Extraction Methodology Research Articles

An integrated method for rapid extraction of fluorescent whitening agents from selected cosmetics by in situ formation of deep eutectic solvent

A novel extraction methodology was developed based on deep eutectic solvent (DES) in situ formation. The solvent was synthesized from a binary mixture of L-cysteine, functioning as a hydrogen bond acceptor, and 2-methylphenol, serving as a hydrogen bond donor. The DES’s inherent molecular structures and extensive hydrogen bonding networks were characterized using the Fourier-transform infrared spectroscopy (FT-IR), while its hydrophobic nature was assessed by determining molecular polarity. Its extraction efficiency was demonstrated through the extraction of fluorescent whitening agents (FWAs) in cosmetic products, acting as a model analytical application. This was accomplished using vortex-assisted emulsification solid–liquid extraction (SLE) method, followed by high-performance liquid chromatography-fluorescence detection. Response surface methodology was employed to optimize extraction conditions in a multivariate experimental design. This approach streamlined the multi-operation extraction process via ingeniously integrating extraction and clean-up into a single step, resulting in significant reductions in extraction time required and high sample throughputs of approximately 40 samples per hour. The methodology was validated and exhibited excellent linearity in concentration ranges of 0.01–24 mg/L for five FWAs and 0.01–40 mg/L for other three FWAs, with correlation coefficients (r) ranging from 0.9985 to 1.000. The limits of detection and quantification were in the range of 0.03–0.26 mg kg−1 and 0.10–0.87 mg kg−1, respectively. The reliability and practicality of the proposed method were affirmed by quantifying eight FWAs in ninety-four commercially available facial masks and whitening creams, yielding recoveries values between 90.0 % and 106.0 % with relative standard deviations (RSDs) of 1.0–4.3 %. Inorganic ions and organic compounds present in the samples had minimal impact on FWAs detection following sample pre-treatment. This procedure showcased exceptional extraction capabilities towards a diverse array of FWAs with the aid of multiple interactions, suggesting significant potential for increasing sample throughput while reducing organic solvent and material consumptions compared with conventional SLE methods. The low-toxicity DES is an excellent alternative to traditional extractants typically applied to SLE techniques.

Ultrasound Image-Based Breast Cancer Detection

Breast cancer is the second most common cause of cancer-related death in women, the definition of breast cancer has drawn attention from the bioinformatics and healthcare communities. Only by removing a sample of breast tissue from the breast, examining, and analyzing it under a microscope would this type of research be possible. In the histopathology lab, issues are found by employing skilled pathologists to examine the specimens; these issues are then further investigated utilizing specialized procedures. However, because they have experience in this field, the ultrasonography may mistakenly reveal abnormal changes or diseases. Recent pattern recognition research has revealed several areas that could be improved, therefore there is now more of an emphasis on building strong image processing experiments to provide a highly-and enhancing current diagnosis. Let's employ deep learning approaches for the image feature and the image feature extraction methodology to detect the disease types of breast tissue using histology and image recognition techniques. Before using ultrasonic feature extraction and the final classification in ultrasound feature extraction, this image can be enlarged utilizing ultrasound processing and CNN approach. Index Terms— Machine learning algorithm, Ultra sound image, MRI.

Development, Validation, and Comparison of a Novel Nociception/Anti-Nociception Monitor against Two Commercial Monitors in General Anesthesia.

In this paper, we present the development and the validation of a novel index of nociception/anti-nociception (N/AN) based on skin impedance measurement in time and frequency domain with our prototype AnspecPro device. The primary objective of the study was to compare the Anspec-PRO device with two other commercial devices (Medasense, Medstorm). This comparison was designed to be conducted under the same conditions for the three devices. This was carried out during total intravenous anesthesia (TIVA) by investigating its outcomes related to noxious stimulus. In a carefully designed clinical protocol during general anesthesia from induction until emergence, we extract data for estimating individualized causal dynamic models between drug infusion and their monitored effect variables. Specifically, these are Propofol hypnotic drug to Bispectral index of hypnosis level and Remifentanil opioid drug to each of the three aforementioned devices. When compared, statistical analysis of the regions before and during the standardized stimulus shows consistent difference between regions for all devices and for all indices. These results suggest that the proposed methodology for data extraction and processing for AnspecPro delivers the same information as the two commercial devices.

