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Machine Learning in Financial Distress: A Scoping Review
Predicting financial distress is crucial for stakeholders, policymakers, governments, and management in decision-making processes. Researchers have developed various prediction models encompassing both traditional and machine-learning approaches. Notably, recent attention has shifted towards employing machine learning models to address the limitations of traditional methods. This study seeks to offer insights into current trends, identify gaps, and suggest future research directions using machine learning models for financial distress prediction, employing the PRISMA Extension for Scoping Reviews methodology. To achieve this, a comprehensive search was conducted across three databasesScience Direct, EBSCO, and ProQuestspanning from 2020 to 2023, identifying 34 relevant articles for analysis. The findings underscore the prevalent use of Support Vector Machine in financial distress prediction, followed by the Random Forest Classifier and Artificial Neural Network, with little attention paid to other models. Furthermore, the study underscores the necessity for more research in developing countries, noting the predominance of studies from developed nations. While machine learning models hold promise for enhancing the accuracy and efficiency of financial distress prediction, additional research is imperative to evaluate their effectiveness and applicability across diverse contexts. This scoping review aims to furnish researchers, policymakers, and institutions with valuable insights and policy recommendations, shedding light on underexplored machine-learning techniques. 2024, Iquz Galaxy Publisher. All rights reserved. -
Predicting Financial Distress in India: A Deep Learning Approach
The present study examines the efficacy of deep learning models in predicting financial distress in India. For this purpose, the study employs three distinct architectures: Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Conventional Neural Network (CNN) models. Utilizing data from companies that filed for bankruptcy under the Insolvency and Bankruptcy Code 2016 for the period of 20162023, the study adopts a balanced sample approach to categorize them into distressed and non-distressed groups. Nineteen financial variables are utilized to predict financial distress. Python is used as the programming language, and Jupyter Notebook facilitates algorithm development. The findings reveal that the LSTM model, when compared to RNN and CNN, achieved 91% accuracy using parameters such as 8 LSTM units with tanh activation and a dense layer with sigmoid activation function, a batch size of 10, 50 epochs, RMSprop optimizer, and binary cross-entropy loss were used. The study suggests that deep learning presents a novel approach that can enhance performance in financial distress prediction studies. This study is believed to be the first to utilize deep learning models for financial distress prediction in India based on single-year data, offering valuable insights for financial institutions and investors seeking more effective risk management strategies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Predicting Financial Distress in India: A Deep Learning Approach
The present study examines the efficacy of deep learning models in predicting financial distress in India. For this purpose, the study employs three distinct architectures: Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Conventional Neural Network (CNN) models. Utilizing data from companies that filed for bankruptcy under the Insolvency and Bankruptcy Code 2016 for the period of 20162023, the study adopts a balanced sample approach to categorize them into distressed and non-distressed groups. Nineteen financial variables are utilized to predict financial distress. Python is used as the programming language, and Jupyter Notebook facilitates algorithm development. The findings reveal that the LSTM model, when compared to RNN and CNN, achieved 91% accuracy using parameters such as 8 LSTM units with tanh activation and a dense layer with sigmoid activation function, a batch size of 10, 50 epochs, RMSprop optimizer, and binary cross-entropy loss were used. The study suggests that deep learning presents a novel approach that can enhance performance in financial distress prediction studies. This study is believed to be the first to utilize deep learning models for financial distress prediction in India based on single-year data, offering valuable insights for financial institutions and investors seeking more effective risk management strategies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Post-quantum Cryptography in Practice: A Survey of Algorithms, Applications, and Deployment Challenges
