Browse Items (14428 total)
Sort by:
-
JP-DAP: An Intelligent Data Analytics Platform for Metro Rail Transport Systems
This paper deals with an intelligent data analytics platform-Jaison-Paul Data Analytics Platform (JP-DAP)-for metro rail transport systems. JP-DAP is intended to ensure smooth functioning, improved customer experience, ridership forecasting, and efficient administration of metro rail transportation systems by integrating and analysing its many data sources. It consists of a middleware which is built on the top of a Hadoop Distributed File System (HDFS) and Spark framework, along with a set of open-source software tools like Apache Hive, Pandas, Google TensorFlow and Spark ML-lib for real-time and legacy data processing. The benchmarking of JP-DAP was conducted using TestDFSIO and have found that it performs well according to industry standards. The specific use case for this project is Kochi Metro Rail Limited (KMRL). The analysis of Automated Fare Collection data from KMRL on JP-DAP framework have produced descriptive statistics visualisation of inflow and outflow analysis, travel patterns during weekdays and weekends, origin-destination matrix, etc.. Moreover JP-DAP framework is capable of producing short term passenger flow predictions using SVR machine learning algorithm with linear, radial basis function and polynomial kernels. Our experiments have shown that SVR linear kernel gives the most accurate results with the least errors in predicting the next day's passenger count using the previous five weekdays data. The station usage (one-to-all) prediction using Long Short-Term Memory (LSTM) is also integrated to this framework. The visualisation as well as analytical outcomes of JP-DAP framework have also been made available to the external world using a rich set of REST APIs and are projected on to a web-dashboard. 2000-2011 IEEE. -
Comparative Analysis of Neural Network Models for Indian Sign Language Hand Gesture Recognition
The recognition of sign language is a crucial element in filling communication gaps that exist in the population. As inclusive communication technologies become more popular, there has been a significant push to develop trustworthy systems for translating sign language into written or visual form. The use of hand gestures and body movements is a fundamental aspect of sign languages, which are commonly used by those who are deaf. The lack of proficiency in sign language makes communication difficult for most people. A project was undertaken to convert Indian Sign Language (ISL) into spoken language through research. The paper presents a comparison of various neural network models. Using OpenCVgenerated real-time images and MediaPipe, it is possible to identify hand movements and collect ISL gesture data in realtime. In the study, it was demonstrated that ResNet50 is 92 per cent accurate in real-time recognition when compared to other models. This work aims to promote inclusivity and communication skills among people who may not have the ability to hear or speak fluently. Adding face recognition to future work may improve accuracy and enable continuous sign language recognition, providing more dynamic and real-time translation capabilities. 2025 IEEE. -
Restrained geodetic domination of edge subdivision graph
For a connected graph G = (V,E), a set S subset of V (G) is said to be a geodetic set if all vertices in G should lie in some u-v geodesic for some u,v S. The minimum cardinality of the geodetic set is the geodetic number. In this paper, the authors discussed the geodetic number, geodetic domination number, and the restrained geodetic domination of the edge subdivision graph. 2022 World Scientific Publishing Company. -
Restrained geodetic domination in graphs
Let G = (V,E) be a graph with edge set E and vertex set V. For a connected graph G, a vertex set S of G is said to be a geodetic set if every vertex in G lies in a shortest path between any pair of vertices in S. If the geodetic set S is dominating, then S is geodetic dominating set. A vertex set S of G is said to be a restrained geodetic dominating set if S is geodetic, dominating and the subgraph induced by V - S has no isolated vertex. The minimum cardinality of such set is called restrained geodetic domination (rgd) number. In this paper, rgd number of certain classes of graphs and 2-self-centered graphs was discussed. The restrained geodetic domination is discussed in graph operations such as Cartesian product and join of graphs. Restrained geodetic domination in corona product between a general connected graph and some classes of graphs is also discussed in this paper. 2020 World Scientific Publishing Company. -
Restrained geodetic domination in the power of a graph
For a graph G = (V,E), S ? V(G) is a restrained geodetic dominating set, if S is a geodetic dominating (gd) set and never consists an isolated vertex. The least cardinality of such a set is known as the restrained geodetic domination (rgd) number. The power of a graph G is denoted as Gk and is obtained from G by making adjacency between the vertices provided the distance between those vertices must be at most k. In this study, we discussed geodetic number and rgd number of Gk. 2024 Author(s). -
Restrained geodetic domination polynomial
For a connected graph G = (V, E), a vertex subset S of G is said to be a restrained geodetic dominating set if S is both geodetic and dominating set of G and also, the subgraph induced by V ? S consists of no vertex with degree zero. From the study of domination polynomial and geodetic domination polynomial, we have initiated the study on restrained geodetic domination polynomial. World Scientific Publishing Company. -
The Role of Prescriptive Analytics on Product Availability Towards Improved Customer Loyalty in Quick Commerce
