Computing and Algorithm Insight serves as a dynamic platform for researchers, scholars, and industry professionals, providing a space for the exploration and dissemination of groundbreaking advancements in computing technologies and algorithmic studies. Computing and Algorithm Insight Journal invites original research articles, comprehensive reviews, and insightful survey papers that contribute to the advancement of knowledge in these dynamic fields. We encourage interdisciplinary approaches that bridge the gap between theoretical concepts and practical applications.
Advanced Osteoporosis Prediction Using Transformer Models and Genetically Optimized Recurrent Neural Networks
While artificial intelligence has revolutionized medical diagnostics, osteoporosis prediction remains constrained by the non-personalized nature of conventional DXA scans and traditional risk assessments. This study addresses these limitations by developing a comparative machine learning framework utilizing datasets from Kaggle and NHANES, each comprising approximately 2,000 patient records. The data were standardized to encompass multifaceted risk factors, including physiological metrics, family history, and lifestyle habits. Ten distinct architectures ranging from traditional classifiers to deep learning models (CNN and RNN) and the BERT transformer were evaluated using 10-fold cross-validation. To handle data imbalance in the NHANES set, the SMOTE technique was applied. Notably, the BERT model demonstrated superior efficacy on the Kaggle dataset, achieving 92% accuracy and an AUC of 0.92. Furthermore, integrating a Genetic Algorithm for parameter optimization significantly enhanced the RNN’s performance, elevating its AUC to 0.932 and matching the transformer’s accuracy. These results suggest that combining deep learning with evolutionary optimization offers a robust, personalized approach for early osteoporosis detection, potentially improving long-term bone health management and preventive strategies.
Copyright (c) 2026 Author(s) https://creativecommons.org/licenses/by-sa/4.0/
A Parallel Hybrid LSTM-FEDformer Framework with CryptoBERT Sentiment Analysis for Cryptocurrency Price Prediction
Predicting cryptocurrency prices is inherently difficult due to extreme volatility influenced by market dynamics and investor sentiment. Traditional models often struggle to synthesize short-term fluctuations with long-range temporal dependencies. This study proposes L-FED, a hybrid deep learning architecture that integrates Long Short-Term Memory (LSTM) networks with the FEDformer framework, further enhanced by sentiment analysis. Utilizing a parallel learning structure, L-FED facilitates bidirectional information interaction through local-global collaborative learning. A robust feature engineering process is employed, incorporating historical trade data, technical indicators, and sentiment features alongside LSTM-derived guiding prices. Experimental results indicate that L-FED significantly outperforms baseline models. On Bitcoin and Ethereum datasets, the model achieved RMSE and MAPE improvements of up to 16% and 12.8%, respectively. Furthermore, incorporating the domain-specific CryptoBERT model for sentiment analysis improved predictive accuracy by up to 19%, demonstrating the critical value of specialized linguistic pre-training in capturing market psychology within the highly volatile cryptocurrency ecosystem.
Enhancing Efficiency and Transparency in Disease Diagnosis via Randomized Machine Learning and Explainable AI
Deep learning has significantly advanced medical diagnostics through big data utilization; however, high computational costs and "black-box" opacity remain critical barriers in resource-constrained clinical settings. This research investigates randomized machine learning frameworks specifically Extreme Learning Machines (ELMs) and Random Vector Functional Link (RVFL) networks as efficient alternatives. By incorporating stochasticity into the training phase, these architectures reduce computational complexity and latency without compromising diagnostic precision. To mitigate interpretability concerns, the study integrates Explainable AI (XAI) tools, including Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP), to elucidate model decision-making. Empirical evaluations involving genitourinary cancers and coronary artery disease datasets reveal that RVFL superiorly balances performance and efficiency, achieving accuracies of 88.29% and 81.64% with significantly reduced processing times (6.22s and 0.0308s, respectively). These findings advocate for the adoption of randomized models to enhance transparency and operational speed in healthcare, ultimately facilitating more accessible, interpretable, and timely diagnostic interventions.
