Browse Items (16481 total)
Sort by:
-
AI-Powered Analytics in IT Services and the Opportunities and Challenges for Scalable Growth
The technological advancement has witnessed high levels of efficiency in operations, decision-making and customer engagement because of the integration of AI enabled analytics into IT-based services. The paper examines the influence of AI-based solutions that are transforming IT service models, with focus on the effect on scalability, predictive maintenance, and intelligent automation. The studies to point out the opportunities presented by AI describe improved personalization of service delivery, fewer instances of downtime, and data-based optimization approaches. Nonetheless, it also addresses such vital issues as the privacy or interpretability of data and the infrastructural requirements of scaling AI solutions. It is suggested to replace these limitations with a hybrid framework resolving limitations by integrating the advantages of cloud-native frameworks with edge-intelligent systems. Experimental study conducted among medium sized IT companies revealed that the speed in delivering the services increased by 5 0%, 6 0% lower error rates and over 55% reduction in downtime. The results hint at the possibility of bearing AI-driven analytics-based IT services when the required control and strategic design are employed. 2025 IEEE. -
AI-Based Security and Privacy Solutions for Edge Computing Using Federated Learning
Edge computing, which reduces latency and bandwidth usage by performing computations on data closer to where they are generated, is generating considerable interest due to the rapid growth of the Internet of Things (IoT) and real-time applications. However, this new architecture brings more security issues, such as cyber-attacks, unauthorized access, and data leakage. This architectural change improves performance, but it also increases the risk for data leakage, unauthorized access and cyberattacks. These distributed/architecturally decentralized scenarios are not something traditional cloud security models are suitable for. Federated learning (FL), a privacy-preserving setting for distributed learning, comes as a new solution which enables edge devices to collaboratively update their models without disclosing locally learned models. When integrated with AI, FL offers intelligent, flexible, and privacy-preserving security to edge ecosystems. This chapter investigates the interplay between edge computing, FL, and AI, providing a detailed analysis of possible future developments, risk mitigation strategies, and existing threats. This chapter studies the pioneering role of AI-supported federated systems in defending the future generation of edge networks through recent research and applied studies. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
A Novel Back-Propagation Neural Network for Intelligent Cyber-Physical Systems for Wireless Communications
Wireless sensor networks, which play a significant role in monitoring complex environments that change rapidly over time, were used in the Artificial Intelligence method. External factors or the device designers themselves are both responsible for this complex behavior. Sensor networks often use machine learning techniques to adapt to such conditions, eliminating the need for excessive redesign. Cyber-physical systems (CPS) appeared as the promising option for improving physical-virtual interactions. The quality of the system containing processing information is primarily determined by the system function. There are many benefits obtained while combining Artificial Intelligence (AI) and Cyber-Physical Systems (CPSs) in buildings. In CPS-based indoor environment has various design schemes containing measurement and intelligent buildings in the control system consisting of detection, tracking, execution, and communication modules. The Multi-Agent System (MAS) is the smallest control unit that simulates among neurons and it flexibly provides the information. To mimic the interactions between human neurons, multi-agents are used. In this paper, the CPSs information world is built on the fundamental principle of granular formal concepts and the theory of granular computing is investigated. The calculation module is used by Back-Propagation Neural Network (BPNN) for pattern recognition and classification by environmental information. Various parameters namely the normalized root mean square error, peak signal-to-noise ratio, mean square error, and the mean absolute error are chosen as the objective assessment criteria to assess the benefits of the proposed method and the effectiveness of the proposed system is proven. 2024 IETE. -
Utilizing Deep Learning Techniques for Lung Cancer Detection
