International Journal of Engineering and Science Research
Reversible Data Hiding In Images Using Dual Embedding And Pixel Prediction
Reversible Data Hiding (RDH) has become an essential research area in digital image security because it allows confidential information to be embedded into digital images while ensuring perfect recovery of both the hidden data and the original cover image. Unlike traditional steganography and watermarking techniques that introduce permanent distortion, RDH guarantees complete reversibility, making it highly suitable for sensitive domains such as medical imaging, military communication, forensic analysis, and digital archiving. Over the years, various RDH techniques such as Difference Expansion, Histogaaram Shifting, and Prediction Error Expansion have been proposed to enhance embedding capacity and minimize distortion [14], [6], [15]. However, achieving an optimal balance between embedding capacity, visual fidelity, and computational simplicity remains a challenge. This research proposes a dual embedding framework that integrates Prediction Error Expansion (PEE) with Least Significant Bit (LSB) substitution to improve embedding efficiency while preserving high image quality. Pixel prediction using a Median Edge Detector (MED) predictor is employed to reduce prediction errors and minimize embedding distortion. The proposed method is evaluated using Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and Structural Similarity Index Measure (SSIM). Experimental results demonstrate that the proposed technique achieves PSNR values above 57 dB even at higher payload capacities, outperforming traditional histogram shifting and standalone LSB approaches [1], [6], [14]. The findings confirm that the proposed dual embedding approach effectively balances capacity, imperceptibility, and reversibility.
Reversible Data Hiding, Prediction Error Expansion, Least Significant Bit, Pixel Prediction, Image Security, PSNR, SSIM, Data Embedding.
Aledhandi Sheshank, Sachin Bemanavena, A. Mohammed Wajid, Chandrakanti Likith Rao
Oil Skimming and Garbage Collection Robot with Bluetooth Control, Water Quality Monitoring
Water pollution caused by oil spills and floating waste represents a persistent environmental challenge affecting aquatic ecosystems, biodiversity, and human health. Mechanical oil skimming remains one of the most reliable large-scale methods for recovering spilled oil from water surfaces, while embedded systems and Internet of Things (IoT) technologies have enabled real-time environmental monitoring and automation. This research presents the design and implementation of a low-cost Oil Skimming and Garbage Collection Robot based on the Raspberry Pi Pico microcontroller, integrating Bluetooth-based remote control and water quality monitoring. The proposed system combines a pump-based oil skimming mechanism, a front-mounted mesh garbage collector, and turbidity and temperature sensors for environmental assessment. Bluetooth Low Energy communication ensures efficient wireless control and data transmission. The system is designed as a compact, energy-efficient, and scalable solution suitable for lakes, ponds, and industrial effluent zones. The integration of mechanical cleaning and IoT-based sensing addresses the limitations of existing standalone systems. Experimental results demonstrate effective debris removal, oil collection, and accurate sensor data transmission, validating the feasibility of the proposed hybrid approach.
Oil skimmer, Raspberry Pi Pico, Bluetooth Low Energy, IoT, Water quality monitoring, Turbidity sensor, Garbage collection robot, Embedded systems.
C. Sowmya, B. Akhil, Ch. Ashwini, B. Sreeshanth
Computational Drug Design and SAR Analysis of Newly Developed Heterocyclic Compounds as Prospective Antidiabetic Molecules
Diabetes mellitus (DM) represents one of the fastest-growing metabolic disorders globally, with type 2 diabetes mellitus (T2DM) accounting for approximately 95% of all cases. The present study investigates the computational drug design approach and structure-activity relationship (SAR) analysis of newly developed heterocyclic compounds as prospective antidiabetic molecules targeting key enzymes, namely α-glucosidase, α-amylase, and dipeptidyl peptidase-IV (DPP-IV). The primary objectives were to evaluate the binding affinity, drug-likeness, and pharmacokinetic profiles of selected heterocyclic scaffolds including thiazolidinediones, oxadiazoles, benzimidazoles, and thiadiazoles through molecular docking and ADMET prediction. The methodology employed in silico approaches comprising molecular docking using AutoDock Vina, SwissADME for drug-likeness, and ProTox-II for toxicity prediction. It was hypothesized that heterocyclic compounds bearing electron-withdrawing substituents would demonstrate superior binding affinity against antidiabetic target enzymes. Results revealed that compounds with para-substituted halogen and nitro groups exhibited binding energies ranging from −7.8 to −10.6 kcal/mol, surpassing the standard drug acarbose (−6.7 kcal/mol). SAR analysis confirmed that the nature and position of substituents critically influence antidiabetic potency. In conclusion, the study validates computational drug design as an effective strategy for identifying potent heterocyclic antidiabetic leads.
