A Modified Neural Network system based on Morphological operations for detection of images with variation in Gray level intensity and facial expressions

We introduce an algorithm based on the morphological shared-weight neural network. Which extract the features and then classify them. This type of network can work effectively, even if the gray level intensity and facial expression of the images are varied. The images are processed by a morphological shared weight neural network to detect and extract the features of face images. For the detection of the edges of the image we are using sobel operator. We are using back propagation algorithm for the purpose of learning and training of the neural network system. Being nonlinear and translation-invariant, the morphological operations can be used to create better generalization during face recognition. Feature extraction is performed on grayscale images using hit-miss transforms that are independent of gray-level shifts. The recognition efficiency of this modified network is about 98%

GENETIC DISORDER ALZHEIMER

Alzheimer's disease (AD), slowly continuous neurological disorder, mostly appears in older >65 age that deals with the memory loss due to death or damage of brain cells and cognitive functions (thinking, reasoning, and behavior abnormalities) due to the accumulation of the specific protein (beta-amyloid protein) which form plaque and fibers (tau tangles) in the brain. Not only the genetic factors are responsible but also most of the non-genetic factors are responsible for AD. Several mutations in the gene (APP, APOE, PENS1, PENS2 on chromosome no. 21, 19, 14, 1) are responsible for causing four types of AD. Memory loss is most common sign of AD. Predisposing factors of AD are hereditary, severe brain injury or traumatic, and metabolic diseases such as diabetes mellitus, hypercholesteremia, and obesity. Although treatment can manage some symptoms in few people, but there is no current mechanism to cure AD or stop its progression. Beta-secretase inhibitor molecule prevents the first step in a chain accumulation which leads to the formation of amyloid plaque in the brain. However, the scientist or researchers have established a compound NIC5-15 they have been found NIC5-15 has safe and effectual treatment which has been used to stabilize cognitive performance in patients with mild to moderate AD

Hysteroscopy in one hundred cases of postmenopausal uterine bleeding, in the detection of uterine cancer and atypical endometrial hyperplasia

Background: Evaluation was done in 100 women presenting with postmenopausal bleeding, (PMB), to discuss the utility of hysteroscopy combined guided endometrial curettage in the diagnosis of uterine cancer and endometrial hyperplasia, and, treat benign lesions, like polyps, synechiae at the same sitting. At MGMH during the years, 2002 to 2006, there were 57 women, and at care, 40 women with PMB during 2011 to 2013, and three in a nursing home, Hyderabad, were investigated.Methods: Evaluation was done in 100 women presenting with PMB by hysteroscopy and curettage to diagnose the cause of PMB and benign lesions like polyps, synechiae were managed by operative hysteroscopy. Bettocchi 5 mm hysteroscope, monopolar instruments and glycine was used for excision of polyps.Results: In one hundred women with PMB, 19% had cancer. Endometrial adenocarcinoma in 14, endocervical carcinoma in 2, uterine carcinosarcoma in 3 cases. All 3 cases of uterine carcinosarcoma on hysteroscopy were large polyps measuring 5×5-6 cm size. Atypical hyperplasia endometrium in 7% and simple hyperplasia in 17%, was reported on histopathology, in cases with hyperplastic endometrium on hysteroscopy. Benign polyps in 41% were managed at the same sitting by operative hysteroscopy.Conclusions: Women with postmenopausal bleeding must have USG, trans vaginal sonography (TVS), endometrial thickness (ET) measurement, preferably endometrial echo complex (EEC). In women with PMB, the risk of uterine cancer would be 19%, i.e., 1 out of 5 women. Atypical hyperplasia in 7%. Hysteroscopy guided curettage, with histopathology, is the gold standard protocol in cases of PMB

ASSESSMENT OF SUSTAINABLE WASTEWATER TREATMENT TECHNOLOGIES USING INTERVAL-VALUED INTUITIONISTIC FUZZY DISTANCE MEASURE-BASED MAIRCA METHOD

Effective wastewater treatment has significant effects on saving water and preventing unnecessary water scarcity. An appropriate wastewater treatment technology (WWTT) brings economic benefits through reuse in different sectors and benefits the society and environment. This study aims to develop a decision-making framework for evaluating the sustainable WWTTs under interval-valued intuitionistic fuzzy set (IVIFS) environment. The proposed MCDM framework is divided into two stages. First, a new Hellinger distance measure is developed to determine the degree of difference between IVIFSs and also discussed its desirable characteristics. Second, an interval-valued intuitionistic fuzzy extension of multi-attribute ideal-real comparative analysis (MAIRCA) model is developed using the proposed Hellinger distance measure-based weighting tool. Further, the proposed model is implemented on an empirical study of sustainable WWTTs evaluation problem. Sensitivity and comparative studies are made. The results indicate that odor impacts, sludge production, maintenance and operation are the most effective sustainable factors and Microbial fuel cell (MFC) technology is the best WWTT followed by natural treatment methods

