Browse Items (14421 total)
Sort by:
-
Handbook of Nutraceuticals: Science, Technology and Engineering
This book explores the complete development cycle of nutraceuticals and nano-nutraceuticals, with particular focus on manufacturing techniques and formulation strategies. It discusses their physicochemical behavior and presents innovative analytical characterization methods. The text also includes a variety of formulation approaches along with pharmacologic and pharmacokinetic evaluations. Several chapters address the controlled delivery of nutraceutical components, and the use of natural and biodegradable polymers in delivery systems is thoroughly reviewed. In vitro evaluation techniques for assessing nutrient delivery effectiveness are covered in detail, along with discussions on bioavailability, food additives, and encapsulation technologies. A dedicated chapter on the future of controlled-release technologies rounds out the volume. Springer Nature Switzerland AG 2026. -
Quantum Network Attacks in Urban IoT Infrastructures
With urban spaces increasingly networked via the internet of things (IoT), the advent of quantum computing brings with it both potential and major security threats. Quantum network attacks are a serious risk to urban IoT systems by targeting weaknesses in conventional cryptographic protocols. In contrast to classical cyber threats, quantum-powered attacks can decrypt commonly used encryption schemes, leaving vital smart city networks, such as traffic management systems, power grids, and public surveillance, vulnerable. The emergent nature of quantum computing calls for an active response to securing urban IoT systems from prospective vulnerabilities that can impair critical services and undermine public security. The inherent complexity of urban IoT infrastructures renders them extremely susceptible to quantum-based cyberattacks. Quantum decryption, man-in-the-middle attacks, and quantum-boosted malware pose risks that can expose sensitive information and facilitate large-scale cyber intrusions. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Reconfigurable Intelligent Surface-Aided Physical Layer Security Techniques: Applications and Future Trends
Reconfigurable Intelligent Surfaces (RIS) are emerging as a groundbreaking technology in the realm of wireless communications, with significant implications for enhancing physical layer security. This chapter delves into the integration of RIS with advanced security techniques, exploring how this innovative technology can be harnessed to address the growing challenges of securing wireless networks. this chapter delves into the future trends and advancements in RIS technology, including next-generation RIS architectures and their potential integration with emerging technologies like 6G. It explores how RIS could pave the way for innovative security protocols and play a pivotal role in advancing secure wireless network infrastructures. RIS technology enables the dynamic and intelligent modification of radio environments through programmable surfaces, which can adjust and optimize signal paths to improve both communication efficiency and security. This chapter provides valuable insights into the current applications and future prospects of RIS in enhancing wireless network security. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Leveraging social media and natural language processing for early detection of depressive disorders
Depression is a prevalent mental health disorder impacting over 280 million people worldwide, according to recent World Health Organization (WHO) estimates. It poses a substantial burden on individuals and societies, emphasizing the need for early detection and timely intervention. Despite the availability of treatment options, many affected individuals do not seek professional help due to barriers such as stigma, lack of awareness, and insufficient access to mental health services. With the widespread adoption of social media, people increasingly share their thoughts, feelings, and daily experiences online, providing an abundant source of user-generated content. This information can be harnessed to detect early signs of depression. In recent years, advancements in Natural Language Processing (NLP) and Machine Learning (ML) have paved the way for innovative approaches to analyzing social media data for mental health insights. By processing text-based content from platforms such as Twitter, Facebook, and Reddit, NLP techniques can identify linguistic patterns. 2025 by IGI Global Scientific Publishing. All rights reserved. -
AI-Powered Threat Detection and Response for Future 6G Networks
As 6G networks come into existence, there will be ultra-low latency, high bandwidth, seamless integration of billions of devices, and a revolution of connectivity. But with these great strides, new challenges about the securing of these systems against very sophisticated cyber attacks have come up. This book discusses how AI can benefit the 6G system to detect and respond threats better. Through the utilization of AI algorithms, machine learning, and deep learning, 6G networks will autonomously identify and mitigate security risks in real time while adapting to dynamic and everevolving attack vectors. AI systems can monitor network traffic in real-time, analyze anomalies, and predict possible vulnerabilities before they are exploited, hence reducing the detection-to-mitigation cycle. As 6G networks become more complex and pervasive, AI will become an indispensable component in maintaining security and enabling a trustworthy digital ecosystem, thereby becoming a core component of the future network defense. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Traffic management and congestion control in vehicular adhoc networks
