Optimal Selection Research Articles

Prediction of annual rice imports emphasizes on systematic error reduction with smoothing series and optimal parameter selection techniques

Prediction of annual rice imports emphasizes on systematic error reduction with smoothing series and optimal parameter selection techniques

Optimal Feature Selection from High-dimensional Fusion of Blood Smear Images for Leukemia Diagnosis

The goal of this study is to improve blood smear image-based blood cancer prediction through medical diagnostic advancements. Blood cancers, particularly leukemia, are challenging to diagnose because of the complexity of biological data and the dimensionality of medical images. There are interpretability and computational problems with each currently in use. We suggest the Random Forest-Recurrent Feature Elimination (RF-RFE) model to increase the precision and dependability of blood cancer diagnosis. This model integrates machine learning and image processing, optimizes feature selection and refinement from high-dimensional data, and applies the XGBoost algorithm to guarantee diagnosis accuracy. Recent model analysis reveals that RF-RFE performs better than them on a wide range of metrics. The RF-RFE offered a sensible, well-rounded strategy. More research on medical diagnostics is made possible by its adaptability in multi-class classification and effectiveness in handling high-dimensional feature values. The optimized feature set and computational efficiency of the model, which may enhance leukemia detection and diagnostics, are highlighted in this study.

Physics-Informed Neural Networks for High-Frequency and Multi-Scale Problems Using Transfer Learning

Physics-Informed Neural Network (PINN) is a data-driven solver for partial and ordinary differential equations (ODEs/PDEs). It provides a unified framework to address both forward and inverse problems. However, the complexity of the objective function often leads to training failures. This issue is particularly prominent when solving high-frequency and multi-scale problems. We proposed using transfer learning to boost the robustness and convergence of training PINN, starting training from low-frequency problems and gradually approaching high-frequency problems through fine-tuning. Through two case studies, we discovered that transfer learning can effectively train PINNs to approximate solutions from low-frequency problems to high-frequency problems without increasing network parameters. Furthermore, it requires fewer data points and less training time. We compare the PINN results using direct differences and L2 relative error showing the advantage of using transfer learning techniques. We describe our training strategy in detail, including optimizer selection, and suggest guidelines for using transfer learning to train neural networks to solve more complex problems.

Investigating lexical categorization in reading based on joint diagnostic and training approaches for language learners

Efficient reading is essential for societal participation, so reading proficiency is a central educational goal. Here, we use an individualized diagnostics and training framework to investigate processes in visual word recognition and evaluate its usefulness for detecting training responders. We (i) motivated a training procedure based on the Lexical Categorization Model (LCM) to introduce the framework. The LCM describes pre-lexical orthographic processing implemented in the left-ventral occipital cortex and is vital to reading. German language learners trained their lexical categorization abilities while we monitored reading speed change. In three studies, most language learners increased their reading skills. Next, we (ii) estimated, for each word, the LCM-based features and assessed each reader’s lexical categorization capabilities. Finally, we (iii) explored machine learning procedures to find the optimal feature selection and regression model to predict the benefit of the lexical categorization training for each individual. The best-performing pipeline increased reading speed from 23% in the unselected group to 43% in the machine-selected group. This selection process strongly depended on parameters associated with the LCM. Thus, training in lexical categorization can increase reading skills, and accurate computational descriptions of brain functions that allow the motivation of a training procedure combined with machine learning can be powerful for individualized reading training procedures.

Investigation into hydraulic fracture propagation behavior during temporary plugging and diverting fracturing in deep coal seam

In this study, we conducted indoor temporary plugging and diverting fracturing (TPDF) experiments on samples of natural deep and shallow coal seams using a small-scale true triaxial fracturing simulation system. By integrating high-precision CT scanning technology and crack reconstruction techniques, and relying on the quantitative characterization of the crack complexity coefficient, we investigated the differences in internal structures between deep and shallow coal seam samples and the variations in the expansion of TPDF cracks. Furthermore, the study primarily focused on exploring the influence of temporary plugging agent (TPA) parameters (quantity and particle size) on the expansion patterns of TPDF cracks in deep coal seam coal-rock samples. The experimental results reveal that in shallow coal samples, artificially induced fractures are notably longer and extend to various surfaces of the samples. Conversely, in deep coal samples, the expansion of artificially induced fractures is influenced by well-developed cleavage and cleats. The crack complexity coefficient of artificial fractures in deep coal samples is 1.54 higher than that in shallow coal samples, with an increased crack width of 98 μm. However, the expansion distance of the fractures is shorter and does not extend to the S3 and S6 surfaces. Increasing the dosage of the TPA is advantageous for inducing frequent redirection of fractures and communication with previously untouched areas. When the dosage of the TPA is increased from 30 g/L to 60 g/L, the expansion distance of artificial fractures significantly increases, extending to various surfaces of the samples. The crack complexity coefficient increases by 1.1, and the crack width enlarges by 84 μm. Appropriate particle sizes of the TPA can effectively form seals. However, small particle sizes (200/400 mesh) struggle to seal initial fractures, while large particle sizes (10/20 mesh) can lead to excessive wellhead blockage, causing rapid shut-in pressure, destructive fracturing in deep coal samples, and the generation of a large amount of coal fines. This study holds significant guidance for the optimal selection of process parameters in the temporary plugging hydraulic fracturing of deep coal rock.

