International Journal of Multidisciplinary Engineering In Current Research
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Intelligent Protection And Fault Management In Electrical Power Systems Using Ai Techniques
Electrical power systems face increasing complexity with renewable energy integration and expanding transmission networks. Traditional protection schemes struggle with dynamic fault scenarios, necessitating advanced intelligent solutions. This research investigates artificial intelligence techniques for enhancing power system protection and fault management. The study examines machine learning algorithms including Neural Networks, Support Vector Machines, and Deep Learning models for fault detection, classification, and localization. Through systematic analysis of AI-based protection systems, this research evaluates performance metrics across various fault scenarios. The hypothesis proposes that AI techniques significantly improve fault detection accuracy and response time compared to conventional methods. Results demonstrate that ensemble machine learning models achieve 99.96% accuracy in fault detection, with neural networks showing 99.52% classification accuracy. Deep learning approaches reduce false alarm rates while maintaining specificity above 98%. Implementation of reinforcement learning for adaptive protection demonstrates substantial improvements in grid resilience. The findings establish AI techniques as transformative tools for modern power system protection, offering enhanced reliability, faster fault isolation, and improved system stability essential for smart grid infrastructure.
Structural and Topological Perspectives on Four-Dimensional Manifolds
Four-dimensional manifolds occupy a uniquely complex position in modern topology. This paper examines their structural and topological properties through intersection forms, Betti numbers, Euler characteristics, and gauge-theoretic invariants. The primary objective is to systematically compare smooth and topological 4-manifolds using standard examples and established invariants. The study employs a descriptive-analytical methodology, drawing on foundational results from Freedman, Donaldson, and Witten to analyze topological invariants of key 4-manifolds including S⁴, CP², S²×S², K3, and the four-torus T⁴. The hypothesis posits that the divergence between smooth and topological categories in dimension four is uniquely determined by the arithmetic of intersection forms and gauge-theoretic constraints. Results demonstrate that Euler characteristics range from 0 (T⁴) to 24 (K3), while Seiberg-Witten invariants sharply distinguish diffeomorphism types within homeomorphism classes. The discussion affirms that no single invariant suffices for complete classification, and that the interplay between intersection forms and gauge theory remains the central framework for understanding 4-manifold topology. The conclusion highlights open problems including the smooth four-dimensional Poincaré conjecture.
Four-dimensional manifolds, intersection forms, Seiberg-Witten invariants, Euler characteristic, smooth topology
A Mathematical Analysis of Diabetes Progression Using Differential Equation Models
Diabetes mellitus is among the fastest-growing chronic metabolic disorders globally, with 589 million adults affected as of 2024. This paper presents a systematic mathematical analysis of diabetes progression employing ordinary differential equations (ODEs) and compartmental modelling frameworks. The primary objective is to simulate glucose-insulin dynamics and disease transition states using the Bergman Minimal Model alongside a population-level compartmental model calibrated to International Diabetes Federation (IDF) and ICMR-INDIAB data. The methodology integrates parametric estimation against real-world clinical and epidemiological datasets sourced from peer-reviewed literature through 2024. The hypothesis posits that ODE-based frameworks can accurately replicate disease progression stages from normoglycaemia through prediabetes to type 2 diabetes with complications and identify mathematically precise intervention thresholds. Results demonstrate that the basic reproduction number (R₀) governs stability of disease-free and endemic equilibria, while insulin sensitivity index (Sᵢ) is the primary physiological predictor. Sensitivity analysis confirms that treatment recovery rates exert the strongest inverse influence on diabetes prevalence. Findings are contextualised within the Indian epidemiological landscape, where over 101 million individuals are currently diabetic. The conclusions affirm ODE models as indispensable predictive instruments for evidence-based national diabetes policy.
Differential equations; diabetes progression; Bergman minimal model; insulin sensitivity index; compartmental epidemiological modelling
Analysis of Surface Wave Generation and Propagation in Layered Geological Media
Surface waves, particularly Rayleigh and Love waves, constitute the dominant component of seismic energy recorded at the Earth's surface and carry crucial diagnostic information about subsurface geological structure. This study investigates the generation mechanisms and propagation behavior of surface waves in horizontally layered geological media through a synthesis of numerical simulation, multi-channel analysis of surface waves (MASW), and dispersion inversion techniques. The primary objective is to characterize how elastic wave parameters shear-wave velocity (Vs), layer thickness, and Poisson's ratio govern surface wave dispersion characteristics across multilayered subsurface profiles. The hypothesis posits that Rayleigh wave phase velocity at high frequencies (>10 Hz) is predominantly sensitive to shallow-layer Vs, whereas low-frequency components penetrate deeper geological horizons. Results demonstrate statistically significant agreement (R² > 0.91) between numerically simulated and field-observed wave parameters across five geological units, with Vs30 values ranging from 145 to 1,750 m/s. Dispersion curves confirm strong depth-dependent phase velocity variation. These findings reinforce the viability of MASW-based inversion for precision subsurface characterization applicable to seismic hazard assessment, foundation engineering, and resource exploration in geologically complex terrains.
