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Blockchain-based Framework: Robust Retail Supply Chain System
Effective supply chain management (SCM) is vital to the operations of an organization survival in the fast-paced, globalized modern business environment. The decentralized, immutable ledger system provided by blockchain technology, an invention, improves supply chains visibility, efficiency, and traceability. Blockchain tackles major issues like fraud, inefficiency, and lack of transparency by doing away with middlemen and enabling transparent, real-time data access. This technology is being used more and more in a variety of industries, such as retail, healthcare, and finance. It revolutionizes operations by enhancing automation, security, and traceability. Notwithstanding its potential, the wider ramifications of blockchain technology are still poorly understood, which calls for a thorough analysis of its efficiency and applicability in supply chains. The design of blockchain, its incorporation into the supply chain, and its effects on different industries are all covered in this chapter. It discusses the difficulties in implementing blockchain technology and highlights how blockchain might improve supply chain operations. The blockchains potential to enhance decision-making, lower costs, and boost customer happiness is covered in detail. A thorough examination of blockchains potential and recommendations for future research provide insightful information for academics and businesspeople who want to fully utilize this game-changing technology. 2025 by Nishant Kumar, Kamal Upreti, Prajwal Deore, Shubham Rajendra Ekatpure, Vishakha Kuwar and Divya Gangwar. -
AI for Fair Recruitment: Balancing Tech and Ethics
Purpose: This chapter's objective is to examine how artificial intelligence (AI) is influencing the development of human resource management (HRM) systems, focusing on recruiting, developing employees, and increasing the diversity of the workforce by reducing bias. Design-methodology-approach: The investigation adopted a two-phase research strategy. First, bibliometric analysis of AI-HRM evidence received attention from most scholars indicating major topics of concern within the AI and HRM corpus. Second, a systematic literature review (SLR) based on these themes and applied focus key strings and PRISMA protocols to ensure satisfactory efforts in locating and discussing the relevant literature. Findings: The bibliometric analysis suggested three points of interest that are popular in the literature: AI in recruitment and diversity, employee development, and bias reduction. These programs can be used from mere training employees to including operational supervision and engagement. The case studies featured some well-known brands such as Unilever, Accenture, and IBM. The results indicate a possibility that AI will assist in the advancement of HRM processes, foster diversity, and inclusiveness and even bias-free recruitment and development of employees. Practical implications: The chapter proposes guidelines for the ethical application of AI in HR, including meticulous data collection, algorithmic design, and routine supervision. It emphasizes that AI possesses transformative potential for achieving diversity and inclusion in workplaces. Originality: This chapter expands on the ongoing discussion of AI in HRM by providing a bibliometric approach and SLR, making a new and substantiated claim on Al's role in promoting diversity and reducing biases. 2025 by Arun Kumar P, Rekha Aranha and Delma Thaliyan. All rights reserved. -
Twin deficit hypothesis: Some recent evidence from India /
Global Business And Economics Review, Vol.18, Issue 3/4, pp.487 - 495, ISSN: 1097-4954. -
High-Performance 15-Level Multilevel Inverter for Renewable and Smart Grid Applications
Multilevel inverters have emerged as a promising solution for improving power quality, reducing switching stress, and enhancing conversion efficiency in renewable energy and smart grid applications. Conventional two-level topologies struggle with high Total Harmonic Distortion (THD), electromagnetic interference, and bulky filters, limiting their suitability for high-power systems. To address these challenges, advanced multilevel architectures have been designed to deliver multiple voltage steps, thereby approximating a sinusoidal waveform with greater precision. This paper investigates a high-performance multilevel inverter with an optimized 15-level configuration that achieves superior harmonic reduction, enhanced voltage boosting, and reduced switching device count compared to conventional alternatives. A detailed switching sequence and operational modes are provided to demonstrate the generation of fifteen distinct output voltage levels. Simulation and analytical results validate the performance in terms of THD minimization, voltage stress reduction, and waveform quality. The proposed configuration is suitable for integration with photovoltaic systems, wind generation, and smart grid applications where reliability, efficiency, and grid compliance are essential. Furthermore, the design demonstrates modularity, scalability, and compatibility with wide bandgap semiconductors, reinforcing its role as a practical solution for modern energy systems. 2025 IEEE. -