Pregnancy outcome in isolated oligohydramnios diagnosed in third trimester

Background: The aim of this study was to compare the outcomes of pregnancies complicated by isolated oligohydramnios with the low risk pregnancies with normal amniotic fluid volume.Methods: The present study is a retrospective cohort study of singleton pregnancies diagnosed with Isolated oligohydramnios (AFI≤5) in their third trimester (N=35). Pregnancy outcome was compared with a matched control group of low risk pregnancies with amniotic fluid volume >5 (N=30).Results: The overall incidence of Isolated oligohydramnios was 0.7-0.8%. In oligohydramnios group, significant association were found in null-parity (60% vs 23.33%, p-value<0.005), Fetal growth retardation (25.71% vs 0% p-value<0.02), preterm delivery (22.85% vs 3.33%, p-value 0.025), rate of Induction of labor (40% vs 10%) and cesarean rate for non-reassuring fetal heart rate (20% vs 3.33%, p-value<0.001). Likewise, the incidence of low birth weight was (54.28% vs 13.33%, p-value<0.001) and NICU admissions was (20% vs 0%, p-value<0.01), but there was no difference in Apgar score finding. NICU stay was of short duration and all babies discharged in stable condition, there were no stillbirth or early neonatal death in both groups.Conclusions: Isolated oligohydramnios has an adverse influence on pregnancy and neonatal outcome in the form of FGR, preterm delivery, increased rate of Induction and cesarean section. Despite the high incidence of low birth weight and NICU admissions, the overall early neonatal outcome was similar to the other low risk pregnancies

An intuitionistic fuzzy entropy-based gained and lost dominance score decision-making method to select and assess sustainable supplier selection

Sustainable supplier selection (SSS) is recognized as a prime aim in supply chain because of its impression on profitability, adorability, and agility of the organization. This work introduces a multi-phase intuitionistic fuzzy preference-based model with which decision experts are authorized to choose the suitable supplier using the sustainability "triple bottom line (TBL)" attributes. To solve this issue, an intuitionistic fuzzy gained and lost dominance score (IF-GLDS) approach is proposed using the developed IF-entropy. To make better use of experts' knowledge and fully represent the uncertain information, the evaluations of SSS are characterized in the form of intuitionistic fuzzy set (IFS). To better distinguish fuzziness of IFSs, new entropy for assessing criteria weights is proposed with the help of an improved score function. By considering the developed entropy and improved score function, a weight-determining process for considered criterion is presented. A case study concerning the iron and steel industry in India for assessing and ranking the SSS is taken to demonstrate the practicability of the developed model. The efficacy of the developed model is certified with the comparison by diverse extant models

A Decision Support System for Assessing and Prioritizing Sustainable Urban Transportation in Metaverse

Blockchain technology and metaverse advancements allow people to create virtual personalities and spend time online. Integrating public transportation into the metaverse could improve services and collect user data. This study introduces a hybrid decision-making framework for prioritizing sustainable public transportation in Metaverse under q-rung orthopair fuzzy set (q-ROFS) context. In this regard, firstly q-rung orthopair fuzzy (q-ROF) generalized Dombi weighted aggregation operators (AOs) and their characteristics are developed to aggregate the q-ROF information. Second, a q-ROF information-based method using the removal effects of criteria (MEREC) and stepwise weight assessment ratio analysis (SWARA) models are proposed to find the objective and subjective weights of criteria, respectively. Then, a combined weighting model is taken to determine the final weights of the criteria. Third, the weighted sum product (WISP) method is extended to q-ROFS context by considering the double normalization procedures, the proposed operators and integrated weighting model. This method has taken the advantages of two normalization processes and four utility measures that approve the effect of benefit and cost criteria by using weighted sum and weighted product models. Next, to demonstrate the practicality and effectiveness of the presented method, a case study of sustainable public transportation in metaverse is presented in the context of q-ROFSs. The findings of this study confirms that the proposed model can recommend more feasible performance while facing numerous influencing factors and input uncertainties, and thus, provides a wider range of application

An Integrated Single-Valued Neutrosophic Combined Compromise Solution Methodology for Renewable Energy Resource Selection Problem

Optimal renewable energy source (RES) selection needs a strategic decision for reducing environmental pollutions, use of conventional resources, and improving economic development. In the process of RESs evaluation, several aspects like environmental, economic, social, and technical requirements play an important role. In addition, diverse factors affect the appropriate RES selection problem which adheres to uncertain and imprecise data. Thus, this selection process can be considered as a complex uncertain multi-criteria decision making (MCDM) problem. This study aims to introduce a novel integrated methodology based on Step-wise Weight Assessment Ratio Analysis (SWARA) and Combined Compromise Solution (CoCoSo) methods within single-valued neutrosophic sets (SVNSs) context, wherein the decision-makers and criteria weights are completely unknown. In the proposed approach, the criteria weights are determined by the SWARA method, and the most suitable RES alternative is determined by an improved CoCoSo method under the SVN context. Further, an illustrative case study of RES selection is considered to demonstrate the thorough execution process of the proposed method. Moreover, a comparison with existing methods is discussed to analyze the validity of the obtained result. This study performs sensitivity analysis with a various set of criteria weights to reveal the robustness of the developed approach. The strength of the proposed method is its practical applicability and ability to provide solutions under uncertain, imperfect, indeterminate, and inconsistent information