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Neuroscience of social understanding
Comprehending human behavior and interactions requires an understanding of the social mind. Social cognitive neuroscience provides a lens to understand these complexities. This chapter explores the core brain mechanisms that control social conduct by exploring the field of social cognitive neuroscience. It examines aspects of social cognition, like the theory of mind, social perception, empathy, and decisionmaking. It explains how the brain helps navigate complex social contexts by looking at complex interactions between neurological processes and social behaviors. Important subjects include the function of the mirror neuron system, temporoparietal junction and prefrontal cortex in mediating social cognition. It discusses the implications of social cognitive neuroscience for understanding diseases such as schizophrenia and autism spectrum disorder, which are characterized by social deficiencies. Through this research, we learn about the social mind and its brain foundations, and it opens the door to novel interventions that improve interpersonal relationships and social well-being. 2024 by IGI Global. All rights reserved. -
Enhancing Network Security with Comparative Study of Machine Learning Algorithms for Intrusion Detection
With the ever-increasing network systems and dependency on digital technologies, ensuring the security and integrity of these systems is of paramount importance. Intrusion detection systems (IDS) play a major role in sheltering such systems. Intrusion detection systems are technologies that are designed to monitor network and system activities and detect suspicious, unauthorized, malicious behavior. This research paper conducts a comprehensive comparative analysis of three popular machine learning algorithmsK-Nearest Neighbors (KNN), Random Forest (RF), and Logistic Regression (LR)in the context of intrusion detection using the renowned NSL-KDD dataset. Preprocessing techniques are applied, and the dataset is split for rigorous evaluation. The findings of this research highlight the effectiveness of Random Forest in detecting intrusions, showcasing its potential for real-world network security applications. This study contributes to the field of intrusion detection and offers valuable insights for network administrators and cybersecurity professionals to enhance network protection. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Pandemic Resilient Organizational Behaviour: From the Lens of Stakeholder and Legitimacy Theory
The Covid-19 pandemic spread on global map with unprecedented speed and created an environment of uncertainty, anxiety and disruption. India, being a densely populated country, had been looked upon with apprehension and later on with great admiration in controlling and managing the pandemic and its devastating effect. The study has built a thematic model for short-term and long-term pandemic resilient organizational practices based on stakeholder and legitimacy theory, which focuses on aligning business with societal values and stakeholder expectations. The foci have been stakeholder groups of employees, customers, suppliers and community. Sustainability reports of selected Indian companies based on GRI standards for FY from 2019 to 2022 are then scored based on the developed model. Further analysis explored changes in risk reporting framework in pandemic and post pandemic. The thematic coverage in sustainability reports for employees and community found a prominent place emphasizing the importance of these groups. The thematic disclosures for suppliers are the least disclosed, indicating areas for improvement in the business practices. Based on this thematic model, suggestions are also made for additional disclosure indicators in the GRI framework for stakeholder group of suppliers and customers. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
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Advancing Gold Market Predictions: Integrating Machine Learning and Economic Indicators in the Gold Nexus Predictor (GNP)
This study employs advanced machine learning algorithms to predict gold prices, using a comprehensive dataset from Bloomberg. The Gold Nexus Predictor (GNP), a key innovation, integrates historical data and economic indicators through advanced feature engineering. Methodologies include exploratory data analysis, model training with various algorithms like Linear regression, Random Forest, Ada Boost, SVM, and ARIMA, and evaluation using metrics like MSC, MAPE, and RMSE. The study's philosophical foundation emphasizes rationalism in economic forecasting and ethical model use. This research offers significant insights for investors and policymakers, enhancing understanding and decision-making in the gold market. 2024 IEEE. -
Duplex functionally graded and multilayered thermal barrier coatings based on 8% yttria stabilized zirconia and pyrochlores
Thermal Barrier Coatings (TBCs) protect gas turbine engine metal components while they serve in a high temperature environment (upto 1200℃). 8% YttriaStabilized Zirconia (8YSZ) is the current state of the art material for TBCs. Typically, 250 to 500 μm (upto 2 mm) thick TBCs can lower the metal temperature by upto 150°C than the service temperature and thereby enhance life to the components. 8YSZ TBCs however, suffer from (a) increased sinterability, (b) phase de-stabilization and (c) poor adhesion with time in service at high temperature. In order to facilitate longer engine running time, research is being directed towards finding (i) newer materials that do not possess these deficiencies or (ii) configurations that can overcome them. In order to further improve the performance efficiency of the engines, TBC materials with extended thermal fatigue life at higher than current service temperatures (>1100℃) are also being actively investigated. In the same area of research, this thesis presents the findings of work on air plasma sprayed (i) duplex, (ii) functionally graded and (iii) multilayered configurations of TBCs synthesized from commercial 8YSZ and lab synthesized pyrochlore (lanthanum zirconate, lanthanum cerate and lanthanum cerium zirconate) compositions with NiCrAlY bond coat.