Deep learning can extract meaningful insights from complex biomedical statistics, which includes Radiographs and virtual tomosynthesis. Traits in contemporary deep studying architectures have enabled faster and more correct mastering of the functions gifted in clinical imagery, main to better accuracy and precision in medical analysis and imaging. Deep studying strategies may be used to pick out patterns within the pics which may be indicative of illnesses like lung cancer. Those ailment patterns, which include small lung nodules, can be used for early detection and prognosis of the sickness. Recent studies have employed deep learning strategies consisting of Convolutional Neural Networks (CNNs) and switch learning to come across most lung cancers in CT pictures. The first step in this manner is to generate datasets of pictures of the lungs, each from wholesome people and those with most lung cancers. Those datasets can then be used to teach a deep knowledge of a set of rules that may be optimized to it should locate those styles. Once educated, the version can be used to come across styles indicative of lung most cancers from new take a look at images with high accuracy. For further accuracy and reliability, extra up-processing techniques, along with segmentation and records augmentation, may be used. Segmentation can be used to detect a couple of lung nodules in a photo, and records augmentation can be used to lessen fake high quality outcomes. 2024 IEEE. -
Synthesis and Characterization of Cyclopentadithiophene and Thienothiophene-Based Polymers for Organic Thin-Film Transistors and Solar Cells
Novel donor-donor type alternating copolymers (8CDT-TT and 16CDT-TT) derived from cyclopentadithiophene (CDT) and thienothiophene (TT) moieties that differ from solubilizing side chains were successfully synthesized and characterized. After the synthesis of CDT-TT-based conjugated polymers with dioctyl and dihexadecyl side chains, their optical, thermal, structural and semiconducting properties were investigated. Organic thin-film transistors fabricated from 8CDT-TT and 16CDT-TT exhibit carrier mobilities as high as 3.920-4 and 1.050-3 cm2V-1s-1, respectively. Bulk heterojunction solar cells fabricated using a polymer:PCBM blend ratio of 1:3 exhibit power conversion efficiencies of 2.12 and 1.84% for 8CDT-TT and 16CDT-TT, respectively. 2018, The Polymer Society of Korea and Springer Nature B.V. -
Online alert system for DDoS attack detection and prevention using machine learning classification algorithms
Distributed Denial of Service (DDoS) attack makes a server inaccessible by flooding it with fallacious traffic. It uses many intermediate devices such as computers, servers, smartphones, and even IoT Devices to generate false traffic. These attacks become more threatening if the attackers use any of these devices to have access to WiFi routers, security cameras, smart devices, etc. This paper proposes a model for DDoS attack detection and mitigation that identifies the DDoS attack and alerts the administrative authorities with the help of machine learning classification algorithms. The paper surveys discrete types of Machine Learning algorithms to identify and mitigate the DDoS attack. Three labeled datasets are employed in this paper to train the model for effective DDOS attack detection with better accuracy. These data sets comprises of benign and malignant attacks to train and test the classification algorithms. Based on the experimental results and performance metrics, it is identified that the XGBoost algorithm provided better accuracy of 99.8% on all three labeled datasets. 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. -
CDADITagger: An Approach Towards Content Based Annotations Driven Image Tagging Integrating Hybrid Semantics
Considering the rapid growth of multimedia data, especially images, image tagging is considered the most efficient way to organize or retrieve images. The significance of image tagging is growing extensively but the frameworks employed for tagging these images aren't sophisticated. These images aren't properly tagged because of a lack of resources for tagging or manual tagging is a challenging task considering such voluminous data. Already existing frameworks take both the image data and tag-related textual data but ultimately resulted in mediocre or unpalatable performance as they are dataset centered. To overcome these limitations in existing frameworks we proposed an image tagging mechanism, CDADITagger capable of automatically tagging images efficiently and much more reliable compared to existing frameworks. This framework can tackle real-world applications like tagging a new unknown image as the framework isn't powered by dataset alone but is designed to inculcate images from search engines like Google, Bing, etc. to have comprehensive knowledge of real-time data. These images are classified using CNN and tag-related textual data is classified using decision trees for enhanced performance. While tagging images from the classified tags, are sorted based on the semantic computation values, only the top 50% of the instances classified are selected. The tags which are more correlated to the image are ranked and finalized. The proposed semantically inclined framework CDADITagger outshined the well-established frameworks with an accuracy of 96.60% and a precision of 95.84% making it a more reliable approach. 2022 IEEE. -
Simulation and Experimental Analysis of L-Section in Reinforced Cement Concrete: Uncertainties in Performance and Strength
