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Role of Artificial Intelligence in Influencing Impulsive Buying Behaviour
This research paper investigates the influence of Artificial Intelligence (AI) on impulsive buying behaviour in the digital commerce domain. The study explores how AI algorithms, data analysis, and customized marketing approaches influence impulsive buying decisions, reshaping traditional understandings of this phenomenon. The analysis draws from a confluence of psychological principles, technological advancements, and marketing strategies, aiming to shed light on how AI not only forecasts but also incites impulsive buying behaviours. The study identifies research gaps, such as the integration of AI with emotional triggers, the comparative effectiveness of AI vs. human influence, and cross-cultural and demographic variability. The research methodology involves a descriptive study with a questionnaire-based survey, and data analysis tools such as ANOVA and paired t-tests. This research contributes to the broader discussion on digital-age consumer behaviors, underscoring the revolutionary role of AI in transforming retail experiences and beyond. 2024 IEEE. -
Role of Blockchain in the Healthcare Sector: Challenges, Opportunities and Its Uses in Covid-19 Pandemic
As the world grapples with the Covid-19 pandemic and major populations are getting vaccinated, increasing realisation processes healthcare industry needs to be augmented. It includes managing supply chains, healthcare records, and patient care. With a scarcity of time and resources, adaptation of blockchain technology will help mitigate the pressures on existing infrastructure. A blockchain distributed ledger helps to exchange health information securely without complex intermediation of trust with secure access. The organisations and persons in the blockchain network can verify and authorise the data, thus protecting patient identity, privacy, medical information system, and reducing transaction costs. The paper examines managing and protecting electronic medical records and personal health records data using blockchain. It also analyses issues in healthcare, blockchain implementation, and its uses in the Covid-19 pandemic. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Role of Data Science in the Field of Genomics and Basic Analysis of Raw Genomic Data Using Python
The application of genomics in identifying the nature and cause of diseases has predominantly increased in this decade. This field of study in life sciences combined with new technologies, revealed an outbreak of certain large amounts of genomic sequences. Analysis of such huge data in an appropriate way will ensure accurate prediction of disease which helps to adopt preventive mechanisms which can ultimately improve the human quality of life. In order to achieve this, efficient comprehensive analysis tools and storage mechanisms for handling the enormous genomic data is essential. This research work gives an insight into the application of data science in genomics with a demonstration using Python. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Role of Filters in Speckle Reduction in Medical Ultrasound Images- A Comparative Study
To diagnose and predict complex disorders in human body, various Medical Imaging Techniques are used. Widely accepted technique among them is the Ultrasound imaging modality, because of its low cost and noninvasive nature. But the images produced by ultrasound scanning are of low quality and amenable to faster degradation due to the presence of speckle noise. This led to various studies for effectively removing speckle noise from ultrasound images. In this paper, an endeavor is made for a comparative analysis of chosen set of post filtering methods for Speckle reduction, VIZ Anisotropic Diffusion, Wavelet, Adaptive Median Filter, Hybrid Algorithm, Modified Fourier Transform and Sparse Code Shrinkage using ICA. The different methods are tested on a collection of ultrasound images and their performance evaluated with the Normalized Cross Correlation metric (NCC), Peak Signal to Noise Ratio (PSNR), Structural Content (SC), Universal Quality Index (UQI), Edge Preservation Index (EPI) and Structural Similarity Index (SSI). Further relative execution time of different approaches are also analyzed. On analysis of the values of different metrics and execution time, Wavelet Based Hybrid Thresholding is found to outperform the other filters considered. 2019 IEEE. -
Role of Machine Learning in the Analysis of Mental Health Data: An Empirical Approach
As funding for mental health research has grown, so too has the body of knowledge about how best to address and alleviate issues related to mental health. However, there is still a lack of certainty and clarity on the precise causes of mental diseases. Discovery of new drugs, analysis of radiological data, forecasting of disease outbreaks, and the diagnosis of illnesses are just some of the medical applications of machine learning algorithms. Machine learning algorithms are commonly used to sift through the mountains of medical data. Since their performance has improved to the point where it can be relied upon, they are now used to aid in medical diagnosis. To assess and address the issues with mental health, numerous new approaches and algorithms had been devised. There are still a lot of issues that can be resolved. So the main purpose of this study is to examine the effectiveness of machine learning in mental health problems. For fulfilling this purpose, this study is descriptive in nature. Primary data is collected with the help of interview method in which 50 individuals suffering from mental illness were asked to answers some questions. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Rotor Dynamics of TurbineGenerator Shaft with Dampers During Subsynchronous Resonance Generated by Series Capacitors