Radiation attenuation parameters and intrinsic efficiency of a few semiconductor crystals for radiation detection applications
This study investigates the effectiveness of nine inorganic semiconductor crystals ? LiGaSe2, LiInSe2, CsHgInS3, SnS, GaTe, BiI3, Sb2Te3, Tl4CdI6, and TlBr ? for radiation detection applications based on photon and charged particle (electrons, protons, and heavy ions) interaction parameters. Mass attenuation coefficient (?/?), half value layer (HVL), relaxation length (?), effective atomic number (Zeff), electron density (Neff), equivalent atomic number (Zeq), and exposure buildup factor (EBF) were computed using PAGEX software. These results, along with their intrinsic efficiencies calculated, were compared with that of standard materials (NaI(Tl), CdZnTe, and CdTe). The ?/? values of the studied semiconducting materials are ranked in the decreasing order as: TlBr, Tl4CdI6, BiI3, CsHgInS3, Sb2Te3, GaTe, SnS, LiInSe2, and LiGaSe2. TlBr, Tl4CdI6, BiI3, and Sb2Te3 show superior photon detection capabilities compared to the reference materials. TlBr and Tl4CdI6 have the highest intrinsic efficiency across nearly all energy regions, while LiGaSe2 has the lowest. Interaction parameters like range and Zeff for charged particles were also computed using standard databases, with SnS and Sb2Te3 showing the least range for all the charged particles studied throughout the entire energy region. The study indicates that TlBr and Tl4CdI6 have strong potential for developing next-generation radiation detectors with enhanced sensitivity, addressing needs in healthcare and national security. 2025 Elsevier Ltd -
Buffer-Induced Electrocatalytic Hydrogen Evolution by a Cobalt Pentadentate Complex in Water
Elucidating proton transfer dynamics in water represents one of the most challenging problems in water splitting reactions due to the presence of multiple proton donors, which complicates the overall reaction kinetics. This study examines the impact of buffer pKa and its concentration on catalytic performance for hydrogen evolution catalyzed by a CoIII complex (1). The results demonstrate that buffer increases the catalytic rate of the hydrogen evolution reaction. This enhanced activity is supported by the number of buffer acids possessing varying pKa values, with 2-(N-morpholino)ethanesulfonic acid yielding the maximum catalytic current. A linear free energy relationship, a characteristic of a Brsted-type mechanism, is observed between the buffers pKa and catalytic rate constants. This substantiates that the rate-limiting step is controlled by the proton delivery mediated by the buffer acids. Moreover, the observed inhibition in catalytic activity at a higher concentration of buffer reveals the possible binding interaction between buffer and the cobalt center, thereby impeding substrate access. These findings underscore the critical role of buffer identity and its concentration in optimizing the proton-dependent catalytic reactions in water. 2026 American Chemical Society -
Convolutional Neural Network based Di-Strategy Cheetah Optimization Algorithm for Automatic Diabetes Prediction
Diabetes is a chronic metabolic disease characterized by elevated blood sugar levels. Diabetes prediction leverages patient data to assess the risk of developing the condition, facilitating early diagnosis and intervention. However, existing models struggle to capture the complex interactions between risk factors due to limited feature representation, leading to inaccurate predictions. This research proposes a Convolutional Neural Network-based Di-Strategy Cheetah Optimization Algorithm (CNN-DS-COA) for automatic diabetes prediction using patient data. The COA is enhanced with tent chaotic mapping and an adaptive search agent, which improves population diversity distribution and convergence speed. Initially, the Pima Indians Diabetes Database (PIMA) and Germany datasets are employed to evaluate the performance of CNN-DS-COA. Min-max normalization is applied to scale the data within a uniform range while preserving relationships among values. The CNN is then used for automatic diabetes prediction, with DS-COA fine-tuning the CNNs parameter values effectively using two strategies. The proposed CNN-DS-COA achieves superior accuracy, with 99.90% and 99.72% on the PIMA and Frankfurt Hospital, Germany datasets, respectively, outperforming existing methods such as stacked ensemble approaches and statistical predictive models. 2025, Research Institute of Intelligent Computer Systems. All rights reserved. -