Computational Drug Design, Structure-Activity Relationship, Heterocyclic Compounds, Antidiabetic, Molecular Docking
An Ethnobotanical Assessment of Plant-Based Veterinary Remedies Practiced by Indigenous Communities in Bastar
The present study was conducted to assess the ethnobotanical knowledge of plant-based veterinary remedies practiced by indigenous communities in Bastar division, Chhattisgarh, India. The primary objectives were to document the diversity of medicinal plants used in ethnoveterinary practices, identify the dominant plant families and plant parts employed, and evaluate the informant consensus on disease categories treated. A cross-sectional ethnobotanical survey was adopted employing semi-structured interviews, focus group discussions, and participatory field walks among 120 informants from Gond, Muria, Maria, and Bhattra tribal communities across 15 villages during 2019–2021. The hypothesis posited that indigenous communities of Bastar possess a rich and structured system of plant-based veterinary healthcare that remains largely undocumented. Data were analyzed using informant consensus factor (Fic), use value (UV), and fidelity level (FL). The results revealed that 58 medicinal plant species belonging to 34 families were utilized for treating 26 veterinary ailments in cattle, goats, and poultry. Fabaceae and Solanaceae emerged as the dominant families, while leaves were the most frequently used plant part (38.6%). The highest Fic was recorded for gastrointestinal disorders (0.89), followed by dermatological ailments (0.85). It is concluded that the Bastar region harbors significant ethnoveterinary knowledge that warrants systematic documentation, pharmacological validation, and integration into primary animal healthcare delivery systems.
Ethnobotany, Ethnoveterinary medicine, Indigenous communities, Bastar, Medicinal plants
The rapid expansion of e-commerce, emergency supply networks, and smart logistics infrastructure has accelerated the demand for reliable heavy-lift autonomous aerial delivery platforms. This paper presents the design, implementation, and performance evaluation of an AI-enabled hexacopter UAV optimized for medium-payload transportation. The proposed system integrates a Pixhawk-based PX4 autopilot, high-thrust BLDC propulsion, and a smart hybrid energy management framework combining Li-Po battery monitoring with predictive energy modeling. An M8N GPS module with sensor fusion-based Extended Kalman Filtering ensures precise waypoint navigation, while an onboard AI-based energy prediction model estimates real-time endurance under dynamic payload conditions. To enhance operational intelligence, the platform incorporates computer vision-based obstacle detection, LoRa telemetry for long-range communication, adaptive PID tuning, and geofencing-enabled failsafe mechanisms. Experimental evaluation demonstrates stable payload lifting up to 2.5 kg with controlled thrust margins and predictable energy consumption behavior. Flight endurance ranged from 19 minutes (no payload) to 9 minutes (2.5 kg payload), validating thrust-to-weight optimization and power management efficiency. The developed prototype demonstrates the feasibility of integrating hybrid energy awareness, AI-driven flight optimization, and autonomous mission intelligence for next-generation smart logistics applications, especially in remote and infrastructure-constrained environments.
Hexacopter UAV, AI-Based Energy Prediction, Hybrid Power Management, Autonomous Delivery, Pixhawk PX4, Smart Logistics, Heavy Payload Drone, Vision-Based Obstacle Avoidance, Multirotor Control.
Badiage Prabhakar, Bobbili Mahith, Akhilesh chennoju, Buddarthi Shiva rama Krishna
Design and Development of a Pixhawk-Based Autonomous Hexacopter for Payload Lifting and GPS-Guided Smart Delivery
The rapid growth of e-commerce and smart logistics has intensified the need for reliable autonomous aerial delivery systems. This paper presents the design and development of a hexacopter-based weight-lifting drone intended for short- to medium-range parcel transportation. The proposed system integrates a Pixhawk open-source flight controller with an F450 hexacopter frame and is powered by a 4200 mAh Li-Po battery. An M8N GPS module ensures accurate positioning and waypoint-based navigation, while a T12 transmitter enables manual override and hybrid control operation. Compared to quadcopters, the hexacopter configuration provides enhanced payload capacity, redundancy, and flight stability, making it suitable for logistics applications. Autonomous functionalities such as waypoint navigation, return-to-home (RTH), and failsafe mechanisms improve operational reliability. The prototype demonstrates the feasibility of UAV-based lightweight parcel delivery and highlights the integration of robust hardware, intelligent flight control, and GPS-guided automation. The proposed system contributes toward next-generation smart delivery solutions aimed at minimizing human intervention, reducing delivery time, and improving accessibility in remote or infrastructure-limited regions.