Road Accessibility during Natural Hazards Based on Volunteered Geographic Information Data and Network Analysis

Natural hazards can present a significant risk to road infrastructure. This infrastructure is a fundamental component of the transportation infrastructure, with significant importance. During emergencies, society heavily relies on the functionality of the road infrastructure to facilitate evacuation and access to emergency facilities. This study introduces a versatile, multi-scale framework designed to analyze accessibility within road networks during natural hazard scenarios. The first module of the framework focuses on assessing the influence of natural hazards on road infrastructure to identify damaged or blocked road segments and intersections. It relies on near real-time information, often provided by citizen science through Volunteered Geographic Information (VGI) data and Natural Language Processing (NLP) of VGI texts. The second module conducts network analysis based on freely available Open Street Map (OSM) data, differentiating between intact and degraded road networks. Four accessibility measures are employed: betweenness centrality, closeness centrality, a free-flow assumption index, and a novel alternative routing assumption measure considering congestion scenarios. The study showcases its framework through an exemplary application in California, the United States, considering different hazard scenarios, where degraded roads and connected roads impacted by the hazard can be identified. The road extraction methodology allows the extraction of 75% to 100% of the impacted roads mentioned in VGI text messages for the respective case studies. In addition to the directly extracted impacted roads, constructing the degraded network also involves finding road segments that overlap with hazard impact zones, as these are at risk of being impacted. Conducting the network analysis with the four different measures on the intact and degraded network, changes in network accessibility due to the impacts of hazards can be identified. The results show that using each measure is justified, as each measure could demonstrate the accessibility change. However, their combination and comparison provide valuable insights. In conclusion, this study successfully addresses the challenges of developing a generic, complete framework from impact extraction to network analysis independently of the scale and characteristics of road network types.

Tuning the extraction methodology targeting protein-enriched fractions from red algae

Alternative protein sources are being studied to substitute animal protein production and decrease the environmental issues that are inherent to the production of animal-based foods.This study investigates the potential of red macroalgae (Gracilaria verrucosa, Porphyra dioica, and Palmaria palmata) as an eco-friendly alternative protein source. Using the Osborne fractionation methodology, proteins were extracted under varying parameters (pH and grinding) and techniques (ultrasound and enzymatic extractions). Seaweed proteins exhibited a strong affinity for water and alkaline environments. Optimal extraction yields were obtained with H2O for G. verrucosa (33.24 ± 1.81 g protein/100 g algae protein) and P. palmata (29.56 ± 1.73 gprotein/100 galgae protein), while P. dioica showed highest yields with aqueous 0.1 M NaOH (26.28 ± 5.90 gprotein/100 galgae protein). Molecular weight patterns revealed aggregates (> 200 kDa) in G. verrucosa and P. palmata in the 0.1 M NaOH fraction. H2O extracts displayed peaks (48–84 kDa) linked to phycobiliproteins, showcasing good emulsifying properties. Solubility varied with solvents, with saline and 70 % EtOH dissolving lower molecular weight proteins. Aqueous 0.1 M NaOH extracts demonstrated promising texturizing capacity.Overall, proteins from red macroalgae show potential for food product development, offering an alternative with lower environmental impact compared to conventional animal-based proteins.

