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NIR properties of Be stars in star clusters in the Magellanic Clouds
Magellanic Clouds are the nearby galaxies which are ideal to study the properties of metal poor stellar population. In this study, we explore the near-IR properties of optically identified classical Be stars in 19 star clusters in the Magellanic Clouds. From an optically identified sample of 835 Be stars we obtained the J, H, K magnitudes of 389 stars from the IRSF MCPS catalog. Among these, 247 stars (36.4%) are found in 9 clusters in the Large Magellanic Cloud and 142 stars (55.5%) in 10 clusters in the Small Magellanic Cloud. After correcting for reddening, we studied their NIR properties in the (HK)0 vs (JH)0 diagram. We identified 14 stars with abnormally large near IR excesses, which were removed from the analysis, there by restricting our study to 355 classical Be stars. We propose an extended area in the near-IR (H-K)0 vs (J-H)0 diagram as the diagnostic location of Classical Be stars in the Magellanic Clouds. We identified 14 stars to have near-IR excess, higher than those seen in classical Be stars. From the analysis based on spectral energy distribution and luminosity estimate, we found that 8 candidate Be stars may be Herbig Ae/Be stars. We identified a new sample of 6 sgB[e] stars, which when added to the sparse existing sample of 15 sgB[e] stars in the Magellanic Clouds can provide insight to understand the evolutionary link between sgB[e] stars and Luminous Blue variables. 2017 Elsevier B.V. -
Quantum tasks using six qubit cluster states
The usefulness of the recent experimentally realized six photon cluster state by C. Y. Lu et al. (Nature 3:91, 2007) is investigated for quantum communication protocols like quantum teleportation and quantum information splitting (QIS) and dense coding. We show that the present state can be used for the teleportation of an arbitrary two qubit state deterministically. Later, we devise two distinct protocols for the QIS of an arbitrary two qubit state among two parties. We construct sixteen orthogonal measurement basis on the cluster state, which will lock an arbitrary two qubit state among two parties. The capability of the state for dense coding is investigated and it is shown that one can send five classical bits by sending only three qubits using this state as a shared entangled resource.We finally show that this state can also be utilised in the remote state preparation of an arbitrary two qubit state. Springer Science+Business Media, LLC 2010. -
Hybrid Approach for Predicting Heart Disease Using Optimization Clustering and Image Processing
Heart disease (cardiovascular disease) is one of the core issues prevalent in this generation. Every year, millions of people die due to various heart diseases. The problem occurs due to hereditary or changes in life styles. Various data mining techniques are used in order to predict heart diseases. Data mining increases the accuracy, precision, and sensitivity of the model being used. In the proposed hybrid approach for predicting heart disease using optimization clustering and image processing (Hy-OCIP) model, a hybrid approach is used to predict heart diseases with the help of optimization, clustering, and image processing. After the heart image is being processed, centroid clustering is used for clustering the processed imaged into a set of chromosomes for optimization. The optimization method used for our model is genetic algorithm. The same methods are performed for both, a healthy and a heart patient. As a result, the model used in this research is able to distinguish between a normal patient and a heart patient by a hybrid combination of image processing, clustering, and optimization. 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Review of Neuropsychological and Electrophysiological Correlates of Callous-unemotional Traits in Children: Implications for EEG Neurofeedback Intervention
Conduct disorder is a significant psychiatric disorder of childhood and adolescence. The Diagnostic and Statistical Manual of Mental Disorders, fifth version (DSM-5), added the limited prosocial specifier to identify those individuals who exhibit a more severe pattern of behavior characterized by a callous and unemotional (CU) interpersonal style across multiple settings and relationships. This review has attempted to summarize the relevant research focusing on the significance of CU interpersonal style in the development of psychopathy. The primary focus was on the electrophysiological and neuropsychological correlates of CU traits and their implication on the treatment protocol using neurofeedback training for children with such traits. The source of the literature search was PubMed, which majorly uses the MEDLINE database. The keywords used included CU traits, conduct disorder, child psychopathy, empathy, electrophysiology, criminal behavior, neuropsychology, neurofeedback training, and so on. Studies from the last 15 years were considered for the review. This review revealed that children with conduct disorder and high-CU traits with a combination of reactive and proactive aggression are more likely to develop psychopathy. Evidence suggests that these children have distinct forms of electrophysiological and neuropsychological correlates. However, research in this area is still not conclusive as they yield variation in findings. Studies on the efficacy of neurofeedback training on reducing symptoms such as impulsivity, hostility, and psychopathy indicate that neurofeedback training can be a promising treatment alternative for children with severe conduct disorder. EEG and Clinical Neuroscience Society (ECNS) 2021. -