A novel attention-based long short term memory and fully connected neutral network approach for production energy consumption prediction under complex working conditions

Continuously growing demands due to the predictable faults or abnormal events of the flexible production line are becoming a challenge to easily cause the energy waste, which needs to accurately predict the real-time energy consumption of the actual production process. By reviewing the related works about prediction model approach and energy consumption analysis in the actual production application, it is necessary to overcome two main challenges: optimal prediction selection and internal relation mining of energy consumption under different equipment working conditions. To deal with the existing challenges, the paper proposes a novel attention-based Long and Short-Term Memory (LSTM) approach to predict energy consumption with temporal series under various equipment working conditions. This method offers forecasting support to form a complete prediction framework for the automatic Miniature Circuit Breaker (MCB) production line, which can be used to predict the specific working condition based on attention-based LSTM approach. By comparing various prediction approaches for energy consumption under the working condition prediction with various equipment of different production line, the Fully Connected Neutral (FCN) network approach can achieve a high accuracy and effectiveness based on MAE, R2 and MSE evaluations.

Study of the adsorption isotherm of 2-mercaptobenzothiazole by activated carbon based on grape marc: linear and non-linear method

The adsorption performance of 2-mercaptobenzothiazole from an aqueous solution on activated carbon synthesized through grape marc was explored. In this research, the agricultural waste was chemically treated with phosphoric acid (40% H3PO4, 170 °C, 3 h), then pyrolyzed at 650 °C for 2 h, to obtain the GMAC-650 to be used as an adsorbent. The resulting material was characterized by Fourier-transform infrared spectroscopy, X-ray diffraction, and Brunauer–Emmett–Teller (BET). Maximum uptake (∼99%) is obtained under ideal conditions: an adsorbent dose of 0.8 g L−1 and pH = 8 for an initial concentration of 10 mg L−1 at 45 °C. The adsorption equilibrium data were fitted to the following isotherm models: Langmuir and Freundlich. For optimal equilibrium isotherm selection, a comparison of linear and non-linear methods for the adsorption of MBT. It was based on four error functions: the coefficient of determination R 2, the sum of squared errors, the Chi-square (χ 2), and the root mean square error. The current work has shown that the non-linear method is a more adequate method for estimating the parameters of the isotherm compared with the linear method. The comparative analysis of the models shows that the Freundlich model is more appropriate for describing equilibrium data.

Optimal patient selection for maze procedure in patients undergoing mitral valve disease

ObjectivesAlthough risk factors for unsuccessful Maze procedure have been demonstrated, an appropriate patient selection is still controversial. In our institute, Maze procedure is indicated for those whom normal sinus rhythm (NSR) was reestablished by intraoperative direct cardioversion (DC) after ventricular unloading by total cardiopulmonary bypass. The purpose of this study was to evaluate the effectiveness of our indication criteria for Maze procedure in patients with mitral valve disease.MethodsBetween October 2012 and October 2021, MAZE was indicated in 55 patients in whom normal sinus rhythm (NSR) was reestablished by intraoperative direct current cardioversion (DC). Three endpoints and predictors were examined: disappearance of atrial fibrillation (AF), NSR, and A-wave detection.ResultsRestoration of NSR by intraoperative DC was confirmed in 43 patients, and these patients underwent MAZE. AF disappeared in 39 patients (90.7%), and F-wave ≥ 0.1 mV was a significant predictive factor (odds ratio (OR) 20.99, 95% CI 1.22–1079.06). NSR was reestablished in 36 patients (83.7%), and F-wave ≥ 0.1 mV (odds ratio 15.62, 95% CI 1.62–359.86) + AF history ≤ 3 years (OR 8.30, 95% CI 1.09–177.04) were significant predictors. A-wave detection was confirmed in 26 patients (60.5%), and left atrial diameter ≤ 55 mm was a significant predictor (OR 5.22, 95% CI 1.28–24.79).ConclusionsIntraoperative DC after ventricular unloading resulted effective patient selection for concomitant Maze procedure. F-wave and AF history were predictive factor of electrical restoration of AF, and left atrial diameter was predictive factor of restoration of atrial function.

