Koneru Lakshmaiah Education Foundation

This paper presents the rigorous study of mobile robot navigation techniques used so far. The step by step investigations of classical and reactive approaches are made here to understand the development of path planning strategies in various environmental conditions and to identify research gap. The classical approaches such as cell decomposition (CD), roadmap approach (RA), artificial potential field (APF); reactive approaches such as genetic algorithm (GA), fuzzy logic (FL), neural network (NN), firefly algorithm (FA), particle swarm optimization (PSO), ant colony optimization (ACO), bacterial foraging optimization (BFO), artificial bee colony (ABC), cuckoo search (CS), shuffled frog leaping algorithm (SFLA) and other miscellaneous algorithms (OMA) are considered for study. The navigation over static and dynamic condition is analyzed (for single and multiple robot systems) and it has been observed that the reactive approaches are more robust and perform well in all terrain when compared to classical approaches. It is also observed that the reactive approaches are used to improve the performance of the classical approaches as a hybrid algorithm. Hence, reactive approaches are more popular and widely used for path planning of mobile robot. The paper concludes with tabular data and charts comparing the frequency of individual navigational strategies which can be used for specific application in robotics. Keywords: Mobile robot navigation, Path planning, Classical approaches, Reactive approaches, Artificial intelligence

Heart disease causes a significant mortality rate around the world, and it has become a health threat for many people. Early prediction of heart disease may save many lives; detecting cardiovascular diseases like heart attacks, coronary artery diseases etc., is a critical challenge by the regular clinical data analysis. Machine learning (ML) can bring an effective solution for decision making and accurate predictions. The medical industry is showing enormous development in using machine learning techniques. In the proposed work, a novel machine learning approach is proposed to predict heart disease. The proposed study used the Cleveland heart disease dataset, and data mining techniques such as regression and classification are used. Machine learning techniques Random Forest and Decision Tree are applied. The novel technique of the machine learning model is designed. In implementation, 3 machine learning algorithms are used, they are 1. Random Forest, 2. Decision Tree and 3. Hybrid model (Hybrid of random forest and decision tree). Experimental results show an accuracy level of 88.7% through the heart disease prediction model with the hybrid model. The interface is designed to get the user's input parameter to predict the heart disease, for which we used a hybrid model of Decision Tree and Random Forest.

A.4 Constraining the flux in the ND A.4.1 Neutrino-electron elastic scattering A.4.2 The low- method A.4.3 Coherent neutrino-nucleus scattering A.4.4 Beam e content A.5 Movable components of the ND and the DUNE-PRISM program A.5.1 Introduction to DUNE-PRISM A.5.2 LArTPC component in the DUNE ND: ArgonCube A.5.3 Multipurpose detector A.5.4 The DUNE-PRISM program A.6 Fixed on-axis component of the DUNE ND A.6.1 Motivation and introduction A.6.2 Three-dimensional projection scintillator tracker spectrometer A.7 Meeting the near detector requirements A.7.1 Overarching requirements A.7.2 Event rate and flux measurements A.7.3 Control of systematic errors B ND hall and construction C Computing roles and collaborative projects C.1 Roles C.2 Specific collaborative computing projects C.2.1 LArSoft for event reconstruction C.2.2 WLCG/OSG and the HEP Software Foundation C.2.3 Evaluations of other important infrastructure

Extraction of complex head and hand movements along with their constantly changing shapes for recognition of sign language is considered a difficult problem in computer vision. This paper proposes the recognition of Indian sign language gestures using a powerful artificial intelligence tool, convolutional neural networks (CNN). Selfie mode continuous sign language video is the capture method used in this work, where a hearing-impaired person can operate the SLR mobile application independently. Due to non-availability of datasets on mobile selfie sign language, we initiated to create the dataset with five different subjects performing 200 signs in 5 different viewing angles under various background environments. Each sign occupied for 60 frames or images in a video. CNN training is performed with 3 different sample sizes, each consisting of multiple sets of subjects and viewing angles. The remaining 2 samples are used for testing the trained CNN. Different CNN architectures were designed and tested with our selfie sign language data to obtain better accuracy in recognition. We achieved 92.88% recognition rate compared to other classifier models reported on the same dataset.

