model differences and significance. The findings bear significant implications for urban planners and policymakers transforming cities into intelligent ecosystems, offering valuable insights for informed decision-making in innovative city development. Improved traffic predictions enhance daily commuting experiences and contribute to the informed development of sustainable urban infrastructure, aligning seamlessly with the ongoing evolution of smart cities toward a more connected and efficient future. Notably, AlexNet exhibits a significant accuracy of 0.931780366 in the context of traffic pattern prediction.

groups

Mohamed Ahmed Kandel mail -

Faris H. Rizk mail -

Lima Hongou mail -

Ahmed Mohamed Zaki mail -

Hakan Khan mail -

El-Sayed M. El-Kenawy mail

link https://doi.org/10.54216/JAIM.060203

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2023

Integrated CNN and Waterwheel Plant Algorithm for Enhanced Global Traffic Detection

Traffic detection is critical in ensuring road safety and efficient traffic management, demanding deploying accurate and practical algorithms. This research explores the fusion of Convolutional Neural Networks (CNNs) and the Waterwheel Plant Algorithm to augment global traffic detection capabilities, utilizing a diverse dataset primarily collected from Turkey. A comprehensive evaluation of prominent CNN architectures, such as VGG19Net, AlexNet, ResNet-50, GoogLeNet, and a generic CNN, underscores substantial efficacy, with the CNN achieving an accuracy of 92.14%. Introducing the Waterwheel Plant Algorithm (WWPA) further enhances performance, as exemplified by the hybrid WWPA-CNN model, exhibiting an impressive accuracy of 97.28%. These findings highlight the promising synergies between traditional optimization algorithms and advanced neural networks, showcasing the potential for innovative developments in traffic monitoring systems and broader applications within computer vision. The statistical analyses, encompassing ANOVA and the Wilcoxon Signed Rank Test, robustly underscore the significance of this integrated approach. As the research contributes to the evolution of traffic monitoring systems, these insights provide a solid foundation for advancements in the field, fostering innovation and shaping the future landscape of computer vision applications.

groups

Faris H. Rizk mail -

Sofia Arkhstan mail -

Ahmed Mohamed Zaki mail -

Mohamed Ahmed Kandel mail -

S. K. Towfek mail

link https://doi.org/10.54216/JAIM.060204

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

Details open_in_new