Chintalaguttu Mallikarjuna, E.Thirumalesh Goud, Bodla Srinivas, Bhandavaram Shashidhar
Soulful Organizations: A Multidimensional Framework For Ethical And Sustainable Leadership In The 21st Century
In a context marked by technological acceleration, pressure for immediate results, and the erosion of trust, many organizations have intensified their discourse on purpose, values, and wellbeing. However, a relevant gap persists between humanistic rhetoric and the day-to-day practice of leadership, governance, and culture. This paper proposes the theoretical construct “Soulful Organizations” as a humanistic paradigm applicable to management and leadership, defining “organizational soul” not as a religious concept, but as an observable cultural quality that integrates ethical coherence, collective human dignity, care, truth, a living purpose, and social responsibility. Through an integrative review of the literature on ethical and responsible leadership, organizational culture, meaningful work, psychological safety, wellbeing, and sustainability, a multidimensional framework with eight dimensions (EAO) is developed and influence mechanisms on key outcomes are described: wellbeing, engagement, trust, reputation, and sustainable performance. In addition, artificial intelligence (AI) is conceptualized as an amplifying factor, and human-centered AI governance is introduced as a critical condition to avoid dehumanization risks. The contribution is threefold: (i) an operational definition and boundaries of the construct, (ii) a conceptual model with testable propositions, and (iii) an empirical blueprint for validation (EAO scale development, psychometric strategies, multilevel longitudinal designs, and culture-intervention studies). Managerial implications are discussed for translating values into systems, incentives, and decisions.
soulful organizations; ethical leadership; organizational culture; dignity; purpose; workplace wellbeing; sustainability; psychological safety; emotional intelligence; spiritual intelligence; artificial intelligence; governance.
Fixed Point Theory: Existence, Uniqueness, and Applications
Fixed point theory is a foundational area of mathematical analysis concerned with conditions under which a mapping admits a point that remains invariant under the action of the mapping. Such points—called fixed points—play a central role across pure and applied mathematics, including nonlinear analysis, differential and integral equations, optimization, game theory, economics, and computer science. This paper presents an elaborated study of fixed point theory with emphasis on existence and uniqueness results, core theorems and methodologies, and a wide spectrum of applications. Beginning with basic definitions and metric-space techniques, the paper advances through contraction principles, topological fixed point theorems, and order-theoretic approaches, and concludes with contemporary applications and extensions. The discussion highlights how fixed point results provide unifying principles for proving solvability, stability, and convergence in mathematical models.
Fixed Point Theory, Contraction Mapping, Banach Fixed Point Theorem, Brouwer Fixed Point Theorem, Schauder Fixed Point Theorem, Nonlinear Analysis, Applications.
Extreme Learning Machine Applied To Software Development Effort Estimation
Reliable estimation of software development effort is essential for effective project planning, scheduling, and resource allocation. Conventional estimation approaches such as expert judgment and algorithm-based models are often affected by human bias and rigid assumptions. This paper presents a machine-learning driven effort estimation framework based on the Extreme Learning Machine (ELM). The proposed framework uses historical project data from the COCOMO-81 repository and models the relationship between project attributes and actual development effort. ELM is implemented and compared with Linear Regression, K-Nearest Neighbour, Support Vector Machine, and Multilayer Perceptron models. Standard error-based performance indicators, including MAE, MSE, RMSE, and MMRE, are employed for quantitative evaluation. In addition, statistical significance of results is verified using Shapiro–Wilk and Wilcoxon signed-rank tests. Experimental observations confirm that the ELM model delivers superior predictive accuracy with significantly lower training time, demonstrating its suitability for practical software project management environments.
Software effort estimation, extreme learning machine, COCOMO-81, machine learning, project analytics.
Alcohol And Drowsiness Sensing Alert, Combining With Emergency Sos And Automatic Engine Off System
Road accidents caused by alcohol consumption and driver fatigue continue to be a major global safety concern. Conventional safety mechanisms mainly focus on post-accident response rather than proactive prevention. This paper presents an intelligent embedded safety system that integrates alcohol detection, drowsiness monitoring, emergency SOS communication and automatic engine cut-off functionality into a unified vehicle safety platform. The proposed system continuously monitors the driver’s breath using an alcohol sensor and detects fatigue conditions through eye-blink and facial behaviour sensing. When unsafe conditions are identified, the system generates immediate alerts and prevents vehicle operation by disabling the engine ignition. In critical scenarios, an emergency SOS message containing real-time location information is transmitted to predefined contacts using a GSM and GPS module. The prototype is developed using a microcontroller-based embedded platform and validated under real-time experimental conditions. The results demonstrate that the proposed solution can significantly enhance accident prevention by enabling early detection and automated safety actions.
Road safety, alcohol detection, drowsiness detection, SOS system, engine cut-off, embedded systems.
AI-Driven FPGA Power Prediction and Performance Optimization for Autonomous Drone Systems
Power consumption has become one of the most critical design constraints in modern Field Programmable Gate Array (FPGA) based systems. With the rapid growth of complex digital applications such as signal processing, embedded control and artificial intelligence accelerators, designers must accurately estimate power at early stages of development. Conventional power estimation tools provided by FPGA vendors usually rely on detailed low-level design information and simulation activity, which are often unavailable during early design phases. This paper presents an AI-based FPGA power prediction system that employs machine learning techniques to estimate dynamic and total power consumption using high-level design and implementation features. The proposed system extracts relevant design attributes such as resource utilization, clock frequency and logic structure statistics, and uses supervised learning models to predict power values. A complete workflow for dataset generation, feature extraction, model training and evaluation is presented. Experimental results demonstrate that the proposed approach can predict FPGA power consumption with high accuracy and significantly reduced estimation time, making it suitable for early-stage design exploration and optimization.
