2692-4048ISSN (Online)
2770-0070ISSN (Print)
This paper applies deep learning techniques in classifying X-ray images to detect osteoporosis. Osteoporosis, a bone weakness condition, increases the risk of fractures; therefore, accurate early diagnosis is essential in management. We have designed a hybrid method called Fuzzy Logic Preprocessed Convolutional Neural Network, or FLPCNN, wherein fuzzy logic is used at the preprocessing step to handle uncertainty and imprecision of features extracted from X-ray images. This paper used a dataset of X-ray images, and the FLPCNN model was applied, classifying them into osteoporotic and non-osteoporotic with quite an accuracy of 100%. Fuzzy logic preprocessing combined with Convolutional Neural Networks (CNN) enhances the model’s classification accuracy and interpretable decisions. The proposed method would be a new way to cut down diagnostic errors and improve patient outcomes, opening ways for further research into deep learning techniques applied in healthcare.
Read MoreDoi: https://doi.org/10.54216/FPA.210201
Vol. 21 Issue. 2 PP. 01-21, (2026)
E-government implementation in developing countries faces obstacles and challenges far beyond being a simple technology., By interviewing citizens through its enhanced PEST (Political, Economic, Social, and Technological) analysis and artificial intelligence algorithms, this study systematically evaluates the experiments of Iraq to accommodate e-government service. 1,081 Iraqi citizens were surveyed using mixed methods to quantify their public acceptance and willingness of e-government services, as well as identifying the obstacles. Our investigation finds that data security (mean = 3.59-3.80), the political situation, economic distress, a lack of enthusiasm for change in society, and shortfalls of technological infrastructure are all serious challenges at present. The research used advanced statistical methods, including correlation analysis (0.634 technology-trust relationship), regression models (R ^ 2 = 0.542), factor analysis (KMO = 0.891), and Multi-Layer Perceptron (MLP) neural network algorithms achieved 89.8% prediction accuracy for e-government acceptance. The AI algorithm supported the conclusions drawn from statistical tests, with Technology Readiness and Security Perception rising up as two most significant predictors (23.4% importance for Technology Readiness and 19.8% importance for Security Perception). The findings also propose a novel methodological framework that integrates traditional statistical analysis with machine learning capabilities, rendering concrete recommendations to developing country policy makers. The study's findings imply that successful e-government implementation requires a holistic approach that factors in political, economic, social and technological aspects together. The composite PEST index score of 0.826 smells widespread resistance on the ground, although AI predictive model greatly facilitates forecasting for future e-government initiatives.
Read MoreDoi: https://doi.org/10.54216/FPA.210202
Vol. 21 Issue. 2 PP. 22-41, (2026)
Managing fuel and energy resources (FER) efficiently is still a major challenge for energy-intensive industries like oil and gas. This paper presents a practical framework that combines mathematical models with easy-to-run algorithms to plan and control FER use in real time. Our twin goals are to cut costs and keep equipment dependable. We first outline the main parts of an energy-management system for an oil-and-gas operation, and then list the key tasks, factors, and decision criteria. The framework has two complementary paths: Path 1 relates FER use to production output via Lagrange optimization, while Path 2 fine-tunes forecasts with a simple least-squares correction based on metered data. Both paths are implemented as executable algorithms and tested on real electricity and fuel-gas datasets. The new method cuts monthly FER-planning errors by up to 80 %, reducing penalties and helping equipment last longer.
Read MoreDoi: https://doi.org/10.54216/FPA.210203
Vol. 21 Issue. 2 PP. 42-55, (2026)
The Fourth Industrial Revolution represents a shift to a more connected, digital world across all industries, including mining. The application of smart sensors will reduce site risks and fuel consumption, reduce equipment breakdowns, improve preventative maintenance, and improve equipment efficiency, including dump truck engines. Dump truck fuel efficiency is influenced by a number of real-world factors, including driver behavior, road and weather conditions, and vehicle specifications. Additionally, potential engine failures and other aspects can impact vehicle outcomes. By using dynamic on-road data to predict fuel consumption per trip, the industry can effectively minimize the expense associated with driving evaluations. Furthermore, analysis of data provides valuable insights into identifying the underlying causes of fuel consumption by analyzing input parameters. This paper proposes and evaluates novel models for predicting dump truck fuel consumption and engine failures in open-pit mining. These models combine the power of features derived from data collected locally by dump truck sensors and their analysis. The fuel consumption prediction architecture for open-top mining trucks using an improved Long Short-Term Memory (LSTM) model and a double-layer thick Deep Neural Network (DNN) forms the basis of the model design, which consists of two separate components. Multi-delay Recurrent Neural Network (RNN) models have been found to be efficient and accurate. The RNN architecture is applied to capture the cyclic components and complex rules in engine consumption data. This research relied on essential factors (route, vehicle speed, engine revolutions, and engine load). The proposed model outperforms existing models, achieving (MAE=0.0210), (RMSE=0.0294), (MSE=0.0009), and accuracy (R²=0.9842), demonstrating that it can produce highly accurate predictions.
