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Most of the current attribute reduction methods use the measure to define the reduct, such as the positive region of rough set theory (RS), information entropy, and distance. However, the size of the reduct based on the measures is still limited. To cope with this problem, we propose a new approach of attribute reduction based on using the intuitionistic fuzzy topology (IFT). Firstly, a new IFT structure based on the pre-order relation and the intuitionistic fuzzy base (IF-base) structure is introduced. Secondly, a new measure is proposed to evaluate the significance of the attribute based on the IF subbase. Finally, the new reduction algorithms based on the IF-base filter and filter-wrapper methods are presented. The theoretical and experimental results show that the proposed method is efficient in terms of size and accuracy of the reduct. Specifically, the reduct of the F_IFT algorithm has an average size of 50% smaller, and the FW_IFT algorithm has an average accuracy of 10% greater than those of the related algorithms. Significantly, the algorithm FW_IFT can very remove noisy attributes. The classification accuracy of the reduct is 15% higher than that of the original set of attributes. [ABSTRACT FROM AUTHOR]

Background: Previous deep learning-based studies were mainly conducted on detecting periapical lesions; limited information in classification, such as the periapical index (PAI) scoring system, is available. The study aimed to apply two deep learning models, Faster R-CNN and YOLOv4, in detecting and classifying periapical lesions using the PAI score from periapical radiographs (PR) in three different regions of the dental arch: anterior teeth, premolars, and molars. Methods: Out of 2658 PR selected for the study, 2122 PR were used for training, 268 PR were used for validation and 268 PR were used for testing. The diagnosis made by experienced dentists was used as the reference diagnosis. Results: The Faster R-CNN and YOLOv4 models obtained great sensitivity, specificity, accuracy, and precision for detecting periapical lesions. No clear difference in the performance of both models among these three regions was found. The true prediction of Faster R-CNN was 89%, 83.01% and 91.84% for PAI 3, PAI 4 and PAI 5 lesions, respectively. The corresponding values of YOLOv4 were 68.06%, 50.94%, and 65.31%. Conclusions: Our study demonstrated the potential of YOLOv4 and Faster R-CNN models for detecting and classifying periapical lesions based on the PAI scoring system using periapical radiographs. [ABSTRACT FROM AUTHOR]

Climate change influences the geographic distribution of species, especially rare and endangered species such as the Vietnamese Golden Cypress (Xanthocyparis vietnamensis). In this study, we determined the key factors influencing modelled distribution of the species, and assessed the influence of climate change on the distribution of the species using the maximum entropy algorithm (MaxEnt) model, occurrence data of the species and environmental variables (climate, topography, and soil conditions). Our results showed that the MaxEnt models were significantly superior to a random prediction (AUC > 0.9). Precipitation of warmest quarter (bio18), elevation, distance to karst areas, and isothermality (bio3) contributed significantly modelled distribution of the species. The predicted size of the current potential distribution is significantly larger than the known range in Vietnam (i.e. Ha Giang, Tuyen Quang, Bac Kan, and Cao Bang provinces). The species' suitable areas were predicted to reduced and shift toward higher elevations in two climate models (ACCESS1-0 and MPI-ESM-LR) under two selected climate change scenarios (medium-low greenhouse gas concentration (RCP 4.5) and high greenhouse gas concentration (RCP 8.5)) in 2050 and 2070. These findings will provide valuable references for managers and decision-makers for the conservation management of rare and threatened species in Vietnam. [ABSTRACT FROM AUTHOR]

Background: Reducing energy consumption and greenhouse gas emissions is a crucial issue in the cassava starch processing industry. In this study, the integrated system combining livestock, cassava cultivation and cassava production in the same area leads to both a zero emission goal and economic efficiency, a typical example of an effective agro-industrial symbiosis. A heat exchange/recovery system was applied including the economizer, heat exchanger tank, biogas tank, and boiler. The economizer attached to the boiler's chimney transfers heat from exhaust gases for pre-heating feed water entering the boiler. The biogas tank recovers energy from the wastewater of starch production and livestock, and the generated biogas was used as fuel for the boiler. Results: The energy and exergy efficiency, energy losses, and exergy destruction for the heat recovery system were analyzed. The specific energy consumption was used to evaluate the overall energy efficiency for a cassava starch factory with a capacity of 20 tons/day. The results show that there is a high potential to recycle waste into energy in the cassava starch industry. The total energy saving and reduced greenhouse gas emissions per year of the cassava starch factory were 0.054%/year and 123,564 kgCO2/per year, respectively. Conclusions: Cassava starch factories can save energy and reduce emissions when applying a heat recovery system in the integrated agro-industrial system. Excess heat from the production was used for evaporating (removal of) NH3 in wastewater flow from the biogas tank, and for heating the biogas system to enhance the efficiency of methane production. A biochar filter was attached to the economizer for adsorption of released ammonium, and the biochar after adsorption was combined with sludge from the biogas tank to produce a solid biofertilizer. [ABSTRACT FROM AUTHOR]

This paper demonstrates the preparation of ZnMn2O4 nanoparticles through thermal hydrolysis in different solvents, such as isopropanol, ethylene glycol, glycerol, and water, combined with pyrolysis. The obtained samples were characterised by using X-ray diffraction (XRD), infrared spectroscopy (FT‒IR), scanning electron microscopy (SEM), energy-dispersive X-ray mapping, nitrogen adsorption/desorption isotherms, and a vibrating sample magnetometer. The electrocatalytic activity of ZnMn2O4 nanoparticles was investigated toward the oxidation of uric acid (UA) and xanthine (XA). The ZnMn2O4-nanoparticle-modified electrode not only enhances the oxidation currents of the two purine derivatives but also successfully separates the voltammetric signals of the analytes in their binary mixture and, hence, is employed for their simultaneous determination. The factors affecting the analysis, such as pH, scan rate, linear range, detection limit, reproducibility, and interferents, were also investigated. The results show that the UA and XA limits of detection are as low as 0.55 and 1.28 µM, and the modified electrodes have satisfactory repeatability and reproducibility. The practical application of the modified electrode was demonstrated by simultaneously determining the concentrations of UA and XA in urine samples with exceptional accuracy. [ABSTRACT FROM AUTHOR]

There have been various researches regarding routing in wireless sensor networks for many years, but underground wireless communication in complex environments like soil-air or water-air is a new and tough problem for researchers. Replacement of the battery of sensor nodes is already a challenging task under normal conditions. However, the difficulty increases significantly when deploying them in environments like cultivation soils; therefore maintaining the network lifetime as well as the total number of living nodes in such an adverse setting is a challenging task for researchers. In this paper, we propose a new energy-efficient routing model in wireless underground sensor networks (WUSN) that considers the path loss energy of the energy model and the energy efficiency of the total network. The proposed FCM-WUSN model is compared against the well-known fuzzy routing method namely FCM in terms of network lifetime, energy consumption, total node alive, and underground node alive. It has been observed that the network lifetime of the proposed method is 1.5 × longer than that of the FCM, and the number of alive nodes including the underground nodes are 1.6 × higher than that of the FCM. The results are better even when running data sets with more sensors in the network and sensor positioning based on the depth of sensors deployed underground. [ABSTRACT FROM AUTHOR]