A privatised approach in enhanced spam filtering techniques using TSAS over cloud networks
Major problem over cloud networks is the effect of malicious code that protrudes its own activity without intend of network user in resource sharing. One such activity is the spam-filtering techniques which assumes the data with training and testing sets and also rely on fundamental classification through distribution. A privatised spam filtering approach is a classic problem which automatically recognises user context and incoming mail information relevance. To filter mail contents learning based methods, probabilistic based method trying to improve their accuracy but they cannot attain an improvement in identifying suspicious contents and also in segregating legitimate mail entries. Here a novel representation of structured abstraction scheme (SAS) used to generate abstraction in e-mail process using HTML tag content in e-mail and its algorithm for filtering such process of spam filtering is depicted. In this SAS methodology near duplicate matching process with HTML tag ordering will be processed and newly assigned position ordering were deliberated. The experimental setup shows that there will be a great improvement while filtering spam in accuracy of e-mail content while sharing in cloud networks. Copyright 2022 Inderscience Enterprises Ltd. -
Prevention and Mitigation of Intrusion Using an Efficient Ensemble Classification in Fog Computing
Cloud services in fog network is a platform that inherits software services to a network to handle cloud-specific problems. A significant component of the security paradigm that supports service quality is represented by intrusion detection systems (IDSs). This work develops an optimization environment to mitigate intrusion using RSLO classifier on a cloud-based fog networks. Here, a three-layer approach namely the cloud, end point, and fog layers is used as a trio to carry out all of the processing. In the cloud layer, three layers of processing are required for handling the dataset metrics which are data transformation metrics, feature selection metrics, and classification processes. With log transformation, data is transformed using KS correlation-based filter which is used to choose a feature. The classification using an ensemble methodology of RideNN classifiers which is a Rider Sea Lion Optimization (RSLO), a created classifier, is used to tune the ensemble classifier. Physical work is carried out at another layer called an end point layer. A trained ensemble classifier is used for intrusion detection in the fog layer. A greater precision, recall, and F-measure were obtained with an accuracy approximately 95%, with all benefits of the suggested RSLO-based ensemble strategy. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
The wellbeing van: A strength-based model for student engagement and promoting student wellbeing
This book chapter takes a proactive approach to address stress by introducing a preventive measure, a relatively new approach implemented on college students in Bangalore, India called the "wellbeing van." The chapter emphasizes the importance of not only managing and coping with stress, but also implementing strategies that promote overall well-being by tapping into the intrinsic factors of the student. The well-being van has shown promising results in reducing stress levels among students in academic institutions. Additionally, the chapter delves into the difficulties students face in maintaining social relationships, conforming to societal expectations, and finding their place. It provides valuable insights on how to foster healthy social dynamics and create supportive environments for students. 2024, IGI Global. All rights reserved. -
Creating a positive school culture through new educational practices
There is an increase in rates of depression and anxiety globally. Schools have moved from a traditional approach of learning and teaching to contemporary forms which includes technology. New revolution has taken place in the world of schooling, with the entry of positive education. Positive education is the application of principles in positive psychology to schooling to enhance the well-being of students and schools. The objective of this review-based chapter is to bring to light the use of various strategies of positive education in classrooms, for middle school students, in order to create a positive learning environment. An evidence-based learning approach is used in this chapter. This chapter address issues and concerns regarding the challenges in applying the strategies of positive education. The main aim of creating a positive school culture in middle school is to enhance the happiness and well-being of students The chapter brings out the need for positive education in the current generation and discusses the implications of the further scope of positive education in India. 2021, IGI Global. -
AI-Driven Home Climate Optimization: The Role of ChatGPT in Enhancing AC Efficiency
The convergence of Internet of Things (IoT) and Artificial Intelligence (AI) has revolutionized home automation, yet traditional air-conditioning (AC) systems still struggle with energy inefficiency. Our research presents a novel solution, integrating AI, IoT, and user-centric design with ChatGPT, to optimize AC systems responsively to occupants' needs. Our methodology employs ChatGPT's capability to analyze historical data, discern patterns, and provide intelligent recommendations for AC operation. This transcends the functions of standard smart thermostats through AI-driven decision-making, optimizing every AC operational moment for both comfort and energy conservation. The system's foundation in data-driven decisions ensures alignment with external and internal conditions, enhancing energy efficiency and user comfort. 2024 IEEE. -
