Advanced Filter Research Articles

Gaussian Denoising for the First Image from The James Webb Space Telescope “Carina Nebula” using Non-Linear Filters

Noise, including Gaussian noise, distorts images during transition or acquisition process, reducing required information. Removing or reducing this noise is crucial in image processing. The James Webb Space Telescope (JWST) is a vital tool for enhancing our understanding of the universe, providing valuable scientific data and inspiring global interest. In this paper we introduce several nonlinear (Non-Local Mean, Bilateral, and classical) filters to remove the Gaussian noise from the Carina Nebula Image, the first image taken by (JWST) on 12 July 2022. These nonlinear filters were therefore selected to highlight the significance of selecting the right technique that can handle, process, and preserve as many details as possible. They also serve to elucidate the degree of advancement achieved in the field of denoising and the distinction between the classical filters and the more sophisticated filters that have evolved to handle finer details. Classic filters deal with the pixels themselves and their neighbors and then perform the desired statistical process. While advanced filters consider the similarities and distances between the central pixel and its neighbors, they preserve the edges of the image as advanced features. Based on quality measurements (PSNR) and (SSIM), the filter results were compared. The results show that the Bilateral filter gives high performance in restoring images under different Gaussian noise densities compared with the other denoising filters where it gives values of (30.65) and (0.93) for (PSNR) and (SSIM) respectively Which is higher than the results of the filters. Paper type :Research paper.

Planning for complex inferior vena cava filter retrievals: the implementation and effectiveness of 3D printed models

BackgroundInferior vena cava filter (IVC) retrieval is most often routine but can be challenging with high morbidity in complex cases, especially those with an extended dwelling time. While risk of morbidity in complex retrievals is decreased with advanced filter retrieval techniques, deciding when and which to use these requires detailed pre-procedural planning. The purpose of our study was to evaluate patient-specific 3D printed anatomic IVC filter models for aiding complex IVC filter retrievals.MethodsAll IVC filter retrieval patients between June 2021 and September 2022 at one academic medical hospital were prospectively screened. Nine met criteria for complex retrieval, and their CT images were used to 3D print patient-specific IVC and filter models. Models were used in pre-procedural planning and clinical utility was assessed using the Anatomic Model Utility Likert Questionnaire and estimations of the procedural and fluoroscopy time saved.ResultsThe usage of 3D printed models in pre-procedural planning had high clinical utility based on the Likert questionnaire (Anatomic Model Utility Points 366.7 ± 103.1). Using a model significantly increased confidence in planning (p = 0.03) and modified the treatment plan in seven cases. It also led to cost-efficient use of resources in the procedure suite with estimated reduction in procedure and fluoroscopy time of 29.0 [20.3] (p = 0.003) and 10.2 [6.7] (p = 0.002) minutes, respectively.Conclusion3D printed anatomic models for patients who require complex IVC filter retrieval demonstrated Likert-based high clinical utility and led to estimated reductions of procedural and fluoroscopy time.

A novel technique to detect and mitigate harmonic during islanding in grid connected PV system

The integration of photovoltaic (PV) systems into the power grid has become increasingly prevalent, providing sustainable and renewable energy solutions. However, the occurrence of islanding events, where a portion of the PV system operates independently from the main grid, presents challenges in maintaining power quality. This research paper investigates the harmonic distortions during islanding in grid-connected PV systems and proposes effective mitigation strategies. The study employs advanced simulation tools to analyse the dynamic behavior of the PV system during islanding scenarios, focusing on the occurrence and characteristics of harmonics. A detailed investigation into the root causes of harmonic distortions is conducted, considering factors such as grid fluctuations, inverter operation, and system impedance. Based on the findings, this paper introduces novel harmonic mitigation techniques tailored for islanding conditions. These techniques encompass advanced control algorithms, filter designs, and modulation strategies to suppress harmonics and enhance the overall system performance during islanded operation. Comparative assessments of different mitigation approaches are presented, evaluating their effectiveness in minimizing harmonic distortions and maintaining grid code compliance. The research contributes valuable insights to the field of grid-connected PV systems, offering a comprehensive understanding of harmonic issues during islanding events and proposing innovative solutions for their mitigation. The outcomes of this study are crucial for advancing the reliability, stability, and power quality of PV systems that are connected to the grid, which makes it possible for green energy sources to be added to the grid without any problems.

