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Data and Its Dimensions
In current times Data is the biggest economic opportunity. As per the studies, it is observed that the world is becoming 2.5 quintillions data-rich every day, with an average of every human contributing 1.7MB of data per second. Every individual has a good appetite for data, as it gives immense insight to explore and expand the business. With the invention of smart devices and innovation in the field of connectivity such as 4G-5G Mobile Networks and Wi-Fi, the generation and consumption of the data are steadily increasing. These smart devices continuously generate data, leading to a bigger pool for better decision-making. This chapter presents data, the various forms and sources, and the concept of Data Science; it discusses how the ownership and value of data are decided; and also highlights the use, abuse, and overuse of the data along with data theft, and a case study to represent data breach. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Data Privacy
Data privacy is a private and public phenomenon and its operations have implications for the individual and the society. This understanding of privacy ceases it from being viewed as a simple technological process and highlights different factors that are associated with it. While on one hand, the right to privacy is seen as integral to the freedom of the individual, on the other hand, it is also seen as the ability to hide certain information for malpractice. This chapter delves into this existing dichotomy of data privacy and simplifies various terms and operations that have emerged in the field of study. A discussion is facilitated on the concept and its associated areas. The chapter looks at privacy regimes in different countries to note emerging developments and also presents a critique of the practice to bring forth shortcomings and enable change. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Challenges of Digital Transformation in Education in India
Online learning has been present since the 1960s and has risen in popularity over time. World-class universities have been using online teaching-learning methodologies to fulfill the needs of students who reside far away from academic institutions for more than a decade. Many people predicted that online education would be the way of the future, but with the arrival of COVID-19, online education was imposed upon stakeholders far sooner and more suddenly than expected. When the COVID-19 pandemic broke out, educational institutions began to explore digital ways to keep students studying even when they couldn't be together in person as governments enacted legislation prohibiting large groups of people from gathering for any reason, including education. The future of such a transition looks promising. However, transitioning from one mode of education to another is not easy. Historically, when educators adopt new tools, learning still continues in the conventional manner. Based on the responses of 176 students, this paper studies the challenges of Digital transformation in the Education sector. The research is extremely beneficial in evaluating the scope of societal opposition to change. 2022 IEEE. -
Emotion Detection Using Machine Learning Technique
Face Emotion Recognition (FER) is an emerging and crucial topic today; since much research has been done in this field, there are still many things to explore. In daily life, where people dont have time to fill out feedback, emotion detection plays an important role, which helps to know customer feedback by analyzing expressions and gestures. Analyzing current studies in emotion recognition demonstrates notable advancements made possible by deep learning. A thorough overview of facial emotion recognition (FER) is provided in this publication. The literature cited in this study is taken from various credible research published in the last 10years. This study has built a model for emotion recognition using photos or a camera. The paper is based on the concepts of Machine Learning (ML), Deep Learning (DL), and Neural Networks (NN). A range of publicly available datasets have been used to evaluate evaluation metrics. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Data: An Anchor for Decision- Making to Build the Future Workforce Management System
In this digital era, the change in business environments and the nature of work lead to skill gaps. Training the workforce on desired skill sets must fill these skill gaps. Data play a crucial role in identifying the skills needed and helping organizations to plan the future workforce. Data is essential for any organizations growth and success in the dynamic market. Knowing the skill set in advance allows organizations and individuals to plan the business and skill requirements well. The way work is done may be impacted by these structural changes as the world is changing swiftly. Building the abilities necessary for the uncertain environments of the present and future environments is also crucial for training the employees. However, such skills must first be acknowledged and appreciated before being developed. Empirical data must support the methodology for valuing such abilities and skills. This chapter outlines the significance of data in skill identification for individuals to be future-ready. Finding the most relevant abilities in a given environment is the first step toward their formalization and acceptance at the systems level. It also presents the importance of creating skill matrices for students and organizations. The skill matrix objectively quantifies skill value for specific occupations and the possible trajectories to acquire those skill sets. This metric will allow policymakers to navigate this fast-changing workforce landscape and focus resources to ensure that skills are needed as students transition into the workforce and have skills that enable them to transition. 2024 selection and editorial matter, Alex Khang, Sita Rani, Rashmi Gujrati, Hayri Uygun, and Shashi Kant Gupta; individual chapters, the contributors. -
A distributed randomization framework for privacy preservation in big data