Nickel ferrite/(N,S)graphene oxide (NF/(N,S)GO) was synthesized by a facile coprecipitation-hydrothermal route using Ni2+ and Fe3+ mixture in (N,S)GO suspension. This material was used as a photocatalyst for the degradation of rhodamine B (RhB) as a dye model in aquatic environment. It was found that NiFe2O4 nanoparticles with a particle size of 11.5 nm was highly dispersed on (N,S)GO matrix, which was prepared from graphite and thiourea. Visible-light-induced photodegradation of RhB over NF/(N,S)GO has been studied where NF/(N,S)GO presented high photodegradation activity toward RhB compared to both nickel ferrite and (N,S)GO. Furthermore, after three cycles of photodegradation of RhB, the catalyst did not exhibit significant loss of activity (with a drop of around 15% in degradation efficiency compared to fresh catalyst), confirming its stability. The chemical oxygen demand (COD) measurement revealed a gradual decrease of COD from 49.4 mg.L−1 at initial time to 4.8 mg.L−1 after 240 min of light illumination, indicating high degree of mineralization of the degradation process. In addition, kinetic and radical scavenger studies suggest that superoxide ions (·O2−), hydroxyl ions (·OH) were the dominant photooxidants, followed by hole (h+) and electron (e−). The degradation mechanism of RhB over NF/(N,S)GO was also addressed. This study provided a possible treatment approach for organic pollutants in aqueous solution by exploiting visible light source. [ABSTRACT FROM AUTHOR]

In this work, Ce3+ (0.5 mol%) and Sm3+ (0.5, 1.0, 1.5 and 2.0 mol%) co-doped borotellurite (BTZN) were successfully fabricated by the melt quenching method. Crystal structure and luminescent characteristics of the synthesized glasses are investigated in detail through X-ray diffraction, SEM images, absorption, excitation, emission spectra and luminescence decay. Result of the XRD patterns shows the amorphous phase nature of the BTZN glass sample. SEM images indicated that surface morphologies of glass samples are highly homogeneous. The analysis of photoluminescence reveals a notable spectral overlap between the Ce3+ emission and the Sm3+ excitation in the means of effective energy transfer (ET): Ce3+ → Sm3+, thus achieving better luminescence properties of Sm3+. The luminescence lifetime of BTZN:Ce/Sm glasses was found to decline on rising Sm3+ concentration, which strongly determine in increasing the ET efficiency by dipole-quadrupole interaction. For the co-doping glasses, the CIE coordinates of the selected glasses can be adjusted from blue to bluish-white colors regions with varying the Sm3+ concentration. [ABSTRACT FROM AUTHOR]

The spherical fuzzy set (SFS) model is one of the newly developed extensions of fuzzy sets (FS) for the purpose of dealing with uncertainty or vagueness in decision making. The aim of this paper is to define new exponential and Einstein exponential operational laws for spherical fuzzy sets and their corresponding aggregation operators. We introduce the operational laws for exponential and Einstein exponential SFSs in which the base values are crisp numbers and the exponents (weights) are spherical fuzzy numbers. Some of the properties and characteristics of the proposed operations are then discussed. Based on these operational laws, some new aggregation operators for the SFS model, namely Spherical Fuzzy Weighted Exponential Averaging (SFWEA) and Spherical Fuzzy Einstein Weighted Exponential Averaging (SFEWEA) operators are introduced. Finally, a decision-making algorithm based on these newly introduced aggregation operators is proposed and applied to a multi-criteria decision making (MCDM) problem related to ranking different types of psychotherapy. [ABSTRACT FROM AUTHOR]

This work describes the synthesis of nickel ferrite/reduced graphene oxide (FN/rGO) and the simultaneous determination of uric acid (UA), xanthine (XT), and caffeine (CF) by using the resultant FN/rGO-modified electrode. The material was characterized by means of X-ray diffraction, scanning electron microscopy, energy-dispersive X-ray microscopy (mapping energy-dispersive X-Ray), adsorption/desorption isotherms of nitrogen, vibrating sample magnetometry, and Raman spectroscopy. The obtained composite with a large surface area comprises reduced-graphene-oxide layers and highly dispersed nickel-ferrite nanoparticles. This composite possesses significant magnetization saturation with a soft magnetic property. The electrocatalytic activity of FN/rGO was studied toward the oxidation of one important purine, UA, and two oxypurines, XT and CF. The electrode successfully separates the voltammetric signals of the three analytes in a trinary mixture and is employed for their simultaneous determination. The peak current varies linearly with the increase of UA, XT, and CF concentration in a range of 4.0–21.5 µM with detection limits of 1.3, 1.6, and 1.4 µM for UA, XT, and CF, respectively. The practical application of the modified electrode was demonstrated by simultaneously determining the concentrations of UA, XT, and CF in urine samples with expectable recoveries. [ABSTRACT FROM AUTHOR]

Nowadays, people use video compression for recreating video without affecting the quality with reduced size. In recent years, the number of video files has increased in social media, smartphones and video recording tools. It is not easy to search and retrieve specific content-based videos. With the use of advanced techniques, the video clips were retrieved based on an object, themes, people and other entities. Video communication tolerates a lot of problems despite using a restricted volume of cutting-edge methodology to accumulate huge-sized videos. For this motivation, the video compression technique is used. Extracting semantic data from a large number of video-based applications is necessary. These advanced techniques were mainly used in digital marketing, social marketing, and video editing. The semantic data analysis is aimed to extract the video by expressing it in text or speech. It is used to understand the content of the video and extract it in the form of footage or video clips through a query. The existing methodologies are insufficient and high amount of optimization cost. The speedy expansion in the available quantity of video information has increased an essential constraint to lengthen. Intellectual methodologies are used to construct and extort the semantic substance. In this paper, Convolutional Neural Network with VGGNet is developed for extorting essential elements in videos and for spatial modification within the frames. Rule-related information employs temporal associations to extort the characterizations. The dynamic movements are extorted by the Optical stream algorithm and it finds the temporal positions. The new algorithm is experimentally validated. [ABSTRACT FROM AUTHOR]

Super-intensive shrimp farming area has been increasing rapidly in the Mekong Delta due to its high expected profit. However, such production practice raises an emerging concern of environmental aspects. Thus, the current study aims at measuring and comparing the environmental or nutrient efficiency among 230 semi-intensive, intensive and super-intensive white shrimp (Litopenaeus vannamei) farmers. To measure the environmental efficiency, the study applied material balance principle and data envelopment analysis. On average, the environmental efficiency of white shrimp farmers was 19.52%, suggesting that the shrimp farmers produce a nutrient surplus of 1216 kg/ha to the aquatic environment. The intensive farming produces the highest amount of nutrient surplus. The study also found that the environmental efficiency of super-intensive shrimp farming is 40%, which is significantly higher than that of the semi-intensive and intensive shrimp farming practices at 23.27% and 17.08%, respectively. The study also offers some policy implications for improving the technical, allocative and environmental efficiencies of white shrimp farming in the Mekong Delta. [ABSTRACT FROM AUTHOR]