Optimized Convolutional Neural Networks for Automated Parkinson’s Disease Detection Using Temporal Speech Biomarkers
Parkinson’s disease (PD) is a progressive neurodegenerative condition characterized by dopamine depletion in the substantia nigra, which manifests through motor impairments and distinct vocal alterations. Recognizing temporal speech variations as critical biomarkers, this study proposes an optimized Convolutional Neural Network (CNN) framework for the early and precise detection of PD. Utilizing a dataset comprising 195 individuals (147 PD patients and 48 healthy controls), the research implements an advanced preprocessing pipeline involving signal denoising, feature normalization, and Bayesian hyperparameter optimization to refine classification efficiency. Experimental results demonstrate that the CNN classifier achieves a superior diagnostic accuracy of 96.85%, complemented by high sensitivity and a robust F1 score. The model maintains consistent predictive reliability even under suboptimal acoustic conditions, highlighting its capability to identify disease-specific vocal signatures effectively. These findings underscore the potential of the proposed deep learning architecture as a scalable clinical tool for early PD screening, facilitating timely therapeutic interventions and improved patient management within integrated diagnostic workflows.
Enhanced Robustness in Automated Skin Cancer Detection via a CNN-Based Ensemble Classifier
Malignant skin lesions, commonly referred to as skin cancer, represent a leading cause of global mortality. The integration of advanced deep learning methodologies facilitates the early diagnosis of these lesions, thereby offering a vital pathway to decrease associated mortality rates. For the automated categorization of dermoscopic images, deep convolutional neural networks (CNNs) have consistently exhibited performance superior to that of traditional machine learning algorithms. The current research focuses on the development of a highly precise ensemble classifier built upon foundational CNN architectures for the purpose of classifying skin lesions. To ensure the broad generalizability of the proposed model, strategic enhancements in diversity were implemented to bolster both systemic resilience and accuracy. Specifically, this necessary variance was introduced at both the dataset and the algorithmic classifier levels. The comprehensive ISIC 2018 challenge dataset, encompassing more than 13,000 dermoscopic images, was utilized across the training, validation, and testing phases. Experimental outcomes indicate that the developed ensemble framework attained an accuracy rate of 90.15%, successfully surpassing baseline CNN configurations and comparable contemporary classifiers in terms of overall accuracy and robust performance.
Enhancing Hypertension Prediction Accuracy Through Hybrid Machine Learning Feature Generation and Long Short-Term Memory Networks
The escalating prevalence of hypertension necessitates the development of sophisticated predictive mechanisms in healthcare. This study introduces a hybrid methodology synergizing machine learning and deep learning for feature augmentation and hypertension prognosis. We investigated five machine learning algorithms Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), XGBoost (XGB), and Gradient Boosting (GB) to generate predictive features, which were subsequently integrated with the original dataset to train a Long Short-Term Memory (LSTM) network. Comparative analysis reveals that the Gradient Boosting-based Feature Prediction combined with LSTM (GB-based FP + LSTM) yields superior performance, achieving a peak accuracy of 98.48% and an equivalent F1-score. Conversely, the LR-based integration demonstrated the lowest accuracy at 89.39%. These results suggest that the proposed GB-based FP + LSTM framework offers a highly reliable diagnostic instrument, providing clinicians and policymakers with an effective pathway for early and accurate hypertension detection.
Copyright (c) 2025 Author(s) https://creativecommons.org/licenses/by-sa/4.0/
Natural Language-Guided Reinforcement Learning for Human-Machine Collaboration in Sparse Reward Environments
Game agents in open environments often struggle with traditional exploration due to sparse environmental rewards. This study adopts deep reinforcement learning to enhance agent decision-making in reward-deficient electronic game settings. We developed a human-machine collaboration model that utilizes natural language instructions to guide the reinforcement learning process through reward construction. To address the sparse feedback problem, Hindsight Experience Replay (HER) was integrated into the architecture. Experimental results show that the natural language reward model achieved a 92% prediction accuracy and a game score of 9.8. Following HER optimization, target instruction accuracy reached 97.8% with a final score of 9.9. These findings demonstrate that combining linguistic guidance with experience replay significantly improves application performance in coefficient reward environments.
