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This paper introduces the concept of rough fermatean neutrosophic sets and investigates their properties. Additionally, a cosine similarity measure between these sets is proposed. By applying this measure to a medical diagnosis example, the paper illustrates how the method can be used in practical situations, highlighting its effectiveness in complex decision-making scenarios. This innovation holds promise for improving decision-making processes, especially in critical areas like medical diagnosis, where making accurate assessments amidst uncertainty is crucial. [ABSTRACT FROM AUTHOR]

Soft clustering techniques are extensively used for segmenting medical images, and in particular, fuzzy c-means (FCM) clustering is employed to cluster the distinctive regions of the medical image. Specifically, a special attention is needed for the segmentation of brain tumor MR images, since it has more uncertainties. To cope with this impreciseness, intuitionistic fuzzy c-means (IFCM) clustering is utilized which improves the accuracy in segmentation. In this framework, a new approach of clustering brain tumor MR image is proposed to segment brain tumor image. Initially, a novel intuitionistic fuzzy generator (IFG) is derived and the input image is enhanced using it to remove uncertainties. Then, kernel distance-based intuitionistic fuzzy c-means clustering is executed for gray-level histogram of the morphologically reconstructed intuitionistic fuzzy image (IFI). Finally, extensive experiment is conducted for the proposed method and other state-of-the-art methods in clustering to show the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

Phase change materials (PCMs) are substances that can change their phase at a specific range of temperatures. This is the case in the temperature range and in the event that there is a brief gap between the supply of a particular quantity of energy and the demand for that quantity, these materials come in quite helpful. As a result, heating, ventilation, and air conditioning (HVAC) for the military, as well as waste heat recovery and thermal management. The simulation result of this proposed method is developed using Matlab and Simulink, the user is classified using a fuzzy logic control which helps to improve the energy consumption patterns which are they exhibit. Using this proposed method, we can perform several modes of operations which are toogled between the control, which achieves more amount of greater flexibility, also at same time it helps to reduce the usage of number of switches which are placed between various OM's. This is demonstrated by the fact that the fuzzy logic control is used. Therefore, the developed control logic reveals a promising potential for boosting the energy efficiency. We achieve nearly 85% of accuracy as a result which are very higher than comparing with the several existing methods. [ABSTRACT FROM AUTHOR]

Background: In this research study, we delve into the effects of smoking cessation programs on the progression of periodontal disease, particularly focusing on the potential benefits experienced by individuals who successfully quit smoking. Materials And Methods: The research involved the participation of 200 individuals, all of whom were active smokers and exhibited varying degrees of periodontal disease. These participants enrolled in a comprehensive smoking cessation program that included regular counseling sessions and, when necessary, the provision of nicotine replacement therapy to facilitate smoking cessation. Over a 12-month period, the progression of their periodontal disease was systematically monitored through a series of dental check-ups and measurements of critical clinical parameters. These parameters included probing depth (PD), which assesses the spaces between teeth and gums, and clinical attachment level (CAL), which evaluates the attachment of gum tissue to tooth surfaces. Results: After 12 months of active participation in the smoking cessation program, several noteworthy results were observed among the participants. On average, participants experienced a reduction in PD by approximately 1.5 mm, indicating a shallower space between the teeth and gums and, thus, healthier periodontal tissues. In addition, the CAL improved by an average of 1.2 mm, signifying enhanced attachment of gum tissue to the tooth surface, which is essential for dental stability. Participants also exhibited a significant reduction in plaque accumulation on tooth surfaces, indicative of improved oral hygiene practices. Furthermore, gingival inflammation, a common symptom of periodontal disease, notably decreased among participants, suggesting an overall improvement in gum health. Conclusion: In conclusion, this study provides compelling evidence supporting the positive impact of smoking cessation programs on the progression of periodontal disease. [ABSTRACT FROM AUTHOR]