The detection of spatial and temporal changes (or change detection) in remote sensing images is essential in any decision support system about natural phenomena such as extreme weather conditions, climate change, and floods. In this paper, a new method is proposed to determine the inference process parameters of boundary point, rule coefficient, defuzzification coefficient, and dependency coefficient and present a new FWADAM+ method to train that set of parameters simultaneously. The initial data are clustered simultaneously according to each data group. This result will be the basis for determining a suitable set of parameters by using the FWADAM+ concurrent training algorithm. Eventually, these results will be inherited in the following data groups to build other complex fuzzy rule systems in a shorter time while still ensuring the model's efficiency. The weather imagery database of the United States Navy (US Navy) is used to evaluate and compare with some related methods using the root-mean-squared error (RMSE), R-squared (R2) measures, and the analysis of variance (ANOVA) model. The experimental results show that the proposed method is up to 30% better than the SeriesNet method, and the processing time is 10% less than that of the SeriesNet method. [ABSTRACT FROM AUTHOR]

The exponential increase in the number of diabetics around the world has led to an equally large increase in the number of diabetic retinopathy (DR) cases which is one of the major complications caused by diabetes. Left unattended, DR worsens the vision and would lead to partial or complete blindness. As the number of diabetics continue to increase exponentially in the coming years, the number of qualified ophthalmologists need to increase in tandem in order to meet the demand for screening of the growing number of diabetic patients. This makes it pertinent to develop ways to automate the detection process of DR. A computer aided diagnosis system has the potential to significantly reduce the burden currently placed on the ophthalmologists. Hence, this review paper is presented with the aim of summarizing, classifying, and analyzing all the recent development on automated DR detection using fundus images from 2015 up to this date. Such work offers an unprecedentedly thorough review of all the recent works on DR, which will potentially increase the understanding of all the recent studies on automated DR detection, particularly on those that deploys machine learning algorithms. Firstly, in this paper, a comprehensive state-of-the-art review of the methods that have been introduced in the detection of DR is presented, with a focus on machine learning models such as convolutional neural networks (CNN) and artificial neural networks (ANN) and various hybrid models. Each AI will then be classified according to its type (e.g. CNN, ANN, SVM), its specific task(s) in performing DR detection. In particular, the models that deploy CNN will be further analyzed and classified according to some important properties of the respective CNN architectures of each model. A total of 150 research articles related to the aforementioned areas that were published in the recent 5 years have been utilized in this review to provide a comprehensive overview of the latest developments in the detection of DR. [ABSTRACT FROM AUTHOR]

Redwater is a highly toxic waste generated during the purification of 2,4,6-trinitrotoluene (TNT), one of the most widely used military explosives. Redwater treatment is difficult because of its high chemical oxygen demand (COD) and strong red color. In this study, we present a comprehensive study on the treatment of the actual TNT redwater by a combination of chemical coagulation and electro-Fenton process. The pretreatment by traditional coagulation using poly-aluminum chloride (PAC) was investigated at different operation conditions of solution pH (4–11), PAC concentration (500–3500 mg/L), flocculant concentration (0–3.5 mg/L), times for coagulation (0–7 min) and flocculation (0–4 min), and stirring speed (100–700 rpm). This pretreatment could removal 52.84% of COD, 53.22% of color, 36.76% of TSS, and 25.88% of total nitrogen under optimal conditions (i.e., pH 7.0, PAC dosage of 2500 mg/L, anion flocculant dosage of 1.25 mg/L, coagulation time of 4 min, flocculation time of 1 min, and stirring speed of 500 rpm). The pretreated redwater was then subjected to an electro-Fenton process using Pt/Ti as anode and carbon felt as the cathode. The effects of solution pH (2–6), iron content (0.05–1 mM), current intensity (8.33–33.33 mA/cm2) and reaction times (0–200 min) on the electro-Fenton treatment were investigated. Under optimal condition (i.e., pH 3.0, Fe(II) concentration of 0.1 mM, current intensity of 16.67 mA/cm2, and reaction time of 120 min), the removal efficiency reached 87.07, 96.2, 72.94, and 50.26% for COD, color, TSS, and total nitrogen, respectively. UV-Vis spectroscopy results confirmed the role of electro-Fenton for the effective removal of aromatic nitro compounds in the redwater. These results suggest that the combination of coagulation and electro-Fenton process could be a promising technology for treating TNT redwater. [ABSTRACT FROM AUTHOR]

In the present work, a nickel-ferrite/reduced-graphene-oxide composite was synthesized by a hydrothermal co-precipitation approach. The obtained materials were characterized by using X-ray diffraction, scanning electron microscopy, energy-dispersive X-ray microscopy (mapping energy dispersive X-Ray), adsorption/desorption isotherms of nitrogen, vibrating sample magnetometer, X-ray photoelectron microscopy, and Raman spectroscopy. The obtained composite with a large surface area consists of highly dispersed nickel-ferrite nanoparticles (7–10 nm) over reduced-graphene-oxide layers. In addition, this composite possesses high magnetization saturation with a soft magnetic property. A sensitive electrochemical sensor for the determination of clenbuterol was manufactured by modifying a glassy-carbon electrode with nickel ferrite/reduced graphene oxide composite film. The peak current measured with differential pulse voltammetry for clenbuterol solutions is linearly proportional to its concentration ranging from 0.99 to 18.03 µM with a limit of detection of 0.17 µM and a limit of quantification of 0.52 µM. Furthermore, the reproducibility, precision, and accuracy of the proposed method were also investigated. This proposed method was applied to detect clenbuterol in pig urine samples with satisfactory results. [ABSTRACT FROM AUTHOR]

Manganese ferrite/rice husk-derived activated carbon (MF/AC) was synthesized in a coprecipitation process followed by hydrothermal treatment. The obtained materials were characterized by using X-ray diffraction (XRD), scanning electron microscopy (SEM), energy-dispersive X-ray mapping (EDX-mapping), vibrating sample magnetometer (VSM), and nitrogen adsorption/desorption isotherms. The MF/AC samples exhibit para-super-magnetic and excellent magnetic properties with high saturation magnetization. The obtained MF/AC was utilized as an electrode modifier for determining methylparaben (MP) in cosmetic products. The pH, amount of modifier, and DPV parameters were investigated to optimize the experimental conditions for the electrochemical process. In the MP concentration range of 14.77–90.91 μM, the peak DPV current exhibits good linearity, with a limit of detection of 4.89 μM and a limit of quantification of 46.55 μM. The data were statistically compared with those obtained from the standard HPLC method. [ABSTRACT FROM AUTHOR]

Identification and repairing holes in ireless sensor networks (WSNs) are challenging with full and low complexity. A local healing approach based on distributed virtual forces will make the healing area of the hole possible. The construction of repairing design is a locally allocated and constructed methodology that will enhance the coverage area in WSNs. In this paper, an enhanced border and hole detection (EBHD) algorithm is proposed for the energy containment of WSN. The performance of the proposed EBHD methodology is compared with those of the existing methods through Network Simulator 2 (NS2). The simulation results prove that the proposed EBHD achieves a high packet reception ratio while improving the residual energy and reducing end-to-end delay. [ABSTRACT FROM AUTHOR]

