Search results

In time and accurate detection of cancer can save the life of the person affected. According to the World Health Organization (WHO), breast cancer occupies the most frequent incidence among all the cancers whereas breast cancer takes fifth place in the case of mortality numbers. Out of many image processing techniques, certain works have focused on convolutional neural networks (CNNs) for processing these images. However, deep learning models are to be explored well.

In this work, multivariate statistics-based kernel principal component analysis (KPCA) is used for essential features. KPCA is simultaneously helpful for denoising the data. These features are processed through a heterogeneous ensemble model that consists of three base models. The base models comprise recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU). The outcomes of these base learners are fed to fuzzy adaptive resonance theory mapping (ARTMAP) model for decision making as the nodes are added to the F_2ˆa layer if the winning criteria are fulfilled that makes the ARTMAP model more robust.

The proposed model is verified using breast histopathology image dataset publicly available at Kaggle. The model provides 99.36% training accuracy and 98.72% validation accuracy. The proposed model utilizes data processing in all aspects, i.e. image denoising to reduce the data redundancy, training by ensemble learning to provide higher results than that of single models. The final classification by a fuzzy ARTMAP model that controls the number of nodes depending upon the performance makes robust accurate classification.

Research in the field of medical applications is an ongoing method. More advanced algorithms are being developed for better classification. Still, the scope is there to design the models in terms of better performance, practicability and cost efficiency in the future. Also, the ensemble models may be chosen with different combinations and characteristics. Only signal instead of images may be verified for this proposed model. Experimental analysis shows the improved performance of the proposed model. This method needs to be verified using practical models. Also, the practical implementation will be carried out for its real-time performance and cost efficiency.

The proposed model is utilized for denoising and to reduce the data redundancy so that the feature selection is done using KPCA. Training and classification are performed using heterogeneous ensemble model designed using RNN, LSTM and GRU as base classifiers to provide higher results than that of single models. Use of adaptive fuzzy mapping model makes the final classification accurate. The effectiveness of combining these methods to a single model is analyzed in this work.

The purpose of this paper is to study the effect of various stoichiometric ratios for synthesised epoxy phenolic novolac (EPN) resins on their physicochemical, thermomechanical and morphological properties.

In the present study, EPN (EPN-1, EPN-2, EPN-3, EPN-4 and EPN-5) resins were synthesised by varying five types of different stoichiometric ratios for phenol/formaldehyde along with the corresponding molar ratios for novolac/epichlorohydrin. Their different physicochemical properties of interest, thermomechanical properties as well as morphological properties were studied by means of cured samples with the variation of its stoichiometric ratios.

The average functionality and reactivity of EPN resin can be controlled by controlling epoxy equivalence as well as cross-linking density upon its curing as all of these factors are internally correlated with each other.

Epoxy resins are characterised by a three-membered ring known as the epoxy or oxirane group. The capability of the epoxy ring to react with a variety of substrates imparts versatility to the resin. However, these resins have a major drawback of low toughness, and they are also very brittle, which limits their application in products that require high impact and fracture strength.

Epoxy resins have been widely used as high-performance adhesives and matrix resins for composites because of their outstanding mechanical and thermal properties. Because of their highly cross-linked structure, the epoxy resin disables segmental movement, making them hard, and it is also notch sensitive, having very low fracture energy.

Epoxy resin is widely used in industry as protective coatings and for structural applications, such as laminates and composites, tooling, moulding, casting, bonding and adhesives.

Systematic study has been done for the first time, as no exact quantitative stoichiometric data for the synthesis of EPN resin were available on the changes of its different properties. Thus, an optimised stoichiometric composition for the synthesis of the EPN resin was found.

Bluetooth wireless technology is a low power, low cost and short‐range RF technology that permits communication between bluetooth enabled devices, and focuses on replacement of cables between electronic devices. Communication between Bluetooth devices follows a strict master‐slave scheme. Each master device can have up to 7 active slaves and forms a so called piconet. In Bluetooth employing conventional scheduling policies such as Round Robin (RR), POLL or NULL packet is sent when the Master or Slave node does not have any data to send which causes a significant waste of resources. The DRR (Deficit Round Robin) scheduling algorithm can avoid the waste of time and slot of the RR scheduling at the sacrifice of fairness. In this paper we propose an improved DRR (IDRR) scheduling algorithm which effectively combines the DRR and bin packing algorithm. Computer simulation reveals that slot utilization is increased up to about 60% while the total number of used slots is decreased up to about 100%. The proposed IDRR scheduling is thus effective for not only basic data transmission but also real‐time multimedia data transmission.

This case depicts a managerial problem faced by Tradition and Trust, a traditional sweet and snack manufacturer, which is 100-year old family business manufacturing, retailing and exporting sweets, snacks and other confectionery products. Like other traditional sweet and snack manufactures, the company is facing the challenge of consumers' shift towards ‘healthy snacking’. Moreover, brands like Haldiram's and Bikanerwala are posing a tough competition to the traditional sweet manufacturing brands. In recent years, the brand has witnessed a decline in sales. In view of the challenges posed to brand, the Chief Marketing Officer, wants to understand the various factors influencing decline of sales and find ways to redesign the strategy for sustaining its current position in the market. The case is written to illustrate how six fundamental steps of conducting a marketing research can be used to address a given managerial problem.

