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Navigating Financial Waters: Exploring the Intersection of Algorithmic Trading and Market Liquidity Dynamics
Algorithmic trading has ushered paradigm shift in trading. The market regulators although welcome this new technological advancement but are still keeping a tight leash. This can be owing to the contradicting and inconclusive evidence of its implications and impact on market microstructure. This study focuses on liquidity which is an integral part of a thriving stock market. We aim to examine if there is a statistical significance between volume of algorithmic orders and market capitalization. The liquidity provision is measured using Amihuds Illiquidity measure which is a proxy for measuring illiquidity. The liquidity measure is examined for chosen 8 stocks based on their market capitalization. The volume of algorithmic orders is examined using the Limit Order Book (LOB) data obtained from the BSE and orders for 23 trading days have been considered. We observe that large capitalization stocks display higher liquidity and algorithmic traders are able to contribute significantly to liquidity when compared to non-algorithmic traders. It was also looked at if there was a big difference in the amount of algorithmic trading done on stocks with big and small capitalization. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
We are Treated as Outsiders in Our Own City: Lived Experiences of Intersectional Stigma Against Sex Workers in Kolkata, India
Introduction: Sex workers in India experience intersectional stigma related to their gender identity, sexuality, and profession. The objective of the present study is to analyze the lived experiences of intersectional stigma against sex workers in Kolkata. Methods: We interviewed 30 cisgender female sex workers in March 2023 in Kolkata, India. Interviews were digitally audio recorded, translated from Bengali into English, and transcribed and coded using thematic analysis. Results: We identified five main themes regarding intersectional stigma: (1) internalized stigma regarding the shame associated with being a female sex worker, (2) perceived stigma of sex work as a dirty profession, associated with lower caste status, (3) enacted stigma against sex workers who are mothers, (4) enacted stigma against the children of sex workers, and (5) reduction of stigma through unionization/labor organizing. Conclusions: Intersectional stigma against sex workersis impacted by negative attitudes regarding gender, caste status, single motherhood, and occupation. We identified internalized stigma as a source of shame for sex workers. Sex workers also were perceived to beengaged in afilthy profession, associated with lower caste status. Those sex workers who were mothers experienced discrimination, as did their children. Respondents reported how collectivization has helped to address these experiences of stigma anddiscrimination. Policy Implications: Addressing the intersectional stigma against sex workers in Kolkata necessitates a shift in social attitudes.Findings underscore the urgent need for stigma reduction interventions and socialpolicies, including (1) labor protections for sex workers, (2) individual/community-level interventions for sex workers, and (3) media campaigns to address stigma reduction. By understanding the lived experiences of sex workers, we may develop better interventions to reduce stigma in the lives of sex workers in Kolkata and throughout India. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Navigating Financial Waters: Exploring the Intersection of Algorithmic Trading and Market Liquidity Dynamics
Algorithmic trading has ushered paradigm shift in trading. The market regulators although welcome this new technological advancement but are still keeping a tight leash. This can be owing to the contradicting and inconclusive evidence of its implications and impact on market microstructure. This study focuses on liquidity which is an integral part of a thriving stock market. We aim to examine if there is a statistical significance between volume of algorithmic orders and market capitalization. The liquidity provision is measured using Amihuds Illiquidity measure which is a proxy for measuring illiquidity. The liquidity measure is examined for chosen 8 stocks based on their market capitalization. The volume of algorithmic orders is examined using the Limit Order Book (LOB) data obtained from the BSE and orders for 23 trading days have been considered. We observe that large capitalization stocks display higher liquidity and algorithmic traders are able to contribute significantly to liquidity when compared to non-algorithmic traders. It was also looked at if there was a big difference in the amount of algorithmic trading done on stocks with big and small capitalization. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Automatic Generation Control of Multi-area Multi-source Deregulated Power System Using Moth Flame Optimization Algorithm