Fuzzy Knowledge Graph (FKG) has recently been emerging as one of the key techniques for supporting classification and decision-making problems. FKG is a novel concept that was firstly introduced in 2020 by integrating approximate reasoning with inference mechanism to find labels of new records, which are impossible for inference by the rule base. However, one of the key limitations of FKG is the use of a single pair to find new records' label that leads to low performance in approximation. This paper presents a novel approach of using FKG pairs instead of a single pair as in the classical model. A novel FKG-Pairs model including a new representing method and an approximation algorithm is presented. Theoretical analysis of the FKG-Pairs model such as identification of a threshold for the best value (k∗) pairs is also investigated. Finally, to validate the proposed model, a numerical example and the experiments on the UCI datasets are presented. In addition, a two-way ANOVA method is also conducted to validate the model statistically. The novel concept about the FKG-Pairs given in this paper exposes new ideas in the effort to realize the much-anticipated decision-making and classification problems in fuzzy systems [ABSTRACT FROM AUTHOR]

Ferromagnetic nanoparticles (FeNPs) have been successfully prepared by using solvothermal reduction method with polyethylene glycol as a solvent. The obtained materials were characterized by X-ray diffraction, scanning electron microscopy, Raman spectroscopy, nitrogen adsorption/desorption isotherms, and vibrating sample magnetometer. The magnetic properties and morphology of iron oxides was found to depend on the solvothermal temperatures. An increase in heating temperature from 120 to 240 °C did not only enhance the saturation magnetization from 21 to 81 emu g−1 but also produced larger, highly crystalline cubic particles of FeNPs. The development of FeNPs-modified glassy carbon electrode (GCE) using Nafion as a binder for sildenafil (SIL) detection was studied. Cyclic voltammetry and differential pulse voltammetry methods were employed for the electrochemical analysis of SIL using the modified electrode. The proposed sensor exhibited excellent electrocatalytic activity towards SIL oxidation with an enhancement in oxidation peak current. Under the optimum conditions, the current response for the determination of SIL was linear in two concentration ranges of 1.33–9.20 and 9.20–80.76 ppm and the limit of detection was 1.01 ppm. This sensor was then used for the determination of SIL in real samples with satisfactory results. [ABSTRACT FROM AUTHOR]

Background: Economic benefit has been analyzed for the yield of farming products when designing a farming system, while waste treatment also generates profitable energy products for this system. The economic factor is decisive in decision-making for applying waste treatment solutions for a small-scale farming system. A household farming system in the Mekong Delta generates many kinds of organic wastes, but most of the agricultural waste resources are disposed of into the environment. Methods: This study approaches an analysis of economic–environmental–energy (EEE) efficiency for waste treatment of an integrated livestock-orchard (LO) system on a household scale in the Mekong Delta. This novel analysis method is based on the energy content of biomass and its cost. The EEE efficiency is optimized to gain objective functions regarding energy yield efficiency, system profit, and CO2 sequestration for the treatment model. The algorithms are built for optimizing these objective functions. Results: The optimization results show the treatment model of pyrolysis and pelleting gain all the objective functions with high efficiency. The model is efficiently applied for the LO system that generates more than 100 kg of orchard residues and 3000 kg of pig manure. The system with a charcoal oven and pellet machine is capable to gain energy efficiency due to its potential biofuel products, such as biochars and pellet products. A treatment model of composting, pyrolysis, and pelleting gives the best performance of overall EEE efficiency. Conclusions: This work has proven economic benefits from integrating biogas tank, charcoal oven, and pellet machine in an integrated LO system. The system contributes not only for reducing CO2 emissions but also for supplementing secondary renewable bioenergy, as well as for increasing incomes and thus supporting livelihoods for the local farming households. [ABSTRACT FROM AUTHOR]

Pentapartitioned neutrosophic sets are a generalization of the single-valued and quadri-partitioned single-valued neutrosophic sets, and utilizes five symbol-valued neutrosophic logic. In this paper, we introduce some novel concepts regarding pentapartitioned neutrosophic graphs (PPNGs), and emphasize the effectiveness at interpreting extremely heterogeneous data that are prevalent in our daily life, particularly data gathered from various different sources which are becoming increasingly common place in the current times. The applicability of the proposed PPNG is demonstrated by applying the PPNGs on a potential real-life scenario on responding to the spread of COVID-19, where PPNGs are used to determine the safest path of travel and the safest place to stay to minimize the chances of getting infected. Both of this information have proven to be vital aspects in the efforts to combat the spread of the COVID-19 pandemic while providing the necessary support to the domestic economies, most of which are currently in recession due to the adverse effects brought upon by the pandemic. Hence, the PPNGs are applicable to all countries around the world and can be used under any circumstances such as pandemics or even in regular situations to optimize the travelling time and distance. [ABSTRACT FROM AUTHOR]

In this world, travelling is the daily routine to reach the desired places such as schools, hospitals, offices and so on. Everyone makes use of their vehicles like two-wheelers or four-wheelers to reach the destination. Vehicle condition plays a major role which includes many parameters such as tyre condition, engine overheating, low fuel mileage. This paper concentrates on the examination of tyre track design for deformities. The mileage of a vehicle relies upon different components. However, it depends on different parameters like street condition, tyre weight and numerous others. For smooth driving and for safe traffic reasons, the auspicious maintenance of tyres plays a vital task. Consequently, the likelihood of prediction of tyre imperfections is assumed to play a vital role to fleet proprietors, government and tyre makers. Researchers make use of recent technologies such as machine learning and deep learning concepts to provide a solution to various real-life problems. The creation of a weighted quality-based convolutional neural network with the purpose of discovery of imperfections on tyres by the examination of pictures of tyre track designs is being proposed. The proposed system with the accessible data-set made public by different tyre producers is included for evaluation. The consequences of our tests demonstrate that it is conceivable to predict the deformities and durability of a tyre from its track designs through a weighted quality-based convolutional neural network. The proposed methodology is used to identify the group of feature values from the tyre image. These kinds of feature values are utilized to implement the dissimilarity of the feature related pixels. The number of epochs is varied as a callback mechanism has been used to validate the proposed model. Binary accuracy and mean absolute error are the best metrics to evaluate the performance which are discussed in the results section. Also, precision, recall and F-measure are used as validation parameters for the proposed model. [ABSTRACT FROM AUTHOR]

Data partition with high confidence is one of the main concentration of researchers in Soft Computing for many years. It is known that there may be some data with less confidence (wrong values, incorrect attribute types, irrelevant domain ranges, etc.) existed in the whole dataset due to the data gathering process. This would degrade the performance of final clustering results because of noises and outliers being occurred. Safe semi-supervised fuzzy clustering has been used extensively in recent years to tackle with this problem by adding the concept of a local graph between labeled and unlabeled data so that wrong labeled data has small impact to the final clusters. However, this process often takes much computational time and sometimes produces unreasonable results. In this research, we propose a new algorithm for the Data partition with confidence problem named as Trusted Safe Semi-Supervised Fuzzy Clustering Method (TS3FCM). The key motivation behind TS3FCM is to handle the drawbacks of the related safe semi-supervised fuzzy clustering algorithms regarding huge computational time. The novelty of TS3FCM against the other safe semi-supervised fuzzy clustering algorithms lies at the isolated processes of finding trusted labeled data and performing semi-supervised fuzzy clustering. The key contributions of the paper are briefly summarized as follows. At first, a new objective function is proposed. This function is incorporated with new weights for each labeled data so that the system can check whether a labeled data point is corrected or not. This function is also optimized to find the cluster centers and the membership matrix. Indeed, the labeled data having small impact after clustering are either set up with very low membership values or removed from the set of labeled data. Furthermore, a new semi-supervised fuzzy clustering model is defined to partition the whole dataset with the additional information being a mixture of the prior membership degrees ( U ¯ ) and labeled data. The whole TS3FCM works through 3 main phases with the main aim to accelerate the computational time and to achieve reasonable clustering quality compared to the related algorithms. TS3FCM is implemented and experimentally compared against the related methods such as the standard Fuzzy C-Means (FCM), the Semi-supervised Fuzzy Clustering method (SSFCM), and the Confidence-weighted safe semi-supervised clustering (CS3FCM) algorithm by both the computational time and the quality of clustering results. The experimental results on the benchmark UCI Machine Learning datasets show that TS3FCM runs faster than the other algorithms while maintaining reasonable clustering quality. We also analyze the results statistically by ANOVA. [ABSTRACT FROM AUTHOR]

