Attribute Reduction Research Articles

Enhancing cybersecurity via attribute reduction with deep learning model for false data injection attack recognition

Cyberattacks have given rise to several phenomena and have raised concerns among users and power system operators. When they are built to bypass state estimation bad data recognition methods executed in the conventional grid system control room, False Data Injection Attacks (FDIA) pose a significant security threat to the operation of power systems. Therefore, real-time monitoring becomes inevitable with the quick implementation of renewables within the grid operator. The state estimation algorithm plays a major role in defining the grid’s operating scenarios. FDIA creates a significant risk to these estimation strategies by adding malicious information to the measurement obtained. Real-time recognition of these attack classes improves grid resiliency and ensures a secure grid operation. This study introduces a novel Attribute Reduction with a Deep Learning-based False Data Injection Attack Recognition (ARDL-FDIAR) technique. The primary goal of the ARDL-FDIAR technique is to improve security via the FDIA detection process. The ARDL-FDIAR technique uses Z-score normalization to scale the input data. The attribute reduction process gets invoked using the modified Lemrus optimization algorithm (MLOA) to choose optimal feature sets. Moreover, the FDIA detection process is performed by modelling an improved deep belief network (IDBN) model. Furthermore, the performance of the IDBN model is improved by the Cetacean Optimization Algorithm (COA)-based hyperparameter tuning process. A series of experiments were performed to ensure the enhancement of the ARDL-FDIAR technique. The results indicated the enhanced security performance of the ARDL-FDIAR technique compared to other DL approaches.

Attribute Reduction in a Hybrid Decision Information System Based on Fuzzy Conditional Information Entropy Using Iterative Model and Matrix Operation

Attribute Reduction in a Hybrid Decision Information System Based on Fuzzy Conditional Information Entropy Using Iterative Model and Matrix Operation

Novel Ensemble Approach with Incremental Information Level and Improved Evidence Theory for Attribute Reduction.

With the development of intelligent technology, data in practical applications show exponential growth in quantity and scale. Extracting the most distinguished attributes from complex datasets becomes a crucial problem. The existing attribute reduction approaches focus on the correlation between attributes and labels without considering the redundancy. To address the above problem, we propose an ensemble approach based on an incremental information level and improved evidence theory for attribute reduction (IILE). Firstly, the incremental information level reduction measure comprehensively assesses attributes based on reduction capability and redundancy level. Then, an improved evidence theory and approximate reduction methods are employed to fuse multiple reduction results, thereby obtaining an approximately globally optimal and a most representative subset of attributes. Eventually, using different metrics, experimental comparisons are performed on eight datasets to confirm that our proposal achieved better than other methods. The results show that our proposal can obtain more relevant attribute sets by using the incremental information level and improved evidence theory.

Role of Rough Neutrosophic Attribute Reduction with Deep Learning-Based Enhanced Kidney Disease Diagnosis

The kidneys have an important role in keeping blood pressure, electrolyte sense, and acid-base sense of body balance to remove toxins from our body. Malfunction is responsible for irrelevant to life-threatening diseases, along with malfunction in the other functional organs. As a result, scholars worldwide have committed to finding methods for effectively diagnosing and accurately treating chronic kidney disease. As machine learning (ML) classifier is widely deployed in the healthcare field for diagnoses, also CKD is now involved in the collection of disorders that could be predicted through the ML classifier. Neutrosophic logic (NL) can be employed as a form of logic that expands classical, fuzzy, and intuitionistic fuzzy logic (IF) by integrating a third constituent: indeterminacy. It enables data handling and representation with three dissimilar membership functions: truth (T), indeterminacy (I), and false (F). The complete set is independent and may differ in the interval [0, 1], providing a convoluted strategy to handle, data incompleteness, vagueness and uncertainty. This makes NL especially relevant in complicated systems where data might be partially unknown, ambiguous, or inconsistent. This article employs a Rough Neutrosophic Attribute Reduction with Deep Learning based Enhanced Kidney Disease Diagnosis (RNSAR-DLKDD) technique. Initially, the RNSAR-DLKDD technique reduces the attributes via the RNSAR technique. Followed by, the detection and classification of kidney disease take place using long short-term memory (LSTM) model. Finally, the hyperparameter selection process is carried out via crow search algorithm (CSA). To highlight the performance of the RNSAR-DLKDD technique, a series of experiments were involved. The extensive results inferred the betterment of the RNSAR-DLKDD technique over other models

