Selection Method Research Articles

Neuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade.

Schizophrenia (SZ) is characterized by significant cognitive and behavioral disruptions. Neuroimaging techniques, particularly magnetic resonance imaging (MRI), have been widely utilized to investigate biomarkers of SZ, distinguish SZ from healthy conditions or other mental disorders, and explore biotypes within SZ or across SZ and other mental disorders, which aim to promote the accurate diagnosis of SZ. In China, research on SZ using MRI has grown considerably in recent years. The article reviews advanced neuroimaging and artificial intelligence (AI) methods using single-modal or multimodal MRI to reveal the mechanism of SZ and promote accurate diagnosis of SZ, with a particular emphasis on the achievements made by Chinese scholars around the past decade. Our article focuses on the methods for capturing subtle brain functional and structural properties from the high-dimensional MRI data, the multimodal fusion and feature selection methods for obtaining important and sparse neuroimaging features, the supervised statistical analysis and classification for distinguishing disorders, and the unsupervised clustering and semi-supervised learning methods for identifying neuroimage-based biotypes. Crucially, our article highlights the characteristics of each method and underscores the interconnections among various approaches regarding biomarker extraction and neuroimage-based diagnosis, which is beneficial not only for comprehending SZ but also for exploring other mental disorders. We offer a valuable review of advanced neuroimage analysis and AI methods primarily focused on SZ research by Chinese scholars, aiming to promote the diagnosis, treatment, and prevention of SZ, as well as other mental disorders, both within China and internationally.

A Standard Penetration Test-Based Step-by-Step Inverse Method for the Constitutive Model Parameters of the Numerical Simulation of Braced Excavation

Numerical simulation is an essential method for predicting soil deformation caused by foundation pit excavation. Its accuracy relies on the constitutive model and its parameters. However, obtaining these parameters through lab tests has limitations like long durations, high costs, and potential errors. To improve the simplicity and accuracy of the selection of constitutive model parameters, this study proposes a step-by-step inverse analytical method. Using the braced excavation project in Hangzhou City, China as a case study, the proposed method firstly determines the ratio of the important constitutive parameters then the values of the key constitutive parameters. The results show that the reference secant stiffness (E50ref) and shear strain (γ0.7) of the hardening soil (HS) and hardening soil–small soil (HSS) models are the key constitutive parameters. The step-by-step inverse method not only reduces the number of parameters, but also improves the predicting accuracy. The established empirical relationship between the E50ref and standard penetration test (SPT) blow counts exhibits a good linear correlation. The parameter selection method proposed in this study is an accurate, practical, and efficient method, which can effectively predict the horizontal displacement and surface settlement of the retaining structure in multiple excavation stages.

A Hybrid Machine Learning Approach for Intrusion Detection and Mitigation on IoT Smart Healthcare

Strong cybersecurity solutions are becoming more and more important as Internet of Things (IoT) technology integration in healthcare settings develops. This study offers a method for feature extraction, selection, and attack classification by fusing the discriminative capacity of feedforward neural networks (FNNs) with the adaptability of fuzzy logic systems. In delicate healthcare database of IoT wearable devices, to reduce false alarm and guaranteeing intrusion detection dependability are the main priorities. The suggested method uses a feature extraction, selection technique, training and testing based on FNN, which allows the model to adjust to the dynamic and varied character of medical data. During the assessment stage, a dataset including a range of healthcare IoT scenarios, including different kinds of attacks, is used to train and evaluate the model, the ToN_IoT dataset was used. Fuzzy logic improves the system's resilience in identifying pertinent features by managing uncertainties and imprecise input. Fuzzy logic is one of the best technique for handling uncertainty, its linguistic representation and rule reasoning helps in better identification and classification. The findings indicate a noteworthy decrease in the frequency of false alarms when juxtaposed with conventional intrusion detection systems. Results obtained from the model are 99.2, 98.8, 99.5, 99.1 & 0.008 for accuracy, precision, recall, F1-Score and False alarm respectively. Promising outcomes in protecting IoT healthcare environments are demonstrated by the suggested system, opening the door to better patient data privacy and system resilience against cyberattacks.