The privacy preservation is a big challenge for data generated from various sources such as social networking sites, online transaction, weather forecast to name a few. Due to the socialization of the internet and cloud computing pica bytes of unstructured data is generated online with intrinsic values. The inflow of big data and the requirement to move this information throughout an organization has become a new target for hackers. This data is subject to privacy laws and should be protected. The proposed protocol is one step toward the security in case of above circumstances where data is coming from multiple participants and all are concerned about individual privacy and confidentiality. 2014 IEEE. -
Secure multi-party computation protocol using asymmetric encryption
Privacy preservation is very essential in various real life applications such as medical science and financial analysis. This paper focuses on implementation of an asymmetric secure multi-party computation protocol using anonymization and public-key encryption where all parties have access to trusted third party (TTP) who (1) doesn't add any contribution to computation (2) doesn't know who is the owner of the input received (3) has large number of resources (4) decryption key is known to trusted third party (TTP) to get the actual input for computation of final result. In this environment, concern is to design a protocol which deploys TTP for computation. It is proposed that the protocol is very proficient (in terms of secure computation and individual privacy) for the parties than the other available protocols. The solution incorporates protocol using asymmetric encryption scheme where any party can encrypt a message with the public key but decryption can be done by only the possessor of the decryption key (private key). As the protocol works on asymmetric encryption and packetization it ensures following: (1) Confidentiality (Anonymity) (2) Security (3) Privacy (Data). 2014 IEEE. -
Decoding HERO: Predicting psychological capital with subjective well-being
The positive psychology movement has gained momentum in recent years and organizations have ascribed great importance to employee well-being in light of the favorable outcomes associated with it. The widely researched Psychological Capital (PsyCap) has been consistently linked to well-being across a variety of contexts but a gap still exists in literature about what lies to the 'left' of psychological capital. The present study attempts to fill this gap by examining subjective well being components- positive and negative affect and life satisfaction, as potential antecedents of PsyCap. The Academic PsyCap questionnaire, the Positive and Negative Affect Schedule (PANAS) and the Satisfaction with Life Scale (SWLS) were administered to participants. Results confirmed the expected associations between affect and PsyCap-positive affect positively predicted PsyCap and its four constituents whereas negative affect emerged as a negative predictor of PsyCap and its dimensions. Life satisfaction positively predicted only individuals' total hope scores. Thus, highlighting the role of subjective well-being components as antecedents of PsyCap, these findings suggest that promoting higher positive affect and lower negative affect can do more than just make individuals feel good, rather, it can bolster their reservoirs of crucial psychological resources as well. 2021 Ecological Society of India. All rights reserved. -
Impact of Expert Academic Teaching Quality and its Performance Based on BiLSTM-Deep CNN Network
Undergraduate and postgraduate students from eight different departments at a UK institution participated in organized conversations about the impact of teachers' research activities on their education. In both samples, positive responses greatly outnumbered negative ones. There was an increase in positive feedback on professors' research when the overall quantity and quality of research in a specific field (as measured by Research Assessment Exercise [RAE] ratings) improved. Undergraduate samples with higher RAE scores were more likely to have negative feedback on research than graduate student samples. Both graduate and undergraduate students agreed that lecturers' research increased the instructor's credibility, relevance, and knowledge, as well as piqued and maintained their own interest, engagement, and drive. Data processing, feature selection, and model training are the first steps in the proposed approach. The data are changed from their raw form into a form suitable for academic use during the data pre-processing phase. They are employing Information Gain and Symmetric Uncertainty for feature selection. Following the feature selection process, the models are trained using BiLSTM-CNN. Both the BiLSTM and the CNN methods are inferior to the proposed method. 2023 IEEE. -
Predicting Work Environment and Job Environment Among Employees using Transfer Learning Approach
Today's enterprises face numerous challenges as a result of the world's rapid evolution. Maintaining a content workforce is crucial to a company's success and survival in today's fast-paced business environment. The efficacy, productivity, efficiency, and dedication of the company's staff are directly associated with the company's capacity to meet the needs of its employees in the workplace. The focus of this system is to identify the factors that contribute to a satisfying work environment for the participants. Preprocessing, feature selection, and model training are the first three steps in the suggested methodology. Data mining systems should get in the habit of normalizing data as a preliminary processing step. The multiple elements assessing company culture and worker satisfaction were consolidated using Principal Components Analysis (PCA) in the feature selection phase. Once features have been selected, KNN-SVM is utilized for model training. When compared to the two most popular alternatives, SVM and KNN, the proposed technique performs better. 2023 IEEE. -
Women in security services: a (post)feminist reading of Lt Col Nitisha v Union of India