The electroencephalogram (EEG) signals are a big data which are frequently corrupted by motion artifacts. As human neural diseases, diagnosis and analysis need a robust neurological signal. Consequently, the EEG artifacts’ eradication is a vital step. In this research paper, the primary motion artifact is detected from a single-channel EEG signal using support vector machine (SVM) and preceded with further artifacts’ suppression. The signal features’ abstraction and further detection are done through ensemble empirical mode decomposition (EEMD) algorithm. Moreover, canonical correlation analysis (CCA) filtering approach is applied for motion artifact removal. Finally, leftover motion artifacts’ unpredictability is removed by applying wavelet transform (WT) algorithm. Finally, results are optimized by using Harris hawks optimization (HHO) algorithm. The results of the assessment confirm that the algorithm recommended is superior to the algorithms currently in use.

Heavy metals occur in immobilized form in sediments and as ores in nature. However due to various human activities like ore mining and industrial processes the natural biogeochemical cycles are disrupted causing increased deposition of heavy metals in terrestrial and aquatic environment. Release of these pollutants without proper treatment poses a significant threat to both environment and public health, as they are non biodegradable and persistent. Through a process of biomagnification, they further accumulate in food chains. Thus their treatment becomes inevitable and in this endeavor, biosorption seems to be a promising alternative for treating metal contaminated waters. This technology employs various types of biomass as source to trap heavy metals in contaminated waters. The biosorbent is prepared by subjecting biomass to various processes like pretreatment, granulation and immobilization, finally resulting in metal entrapped in bead like structures. These beads are stripped of metal ions by desorption which can be recycled and reused for subsequent cycles. This technology out- performs its predecessors not only due to its cost effectiveness but also in being eco-friendly i.e., where other alternatives fail.   Key words: Biosorption, biomass, biosorbents, pretreatment, immobilization.

INTRODUCTION: Image segmentation in medical physics plays a vital role in image analysis to identify the affected tumour. The process of subdividing an image into its constituent parts that are homogeneous in feature is called Image segmentation, and this process concedes to extract some useful information. Numerous image segmentation techniques have been developed, and these techniques conquer different restrictions on conventional medical segmentation techniques. This paper presents a review of medical image segmentation techniques and statistical mechanics based on the novel method named as Lattice Boltzmann method (LBM). The beauty of LBM is to augment the computational speed in the process of medical image segmentation with an accuracy and specificity of more than 95% compared to traditional methods. As there is not much information on LBM in medical physics, it is intended to present a review of the research progress of LBM.OBJECTIVE: As there is no review paper on the research progress of the LB method, this paper presents a review with an objective to give some thought regarding the different segmentation for medical image and novel LB method to advance interest for future investigation and exploration in medical image segmentation.METHODS: This paper in attendance a short review of medical image segmentation techniques based on Thresholding, Region-based, Clustering, Edge detection, Model-based and the novel method Lattice Boltzmann method (LBM).CONCLUSION: In this paper, we outlined various segmentation techniques applied to medical images, emphasize that none of these problem areas has been acceptably settled, and all of the algorithms depicted are available for broad improvement. Since LBM has the benefits of speed and adaptability of modelling to guarantee excellent image processing quality with a reasonable amount of computer resources, we predict that this method will become a new research hotspot in image processing.

Abstract The electroencephalography (EEG) signal is corrupted with some non-cerebral activities due to patient movement during signal measurement. These non-cerebral activities are termed as artifacts, which may diminish the superiority of acquired EEG signal statistics. The state of the art artifact elimination approaches applied canonical correlation analysis (CCA) for confiscating EEG motion artifacts accompanied by ensemble empirical mode decomposition (EEMD). An improved cascaded approach based on Gaussian elimination CCA (GECCA) and EEMD is applied to suppress EEG artifacts effectively. However, in a highly noisy environment, a novel addition of median filter before the GECCA algorithm is suggested for improving the accuracy of onslaught the EEG signal. The median filter is opted due to its edge preserving nature and speed. This proposed approach is appraised using efficacy grounds for instance Del signal to noise ratio, Lambda (λ), root mean square error and receiver operating characteristic (ROC) parameters and verified contrary to presently obtainable EEG artifacts exclusion methods. The primary concern is to improve the efficacy and precision of the proposed artifact elimination technique. The elapsed time is also calculated to evaluate the computation efficiency. Results show that the proposed algorithm is appropriate to be used as an addition to existing algorithms in use.