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Synthesis and Studies on Partially Stabilized Zirconia and Rare-Earth Zirconate Pyrochlore Structured Multilayered Coatings
This work is focused on the thermal fatigue behaviour studies of ceramic coatings, as TBC (Thermal Barrier Coating) system, its importance in determining the thermo-mechanical properties and service-life estimation of the coatings when exposed to elevated operating temperatures. Commercial 6-8%Yttria stabilized zirconia (YSZ) top coat (TC) and NiCrAlY bond coat (BC) in (a) conventional YSZ (BC and TC), (b) multi-layered functionally graded materials (FGM) i.e., BC-blend (50BC+50TC)-(TC) configuration and (c) lab synthesized Zirconia based pyrochlore (Lanthanum Zirconate-LZ) were the coating materials involved. Nickel based super alloy Inconel 718 substrates were coated by using Atmosphere Plasma Spray (APS) system with three different (varying power) plasma spray parameters. All the sides of the 25mm x 10mm x 5mm thick substrates were completely covered with the bond coat and ceramic coating. FGM configuration was spray coated only on one side of the Inconel flat plates. Thermal shock cycle tests were performed on the coated specimen by following the ASTM B214-07 guidelines which comprised of introducing the coated specimen in a muffle furnace at 1150C, held in it for 2 minutes before removing from furnace followed by forced fan air cooling (one shock cycle). The specimen were periodically subjected to visual inspection for faults, before continuing the shock cycles, until the coating flaked off or cracked or detached from substrate. Cross section metallographic samples were prepared and analysed under SEM (Scanning Electron Microscope) and Energy Dispersive spectroscope (EDS) to study the as-sprayed coating morphology and interface quality, measure coating thickness, study defects characteristics and the chemical composition. Crystal structural phases were analysed using X-Ray Diffraction (XRD). 2019 Elsevier Ltd. -
Deep Learning for Arrhythmia Classification: A Comparative Study on Different Deep Learning Models
Arrhythmias, or irregular heart rhythms, are a major global health concern. Since arrhythmias can cause fatal conditions like cardiac failure and strokes, they must be rapidly identified and treated. Traditional arrhythmia diagnostic techniques include manual electrocardiogram (ECG) image interpretation, which is time consuming and frequently required for expertise. This research automates and improves the identification of heart problems, with a focus on arrhythmias, by utilizing the capabilities of deep learning, an advanced machine learning technique that performs well at recognizing patterns in data. Specifically, we implement and compare Custom CNN, VGG19, and Inception V3 deep learning models, which classify ECG images into six categories, including normal heart rhythms and various types of arrhythmias. The VGG19 model excelled, achieving a training accuracy of 95.7% and a testing accuracy of 93.8%, showing the effectiveness of deep learning in the comprehensive diagnosis of heart diseases. 2023 IEEE. -
Smart Facial Emotion Recognition with Gender and Age Factor Estimation
Human-Computer Interaction (HCI) in an intelligent way, which aims at creating scalable and flexible solutions. Big tech firms and businesses believe in the success of HCI as it allows them to profit from on-demand technology and infrastructure for information-centric applications without having to use public clouds. Because of its capacity to imitate human coding abilities, facial expression recognition and software-based facial expression identification systems are crucial. This paper proposes a system of recognizing the emotional condition of humans, given a facial expression, and conveys two methods of predicting the age and gender factors from human faces. This research also aims in understanding the influences posed by gender and age of humans on their facial expressions. The model can currently detect 7 emotions based on the facial data of a person - (Anger, Disgust, Happy, Fear, Sad, Surprise, and Neutral state). The proposed system is divided into three segments: a.) Gender Detection b.) Age Detection c.) Emotion Recognition. The initial model is created using 2 algorithms - KNN, and SVM. We have also utilized the architectures of some of the deep learning models such as CNN and VGG - 16 pre-trained models (Transfer Learning). The evaluation metrics show the model performance regarding the accuracy of the Recognition system. Future enhancements of this work can include the deployment of the DL and ML model onto an android or a wearable device such as a smartphone or a watch for a real-time use case. 2022 Elsevier B.V.. All rights reserved. -
Architecture of monophase InSe thin film structures for solar cell applications
