Analysis of Dimensionality Reduction Techniques on Big Data

Due to digitization, a huge volume of data is being generated across several sectors such as healthcare, production, sales, IoT devices, Web, organizations. Machine learning algorithms are used to uncover patterns among the attributes of this data. Hence, they can be used to make predictions that can be used by medical practitioners and people at managerial level to make executive decisions. Not all the attributes in the datasets generated are important for training the machine learning algorithms. Some attributes might be irrelevant and some might not affect the outcome of the prediction. Ignoring or removing these irrelevant or less important attributes reduces the burden on machine learning algorithms. In this work two of the prominent dimensionality reduction techniques, Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) are investigated on four popular Machine Learning (ML) algorithms, Decision Tree Induction, Support Vector Machine (SVM), Naive Bayes Classifier and Random Forest Classifier using publicly available Cardiotocography (CTG) dataset from University of California and Irvine Machine Learning Repository. The experimentation results prove that PCA outperforms LDA in all the measures. Also, the performance of the classifiers, Decision Tree, Random Forest examined is not affected much by using PCA and LDA.To further analyze the performance of PCA and LDA the eperimentation is carried out on Diabetic Retinopathy (DR) and Intrusion Detection System (IDS) datasets. Experimentation results prove that ML algorithms with PCA produce better results when dimensionality of the datasets is high. When dimensionality of datasets is low it is observed that the ML algorithms without dimensionality reduction yields better results

A metaheuristic optimization approach for energy efficiency in the IoT networks

© 2020 John Wiley & Sons, Ltd. Recently Internet of Things (IoT) is being used in several fields like smart city, agriculture, weather forecasting, smart grids, waste management, etc. Even though IoT has huge potential in several applications, there are some areas for improvement. In the current work, we have concentrated on minimizing the energy consumption of sensors in the IoT network that will lead to an increase in the network lifetime. In this work, to optimize the energy consumption, most appropriate Cluster Head (CH) is chosen in the IoT network. The proposed work makes use of a hybrid metaheuristic algorithm, namely, Whale Optimization Algorithm (WOA) with Simulated Annealing (SA). To select the optimal CH in the clusters of IoT network, several performance metrics such as the number of alive nodes, load, temperature, residual energy, cost function have been used. The proposed approach is then compared with several state-of-the-art optimization algorithms like Artificial Bee Colony algorithm, Genetic Algorithm, Adaptive Gravitational Search algorithm, WOA. The results prove the superiority of the proposed hybrid approach over existing approaches

Load Balancing of Energy Cloud using Wind Driven and Firefly Algorithms in Internet of Everything

The smart applications dominating the planet in the present day and age, have innovatively progressed to deploy Internet of Things (IoT) based systems and related infrastructures in all spectrums of life. Since, variety of applications are being developed using this IoT paradigm, there is an immense necessity for storing data, processing them to get meaningful information and render suitable services to the end-users. The “thing” in this decade is not only a smart sensor or a device; it can be any physical or household object, a smart device or a mobile. With the ever increasing rise in population and smart device usage in every sphere of life, when all of such “thing”s generates data, there is a chance of huge data traffic in the internet. This could be handled only by integrating “Internet of Everything (IoE)” paradigm with a completely diversified technology - Cloud Computing. In order to handle this heavy flow of data traffic and process the same to generate meaningful information, various services in the global environment are utilized. Hence the primary focus revolves in integrating these two diversified paradigm shifts to develop intelligent information processing systems. Energy Efficient Cloud Based Internet of Everything (EECloudIoE) architecture is proposed in this study, which acts as an initial step in integrating these two wide areas thereby providing valuable services to the end users. The utilization of energy is optimized by clustering the various IoT network using Wind Driven Optimization Algorithm. Next, an optimized Cluster Head (CH) is chosen for each cluster, using Firefly Algorithm resulting in reduced data traffic in comparison to other non-clustering schemes. The proposed clustering of IoE is further compared with the widely used state of the art techniques like Artificial Bee Colony (ABC) algorithm, Genetic Algorithm (GA) and Adaptive Gravitational Search algorithm (AGSA). The results justify the superiority of the proposed methodology outperforming the existing approaches with an increased -life-time and reduction in traffic

An enhanced algorithm for frequent pattern mining from biological sequences

Bio-data analysis deals with the most vital discovering problem of similarity search and finding relationship among bio sequences and structures. In this paper, we are trading the problem of discovering the most recurrently occurring patterns in a given DNA or protein sequence. Several on hand tools need the user to spell out gap constraints in advance in turn to find specific patterns. Practically it is not possible for the user to provide the gap constraints. So the need arises of budding an algorithm to obtain the patterns easily on its own without the need of user intervention in the form of mentioning of gap constraints. We have got two analytical methods to find out the recurrent subsequences and guesstimate the maximum support for data with complexity O(|T|.Sup) where |T| stands for text sequence length and Sup represents the number of occurrences of the pattern. We are proposing an altered version of the previously proposed algorithm with complexity O(|T|)