Surface wave dispersion, Rayleigh waves, layered geological media, MASW inversion, shear-wave velocity
Trust-Aware Clustering for Enhanced Routing Security and Performance in Sensor-Enabled Mobile Ad Hoc Networks
Mobile Ad Hoc Networks (MANETs) play a vital role in sensor-based and Internet of Things (IoT) applications operating in infrastructure-less and dynamically changing environments such as disaster recovery, military operations, and remote monitoring. However, frequent topology variations, limited node energy, and the absence of centralized control make MANETs highly vulnerable to routing inefficiencies and security threats, leading to degraded sensor data delivery performance. This paper proposes a trust-aware clustering-based routing framework for sensor-enabled MANETs aimed at enhancing both routing security and network performance. The proposed approach employs a hybrid cluster head (CH) election mechanism that integrates node degree, mobility, residual energy, and a dynamically computed trust value to ensure stable, energy-efficient, and secure cluster formation. The trust model evaluates node behavior based on packet forwarding reliability and communication consistency, enabling the identification and isolation of malicious or selfish nodes. Secure inter-cluster communication is further strengthened through lightweight symmetric encryption and authentication mechanisms, while a trust-assisted intrusion detection component at the CH level facilitates early threat detection. The proposed protocol is evaluated using NS-3 simulations under varying node densities and mobility conditions. Performance analysis demonstrates significant improvements in packet delivery ratio, routing overhead, end-to-end delay, and malicious node detection accuracy compared to AODV, CBRP, and Secure-AODV protocols. The results indicate that integrating trust-aware clustering with lightweight security mechanisms provides an effective and scalable solution for reliable and secure data transmission in sensor-enabled MANET and IoT environments.
A Comprehensive Energy–QoS Trade-off Modeling Framework for Heterogeneous Cloud Data Centers
Modern cloud data centers operate under constant pressure to deliver high performance while simultaneously reducing energy consumption and operational costs. As cloud infrastructures grow increasingly heterogeneous—comprising diverse servers, virtualization layers, and multi-tier architectures—maintaining an optimal balance between Quality of Service (QoS) and energy efficiency has become a significant challenge. Traditional resource management strategies often focus on either minimizing power usage or satisfying Service Level Agreements (SLAs), but rarely address the dynamic trade-off between these two competing objectives in an integrated manner. This research proposes a comprehensive Energy–QoS trade-off modeling framework tailored for heterogeneous cloud data centers. The framework establishes mathematical relationships between workload characteristics, resource utilization, energy consumption, and SLA compliance. By incorporating multi-objective optimization principles, the model quantifies the impact of resource allocation decisions on both performance and power efficiency. It also introduces a penalty-based SLA violation function to capture real-world service constraints. The expected outcome of this study is a flexible modeling foundation that enables cloud operators to make informed, adaptive decisions that balance energy savings with service reliability. The framework aims to reduce operational costs, minimize SLA violations, and enhance sustainability, thereby supporting the development of intelligent and environmentally responsible cloud computing systems.
Carbon-aware computing, Cloud data centers, Energy efficiency, Energy modeling Heterogeneous systems, multi-objective optimization, Quality of Service (QoS), Resource allocation Service Level Agreement (SLA), Workload characterization
Improving Medical Diagnosis Systems using Efficient Machine Learning System
This paper proposes a unified and mathematically grounded hybrid framework for disease prediction by integrating machine learning, applied mathematics, and fuzzy logic. The study focuses on predictive modeling of infectious and non-communicable diseases, including COVID-19, lung cancer, swine flu, and dengue, using structured clinical, epidemiological, and climatic datasets. Data preprocessing techniques such as cleaning, normalization, and train–test splitting are employed to ensure reliability and consistency. Multiple supervised learning algorithms Support Vector Machine, Random Forest, k- Nearest Neighbors, Decision Tree, and Artificial Neural Network are implemented and comparatively evaluated using accuracy, precision, recall, F1-score, mean squared error, and confusion matrices. To address uncertainty in epidemiological parameters, fuzzy mathematical modeling and bifurcation analysis are incorporated, enabling uncertainty-aware interpretation of disease dynamics through the fuzzy basic reproduction number. Experimental results indicate that Random Forest achieves the most stable and reliable performance, while SVM and ANN show competitive outcomes. Overall, the proposed hybrid analytical–ML framework enhances interpretability, robustness, and scalability, making it suitable for epidemiological studies and clinical decision support systems.
Machine Learning, Fuzzy Logic, Disease Prediction, Epidemiological Modeling, Clinical Decision Support.