FPGA power estimation, machine learning, hardware design analytics, low-power design, artificial intelligence.
Groundwater Contamination Assessment Using GIS and Remote Sensing Techniques
Groundwater contamination has emerged as a critical environmental concern in India, threatening drinking water safety and public health. This study assesses groundwater contamination using Geographic Information System (GIS) and Remote Sensing (RS) techniques, with the objective of mapping spatial distribution of contaminants, evaluating groundwater vulnerability, and determining the Water Quality Index (WQI) across selected contamination-prone regions of India. The methodology integrates satellite-derived data from Landsat-8 OLI and Sentinel-2, combined with field-collected hydrochemical data analyzed through the DRASTIC vulnerability model and weighted arithmetic WQI within an ArcGIS 10.8 framework. It is hypothesized that areas with intensive agricultural and industrial land use exhibit significantly higher groundwater contamination levels. Results reveal that approximately 55% of the studied area falls under poor to unfit water quality categories, with Total Dissolved Solids (TDS) ranging from 252 to 2065 mg/L and fluoride concentrations exceeding the WHO permissible limit of 1.5 mg/L in 43–49% of samples. The DRASTIC model identified high vulnerability zones concentrated near urban-industrial corridors and intensively irrigated agricultural belts. The study concludes that integrated GIS-RS approaches provide robust, cost-effective frameworks for groundwater contamination assessment, enabling spatially explicit decision-making for sustainable groundwater management in India.
Groundwater contamination, GIS, Remote sensing, Water Quality Index, DRASTIC model
The Impact of Spirituality Management on New Private Sector Banks in India: An Empirical Investigation
This empirical study examines the influence of spirituality management practices on employee and organizational outcomes in new private sector banks in India. Using a mixed-methods approach, data was collected from 384 employees across six major private sector banks through structured questionnaires and semi-structured interviews. The research tested three hypotheses regarding the relationships between spirituality management dimensions and job satisfaction, organizational commitment, and customer service quality. Statistical analysis revealed significant positive correlations between spirituality management practices and all three outcome variables, with meaningful work emerging as the strongest predictor of job satisfaction (β = 0.63, p < 0.001) and sense of community showing the strongest relationship with organizational commitment (β = 0.58, p < 0.001). The findings indicate that banks implementing comprehensive spirituality management frameworks demonstrated higher customer service ratings and employee retention. This research contributes to understanding the operational benefits of spirituality management in the Indian banking context and provides evidence-based recommendations for banking executives seeking to enhance organizational effectiveness through spirituality-centered approaches.
Spirituality management, workplace spirituality, private sector banks, job satisfaction, organizational commitment.
Relationship Between Diesel Equipment Use and Ventilation Efficiency in Underground Mines
Underground mining operations heavily rely on diesel-powered equipment for ore extraction and material transportation, which significantly impacts ventilation system requirements and efficiency. This study investigates the relationship between diesel equipment utilization and ventilation efficiency in underground mines through comprehensive literature review and data analysis. The research examined multiple underground mining facilities to establish correlations between diesel equipment characteristics including power ratings, emission profiles, and operational patterns with ventilation system performance parameters such as airflow velocity, contaminant dilution rates, and energy consumption. Results indicate that diesel equipment contributes to approximately 50-60% of total heat load and is the primary source of particulate matter and noxious gases in underground mine atmospheres. Analysis reveals that ventilation airflow requirements increase proportionally with diesel equipment power, with minimum standards ranging from 0.04 to 0.067 cubic meters per second per kilowatt across different jurisdictions. The study found significant correlations between diesel particulate matter concentrations and ventilation efficiency, with properly designed ventilation systems achieving 70-85% reduction in worker exposure levels. Statistical analysis demonstrated that increasing ventilation rates by 30% resulted in 45% reduction in diesel particulate matter concentrations in underground headings. This research provides valuable insights for mine ventilation engineers to optimize system design, improve air quality, reduce energy costs, and enhance worker safety in diesel-intensive underground mining operations.
Underground mining, Diesel equipment, Ventilation efficiency, Air quality, Diesel particulate matter.
Assessment of Fly Ash and Mine Overburden Blends for Underground Backfilling
Underground mining operations generate substantial volumes of overburden waste while coal-fired thermal power plants produce massive quantities of fly ash, creating significant environmental management challenges. This study evaluates the geotechnical performance of fly ash and mine overburden blends as sustainable backfilling materials for underground mine voids. The research examines various blend proportions through comprehensive laboratory testing including unconfined compressive strength, California bearing ratio, shear strength parameters, and compaction characteristics. Results demonstrate that optimum blend ratios of 70% overburden with 30% fly ash and 6% cement binder achieve unconfined compressive strength values of 4.01 MPa at 56 days curing period. The investigation reveals significant improvements in mechanical properties, with blends containing 20-30% fly ash exhibiting enhanced strength characteristics suitable for underground backfilling applications. Statistical analysis confirms the effectiveness of fly ash incorporation in modifying engineering properties while providing economical and environmentally sustainable solutions for waste utilization. The findings establish technical feasibility for large-scale implementation of these composite materials in underground mining operations, contributing to waste management, ground control, and subsidence prevention objectives.