Background: Periodontal health is a critical aspect of overall oral health, yet public awareness and education on this topic remain limited. With the increasing prevalence of online health education platforms, it is essential to evaluate the effectiveness of online periodontal health workshops compared with traditional in-person workshops on improving public awareness. Materials and Methods: Study Design: This randomized controlled trial (RCT) aimed to assess the impact of online and in-person periodontal health workshops on public awareness. Participants (N = 500) were randomly assigned to one of two groups: the online workshop group or the in-person workshop group. Online Workshop: Participants in this group accessed an interactive online periodontal health workshop, consisting of video presentations, animations, and quizzes. The workshop covered topics, such as gum disease prevention, oral hygiene, and the importance of regular dental checkups. In-Person Workshop: Participants in this group attended a traditional in-person periodontal health workshop conducted by dental professionals. The content and duration of this workshop mirrored the online version. Pre- and Postworkshop Assessments: Both groups completed pre-workshop and postworkshop assessments, including a knowledge questionnaire and a self-assessment of oral health habits. Arbitrary scores were assigned to quantify knowledge gain (0–100%). Results: Participants in the online workshop group showed a mean knowledge gain of 30% (standard deviation (SD) = 5.2), while those in the in-person workshop group exhibited a mean knowledge gain of 35% (SD = 4.7). The self-assessment of oral health habits indicated an improvement in both groups, with 60% of participants reporting better oral hygiene practices. Conclusion: Both online and in-person periodontal health workshops demonstrated effectiveness in improving public awareness and promoting better oral health habits. Combining both modalities could be an effective strategy for comprehensive public education on periodontal health. [ABSTRACT FROM AUTHOR]

This paper deals with the combination of rough sets and interval neutrosophic sets. We introduce the interval neutrosophic hyper-ideals in semi-hyper-rings. Also we study the rough interval neutrosophic hyper- ideals in semi-hyper-rings. [ABSTRACT FROM AUTHOR]

Quantum image processing is the budding branch in image processing for processing images in the quantum computer with the aid of quantum mechanics and linear algebra. Also, in recent decades, fuzzy has played a vital role in image processing as digital images are acquired with different uncertainties due to various factors. Hence, combining fuzzy and quantum in image processing techniques can bring effective results and has significant applications in different fields. In order to process the fuzzy image in a quantum computer, a new approach is derived to represent the fuzzy image as a quantum image. Thus, the quantum representation of fuzzy image derived in this paper enables storing and processing the fuzzy image using the basis states of qubits in the quantum circuit. Moreover, the experiment was made with a sample image to prepare a quantum image through the IBM quantum experience platform. Finally, through the measurement of the quantum circuit, the visual interface of quantum information for the proposed method is successfully established. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a deep neural network (DNN) for forecasting the intra-day solar irradiance, photovoltaic PV plants, regardless of whether or not they have energy storage, can benefit from the work being done here. The proposed DNN utilises a number of different methodologies, two of which are cloud motion analysis and machine learning, in order to make forecasts regarding the climatological conditions of the future. In addition to this, the accuracy of the model was evaluated in light of the data sources that were easily accessible. In general, four different cases have been investigated. According to the findings, the DNN is capable of making more accurate and reliable predictions of the incoming solar irradiance than the persistent algorithm. This is the case across the board. Even without any actual data, the proposed model is considered to be state-of-the-art because it outperforms the current NWP forecasts for the same time horizon as those forecasts. When making predictions for the short term, using actual data to reduce the margin of error can be helpful. When making predictions for the long term, however, weather information can be beneficial. [ABSTRACT FROM AUTHOR]

Due to increase of data in online web resources Recommendation System plays a vital role. In many platforms such as music, movie, books, videos, ecommerce for recommending the useful data. Video recommendation system provides users a very highly satisfying recommendations which in turn increase the user stickiness to the site. A personalized recommendation system with collaborative filtering technique is implemented to increase the user satisfaction. This method is used to recommend movies to users depending on their historical data on the streaming site and also by exploring the content watched by the users with similar preferences. A personalized recommendation system using collaborative filtering with multi-layer perceptron is implemented to increase the accuracy of the system. [ABSTRACT FROM AUTHOR]

