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Induced acyclic path decomposition in graphs
A decomposition of a graph G is a collection ? of graphs H1, H2,...,Hr of G such that every edge of G belongs to exactly one Hi. If each Hi is either an induced path in G, then ? is called an induced acyclic path decomposition of G and if each Hi is a (induced) cycle in G then ? is called a (induced) cycle decomposition of G. The minimum cardinality of an induced acyclic path decomposition of G is called the induced acyclic path decomposition number of G and is denoted by ?ia(G). Similarly the cyclic decomposition number ?c(G) is defined. In this paper we begin an investigation of these parameters. -
AI Based Seamless Vehicle License Plate Recognition Using Raspberry Pi Technology
This research presents the implementation of an innovative Vehicle Management System designed specifically for the Christ University Project 'CampusWheels.' The system incorporates cutting-edge technologies, including YOLOv8 and Tesseract OCR, for robust license plate recognition. Addressing the unique challenges faced by Christ University in managing and securing vehicular movements within the campus, this project becomes crucial as the number of vehicles on campuses continues to grow. It not only provides an effective solution to these challenges but also introduces innovative methodologies, marking a significant departure from conventional campus management practices. The paramount importance of this project lies in its ability to enhance campus security through real-time vehicle monitoring and identification. The utilization of YOLOv8 for vehicle detection and Tesseract OCR for license plate recognition ensures a high level of accuracy in identifying and tracking vehicles entering and leaving the campus. This precision significantly contributes to the prevention of unauthorized vehicle access, a common security concern on educational campuses. Moreover, the system's ability to streamline traffic flow and improve efficiency in parking and access control addresses practical issues faced by campus administrators and security personnel. 2024 IEEE. -
Treexpan instantiation of xpattern framework
Most of the data generated from social media, Internet of Things, etc. are semi-structured or unstructured. XML is a leading semi-structured data commonly used over cross-platforms. XML clustering is an active research area. Because of the complexity of XML clustering, it remains a challenging area in data analytics, especially when Big Data is considered. In this paper, we focus on clustering of XML based on structure. A novel method for representing XML documents, Compressed Representation of XML Tree, is proposed following the concept of frequent pattern tree structure. From the proposed structure, clustering is carried out with a new algorithm, TreeXP, which follows the XPattern framework. The performances of the proposed representation and clustering algorithm are compared with a well-established PathXP algorithm and found to give the same performance, but require very less time. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
Coming out of the desi closet: disclosure of same-sex sexuality in metropolitan-India
Coming out of the closet is a psychosocial process that entails the disclosure of ones non- heteronormative sexual orientation to family, peers, and the wider publica phenomenon that is necessitated by the prevalence of societal heteronormativity. With the recent legal decriminalization of consensual same-sex sexual relationships in India, there is renewed interest in and emergent necessity to expand upon the existing academic discourse on the lives, rights, health and well-being of same-sex attracted individuals in India. The present study accumulates detailed narratives of disclosure of sexual orientation of five male and five female young-adults of same-sex sexuality from ages 18 to 25 in metropolitan cities of India. Thematic narrative analysis is used to gain insight into the factors of being in the closet, those underlying coming out of the closet, and the expectations from and impact of coming out to ones family. Five major themes have emergedthree restraint factors and two propulsion factors influencing sexual identity disclosure. Restraint factors are those that reduce the probability of coming out and these arean incessant pressure to conceal, perceived lack of stability and support, and anticipated disintegration of long-standing familial tradition. Propulsion factors act as catalysts of disclosure and these are target congeniality i.e. approachability of the target of disclosure, and parental validationwhich, when attained, enables the individual to come out more easily to others. The findings have been critically compared and contrasted with the existing body of literature in the domain, which sets the agenda for further inquiry. 2021 Taylor & Francis Group, LLC. -
Dynamics of fractional model of biological pest control in tea plants with beddingtondeangelis functional response
In this study, we depicted the spread of pests in tea plants and their control by biological enemies in the frame of a fractional-order model, and its dynamics are surveyed in terms of boundedness, uniqueness, and the existence of the solutions. To reduce the harm to the tea plant, a harvesting term is introduced into the equation that estimates the growth of tea leaves. We analyzed various points of equilibrium of the projected model and derived the conditions for the stability of these equilibrium points. The complex nature is examined by changing the values of various parameters and fractional derivatives. Numerical computations are conducted to strengthen the theoretical findings. 2021 by the authors. Licensee MDPI, Basel, Switzerland. -
A Neural Network Based Customer Churn Prediction Algorithm for Telecom Sector
For telecommunication service providers, a key method for decreasing costs and making revenue is to focus on retaining existing subscribers rather than obtaining new customers. To support this strategy, it is significant to understand customer concerns as early as possible to avoid churn. When customers switch to another competitive service provider, it results in the instant loss of business. This work focuses on building a classification model for predicting customer churn. Four different deep learning models are designed by applying different activation functions on different layers for classifying the customers into two different categories. A comparison of the performance of the different models is done by using various performance measures such as accuracy, precision, recall, and area under the curve (AUC) to determine the best activation function for the model among tanh, ReLU, ELU, and SELU. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Securing International Law Against Cyber Attacks through Blockchain Integration