As quantum computing becomes more practical, it significantly threatens the conventional cryptographic systems, particularly RSA and ECC, that are critical to worldwide digital security. Post-quantum cryptography (PQC) has emerged as a strong alternative as a response. NIST recently standardized algorithms such as CRYSTALS-Kyber and Dilithium. This survey brings together findings from ten key papers that examine PQC across different fields, including telecommunications, finance, healthcare, IoT, smart cards, and blockchain voting systems. The chosen studies include direct comparisons of digital signature schemes, real-world protocol integration on smart cards, hybrid cryptographic models using AES and blockchain, and strategies for transition based on policy frameworks like NIST CSF 2.0. The survey examines cryptographic flexibility, hardware practicality, readiness for adoption, and the social and economic effects of quantum breaches. It compares algorithm performance, deployment challenges, and specific needs for various areas. This paper is an overview of the current state and future directions for PQC implementation in critical infrastructure. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Innovative Power Conversion Solutions for Renewable Energy and Electric Mobility
The global transition to renewable energy sources and electrification demands efficient power conversion systems for applications like hybrid electric vehicles (HEVs) and energy storage systems. This paper introduces a novel Multi-Port Bidirectional DC-DC/DC-AC Converter (MBPC) with high efficiency, compact design, and versatile functionality. The MBPC supports two input and two output ports, enabling energy flow between renewable energy sources, storage systems, and loads. Its efficiency exceeds 95%, with a power density of over 10W/cm2. The innovative design minimizes component count, reducing manufacturing costs by 30% compared to conventional converters. Extensive experimentation validates its ability to handle varying current-voltage profiles in multiple operational modes, including DC-DC and DC-AC conversions. With applications in grid-tied systems and electric vehicles, the MBPC addresses efficiency, cost, and flexibility challenges in modern power systems. This work contributes to advancing renewable energy integration and efficient electrification solutions. 2025 IEEE. -
Functional characterization of Malabar grouper (Epinephelus malabaricus) interferon regulatory factor 9 involved in antiviral response
IRF9 is a crucial component in the JAK-STAT pathway. IRF9 interacts with STAT1 and STAT2 to form IFN-I-stimulated gene factor 3 (ISGF3) in response to type I IFN stimulation, which promotes ISG transcription. However, the mechanism by which IFN signaling regulates Malabar grouper (Epinephelus malabaricus) IRF9 is still elusive. Here, we explored the nd tissue-specific mRNA distribution of the MgIRF9 gene, as well as its antiviral function in E. malabaricus. MgIRF9 encodes a protein of 438 amino acids with an open reading frame of 1317 base pairs. MgIRF9 mRNA was detected in all tissues of a healthy M. grouper, with the highest concentrations in the muscle, gills, and brain. It was significantly up-regulated by nervous necrosis virus infection and poly (I:C) stimulation. The gel mobility shift test demonstrated a high-affinity association between MgIRF9 and the promoter of zfIFN in vitro. In GK cells, grouper recombinant IFN-treated samples showed a significant response in ISGs and exhibited antiviral function. Subsequently, overexpression of MgIRF9 resulted in a considerable increase in IFN and ISGs mRNA expression (ADAR1, ADAR1-Like, and ADAR2). Co-immunoprecipitation studies demonstrated that MgIRF9 and STAT2 can interact in vivo. According to the findings, M. grouper IRF9 may play a role in how IFN signaling induces ISG gene expression in grouper species. 2024 Elsevier B.V. -
Transfer Learning based Analysis of Chest X-rays for Accurate Lung Disease Detection and Interpretation
This is a research paper based on a transfer learning approach with a primary aim at the analysis of chest Xrays for accurate detection and interpretation of lung diseases. The proposed method relies heavily on the use of pretrained deep learning models to enhance diagnostic accuracy and reduce the time and computational resources taken during training. Applying transfer learning to a large chest X-ray dataset, the model successfully detects key patterns associated with common lung diseases, such as pneumonia and tuberculosis. The manuscript encompasses data preprocessing, model finetuning, and performance evaluation and demonstrates huge improvements over the traditional methods both in terms of accuracy and interpretability. It has been experimentally proven that the model is competent enough to provide localization of disease areas, as it can be visualized through heatmaps obtained from predictions, which might further help the radiologists perform their diagnosis tasks. This work advocates for medical imaging automation for the early and efficient detection of lung disease. 2025 IEEE. -
Enhancements to greedy web proxy caching algorithms using data mining method and weight assignment policy
A Web proxy caching system is an intermediary between the users and servers that tries to alleviate the loads on the servers by caching selective Web objects and behaves as the proxy for the server and service the requests that are made to the servers by the users. In this paper the performance of a proxy system is measured by the number of hits at the proxy. A higher number of hits at the proxy server reflects the effectiveness of the proxy system. The number of hits is determined by the replacement policies chosen by the proxy systems. Traditional replacement policies that are based on time and size are reactive and do not consider the events that will possibly happen in the future. The outcomes of the paper are proactive strategies that augment the traditional replacement policies with data mining techniques. In this paper, the performances of the greedy replacement policies such as GDS, GDSF and GD* are adapted by the data mining method and weight assignment policy. Experiments were conducted on various data sets. Hit ratio and byte hit ratio were chosen as parameters for performance. 2018 ISSN. -