Advanced Filtering and Enhancement Techniques for Diabetic Retinopathy Image Analysis

Diabetic retinopathy is a leading cause of visual impairment and blindness in diabetes sufferers. Early detection is crucial to prevent severe outcomes. This study presents an image processing method for retinal images to aid early detection. The method involves four steps: image enlargement, preprocessing, enhancement, and convolution. First, an algorithm enlarges the retinal image to increase resolution and reveal finer details. Preprocessing uses a min-max filtering algorithm to reduce noise and improve image quality. Next, specific pixel range enhancement techniques further refine the image and highlight relevant features. Finally, convolution with customized kernels detects and emphasizes areas indicating diabetic retinopathy, such as aneurysms and hemorrhages. Experimental results show improvement in image clarity and detail, enabling more accurate detection of diabetic retinopathy features. The correlation results are as follows: Filtering (0.35275, 0.20157, 0.4345), Enhancement (0.3214, 0.15823 0.34674), and Convolution (0.33542, 0.15758, 0.36826). The proposed algorithm enhances early detection and diagnosis by improving retinal image quality. Future work can optimize the algorithm and validate results with larger datasets, aiming to refine the determination of areas or pixel values relevant to diabetic retinopathy.

Synthesis of blue-sparkling N, S-doped carbon dots for effective detection of nitro explosive and Fe3+ ion and anti-counterfeiting studies

Highly blue-luminescent nitrogen & sulfur-doped carbon dots (N, S-CDs) made with homocysteine and ethylene diamine by simple single-step microwave-assisted style. The prepared carbon dots showed a higher photoluminescent quantum yield (PL-QY), which is found to be 45 %. FTIR and XPS analysis reveals that these N, S-CDs contain rich nitrogen, and sulfur as well as C=O and C=C functional groups. N, S-CDs had a mean size around 3-4 nm, very interestingly, obtained carbon dots exhibit excitation-dependent photoluminescent character, are water-soluble, and restore luminescent stability under UV-light exposure and a wide range of pH. The crafted N, S-CDs are hired for precise and discriminating detection of multiple water impurities such as Fe3+ ion and Picric acid. The complexation between oxygen species of N, S doped CDs, and Fe3+ ion follows the inner-filteration effect (IFE) and with Picric acid follows Forster Resonance Energy Transfer (FRET) for the doped N, S-CDs are found to be the utmost possible mechanism for the observed sensing performance. The lower detection limit (LOD) of Fe3+ sensors is about 0.11 µM and for picric acid is 0.07 µM. Moreover, these blue-emissive N, S-CDs as competent fluorescence sensors, have been used as fluorescent inks, as well as in advanced portable devices information security and filter paper-based probes for visual recognition might also hold prominent ability to make wider applications in biological systems.

Adaptive Aberrance Repressed Correlation Filters with Cooperative Optimization in High-Dimensional Unmanned Aerial Vehicle Task Allocation and Trajectory Planning

In the rapidly evolving field of unmanned aerial vehicle (UAV) applications, the complexity of task planning and trajectory optimization, particularly in high-dimensional operational environments, is increasingly challenging. This study addresses these challenges by developing the Adaptive Distortion Suppression Correlation Filter Cooperative Optimization (ARCF-ICO) algorithm, designed for high-dimensional UAV task allocation and trajectory planning. The ARCF-ICO algorithm combines advanced correlation filter technologies with multi-objective optimization techniques, enhancing the precision of trajectory planning and efficiency of task allocation. By incorporating weather conditions and other environmental factors, the algorithm ensures robust performance at low altitudes. The ARCF-ICO algorithm improves UAV tracking stability and accuracy by suppressing distortions, facilitating optimal path selection and task execution. Experimental validation using the UAV123@10fps and OTB-100 datasets demonstrates that the ARCF-ICO algorithm outperforms existing methods in Area Under the Curve (AUC) and Precision metrics. Additionally, the algorithm’s consideration of battery consumption and endurance further validates its applicability to current UAV technologies. This research advances UAV mission planning and sets new standards for UAV deployment in both civilian and military applications, where adaptability and accuracy are critical.

