A Survey on Feature Selection Algorithms

One major component of machine learning is feature analysis which comprises of mainly two processes: feature selection and feature extraction. Due to its applications in several areas including data mining, soft computing and big data analysis, feature selection has got a reasonable importance. This paper presents an introductory concept of feature selection with various inherent approaches. The paper surveys historic developments reported in feature selection with supervised and unsupervised methods. The recent developments with the state of the art in the on-going feature selection algorithms have also been summarized in the paper including their hybridizations. DOI: 10.17762/ijritcc2321-8169.16043

Modelling and optimization of injection molding process for PBT/PET parts using modified particle swarm algorithm

In the present study, a systematic methodology has been presented to determine optimal injection molding conditions for minimizing warpage and shrinkage in a thin wall relay part using modified particle swarm algorithm (MPSO). Polybutylene terephthalate (PBT) and polyethylene terephthalate (PET) have been injected in thin wall relay component under different processing parameters: melt temperature, packing pressure and packing time. Further, Taguchi’s L9 (32) orthogonal array hasbeen used for conducting simulation analysis to consider the interaction effects of the above parameters. A predictive mathematical model for shrinkage and warpage has been developed in terms of the above process parameters using regression model. ANOVA analysis has been performed to establish statistical significance among the injection molding parameters and the developed model. The developed model has been further optimized using a newly developed modified particle swarm optimization (MPSO) algorithm and the process parameters values have been obtained for minimized shrinkage and warpage. Furthermore, the predicted values of the shrinkage and warpage using MPSO algorithm have been reduced by approximately 30% as compared to the initial simulation values making more adequate parts

Modelling and optimization of injection molding process for PBT/PET parts using modified particle swarm algorithm

603-615In the present study, a systematic methodology has been presented to determine optimal injection molding conditions for minimizing warpage and shrinkage in a thin wall relay part using modified particle swarm algorithm (MPSO). Polybutylene terephthalate (PBT) and polyethylene terephthalate (PET) have been injected in thin wall relay component under different processing parameters: melt temperature, packing pressure and packing time. Further, Taguchi’s L9 (32) orthogonal array has been used for conducting simulation analysis to consider the interaction effects of the above parameters. A predictive mathematical model for shrinkage and warpage has been developed in terms of the above process parameters using regression model. ANOVA analysis has been performed to establish statistical significance among the injection molding parameters and the developed model. The developed model has been further optimized using a newly developed modified particle swarm optimization (MPSO) algorithm and the process parameters values have been obtained for minimized shrinkage and warpage. Furthermore, the predicted values of the shrinkage and warpage using MPSO algorithm have been reduced by approximately 30% as compared to the initial simulation values making more adequate parts

Hybrid Feature Selection Methods for High-Dimensional Multi-Class Datasets

Hybrid methods are very important for feature selection in case of the classification of high-dimensional datasets. In this paper, we proposed two hybrid methods which are the combination of filter-based feature selection, genetic algorithm, and sequential random search methods. The first proposed method is hybridisation of information gain and genetic algorithm. In this, first, the features are ranked based on the information gain and then a user defined features are selected from the ranked features. Genetic algorithm with these selected features is applied for the selection of optimal feature subset. It is applied for feature selection with two types of fitness functions which are single objective and multi-objective in nature. The second feature selection model is the hybridisation of information gain and sequential random K-nearest neighbour (SRKNN). In this method, again information gain is used to rank the features and a user defined top ranked number of features are selected. A set of binary population (having all feature selected by users) are generated and on each population sequential search method is applied for maximising the classification accuracy. These methods are applied to 21 high-dimensional multi-class datasets. Obtained results show that on some datasets first method\u27s performance is good and on some datasets second method\u27s performance is good. The results obtained by proposed methods are compared with results registered for other methods

Deep learning-based robust analysis of laser bio-speckle data for detection of fungal-infected soybean seeds

T Seed-borne diseases play a crucial role in affecting the overall quality of seeds, efficient disease management, and crop productivity in agriculture. Detection of seed-borne diseases using machine learning (ML) and deep learning (DL) can automate the process at large-scale industrial applications for providinghealthy and high-quality seeds. ML-based methods are accurate for detecting and classifying fungal infectionin seeds; however, their performance degrades in the presence of noise. In this work, we propose a laser biospeckle based DL framework for detection and classification of disease in seeds under varying experimental parameters and noises. We develop a DL-based spatio-temporal analysis technique for bio-speckle data using DL networks, including neural networks (NN), convolutional neural networks (CNN) with long-short-termmemory (LSTM), three-dimensional convolutional neural networks (3D CNN), and convolutional LSTM (ConvLSTM). The robustness of the DL models to noise is a key aspect of this spatio-temporal analysis. In this study, we find that the ConvLSTM model has an accuracy of 97.72% on the test data and is robust to different types of noises with an accuracy of 97.72%, 94.31%, 98.86%, and 96.59% . Furthermore, the robust model (ConvLSTM) is evaluated for variations in experimental data parameters such as frame rate, frame size, and number of frames used. This model is also sensitive towards detecting bio-speckle activity of different order, and it shows average test accuracy of 99% for detecting four different classes. CC BY 4.0This work was supported by Government of India, being implemented by Digital India Corporation, and Science and Engineering ResearchBoard project grant (CRG/2021/001215 and CRG/2018/002697), and the staff and student mobility funded by Erasmus+ project betweenIIT Indore and University West.</p

Assessment of indoor Radon levels in Residences of Jind District, Haryana using Solid-State Nuclear Track Detectors: Implications for Human Health