This article presents a stability analysis of fractional-order tumor system that correlates tumor-immune with chemotherapy under Lyapunov's theory. More particularly, a finite-time stabilization of prescribed system with the consequence of quantized input and actuator fault is investigated. Further, the nonlinear tumor system is converted to Takagi-Sugeno(T-S) fuzzy model. Then based on the utilization of Gronwall's inequality, the necessary and sufficient conditions are derived with the assist of Lyapunov's candidates. To overcome the effect of external disturbances such as unwanted smoke, intake habits and tower radiations, the optimal level of performance which is reduced from the generalized performance extended dissipative is included. Finally, a numerical simulation is presented with graphical results to expound the superiority and validity of the prescribed controller. [ABSTRACT FROM AUTHOR]

In this study we introduce the concept of rough bipolar interval neutrosophic sets which is a combination of rough sets and bipolar interval neutrosophi sets. Also we define union, complement, intersection and some interesting properties of this set. [ABSTRACT FROM AUTHOR]

In the present study, the deflocculated sludge was disintegrated through thin layer immobilized titanium dioxide (TiO 2 ) as photocatalyst under solar irradiation. The deflocculation of sludge was carried out by 0.05 g/g SS of sodium citrate aiming to facilitate more surface area for subsequent TiO 2 mediated disintegration. The proposed mode of disintegration was investigated by varying TiO 2 dosage, pH and time. The maximum COD solubilization of 18.4% was obtained in the optimum 0.4 g/L of TiO 2 dosage with 5.5 pH and exposure time of 40 min. Anaerobic assay of disintegrated samples confirms the role of deflocculation as methane yield was found to be higher in deflocculated (235.6 mL/gVS) than the flocculated sludge (146.8 mL/gVS). Moreover, the proposed method (Net cost for control – Net cost for deflocculation) saves sludge management cost of about $132 with 53.8% of suspended solids (SS) reduction. [ABSTRACT FROM AUTHOR]

In this paper, a neural network (NN)-bacterial foraging algorithm (BFA) is proposed to control the load frequency of the power system. Traditionally, the tumbling decision element of BFA is defined by the stochastic values. But, these values are changed as per the variations of chemotactic step size so that the convergence time is increased. Here, NN is used to ensure the distribution of stochastic value with respect to the chemotactic step size and thus the performance of BFA is enhanced. The feed forward NN is used here with back propagation training algorithm. Using the proposed controller, the controller error, load changes, and speed changer position are tuned and the stability of the interconnected power system is improved. The proposed tuning controller is implemented in MATLAB/Simulink platform and the load frequency control responses are evaluated. The performances of proposed controller are compared with those of the PID controller and the BFA-PID controller. [ABSTRACT FROM AUTHOR]

Image segmentation has attracted a lot of attention due to its potential biomedical applications. Based on these, in the current research, an attempt has been made to explore object enhancement and segmentation for CT images of lungs infected with COVID-19. By implementing Pythagorean fuzzy entropy, the considered images were enhanced. Further, by constructing Pythagorean fuzzy measures and utilizing the thresholding technique, the required values of thresholds for the segmentation of the proposed scheme are assessed. The object extraction ability of the five segmentation algorithms including current sophisticated, and proposed schemes are evaluated by applying the quality measurement factors. Ultimately, the proposed scheme has the best effect on object separation as well as the quality measurement values. [ABSTRACT FROM AUTHOR]

Abstract: In the age of digital information, audio data has become an important part in many modern computer applications. Audio classification and indexing has been becoming a focus in the research of audio processing and pattern recognition. In this paper, we propose effective algorithms to automatically classify audio clips into one of six classes: music, news, sports, advertisement, cartoon and movie. For these categories a number of acoustic features that include linear predictive coefficients, linear predictive cepstral coefficients and mel-frequency cepstral coefficients are extracted to characterize the audio content. The autoassociative neural network model (AANN) is used to capture the distribution of the acoustic feature vectors. Then the proposed method uses a Gaussian mixture model (GMM)-based classifier where the feature vectors from each class were used to train the GMM models for those classes. During testing, the likelihood of a test sample belonging to each model is computed and the sample is assigned to the class whose model produces the highest likelihood. Audio clip extraction, feature extraction, creation of index, and retrieval of the query clip are the major issues in automatic audio indexing and retrieval. A method for indexing the classified audio using LPCC features and k-means clustering algorithm is proposed. [ABSTRACT FROM AUTHOR]