Enhancing Greedy Web Proxy caching using Weighted Random Indexing based Data Mining Classifier
Web Proxy caching system is an intermediary between the Web users and servers that try to alleviate the loads on the origin servers by caching particular Web objects and behaves as the proxy for the server and services the requests that are made to the servers. In this paper, the performance of a Proxy system is measured by the number of hits at the Proxy. Higher number of hits at the Proxy server reflects the effectiveness of the Proxy system. The number of hits is determined by the replacement policies chosen by the Proxy systems. Traditional replacement policies that are based on time and size are reactive and do not consider the events that will possibly happen in the future. The performance of the web proxy caching system is improved by adapting Data Mining Classifier model based on Web User clustering and Weighted Random Indexing Methods. The outcome of the paper are proactive strategies that augment the traditional replacement policies such as GDS, GDSF, GD? which uses the Data Mining techniques. 2019 -
Creating a Logic Divider Based on BCD and Utilizing the Vedic Direct Flag Method
Reversible logic has potential for a variety of applications demanding low energy usage since it prevents information loss and energy waste. The purpose of this work is to design a new Vedic divider circuit with reversible gates. Efficiency in quantum and ASIC parameters is demonstrated by the Reversible Direct Flag Vedic Division Method (RDFVDM), which has been devised. Block-level reversible gates are used in the RDFVDM to provide benefits including lower quantum costs and less trash outputs. The performance of Cadence EDA Tool is validated by simulation trials. Based on a comparative examination utilizing current methodologies, RDFVDM performs better than comparable designs. Interestingly, it improves energy usage by 26%. Moreover, RDFVDM performs exceptionally well in terms of quantum cost while employing the RSA cryptographic technique, efficiently managing 1276,293 constant inputs and 311 garbage outputs. 2024 by the Perumal B, Balamanikandan A, Arunraja A, Venkatachalam K, Shaik Rahamtula, Dhanalakshmi M. -
Fault analysis in the 5-level multilevel NCA DCAC converter
The existing neutral clamped active inverter has common mode voltage with the high frequency which can reduce the severity with less voltage gain. The traditional active neutral point clamped (APC) DCAC converter maintains great common mode voltage with high-frequency (CMV-HF) reduction capability so, it has limited voltage gain. The paper presents a new 5-level active neutral point clamped DCAC converter that can change voltage step-up in a single-stage inversion. In the suggested design, a common ground not only reduces the CMV-HF but also improves DC link voltage use. Compared with the traditional two-stage 5-level APC DCAC converter, the proposed design has lower voltage stresses and greater uniformity. While improving the overall efficiency, the suggested clamped DCAC converter saves three power switches and a capacitor. Modelling and actual tests have proven the suggested active neutral point clamped inverters overall operation, efficacy and achievability. The proposed circuit is finally tested with fault clearance capability. 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. -
VLSI Implementation of High-Speed and Area-Efficient Multiplierless Address Generation Architecture for Deinterleaver in WiMAX Applications
This paper presents a VLSI implementation of a high-speed, area-efficient, multiplierless address generation architecture for the WiMAX deinterleaver, conforming to the IEEE 802.16e standard. The primary motivation of this work is to reduce hardware complexity and delay by eliminating multipliers, which are traditionally used in address generation. The proposed architecture is designed for FPGA and ASIC platforms, emphasizing simplicity, reduced latency, and efficient hardware utilization. The design supports standard modulation schemesQPSK, 16-QAM, and 64-QAMwith their respective code rates. Two key performance evaluations were conducted: Score 1, which refers to FPGA implementation on the Xilinx XC3S400, demonstrated a 13% increase in speed, and Score 2, based on ASIC analysis using 45-nm CMOS technology, and achieved improvements of 17% in power delay product (PDP) and 22% in area delay product (ADP) over existing architectures. These results confirm the architectures effectiveness for high-speed, low-power applications in modern communication systems. Copyright 2025 Vivek Karthick Perumal et al. Journal of Electrical and Computer Engineering published by John Wiley & Sons Ltd. -
AdaptiveNet: A Novel Architecture for Reducing Computation Complexity to Fake Review Classification