429-434The present study reports the indoor radon levels in the ambience of some residences in Jinddistrict, Haryana. Assessment of absorbed dose due to inhalation of these radioactive gases is critical as a high dose of such gases is hazardous to human health. Pinhole dosimeters with LR-115 solid-state nuclear track detectors were suspended in 50 dwellings for passive measurement of indoor radon and thoron gas. Direct radon/thoron progeny sensors were also attached with dosimeters for equivalent equilibrium radon/thoron concentration measurements. This study calculates the annual absorbed ingestion dose from indoor radon/thoron concentration. From the results, it can be concluded that the absorbed doses are within the permissible limit. The average radon concentration is also below the ICRP recommendations of 200 Bq/m3- 300 Bq/m3

Magnetic Cellulose Nanocrystal Based Anisotropic Polylactic Acid Nanocomposite Films: Influence on Electrical, Magnetic, Thermal, and Mechanical Properties

This paper reports a single-step co-precipitation method for the fabrication of magnetic cellulose nanocrystals (MGCNCs) with high iron oxide nanoparticle content (∼51 wt % loading) adsorbed onto cellulose nanocrystals (CNCs). X-ray diffraction (XRD), Fourier transform infrared (FTIR), and Raman spectroscopic studies confirmed that the hydroxyl groups on the surface of CNCs (derived from the bamboo pulp) acted as anchor points for the adsorption of Fe₃O₄ nanoparticles. The fabricated MGCNCs have a high magnetic moment, which is utilized to orient the magnetoresponsive nanofillers in parallel or perpendicular orientations inside the polylactic acid (PLA) matrix. Magnetic-field-assisted directional alignment of MGCNCs led to the incorporation of anisotropic mechanical, thermal, and electrical properties in the fabricated PLA–MGCNC nanocomposites. Thermomechanical studies showed significant improvement in the elastic modulus and glass-transition temperature for the magnetically oriented samples. Differential scanning calorimetry (DSC) and XRD studies confirmed that the alignment of MGCNCs led to the improvement in the percentage crystallinity and, with the absence of the cold-crystallization phenomenon, finds a potential application in polymer processing in the presence of magnetic field. The tensile strength and percentage elongation for the parallel-oriented samples improved by ∼70 and 240%, respectively, and for perpendicular-oriented samples, by ∼58 and 172%, respectively, in comparison to the unoriented samples. Furthermore, its anisotropically induced electrical and magnetic properties are desirable for fabricating self-biased electronics products. We also demonstrate that the fabricated anisotropic PLA–MGCNC nanocomposites could be laminated into films with the incorporation of directionally tunable mechanical properties. Therefore, the current study provides a novel noninvasive approach of orienting nontoxic bioderived CNCs in the presence of low magnetic fields, with potential applications in the manufacturing of three-dimensional composites with microstructural features comparable to biological materials for high-performance engineering applications

A prospective, observational study of osteoporosis in men

Context: The number of men afflicted with osteoporosis is unknown. Aims: This study aims to determine the prevalence of osteoporosis in men. Settings and Design: This was a prospective, observational study. Subjects and Methods: A total of 200 male attendants of patients attending endocrine outpatient department and who were >55 years were recruited for the study. All the patients with osteopenia and osteoporosis were advised lifestyle interventions, supplementation with calcium carbonate (1000–1500 mg/day) and 25-hydroxyl-Vitamin D (400–600 IU/day) and bisphosphonates if indicated. Vitamin D3 60,000 IU once a week for 8 weeks and once a month thereafter was prescribed to Vitamin D-deficient patients. Androgen-deficient patients were given replacements of either injectable testosterone or oral testosterone undecanoate. Statistical Analysis Used: Two sample t-test and paired t-test were used to compare pre- and post-test parameters. Results: Overall 80 (40%) subjects had low bone mass, 93 (43.5%) had Vitamin D deficiency/insufficiency, and 39 (19.5%) had androgen deficiency. Osteoporosis was found in 8.5% patients. All patients were above 70 years (Mean age: 73.82 ± 2.79 years). Seventy percentage of these patients had low serum testosterone and 70% of patients had Vitamin D deficiency/insufficiency. About 31.5% of patients had osteopenia (mean age of 67.47 ± 6.35 years). Thirty-five percentage of these patients were androgen deficient and 25% were Vitamin D-deficient/insufficient. Age >70 years, serum testosterone 70 years, low androgen (<3 ng/ml), steroid use, and low Vitamin D (<20 ng/ml) were independent risk factors of male osteoporosis. Calcium and Vitamin D are effective in improving BMD. Androgen replacement has beneficial effect on BMD in hypogonadism patients

A historical review and analysis on MOORA and its fuzzy extensions for different applications

Multi-criteria decision-making (MCDM) methods have been widely used among researchers to provide a trade-off solution between best and worst, considering conflicting criteria and sets of preferences. An efficient and systematic literature review of these methods is needed to maintain their application in distinctive domains. To this end, this paper presents a comprehensive and systematic literature survey on “multi-objective optimization by ratio analysis” (MOORA) method and its fuzzy extensions developed and discussed in recent years. This review includes articles categorized based on the publication name, publishing year, journal name, type of applications, and type of fuzzy extensions. In addition, this review will enhance the understanding of practitioners and decision-makers on the MOORA method, its development, fuzzy hybridization, different application areas, and future work. The study revealed that the MOORA technique was predominantly used with the TOPSIS approach, followed by the AHP and COPRAS methods. Furthermore, 76.28 % use single and hybridization approaches among all MOORA studies, while 23.72 % use MOORA in a fuzzy environment