Systematic Review of Interparental Conflict and Intimate Relationship Satisfaction of Adult Offspring
Purpose: The objective of this paper is to synthesize a systematic review and showcase the findings of how interparental conflict affects the adult offsprings relationship satisfaction. Approach: The paper follows a systematic review approach adhering to PRISMA Guidelines. Findings: Three major themes were identified by the researcher that explained the consequences of interparental conflict on adult offspring. The themes are-Offspring Factors, Offspring factors feature the significant components and factors of offspring that highlight the differences in impact of interparental conflict on ones own romantic relationship; Consequences of Interparental Conflict on Offspring, which highlights the various corollaries on offspring of divorce; and Parent Factors help understand the different parental elements that affect relationship satisfaction of adult offspring. Limitations: 4 full text articles could not be accessed by the author, and hence were not accounted for in the study. Practical Implications: The study helps practitioners comprehend the extensive effects of interparental conflict for usage in therapeutic setups. It also highlights research gaps in existing literature such as lack of studies focusing on men, LGBTQ+ population, children from polygamous families and multicultural cohorts. Value/Originality: Despite the numerous studies published on the topic interparental conflict and its effects on adult offspring, no review has been done to the authors knowledge, thereby limiting the accessibility of precise information. The research aims to bridge this gap, and make comprehensive findings easily accessible. 2022 RESTORATIVE JUSTICE FOR ALL. -
Educate, enable and empower future leaders: A model for community development through the child sponsorship program
The Child Sponsorship Program is an attempt by development organizations to reinstate the rights of a child to education, focusing on the overall well-being of a child and the community. The Sustainable Development Goals view Child Sponsorship Program as a tool for contributing towards development goals and targets. While the conventional models of the Child Sponsorship Program focused on the scholastic performance of children up to the elementary level, several progressive sponsorship programs aim at the whole personal development of the children and their community by ensuring community participation and a development-based approach targeting education, livelihood, empowerment, etc. This program channelized by the Centre for Social Action (CSA), CHRIST (Deemed to be) University), Bengaluru, is a child development program that engages with the goals of holistic development of the child and community development through the Child Sponsorship Program in two urban slum communities in Bengaluru. The research aims to study the change brought in the indicators, such as education, behaviour and attitude change, leadership and their holistic development, by the program. It also intends to assess the impact of the program on the development of the family, community and participation of the community members in the academic development of children. The study follows a qualitative study employing in-depth interviewing and Focused Group Discussions with parents and child participants of the communities where the Child Sponsorship Program is implemented. The data is analyzed through qualitative and quantitative software. 2024 Nova Science Publishers, Inc. -
Removal of Artifacts from Electroenchaphalography Signal using Multiwavelet Transform
The signal from the brain can be recorded using Electroenchaphalography (EEG). The proposed work summarizes a unique method which is used for the removal of mixed artifacts presented in the electroencephalography signal during the acquisition process. Artifacts comprises of various bio-potential unit such as electrooculogram, electrocardiogram, and electromyogram. These artifacts are referred as a noise sources which is responsible for the complexity of the EEG signal. The artifacts obtained from the EEG signal leads towards improper diagnosis of pathological conditions. The EEG signal which is obtained from the brain is the multi-dimensional signal with the various statistical properties. Time consumption of the EEG signal is not reproducible due to the biological properties of the signal. The information of the EEG signal consists of the data of the neuron levels which is collected for every millisecond with the temporal resolution scale. In account of special cases, EEG signal contains noise and artifacts where information is collected using the extraction of signals. To obtain the information of the artifacts the proposed technique is used to maintain higher accuracy in the extraction process. The proposed technique consists of multiwavelet transform to remove the artifacts from the input EEG signal. In the proposed multiwavelet transform, the signal which consists of noisy features can be decomposed using GHM and thresholding technique. This experimental analysis shows the removal of artifacts from the EEG signals. The pathological conditions are removed which leads to the increase in the accuracy of the system. Also, this research findings shows that the proposed multiwavelet transform based approach outperforms significantly with respect to conventional approaches. The reconstructed EEG signal has the lesser reliability range which is measured in-terms of signal to noise ratio and power spectral density. Published under licence by IOP Publishing Ltd. -