Secure Cloud Enabled Platform for AI-Powered Computer Vision Syndrome Detection
Computer vision syndrome detection has become a significant discovery in the current healthcare system, as a way of diagnosing genetic and development disorders at an early stage with the help of facial features. Nevertheless, in practice, such systems must have high-level security, why privacy protection, and scalability in the case of sensitive patient information. This paper describes a safe cloud-based system that incorporates AI-based computer vision algorithms to detect syndromes automatically. The given framework makes use of using convolutional neural networks (CNNs) to extract facial features and make deep learning classifications using the Softmax probability-based syndrome prediction. Experimental evaluation was done using a 90 sample of faces images that consisted of Down Syndrome (33.3%), Williams Syndrome (22.2%), Turner Syndrome (16.7%), and other syndromes (27.8%). The gender representation was also fairly equal at 53.3/46.7 % males/females respectively. The site also has security features like encryption and privacy learning methods to secure the information of the patients in the cloud implementation.
Deep Neural Intelligence for Proactive Cyber Defence: A Comprehensive Review of Emerging Machine Learning Architectures
The growing sophistication of cyber threats has exposed the limitations of traditional rule-based security systems, necessitating the development of intelligent and proactive defence mechanisms. Recent advances in machine learning and deep learning have significantly improved intrusion detection capabilities by enabling automated analysis of large-scale network traffic and early identification of anomalous behavior. This paper presents a comprehensive review of emerging deep neural architectures for proactive cyber defence, focusing on Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, hybrid CNN–LSTM models, and Graph Neural Networks (GNN). The study examines their architectural characteristics, implementation parameters, and comparative performance in detecting advanced cyber threats, including zero-day attacks and persistent intrusions. Furthermore, the paper proposes a hybrid deep neural framework integrated with threat intelligence and automated response mechanisms to enhance detection accuracy and reduce response latency. Key challenges such as computational complexity, false positives, and real-time deployment constraints are critically analyzed. The findings indicate that hybrid and graph-based models demonstrate superior capability in capturing both spatial and relational attack patterns, making them suitable for next-generation Intrusion Detection Systems. This review contributes to the existing body of knowledge by consolidating recent advancements, identifying research gaps, and outlining future directions for scalable, adaptive, and intelligent cyber security solutions capable of addressing the evolving threat landscape.
Design And Implementation Of A Real-Time Digital Twin System For Underground Mine Monitoring And Control
Digital twin technology has emerged as a transformative solution for underground mine safety and operational efficiency, addressing critical challenges in real-time monitoring and predictive control. This research presents the design and implementation of a comprehensive digital twin system integrating Internet of Things sensors, wireless communication networks, and advanced data analytics for underground mine environments. The study employs a multi-sensor framework incorporating environmental monitoring systems capable of detecting hazardous gases like methane, carbon monoxide, and oxygen levels, alongside temperature, humidity, and ground displacement sensors. Utilizing LoRaWAN wireless technology and 3D visualization platforms, the system achieves near real-time data transmission with latency below 500 milliseconds. Implementation at a pilot underground mining facility demonstrated 87.5% sensitivity in hazard detection, 15% reduction in maintenance costs through predictive analytics, and 40% improvement in emergency response efficiency. Statistical analysis confirms significant enhancements in worker safety metrics and operational productivity. The digital twin framework successfully bridges physical and virtual mining operations, enabling proactive decision-making and reducing accident risks by 65%. This research contributes validated methodologies for scalable digital twin deployment in challenging underground environments, advancing Industry 4.0 adoption in the mining sector.
Digital Twin, Underground Mine Monitoring, IoT Sensors, Real-Time Control, Mine Safety.
Optimized Blasting Techniques For Enhanced Rock Breakage In Hard Rock Metal Deposits
Blasting operations in hard rock metal deposits represent a critical phase in mining activities, directly influencing downstream processes and overall mine productivity. This study examines optimized blasting techniques aimed at enhancing rock breakage efficiency in hard rock metal deposits through comprehensive analysis of blast design parameters, rock properties, and fragmentation outcomes. The research investigates the relationship between controllable blast parameters including burden, spacing, powder factor, and stemming with resultant rock fragmentation. Field investigations conducted across multiple hard rock mining operations revealed that optimization of blast design parameters can significantly improve fragmentation quality while reducing operational costs. Results demonstrate that optimal burden values ranging from 3.5-4.2 m combined with spacing ratios of 1.2-1.4 achieved superior fragmentation with mean fragment sizes between 250-350 mm. Statistical analysis indicates strong correlation between powder factor optimization and downstream productivity improvements. The study establishes that integrated approaches combining rock mass characterization, precise explosive energy distribution, and controlled initiation sequences yield enhanced breakage efficiency. Findings provide valuable insights for mining professionals to optimize blasting operations, reduce production costs, and improve overall mining efficiency in hard rock metal deposit extraction.
Blasting optimization, Hard rock fragmentation, Powder factor, Blast design parameters, Metal deposits.