A single neural network global I-V and C-V parameter extractor for BSIM-CMG compact model

A global I-V and C-V BSIM-CMG parameter extraction methodology based on deep learning is proposed. 100 k training datasets were generated through Monte Carlo simulation varying 28 IV and CV model parameters in the industry-standard BSIM-CMG FinFET model. For each of the 100 k Monte Carlo-selected BSIM-CMG parameter dataset, the ID-VG and CGG-VG characteristics of seven Monte Carlo-selected gate lengths ranging from 14 nm to 110 nm were generated as the input to train the parameter extraction neural network. The neural network outputs for training are the 28 model parameters’ values. The neural network's capability to extract BSIM-CMG model parameters that accurately fit TCAD-generated ID-VG and CGG-VG data over a range of gate lengths was demonstrated. This marks the first time a deep learning compact model parameter extraction flow, employing a single neural network for both I-V and C-V parameters and for a range of gate length, is presented.

Prediction of Certain examinations on AI procedures for Text rundown

Making text synopses from huge volume of unstructured text like client surveys, web log posts and online entertainment posts is a significant assignment in text mining applications. These synopses uncover the valuable data which depicts the whole report or surveys. Rundown errand could be performed utilizing two significant methodologies: Extractive and abstractive methodology. Extractive methodology distinguishes the huge piece of the report that uncovered the whole happy and removes it to shape synopsis. Abstractive approach makes synopsis in light of catchphrases and semantics from the archive, which makes it troublesome when contrasted with the other methodology. The extractive synopsis task is perplexing because of overt repetitiveness, enormous volume of text, changeability and semantics of regular language in the text. The wide pertinence as well as trying nature of the errand has enlivened dynamic research in the space by both scholar and industry specialists. This exploration centers around the plan of AI based frameworks for two center regions connected with text mining, specifically, Component based text synopsis and text comparability location. Existing element positioning and outline frameworks utilize an assortment of strategies including inactive semantic ordering; Innocent Bayes' and other semantics based approaches. Because of the intricacy of the undertaking there is a requirement for creating proficient frameworks. In this proposal, an element positioning framework in light of client inclinations have been created. Three different AI approaches have been embraced for highlight based miniature level extractive text synopsis development.

Small-Signal Modeling and Parameter Extraction Method for Photovoltaic Cell Integration in Indoor Visible Light Communication Systems

Photovoltaic (PV) cells are being adopted as a viable and cost-effective option for implementing receivers within Visible Light Communication (VLC) systems, primarily in indoor environments. Accurately estimating the generated current and voltage of the PV cell based on incident light is crucial when designing VLC systems.For this assessment, the 1D2R electrical equivalent model, which incorporates a diode and two resistors, is employed.In AC small signal analysis, the diode is substituted by its dynamic counterpart, which comprises a dynamic resistance in parallel with an equivalent capacitance. This study introduces an approach to measure and characterize the small-signal parameters of a PV cell operating at the maximum power point (MPP), open circuit (OC), and short circuit (SC) bias points. This is achieved through a closed-loop frequency response system, calibrated to encompass illuminance levels ranging from 50 to 500 lux.The procedure for estimating the AC response of the PV cell is outlined, and the outcomes are subsequently employed in an analytical parameter extraction methodology. Experimental results from a 20 x 40 mm PV cell reveal that MPP represents the least favorable bias point in terms of bandwidth, whereas the SC bias point exhibits the most favorable performance. This observation validates the hypothesis that the optimal bias point for energy harvesting in PV cells is the worst bias point for communication purposes.

Methodology for parameters extraction with undoped junctionless EZ-FETs

The junctionless EZ-FET is a simple FDSOI-like device that requires only two lithography levels and standard processing steps. With its simplified architecture and fabrication flow, and using undoped source and drain terminals, the device allows for a fast electrical evaluation of semiconductor films on insulators (SOI) and gate stacks. This paper describes an electrical model that reproduces the peculiar transfer characteristics of a junctionless EZ-FET. The model is then simplified to develop a pragmatic parameter extraction methodology. This methodology is experimentally validated and provides the electrical properties of SOI films (mobility, threshold voltage) for both electrons and holes.