Fly ash, Mine overburden, Underground backfilling, Geotechnical properties, Waste utilization.
New Distributional Record Of The Indian Species Of The Genus Testudinella Bory De Saint-Vincent, 1822 (Rotifera: Flosculariacea: Testudinellidae) From The Western Ghats Of Tamil Nadu, India.
The present paper reports the new distributional record of a littoral -periphytic rotifer species of the family Testudinellidae Harring, 1913 from the state of Tamil Nadu as well as to the Western Ghats. The species Testudinella greeni Koste, 1981 is an interesting Palaeotropical rotifer element which is reported earlier from North East India only. From the state of Tamil Nadu four species of the genus Testudinella viz. Testudinella parva (Ternetz, 1892), Testudinella incisa, Testudinella mucronata (Gosse, 1886) and Testudinella patina (Hermann, 1783) were earlier reported. The present study also provided brief descriptions of this species, including images taken with 80i Nikon microscope, illustrated and comments are made on their distribution.
Western Ghats, Tamil Nadu, Testudinellidae, Palaeotropical, Littoral-periphytic rotifer.
Advances in Biomedical Optical Sensing through Machine Learning Techniques
Biomedical optical sensing has revolutionized medical diagnostics through non-invasive imaging and real-time monitoring capabilities. This paper examines the integration of machine learning (ML) techniques with optical sensing technologies including optical coherence tomography (OCT), spectroscopy, and fluorescence imaging. The primary objective is to analyze how ML algorithms enhance diagnostic accuracy, image processing, and disease detection in biomedical applications. A comprehensive literature review methodology was employed, analyzing 45 peer-reviewed studies from 2018-2023. The hypothesis posited that ML integration significantly improves sensitivity and specificity in optical sensing applications. Results demonstrate that deep learning algorithms achieved 92-97% accuracy in retinal disease detection through OCT, while support vector machines showed 89% accuracy in cancer tissue classification using Raman spectroscopy. Convolutional neural networks reduced image processing time by 75% compared to traditional methods. Discussion reveals that ML techniques address challenges of data interpretation, artifact removal, and real-time analysis in optical sensing. The study concludes that ML-enhanced optical sensing represents a paradigm shift in personalized medicine, offering improved diagnostic capabilities, reduced human error, and faster clinical decision-making in healthcare systems.
Biomedical optical sensing, Machine learning, Optical coherence tomography, Deep learning, Medical diagnostics.
Parental Involvement And School Environment As Determinants Of Quality Primary Education: Policy And Practice Perspectives
Quality primary education remains a critical challenge in India despite significant policy interventions. This study examines parental involvement and school environment as key determinants of educational quality through analysis of UDISE+ 2024-25 and NAS 2021 data encompassing 1.47 million schools and 3.4 million students. The research reveals that parental engagement significantly correlates with improved learning outcomes, while infrastructure gaps persist with only 65% schools having functional computers and 35% lacking electricity. Primary-level learning achievement stands at 59% nationally, declining to 49% by Class 5, indicating systemic challenges. The study employs mixed-methods analysis examining policy frameworks including RTE Act 2009 and NEP 2020 alongside empirical data on school infrastructure, pupil-teacher ratios, and parental participation patterns. Findings demonstrate that schools with active School Management Committees and regular parent-teacher engagement show 23-31% higher retention rates. The research concludes that integrated policy implementation addressing both parental involvement mechanisms and physical learning environment is essential for achieving universal quality primary education by 2030 as envisioned under NEP 2020. DOI: https://doi-ds.org/doilink/01.2026-91377747
Parental Involvement, School Environment, Quality Primary Education, RTE Act, National Education Policy
FloraFusion: Valorization of Agro-Waste-Based Natural Dyes for Sustainable Textile Applications
The global textile industry is a major contributor to environmental degradation, primarily due to the use of synthetic dyes, which are derived from petrochemicals and pose severe risks to ecosystems and human health. Synthetic dye effluents contaminate water bodies, disrupt aquatic ecosystems, and often require expensive wastewater treatment processes. As a result, there is a growing interest in identifying biodegradable and renewable alternatives. This study explores the dyeing potential of agro-waste-based natural dyes derived from betel nut, marigold, and pomegranate peel on three fabric substrates: cotton knit, cotton woven, and silk. The primary objectives are to assess the color strength (K/S values), fastness properties (washing, rubbing, light, saliva), changes in fabric GSM after dyeing, and the antibacterial properties of the dyed fabrics. Aqueous dye extracts were obtained from dried agro-waste powders and applied to pre-mordanted fabrics using an alum mordant. Standard ISO testing protocols were employed to evaluate color fastness, while the zone of inhibition method was used to assess antibacterial efficacy. Spectrophotometric analysis revealed that silk consistently exhibited the highest dye uptake, followed by cotton woven and cotton knit. Among the dyes, pomegranate peel not only produced a strong color yield but also demonstrated significant antibacterial activity. The results demonstrate that agro-waste-derived dyes can offer performance comparable to conventional synthetic dyes. These findings support the valorization of agricultural by-products as sustainable, cost-effective, and functional dye sources. This research contributes to the advancement of environmentally responsible textile processing and aligns with global sustainability objectives in the fashion and apparel industry. DOI: https://doi-ds.org/doilink/01.2026-93288788
