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Smart songs selection in playlists using parallel k-means clustering
Most songs today are of different tempo, pitch and time signature. In a music player application, the typical shuffle picks the succeeding song or preceding song at random with no parameters to choose the songs. Different songs from different genres can have a tempo range anywhere between forty beats per minute and three hundred beats per minute. In this paper, the quick and efficient parallel k means clustering algorithm is implemented in Hadoop on the million-song dataset subset to form clusters for the songs based on tempo and pitch. The aim of this paper is to reduce the variation that occurs when a typical shuffle picks the succeeding song at random. This variation can be in the form of tempo or other parameters. The formation of clusters and intern the reduction in the variation of tempo can be used in a new 'smart shuffle'. After the clusters have been formed, the smart shuffle picks the songs within that specific cluster. This paper aims at reducing the variation by 50%. This would have many musical benefits and would also be more pleasing to the listener. 2018 IAEME Publication. -
Synthesis and characterization of porous, mixed phase, wrinkled, few layer graphene like nanocarbon from charcoal /
Russian Journal Of Physical Chemistry A, Vol.89, Issue 13, pp.2438-2442, ISSN No: 0036-0244. -
Export performance of Indian textile industry in the post multi fibre agreement regime /
Artha Journal Of Social Science, Vol.13, Issue 4, pp.63-86, ISSN No: 0975-329X. -
Machine Learning-Based Classical Dance Mudra Recognition Model
In this research, symbolic hand mudras of the Indian traditional dance style of Bharatanatyam are recognized and categorized using deep learning techniques. The three main goals are establishing baseline datasets to identify and categorize hasta mudras, designing an automated tutoring program for prospective students, and constructing a system for recommending videos that support cultural heritage. The research achieves a real-time recognition accuracy of 85% to 95% using convolutional neural networks (CNNs) and the Mobile Net architecture. This activity greatly aids virtual learning during pandemics, worldwide cultural relations, and preserving intangible cultural assets. The three main goals of this research are to establish baseline datasets for accurate mudra identification, create an automated tutoring program for participants, and build a video recommendation system to promote cultural heritage globally. The benchmark datasets that are used to train the models are made up of high-quality photos and videos of mudras that are taken and annotated under the direction of experts. While the video recommendation system supports attempts to preserve culture and advance education, the automated tutoring system provides participants with a comprehensive virtual learning environment and tailored feedback. To ensure the survival and continued appreciation of Bharatanatyam around the world, our endeavor substantially enhances virtual education, deep learning, and cultural preservation. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Analysis of skip-lot sampling plan of Type 3 with multiple reference criteria
In the modern quality control segment, the skip-lot sampling plan is still significant among all others plans due to rising production volumes and the demand for cost-effective inspection methods that will yield high-quality outputs. Unlike other sampling plans, while inspecting a submitted lot, a skip-lot plan is economically advantageous and ensures high quality. The skip-lot sampling plan utilizes single sampling plan (SSP) or double sampling plan (DSP) as the reference plan during both normal and skipping inspections. However, using these plans as the reference can lead to bias, favouring either the producer or the consumer. In this paper, a novel approach is illustrated where the skip-lot sampling plan of type 3 is having the provision of two different reference plans in the normal and skipping phases. The proposed plan is termed as the Multi-Reference Skip-lot Sampling Plan of type 3 (MR-SkSP-3). The plan is then compared with the help of performance measures such as operational characteristic (OC) function and average sample number (ASN). The comparison is done between the proposed plan and existing skip-lot sampling plans which use single sampling plan or double sampling plan as reference plan in both inspection phases. The comparison is made based on performance measures with graphical and tabulated illustrations. The comparative analysis proves that the proposed plan successfully balances the satisfaction of both producers and consumers. By leveraging the strengths of conventional skip-lot sampling plans that use single reference plans, it achieves superior performance. 2026 The Korean Statistical Society, and Korean International Statistical Society. All rights reserved. -
MULTI REFERENCE SKIP-LOT SAMPLING PLAN