Urban traffic congestion is a growing concern worldwide. Vehicular Adhoc Networks (VANETs) offer a glimpse into a future with smoother traffic flow and reduced congestion. These networks enable real- time communication between vehicles and infrastructure, creating a dynamic traffic management system. Imagine traffic signals that adjust based on real- time data, congestion being predicted and alleviated before it builds, and emergency services receiving faster response times. This is the potential of VANETs. Ensuring reliable communication and data integrity among constantly moving vehicles is crucial. Researchers are developing protocols and algorithms to address this, focusing on efficient routing, data dissemination, and network stability. The integration of emerging technologies like 5G, edge computing, and artificial intelligence holds promise for further enhancing network performance and robustness. While significant progress has been made, widespread adoption of VANETs faces hurdles. Scalability, security, privacy, and infrastructure development costs are significant concerns. 2025, IGI Global Scientific Publishing. All rights reserved. -
AI Solutions for Complex Communication Network Challenges
As communication networks balloon in size and complexity, managing them effectively becomes a monumental task. This chapter explores how Artificial Intelligence (AI) offers a powerful toolkit for tackling the intricate challenges faced by these systems. By leveraging machine learning, deep learning, and neural networks, AI can significantly enhance network performance, optimize resource allocation, and bolster security. Outlining the major hurdles plaguing modern communication networks, such as scalability limitations, latency issues, congestion bottlenecks, and ever- evolving cybersecurity threats. The chapter also acknowledges the ethical considerations and potential risks associated with AI deployment, emphasizing the need for responsible practices. Ultimately, this chapter provides a comprehensive perspective on how AI can become the cornerstone of resilient and efficient communication networks, paving the way for future advancements in this critical field. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Reconfigurable Intelligent Surface for mMIMO and NOMA Networks: Applications and Research Challenges
Reconfigurable Intelligent Surfaces are emerging as a transformative technology in wireless communication, particularly in the context of massive Multiple-Input Multiple-Output (mMIMO) systems and Non-Orthogonal Multiple Access (NOMA) networks. This chapter provides an in-depth exploration of how RIS can enhance the performance of mMIMO and NOMA networks, focusing on both practical applications and research challenges. RIS technology enables dynamic control over the wireless environment by adjusting signal reflections and enhancing signal propagation, which can significantly improve the efficiency and effectiveness of mMIMO and NOMA systems. For mMIMO, RIS can optimize spatial beamforming and mitigate interference, leading to enhanced capacity and coverage. In NOMA networks, RIS can This chapter offers a comprehensive overview of the potential of RIS to revolutionize mMIMO and NOMA networks, while also addressing the critical research challenges that must be overcome to fully realize its benefits. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Improved Random Forest Algorithm for Cognitive Radio Networks' Distributed Channel and Resource Allocation Performance
Modified Random Forest (MRF) machine learning algorithm aimed at improving the distributed channel allocation and resource allocation performance in cognitive radio networks (CRNs). The purpose of this research is to enhance the efficiency and effectiveness of CRNs by optimizing the allocation of channels and resources. The proposed MRF algorithm is developed by adapting and modifying the random forest technique to address the specific challenges of CRN allocation. Experimental evaluations demonstrate that the MRF algorithm achieves higher accuracy and efficiency compared to existing routing techniques and channel allocation algorithms like ACO and PSO. It exhibits a high packet delivery ratio, increased throughput, and reduced delay in channel selection, thus improving the overall performance of CRNs.The implications of this research are twofold. On a theoretical level, this study contributes to the field by extending the capabilities of the random forest algorithm and adapting it to the domain of CRNs. The modified algorithm demonstrates the potential of machine learning techniques in addressing allocation challenges in wireless communication systems. The findings emphasize the importance of advanced algorithms in improving the efficiency and effectiveness of channel and resource allocation processes. 2023, Success Culture Press. All rights reserved. -
Stiffness of a single layered cable assembly over a sheave with internal friction
The stiffness response of a single layered helical strand with a straight core surrounded by a layer of six helical wires has been made with improved relations of wire curvatures & twist and with internal friction considerations. The stranded cable undergoes a constant curvature bending over a sheave/pulley under static loading conditions and experiences the combinations of tension, torsion and bending loadings. A new analytical model has been developed for the cable in contact with the pulley/sheave using thin rod theory under linear elastic conditions. The stiffness coefficients of the cable are evaluated in free bending and constrained bending modes. The resulting wire strains are evaluated and compared with the experimental results. IAEME Publication -