Proficient technique for satellite image enhancement using hybrid transformation with FPGA
Visual quality of images is improved by digital techniques for the improvement of photographs. The main purpose of image improvements is to process an image to make the output more desirable for a particular use than the original image. This paper proposes a new approach, which improves the picture of the satellite by the use of the SVD DWT concept, the Gaussian transformation DWT and multiwavelet transformation. This suggested approach would convert and approximate the single-colour value matrix of the low-flowing sub-band into one low-frequency and 15 high-frequency sub-bands, and then recreate the improved picture using the inverse transformation. In terms of technical criteria as PSNR, RMSE and CC, this approach can have higher quality and quantitative performance. This paper introduces strategies for improving hardware images using a programmable door array in real-time (FPGA). The suggested algorithm is implemented successfully with Xilinx ISE, MATLAB and ModelSim on different scale satellite images in Verilog HDL. In this article, these algorithms should be simulated and implemented using Verilog HDL. The Spartan-3E from Xilinx is the unit chosen here. 2021 IEEE -
Diagnosis of Retinal Disease Using Retinal Blood Vessel Extraction
The eye is one of the important organs of the human body. In recent times, major parts of the eye are damaged due to various retinal diseases. Major diseases related to the retina are glaucoma, papilledema, retinoblastoma, diabetic retinopathy, and macular degeneration. These diseases can be detected using image processing techniques. These diseases can cause damage to the eye; hence the early diagnosis can prevent the loss of vision. Thus the early stage of rectification may lead to smaller damage than the risky ones. By extracting the blood vessels, various retinal diseases can be identified, and the severity of the disease can also be identified. Some of these diseases in the retina will occur due to hypertension, blood pressure, and diabetics. Thus, the tear in the blood vessels leads to the loss of visuality in human beings. The proposed work consists of image processing techniques such as segmentation, feature extraction, and boundary extraction which lead to the identification of various retinal diseases with a certain level of accuracy, sensitivity, and specificity by using image processing techniques. The training and testing of retinal images are carried out by using the artificial neural network (ANN) classifier for glaucoma detection and support vector machine (SVM) classifier for detecting diabetic retinopathy. 2021, Springer Nature Switzerland AG. -
Equalization of Finite-Alphabet MMSE for All-Digital Massive MU-MIMO mm-Wave Communication
For more than twenty years, growing the performance and efficiency of wireless communications systems using antenna arrays has been an active field of study. Wireless networks with multiple-input multiple-output are also part of the current norms and are implemented around the world. Access points or BSs with comparatively few antennas are used for standard MIMO systems, and the resulting increase in spectral efficiency was relatively modest. A Multiple-Input Multiple-Output platform's capacity is researched where the transmitter outputs are processed and quantified by a set of limit quantizes through an analogue linear combining network. The linear mixing weights and cutoff levels are chosen from with a collection of possible combinations as a function of the transmitted signal. Millimetre-wave networking requires optimum data transmission to various computers on same moment network in combination with large multi-user actually massive. In order to guarantee efficient data transmission, the heavy insertion loss of wave propagation at su ch a faster speed needs proper channel estimation. A new channel estimation algorithm called Beam space Channel Estimation is suggested. From a set of possible configurations, the capacity of a massive stream from which antennas signals are handled by an analog channel as a part of the channel matrix, linear mixture weights and frequency modulation levels are selected. Probable implementations of specific analogue receiver designs for the combined network model, such as smart antenna selection, sign antennas output thresholding or linear output processing. To demonstrate the effectiveness of BEACHES in service and have FPGA implementation results, we are developing VLSI architecture. Our results show that for large MU-MIMOs, mm-wave communications with hundreds of antennas, specially made denoising can be done at maximum bandwidth and in an equipment format. Published under licence by IOP Publishing Ltd. -
An Improvised Mechanism for Optimizing Fault Detection for Big Data Analytics Environment