Study on Groundwater Alteration Due to Surface Mining and Its Mitigation Framework
Surface mining operations significantly compromise groundwater quality through multiple contamination pathways including acid mine drainage, heavy metal leaching from overburden dumps, and disruption of natural aquifer systems. This study comprehensively examines groundwater alteration mechanisms in Indian coal mining regions, focusing on physicochemical parameter changes and heavy metal accumulation patterns. The investigation employed systematic sampling protocols across major coalfields including Korba, Singrauli, Jharia, and Talcher, analyzing fourteen heavy metals and key water quality parameters. Results demonstrate that iron, manganese, and aluminum concentrations exceeded Bureau of Indian Standards limits in approximately 59%, 56%, and 48% of groundwater samples respectively near active mining zones. Heavy Metal Pollution Index values ranged from 1.5 to 99.1 across different regions, indicating low to critical contamination levels. Statistical analysis revealed significant seasonal variations with post-monsoon periods showing elevated metal concentrations due to leaching processes. The study proposes an integrated mitigation framework incorporating passive treatment systems, phytoremediation technologies, managed aquifer recharge, and advanced monitoring protocols. Implementation of these strategies can reduce heavy metal concentrations by 65-85% and restore pH levels to acceptable ranges within 18-24 months of intervention.
Groundwater contamination, Surface mining, Heavy metal pollution, Acid mine drainage, Mitigation strategies.
AI-Enabled Optical Sensing Technologies for Emerging Biomedical Applications
Artificial intelligence (AI)-enabled optical sensing technologies represent a transformative paradigm in biomedical diagnostics, offering unprecedented accuracy and real-time disease detection capabilities. This study investigates the integration of machine learning and deep learning algorithms with optical biosensors for enhanced biomedical applications. The objective was to systematically analyze the performance metrics of AI-enhanced optical sensors across various detection modalities including surface plasmon resonance, fluorescence, Raman spectroscopy, and colorimetric sensing. A comprehensive review methodology was employed, analyzing peer-reviewed literature from 2020-2022 to extract quantitative performance data. The hypothesis posited that AI integration significantly improves sensitivity, specificity, and detection accuracy compared to conventional optical sensing methods. Results demonstrated that AI-enhanced optical biosensors achieved detection accuracies ranging from 91% to 98%, with sensitivities between 5,000-24,000 nm/RIU for SPR-based systems. Discussion revealed that convolutional neural networks and support vector machines emerged as the most effective algorithms for spectral data analysis. The study concludes that AI-enabled optical biosensors represent a promising frontier for point-of-care diagnostics, early cancer detection, and personalized medicine applications.
Artificial intelligence, Optical biosensors, Machine learning, Deep learning, Biomedical diagnostics.
Exploring Big Data Technologies: Navigating through Storage Capacities, Big Data Management Tools, Opportunities, Challenges, and Future Outlooks
The extraordinary proliferation of digital data from diverse sources, alternating from social media communications and sensor networks to enterprise systems and scientific evidence-based research, has transformed big data into a pivotal asset for innovation and decision-making in a meaningful manner. This paper provides a wide-ranging exploration of big data technologies, focusing on the evolution of storage capacities, big data characteristics, the functionality of management tools, and the broader opportunities, challenges, and future paths of big data. It importantly examines the essential architecture and infrastructures that facilitate large-scale data storage and processing, incorporating distributed file systems, hybrid models and cloud computing. The key big data management frame works, including Hadoop, NoSQL databases, and Spark, are analysed with respect to the scalability, suitability, and fault tolerance for diverse data environments. The paper further examines the strategic prospects of big data advances across areas, intensely in real-time intelligence, predictive analytics, and the enhancement of Internet of Things (IoT) and artificial intelligence (AI) applications. However, it also addresses persistent challenges including data quality, security, privacy, governance, and the growing insistence on specialised technical expertise. In conclusion, the paper portrays future outlooks emphasising the merging of big data with emerging patterns, including edge computing, automation, quantum computing, and ethical AI. The study emphasises the necessity of vigorous frameworks and interdisciplinary cooperation to fully realise the transformative capability of big data in a progressively complex digital ecosystem.
Big Data, Storage Capacities, Big Data Technologies, Big Data Characteristics, Data Management Tools, and Big Data Tools
Using Machine Learning To Detect And Prevent Cyber Attacks
Cyberattacks pose a significant challenge in the age of digital advancements, causing various sectors to experience financial losses and data breaches alongside security vulnerabilities. This paper researches the use of machine learning models in cyber threat detection and prevention by focusing on publicly accessible intrusion detection datasets, such as NSL-KDD, CIC-IDS 2017, and UNSW-NB15. A comparative evaluation of supervised and unsupervised learning techniques is done, where key performance metrics such as False Positive Rate (FPR) and Attack Mitigation Efficiency are taken into account. Based on the results, it shows that Neural Networks have achieved the highest mitigation efficiency in attacks (94.2%) and the lowest FPR (3.8%), making them the most effective model for cybersecurity applications. Random Forest also performed well with a mitigation efficiency of 90.5% and an FPR of 4.5%. K-Means Clustering, on the other hand, has a problem in that it has a high false positive rate of 7.4% and low detection accuracy. In conclusion, AI-driven security frameworks have the potential to improve the mechanisms of cyber defense and support the inclusion of advanced ML models to enhance the detection and prevention of threats.