An Intelligent Stroke Detection Framework Utilizing Genetic Algorithm-Based Feature Selection and Bidirectional LSTM on CT Imagery
Cerebrovascular pathologies, particularly strokes, are a leading cause of global mortality and morbidity, where timely diagnosis is critical for effective clinical intervention. This research introduces an advanced diagnostic framework for early stroke identification by integrating Computed Tomography (CT) imaging with a hybrid computational approach. The system utilizes a Genetic Algorithm (GA) to optimize feature selection from neural network outputs, identifying the most discriminative biomarkers within the image data. These refined features are subsequently classified using a Bidirectional Long Short-Term Memory (BiLSTM) network. Rigorous validation through cross-validation showed that the proposed model achieved a 96.5% accuracy, significantly outperforming traditional classifiers like Random Forests and Support Vector Machines. By utilizing metrics such as AUC-ROC and F1-score, the study demonstrates that this architecture serves as a robust decision-support tool, enabling medical practitioners to implement more precise and rapid stroke management strategies.
A Hybrid Malware Detection Framework Utilizing Natural Language Processing and Surface Analysis Features
The escalating frequency of malware attacks necessitates the development of robust detection models, predominantly relying on features derived from surface analysis and machine learning. While prior research in surface analysis has established image-based methods via ensemble learning, there remains a significant deficiency in natural language processing (NLP) methodologies that effectively integrate multiple features. Existing NLP-based detection schemes typically utilize singular features, as the amalgamation of hybrid features into a unified data point disrupts word sequence integrity, thereby impeding detection accuracy. Addressing this gap, this paper introduces a novel hybrid model that leverages three distinct features obtained through surface analysis for malware identification. This study validates the efficacy of applying NLP techniques in conjunction with hybrid features, overcoming previous sequential data limitations. Empirical results demonstrate the superior performance of this combined approach, achieving an F-measure of 0.927.
Advanced Path Planning for Autonomous Vehicles via Deep Reinforcement Learning with Dynamic Reward Optimization
In the realm of intelligent transportation, autonomous driving relies heavily on robust path planning. This study introduces a novel Deep Reinforcement Learning algorithm for Path Planning (DRL-PP) to address limitations in current methodologies. To navigate complex environments, DRL-PP is engineered to determine optimal actions while minimizing overfitting. The algorithm employs neural networks to identify advantageous state-specific actions, constructing an optimized trajectory from origin to destination. A critical innovation lies in the enhanced reward function, which integrates objective-specific data to dynamically calibrate reward metrics. This refinement significantly augments decision-making efficiency. Empirical evaluations demonstrate that DRL-PP stabilizes reward accumulation and minimizes exploratory iterations. Comparative analysis confirms that the proposed algorithm consistently outperforms benchmark models in navigational efficacy, offering a robust solution for the evolutionary advancement of autonomous vehicle technology.
Simultaneous Prediction of Hardness and Phase Fractions in Micro-Alloyed Steels Using an Ensemble-Enhanced Feed-Forward Neural Network
This study presents an AI-driven framework designed to concurrently forecast the hardness and constituent phase fractions of micro-alloyed steels, accounting for specific chemical compositions and thermomechanical processing parameters. The proposed architecture utilizes a feed-forward neural network (FNN) integrated with an ensemble learning technique to bolster predictive reliability and precision. Training was conducted using a comprehensive empirical dataset derived from the continuous cooling transformation (CCT) diagrams of 39 distinct steel grades. Model inputs incorporate the alloy’s elemental constituents and a cooling profile defined by discrete time-temperature coordinates. To evaluate the influence of alloying elements and processing conditions, a sensitivity analysis was performed on the validated model. This assessment, quantifying output variance relative to input fluctuations, demonstrated high fidelity with both experimental observations and established metallurgical literature. Consequently, the model serves as a robust tool for predicting steel properties under diverse cooling regimes, aligning consistently with fundamental theoretical principles and experimental evidence.