In the applications of fault detection, the inputs are the data reflected from health state of the observed system. A major challenge to finding errors is the nonlinear relationship between the data. Big data has other drawbacks, and the volume and speed with which it is generated are reflected in the data streams themselves. In this paper, we develop a deep learning model that aims to provide fault detection in big data analytics engine. This investigation develops an approach for fault detection in large datasets using unsupervised learning. In this research, an unsupervised method of learning is developed specifically for the task of classifying large datasets. To discover regular textual patterns in large datasets, this research use data visualization methods. In this virtual environment, we employ an unsupervised learning method of machine learning that does not require human oversight. Instead, the system should be allowed some leeway to work and find things on its own. The unsupervised learning approach utilizes data that has not been tagged. In contrast to supervised learning, this approach can handle complex tasks. 2024, Ismail Saritas. All rights reserved. -
Liquid gold: assessing groundwater quality at the historic Kolar gold fields, Karnataka, India
To assess ecological sustainability and resilience, it is necessary to periodically examine various ecological properties in areas with high pollution and contaminant risks. Kolar Gold Fields (KGF) in Kolar, Karnataka, showcases one of India's most contaminated zones because of the extensive gold mining and its lingering effects. In KGF, the quality of groundwater has been severely reduced as there exist extensive mining tailings, locally referred to as cyanide dumps, which have been neglected for several preceding years without proper disposal strategies. The current approach focuses on the water pollution caused by heavy metal deposits in the KGF region. Groundwater samples were sampled from Oorgam, an abandoned region in KGF, and subsequently filtered for water quality examinations. The investigation documented concentrations of several metals, including cadmium (0.068 0.0024 ppm), lead (0.288 0.0016 ppm), nickel (0.058 0.0047 ppm), and chromium (0.23 0.0235 ppm) and have met the standard specifications in accordance with World Health Organization (WHO). Prominent pH disparity was documented amongst the experimental samples, with a detectable pH drop in the aqua-purified water in comparison to the positive control. The test results imply that the water samples collected from KGF remain unpotable for consumption or irrigation due to the persistence of high levels of heavy metal concentration. This study underscores the urgent requirement for a remedial approach to ensure water safety for drinking and irrigation in the area. 2025 Brawijaya University. All rights reserved. -
A Relative Reference Responsive LRU based Page Replacement Algorithm for NAND Flash Memory
The acceptance of NAND flash memories in the electronic world, due to its non-volatility, high density, low power consumption, small size and fast access speed is hopeful. Due to the limitations in life span and wear levelling, this memory needs special attention in its management techniques compared to the conventional techniques used in hard disks. In this paper, an efficient page replacement algorithm is proposed for NAND flash based memory systems. The proposed algorithm focuses on decision making policies based on the relative reference ratio of pages in memory. The size adjustable eviction window and the relative reference based shadow list management technique proposed by the algorithm contribute much to the efficiency in page replacement procedure. The simulation tool based experiments conducted shows that the proposed algorithm performs superior to the well-known flash based page replacement algorithms with regard to page hit ratio and memory read/write operations. 2021, Webology. All Rights Reserved. -
Review on developments in nand flash page replacement algorithms
The non volatility, low power consumption and high density of NAND flash memories, made them an inevitable part of electronic industry. Due to the high wear out nature exhibited by flash systems, the algorithms used for page replacement in traditional memory systems are not suitable for flash page replacement. Along with the objective to maintain high hit rate, flash page replacement algorithms should aim at decreasing the page write count and maintain wear levelling. This paper presents major algorithms proposed for flash memory page replacement. The major contribution of this work is a relative study on various strategies, performance matrices and evaluation tools used for flash page replacement algorithm. The study shall help the researchers to identify the pros and cons of various flash page replacement algorithms, to identify the major gaps in between and to identify some commendable tools that can be used for flash page replacement algorithm evaluation. The gaps identified need to be addressed seriously in the near future. 2020, Engg Journals Publications. All rights reserved. -
A Relative Analysis on the Spotting of Cardiovascular Disease Employing Machine Learning Techniques
Heart is one of the significant segments in the human body since it powers blood to the all the pieces of the body. Blood courses through the vein. Cardiovascular sickness is corresponded with the blockage of vein. The sign of heart sickness depends whereupon condition is impacting an individual. The term coronary illness is ordinarily utilized instead of cardiovascular infection. Dilated cardiomyopathy, Heart failure, Arrhythmia, Pulmonary stenosis, Mitral regurgitation, Coronary artery disease, Myocardial infraction, Mitral valve prolapse, Hypertrophic cardiomyopathy are the sorts of coronary illness. The several machine learning techniques are analyzed to spot heart disease. This paper gives relative investigation of coronary illness expectation utilizing machine learning. 2021 IEEE.