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UAV Security Analysis Framework
This study presents a framework that allows for various types of checks to detect weaknesses in UAV subsystems. The UAV testing process is automated and allows the operator only to select the types of checks or types of structural and functional characteristics that the operator wants to test. To ensure the possibility of automated verification, implemented databases are used, which include a catalog of structural characteristics, threats, vulnerabilities, and attacks. These catalogs are many-to-many related, and thanks to these links, it is possible to identify threats or vulnerabilities specific to a particular structural characteristic. In essence, such an architecture is a knowledge base based on an ontological model. Thanks to this architecture of the system, it is enough for the operator to determine what types of structural characteristics need to be checked and the system will give him information about the vulnerabilities of the UAV. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
New Paradigm of Marketing-Financial Integration Modelling for Business Performance: An IMC Model
When it comes to the provision of financial services, the integrated marketing communication (IMC) process is crucial in the creation and maintenance of client-provider bonds. This research presents a literature assessment on the theoretical basis for using marketing communication tools in the provision of financial services. This research is an attempt to bolster the little theoretical literature on the effectiveness of marketing communication techniques in the provision of financial services. Financial service providers use marketing communication as a channel for two-way exchanges with their clientele, with the ultimate goal of maximising the benefits their customers bring to the company. When it comes to providing financial services, an organisations success hinges on its ability to effectively manage its relationships with both current and potential consumers. As a result, it is important for practical reasons to be guided by well-defined marketing communications goals to identify the extent of usage and within the constraints of available resources. In this regard, businesses are free to establish specific communications objectives in accordance with their unique situations to direct the implementation of their IMC plan. This study aims to find out an impact of financial integration with IMC on business performance. This study is descriptive in nature. Primary data is collected with the help of questionnaire. The study finds that the financial integration in the IMC model has a statistically significant impact on business success. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
War Strategy Optimization for Optimal Integration of Public Fast Charging Stations in Radial Feeders
In light of rising pollution and global warming, there is need for raising the acceptance rate of electric vehicles (EVs) across the globe for sustainable and clean transportation. However, low-voltage electrical distribution networks (EDN) are necessary to provide the electrical power needed to charge the EV batteries. Due to their radial construction and high r/x ration branches; these networks typically suffer from significant energy losses, inadequate voltage profiles, and low stability margins. Therefore, the performance of EDNs shouldnt be further compromised by the incorporation of EV charging stations. In light of this, this work presents a unique heuristic war strategy optimization (WSO) for integrating fast charging stations (FCS) as efficiently as possible utilizing the voltage stability index (VSI). The effect of equivalent EV load penetration in EDS is initially evaluated in terms of loss, voltage profile, and voltage stability for a certain number of EVs. Simulations are executed for IEEE 15-bus system for three different scenarios. The technological advantages seen in the simulations illustrate the efficiency of the suggested technique for real-time adaptation. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
A Secure Data Encryption Mechanism in Cloud Using Elliptic Curve Cryptography
Cloud computing is undergoing continuous evolution and is widely regarded as the next generation architecture for computing. Cloud computing technology allows users to store their data and applications on a remote server infrastructure known as the cloud. Cloud service providers, such Amazon, Rackspace, VMware, iCloud, Dropbox, Google's Application, and Microsoft Azure, provide customers the opportunity to create and deploy their own applications inside a cloud-based environment. These providers also grant users the ability to access and use these applications from any location worldwide. The subject of security poses significant challenges in contemporary times. The primary objective of cloud security is to establish a sense of confidence between cloud service providers and data owners inside the cloud environment. The cloud service provider is responsible for ensuring user data's security and integrity. Therefore, the use of several encryption techniques may effectively ensure cloud security. Data encryption is a commonly used procedure utilised to ensure the security of data. This study analyses the Elliptic Curve Cryptography method, focusing on its implementation in the context of encryption and digital signature processes. The objective is to enhance the security of cloud applications. Elliptic curve cryptography is a very effective and robust encryption system due to its ability to provide reduced key sizes, decreased CPU time requirements, and lower memory utilisation. 2024 IEEE. -
Digital Watermarking Techniques for Secure Image Distribution