A Hybrid CPFD and Machine Learning Approach for Predictive Modeling of Industrial FCC Riser Reactors
This study presents a novel hybrid model designed to predict six key process variables in an industrial-scale fluid catalytic cracking (FCC) riser reactor: vacuum gas oil (VGO) conversion, outlet temperature, and yields of gasoline, LCO, light gases, and coke. The methodology integrates computational particle-fluid dynamics (CPFD) with artificial intelligence, using Barracuda Virtual Reactor 22.0® to solve first principle model (FPM) equations. Based on 216 simulations incorporating CREC-UWO catalytic kinetics, a comprehensive dataset was generated to train a machine learning algorithm. A multiple output feedforward neural network (FNN) was utilized to correlate reactor feeding conditions with output parameters, achieving an average regression coefficient of 0.83 and an overall RMSE of 1.93. This research demonstrates that combining CPFD simulations with ML significantly enhances predictive accuracy and process optimization, offering substantial advancements for industrial FCC operations.
A Deep Learning Approach to the Classification of Chinese Calligraphy Styles and Artistic Identity Using Convolutional Neural Networks
Chinese calligraphy is globally esteemed for its therapeutic and mindfulness benefits, encompasses foundational scripts such as regular (Kai Shu), running (Xing Shu), official (Li Shu), and cursive (Cao Shu). Beginners typically master the structured regular script before advancing to the complexities of cursive forms. Each aesthetic style is defined by unique historical contributions, requiring learners to discern subtle stylistic nuances. This study introduces an innovative convolutional neural network (CNN) architecture, pioneering the application of deep learning in the classification of these calligraphic traditions. Focusing on the four primary scripts from the Tang Dynasty (690–907 A.D.), a period marking the zenith of regular script refinement, a comprehensive dataset of 8,282 samples was compiled for model training. Our approach distinguishes individual artistic styles with superior performance over existing networks, achieving 89.5–96.2% accuracy. This research highlights the efficacy of CNNs in categorizing both font and artistic styles, advancing cultural and technical calligraphy studies.
AI-Driven Precision Agriculture: Optimizing Robotic Crop Farming for Sustainable Urban Food Security
Global warming significantly threatens water sustainability, creating an urgent crisis for irrigation systems. As the global population rises, innovative strategies are required to ensure food security amid extreme weather conditions. This paper investigates how artificial intelligence (AI)-powered precision agriculture can transform urban crop farms (CF) to enhance sustainability. We developed a robotic CF prototype utilizing deep reinforcement learning to optimize seeding, irrigation, and maintenance based on real-time sensor data. Experimental results demonstrated a 26% increase in crop yield, a 41% reduction in water consumption, and a 33% decrease in chemical use. Beyond technical efficiency, qualitative assessments through stakeholder interviews revealed that AI-enabled farming strengthens social cohesion and urban well-being. The adoption of such technologies fosters resilient food supplies while minimizing agriculture’s environmental footprint, providing a scalable model for sustainable food production within modern smart cities.
Developing a Real-Time Ecological Map for Vehicle Emissions: Insights from the Vehicle Activity Dataset (VAD)
In the domain of smart mobility, Artificial Intelligence (AI) methodologies play a pivotal role, offering substantial potential for transformative impact. This project focuses on the development of a real-time ecological map of road traffic, designed to enable electric vehicles (EVs) and thermal vehicles (TVs) to visualize energy consumption costs and CO2 emissions across various road segments. In urban settings, vehicle-related emissions significantly contribute to environmental degradation, underscoring the need for a detailed analysis of vehicle operational dynamics across diverse road infrastructures. This study introduces the Vehicle Activity Dataset (VAD), a novel and comprehensive dataset aimed at evaluating vehicle emissions and fuel consumption in relation to their operational contexts. Compiled from extensive real-world driving scenarios, VAD integrates emission data gathered via an industrial Portable Emission Measurement System (PEMS), road imagery captured by an RGB camera, and the identification of various object classes within these images. The primary goal of VAD is to elucidate the interplay between vehicle emissions and the multitude of objects encountered on roadways. Empirical evaluations conducted in authentic road traffic conditions across multiple studies affirm the robustness and reliability of the dataset.
