International Journal of Multidisciplinary Engineering In Current Research
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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
An Enhanced OBS Core Node Design Incorporating Wavelength Conversion and Deflection Routing Mechanisms
The primary challenge in Optical Burst Switching (OBS) networks is the occurrence of burst contention. To address this issue, techniques such as wavelength conversion and deflection routing are widely used within switch fabrics. This study introduces a mathematical model for a newly proposed OBS core-node architecture. The system’s performance is evaluated by analyzing burst loss probability, relying on steady-state occupancy analysis and a Poisson arrival process. The results of the performance assessment are provided for various average burst arrival rates while considering key node parameters, including wavelength conversion capability and support for deflection routing. DOI: https://doi-ds.org/doilink/12.2025-27862832
burst loss probability, deflection routing
AI in Cybersecurity: Enhancing Digital Defenses with Anomaly Detection and LSTM-Based Threat Prediction
These are the key future areas of research in Applied Artificial Intelligence. In this era of increasingly fast, internet-connected systems, traditional defenses have become inefficient at detecting and responding to various types of sophisticated threats such as malware, phishing, ransomware, distributed denial of service attacks, and more. Such conventional security approaches are not effective with zero-day attacks or new-age evolving attacks. AI-based methods have developed into serious competitors through LSTM networks-an advanced adaptive technology-for continuous improvement of digital defense against anomalous access into network traffic patterns to provide maximum efficiency. To this end, the current research revolves around LSTM-based VAE anomaly detection methodology, utilizing onboard data from live network detection for prediction of future threats. The methodology involves necessary steps that include data pre-processing of Z-score normalization, encoding network traffic into latent representations and then reconstructing the pattern to realize anomaly detection through reconstruction loss and KL divergence. LSTM-based classifier with SoftMax activation is used for threat classification. Testing results indicated improvement in detection accuracy over 90% after 20 epochs of training. This is an AI-driven system that increases real-time monitoring in cybersecurity while reducing false positives associated with it. It future work concentrates on improving the efficiency of the models and embedding them within large-scale security frameworks.
Cybersecurity, Anomaly Detection, Long Short-term Memory - Variational Auto-Encoder, Threat Prediction, AI-Based Security, Network Traffic Analysis
A Study On Synthesis With Respect To Characterizations And Applications Of Various Types Of Nanoparticles And Composites
Nanoparticles and nanocomposites have emerged as transformative materials due to their superior physicochemical, mechanical, catalytic, and biological properties. This study presents a systematic investigation into the synthesis, characterization techniques, and multi-dimensional applications of metallic, metal oxide, polymeric, carbon-based nanoparticles, and hybrid nanocomposites. Emphasis is placed on green synthesis approaches, physiochemical modifications, and structure–property relationships. Characterization tools such as XRD, FTIR, UV-Vis, SEM, TEM, AFM, TGA, DLS, and Raman spectroscopy are examined in detail to understand morphological, structural, optical, and thermal behaviors. The study also reviews cutting-edge applications in catalysis, drug delivery, energy storage, environmental remediation, biosensing, antimicrobial activity, and smart materials. The findings underscore the tremendous potential of nanoparticles and composites in developing sustainable, efficient, and technologically advanced systems.
Nanoparticles; Nanocomposites; Green Synthesis; Chemical Reduction; Sol–Gel Method; Hydrothermal Synthesis; Metal Oxide Nanoparticles; Polymer Nanocomposites; Carbon-Based Nanomaterials etc.
Design Of A Low-Latency Pipelined Fir Filter Through Advanced Critical Path Analysis
A filter with a finite period impulse response that settles to zero in a finite amount of time is known as a FIR in digital signal processing. This is frequently contrasted with IIR filters, which may respond endlessly even if they have internal feedback. In this study, we use fine-grained seamless pipelining to propose a novel hardware design for a very high-speed finite impulse response (FIR) filter. By positioning the pipeline registers both across and in between components, the suggested full-parallel pipeline FIR filter may provide an output sample in a few gate delays. Depending on the throughput need, a suitable pipelining strategy may be created with the help of a detailed critical path analysis at the gate level. Two other designs are also shown in this project, each with a unique trade-off between throughput rate and area. The suggested FIR filters are created to measure the highest throughput while striking a compromise between speed and complexity. Model sim software is used for simulation, while Xilinx is used for implementation. DOI: https://doi-ds.org/doilink/11.2025-32813615
FIR filter, FFA, EKG, DSP, WRT, PP, Carry adder.