Control of microstructural evolution during the crystallization of InSe thin films is an inevitable strategy to mold their fundamental properties and potential for the fabrication of solar cells. Impact of annealing as well as substrate temperature on the crystallization progress and physical characteristics of thermally evaporated InSe was examined systematically, which eventually dictates the overall performance of resulting device. Structural and compositional characterizations have been thoroughly investigated by X-ray diffraction and energy dispersive X-ray analyses. InSe films form hexagonal structure with a preferred orientation of crystallites along the (004) direction upon crystallization. The layer of InSe is formed by two concomitant processes, deposition and recrystallization. Application of heat treatment resulted in topographical modification, which was probed by an atomic force microscope. Surface roughness was enhanced due to the influence of temperature and thereby the growth of grains. Investigations of electrical and optical properties, thus provided ample evidence for the use of crystallized monophase InSe as an absorber layer in photovoltaic conversion devices. Carrier concentration and mobility of charge carriers estimated from the Hall measurements were found to be 19.43020cm?3 and 2.01cm2V?1s?1 respectively. Moreover, this research work explores power conversion efficiency of p-InSe/n-CdS heterojunction solar cells. 2017 Elsevier B.V. -
Grain-growth engineering and mechanical properties of physical-vapour-deposited InSe platelets
The present work demonstrates a novel use of physical vapour deposition for grain-growth engineering by optimizing supersaturation, which led to the evolution of stoichiometric indium monoselenide crystals, employing a custom-fabricated dual-zone furnace. The growth zone was kept at a constant temperature for different experimental runs (673-883K), while the source zone was kept at a stable temperature of 1123K. In this way, the temperature difference ?T = 240-450K resulted in a significant increase of the mass transport between the zones so as to accomplish bulk crystallization. At comparatively low supersaturation (?T = 240K), the presence of nodules and flakes was observed. When ?T = 250K, multiple grains were formed owing to temperature asymmetry at the rough vapour-solid interface. A further increase in supersaturation (?T = 330K) facilitated polyhedral grain growth, with distinct grain boundaries. A subsequent increment in ?T (400K) led to evolution of the polycrystalline morphology to well developed hexagonal platelets owing to adsorption of atoms on surface steps and kinks in accordance with the leading-edge growth mechanism. Energy-dispersive analysis by X-rays and X-ray diffraction experiments were carried out to confirm the structure and phase of crystals. Microindentation studies were done to assess the hardness and mechanical stability of the as-grown crystals in response to external loads in order to explore their suitability for solar cell applications. The investigations of bulk vapour phase transport, morphology and strengthening of InSe platelets provide pathways for the production of crystalline textures with versatile properties. International Union of Crystallography 2017. -
Crystal shape engineering and studies on the performance of vapour deposited InSe platelets
The influence of growth conditions on the morphology of stoichiometric indium monoselenide (InSe) crystals has been explored. Crystalline habits such as microfibres, needles, platelets and spherulites were obtained from physical vapour deposition by optimizing supersaturation, which sturdily depends on the temperature difference between charge (TC) and substrate (TS) zones ?T, (= TC ? TS). Morphology and growth mechanism were investigated with the aid of scanning electron microscopy and high-resolution transmission electron microscopy, which justified the layer by layer addition of atoms as per the KosselStranskiVolmer model. Thermogravimetric measurements revealed the stability of InSe, confirming its melting point, M.P. = 611C, which reflects the formation of monophase. The mobility and carrier concentration calculated from the Hall effect experiment are found to be 11.14cm2V?1s?1 and 1.52 1020cm?3 respectively. Furthermore, optical characterizations such as UVVisNIR and photoluminescence spectrometric analysis established the value of band gap as 1.45eV, manifesting the versatility of the grown semiconducting platelets for photovoltaic applications. 2018, Springer Science+Business Media, LLC, part of Springer Nature. -
Real-time architectural efforts in building a social network using NOSQL databases
Relational database management systems (RDBMS) today are the predominant technology for storing structured data in web and business applications. Along with the increasing size of the datasets, the number of accesses and operations performed increases. This growth, enhanced by the proliferation of social networks, led to a depletion of traditional relational databases that were commonly used to solve a wide range of problems. -
Convergent replicated data structures that tolerate eventual consistency in NoSQL databases
The Eventual consistency is a new type of database approach that has emerged in the field of NoSQL, which provides tremendous benefits over the traditional databases as it allows one to scale an application to new levels. The consistency models used by NoSQL database is explained in this paper. It elaborates how the eventually consistent data structure ensures consistency on storage system with multiple independent components which replicate data with loose coordination. The paper also discusses the eventually consistent model that ensures that all updates are applied in a consistent manner to all replicas. 2013 IEEE. -
Effect of Mindfulness Based Dialectical Behavioral Therapy (MBDBT) Training on it Employees: An Intervention Based Approach