The design and construction of reinforced cement concrete (RCC) flooring play a crucial role in the overall stability of a structure, particularly in regions prone to tectonic activity. RCC floors comprise various beams, including intermediate T-sections and specific L-sections at critical points such as corners and around staircases or lift openings. This paper identifies a key challenge in building frameworks to resist tectonic loads. It further explores the components of the structure that provide potential for interruption, capability, and the safe transfer of tectonic loading to the array connection, all while maintaining sufficient strength. The L-sections were experimented on using various grades of concrete and sizes to reinforce connections under diverse loading conditions. L-sections contribute to reducing floor height, solving economic and technical problems, and creating advanced composite connections that integrate the proposed structural system. The analysis was conducted both analytically and experimentally to assess methods to resist earthquake forces based on stiffness, building strength, and elasticity capacity. These approaches have been identified to safeguard buildings during substantial seismic events. The development of the L-section is detailed, highlighting the loading process and the capacity to overcome various structural challenges. 2024 by the authors. Licensee MDPI, Basel, Switzerland. -
System Design for Financial and Economic Monitoring Using Big Data Clustering
Economic data executives are becoming increasingly important for the longevity and improvement of ventures due to the constant expansion in the influence of data innovation. This study lays out an undertaking economic data the executive's structure for the intricate internal undertaking economic data the board business. It also includes the application of web-based big data technology to understand the fairness, reliability, and security of system database calculations, mainly to improve office capabilities and solve daily project management problems. used in the project. The aim is to evaluate the suitability of transfer clustering computation (DCA) for managing large amounts of data in energy systems and the suitability of data economics dispatch methods for harnessing new energies. Then, combine day-ahead shipping plans with continuous shipping plans to create a multi-period, data-economic shipping model. Consider how the calculations are performed using a case study on the use of new energies. This will enable new energy in multi-period data economics shipping models while meeting his DR requirements on the customer side. 2023 IEEE. -
AI-Controlled Wind Turbine Systems: Integrating IoT and Machine Learning for Smart Grids
Advances in renewable energy technologies are pivotal in addressing the challenges posed by the depletion of traditional energy sources and their associated environmental impacts. Among these, wind energy stands out as a promising avenue, with wind turbine farms proliferating globally. However, the unpredictable nature of wind and intricate interplay between turbines necessitate innovative solutions for efficient operation and maintenance. This paper reviews advancements in intelligent control systems, notably those proposed by Smart Wind technologies. These systems leverage a network of sensors and IoT devices to gather real-time data, such as wind speed, temperature, and humidity, to optimize turbine performance. A significant focus is on turbines employing doubly-fed induction generators, which offer benefits like adjustable speed and consistent frequency operation. Their integration into smart grids introduces challenges concerning power system dynamics'security and reliability. This review delves into the dynamics, characteristics, and potential instabilities of such integrations, emphasizing the uncertainties in wind and nonlinear load predictions. A noteworthy finding is the rising prominence of artificial intelligence, particularly machine and deep learning, in predictive diagnostics. These methodologies offer costeffective, accurate, and efficient solutions, holding potential for enhancing power system stability and accuracy in the smart grid context. The Authors, published by EDP Sciences, 2024. -
Automatic Classification of Normal and Affected Vegetables Based on Back Propagation Neural Network and Machine Vision
This article presents a neural network and machine vision-based approach to classify the vegetables as normal or affected. The farmers will have great difficulty if there is a change from one disease control to another. The examination through an open eye to classify the diseases by name is more expensive. The texture and color features are used to identify and classify different vegetables into normal or affected using a neural network and machine vision. The mixture of both the features is proved to be more effective. The results of experiments show that the proposed methodology extensively supports the accuracy in automatic detection of affected and normal vegetables. The applications in packing and grading of vegetables are the outcome of this research article. 2019, Springer Nature Singapore Pte Ltd. -
Recognition of Green Colour Vegetables' Images Using an Artificial Neural Network
