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Efficient Pathfinding in a Maze to overcome Challenges in Robotics and AI Using Breadth-First Search
Efficient pathfinding in a maze is a key obstacle in robotics, computer science, and artificial intelligence. The article is proposing a strategy using the Breadth-First Search (BFS) algorithm to establish the shortest path for a robot navigating from the top-left to the bottom-right corner of a maze depicted as a two-dimensional grid. The maze comprises open pathways and obstructions, signified by 0 and 1, respectively. The robot's permissible actions include up, down, left, and right, restricted by the boundaries of the grid and the position of obstacles. BFS, an approach well-suited for unweighted graphs, sequentially examines all available routes, ensuring that the first observed path to the goal is the shortest. A visited set removes redundant cell visits, reducing infinite loops and inefficient processing. The algorithm's efficiency is dramatically upgraded by harnessing a queue structure to maintain live routes and their associated steps. This approach assures effectiveness and extensiveness for grid-based navigation problems, making it especially appropriate for real-world robotic applications where minimizing traversal cost is critical. Additionally, the paper discusses the algorithm's execution, complexities, and potential upgrades for larger grids or dynamic environments. Experimental results demonstrate BFS's resilience and efficacy in solving pathfinding challenges in various maze configurations. This work contributes to developing stable navigation techniques, integral to advancing autonomous robotic navigation and related fields. 2025 IEEE. -
Ensemble Hybrid LSTM Architectures for Robust Multi-Currency Forex Forecasting
The analysis of financial time series presents a longlasting obstacle regarding currency exchange rate forecasting because volatility and nonlinearity and non-stationarity characterize currency markets. The research presents an ensemble forecasting system which combines various deep learning and hybrid predictive models such as LSTM and GRU-LSTM and CNN-LSTM and Attention-LSTM and XGBoost-LSTM for scalable integration. The ensemble methodology follows a dynamic weighted averaging technique which bases its priority on assigning weights through the reciprocal calculation of Mean Squared Errors from individual models to identify accurate forecasters. A representative study based on the EUR/USD exchange rate took place as part of extensive evaluations that spanned various currency pairs. The standalone XGBoost-LSTM model proved most effective in terms of MSE and R2 values at 0.000088 and 0.9778 respectively. The ensemble model proved to be highly robust and generalizable through its outcomes which produced an MSE of 0.000142 along with MAE of 0.009204 and R2 of 0.9643. The ensemble approach stands as an effective and reliable method to increase both stability and predictive power of forex forecasting systems. The conceptual structure offers sound potential applications for algorithmic trading as well as financial risk management and multi-currency strategic decision-making systems. 2025 IEEE. -
Early Warning System for Engine Failure Detection in Aircraft Engines Using Machine Learning
Aviation has a problem with engine defects which are a major concern. Unforeseen causes might render them expensive on the ground and hazardous in the air. We present a system that signals when an aircraft engine is about to fail. Our AdvancedModelTrainer checks a collection of models - Random Forest, XGBoost, Gradient Boosting, LightGBM, Ridge, Lasso, ElasticNet, and a simple neural network - through a dataset of 10,000 engine cycles along with 25 engineered features. Hyperparameter tuning and Remaining Useful Life (RUL) metrics help to select the top two (Gradient Boosting and XGBoost, RMSE 39.99, R2=0.7715). A complete MLOps structure keeps an eye on the drift, initiates the retraining process, and sets up dashboards that are user-friendly for the mechanics. The system has detected on 1,433 new engines, 1,126 were classified as Safe, 106 as Warning, and 201 as Critical, which is indicating the coverage of 93.44The dataset used was completely anonymized in order to safeguard sensitive operational data and to not conflict with the aviation data privacy regulations. 2025 IEEE. -
Water purification membranes: state of the art, fundamentals, challenges, and opportunities