The exponential rise of e-commerce platforms has resulted in a dramatic increase in online reviews, which creates a challenge in distinguishing fake reviews that erode consumer confidence and harm commerce ecosystems. Traditional approaches for fake review detection employ computationally expensive deep learning networks which are resource-intensive and difficult to use in practice. In this paper, we describe AdaptiveNet, a new lightweight neural architecture that achieves fake review detection with much lower computational resources while maintaining a higher detection and classification precision. The model proposed in this paper is based on three original innovations: a Multi-Scale Semantic Fusion (MSSF) layer for hierarchical feature extraction, Dynamic Attention Scaling (DAS) with complexity measure attention, and Adaptive Parameter Sharing (APS) context-gated networks. With thorough evaluation on Amazon, Yelp, and TripAdvisor datasets of reviews totalling 1.2 million reviews, AdaptiveNet attains 94.8% accuracy while achieving 65% computational overhead in comparison to traditional models. The architecture outperformed all other state-of-the-art models, BERT-base (92.1%), RoBERTa (91.8%), and other more recent efficient models, requiring 70% lower parameters and 60% lower energy consumption. This work markedly advances the other efficient deep learning architectures for text classification and allows for the practical implementation of fake review detection systems in resource-limited settings as process innovation. 2026 by the authors. -
Quality of life of children and adolescents living with HIV in India: a systematic review and meta-analysis
Children and adolescents living with HIV (CALHIV) encounters compromised health and well-being especially in developing countries. Understanding the health-related quality of life (HRQOL) of CALHIV living in India is vital in planning and developing comprehensive care approach. A systematic review and meta-analysis were conducted to explore and examine the HRQOL of CALHIV in India. Five electronic databases were searched, retrieving 2,729 citations with a final eight studies that met the inclusion criteria. Methodological quality was assessed using the Joanna Briggs Institute (JBI) critical appraisal tools. The included studies predominantly evaluated quality of life using the Paediatric Quality of Life Inventory, with a mean self-reported HRQOL score of 77.62 (95% CI 72.9182.34, I 2 = 93%). HRQOL of CALHIV observed to be better than other chronically ill children. However, CALHIV demonstrated lower HRQOL than the matched general population. Younger age children and boys reported better HRQOL. Poor socio-economic status, immunological status and advanced clinical stages noted to be adversely affecting HRQOL. HRQOL of children reared in institutional care reported to better or in par with family reared children. The review highlights the sparse evidence investigating the HRQOL of children with HIV in India, and the need for further well-designed studies in this population. A population-specific holistic-care approach recommended to be benefiting the well-being of CALHIV. 2023 Informa UK Limited, trading as Taylor & Francis Group. -
A Space Vector Modulated Direct Torque Control of Induction Motor with Improved Transient Performance and Reduced Parameters Dependency
Direct torque control (DTC) of induction motors is hampered by high torque and current ripple. Integrating DTC with space vector pulse width modulation (DTC-SVPWM) is one of the frequently used approaches to solve this problem. However, it adds to the computational complexity, increases the number of necessary motor parameters needed for control scheme implementation, and also affects the transient performance of the induction motor; this approach compromises the robustness and simplicity of DTC scheme. To get around these restrictions, a novel control strategy is put forth in this paper. The suggested scheme enhances the steady-state performance and transient response of the motor while preserving the simplicity and robustness of the DTC scheme. To accomplish this, the proposed control scheme operates at varying switching frequencies during transient conditions and constant switching frequencies during steady-state. The suggested speed control method does not employ any rotating reference frame transformations or usage of many rotor parameters for computation, nor does it call for sector identification and operates with a single PI controller. The suggested topology also uses a bus-clamped PWM modulation technique, which lowers the average switching frequency to 2/3 times the actual switching frequency. Thus, switching losses are also decreased. Simulation results show the effectiveness of the proposed topology in enhancing the transient and steady-state performance of the induction motor. The results are compared with the traditional DTC and DTC-SVPWM scheme. 2023 IEEE. -
CNN based Model for Severity Analysis of Diabetic Retinopathy to aid Medical Treatment with Ayurvedic Perspective
One among the major modern life-style diseases is Diabetes. Diabetic Retinopathy is a major cause for blindness even at an early age. Clinical assessments for eye disease are done using visual examinations and probing. Retinal vessel segmentation is an important technique which helps in detection of changes that happens in blood vessel as well as gives information regarding the location of vessels. The work presented in this paper tries to detect and analyze the changes occurred in the blood vessels of human retina caused by diabetic retinopathy. Using digital imaging techniques, the severity screening technique facilitates the diagnosis of diabetic retinopathy. The model works in such a way that it helps the Ayurvedic treatment methodology for Diabetic Retinopathy. Results are obtained to categorize the data elements according to the severity of the disease and different classifications. 2022 IEEE. -
