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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. -
Hybrid Model Using Interacted-ARIMA andANN Models forEfficient Forecasting
When two models applied to the same dataset produce two different sets of forecasts, it is a good practice to combine the forecasts rather than using the better one and discarding the other. Alternatively, the models can also be combined to have a hybrid model to obtain better forecasts than the individual forecasts. In this paper, an efficient hybrid model with interacted ARIMA (INTARIMA) and ANN models is proposed for forecasting. Whenever interactions among the lagged variables exist, the INTARIMA model performs better than the traditional ARIMA model. This is validated through simulation studies. The proposed hybrid model combines forecasts obtained through the INTARIMA model from the dataset, and those through the ANN model from the residuals of INTARIMA, and produces better forecasts than the individual models. The quality of the forecasts is evaluated using three error metrics viz., Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Empirical results from the application of the proposed model on the real dataset - lynx - suggest that the proposed hybrid model gives superior forecasts than either of the individual models when applied separately. The methodology is replicable to any dataset having interactions among the lagged variables.. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
5G Technology Empowering Wireless Technology
Wireless Communication is the means of transferring data from one point to another without the use of any wired means. With reference to wireless communication, wireless sensor Networks (WSN) have also developed in recent times. It can be referred as an infrastructure-less system of wireless devices which can gather and exchange information with the help of a wireless link. The information which is gathered is sent respectively to the base stations and sinks for further developments. Recently, the 5G generation network, the latest Wireless Communication Network operates at a higher frequency range than its predecessor. In this paper, a detailed analysis on the 5G generation cellular network, which is expected to be a key instrument of wireless technologies in the near future is outlined. Also a comparative analysis of different kinds of networks in context to wireless scenario is discussed. It was found that 5G provides the best outcome in terms of high speed and network spectrum bandwidth. 2023 IEEE. -
Recent trends in the electrochemical sensors on ?- and calcium channel blockers for hypertension and angina pectoris: A comprehensive review
Stress, ingrained human behaviors, an inactive lifestyle, and poor dietary decisions are the primary causes of hypertension and the related coronary artery disease (CAD), which is also commonly referred to as angina pectoris. Effective high blood pressure (BP) treatment represents a substantial approach to reducing the burden of hypertension-related cardiovascular and renal diseases. A group of drugs known as ?-blockers and calcium channel blockers (CCBs) are frequently used to treat diseases like hypertension (high blood pressure), cardiac arrhythmias and heart failure. For efficient therapeutic use and to reduce potential side effects, ?-blocker concentration monitoring is essential. Chromatographic techniques are employed in a wide range to detect ?-blockers and CCBs without interference, among other analytical methods that have been described. For the detection of ?-blockers and CCBs, electrochemical sensors provide numerous benefits including sensitivity, selectivity, rapidity, and cost-effectiveness. These sensors can help with patient monitoring in clinical settings, ensuring that the prescription ?-blocker dosage is within the therapeutic range. Since ?-blockers are frequently consumed by people, the contamination can be occurred through discharge of wastewater. The presence and measurement of ?-blockers in water samples enables researchers to evaluate potential risks to aquatic life and public health. In this regard, this review addresses recently developed electrochemical (voltammetric) methodologies and measurement protocols for the determination of both ?-blockers and CCBs in pharmaceuticals, biological fluids, and environmental samples. Additionally, this review also provides an overview of the various advanced nanomaterials such as carbon nanotubes, graphene oxide, metal and metal oxide nanoparticles, polymeric structures, zeolite materials, ionic liquids, perovskite semiconductor-based materials, MXenes, Quantum dots, Nano MIPs and various dimensional materials applied to fabricate chemically modified electrodes/electrochemical sensors to determine the ?-blockers and CCBs. Moreover supplied are tables listing the analyte, modified electrode, measurement method, measuring medium pH, linear detection range (LDR), limit of detection (LOD) and sensitivity as they are cited in the original research. Furthermore, important conclusions are made from the published reports in the last decade and some future perspectives are also suggested. 2023 Elsevier B.V. -
New frontiers in polyphenol analysis: A review of electrochemical sensors and commercial devices enhancing food and beverage analysis