A Comprehensive Feature Engineering Approach for Breast Cancer Dataset

Breast cancer continues to pose a significant challenge in the field of healthcare, serving as the primary cause of cancer-related deaths in women on a global scale. The present study aims to investigate the intricate relationship between breast cancer, statistical analysis, and feature engineering. By conducting an extensive analysis of a comprehensive dataset and employing sophisticated statistical methodologies, this research endeavor aims to unveil concealed insights that can enrich the medical community's existing knowledge base. Through the implementation of rigorous feature selection and extraction methodologies, the overarching aim is to augment the comprehension of breast cancer. Moreover, the study showcases the successful incorporation of univariate and bivariate analysis in order to enhance the accuracy of diagnostic procedures. The convergence of these disciplines exhibits considerable promise in the realm of breast cancer detection and prediction, facilitating cooperative endeavours aimed at addressing this widespread malignancy.

Tips and tools to obtain and assess mosquito viromes.

Due to their vectorial capacity, mosquitoes (Diptera: Culicidae) receive special attention from health authorities and entomologists. These cosmopolitan insects are responsible for the transmission of many viral diseases, such as dengue and yellow fever, causing huge impacts on human health and justifying the intensification of research focused on mosquito-borne diseases. In this context, the study of the virome of mosquitoes can contribute to anticipate the emergence and/or the reemergence of infectious diseases. The assessment of mosquito viromes also contributes to the surveillance of a wide variety of viruses found in these insects, allowing the early detection of pathogens with public health importance. However, the study of mosquito viromes can be challenging due to the number and complexities of steps involved in this type of research. Therefore, this article aims to describe, in a straightforward and simplified way, the steps necessary for obtention and assessment of mosquito viromes. In brief, this article explores: the capture and preservation of specimens; sampling strategies; treatment of samples before DNA/RNA extraction; extraction methodologies; enrichment and purification processes; sequencing choices; and bioinformatics analysis.

Relative egg extraction efficiencies of manual and automated fecal egg count methods in equines.

The World Association for the Advancement of Veterinary Parasitology recently released new recommendations for the design of fecal egg count (FEC) reduction tests for livestock. These provide suggestions as to the number of animals to be sampled and the minimum number of eggs that must be counted to produce statistically meaningful results. One of the considerations for study design is the multiplication factor of the FEC method to be used; methods with lower multiplication factors require fewer animals to be sampled because they are presumed to count more eggs per test. However, multiplication factor is not the sole determinant of the number of eggs counted by any given method, since different techniques use very different sample extraction methodologies that could affect the number of eggs detected beyond just the amount of feces examined. In this light, we compared three commonly used manual FEC methods (mini-FLOTAC, McMaster and Wisconsin) and two automated methods (Imagyst and Parasight All-in-One) with respect to how many equine strongylid and ascarid eggs they counted in the same samples. McMaster and mini-FLOTAC (multiplication factors of 25x and 5x, respectively) produced the most accurate results of the methods tested but mini-FLOTAC counted approximately 5-times more eggs than McMaster. However, Wisconsin and Parasight (multiplication factor = 1x) counted 3-times more ova than mini-FLOTAC, which was less than the 5-fold difference in their multiplication factors. As a result, these tests perform with multiplication factors more akin to 1.6x relative to mini-FLOTAC. Imagyst, due to its unique sample preparation methodology, does not have a traditional multiplication factor but performed similarly to McMaster with respect to egg recovery.