In this paper, a novel nature motivated optimization technique known as moth flame optimization (MFO) technique is proposed for a multi-area interrelated power system with a deregulated state with multi-sources of generation. A three-area interrelated system with multi-sources in which the first area consists of the thermal and solar thermal unit; the second area consists of hydro and thermal units. The third area consists of gas and thermal units with AC/DC link. System performances with various power system transactions under deregulation are studied. The dynamic system executions are compared with diverse techniques like particle swarm optimization (PSO) and differential evolution (DE) technique under poolco transaction with/without AC/DC link. It is found that the MFO tuned proportional-integral-derivative (PID) controller superior to other methods considered. Further, the system is also studied with the addition of physical constraints. The present analysis reveals that the proposed technique appears to be a potential optimization algorithm for AGC study under a deregulation environment. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Valorisation of starfruit waste derived pectin for biodegradable sheet fabrication: A comprehensive study on extraction and characterization
This research work focuses on the extraction and characterization of pectin from starfruit peel and its application for fabrication of pectin film. Starfruit is chosen as the source for pectin extraction as the data regarding pectin extraction starfruit is relatively scarce in the available literature. Conventional organic acid based extraction using citric acid is employed for pectin extraction as it is eco-friendly and cost effective. The yield of pectin was found to be 8.22 1.018 (w/w). Fourier-transform infrared spectroscopy (FT-IR), analysis is used to identify functional groups present in the extracted pectin and X-ray Powder Diffraction (XRD) is done to check its crystallinity. Furthermore, scanning electron microscopy (SEM) characterization was performed to deduce the morphological characteristics of the extracted biopolymer. The particle size was found to be between 1m and 20 m. Fabrication of pectin based film was done using solvent cast method. The biodegradable film developed was found to be transparent and flexible. This work highlights the use of starfruit as a cost effective substrate for pectin extraction. Future studies should aim at exploring various applications of pectin and utilizing its potential in diverse applications. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). -
Food Recommendation System using Custom NER and Sentimental Analysis
In today's fast-paced lifestyle, the need for efficient and personalized solutions is paramount, especially in the category of dining experiences. This research responds to this demand by proposing a better food recommendation system for Zomato reviews. It targets the audience who are not aware of the best cuisines and search for user reviews online. Utilizing custom Named Entity Recognition (NER) and sentiment analysis, the system seeks to understand and cater to individual food preferences extracted from user Reviews. Specifically, improving the analysis by extracting reviews for ten restaurants in the city of Kolkata. By providing a specific solution to address the current research gap in the area of restaurants recommendation systems, the system recommends top choices for neighboring restaurants and best food based on the sentimental analysis of the chosen menu items. 2024 IEEE. -
Integrating Machine Learning with Financial Risk Modeling for Portfolio Management
Financial markets may be unpredictable and volatile; the ability to perform proper risk forecasting and effectiveness in performing an efficient portfolio is of primary importance when making wise investment choices. The nonlinear trends, and time dependence applied in financial data are usually not captured in conventional predictive models. The research is suggesting a new hybrid architecture LSTXplain that combines with and is afforded capabilities of SHAP, and exogenized with LSTM networks as well as Experimental learning. The aim of this paper, which is entitled Integrating Machine Learning with Financial Risk Modeling to Portfolio Management is to combine sequential learning with interpretability in an attempt to deepen financial risk prediction and portfolio optimization. The model is intended to forecast various measurements of financial risk, such as volatility and Value-at-Risk, and is also likely to establish the causes of each of these estimates. LSTXplain uses historical stock prices, technical features and optionally, sentiment scores designed using financial news to train a robust deep learner. Model outputs are then fed through SHAP that allocates a value of importance of a feature and discover that this allows analysts to know and trust what the model does. In order to compare the framework, Yahoo Finance data was applied, and the findings were compared to the traditional models ARIMA, SVM, Random Forest, and MLP. It has a prediction accuracy of over 98 percent which does not just complement the risk forecasting but enables a portfolio management to act. The analysis is a bridge between the performance of DL and explainable AI in the financial risk prediction. Statistical significance were applied to prove that such improvements are significant, and it is established that results are significant at p<0.05. 2025 IEEE. -
