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Extended Slash Modified Lindley Distribution to Model Economic Variables Showing Asymmetry
This article introduces a novel probability distribution to model economic variables with high kurtosis and heavy tails showing a decreasing trend. From a mathematical viewpoint, it corresponds to the distribution of the ratio of two independent random variables, one with the modified Lindley distribution and another with the beta distribution. In some sense, it can be described as an extended three-parameter version of the Lindley distribution that has the ability to model data with high kurtosis. After presenting this distribution in more in-depth details, a comprehensive analysis is given, including its associated functions, moments, skewness, and kurtosis characteristics. Furthermore, a parametric estimation work is carried out. A simulation approach is used to validate the performance of the obtained estimates. The applicability of the proposed distribution is demonstrated by fitting real-world data into various socioeconomic scenarios. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
An Accurate Multiple Data Based Stock Prediction and Sentiment Analysis Using Synergic Deep Info Convolutional Neural Network
Sentiment analysis is one of the most widely used methods for forecasting stock market action from consumer feedback. Most of the methods associated with sentiment analysis are limited due to low accuracy and enhanced error rate. This is addressed by proposing a synergic squeeze deep info convolutional neural network-advanced variable capsule equilibrium auto encoder (SSDCNN-AVCEAE) for sentiment analysis and accurate multiple data-based stock prediction. Stock market data from NSE Nifty 50 (Mar 2, 2020May 10, 2021) and real-time twitter sentiment analysis are pre-processed through data cleaning and sentiment analyzer lexicon processes. Merging features using SSDCNN, optimized with random search algorithm, mitigates overfitting. SSDCNN eliminates redundant features. Selected features undergo classification by AVCEAE, a fusion of advanced capsule auto encoder (ACAE) and variable equilibrium optimization algorithm, enhancing prediction accuracy for rising or falling stock market movements while minimizing errors. Variable equilibrium optimization refines ACAE parameters. The proposed framework demonstrates exceptional performance with F1-Score, accuracy, false alarm rate, sensitivity, precision, specificity, and error rate reaching 98%, 99%, 0.1%, 99%, 99%, and 0.2%, respectively. The measurements highlight the model's ability to handle a variety of issues, making it a reliable option for precise stock prediction. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
The development and validation of the digital intelligence scale for students
Digital intelligence is increasingly recognized as a vital skill in navigating both academic and personal digital environments, yet existing tools often use multidimensional or adult-oriented frameworks. This study aims to develop and validate the Digital Intelligence Scale for Students (DISS), a unidimensional self-report instrument designed to assess the general digital intelligence of school, college, and university students. The study was conducted in two phases. In the first phase, data was collected from 786 students in India to examine the factor structure of the model. The analysis supported a unidimensional model, indicating that all items measured a single underlying construct. In the second phase, data was collected from 611 students in India to confirm the unidimensional model. Results supported a robust unidimensional structure, with excellent internal consistency (? = 0.954). The DISS was found to be significantly correlated with Internet Skills Scale and Digital Literacy Scale, providing evidence for convergent validity. Divergent validity was assessed using State-Trait Anxiety Inventory and Big Five Personality Inventory. This scale provides a practical framework for evaluating digital readiness in educational settings and guiding interventions. Subsequent studies could validate its relevance across cultural contexts and examine developmental trajectories in digital intelligence. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026. -
Semi-analytical framework for dynamic stress concentration in semi-elliptical notches of thin walled piezoelectric media under SH-wave excitation and KNN