Skip-lot sampling plans have become significant in modern quality control due to rising production volumes and the demand for cost-effective inspection methods that will yield high-quality outputs. When inspecting a submitted lot, a skip-lot plan is economically favourable and guarantees high quality. Thus, this approach benefits both producers and consumers. The skip-lot sampling plan generally utilizes the same sampling plan as the reference plans for both skipping and normal inspection. However, using the same plan in both phase favours either the producer or the consumer in the most essential situations. This article introduces a novel approach, the Multi Reference Skip-Lot Sampling Plan with the provision of having two different reference plans in the normal and skipping phases of the skip-lot plan. The paper explores the efficacy of this approach by deriving performance measures using a power series approach. To evaluate the proposed plan, a comparison is made with existing skip-lot sampling plans that use single sampling plans or double sampling plans as reference plans. This comparison is based on operational characteristics and average sample number values, accompanied by graphical representations. The comparative analysis demonstrates that the new plan effectively balances the satisfaction of both producers and consumers. Additionally, the study offers a strategy for selecting the plan parameters using the unity value approach, supported by a table providing unity values. 2025, Gnedenko Forum. All rights reserved. -
MULTI-REFERENCE SKIP-LOT SAMPLING OF TYPE 3 (MR-SkSP-3)
In the current industrial sector, the rate of defective products present in the lots has been decreasing and most of the products keeps up a good history of quality throughout the production also. Skip-lot sampling plans are the suitable acceptance sampling plan for the situations where the series of products shows a stable and excellent quality. The skip-lot sampling plans are still widely used because of its reduced sampling cost and efforts, because the plan only needs to inspect a fraction of the lots submitted after a continues series of lots with excellent quality. This approach makes the skip-lot plan more cost-effective than the other sampling plans, thus making it an economically important plan. The current study incorporated a modification on the skip-lot sampling of type 3 and designated it as multi-reference skip lot sampling of type 3. The proposed plan has the provision of having multiple reference plans in normal and skipping inspection of a skip-lot sampling plan, unlike the traditional skip-lot plans which has the same reference plan in all phases. The performance measures of the proposed plan are derived using the power series approach. A designing methodology to determine the optimal parameters for the plan using the unity value approach is also described with the help of a numerical illustration. Behaviour of the operating characteristic curves for varying set of parameters are also analysed for the plan. Comparison of the proposed plan is done between the conventional plans using performance measure values and graphical representations. This analysis shows that the new plan is able to effectively optimize the preferences of producer and consumer simultaneously, where the traditional plans fail to. The analysis is supported with the help of graphical representations and tabulated values. 2025, Gnedenko Forum. All rights reserved. -
AI Trust, Risk, and Security Management: Framework, Principles, and Practices
For industry practitioners, academic researchers, and governance professionals alike, this book offers both clarity and depth in one of the most important domains of modern technology. As AI matures, trust and risk management will define its success-and this book lays the groundwork for achieving that vision. As AI continues to permeate sectors ranging from healthcare to finance, ensuring that these systems are not only powerful but also accountable, transparent, and secure, is more critical than ever. This book offers a vital exploration into the intersection of trustworthiness, risk mitigation, and security governance in artificial intelligence systems, serving as a definitive guide for professionals, researchers, and policymakers striving to build, deploy, and manage AI responsibly in high-stakes environments. Using a comprehensive approach, it explores how to integrate technical safeguards, organizational practices, and regulatory alignment to manage the unique risks posed by AI, including algorithmic bias, data misuse, adversarial attacks, and opaque decision-making. The result is a strategic approach that not only identifies vulnerabilities, but also promotes resilient, auditable, and trustworthy AI ecosystems. At its core, AI TRiSM is a forward-looking concept that embraces the realities of AI in production environments. The framework moves beyond traditional static models of governance to propose dynamic, adaptive controls that evolve alongside AI systems. Through real-world case studies, the book outlines how tools like model cards, bias audits, and zero-trust architectures can be embedded into the AI development lifecycle. Readers will find the volume: Introduces concepts to stay ahead of regulations and build trustworthy AI systems that customers and stakeholders can rely on; Addresses security threats, bias, and compliance gaps to avoid costly AI failures; Explores proven frameworks and best practices to deploy AI responsibly and strategies to outperform; Provides comprehensive guidance through real-world case studies and contributions from industry and academia. Audience AI and machine learning engineers, data scientists, cybersecurity and risk management specialists, academics, researchers, and policymakers specializing in AI ethics, security, and risk management. 2026 Scrivener Publishing LLC. -
Advancements in Automated Spine Disorder Detection Using CT Scans: A Decade of Progress (20142024)