Enhancing bridge damage assessment: Adaptive cell and deep learning approaches in time-series analysis

In recent years, the application of Deep Learning (DL) for damage detection in Structural Health Monitoring (SHM) using time-series data has garnered significant attention from the global scientific community. By incorporating numerous filters and units in their architecture, these models facilitate feature extraction making the processing of large time-series datasets in SHM more effective. However, optimizing of the number of filters and units is a challenge, often relying on scientists' experience or previous models without a systematic method for measurement or automatic selection. Therefore, this study proposes a novel framework to enhance the accuracy and efficiency of SHM for damage detection in bridge structures by using metaheuristic algorithms, Electric Eel Foraging Optimization (EEFO) algorithm, to optimize hyperparameters of DL model. This DL model combines the advantages of each traditional model. Specifically, 1D Convolutional Neural Network (1DCNN) is employed for features extraction, Gated Recurrent Units (GRU) for identifying long-term dependencies, and Residual Networks (ResNet) for avoiding vanishing gradient problem which often happens in DL model during training, referred to as 1DCNN-GRU-ResNet. The time-series dataset generated from Cua Rao bridge is used to demonstrate the effectiveness of the proposed method. 1DCNN-GRU-ResNet after optimization (1DCNN-GRU-ResNet-opt) achieves 91.6 %, significantly surpassing traditional 1DCNN (82.5 %), GRU (79.9 %), 1DCNN-GRU (85.7 %), and 1DCNN-GRU-ResNet (89.3 %) in the test set. The proposed 1DCNN-GRU-ResNet-opt approach demonstrates considerable potential in practical applications for SHM, offering high accuracy and efficiency.

A Hybrid Machine Learning Approach for Intrusion Detection and Mitigation on IoT Smart Healthcare

Strong cybersecurity solutions are becoming more and more important as Internet of Things (IoT) technology integration in healthcare settings develops. This study offers a method for feature extraction, selection, and attack classification by fusing the discriminative capacity of feedforward neural networks (FNNs) with the adaptability of fuzzy logic systems. In delicate healthcare database of IoT wearable devices, to reduce false alarm and guaranteeing intrusion detection dependability are the main priorities. The suggested method uses a feature extraction, selection technique, training and testing based on FNN, which allows the model to adjust to the dynamic and varied character of medical data. During the assessment stage, a dataset including a range of healthcare IoT scenarios, including different kinds of attacks, is used to train and evaluate the model, the ToN_IoT dataset was used. Fuzzy logic improves the system's resilience in identifying pertinent features by managing uncertainties and imprecise input. Fuzzy logic is one of the best technique for handling uncertainty, its linguistic representation and rule reasoning helps in better identification and classification. The findings indicate a noteworthy decrease in the frequency of false alarms when juxtaposed with conventional intrusion detection systems. Results obtained from the model are 99.2, 98.8, 99.5, 99.1 & 0.008 for accuracy, precision, recall, F1-Score and False alarm respectively. Promising outcomes in protecting IoT healthcare environments are demonstrated by the suggested system, opening the door to better patient data privacy and system resilience against cyberattacks.

Cathode Electrochemical Behavior of Aluminum Electrolysis in NaF-NaCl Based Molten Salt

Abstract Selective and efficient electrochemical analytical methods for the depositing process of metal aluminum are significant and necessary. The electrochemical measurement methods are based on the means of cyclic voltammetry, square wave voltammetry and constant potential electrolysis. The above methods are even never used to analyze the depositing process of metal aluminum in NaF-NaCl-Al2O3 and NaF-NaCl-NaAlO2 molten salt. Cyclic voltammetry analysis showed that Al3+ ions were reduced to metal aluminum through two consecutive steps. From the analysis of square wave voltammetry curve, the main reaction on the electrode was the reduction of metal sodium in the NaF-NaCl system, and the electron transfer number of the reaction was 0.80. With the addition of NaAlO2 and Al2O3 in the electrolyte, the electron transfer numbers of the two reduction steps were about 3.0. The process of reduction of Al3+ ions to aluminum was further proved by the analysis of the electrolysis curve at constant potential. The diffusion rate of Al3+ions and the rate of Al3+ reduction reaction increased with the addition of NaAlO2 and Al2O3 and the change of the potential.

Investigating the Anticancer Potential of Zinc and Magnesium Alloys: From Base Materials to Nanocoated Titanium Implants.