The rising global demand for clean and safe water has intensified the necessity for effective and sustainable purification technologies. This chapter thoroughly overviews membrane-based water purification systems, highlighting their basic principles, material types, operational mechanisms, and evolving roles in addressing water scarcity. It begins with the historical progression of membrane technology, discussing the various membrane types alongside essential separation processes including ultrafiltration, nanofiltration, reverse osmosis, and membrane distillation. The text also covers recent innovations in nanocomposite membranes, cutting-edge material design, and membrane module configurations, focusing on enhancing performance and energy efficiency. Special focus is placed on membrane fouling, its causes, effects, and strategies for mitigation, backed by computational modeling and machine learning insights. The chapter begins by exploring emerging trends, such as the development of fit-for-purpose membranes, their integration into zero liquid discharge systems, and scalable fabrication methods. Together, this information highlights the transformative capacity of membrane technology in tackling global water issues. 2026 Elsevier Inc. All rights reserved.. -
Aritificial intelligence in investment and wealth management
Artificial Intelligence (AI) has emerged as a transformative force in various industries, revolutionizing the fields of investment and wealth management. This study explores how AI technologies, including machine learning, natural language processing, and robotic process automation, have enhanced decision-making processes, risk management, and portfolio optimization within financial services. Early developments in AI were limited by rule-based systems, but advancements in deep learning and access to large datasets have enabled sophisticated real-time analysis and personalized financial solutions. However, challenges related to data privacy, algorithmic bias, and ethical considerations persist, necessitating ongoing innovation in AI system transparency and accountability. This research analyzes the impact of AI on investment strategies, compares AI-driven portfolios with traditional approaches, and evaluates AI's role in reducing market volatility and improving return on investment. 2025, IGI Global Scientific Publishing. -
Financial Vulnerability in Households: Dissecting the Roots of Financial Instability
The phenomenon of household financial vulnerability, defined by unexpected shocksin income and expenditures, carries major implications for both individual households and the overall economy of a nation. For a considerable time, household debt has been widely acknowledged as the primary determinant of household financial vulnerability. This study aims to extend the analysis beyond the scope of household debt. Middle-income households may experience financial difficulties when faced with unexpected changes in income and expenses. These challenges can arise from several circumstances, including the inability to engage in discretionary activities such as dining out or vacations. For a very long time, it has been posited that low-income households exclusively experience financial vulnerability. Hence, it is imperative to thoroughly examine the concept of household financial vulnerability and its underlying factors to enhance households' ability to withstand adversities and better clarify the matter. In light of the prevailing economic recession triggered by the global pandemic and the ongoing confrontation between Russia and Ukraine, the significance of the matter is further underscored. This study aims to comprehensively define household financial vulnerability and examine its relationship with financial capability, digitalized payments, financial stress, and financial socialization. The current study anticipates establishing a foundational framework for future research endeavors in this specific field. Moreover, this paper also explores potential avenues for future research. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Dreamscapes and Virtual Realms: Exploring VRs Impact on Dream Patterns
In recent years, psychologists have been exploring the impacts of virtual reality on mental health, discovering its potential toward curing diseases and therapeutic tools for many such mental conditions. In conventional exposure therapy, the ability of patients to successfully visualize particular feared stimuli is a prerequisite for imaginal exposures. On the flip side, various VR-related tasks have been conducted to test the impact on dreams. This study examines the impact of VR on dreams and analyze the changes in its patterns. By reviewing the recent papers, we concluded that the potential harm caused by VR is well established, with negative side effects reported since the early 1990s. Seven of these twenty-three studies either did not report global incorporation rates or failed to provide sufficient data to determine them. The side effect profile associated with the clinical use of VR and AR remains largely unknown; therefore, we systematically reviewed available evidence of their adverse effects. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Deep Learning Approaches for Detection and Classification of Microplastics in Water for Clean Water Management