Food safety concerns arise from outbreaks of foodborne illnesses and contamination within the food supply. Polyphenols, naturally occurring compounds in plants, are characterized by multiple phenolic (hydroxyl) groups and are prevalent in fruits, vegetables, tea, coffee, and wine. While beneficial in moderation, excessive polyphenol intake is harmful, and they classified as secondary pollutants in environment. Therefore, accurate quantification of polyphenols is essential for ensuring product safety, quality, and nutritional value, which is the focus of this review. Electrochemical sensors offer a sensitive, selective, and cost-effective method for detecting polyphenols in food and beverages. The review examines advanced voltammetric techniques for identifying polyphenols in various food samples, including beverages and dietary products. Additionally, total antioxidant capacity (TAC) sensors are highlighted as valuable tools for assessing the antioxidant potential of foods, aiding in nutritional analysis and quality control. This review, for the first time, catalogs around ten commercially available devices and twenty assay kits for detecting antioxidant polyphenols, highlighting their significance in advancing food safety, bolstering consumer confidence, and supporting ongoing nutritional research. Additionally, made efforts to bridge a crucial gap between conventional research and industry needs by expanding the existing body of knowledge and providing fresh insights into polyphenol analysis. 2025 Elsevier Inc. -
A Compartmental Mathematical Model of Novel Coronavirus-19 Transmission Dynamics
The COVID-19 pandemic has spread quickly throughout the world, posing a serious threat to human-to-human transmission. The novel coronavirus pandemic is described quantitatively in this paper using a mathematical model of COVID-19 driven by a system of ordinary differential equations. The suggested model is used to provide predictions regarding the behavior of a COVID-19 outbreak over a shorter time frame. It is demonstrated that the system of model equations has a unique and existing solution. Furthermore, the answer is positive and bounded. Thus, it is argued that the model created and discussed in this work is both mathematically and biologically sound. A threshold parameter that controls the disease transmission is used in a qualitative analysis of the model to confirm the existence and stability of disease-free and endemic equilibrium points. Additionally, the key parameters undergo sensitivity analysis to ascertain their relative significance and potential influence on the COVID-19 virus dynamics. 2024 NSP Natural Sciences Publishing Cor. -
Initial Public Offerings (IPOs) Performance: Bibliometric Analysis of Scholarly Articles on Short-Run Performance
Initial Public offerings are used by the companies to raise capital from the public and to enter in to the public markets. To understand the concept of short-run performance and long-run performance of the company is essential for the investors, regulators and market analysts to evaluate market efficiency. there are many researches were conducted on IPO from the year 1991 to till date, shows the importance of the IPOs. This paper conducted a bibliometric analysis on IPO performance landscape. The data were collected using the Dimensions data base. The articles so collected were from the period 1991 to 2024. Total of 126 articles were identified and considered for the current research. The extracted database was analyzed using VOSview software. Using the software, the current research identified the key authors in the field of IPO research, their organizations, countrywide research contributed in the field of IPO performance both short-run and long-run, especially short-run. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Unveiling the Dynamics of Initial Public Offerings: A Comprehensive Review of IPO Pricing, Performance, and Market Trends
Initial Public Offerings (IPOs) serve as pivotal moments in the financial markets, representing a company's transition from private to public ownership. The importance of IPOs lies in their capacity to raise substantial capital, facilitating business expansion and development. This paper conducts an in-depth analysis of Initial Public Offerings (IPOs) in India spanning the period from 2018 to 2022, with a particular focus on their listing day performance. The study categorizes IPOs into various issue price ranges, revealing substantial variability in listing day returns across these categories. It underscores the importance of pricing strategy, emphasizing the need for companies to carefully assess their issue prices to align with market demand. Furthermore, the analysis highlights the varying levels of risk associated with IPO investments based on issue price ranges, advocating for diversification and thorough due diligence. In addition, the paper emphasizes the dynamic nature of IPO markets, influenced by factors beyond pricing, and encourages a balanced approach that considers both potential rewards and challenges. This research provides valuable insights for stakeholders, guiding companies, investors, and analysts in making informed decisions in the dynamic world of IPOs. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Enhancing mobility management in 5G networks using deep residual LSTM model