Attribute reduction based on adaptive neighborhood rough sets and three-way pied kingfisher optimizer

Attribute reduction based on adaptive neighborhood rough sets and three-way pied kingfisher optimizer

Attribute reduction based on weighted neighborhood constrained fuzzy rough sets induced by grouping functions

Attribute reduction based on weighted neighborhood constrained fuzzy rough sets induced by grouping functions

Attribute reduction method based on fuzzy relational equations and inequalities

Attribute reduction method based on fuzzy relational equations and inequalities

Menadiol diacetate mediated subcellular Cd accumulation and nutrients uptake alleviates Cd toxicity and increases growth and yield of summer squash

Cadmium (Cd) has shown toxicity to reduce growth and productivity in different plants. The Present study investigated the efficacy of menadiol diacetate (MD) to reduce Cd stress on growth and yield of summer squash plants. The experiment was performed under saturated Hoagland’s nutrient solution (control) while the other group was supplemented with 0.1 mM CdCl2 (Cd stress). Surface sterilized seeds of summer squash were primed in different concentrations (10, 20 µM) of MD as well as in distilled water for 24 h and sown in the pots. Different morphological and physio-biochemical attributes were determined after 35 d of growth whereas the data for yield attributes was collected after 70 d. Cd concentration was determined in various subcellular compartments i.e., cell walls and cell wall debris, chloroplast, cell membrane and other organelles including vacuoles. The Cd stress decreased photosynthetic pigments, osmoprotectants and ultimately caused reduction in the yield attributes. Further, it increased the secondary metabolites and oxidants (MDA and H2O2) in the summer squash tissues. Cd exposure also altered ions accumulation in the summer squash tissues by increasing the root and shoot Ca2+ (24–93%) and Fe (4–18%) ions while decreasing the Mg2+ (31–39%) ions. The MD-priming, particularly at 10 µM concentration mediated increase in the total phenolics, ascorbic acid, and anthocyanins concentration, and thus enhanced growth and yield attributes of summer squash exposed to Cd toxicity. Further, 10 µM MD-priming facilitated Cd compartmentalization in the subcellular compartments mainly in the cell wall (58%) rather than in the chloroplast (18%), cell membrane (7%) and soluble fractions (18%). In this context, cell wall and vacuole were the key compartments for Cd sequestration. This study highlights MD-priming as a potential strategy to counter Cd toxicity in summer squash plants.

Powder dosing within continuous manufacturing: A lean approach for gravimetric dosing configuration and equipment selection

This work presents a novel approach to select gravimetric dosing configurations for continuous unit operations in pharmaceutical processing. It optimizes material, time, and personnel use, and allows selections for configurations of materials not included in the model by robust material attribute-based interpolation. The approach does not apply Principal Component Analysis (PCA) and Partial Least Squares (PLS). Instead, a reduced set of material attributes that require measurement was determined from existing material attribute libraries found in literature. Only those identified 17 attributes were measured for 13 materials and employed in this study. The established model proved to be predictive for an even further reduced attribute set of 12 attributes. The model introduces a simplified method for selecting feeding equipment combining precision metrics (relative mean intra-bin variability) and model parameters (fmax, fmin, β). The reduction of Material Attributes (MAs) in this short-cut method enables fast evaluation and enhances resource efficiency by incorporating only a few selected MAs into the model and therefore introduces a different line of thinking. It was also possible to compare feeding of different hopper geometries, showing that flat bottom-hoppers have higher dosing precision than round bottom-hoppers. In conclusion, this work introduces a smart and innovative model that simplifies the equipment selection process and brings objectivity through a comprehensive numerical description of the discharge characteristics, and provides new options for continuous dosing in pharma through combining precision and curve fitting parameters.