A Review on the Classification of Partial Discharges in Medium-Voltage Cables: Detection, Feature Extraction, Artificial Intelligence-Based Classification, and Optimization Techniques

Medium-voltage (MV) cables often experience a shortened lifespan attributed to insulation breakdown resulting from accelerated aging and anomalous operational and environmental stresses. While partial discharge (PD) measurements serve as valuable tools for assessing the insulation state, complexity arises from the presence of diverse discharge sources, making the evaluation of PD data challenging. The reliability of diagnostics for MV cables hinges on the precise interpretation of PD activity. To streamline the repair and maintenance of cables, it becomes crucial to discern and categorize PD types accurately. This paper presents a comprehensive review encompassing the realms of detection, feature extraction, artificial intelligence, and optimization techniques employed in the classification of PD signals/sources. Its exploration encompasses a variety of sensors utilized for PD detection, data processing methodologies for efficient feature extraction, optimization techniques dedicated to selecting optimal features, and artificial intelligence-based approaches for the classification of PD sources. This synthesized review not only serves as a valuable reference for researchers engaged in the application of methods for PD signal classification but also sheds light on potential avenues for future developments of techniques within the context of MV cables.

Hydrolyzable polyphenols contained in extracts from Mentha piperita L. and antioxidant capacity

Mentha piperita L., is one of the most popular and consumed species worldwide because of its secondary metabolites with antioxidant properties, among these metabolites are polyphenolic molecules. These molecules can be extracted using different methodologies. However, the range of bioactive compounds in an extract can vary because of different factors, including the extraction methodology. Recently, use of hybrid technologies for extraction of plant compounds has been increased since a empowerment of various properties in the obtained extracts has been demonstrated. In this study was performed an analysis of the polyphenolic compounds and antioxidants from Mentha piperita L. Polyphenolic compounds were extracted using different extraction techniques which are reported as efficient and friendly to the environment, like ultrasound and microwave, which were compared with the hybrid technology (ultrasound-microwave). Results showed a significant increase on the obtaining of hydrolyzable polyphenolic compounds and antioxidant capacity, highlighting that using extraction by a hybrid technique (ultrasound-microwave) showed a greater % yield of extraction compared to ultrasound or microwave used separately.

Exploring the Performance of PySpark and Scikit-Learn Libraries in Developing Fall Detection Systems

Falls pose a significant risk for the elderly population, often resulting in serious injuries and decreased quality of life. Accurate and effective fall detection systems can play an important role in reducing these risks. This study conducts a comparative analysis of the performance of PySpark and Scikit-Learn libraries in the development of fall detection models. PySpark offers a distributed computing environment specifically designed for big data processing, while Scikit-Learn is a Python-based library widely used for data analysis and machine learning. Five popular machine learning algorithms, including logistic regression, gradient boosting classifier, random forest, support vector machine, and Decision Tree, were used to build fall detection models using both PySpark and Scikit-Learn. Models were evaluated using a comprehensive set of measurements including accuracy, sensitivity, specificity, and confusion matrix. In addition to the five popular machine learning algorithms included in the study, 26 different features were extracted from the Sisfall dataset, one of the most comprehensive fall and daily life activity datasets, in five main categories: basic statistical features, frequency domain features, time series features, motion features, and relational features. These features were incorporated into fall detection models to improve their ability to accurately identify falls. The findings reveal that both PySpark and Scikit-Learn provide strong and effective results in fall detection. Logistic regression emerged as the best performing algorithm in both libraries, achieving the highest accuracy rates. However, PySpark exhibited slightly longer training times compared to Scikit-Learn, which performed better in testing. Ultimately, this study will benefit researchers and practitioners involved in the development of robust and effective fall detection systems, thereby enabling improved safety and well-being for the elderly population. Additionally, it contributes to the literature by presenting a comprehensive feature extraction methodology for fall detection, which has not been explored extensively in previous research works.