Study on Rock Load Variation Considering Weatherability in Bord and Pillar Method of Underground Coal Extraction
In India, bord and pillar is the most commonly used method in the extraction of coal which contributes approximately 98% production from underground. Estimation of the rock load on the roof is important in providing sufficient support systems and mine safety. This research is carried out for the evaluation of rock load variation with weathering, which has been treated as a key parameter in CMRI-ISM RMR classification. A relationship was established between weather ability as determined by slake durability index (SDI) and rock load mobilization due to development drivages in Indian coalfields. The approach combined field technique and geotechnical characterization. Findings indicated that weather ability plays an important role in rock load estimation; for example, first-cycle SDIs are highly inversely correlated to the rock load magnitude. Strong coefficients of determination (R² > 0.85) were found between CMRI-ISM RMR and rock loads for galleries at various widths (3.6-4.8 m). Results have demonstrated that the bulk rock load under weather ability dependent is heavier than that of mechanically dependent, which should be taken into account for support design, especially in moisture-sensitive coal measure rocks, and provide reliable guidance on safety regulation of underground coal mining. DOI: https://doi-ds.org/doilink/11.2025-86468638
Rock loading effect, Weathering, Bord and pillar method, Slake durability index, CMRI-ISM RMR
A Critical Analysis of Thematic Depth, Narrative Techniques, and Cultural Representation in the Writings of Tagore, Bandyopadhyay, Devi, and Basu
This empirical study examines the thematic patterns, narrative techniques, and cultural representations in the works of four distinguished Bengali writers: Rabindranath Tagore, Bibhutibhushan Bandyopadhyay, Mahasweta Devi, and Buddhadeva Basu. Through systematic textual analysis of 120 literary works spanning 1890-1990, this research employs quantitative content analysis, thematic coding, and statistical correlation methods to identify distinctive literary characteristics and evolving cultural paradigms. The study analyzes 45,000 pages of text across multiple genres including poetry, novels, short stories, and essays. Data collection involved manual coding of thematic elements, narrative structure assessment, and cultural motif identification validated through inter-rater reliability testing (κ = 0.87). Results reveal significant variations in thematic focus: Tagore emphasizes philosophical humanism (42%), Bandyopadhyay foregrounds nature and rural life (51%), Devi concentrates on social marginalization (63%), and Basu explores psychological modernism (48%). Narrative style analysis demonstrates distinct temporal structures and point-of-view preferences across authors. Statistical tests including chi-square analysis and ANOVA confirm significant differences in cultural representation patterns (p < 0.001). The findings contribute to comparative literary studies by providing empirical evidence of stylistic evolution in Bengali literature and establishing quantifiable parameters for cross-cultural literary analysis.
Bengali literature, comparative textual analysis, thematic coding, narrative structure, cultural representation, quantitative literary studies, Indian regional literature
Comprehensive Analysis of Preparation Techniques, Structural Features, and Properties in Composite Multiferroic Systems
This empirical study provides a comprehensive exploration of synthesis methodologies, characterization techniques, and fundamental properties in composite multiferroic materials. Multiferroics, exhibiting simultaneous ferroelectric and magnetic ordering, represent a frontier class of multifunctional materials with significant technological potential. Through systematic investigation of 127 distinct composite samples synthesized via solid-state reaction, sol-gel, and hydrothermal methods, we establish critical correlations between processing parameters and resultant multiferroic properties. X-ray diffraction, scanning electron microscopy, and vibrating sample magnetometry analyses reveal distinct phase formation behavior dependent on synthesis route, with optimal magnetoelectric coupling coefficients (up to 18.7 mV/cm·Oe) achieved in samples with controlled interfaces between ferroelectric and ferromagnetic components. Statistical analysis demonstrates significant variations in multiferroic performance across synthesis methods (p<0.001), with hydrothermal processing yielding superior magnetization (1.27±0.11 emu/g) while maintaining comparable ferroelectric properties to conventional techniques. These findings establish comprehensive synthesis-structure-property relationships in composite multiferroics, providing quantitative guidance for optimizing magnetoelectric coupling in next-generation multifunctional devices.
Composite multiferroics; magnetoelectric coupling; synthesis-structure-property relationship; hydrothermal synthesis; phase interface; ferroelectricity; ferromagnetism.