Cyberattacks, Digital Age, Financial Losses, Data Breaches, Security Vulnerabilities, Machine Learning (ML), Cyber Threats.
A Comprehensive Literature Review on the Role of Machine Learning Across Domains: Advances, Applications, and Future Directions
Machine Learning (ML) has become one of the most disruptive technologies in such fields as finance, healthcare, disaster management, big data analytics, intelligent transportation systems, behavioral economics, radiomics, and human-computer interaction. The literature review is based on over forty peer-reviewed publications and provides one of the unified views of applications, advancements, challenges, and emergent trends of ML. Improvements in deep learning oil, generative AI, anomaly detection, supervised and unsupervised learning, multimodal learning, GAN-based data generation, ethical AI, and optimization methods are also covered in the review. This review shows the impact of machine learning on industries, positioning it as a predictive analytics tool, decision-making tools, automation, and real-time intelligent systems and highlights it through compiling studies on how machine learning is transforming industries published in 2017 to 2025. Some of the critical technical problems such as interpretability, fairness, data quality, scalability and real time deployment are also discussed. The research directions are outlined in the future in the context of explainable ML, hybrid AI systems, integrated multimodal architectures, and responsible AI frameworks. DOI: https://doi-ds.org/doilink/01.2026-34624146
Artificial Intelligence, Behavioral Economics, Machine Learning, VANET.
Evolutionary Strategies for Parameter Optimization in Deep Learning Models: A Meta-Heuristic Approach to Hyperparameter Tuning
The effectiveness of Deep Learning (DL) models is fundamentally determined by the set of their hyperparameters, which include the settings, which control the learning process and the structure, i.e. the learning rate, network depth, dropout rate, and the choice of the optimizer. Hyperparameter optimization (HPO) may use manual and grid search techniques, which are often computationally infeasible and incapable of solving complex and high-dimensional search spaces. In this paper, Evolutionary Strategies (ES) is reviewed and analyzed as a powerful, population-based meta-heuristic method of automated HPO in DL. Based on biological evolution, ES uses selection, crossover and mutation mechanisms to repeatedly modify a population of candidate hyperparameter combinations to areas of the performance landscape that are optimal. The underlying methodology that we synthesize includes the knowledge of other adjacent areas such as Bayesian optimization in tuning of SVM, multimodal learning problems, or any other area where efficient model tuning is needed. The article has presented the most important types of ES variants in a systematized discussion (e.g., Genetic Algorithms, Covariance Matrix Adaptation Evolution Strategy), how they can be combined with DL training pipelines and have missed the comparison of the performance of these two categories of HPO with other types of HPO such as Random Search, Bayesian Optimization, and Gradient-Based Optimization. Empirical studies in a wide variety of fields (including image registration (where CNNs are used) and time-series prediction (where LSTMs are used) and radiomics-based medical disease diagnostics show that ES is always able to determine high-performing models, and in many cases, it has a better exploration behavior in rugged, non-convex search spaces. Nonetheless, they have issues, such as, the cost per evaluation is too high, they have to be parallelized, and developed efficient fitness functions and genetic representation of neural architectures. The paper ends by giving a list of future research directions on the border of evolutionary computation and DL, including neuroevolution to optimize joint architecture and parameter search and multi-objective optimization to find balance between accuracy and efficiency, and the generalization of such concepts to apply to emergent models in finance, IoT systems, and multimodal AI. DOI: https://doi-ds.org/doilink/01.2026-59167436
Evolutionary Strategies, Hyperparameter Optimization, Deep Learning, Genetic Algorithms, Meta-Heuristics, Neural Architecture Search, Automated Machine Learning (AutoML), Model Tuning.
Experimental Investigation Of Phase Change Material–Integrated Solar Water Heaters With Varying Structural And Thermal Storage Designs
Phase change material-integrated solar water heaters (PCM-ISWHs) incorporate latent heat storage to extend usable hot water supply beyond daylight hours, mitigating solar intermittency. This article details experimental investigations into structural variations such as encapsulation types (cylindrical, slab), fin enhancements, and cascaded PCM layers and thermal storage designs, including HTF flow rates and tank geometries. Paraffin waxes (RT42, RT50, RT58) dominate due to melting points (42-58°C) aligning with domestic needs (45-60°C). Key experiments show single PCM boosting temperatures by 9-23°C over sensible storage, cascaded setups achieving 3-7% higher collection efficiency via uniform phase transitions, and finned designs accelerating charging by 20-30%. Productivity rises 35-92% in hybrid configurations, with exergy efficiencies up to 71%. Challenges like low conductivity (addressed by nano-additives) and leakage are explored, alongside CFD validations. Findings advocate cascaded, fin-augmented cylindrical encapsulations for 52-85°C delivery, reducing payback to 2-3 years in tropical climates. Future hybrid nano-PCMs promise further gains.