Assessing Satellite-Derived Shoreline Detection on a Mesotidal Dissipative Beach

The accuracy and robustness of the shoreline definition from satellite imagery on different coastal types are crucial to adequately characterising beach morphology and dynamics. However, the generic and widespread application of satellite-derived shoreline algorithms is limited by the lack of robust methods and parameter assessments. This work constitutes a quantitative and comprehensive assessment of the satellite-derived waterlines from Sentinel-2 by using the novel SAET tool (Shoreline Analysis and Extraction Tool) on the exposed and mesotidal beach of La Victoria (Cádiz, SW Spain). The diverse parameters available in SAET, such as water indexes, thresholding methods, morphological filters, and kernel sizes, were combined to define water/land interface positions that were compared against coincident video-derived waterlines. Satellite-derived waterline errors are found to be affected by extraction parameters, as well as by the oceanographic and morphological conditions at the time of the image acquisition. The application of a morphological erosion filter on the water mask, which tends to shift the extracted waterline seawards and reduce bias, is the best solution at the dissipative site of La Victoria Beach. Moreover, using a 3 × 3 kernel size consistently shows higher accuracies than a larger kernel. Although there was no parameter combination showing the best skill for all dates, the employment of the Automated Water Extraction Index for images with no shadows (AWEInsh) with a threshold = 0, erosion morphological filter, and 3 × 3 kernel was, overall, the best combination of extraction parameters for this beach (average waterline RMSE of 5.96 m). The combination of the Modified Normalised Difference Water Index (MDNWI) with the Otsu thresholding also led to similar positions of the resulting waterlines and offered good accuracies. In line with other recent research efforts, our work stresses the lack of generic shoreline extraction solutions that can be applied automatically at a global level and the necessity to adapt and validate the extraction methodologies to the different types of coastlines.

Antioxidant and Anticancer Activity of Vitis vinifera Extracts in Breast Cell Lines.

Vitis vinifera extracts have been shown to possess antioxidant activity because of their polyphenol content. In addition, their therapeutic potential against several diseases, including cancer, has been reported. In this study, we produced twelve extracts from the seeds, fruit, leaves, and wood of the Vitis vinifera Airen variety using different extraction methodologies and measured their total polyphenol content (TPC). We also determined their antioxidant and antiproliferative effects against normal cells and evaluated the most potent extract against a panel of breast cancer cell lines. We found that the extracts produced by the seeds of Vitis vinifera had a higher TPC compared to the other parts of the plant. Most extracts produced from seeds had antioxidant activity and did not show cytotoxicity against normal breast cells. The extract produced from whole organic seeds of white grape showed the best correlation between the dose and the ROS inhibition at all time points compared to the other seed extracts and also had antiproliferative properties in estrogen-receptor-positive MCF-7 breast cancer cells. Its mechanism of action involves inhibition of proteins Bcl-2, Bcl-xL, and survivin, and induction of apoptosis. Further investigation of the constituents and activity of Vitis vinifera extracts may reveal potential pharmacological applications of this plant.

Hesperidin from Orange Peel as a Promising Skincare Bioactive: An Overview.

The pursuit for better skin health, driven by collective and individual perceptions, has led to the demand for sustainable skincare products. Environmental factors and lifestyle choices can accelerate skin aging, causing issues like inflammation, wrinkles, elasticity loss, hyperpigmentation, and dryness. The skincare industry is innovating to meet consumers' requests for cleaner and natural options. Simultaneously, environmental issues concerning waste generation have been leading to sustainable strategies based on the circular economy. A noteworthy solution consists of citrus by-product valorization, as such by-products can be used as a source of bioactive molecules. Citrus processing, particularly, generates substantial waste amounts (around 50% of the whole fruit), causing unprecedented environmental burdens. Hesperidin, a flavonoid abundant in orange peels, is considered to hold immense potential for clean skin health product applications due to its antioxidant, anti-inflammatory, and anticarcinogenic properties. This review explores hesperidin extraction and purification methodologies as well as key skincare application areas: (i) antiaging and skin barrier enhancement, (ii) UV radiation-induced damage, (iii) hyperpigmentation and depigmentation conditions, (iv) wound healing, and (v) skin cancer and other cutaneous diseases. This work's novelty lies in the comprehensive coverage of hesperidin's promising skincare applications while also demonstrating its potential as a sustainable ingredient from a circular economy approach.