In this work, we show the in vitro anticancer potential of surgical wires, obtained from zinc (ZnMg0.004) or magnesium (MgCa0.7) alloys by spatial technology comprising casting, extrusion, and final drawing processes. We also present the selective anticancer effects of applied soluble multilayer nanocoatings of zinc and magnesium onto titanium surfaces using the pulse laser deposition method. In the latter, the titanium samples were produced via 3D printing using the selective laser melting method and coated with various combinations of zinc and magnesium layers. For cytotoxicity studies, human dental pulp-derived stem cells (hDPSCs) and human osteosarcoma SaOS-2 cell line were used as representatives of healthy and cancer cells. Cells were examined against the 0.3-3.0 cm2/mL material extract ratios obtained from experimental and steel surgical wires, the latter being the current clinical industry standard. The MgCa0.7 alloy wires were approx. 1.5 times more toxic to cancer cells at all examined extract ratios vs. the extracts from steel surgical wires that exhibited comparable toxicity towards healthy and cancer cells. The ZnMg0.004 alloy wires displayed increased toxicity towards cancer cells with decreasing extract ratios. This was also reflected in the increased anticancer effectiveness, calculated based on the viability ratio of healthy cells to cancer cells, from 1.1 to 4.0 times. Healthy cell viability remained at 80-100%, whereas cancer cell survival fluctuated at 20-75%, depending on the extract ratio. Furthermore, the culture of normal or cancer cells on the surface of Zn/Mg-coated titanium allowed us to select combinations of specific coating layers that yielded a comparable anticancer effectiveness to that observed with the experimental wires that ranged between 2 and 3. Overall, this work not only demonstrates the substantial anticancer properties of the studied wires but also indicates that similar anticancer effects can be replicated with appropriate nanocoatings on titanium samples. We believe that this work lays the groundwork for the future potential development of the category of new implants endowed with anticancer properties.

Comparison of WAIC and posterior predictive approaches for N-mixture models

Hierarchical models are common for ecological analysis, but determining appropriate model selection methods remains an ongoing challenge. To confront this challenge, a suitable method is needed to evaluate and compare available candidate models. We compared performance of conditional WAIC, a joint-likelihood approach to WAIC (WAICj), and posterior-predictive loss for selecting between candidate N-mixture models. We tested these model selection criteria on simulated single-season N-mixture models, simulated multi-season N-mixture models with temporal auto-correlation, and three case studies of single-season N-mixture models based on eBird data. WAICj proved more accurate than the standard conditional formulation or posterior-predictive loss, even when models were temporally correlated, suggesting WAICj is a robust alternative to model selection for N-mixture models.

Comparison of WAIC and posterior predictive approaches for N-mixture models

Hierarchical models are common for ecological analysis, but determining appropriate model selection methods remains an ongoing challenge. To confront this challenge, a suitable method is needed to evaluate and compare available candidate models. We compared performance of conditional WAIC, a joint-likelihood approach to WAIC (WAICj), and posterior-predictive loss for selecting between candidate N-mixture models. We tested these model selection criteria on simulated single-season N-mixture models, simulated multi-season N-mixture models with temporal auto-correlation, and three case studies of single-season N-mixture models based on eBird data. WAICj proved more accurate than the standard conditional formulation or posterior-predictive loss, even when models were temporally correlated, suggesting WAICj is a robust alternative to model selection for N-mixture models.

Mask-Guided Vision Transformer for Few-Shot Learning.

Learning with little data is challenging but often inevitable in various application scenarios where the labeled data are limited and costly. Recently, few-shot learning (FSL) gained increasing attention because of its generalizability of prior knowledge to new tasks that contain only a few samples. However, for data-intensive models such as vision transformer (ViT), current fine-tuning-based FSL approaches are inefficient in knowledge generalization and, thus, degenerate the downstream task performances. In this article, we propose a novel mask-guided ViT (MG-ViT) to achieve an effective and efficient FSL on the ViT model. The key idea is to apply a mask on image patches to screen out the task-irrelevant ones and to guide the ViT focusing on task-relevant and discriminative patches during FSL. Particularly, MG-ViT only introduces an additional mask operation and a residual connection, enabling the inheritance of parameters from pretrained ViT without any other cost. To optimally select representative few-shot samples, we also include an active learning-based sample selection method to further improve the generalizability of MG-ViT-based FSL. We evaluate the proposed MG-ViT on classification, object detection, and segmentation tasks using gradient-weighted class activation mapping (Grad-CAM) to generate masks. The experimental results show that the MG-ViT model significantly improves the performance and efficiency compared with general fine-tuning-based ViT and ResNet models, providing novel insights and a concrete approach toward generalizing data-intensive and large-scale deep learning models for FSL.