Reinforcement Learning for Language Grounding: Mapping Words to Actions in Human-Robot Interaction
Within the domain of human-robot communication, effective communication is paramount for seamless and smooth collaboration between humans and robots. A promising method for improving language grounding is reinforcement learning (RL), which enables robots to translate spoken commands into suitable behaviors. This paper presents a comprehensive review of recent advancements in RL techniques applied to the task of language grounding in human-robot interaction, focusing specifically on instruction following. Key challenges in this domain include the ambiguity of natural language, the complexity of action spaces, and the need for robust and interpretable models. Various RL algorithms and architectures tailored for language grounding tasks are discussed, highlighting their strengths and limitations. Furthermore, real-world applications and experimental results are examined, showcasing the effectiveness of RL-based approaches in enabling robots to understand and execute instructions from human users. Finally, promising directions for future research are identified, emphasizing the importance of addressing scalability, generalization, and adaptability in RL-based language grounding systems for human-robot interaction. 2024 IEEE. -
A Reverse Firewall & Re-Encryption Model Supported Peks Model for Security in Health Based Applications
Combining reverse firewalls and reencryption in a PEKS (Public Key Encryption with Keyword Search) model introduces a powerful way to mitigate malicious client behaviour and enhance privacy, especially in untrusted or semi-trusted environments. A reverse firewall is a client-side monitor or proxy that sits between a cryptographic application and the network. Its goal is to ensure that compromised software does not leak any information, even if the software is compromised. In this context a reverse firewall supported with Proxy re-encryption (PRE) supports a secured scheme for applications which require high level of security in public domain scenarios like Cloud for data sharing forms. For schemes which require good level of Identity applications and also needs the support of data Integrity, reverse firewall associated with Re encryption may be a suitable choice. The model is refined with conditional time stamp in accepting the keys for re encryption process. As this model can use different random numbers at different levels of Reverse firewall and re encryption, makes the work free from Chosen cipher text attacks. Also, the proposed model supports Design, Modelling and security analysis in a real time environment. 2025 IEEE. -
Enhancing authentication in blockchain bridges: A smart contract-based approach leveraging polynomial interpolation
This work focuses on the integration of blockchain for enhancing the security, privacy, and trust management within Vehicle Ad Hoc Networks (VANETs). In the context of smart transportation, VANETs offer essential safety but the open and dynamic nature of these networks makes secure, anonymous authentication a major challenge. Blockchain's decentralized nature can provide a secure, tamper- resistant ledger for managing data across the network nodes, helping address these security concerns. Cross- chain bridges enable the transfer of data, money and assets across blockchains. It has thus become important to enhance existing authentication mechanisms in blockchain bridges. In this research, we analyze existing authentication approaches, highlighting their limitations, such as reliance on centralized entities, private key leaks and weakness in smart contract functions. We then propose a novel approach to strengthen existing authentication mechanisms with the combined capabilities of Smart Contracts and Polynomial Interpolation, to establish a secure authentication layer. 2025, IGI Global Scientific Publishing. All rights reserved. -
The Impact of Digital Marketing Strategies on Customer Attitude and Purchase Intention Towards Electronic Gadgets: A Study on Indian Students
A portion of a companys long-term strategy should be devoted to digital marketing transformation. It is a challenging task to select the effective marketing strategy when conducting business in modern digital world. This study seeks to elucidate the influences of digital marketing strategy forms on customer attitude and purchase intention of students towards electronic gadgets. The relationship between four digital marketing strategies such as search engine advertising, social media, content marketing and email marketing towards customer attitudes and purchase intention was investigated in accordance with hypotheses, 225 students from Bangalore city, India, who had experience in online purchase of electronic gadgets comprised as a research sample. The relationship among the selected variables are tested with help of Correlation, ANOVA and regression analysis. The study conclude that there is an impact of various forms of digital marketing strategies on customers attitude and the purchase intention of young (Students) customer. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
The Impact of Digital Marketing Strategies on Customer Attitude and Purchase Intention Towards Electronic Gadgets: A Study on Indian Students
