Selection Method Research Articles

Retraction Note: A novel feature selection method for brain tumor MR image classification based on the Fisher criterion and parameter-free Bat optimization

Retraction Note: A novel feature selection method for brain tumor MR image classification based on the Fisher criterion and parameter-free Bat optimization

Enhancing speech emotion recognition through deep learning and handcrafted feature fusion

In this paper, we introduce an innovative investigation in speech emotion recognition (SER). The proposed model combines deep learning-based and handcrafted audio features to achieve optimal accuracy. The proposed model employs an iterative feature selection and majority voting pipeline to obtain better results by fusing deep learning-based and handcrafted features. The wav2vec2 model and the openSmile audio processing library are used in order to extract audio features from audio data. Then the feature selection and majority voting techniques are used to identify the optimal feature selection methods for diverse feature sets and to combine their strengths. The experiments are performed using a diverse and extensive corpus to ensure the robustness of the proposed method. In the construction of this multi-corpus dataset, we used four well-known benchmark datasets, namely Ravdess, Savee, Crema-D, and Tess. All of these datasets are publicly available. These datasets are combined on six common emotions: sadness, happiness, fear, anger, surprise, and disgust. The resultant dataset comprises 11,511 samples across these categories. The proposed method has been shown to achieve results comparable to those reported in the existing literature. The experimental results indicate that the proposed pipeline leads to a 3 % improvement in classification accuracy. The highest achieved accuracy on the multi-corpus dataset is 92.55 %.

Estimation of distribution algorithms with solution subset selection for the next release problem

Abstract The Next Release Problem (NRP) is a combinatorial optimization problem that aims to find a subset of software requirements to be delivered in the next software release, which maximize the satisfaction of a list of clients and minimize the effort required by developers to implement them. Previous studies have applied various metaheuristics, mostly genetic algorithms. Estimation of Distribution Algorithms (EDA), based on probabilistic modelling, have been proved to obtain good results in problems where genetic algorithms struggle. In this paper we propose to adapt three EDAs to tackle the multi-objective NRP in a fast and effective way. Results show that EDAs can be applicable to solve the NRP with rather good quality of solutions. Furthermore, we prove that their execution time can be significantly reduced using a per-iteration solution subset selection method while maintaining the overall quality of the solutions obtained, and they perform the best when limiting the search time as in an interactive tool that requires fast responsiveness. The experimental framework, code and datasets have been made public in a code repository.

Establishing a Hyperspectral Model for the Chlorophyll and Crude Protein Content in Alpine Meadows Using a Backward Feature Elimination Method

Establishing a Hyperspectral Model for the Chlorophyll and Crude Protein Content in Alpine Meadows Using a Backward Feature Elimination Method

An Improved Genetic Method for Satellite Selection in Multi-Global Navigation Satellite System Positioning for Mobile Robots

An Improved Genetic Method for Satellite Selection in Multi-Global Navigation Satellite System Positioning for Mobile Robots

On-Chip Annealing Using Embedded Micro-Heater for Highly Sensitive and Selective Gas Detection.

The demand for gas sensing systems that enable fast and precise gas recognition is growing rapidly. However, substantial challenges arise from the complex fabrication process of sensor arrays, time-consuming data transmission to an external processor, and high energy consumption in multi-stage data processing. In this study, a gas sensing system using on-chip annealing for fast and power-efficient gas detection is proposed. By utilizing a micro-heater embedded in the gas sensor, the sensing material of adjacent sensors in the same substrate can be easily varied without further fabrication steps. The response to oxidizing gas is constrained in metal oxide (MOX) sensing material with small grain sizes, as the depletion width of grain cannot extend beyond the grain size during the gas reaction. On the other hand, the response to reducing gases and humidity, which decrease the depletion width, is less affected by grain sizes. A readout circuit integrating a differential amplifier and dual FET-type gas sensors effectively emphasizes the response to oxidizing gases by canceling the response to reducing gases and humidity. The selective on-chip annealing method is applicable to various MOX sensing materials, demonstrating its potential for application in commercial fields due to its simplicity and expandability.