Analysis of Ventilation Requirements Affected by Diesel Machinery in Underground Mines
Underground mines extensively utilize diesel-powered equipment for material handling and transportation, resulting in significant emissions of toxic gases and diesel particulate matter (DPM) that pose serious health risks to miners. This research analyzes ventilation requirements in underground mining operations with a focus on diesel machinery emissions and their control through optimized ventilation systems. The study examines current ventilation standards, emission characteristics, and airflow requirements across various mining jurisdictions. Through comprehensive literature review and data analysis, this research identifies that ventilation systems consume approximately 30-40% of total underground mine energy, with airflow requirements ranging from 0.047 to 0.067 m³/s per kilowatt of diesel engine power depending on regulatory frameworks. The study reveals that diesel equipment contributes over 50% of heat load in deep mines while emitting carcinogenic DPM, carbon monoxide, and nitrogen oxides. Results demonstrate that optimizing auxiliary ventilation systems through computational fluid dynamics modeling and implementing ventilation-on-demand strategies can reduce energy consumption by 31-53% while maintaining acceptable air quality standards. The research concludes that integrated approaches combining improved engine technologies, after-treatment devices, and advanced ventilation control systems are essential for ensuring miner safety and operational efficiency in modern underground mining operations. DOI: https://doi-ds.org/doilink/10.2025-53564516
Underground mine ventilation, Diesel particulate matter, Airflow requirements, Emission control, Computational fluid dynamics
Use of Industrial and Mining Wastes for Backfill in Subsurface Mining Operations
The escalating generation of industrial and mining wastes poses significant environmental challenges globally, necessitating sustainable disposal solutions. This research investigates the utilization of various industrial wastes including fly ash, slag, coal gangue, construction and demolition waste, and mine tailings as backfill materials in subsurface mining operations. The study aims to evaluate the mechanical properties, environmental sustainability, and economic feasibility of waste-based backfill composites. A comprehensive experimental methodology was employed involving laboratory testing of different waste combinations with varying cement ratios, curing periods, and compositional parameters. Results demonstrate that cemented paste backfill incorporating 30% fly ash with 8% cement content achieved uniaxial compressive strength of 4.95 MPa after 28 days curing, meeting mining industry requirements. Statistical analysis of five experimental datasets revealed significant correlations between waste content, binder ratio, and mechanical performance. The findings confirm that waste-based backfills effectively control ground subsidence while promoting circular economy principles. This research contributes to sustainable mining practices by transforming hazardous waste streams into functional engineering materials, simultaneously addressing environmental concerns and improving mine stability. DOI: https://doi-ds.org/doilink/10.2025-87317652
Backfill mining, Industrial waste, Fly ash, Mine tailings, Cemented paste backfill
Optimization Techniques and Performance Assessment of IoT-Enabled WBANs in Healthcare Systems
Wireless Body Area Networks (WBANs) integrated with Internet of Things (IoT) technology have emerged as transformative solutions for continuous healthcare monitoring systems. This research investigates optimization techniques and performance assessment mechanisms for IoT-enabled WBANs in healthcare applications. The primary objective focuses on evaluating energy efficiency, quality of service parameters, and network reliability through advanced optimization algorithms. The methodology encompasses systematic analysis of IEEE 802.15.6 standard implementation, examining multiple performance metrics including throughput, packet delivery ratio, latency, and energy consumption across various network configurations. Experimental results demonstrate that optimized WBANs achieve 35% reduction in energy consumption, 97.5% packet delivery ratio, and latency below 92 milliseconds. The study employs machine learning algorithms, ant colony optimization, and particle swarm optimization techniques for cluster head selection and routing protocols. Statistical analysis reveals significant improvements in network lifetime extending up to 40% compared to traditional approaches. The findings confirm that integrated optimization strategies effectively balance energy efficiency with quality of service requirements, providing robust and scalable solutions for next-generation healthcare monitoring systems.
Wireless Body Area Networks, IoT Healthcare, Energy Optimization, Quality of Service, Performance Metrics
Experimental Analysis of Underground Mine Ventilation System of Indian Metal Mines
Underground mine ventilation systems in Indian metal mines represent critical infrastructure for ensuring miner safety, productivity, and operational efficiency. This experimental study examines ventilation parameters across five major zinc-lead metal mining operations in India, specifically analyzing Rampura Agucha, Rajpura Dariba, Zawar, Sindesar Khurd, and Kayad mines in Rajasthan state. The research investigates airflow distribution, fan performance, air quality parameters, and energy consumption patterns in deep underground metal mining environments. Primary objectives include quantifying ventilation efficiency, assessing thermal comfort conditions, evaluating contaminant dilution effectiveness, and determining optimal fan operation strategies. Methodology employed field measurements, computational fluid dynamics simulations, and statistical analysis of ventilation data collected over twelve months during financial year 2023-24. Results demonstrate that Indian metal mines maintain airflow rates between 1450-2024 m³/sec, with primary ventilation consuming 32-34% of total mine electricity. Temperature regulation challenges persist at depths exceeding 600 meters, necessitating auxiliary cooling systems. Discussion reveals significant energy optimization potential through variable speed drives and ventilation-on-demand systems, achieving 25-31% energy reduction. The study concludes that modernization of ventilation infrastructure, implementation of intelligent monitoring systems, and adoption of international best practices can substantially enhance both safety parameters and operational economics in Indian underground metal mining sector. DOI: https://doi-ds.org/doilink/10.2025-74516576
Underground mine ventilation, metal mines India, airflow optimization, fan efficiency, thermal management
Toward Sustainable Development Through Green Finance
Environmental change, energy constraints, and financial crisis are the three largest threats to the global economy in this era of fast technological advancement.. The reason for this is that governments incur costs associated with environmental deterioration as a result of financial development. To achieve a balance between the market and the natural world, green financing is the way to go. Financial backing for environmentally friendly development practices, sometimes known as "green finance," significantly cuts down on emissions of pollutants that deplete the ozone layer and other harmful air pollutants. If the country wants to enhance its finances, it should invest more in green gardening, green buildings, green security, and other green initiatives. The goal of this study is to survey what is been written on green finance and to speculate on its potential future development in India. DOI: https://doi-ds.org/doilink/10.2025-93865331
Environment; Green finance; Green building; Green projects; Renewableenergy.