Cosine similarity knowledge distillation for surface anomaly detection.

The current state-of-the-art anomaly detection methods based on knowledge distillation (KD) typically depend on smaller student networks or reverse distillation to address vanishing representations discrepancy on anomalies. These methods often struggle to achieve precise detection when dealing with complex texture backgrounds containing anomalies due to the similarity between anomalous and non-anomalous regions. Therefore, we propose a new paradigm-Cosine Similarity Knowledge Distillation (CSKD), for surface anomaly detection and localization. We focus on the superior performance of the same deeper teacher and student encoders by the distillation loss in traditional knowledge distillation-based methods. Essentially, we introduce the Attention One-Class Embedding (AOCE) in the student network to enhance learning capabilities and reduce the effect of the teacher-student (T-S) model on response similarity in anomalous regions. Furthermore, we find the optimal models by different classes' hard-coded epochs, and an adaptive optimal model selection method is designed. Extensive experiments on the MVTec dataset with 99.2% image-level AUROC and 98.2%/94.7% pixel-level AUROC/PRO demonstrate that our method outperforms existing unsupervised anomaly detection algorithms. Additional experiments on DAGM dataset, and one-class anomaly detection benchmarks further show the superiority of the proposed method.

Automated real-time prediction of geological formation tops during drilling operations: an applied machine learning solution for the Norwegian Continental Shelf

AbstractAccurate prediction of geological formation tops is a crucial task for optimizing hydrocarbon exploration and production activities. This research investigates and conducts a comprehensive comparative analysis of several advanced machine learning approaches tailored for the critical application of geological formation top prediction within the complex Norwegian Continental Shelf (NCS) region. The study evaluates and benchmarks the performance of four prominent machine learning models: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest ensemble method, and Multi-Layer Perceptron (MLP) neural network. To facilitate a rigorous assessment, the models are extensively evaluated across two distinct datasets - a dedicated test dataset and a blind dataset independent for validation. The evaluation criteria revolve around quantifying the models' predictive accuracy in successfully classifying multiple geological formation top types. Additionally, the study employs the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm as a baseline benchmarking technique to contextualize the relative performance of the machine learning models against a conventional clustering approach. Leveraging two model-agnostic feature importance analysis techniques - Permutation Feature Importance (PFI) and Shapley Additive exPlanations (SHAP), the investigation identifies and ranks the most influential input variables driving the predictive capabilities of the models. The comprehensive analysis unveils the MLP neural network model as the top-performing approach, achieving remarkable predictive accuracy with a perfect score of 0.99 on the blind validation dataset, surpassing the other machine learning techniques as well as the DBSCAN benchmark. However, the SVM model attains superior performance on the initial test dataset, with an accuracy of 0.99. Intriguingly, the PFI and SHAP analyses converge in consistently pinpointing depth (DEPT), revolution per minute (RPM), and Hook-load (HKLD) as the three most impactful parameters influencing model predictions across the different algorithms. These findings underscore the potential of sophisticated machine learning methodologies, particularly neural network-based models, to significantly enhance the accuracy of geological formation top prediction within the geologically complex NCS region. However, the study emphasizes the necessity for further extensive testing on larger datasets to validate the generalizability of the high performance observed. Overall, this research delivers an exhaustive comparative evaluation of state-of-the-art machine learning techniques, offering critical insights to guide the optimal selection, development, and real-world deployment of accurate and reliable predictive modeling strategies tailored for hydrocarbon exploration and reservoir characterization endeavors in the NCS. Graphical abstract

Optimal Planning for Wind Turbines in Mega Seaports Considering Practical Application Constraints: A Case Study of Ningbo-Zhoushan Port