Captcha: Visual Passwords For Robust Authentication Through Difficult AI Tasks
Many security primitives are based on hard mathematical problems. Using hard AI problems for security is emerging as an exciting new paradigm, but has been under-explored. In this project, we present a new security primitive based on hard AI problems, namely, a novel family of graphical password systems built on top of Captcha technology, which we call Captcha as graphical passwords (CaRP). CaRP is both a Captcha and a graphical password scheme. CaRP addresses a number of security problems altogether, such as online guessing attacks, relay attacks, and, if combined with dual-view technologies, shoulder-surfing attacks. Notably, a CaRP password can be found only probabilistically by automatic online guessing attacks even if the password is in the search set. CaRP also offers a novel approach to address the well-known image hotspot problem in popular graphical password systems, such as Pass Points, that often leads to weak password choices. CaRP is not a panacea, but it offers reasonable security and usability and appears to fit well with some practical applications for improving online security.
Application of Fuzzy Analytic Hierarchy Process to Assess Key Financial Indicators Influencing Indian Stock Market Investments
This research examines the principal financial factors affecting investment decision-making in the Indian stock market through the application of the Fuzzy Analytic Hierarchy Process (FAHP). Financial data were obtained from the top ten companies listed on the Bombay Stock Exchange (BSE), while expert evaluations were gathered from two senior specialists—an experienced investor and a finance professor—each possessing more than fifty years of professional expertise. The analysis focused on four key financial criteria: Return on Equity (ROE) (C1), Earnings Per Share (EPS) (C2), Price-to-Earnings (P/E) ratio (C3), and Debt-to-Equity (D/E) ratio (C4). The FAHP results indicate that Return on Equity (ROE) (C1) is the most influential criterion in investment decision-making. The findings further reveal that indicators related to profitability carry greater weight in investor preferences, while liquidity-related measures have comparatively limited impact. Overall, the study offers valuable insights for investors by underscoring the importance of prioritizing profitability and growth-oriented metrics over short-term solvency considerations when constructing investment portfolios.
Fuzzy Analytic Hierarchy Process (FAHP); Investment Decision-Making; Indian Stock Market; Financial Performance Indicators; Return on Equity; Multi-Criteria Decision Making (MCDM).
Anomaly Detection For Industrial Application: Spotlight On Patchcore And Autoencoder Insights With Resnet Backbone
Ensuring product quality is one of the key requirements in industrial manufacturing, where early detection of defects helps in reducing losses, improving safety, and maintaining customer satisfaction. Manual inspection is often time-consuming and prone to human error, which has motivated the use of Artificial Intelligence (AI) for automated anomaly detection. This project focuses on developing a basic yet effective anomaly detection system using image data. Two simple deep learning approaches are employed: an Autoencoder and a Patch-based analysis method. This project focuses on building a simple anomaly detection system for industrial applications. The goal is to identify whether a product image is normal or defective. To achieve this, we use a basic Convolutional Neural Network (CNN) for feature extraction and apply two easy approaches. A small dataset of normal and defective product images is used for testing. The project demonstrates that even with basic deep learning techniques, it is possible to detect defects in industrial images with good accuracy. This highlights the usefulness of AI in improving product quality and reducing manual inspection effort.
Graphical User Authentication: Enhancing Multi-Factor Authentication With Images
The aim project is to develop a robust and user-friendly Graphical Password Authentication that enhances the security of digital access by incorporating image-based authentication alongside traditional alphanumeric credentials.In the current digital landscape, where cyber threats are becoming increasingly sophisticated, traditional alphanumeric passwords often fall short of providing sufficient protection. This project addresses these vulnerabilities by introducing an innovative authentication method that leverages graphical passwords. Users are required to select specific images from a predefined set, creating a personalized and unique password. This graphical layer works in tandem with conventional passwords, significantly improving security and making unauthorized access more challenging.Additionally, the project focuses on striking a balance between security and usability. While many advanced authentication systems can compromise the user experience due to their complexity, the graphical password system offers a simple yet effective solution. By providing a visual, intuitive method for authentication, users can easily recall and utilize their passwords without sacrificing security. Graphical Password Authentication highlights the potential of combining innovative image-based techniques with modern technologies, offering a more secure, personalized, and user-friendly alternative to traditional authentication methods. The system aims to reduce the risks associated with unauthorized access to sensitive information and ensure a more secure online environment for users.
