Enhanced identification of illicit bitcoin transactions through genetic algorithm-based feature selection

Traditional crop cutting experiment-based yield estimation method captures the regional yield variability but lacks field-level information. Satellite images hold enormous crop information at finer spatial resolution. Crop yield mapping with optical images is particularly challenging if cloud-free images are unavailable during the crucial crop developmental stages. All-weather availability and sensitivity to crop structure, dielectric properties make synthetic aperture radar (SAR) images an excellent resource for yield estimation. Both types of data provide complementary information about crop conditions. A random forest regression model with genetic algorithm-based feature selection is developed to exploit the Sentinel-2 optical and Sentinel-1 SAR images for yield estimation. We utilized the crop harvest and quality survey (BEE) yield data set collected by the Hessisches Statistisches Landesamt (HSL), Wiesbaden, Germany, over 490 fields. We prepared 20 m resolution yield maps for winter wheat, winter barley, winter rye and winter rapeseed. Input features for the yield estimation model are selected based on the prior knowledge of remote sensing of vegetation. Baseline random forest regression models are developed for all the four crop types with optical and SAR input features. An optimized random forest regression model with genetic algorithm-based feature selection results in performance improvement. Dissimilarity in genetic algorithm selected image features highlights the significance of crop-specific feature selection for yield estimation. The optimized models reliably estimate yield by achieving correlation coefficient (r) of 0.65–0.86, mean absolute error 0.93–1.16 t ha–1 and root mean square error 1.12–1.56 t ha–1 with BEE yield on testing data set. The proposed models could estimate the intra-field yield variation when winter wheat, winter barley, winter rye were in the shooting phase to the beginning of ear-shifting, and winter rapeseed began to flower or was already flowering. These results demonstrate the merits of our model for early-stage crop yield estimation at the field level with mono-temporal image and adaptability for the cropping season with high cloud cover.

Machine learning techniques are very useful in extracting useful patterns from customer datasets. Although Machine Learning techniques give good results in most of the cases, there is a need to improve the efficiency of ML models in different ways. Feature selection is one of the most important tasks in machine learning. A genetic algorithm is a heuristic method that simulates the selection process. Genetic algorithms come under the category of evolutionary algorithms, which are generally used for generating solutions to optimization problems using selection, crossover, mutation methods. In this paper, we proposed a genetic algorithm-based feature selection model to improve the efficiency of Machine Learning techniques for customer related datasets. We applied a genetic algorithm-based feature selection for two different customer information datasets from UCI repository and achieved good results. All the experiments are implemented in Python language which provides vast packages for machine learning tasks.KeywordsMachine learningGenetic algorithmFeature selectionPython

Singer identification is one of the important aspects of music information retrieval (MIR). In this work, traditional feature-based and trending convolutional neural network (CNN) based approaches are considered and compared for identifying singers. Two different datasets, namely artist20 and the Indian popular singers’ database with 20 singers are used in this work to evaluate proposed approaches. Cepstral features such as Mel-frequency cepstral coefficients (MFCCs) and linear prediction cepstral coefficients (LPCCs) are considered to represent timbre information. Shifted delta cepstral (SDC) features are also computed beside the cepstral coefficients to capture temporal information. In addition, chroma features are computed from 12 semitones of a musical octave, overall forming a 46-dimensional feature vector. Experiments are conducted with different feature combinations, and suitable features are selected using the genetic algorithm-based feature selection (GAFS) approach. Two different classification techniques, namely artificial neural networks (ANNs) and random forest (RF), are considered on the features mentioned above. Further, spectrograms and chromagrams of audio clips are directly fed to CNN for classification. The singer identification results obtained using CNNs seem to be better than the traditional isolated and ensemble classifiers. Average accuracy of around 75% is observed with CNN in the case of Indian popular singers database. Whereas, on artist20 dataset, the proposed configuration of feature-based approach and CNN could not give better than 60% accuracy.

Medical organizations struggle to deal with huge high-dimensional datasets that need powerful machine learning systems to produce precise healthcare outcomes. Traditional analytical techniques prove inadequate when dealing with extraction from features and performance of classifiers in this specific setting. The research introduces an algorithm which enhances Stacked Autoencoders (SAEs) by combining them with a customized Logistic Regression model intended for medical high-dimensional data analysis. This approach implements a Hybrid Imputation Method using MICE and KNN Imputation which precedes other stages and helps process missing values and outliers in medical data. We use CNNs and SAEs together for deep feature extraction before using Feature Fusion to assemble a robust feature collection. A set of the most important features is identified by executing Advanced Ensemble Feature Selection (EFS) procedures which include Few-shot Learning and Model-Agnostic Meta-Learning Algorithm (MAML) and Genetic Algorithm-Based Feature Selection (GAFS). The procedure of fine-tuning pre-trained models represents an effective enhancement for classification tasks particularly in situations of limited dataset availability. The experimental outcomes demonstrate remarkable performance gains in terms of accuracy and sensitivity and specificity as well as reduced execution time as compared to current techniques. Upcoming work for this study involves speeding up algorithm processing abilities and scalability alongside the integration of robust deep learning structures with self-supervised learning methodologies together with upgrade transfer learning approaches for medical dataset variety applications. The study will concentrate on enhancing model transparency through explainable AI and real-time validation for clinical deployment and ethical and regulatory compliance to develop this technique for practical healthcare settings.