AI-Driven Predictive Modeling of Athlete Fatigue and Stamina Using IMU Data
Optimal athletic performance requires balancing intensive training with adequate recovery. Inertial Measurement Units (IMUs) provide objective data to monitor training loads, overcoming limitations of subjective assessments that risk overtraining. Using AI, this study analyzes IMU-derived multivariate time series data from 19 athletes, capturing triaxial acceleration, angular velocity, and magnetic orientation during standardized and fatigue-inducing sessions. A supervised machine learning model, comparing Random Forest, Gradient Boosting Machines, and LSTM networks, predicts fatigue and stamina with high accuracy. Real-time feedback and bias correction enhance model reliability, enabling personalized training adjustments that align with physiological thresholds. The model anticipates fatigue onset, reduces overtraining risks, and optimizes training periodization, improving performance. This AI-driven approach transforms sports analytics, supporting real-time monitoring and data-driven decision-making for tailored training and enhanced outcomes.
Copyright (c) 2024 Author(s) https://creativecommons.org/licenses/by-sa/4.0/
Enhancing Facial Beauty Prediction Using Multi-Task Learning and Attentional Feature Fusion
Facial beauty prediction (FBP) is a cutting-edge research area in artificial intelligence (AI), enabling computers to evaluate facial attractiveness similarly to human perception. Despite advancements in deep neural networks for FBP, challenges like limited label information and overfitting remain. Our study introduces a novel approach combining multi-task learning with an adaptive sharing policy and attentional feature fusion (AFF) to address these issues. Built on the AdaShare network with a ResNet18 backbone, the method enhances label utilization by leveraging multiple datasets and reduces overfitting through AFF’s attention mechanism, which integrates semantic information. This innovative framework significantly improves FBP accuracy by tackling insufficient label data and overfitting risks. Experimental results on the Large-Scale Asia Facial Beauty Database (LSAFBD) and SCUT-FBP5500 datasets demonstrate superior performance compared to single-database, single-task baselines. The proposed method not only advances facial beauty prediction but also shows promise for broader applications in image classification and AI-driven tasks. This approach offers a robust solution for researchers seeking reliable and scalable AI models.
Energy-Efficient Event Detection Using Low-Power Microcontrollers for Sustainable Carbon Emission Reduction
This research explores the convergence of low-power microcontroller systems and binary event classification within the framework of carbon emission reduction efforts. The study proposes a novel methodology that utilizes microcontrollers for real-time event detection, implemented in a uniform hardware/firmware configuration under constrained computational resources. The approach demonstrates the capability of microcontrollers to efficiently process sensor inputs while significantly minimizing energy usage, all without reliance on large-scale training datasets. Two illustrative case studies one on CO₂ emissions from landfills and another on household energy consumption validate the practicality and efficacy of the method. The results underscore notable reductions in energy consumption, with data transmission during inactive periods reduced by 94.8% to 99.8%. Furthermore, the approach offers a sustainable alternative to conventional AI/ML platforms, which are substantially more resource-intensive consuming and emitting approximately 20,000 times more power and carbon, respectively.
Revolutionizing Deep Learning Denoising Model Auditing for Low-Dose CT Imaging
The reliability of deep learning (DL) denoising models in low-dose computed tomography (CT) is vital to prevent hallucinated structures and ensure clinical trust. This study introduces AntiHalluciNet, a novel framework designed to detect spurious structural components in residual noise from DL denoising models. Trained on paired structure-embedded and pure noise images, AntiHalluciNet identifies unwanted artifacts using the Residual Structure Index (RSI), a new metric for quantifying prediction confidence. Its ability to assess denoised image fidelity using only noise residuals offers a practical alternative to the Structural Similarity Index (SSIM), which requires reference images. Applied to audit RED-CNN, CTformer, and ClariCT.AI (version 1.2.3), AntiHalluciNet achieved RSI values of 0.57 ± 0.31 for structure-embedded noise and 0.02 ± 0.02 for pure noise in a 50% low-dose dataset (p < 0.0001). For degraded images, RSI outperformed SSIM across 25%–100% degradation levels, showing greater differentiation. Distinct RSI distributions 0.28 ± 0.06 (RED-CNN), 0.21 ± 0.06 (CTformer), and 0.15 ± 0.03 (ClariCT.AI) highlight AntiHalluciNet’s precision in auditing DL denoising models, enhancing clinical reliability by detecting structural artifacts in residual noise.