Abstract The International Journal of Consumer Studies (IJCS) is a distinguished 45‐year‐old peer‐reviewed international journal in the field of multidisciplinary consumer research. This paper takes stock of the work published since 1977 ( n = 2088) and examines the impact of published research by applying network analysis technique using VOSviewer. In particular, it examines the overall citations received, the most cited papers and authors and their contributions to the field of consumer research. The advancement of research in the field of consumer research is discussed and directions for future research is provided to undertake novel research.

Image processing is largely used for gathering more knowledge / understanding either by human or by machines like computer. Segmentation, Thresholding and Edge detection are an important technique in Computer vision and Image processing. In digital images feature detection or extraction can be done for finding the irregularities in the image maybe in the rightness etc. This paper is a small review on Otsu"s method. This is proposed for improving the efficiency of computation for the optimal thresholds of an image. This paper gives thresholding technique and Otsu"s method of thresholding, also expresses its algorithm and working. This method gives satisfactory results when the numbers of pixels in each class are close to each other. It is the most referenced thresholding methods, as it directly operates on the gray level histogram, so it"s fast and computes an optimized threshold value. It automatically performs clustering-based image thresholding as its one of many binarization algorithms. 21778

The Deep Underground Neutrino Experiment (DUNE) is an international, world-class experiment aimed at exploring fundamental questions about the universe that are at the forefront of astrophysics and particle physics research. DUNE will study questions pertaining to the preponderance of matter over antimatter in the early universe, the dynamics of supernovae, the subtleties of neutrino interaction physics, and a number of beyond the Standard Model topics accessible in a powerful neutrino beam. A critical component of the DUNE physics program involves the study of changes in a powerful beam of neutrinos, i.e., neutrino oscillations, as the neutrinos propagate a long distance. The experiment consists of a near detector, sited close to the source of the beam, and a far detector, sited along the beam at a large distance. This document, the DUNE Near Detector Conceptual Design Report (CDR), describes the design of the DUNE near detector and the science program that drives the design and technology choices. The goals and requirements underlying the design, along with projected performance are given. It serves as a starting point for a more detailed design that will be described in future documents.

ABSTRACT Recent advancements in technology have emerged the requirements of hardware and software applications. Along with this technical growth, software industries also have faced drastic growth in the demand of software for several applications. For any software industry, developing good quality software and maintaining its eminence for user end is considered as most important task for software industrial growth. In order to achieve this, software engineering plays an important role for software industries. Software applications are developed with the help of computer programming where codes are written for desired task. Generally, these codes contain some faulty instances which may lead to the buggy software development cause due to software defects. In the field of software engineering, software defect prediction is considered as most important task which can be used for maintaining the quality of software. Defect prediction results provide the list of defect-prone source code artefacts so that quality assurance team scan effectively allocate limited resources for validating software products by putting more effort on the defect-prone source code. As the size of software projects becomes larger, defect prediction techniques will play an important role to support developers as well as to speed up time to market with more reliable software products. One of the most exhaustive and pricey part of embedded software development is consider as the process of finding and fixing the defects. Due to complex infrastructure, magnitude, cost and time limitations, monitoring and fulfilling the quality is a big challenge, especially in automotive embedded systems. However, meeting the superior product quality and reliability is mandatory. Hence, higher importance is given to V&V (Verification & Validation). Software testing is an integral part of software V&V, which is focused on promising accurate functionality and long-term reliability of software systems. Simultaneously, software testing requires much effort, cost, infrastructure and expertise as the development. The costs and efforts elevate in safety critical software systems. Therefore, it is essential to have a good testing strategy for any industry with high software development costs. In this work, we are planning to develop an efficient approach for software defect prediction by using soft computing based machine learning techniques which helps to predict optimize the features and efficiently learn the features.