This study develops a semi-analytical framework to investigate the dynamic response of semi-elliptical notches in piezoelectric half-spaces subjected to shear-horizontal (SH) wave excitation. By employing wave function expansions in elliptical coordinates and Mathieu functions, the model efficiently solves boundary value problems in electromechanically coupled media and demonstrates greater versatility compared to conventional techniques. The analysis highlights how notch depth, wave incidence angle, and excitation frequency govern surface displacement and stress amplification. In particular, deeper notches under high-frequency excitation yield pronounced dynamic stress concentration, which raises concerns regarding the structural integrity of piezoelectric devices. Comparative results further reveal that materials with stronger piezoelectric coupling, such as PZT-5H, exhibit more severe stress localization than PZT-6B or BaTiO?. The study also examines the role of weak interfaces and nanoscale surface effects. Weak interfaces are shown to reduce stiffness in phonon and phason fields while increasing stiffness in the electric field for Rayleigh waves, with such effects becoming most prominent under strongly dispersive conditions. At the nanoscale, surface and interface influences effectively mitigate dynamic stress concentration, with diffraction stress concentration factor (DSCF) decreasing monotonically as the nano-influence factor increases, eventually tending to vanish in the limit of diminishing defect size. To complement the analytical formulation, a K-Nearest Neighbors (KNN) machine learning (ML) model was implemented using the analytical DSCF dataset. The classifier achieved nearly 90% accuracy in distinguishing between low and high stress concentration regimes. Decision maps highlighted frequencygeometry combinations most prone to defect amplification, while the confusion matrix confirmed reliable detection of critical hot-spots. This integration of ML provides a rapid surrogate framework that complements the semi-analytical method, enabling efficient prediction, defect screening, and design optimization in advanced piezoelectric systems. The Author(s), under exclusive licence to Springer Nature B.V. 2025. -
Maximised bioethanol extraction from bamboo biomass through alkali pretreatment and enzymatic saccharification by application of ANN-NSGA-II-based optimisation method
The demand for alternative fuels is growing due to the depletion of fossil fuel resources. Non-edible resources are explored as alternatives, and a bamboo is an up-and-coming option for producing ethanol. The extraction process for bioethanol from bamboo involves alkali pretreatment, enzymatic saccharification, and ethanol production. The bamboo biomass is treated with alkali at high temperatures and pressure. This treatment helps break the lignin bonds that hinder the reaction between cellulose and enzymes. As a result, the pretreated biomass contains 40% less lignin than its raw form. Next, the air-dried pretreated biomass undergoes saccharification using Supercut Acid Cellulose. The saccharification process is optimised to achieve the shortest possible time, determined through prediction models based on artificial neural networks and optimisation techniques like Non-dominated Sorting Genetic Algorithm-II. The optimised process involves specific biomass and enzyme loading, producing reducing sugars estimated using the DNS method. Following enzymatic Saccharification, the hydrolysate is fermented using Saccharomyces cerevisiae, a type of yeast. This fermentation process yields ethanol with a 1614.26mg/kg concentration. The Author(s), under exclusive licence to Springer Nature B.V. 2023. -
Investigating and analyzing the causality amid tourism, environment, economy, energy consumption, and carbon emissions using TodaYamamoto approach for Himachal Pradesh, India
Himachal Pradesh is a preferred tourist destination with a Compound Annual Growth Rate (CAGR) of 10.76% between 201112 and 202021. The increasing trend of CAGR has boosted the tourism economy in the state while impacting the local environment. The negative impacts have recently increased due to changes in climatic patterns and increased tourism influx during the post-pandemic period. In this context, the present study analyzed the impact of tourism on the environment, economy, and energy consumption using the Environmental Kuznets Curve (EKC) hypothesis. The novelty of this study is to the existing literature on sustainable tourism development through investigating the interrelationship between tourism, environment, economy, energy consumption, and carbon emissions by employing the TodaYamamoto (TY) technique. This study will be a pioneering scientific investigation with quantitative results in the western Himalayan states of India, encompassing Jammu & Kashmir, Uttarakhand, and Himachal Pradesh. The annual data for each variable, such as per capita carbon emission (CEP), per capita Gross State Domestic Product (GSDP), per capita GSDP square, per capita energy consumption (ECP), and per capita tourism receipts (TRP), was collected from 2010 to 2021. This study exhibited an inverted-U EKC in the state, signifying the initial stage of economic development and extensive exploitation of natural resources for tourism. The TY results indicated an inter-causal relationship and feedback association among the variables in the study area. Thus, increased TRP would lead to an upsurge in energy consumption affecting the environmental quality due to increased carbon emissions. Such environmental degradation in the state would negatively impact the tourism sector in the long run. The research findings would guide planners and policymakers in promoting sustainable tourism. The Author(s), under exclusive licence to Springer Nature B.V. 2023. -