Read MoreDoi: https://doi.org/10.54216/FPA.210204
Vol. 21 Issue. 2 PP. 56-69, (2026)
Generative AI has made significant strides over the past few years, and this progress has accelerated the development of deepfake techniques, which can unfortunately be used for harmful purposes. It is essential to stay up-to-date with this advancement. In this paper, we present an explainable weighted average fusion deepfake detection system that combines Vision Transformer (ViT) and InceptionResNetV1 to improve classification accuracy. We also employed LIME and GradCAM++ to provide interpretability for the model decision. ViT utilizes self-attention modules to extract features, whereas InceptionResNetV1 employs convolutional layers to extract spatial features. Grad-CAM++ highlights the important regions influencing classification, and LIME examines the regional contributions. Together, these tools offer a deeper understanding of the model's decision-making process. Our fusion technique combines the outputs of both models by assigning specific weights that users can adjust interactively through the user interface. The use of these tools gives a better understanding of how the model classifies, which improves transparency and reliability in the models. The performance of the fusion strategy is tested with accuracy, precision, recall, and F1-score. Our proposed model achieves a classification accuracy of 99.19%, surpassing both ViT and InceptionResNetV1 when we evaluated them individually. To the best of our knowledge, this work represents the first deepfake detection model that combines Vision Transformer (ViT) and InceptionResNetV1 using a weighted averaging fusion approach with dual explainability techniques.
Read MoreDoi: https://doi.org/10.54216/FPA.210205
Vol. 21 Issue. 2 PP. 70-92, (2026)
Automated detection (AD) techniques are essential for early recognition of skin cancer. Hybrid models using feature fusion, which combine pre-trained CNNs with customized models, have shown superiority in real-time skin cancer pathology classification. This study combines VGG19 feature maps with a novel learning network based framework called AD_Net to enhance classification accuracy. VGG19 facilitated robust low-level feature extraction, while AD_Net brilliantly extracts specialized patterns. This strategy provided a flexible and fast architecture, suitable for real-time medical applications. This work led to the classification of three of the most lethal skin cancer types. The model was trained and validated using experiments on the publicly available ISIC2019 dataset. In order to improve the interpretability of the model's predictions, interpretable artificial intelligence (XAI) techniques particularly Grad-CAM were applied. Four baseline models EfficientNetB0, MobileNetV2, Inception-v3, and VGG16, were used to assess the proposal's efficacy. The suggested model outperformed the four baseline models with 99.18% accuracy, 99.0% precision, 99.0% recall, and 99.0% F1 score. Dermatologists and other medical professionals can use this method to detect skin cancer early.
Read MoreDoi: https://doi.org/10.54216/FPA.210206
Vol. 21 Issue. 2 PP. 93-103, (2026)
Cloud communication faces numerous disruptive cybersecurity threats. Various issues related to such disruption have been the subject of previous research, but detection attacks in the blade server (BS) in the cloud have not been studied. Therefore, this paper proposes an efficient intrusion detection system (IDS) framework for BS in the cloud. This framework uses Kerberos authentication-based exponential Mestre-Brainstrass curve cryptography, Sechsoftwave and sparsely centric gated recurrent unit (SSGRU). In this framework, cloud users are firstly registered to the network, and then incoming data are encrypted. The BS is then used to balance the incoming loads, and IDS is applied to detect attacks in the BS, with the data being pre-processed firstly and the big data being handled in the IDS. Afterwards, the features are extracted, from which optimal features are selected. Attacked and normal blades are classified by using the SSGRU classifier and then differentiated by generating a Sankey diagram. The attacked blades are then isolated, and the normal blades are used for load balancing on the cloud. Results indicate that this model achieved 99.43% accuracy, thus demonstrating superior performance to other models.