Multi-Scale Attention Network for Building Extraction from High-Resolution Remote Sensing Images

The precise building extraction from high-resolution remote sensing images holds significant application for urban planning, resource management, and environmental conservation. In recent years, deep neural networks (DNNs) have garnered substantial attention for their adeptness in learning and extracting features, becoming integral to building extraction methodologies and yielding noteworthy performance outcomes. Nonetheless, prevailing DNN-based models for building extraction often overlook spatial information during the feature extraction phase. Additionally, many existing models employ a simplistic and direct approach in the feature fusion stage, potentially leading to spurious target detection and the amplification of internal noise. To address these concerns, we present a multi-scale attention network (MSANet) tailored for building extraction from high-resolution remote sensing images. In our approach, we initially extracted multi-scale building feature information, leveraging the multi-scale channel attention mechanism and multi-scale spatial attention mechanism. Subsequently, we employed adaptive hierarchical weighting processes on the extracted building features. Concurrently, we introduced a gating mechanism to facilitate the effective fusion of multi-scale features. The efficacy of the proposed MSANet was evaluated using the WHU aerial image dataset and the WHU satellite image dataset. The experimental results demonstrate compelling performance metrics, with the F1 scores registering at 93.76% and 77.64% on the WHU aerial imagery dataset and WHU satellite dataset II, respectively. Furthermore, the intersection over union (IoU) values stood at 88.25% and 63.46%, surpassing benchmarks set by DeepLabV3 and GSMC.

IProm-Yeast: Prediction Tool for Yeast Promoters Based on ML Stacking

Background and Objective: Gene promoters play a crucial role in regulating gene transcription by serving as DNA regulatory elements near transcription start sites. Despite numerous approaches, including alignment signal and content-based methods for promoter prediction, accurately identifying promoters remains challenging due to the lack of explicit features in their sequences. Consequently, many machine learning and deep learning models for promoter identification have been presented, but the performance of these tools is not precise. Most recent investigations have concentrated on identifying sigma or plant promoters. While the accurate identification of Saccharomyces cerevisiae promoters remains an underexplored area. In this study, we introduced “iPromyeast”, a method for identifying yeast promoters. Using genome sequences from the eukaryotic yeast Saccharomyces cerevisiae, we investigate vector encoding and promoter classification. Additionally, we developed a more difficult negative set by employing promoter sequences rather than nonpromoter regions of the genome. The newly developed negative reconstruction approach improves classification and minimizes the amount of false positive predictions. Methods: To overcome the problems associated with promoter prediction, we investigate alternate vector encoding and feature extraction methodologies. Following that, these strategies are coupled with several machine learning algorithms and a 1-D convolutional neural network model. Our results show that the pseudo-dinucleotide composition is preferable for feature encoding and that the machine- learning stacking approach is excellent for accurate promoter categorization. Furthermore, we provide a negative reconstruction method that uses promoter sequences rather than non-promoter regions, resulting in higher classification performance and fewer false positive predictions. Results: Based on the results of 5-fold cross-validation, the proposed predictor, iProm-Yeast, has a good potential for detecting Saccharomyces cerevisiae promoters. The accuracy (Acc) was 86.27%, the sensitivity (Sn) was 82.29%, the specificity (Sp) was 89.47%, the Matthews correlation coefficient (MCC) was 0.72, and the area under the receiver operating characteristic curve (AUROC) was 0.98. We also performed a cross-species analysis to determine the generalizability of iProm-Yeast across other species. Conclusion: iProm-Yeast is a robust method for accurately identifying Saccharomyces cerevisiae promoters. With advanced vector encoding techniques and a negative reconstruction approach, it achieves improved classification accuracy and reduces false positive predictions. In addition, it offers researchers a reliable and precise webserver to study gene regulation in diverse organisms.