A portion of a companys long-term strategy should be devoted to digital marketing transformation. It is a challenging task to select the effective marketing strategy when conducting business in modern digital world. This study seeks to elucidate the influences of digital marketing strategy forms on customer attitude and purchase intention of students towards electronic gadgets. The relationship between four digital marketing strategies such as search engine advertising, social media, content marketing and email marketing towards customer attitudes and purchase intention was investigated in accordance with hypotheses, 225 students from Bangalore city, India, who had experience in online purchase of electronic gadgets comprised as a research sample. The relationship among the selected variables are tested with help of Correlation, ANOVA and regression analysis. The study conclude that there is an impact of various forms of digital marketing strategies on customers attitude and the purchase intention of young (Students) customer. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Using Document Similarity Algorithms for Suicidal Detection in Social Media: A Case Study of User Tweets
Suicidal detection and treatment from the clinical and public health perspective are reactive. For an action whose consequences are irreversible, a reactive approach to the problem cannot be the answer. A proactive approach is needed to solve and detect suicidal intent. Social media has become the television and diary of millennials and Gen z alike; hence, it is imperative to create techniques and approaches to study their actions in this particular space. This research involved creating document similarity algorithms from Corpora mined from the Twitter Developer API. Making the data unique to this platform, a methodology design involving validating data at various spectrum and selecting an appropriate threshold to classify the similarity levels were created as well as a lexicon unique to the Twitter Dataset. With an accuracy score of 84%, the Jaccard document similarity algorithm was able to spot suicidal intent from users tweets, and with an accuracy of 93%, it was also able to spot non-suicidal intent. The Jaccard model seemed to be the most durable and computationally efficient for the problem and was chosen as the algorithm for detecting suicidal tendencies in users tweets. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
GrapheneLiquid Crystal Synergy: Advancing Sensor Technologies across Multiple Domains
This review explores the integration of graphene and liquid crystals to advance sensor technologies across multiple domains, with a focus on recent developments in thermal and infrared sensing, flexible actuators, chemical and biological detection, and environmental monitoring systems. The synergy between graphenes exceptional electrical, optical, and thermal properties and the dynamic behavior of liquid crystals leads to sensors with significantly enhanced sensitivity, selectivity, and versatility. Notable contributions of this review include highlighting key advancements such as graphene-doped liquid crystal IR detectors, shape-memory polymers for flexible actuators, and composite hydrogels for environmental pollutant detection. Additionally, this review addresses ongoing challenges in scalability and integration, providing insights into current research efforts aimed at overcoming these obstacles. The potential for multi-modal sensing, self-powered devices, and AI integration is discussed, suggesting a transformative impact of these composite sensors on various sectors, including health, environmental monitoring, and technology. This review demonstrates how the fusion of graphene and liquid crystals is pushing the boundaries of sensor technology, offering more sensitive, adaptable, and innovative solutions to global challenges. 2024 by the authors. -
Predicting Job Risk from Artificial Intelligence in London Using Supervised Machine Learning Models
This study investigates the risk of job automation in London due to artificial intelligence (AI), applying supervised machine learning techniques to identify occupations most at risk. Leveraging a dataset encompassing job-specific features such as primary tasks, industry domains, and associated AI models, the research develops two predictive models. A Random Forest Classifier is used to categorize jobs as low, medium, or high automation risk, while a Linear Regression model estimates the proportion of each occupation's workload likely to be automated. The Random Forest model achieved a high accuracy rate of 97% in classifying job risk, indicating strong predictive capability. Meanwhile, the regression model explained 85% of the variance in the AI workload ratio, highlighting a significant relationship between job attributes and automation potential. These results suggest that job characteristics are reliable indicators of AI impact, particularly in routine, repetitive, and low-skilled roles that are more easily codified and replicated by algorithms. The findings align with broader economic theories such as creative destruction and technological waves, suggesting that AI not only displaces certain roles but also drives structural transformation within the labor market. By focusing on London, this study provides a localized understanding of how AI is reshaping employment patterns. It underscores the growing urgency for strategic workforce re-skilling and adaptive policy frameworks to mitigate negative outcomes and maximize opportunities presented by AI. Ultimately, this research contributes valuable insights into the interaction between AI technologies and employment, helping policymakers, employers, and educators anticipate change and prepare for a more resilient, inclusive labor market. 2025 IEEE. -