In the contemporary era of digital advancements, it is of utmost importance to prioritize the establishment of robust security measures and traceability protocols for photos. This necessity arises from the inherent risk associated with the effortless diffusion of unlicensed information. Digital watermarking, which implants hidden data into digital photographs to verify their validity, is frequently used. This level emphasizes the need of safe photo distribution, digital platform problems, and unauthorized reproductions. The purpose of this research is to explain digital watermarking fundamentals. It emphasizes verification, IP protection, and digital watermarking monitoring. This research compares spatial and frequency domain watermarking approaches. Direct pixel manipulation in spatial domain techniques is vulnerable to attacks. Integrating watermarks with transform domains like Discrete Cosine Transform improves robustness in frequency domain techniques. The study also studies adaptive watermarking, which adjusts the watermark to the image's content to balance visibility and durability. The purpose of this research is to explore watermark identification methods. These methods use blind and non-blind watermarking. We discuss the security risks that might compromise watermarked photographs and the ways to reduce their likelihood. 2024 IEEE. -
A Deep Learning Method for Autism Spectrum Disorder
The present study uses deep learning methods to detect autism spectrum disorder (ASD) in patients from global multi-site database Autism Brain Imaging Data Exchange (ABIDE) based on brain activity patterns. ASD is a neurological condition marked by repetitive behaviours and social difficulties. A deep learning-based approach using transfer learning for automatic detection of ASD is proposed in this study, which uses characteristics retrieved from the intracranial brain volume and corpus callosum from the ABIDE data set. T1-weighted MRI scans provide information on the intracranial brain volume and corpus callosum. ASD is detected using VGG-16 based on transfer learning. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Advancing Credit Card Fraud Detection Through Explainable Machine Learning Methods
The world of finance has experienced a significant shift in the way money flows, due to the advancements in technologies such as online banking, card payments, and QR-based payment systems. These innovative banking payment facilities are offered by ensuring the safety of the transaction and ensuring that only the authorized customer can access and utilize these banking services. Credit card fraud is innovative way to cheat the user of the card. Government all over the word encouraging to the people for the uses of digital money. This research work focuses on analyzing the machine learning database by using a labelled dataset to classify legitimate and fraudulent business transactions with explainable AI. This study is based on decision tree, logistic regression, support vector machine and random forest machine learning techniques. 2024 IEEE. -
Integrating AI Tools into HRM to Promote Green HRM Practices
The image of Human Resource Management (HRM) is undergoing a drastic transformation. The conventional methods are evolving due to the emergence of technology, especially with the integration of Artificial Intelligence (AI) and data analytics into the HR processes. With the rapidly changing concept of the overall growth of an organization, AI is becoming a vital stimulant for sustainable growth. AI-powered tools promote data-driven decision-making for talent acquisition, performance management, workforce training and development, optimization of energy consumption and waste reduction. Green HRM aligns these efforts by integrating sustainability considerations into talent management strategies, nurturing employees eco-engagement, and promoting environmentally responsible practices within the workforce. This research paper aims to explore the synergies between AI tools and Green HRM practices, investigating how the integration of AI technologies into HR processes can contribute to the promotion of environmental sustainability. By examining real-world case studies, this study aims to investigate the potential of AI-powered solutions in shaping the future of HRM through the lens of sustainability. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
An Effective BiLSTM-CRF Based Approach to Predict Student Achievement: An Experimental Evaluation
Currently, massive volumes of data are accumulated in databases when people configure new requirements and services. Data mining techniques and intelligent systems are emerging for managing large amounts of data and extracting actionable insights for policy development. As digital technology has grown, it has naturally become intertwined with e-learning practices. In order to facilitate communication between instructors and a diverse student body located all over the world, distance learning programs rely on Learning Management Systems (LMSs). Colleges can better accommodate their students' individual needs by using and analyzing interaction data that reveals variances in their learning progress. Predicting pupils' success or failure is a breeze with the help of learning analytics tools. Better learning outcomes might be achieved through early prediction leading to swift focused action. Preprocessing, feature selection, and model training are the three components of the proposed method. Data cleansing, data transformation, and data reduction are the preprocessing steps used here. It used a CFS to enable feature selection. This study has used a BiLSTM-CRF hybrid approach to train the model. When compared to tried-and-true techniques like CNN and CRF, the proposed method performs effectively. 2024 IEEE. -
Optimizing Portfolio for Highly Funded Industries Within Budget Constraints for the Period of 20232024
This research paper aims to analyze and optimize portfolios for the top funded industries based on the budget23. The study uses a data-driven approach to identify the best investment opportunities within these industries. The methodology involves collecting financial data, conducting market analysis, and using optimization techniques to create an optimal portfolio. The results of the study show that the top funded industries have a high potential for growth, and the optimized portfolios can maximize returns while minimizing risk. The findings can provide valuable insights for investors and fund managers who are seeking to make informed investment decisions in these industries. The study also highlights the importance of considering the budget constraints while optimizing portfolios. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
A Novel Two-Step Bayesian Hyperparameter Optimization Strategy for DoS Attack Detection in IoT