Color matching of bicomponent (PET/PTT) filaments with high performances using genetic algorithm

In recent years, a great interest has focused on the use of bicomponent filaments in several high-performance textile articles such as swimwear, sportswear and even high-quality denim. To dye fabrics containing these filaments, it is necessary to establish appropriate dye recipes allowing to obtain desired shades. In this article, we developed a genetic algorithm to optimize the color matching step of these bicomponent filaments, especially (PET/PTT) filaments. Three disperse dyes with different molecular weights were used for dyeing. The objective is to reproduce the reference color by choosing the appropriate disperse dyes among the available dyestuffs and their corresponding quantities to use on the mixture. For modeling, two sets of parameters (lied to the color formulation problem and the genetic algorithm), the objective function as well as the different stages of the algorithm were defined and described. In addition, different techniques of selection and mutation were applied and evaluated. The optimization criterion is to reduce the CMC color difference between the desired reference colors and the colors proposed by the algorithm. The developed algorithm showed good performances with very small color differences. The results indicate that the roulette wheel selection technique outperforms both rank and uniform selection methods. Moreover, employing a simple mutation strategy yields favorable outcomes with CMC color differences all lower than 1.

AIE-based fluorescent sensing of ciprofloxacin using non-fluorescent 2D silver dimolybdate nanosheets: The synergistic sensor method (SGSM) to switch on non-fluorescent 2D nanosheets into an effective fluorescent biosensor

The development of an efficient method for detecting ciprofloxacin (CIP) is critical due to the potential for significant harm to humans, including the danger of severe liver damage and hematuria associated with increased levels of this drug. In this study, we offer a highly sensitive and highly selective method for fluorescence sensing of the antibiotic ciprofloxacin employing the properties of Aggregation-Induced Emission (AIE) with silver dimolybdate nanosheets (Ag2Mo2O7 NSs). Outstanding structural characteristics of the Ag2Mo2O7 NSs achieved using the hydrothermal method. By carefully controlling the aggregation of the Synergistic Sensor Method (SGSM), we are able to produce a very bright fluorescence response with the ciprofloxacin concentration range from 0 to 250 nM with a low detection limit of 1.1 nM. Quick and ultrasensitive detection has been achieved by the Synergistic effect (SGE) and AIE from the current approach. The SGSM has been shown to be a novel platform for human health monitoring and pharmaceutical quality control. With broad-ranging practical implications, the combination of AIE and silver dimolybdate nanosheets offers a platform for reliable and ciprofloxacin detection. We believe the SGSM is a role model paper for future nano-biosensor detection methods due to the synergistic effect resulting in the AIE and turn-on sensor approach for many non-fluorescent 2D nanosheets. This can open many new doors for the intensive development of 2D nanosheets for future fluorescent sensors without the need to produce fluorescence by the nanosheet itself.

Photocatalytic application of nickel oxide/zinc oxide nanoparticles decorated kaolinite nanocomposite for the degradation of crystal violet

The photocatalysis process, which is effective, economical, and environmentally friendly can overcome rapidly increasing environmental problems, has shown rapid development in recent years. Nontoxic, low-cost natural minerals are often used in photocatalysis processes that remove organic pollution from wastewater. In this study, a selective method for the removal of crystal violet (CV) from aqueous solutions using nanocomposite of nickel oxide/zinc oxide nanoparticles (NiO/ZnO) supported on kaolinite photocatalyst was investigated. The raw kaolinite, bare NiO, bare ZnO and nanocomposite samples were characterized by the XRD, TEM, SEM-EDX, XPS and FTIR techniques. The photocatalytic performances of kaolinite-NiOZnO (K-NiO/ZnO) nanocomposite and bare samples were also investigated and comparatively evaluated by the photodegradation of CV (cationic dye) dye under visible light irradiation. The effects of various parameters such as the dosage of photocatalysis, contact time, and initial dye concentration on the rate of removal of dye were investigated and optimized. The kinetics of decolorization by samples can be well described by the pseudo-first-order model. Finally, it can be used in advanced oxidative processes for the degradation of organic pollutants.