In the context of global carbon neutrality, ports face significant electricity demand for cargo handling and pressure to reduce carbon emissions. The abundant wind energy resources in port areas make wind power highly promising for port applications. The optimal selection of site and turbine types for wind power systems can effectively reduce emissions in ports, achieving sustainability and improving economic benefits. The practical implementation of wind energy systems considering practical constraints holds significant research significance. Taking Ningbo-Zhoushan Port as an example, this paper analyzes the wind energy resources in the port area and provides an overview of wind power system construction sites. Based on the actual conditions of the port area, this paper comprehensively reviews the site selection of wind turbines from the perspectives of wind resources, specific climates, and noise impacts. With the consideration of engineering preferences, this paper selects performance indicators based on the four mainstream turbine models and proposes a comprehensive weight determination method using the entropy weight method and analytic hierarchy process (AHP) to determine the weights of the indicators. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is then employed to score and compare four turbine plans, enabling the turbine selection process to consider both engineering preferences and objectivity, thereby enhancing the accuracy and reliability of wind turbine planning and achieving significant ecological and economic benefits through benefit analysis.

Highly Sensitive and Selective Toluene Gas Sensors Based on ZnO Nanoflowers Decorated with Bimetallic AuPt.

Efficient sensors for toluene detecting are urgently needed to meet people's growing demands for both environment and personal health. Metal oxide semiconductor (MOS)-based sensors have become brilliant candidates for the detection of toluene because of their superior performance over gas sensing. However, gas sensors based on pure MOS have certain limitations in selectivity, operating temperature, and long-term stability, which hinders their further practical applications. Noble metals (including Ag, Au, Pt, Pd, etc.) have the ability to enhance the performance of MOS-based sensors via surface functionalization. Herein, ZnO nanoflowers (ZNFs) modified with bimetallic AuPt are prepared for toluene detection through hydrothermal method. The response of a AuPt@ZNF-based gas sensor can reach 69.7 at 175 °C, which is 30 times, 9 times, and 10 times higher than that of the original ZNFs, Au@ZNFs, and Pt@ZNFs, respectively. Furthermore, the sensor also has a lower optimal operating temperature (175 °C), good stability (94% of previous response after one month), and high selectivity towards toluene, which is the result of the combined influence of the electronic and chemical sensitization of noble metals, as well as the unique synergistic effect of the AuPt alloy. In summary, AuPt@ZNF-based sensors can be further applied in toluene detection in practical applications.

A genetic algorithm-based optimal selection and blending ratio of plastic waste for maximizing economic potential

Pyrolysis of plastic waste presents an innovative solution to convert plastics into valuable fuels like oil and gas, thereby contributing to circular economy and sustainability. The efficiency of this process highly depends on the types and compositions of plastics utilized, as each type reacts uniquely during pyrolysis, affecting fuel yield and quality. However, previous studies have not thoroughly examined the impact of plastic selection and blending ratios on the pyrolysis outcome, as this requires extensive experimentation due to the numerous possible combinations. Optimizing these factors, along with operational conditions, is essential for enhancing the economic viability of plastic recycling via pyrolysis. This study introduces an optimal selection and blending ratio for plastic waste in pyrolysis fuel production to enhance economic viability using the surrogate model-based genetic algorithm (GA). The optimization process involves (1) generating data via a process model of the plastic waste pyrolysis reactor, (2) preprocessing data, (3) developing a surrogate model based on deep neural networks (DNNs), and (4) deriving the optimal solution using GA. The results reveal an optimal blending ratio: 4 wt% LDPE, 30 wt% HDPE, 43 wt% PP, and 24 wt% PS. Additionally, the optimal pyrolysis temperature is 607 ℃. By adopting this optimal solution, the net profit could increase by 20% compared to the conventional scenario, resulting in an additional annual net profit of US$ 407,811, thus highlighting the potential this approach offers to attain maximum economic feasibility in industrial applications.

A Study of Quantum Game for Low-Carbon Transportation with Government Subsidies and Penalties

Traditional classical game theory struggles to effectively address the inefficiencies in subsidizing and penalizing the R&D and production of low-carbon transportation vehicles. To avoid the shortcomings of classic game theory, this research integrates quantum game theory with Nash games to explore the characteristics of automakers’ behavior for low-carbon transportation with government subsidies and penalties. We first constructed a low-carbon transportation game model between the government and automakers. Then, the optimal profit strategies for both parties in a quantum entangled state were analyzed. Finally, the impact of quantum superposition states and the initial entangled state on the profit strategies of both parties was simulated and analyzed using Monte Carlo simulations. We find that under the joint effects of government subsidies and penalties, quantum game states and the initial quantum entangled state have a crucial influence on the game’s outcomes. They can encourage the realization of Nash equilibrium and perfect coordination in the quantum game, significantly increasing the profits for both parties. This in turn effectively stimulates automakers to research and produce low-carbon transportation solutions, promoting the rapid development of low-carbon transportation technology. In theory, this research can enrich the Quantum game for improvements in the Nash equilibrium solution for the government to subsidize and penalize the low-carbon transportation problem. Meanwhile, in practice, it can provide guidance and reference in optimal strategy selection conditions for government policymakers and automakers for low-carbon transportation.