Selection of the most informative wavenumbers to improve prediction accuracy of milk fatty acid profile based on milk mid‐infrared spectra data

AbstractMilk mid‐infrared (MIR) spectra have been shown to provide valuable information on a wide range of traits to be used in dairy cattle breeding programs. Selecting the most informative variables from complex data can improve the prediction accuracy and model robustness and, consequently, the interpretability of MIR spectra. Thus, we aimed to investigate the prediction performance of feature selection methods based on MIR spectra data, using the milk fatty acid (FA) profile as an example to illustrate the evaluated procedure. Data of MIR spectra, milk test‐day records, and reference FA concentrations of 155 first‐parity Holstein cows were used in the analyses. Four models comprising different explanatory variables and three feature selection methods were evaluated. The results indicated that competitive adaptive reweighted sampling (CARS) method can effectively select the most informative variables from the MIR spectra, resulting in higher prediction accuracies than other variable selection approaches. The model including selected MIR spectra and cow information variables yielded the best FA profile predictions based on partial least square regression. C8:0, C10:0, C14:1, C17:0 isomers, C18:1, C18:1 isomer, medium‐chain FA, unsaturation FA, monounsaturated FA, and polyunsaturated FA presented accuracies based on the determination coefficient ranging from 0.66 to 0.85 in internal validation and from 0.65 to 0.84 in external validation. The most related wavenumbers to 35 FAs were found within 1003 to 1145 cm−1. Generally, using CARS and cow information improved predictions of FAs based on MIR spectra in Chinese Holstein dairy cows. Additional validation studies should be conducted as larger datasets become available.

Selection of the most informative wavenumbers to improve prediction accuracy of milk fatty acid profile based on milk mid‐infrared spectra data

AbstractMilk mid‐infrared (MIR) spectra have been shown to provide valuable information on a wide range of traits to be used in dairy cattle breeding programs. Selecting the most informative variables from complex data can improve the prediction accuracy and model robustness and, consequently, the interpretability of MIR spectra. Thus, we aimed to investigate the prediction performance of feature selection methods based on MIR spectra data, using the milk fatty acid (FA) profile as an example to illustrate the evaluated procedure. Data of MIR spectra, milk test‐day records, and reference FA concentrations of 155 first‐parity Holstein cows were used in the analyses. Four models comprising different explanatory variables and three feature selection methods were evaluated. The results indicated that competitive adaptive reweighted sampling (CARS) method can effectively select the most informative variables from the MIR spectra, resulting in higher prediction accuracies than other variable selection approaches. The model including selected MIR spectra and cow information variables yielded the best FA profile predictions based on partial least square regression. C8:0, C10:0, C14:1, C17:0 isomers, C18:1, C18:1 isomer, medium‐chain FA, unsaturation FA, monounsaturated FA, and polyunsaturated FA presented accuracies based on the determination coefficient ranging from 0.66 to 0.85 in internal validation and from 0.65 to 0.84 in external validation. The most related wavenumbers to 35 FAs were found within 1003 to 1145 cm−1. Generally, using CARS and cow information improved predictions of FAs based on MIR spectra in Chinese Holstein dairy cows. Additional validation studies should be conducted as larger datasets become available.

Machine Learning for Predicting Chronic Renal Disease Progression in COVID-19 Patients with Acute Renal Injury: A Feasibility Study

This study aimed to determine the feasibility of applying machine-learning methods to assess the progression of chronic kidney disease (CKD) in patients with coronavirus disease (COVID-19) and acute renal injury (AKI). The study was conducted on patients aged 18 years or older who were diagnosed with COVID-19 and AKI between April 2020 and March 2021, and admitted to a second-level hospital in Mérida, Yucatán, México. Of the admitted patients, 47.92% died and 52.06% were discharged. Among the discharged patients, 176 developed AKI during hospitalization, and 131 agreed to participate in the study. The study’s results indicated that the area under the receiver operating characteristic curve (AUC-ROC) for the four models was 0.826 for the support vector machine (SVM), 0.828 for the random forest, 0.840 for the logistic regression, and 0.841 for the boosting model. Variable selection methods were utilized to enhance the performance of the classifier, with the SVM model demonstrating the best overall performance, achieving a classification rate of 99.8% ± 0.1 in the training set and 98.43% ± 1.79 in the validation set in AUC-ROC values. These findings have the potential to aid in the early detection and management of CKD, a complication of AKI resulting from COVID-19. Further research is required to confirm these results.

