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Ideal co-secure domination in graphs
A set S ? V of a graph G = (V, E) is a co-secure dominating set if for every u ? S, there exists v ? V \ S such that uv ? E and (S \ {u}) ? {v} is a dominating set of G. The minimum cardinality of a co-secure dominating set of G is the co-secure domination number and is denoted by ?cs(G). In this paper we initiate the evaluation of a domination parameter known as the ideal co-secure domination and is defined as follows: A set D ? V is an ideal co-secure dominating set of a graph G = (V, E) if for every u ? D and for every v ? V \ D such that uv ? E, (D \ {u}) ? {v} is a dominating set of G. The minimum cardinality of an ideal co-secure dominating set of G is the ideal co-secure domination number and is denoted by ?ics(G). We look to determine the ideal co-secure domination number of some families of standard graphs and obtain sharp bounds. We also provide the conditions necessary for the trees to have ideal co-secure domination number equal to n - 2. 2020 Author(s). -
An analysis of load balancing algorithms in the cloud environment
The emerging area in an IT environment is Cloud Computing. There are many advantages of the computing but unfortunately, allocation of the job request effectively is a trouble. It requires lots of infra structural commitments and the quality inputs of the resources. Also, in the cloud computing environment, Load Balancing is an important aspect. Efficient load balancing algorithm helps the resource to have optimized utilization with the proper dissemination of the resources to the cloud user in pay-as-you-say-manner. It also supports ranking the job request based on the priority with the help of scheduling technique. We present the various types of Load Balancing Techniques in the different platform of Cloud Environment specified in SLA (Service level Agreement). 2016 IEEE. -
An integration of big data and cloud computing
In this era, Big data and Cloud computing are the most important topics for organizations across the globe amongst the plethora of softwares. Big data is the most rapidly expanding research tool in understanding and solving complex problems in different interdisciplinary fields such as engineering, management health care, e-commerce, social network marketing finance and others. Cloud computing is a virtual service which is used for computation, data storage, data mining by creating flexibility and at minimum cost. It is pay & use model which is the next generation platform to analyse the various data which comes along with different services and applications without physically acquiring them. In this paper, we try to understand and work on the integration model of both Cloud Computing and Big Data to achieve efficiency and faster outcome. It is a qualitative paper to determine the synergy. Springer Science+Business Media Singapore 2017. -
Multi-view video summarization
Video summarization is the most important video content service which gives us a short and condensed representation of the whole video content. It also ensures the browsing, mining, and storage of the original videos. The multi- view video summaries will produce only the most vital events with more detailed information than those of less salient ones. As such, it allows the interface user to get only the important information or the video from different perspectives of the multi-view videos without watching the whole video. In our research paper, we are focusing on a series of approaches to summarize the video content and to get a compact and succinct visual summary that encapsulates the key components of the video. Its main advantage is that the video summarization can turn numbers of hours long video into a short summary that an individual viewer can see in just few seconds. Springer India 2016. -
The Role of IoT in Revolutionizing Payment Systems and Digital Transactions in Finance
The revolutionary impact of the Internet of Things (IoT) on payment systems and digital transactions within the financial industry is investigated so as to better understand its implications. During this period of unparalleled digitalization in the financial environment, the Internet of Things has emerged as a crucial participant in the process of altering traditional payment paradigms. For the purpose of improving efficiency, security, and the overall user experience, this article analyzes the incorporation of Internet of Things (IoT) devices into financial transactions. These devices include smart cards, wearables, and linked appliances. The paper elucidates how Internet of Things-driven innovations are expediting payment processes, reducing transaction costs, and mitigating fraud risks. This is accomplished through a comprehensive investigation of case Researches, technology breakthroughs, and regulatory frameworks. In addition to this, the article investigates the implications of the Internet of Things (IoT) in terms of promoting financial inclusion by providing digital payment services to groups that were previously underserved. This research gives useful insights for policymakers, financial institutions, and technologists who are looking to navigate and harness the potential of the Internet of Things in transforming payment systems. These insights are gained through an examination of the obstacles and opportunities related with the adoption of IoT in the financial sector. 2024 IEEE. -