Quick commerce (Q-commerce) has transformed retail by enabling ultra-fast deliveries, requiring optimised product assortment and inventory management. While traditional e-commerce offers a broad product range and competitive pricing, its delivery limitations led to Q-commerces emergence, ensuring fulfilment within 30min to a few hours. This study applies prescriptive analytics, machine learning and optimisation algorithms to enhance decision-making in Q-commerce. Advanced forecasting models, such as LSTM networks, improved demand forecasting with a Mean Absolute Error (MAE) of 0.25 and Root Mean Square Error (RMSE) of 0.35, reducing inventory costs by 10%. Linear programming optimised product mix, increasing sales by 15%. LSTM demonstrated high accuracy in predicting demand patterns, ensuring the availability of high-demand products while minimising overstock. Market Basket Analysis (MBA) revealed significant product associations, streamlining fulfilment centre operations and enhancing cross-selling strategies. Market Basket Analysis (MBA) using the Apriori algorithm identified key product associations, reducing picking times by 20% and boosting order value by 12%, contributing to a 15% rise in overall sales. Personalised recommendation systems using collaborative and content-based filtering increased conversion rates by 20% and customer retention by 15%. Despite these advancements, challenges in computational feasibility and synthetic data applicability persist. Future research should focus on real-time analytics and adaptive inventory strategies to enhance scalability and efficiency. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
The role of IoT in optimizing quick commerce operations: a comprehensive analysis of micro-fulfillment centers and consumer satisfaction
This research focuses on integrating Internet of Things (IoT) and AI-driven machine learning algorithms to optimize operations within micro-fulfillment centers for quick commerce in India. The study addresses the growing demand for faster delivery times, driven by the rise of e-commerce, and the challenges associated with managing inventory, logistics, and last-mile delivery. By implementing IoT-enabled devices to monitor real-time data and deploying machine learning algorithms for demand forecasting and route optimization, the study aimed to enhance operational efficiency and reduce delivery times. The research utilized a dataset comprising various operational metrics from micro-fulfillment centers across major Indian cities. A comparative analysis revealed that the AI and IoT integration led to a 22% reduction in delivery time and a 15% improvement in order accuracy. Additionally, the predictive maintenance system, powered by IoT sensors, resulted in a 30% decrease in equipment downtime. The results demonstrate that combining IoT with machine learning optimizes supply chain operation and significantly contributes to meeting consumer expectations in the quick commerce sector. This research provides a comprehensive framework for leveraging technology to address the unique challenges of the Indian market, offering valuable insights for industry stakeholders. 2026 Elsevier Inc. All rights reserved.. -
Automate Threat Detection and Analysis Through Intelligent Data Mining Techniques for Network Traffic and Cybersecurity
Today, we are constantly surrounded by vast amounts of data, a trend that is expected to grow significantly over the next decade. The abundance of data presents challenges for thorough analysis and extraction of valuable insights buried within unstructured information. Advanced tools like data mining are crucial in uncovering this useful information and making full use of it. In light of the increasing number of security threats in networks, there is a need for robust security solutions. While traditional network security measures have been primarily managed locally, concerns about internet-based security have grown due to heightened computer usage leading to cybercriminal activities previously limited to physical intrusions. A threat intelligence program aims to enhance analytical and preventive capabilities by acquiring knowledge about potential or existing threats based on evidence. As most devices are interconnected with the Internet, many organizations prioritize cybersecurity as they acknowledge the vulnerabilities arising from this connectivityproviding opportunities for cyber-attacks. Effective threat intelligence concerning network traffic necessitates a comprehensive understanding supported by thoughtful representation techniques. This paper proposes an extensive exploration of various machine learning methods aimed at identifying weaknesses in detecting invasive activity using different approaches and evaluating their performance against the KDD 99 benchmark dataset. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Fraud Prevention in Banking: Machine Learning-driven Approaches for Detecting Payment Anomalies
The fast-paced development of digital banking has brought with it new convenience but also tremendous challenges in maintaining transaction security. Banks are confronted with mounting threats from malicious activities like identity theft, account takeover, and unauthorized access, which can lead to huge financial losses and loss of customer confidence. This study investigates the formulation of a cybersecurity framework for fraud prevention in banking through machine learning algorithms. A transactional real-world dataset of 200,000 instances from LOL Bank Pvt. Ltd. was used to construct and evaluate predictive models. Preprocessing included categorical encoding, temporal feature engineering, and synthetic minority oversampling (SMOTE) for class imbalance handling. Three machine learning classifiers - Logistic Regression, Random Forest, and XGBoost - have been compared using measures of accuracy, precision, recall, F1-score, and ROC-AUC. Results show that ensemble models significantly outperformed logistic regression by a wide margin, with Random Forest and XGBoost both achieving over 91% accuracy and very good discrimination power. The study emphasizes how well machine learning-based systems detect theft in real time and outlines avenues for future research to enhance detection using adaptive and interpretable AI models. 2025 IEEE. -