Numerical simulation of an in-fiber broadband plasmonic polarization filter using photonic crystal fiber with dual elliptical gold layers

The demand for advanced and compact photonic filters is increasing in today's optical communication systems. This work introduces a new all-fiber broadband polarization filter using photonic crystal fiber with dual elliptical gold layers. The filtering performance is evaluated and analyzed by the finite element method. The simulation results demonstrate that the two elliptical gold layers can effectively induce a significant change in the energy of x-polarized light, leading to a great difference between the two polarized lights. By selecting reasonable structural parameters of the proposed PCF, the 1 mm-long in-fiber filter has a maximum extinction ratio of -108.90 dB, a low insertion loss of 0.64 dB around 1.31 µm and an ultra-broad bandwidth of 1020 nm, covering two common communication windows of 1.31 µm and 1.55 µm. Additionally, the filtering experimental scheme is discussed. This filter is expected to drive the advancement of optical integrated chips and all-optical communication networks.

Converting From Laparoscopic Cholecystectomy to Open Cholecystectomy: A Systematic Review of Its Advantages and Reasoning.

Cholecystectomy is the standard treatment for symptomatic cholelithiasis and asymptomatic impending biliary obstruction, which is typically carried out laparoscopically. However, difficult gallbladders, due to distorted anatomy or increased risk of bleeding, can necessitate conversion to open surgery. This systematic review evaluates the advantages, disadvantages, complications, and outcomes of laparoscopic versus converted open cholecystectomy. We screened articles published from 2011 to 2024 by utilizing advanced filters of PubMed, Cochrane, and Scholar databases. Exclusion criteria included non-English language articles, duplicates, and animal studies. After analyzing relevant articles, 31 articles were included in this study. The total number of participants who underwent laparoscopic procedures was 28,054, of which 5,847 were converted from laparoscopic to open procedures. Conversions were primarily due to bleeding, adhesions, and obscured anatomy, with bile leakage being the most common short-term complication. Converted cases showed higher rates of long-term complications, increased hospital stays, and higher morbidity and mortality. Laparoscopic cholecystectomy remains safe and effective, but identifying high-risk patients for conversion is important. Preoperative identification of high-risk patients and recognizing predictive factors for conversion can enhance surgical outcomes and cost-effectiveness. While laparoscopic cholecystectomy is generally preferred, timely conversion to open surgery is essential for patient safety.

Design of a logistics warehouse robot positioning and recognition model based on improved EKF and calibration algorithm

Automatic guided vehicles for logistics warehousing are a key link in the construction of intelligent logistics. To improve the positioning accuracy of warehouse robots, we designed an advanced extended Kalman filter method integrating multiple synchronous positioning techniques and map construction methods, and completed the calibration and detection of pallets based on color image information. The results revealed that the proposed multi-innovation enhanced model achieved minimum relative rotation and absolute trajectory errors of 0.13 and 0.09, outperforming existing models. It showcased excellent mapping fidelity and integrity (above 0.9) across various datasets, with a high loop detection success rate (0.91) enhancing map precision. The tray fusion detection algorithm's AUC (area under the curve) reached 0.92, reflecting a balanced accuracy-recall tradeoff. This research offers robust positioning and mapping capabilities in logistics warehousing environments, effectively identifying errors and ensuring pallet accuracy. The detection error and accuracy of this method are better than the other three models, with the lowest average absolute error of 0.32 and the lowest root-mean-square error of 0.27, and the overall error in the detection of pallets is small. The findings provide strong theoretical backing and technical support for advancing intelligent logistics warehousing technology. Precise positioning and identification capabilities enable logistics and warehousing robots to accurately and quickly complete tasks such as access, handling and sorting of goods, greatly improving the efficiency of warehousing operations, promoting the digital transformation and intelligent development of the logistics and warehousing industry, and improving the competitiveness of the industry and the level of service.

ENHANCING TECHNICIAN & PLUMBER HIRING STRATEGIES WITH PYTHON: A TRENDSETTING COMPUTER SCIENCE ANALYSIS

Abstract: The Online Platform for Technician and Plumber Hiring is a digital marketplace designed to streamline the process of connecting individuals and businesses with skilled technicians and plumbers for maintenance and repair services. The platform offers an intuitive interface accessible via web browsers and mobile applications, allowing users to search, compare, and hire technicians and plumbers based on their specific needs and preferences. Service providers create detailed profiles showcasing their qualifications, experience, areas of expertise, and customer reviews, allowing service seekers to evaluate the suitability of technicians and plumbers for their specific requirements. The platform also offers advanced search and filter functionality, allowing users to narrow down their options based on location, services offered, availability, pricing, and ratings. It facilitates seamless booking and scheduling of service appointments, allowing users to specify the date, time, and nature of the required services. Clear pricing and payment information are provided for each service, ensuring transparency and eliminating ambiguity for both service seekers and providers. After service completion, users can rate and provide feedback on the quality of work and customer service received from technicians and plumbers, maintaining accountability and encouraging continuous improvement within the service provider community. The platform also offers customer support services to address inquiries, resolve issues, and facilitate communication between parties if disputes arise during or after service delivery. Mediation mechanisms are in place to ensure fair resolution of conflicts. IndexTerms – HTML, CSS, AngularJs , NodeJs , Bootstrap, MySQL

Inferior vena cava filter thromboprophylaxis in surgical cancer patients.