Robotics is one of the most emerging technologies today, and are used in a variety of applications, ranging from complex rocket technology to monitoring of crops in agriculture. Robots can be exceptionally useful in a smart hospital environment provided that they are equipped with improved vision capabilities for detection and avoidance of obstacles present in their path, thus allowing robots to perform their tasks without any disturbance. In the particular case of Autonomous Nursing Robots, major essential issues are effective robot path planning for the delivery of medicines to patients, measuring the patient body parameters through sensors, interacting with and informing the patient, by means of voice-based modules, about the doctors visiting schedule, his/her body parameter details, etc. This paper presents an approach of a complete Autonomous Nursing Robot which supports all the aforementioned tasks. In this paper, we present a new Autonomous Nursing Robot system capable of operating in a smart hospital environment area. The objective of the system is to identify the patient room, perform robot path planning for the delivery of medicines to a patient, and measure the patient body parameters, through a wireless BLE (Bluetooth Low Energy) beacon receiver and the BLE beacon transmitter at the respective patient rooms. Assuming that a wireless beacon is kept at the patient room, the robot follows the beacon's signal, identifies the respective room and delivers the needed medicine to the patient. A new fuzzy controller system which consists of three ultrasonic sensors and one camera is developed to detect the optimal robot path and to avoid the robot collision with stable and moving obstacles. The fuzzy controller effectively detects obstacles in the robot's vicinity and makes proper decisions for avoiding them. The navigation of the robot is implemented on a BLE tag module by using the AOA (Angle of Arrival) method. The robot uses sensors to measure the patient body parameters and updates these data to the hospital patient database system in a private cloud mode. It also makes uses of a Google assistant to interact with the patients. The robotic system was implemented on the Raspberry Pi using Matlab 2018b. The system performance was evaluated on a PC with an Intel Core i5 processor, while the solar power was used to power the system. Several sensors, namely HC-SR04 ultrasonic sensor, Logitech HD 720p image sensor, a temperature sensor and a heart rate sensor are used together with a camera to generate datasets for testing the proposed system. In particular, the system was tested on operations taking place in the context of a private hospital in Tirunelveli, Tamilnadu, India. A detailed comparison is performed, through some performance metrics, such as Correlation, Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), against the related works of Deepu et al., Huh and Seo, Chinmayi et al., Alli et al., Xu, Ran et al., and Lee et al. The experimental system validation showed that the fuzzy controller achieves very high accuracy in obstacle detection and avoidance, with a very low computational time for taking directional decisions. Moreover, the experimental results demonstrated that the robotic system achieves superior accuracy in detecting/avoiding obstacles compared to other systems of similar purposes presented in the related works. [ABSTRACT FROM AUTHOR]

This paper proposes a novel Z-source boost derived hybrid converter (ZSBDHC) that produces high gain dual output from a single DC input, which is the choice of a solar photovoltaic system. This has multiple advantages as lesser count of switches, high power processing capability and high reliability result in reduction of overall cost. Besides, the prime advantage of this topology is achieving high voltage gain for a low duty ratio. The system transfer function is derived and the stability of the system is studied with the aid of average state model of the converter under continuous conduction mode. The transfer function of the system exhibits the sixth order dynamic characteristics, which is reduced to second order approximation to design the control parameters and to develop the closed loop scheme. To prove the robustness of the ZSBDHC with voltage feedback control, it is examined with line, load regulation and set point variation. Computer simulation is done for the dynamic characteristics of the ZSBDHC. The experimental operation is also demonstrated to validate the simulated results. [ABSTRACT FROM AUTHOR]

This article reports the synthesis of the University of Oslo-66 (UiO-66) and its application to simultaneous voltammetric determination of ascorbic acid (AA) and acetaminophen (AC). The zirconium terephthalate samples synthesized in different solvents (the N,N′-dimethylformamide, acetonitrile, and ethanol) are characterized with XRD, SEM, TG, nitrogen adsorption/desorption isotherm, and XPS techniques. Among solvents the N,N′-dimethylformamide solvent is favorable for the UiO-66 phase formation. The synthesized UiO-66 material with spherical particles with round 100–150 nm in diameters possesses a large specific surface area. The UiO-66 is used as electrode modifier to develop UiO-66 modified glassy carbon electrode for determining simultaneous AA and AC. The UiO-66 modified electrode demonstrates excellent electrocatalytic performance towards AA and AC oxidation compared with bare GCE. The anodic peak current varies linearly to AA and AC concentration in the range of 0.02 to 2.92 μM. The level of detection (S/N = 3) was found to be 0.019 and 0.018 μM, respectively. The proposed differential pulse voltammetry method was used to determine individually or simultaneously AA and AC in pharmaceutical formulations. The results are not significant different from those analyzed by high-performance liquid chromatography. [ABSTRACT FROM AUTHOR]

In this paper we study a generalization of Clifford algebra depending on parameters introduced by Tutschke and Vanegas in 2008. We also introduce some related notions, a Cauchy–Pompeiu integral formula, and two boundary value problems for monogenic functions with values in this Cliford algebra. [ABSTRACT FROM AUTHOR]

With the consistent growing maneuver and applications of mobile technologies, dynamic handling of queries related to information on electronic media (Broadcast Queries) plays a vital role. Broadcast Queries (BQs) consists of some authentic characteristics that are not addressed when compared to conventional spatial query processing. It is possible to achieve a considerable reduction in latency while maintaining the highest accuracy and scalability related to queries related to peer-to-peer allocation and thereby allows finishing queries with no interruption at a mobile congregation. In this paper, we propose a hybrid cache management algorithm in order to proficiently encourage accessing of data in ad hoc networks. The verification algorithm gathers the neighboring peer data items to provide the spatial query solutions. The network utilization in the network is related with the available bandwidth and the free space through efficient cache management. The hybrid cache management is combined with C a c h e Data and C a c h e Path techniques utilizing the concept of cache replacement policies with the motivation to further improve the performance of BQs. The experimental results indicate that the proposed scheme works well in reduction of query delay and also message complexity than the related techniques. [ABSTRACT FROM AUTHOR]