Nine Level Quadra Boost Inverter with Modified Level Shifted Pulse Width Modulation Technique
This research initiatives to introduce a switched capacitor based nine level boost inverter (SC-9LBI) powered by modified level shifted pulse width modulation (PWM) technique. The SC-9LBI equipped with single DC source along with three capacitors and eight controlled switches to develop nine level inverter output voltage. The suggested inverter configuration has the ability of boosting the inverter input voltage into 1:4 ratio. Also, this research involves modified level shifted PWM technique to enhance the quality of inverter output voltage. The effectiveness of the NLMLI is assessed through parameters such as harmonic distortion, peak voltage, and output voltage root mean square value (rms). Simulation studies have been conducted using MATLAB/Simulink to evaluate the proposed inverter's performance. 2024 IEEE. -
Level Shifted Phase Disposition PWM Control for Quadra Boost Multi Level Inverter
This article introduces a novel boost switched capacitor Inverter (NBSCI) that significantly advances existing designs. Many recently developed multilevel voltage source inverters stand out for their ability to reduce the number of DC sources while markedly improving voltage levels with fewer switching devices. Building on these advancements, our work proposes an innovative inverter arrangement that, utilizing 1 DC source, eight switches and 3 capacitors, achieves 9-level output voltage waveforms. The increased range of voltage levels facilitates the generation of high-quality sine wave output signals with minimal Total Harmonic Distortion (THD). Also, this work employs Level shifted - Phase Disposition (LS-PD) pulse width modulation techniques to generate gating signals, ensuring the production of superior output waveforms. The article also presents various simulation results conducted using MATLAB-SIMULINK, providing a comprehensive assessment of the proposed configuration's precise effectiveness under diverse modulation index. 2024 IEEE. -
Gen AI Gen Z: understanding Gen Zs emotional responses and brand experiences with Gen AI-driven, hyper-personalized advertising
Introduction: Gen Z, a tech-savvy consumer group, has highly evolved in its approach to new-age advertising. The rise of Generative Artificial Intelligence (Gen AI) has revolutionized advertising by enabling hyper-personalized content, making it essential to understand its influence on Generation Z (Gen Z) population. This study explores the responses of Gen Z participants in India to Generative Artificial Intelligence based, hyper-personalized advertisements, with a specific focus on emotional responses and brand interactions which are significant predictors of advertisement success. Methods: Using qualitative research methods, semi-structured interviews were conducted with 40 Gen Z participants. Thematic analysis of the data was performed to understand the major themes pertaining to emotional responses and brand interactions to this form of Gen AI-driven advertising. Results: Two major themes and five sub-themes were revealed through thematic analysis. The first theme, diverse emotional responses, encompassed two sub-themes, curiosity and interest as well as fear and suspicion. The second major theme, enhanced brand experience, encompassed three sub-themes of advanced targeted marketing; initial attraction and brand engagement; and brand connection and loyalty, as perceived by the participants. Discussion: Findings imply that brands can harness Gen AI-driven, hyper-personalized advertisements to evoke meaningful emotions, enhancing consumer loyalty and building stronger, more personal connections with their audience. Copyright 2025 Peter, Roshith, Lawrence, Mona, Narayanan and Yusaira. -
Significance of extra-framework monovalent and divalent cation motion upon CO2 and N2 sorption in zeolite X
Experimental observations and the GCMC (Grand Canonical Monte Carlo) simulations with fixed and mobile cations in their cavities have been used to study nitrogen and carbon dioxide sorption in divalent cation (Ca, Sr, and Ba) exchanged Zeolite X. Simulations of carbon dioxide and nitrogen adsorption isotherms and the heat of adsorption in divalent cation exchanged zeolite X produced results that were similar to those found in experimental results. Both experimental and simulated isotherms showed that carbon dioxide adsorption capacity is saturated at lower pressure with high adsorption capacity than the nitrogen isotherm in all zeolite samples. In the order of electronegativity of the extra-framework cations, the isosteric heat of sorption results show that carbon dioxide as well as nitrogen molecules interact more with divalent metal ion exchanged zeolites. Simulations of carbon dioxide and the nitrogen sorption in zeolite -X revealed that the mobile extra-framework cations in the cages of zeolite X had a significant advantage over zeolite molecular sieves in the separation process. The simulation with mobile cations can be a good tool for developing selective and purposeful zeolite-based adsorbents by knowing the cation position and its migration upon the adsorption of various gases. 2022