Development and Validation of a Novel Rock Brittleness Index for Predicting Rock Bursts in Water-Saturated Sandstones
The brittleness index is a critical parameter for evaluating rock bursts and catastrophic failures in deep underground mining. Accurate prediction of this index is vital for monitoring rock bursts, which pose risks to miners and structural integrity in engineering projects. This study introduces a new mathematical brittleness index, incorporating crack initiation, crack damage, and peak stress, tailored for sandstones with varying water contents. The proposed index is benchmarked against established brittleness indices (B1, B2, B3, B4) using infrared radiation (IR) characteristics, specifically the variance of infrared radiation temperature (VIRT), and advanced artificial intelligence (AI) models, including k-nearest neighbor (KNN), extreme gradient boost (XGBoost), and random forest (RF). Key findings include: (1) crack initiation, elastic modulus, crack damage, and peak stress decrease as water content increases; (2) brittleness indices B1, B3, and B4 exhibit a strong positive exponential correlation (R² = 0.88) with water content, while B2 shows a negative exponential correlation (R² = 0.82); (3) the new brittleness index demonstrates robust linear correlations with B1, B3, and B4 (R² > 0.85) but a weaker negative correlation with B2 (R² = 0.61); (4) the RF model outperforms others in predicting the brittleness index, achieving R² = 0.999, RMSE = 0.383, MSE = 0.007, and MAE = 0.002. Thus, the RF model is recommended for precise brittleness prediction. These findings provide valuable insights for developing effective brittleness indices to mitigate rock burst risks in water-influenced rock engineering projects.
Assessing Data Independence in Sequential Ultrasound Imaging for Machine Learning-Based Liver Disease Diagnosis
Machine learning applications in medical imaging demand sufficient and independent data for effective training and testing. However, sequential imaging data, such as time-series images, often exhibit inherent correlations, leading to data interdependence. This study investigates statistical methods to assess the independence of sequential B-mode liver ultrasound images. We analyzed 1,180 ultrasound images containing 5,903 regions of interest from patients with liver fibrosis, steatosis, or normal liver conditions. Texture features extracted from these images were used to train machine learning models for computer-aided diagnosis. The models achieved strong performance, with logistic regression yielding an AUC of 0.928 for binary classification and random forest achieving an AUC of 0.917 for multiclass classification. To evaluate data independence, we applied Jensen Shannon (JS) divergence, which revealed that images from normal livers were independent, while those from diseased livers showed interdependence. These findings highlight the importance of testing for data independence in sequential medical imaging to ensure model generalizability and prevent data leakage. Such statistical tests can guide the use of same-subject images in training machine learning models for real-world medical imaging applications.
Identifying Responsible Raman Spectral Regions for Microbial Classification via Gradient-Weighted Class Activation Mapping
A growing body of literature demonstrates the feasibility of developing robust predictive models for microorganisms by integrating Raman micro-spectroscopy with chemometric techniques, frequently resulting in high-precision outcomes. While advancements in machine learning and broader software accessibility have simplified the generation of predictive models from intricate datasets, the underlying mechanisms driving such high accuracy remain insufficiently explored. This research utilizes Raman spectroscopic data from fungal spores and carotenoid-bearing microorganisms to reveal that precise classification often does not depend on specific peak positions or subtle variations in band ratios related to chemical composition. Instead, characteristic baseline effects in biochemically analogous microorganisms, which may be intensified by particular data pretreatment methods, or even seemingly neutral spectral regions, can hold significant weight for convolutional neural networks (CNNs). By employing Gradient-weighted Class Activation Mapping (Grad-CAM), this study seeks to demystify the "black box" of CNNs in microbiological contexts, identifying the specific Raman spectral regions fundamentally responsible for successful classification.