This study evaluated the utilization and outcomes of inferior vena cava (IVC) filters as thromboprophylaxis in cancer patients undergoing surgery. This single-center retrospective study analyzed baseline patient characteristics and clinical outcomes of surgical cancer patients who received perioperative prophylactic IVC filters. Primary clinical endpoints included venous thromboembolism (VTE) incidence and filter complications. A statistical correlative analysis was conducted to identify risk factors related to pulmonary embolism (PE), deep vein thrombosis (DVT), and filter thrombi, as well as advanced technique filter removal and mortality at 6 months. A total of 252 surgical oncology patients (median age, 59; female 51%) received IVC filters for the perioperative prevention of PE. Primary surgical sites included spine (n = 91, 36%), orthopedic extremity/joint (n = 49, 19%), genitourinary (n = 47, 19%), brain/cranial (n = 40, 16%), abdominal (n = 18, 7%), multisite (n = 4, 2%), and chest (n = 3, 1%). Moreover, 15% of patients experienced DVTs in the postplacement preretrieval period, while 2% (n = 6) of patients experienced definitive PEs. A total of 36% of IVC filters were ultimately retrieved, with an average filter dwell time of 7.4 months. Complications occurred in one retrieval. Prophylactic perioperative IVC filters in surgical cancer patients resulted in minimal complications while ultimately resulting in a low incidence of PE.

ENHANCING TECHNICIAN & PLUMBER HIRING STRATEGIES WITH PYTHON: A TRENDSETTING COMPUTER SCIENCE ANALYSIS

Abstract: The Online Platform for Technician and Plumber Hiring is a digital marketplace designed to streamline the process of connecting individuals and businesses with skilled technicians and plumbers for maintenance and repair services. The platform offers an intuitive interface accessible via web browsers and mobile applications, allowing users to search, compare, and hire technicians and plumbers based on their specific needs and preferences. Service providers create detailed profiles showcasing their qualifications, experience, areas of expertise, and customer reviews, allowing service seekers to evaluate the suitability of technicians and plumbers for their specific requirements. The platform also offers advanced search and filter functionality, allowing users to narrow down their options based on location, services offered, availability, pricing, and ratings. It facilitates seamless booking and scheduling of service appointments, allowing users to specify the date, time, and nature of the required services. Clear pricing and payment information are provided for each service, ensuring transparency and eliminating ambiguity for both service seekers and providers. After service completion, users can rate and provide feedback on the quality of work and customer service received from technicians and plumbers, maintaining accountability and encouraging continuous improvement within the service provider community. The platform also offers customer support services to address inquiries, resolve issues, and facilitate communication between parties if disputes arise during or after service delivery. Mediation mechanisms are in place to ensure fair resolution of conflicts. IndexTerms – HTML, CSS, AngularJs , NodeJs , Bootstrap, MySQL

Hybrid structured silk-rPET nanotechnical cloth for advanced air purification

AbstractAir pollution has become a significant global issue due to its detrimental environmental and human health effects. In this study, a novel approach was taken to address these challenges by developing a recycled polyethylene terephthalate (rPET) nano-coated silk technical cloth embedded with green-synthesized silver nanoparticles (AgNPs) using a solution electrospinning technique. The filtration performance of the developed material was assessed through particle filtration efficiency (PFE) tests, while differential pressure (DP) tests were conducted to evaluate pressure drop. SEM, FTIR, tensile, antibacterial, radiative heat barrier performance, and moisture management properties of the developed samples were also performed. Maximum 96.58% of filtration performance was observed with corresponding low differential pressures of 29.1 Pa/cm2; maximum tensile force and elongation% were 157.47 N and 15.32%, respectively of the developed samples. FTIR analysis confirmed the presence of silk, rPET, sodium alginate, and AgNPs in the developed sample. Antibacterial assays demonstrated inhibition against Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli). Moisture management property revealed water penetration resistance and radiative heat barrier testing showed good barrier performance. These results make the promising potential of the developed material as an advanced air filter. Graphical Abstract