Variations of Hyperparameter in Machine Learning (ML) algorithm effectively strikes the model's performance in terms of accuracy, loss, F1 score and many others. In the current study a two-step hyperparameter optimization approach is represented to analyse selected ML models' performance in detecting specific Denial of Service attacks in IoT. These attacks are Synchronization Flooding Attack at Transport layer, DIS Flooding attack and Sinkhole attack at Network layer. The two-step approach is a combination of Manual Hyperparameter tuning followed by Bayesian Optimization technique. The first stage manually analyses the hyperparameters of ML algorithms by considering the nature of the attack datasets. This technique is quite rigorous as it demands thorough analysis of the dependencies of the nature of datasets with hyperparameter types. At the same time this process is time consuming. The output of the first stage is the ranges of independent hyperparameter values that give maximum accuracy (minimum error rate). In the next stage Bayesian Hyperparameter tuning is used to specifically derive the single set of all hyperparameters values that give optimized accuracy faster than the BO. The input to the second stage is the ranges of individual hyperparameters that gave maximum accuracy in the first stage. The efficiency of the approach is depicted by comparative analysis of training time between the proposed and existing BO. NetSim simulator is used for generating attack datasets and Python packages are used for executing the two-step approach. 2024 IEEE. -
Pattern Recognition: An Outline of Literature Review that Taps into Machine Learning to Achieve Sustainable Development Goals
The sustainable development goals (SDGs) as specified by the United Nations are a blueprint to make the Earth to be more sustainable by the year 2030. It envisions member nations fighting climate change, achieving gender equality, quality education for all, and access to quality healthcare among the 17 goals laid out. To achieve these goals by the year 2030, member nations have put special schemes in place for citizens while experimenting with newer ways in which a measurable difference can be made. Countries are tapping into ancient wisdom and harnessing newer technologies that use artificial intelligence and machine learning to make the world more liveable. These newer methods would also lower the cost of implementation and hence would be very useful to governments across the world. Of much interest are the applications of machine learning in getting useful information and deploying solutions gained from such information to achieve the goals set by the United Nations for an imperishable future. One such machine learning technique that can be employed is pattern recognition which has applications in various areas that will help in making the environment sustainable, making technology sustainable, and thus, making the Earth a better place to live in. This paper conducts a review of various literature from journals, news articles, and books and examines the way pattern recognition can help in developing sustainably. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Modelling Networks withAttached Storage Using Perfect Italian Domination
Network-attached storage (NAS) is how data is stored and shared among hosts through a configured network. This is cheaper yet the best solution for sharing and using any huge unstructured data in an organization. Optimal distribution of NAS in a network of servers can be done using the concept of Perfect Italian Domination (PID). PID is a vertex labelling where the vertices of a graph G are labelled by 0, 1, 2 such that a vertex with label 0 should have a neighbourhood where the summation of the labels is exactly 2. The minimum possible sum of the labels obtained for graph G is its PID number. A network in an organization can have any structure. It can be highly interconnected, like a graph obtained from the Join of two graphs or the Corona product of two graphs. Hence, this paper discusses the PID of different graphs generated by the Join and the Corona products. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
A Lesion Feature Engineering Technique Based on Gaussian Mixture Model to Detect Cervical Cancer
Latest innovations in technology and computer science have opened up ample scope for tremendous advances in the healthcare field. Automated diagnosis of various medical problems has benefitted from advances in machine learning and deep learning models. Cancer diagnosis, prognosis prediction and classification have been the focus of an immense amount of research and development in intelligent systems. One of the major concerns of health and the reason for mortality in women is cervical cancer. It is the fourth most common cancer in women, as well as one of the top reasons of mortality in developing countries. Cervical cancer can be treated completely if it is diagnosed in its early stages. The acetowhite lesions are the critical informative features of the cervix. The current study proposes a novel feature engineering strategy called lesion feature extraction (LFE) followed by a lesion recognition algorithm (LRA) developed using a deep learning strategy embedded with a Gaussian mixture model with expectation maximum (EM) algorithm. The model performed with an accuracy of 0.943, sensitivity of 0.921 and specificity of 0.891. The proposed method will enable early, accurate diagnosis of cervical cancer. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Certificate Generation and Validation Using Blockchain
Verifying academic credentials is a standard procedure for employers when making job offers. After the interview procedure is complete, the employer takes a long time to supply the offer letter. The employer must have the certificate authenticated by the organization that issued it to confirm its originality. While confirming the authenticity of a certificate, the employer takes a long time. The selection procedure takes longer overall because of the long process involved in certificate verification. Blockchain offers a verified distributed ledger with a cryptography technique to combat academic certificate forgery to address this issue. The blockchain also offers a standard platform for document storage, access, and minimization of verification time. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Hybrid sparse and block-based compressive sensing algorithm for industry based applications