Mindfulness Based Dialectical Behavioral Therapy (MBDBT) is a recent advancement in mindfulness-based interventions, focusing on helping clients observe their experiences, describe them using verbal labels, and be fully present in the moment and their actions without self-consciousness. This study investigated the effectiveness of MBDBT training on young employees using a mixed-method approach. The research was conducted on a sample of 10 newly joined IT employees in Bangalore, aged 25-30 years, over a 6-week period. The methodology included regular interviews for MBDBT skill training and standardized assessments measuring perceived stress, mindfulness, emotion regulation, and general self-confidence. Assessments were conducted at pre-, mid-, post-, and one-month follow-up sessions. The findings indicate that MBDBT has a significant effect on enhancing mindfulness and cognitive reappraisal while reducing expressive suppression with practice. These results suggest potential benefits of implementing MBDBT training programs for young professionals in the IT sector, with implications for improving their mental well-being and work performance. 2024 selection and editorial matter, Dr. Sundeep Katevarapu, Dr. Anand Pratap Singh, Dr. Priyanka Tiwari, Ms. Akriti Varshney, Ms. Priya Lanka, Ms. Aankur Pradhan, Dr. Neeraj Panwar, Dr. Kumud Sapru Wangnue; individual chapters, the contributors. -
Emotion Regulation and Psychological Well-being as Contributors Towards Mindfulness Among Under-Graduate Students
Emotion regulation is generally described as the ability of an individual not only to manage emotions effectively but also to respond effectively to the emotional experience. It has also been viewed as a crucial aspect for psychological well-being. It is a psychological state which means more than just being free from stress and not having any other psychological disorder reported by the individual. At the same time, students with higher emotion regulation and psychological well-being are expected to be more attentive and able to observe, describe and participate in the present moment, effectively, with non-judgmental awareness, which is in turn defined as mindfulness. Hence, it has been expected that participants with higher emotion regulation and psychological well-being would also report higher levels of mindfulness. Therefore, the present empirical investigation has been conducted with an objective of assessing the level of emotion regulation, psychological well-being and mindfulness among under-graduate students. Additionally, it was also expected that all the said variables would be positively correlated and emotion regulation and psychological well-being would predict mindfulness positively for under-graduate students. For this purpose, ex post facto research design was adopted, and standardized tools pertaining to emotion regulation, psychological well-being and mindfulness were administered on a sample of 104 under-graduate students. The results of correlation statistics revealed that emotional regulation (r=0.27; p<0.01) and psychological well-being (r=0.21; p<0.01) are the positive and significant correlates of mindfulness. Additionally, statistical outcomes of stepwise multiple regression analysis confirm that emotion regulation and psychological well-being are the significant predictors of mindfulness and contribute collectively towards a 11% variance towards the same. 2020, Springer Nature Switzerland AG. -
Exploring BERT and Bi-LSTM for Toxic Comment Classification: A Comparative Analysis
This study analyzes on the classification of toxic comments in online conversations using advanced natural language processing (NLP) techniques. Leveraging advanced natural language processing (NLP) techniques and classification models, including BERT and Bi-LSTM models to classify comments into 6 types of toxicity: toxic, obscene, threat, insult, severe toxic and identity hate. The study achieves competitive performance. Specifically, fine-tuning BERT using TensorFlow and Hugging Face Transformers resulted in an AUC ROC rate of 98.23%, while LSTM yielded a binary accuracy of 96.07%. The results demonstrate the effectiveness of using transformer-based models like BERT for toxicity classification in text data. The study discusses the methodology, model architectures, and evaluation metrics, highlighting the effectiveness of each approach in identifying and classifying toxic language. Additionally, the paper discusses the implementation of a userfriendly interface for real-time toxic comment detection, leveraging the trained models for efficient moderation of online content. 2024 IEEE. -
Employing Deep Learning in Intraday Stock Trading
Accurate stock price prediction is a significant benefit to the Stock investors. The future Stock value of any company is determined by Stock market prediction. A successful prediction of the stock's future price could result in a significant profit; Hence investors prefer a precise Stock price prediction. Although there are many different approaches to helps in forecasting stock prices, this paper will briefly look into the deep learning models and compare LSTM model and its variants. The key intention of this study is to propose a model that is best suitable and can be implemented to forecasting trend of stock prices. This paper focuses on binary classification problem, predicting the next-minute price movement of SPDR SP 500 index The testing experiments performed on the SPDR SP 500 index reveals that the variants of LSTM models, Slim LSTM1, slim LSTM2, and Slim LSTM3 with less parameters, provide better performance when compared to the Standard LSTM Model. 2020 IEEE.