The agricultural sector generates a significant amount of waste, the majority of which is not productively used and is becoming a danger to both world health and the environment. Because of the promising relevance of agro-residues in the agri-food-pharma sectors, various bioproducts and novel biologically active molecules are produced through valorization techniques. Valorization of agro-wastes involves physical, chemical, and biological, including green, pretreatment methods. Bioactives and bioproducts development from agro-wastes has been widely researched in recent years. Nanocapsules are now used to increase the efficacy of bioactive molecules in food applications. This review addresses various agri-waste valorization methods, value-added bioproducts, the recovery of bioactive compounds, and their uses. Moreover, it also covers the present status of bioactive micro- and nanoencapsulation strategies and their applications.

In India, we all know that Agriculture is the backbone of the country. This paper predicts the yield of almost all kinds of crops that are planted in India. This script makes novel by the usage of simple parameters like State, district, season, area and the user can predict the yield of the crop in which year he or she wants to. The paper uses advanced regression techniques like Kernel Ridge, Lasso and ENet algorithms to predict the yield and uses the concept of Stacking Regression for enhancing the algorithms to give a better prediction.

Wnt/β-catenin (WβC) signaling pathway is an important signaling pathway for the maintenance of cellular homeostasis from the embryonic developmental stages to adulthood. The canonical pathway of WβC signaling is essential for neurogenesis, cell proliferation, and neurogenesis, whereas the noncanonical pathway (WNT/Ca2+ and WNT/PCP) is responsible for cell polarity, calcium maintenance, and cell migration. Abnormal regulation of WβC signaling is involved in the pathogenesis of several neurodegenerative diseases such as Alzheimer’s disease (AD), Parkinson’s disease (PD), Huntington’s disease (HD), amyotrophic lateral sclerosis (ALS), multiple sclerosis (MS), and spinal muscular atrophy (SMA). Hence, the alteration of WβC signaling is considered a potential therapeutic target for the treatment of neurodegenerative disease. In the present review, we have used the bibliographical information from PubMed, Google Scholar, and Scopus to address the current prospects of WβC signaling role in the abovementioned neurodegenerative diseases.

Peanut is a legume crop that belongs to the family of Fabaceae, genus Arachis, and botanically named as Arachis hypogaea. Peanuts are consumed in many forms such as boiled peanuts, peanut oil, peanut butter, roasted peanuts, and added peanut meal in snack food, energy bars and candies. Peanuts are considered as a vital source of nutrients. Nutrition plays an important role in growth and energy gain of living organisms. Peanuts are rich in calories and contain many nutrients, minerals, antioxidants, and vitamins that are essential for optimum health. All these biomolecules are essential for pumping vital nutrients into the human body for sustaining normal health. This paper presents an overview of the peanut composition in terms of the constituent biomolecules, and their biological functions. This paper also discusses about the relationship between consumption of peanuts and their effect on human metabolism and physiology. It highlights the usefulness of considering peanuts as an essential component in human diet considering its nutritional values.

Abstract In this study, different types of magnetic biochar nanocomposites were synthesized using the co-precipitation method. Two biochar materials, namely, sewage sludge biochar and woodchips biochar, were prepared at two different temperatures, viz., 450 and 700 °C. These biochars were further modified with magnetic nanoparticles (Fe 3 O 4 ). The modified biochar nanocomposites were characterized using field emission–scanning electron microscopy (FE-SEM), X-ray diffraction (XRD), transmission electron microscopy (TEM), Brunauer–Emmett–Teller (BET), SQUID analysis, X-ray photoelectron spectroscopy (XPS), and Fourier-transform infrared spectroscopy (FTIR). The potential of prepared adsorbents was examined for the removal of hexavalent chromium (Cr(VI)) and Acid orange 7 (AO7) dye from water as a function of various parameters, namely, contact time, pH of solution, amount of adsorbents, and initial concentrations of adsorbates. Various kinetic and isotherm models were tested to discuss and interpret the adsorption mechanisms. The maximum adsorption capacities of modified biochars were found as 80.96 and 110.27 mg g -1 for Cr(VI) and AO7, respectively. Magnetic biochars showed high pollutant removal efficiency after 5 cycles of adsorption/desorption. The results of this study revealed that the prepared adsorbents can be successfully used for multiple cycles to remove Cr(VI) and AO7 from water.