Efficient fuzzy multi-criteria decision-making for optimal college location selection: A comparative study of min–max fuzzy TOPSIS approach

Ranking fuzzy values is often essential in fuzzy multi-criteria decision-making (FMCDM) systems since it is established on the comparison of hazy data pairs, even though the comparison needs temporal complexity. Then to provide a fuzzy MCDM approach under min–max operations for avoiding the fuzzy comparison. By using fuzzy TOPSIS’s min and max operations, the suggested method produces two positive and negative ideal solutions of each alternative. Here, before discussing the effectiveness of the matrices between the alternatives, first to analyze the weighted strength and weakness indices from both positive and negative indices. The results of the method have been described using a straightforward case study of a proper location for creating a college. The best location can be identified using this method based on various criteria. The outcomes are also compared with the suggested method for its effectiveness. Additionally, this problem analyzes the fuzzy TOPSIS approach in an interval-valued number for greater efficiency.

Impact of disease volume on survival efficacy of triplet therapy for metastatic hormone-sensitive prostate cancer: a systematic review, meta-analysis, and network meta-analysis.

Triplet therapy, androgen receptor signaling inhibitors (ARSIs) plus docetaxel plus androgen-deprivation therapy (ADT), is a novel guideline-recommended treatment for metastatic hormone-sensitive prostate cancer (mHSPC). However, the optimal selection of the patient most likely to benefit from triplet therapy remains unclear. We performed a systematic review, meta-analysis, and network meta-analysis to assess the oncologic benefit of triplet therapy in mHSPC patients stratified by disease volume and compare them with doublet treatment regimens. Three databases and meeting abstracts were queried in March 2023 for randomized controlled trials (RCTs) evaluating patients treated with systemic therapy for mHSPC stratified by disease volume. Primary interests of measure were overall survival (OS). We followed the PRISMA guideline and AMSTAR2 checklist. Overall, eight RCTs were included for meta-analyses and network meta-analyses (NMAs). Triplet therapy outperformed docetaxel plus ADT in terms of OS in both patients with high-(pooled HR: 0.73, 95%CI 0.64-0.84) and low-volume mHSPC (pooled HR: 0.71, 95%CI 0.52-0.97). There was no statistically significant difference between patients with low- vs. high-volume in terms of OS benefit from adding ARSI to docetaxel plus ADT (p = 0.9). Analysis of treatment rankings showed that darolutamide plus docetaxel plus ADT (90%) had the highest likelihood of improved OS in patients with high-volume disease, while enzalutamide plus ADT (84%) had the highest in with low-volume disease. Triplet therapy improves OS in mHSPC patients compared to docetaxel-based doublet therapy, irrespective of disease volume. However, based on treatment ranking, triplet therapy should preferably be considered for patients with high-volume mHSPC while those with low-volume are likely to be adequately treated with ARSI + ADT.

Modeling the Nexus between European Carbon Emission Trading and Financial Market Returns: Practical Implications for Carbon Risk Reduction and Hedging

The carbon–financial nexus helps firms evaluate susceptibility to carbon risk more effectively. This is the first research article to model the short- and long-run co-integrating association between European financial markets, the CBOE oil price volatility index (OVZ) and the European carbon emission trading system (EU-ETS) by using the daily returns from 1 October 2013 to 1 October 2023. We utilize co-integration test followed by the ARDL framework with an error correction mechanism (ECM). Moreover, we utilize the DCC-GARCH-t copula framework to estimate the hedge ratio and to select an optimal portfolio weight for carbon risk hedging. Overall, the findings suggested that EU-ETS (OVZ) has a consistent positive (negative) short-term influence on all the equity returns of Belgium, Denmark, Finland, France, Germany, Ireland, Italy, Netherlands, Spain and the stock indices of the whole Eurozone. However, in the long term, EU-ETS has a positive (negative) effect on the stock returns of France and the Eurozone (Belgium and Spain). Belgian and Spanish companies could implement long-term carbon reduction policies. Belgian and Spanish firms should focus on the utilization of green energy resources and the internalization of carbon emission-free mechanical processes as this may offer a safeguard against the additional pressure arising from escalating carbon prices. Finally, an optimal portfolio weight selection strategy based upon the DCC-GARCH-t copula approach aims for higher hedging effectiveness (HE) than the hedge ratio strategy when adopting short-term positions in Italian and Danish equity markets to reduce the risk of long-term EU-ETS volatility.