Risk-Aware and Trustworthy Artificial Intelligence Systems: A Review of Learning, Generative, and Governance-Driven Approaches for High-Impact Applications
The growing number of Artificial Intelligence (AI) systems used in high-impact sectors of finance, healthcare, smart transportation, cybersecurity, and critical infrastructure has increased the desire to implement risk-conscious, trustworthy, reliable, and effective models of decision-making. Although the conventional machine learning models focus on predictive reliability, they tend to be unable to deal with uncertainty, rare, adversarial, and moral responsibility. Consequently, recent studies in AI have been moving towards those architectures which explicitly support risk modeling, robustness, interpretability, and governance practices. The current review gives a syntactic overview of the recent developments in risk-conscious and trustworthy AI systems. The paper discusses the concept of anomaly detection by using deep learning, uncertainty-sensitive predictive models, generative AI-driven stress simulation, optimization-focused learning pipelines, and explainable AI models. Several applications in finance, healthcare, cyber-physical systems, and intelligent networks are critically examined in order to point out the frequent obstacles and design principles. The review also addresses the issue of ethical concerns, privacy protection, and compliance with regulations as the inseparable elements of the reliable AI systems. Lastly, the research directions are put forward to help in developing resilient transparent and human-friendly AI architectures that can be applied in the real world. DOI: https://doi-ds.org/doilink/01.2026-65995577
Risk-Aware Artificial Intelligence; Trustworthy AI; Anomaly Detection; Generative AI; Explainable AI; Secure Machine Learning; Ethical AI Governance.
A Comprehensive Review of Artificial Intelligence, Machine Learning, and Generative Models for Intelligent Decision-Making Across Modern Computational Domains
Machine Learning (ML) and Artificial Intelligence (AI) have found their way into the perennial categories of valuable tools used to make intelligent decisions in data-intensive and complex spaces. The ongoing accelerated research and development in the supervised learning field, deep learning, generative algorithms, and signal processing applications have facilitated effective solutions to financial, medical, intelligent transportation system, Internet of Things (IoT), and cyber-physical infrastructures. Rule-based and statistical methods prove often to be not scalable or generalizable in regard to heterogeneous, in large dimensional, and dynamically changing data. This in turn is leading to the adoption of data-driven and learning-based solutions that can be used to aid in prediction, automation and adaptive intelligence. The current review paper provides a synthesis of the most recent research advances in the field of AI-based decision-support systems and dwells upon the algorithmic core, specific areas of application, and trends. The article summarizes the findings of papers dealing with financial anomaly detection, learning analytics, radiomics-aided medical imaging, IoT-powered intelligent systems and generative artificial intelligence as well as cutting-edge signal processing systems. The most critical challenges associated with the interpretability, data quality, scalability, security, and ethical governance are under consideration. The review ends with an overview of the future research directions to explainable, multimodal, and responsible AI systems that will be able to work properly in the real-world settings. DOI: https://doi-ds.org/doilink/01.2026-18381397
Artificial Intelligence; Machine Learning; Deep Learning; Generative AI; Intelligent Systems; Financial Analytics; Medical Imaging; IoT; Responsible AI
An Inventory Model Incorporating Deterioration And Partial Backordering
This study develops an inventory model for deteriorating items that incorporates partial backordering under realistic operational and financial conditions. The model addresses the practical challenges of managing inventory where products experience time-dependent deterioration and shortages occur due to demand uncertainty or supply disruptions. Unlike traditional inventory models, this framework integrates exponential time-dependent demand, variable deterioration rates, and partial backordering to reflect real-world customer behavior where only a fraction of unmet demand is willing to wait. The mathematical model employs differential equations to determine optimal replenishment times, cycle lengths, and order quantities that minimize total average costs including holding costs, deterioration costs, shortage costs, and lost sales. The analysis considers multiple scenarios involving trade credit policies, interest rates, and payment delays between suppliers and retailers in a two-echelon supply chain. Numerical illustrations demonstrate that optimal solutions differ between individual stakeholders and the overall supply chain, with system-wide cost minimization typically achieved at moderate order frequencies. Results indicate that progressive credit periods marginally increase order quantities but significantly reduce annual total costs. The model provides decision-makers with a practical tool for optimizing inventory policies under deterioration, shortages, and financial constraints in contemporary market conditions. DOI: https://doi-ds.org/doilink/01.2026-68834452
Deteriorating inventory, Partial backordering, Time-dependent demand, Trade credit policy, Supply chain optimization
Influence of Spiritually Enriched Family Environment on Emotional Regulation and Psychological Adjustment of Adolescents
Adolescence is a very important stage of development when the family setting plays a major role in determining psychological well-being. This paper has explored the connection between spiritually enriched family setting, emotional control and psychological adaptation of Indian adolescents. The survey was cross-sectional and involved 420 teenagers between the age of 14-18 years living in Northern Indian urban and semi-urban regions. The standardized measures were standardized such as the Family Environment Scale, Difficulties in Emotion Regulation Scale-Short Form, Bell Adjustment Inventory, and Index of Core Spiritual Experiences. The hypothesis was that adolescents raised in spiritual enhanced family conditions would show excellent emotional regulation and psychological adaptation than their colleagues. Findings indicated strong positive correlations existed between family spiritual practices (r = 0.68, p < 0.001), emotional regulation abilities (r = 0.72, p < 0.001) and psychological adjustment scores (r = 0.64, p < 0.001). The results indicate that family spirituality is a protective factor to improve adolescent mental health, and it has implications on family-based interventions and counseling programs in Indian cultural settings.