Mobility management is an essential component of 5G networks to provide mobile users with seamless connectivity and efficient cell transition. However, increasing user mobility, device density, and the diversity of service requirements all pose significant challenges to achieving optimal mobility management. This article describes a novel method for improving mobility management in 5G networks that employs a deep residual Long Short-Term Memory model. Deep learning and LSTM, a type of recurrent neural network, are used in the proposed model to identify temporal dependencies and patterns in user mobility data. The model learns to predict future user locations and mobility patterns by training on historical mobility data, allowing for proactive resource allocation and handover decisions. We incorporate residual connections into the LSTM architecture, inspired by the residual learning framework, to address the inability of traditional LSTM models to capture complex temporal dynamics. This allows the model to effectively incorporate long-term dependencies and improves prediction accuracy. Furthermore, we incorporate the mLSTM model into the mobility management framework of 5G networks. The model continuously obtains real-time user location updates and predicts future user positions, allowing for proactive handover decisions. The network can optimize resource allocation, reduce handover latency, and improve user experience by leveraging anticipated mobility patterns. We test the proposed method by simulating it extensively with real-world mobility traces. The results show that the mLSTM model accurately predicts user mobility and outperforms conventional methods in transition performance. The model is not affected by changing network conditions, user mobility patterns, or service specifications. 2024 Elsevier B.V. -
Citizen data in distributed computing environments: Privacy and protection mechanisms
Data security is paramount in the increasingly connected world. Securing data, while in transit and rest, and while under usage, is essentialfor deriving actionable insights out of data heaps. Incorrect or wrong data can lead to incorrect decisions. So, the confidentiality and integrity of data have to be guaranteed through a host of technology-inspired security solutions. Organizational data is kept confidentially by the businesses and governments, often in distant locations (e.g., in cloud environments), though more sensitive data is normally kept in house. As the security mechanisms are getting more sophisticated, cyber security attacks are also becoming more intensive, so there is a constant battle between the organisations and the hackers to be one step ahead of the other. In this chapter, the aim is to discuss various mechanisms of accomplishing citizens ' data confidentiality and privacy and to present solution approaches for ensuring impenetrable security for personal data. 2021 by IGI Global. All rights reserved. -
Load balancing with availability checker and load reporters (LB-ACLRs) for improved performance in distributed systems
Distributed system has quite a lot of servers to attain increased availability of service and for fault tolerance. Balancing the load among these servers is an important task to achieve better performance. There are various hardware and software based load balancing solutions available. However there is always an overhead on Servers and the Load Balancer while communicating with each other and sharing their availability and the current load status information. Load balancer is always busy in listening to clients' request and redirecting them. It also needs to collect the servers' availability status frequently, to keep itself up-to-date. Servers are busy in not only providing service to clients but also sharing their current load information with load balancing algorithms. In this paper we have proposed and discussed the concept and system model for software based load balancer along with Availability-Checker and Load Reporters (LB-ACLRs) which reduces the overhead on server and the load balancer. We have also described the architectural components with their roles and responsibilities. We have presented a detailed analysis to show how our proposed Availability Checker significantly increases the performance of the system. 2014 IEEE. -
A Secure Deep Q-Reinforcement Learning Framework for Network Intrusion Detection in IoT-Fog Systems
IoT-Fog system security depends on intrusion detection system (IDS) since the growing number of Internet-of-Things (IoT) devices has increased the attack surface for cyber threats. The dynamic nature of cyberattacks often makes it difficult for traditional IDS techniques to stay up to date. Because it can adapt to changing threat landscapes, deep Q-reinforcement learning (DQRL) has become a potential technique for ID in IoT-Fog situations. In this paper, an IDS system for IoT-Fog networks based on DQRL is proposed. The suggested solution makes use of fog nodes' distributed computing power to provide real-time IDS with excellent accuracy and minimal latency. With feedback from the network environment, the DQRL agent learns to recognize and categorize network traffic patterns as either normal or intrusive. Adaptive exploration techniques, effective reward functions, and deep neural networks for feature extraction are adopted by the system to improve predictive performance. The evaluation findings show that, in terms of detection accuracy, precision, recall and f-measure, the proposed DQRL provides flexibility to changing threat patterns as compared to conventional IDS techniques. A vast array of cyberattacks, such as malware infections, denial-of-service (DoS) attacks, and command-and-control communications, are successfully recognized and categorized by the system. It is possible that the suggested solution will be crucial in safeguarding IoT-Fog networks and preventing cyberattacks 2024 IEEE. -
Experimental Augmentation of Heat Transfer in a Shell and Tube Heat Exchanger using Twisted Tape with baffles and hiTrain Wire Matrix Inserts - A Comparative Study