Abstract: In the age of digital information, audio data has become an important part in many modern computer applications. Audio classification has been becoming a focus in the research of audio processing and pattern recognition. Automatic audio classification is very useful to audio indexing, content-based audio retrieval and on-line audio distribution, but it is a challenge to extract the most common and salient themes from unstructured raw audio data. In this paper, we propose effective algorithms to automatically classify audio clips into one of six classes: music, news, sports, advertisement, cartoon and movie. For these categories a number of acoustic features that include linear predictive coefficients, linear predictive cepstral coefficients and mel-frequency cepstral coefficients are extracted to characterize the audio content. Support vector machines are applied to classify audio into their respective classes by learning from training data. Then the proposed method extends the application of neural network (RBFNN) for the classification of audio. RBFNN enables nonlinear transformation followed by linear transformation to achieve a higher dimension in the hidden space. The experiments on different genres of the various categories illustrate the results of classification are significant and effective. [Copyright &y& Elsevier]

Biophotonics, an interdisciplinary field merging biology with photonics, has transformed dentistry by offering innovative techniques and tools for diagnosis, treatment, and research. This overview explores the applications and benefits of biophotonics in dentistry, including early disease detection, precision in procedures, restorative dentistry assessment, real-time monitoring, and teeth whitening. We discuss how biophotonics improves patient care and the potential for future developments in personalized treatment, targeted therapy, enhanced imaging, and pain management. Biophotonics promises to continue revolutionizing oral healthcare, leading to better patient outcomes worldwide. [ABSTRACT FROM AUTHOR]

The paper reports the design and development of wrought Al-Cu alloys that retain high strength until 250 °C. It is achieved by minor alloying of Zr (<0.15 at%) and Nb (<0.1 at%) and inducing precipitation of stable nanometric dispersion of L1 2 ordered precipitates during controlled thermomechanical treatment (rolling) of cast alloys before conventional heat treatment (solutionizing and ageing). The rolling temperature was optimized to 450 °C by quantitatively evaluating the size distribution of the dispersions at different rolling temperatures. The dispersions influence the microstructure and the nature of the precipitation of the strengthening θ′ plates during subsequent conventional heat treatment. The microstructure of the processed alloy show stability at high temperatures. The atom probe tomography reveals Zr enrichment at the broad faces of θ′ plates. The presence of Zr plays a critical role in the high-temperature strength by promoting the stability of θ′ precipitates and resisting its coarsening. The composite microstructure of Al-Cu-Nb-Zr alloy displayed yield strength (YS) of 415 MPa and 220 MPa at room temperature and at 250 °C, which are higher by 26 % and 45 % respectively as compared to a commercial Al-Cu based alloy (2219-T851). The present results are promising for developing wrought Al alloys for high-temperature applications. • Effect of Zr and Nb addition in Al-Cu alloy processing through thermo-mechanical route. • Dynamic precipitation of nanometric L1 2 ordered phase in Al matrix during wrought processing • L1 2 ordered precipitate promotes the nucleation of θ′ and retard the growth and coarsening. • APT study reveals solute (Zr/Nb) segregation at the Al/θ′ interface. [ABSTRACT FROM AUTHOR]

In this paper, the output tracking control problem for a class of switched nonlinear systems with multiple time-varying delays is studied based on equivalent-input-disturbance (EID) approach. More precisely, with the use of suitable Lyapunov-Krasovskii functional together with average dwell-time technique, an output feedback tracking controller is designed which makes that the states of resulting system can asymptotically track the desired trajectory. Further, the EID estimator is implemented to reject both matched and unmatched disturbances effectively without requiring any prior knowledge of the disturbances. Simulation results are presented to illustrate the effectiveness and potential of the developed EID-based output tracking control design technique. The results reveal the fact that the tracking controller based on EID provides a better tracking performance than the feedback controller based on sliding mode technique. [ABSTRACT FROM AUTHOR]