Design and implementation of a new tuned hybrid intelligent model to predict the uniaxial compressive strength of the rock using SFS-ANFIS

This study proposes a novel design to systematically optimize the parameters for the adaptive neuro-fuzzy inference system (ANFIS) model using stochastic fractal search (SFS) algorithm. To affirm the efficiency of the proposed SFS-ANFIS model, the predicting results were compared with ANFIS and three hybrid methodologies based on ANFIS combined with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO). Accurate prediction of uniaxial compressive strength (UCS) is of great significance for all geotechnical projects such as tunnels and dams. Hence, this study proposes the use of SFS-ANFIS, GA-ANFIS, DE-ANFIS, PSO-ANFIS, and ANFIS models to predict UCS. In this regard, the fresh water tunnel of Pahang–Selangor located in Malaysia was considered and the requirement data samples were collected. Different statistical metrics such as coefficient of determination (R²) and mean absolute error were used to evaluate the models. Referring to the efficiency results of SFS-ANFIS, it can be found that the SFS-ANFIS (with the R² of 0.981) has higher ability than PSO-ANFIS, DE-ANFIS, GA-ANFIS, and ANFIS models in predicting the UCS.

     

Modeling and Analysis of Temperature Compensation for Multi-temperature Zone Sintering Furnace Temperature Sensing

International Journal of Control, Automation and Systems - The surface temperature of workpieces in a multi-temperature zone sintering furnace is an important parameter to characterize the...     

Theoretical calculation simulation studies of ABV nuclear reactor coupled with desalination system

In this paper, research has been conducted on the floating type nuclear power plant named as ABV reactor which is designed for district heating, power, and sea water desalination by OKBM facility at Russia. This reactor was tested under different thermal loads during the designing phase, and three modules have been investigated. Theoretical calculations and simulation studies have been performed on these three modules having specifications as ABV‐6M with 47MW_th, ABV‐6 with 38MW_th, and ABV‐3 with 18MW_th.The results obtained from these modules have been calculated mathematically and verified by simulation. We have compared the originally derived data of ABV desalination system with our theoretical and simulation analysis. The results from two desalination techniques including RO and RO + MED have been calculated and are presented in this paper with details. The results obtained from both analysis show that the efficiency of ABV nuclear reactor desalination system increases with the decrease in corresponding water cost ratio. Copyright © 2015 John Wiley & Sons, Ltd.     

Computational intelligence for microarray data and biomedical image analysis for the early diagnosis of breast cancer

The objective of this paper was to perform a comparative analysis of the computational intelligence algorithms to identify breast cancer in its early stages. Two types of data representations were considered: microarray based and medical imaging based. In contrast to previous researches, this research also considered the imbalanced nature of these data. It was observed that the SMO algorithm performed better for the majority of the test data, especially for microarray based data when accuracy was used as performance measure. Considering the imbalanced characteristic of the data, the Naive Bayes algorithm was seen to perform highly in terms of true positive rate (TPR). Regarding the influence of SMOTE, a well-known imbalanced data classification technique, it was observed that there was a notable performance improvement for J48, while the performance of SMO remained comparable for the majority of the datasets. Overall, the results indicated SMO as the most potential candidate for the microarray and image dataset considered in this research.     

Civil Liabilities for Unsafe Foods in Bangladesh and Australia: A Comparative Perspective on Consumer Protection

The statutory laws concerning food safety, which allow the consumers affected by unsafe foods to claim compensation for their loss or damage in Bangladesh, are flawed in several respects. These flaws are argued to have harmful impacts on consumer protection. The ineffective legislation in the absence of application of the common law principles of negligence has left the consumers virtually helpless in securing remedies for their injuries. This article endeavors to critically examine the ambiguities and shortcomings in the Consumer Rights Protection Act 2009 (Bangladesh) looking through the prisms of the Australian Consumer Law 2010 and relevant case law from major common law jurisdictions. It discovers a number of weaknesses in the legislation of Bangladesh and provides specific suggestions for strengthening the civil liability provisions from the perspective of consumer protection by preventing their contraventions and providing adequate compensation.     

Egg white proteins and β-cyclodextrin: effective cryoprotectant mixture against oxidative changes in the myofibrillar proteins of Culter alburnus

The effect of egg white protein (EWP) and β-cyclodextrin (β-CD) mixture was analysed on the myofibrillar proteins (MP) of Culter alburnus during a frozen storage at −18 °C for 60 days. Different proportions of EWP:β-CD mixture (0%, 2%, 4% and 6%) were added into MP to analyse their effect against oxidative changes. During the study, sulphydryl contents and Ca-ATPase activity (0.297 to 0.136 mmol g⁻¹) decreased. Moreover, protein denaturation also prompted the surface hydrophobicity (11.47 to 32.06 μg) and carbonyls (26.36 to 49 mg.28 nmol mg⁻¹) of control MP. A significant decline was observed in emulsifying properties. Besides, EWP:β-CD showed remarkable stability against oxidative changes, by significantly reducing the carbonyls (26.26 to 37.69 nmol mg⁻¹) and surface hydrophobicity (11.51 to 20.31 μg) and also the decline of Ca-ATPase activity (0.29 to 0.19 mmol g⁻¹). It can be concluded that EWP:β-CD (6%) is an efficient approach against oxidative changes in MP from Culter alburnus.     

Dynamic analysis of spinning triangle geometry part 2: spinning triangle geometry and yarn quality

This paper examines empirical relationships between the geometrical parameters of the spinning triangle and the quality parameters of ring spun yarns. For this purpose, controlled variations in the geometry of the spinning triangle were induced using two existing modifications in the ring spinning process and by varying the yarn twist level. The geometrical parameters of the spinning triangle were measured using the clear rubber roller combined with digital image processing techniques as described in the first part of this study. The yarn quality parameters measured included yarn hairiness, strength, elongation and evenness. Yarn hairiness showed more noticeable sensitivity towards the physical shape of the spinning triangle than yarn tenacity, while yarn evenness and elongation were not significantly influenced by changes in spinning triangle geometry.     

Simplification of genetic programs: a literature survey

Data Mining and Knowledge Discovery - Genetic programming (GP), a widely used evolutionary computing technique, suffers from bloat—the problem of excessive growth in individuals’ sizes....     

Suggestion Mining from Opinionated Text of Big Social Media Data

Social media data are rapidly increasing and constitute a source of user opinions and tips on a wide range of products and services. The increasing availability of such big data on biased reviews and blogs creates challenges for customers and businesses in reviewing all content in their decision-making process. To overcome this challenge, extracting suggestions from opinionated text is a possible solution. In this study, the characteristics of suggestions are analyzed and a suggestion mining extraction process is presented for classifying suggestive sentences from online customers’ reviews. A classification using a word-embedding approach is used via the XGBoost classifier. The two datasets used in this experiment relate to online hotel reviews and Microsoft Windows App Studio discussion reviews. F1, precision, recall, and accuracy scores are calculated. The results demonstrated that the XGBoost classifier outperforms—with an accuracy of more than 80%. Moreover, the results revealed that suggestion keywords and phrases are the predominant features for suggestion extraction. Thus, this study contributes to knowledge and practice by comparing feature extraction classifiers and identifying XGBoost as a better suggestion mining process for identifying online reviews.