Purpose In this paper, an electromechanical approach to study the turbinegenerator shaft stability with and without dampers is made. The shaft is subjected to electrical disturbances created by series capacitors. The high power capacitors help the electric power system to improve the reactive power in high voltage transmission lines. Methods Finite element method is used to study the stability of the shaft under subsynchronous resonance when compared to the traditional methods like eigenvalue analysis, frequency scanning method and digital time simulation techniques. At the same time, it leads to subsynchronous resonance. Results Electromechanical stress in the rotating shaft arises when the resonance is created in the system. Maximum stress and strain of the shaft are calculated with other necessary parameters to prove the system instability. In order to maintain stability, dampers are installed at an optimum location. Conclusion Best location of installing damper is found using ANSYS 16.0 by modal analysis, harmonic and phase response analysis. The damper installed at the point reduces the stress caused by subsynchronous resonance and maintains the stability of the system. 2021, Springer Nature Singapore Pte Ltd. -
Rough Set Based Ant-Lion Optimizer for Feature Selection
As the area of computational intelligence evolves, the dimensionality of any sort of data gets expanded. To solve this issue, Rough Set Theory (RST) has been successfully used for finding reducts as it requires only supplied data and no additional information. This paper investigates a novel search strategy for minimal attribute reduction based on rough sets and Ant Lion Optimization (ALO). ALO is a nature-inspired algorithm that mimics the hunting mechanism of ant lions, and this is inspired to find the minimum reducts. Datasets from the UCI repository are used in this paper. The experimental results show that the features selected by the proposed method are well classified with reasonable accuracy. 2020 IEEE. -
Rubitics: The Smarter GCMS for Mars
A GCMS stands for a Gas Chromatograph and Mass Spectrometer. These two instruments are used to identify compounds from both soil and atmospheric samples. The GCMS usually has a mass of around 40 kilograms and is the size of a microwave oven, but what if we could downsize it? Downsizing the GCMS means that the number of equipment and instruments that can be used and carried by a rover can drastically increase. Rubitics is essentially a GCMS, only smaller and more efficient. This paper discusses the way Rubitics functions and how a GCMS can be remodeled and used to its fullest potential. The column of the Gas Chromatograph is replaced with composite materials to increase the flexibility of the tube, thereby increasing the number of columns along with finger-like projections on the interiors, which will aid in a much more precise separation of compounds. The inert carrier gas container is changed with a more durable, strong composite that will be instrumental in reducing the mass of the cylinder, and a safer chemically unreactive material will ensure complete pure storage. Rubitics will also contain a cooling system so as to be more power-efficient and aid in obtaining precise results. The material of the oven used in the gas chromatograph will be of much more insulating capacity (thermal resistance), lighter in mass, and smaller in size. Rubitics maintains the optimum shape to provide the most temperature and energy-efficient GCMS ever. Rubitics houses a compact electronic bay with sensors and a microprocessor for analysing the different components. The detectors' values are processed in the onboard microprocessor with the help of TinyML. This light algorithm can help in reducing the bandwidth consumed in transmitting unnecessary data to the ground station through providing in-situ data filtration. The paper also contemplates using such an algorithm to improve the efficiency of GCMS. In conclusion, Rubitics will be the future of GCMS technologies and sample analysis on different planetary terrains. Due to its re-engineered structure, it occupies lesser weight, size, and space. Rubitics thereby changes the number and quality of experiments that can be performed on Mars, leading to better insights for successful future habitation. Copyright 2022 by Ms. Harshini K Balaji. Published by the IAF, with permission and released to the IAF to publish in all forms. -
Ryu controller's scalability experiment on software defined networks
Software defined networks is the future of Computer networks which claims that traditional networks are getting replaced by SDN. Considering the number of nodes everyday connecting to the global village of internet, it becomes inevitable to adapt to any new technology before testing its scalability in presence of dynamic circumstances. While a lot of research is going on to provide solution as SDN to overcome the limitations of the traditional network, it gives a call to research community to test the applicability and caliber to withstand the fault tolerance of the provided solution in the form of SDN Controllers. Out of the existing multiple controllers providing the SDN functionalities to the network, one of the basic controllers is Ryu Controller. This paper is a contribution towards performance evaluation of scalability of the Ryu Controller by implementing multiple scenarios experimented on the simulation tool of Mininet, Ryu Controller and iPerf. Ryu Controller is tested in the simulation environment by observing throughput of the controller and checked its performance in dynamic networking conditions over Mesh topology by exponentially increasing the number of nodes until it supported tested on high end devices. 2018 IEEE. -
Safe cloud: Secure and usable authentication framework for cloud environment