Image reconstructions are a challenging task in MRI images. The performance of the MRI image can be measure by following parameters like mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). Compromising the above parameters and reconstructing the MRI image leads to false diagnosing. To avoid the false diagnosis, we have combined sparse based compressive sensing and block-based compressive sensing algorithm, and we introduced the hybrid sparse and block-based compressive sensing algorithm (HSBCS). In compressive stage, however, image reconstruction performance is decreased, hence, in the image reconstruction module, we have introduced convex relaxation algorithm. This proposed algorithm is obtained by relaxing some of the constraints of the original problem and meanwhile extending the objective function to the larger space. The performance is compared with the existing algorithm, block-based compressive sensing algorithm (BCS), BCS based on discrete wavelet transform (DWT), and sparse based compress-sensing algorithm (SCS). The experimentation is carried out using BRATS dataset, and the performance of image compression HSBCS evaluated based on SSIM, and PSNR, which attained 56.19 dB, and 0.9812. Copyright 2024 Inderscience Enterprises Ltd. -
Integral Transforms andGeneralized Quotient Space ontheTorus
In this chapter, we discuss one of the recent generalization of Schwartz distributions that has significantly influenced the expansion of various mathematical disciplines. Here, we study the space of generalized quotient on the torus. Different integral transforms are investigated on the space of generalized quotients on the torus BS?(Td). The space BS?(Td) is made of both distributions as well as space of hyperfunctions on the torus. Further, by introducing the relation between the Fourier and other integral transforms, the conditional theorems are proved for generalized quotients on tours. Moreover, we study the convergence structure of delta-convergence on the generalized quotient space, and an inversion theorem is proved. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Evaluation of Virtual Reality Experiential Dimensions using Sentiment Analysis
Experiential technologies like Virtual Reality (VR) are revitalizing the gaming industries through higher immersive and interactive gaming experiences. The immersive technology has a considerable impact on the industry and will evolve simultaneously as the technology continues to update and improve further. Indian tech cities Bangalore, Delhi NCR, Mumbai, Kolkata, Chennai, Pune, and Hyderabad were chosen for the study and the user-generated content was scraped from the top gaming centers of each city. User Generated Content analysis is gaining immense interest among businesses for devising better decision-making and marketing strategies. The study devised an integrated framework comprised of web data scraping, data cleaning, data pre-processing, AI model designing, sentiment analysis, logistic regression model, and support vector machine model. Logistic Regression predicted the sentiment of the text and the Support Vector Machine classified the VR experiential dimensions and helped in understanding the most important dimension for customer satisfaction. The study has found that VR experiences are gaining positive responses among the customers and illusion emerges as the most significant dimension for their satisfaction. 2024 IEEE. -
ICT Policy Reforms for Innovation and Economic Development: A Comparative Study of India and China
The widespread adoption of Information and Communication Technologies (ICTs) has become essential for economic and social growth across the world. This paper aims to examine the impact of ICT policies and reforms on the level of economic development and adoption of ICTs in two countries, India and China. Previous studies have shown the positive impact of ICT adoption on economic growth, productivity, and innovation. However, the effectiveness of specific policy measures in promoting ICT adoption and economic development remains ambiguous to the users of ICT. This paper presents a comparative analysis of the ICT policies and reforms implemented in India and China from 2010 to 2021 and their impact on GDP per capita and internet usage. The study aims to identify and analyze the key ICT policies and reforms implemented in the two countries and examine their impact on economic development. The data for this study have been collected from the World Bank indicators database. The sample consists of the two fastest-growing economies in the world, India and China. The data analysis involves conducting descriptive statistics, correlation, and regression analysis to examine the relationship between ICT policies and reforms and their impact on GDP per capita, internet usage, and research and development expenditure. The findings of this study will contribute to the existing literature on the relationship between ICTs and economic development and provide insights into the policy measures that can promote ICT adoption and economic growth in different contexts. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Multimodal Early Fusion Strategy Based on Deep Learning Methods for Cervical Cancer Identification
It is essential to enhance the accuracy of automatic cervical cancer diagnosis by combining multiple forms of information obtained from a patients primary examination. However, existing multimodal systems are not very effective in detecting correlations between different types of data, leading to low sensitivity but high specificity. This study introduces a deep learning system for automatic diagnosis of cervical cancer by incorporating multiple sources of data. First, a convolutional neural network (CNN) to transform the image database to a vector that can be combined with non-image datasets is used. Subsequently, an investigation of jointly the nonlinear connections between all image and non-image data in a deep neural network is performed. Proposed deep learning-based method creates a unified system that takes advantage of both image and non-image data. It achieves an impressive 89.32% sensitivity at 91.6% specificity when diagnosing cervical intraepithelial neoplasia on a wide-ranging dataset. This result is far superior to any single-source system or prior multimodal approaches. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.