The role of women in Indian security services was debated in the recent judgement, Lt Col Nitisha v Union of India. The Supreme Court invalidated the impugned selection criteria and their retrospective application upon female staff as instances of indirect discrimination. This case note identifies three major implications regarding the gendered nature of the debate. Firstly, the Courts findings reveal that the government adopted certain strategic modes of postfeminist governmentality to utilize the discourse of women empowerment for departmental ends. Secondly, popular readings of the judgement risk being reduced to postfeminist cultural sensibilities, which romanticize individualistic agency and manufacture isolated struggling heroes to eventually dull the cross-sectoral feminist solidarity. Lastly, the Courts progressive doctrinal turn still construed women officials as mere employees-beneficiaries mostly claiming careerist interests. It abandoned any holistic appraisal of the role of women in military service, thereby articulating an impoverished account of female agency, dignity, and personhood. 2025 Informa UK Limited, trading as Taylor & Francis. -
Photoaligned nematic liquid crystals doped with palladium-immobilised carbon nanospheres for advanced low-voltage display and energy storage devices
This study presents a nematic liquid crystal (NLC), D30-17, doped with palladium-immobilised carbon nanospheres (CNS) Pd/ON10 at two different concentrations. The composites were prepared with 0.1 and 0.4 wt/wt% dopant concentrations and are referred to as Mix 1 and Mix 2, respectively. The palladium-immobilized carbon nanospheres were employed because they function as advanced materials for catalysis and energy applications owing to the catalytic properties of palladium. The sample holder used in this experiment consisted of photo-aligned cells coated with a photosensitive alignment layer, Cibacron brilliant yellow (CBY). The textural studies revealed improved alignment in the doped mixtures. The frequency- and temperature-dependent dielectric behaviour was analysed for the pure and doped systems in both the planar (at 0 V) and homeotropic (at 12 V) states. Dielectric studies showed that the relative permittivity, dielectric loss, and conductivity of the doped material increased with increasing dopant concentration compared with that of pure NLC. Compared with the pure NLC and Mix 2, Mix 1 exhibited greater dielectric anisotropy, leading to a lower threshold voltage. A reversal in the dielectric anisotropy was also observed, which was attributed to the bistable inversion in the CBY alignment layer of the photo-aligned cells. Optical studies indicated that there was no significant shift in wavelength with respect to the dopant concentration. These composites are expected to find applications in liquid-crystal-based electronic and photonic devices. This journal is The Royal Society of Chemistry, 2026 -
The Role of Regular Meditation Practice, Trait Mindfulness, and Psychological Characteristics in Affective Startle Modulation: A Psychophysiological Study
Meditation practices, including mindfulness, are linked with adaptive emotional processing and regulation. Although startle response modulation among meditators has been studied using habituation and prepulse-induced startle inhibition paradigms, affective startle modulation, which refers to potentiation by negative stimuli and attenuation by positive stimuli (both relative to neutral stimuli), remains unexplored. This study examined how regular meditation practice, dispositional mindfulness, and affective difficulties influence affective modulation of the acoustic startle reflex. Seventeen meditators and thirty non-meditators were exposed to pleasant, neutral, and unpleasant images while their eye-blink startle responses were recorded. Participants also completed self-report measures of dispositional mindfulness, alexithymia, emotion regulation difficulties, depression, anxiety, and stress. Meditators, compared to non-meditators, reported higher dispositional mindfulness, particularly in the Observing and Non-reactivity domains, lower stress, and fewer difficulties in goal-oriented behaviour during negative emotions; they also had longer startle onset latencies, potentially indicating lower state anxiety, across the entire experiment regardless of the valence of visual images. Higher dispositional mindfulness correlated with lower scores on alexithymia, emotion regulation difficulties, depression, anxiety, and stress across the pooled sample. These findings suggest that mindfulness, whether cultivated through meditation or as a trait, reduces negative emotionality, highlighting its potential for emotional regulation and stress reduction. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026. -
A system for simulation of collision resistent secure sum protocol and method thereof /
Patent Number: 202021055655, Applicant: Dr. Samiksha Shukla.
The present invention discloses a system for simulation of collision resistant secure sum protocol and method thereof. The present invention discloses a simulation apparatus, system and method thereof having a computation system conjugated with a processor and a Trusted Third Party (TTP) system provided on a computation server system, in which computing, by the Trusted Third Party (TTP) system having an initiator, and via the processor, for number of party, packets per party and anonymizers. -
A system for secure collaborative computation and method thereof /
Patent Number: 202121003561, Applicant: Dr. Samiksha Shukla.
The present invention discloses a system for Secure Collaborative Computation and method thereof. The present invention discloses a computation system conjugated with a processor and, the processor is to: provide input data by one or more computer system using an input device and further recognizing the input data. -
A storage system for data encryption and decryption using line and method thereof /
Patent Number: 202141013775, Applicant: Sanjana Theresa.
The present invention discloses a storage system in a computer network for data encryption and decryption using line graphs and method thereof. The system includes, but not limited to, a processing unit in a computer network that includes at least one hardware processor, the processing system configured to perform: to represent data as vertices in a graph; wherein each character is correspond to a vertex while all adjacent characters in the plaintext will be represented as adjacent vertices in the graph.