Response of host Indian Musa germplasm against artificially induced root lesion nematodes (Pratylenchus coffeae)

Under greenhouse condition, the host response of fifty-nine banana genotypes (Musa spp.) from the Eumusa section to the root-lesion nematode (Pratylenchus coffeae) was assessed. In a factorial completely randomized design (FCRD) with five replications, healthy banana suckers of the diploid and triploid accessions were planted in cement pots. Uninoculated controls were included in the experiment as comparison to study the sensitivity of the genotypes. Two varieties (Pisang Lilin and Nendran) with known reaction to lesion nematode were also included as reference clones because of their resistance and susceptibility to P. coffeae. Banana accessions maintained in the pots were inoculated with infective juveniles of root-lesion nematode, P. coffeae at 45 days after planting at the rate of 400 nematodes/pot. Ninety days after inoculation, the plants were harvested to observe the response of the different banana genotypes to P. coffeae. Data were recorded on plant growth (plant height, girth at the base, number of standing leaves, number of roots and weight of the root system), root damage assessment (percentage of infected roots and percentage necrosis) and nematode reproduction. When inoculated with nematodes, the reduction in plant growth attributes was relatively higher in genotypes that were susceptible. The reduction in growth characters viz., plants height, number of roots and root fresh weight were maximum in the diploid genotypes Manguthamng (26.80, 27.2, 33.0 %) and Manohar (25.70, 29.2, 35.2 %) and in the triploid genotypes Kaali, Rajthali, Digjowa, Saapkal, Cheenichampa, Dasaman, Borchampa, Jahaji, Manjahaji, Barjahaji and Sabri when inoculated with Pratylenchus coffeae. Even after nematode inoculation, root investigations showed that resistant and tolerant genotypes had greater numbers of roots as well as high fresh and dry weight of roots. Root and soil population of nematodes assessed at 90 days after inoculation indicated very high population buildup of Pratylenchus coffeae (>28) in diploids Manguthamng and Nendrapadathi and in triploids Jahaji, Manjahaji, Saapkal, Borchampa, Therahaw-1163 and 1164, Ankur-I and Bersain. The rate was the lowest (<9) in the diploid genotypes Kanai Bansi, Kechulepa, M.balbisiana, Athiakol, Bhimkol and Aittakola and in the triploids Karthobiumtham and Ankur-II. The results of the experiment showed that the diploids Musa balbisiana (BB), Aittakola (BB), Bhimkol (BB), Kechulepa (BB), Kanai Bansi (AA) and Athiakol (BB) and the triploid genotypes Kachkel (ABB), Karthobiumtham (ABB) and Ankur-II (ABB) were resistant to Pratylenchus coffeae.

Attribute reduction based on directional semi-neighborhood rough set

Attribute reduction based on directional semi-neighborhood rough set

DCE attribute development investigating public policy for the provision of medicinal cannabis

Background Defining attributes and attribute levels for a discrete choice experiment (DCE) poses a significant challenge for practitioners exploring preferences for new or unfamiliar products due to the dearth of available information and limits in stakeholder knowledge. This study outlines a comprehensive process for identifying attributes and levels in a DCE aimed at gauging public preferences for health policies related to medicinal cannabis (MC). Methods A rigorous four-stage attribute development process was utilized and included i) the formulation of a preliminary attribute list from a scoping review and document analysis, ii) reduction of attributes via focus groups, iii) removal of inappropriate attributes using Delphi studies and research team knowledge, and iv) refinement of attribute language based on the feedback from think-aloud interviews. Results A base attribute list formed from the scoping review and document analysis served as effective discussion stimuli in focus groups, especially for participants with limited subject knowledge. Structured focus group activities proved more effective than open-ended discussions in engaging naive participants. Delphi studies were found to be overcomplex for expert-led attribute prioritization. Think-aloud interviews during a pilot DCE were essential for assessing attribute language and clarity and understanding participants’ decision-making processes. Conclusion The development of DCE attributes measuring preferences for an unfamiliar health product requires a multi-method approach. Evaluating the outcomes from various methods of attribute development yielded a refined list of attributes that were significant to stakeholders, allowed for meaningful trade-offs, and were presented in language accessible to the target population.

Transcriptome analysis of the effect of HERV-K env gene knockout in ovarian cancer cell lines.