The Internet is being extensively used in various fields to serve billions of users, which leads to the number of network security issues. Here, Internet Protocol Spoofing is considered the main threat for masquerade of the packet identity. An IP packet, which contains the header with the source IP address, lacks source verification. The invaders to spoof the network address of the packet use this vulnerability. To overcome this, verification of source is performed by marking the packets and tracing back to the source. Existing schemes make use of either packet marking or packet logging for trace back to the source with high computational and storage overhead. This paper proposed a scheme to minimize both the overheads by using a Combined IP Traceback procedure. Packet marking is done efficiently by using the 16-bit ID field of the packet header and packet logging is completed more effectively by using the hash table. The path reconstruction is done using the mark value in the packet, which traces back to the original source border router. The proposed method is empirically validated against the related ones. [ABSTRACT FROM AUTHOR]

The single-valued neutrosophic set (SVNS) is a well-known model for handling uncertain and indeterminate information. Information measures such as distance measures, similarity measures and entropy measures are very useful tools to be used in many applications such as multi-criteria decision making (MCDM), medical diagnosis, pattern recognition and clustering problems. A lot of such information measures have been proposed for the SVNS model. However, many of these measures have inherent problems that prevent them from producing reasonable or consistent results to the decision makers. In this paper, we propose several new distance and similarity measures for the SVNS model. The proposed measures have been verified and proven to comply with the axiomatic definition of the distance and similarity measure for the SVNS model. A detailed and comprehensive comparative analysis between the proposed similarity measures and other well-known existing similarity measures has been done. Based on the comparison results, it is clearly proven that the proposed similarity measures are able to overcome the shortcomings that are inherent in existing similarity measures. Finally, an extensive set of numerical examples, related to pattern recognition and medical diagnosis, is given to demonstrate the practical applicability of the proposed similarity measures. In all numerical examples, it is proven that the proposed similarity measures are able to produce accurate and reasonable results. To further verify the superiority of the suggested similarity measures, the Spearman's rank correlation coefficient test is performed on the ranking results that were obtained from the numerical examples, and it was again proven that the proposed similarity measures produced the most consistent ranking results compared to other existing similarity measures. [ABSTRACT FROM AUTHOR]

Heuristic computational intelligence techniques are widely used in combinatorial optimization problems, essentially in large size configurations. Bio-inspired heuristics such as PSO, FA or FPA showed their capacities to solve such problems. Bi-heuristic optimization consists of using a couple of techniques and a collaboration mechanism. This paper reviews the major contributions in solving TSP with bi-heuristics and presents a new hybridization scheme based on FPA, ACO with Ls, ant supervised by flower pollination with local search, ASFPA-Ls; as well as the impact of social and cognitive PSO for the ant supervised by PSO with local search, ASPSO-Ls. AS-chaotic-PSO-Ls which stands for ant supervised by chaotic PSO local search is also investigated. The meta-heuristic algorithms (FPA, PSO or chaotic PSO) and ant colony optimization are used with a hierarchical collaboration schema in addition to a local search mechanism. In this work, the local search strategy, Ls, used is the 2-opt method; the proposals are called, respectively, ASFPA-Ls, cognitive ant supervised by PSO with local search, Co-ASPSO-Ls, social-ASPSO-Ls and AS-chaotic-PSO-Ls; where ACO is coupled with a local search heuristic mechanism called 2-opt, and a set of meta-heuristics, FA, FPA and PSO asked to adapt ACO parameters while running. Comparative experimental investigations are conducted using the TSP test bench. Proposed hybridizations attended fair solutions for the TSP problems used in the experimental investigations including berlin52, St70, eil76, rat 99, eil101, KroA100, Ch150 and kroA200. A good balance performance/time is found with the social ant supervised by PSO with local search, So-ASPSO-Ls. The cognitive ant supervised by PSO with local search, Co-ASPSO-Ls comes in the second position in terms of time effectiveness with close performances to AS-chaoticPS0-LS, all proposed approaches returned low rate errors or BKS for used test benches: berlin52, st70, eil76, rat99, kroA100 and kroA200. [ABSTRACT FROM AUTHOR]

Detection and clustering of commercial advertisements plays an important role in multimedia indexing also in the creation of personalized user content. In existing methodologies, the mining techniques were text, image, audio retrieval based on knowledge based environment and commercial video retrieval based on rule-based algorithms, logo-based algorithms, recognition based methods. The quality video with enhanced accuracy has been detected using the automated commercial and general program for video detection technique. The clustering technique implements the clustering process for the entire video to the frames and the required main frames are depended on the total amount of frames. The key frames are extracted from the video sequences and the duplicate key frames are eliminated from the video sequences. The video compression feature has been optimized using hybrid end-to-end compression technique to extract the features and reconstruct the video frames. The main encoding algorithm performs the optimization to produce the encoded frames and it is further compressed using the additional encoding algorithm. The performance results show that the proposed technique has the improved performances in terms of MSE, PSNR, SSIM, compression ratio and the computation time which is compared with the related techniques. [ABSTRACT FROM AUTHOR]

Inaccurate prediction would cause the insurance company encounter catastrophic losses and may lead to overpriced premiums where low-earning consumers cannot afford to insure themselves. The ability to forecast mortality rates accurately can allow the insurance company to take preventive measures to introduce new policies with reasonable prices. In this paper, several Lee–Carter (LC) based models are used to forecast the mortality rates in a case study of the Malaysian population. The LC-ARIMA model and also a combination of the LC model with two machine learning (ML) methods, namely the random forest (RF) and artificial neural network (ANN) methods are utilized on the prediction of mortality rates for males and females in Malaysia, whereby the LC-Random Forest (LC-RF) hybrid model is a new model that is introduced in this paper. Seventeen years of mortality data in Malaysia are selected as the dataset for this research. To analyze how the forecasting models perform for other countries, we have determined the model that has the best fit and produced the best forecasted mortality rates for all the other countries that are studied. This research has showed that LC-ANN and LC-ARIMA are the best model in predicting the mortality rates of males and females in Malaysia, respectively. This study has also found that the LC-ARIMA model is the best performing model in forecasting the mortality rates in countries that have longer life expectancy and a good healthcare system such as Sweden, Ireland, Japan, Hong Kong, Norway, Switzerland and Czechia. In contrast, the LC-ANN model is the best performing model in forecasting the mortality rates in countries that have a less efficiency, less accessibility healthcare system, and bad personal behavior such as Malaysia, Canada and Latvia. [ABSTRACT FROM AUTHOR]

Hospital cost analysis (HCA) becomes a key topic and forefront of politics, social welfare and medical discourse. HCA includes a wide range of expenses; yet the foremost attention relates to the money expense in which hospital managers would like to draw a figure of incomes in the past and future. Based on the HCA results, they can develop many plans for improving hospital's service quality and investing in potential healthcare services in order to deliver better services with lower costs. Machine learning methods are often opted for prediction in HCA. In this paper, we propose a new method for HCA that uses genetic algorithm (GA) and artificial neural network (ANN). Operators of GA are used to boost up calculation to get optimal weights in the forward propagation of ANN. Experiments on a real database of Hanoi Medical University Hospital (HMUH) including calculus of kidney and ureter inpatients show that the new method achieves better accuracy than the relevant ones including linear regression, K-nearest neighbors (KNN), ANN and deep learning. The mean squared error of the proposed model gets the lowest value (0.00360), compared to those of deep learning, KNN and linear regression which are 0.00901, 0.01205 and 0.01718 respectively. [ABSTRACT FROM AUTHOR]

