Varietal variations in content,characteristics and composition of mango seeds and fat

Wide variations were found in the content, characteristics and composition of seed and fat of 43 varieties of mango (Mangifera indica) fruit. The seed in fruit amounted to 3–25% and kernel in seed 54–85% on an as-is basis. The contents of fat, protein and ash in kernel ranged 3.7–12.6, 4.0–8.1 and 1.0–3.7 (% on dry basis), respectively. Acid value of fat varied from 2.1 to 8.8 and unsaponifiables from 1.0 to 5.3%. The ranges for fatty acids, as determined by gas chromatography, were: 16:0, 3–18; 17:0, traces-2.0; 18:0, 24–57; 18:1, 34–56; 18:2, 1–13 and 20:0, traces-4 (area %). Presented at the Convention of the Oil Technologists’ Association of India, Bombay, February 1980.     

A process for solid fat from castor oil by simultaneous hydrogénation and dehydration

A single step process has been developed on a pilot plant scale (15 kg/batch) for simultaneous hydrogenation and dehydration of castor oil using nickel catalyst and attapulgite to yield solid fats having a hydroxyl value of 7–33 and an iodine value of 41–56. The polymer and keto fatty acid contents in the products were up to 2% and 3.2%, respectively. The product can be used in the manufacture of soap as the hard fat component and in textile sizing.     

A PSO Based Routing with Novel Fitness Function for Improving Lifetime of WSNs

Wireless Personal Communications - Wireless sensor networks (WSNs) consist of spatially distributed low power sensor nodes and gateways along with base station to monitor physical or environmental...     

Shuffled Complex Evolution Approach for Load Balancing of Gateways in Wireless Sensor Networks

Wireless Personal Communications - Energy consumption is one of the important factor of Wireless Sensor Networks (WSN). It has much attention in many fields. From recent studies, it is observed...     

SCE-PSO based clustering approach for load balancing of gateways in wireless sensor networks

Wireless Networks - Wireless sensor networks (WSNs) consist of spatially distributed low power sensor nodes and gateways along with sink to monitor physical or environmental conditions. In...     

Lie detection using extreme learning machine: A concealed information test based on short-time Fourier transform and binary bat optimization using a novel fitness function

Lie detection is one of the major challenges that is being faced by the forensic sciences. Identification of lie on the basis of a person's mental behavior is a tedious task. Brain-computer interface is one such medium which provides a solution to this problem by displaying visual stimuli and recording subject's brain responses. A P300 response is elicited whenever a person comes across a familiar stimuli in a series of rare stimuli. This P300 response is used for the lie detection method. In the proposed concealed information test, acquired signals are preprocessed to discard noise. Then, short-time Fourier transform method is applied to extract features from the preprocessed electroencephalogram signals. To avoid the curse of dimensionality and to reduce computational overhead, binary bat algorithm is applied, which helps in choosing optimal subset of features. The obtained set of features is given as an input to the extreme learning machine classifier for training of guilty and innocent samples. The performance of the system is assessed using 10-fold cross-validation. The resultant accuracy obtained from the proposed lie detection system is 88.3%. The system has provided efficient results in contrast with most of the state-of-the-art lie detection methods.     

A novel hybrid credit scoring model based on ensemble feature selection and multilayer ensemble classification

Credit scoring focuses on the development of empirical models to support the financial decision‐making processes of financial institutions and credit industries. It makes use of applicants' historical data and statistical or machine learning techniques to assess the risk associated with an applicant. However, the historical data may consist of redundant and noisy features that affect the performance of credit scoring models. The main focus of this paper is to develop a hybrid model, combining feature selection and a multilayer ensemble classifier framework, to improve the predictive performance of credit scoring. The proposed hybrid credit scoring model is modeled in three phases. The initial phase constitutes preprocessing and assigns ranks and weights to classifiers. In the next phase, the ensemble feature selection approach is applied to the preprocessed dataset. Finally, in the last phase, the dataset with the selected features is used in a multilayer ensemble classifier framework. In addition, a classifier placement algorithm based on the Choquet integral value is designed, as the classifier placement affects the predictive performance of the ensemble framework. The proposed hybrid credit scoring model is validated on real‐world credit scoring datasets, namely, Australian, Japanese, German‐categorical, and German‐numerical datasets.