Increasing the Efficiency of Mobile Robot Navigation Using Slam with Several Advanced Algorithms and Filters

Increasing the Efficiency of Mobile Robot Navigation Using Slam with Several Advanced Algorithms and Filters

Critical Analysis of Active Filter for Harmonic Reduction under Varying Load

In this paper, we give the main focus on the proliferation of non-linear loads in power systems, the mitigation of harmonics has become a critical concern to ensure stable and efficient operation. This study presents an analysis of an advanced control active filter (ACAF) designed for harmonic reduction under varying load conditions. The ACAF is a sophisticated solution that employs advanced control algorithms to effectively suppress harmonics and maintain power quality. The research investigates the performance of the ACAF under varying load conditions, which represent real-world scenarios where power consumption fluctuates. A comprehensive analysis is conducted to evaluate the filter's ability to adapt and maintain harmonic suppression efficiency across different load profiles. The study considers various load scenarios, including steady-state operation, transient load changes, and dynamic load fluctuations. The findings of this research contribute to the understanding of advanced control active filters in practical power systems with non-linear loads and varying operating conditions. The analysis provides valuable insights into the design, optimization, and implementation of ACAFs for harmonic mitigation, ultimately improving power quality and system reliability in modern electrical networks. A classified list of 38 publications on this topic is also given for the quick reference.

Solar Powered Air Purification and Cooling

This paper presents a pioneering approach to address indoor air quality challenges in urban residential spaces through the integration of solar power and advanced technology. The developed system leverages dual-axis solar tracker technology for efficient energy harvesting and storage, ensuring continuous operation. Automation driven by energy-efficient algorithms orchestrates indoor air filtration using sensors and advanced filters to remove dust and toxic gases effectively. A key innovation is the integration of renewable energy with air purification, contributing to sustainable urban practices and healthier living environments. Central to this system is an intelligent air quality monitoring framework, based on the Arduino Uno microcontroller, facilitating real-time assessment of indoor air quality. Rigorous testing and residential experiments validated the system's capability in accurately monitoring air quality indicators. The findings highlight the potential impact of this system in mitigating the transmission of airborne viruses and pollutants, thereby enhancing indoor air quality significantly. This project represents a crucial step towards creating sustainable and healthier urban living environments. Key Words: indoor air quality, solar power, air purification, intelligent monitoring, urban sustainability.

WAYS TO REDUCE THE IMPACT OF PULSATIONS IN LOCAL POWER SUPPLY ROBOTIC SYSTEMS

An analysis of the changes in the flow of pulsation transforming the energy-dynamic indicators of on-board power electronics systems has shown that there are several options for solving this problem.
 The article outlines the main options for the current problem of pulsation in the energy systems of autonomous electrical systems, such as UAVs, robotic complexes, and other systems. The effectiveness of changes in pulsation is examined, in addition to directly assessing the effectiveness of stagnation strategies and methods. Also in the statistics, the optimal technical solutions for specific operating minds were identified.
 The prospects and future directives have been predicted based on the proposals for further research in the sphere of changes in pulsation in local life systems.
 In the future, it is predicted and supported by the development of technology and innovation in this country.
 To sum up, we can say that the statistics have analyzed the changes in pulsation in robotic local power supply systems, which is one of the key directions in the field of electrical engineering and electronics. Increased attention to this problem is due to the need for assured stability, reliability and greater energy efficiency of electronic systems. Research in this area is aimed at developing more comprehensive methods and strategies for reducing noise and pulsation in electrical systems.
 Assessing the effectiveness of stagnant methods, promoting the development of new technologies and innovation, as well as developing promising directions in this field opens the way to creating more stable and adaptive living systems. Replacement of advanced electronic filters, voltage stabilizers, selection of new materials and components, integration of intelligent energy management systems and optimization of essential energy resources local power supply systems – the whole purpose is to directly conduct further investigations.
 The growing demand for stable and efficient electronic systems is driven by current research and the introduction of new technologies to change pulsation. Technological developments and scientific research in this area open up broad prospects for reducing the power supply and ensuring the stability of the functioning of modern electronic devices.

