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The essential goal of the manuscript is to initiate lattice ordered multi-fuzzy soft matrix ( ℒ ℳ ℱ S ℳ ) using lattice ordered multi-fuzzy soft set ( ℒ ℳ ℱ S S ) and discuss the operations and depict rudimentary properties on ℒ ℳ ℱ S ℳ . Moreover, we originate the various compositions of ℒ ℳ ℱ S ℳ and propose algorithm for medical diagnosis by applying score matrix which have been implemented with the numerical example. Finally, the comparitive analysis of three composition methods is also included for improved reasonability and reliability.

The most serious sort of cancer that affects women most frequently in modern times is breast cancer (BC). If it is not detected in the beginning stages, the death rate is significant. Breast cancer affects around 12% of woman, and the percentage is continually rising. The invention of a ML-based breast cancer classification system that can identify the disease from a patient's digital scan is artificial intelligence's greatest contribution to healthcare. Breast cancer is found using the mammography method however, radiologists' interpretations vary greatly. Fine needle aspiration cytology is commonly used in the diagnosis of breast cancer (FNAC). Uniform cell size, if the tumour has a consistent cell shape and other factors are taken into account, the prediction will determine if the tumour is benign or malignant. We have gathered both the characteristics of breast cancer cells and cells from healthy individuals. We were able to differentiate between malignant and benign employing a supervised machine learning classifier system to identify tumours. However, by taking the right medications, needless therapy can be avoided if patients are correctly recognised early on employing ML approaches. Though computer vision, ML technologies have demonstrated a high level of accuracy in healthcare applications, Physical examinations shouldn't solely be conducted using these systems. These are meant to support doctors, not replace them. Machine learning has a distinct advantage in that it can find relevant breast cancer features in large datasets. In predictive modelling and pattern recognition, the technique is extensively used. Assessing each classifier's effectiveness in terms of accuracy, precision, and recall is the project's main objective. Examining the effectiveness, accuracy, and early identification of breast cancer using different machine learning classifiers is the goal.

This article deals with a fuzzy hypercompositional structure called fuzzy hyperlattice ordered δ- group ( FHLO δ - G ) , the extension of the fuzzy hypercompositional structure namely fuzzy hyperlattice ordered group (FHLOG). Using FHLO δ - G , we can involve one additional non-empty set δ with FHLOG, which helps to develop new results and applications. The structural characteristics and properties of FHLO δ - G are analysed. Furthermore, an application of FHLO δ - G for ABO blood group system is proposed.

This paper introduces the concept of homomorphism on fuzzy hyperlattice ordered group ( FHLOG ) . It studies how the binary and the fuzzy hyperoperations of a FHLOG can be transformed into the binary and the fuzzy hyperoperations of another FHLOG . The notion of fuzzy hypercongruence relation on FHLOG is also defined. The paper also establishes the redox reaction of copper, gold and americium forms three FHLOG s. Besides, homomorphism and composition function of FHLOG s using the redox reactions are developed. Therefore, the paper develops a relation among three different metal’s redox reactions in which the binary and the fuzzy hyperoperations, are preserved.

This paper introduces some new operations on complex intuitionistic fuzzy lattice ordered groups such as sum, product, bounded product, bounded difference and disjoint sum, and verifying its pertinent properties. The research exhibits the CIFS-COPRAS algorithm in a complex intuitionistic fuzzy soft set environment. This method was furthermore applied for the equipment selection process.

The field of medical analysis follows an exponential curve in the area of scientific knowledge, yet things are not very optimistic and assured from the perspective of patients as many diseases are discovered quite late by doctors. The lack of comprehensive data on relevant risk factors for coronary artery disease (CAD) has limited the ability to predict the risk of developing CAD in large populations. The challenge lies in the data complexity and correlations in terms of prediction using conventional techniques. Coronary artery disease is predicted by uploading the electrocardiogram (ECG) reports of patients into the machine learning model. Multiple classifiers like k-nearest neighbors, Logistic Regression, Support Vector Machine, and Voting Based Ensemble Classifier are implemented, and based on the acceptable criteria on the accuracy, the model will be finalized. This model will help medical professionals in diagnosis of cardiac diseases, to detect whether a patient has/had Myocardial Infarction, Abnormal Heartbeat, or the patient is hale and healthy by inferring the ECG reports.

Inspired by the analysis of soft sets and fuzzy sets, in this paper, we study the relevance and interrelationship of the equivalence and congruence relation of lattice ordered fuzzy soft groups(l-FSGs). Moreover, we analyze some of the innate algebraic results accrued out of it theoretically. We construct l-FSG congruence class and l-FSG quotient set on a group X. We analyze the relationship between l-FSG congruence relation and l-FSG normal subgroups. In particular, we prove that in a group X, l-FSG normal subgroups form a modular lattice.