User Authentication with Graphical Passwords using Hybrid Images and Hash Function
As per human psychology, people remember visual objects more than texts. Although many user authentication mechanisms are based on text passwords, biometric characteristics, tokens, etc., image passwords have proven to be a substitute due to its ease of use and reliability. The technological advancements and evolutions in authentication mechanisms brought greater convenience but increased the probability of exposing passwords through various attacks like shoulder-surfing, dictionary, key-logger, and social engineering attacks. The proposed methodology addresses these vulnerabilities and ensures to keep up the usability of graphical passwords. The system displays hybrid images that users need to recognize and type the randomly generated alphanumeric or special character values associated with each of them. A mechanism to generate One Time Password (OTP) is included for additional security. As a result, it is difficult for an attacker to capture and misuse the password. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Activity Classifier: A Novel Approach Using Naive Bayes Classification
Activity movements have been recognized in various applications for elderly needs, athletes activities measurements and various fields of real time environments. In this paper, a novel idea has been proposed for the classification of some of the day to day activities like walking, running, fall forward, fall backward etc. All the movements are captured using a Light Blue Bean device incorporated with a Bluetooth module and a tri-axial acceleration sensor. The acceleration sensor continuously reads the activities of a person and the Arduino is designed to continuously read the values of the sensor that works in collaboration with a mobile phone or computer. For the effective classification of a persons activity correctly, Nae Bayes Classifier is used. The entire Arduino along with acceleration sensor can be easily attached to the foot of a person right at the beginning of the user starts performing any activity. For the evaluation purpose, mainly four protocols are considered like walking, running, falling in the forward direction and falling in the backward direction. Initially five healthy adults were taken for the sample test. The results obtained are consistent in the various test cases and the device showed an overall accuracy of 90.67%. Springer Nature Switzerland AG 2020. -
Process scheduling in heterogeneous multicore system using agent based graph coloring algorithm
In any heterogeneous multicore system, there are numerous amount of processors with different platform and all the processing units are fabricated on a common single unit preferably on a System on Chip. As there is a tremendous amount of parallelism encompassed in a multicore system, proper utilization of the cores is a big challenge in the current era. Hence a more automated software approach is required like an agent based graph coloring algorithm to find the free processor and schedule the tasks on the respective cores. Predominantly the entire process of scheduling the tasks on multicore system is based on arrival time of process. This paper incorporates the scheduling on the linux 2.6.11 kernel and GEMS simulator for multicore implementation. The core utilization in this type of agent scheduling is 50% more than the existing scheduling mechanism. BEIESP. -
Captcha-Based Defense Mechanism to Prevent DoS Attacks
The denial of service (DoS) attack, in the current scenario, is more vulnerable to the banking system and online transactions. Conventional mechanism of DoS attacks consumes a lot of bandwidth, and there will always be performance degradation with respect to the traffic in any of the communication networks. As there is an advent over the network bandwidth, in the current era, DoS attacks have been moved from the network to servers and API. An idea has been proposed which is CAPTCHA-based defense, a purely system-based approach. In the normal case, the protection strategy for DDoS attacks can be achieved with the help of many session schedulers. The main advantage is to efficiently avoid the DoS attacks and increase the server speed as well as to avoid congestion and data loss. This is majorly concerned in a wired network to reduce the delays and to avoid congestion during attacks. 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Multilevel Security and Dual OTP System for Online Transaction Against Attacks
In the current internet technology, most of the transactions to banking system are effective through online transaction. Predominantly all these e-transactions are done through e-commerce web sites with the help of credit/debit cards, net banking and lot of other payable apps. So, every online transaction is prone to vulnerable attacks by the fraudulent websites and intruders in the network. As there are many security measures incorporated against security vulnerabilities, network thieves are smart enough to retrieve the passwords and break other security mechanisms. At present situation of digital world, we need to design a secured online transaction system for banking using multilevel encryption of blowfish and AES algorithms incorporated with dual OTP technique. The performance of the proposed methodology is analyzed with respect to number of bytes encrypted per unit time and we conclude that the multilevel encryption provides better security system with faster encryption standards than the ones that are currently in use. 2019 IEEE. -