Review Paper - Recruitment and Selection Process at HDFC Bank

This research explores the complexities of the recruiting and selection process at HDFC Bank, a prominent financial organization in India. The main goal is to provide a thorough understanding of the techniques, processes, and difficulties associated with acquiring, evaluating, and selecting skilled individuals inside the bank. Data was collected using a mixed-methods approach that included qualitative and quantitative research approaches. The data was received from several sources, including HR professionals, existing workers, and relevant paperwork. The study emphasizes the significance of efficient recruiting and selection methods in preserving a competitive advantage in the banking industry. The analysis focuses on the congruence between HDFC Bank's recruiting tactics and its organizational goals, highlighting the significance of talent acquisition in propelling corporate performance and attaining long-term expansion. Moreover, the research investigates the use of technology and advanced approaches to make the recruiting process more efficient and improve the experience of candidates. The key results highlight the importance of cultural compatibility, alignment of talents, and inclusion of diversity in the selection process, demonstrating HDFC Bank's dedication to creating an inclusive work environment. In addition, the report highlights obstacles such as a shortage of skilled workers, growing competition, and changing market conditions, which compel the bank to constantly adjust and improve its recruiting methods. In summary, this study adds to the current understanding of recruiting and selection techniques in the banking industry. It provides valuable insights and suggestions for HR professionals and organizational leaders who want to improve their talent acquisition methods. The statement highlights the essential significance of recruiting and selection in influencing the achievement of an organization and promoting a culture of exceptional performance within HDFC Bank.

Hybrid feature selection in a machine learning predictive model for perioperative myocardial injury in noncoronary cardiac surgery with cardiopulmonary bypass.

Perioperative myocardial injury (PMI) is associated with increased mobility and mortality after noncoronary cardiac surgery. However, limited studies have developed a predictive model for PMI. Therefore, we used hybrid feature selection (FS) methods to establish a predictive model for PMI in noncoronary cardiac surgery with cardiopulmonary bypass (CPB). This was a single-center retrospective study conducted at the Fuwai Hospital in China. Patients aged 18-70years who underwent elective noncoronary surgery with CPB at our institution from December 2018 to April 2021 were enrolled. The primary outcome was PMI, defined as the postoperative cardiac troponin I (cTnI) levels exceeding 220 times of upper reference limit (URL). Statistical analyses were conducted by Python (Python Software Foundation, version 3.9.7 and integrated development environment Jupyter Notebook 1.1.0) and SPSS software version 26.0 (IBM Corp., Armonk, New York, USA). A total of 1130 patients were eventually eligible for this study. The incidence of PMI was 20.3% (229/1130) in the overall patients, 20.6% (163/791) in the training dataset, and 19.5% (66/339) in the testing dataset. The logistic regression model performed the best AUC of 0.6893 (95 CI%: 0.6371-0.7382) by the traditional selection method, and the random forest model performed the best AUC of 0.6937 (95 CI%: 0.6416-0.7423) by the union of Wrapper and Embedded method, and the CatBoost model performed the best AUC of 0.6828 (95 CI%: 0.6304-0.7320) by the union of Embedded and forward logistic regression technique, and the Naïve Bayes model achieved the best AUC with 0.7254 (95 CI%: 0.6746-0.7723) by forwarding logistic regression method. Moreover, the decision tree, KNeighborsClassifier, and support vector machine models performed the worse AUC in all selection forms. Furthermore, the SHapley Additive exPlanations plot showed that prolonged CPB, aortic clamp time, and preoperative low platelets count were strongly related to the PMI risk. In total, four category feature selection methods were utilized, comprising five individual selection techniques and 15 combined methods. Notably, the combination of logistic regression and embedded methods demonstrated outstanding performance in predicting PMI risk. We also concluded that the machine learning model, including random forest, catboost, and Naive Bayes, were suitable candidates for establishing PMI predictive model. Nevertheless, additional investigation and validation are imperative for substantiating these finding.