Enhancing MRI Image Classification for Alzheimer’s Disease and Brain Tumor Detection Using a Hybrid CNN-PSO Model
Convolutional Neural Networks (CNNs) have shown significant promise in addressing the complex challenge of classifying MRI images for detecting Alzheimer’s disease and identifying brain tumors. Despite their ability to automatically optimize parameters during training, determining the optimal hyper-parameter values remains difficult due to the vast and intricate search space, often leading to suboptimal outcomes. Researchers frequently resort to trial-and-error or rely on expert knowledge to navigate this challenge, as the training process does not ensure globally optimal parameter settings. This limitation hinders the development of effective real-world applications for MRI image analysis. To overcome this, we propose a novel hybrid model that integrates Particle Swarm Optimization (PSO) with CNNs to improve classification performance. The PSO algorithm is employed to identify the optimal CNN hyper-parameter configuration, which is then applied to the CNN architecture for enhanced classification. Our approach achieves higher prediction accuracy and minimizes loss in function value for brain disease detection. We evaluated the model using three benchmark datasets: the Alzheimer’s Disease Neuroimaging Initiative (ADNI), a Kaggle international dataset for Alzheimer’s disease, and a brain tumor dataset. Experimental results highlight the model’s superior performance, with accuracy rates of 98.50%, 98.83%, and 97.12% for the respective datasets, demonstrating its effectiveness in MRI-based disease classification.
Privacy-Preserving Deep Learning Framework for Analyzing Homomorphically Encrypted Time-Series Medical Images
Deep learning (DL) algorithms have significantly advanced healthcare applications, particularly in medical imaging for diagnosis, treatment planning, and disease management. However, the application of DL to sensitive medical images raises critical privacy and data security concerns. Balancing enhanced medical imaging analysis with the protection of patient confidentiality remains a formidable challenge. Consequently, privacy-preserving techniques for training and inferring DL models are increasingly vital. This study introduces a novel convolutional Bi-LSTM network designed to analyze fully homomorphically encrypted (HE) time-series medical images. Convolutional blocks extract selective spatial features from encrypted image sequences, while Bi-LSTM layers encode temporal dynamics. A weighted unit and sequence voting layer integrates spatial and temporal information with adaptive weighting to enhance diagnostic accuracy and minimize errors. The proposed framework is evaluated on two rigorous public benchmarks, CheXpert and BreaKHis, achieving an accuracy exceeding 0.99 on both datasets. These results surpass several competing methods, demonstrating the framework’s capability to effectively extract visual and sequential features from encrypted medical images while preserving patient privacy and delivering superior performance in medical image analysis.
Optical High-Resolution Fully Convolutional Neural Network for Accelerated Image Classification
This study presents the development of FatNet, an optical fully convolutional neural network designed to transform traditional in silico architectures into a format optimized for high-resolution optical processing. By leveraging a free-space system, the network utilizes high-resolution feature maps and kernels without compromising frame rates. Unlike standard classifiers, FatNet integrates feature extraction and classification into a single fully convolutional framework, maximizing the parallelism of optical systems and reducing electronic-optical conversion overhead. Evaluation using the CIFAR100 dataset demonstrates that FatNet performs 8.2 times fewer convolution operations than ResNet-18 with only a marginal 6% reduction in accuracy. These results indicate that the optical implementation of FatNet achieves significantly faster inference than ResNet-18. This research provides a promising foundation for the advancement of deep learning models specifically tailored for the burgeoning era of optical computing through high-resolution kernel training.
Copyright (c) 2024 Computing and Algorithm Insight https://creativecommons.org/licenses/by-sa/4.0/
Generative Adversarial Networks for Mitigating Partial Coherence in Coherent Diffractive Imaging
This paper presents a generative deep learning approach to mitigate image degradation caused by partial coherence in lensless Coherent Diffractive Imaging (CDI). While high-resolution CDI relies on ideal X-ray coherence, experimental constraints frequently introduce partial coherence, leading to blunted diffraction patterns. Unlike existing methods that require expensive computation or pre-calibration to model illumination coherence, our proposed Generative Adversarial Network (GAN) directly restores sharp structural features from degraded images without explicit coherence characterization. The effectiveness of this approach is validated through applications in both CDI and ptychography, indicating its potential utility for a wide spectrum of phase-retrieval-based imaging techniques.