Role of energy sources in promotion of sustainable development: moderating implications of globalisation
This study empirically investigates the impact of renewable and non-renewable energy generation on sustainable development for a balancedpanel of 68 developed and developing economies from 1990 to 2019. This is done to scrutinise the intricate interplay between energy sources and sustainable development outcomes at the global level. The estimated models also control for the effects of globalisation, urbanisation, and government expenditure. The Westerlund cointegration establishes a significant long-run relationship between the variables under consideration. In this regard, the two-step dynamic system-generalised moment method (system GMM) demonstrates a positive impact of renewable energy, globalisation, and government expenditure on sustainable development. In contrast, non-renewable energy and urbanisation exert detrimental influences on it. However, both the energy sources demonstrate an amplified positive impact on sustainable development under the moderating influence of globalisation. The Feasible Generalised Least Squares estimation also confirms the long-run reliability of these baseline findings. Furthermore, Granger based non-causality test establishes a significant causal relationship between the variables under consideration. Potential policy suggestions for promoting the sustainable development are also discussed. The Author(s), under exclusive licence to Springer Nature B.V. 2024. -
A bibliometric analysis of sustainability and organizations performance
The incorporation of sustainability into an organizations performance is becoming an emerging topic to work upon. Moreover, conventional economic systems have had significant negative consequences for sustainable management, as well as imbalanced wealth distribution, which has resulted in natural catastrophes and population disparity. Sustainability practices in the current environment represent better quality performances and affect organizations performance. This research highlights the key areas and current evolution in the notion of sustainable development and organizational performance, as well as recommendations for further studies. Using the bibliometric analysis we examine a sample of 1442 articles published in Scopus between 1994 till 2021. The researcher identifies prominent authors, publications, and journals by employing a variety of network analysis techniques such as term co-occurrence, co-citation, and bibliography coupling with the help of VOS viewer. To the best of the authors knowledge, no other study has examined bibliographic data on sustainability and organizations performance; hence, this research is a one-of-a-kind addition to the literature. The Author(s), under exclusive licence to Springer Nature B.V. 2024. -
Integrated biogasification and carbon capture pathways: a system-level review of technologies, storage options, and deployment challenges
Carbon-negative energy systems that integrate bioenergy production with permanent carbon dioxide (CO2) sequestration are increasingly recognized as essential for achieving global net-zero and beyond-zero climate targets. While extensive research exists on individual components such as biogasification, carbon capture technologies, and geological storage, a coherent system-level synthesis linking these pathways remains fragmented. This review addresses this gap by providing an integrated assessment of biogasification-based carbon capture and storage (CCS) systems, with particular emphasis on techno-economic performance, capture efficiency, subsurface storage options, and deployment challenges. Following the PRISMA 2020 guidelines, 112 studies were systematically selected from an initial pool of 780 publications and analyzed to compare advanced gasification routes, emerging capture technologies, and storage strategies. The results indicate that hybrid gasificationsolid oxide fuel cell systems can achieve efficiencies of up to 55%, while cryogenic carbon capture consistently delivers CO? purities above 95% with reduced energy penalties. Supercritical water gasification and hydrothermal pathways demonstrate strong potential for wet biomass conversion, achieving hydrogen yields exceeding 1150 mmol/L and carbon efficiencies above 80%. Despite these technical advances, large-scale deployment remains constrained by high costs (USD 8001350 per tonne CO2), infrastructure limitations, and policy uncertainty. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026. -
Adapting Employee Engagement Strategies Amid Crisis: Insights from the COVID-19 Pandemic