Symmetry Reductions and Approximate Analytical Solutions for Boundary-Layer Flows
Boundary-layer flows play a vital role in fluid mechanics, yet exact analytical solutions are scarce for many realistic configurations. This study develops a symmetry-based reduction strategy using Lie group analysis to simplify boundary-layer governing equations into tractable ordinary differential forms. The resulting nonlinear models are investigated through semi-analytical techniques, including homotopy-based methods, rational approximations, and asymptotic expansions. The approach is validated using classical flat-plate and wedge-flow problems and further applied to advanced cases involving magnetohydrodynamic nanofluids and porous media. Accuracy and convergence are evaluated against numerical benchmarks, demonstrating the method’s effectiveness in predicting shear stress and heat-transfer behavior for engineering applications. DOI: https://doi-ds.org/doilink/01.2026-52256462
A Review: Face Recognition Using Local Tetra Pattern
Face recognition is a critical technology in security, authentication, and surveillance systems. Traditional methods like Local Binary Pattern (LBP) and Principal Component Analysis (PCA) have limitations under variations in lighting, facial expression, and pose. This project proposes a robust face recognition system using Local Tetra Pattern (LTrP), a texture-based descriptor that encodes directional information from image gradients. The proposed method extracts LTrP features from facial images and employs machine learning for classification, ensuring high recognition accuracy under varying conditions.
Local Binary Pattern (LBP), Local Ternary Pattern (LTP), Local Derivative Pattern (LDP), Local Tetra Pattern (LTrP)
Deep Insights into Neuroprotective Effects of some Imperative Herbal Resources: A Compressive Review Perspective
Even the simplest medical systems have a history of using medications derived from plants. Due to wide spread acceptance and the common impression of their safer profile compared to traditional pharmaceuticals, they are the most often used medicines. Herbal supplements and medications are growing in popularity throughout the globe as people look for caffeine-free options and healthy ways to cut calories. There has been a rise in interest in traditional medicine in recent years. Alzheimer's disease and other nerves system illnesses are treated with a variety of herbs in traditional medicine. The term “neuroprotection” is used to describe measures used to safeguard neurons in the CNS against demage. The remedies found in nature are still available to humans. Drug like vincristine, artemisinin and gentamcine own thire effectiveness in part to their origins in nature or to their structural similarities to naturally occurring compound. For countries several plants extract have been used in traditional medicine for their purported neuroprotective, memory-enhancing, and anti-aging properties. Several example of such as herbs include ginseng, ginkgo, bacopa, curcuma, and sage this traditional clam was investigated to determine whether it held water, and the herbs’ neuroprotective benefits and their method of action were also explored. DOI: https://doi-ds.org/doilink/01.2026-81278192
Neurodegenerative, Plants, Neuroinflammation, Neuroprotection, Herbal, Hallmarks.
The Interplay of Project Management, Organizational Structure, and Cultural Dimensions in Enhancing Construction Firm Performance in Delhi-NCR
This study examines the complex relationships between project management practices, organizational structure, and cultural dimensions in enhancing construction firm performance in Delhi-NCR. The Indian construction market, valued at USD 740.08 billion in 2025 and projected to reach USD 1,031.71 billion by 2030, faces significant performance challenges. Research indicates construction output grew at 7% CAGR between FY 2018-23, yet 33% of projects experience delays averaging 47 months. This research employs a quantitative approach using structured questionnaires distributed to 312 construction professionals across Delhi-NCR during January-June 2024. The hypothesis posits that integrated project management practices, adaptive organizational structures, and culturally aligned management approaches significantly enhance construction firm performance. Results reveal that matrix organizational structures combined with participative project management styles yield 28% higher project success rates. Cultural dimensions, particularly power distance (PDI=77) and collectivism orientation (IDV=48), significantly moderate the relationship between organizational structure and performance. Statistical analysis demonstrates that firms adopting decentralized decision-making frameworks achieve 35% better schedule adherence and 31% improved cost performance. The study concludes that construction firms in Delhi-NCR must synchronize their organizational design with India's cultural context while implementing robust project management frameworks to achieve superior performance outcomes and competitive advantage in the rapidly expanding infrastructure sector.
Project management, Organizational structure, Cultural dimensions, Construction performance, Delhi-NCR
Impact of Weatherability on the Geomechanically Stability of Coal Pillars in Underground Mining
Roof stability in bord and pillar coal mines is critically influenced by the dynamic interplay of geological and operational factors. This study investigates the detrimental impact of weathering and repetitive blasting vibrations on the temporal degradation of Rock Mass Rating (RMR) and the consequent escalation of dynamic rock loads. Field investigations at operational mines were conducted to analyze these time-dependent interactions. A modified rock load equation, incorporating time-decaying RMR, was developed. Regression analysis established empirical models correlating weathering, RMR, and increasing rock loads for standard gallery widths. The results confirm a significant temporal increase in rock load due to strength reduction from moisture, chemical weathering, and blast-induced micro-fracturing. Conventional support systems, designed on static rock mass assumptions, are shown to be inadequate for these dynamic conditions. This research proposes integrating time-dependent factors into support design to mitigate roof fall risks. These models enable a proactive framework for stability management, facilitating timely reinforcement strategies that enhance safety and optimize resource utilization. By addressing a primary cause of mining fatalities, this work offers actionable strategies to enhance operational safety and sustainability. The findings provide critical guidance for engineers and policymakers in developing resilient adaptive practices for underground coal mines. DOI: https://doi-ds.org/doilink/12.2025-82661628
Bord and Pillar Mining, Roof Stability, Weathering, Rock Mass Rating (RMR), Dynamic Rock Load, Support Design, Mine Safety.