Cloud computing an emerging computing model having its roots in grid and utility computing is gaining increasing attention of both the industry and laymen. The ready availability of storage, compute, and infrastructure services provides a potentially attractive option for business enterprises to process and store data without investing on computing infrastructure. The attractions of Cloud are accompanied by many concerns among which Data Security is the one that requires immediate attention. Strong user authentication mechanisms which prevent illegal access to Cloud services and resources are one of the core requirements to ensure secure access. This paper proposes a user authentication framework for Cloud which facilitates authentication by individual service providers as well as by a third party identity provider. The proposed two-factor authentication protocols uses password as the first factor and a Smart card or Mobile Phone as the second factor. The protocols are resistant to various known security attacks. Springer India 2016. -
Safety of Unmanned Systems
The safety risk management process describes the systematic application of management policies, procedures and practices to the activities of communicating, consulting, establishing the context, and assessing, evaluating, treating, monitoring and reviewing risk. This process is undertaken to provide assurances that the risks associated with the operation of unmanned aircraft systems have been managed to acceptable levels. Active efforts should be made to develop rules to ensure the safe operation of unmanned aerial vehicles. For the safe integration of operations with unmanned aerial vehicles, it is important to take into account the influence of different levels of control and autonomous capabilities, as well as the source of movement monitoring in the system. This article discusses the security issues of unmanned systems, the main directions of ensuring the information security of unmanned systems, software and hardware vulnerabilities have been identified. The methods of information protection are given, the disadvantages are indicated. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Sales Prediction Scheme Using RFM based Clustering and Regressor Model for Ecommerce Company
Machine learning models are being used for better insights and decision making across many industries today. It shows to be quite useful for businesses in the ecommerce industry as well due to the vast amount of data generated and its potential. This research aimed to find insights on future sales of an ecommerce company [1]. The vast number of variables including both categorical and continuous variables under product data, customer information, transaction information, led us to implement a prediction model using regressors rather than just time series forecasting techniques. First an RFM (Recency, Frequency and Monetary) based clustering algorithm was used to get customer related information and then integrate those results into a regressor to achieve the desired goal of prediction of sales. Two schemes were tested one being predictions on individual clusters and the other where the clusters were one hot encoded back into the main data. Results show quite high accuracy of prediction. The high R-squared also indicated that our hypothesis of including the variables contributed significantly to the predicted sales values was correct in this case. This research fulfills an identified need to understand how machine learning algorithms can be implemented by multiple algorithms being integrated in sequential and logical orders thus helping derive business specific strategies rather than making it a mere technical process by providing empirical results about how the predicted sales values along with given inputs can contribute in business decision making relating to marketing, inventory management, dynamic pricing or many more such strategies. 2022 ACM. -
SARIMA Techniques for Predictive Resource Provisioning in Cloud Environments
Seasonal Autoregressive Integrated Moving Average (SARIMA) models for dynamic cloud resource provisioning are introduced and evaluated in this work. Various cloud-based apps provided historical data to train and evaluate SARIMA models. The SARIMA(1,1,1)(0,1,1)12 model has an MAE of 0.056 and an RMSE of 0.082, indicating excellent prediction ability. This model projected resource needs better than other SARIMA settings. Sample prediction vs. real study showed close congruence between projected and observed resource consumption. MAE improved with hyperparameter adjustment, according to sensitivity analysis. Moreover, SARIMA-based resource allocation improved CPU usage by 12.5%, RAM utilization by 20%, and storage utilization by 21.4%. These data demonstrate SARIMA's ability to forecast cloud resource needs. SARIMA-based resource management might change dynamic cloud resource management systems due to cost reductions and resource usage efficiency. This research helps industry practitioners improve cloud-based service performance and cost. 2023 IEEE. -
Scalability of software defined network on floodlight controller using OFNet
Software Defined Network is the thriving area of research in the realm of networking. With growing number of devices connecting to the global village of internet, it becomes inevitable to adapt to any new technology before testing its scalability in presence of dynamic circumstances. While a lot of research is going on to provide solution to overcome the limitations of the traditional network, it gives a call to research community to test the competence and applicability to hold up the fault tolerance of the solution offered in the form of SDN Controllers. Out of the accessible multiple controllers with enabled the SDN functionalities to the network infrastructure, one of the best choice in controllers is Floodlight Controller. This research article is a contribution towards performance evaluation of scalability of the Floodlight Controller by implementing dual scenarios implemented, experimented and analyzed on the emulation tool of OFNet. Floodlight Controller is tested in the emulation environment by observing eight different parameters of the controller and checked its performance in scalable networking conditions over linear topology by gradually increasing the number of nodes. 2017 IEEE. -
Scalable, Cost Effective IoT Based Medical Oxygen Monitoring System for Resource Constrained Hospital Environment