Integration of cyber-physical systems with wearable devices:A new paradigm for patient monitoring
The healthcare sector has witnessed significant changes as cyber-physical systems (CPS) bring embedded technologies that are developed from human and physical surroundings to smart objects. Wearable technologies such as wearable fitness trackers, biosensors, and smartwatches stand as good examples of smart healthcare that may improve decision-making and real-time patient monitoring. Through advances in personal health technology, mobile medication, and smart sensing, these technologies aid the medical treatment of the patient. Such advancements enable monitoring and programming health data streams continually, which supports early diagnosis and better care. However, challenges persist in integrating machine learning into health wearables; it still poses a limitation, improving algorithm accuracy and reliability so that the use can be widespread. Advances in skin-based, textile-based, and biofluidic designs have allowed medical wearables to monitor neuro, cardiovascular, and metabolic disorders, which are being further extended to drug delivery systems. The present study identifies gaps and advances in the field using secondary data from articles, journals, and research papers. It highlights future research directions on the clinical applications of wearable technology and its role in routine safety and health monitoring. Findings have indicated that regulated data privacy, equity, and fairness must be pursued to fully realize CPS-enabled wearables in terms of a healthcare revolution. 2026 selection and editorial matter, Jossy George, Kamal Upreti, Ramesh Chandra Poonia, Ankit Gautam, and Danish Nadeem; individual chapters, the contributors. -
Three-level biometric digital voting system
The aim of this research paper is to design and develop a new model of electronic voting machine (EVM) with enhanced features, that are not present in the current EVM model. The paper proposes the new model of EVM that uses biometric identifiers, such as fingerprints, iris recognition, and facial recognition, to make voting more secure and efficient. Previously, voting was done with paper ballots. This method suffered from issues like over voting, lost or misplaced ballots, and it consumed a lot of time in counting paper ballots and was also harmful for the environment due to the use of papers. The new EVM system aims to solve those issues because many security levels are added to this EVM system. It can use biometric identifiers to ensure each voter is authenticated securely and records their votes accurately. This EVM system can prevent fraud, such as voter casting multiple votes or a person illegally voting on behalf of others. The system first verifies the voters, and then the voters can cast their votes. 2026 selection and editorial matter, Dr. Poonam Nandal, Dr. Mamta Dahiya, Dr. Meeta Singh, Dr. Arvind Dagur, Dr. Brijesh Kumar. All rights reserved. -
An efficient ZnO and Ag/ZnO honeycomb nanosheets for catalytic green one-pot synthesis of coumarins through Knoevenagel condensation and antibacterial activity
This study pioneers the synthesis of porous Ag/ZnO nanosheets, focusing on their role as a catalyst in Knoevenagel condensation. Notably, these nanosheets display exceptional catalytic efficacy and captivating antibacterial properties. The research delves into the Ag/ZnO catalysts recyclability and proposes a potential reaction mechanism, marking the first comprehensive exploration of Knoevenagel condensation on porous Ag/ZnO nanosheets. Key findings underscore the successful synthesis of coumarin derivatives using various o-hydroxy benzaldehyde and 1,3-dicarbonyl compounds, with nano-Ag/ZnO serving as a catalyst via a monomode microwave-assisted approach. X-ray diffraction (XRD), Field Emission Scanning Electron Microscopy (FE-SEM), Transmission Electron Microscopy (TEM) and UVVis spectroscopy were used in conjunction with other physicochemical methods to characterize the synthesized catalytic samples. The method boasts advantages such as high product yields, brief reaction durations, and the ability to reuse the catalyst for multiple cycles. The Ag/ZnO nanosheets, functioning as an acid catalyst, activate carbonyl groups and facilitate their interaction with methylene-containing active molecules. In addition, antibacterial activity assessments demonstrate the superior effectiveness of Ag/ZnO nanocomposites compared to ZnO nanosheets against Staphylococcus aureus germs. This multifaceted study not only advances catalytic synthesis but also unveils promising biological applications of porous Ag/ZnO nanosheets. 2024 Walter de Gruyter GmbH, Berlin/Boston. -
Balancing patient privacy and predictive accuracy through data anonymization in healthcare