Microplastic pollution is a growing environmental concern, threatening aquatic ecosystems and human health. This study presents a dual deep learning approach for microplastic detection and classification using two datasets. For water microplastics, YOLOv8 and YOLOv11 were employed for object detection. InceptionV3, VGG19, ResNet50, ResNet152, DenseNet121, EfficientNetB0, and a custom CNN were applied for classification, classifying three distinct microplastic types in non-aquatic environments. Experimental findings display high accuracy, and indicate the potential of AI-enabled solutions for environmental monitoring. This research contributes to SDG 6 Clean Water and Sanitation, promoting sustainable management of water. 2025 IEEE. -
Spatiotemporal Forecasting and Environmental Driver Modeling of Marine Microplastic Pollution: an Interpretable Deep Learning Approach for Sustainable Ocean Policy
Marine microplastic contamination presents a significant risk to ocean health, necessitating precise spatiotemporal predictions for effective marine policy development. This study introduces a transparent deep learning model to examine and forecast microplastic levels in global oceans by leveraging historical sampling data, seasonal variations, and climatic factors. A comprehensive global dataset is curated and analyzed, integrating environmental indices such as ENSO, PDO, NAO, and MEI to model the influence of large-scale ocean-atmosphere interactions. Temporal decomposition, Mann-Kendall trend testing, Theil-Sen regression, and seasonal analysis reveal statistically significant monthly and interannual variations in microplastic concentration. Correlations with climate drivers underscore the dynamic environmental control on pollutant distribution. By incorporating interpretable environmental modeling, the proposed framework supports data-driven marine pollution mitigation and policy strategies aligned with UN Sustainable Development Goal 14 (Life Below Water). This work establishes a foundation for future extensions involving LSTM- and Transformer-based time series forecasting combined with SHAP-based explainability for enhanced decision-making. Furthermore, anomaly detection employing Prophet residuals and Isolation Forest reveals sudden increases in pollutants, providing early warning systems for disturbances to marine ecosystems. High-risk areas that need focused regulatory actions are further identified using clustering analysis. All things considered, the model makes it possible to forecast marine plastic pollution in a comprehensive, comprehensible, and scalable manner-a crucial component of sustainable ocean governance. 2025 IEEE. -
Escape velocity backed avalanche predictor neural evidence from nifty /
International Journal of Recent Technology And Engineering, Vol.8, Issue 4, pp.486-490, ISSN No: 2277-3878. -
Multifractal analysis of volatility for detection of herding and bubble: evidence from CNX Nifty HFT /
Investment Management And Financial Innovations, Vol.16, Issue 3, pp.182-193 -
Power law in tails of bourse volatility – evidence from India /
Investment Management And Financial Innovations, Vol.16, Issue 1, pp.291-298 -
Hydrogen-enriched dual-fuel CI engine fueled with Mahua biodiesel and hybrid nano-additives: Integrated experiments, explainable machine learning, and multi-objective optimization
Hydrogen-enriched dual-fuel compression-ignition (CI) engines are a potential pathway towards higher efficiency and lower carbon-intensive emissions. Studies conducted so far have considered hydrogen enrichment, biodiesel fuels, nano-additives, and data-driven optimization as separate entities; hence, there is no integration or comprehensive understanding about them, which leads to an efficiency-nitrogen oxides trade-off. This study presents an integrated experimental-machine learning-explainable artificial intelligence-multi-objective optimization framework for a hydrogen-assisted dual-fuel CI engine fueled with a Mahua biodiesel-diesel (B20) blend and hybrid nano-additives (Al2O3TiO2 and CeO2-MWCNT, 50-100ppm). Experimental results indicated that hydrogen enrichment hybridized with nano-additives improves brake thermal efficiency by 8-14% and reduces brake-specific fuel consumption by 10-18%. HC, CO, and smoke emissions are reduced by up to 35%, 32%, and 45%, respectively. There is a moderate increase in NOx by 12-28%. Machine-learning models achieved high predictive accuracy (R2>0.99). The XGBoost exhibited superior generalization. The SHapley Additive exPlanations analysis found that the dominant factors were engine load, the hydrogen energy share, and the concentration of nano-additives. The XGBoost-Multi-Objective Grey Wolf Optimizer (XGBMOGWO) framework created Pareto-optimal solutions showing a strong and interpretable pathway for advancing trade-offs between efficiency and emissions in dual-fuel engines. 2026 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Medical Tourism in South India - A Relative Study of the Principal participants in hospital and hospitality industry in South India