Fe-rich X-ray amorphous material records past climate and persistence of water on Mars

X-ray amorphous material comprises 15-73 wt.% of sedimentary rocks and eolian sediments in Gale crater. This material is variably siliceous and iron rich but aluminum poor. The presence of volatiles is consistent with the existence of incipient weathering products. To better understand the implications of this material for past aqueous conditions on Mars, here we investigate X-ray amorphous material formation and longevity within terrestrial iron rich soils with varying ages and environmental conditions using bulk and selective dissolution methods, X-ray diffraction, and transmission electron microscopy. Results indicate that in situ aqueous alteration is required to concentrate iron into clay-size fraction material. Cooler climates promote the formation and persistence of X-ray amorphous material whereas warmer climates promote the formation of crystalline secondary phases. Iron rich X-ray amorphous material formation and persistence on Mars are therefore consistent with past cool and relatively wet environments followed by long-term cold and dry conditions.

An Integrated Geometric Obstacle Avoidance and Genetic Algorithm TSP Model for UAV Path Planning

In this paper, we propose an innovative approach for the path planning of Uninhabited Aerial Vehicles (UAVs) that combines an advanced Genetic Algorithm (GA) for optimising missions in advance and a geometrically based obstacle avoidance algorithm (QuickNav) for avoiding obstacles along the optimised path. The proposed approach addresses the key problem of determining an optimised trajectory for UAVs that covers multiple waypoints by enabling efficient obstacle avoidance, thus improving operational safety and efficiency. The study highlights the numerous challenges for UAV path planning by focusing on the importance of both global and local path planning approaches. To find the optimal routes, the GA utilises multiple methods of selection to optimise trajectories using the Cartesian Coordinate System (CCS) data transformed from a motion capture system. The QuickNav algorithm applies linear equations and geometric methods to detect obstacles, guaranteeing the safe navigation of UAVs and preventing real-time collisions. The proposed methodology has been proven useful in reducing the total distance travelled and computing times and successfully navigating UAVs across different scenarios with varying numbers of waypoints and obstacles, as demonstrated by simulations and real-world UAV flights. This comprehensive approach provides advantageous perspectives for real-world applications in a variety of operational situations and improves UAV autonomy, safety, and efficiency.

Optimal Cut-Point Selection Methods Under Binary Classification When Subclasses Are Involved.

In practice, we often encounter binary classification problems where both main classes consist of multiple subclasses. For example, in an ovarian cancer study where biomarkers were evaluated for their accuracy of distinguishing noncancer cases from cancer cases, the noncancer class consists of healthy subjects and benign cases, while the cancer class consists of subjects at both early and late stages. This article aims to provide a large number of optimal cut-point selection methods for such setting. Furthermore, we also study confidence interval estimation of the optimal cut-points. Simulation studies are carried out to explore the performance of the proposed cut-point selection methods as well as confidence interval estimation methods. A real ovarian cancer data set is analyzed using the proposed methods.

A Fusion Positioning System Based on Camera and LiDAR for Unmanned Rollers in Tunnel Construction

As an important vehicle in road construction, the unmanned roller is rapidly advancing in its autonomous compaction capabilities. To overcome the challenges of GNSS positioning failure during tunnel construction and diminished visual positioning accuracy under different illumination levels, we propose a feature-layer fusion positioning system based on a camera and LiDAR. This system integrates loop closure detection and LiDAR odometry into the visual odometry framework. Furthermore, recognizing the prevalence of similar scenes in tunnels, we innovatively combine loop closure detection with the compaction process of rollers in fixed areas, proposing a selection method for loop closure candidate frames based on the compaction process. Through on-site experiments, it is shown that this method not only enhances the accuracy of loop closure detection in similar environments but also reduces the runtime. Compared with visual systems, in static positioning tests, the longitudinal and lateral accuracy of the fusion system are improved by 12 mm and 11 mm, respectively. In straight-line compaction tests under different illumination levels, the average lateral error increases by 34.1% and 32.8%, respectively. In lane-changing compaction tests, this system enhances the positioning accuracy by 33% in dim environments, demonstrating the superior positioning accuracy of the fusion positioning system amid illumination changes in tunnels.