In mobile ad hoc networks (MANETs), obstacle happens owing to the loss of packet, which could be effectively minimized by linking congestion control schemes (comprising of routing methods) and control inflow at the network layer. Here, we propose a link matrix technique for MANETs that minimizes congestion links based on the range before the distortion level of each selected transmission node to get better system ability. The effectiveness of the chosen traffic matrix technique is analyzed by comparing its performance with Capacity Optimized COoperative communication (COCO). Experiments suggest that the traffic matrix technique significantly improves the accuracy to derive traffic patterns in MANETs. The simulation results proved that the proposed method is performed well compared to the COCO method in terms of throughput (41%), delay (28%), energy (60%), overhead (48%) and packet delivery ratio (5%). [ABSTRACT FROM AUTHOR]

Due to increased demand for video data by heterogeneous Internet users, the demand of multicasting is increasing day by day. Multicasting is an efficient group communication technique, which is widely used in various applications such as bloggers, Internet group, forums, conferences, YouTube and online TV. Because of the heterogeneous nature of receivers, the network become congested that results in high packet loss, less throughput and reduced QoS. The multicast congestion control seems to be an effective solution to tackle the congestion issue in which the reception rate is adjusted according to the feedback of receivers. This paper provides a new congestion control scheme for multicast communication called Extended Logarithmic Increase and Multiplicative Decrease (ELIMD) to reduce packet loss, increase throughput, QoS and fairness during group communication. The entire research work is classified and elaborated in the key components namely architecture, newly designed equations, and flow charts. Experimental validation in NS-2.35 has affirmed the efficiency of the proposed scheme ELIMD against the existing schemes. [ABSTRACT FROM AUTHOR]

Information in a signal is often followed by undesirable disturbance which is termed as noise. Preventing noise in the signal leads to signal integrity, which also leads to better signal quality. The previous related works have the major issues while reducing noise in signals regarding assumptions, frequency and time domain, etc. This paper proposes a new Neutrosophic approach to reduce noises and errors in signal transmission. In the proposed method, confidence function is used as the truth membership function, which is associated with sampled time intervals. Then, we define a Dependency function at each time interval for the frequency of transmitted signal. Finally, a Falsehood function, which indicates the loss in information due to amplitude distortion, is defined. This function shows how much information has been lost. Our objective is to minimize the falsehood function using several neutrosophic systems. Experimental results show 1% decrease in loss compared to the original signal without PAM. It is shown the decrease of 0.1% if the frequency is shifted to a higher range. [ABSTRACT FROM AUTHOR]

Neutrosophic theory studies objects whose values vary in the sets of elements and are not true or false, but in between, that can be called by neutral, indeterminate, unclear, vague, ambiguous, incomplete or contradictory quantities. In this paper, we firstly introduce preliminaries on granular calculus and analysis related to single-valued neutrosophic functions. Based on horizontal membership functions approach, we establish some basic arithmetic operations of single-valued neutrosophic numbers, that red allow us to directly introduce the terms of neutrosophic function in usual mathematical formulas. Additionally, we build metrics on the space of single-valued neutrosophic numbers induced from Hamming distance. Then, we define some backgrounds on the limit, derivative and integral of single-valued neutrosophic functions. Finally, in order to demonstrate the usable of our theoretical results, we present some applications to well-known problems arising in engineering such as logistic model, the inverted pendulum system, Mass - Spring - Damper model. [ABSTRACT FROM AUTHOR]

Global average temperature increases and causes extreme weather events, killing millions of people and affecting economic stability. Municipal solid waste (MSW) is one of the primary factors that contribute to climate change that is the main concern of various municipalities in the world. In this paper, a two-step systematic approach is proposed to analyze municipal solid waste collection (MSWC) in Sfax, the second most populated city in Tunisia. In the first step, three feasible waste collection scenarios were suggested and have gained the consensus of decision makers and stakeholders. In the second, a geographic information system–based multi-criteria decision aid approach is proposed to identify the most appropriate scenario. Firstly, the Network Analyst function in ArcGIS is used to determine the optimal routes with the traveling distances and operational time of vehicles associated to each scenario. Secondly, a ranking of scenarios is produced by ELECTRE III method. Six pseudo criteria such as fuel consumption cost, gas emission, reliability, road length, and compatibility with the national energy policy were taken into account. The results suggest a relatively efficient and environment-friendly scenario where all vehicles start their trips from the depot, collect garbage from the gather sites, and unload waste at a transfer station then travel back to the depot. The methodology can be adopted into similar contexts to improve the effectiveness of MSW collection through the systematic use of GIS. [ABSTRACT FROM AUTHOR]

Botnet is regarded as one of the most sophisticated vulnerability threats nowadays. A large portion of network traffic is dominated by Botnets. Botnets are conglomeration of trade PCs (Bots) which are remotely controlled by their originator (BotMaster) under a Command and-Control (C&C) foundation. They are the keys to several Internet assaults like spams, Distributed Denial of Service Attacks (DDoS), rebate distortions, malwares and phishing. To over the problem of DDoS attack, various machine learning methods typically Support Vector Machine (SVM), Artificial Neural Network (ANN), Naïve Bayes (NB), Decision Tree (DT), and Unsupervised Learning (USML) (K-means, X-means etc.) were proposed. With the increasing popularity of Machine Learning in the field of Computer Security, it will be a remarkable accomplishment to carry out performance assessment of the machine learning methods given a common platform. This could assist developers in choosing a suitable method for their case studies and assist them in further research. This paper performed an experimental analysis of the machine learning methods for Botnet DDoS attack detection. The evaluation is done on the UNBS-NB 15 and KDD99 which are well-known publicity datasets for Botnet DDoS attack detection. Machine learning methods typically Support Vector Machine (SVM), Artificial Neural Network (ANN), Naïve Bayes (NB), Decision Tree (DT), and Unsupervised Learning (USML) are investigated for Accuracy, False Alarm Rate (FAR), Sensitivity, Specificity, False positive rate (FPR), AUC, and Matthews correlation coefficient (MCC) of datasets. Performance of KDD99 dataset has been experimentally shown to be better as compared to the UNBS-NB 15 dataset. This validation is significant in computer security and other related fields. [ABSTRACT FROM AUTHOR]

Nowadays, routing in three-dimensional environments is necessary since sensor nodes are organized in those kinds of areas. In this routing mechanism, data packets are routed using geographic routing by constructing a forwarding area. It is assumed that nodes in the network are homogeneous which contain the same energy level and sensing parameter. In this paper, we propose a new method to reduce the void node problem called Adaptive Void Recovery Mechanism, which is implemented by two folds namely position management and forwarding management concepts. Position management is implemented by sensing the surroundings using the base station and location management. Forwarding management is implemented using the assured factor value and cumulative value from the gathered data. The sensor nodes are elected with a minimized congestion packet latency value. Cluster-based routing technique is implemented to improve the network lifetime and network throughput. The proposed method is evaluated by simulation against the related methods like CREEP, EECS, FABC-MACRD in terms of End to End Delay, Residual Energy, Energy Consumption, Routing Overhead, Network Lifetime, and Network Throughput. [ABSTRACT FROM AUTHOR]

