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Parity labeling in Signed Graphs
Let S = (G; ?) be a signed graph where G = (V;E) is a graph called the underlying graph of S and ?: E(G) ? {+; -}. Let f: V(G) ? {1, 2, ..., |V(G)|} such that ?(uv) = + if f(u) and f(v) are of same parity and ?(uv) = - if f(u) and f(v) are of opposite parity. The bijection f induces a signed graph Gf denoted as S, which is a parity signed graph. In this paper, we initiate the study of parity labeling in signed graphs. We define and find `rna' number denoted as ?-(S) for some classes of signed graphs. We also characterize some signed graphs which are parity signed graphs. Some directions for further research are also suggested. 2021, Journal of Prime Research in Mathematics. All rights reserved. -
Characterizations of some parity signed graphs
We describe parity labellings of signed graphs: equivalently, cuts of the underlying graph that have nearly equal sides. We characterize the bal-anced signed graphs which are parity signed graphs. We give structural characterizations of all parity signed stars, bistars, cycles, paths and com-plete bipartite graphs. The rna number of a graph is the smallest cut size that has nearly equal sides; we find this for a few classes of graphs. The author(s). -
C-CORDIAL LABELING OF BIPARTITE SIGNED GRAPHS
Let ?:= (V, E) be a graph and ?:= (?, ?) be a signed graph with underling graph ?. Let : V (?) ?? {+, ?} be a C-marking. Then the function is called C-cordial labeling of signed graph ?, if |e? (?1) ?e? (1)| ? 1 and |v (?) ?v (+)| ? 1, where v (+) and v (?) are the number of vertices of ? having label + and ?, respectively under . In this paper, we have characterized signed cycles with given number of negative sections, which admit C-cordial labeling. We have also obtained a characterization of signed bistars which admit C-cordial labeling. 2021 Allahabad Mathematical Society. -
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. -
Algorithmic and Non-Algorithmic Trading Activity in the BSE Using Limit Order Book of Select Stocks
With the existence of a heterogeneous market compounded by asymmetric information, technology has become one of the major newlineenablers in stock market development. Introduction of algorithms for trading gave a fillip to many stock market participants and allowed them to trade rapidly and profitably. In the present day in Indian stock market, newlinewe have two types of market players; algorithmic traders and nonalgorithmic traders. The algorithmic traders are playing a dominant role in order placement, order modification and order execution while the newlinenon-algorithmic traders still continue to use their intuition. This study aims to understand the trading activity of both the market participants. The study uses the Limit Order Book data from Bombay Stock Exchange. newlineThe LOB data of selected nine stocks is considered for the study whose variables namely Order Added, Order Updated and Order Deleted data along with the Bid Ask Quotes are considered for measurement. Based on newlinethe Limit Orders it is observed that there is a statistically significant difference in the trading behavior of algorithmic and non-algorithmic traders based on stock market session timings and market capitalization. newlineThe market making ability of the algorithmic traders was examined using Order-to trade Ratio and it is observed that large number of orders are not executed indicating that there is no significant Market Making happening. newlineThe algorithmic traders possess an edge over the non-algorithmic traders in Order Modification resulting in dominance in the Stock market. The Mann Kendal Trend test indicates upward and downward trend in newlinevolume adjusted spread indicating that market making is happening especially in the stocks where algorithmic activity is high. This study enables regulatory authorities to monitor stock market activity especially during pre- open session. This study provides sufficient scope for further research on future of algorithmic trading activity and its ramifications on non-algorithmic trading activity in the future. -
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. -
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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. -
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. -
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. -
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. -
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. -
The Effect of Short-Term Training of Vipassanas Body-Scan on Select Cognitive Functions
This experiment examined the effect of a short-term body-scan meditation technique of vipassana practice on select cognitive functions. Participants (n = 77) were randomly divided into an experimental group (n = 37) and an active control group (n = 40). The average age of participants in the experimental group and the active control group was 21.67 1.16 and 21.40 3.14years, respectively. The experimental group practiced body-scan mindfulness, one session per day for 6 days with each session lasting for 25min. Participants in the active control group spent an equal amount of time reading fiction of their choice and listening to soothing music. Variables that were studied included five cognitive functions, namely reaction time, attention, learning, working memory, and social-emotional cognition. Results showed that short-term mindfulness meditation decreased reaction time and increased attention, with mild effect size. It may be concluded that short-term mindfulness practice might be an alternative for individuals who, due to various reasons, cannot practice long-term courses. 2018, National Academy of Psychology (NAOP) India. -
Concept Mapping of Issues of Students Life in University
The undergraduate student body forms around 85.9% of the total number of students enrolled in India, which is a significant population. It has become imperative to understand the issues that these students face during their undergraduate years as a precursor to developing mechanisms and strategies to enable student progress, both academically and developmentally. This study aimed at developing a concept map to outline the various aspects and issues of the undergraduate students life in India utilizing the concept mapping method. Data from participants (n = 141) at different phases was analysed resulting in 49 unique life issues and aspects and 8 clusters. The emerging issues have relevance and implications for teachers, parents, administrators and other stakeholders in structuring and developing services targeted towards undergraduate students in India. 2015, National Academy of Psychology (NAOP) India. -
Study of Effect of Vipassana on Anxiety and Depression
International Journal of Psychology and Behavioral Sciences, Vol-2 (6), pp. 274-276. -
The Search for Universal Values
IOSR Journal of Humanities and Social Science Vol. 2, Issue 1, pp 69-72, ISSN No. 2279-0837 -
The Effect of short format body-scan mindfulness meditation on cognitive function and affect
Studies of mindfulness are consistent in their finding indicating that mindfulness can serve as a therapy model to deal with many psychological and physical problems, and improve wellbeing. Further, mindfulness is also found to enhance the moment to moment experience of individuals with better clarity of phenomenon. Interestingly, most of these studies have been conducted among seasoned practitioners or as long-format courses. Although long-format can be an ideal practice, it often proves to be expensive for many. Also, some people may not be able to practice in long-format due to several reasons. Thereby, it is imperative to investigate the benefits of short-format mindfulness exploring its utility and scalability. This experimental study examined the effect of short format mindfulness especially on affect and cognitive function. Participants (N=72; F=40, M=32) were randomly divided into an experimental group (N=35; M=15, F=20) and an active control group (N=37; M=17, F=20). The average age of participants in the experimental group and the active control group was 21.79 and 21.59 respectively. The experimental group practiced body-scan mindfulness, one session per day for six days, each session lasting for 25 minutes. Participants in the active control group spent an equal amount of time while reading fiction of their choice and listening to soothing music. Variables that were considered include positive and negative affects, and five cognitive functions namely psychomotor function, attention, learning, working memory-simple, and social-emotional cognition. Results showed an increase in positive affect and a decrease in negative affect, and an increase in performance in all five cognitive functions in the experimental group with an effect size ranging from mild to moderate, in comparison to active control. The study concluded that short format mindfulness practice, although may not be ideal, might be an alternative for individuals who due to various reasons cannot practice long format courses.