Multivariate wind speed forecasting with genetic algorithm-based feature selection and oppositional learning sparrow search

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The increasing use of pesticides in modern agriculture has raised serious concerns regarding food safety and public health due to the presence of hazardous residues in fresh produce. Traditional detection methods, such as chromatography, though accurate, are time-consuming, expensive, and require complex sample preparation. This dissertation introduces a novel and efficient approach for the rapid, non-destructive, and multiplex detection of pesticide residues by integrating surface-enhanced Raman spectroscopy (SERS) with advanced transformer-based machine learning models. This dissertation explores the development of novel SERS substrates through electrospinning. Electrospun polyacrylonitrile (PAN) nanofibers were functionalized and coated with Au@Ag core–shell nanoparticles to create high-surface-area scaffolds with strong plasmonic enhancement. These electrospun substrates demonstrated excellent uniformity and reproducibility, enabling the sensitive detection of the fungicide thiabendazole in soy-based foods with detection limits down to parts-per-billion levels (LOD = 23.1 ppb for soy milk; 79.4 ppb for soy sauce). The optimized electrospinning parameters produced nanofibers with a mean diameter of ~508 nm, offering a stable and tunable platform for SERS applications. This complementary approach highlights how nanofiber-based substrate engineering can significantly enhance the sensitivity and applicability of SERS sensors, further advancing the design of versatile detection platforms for monitoring food contaminants. Two innovative models, SERSFormer and SERSFormer-2.0, were developed and evaluated. Gold-silver core-shell nanoparticles (Au@Ag NPs) were synthesized to serve as SERS substrates, offering enhanced Raman signal amplification and reproducibility. The SERSFormer model was designed for single-pesticide detection in spinach samples and achieved 98.4 percent classification accuracy with a mean absolute error (MAE) of 0.966 in quantification. Building upon this foundation, SERSFormer-2.0 addressed the complex challenge of detecting multiple co-existing pesticide residues in real-world produce such as strawberries and spinach. This model employed a multitask attention-based transformer architecture capable of performing both multilabel classification and multiregression simultaneously. It achieved near-perfect performance across all metrics (accuracy = 0.999; F1 score = 0.992; precision = 0.990; recall = 0.996) and demonstrated strong regression capabilities with an MSE of 0.136 and R² = 0.804. These models utilize shared self-attention mechanisms and task-specific embeddings to capture complex spectral features, even in the presence of overlapping Raman signals. Preprocessing techniques, including denoising, baseline correction, normalization, and genetic algorithm-based feature selection, further enhanced model robustness. The comprehensive framework developed in this work not only improves detection sensitivity and accuracy but also allows real-time assessment of contamination in complex food matrices. This dissertation demonstrates that the synergistic combination of SERS and deep learning, particularly transformer architectures, offers a transformative solution for food safety monitoring. The findings have strong implications for agricultural management, public health, and the development of portable, AI-powered sensors for rapid on-site pesticide screening.

Enhanced Software Bug Prediction Using Ensemble Learning and Genetic Algorithm-Based Feature Selection

Patients with Barrett's esophagus (BE) undergo periodic endoscopic surveillance with random biopsies in an effort to detect dysplastic or early cancerous lesions. Surveillance may be enhanced by near-infrared Raman spectroscopy (NIRS), which has the potential to identify endoscopically-occult dysplastic lesions within the Barrett's segment and allow for targeted biopsies. The aim of this study was to assess the diagnostic performance of NIRS for identifying dysplastic lesions in BE in vivo. Raman spectra (Pexc=70 mW; t=5 s) were collected from Barrett's mucosa at endoscopy using a custom-built NIRS system (λexc=785 nm) equipped with a filtered fiber-optic probe. Each probed site was biopsied for matching histological diagnosis as assessed by an expert pathologist. Diagnostic algorithms were developed using genetic algorithm-based feature selection and linear discriminant analysis, and classification was performed on all spectra with a bootstrap-based cross-validation scheme. The analysis comprised 192 samples (112 non-dysplastic, 54 low-grade dysplasia and 26 high-grade dysplasia/early adenocarcinoma) from 65 patients. Compared with histology, NIRS differentiated dysplastic from non-dysplastic Barrett's samples with 86% sensitivity, 88% specificity and 87% accuracy. NIRS identified 'high-risk' lesions (high-grade dysplasia/early adenocarcinoma) with 88% sensitivity, 89% specificity and 89% accuracy. In the present study, NIRS classified Barrett's epithelia with high and clinically-useful diagnostic accuracy.