Data anonymization in healthcare is essential for protecting sensitive patient information while enabling secure usage for research, analytics, and AI-driven clinical decision-making. In this study, the MIMIC-III - Deep Reinforcement Learning dataset was used, which contains comprehensive electronic health records (EHRs) of ICU patients. Data preprocessing was performed using Min-Max Normalization to scale numerical features and ensure consistency. Anonymization techniques such as pseudonymization, generalization, suppression, data masking, and statistical methods like k-anonymity, l-diversity, and t-closeness were applied to safeguard patient privacy. The anonymized dataset was then utilized for predictive modelling using AI techniques including Random Forest and LSTM. Results demonstrated that privacy was maintained with 0% PII leakage, while predictive accuracy remained high, achieving accuracy of 94.6%, precision of 93.8%, recall of 92.5%, and F1-score of 93.1%. This study highlights that effective data anonymization ensures compliance with HIPAA and GDPR while retaining the utility of healthcare data for advanced analytics and AI applications. 2026 Techno-Press -
Early Detection and Analysis of Potato Leaf Diseases Using Deep Learning based CNN Models
Potato diseases pose a significant threat to global agricultural productivity, leading to severe economic losses. Early and accurate disease detection is crucial for effective disease management and improved crop yield. This research explores deep learning techniques for automated potato disease prediction using convolutional neural networks (CNNs). A large dataset of potato leaf images is used to train and validate the model, ensuring robustness and accuracy. The proposed deep learning model efficiently classifies common potato diseases, such as late blight and early blight, with high precision. Performance evaluation metrics, include accuracy, The integration of deep learning in disease prediction minimizes the reliance on manual inspection, providing farmers with a cost-effective and scalable solution. Additionally, we analyze the impact of transfer learning and data augmentation on model performance. The results highlight the potential of AI-driven approaches in precision agriculture, offering real-time disease diagnosis and early intervention strategies. This research contributes to the advancement of smart farming technologies, ensuring sustainable crop protection and food security. Future work will focus on optimizing the model for real-world deployment through mobile applications and IoT-based systems. 2025 IEEE. -
Approaches To Improve Performance of K-Means Clustering
In this research, we present an enhanced K-Means clustering approach utilizing Neural Engine processors integrated within distributed smartphone networks. Each smartphone runs the K-Means algorithm locally using its Neural Engine to compute centroids efficiently, and these local centroids are then combined to form global clusters on a cloud server. Our implementation significantly reduces computation time while maintaining high clustering accuracy. Experimental evaluation on large datasets demonstrates improved performance over traditional K-Means, proving its suitability for big data analytics in healthcare, IoT, and smart mobile applications. This approach ensures faster processing, lower energy consumption, and effective resource utilization within distributed environments. Further, the proposed method addresses challenges in data privacy by performing local computation and only sharing centroid information. The results indicate potential for scalable clustering solutions in real-time scenarios, opening new directions for edge-cloud integrated machine learning frameworks that harness device-level AI accelerators for complex data-driven tasks efficiently. 2025 IEEE. -
Fortifying Networks based AI Models for Early Vulnerability Detection
Early identification of network vulnerabilities is now essential for protecting sensitive data and guaranteeing system resilience due to the increasing complexity of digital infrastructures. An extensive analysis of artificial intelligence (AI) models intended for the early identification of network vulnerabilities is presented in this research. The study examines current approaches, assesses their efficacy, and pinpoints research gaps while drawing on ideas from recent studies and cutting-edge academic research tools. The results show how AI has the ability to revolutionize cybersecurity tactics and point to new avenues for improving vulnerability detection systems. 2025 IEEE. -
Deep Reinforcement Learning with Meta-Learning and Signal Bands for Indian Equity Portfolio Management