The preponderance of matter over antimatter in the early universe, the dynamics of the supernovae that produced the heavy elements necessary for life, and whether protons eventually decay—these mysteries at the forefront of particle physics and astrophysics are key to understanding the early evolution of our universe, its current state, and its eventual fate. DUNE is an international world-class experiment dedicated to addressing these questions as it searches for leptonic charge-parity symmetry violation, stands ready to capture supernova neutrino bursts, and seeks to observe nucleon decay as a signature of a grand unified theory underlying the standard model. Central to achieving DUNE's physics program is a far detector that combines the many tens-of-kiloton fiducial mass necessary for rare event searches with sub-centimeter spatial resolution in its ability to image those events, allowing identification of the physics signatures among the numerous backgrounds. In the single-phase liquid argon time-projection chamber (LArTPC) technology, ionization charges drift horizontally in the liquid argon under the influence of an electric field towards a vertical anode, where they are read out with fine granularity. A photon detection system supplements the TPC, directly enhancing physics capabilities for all three DUNE physics drivers and opening up prospects for further physics explorations. The DUNE far detector technical design report (TDR) describes the DUNE physics program and the technical designs of the single- and dual-phase DUNE liquid argon TPC far detector modules. Volume IV presents an overview of the basic operating principles of a single-phase LArTPC, followed by a description of the DUNE implementation. Each of the subsystems is described in detail, connecting the high-level design requirements and decisions to the overriding physics goals of DUNE.

Agriculture plays a significant role in most countries and there is an enoromous need for this industry to become “Smart”. The Industry is now moving towards agricultural modernization by using modern smart technologies to find solutions for effective utilization of scarce resources there by meeting the ever increasing consumtion needs of global population. With the advent of Internet of Things and Digital transformation of rural areas, these technologies can be leveraged to remotely monitor soil moisture, crop growth and take preventive measures to detect crop damages and threats. Utilize artificial intelligence based analytics to quickly analyze operational data combined with 3rd party information, such as weather services, expert advises etc., to provide new insights and improved decision making there by enabling farmers to perform “Smart Agriculture”. Remote management of agricultural activities and their automation using new technologies is the area of focus for this research activity. A solar powered remote management and automation system for agricultural activities through wireless sensors and Internet of Things comprising, a hardware platform based on Raspberry Pi Micro controller configured to connect with a user device and accessed through the internet network. The data collection unit comprises a set of wireless sensors for sensing agricultural activities and collecting data related to agricultural parameters; the base station unit comprising: a data logger; a server; and a software application for processing, collecting, and sending the data to the user device. The user device ex: mobile, tablet etc. can be connected to an internet network, whereby an application platform (mobile-app) installed in the user device facilitates in displaying a list of wireless sensor collected data using Internet of Things and a set of power buttons. This paper is a study and proposal paper which discusses the factors and studies that lead towards this patent pending invention, AGRIPI.

It is known to us that the problem of freshwater shortage is constantly exacerbated by the rapid population increase and the great diversity of different industries that need water to continue their work. It is also well known that solar stills are one of the thermal solutions to this problem, but they are disadvantaged by their low productivity. So, this study presents an experimental study to improve the performance of the tubular solar still with rotating drum (TDSS) using nanoparticles' coating, parabolic solar concentrator (PSC), and phase change material (PCM). Different operating variables were tested in this study. This combination of using PSC and PCM is investigated for the first time in TDSS. PSC was used to concentrate the solar rays on the back side of the drum, which raised the evaporation rate. The nanoparticles coating was used to paint the surfaces of the drum and basin still to change the film-wise mechanism of water to drop-wise. In addition, the effect of various rotating speeds of drum on the performance of TDSS was investigated. The experimental results revealed that using the rotating cylinder inside the tubular solar still (TSS) increased the productivity of the distiller as compared to that of the conventional solar still (CSS). Furthermore, the TDSS with nanoparticles’ coating provided a productivity of 6650 mL/m2.day compared to 2800 mL/m2.day for the CSS with an enhancement by 137%. Besides, the maximum increase in productivity of TDSS when using PSC and PCM was obtained at 0.3 rpm, where the productivity improvement was around 195% and 218%, respectively. The thermal efficiency of the CSS was around 32–34%. Highest thermal efficiency of the TDSS with PCM was 63.8% at 0.3 rpm. Also, the cost of distilled water of TDSS with nanoparticles' coating, PSC, and PCM is 0.024 $/L compared to 0.029 $/L for the CSS, and the payback time was around 5 and 3 months, respectively.