Human endogenous retroviruses (HERVs) have been implicated in the pathogenesis of various diseases, particularly cancers. Previous investigations from our group demonstrated that targeted knockout (KO) of the HERV-K env gene led to a significant reduction in tumorigenic attributes, including proliferation, migration, and invasion of ovarian cancer cells. In this study, we aimed to elucidate the impact of HERV-K env KO on gene expression in ovarian cancer cell lines through comparative RNA sequencing (RNA-Seq) analysis with two distinct HERV-K env KO ovarian cancer cell lines, SKOV3 and OVCAR3. HERV-K env gene KO was achieved in SKOV3 and OVCAR3 ovarian cancer cell lines using the CRISPR-Cas9 system. Next-generation mRNA sequencing was employed to assess the gene expression profiles of both mock and HERV-K env KO ovarian cancer cells. Furthermore, comprehensive analyses involving gene ontology and pathway assessments were conducted. Transcriptome analysis revealed that 23 differentially expressed genes (DEGs) were upregulated and 17 DEGs were downregulated in SKOV3 cells. In OVCAR3 cells, 198 DEGs were upregulated, and 17 DEGs were downregulated. Notably, 53 DEGs exhibited statistically significant differences among the 1,612 DEGs identified. Our findings indicate that HERV-K env gene KO exerts a profound influence on gene expression patterns in OVCAR3 cells, while genetic alterations in expression were relatively modest in SKOV3 cells. Nevertheless, genes ND1, ND2, and CYTB displayed a common increase in expression, while ERRFI1 and NDRG1 exhibited a decrease in expression in both cell lines. Our study demonstrates that KO of the HERV-K env gene in ovarian cancer cell lines has a substantial impact on gene expression patterns and can be used to identify potential therapeutic targets for ovarian cancer and related diseases.

Rapid and optimized parallel attribute reduction based on neighborhood rough sets and MapReduce

Attribute reduction is a crucial step in data pre-processing and feature engineering. It is the selection of a subset of relevant data attributes to reduce the computational complexity of machine learning models and improve their performance. Neighborhood rough set (NRS) theory provides a valuable framework for attribute reduction. It leverages neighborhood information to identify non-redundant and informative attributes for data analysis and machine learning tasks. Attribute subsets based on NRS theory are highly qualitative, producing effective prediction accuracies in Euclidean space. However, existing NRS-based solutions are resource-intensive because of the large search space required for finding neighborhoods and redundant computations. To overcome these limitations, we propose the rapid and optimized attribute reduction (ROAR) algorithm that optimizes the current state-of-the-art attribute-reduction method in NRS theory. The strength of ROAR lies in its ability to accelerate computations by rapidly determining the neighborhood consistency of data samples and consequently expediting the identification of both positive and boundary regions. This efficiency significantly enhances the overall processing time for the data analysis tasks. Experimental results on 12 standard datasets demonstrate that the ROAR algorithm exhibits high efficiency by obtaining accurate reduction results with rapid response times. To ensure that the ROAR algorithm is suitable for high-dimensional datasets, we provide a parallel implementation, namely, the P-ROAR algorithm. The P-ROAR algorithm is the first parallel attribute-reduction algorithm in the classical NRS theory. Computational speeds and scalability metrics establish that P-ROAR is much faster and more scalable for datasets with an enormous attribute space. These algorithms provide a tool for handling feature reduction in data engineering without compromising accuracy and performance.

Attribute reduction using self-information uncertainty measures in optimistic neighborhood extreme-granulation rough set

The pessimistic and optimistic neighborhood multi-granulation rough sets (PNMRS and ONMRS) have been applied to attribute reduction. Nonetheless, the setting of the granulation space in both models is usually based on expert experience or continuous trials, which are poorly interpretable. Besides, the dependency, an important attribute evaluation function in PNMRS and ONMRS, only considers classification information in the lower approximation and ignores that of the upper approximation, affecting its evaluation effect. Thus, we firstly propose the pessimistic and optimistic neighborhood extreme-granulation rough sets (PNERS and ONERS) based on the minimum granulation space, avoiding the unobjective setting of the granulation space. However, PNERS describes knowledge with low accuracy, and therefore we construct two self-information uncertainty measures in ONERS and propose the joint self-information that is more suitable for attribute reduction as it takes full consideration of classification information in the lower and upper approximations. Then, an attribute reduction method using the joint self-information is designed, and Fisher Score is introduced into our method to decrease time cost when processing high dimensional data. Extensive experiments validate that our method is able to achieve superior reduction and classification results on different dimensional datasets.