The portfolio is a collection of assets belonging to an investor. Managing the portfolio depends on the goal of the portfolio management. This paper proposed a new portfolio managing technique using a deep reinforcement learning framework combined with meta-learning and signal bands to optimize the returns and risk of the Nifty 50 index. The objective is to maximize portfolio returns by minimizing the risk, portfolio volatility, and drawdowns with constraints of transaction cost, maximum and minimum allocation, and availability of cash and holdings. The model executes the actions of buy, sell, and hold with the constraints, and the model executes any of those actions depending on the situation and model training. Proposed model recorded a 4.68 Sharpe ratio and 7.53 Sortino ratio while training the model. While testing the model, it recorded a 4.5 Sharpe ratio and 7.64 Sortino ratio, which aligns with the aim to achieve a higher Sortino and Sharpe ratio to build a robust model for risk-adjusted returns. Proposed approach aims to create a strong model for a portfolio management system that adapts to dynamic market conditions and optimizes investment strategies by integrating these techniques. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
An Efficent Deep Learning Framework for Cyberbullying Detection Using DistilBERT and Sentiment Analysis
Particularly because of the complex and changing character of online communication, which hampers conventional detection strategies, the frequency of cyberbullying presents a significant threat to mental health and well-being in the digital age. This article presents a fast deep learning approach to improve cyberbullying detection by combining sentiment analysis with a lightweight transformer model, DistilBERT. This work intends to increase classification performance by using sentiment-based features and using DistilBERT's language and contextual awareness. Unlike conventional approaches and simpler machine learning methods, which can depend on feature extraction techniques like Bag of Words (BOW) or TF-IDF, the proposed model directly leverages contextual embeddings. Moreover, DistilBERT provides a balance between speed and performance unlike deep learning models like CNN, BLSTM, and LSTM, which could suffer with computational efficiency. Experimental results demonstrating remarkable accuracy and recall on many different datasets indicate the effectiveness of our hybrid approach. demonstrating a significant rise in cyberbullying detection over conventional methods, to evaluate performance criteria including computational efficiency, accuracy, and F1-score. With an outstanding 93.7 % accuracy, the proposed model exceeded earlier evaluated methods on this dataset. 2025 IEEE. -
Impact of Smart Phones and Social Media Consumer Addiction Affecting Interpersonal Relationship
In this Chapter we will discuss the addiction of smartphones and Social Media Addiction. In this generation accessing the internet and getting connected online with people is very easy now people carry their smart devices with them everywhere and today people use mobile data more than calling someone because each and every individual with all age categories from a kid to senior citizen everyone is being Addicted. While the smartphone and social media platforms have played a crucial role in connecting people in a very easy manner. When people meet social gathering or in a parties they should interact with the people and have should socialise with the people but instead of that a notification from social media apps on smartphones pop's up and within a sec person get engaged with their phones or checking the notification. This is a major issue that is happening in today's time. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Effect of Impulse Buying on Socio-economic factors and Retail Categories
Indian Journal of Marketing, Vol. 46, Issue 9, pp. 24-34, ISSN No. 0973-8704 -
Factors Affecting the Performance of Private Label Brands in Indian Online Market: An Assessment of Reliability and Validity
Asian Journal of Management, Vol. 7, Issue 3, pp. 223-230, ISSN No. 0973-8705 -
Detection and classification of lung cancer using deep neural network
Lung cancers hold a critical spot among the reasons for most cancer deaths among humans. The better way to maximise the survival rate is the detection of cancer at the earliest. But existing traditional techniques are time-consuming and error-prone. This study is a significant and efficient method for the detection and classification of lung cancer into large cell carcinomas, small cell, adenocarcinoma, squamous cell carcinomas, or benign respectively. In the proposed technique, a novel algorithm is implemented to generate the Image patches from whole slide histopathological images. Then, histogram normalisation is carried out to remove noise and enhance the image. Then a novel extended Mobius transformation technique is used for image augmentation. Finally, Dense EfficientNetB7 is trained to extract the features for the detection and classification of lung cancer. The performance of the proposed technique is proved more efficient and par with histologists attaining an accuracy of 98.87%. Copyright 2025 Inderscience Enterprises Ltd.