The Transformation of Shape Movement Structure in the Drawings of Wasma Al-Agha

Formal formations, linear arrangements, variations in spaces, color contrasts, proximity and spacing between shapes, and even the repetition of shapes in some cases, and differences in sizes, make the painting a full-fledged compositional dimension. Therefore, we find through that importance a pressing need to conduct a study that sheds light on an important aspect of the formation represented by the experience of the artist Wasma Al-Agha. From this starting point, the researcher found a logical justification to address the subject of this research, to study and deepen the idea of shape movement and its relationship to the paintings of the artist Wasma Al-Agha. Through the foregoing, the researcher was able to formulate the following question: To what extent does the structure of shape movement transform in the drawings of Wasma Al-Agha?As the current research aims to understand the mechanisms of formation, types of shapes, and their movement in the drawings of the artist. Therefore, the researcher relied on the descriptive-analytical method (descriptive analysis) due to its suitability for the subject of the current study and what it allows for in terms of analysis procedures in order to achieve the research goal. The sample was selected using the intentional selection method for the representative sample of the research community, where the researcher identified his research models (3) models from the original community. Also the research came out with a set of results, the most important of which are: The artist tried to represent a sad and dramatic story of the suffering of immigrants in the Iraqi provinces, as in sample number (1). She relied on her own style in embodying the shapes to convey the scene with an illusory movement of the shapes, like an old inherited novel that she tells through colors and shapes, as in all the samples. As for the research Conclusions: 1. The artist relied on multiple pure colors that express a festive dimension that evokes joy in the viewer's soul. She depicted the Baghdadi scene that was repeated in all her paintings and her representation of the Iraqi woman as an old inherited novel that she tells through colors and shapes.

Improving prediction of blood cancer using leukemia microarray gene data and Chi2 features with weighted convolutional neural network

Blood cancer has emerged as a growing concern over the past decade, necessitating early diagnosis for timely and effective treatment. The present diagnostic method, which involves a battery of tests and medical experts, is costly and time-consuming. For this reason, it is crucial to establish an automated diagnostic system for accurate predictions. A particular field of focus in medical research is the use of machine learning and leukemia microarray gene data for blood cancer diagnosis. Even with a great deal of research, more improvements are needed to reach the appropriate levels of accuracy and efficacy. This work presents a supervised machine-learning algorithm for blood cancer prediction. This work makes use of the 22,283-gene leukemia microarray gene data. Chi-squared (Chi2) feature selection methods and the synthetic minority oversampling technique (SMOTE)-Tomek resampling is used to overcome issues with imbalanced and high-dimensional datasets. To balance the dataset for each target class, SMOTE-Tomek creates synthetic data, and Chi2 chooses the most important features to train the learning models from 22,283 genes. A novel weighted convolutional neural network (CNN) model is proposed for classification, utilizing the support of three separate CNN models. To determine the importance of the proposed approach, extensive experiments are carried out on the datasets, including a performance comparison with the most advanced techniques. Weighted CNN demonstrates superior performance over other models when coupled with SMOTE-Tomek and Chi2 techniques, achieving a remarkable 99.9% accuracy. Results from k-fold cross-validation further affirm the supremacy of the proposed model.

Improving prediction of blood cancer using leukemia microarray gene data and Chi2 features with weighted convolutional neural network

Blood cancer has emerged as a growing concern over the past decade, necessitating early diagnosis for timely and effective treatment. The present diagnostic method, which involves a battery of tests and medical experts, is costly and time-consuming. For this reason, it is crucial to establish an automated diagnostic system for accurate predictions. A particular field of focus in medical research is the use of machine learning and leukemia microarray gene data for blood cancer diagnosis. Even with a great deal of research, more improvements are needed to reach the appropriate levels of accuracy and efficacy. This work presents a supervised machine-learning algorithm for blood cancer prediction. This work makes use of the 22,283-gene leukemia microarray gene data. Chi-squared (Chi2) feature selection methods and the synthetic minority oversampling technique (SMOTE)-Tomek resampling is used to overcome issues with imbalanced and high-dimensional datasets. To balance the dataset for each target class, SMOTE-Tomek creates synthetic data, and Chi2 chooses the most important features to train the learning models from 22,283 genes. A novel weighted convolutional neural network (CNN) model is proposed for classification, utilizing the support of three separate CNN models. To determine the importance of the proposed approach, extensive experiments are carried out on the datasets, including a performance comparison with the most advanced techniques. Weighted CNN demonstrates superior performance over other models when coupled with SMOTE-Tomek and Chi2 techniques, achieving a remarkable 99.9% accuracy. Results from k-fold cross-validation further affirm the supremacy of the proposed model.