Spirituality, Family Environment, Emotional Regulation, Psychological Adjustment, Adolescents
Influence of Optical Properties of Glazing and Reflector Configurations on The Thermal Efficiency Of Integrated Solar Water Heating Systems
Integrated solar water heating systems (ISWHS) rely on flat-plate or evacuated tube collectors to harness solar energy for domestic and industrial hot water needs. Glazing materials, with their transmittance, absorptance, and reflectance properties, govern the amount of solar radiation reaching the absorber plate, while reflector configurations like compound parabolic concentrators (CPCs) or V-troughs enhance incident radiation. This article explores how variations in glass thickness, anti-reflective coatings, and reflector geometries such as mirror spacing, angles, and types affect overall thermal performance. Drawing from experimental and simulation studies, it examines heat transfer mechanisms, optical efficiency gains, and practical optimizations. Key findings indicate that 4 mm low-iron glass yields peak efficiencies around 35%, while reflectors at 5 cm spacing and 30-degree angles can elevate temperatures to 318 K and efficiencies to 85%. Challenges like overheating and angle-dependent losses are addressed, alongside future directions involving nanofluids and advanced simulations. The analysis underscores the potential for 20-50% efficiency improvements, promoting ISWHS adoption in diverse climates.
A Comparative Study of Care Leadership Models and Their Influence on Older Adults' Health and Well-Being
The aging global population necessitates effective leadership models in elder care settings to optimize health outcomes and well-being. This comparative study examines three predominant care leadership models transformational, servant, and person-centered leadership and their differential impacts on older adults' health, quality of life, and satisfaction outcomes. A cross-sectional comparative design was employed involving 302 nursing home staff and 620 care facility assessments across multiple settings. The study hypothesizes that relationship-oriented leadership models demonstrate superior outcomes compared to passive-avoidant approaches. Results indicate that transformational leadership explained 18-23% variance in staff satisfaction and care quality, while servant leadership contributed an additional 15-26% variance in organizational commitment and job satisfaction. Person-centered care models showed 90% satisfaction rates among residents regarding family contact and autonomy. Statistical analyses revealed that transformational and servant leadership significantly improved patient safety culture, reduced adverse events, and enhanced residents' quality of life scores by 31.82±7.18 compared to institutional care. The discussion emphasizes that relationship-oriented leadership fosters collaborative environments, staff empowerment, and resident-centered approaches. This study concludes that integrating transformational and servant leadership principles with person-centered care frameworks optimizes elder care quality, staff well-being, and resident health outcomes.
ransformational leadership, Servant leadership, Person-centered care, Older adults, Health outcomes
Distribution of Prime Numbers and Recent Advances Toward the Riemann Hypothesis
The distribution of prime numbers lies at the heart of analytic number theory. The Prime Number Theorem (PNT) gives the first-order asymptotic behaviour of the prime-counting function (\pi(x)), while the finer structure of the primes appears to be governed by the zeros of the Riemann zeta function (\zeta(s)). The Riemann Hypothesis (RH), asserting that all nontrivial zeros of (\zeta(s)) lie on the critical line (\Re(s)=\tfrac12), would yield near–optimal error terms in the PNT and far-reaching consequences for the distribution of primes in short intervals, arithmetic progressions, and many other problems. This paper reviews the classical framework connecting primes and zeta zeros via explicit formulas and discusses the role of zero-free regions and zero-density estimates. It then surveys selected recent advances connected—directly or indirectly—to RH: improved explicit zero-free regions, refinements in the proportion of zeros known to lie on the critical line, results on the pair correlation of zeros and its implications, developments around the de Bruijn–Newman constant, and progress on bounded gaps between primes. While a proof of RH remains elusive, these developments significantly sharpen our understanding of both prime distribution and the landscape of zeta zeros, suggesting promising directions for future research.
prime number theorem, Riemann zeta function, Riemann Hypothesis, zeros of zeta, zero-free region, pair correlation, bounded gaps between primes