Prevention of Data Breach by Machine Learning Techniques
In today's data communication environment, network and system security is vital. Hackers and intruders can gain unauthorized access to networks and online services, resulting in some successful attempts to knock down networks and web services. With the progress of security systems, new threats and countermeasures to these assaults emerge. Intrusion Detection Systems are one of these choices (IDS). An Intrusion Detection System's primary goal is to protect resources from attacks. It analyses and anticipates user behavior before determining if it is an assault or a common occurrence. We use Rough Set Theory (RST) and Gradient Boosting to identify network breaches (using the boost library). When packets are intercepted from the network, RST is used to pre-process the data and reduce the dimensions. A gradient boosting model will be used to learn and evaluate the features chosen by RST. RST-Gradient boost model provides the greatest results and accuracy when compared to other scale-down strategies like regular scaler. 2022 IEEE. -
Advancing Credit Card Fraud Detection Through Explainable Machine Learning Methods
The world of finance has experienced a significant shift in the way money flows, due to the advancements in technologies such as online banking, card payments, and QR-based payment systems. These innovative banking payment facilities are offered by ensuring the safety of the transaction and ensuring that only the authorized customer can access and utilize these banking services. Credit card fraud is innovative way to cheat the user of the card. Government all over the word encouraging to the people for the uses of digital money. This research work focuses on analyzing the machine learning database by using a labelled dataset to classify legitimate and fraudulent business transactions with explainable AI. This study is based on decision tree, logistic regression, support vector machine and random forest machine learning techniques. 2024 IEEE. -
Customer Lifetime Value Prediction: An In-Depth Exploration with Regression, Regularization and Hyperparameter Tuning
In today's dynamic business environment, companies have been strategically shifting towards a customer-centric approach from their traditional product-centric focus. The main goal of this paper is to estimate customer lifetime value of 5,000 customers in the retail industry. This research follows a step-by-step approach to construct a multiple regression machine learning model. The model used in the study is based on the nine features to predict the customer life time value. First basic train-test split model is developed, which predicted 74% of variation in the customer lifetime value. This necessitates to improve the model performance, hence to address the multicollinearity problem lasso regularization is used. After lasso regularization , final model is trained with hyperparameter turning for further model performance improvement. The results show significant improvements in predicting customer lifetime value with the final model. This study suggests that the machine learning regression models can help to businesses to better understand how much value they can generate from individual customer.This deep understanding about customers helps retail businesses to align their customer engagement strategies to create a positive impact on the profitability and maximizing overall value offered to the customers. 2024 IEEE. -
Decoding Customer Lifetime Value to Unlock Business Success with Predictive Machine Learning Approach
This study highlights how crucial customers are for a company's success who directly impacts revenue and overall business value. This study focuses on analysis of customer lifetime value, the research uses data from 5000 customers with 8 important features with the main goal of predicting customer lifetime value. Business leaders often face choices about where to invest in marketing, like loyalty programs, incentives and ads or nothing. The study suggests that customer lifetime value is a key metric for making smart decisions, which measures how much a customer spends over their time with a company. To predict this value, the research explored different machine learning models - linear regression, decision tree regressor, random forest, and AutoML regressor. Each model is checked for how well it predicts customer spending habits. The results show that AutoML regression stands out for its accuracy without overcomplicating things. This study offers insights for businesses looking to improve their customer-focused strategies and long-term success. 2024 IEEE.