Effect of Personalized Social Media Advertising on Subscription Retention in Urban and Rural Markets
Personalized social media advertising has emerged as a significant driver of customer engagement and retention in subscription-based digital services. This paper investigates the influence of personalized advertising on subscription retention in urban and rural markets, with emphasis on comparative effectiveness across demographic segments. A mixed-method research design was adopted, incorporating quantitative survey data and social media engagement metrics collected from 1,000 subscribers across urban and rural regions. Key parameters analyzed include advertisement relevance, consumer trust, interaction frequency, and renewal behavior. Empirical results indicate that personalized social media advertising positively impacts subscription retention in both market contexts. However, the magnitude of impact is considerably higher in urban markets, where enhanced digital infrastructure, greater social media exposure, and higher digital literacy levels support stronger engagement with personalized content. Conversely, rural markets exhibit lower retention outcomes due to challenges such as limited connectivity, privacy concerns, and preference for minimalistic communication. The study demonstrates that personalization effectiveness is strongly influenced by socio-economic and technological factors. These findings provide valuable insights for digital marketers and service providers, suggesting the need for differentiated personalization strategies tailored to specific market environments. By aligning advertisement complexity and delivery mechanisms with regional consumer characteristics, organizations can improve retention performance. This research contributes to the existing body of knowledge by presenting a structured comparison of urban and rural consumer responses to personalized digital advertising, offering implications for scalable and inclusive marketing practices.
Digital India, Subscription Economy, Social Media Marketing, Regional Content, WhatsApp Commerce, Influencer Marketing, Price Sensitivity, Tier 2/3 Markets
A Study On Ethical Challenges And Bias In Machine Learning Algorithms
In the realm of consumer behaviour analysis, the confluence of machine learning methodologies and abundant data resources has propelled the discipline to new heights. This comprehensive review delves into the latest frontiers of deploying machine learning algorithms to decode the intricacies of consumer behaviour. The investigation initiates with an in-depth exploration of data acquisition and optimization, elucidating the meticulous process of feature engineering, and scrutinizing the practical application of diverse machine learning paradigms, encompassing regression, classification, clustering, and recommendation systems. Furthermore, the discourse encompasses the tangible ramifications of machine learning derived insights, elucidating their utility in optimizing pricing strategies, guiding product development, orchestrating targeted marketing campaigns, and enhancing the Caliber of customer service. It is essential to underscore the complexity of the challenges and ethical considerations intrinsic to the computational analysis of consumer behaviour. In conclusion, this review casts a forward-looking perspective, highlighting the imperative need for an ever-evolving data landscape to adapt to the ever-shifting consumer preferences and market dynamics. This paper serves as a paramount reference for researchers, businesses, and policymakers aiming to harness the multifaceted potential of machine learning in the realm of consumer behaviour understanding and influence. DOI: https://doi-ds.org/doilink/12.2025-55337411
Machine learning, Consumer behaviour analysis, Data optimization, Feature engineering, Ethical considerations
AI-Based Approaches for Predicting Employee Success and Retention
Employee retention has emerged as a critical challenge for organizations globally, with attrition rates reaching 57.3% in 2021 and turnover costs estimated at 200% of an employee's annual salary. This research investigates AI-based approaches for predicting employee success and retention through advanced machine learning algorithms. The study employs a comprehensive methodology incorporating predictive analytics, natural language processing, and classification models to analyze employee behavior patterns. The hypothesis posits that AI-driven predictive models can achieve accuracy rates exceeding 80% in identifying at-risk employees. Results demonstrate that Random Forest and XGBoost classifiers achieved 98.7% and 98.8% accuracy respectively in predicting employee turnover. Key findings reveal that overtime, job satisfaction, monthly income, age, and distance from home are primary attrition predictors. Implementation of AI-powered retention strategies resulted in 15-30% reduction in turnover rates across analyzed organizations, with IBM saving $300 million through predictive attrition models. The study concludes that AI-based approaches significantly enhance retention strategies through early risk detection, personalized interventions, and real-time monitoring capabilities.