Copyright (c) 2023 Author(s) https://creativecommons.org/licenses/by-sa/4.0/
LSTM-Based Digital Assistance System for Enhanced Situational Awareness and Conflict Detection in Next-Generation Air Traffic Management
Modern air traffic management (ATM) systems rely on human-centric coordination between controllers and pilots, a framework currently challenged by increasing flight densities and diverse aircraft types in European airspace. This saturation results in heightened congestion, safety risks, and environmental impact. To address these limitations, this paper proposes a transition toward a "next-generation" ATM through enhanced airspace abstraction and situational awareness. This study contributes an analysis of contemporary ATM complexity and introduces a digital assistance system for conflict detection. Utilizing Long Short-Term Memory (LSTM) networks, the system evaluates temporal dynamics in surveillance data to classify error patterns. Training was conducted on large-scale models comprising thousands of flights, utilizing a 20–10–1 architecture with leaky ReLU and sigmoid activations, optimized via the ADAM algorithm and binary cross-entropy loss. Numerical findings demonstrate the efficacy of LSTMs in predicting critical disruptions, including adverse weather, cyber-attacks, and human-factor-related emergencies.
Real-Time Weather Classification for Autonomous Vehicles: A Deep Learning Framework via Transfer Learning
Accurate weather classification is essential for the safety and adaptability of autonomous vehicles (AVs). This paper proposes a deep learning (DL) detection framework leveraging transfer learning to categorize outdoor weather conditions in both normal and adverse scenarios. We evaluated the performance of three specific CNN architectures SqueezeNet, ResNet-50, and EfficientNet utilizing Nvidia GPU hardware. The study employed a combined dataset from DAWN2020 and MCWRD2018 to train the models on six weather classes: cloudy, rainy, snowy, sandy, shine, and sunrise. Experimental results identified ResNet-50 as the superior model, achieving detection accuracy, precision, and sensitivity of 98.48%, 98.51%, and 98.41%, respectively. Additionally, the model exhibited a low inference time of 5 ms. When compared to current state-of-the-art methods, our approach improved classification accuracy by a factor of 0.5–21%. Consequently, this framework is well-suited for real-time implementation, providing AVs with on-demand, high-precision decision support.
Copyright (c) 2023 Computing and Algorithm Insight https://creativecommons.org/licenses/by-sa/4.0/
A Hybrid ANN Architecture Optimized by Genetic and Bat Algorithms for Predicting Pregnancy Success Rates
The diagnosis of gynecological conditions remains a complex medical challenge, encompassing issues from routine pregnancy to disorders like PCOS and endometritis. This research proposes a hybrid Artificial Neural Network (ANN) designed to assist gynecologists in predicting pregnancy success rates by analyzing serum hormone ratios. Unlike standard architectures, this model integrates the Genetic Algorithm (GA) and Bat algorithm within the final hidden layer. The GA is utilized to optimize testing costs, while the Bat algorithm refines training costs. This synergistic metaheuristic approach enhances model performance and predictive precision. The methodology was validated using a dataset of 35,207 patient records from the Hospital University of Jordan (HUJ). The results yielded a classification accuracy of 96.5%, demonstrating the system's robustness in handling diverse clinical profiles, including patients with normal hormonal ranges and those with complications like diabetes or infections. This tool offers significant potential for enhancing clinical decision-making in reproductive medicine.
Secure IoT-Based Smart Healthcare: Integrating Blockchain and Deep Learning for Autonomous Clinics and Pharmaceutical Management
The Internet of Things (IoT) has revolutionized smart healthcare, proving indispensable during the COVID-19 pandemic. However, the resource-constrained nature of IoT devices often precludes robust encryption, rendering systems vulnerable to privacy breaches and cyberattacks that threaten medical data integrity. To mitigate these risks, this study explores the synergy between IoT and blockchain technology to bolster security and transparency. Building on existing research, this paper outlines three key contributions: (1) an architecture for an autonomous, unsupervised medical clinic designed to deliver rapid care while minimizing infection exposure for staff; (2) a deep learning diagnostic model utilizing ResNet152 transfer learning for automated COVID-19 detection from X-ray images; and (3) a secure blockchain-enabled pharmaceutical distribution system. Experimental validation confirms that the proposed frameworks demonstrate the necessary efficacy, security, and robustness for reliable deployment in clinical environments.