Research and application of rough set attribute reduction algorithm based on genetic algorithm in pipeline network

Abstract Explosion accidents often occur in urban underground pipeline network systems, which has caused safety issues in public life and industrial production. GIS technology can visually manage pipeline equipment and solve the problem of valve closure analysis in the event of current accidents, avoiding the escalation of the situation, but it cannot make predictions. Attribute reduction is one of the core contents of rough set theory and is an NP-hard problem. This article proposes a custom attribute reduction algorithm based on an algorithm and uses the algorithm in the historical burst pipeline dataset of the water supply pipeline industry. The results have shown that this method combined with a decision tree can make predictions before water pipe leakage. The algorithm provides the best result of attribute reduction for decision tree methods, which will improve the speed and accuracy of decision-making.

BiFuG2-Spark: Bi-Directional Fuzzy Granular-Cabin Parallel Attribute Reduction Accelerator With Granular-Group Collaboration

BiFuG2-Spark: Bi-Directional Fuzzy Granular-Cabin Parallel Attribute Reduction Accelerator With Granular-Group Collaboration

Attribute Reduction Based on Fuzzy Distinguishable Pair Metric Considering Redundancy Upper and Lower Bounds

Attribute Reduction Based on Fuzzy Distinguishable Pair Metric Considering Redundancy Upper and Lower Bounds

Quantifying assumptions underlying peak oxygen consumption equations across the body mass spectrum.

The goal of this study is toquantify the assumptions associated with the Wasserman-Hansen (WH) and Fitness Registry and the Importance of Exercise: A National Database (FRIEND) predictive peak oxygen consumption (pVO2) equations across body mass index (BMI). Assumptions in pVO2 for both equations were first determined using a simulation and then evaluated using exercise data from the Stanford Exercise Testing registry. We calculated percent-predicted VO2 (ppVO2) values for both equations and compared them using the Bland-Altman method. Assumptions associated with pVO2 across BMI categories were quantified by comparing the slopes of age-adjusted VO2 ratios (pVO2/pre-exercise VO2) and ppVO2 values for different BMI categories. The simulation revealed lower predicted fitness among adults with obesity using the FRIEND equation compared to the WH equations. In the clinical cohort, we evaluated 2471 patients (56.9% male, 22% with BMI >30 kg/m2, pVO2 26.8 mlO2/kg/min). The Bland-Altman plot revealed an average relative difference of -1.7% (95% CI: -2.1 to -1.2%) between WH and FRIEND ppVO2 values with greater differences among those with obesity. Analysis of the VO2 ratio to ppVO2 slopes across the BMI spectrum confirmed the assumption of lower fitness in those with obesity, and this trend was more pronounced using the FRIEND equation. Peak VO2 estimations between the WH and FRIEND equations differed significantly among individuals with obesity. The FRIEND equation resulted in a greater attributable reduction in pVO2 associated with obesity relative to the WH equations. The outlined relationships between BMI and predicted VO2 may better inform the clinical interpretation of ppVO2 values during cardiopulmonary exercise test evaluations.

Attribute reduction with fuzzy divergence-based weighted neighborhood rough sets

Neighborhood rough sets are well-known as an interesting approach for attribute reduction in numerical/continuous data tables. Nevertheless, in most existing neighborhood rough set models, all attributes are assigned the same weights. This may undermine the capacity to select important attributes, especially for high-dimensional datasets. To establish attribute weights, in this study, we will utilize fuzzy divergence to evaluate the distinction between each attribute with the whole attributes in classifying the objects to the decision classes. Then, we construct a new model of fuzzy divergence-based weighted neighborhood rough sets, as well as propose an efficient attribute reduction algorithm. In our method, reducts are considered under the scenario of the α-certainty region, which is introduced as an extension of the positive region. Several related properties will show that attribute reduction based on the α-certainty region can significantly enhance the ability to identify optimal attributes due to reducing the influence of noisy information. To validate the effectiveness of the proposed algorithm, we conduct experiments on 12 benchmark datasets. The results demonstrate that our algorithm not only significantly reduces the number of attributes compared to the original data but also enhances classification accuracy. In comparison to some other state-of-the-art algorithms, the proposed algorithm also outperforms in terms of classification accuracy for almost all of datasets, while also maintaining a highly competitive reduct size and computation time.