Crises are unpredictable events that have the potential to strike at any moment, causing significant disruptions to work, daily routines, and the normal course of life. The COVID-19 Pandemic served as a facilitator for transformative changes in the way we work, shifting to an era of remote and flexible work arrangements across industries. This crisis underlined the importance of employee engagement and organizational culture-building in navigating unforeseen situations. As organizations prepare for the future, it becomes crucial to anticipate and adapt to potential crises that may arise. The effect of the pandemic varied from industry to industry. When the technology industry worked towards creating a virtual workspace, the production industry strived to continue production without disruption. However, irrespective of the industry, HR teams across the board were dedicated to identifying and addressing the challenges posed by the crisis. They have worked tirelessly to ensure employee engagement remains a priority. This qualitative study explores the challenges encountered by HR teams during the pandemic and explores the strategies and policies they adopted to foster employee engagement. The data was collected through an in-depth interview with 39 HR Practitioners from different industries. The significant challenges included the need to cultivate a sense of community, navigate muddled up HR processes, sustain productivity amid disruptions, and prioritize employee wellness. To provide a comprehensive analysis, this study examined industry-specific approaches, employing within-case analysis to understand key strategies in communication, rewards and recognition, employee benefits, wellness initiatives, and fostering an enjoyable virtual workplace. This study offers a forward-looking perspective and serves as a guide for organizations aiming to thrive in times of uncertainty, ensuring that employee engagement remains a strategic priority regardless of the crisis at hand. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. -
Digital micromirror device characterization in optical band for astronomical multi-object spectrograph
The Digital Micromirror Device (DMD), a micro-electro-mechanical system (MEMS) consisting of individually controllable micromirrors, has emerged as a versatile tool for astronomical instrumentation, particularly in multi-object spectroscopy (MOS). Unlike traditional slit masks or fiber-based systems, DMDs offer dynamic reconfigurability, enabling efficient light modulation and enhanced spectral acquisition. Their adaptability has led to widespread adoption in ground-based spectrographs (e.g., RITMOS, BATMAN, SAMOS, IRMOS) and feasibility studies for space missions (e.g., EUCLID, CASTOR, SUMO, SIRMOS). DMDs have demonstrated robustness in space qualification tests, including radiation exposure, thermal cycling, and mechanical stress, making them viable for space-based applications. Recent advancements, such as UV-transparent windows and enhanced coatings, further expand their potential for ultraviolet astronomy. In India, the success of AstroSats Ultra Violet Imaging Telescope (UVIT) has motivated the development of the next-generation INdian Spectroscopic and Imaging Space Telescope (INSIST), which includes a DMD-based MOS for UV/optical observations. To advance its Technology Readiness Level (TRL), we evaluated the Texas Instruments DLP9500 DMD (1920 1080 micromirrors, 10 m pitch) in the optical band, assessing key parameters such as diffraction efficiency, reflectivity, contrast, micromirror repeatability, and Point Spread Function (PSF) alignment. This study establishes a foundation for future UV-optimized DMD applications in INSIST and other astronomical missions. The Author(s), under exclusive licence to Springer Nature B.V. 2025. -
DMD-based Multi-Object Spectrograph (D-MOS): AIV and first light results
A Digital Micromirror Device (DMD)-based Multi-Object Spectrograph (D-MOS) with an integrated imager has been developed. The optical performance of the MOS is evaluated through comprehensive laboratory calibration and on-sky observations using the 1.3-meter J.C. Bhattacharya (JCB) Telescope at the Vainu Bappu Observatory (VBO). The system is designed to assess the viability of using a DMD as a programmable slit mechanism for future ultraviolet-optical space missions. A complete imager-cum-spectrograph assembly was constructed using off-the-shelf optical components and configured for operation in the optical band, employing a DLP9500 DMD with a 19201080 micromirror array. Calibration experiments established the DMD-to-detector coordinate mapping and validated the strategies for object selection and slit placement. On-sky tests in crowded stellar fields confirmed successful slit targeting, precise object alignment, and multiplexed spectral acquisition. The spectrograph achieved a peak efficiency of 32%, a spectral resolving power of R1000 at 6000 a multiplexing capability of up to 46 slits (extendable to 85), and a contrast ratio of 6000. These results demonstrate the robustness and effectiveness of the DMD MOS system under real observational conditions and raise its TRL level for use in next-generation spectroscopic space missions. The Author(s), under exclusive licence to Springer Nature B.V. 2025. -
The Role of Real Exchange Rate in Indias Service Export: Do Remittances Inflows Matter in Post Liberalization-Era?