Automated spine disorder detection has transformed a lot in the last decade, from classic segmentation techniques to advanced deep learning models. Remarkable developments can be noticed in this field, especially in developing hybrid architectures combining CNNs with LSTM networks to increase diagnostic accuracy. Recent implementations reach an accuracy of up to 97.46% and a precision of 99.72%, highlighting the achievement of impressive performance metrics by modern systems in detecting spinal deformity. Integrating U-net architectures for detecting accurate cervical spine fracture and developing two-tier detection pipelines which efficiently balance specificity and sensitivity are significant innovations. Early approaches concentrated on detecting basic anatomical features, and the latest methods comprise advanced deep learning models for comprehensive analysis. From traditional segmentation tasks to managing complicated challenges and iterative random walks, the field of automated spine disorder detection has improved significantly. However, issues regarding data standardization and model generalization persist, despite this growth. Future research should focus on the development of more robust, system-independent frameworks that are capable of handling various imaging conditions and patient populations. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Cyber-Secure Framework for the Insecure Designs in Healthcare Industry
Sensitive data protection has been a top priority in the healthcare industry. This has led to the investigation of safe data storage and transaction. Despite various attempts to address this issue, data breaches continue to plague the healthcare industry. This study aims to investigate prevalent storage practices and security methodologies in the healthcare, recognizing the need for a robust framework. The work further extends with design of new security framework for healthcare industry. This framework identifies critical data and implement measures to prevent unauthorized access and data tempering. The industrial hype towards the implementation of adaptive machine learning craves the need for hybrid machine learning approaches to be adapted in the cyber secure framework. In order to improve security and confidentiality in the healthcare sector. Blockchain is used in the proposed cyber secure framework promising integrity of data with the features of immutability. This proposal aims to provide a comprehensive solution to the ongoing problem of protecting medical data. Grenze Scientific Society, 2024. -
Game Rules Prediction Winning Strategies Using Decision Tree Algorithms
With the availability of extensive data spanning over the years, sports have become an emerging field of research. The application of analytics in cricket has become prominent over the years. Cricket, the most loved sport in India, draws the attention of fans worldwide. The Indian Premier League is no exception. Created in 2008, this franchise-based T20 format of cricket has gripped the attention of cricket enthusiasts. With ardent fans cheering for their favorite teams, teams have mounting pressure to maintain their winning streak. One such team is the beloved Chennai Super Kings. Statistical techniques for winner prediction have become popular over the last decade. In this study, we try to frame decision rules for IPL teams to win a series using the CART algorithm. By considering Chennai Super Kings, this study aims to understand the criteria for winning and identify potential weaknesses, allowing the team to predict the likelihood of winning the IPL series. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Industry 5.0 - The co-creator in marketing
The unavoidable connection between automation and digitalization is already in the business horizon in the name of Industry 5.0. Industry 4.0, the robotic and technological revolution were largely hit among the manufacturing industries, but Industry 5.0 is meant for all sectors across ranges from manufacturing to services. Evolution from the days of mechanization (Industry 1.0) to smart factory (Industry 4.0) witnessed the improvisation of metrics related to efficiency and optimization. And now its turn for the balance between optimization and efficiency with the support from robots in assisting the smarter generation's technologies and machineries and tools through Industry 5.0 in the domain of marketing too where the change is constant and dynamic would be more accommodative to opportunities and challenges through the next wave of 5.0. The disruption by Industry 5.0 will change existing nature of marketing in terms of customer experience, supply chain, procurement, product development, retail operations, etc. The market which predominantly flourishes with the help of customers in co-creation is going to have robot as bystander with the intervention of this Revolution 5.0 which will level up the existing customer experience. Marketing by its nature demands the cooperation at multiple levels and is becoming easier prey for the Industry 5.0 revolution as it's expected to create the cooperation between the humans and machines. Product development, customer engagement and customer experience will undergo the transformation due to this industry revolution and also there are other areas in the marketing domain to go through the impact are addressed in this chapter. 2023 by A. Mansurali, V. Harish and Swamynathan Ramakrishnan. All rights reserved. -
Employee attrition and absenteeism analysis using machine learning methods: Application in the manufacturing industry