Global probability distribution structure-sparsity filter pruning for edge fault diagnosis in resource constrained wireless sensor networks

In this paper, a global probability distribution structure-sparsity filter pruning is proposed to address the problem of difficult deployment of diagnostic models in resource constrained wireless sensor networks (WSNs) for edge fault diagnosis. Firstly, local and global weight distributions are analyzed. A global probability distribution weight sparsity method is proposed to obtain the global sparse boundary and the important features of the model. Secondly, according to the distribution of weight amplitude, the problem of uneven weight distribution is disclosed. A computational acceleration sensing method is proposed to reduce the high computational cost caused by small weight amplitude with low importance estimation. Moreover, a novel layer filter clustering pruning scheme is proposed. The intra-layer filters are classified and redundant filters are removed by using the clustering idea according to the predetermined pruning rate. Thus, the structure-sparsity filter pruning is realized. Compared with other advanced filter pruning methods, the proposed method analyzes the global and local model weight distribution and the influence of weight importance on the computational complexity of the model. The proposed method then performs filter pruning on the importance of overall filters for each network layer rather than individual filters. The accuracy of the proposed method on experimental dataset for pruned ResNet8 model under pruning rate at 0.95 is 99.12% with 3.668K parameters and 3.636MFLOPs. The experimental results elucidate that the proposed method is more suitable for deployment in resource-constrained WSNs for fault diagnosis of rotating machinery. This provides a potential solution for practical engineering applications.

Harnessing the power of radiomics and deep learning for improved breast cancer diagnosis with multiparametric breast mammography

Breast cancer, with its high mortality, faces diagnostic challenges due to variability in mammography quality and breast densities, leading to inconsistencies in radiological interpretations. Computer-aided diagnostic (CAD) systems, while helpful, struggle with accurately interpreting lesion characteristics such as morphology, density, and size. To address this, our study developed advanced deep-learning algorithms to improve the detection, localization, risk assessment, and classification of breast lesions, aiming to reduce false positives on human intervention and tackle slow convergence rates. Key innovations of the approach include preprocessing techniques, advanced filtering, and data augmentation strategies to optimize model performance, mitigating over- and under-fitting concerns. A significant development is the Chaotic Leader Selective Filler Swarm Optimization (cLSFSO) method, which effectively detects breast-dense lesions by extracting textural and statistical features. Additionally, the study adapted deep learning models like modified VGGNet and SE-ResNet152 through transfer learning, significantly enhancing their capability to distinguish between normal and suspicious mammography regions. The study also introduces hybrid deep neural network-based approaches, including CNN+LSTM and CNN+SVM, for diagnosing and grading cancerous polyps from the pre-segmented ROIs. Besides, the transfer learning paradigm is employed to boost the efficacy in classifying breast masses and reducing computing time by modifying the final layer of the proposed pre-trained models. The integration of Grad-CAM techniques further refines our analysis, leading to more accurate assessments and improved diagnosis of breast anomalies. Evaluated using benchmark and private datasets, our algorithms demonstrated a sensitivity of 0.99 and an overall AUC of 0.99, indicating significant improvements in mammogram analysis. These advancements aid radiologists, potentially improving patient outcomes and contributing to medical imaging and AI in healthcare.

A simple and efficient filter feature selection method via document-term matrix unitization

Text processing tasks commonly grapple with the challenge of high dimensionality. One of the most effective solutions to this challenge is to preprocess text data through feature selection methods. Feature selection can select the most advantageous features for subsequent operations (e.g., classification) from the native feature space of the text. This process effectively trims the feature space’s dimensionality, enhancing subsequent operations’ efficiency and accuracy. This paper proposes a straightforward and efficient filter feature selection method based on document-term matrix unitization (DTMU) for text processing. Diverging from previous filter feature selection methods that concentrate on scoring criteria definition, our method achieves more optimal feature selection by unitizing each column of the document-term matrix. This approach mitigates feature-to-feature influence and reinforces the role of the weighting proportion within the features. Subsequently, our scoring criterion subtracts the sum of weights for negative samples from positive samples and takes the absolute value. We conduct numerical experiments to compare DTMU with four advanced filter feature selection methods: max–min ratio metric, proportional rough feature selector, least loss, and relative discrimination criterion, along with two classical filter feature selection methods: Chi-square and information gain. The experiments are performed on four ten-thousand-dimensional feature space datasets: book, dvd, music, movie and two thousand-dimensional feature space datasets: imdb, amazon_cells, sourced from Amazon product reviews and movie reviews. Experimental findings demonstrate that DTMU selects more advantageous features for subsequent operations and achieves a higher dimensionality reduction rate than those of the other six methods used for comparison. Moreover, DTMU exhibits robust generalization capabilities across various classifiers and dimensional datasets. Notably, the average CPU time for a single run of DTMU is measured at 1.455 s.