Heat transfer, a mere process of exchange of heat due to a temperature gradient, plays a vital role in industries and domestic applications. Among all the heat exchangers, Shell and Tube Exchanger are used predominantly due to their compact and robust design. For a given design to increase the heat transfer characteristics needs a research investigation. Among all augmentation techniques, a passive method found widely used as it avoids mechanical modification of the existing heat exchanger and addresses only on flow geometry. Twisted tape inserts are extensively used to change the flow geometry of fluid on the tube side. The present research work intended on utilising twisted tape, twisted tape with baffles and hiTrain wire matrix inserts. Experimental investigation reveals that inserts efficiently disturb the tube side fluid flow, in turn, increases pressure drop which increases the fluid wall shear and hence enhances the substantial increase in tube side heat transfer rate. At lower Reynolds number twisted tape with baffles has comparatively higher heat transfer coefficient, and at higher Reynolds, number hiTrain wire has comparatively higher heat transfer coefficient. Friction factor decreases linearly from twisted tape with baffles to hiTrain wire matrix as Reynolds number increases. Published under licence by IOP Publishing Ltd. -
Blind separation of speech from aortic regurgitation signals using Dhoulaths method
Conducting auscultation of traumatically distressed patients has always been demanding for medical professionals. The challenge calls for an innovative solution enabling doctors to conduct precise diagnoses despite other sound interference. This suggested study presents an entirely non-invasive and convenient method designed to aid doctors in routine diagnostic procedures. This study is centred on the segregation of aortic regurgitation heart sounds from speech. The mixture utilised for the study is a combination of speech and aortic regurgitation signals. The method applied for the study is a revised procedure of Blind Source component separation utilising a solo sensor method. With this technique, doctors are not compelled to prevent patients from articulating their pain or discomfort while diagnosing heart sounds. Doctors can offer a consoling word to patients while the auscultation is in progress without worrying about how the speech sounds affect the diagnosis. For babies, timely detection of heart-related issues can be life-saving. With Dhoulaths method, the distressing sounds of a babys cries can be effectively separated, thereby offering doctors clear audio of heartbeats. The study was conducted to ascertain if heartbeats can be segregated from the signals of speech or cries. This segregation procedure has succeeded in arriving at an enhanced level of clarity. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Industrial Applications of Hybrid Nanocatalysts and Their Green Synthesis
Abstract: The era of industrial revolution has been hugely dependent on a myriad of catalysts. The present era has contributed another dimension to this by the advent of nanocatalysts. The last decades saw even more fine tuning with the use of hybrid nanocatalysts by the integration of a plethora of functionalities into a single nanoparticle. The extremely high surface area, low toxicity, easy recovery and reusability, high product output and possibilities of green synthesis makes hybrid nanocatalysts significant in various fields like bioremediation, fuel cell production, cleaner energy production, dye degradation etc. Metal based hybrid nanocatalysts are highly appealing due to their extremely high surface over volume ratio, entailing unique electronic properties and access to more reaction sites. The recent years have seen more thrust being given to greener modes of synthesis of nanocatalysts, rather than the classical modes (which uses hazardous chemicals), aligning with sustainability goals.The current review is an attempt to explore the myriad uses of magnetic, metal and metal oxide hybrid nanocatalysts and their green synthesis methods. Optimizing the use of hybrid nanocatalysts in various domains would definitely help us achieve the SDGs of the United Nations for a more sustainable life on this planet. Graphical Abstract: [Figure not available: see fulltext.] Highlights: Types of hybrid nanocatalysts have been described. Industrial applications of hybrid nanocatalysis has been summarized. Ways of greener synthesis of hybrid nanocatalysts for environmental sustainability depicted. Advantages and limitations of hybrid nanocatalysts have been evaluated. 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
The Role of Corporations in Achieving Ecological Sustainability: Evaluating the Environmental Performance of Corporations
Industrial development of the past 200 years has brought immeasurable wealth and prosperity. However, it has also caused an unintended eco-logical degradation. Hence, the focus of environmental law has shifted from the creation of global frameworks to deal with environmental problems to comply with those frameworks. As a result, the primary actors in environmental law have shifted from the state and the global community to corporations. As a consequence, environmental policies must develop along legally holistic lines. The role corporations have had in achieving ecological sustainability is poorly understood. In the backdrop of the above issues, the chapter examines the implications of ecologically sustainable development for corporations. It articulates corporate ecological sustainability through the concepts of environmental management and ecologically sustainable competitive strategies. It further examines the implications that these concepts have for a corporation in the long run. 2020 by IGI Global. -