Medical Geography today draws on the concepts and techniques of geography, and epitomes the interdisciplinary nature of urban health discipline. Chikungunya is one of the Vector-borne diseases and today it continues to remain an important public health problem in Madurai city. It is a mosquito-borne viral infection caused by the Chikungunya Virus (CHKV). The sudden onset of very high fever along with a rash and severe arthralgia especially in the small joints of hands and toes are the characteristics of the disease. The study area, Madurai is the oldest and second-largest city in Tamil Nadu situated on the banks of river Vaigai in the south-central part of Tamil Nadu, India. The main objectives were to identify the major disease zones concerning Chikungunya cases and to observe the related urban ecological and environmental conditions. The study also aimed to derive a conceptual framework towards strengthening, control and sustainable development of the urban landscape. The data collected were taken by using the method of stratified Random sampling and a total of 600 samples were collected. Standard score (Z-score) technique was used to describe the relationship between the sets of variables and total conditions of selected urban environmental variables. It includes mapping of the disease using GIS software of Arc GIS. Apart from this, the multi-variant statistical technique, factor analysis is used to find the major associations and interrelationship between Chikungunya and people. The twelve factors rotated by explaining the varimax procedure registered more than 89.83 percent of the total variance. Among these, the first factor alone accounted for 33.52 percent of the total variance and eigenvalue of 21.45 was qualified as the primary factor. Our study concludes that in Madurai city’s built-up area, a landscape environment of transmission of Chikungunya epidemics with a high prevalence is noted since it is associated with urban environmental factors.

There is an open problem in [5] stated as follows: “Is there a suitable and useful definition of associative filter in residuated lattice?” Yes, here the concept is defined as follows: filter F is associative of L if α → (β → ω) ∈ F ⇔ (α → β) → ω ∈ F for any α,β,ω ∈ L. Also this concept is developed in the context of multisets and thus the notions of multiset associative filter, multiset pre-associative filter, multiset associative pseudofilter have been introduced followed by the relationship between multiset associative filter and other multiset filters have been found.

In this paper, we introduce the concept of multiset implicative filter and multiset positive implicative filter of residuated lattice. Also, we discuss their characterizations and we show t...

Soft set theory has been evolved as a completely new mathematical tool for modeling vague concepts. Yong Yang et.al propounded the theory of Multi-fuzzy soft set and its application to deci...

The present work describes the effect of processing route on the densification of W-10 wt% Cu nanocomposite. The spark plasma sinterability of the nanocomposites prepared through four different routes has been investigated. Out of the four routes, polyol process resulted in the highest density of 17.20 g/cc, which amounts to 99.7% of theoretical density. Mechanical milling was the second best yielding a density of 16.83 g/cc. The correlation between densification vis-a-vis mechanical and electrical properties were studied and it was observed that as the density increases, both mechanical strength and electrical conductivity increase.

Criminal activities, be it violent or non-violent are major threats to the safety and security of people. Frequent Crimes are the extreme hindrance to the sustainable development of a nation and thus need to be controlled. Often Police personnel seek the computational solution and tools to realize impending crimes and to perform crime analytics. The developed and developing countries experimenting their tryst with predictive policing in the recent times. With the advent of advanced machine and deep learning algorithms, Time series analysis and building a forecasting model on crime data sets has become feasible. Time series analysis is preferred on this data set as the crime events are recorded with respect to time as significant component. The objective of this paper is to mechanize and automate time series forecasting using a pure DL model. N-Beats Recurrent Neural Networks (RNN) are the proven ensemble models for time series forecasting. Herein, we had foreseen future trends with better accuracy by building a model using NBeats algorithm on Sacremento crime data set. This study applied detailed data pre-processing steps, presented an extensive set of visualizations and involved hyperparameter tuning. The current study has been compared with the other similar works and had been proved as a better forecasting model. This study varied from the other research studies in the data visualization with the enhanced accuracy.

The structure of lattice in interval valued hesitant fuzzy soft set was implemented in 2018. In our previous work, the pivotal properties of L-IVHFSS and the effect of its nature in the problem of decision making was explored. In this paper, we appraised the operations of L-IVHFSS and the new conception of Contra-L-IVHFSS. Also the inference of duality in L-IVHFSS and Contra-L-IVHFSS were examined.

In 2019, we have initiated the theory of lattice ordered multi-fuzzy soft sets (ℒℳℱ𝒮 𝒮 ) and its application in Forecasting process. In this paper, our aim is to propose the idea of Anti-lattice ordered multi-fuzzy soft sets (𝒜 ℒℳℱ𝒮 𝒮 ) and also establish the operations of 𝒜 ℒℳℱ𝒮𝒮. Moreover, as an application of 𝒜 ℒℳℱ𝒮𝒮 is developed.

Plithogenic set is an extension of the crisp set, fuzzy set, intuitionistic fuzzy set, and neutrosophic sets, whose elements are characterized by one or more attributes, and each attribute can assume many values. Each attribute has a corresponding degree of appurtenance of the element to the set with respect to the given criteria. In order to obtain a better accuracy and for a more exact exclusion (partial order), a contradiction or dissimilarity degree is defined between each attribute value and the dominant attribute value. In this paper, entropy measures for plithogenic sets have been introduced. The requirements for any function to be an entropy measure of plithogenic sets are outlined in the axiomatic definition of the plithogenic entropy using the axiomatic requirements of neutrosophic entropy. Several new formulae for the entropy measure of plithogenic sets are also introduced. The newly introduced entropy measures are then applied to a multi-attribute decision making problem related to the selection of locations. Web of Science 8 6 art. no. 965

A green chemical route is reported for the synthesis of cobalt and cobalt carbide nanostructures using decomposition of cobalt acetate salt in a carbon matrix in situ during spark plasma sintering (SPS). The effect of SPS pressure on the phase formation was studied in detail and it revealed that with increase in pressure from 30 to 60 MPa the interaction between Co and C atoms increases resulting in the formation of only pure face centered cubic Co at 30 MPa to predominantly Co2C phase at 60 MPa. The magnetization measurements revealed a decrease in magnetization (M) value accompanied by an increase in coercivity (Hc) value with increase in pressure. For a sample with predominant Co2C phase a M value of 35 emu/g and Hc value of 786 Oe was obtained.