This study assesses the effects of real exchange rate and remittance inflows on India's total service exports, comprising traditional and modern service exports, spanning the annual data from 1990 to 2020. The control variables for the service export function include developments in the banking sector and the stock market and net inflows of foreign direct investment. The ARDL model is the estimating technique of the present study. The real exchange rate has an adverse effect on total, traditional, and modern service exports, according to the long-run outcomes of the ARDL model. Remittance inflows are interestingly shown to support modern service exports while impeding total and traditional service exports. The growth of the banking sector is beneficial for traditional and total service exports, but it has a negative impact on modern service exports. All service exports are benefited by stock market development; however, net FDI inflows negatively impact all forms of service exports. Based on these results, thepolicymakers in India are advised to maximize the effective utilization of remittance inflows in traditional service exports. Additionally, proactive intervention by the central bank is recommended to mitigate the adverse effects of the real exchange rate on traditional and modernservice exports. This study also provides valuable insights for thepolicymakers and practitioners seeking to enhance India's service export performance while navigating the complexities of real exchange rates, remittance inflows, and financial factors. The Author(s), under exclusive licence to Springer Japan KK, part of Springer Nature 2024. -
Mushroom Farming and Rural Youth: Analyzing a Sustainable Livelihoods Framework
We examine mushroom farming as an alternative livelihood for rural youth, utilizing the Sustainable Livelihoods Framework (SLF) to assess its impact on economic resilience and rural development. A qualitative study was conducted in Uttarakhand, India, involving 20 participants aged 1835 who were engaged in mushroom cultivation. Data from semi-structured interviews were analyzed thematically, revealing that rural youth are motivated by their hopes for financial independence and a growing interest in sustainable agriculture. Mushroom farming enhances human capital through skill development, cultivation, and business management, which in turn boosts self-esteem. The income they earn provides financial capital to support their families and reinvestment in their businesses. In addition to empowering marginalized groups, mushroom farming bolsters all five capitals of the SLF: human capital through skills and education; financial capital through income generation; natural capital via the use of agricultural waste and biological resources; physical capital through infrastructure and tools; and social capital through peer networks and community support. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
Power law coefficient effects on buoyant heat transfer in porous trapezoidal enclosures
The investigation of steady, incompressible, laminar mixed convective fluid flow within two different types of trapezoidal enclosures filled with saturated water and study explores how the power-law index governs buoyancy-driven heat transfer in a porous trapezoidal cavity filled with non-Newtonian fluids. Unlike Newtonian fluids, non-Newtonian fluids exhibit flow behavior that directly depends on the power-law index, which characterizes their shear-dependent viscosity. We formulate the governing equations in terms of the stream function and temperature and solve them using a validated, in-house MATLAB solver. Embedding a porous matrix within a trapezoidal enclosure creates intricate interactions between convective currents and conductive resistance. By performing numerical simulations across a range of Rayleigh numbers (Ra = 102 to 2 103) and boundary conditions, we systematically assess how variations in the power-law index alter local velocity fields, temperature distributions and overall heat-transfer rates. Our results reveal that increasing the power-law index strengthens convective flow and raises the average Nusselt number, whereas decreasing the index shifts the balance toward diffusion-dominated transport. These findings offer practical guidance for enhancing thermal management in industrial systems that employ both Newtonian and non-Newtonian fluids within porous structures. The study presents new empirical correlations linking Nu, Ra and power law co-efficients offering a practical tool for engineering design. Unlike previous works that focused primarily on Newtonian fluids or simplified geometries, this work provides a detailed analysis of non-Newtonian effects in realistic porous enclosures. These results contribute to a deeper understanding of convective mechanisms in complex therm-ofluid systems and offer guidance for optimizing thermal performance in engineering applications. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2026. -
Mathematical and prosodic analysis of intonation in Malayalam
This study investigates the intonation patterns of Malayalam, a language spoken in Kerala, India, using polynomial regression and Kernel Density Estimation. Malayalam has distinct pitch modulation patterns across genders and question types, with variations in acoustic features, syllables, and stress structures. We examine the mean pitch characteristics of different question types and analyze the correlation between stress patterns and syllable structure counts in the language. Additionally, we perform clustering analysis on Malayalam words to highlight similarities and diversities in acoustic features, which helps us understand the phonetic diversity within the language. Our analysis shows that the overlap of KDE curves in the feature space allows us to analyze the linguistic factors that influence variability in Malayalam speech. This suggests a need for further research on regions where syllable complexity and phonological patterns are notably concentrated. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
Waves, Velocity Addition and Doppler Effect in Light of EPRs Completeness Condition
It is a standard practice to derive velocity addition rules for point particles from Galilean and Lorentz transformations in point (classical) mechanics, and to apply such rules to wave velocities for explaining Doppler effect. However, in such standard practice, it is never shown whether the equation for wave propagation actually transforms in a way such that the velocity addition rules get manifested through the equation itself. We address this gap in the literature as follows. We claim that the velocity addition for waves, being the one and only mean to explain the empirically verified Doppler effect, should be considered as an element of physical reality in accord with EPRs completeness condition of a physical theory. Therefore, the equation for wave propagation should manifest such velocity addition so as to be considered as a part of the respective physical theory of waves. We show that such manifestation is possible if and only if wave propagation is modeled with first order partial differential equations. From a historical point of view, this work settles the Doppler-Petzval debate which originated from Petzvals demand for an explanation of Doppler effect in terms of differential equations. From the foundational perspective, this work sets the stage for a renewed focus on the mathematical modeling of wave phenomena, especially in the context of various Doppler effects. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
Navigating uncertainty: how do parenting and grit influence career indecisiveness among Indian emerging adults?