HR analytics has been envisaged as recent research trend for providing a comprehensive decision support system to the top level management in terms of employee's performance, recruitment and behaviour analysis. Globally, organizations are using technology to support and ease HR processes. Every organization should give maximum value to every available human resource, and they should minimize the attrition and absenteeism rate and ensure what are the factors that contribute towards employee attrition as well as the causes for workmen absenteeism. The ultimate objective is to correctly identify attrition and absenteeism in order to assist the company to improve retention tactics for key personnel and increase employee satisfaction. Through this chapter, a machine learning-based model is proposed to get quick results for such employee attrition and workmen absenteeism. The model is trained and tested for its accuracy. The result shows that the proposed model has high sensitivity. The managerial implications are also discussed for taking informed decisions. 2023, IGI Global. All rights reserved. -
Framing Conflict And Development: Media Narratives, Security Planning, And Regional Recovery In Post-Article 370 Jammu And Kashmir
The repeal of Article 370 in 2019 has brought about a drastic change in the political, security, and media situation in Jammu and Kashmir, changing the way the events related to the conflict are framed and perceived. This paper will analyze the reporting of the 2025 Pahalgam terror attack and the following Operation Sindoor in two of the most popular regional dailies, Greater Kashmir and Daily Excelsior. The study is based on the qualitative comparative methodology that is supported by the framing theory to compare the tone, stress, and editorial strategy through the purposive analysis of the front-page coverage of April 23-May 8, 2025. The results are contrasting: Daily Excelsior adopts nationalist and security-centered frame that highlights military heroism and state intervention whereas Greater Kashmir adopts humanitarian frame that highlights civilian victimization, emotional appeal and community healing. Such competing frames not only affect the perception of the population, but also the discourses of security planning, tourism recovery, and regional development. The study suggests the significance of the media discourses as a dynamic element of the process of defining policy directions and planning outcomes in conflict-sensitive environments. 2025, Green Publication. All rights reserved. -
Through the Lens of Recession 2.0: Diversification Dynamics Between the Leading Asian Stock Markets
The focus of this article is to analyse the inter-linkages between eight leading stock markets in Asian continent from the period of July 2011 to February 2018. This period holds relevance as this was the time when Recession 2.0 set in, which adversely affected the developed economies; however, the developing economies withstood the crisis without much of an impact. Co-integration and Granger causality tests were conducted to probe the inter-linkages. Study revealed a positive impact on Asian stock market indices collectively on each of the indexes. The highest number of unidirectional causalities was to KOPSI and NIFTY from rest of the stock indices. Results confirmed that no co-integration relationship existed among the selected indices indicating favourable diversification opportunities. Thus, the study fosters global market participants and policymakers to consider the nitty-gritties of stock market integration so as to benefit from international stock market diversification in the Asian region. 2022 Management Development Institute. -
Unleashing the Potential: AIs Impact on Sustainable Finance in a Changing Global Economy
The world is becoming more conscious of sustainability, and the Finance sector is also influenced by this shift. Sustainable finance helps investors to make investment decisions by considering Environmental, Social and Governance (ESG) factors. There is a rapid change in this investment decision process. The main key reason for this change is the adoption of Artificial Intelligence (AI). The study pertains to understanding the impact of AI-based technologies on sustainable finance. AI-based technologies play a significant role in the transformation of sustainable finance by identifying quality data, doing better analysis and predicting ESG trends and investment risks. AI-driven technologies help investors to understand the disclosures on ESG practices, framework and policy mechanisms of the companies so that they make informed decisions and achieve Sustainable Development Goals (SDGs). However, use of AI-based technologies in sustainable finance is expected to be transparent, ethical, accountable and accurate. The study discussed the various applications of AI in achieving SDG goals, the use cases of AI and ESG investment and the relation of green finance and green economy. Few challenges in integrating AI and sustainability are also discussed in the chapter. 2026 selection and editorial matter, A.V. Senthil Kumar, Ankita Chaturvedi, Atul Bansal, and Rohaya Latip; individual chapters, the contributors. -
Blockchain and the Evolving Internal Audit Function