Using interpretative structure model and analytical network process for optimum site selection of airport locations in Delta Egypt

Abstract Airports are among the constructions that must meet international standards and specifications established by the International Civil Aviation Organization (ICAO). Some of these parameters and criteria include topography, environmental, and operating circumstances. Navigation operations are also affected by terrain and human barriers, while noise, infrastructure, and weather factors affect the environment around airports. This article investigates using the interpretative structure model (ISM) and analytical network process (ANP) together as a new technique to select and determine the optimum site selection for constructing new international airports in the Nile Delta, Egypt. The criteria used (16 international criteria) are selected depending on the standards of the ICAO, field surveys, and previous studies. Nile Delta, Egypt, is chosen as a case study because it suffers from the lack of international airports and has a population of approximately 23 million. Therefore, new international airports must be established to serve this region, boost tourism, improve transportation, and stimulate commercial traffic. The results using the suggested new technique are compared with the traditional used methods for site selection, such as fuzzy-analytic hierarchy process. Landsat 8 images are used in this research. A quality test using the area under the curve and the receiver operating characteristic curve was applied to evaluate the new technique for site selection of international airports, depending on calculating the highest suitability index for each proposed site. From the quality tests, it is deduced that the suggested method (ISM–ANP) for airport site selection is more accurate than any other traditional method. ArcGIS 10.5 software is used to draw the final digital maps containing the proposed resulted sites. As a result, three new locations for the construction of international airports were found and selected throughout the research region (Delta Nile, Egypt) based on the used mathematical models. Therefore, the proposed novel method for determining the locations of international airports is thought to be effective and feasible, and it can be used to determine the locations of any development projects in general, particularly in developing countries, which benefits the decision-makers in making the right decisions.

A Human-In-One-Loop Active Domain Adaptation Framework for Digit Recognition

ABSTRACT Domain adaptation can effectively enhance a model’s performance on target domain data with limited data. However, when some target domain labels are obtainable, training the model with both source and target domain data simultaneously can lead to worse performance due to the lower density of target domain data. If a large amount of target domain data is labeled without discrimination, it will necessitate a considerable expenditure of human resources. To address this issue, this paper proposes a human-in-one-loop active domain adaptation framework based on Target Domain Feature Generation to solve the problems. The oracle participates in only one iteration of data labeling, and a target domain classifier will take over the subsequent rest iterations. An image generator based on multiple CycleGANs forms an iterative co-training mechanism, which can continuously generate more high-quality labeled fake target domain data in iterations to improve the performance of the target domain classifier. The Top-N labeled data selection method with high confidence is devised to select the most accurately predicted data for labeling, reducing manual labeling workload. This framework can achieve an average accuracy of 0.8869 on six domain pairs, doubling the classical domain adaptation method DSN, requiring only a small amount of manual labeling.

Enhancing a machine learning model for predicting agricultural drought through feature selection techniques

This study aims to determine the crucial variables for predicting agricultural drought in various climates of Iran by employing feature selection methods. To achieve this, two databases were used, one consisting of ground-based measurements and the other containing six reanalysis products for temperature (T), root zone soil moisture (SM), potential evapotranspiration (PET), and precipitation (P) variables during the 1987–2019 period. The accuracy of the global database data was assessed using statistical criteria in both single- and multi-product approaches for the aforementioned four variables. In addition, five different feature selection methods were employed to select the best single condition indices (SCIs) as input for the support vector regression (SVR) model. The superior multi-products based on time series (SMT) showed increased accuracy for P, T, PET, and SM variables, with an average 47%, 41%, 42%, and 52% reduction in mean absolute error compared to SSP. In hyperarid climate regions, PET condition index was found to have high relative importance with 40% and 36% contributions to SPEI-3 and SPEI-6, respectively. This suggests that PET plays a key role in agricultural drought in hyperarid regions because of very low precipitation. Additionally, the accuracy results of different feature selection methods show that ReliefF outperformed other feature selection methods in agricultural drought modeling. The characteristics of agricultural drought indicate the occurrence of drought in 2017 and 2018 in various climates in Iran, particularly arid and semi-arid climates, with five instances and an average duration of 12 months of drought in humid climates.

Differentiation of Polygonatum Cyrtonema Hua from Different Geographical Origins by near-Infrared Spectroscopy with Chemometrics.