IoT-Driven Credit Scoring Models: Improving Loan Decision Making in Banking
By the game-changing possibilities of credit scoring models driven by the Internet of Things, this hopes to shed light on how the banking sector may enhance its loan decision-making procedures. Financial organisations are putting more and more faith in Internet of Things technologies to improve their risk assessment and lending processes. These IoT-driven models provide a more accurate and thorough assessment of creditworthiness by including real-time and detailed data on borrowers' activities, spending habits, and asset utilisation. This research examines the practicality and accuracy of Internet of Things (IoT) credit scoring by comparing it to conventional methods, looking closely at case researches, and analysing empirical data. The findings shed light on potential ways to enhance the loan approval and risk prediction procedures while also addressing concerns and considerations related to data privacy, security, and regulatory compliance. It is possible that decision-making frameworks could be altered by IoT-driven credit scoring algorithms, which could lead to a more inclusive and informed lending atmosphere. The contributes to the growing area of banking credit evaluation by showing that these models have promise. 2024 IEEE. -
AI-Enhanced IoT Data Analytics for Risk Management in Banking Operations
Using IoT data analytics in conjunction with artificial intelligence (AI) has the potential to improve banking operations' risk management. Sophisticated analytical methods are necessary for the detection and management of possible risks due to the increasing complexity and amount of data generated by the banking industry. This research proposes a novel method for analysing real-time data from IoT devices by employing artificial intelligence algorithms. The risks associated with financial transactions and operations can be better and more accurately assessed using this method. Through the integration of AI's pattern recognition, anomaly detection, and predictive modelling capabilities with the massive amounts of data generated by Internet of Things devices, this project aims to substantially enhance the efficacy and efficiency of risk management approaches in the banking sector. Research like this could lead to innovative solutions that make financial institutions more resistant to rising risks by enhancing decision-making, reducing operational weaknesses, and so on. 2024 IEEE. -
Enhancing Banana Cultivation: Disease Identification through CNN and SVM Analysis for Optimal Plant Health
Detection and effective remedies play a crucial role in revolutionizing banana crop health. The banana industry faces numerous challenges, including the prevalence of diseases and pests that can lead to significant yield losses. This paper explores the potential impact of detection techniques and remedies on improving banana crop management. Disease detection models based on machine learning, image processing and deep learning offer high accuracy in identifying diseases like Fusarium Wilt, Yellow Sigatoka, and Black Sigatoka. Implementing detection and targeted treatments can enhance crop productivity, reduce pesticide usage, and ensure sustainable banana production. 2024 IEEE. -
Analysis and Forecasting of Crude Oil Price Based on Univariate and Multivariate Time Series Approaches
This paper discusses the notion of multivariate and univariate analysis for the prediction of crude oil price in India. The study also looks at the long-term relationship between the crude oil prices and its petroleum products price such as diesel, gasoline, and natural gas in India. Both univariate and multivariate time series analyses are used to predict the relationship between crude oil price and other petroleum products. The Johansen cointegration test, EngleGranger test, vector error correction (VEC) model, and vector auto regressive (VAR) model are used in this study to assess the long- and short-run dynamics between crude oil prices and other petroleum products. Prediction of crude oil price has also been modeled with respect to the univariate time series models such as autoregressive integrated moving average (ARIMA) model, Holt exponential smoothing, and generalized autoregressive conditional heteroskedasticity (GARCH). The cointegration test indicated that diesel prices and crude oil prices have a long-run link. The Granger causality test revealed a bidirectional relationship between the price of diesel and the price of gasoline, as well as a unidirectional association between the price of diesel and the price of crude oil. Based on in-sample forecasts, accuracy metrics such as root mean square logarithmic error (RMSLE), mean absolute percentage error (MAPE), and mean absolute square error (MASE) were derived, and it was discovered that VECM and ARIMA models can efficiently predict crude oil prices. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Global Analysis of Quantum Technology Discourse