Artificial Intelligence, Employee Retention, Machine Learning, Predictive Analytics, Turnover Prediction
Enhancing Liver Disease Detection Using Cloud Computing And Autoencoders In Healthcare Systems
Liver disease is a dangerous medical condition in need of an early diagnosis for proper treatment to prevent complications. This paper presents a prediction framework for liver disease based on Autoencoders and Cloud Infrastructure. The framework commences with the preprocessing of the data through cleaning, augmentation, and Z-score scaling, after which feature extraction from a Deep Belief Network (DBN) and Autoencoder-based classification are performed. The performance of the proposed model is evaluated using accuracy, precision, recall, F1-score, and AUC-ROC. The Proposed Autoencoders model performs better than the existing methods like Ensemble Method, HGB+MARS+SoftMax and SVC as per the experiment results. The Proposed Autoencoders have excellent 99.45% accuracy, 95.11% precision, 96.31% recall, 97.82% F1-score, and 96.51% AUC-ROC, and they exhibit superior performance in predicting liver disease. The suggested framework, with its cloud infrastructure and deep learning architecture, provides a strong solution for accurate and efficient diagnosis of liver disease.
Liver Disease, Cloud Infrastructure, Deep Belief Network, Autoencoders, Healthcare Systems
Integrated Design And Analysis Of Liquid Pvt Technology For Renewable Energy-Enabled Buildings
The integration of liquid Photovoltaic-Thermal (PVT) technology represents a significant advancement in renewable energy systems for sustainable buildings. This study investigates the integrated design and performance analysis of liquid-based PVT collectors for renewable energy-enabled buildings. The primary objective is to evaluate the thermal and electrical efficiency of liquid PVT systems and their applicability in building energy management. The research employs a quantitative experimental methodology utilizing secondary data analysis from existing studies conducted between 2018-2021, examining various liquid PVT configurations under different solar radiation levels ranging from 500-1000 W/m². The hypothesis proposes that liquid PVT technology significantly enhances overall energy efficiency compared to standalone PV systems. Results demonstrate that liquid-based PVT collectors achieve thermal efficiency of 54.6-58.64%, electrical efficiency of 11.5-13.8%, and combined efficiency of 65-68.4% under optimal conditions. The integration of spiral flow absorbers with water-based cooling systems demonstrated superior performance at mass flow rates of 0.041 kg/s. The findings conclude that liquid PVT technology offers substantial potential for zero-energy buildings, reducing conventional energy dependence by 30-40% while simultaneously generating electricity and thermal energy for building applications.
Liquid PVT Technology, Renewable Energy Buildings, Thermal Efficiency, Electrical Performance, Building Integration
Personalized Learning in Indian Schools through Artificial Intelligence (AI) in Present Scenario
The National Education Policy (NEP) 2020 emphasises the inclusion of contemporary subjects like Artificial Intelligence (AI) in school curricula, at appropriate stages, to foster innovation and digital literacy among students. Personalized learning enabled by Artificial Intelligence (AI) represents a transformative shift in Indian school education, offering adaptive, student-cantered experiences that respond to individual abilities, interests, and backgrounds. With the National Education Policy (NEP) 2020 encouraging technology integration and innovations, AI-driven personalized learning tools have rapidly emerged across urban and rural landscapes. These include intelligent tutoring systems, adaptive platforms, predictive analytics, automatic assessment, and multilingual content delivery. India’s government and private edtech sector have jointly launched initiatives such as SOAR and integrated AI capabilities into the CBSE curriculum to democratize access and raise educational outcomes. Recent data show over 60% of Indian higher education institutions now permit AI-based learning tools, and leading platforms have reached millions of students across diverse settings. Despite strong momentum and clear benefits in engagement, efficiency, and equity, challenges persist regarding infrastructure, teacher training, data privacy, and linguistic inclusivity. This article reviews theoretical foundations, case studies, current progress, and evolving challenges in AI-driven personalized learning in Indian schools. Recommendations emphasize a collaborative, ethical, and scalable approach to ensure inclusivity and maximize the impact of this technological revolution on future generations.
Comparative Review of Silicon-Based and Non-Silicon Solar Cells
Solar photovoltaic (PV) technology has emerged as one of the most critical renewable energy solutions in combating climate change and ensuring long-term energy security. Over the past six decades, silicon-based solar cells have dominated the PV market due to their high efficiency, long-term stability, and well-established manufacturing processes. However, their relatively high manufacturing energy requirements, rigidity, and cost have prompted research into alternative non-silicon technologies such as cadmium telluride (CdTe), copper indium gallium selenide (CIGS), perovskites, organic photovoltaics (OPVs), dye-sensitised solar cells (DSSCs), and quantum dot solar cells (QDSCs). This chapter presents a comprehensive comparative review of these technologies, analysing their material properties, fabrication techniques, performance records, cost structures, environmental implications, and market prospects. The review also discusses the emergence of hybrid and tandem architectures that combine the advantages of different materials to achieve record efficiencies exceeding 33%. The chapter concludes with future research directions and strategies for large-scale sustainable deployment of both silicon-based and non-silicon PV technologies. DOI: https://doi-ds.org/doilink/12.2025-61124448