National Education Policy 2020: Equity and inclusion in India's education system
The term "equity" in education refers to justice and fairness in the allocation of educational resources and opportunities. In order to achieve educational equity, it is necessary to remove the structural obstacles that prevent students from realizing their full potential. Obstacles like socioeconomic inequalities, prejudice, and unequal resource distribution often act as barriers to quality education. In this background, the present chapter will critically analyze a few significant opportunities offered by the New Education Policy 2020, such as three language formulas, privatization, NEP financing, special education zones policy implications, and challenges in implementation. Even though the opportunities and milestones offered by NEP 2020 are irrefutable, apprehensions pertaining to its scope and usefulness also exist, questioning the sanguinity of the policy. 2024, IGI Global. All rights reserved. -
The mediating influence of leadership behaviour on the relationship between organizational health and work engagement
Efficient leadership and a healthy teaching environment are the two factors that determine how school teachers conduct themselves professionally. Work engagement not only reflects the teachers' performance but also implies the performance of the pupils and the school and it depends on how congenial their working conditions are. The present study intended to assess the extent to which the leadership behaviour of principals mediated the effect of organizational health on the work engagement of 516 secondary school teachers working in Bengaluru, India. The organizational health inventory was employed to quantify the organizational health of schools at institutional, managerial and technical levels, the Utrecht tool was used to measure teachers' work engagement through their vigor, dedication and absorption, and the leadership behaviour of principals was measured in terms of consideration and initiating behaviour. The findings implied a positive relationship between organizational health and teachers' work engagement. Further, while leadership behaviour indeed mediated the impact of organizational health on work engagement, the mediating effect was only partial. The results imply that the teachers' work engagement cannot be entirely attributed to the school management and working conditions, which implies scope for further research on the factors affecting work engagement among the teachers. 2020 by authors. -
Cognitive outcomes prediction in children using machine learning and big data analytics
This study explores the potential of machine learning (ML) and big data analytics in predicting cognitive outcomes in children, aiming to enhance early identification and intervention strategies. Leveraging a diverse dataset comprising neurocognitive assessments, genetic markers, socio-economic factors, and environmental variables, our research employs advanced ML algorithms to develop predictive models. The interdisciplinary approach integrates neuroscience, psychology, and data science to discern patterns and correlations within the expansive dataset. The study emphasizes the importance of early cognitive assessment for optimal child development and academic success. By harnessing the power of big data, our models seek to uncover nuanced relationships that traditional methodologies may overlook. Preliminary results indicate promising accuracy in predicting cognitive outcomes, offering a valuable tool for educators, healthcare professionals, and policymakers. Additionally, the model's interpretability allows for a deeper understanding of the factors influencing cognitive development. Ethical considerations, privacy safeguards, and data governance are integral components of this research, ensuring responsible use of sensitive information. The implications of this study extend beyond academia, with the potential to inform educational policies, personalized learning strategies, and targeted interventions for at-risk populations. As technological advancements continue, the integration of ML and big data analytics in predicting cognitive outcomes heralds a new era in pediatric research, promoting proactive approaches to support children's cognitive well-being. 2024 IEEE. -
Artificial immune system based frameworks and its application in cyber immune system: A comprehensive review
Computer science has always mixed the concepts of biology and computers to enhance the way in which systems are designed. Artificial Immune System (AIS) is a Computational Intelligence strategy dependent on an organically enlivened computational system that can be utilized for taking care of complex computational issues. It tends to be seen that AIS is an incredibly various locale of research, going from the modeling immune systems to complex algorithms for specific applications. This paper exhibits an exhaustive survey of different frameworks developed in the artificial immune system and its application. Reviews of frameworks in AIS are uncommon and henceforth this paper gives an inside out audit of progressing research and challenges in AIS. We start by presenting AIS and give a thorough survey of different systems in AIS and its application in anomaly detection. We investigate the utilization of AIS in the Intrusion Detection System named the Cyber Immune System(CIS) and compares various AIS works applied to CIS. We conclude with various future extensions in the area of AIS research. 2019 by Advance Scientific Research. This is an open-access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)