In a developed society, people have more concerned about their health. Thus, improvement of medical field application has been one of the greatest active study areas. Medical statistics show that heart disease is the main reason for morbidity and death in the world. The physician's job is difficult because of having too many factors to analyze in the diagnosis of heart disease. Besides, data and information gained by the physician for diagnosis are often partial and immersed. Recently, health care applications with the Internet of Things (IoT) have offered different dimensions and other online services. These applications have provided a new platform for millions of people to receive benefits from the regular health tips to live a healthy life. In this paper, we propose a novel framework based on computer supported diagnosis and IoT to detect and monitor heart failure infected patients, where the data are attained from various other sources. The proposed healthcare system aims at obtaining better precision of diagnosis with ambiguous information. We suggest neutrosophic multi criteria decision making (NMCDM) technique to aid patient and physician to know if patient is suffering from heart failure. Furthermore, through dealing with the uncertainty of imprecision and vagueness resulted from the symmetrical priority scales of different symptoms of disease, users know what extent the disease is dangerous in their body. The proposed model is validated by numerical examples on real case studies. The experimental results indicate that the proposed system provides a viable solution that can work at wide range, a new platform to millions of people getting benefit over the decreasing of mortality and cost of clinical treatment related to heart failure. [ABSTRACT FROM AUTHOR]

Fuzzy minimum spanning tree (FMST) has emerged from various real-life applications in different areas by considering uncertainty that exists in arc lengths of a fuzzy graph. In most relevant studies regarding FMST, type 1 fuzzy set was used to represent edge weights. Nonetheless, its membership values are totally crisp which is hard to determine its exact value by human perception. Interval type 2 fuzzy set (IT2FS) increases the number of degrees of freedom to express uncertainty of the edge weight and has more capacity to describe fuzzy information in a logically correct manner. In this paper, we propose the minimum spanning tree problem with undirected connected weighted interval type 2 fuzzy graph (FMST-IT2FS). Herein, the interval type 2 fuzzy set is used to represent the arc lengths of a fuzzy graph. Then, a new genetic algorithm is proposed to solve the FMST-IT2FS problem with the addition, ranking and defuzzification of IT2FSs being used. Illustrative examples are included to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

A wireless sensor network is designed to facilitate various real time applications, constituting a wide range of sensor nodes. In order to provide energy efficient transmissions, a novel congestion control mechanism is proposed on optimized rate. Here, the rate-based congestion control algorithm based on cluster routing is introduced to reduce energy consumption throughout the network. Rate control process reduces the end to end delay to improve network life time for large simulation period. Initially, nodes are clustered by the hybrid K-means and Greedy best first search algorithms. After that, the rate control is performed using firefly optimization strategy which is suitable for high packet delivery ratio. Finally, packets are sent with maximum throughput using Ant Colony Optimization-based routing. The simulation is performed on the MATLAB simulation platform. Finally, performances are evaluated with respect to, average delay in end to end node, delivery ratio of packets, throughput, energy efficiency, energy consumption and reliability. [ABSTRACT FROM AUTHOR]

Routing is the process of identifying the best path from source to sink nodes. The lifetime of nodes in the network is crucial and has to be increased by considering energy of the node. In this paper, Dynamic routing protocol is proposed to improve the Quality of Service by increasing the lifetime of the Wireless Sensor Networks. When a node requires sending the data, it searches for an intermediate node. It is assumed that nodes in network must have the equal sensing range, identical speed and also same energy. The forwarding node is selected by parameters such as the number of hops towards the sink, remaining energy and distance towards the sink node. The source or intermediate node considers itself as located in the origin and divides the area into four quadrants. The nodes that are in the sink quadrant are conceived as eligible forwarding nodes. One of the nodes is selected and used to forward data packet towards the sink. The proposed method performs better than the other existing methods. [ABSTRACT FROM AUTHOR]

Mobile Ad hoc Networks (MANETs) have gained great interests owing to their dynamic and smoothness of exploitation. Conversely, the wireless and energetic nature adds exposed to different types of protection attacks than the other kind of networks. In this kind of attacks, it is essential to expand proficient intrusion-detection system to prevent MANET from different attacks. In this paper, we recommend a new intrusion-detection system called Modified Zone Based Intrusion Detection System (MZBIDS) for MANETs. Evaluated to contemporary methodologies, MZBIDS exhibits superior malicious behavior-detection ratios in convinced situations whereas it may not significantly influence performance of entire network. [ABSTRACT FROM AUTHOR]

The uncertainty principle is one of the most important features in modeling and solving linear time-invariant (LTI) systems. The neutrality phenomena of some factors in real models have been widely recognized by engineers and scientists. The convenience and flexibility of neutrosophic theory in the description and differentiation of uncertainty terms make it take advantage of modeling and designing of control systems. This paper deals with the controllability and stabilizability of LTI systems containing neutrosophic uncertainty in the sense of both indeterminacy parameters and functional relationships. We define some properties and operators between neutrosophic numbers via horizontal membership function of a relative-distance-measure variable. Results on exponential matrices of neutrosophic numbers are well-defined with the notion e tA deployed in a series of neutrosophic matrices. Moreover, we introduce the concepts of controllability and stabilizability of neutrosophic systems in the sense of Granular derivatives. Sufficient conditions to guarantee the controllability of neutrosophic LTI systems are established. Some numerical examples, related to RLC circuit and DC motor systems, are exhibited to illustrate the effectiveness of theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, we design a planar monopole UWB antenna among two notched. Two semi-elliptical geometries are introduced at the lower and upper side's rectangular patch. For increasing the antenna's impedance bandwidth, stepped ground plane is used. The interference because of Worldwide Interoperability for Microwave Access (WiMAX 3.3–3.8 GHz) and Wireless Local Area Network (WLAN 5–6 GHz) bands are eliminated by introducing half wavelength U-shaped slot in radiator and couple of quarter wavelength L-shaped slots in stepped floor plane, respectively. Results are obtained from the fabricated UWB antenna. The proposed antennas are established from Omni-directional radiation pattern. [ABSTRACT FROM AUTHOR]

The foremost objective of this article is to develop a novel hybrid powerful meta-heuristic that integrates the salp swarm algorithm with sine cosine algorithm (called HSSASCA) for improving the convergence performance with the exploration and exploitation being superior to other comparative standard algorithms. In this method, the position of salp swarm in the search space is updated using the position equations of sine cosine; hence the best and possible optimal solutions are obtained based on the sine or cosine function. During this process, each salp adopts the information sharing strategy of sine and cosine functions to improve their exploration and exploitation ability. The inspiration behind incorporating changes in salp swarm optimizer algorithm is to assist the basic approach to avoid premature convergence and to rapidly guide the search towards the probable search space. The algorithm is validated on 22 standard mathematical optimization functions and 3 applications namely the 3-bar truss, tension/compression spring and cantilever beam design problems. The aim is to examine and confirm the valuable behaviors of HSSASCA in searching the best solutions for optimization functions. The experimental results reveal that HSSASCA algorithm achieves the highest accuracies with least runtime in comparison with the others. [ABSTRACT FROM AUTHOR]