Blockchain Technology indicates a transformative era for internal audit practices in the evolving digital finance and operations landscape. This research explores the internal audit function in a Blockchain-driven world, emphasizing the changing perspectives and methodologies necessitated by this disruptive technology. With its foundational principles of transparency, immutability, and decentralization, Blockchain presents challenges and opportunities for internal auditors. The paper delves into how Blockchain is poised to redefine traditional audit practices, moving towards more real-time and continuous auditing techniques. It examines the implications of Blockchain for risk assessment, fraud detection, and compliance, highlighting the shift towards proactive rather than reactive audit strategies. Furthermore, the research examines Blockchains opportunities and challenges to the internal audit function. This study provides insights into integrating Blockchain Technology in internal auditing through a comprehensive secondary data analysis. It proposes a roadmap for auditors to adapt and thrive in this new era. The findings underscore the importance of embracing technological advancements, advocating for a dynamic approach to audit practices that aligns with the complexities of a blockchain-driven world. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
AI in IA: Impact of Artificial Intelligence in Internal Audit: A Qualitative Study
Internal auditing is becoming more crucial as businesses become more complex and extensive. Artificial intelligence (AI) in internal auditing is a trend change that promises to revolutionize how internal auditing functions are performed and delivered through significant improvements in audit quality and operational discipline. This paper reflects on many of the multifaceted impacts of AI on internal auditing functions. This paper intends to investigate how this AI will impact the audit profession. By interviewing ten individual internal audit experts qualitatively, the study shows that AIs implementation will impact the following six critical levels. AI makes it possible for an auditor to (1) spend less time and make the audit more productive, (2) increase coverage, (3) real-time auditing, (4) enhance decision-making, (5) risk assessment and management, and (6) create new advisory services. The findings thus imply a need for a well-defined and consistent audit structure that is flexible enough for auditors to improve their audits. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Phytogenic synthesis and antimicrobial activity of ZnO nano bow ties (ZnO NBTs): An experimental and computational study
Phytogenic synthesis is a sustainable and eco-friendly approach for producing nanoscale particles, using biological entities such as plants and their byproducts. In this study, Allium sativum extract was selected as a capping and reducing agent due to the presence of phytochemicals such as allicin, diallyl disulfide (DADS), vinyl dithiins, ajoene (E- and Z-ajoene), diallyl trisulfide (DATS), and thiol (sulfhydryl) groups. The resulting ZnO Nano Bow Ties (ZnO NBTs) were characterized using FE-SEM, XRD, EDX, DLS, zeta potential, FTIR, and UV-Vis spectroscopy to evaluate the size, morphology, and crystallinity. The obtained XRD, SEM, and DLS results suggested an average longitudinal length of ?372 nm with a maximum lateral width of ?64 nm and a Bow Tie shape. Gas Chromatography-Mass Spectroscopy (GC-MS) analysis was employed to elucidate the prominent phytochemical constituents of the Allium sativum extract. Preliminary antibacterial assays reveal significant inhibition zones and growth inhibition effects against gram-negative bacteria of both Klebsiella pneumoniae and Escherichia coli, suggesting the promising antimicrobial potential of these ZnO NBTs. Monte Carlo simulations revealed that the cone-shaped ZnO NBTs bind strongly to the active sites of the target proteins with binding affinities of ?36.20 and ?32.14 kcal/mol for Klebsiella pneumoniae and Escherichia coli respectively, which correlates with their activities. The ZnO NBTs complexes formed stronger hydrophobic interactions and hydrogen bonds with amino acid residues of Escherichia coli than with Klebsiella pneumoniae. This integrated experimental and computational study underscores the potential of the use of ZnO NBTs as a sustainable and effective strategy to combat bacterial pathogens. The findings of this study indicate that efficient morphology (shape) is a major contributor to the protein binding affinities of ZnO NBTs, with promising implications for the design of antibacterial drugs in nanomedicine. 2024 The Authors -
Review Article: A Review on Starch and CelluloseEnhanced Superabsorbent Hydrogel
Superabsorbent hydrogels are hydrophilic polymer units that can absorb water and organic fluids into the three-dimensional network and mimic biological cells when swollen. Hydrogels are categorized as natural, synthetic, and hybrid, depending on their constituent polymer. The novel green synthesis includes the combination of natural polymers with synthetic ones to produce eco-friendly Hydrogels. The networks are established using crosslinkers formed chemically as covalent bonds or ionic bonds and physically if intermolecular forces are involved. Starch and cellulose are naturally occurring biopolymers that make significant applications for hydrogel production. This article reviews hydrogel, its properties, classification, synthesis mechanism, and application in various sectors using starch and cellulose as copolymers. Due to the high range of availability, nontoxic nature, and biodegradability, starch and cellulose-based hydrogels find high regard in the present research era. The biopolymers beneficiation can result in the evolution of economic and sustainable methods for transforming this natural biopolymer into utilitarian organic products. 2023, Sami Publishing Company. All rights reserved.