Presently gender inequality and women's nutrition are the most concerning area of any development policy. Recent empirical evidence emphasizes that gender inequality decreases over time and the on the other hand percentage of overweight (OW) and obesity for women, especially in developing countries, increases over time. However, the relationship between these two phenomena (gender inequality and obesity) has rarely been investigated. Using time series yearly data (1990–2016) from the Nutrition Landscape Information Systems (NLiS) database of World Health Organisation (WHO) for India, we apply standard time series analysis including break test, stationarity test, cointegration test, and vector error correction model (VECM) to estimate the relationship between gender inequalities and percentage of females OW and obese. Our results show that there is a long-run relationship between these variables. Moreover, we also find that a decrease in gender inequality influences the increase in the number of females under OW and obese. In conclusion, the findings of this study reveal that, while elevating the position of women in society may be an important step toward combating the epidemic of OW and obesity, strategies must also tackle unhealthy habits that promote obesity.

In the present study, wire electro-discharge machining (WEDM) of Inconel 625 (In-625) is performed with the machining parameter such as spark-on time, spark-off time, wire-speed, wire tension and servo voltage. The purpose of this study is to find the most favorable machining parameter setting with respect to WEDM performance such as material removal rate (MRR) and surface roughness (RA).

Taguchi’s L27 orthogonal array has been used to design the experiments with varying machining parameters into three-level four factors. A hybrid multi-optimization technique has been purposed with grey relation analysis and fuzzy inference system integrated with teaching learning-based optimization to achieve optimum machinability (MRR and RA in present case). The obtained result has been compared with two evolutionary optimization tools via a genetic algorithm and simulated annealing.

It has been found that proposed hybrid technique taking minimum computational time, provide better solution and avoid priority weightage calculation by decision-makers. A confirmation test has been performed at single and multi-optimal parameter settings. The decision-makers have been chosen to select any single or multi-parameter setting as per the industry’s demand.

The proposed optimization technique provides better machinability of In-625 using zinc-coated brass wire electrode during WEDM operation.

The case describes the situation of Vasudha Kumar, Manager – Background Vetting at Praxum Services Limited. She is surprised by the unexpected resignation of her team member a few days before she has to leave for her maternity break. In a small team, how does she deal with this unanticipated resignation? She wonders if this is ethical. Kumar, the protagonist of the case had to handle multiple crisis situations along her journey. How will she overcome challenges of managing the new gen workforce?

One of the major concerns of the constrained-surface stereolithography (SLA) process is that the built-up part may break because of the force resulting from the pulling-up process. This resultant force may become significant if the interface mechanism between the two contact surfaces (i.e. newly cured layer and the bottom of the resin vat) produces a strong bonding between them. The purpose of this paper is to characterize the separation process between the cured part and the resin vat by adopting an appropriate and simple mechanics-based model that can be used to probe the pulling-up process.

In this paper, the time-histories of the pulling-up forces are measured using FlexiForce® force sensors. The experimental data are analyzed and used to estimate the constitutive parameters of the separation mechanism. Here, the separation mechanism is modeled based on the concept of cohesive zone model (CZM) that is well-studied in the field of fracture mechanics. By using the experimentally measured pulling-up force, this paper proposes a very efficient inverse technique to estimate the constitutive parameters for the CZM. The constitutive laws for the CZM facilitate in relating the separation force at the interface between the cured part and the resin vat in terms of the pulling-up velocity. Unlike work proposed earlier, computationally expensive full-scale finite element runs are not essential in the current work while estimating the required parameters of the constitutive laws. Instead, mechanics-based computationally efficient surrogate model is proposed to readily estimate these constitutive parameters.

Two constitutive laws are compared on the basis of their predictions of the separation force profile. Excellent match is obtained between the measured and the predicted separation force profiles.

This paper selects a suitable mechanics-based model that can characterize the separation process and proposes a computationally efficient scheme to estimate the required constitutive parameters. The proposed scheme can be used to reliably predict the separation force for the constrained-surface SLA process, leading to improved productivity and reliability of the SLA processes in fabricating the built-up parts.

The purpose of this paper is to investigate the effect of corrugation, wave number, initial stress and the heterogeneity of the media on the phase velocity of the Love-type wave. Moreover, the paper aims to have a comparative study of the presence and absence of anisotropy, heterogeneity, corrugation and initial stress in the half-space, which serve as a focal theme of the study.

The present paper modelled the propagation of the Love-type wave in a corrugated heterogeneous monoclinic layer lying over an initially stressed heterogeneous transversely isotropic half-space. The method of separation of variables is used to procure the dispersion relation.

The closed form of dispersion relation is obtained and found to be in well agreement to the classical Love wave equation. Neglecting the corrugation at either of the boundary surfaces, expressions of the phase velocity of the Love-type wave are deduced in closed form as special cases of the problem. It is established through the numerical computation of the obtained relation that the concerned affecting parameters have significant impact on the phase velocity of the Love-type wave. Also, a comparative study shows that the anisotropic case favours more to the phase velocity as comparison to the isotropic case.

Although many attempts have been made to study the effect of corrugated boundaries on reflection and refraction of seismic waves, but the effect of corrugated boundaries on the dispersion of surface wave (which are dispersive in nature) propagating through mediums pertaining various incredible features still needs to be investigated.