Comparative Study on the Experimental Results on Low-Velocity Impact Characteristics of GLARE Laminates with Simulation Results from LS Dyna
Fiber reinforcement with metallic face sheets is one of the recently implemented materials for distinctive applications in automotive and aerospace sectors. While the reinforcement enhances the sustenance property of the laminate, the face sheets provide resistance to impact force. In most automotive sectors, drop weight analysis at varying velocity ranges is performed to evaluate the damage characteristics of the vehicle body. The present work is aimed at studying the influence of low-velocity impact (LVI) on Glass Laminate Aluminum-Reinforced Epoxy (GLARE) laminate. Three distinct thicknesses of Al-2024 T3 aluminum alloy (0.2, 0.3 and 0.4mm) were chosen as the face sheet and E-glass fiber was used as intermediate layers. Epoxy resin LY556 with a HY951 hardener was used to fabricate the GLARE structure and the overall thickness was maintained at 2.0mm for all the cases. Energy absorbed by GLARE laminates for different energy was determined using Drop weight Impact test experimentally and analytically. The laminate and the dart were modeled by ANSYS ACP tool and the simulation was performed using LS Dyna software. It was evident that laminate can sustain impact at a velocity of 3.13m/s and beyond which leads to surface delamination. The simulation results were in close agreement with the experimental values for the absorbed energy, with less than 10% error. 2022, The Institution of Engineers (India). -
A Study on the Impact of Brick and Mortar Stores and e-Commerce on Impulsive Buying Behaviour of Consumers
In the changing business landscape in retail and e-commerce, impulsive buying behaviour has played a significant role in the field of marketing and consumer psychology. The study aims to compare the impulsive buying behaviour in brick-and-mortar stores and e-commerce platforms. To execute the research, the primary data was collected through a structured questionnairethe data from collected from 300 respondents who were living in urban Bengaluru. The findings revealed that the physical stores stimulate impulsive buying through the sensory cues, through various internal promotional activities, and also through product gratification. The e-commerce also triggers impulsive buying with the help of recommendations, which are driven by algorithms, with limited-period offers, lucrative deals, discounts, and bundle offers. On the other hand, the demographic variables also have an impact on impulsive buying behavior. The research highlights the ever-evolving nature of consumer behaviour in the digital era and provides quality information that would be used as tactics to trigger impulsive buying. The research paper concludes with practical inputs for companies and marketing agencies. The research can be further extended to various other Omni-channel. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Exploring the Influence of Cause-Related Brand Partnerships on Consumer Attitudes and Purchase Decisions
This study examines how cause-related brand partnerships influence consumer attitudes and purchase decisions. Consumers increasingly value social responsibility, so brands align with charitable causes to enhance their image and foster loyalty. The research investigates whether these partnerships lead to positive consumer perceptions, increased trust, and stronger emotional connections with the brand. By analyzing both qualitative and quantitative data, the study explores the extent to which cause-related marketing (CRM) impacts actual purchasing behavior and long-term brand loyalty. The results suggest that while CRM can improve consumer attitudes, its effectiveness depends heavily on the perceived authenticity of the brands commitment to the cause. Brands that genuinely align with causes that resonate with their target audience are more likely to see a positive impact on both consumer sentiment and purchase behavior. The findings highlight the importance of consistent and sincere engagement for brands aiming to leverage social causes for business growth. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Urban Heat Island in Bengaluru: Built-up Growth and Temperature Trends
Bangalore, once renowned as India's "Garden City"has transformed into a "Silicon Valley"metropolis over the past five decades, experiencing profound environmental changes. This study investigated the direct correlation between the city's extensive built-up area expansion and rising temperatures, drawing upon comprehensive literature and climate data. The analysis revealed a dramatic 1055% increase in built-up areas, from 7.97% in 1973 to 93.3% in 2023.Concurrently, vegetation cover has plummeted by 88% (from 68.27% to approximately 6%), and water bodies have decreased by 79%. These significant land-use alterations have led to notable thermal shifts in the region's climate. Land Surface Temperatures (LST) increased by 7.9 C, from 33.08 C in 1992 to 41 C in 2017, while average air temperatures rose by 0.23 C per decade since 1975.The urban heat island (UHI) effect was pronounced, with an average annual nighttime surface UHI of 0.99 C. A strong inverse relationship between vegetation cover and LST (R =-0.74 in dry seasons,-0.34 in wet seasons) confirms the critical role of green spaces in urban areas. This evidence unequivocally attributes the escalating UHI effect to the rapid, unplanned urban growth of Bangalore, underscoring the urgent need for sustainable urban planning. The Authors, published by EDP Sciences, 2025.