Oxygen therapy is one of the critical treatments employed in epidemics, pandemics, and natural calamities. Recent covid pandemic worldwide witnessed many deaths due to improper management, delayed delivery, and wastage of medical oxygen. Therefore, efficient utilization of available oxygen is very important. To monitor and manage oxygen, several hospitals employ IoT-based systems. Scalability is an essential feature in such monitoring systems in order to cater to the needs of a sudden surge in the number of patients requiring oxygen. The most commonly employed technique to monitor and manage an oxygen cylinder uses a pressure sensor where scaling up is an issue. Therefore, in this paper, a scalable solution that efficiently measures and monitors the available oxygen in the cylinder is proposed. The approach measures oxygen level using a weight sensor module and raises alerts during critical conditions such as low oxygen level and blockage or leakage of oxygen. The proposed system is a cost-effective, plug-and-play system that aids rapid deployment thereby providing timely care to the patients. Also, it does not require any change in the existing infrastructure making it suitable for a resource-constrained environment. The proposed system supports a web-based dashboard and mobile app that can be remotely accessed. 2022 IEEE. -
Scrutiny In-Utero to recognize Fetal Brain MRI Anomalies
In utero MRI distinguishes fbrain irregularities high precisely compared to ultrasonography as well as gives extra medical data during the pregnancies. fMRI is medically performed to get the knowledge of the brain in conditions where the inconsistency are perceived with the help of pre-birth sonography. These are common regularly solidify ventriculomegaly, not regular of the corpus callosum, and oddities of the back fossa. Fbrain inconsistencies can cause authentic brain hurt. Therefore, it is vital to recognize them from the get-go in their course so treatment can be managed to the mother, if conceivable. The job of imaging is to decide the presence, assuming any, and the degree of brain harm in the contaminated hatchling. Even though MRI is most generally utilized as a subordinate to sonography when clinical doubt is high in the setting of a typical ultrasound or to all the more likely characterize irregularities recognized by ultrasound, MRI is regularly utilized in toxoplasmosis seroconversion to conclusively preclude brain injuries, in any event, when the ultrasound examination is viewed as ordinary. X-ray is likewise utilized sequentially all through the pregnancy to check for the improvement of brain anomalies; clinical treatment brings about the astounding clinical result if the brain is typical. Intracranial irregularities are ordinarily speculated discoveries on antenatal US that are needed for assessment which is used by MRI. This audit portrays numerous irregularities imaged as a way to direct clinicians' inappropriate determination. 2021 IEEE. -
Search Engine Optimization for Digital Marketing to Raise the Rank, Traffic, and Usability of the Website
According to the Content Marketing Institute, 93% of online experiences start with search. That is the explanation search. Thats why search promoting is a crucial procedure for all organizations to improve and develop their organizations. At that time the marketers and the clients who paid for advertisements started analyzing SEO and SEM. Web crawler promoting expands the perceivability of sites through SEO or through paid publicizing with the plan of expanding traffic to the site. SEM eludes to all advertising exercises that utilization web index innovation for promoting purposes. These incorporate SEO, paid postings and advertisements, and other web crawler related administrations and capacities that will expand reach and introduction of the site, bringing about more prominent traffic. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Secure Authenticated Communication Via Digital Signature And Clear List In VANETs
Vehicular ad hoc network (VANET) plays a vital role in the intelligent transportation system(ITS), When a vehicle receives a message through network, the CRL (certificate revocation list) checking process will operate before certificate and signature verification. After successful authentication,a CRL list is created based on authentication. This CRL is used to verify whether a vehicle node can be permitted for communication in the VANET network. But when using CRL, a huge amount of storage space and checking time is needed. So we proposed a method without CRL list, but mentions a key management list to overcome large storage space and checking time even it reduce the access delay too. For the access permission we can do an authentication system based digital novel signature authentication(DNSA) for each vehicles in the vanet with the RSU unit or with other participant node vehicles in the communication as per the Topology.So we can perform an efficient and secured communication in VANET. The Electrochemical Society -
Secure Bitcoin Transaction and IoT Device usage in Decentralized Application
In the recent years, there has been a boom in the number of connected devices due to developments in the field of Internet of things. This has also increased the requirements of security specification. The proposed method is introducing a secure information transmission system by using Blockchain technology. Blockchain is a relatively new technology which was introduced by stoshi nakamoto, which was also the basis for developing crypto currency [bitcoin]. Crypto currencies are made transparent and secure using their network architecture, which is a combo of a decentralized and distributed network. In this paper is try to exploit the same methodology used in crypto currencies to develope an IOT network, where the devices can talk to their peers in a secure manner. They explored all the different networks and features of developing a Decentralized application that is named as Dapp. 2018 IEEE. -
Secure Data Processing System Using Decision Tree Architecture
[No abstract available]