Organic matter from benthic foraminifera (Ammonia beccarii) shells by FT-IR spectroscopy: A study on Tupilipalem, South east coast of India

Fourier Transform Infrared Spectroscopy (FTIR) was used to study the variations in organic matters of benthic foraminifera (Ammonia beccarii) from four samples collected from beach environments from brackish environments along Tupilipalem coast (South east coast of India). Common absorption bands were observed as peaks in the range of 3600–3400 cm−1, 3000–2850 cm−1, 1750–1740 cm−1, 1640–1600 cm−1, 1450–1350 cm−1, 885–870 cm−1 and 725–675 cm−1 in all the shells of Ammonia beccarii. The FTIR spectrum of station-1 represents the presence of alkanes (CH3) and alkyl halide (CF stretching) with absorptions at the range 1385–1255 and 1350–1150 cm−1 were observed and ether (CO stretching) absorption band was observed at stations 1 and 3 with wavenumber of 1115 cm−1 and 1117 cm−1 respectively. Alkynes CH bend was observed at station-1 with the wavenumber of 667.43 cm−1. The shifting of peak positions in all the samples is could be due to presence of organic matter in the samples. Satellite remote sensing and field observation data revealed that the river mouth at Tupilipalem coast was closed by a sand bar. Consequentially, this waterbody may affect the species diversity. • Positions of the sampling locations were identified using a hand-held Garmin Global Positioning System (GPS). • Foraminifera from the sediment were obtained using a mixture of Bromoform and Acetone. • The functional groups present in the benthic foraminifera shells were recorded in the spectral range of 4000–400 cm−1 using an FT-IR Spectrophotometer

Dynamics of Pulicat Lake mouth analysis using geospatial data, east coast of India: Implications to socio-economic scenarios

Pulicat Lake is one of the major wetlands in India. It is the second largest brackish water lagoon in India next to Chilika Lake in Orissa state. Pulicat Lake sits beside the Bay of Bengal so, the study on the mouth is vital. The investigations were carried out by using multi-temporal satellite imageries of IRS P6, LISS III data for four years viz., 2009, 2011, 2012 and 2013. Subsequent changes in the width of the lake at the southern side were measured. It is found that the lake mouth is not static but dynamic predominantly fluctuating year by year. Obviously, this poses threat to the lake biodiversity. Hence, it is high time to mitigate, manage, monitor and protect the existing width of the sea mouth to keep the lake biological, ecological, economically active. This paper noticed a considerable change in the mouth of the lake studied using satellite imageries and socio-economic settings Keywords: Pulicat Lake, IRS LISS III data, Multi-temporal satellite imageries, Socio economic setting

Mobile phone and base stations radiation and its effects on human health and environment: A review

A review of the impact of mobile phone and base station radiation on human health and the environment has been presented here. Cell phone is an important invention in human history that has revolutionized people's lifestyles. As mobile phones have become an integral part of human daily routine, the quality of life around the world has improved significantly. However, concerns about the exposure of people, flora and fauna to radio frequencies are not new. The satisfaction and convenience derived from the use of cellular phones is threatened by claims that the radiation emitted by the devices has unfavarable impacts on human health. The effects of radiation may be classified into non-thermal and thermal. Thermal effects are similar to those of cooking in a microwave oven. The non-thermal effects are not properly defined, but it is been learnt that the these effects are three to four times more hazardous than the thermal, which remains controversial. A brief picture of the Indian scenario of cell phone industry and the number of mobile towers in India was discussed. The effects of radiation emitted from cell phones and base stations on wildlife, humans and the environment were summarized with suitable examples and studies conducted by various voluntary organizations

River mouth dynamics of Swarnamukhi estuary, Nellore coast, southeast coast of India

Swarnamukhi is an east flowing river having a total length of 130 km. This is an independent river which rises at an elevation of 300 m in the eastern Ghats ranges near Pakala village in Chittoor district of Andhra Pradesh, India. This study was carried out using multitemporal satellite images of IRS P6 LISS-III and Landsat 8 OLI/TIRS data from 2011 to 2015. The subsequent short term river mouth dynamics, coastal erosion and accretion rates have been calculated for the years between 2011 and 2015. Low river inflow, wind, tides, movement of the waves and littoral currents play a key role in the dynamic activities of erosion and accretion. The erosion rate from 2011 to 2015 was slightly decreased from 0.081 to 0.027 km2. The total net rate of accretion was estimated at 0.438 km2. The study shows during last five years (2011–2015) accretion is more than the erosion. High fluctuation of erosion and accretion are characteristics for the short term scale at river mouth