Image processing is used in all the domains including agriculture. In this paper, we have introduced a computationally simple and small feature vector, as a tool for the recognition of green colour vegetable images. The RGB colour system is used and the feature set is computationally economic and performs well on locally available vegetable images. For recognition of vegetable images, an ANN-based classifier is deployed. The recognition percentage is in the scale of 74-100 for 15 vegetable types. This work finds application in the packing of vegetables, food processing, automatic vending. 2019 IEEE. -
Climate Risks in an Unequal Society: The Question of Climate Justice in India
Over the past few decades, India has witnessed the brunt of climate change impacts in multiple dimensions. Notably, recent years' experiences prove that there has been a substantial increase in the intensity, frequency, and duration of climate-related risks and extreme events, resulting in an acceleration of the nexus between climate change and inequities. Despite the growing advancements of socioeconomic research on current and future climate change risks in India, the explorations through the lens of legal perspectives are still limited and have not met the demands. This chapter argues for rethinking legal perspectives of climate justice in India by drawing insights from two recent climate extreme events. To begin with, this chapter briefly reviews the historical background of the global actions to combat climate inequities and injustices and identifies the ways in which climate injustices perpetuate. For this, it adopts three main principles of climate justice, consisting of equity, a rights-based approach, and sustainability. Following this, it discusses India's climate policy and the existing institutional framework and actions to respond to climate change at the national and state levels. Then, by focusing on the climate change impacts on India, it introduces two recent climate-related risk events in India, and it discusses the unequal structuration of climate risks and the resulting more vulnerable and precarious situation of the marginal sections of the society that already faces multiple social injustices of Indian society. At the end of the cases, it briefly offers a critique of the climate change action plans of the respective states. This chapter concludes by outlining a few strategies to create a more sustainable and equitable approach toward climate governance and justice by strengthening the legal and institutional dispensations of the climate regime in India. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022. -
Characterization of Negative Exponential Distribution Based on Patil-Seshadri Condition
In this paper, the different characterizations of the negative exponential distribution in the context of the Patil-Seshadri (P-S) condition are analyzed. To support this conclusion, we next show in the case of several continuous probability distributions including the generalized logistic, Laplace, lognormal and more, that under certain conditions they can be seen as a damaged rendition of the negative exponential distribution. The results offer new insights into the ways to continue previous research on how damaged data may appear to follow simpler exponential forms. The paper also presents the theoretical judgments of these characterizations and practical uses in biological frameworks, fund and signal handling where exponential developments and decays are usual scenes. Our research aims to have the following implications to these fields; it provides a fresh view to exponential model. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Genetic Modification of Enzymes for Biomass Hydrolysis
Lignocellulose biomass is an economically viable and most abundant energy source. The synthesis of renewable energy-based fuel from lignocellulosic biomass is a replacement for fossil fuel. Cellulases are the biocatalysts that hydrolyze the ?-1,4-glycosidic bond in cellulose to release carbohydrate moieties that can be converted to ethanol, butanol, and other compounds. However, little enzymatic activity and product yield, and thermal stability are hurdles in the deconstruction of lignocellulose. Current progress in synthetic and omics technologies has resulted in several works in metabolic and genetic engineering that have paved the way for efficient conversion of lignocellulose to fuel in the last decades. Several works have attempted to apply genetic and metabolic engineering in the synthesis of stable and highly active cellulases at lower cost. This chapter reviews various genetic engineering technologies for enhancing cellulase synthesis and catalytic efficiency. 2024 selection and editorial matter, Reeta Rani Singhania, Anil Kumar Patel, Htor A. Ruiz, Ashok Pandey; individual chapters, the contributors. -
Bacterial Pigments as Antimicrobial Agents
In this chapter, we discuss various bacterial derived secondary metabolites pigments which has antimicrobial properties. Though these metabolites were identified more than several decades ago, attention into their bioactivities has emerged in the last few decades. Their increasing acceptance is an outcome of their cost-effectiveness, biodegradability, noncarcinogenic property, and eco-friendly characteristics. This chapter has made an attempt to take an in-depth observation into the current bacterial derived pigments and their bioactivity against various microorganisms. 2024 selection and editorial matter, Mohammed Kuddus, Poonam Singh, Raveendran Sindhu and Rachana Singh; individual chapters, the contributors. -