The identification of the geographical origin of Polygonatum cyrtonema Hua is of particular importance because the quality and market value of Polygonatum cyrtonema Hua from different production areas are highly variable due to differences in the growing environment and climatic conditions. This study utilized near-infrared spectra (NIR) of Polygonatum cyrtonema Hua (n = 400) to develop qualitative models for effective differentiation of Polygonatum cyrtonema Hua from various regions. The models were produced under different conditions to distinguish the origins distinctly. Ten pre-processing methods have been used to pre-process the original spectra (OS) and to select the most optimal spectral pre-processing method. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA) were employed to determine appropriate models. For simplicity, the pretreated full spectrum was calculated by different wavelength selection methods, and the four most significant variables were selected as discriminant indicator variables. The results show that Polygonatum cyrtonema Hua from different regions can be effectively distinguished using spectra from a series of samples analyzed by OPLS-DA. The accuracy of the OPLS-DA model is also satisfactory, with a good differentiation rate. The study findings indicate the feasibility of using spectroscopy in combination with multivariate analysis to identify the geographical origins of Polygonatum cyrtonema Hua. The utilization of near-infrared spectroscopy combined with chemometrics exhibits high efficacy in discerning the provenance of herbal medicines and foods, thereby facilitating quality assurance measures.

Some problems of research of the Yuan Dynasty copper red enamel porcelain (you’li-hong)

In the scholarly exploration of Chinese ceramic history, the Yuan Dynasty emerges as a pivotal era, distinguished by its remarkable advancements in porcelain artistry, culminating in the mastery of porcelain craft. This period is notably marked by the emergence and refinement of underglaze red porcelain, a quintessential embodiment of Yuan ceramic excellence. The inception of underglaze red porcelain, a technique entailing the application of copper-containing colorant to porcelain before overlaying it with a transparent glaze and firing at temperatures exceeding 1350°C in a reducing kiln atmosphere, is historically attributed to the Yuan Dynasty. This technique, which flourished subsequently during the Ming and Qing Dynasties, represents a significant evolution in ceramic art. Each Chinese dynasty contributed uniquely to the ceramic arts, with the Yuan Dynasty particularly enhancing the underglaze red porcelain technique during its middle and late periods, as evidenced by the sophisticated coloration in surviving artifacts. This study aims to thoroughly examine the underglaze red porcelain of the Yuan Dynasty, highlighting its artistic uniqueness and exceptional craftsmanship. The paper commences with an overview of the Yuan Dynasty’s historical context—its political, economic, and cultural landscape—setting the stage for a deeper understanding of the underglaze red porcelain’s development. This is followed by an exploration of the porcelain’s creation process, including material selection and firing methods, to underscore the intricacies of its craftsmanship. Furthermore, a detailed analysis of the artistic and decorative elements of the underglaze red porcelain will be undertaken, focusing on aspects such as form, design motifs, and glaze hues, to unpack its stylistic and aesthetic dimensions. The culmination of this paper will be an evaluation of the historical significance and artistic merit of the Yuan Dynasty’s underglaze red porcelain, offering insights into its enduring legacy and influence on subsequent ceramic traditions. Through this comprehensive investigation, the study not only enriches our understanding of the Yuan Dynasty’s ceramic artistry but also underscores the importance of preserving and promoting traditional Chinese ceramic heritage. Additionally, it provides a foundation for further scholarly inquiry and conservation efforts in the realm of ancient Chinese ceramics.

Colorimetric Detection of Sulfur Mustard with 4-(p-Nitrobenzyl)pyridine and Its Derivatives.

In this study, we present a simple, highly sensitive, and selective colorimetric method for detecting sulfur mustard (SM) and its simulants. This method relies on a nucleophilic substitution reaction between derivatives of 4-(p-nitrobenzyl)pyridine (NBP) and SM and subsequent treatment with an external base, resulting in a visible response. This reaction exhibits an impressively low detection threshold by the naked eye, as low as 10 ppm at room temperature. In contrast to the conventional use of NBP for detecting other alkylating agents, such as nitrogen mustard, our approach eliminates the need for prolonged heating or intricate extraction processes. Both computational and experimental investigations underscore the significance of water within our detection medium as it stabilizes crucial episulfonium cation intermediates. Furthermore, we demonstrate the practical applicability of this sensor by incorporating it onto cellulose and silica surfaces, which may provide guidance for the design and development of solid-state SM detectors.