Read MoreDoi: https://doi.org/10.54216/FPA.210207
Vol. 21 Issue. 2 PP. 104-118, (2026)
The generation of cryptographic keys from biometric traits presents an opportunity to replace traditional password-based systems with mechanisms grounded in individual physiology. Nonetheless, reliably deriving secure and reproducible keys from modalities such as fingerprints and irises remains a significant challenge, particularly under varying input conditions and constraints on entropy. In this work, we present a hybrid dual-path deep learning architecture that combines Gated Linear Units (GLUs) with Squeeze-and-Excitation (SE) modules to extract rich, multimodal embeddings from iris and fingerprint images. The model, trained on an augmented cross-modal dataset, achieved a test accuracy of 99.92% and consistently high F1-scores across 50 subjects. To derive the cryptographic key, we apply a multi-stage pipeline that blends principal component projections, distance-based feature encoding, chaotic sequence modeling based on Lorenz-like dynamics, and a lightweight error-correcting routine. These representations are fused via a custom mixing function, producing a 512-bit binary vector subsequently refined using a SHA-256-based HKDF. Evaluation of the generated keys indicates near-ideal entropy, high inter-user separation, and strong avalanche characteristics. The system also passed multiple NIST statistical randomness tests and achieved a near-zero false acceptance rate. These results support the feasibility of the proposed method for secure and repeatable biometric key generation.
Read MoreDoi: https://doi.org/10.54216/FPA.210208
Vol. 21 Issue. 2 PP. 119-148, (2026)
Early and accurate diagnosis of Autism Spectrum Disorder (ASD) using neuroimaging has become increasingly viable with the advent of deep learning (DL) technologies. Current clinical diagnostic processes for ASD are largely subjective and time-intensive, creating an urgent need for objective diagnostic tools. This study presents a comprehensive comparison of three prominent functional Magnetic Resonance Imaging (fMRI) feature extraction methods, ALFF (Amplitude of Low-Frequency Fluctuations), fALFF (fractional ALFF), and ReHo (Regional Homogeneity), alongside structural Magnetic Resonance Imaging (sMRI) data, to evaluate their effectiveness in classifying ASD using various deep learning architectures. Preprocessed data from the ABIDE dataset were utilized, with uniform preprocessing pipelines applied, followed by feature extraction using the AAL (Automated Anatomical Labeling) atlas. Synthetic data augmentation was performed using Generative Adversarial Networks (GANs) to mitigate class imbalance. We trained and tuned multiple models, including 1-dimensional Convolutional Neural Networks (1D CNNs) with multi-head attention, Long Short-Term Memory (LSTM), and Vision Transformers (ViTs), with and without hyperparameter optimization. The findings indicate that the highest classification performance was attained using ALFF features with a hyperparameter-optimized CNN enhanced by attention mechanisms, achieving an accuracy of 0.83. Similarly, ReHo features yielded an equal accuracy of 0.83 when analyzed using a Vision Transformer (ViT) model. Across all experiments, functional neuroimaging features consistently outperformed structural features in classifying ASD. Notably, systematic hyperparameter tuning led to substantial improvements, particularly for ALFF-based models, where accuracy increased markedly from 59% to 83% using the CNN+Attention architecture. This study presents a comprehensive evaluation of feature types and model architectures across neuroimaging modalities, offering critical insights into their relative diagnostic value for ASD. The achieved accuracy of 83% using both ALFF and ReHo features marks a meaningful advancement in the field, setting realistic benchmarks for future research while adhering to stringent methodological rigor.
Read MoreDoi: https://doi.org/10.54216/FPA.210209
Vol. 21 Issue. 2 PP. 149-158, (2026)
Vitreoretinal surgery is highly dependent on good visualization of fragile retinal surfaces for the purpose of accurate and safe operation. However, the image quality of current 3D heads-up display systems is often suboptimal, such as low contrast or inadequate sharpness, which is likely to decrease the accuracy of operation and prolong the operation duration. Improving intraoperative image quality continues to be a challenge for the advancement of the surgical results. In this paper, we advocate a deep learning-based solution to optimal imaging parameter guidance for the prospect of 3D HU-image guided VR surgery, seeking to improve vitreoretinal surface visibility during the surgery. A hybrid model that combines a U-Net-based image enhancement with a ViT for feature refinement has been learned using 212 manually optimized still frames (extracted from the ERM surgical video). The performance of the algorithm was quantitatively assessed through peak signal-to-noise ratio (PSNR) and the structural similarity index map (SSIM) and qualitatively evaluated in terms of the improvement in sharpness, brightness, and contrast. Moreover, the in-cabin usability of optimized images was investigated in an intraoperative survey. For in-vitro validation, 121 anonymous high-resolution ERM fundus images were analyzed with a 3D display coupled with the algorithm. The SSIM and PSNR of the model were 36.45±4.90 and 0.91±0.05, respectively, which indicated considerable improvements in image sharpness, brightness, and contrast. Visible ERM size and color contrast ratio were significantly enhanced in optimized images in the in-vitro studies. The results demonstrate that the developed algorithm can perform digital image enhancement effectively and has promise in the real-time applications during the 3D heads-up vitreoretinal surgeries.
Read MoreDoi: https://doi.org/10.54216/FPA.210210
Vol. 21 Issue. 2 PP. 159-169, (2026)