Design and genome engineering of microbial cell factories for efficient conversion of lignocellulose to fuel
The gradually increasing need for fossil fuels demands renewable biofuel substitutes. This has fascinated an increasing investigation to design innovative energy fuels that have comparable Physico-chemical and combustion characteristics with fossil-derived fuels. The efficient microbes for bioenergy synthesis desire the proficiency to consume a large quantity of carbon substrate, transfer various carbohydrates through efficient metabolic pathways, capability to withstand inhibitory components and other degradation compounds, and improve metabolic fluxes to synthesize target compounds. Metabolically engineered microbes could be an efficient methodology for synthesizing biofuel from cellulosic biomass by cautiously manipulating enzymes and metabolic pathways. This review offers a comprehensive perspective on the trends and advances in metabolic and genetic engineering technologies for advanced biofuel synthesis by applying various heterologous hosts. Probable technologies include enzyme engineering, heterologous expression of multiple genes, CRISPR-Cas technologies for genome editing, and cell surface display. 2022 Elsevier Ltd -
Murraya koenigii extract blended nanocellulose-polyethylene glycol thin films for the sustainable synthesis of antibacterial food packaging
Non-biodegradable plastics are a worldwide problem that have a negative impact on all living things, including humans. Nanocellulose, an excellent biopolymer is known for their increasing uses in food, healthcare, cosmetics, and various other fields. Nanocellulose is readily biodegradable, bioderived, and useful for creating innovative bioplastics that are employed in the production of food packaging and wound dressing. Curry leaves (Murraya koenigii) belongs to the rutaceae family and has many health benefits. Synthesis of Murraya koenigii incorporated nanocellulose thin films, and its characterisation using FT-IR, and XRD is discussed in detail. The source of nanocellulose in this study is sugar cane bagasse, an easily available agricultural residue in Kerala. Also, a biocompatible plasticizer is utilised to produce antibacterial packaging for food. The synthesised nanocomposites showed non-toxicity against THP1-derived macrophage cells and significant antibacterial activity against gram positive and gram-negative bacteria suggesting the possible application as a viable alternative for food packaging materials. 2023 Elsevier B.V. -
Non-Contact Vital Prediction Using rPPG Signals
In this paper, we present the clinical significance of various cardiac symptoms with the use of heart rate detection, ongoing monitoring and present emotions. The development of algorithms for remote photoplethysmography has drawn a lot of interest during the past decade (rPPG). As a result, using data gathered from the video feed, we can now precisely follow the heart rate of individuals who are still seated. rPPG algorithms have also been developed, in addition to technique based on hand-crafted characteristics. Deep learning techniques often need a lot of data to train on, but biomedical data frequently lacks real-world examples. The experiment described in this work, we looked at how illumination affected the rPPG signals' SNR. The findings show that the SNR in each RGB channel varies depending on the colour of the light source. Paper describes development in video filtering for recognising the comprehending human face emotions. In our method, emotions are deduced by identifying facial landmarks and analysing their placement. 2023 IEEE. -
Performance of second law in Carreau fluid flow by an inclined microchannel with radiative heated convective condition
This investigation addresses the novel characteristics of entropy production in the fully-developed heat transport of non-Newtonian Carreau fluid in an inclined microchannel. The physical effects of Roseland thermal radiation and viscous heating are included in the energy equation. The no-slip boundary condition for velocity and convective type heating boundary conditions for temperature are also accounted. Mathematical modeling included the non-Newtonian Carreau fluid model. The dimensionless two-point boundary value problem acquired from governing equations via dimensionless variables. The nonlinear system is tackled by using the Finite Element Method. A detailed discussion of the significance of effective parameters on Bejan number, entropy generation rate, temperature and velocity is presented through graphs. Our analysis established that the entropy generation is reduced at the left and right phase of the channel while the Bejan number is improved at both phases of the channel and is maximum at the center of channel by the incrementing values of Weissenberg number. 2020 Elsevier Ltd