he study provides a thorough exploration of the global quantum technology landscape, offering valuable insights for researchers, policymakers, and industry stakeholders. It employs advanced analytical methods such as Latent Dirichlet Allocation (LDA) and Non-Negative Matrix Factorization (NMF) for topic modeling. The research focuses on understanding discussion intensity, geographical distribution, co-mentioning patterns among countries, prevalent topics, and keyword-based trends. Utilizing diverse datasets, the study employs heatmaps, network analysis, and thematic analysis to categorize textual data. Evaluation metrics like Topic Coherence and Network Centrality Measures contribute to a robust methodology.Key findings include dominant discussions on quantum computing and investment strategies, with focused attention on governmental roles in R&D and specific quantum computer research. Notably, there is a niche focus on quantum algorithmic risks in Australia. Document characteristics vary, with some blending multiple themes and others centered around a single topic. LDA topic modeling and network analysis identify key countries, showcasing global hotspots and potential collaborations in quantum technology discussions. 2024 IEEE. -
Comparative study of recommender systems
Recommendation System is a quickly progressing study area. Many new approaches are offered so far. In this particular paper we have researched on various applications of recommender system and various techniques used in recommender system like collaborative filtering, content-based filtering and hybrid filtering. Collaborative filtering is amongst the common methods utilized in recommending process. So comparative study on various collaborative filtering is done and the results are plotted graphically. 2016 IEEE. -
The Effect of Bloom's Taxonomy on Random Forest Classifier for cognitive level identification of E-content
With the advancement in internet, the efficiency of e-learning increased and currently e-learning is one of the primary method of learning for most learners after the regular academics studies. The knowledge delivery through e-learning web sites increased exponentially over the years because of the advancement in internet and e-learning technologies. The learner can find many website with lots of information on the relevant domain. However learners often found it difficult to Figure out the right leaning content from the humongous availability of e-content. In the proposed work an intelligent framework is developed to address this issue. The framework recommend the right learning content to a user from the e-learning web sites with the knowledge level of the user. The e-contents available in web sites were divided in to three cognitive levels such as beginner, intermediate and advanced level. The current work uses Blooms Taxonomy verbs and its synonyms to improve the accuracy of the classifier used in the framework. 2020 IEEE. -
ThermAI: Exploring Temperature Analysis Through Diverse Machine Learning Models
Meteorological forecasting is crucial in multiple industries, including agriculture, aviation, and daily routines. The objective of this inquiry is to improve temperature predictions by examining and comparing several machine learning methods, such as linear regression, decision trees, and random forests. This work aims to fill the gap in assessing machine learning models for temperature forecasting on a broader scale by utilising the comprehensive Indian meteorological dataset, which covers a wide range of geographical regions. The research utilises a thorough technique that includes gathering data, selecting relevant features, choosing appropriate models, and evaluating the results using R-squared and Mean Square Error metrics. The findings demonstrate that the Random Forest model surpasses both multiple linear regression and decision trees in terms of performance, displaying superior accuracy and reduced prediction errors. This study enhances proactive weather management and decision-making processes by offering valuable insights and tools to stakeholders in various industries. The work is organised into distinct sections that encompass a literature review, methodology, results, and conclusions, providing a comprehensive viewpoint on developments in temperature forecasting. 2024 IEEE. -
Customer Segmentation in the Field of Marketing
The motive of this work is to classify and categorize customers depending on their familiar traits/characteristics so as to enable a company or a firm to adequately market their products to each category more attractively and competently. It is imperative for a firm to educate themselves with each and every detail about the customer, such as age group, sexuality, social class, purchase pattern etc as it paves way for customer segmentation. Businesses may utilize segmentation to make better use of their marketing resources, get a competitive advantage over competitors, and, most importantly, display a deeper understanding of their consumers' requirements and desires. Customer segmentation, when combined with customer targeting and positioning, creates the foundation for strategic marketing. A manager can find new marketing