International Journal of Management, IT and Engineering Vol.3, ISSUE 1, pp. 613-626 ISSN No. 2249-0558 -
A literature review on destination management organization /
Zenith International Journal of Multidisciplinary Research, Vol.4, Issue 12, pp.675-681, ISSN No: 2231-5780. -
Setting benchmarks through Destination Management Organizations (DMOs): A study on the tourism policy of Karnataka, India /
Asian Journal of Management Sciences, Vol.2, Issue 6, pp.33-39, ISSN No: 2348-0351. -
Formation and photoluminescence of ZnS:Tb nanoparticles stabilized by polyethylene glycol
ZnS nanoparticles doped with 1 mol.% of Tb have been prepared at 70 C by simple chemical precipitation method using poly ethylene glycol (PEG) as capping agent. The synthesized nanoparticles have been analysed using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), photoluminescence (PL) and UV-Vis absorption spectroscopy. From X-ray diffraction analysis, it was found that nanostructured ZnS:Tb particles exhibited cubic structure with an average crystallite size of 2.75 nm. Room temperature photoluminescence (PL) spectrum of the doped sample exhibited broad emission in the visible region with multiple peaks at 395 and 412 nm due to 5D3?7F6and 7F5transitions and 492, 536, 600, 653 and 680 nm due to 5D4?7F67F57F4,7F1and 7F0transitions. 2020 Elsevier Ltd. All rights reserved. -
Highly luminescent ZnS:Mn quantum dots capped with aloe vera extract
This study demonstrates the optical properties of ZnS:Mn2+ qquantum dots synthesized by simple and eco-friendly chemical precipitation method using aloe vera (AV) extract as the stabilizing agent. The nanoparticles have been characterized by transmission electron microscopy (TEM), Fourier transform infrared (FTIR) spectroscopy, diffuse reflectance spectroscopy (DRS), photoluminescence (PL) and time-resolved PL spectroscopy. Increase in band gap energy with decrease in particle size was observed from DRS studies due to quantum confinement effect. Dominant yellow emission was observed from characteristic 4T1?6A1 transitions of the Mn2+ions in the ZnS:Mn/AV nanoparticles. The results provide insight to the quantum confinement effect that occur and how it affect decay life time of the ZnS:Mn2+/AV nanoparticles. 2020 Elsevier Ltd -
Pure red luminescence and concentration-dependent tunable emission color from europium-doped zinc sulfide nanoparticles
Nano-sized Eu3+-doped ZnS particles were prepared by chemical precipitation method using polyethylene glycol as capping agent. The structural and morphological studies of ZnS:Eu3+ nanoparticles were carried out using X-ray diffraction (XRD), transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM). XRD results show that ZnS:Eu3+ nanoparticles have a cubic structure for all Eu3+ concentrations. Dependence of doping concentration on the photoluminescence (PL) of ZnS:Eu3+ nanophosphor was studied for excitations at 395nm and 465nm. At 395-nm excitation, emission colors of ZnS:Eu3+ nanophosphor lie in blue, green, yellow, and red regions of chromaticity diagram for different doping concentrations. But for all doping concentrations we got red emission when the excitation wavelength was 465nm and the color purity was 92% for 0.03M doped sample. 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Securing patient information: A multilayered cryptographic approach in IoT healthcare
The increasing integration of devices utilising the of Internet of Things (IoT) in healthcare has resulted in the collection of an unparalleled volume of patient data. Personal identifiers, insurance information, medical history, and health monitoring measures are all included in a complete dataset. Ensuring security and privacy of IoT devices is crucial in the healthcare sector. The goal of this project is to combine steganography with three different cryptographic algorithms to develop a hybrid cryptographic technique. Among the algorithms under investigation are steganography, Caesar cipher, columnar transposition cipher, and one-time pad. Every encryption scheme uses three keys to encrypt patient data. The encrypted data is subsequently encoded into an image file through image-based steganography. To ensure confidentiality and authentication, an authorised user can decrypt the file through a designated decryption process, maintaining the integrity of patient data. 2025 selection and editorial matter, Keshav Kumar and Bishwajeet Kumar Pandey; individual chapters, the contributors.