This paper examined how perceived parenting styles (permissive, authoritative, and authoritarian) influenced career indecisiveness among Indian emerging adults, with grit as a mediating factor. A total of 420 Indian emerging adults (18-25 years) were administered self-reported questionnaires. Both PROCESS mediation analyses and path analysis using SPSS AMOS were computed. Both parents authoritative and authoritarian parenting styles predicted career indecisiveness. The passion component of grit emerged as a significant mediator, as supported by the PROCESS mediation analysis. These findings offer new methodological and practical insights, highlighting the significance of culturally relevant career counseling interventions, integrating both familial influences and noncognitive traits. The Author(s), under exclusive licence to Springer Nature B.V. 2025. -
A web forensic optimization framework for investigating false information on social media using the ForenOptiNet model
Todays technological advancements in the field of digital media have resulted in the unprecedented transmission of information leading to unauthorized exploitation. Businesses use social media as the primary marketing platform. Considering the severity of spreading misinformation and fake news in our society due to false marketing by bogus businesses, there is a great need to demystify this propagation using web forensics-based frameworks. In order to increase consumer equity, the rapid spreading of malicious information makes it hard for users to differentiate between real and false information. This research intends to design an effective and adaptable framework for detecting false information campaign carried out by criminals affecting online social network (ONS). A novel ForenOptiNet model is designed and diverse data gathered from the Reddit and INFD dataset is used to train the suggested model. The Web Forensic-Based Investigation Optimization (WFBIO) algorithm provides a high accuracy classification of malicious content from the web. Moreover, the WFBIO framework enhances the robustness of the ForenOptiNet model and ensures that the proposed approach can effectively identifies misinformation and fake news to validate factual claims. Results of the simulation analysis provides a muti-level mechanism combining anomaly detection and ForenOptiNet model together outperforming other state-of the-art optimization algorithms trained against CNNs with SGD, Adagrad and AdaDelta. While these baselines yielded accuracies between 55 and 92%, our proposed model achieved highest accuracy of 99% accuracy with an effective front-end design integration. The Author(s) 2025. -
Multi-stage spatial temporal ensemble model with integrated learning methods for robust deepfake detection
Deepfake detection remains a significant challenge as modern generative models increasingly minimize visible artefacts, and many existing approaches rely solely on either spatial or temporal cues, which limits their robustness and generalization. Many existing hybrid approaches integrate mature learning models in linear or stacked pipelines, which often suffer from error propagation, reduced interpretability, and suboptimal generalization. Unlike prior hybrid approaches that primarily stack spatialtemporal learners, the proposed multi-stage hybrid Integrated Learning Method (ILM) introduces a validation-aware dual-detection mechanism, an independent dual-path spatial-temporal learning design, and a decision-level nonlinear ensemble fusion strategy, explicitly mitigating face mislocalization, temporal dilution, and false-positive propagation observed in existing deepfake detection pipelines. The ILM framework structurally coordinates facial region localization and validation using YOLOv5 and Haar Cascade, deep spatial feature extraction using ResNet-50, frame-level spatial classification via LightGBM, and temporal sequence modeling using LSTM networks. The outputs from the spatial and temporal pathways are subsequently fused using a Random Forest classifier, enabling nonlinear aggregation of complementary evidence while preserving interpretability. Experimental results on the FaceForensics + + and Celeb-DF (v2) benchmark datasets show that ILM achieves 98.30% accuracy, 97.90% precision, and 98.70% recall, outperforming recent state-of-the-art CNNLSTM, ViT-based, and CNNTransformer models by 16%. Ablation studies confirm that each module contributes incrementally to performance stability and false-positive reduction, demonstrating the importance of ILMs multi-stage architecture rather than the individual algorithms alone. Overall, ILM provides a modular, accurate, and computationally efficient solution suitable for deployment in digital forensics, media authentication, and AI governance. Future work will extend ILM with transformer-based global encoders and explainable AI techniques to further improve interpretability and robustness against emerging deepfake models. The Author(s) 2026.