Lung Cancer Detecting using Radiomics Features and Machine Learning Algorithm
Lung Cancer Incidence across the globe is the second leading cancer type tallying to about 2,206,771 during 2020 and is estimated to rise to about 3,503,378 by 2040 for both male and female sexes and for all ages accounting to 11.4% as per Globocan 2020 [1]. It is the leading death-causing cancer. Lung Cancer [2] in broad terms encompasses Trachea, bronchus as well as lungs. Purpose: The study is aimed to understand Radiomics based approach in the identification as well as classification of CT Images with Lung Cancer when Machine Learning (ML) algorithms are applied. Method: CT Image from LIDC-IDRI [4] Dataset has been chosen. CT Image Dataset was balanced and image features by PyRadiomics library were collected. Various ML features classification algorithms are utilized to create models and matrices adopted in judging their accuracies. The models, distinctive capacity is assessed by receiver operating characteristics (ROC) analysis. Result: The Accuracy scores and ROC-AUC values obtained for various Classification Model are as follows, for Ada Boosting, the accuracy score was 0.9993 ROC-AUC was 0.9993 and followed by GBM, the accuracy score was 0.9993, was 0.9992. Conclusion: Extracting texture parameters on CT images as well as linking the Radiomics method with ML would categorize Lung Cancer commendably. 2023 IEEE. -
Testing of long run association between crude oil and gold commodities: An empirical study in India /
Test engineering & Management, Vol.82, pp.2902-2906, ISSN No: 0193-4120. -
Corporate diversification and firms financial performance: an empirical evidence from Indian IT sector
The aim of this research paper is to provide empirical evidence on the effect of geographic and segment diversification on the financial performance of the Indian IT sector. The study was done on 12 listed IT firms representing 93% market share on BSE/NSE. Standard econometric regression analysis on panel data was carried out to find the stated relationship. The results of the regression analysis revealed that international/geographic diversification impacted strongly on IT firms profitability whereas product/segment diversification had no significant impact on the firms profitability. This study also proves the existence of demand for Indian IT sector in other countries. These results could be useful in decision making for top managers of IT companies as they advocate the need for diversification (specialisation) and growth in size and also provide encouragement to small-scale Indian IT companies to undertake international diversification activities with confidence. Copyright 2023 Inderscience Enterprises Ltd. -
Kakkot List- An Improved Variant of Skip List
Kakkot list is a new data structure used for quick searching in a well ordered sequence of list like Skip list. This ordered sequence of list is created using linked list data structure and the maximum number of levels here will be limited to log n in all input behavioral cases. The maximum number of items in each level is halved to that of previous levels and thus guarantees a fast searching in a list. The basic difference between Kakkot list and Skip list lies in the creation of levels and decision of when an item has to be included in the higher levels. In skip list the levels are created and items are added to each level during the insertion of an item where as in Kakkot list this will be done at the time of searching an item. This modification have made drastic impact in searching time complexity in the Kakkot list. Another issue in Skip list is that it is not cache friendly and does not optimize locality of reference wherein this problem is also addressed in Kakkot List. 2020 IEEE. -
Impact of macroeconomic variables on the prices of gold /
Journal of Emerging Technologies And Innovative, Vol.6, Issue 2, pp.569-576, ISSN No: 2349-5162. -
The quantum key distribution, attenuation and data loss over foggy, misty and humid environment
The quantum encryption is a method of key transfer in cryptography by using quantum entanglement of photons. The real power of quantum entanglement is instantaneous communication that is non interceptable. The advantage of quantum encryption method is, it can be incorporated with conventional encryption methods safely. The quantum cryptography can replace conventional key exchange mechanism with the polarized photons using channels like optic fiber cables. Quantum cryptographic can also provide far and secure data communication. The present day experiments clearly proved that the quantum cryptography can be implemented through medium like optic fiber cable or air. But the distance of transmission through the air is limited by rule of line of sight propagation. The quantum key distribution will have uses in different types of communication between distant parts of earth. So this paper discussing various aspects of Quantum key distribution and successfully calculated polarized photon loss during transmission of Quantum cryptography link, while using in various type of atmospheric conditions like Mist Fog Haze. Also successfully calculated probability of single polarized photon missing by successfully utilizing the Light transmission characteristics and power measurements in various Atmospheric conditions. 2019, Institute of Advanced Scientific Research, Inc.. All rights reserved. -
A new assessment of quantum key distribution, attenuation and data loss over foggy, misty and humid environment