possibilities and create or adjust the product to satisfy the demands of potential clients using the notion of strategic marketing. The product's quality level determines its position in the market's overall offering. It's a crucial aspect in selecting which market segment a collection will target. The commercial world has gotten more competitive over time, as enterprises like these have to fulfil their consumers' demands and aspirations, attract new customers, and enhance their bottom lines. In this research, I have put the spotlight on the information used by firms for the purpose of customer segmentation in the most valuable manner. In addition to that, I have portrayed different models of customer segmentation and the benefits reaped by a business in implementing them. 2022 IEEE. -
Human heart disease prediction system using data mining techniques
Nowadays, health disease are increasing day by day due to life style, hereditary. Especially, heart disease has become more common these days, i.e. life of people is at risk. Each individual has different values for Blood pressure, cholesterol and pulse rate. But according to medically proven results the normal values of Blood pressure is 120/90, cholesterol is and pulse rate is 72. This paper gives the survey about different classification techniques used for predicting the risk level of each person based on age, gender, Blood pressure, cholesterol, pulse rate. The patient risk level is classified using datamining classification techniques such as Nae Bayes, KNN, Decision Tree Algorithm, Neural Network. etc., Accuracy of the risk level is high when using more number of attributes. 2016 IEEE. -
Gender Identification of Silkworm Pupa and Automated Cocoon Cutting Machine for Benefiting the Sericulture Grainages in Karnataka
Sericulture is the backbone of a mediocre farmer family in India. Sericulture provides a major financial support to the farmers with minimum infrastructure and maintenance. Farmers collect the seed cocoons from the grainages also known as seed factories. Grainages produce high quality seeds by mating the male and female cocoons. There is a huge demand of labor in these seed factories to process cocoons. The process includes deflossing, removal of pupa from the cocoon, gender identification, mating, storage of seeds, dispersal of eggs to farmers. Removal of pupa from the cocoon requires the labor to cut open a small portion of the cocoon and remove the pupa from inside. Presently in India, most of the grainages induce female laborers to perform the above job. Pupa is removed from the cocoon by cutting the cocoon using a stainless-steel blade. Each labour is given certain amount of cocoons to cut in a day. This requirement would force the laborers to perform the job at a higher speed which poses a threat of getting wounded by the blade. Hence the process of removal of pupa from the cocoon and sex identification of pupa to be automated. Thereby it is important to automate the possible processes in the grainages which could reduce human intervention and increase productivity. Bivoltaine hybrid race of silkworm namely FC1 and FC2 are the varieties under consideration for the research. Theses silkworm varieties are majorly used in grainages for seed production and hence the proposed machine was introduced. This semi automated cocoon cutting machine identifies the gender of the cocoon and later cut the required amount of cocoons minimally. This process would help in maintaining the maximum reliability of silk thread. Thereby the silkworm gender identification has to be non destructive. Image classification were done using Convolution Neural Network (CNN), Visual Geometry Group 16 (VGG16) and Efficient net methods, among which the latter produced highest accuracy. The Efficient Net method has produced the validation accuracy of 98.99% for FC1 and 99.9 for FC2 variety. An automated cocoon cutting machine was developed to cut open the cocoons at a high speed. It is important to automate the possible processes in the grainages which could reduce human intervention and increase productivity. This paper focuses on automating the gender identification and removal of the pupa from the cocoon. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
A GPS-Gradient Mapped Database-Based Fuzzy Energy Management System for a SeriesParallel Hybrid Electric Vehicle
The Energy Management System developed for the hybrid electric vehicle operates using a database with GPS co-ordinates and corresponding altitudes mapped, thereby giving a predictive control to optimize the operation of the seriesparallel hybrid system. The system aims at extracting the maximum potential of the seriesparallel hybrid power train architecture. The mapping of the latitude and longitude obtained from a global positioning system (GPS) to the altitude measured to create a database which generates a predefined driving cycle prior to the actual motion of the vehicle. The created database is then used in a MATLAB/Simulink model to simulate the operation of the seriesparallel hybrid system and implement the Energy Management System. The validated data is then tested in a Raspberry Pi (RPi)-based prototype. The Energy Management System regulates the vehicle dynamics based on the input drive cycle. The fuzzy logic-based control mechanism is implemented in the RPi to optimize the load sharing between the IC engine and the brushless DC motor. 2020, Springer Nature Singapore Pte Ltd. -