A methodological integration of fisher score technique with intelligent machine learning methods for ball bearing fault investigation

This paper presents an efficient approach to detect, diagnose and estimate the severity of failures in various components of bearings in induction motors using vibration signature analysis. This automated method integrates the Fisher Score feature selection method and an efficient hyperparameter tuning model with machine learning models, including Support Vector Machine (SVM), k-Nearest Neighbor (k-NN) and Decision Tree (DT), to accurately classify defects in bearings. This approach ensures accurate classification of bearing defects within less computation time. This work is carried out with vibration signals, recorded from a laboratory experimental setup on Machine Fault Simulator (MFS), focusing on ball bearing with defects in inner race, outer race and ball itself. Time and Frequency domain analysis are employed to compute the features for fault investigation in ball bearings using machine learning models. The computed results demonstrate that the proposed feature selection method with hyperparameter tuning achieved remarkable maximum accuracy among X, Y and XY combinations of datasets, with 97% in DT, 94% in SVM and 95.23% in k-NN models during the frequency domain analysis. Notably, these model accuracies improved to 99.04% in DT, 98% in SVM and 98% in k-NN during further analysis with Fisher Score technique. Consequently, the testing loss using this hyperparameter tuning function remains very low. Overall, this paper compares the results of time and frequency domain analysis and introduces a promising approach for both efficient and accurate fault detection and severity estimation in bearings of induction motors, potentially reducing the need for extensive manpower and sensor usage.

Biosensor-Driven IoT Wearables for Accurate Body Motion Tracking and Localization

The domain of human locomotion identification through smartphone sensors is witnessing rapid expansion within the realm of research. This domain boasts significant potential across various sectors, including healthcare, sports, security systems, home automation, and real-time location tracking. Despite the considerable volume of existing research, the greater portion of it has primarily concentrated on locomotion activities. Comparatively less emphasis has been placed on the recognition of human localization patterns. In the current study, we introduce a system by facilitating the recognition of both human physical and location-based patterns. This system utilizes the capabilities of smartphone sensors to achieve its objectives. Our goal is to develop a system that can accurately identify different human physical and localization activities, such as walking, running, jumping, indoor, and outdoor activities. To achieve this, we perform preprocessing on the raw sensor data using a Butterworth filter for inertial sensors and a Median Filter for Global Positioning System (GPS) and then applying Hamming windowing techniques to segment the filtered data. We then extract features from the raw inertial and GPS sensors and select relevant features using the variance threshold feature selection method. The extrasensory dataset exhibits an imbalanced number of samples for certain activities. To address this issue, the permutation-based data augmentation technique is employed. The augmented features are optimized using the Yeo–Johnson power transformation algorithm before being sent to a multi-layer perceptron for classification. We evaluate our system using the K-fold cross-validation technique. The datasets used in this study are the Extrasensory and Sussex Huawei Locomotion (SHL), which contain both physical and localization activities. Our experiments demonstrate that our system achieves high accuracy with 96% and 94% over Extrasensory and SHL in physical activities and 94% and 91% over Extrasensory and SHL in the location-based activities, outperforming previous state-of-the-art methods in recognizing both types of activities.

Analysis of the outage performance of energy-harvesting cooperative-NOMA system with relay selection methods

Recent years have witnessed the remarkable progress in wireless communication systems due to the escalating demand for higher data rates, improved reliability, and increased energy efficiency. In this regard, Non-Orthogonal Multiple Access (NOMA) has emerged as a promising technology, enhancing spectral efficiency and accommodating multiple users concurrently within the same time and frequency resources. Simultaneously, the energy harvesting has surfaced as a sustainable solution, converting ambient environmental energy into usable electrical power for operating communication nodes. This paper proposes a cooperative NOMA transmission scheme integrating energy harvesting and utilizing Least Squares (LS) channel estimation for precise Channel State Information (CSI) acquisition. The objective is to establish an optimal communication path from source to destination. Relay selection methods: Optimal Relay Selection (ORS) and Max-Min Relay Selection (MMRS), are compared, focusing on their impact on the system performance. The analysis considers the influence of the number of relays and power allocation factor on the system, with a specific emphasis on the outage probability expressions. Comparative analysis between the cooperative-NOMA and the traditional cooperative relaying without NOMA reveals the superior performance of the cooperative-NOMA. Additionally, the ORS scheme outperforms MMRS in terms of the outage performance.