Quantum encryption is a method of key transfer in cryptography by using quantum entanglement of photons. The real power of quantum entanglement is instantaneous communication that is non intercept able. The advantage of quantum encryption method is, it can be incorporated with conventional encryption methods safely. The quantum cryptography can replace conventional key exchange mechanism with the polarized photons using channels like optic fiber cables. Quantum cryptographic can also provide far and secure data communication. The present day experiments clearly proved that the quantum cryptography can be implemented through medium like optic fiber cable or air. But the distance of transmission through the air is limited by rule of line of sight propagation. The quantum key distribution will have uses in different types of communication between distant parts of earth. So this paper discussing various aspects of Quantum key distribution and successfully calculated polarized photon loss during transmission of Quantum cryptography link, while using in various type of atmospheric conditions like Mist Fog Haze. Also successfully calculated probability of single polarized photon missing by successfully utilizing the Light transmission characteristics and power measurements in various Atmospheric conditions. 2018, UK Simulation Society. All rights reserved. -
Machine Learning Based Crime Identification System using Data Analytics
Poverty is known to be the mother of all crimes, and a vast percentage of people in India live below the poverty line. In India, the crime rate is rapidly rising. The police officers must spend a significant amount of time and personnel to identify suspects and criminals using current crime investigation. In this research, the method presented for designing and implementing crime identification and criminal recognition systems for Indian metropolitans is utilizing techniques of data mining. These occurrences are represented by 35 predefined crime attributes. Access to the crime database is protected by safeguards. The pending four subjects are important for crime unmasking, identification and estimation of criminals, and crime authentication, in that order. The detection of crime is investigated with the help of K-Means clustering, which iteratively builds two crime batches based on congruent criminal features. Google Maps is to enhance the k-means visualization. K-Nearest Neighbor classification is used to examine criminal identification and forecasting. This is used for the authentication of the results. The technique benefits society by helping investigative authorities in crime solving and criminal recognition, resulting in lower crime rates. This research study describes a way for creating and deploying crime solving and criminal recognition systems for Indian metro's using data mining tools in this study. The method consists of data evulsion, data pre- processing, clustering, Google map delegation and classification. The first module, data evulsion, retrieves unformed or unrecorded crime datasets from several criminal sources online from 2000 to 2012. In the second module, Data pre-processing cleans, assimilates, and reduces the obtained criminal data into organized 5,038 crime occurrences. Several predefined criminal traits represent these instances. Safeguards are in place to prevent unauthorized access to the crime index. The remaining components are critical for detecting crimes, criminal identity and prediction, and crime verification, in that sequence. The investigation of crimes is investigated using k-means clustering, which gives results repeatedly. 2023 IEEE. -
Artificial Intelligence and Deep Learning Based Brain Tumor Detection Using Image Processing
In the field of medical science, applications that are particularly used for diagnostic purposes, are used in the detection of brain tumors since detecting an error in MRI scanning is becoming a major task for radiologists and requires a lot of their focus. Flaws that are prevalent during tumor detection must be taken care of to avoid further complications. MRI scanning is one of the most recently developing technologies. The radiologist is a key player in the identification of the brain tumor. Radiologists have to check every image perfectly to avoid the errors in identifying the brain tumor. There is a probability that sometimes cerebral fluid may also appear as mass tissue during the MRI scan. The model that is proposed in this research uses a machine learning algorithm which helps to improve the validity of the classification of the images that are taken in MRI scans. The study focuses on having an automated system that carries out an essential role in determining whether a lump is present in the brain or not. The study tries to resolve the primary flaws in detection necessary to evade further complications in MRI images in brain detection. The main aim of this study is to train the algorithm in a more extensive dataset and to check the patient-level validity with the help of various new datasets. 2023 IEEE. -
Hedging with the Indo-Pacific: why Southeast Asia might benefit from embracing the construct
ASEANs engagement with the Indo-Pacific is often framed as a strategic shift, but this paper argues it is instead a continuation of its established hedging strategy. Drawing historical parallels, particularly Thailands colonial-era diplomacy, the article examines how ASEAN balances major power competition while preserving autonomy. By assessing ASEANs economic and security engagements with China and the United States, the paper highlights how hedging remains essential amidst intensifying geopolitical tensions. The Indo-Pacific framework does not require ASEAN to choose sides but reinforces its flexibility. ASEAN centrality, as enunciated by many global powers, has given much impetus to the organisation to continue with the hedging strategy. As the USChina rivalry deepens, hedging offers ASEAN the best path to stability and strategic relevance. 2025 The Round Table Ltd.