Deep Learning-based Gender Recognition Using Fusion of Texture Features from Gait Silhouettes
The gait of a person is the manner in which he or she walks. The human gait can be considered as a useful behavioral type of biometric that could be utilized for identifying people. Gait can also be used to identify a persons gender and age group. Recent breakthroughs in image processing and artificial intelligence have made it feasible to extract data from photographs and videos for various classifying purposes. Gender can be regarded as soft biometric that could be useful in video captured using surveillance cameras, particularly in uncontrolled environments with erratic placements. Gender recognition in security, particularly in surveillance systems, is becoming increasingly popular. Popularly used deep learning algorithms for images, convolutional neural networks, have proven to be a good mechanism for gender recognition. Still, there are drawbacks to convolutional neural network approaches, like a very complex network model, comparatively larger training time and highly expensive in computational resources, meager convergence quickness, overfitting of the network, and accuracy that may need improvement. As a result, this paper proposes a texture-based deep learning-based gender recognition system. The gait energy image, that is created by adding silhouettes received from a portion of the video which portrays an entire gait cycle, can be the most often utilized feature in gait-based categorization. More texture features, such as histogram of oriented gradient (HOG) and entropy for gender identification, have been examined in the proposed work. The accuracy of gender classification using whole body image, upper body image, and lower body image is compared in this research. Combining texture features is more accurate than looking at each texture feature separately, according to studies. Furthermore, full body gait images are more precise than partial body gait images. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
University-Community Collaboration for A Sustainable School-Based Program for The Holistic Education and Wellness of Adolescents
Adolescents have been particularly affected by the COVID-19 pandemic and the closure of schools that are already struggling to carry out their mission of quality education and holistic well-being of students. Research suggests that community-collaborative schools are improving students' academic engagement and reducing learning barriers. When communities and universities are involved in holistic education, it benefits all the stakeholders by enhancing mutual learning and strengthening both. Community members' involvement for student development encourages students and their families to be more involved in community-service initiatives. The paper reports DREAMS, a multi-stakeholder partnership (schools, universities and communities) after-school mentoring model's sustainability. The study identifies and delineates how the model has incorporated the Sustainable Development Goals (SDGs) calling for Good Health and Well-being (SDG-3), Quality Education (SDG-4), Sustainable Cities and Communities (SDG-11) through Partnerships to Achieve its Goals (SDG-17) and proposes it as a sustainable afterschool plan for the post COVID scenario. The Electrochemical Society -
Kho Kho Model: A Novel Technique for Efficient Handoff in Vehicular Ad-hoc Networks
The highly mobile nature of VANET implies that the nodes involved are constantly disconnecting and reconnecting as they switch between access points or move out of the range of their access points. In such scenarios, seamless connectivity is essential, especially when emergency services are involved. Handoff is a process in wireless communication that takes care of the switching process that happens between access points whenever a mobile device moves from one point to another. In a dynamic scenario involving vehicular nodes, this switching needs to take place between a mobile node or a fixed access point (known as RSUs), as quickly as possible. To this end, this research work proposes a novel handoff method known as the Kho Kho Model - which is loosely based on the traditional Indian sport of the same name. The model groups together nodes that are moving in the same direction, thereby effectively reducing the amount of processing required to perform handoff for a set of nodes. The use of ANN have helped to improve handoff since it can help in making decisions quickly by making use of multiple parameters including signal strength, noise, direction, and others. To improve the efficiency of the proposed handoff model, RBFNN has been used in this research. The proposed model was implemented using NS-3 simulator. The results have shown that the proposed method has a slightly better improvement in the overall NRO, a reduced average delay and reduced jitter compared to the existing handoff method employed by the IEEE 802.11p standard. 2023 IEEE.