Monkeypox Detection using CSA Based K-Means Clustering with Swin Transformer Model

Despite the global COVID-19 pandemic, public health professionals are also concerned about a possible new monkeypox epidemic. Similar to vaccinia, cowpox, and variola, the orthopoxvirus that causes monkeypox has two strands that are double-stranded. Many people have propagated the current pandemic through sexual means, particularly those who identify as bisexual or gay. The speed with which monkeypox was detected is the most important element here. In order to catch monkeypox before it infects more people, machine learning could be a huge help in making a quick and accurate diagnosis. Finding a solution is the driving force behind this project, which aims to develop a model for detecting monkeypox using deep learning and image processing. For optimal cluster selection during photo segmentation, the Chameleon Swarm Algorithm (CSA) employs K-means clustering. Examining the accuracy with which the Swin Transformer model identified instances of monkeypox was the driving force for this study. The proposed techniques are evaluated on two datasets: Kaggle Monkeypox Skin Lesion Dataset (MSLD) besides the Monkeypox Skin Image Dataset (MSID). We assessed the outcomes of various deep learning models using sensitivity, specificity, and balanced accuracy. Positive results from the projected process raise the possibility of its widespread application in monkeypox detection. This ingenious and cheap method can be put to good use in economically deprived communities that may not have access to proper laboratory facilities.

A comparison of channel selection for the assimilation of CrIS radiances for NWP

Abstract With hyperspectral instruments measuring radiation emitted by the earth and its atmosphere in the thermal infrared range in multiple channels, several studies were made to select a subset of channels in order to reduce the number of channels to be used in a data assimilation system. An optimal selection of channels based on information content depends on several factors related to observation and background error statistics and the assimilation system itself. An optimal channel selection for CrIS was obtained and then compared to selections made for different NWP systems. For instance, the channel selection of Carminati (2022) has 224 channels also present in to our optimal selection which includes 455 channels. However, in terms of analysis error variance, the difference between the two selections is small. Integrated over the whole profile, the relative difference is equal to 15.3 % and 4.5 % for temperature and humidity respectively. Also, different observation error covariance matrices were considered to evaluate the impact of this matrix on channel selection. Even though the channels selected optimally were different in terms of which channels were selected for the various R matrices.

Optimizing Disease Prediction with Artificial Intelligence Driven Feature Selection and Attention Networks

The rapid integration of machine learning methodologies in healthcare has ignited innovative strategies for disease prediction, particularly with the vast repositories of Electronic Health Records (EHR) data. This article delves into the realm of multi-disease prediction, presenting a comprehensive study that introduces a pioneering ensemble feature selection model. This model, designed to optimize learning systems, combines statistical, deep, and optimally selected features through the innovative Stabilized Energy Valley Optimization with Enhanced Bounds (SEV-EB) algorithm. The objective is to achieve unparalleled accuracy and stability in predicting various disorders. This work proposes an advanced ensemble model that synergistically integrates statistical, deep, and optimally selected features. This combination aims to enhance the predictive power of the model by capturing diverse aspects of the health data. At the heart of the proposed model lies the SEV-EB algorithm, a novel approach to optimal feature selection. The algorithm introduces enhanced bounds and stabilization techniques, contributing to the robustness and accuracy of the overall prediction model. To further elevate the predictive capabilities, an HSC-AttentionNet is introduced. This network architecture combines deep temporal convolution capabilities with LSTM, allowing the model to capture both short-term patterns and long-term dependencies in health data. Rigorous evaluations showcase the remarkable performance of the proposed model. Achieving a 95% accuracy and 94% F1-score in predicting various disorders, the model surpasses traditional methods, signifying a significant advancement in disease prediction accuracy. The implications of this research extend beyond the confines of academia. By harnessing the wealth of information embedded in EHR data, the proposed model presents a paradigm shift in healthcare interventions. The optimized diagnosis and treatment pathways facilitated by this approach hold promise for more accurate and personalized healthcare, potentially revolutionizing patient outcomes