Genetic algorithm-based feature selection in high-resolution NMR spectra

Recent monsoon failures and reduced rain falls urge the environmental and ecology researchers to concentrate on the land cover changes. Significant and efficient way to monitor the land cover changes is satellite image classification. This work describes the combination of remotely sensed data, LANDSAT and ENVISAT images, to improve the classification accuracy. Instead of predictor space, embedding space is considered in the proposed KNNES and SVMES methods and applied for the classification of combined LANDSAT and ENVISAT datasets. Genetic algorithm-based (GA) feature selection is adopted to enhance the proposed classification methods. Classification of land cover changes of the study area are identified as used land, unused land, forest and vegetation. Proposed methods are evaluated by an accuracy analysis which follows good practice recommendations. Accuracy is quantified by reporting standard errors, i.e., producer accuracy, user accuracy, omission error and commission error. Performance of the proposed SVM and KNN-based methods using GA-based feature selection for combined dataset is improved significantly and provide overall accuracy 80% and 76% respectively.

BackgroundWe consider both univariate- and multivariate-based feature selection for the problem of binary classification with microarray data. The idea is to determine whether the more sophisticated multivariate approach leads to better misclassification error rates because of the potential to consider jointly significant subsets of genes (but without overfitting the data).MethodsWe present an empirical study in which 10-fold cross-validation is applied externally to both a univariate-based and two multivariate- (genetic algorithm (GA)-) based feature selection processes. These procedures are applied with respect to three supervised learning algorithms and six published two-class microarray datasets.ResultsConsidering all datasets, and learning algorithms, the average 10-fold external cross-validation error rates for the univariate-, single-stage GA-, and two-stage GA-based processes are 14.2%, 14.6%, and 14.2%, respectively. We also find that the optimism bias estimates from the GA analyses were half that of the univariate approach, but the selection bias estimates from the GA analyses were 2.5 times that of the univariate results.ConclusionsWe find that the 10-fold external cross-validation misclassification error rates were very comparable. Further, we find that a two-stage GA approach did not demonstrate a significant advantage over a 1-stage approach. We also find that the univariate approach had higher optimism bias and lower selection bias compared to both GA approaches.

Genetic Algorithm-Based Feature Selection and Self-Organizing Auto-Encoder (Soae) for Snp Genomics Data Classifications

Thanks to our daily available open source characteristics, Android Platform has the largest Market Share. Android has gathered most of the attentions of cybercriminals as it’s the most popular Operating System in the world, they operate through wide distributed malicious application. This article approaches a Machine learning-based Malware detection of Android using Genetic Algorithms-based feature selection. Genetic Algorithm is used to detect malwares by training selected feature to ML classifiers and to check the identification capacity before and after feature selection. This experiment showed that genetic algorithm produces the most optimized feature result and helps in reducing the feature dimension to half of the original feature. After feature selection and maintaining the post-feature selection of ML classifiers, 97% accuracy is gained, while working on reduced dimension, thereby a positive impact on computational complexity is gained.KeywordsAndroidMalwareCyber crimeGeneticNeural networkAISVM

Artificial intelligence (AI)-supported agricultural disease detection has become increasingly important for addressing global food security challenges. In this study, a hybrid meta-heuristic optimization-based feature selection approach is proposed for the detection of peacock eye disease (Venturia oleaginea) on olive leaves. The proposed method combines Artificial Rabbit Optimization (ARO) and Genetic Algorithm (GA) strategies to balance global exploration and local exploitation during feature selection. Comprehensive experiments conducted on a dataset of 954 olive leaf images demonstrate that the proposed approach achieves an F1-score of 99.7% while reducing the feature dimensionality by 95%, selecting only 100 features from ResNet101. Statistical analysis confirms that the method significantly outperforms standalone GA and ARO approaches (p<0.05, paired t-tests), demonstrating superior long-term convergence behavior and a 47–56% reduction in performance variance across repeated runs. Compared to existing approaches in the literature, the proposed method attains competitive or superior accuracy with substantially fewer features, indicating a marked reduction in computational complexity. These results suggest that the proposed hybrid feature selection framework has strong potential for deployment in resource-constrained agricultural monitoring scenarios, where efficient inference and reduced model complexity are critical.