Cyber-attacks have become a growing concern for governments, organizations, and individuals worldwide. In this paper, we explore the use of blockchain technology to secure international law against cyber-attacks. We discuss the advantages of blockchain technology in providing secure and transparent data storage and transmission, and how it can enhance the security of international law. We also review the current state of international law regarding cyber-attacks and the need for a robust and effective legal framework to address cyber threats. The study proposes a blockchain-based approach to secure international law against cyber-attacks. We examine the potential of blockchain technology in providing a decentralized and tamper-proof database that can record and track the implementation of international laws related to cyber-attacks. We also discuss how smart contracts can be utilized to automate compliance with international laws and regulations related to cybersecurity. The study also discusses the challenges and limitations of using blockchain technology to secure international law against cyber-attacks. These include the need for interoperability between different blockchain networks, the high energy consumption of blockchain technology, and the need for international cooperation in implementing and enforcing international laws related to cybersecurity. Overall, this study provides a comprehensive overview of the potential of blockchain technology in securing international law against cyber-attacks. It highlights the need for a robust legal framework to address cyber threats and emphasizes the importance of international cooperation in implementing and enforcing international laws related to cybersecurity. 2023 IEEE. -
An Innovative Method for Election Prediction using Hybrid A-BiCNN-RNN Approach
Sentiment, volumetric, and social network analyses, as well as other methods, are examined for their ability to predict key outcomes using data collected from social media. Different points of view are essential for making significant discoveries. Social media have been used by individuals all over the world to communicate and share ideas for decades. Sentiment analysis, often known as opinion mining, is a technique used to glean insights about how the public feels and thinks. By gauging how people feel about a candidate on social media, they can utilize sentiment analysis to predict who will win an upcoming election. There are three main steps in the proposed approach, and they are preprocessing, feature extraction, and model training. Negation handling often requires preprocessing. Natural Language Processing makes use of feature extraction. Following the feature selection process, the models are trained using BiCNN-RNN. The proposed method is superiorto the widely usedBiCNN and RNN methods. 2023 IEEE. -
Nonlinear Dynamics in Distributed Ledger Blockchain and analysis using Statistical Perspective
More and more in healthcare is blockchain technology applied for safe and open data storage. Still, it is understudied how deeply regression analysis combined with nonlinear dynamics into distributed ledger systems performs. This kind of approach may help to increase data transfer efficiency and help storage management in blockchain systems. Data speed and storage efficiency restrictions make current blockchain systems difficult to handle for large amounts of healthcare data. Conventional methods find poor data retrieval and transfer due to the great complexity and nonlinear characteristics of healthcare data. Combining nonlinear dynamics with deep regression analysis, this paper proposes a fresh approach for maximizing data transfer and storage in blockchain systems. Inspired by nonlinear dynamics ideas, a deep regression model aimed at maximizing block storage and forecast data transmission requirements was assessed on a simulated healthcare dataset using a distributed ledger system with 1,000 blocks and a 500 GB total dataset size. Performance criteria covered transmission efficiency and storage consumption. The proposed technique improved data transmission efficiency by thirty percent over current techniques. Another clear improvement was using storage; block size needs fell 25%. The best model, according to numerical research, lowered an average transmission time from 120 to 84 minutes and storage overhead from 200 to 150 GB. 2024, International Publications. All rights reserved. -
Artificial Intelligence (AI) in CRM (Customer Relationship Management): A Sentiment Analysis Approach
The use of customer relationship management (CRM) in marketing is examined in this essay. It looks at how CRM makes it possible to use reviews, integrate AI, conduct marketing in real time, and conduct more regular marketing operations. CRM tactics are illustrated through case studies of businesses like Uber, T-Mobile, Amazon, Apple, and Apple. CRM offers centralized data, better marketing and sales, and better customer support. There is also a discussion of the ethical, private, security, adoption, and scalability challenges of AI in CRM. In general, CRM makes data-driven decisions and customer insights easier to achieve to increase growth, loyalty, and engagement. 2024 IEEE. -
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. -
Design and development of a method for detecting sleep roll-over counts using accelerometer ADXL335
Sleep plays an important role as it helps human body to rejuvenate, boosts mental function and manage stress. Sleep is restorative function which enhances muscle growth, repairs tissues, maintains health and make physical appearance look or feel better. The lack of sleep in human body can increase the risk of diseases which are asthma, diabetes, depression. For healthy physiological function, sleep is essential and has strong relation to mental condition. Easy way of sleep management is considered for maintaining good mental health. Numerous scientists, doctors and researchers have proposed various ways to monitor sleep, some of those best tests are polysomnography test and actigraphy test. However, taking sleep test covering the whole body with wires and electrodes which is polysomnography test is uncomfortable for patients, and sensors used for different approaches like this are costly and often require overnight treatment and expert monitoring in clinics. Therefore, easy way of detecting roll-over movements which is convenient for patients to wear is proposed. Accelerometer ADXL335 sensor is taped on socks during sleep which is comfortable for patients to wear and do not cause any inconvenience during sleep. Algorithm is proposed to read the dataset and count the roll-over during the sleep based on threshold. Resulting the number of roll-over detected during a sleep period. Copyright 2020 Institute of Advanced Engineering and Science. All rights reserved. -
Characterization of product cordial dragon graphs
The vertices of a graph are to be labelled with 0 or 1 such that each edge gets the label as the product of its end vertices. If the number of vertices labelled with 0's and 1's differ by at most one and if the number of edges labelled with 0's and 1's differ by at most by one, then the labelling is called product cordial labelling. Complete characterizations of product cordial dragon graphs is given. We also characterize dragon graphs whose line graphs are product cordial. 2024 Azarbaijan Shahid Madani University. -
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. -
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. -
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.