Category Archives: Uncategorized

Authors: Maaz Bahauddin Naveed

Abstract: Research on composite materials has gradually replaced traditional materials and alloys in an effort to create effective and cost-effective solutions for a range of applications. Metal Matrix Composites (MMCs) offer several benefits over traditional composites, including a lower thermal expansion coefficient, improved resistance to wear and abrasion, a higher strength-to-weight ratio, and reduced density. This review focuses on MMCs based on aluminium, magnesium, copper, titanium, and zinc as well as their alloys. It examines their physical and mechanical properties, manufacturing techniques (such as in-situ, liquid-phase, and solid-phase manufacture), and recent advancements. The properties of MMCs have been enhanced. As a result, they are suitable for critical applications in electronics, autos, and aerospace. Significant findings show how they can overcome the shortcomings of conventional materials by improving a range of mechanical properties. Their tensile characteristics, density, thermal expansion coefficient, elasticity modulus, hardness and fracture resistance, fatigue resistance, creep stiffness, and electrical conductivity can all be enhanced by using reinforcing techniques. The text also emphasises how important it is to select the right production processes to obtain the necessary characteristics while lowering costs and defects. This evaluation is a comprehensive resource for researchers and business professionals. It highlights current advancements, challenges, and the vast potential of MMCs as future resources.

DOI: http://doi.org/10.5281/zenodo.21473843

Published by: vikaspatanker

Authors: Manjusha Chennareddy, Assistant Professor Ch. Lavanya

Abstract: The integration of Artificial Intelligence in Decision Support Systems has revolutionized the process of business strategic management through making data-driven, predictive, and adaptive decisions possible. The following paper provides an extensive analysis of AI-Driven Decision Support Systems (AI-DSS) for strategic business management, with the focus on the system architecture, implementation strategies, and results of the performance. The paper proposes a hybrid approach to implementing AI-DSS, using such methods as machine learning predictive analytics, Explainable AI, and multi-criteria decision-making. The empirical analysis shows that the implementation of AI-DSS results in improving the decision accuracy by 16.4%, decreasing decision time by 35.6%, and increasing user satisfaction by 22.8%. The comparative analysis proves that the implementation of AI-DSS outperforms the traditional approach of using spreadsheets on all measures.

DOI: https://doi.org/10.5281/zenodo.21473763

Published by: vikaspatanker

Authors: Sukeshni Moon

Abstract: Artificial Intelligence (AI) has become an important part of modern households, transforming the way people communicate, work, learn, and manage daily activities. Once considered a technology limited to research laboratories and large organizations, AI is now available to common people through smartphones, smart home devices, virtual assistants, recommendation systems, healthcare applications, and educational tools. This paper examines the evolution of artificial intelligence in homes, its advantages and disadvantages, and how it has improved the lives of ordinary individuals. It also discusses concerns related to privacy, security, dependence on technology, and ethical challenges. The study highlights that AI has the potential to make life more convenient and efficient, but responsible use and awareness are necessary to maximize its benefits.

DOI: https://doi.org/10.5281/zenodo.21469831

Published by: vikaspatanker

Authors: R.Kanimozhi, Dr.V.Maniraj

Abstract: Coconut is an economically important plantation crop whose productivity is significantly affected by leaf diseases such as Leaf Rot, Grey Leaf Spot, and Bud Rot. Early detection of these diseases is essential to reduce crop losses and improve yield. Conventional disease diagnosis through manual inspection is time-consuming, subjective, and unsuitable for large-scale plantations. This paper proposes an Attention-Based Deep Learning Framework for the early detection and classification of coconut leaf diseases. The proposed framework integrates image preprocessing, data augmentation, transfer learning, and a Convolutional Block Attention Module (CBAM) to enhance feature extraction. The attention mechanism enables the model to focus on disease-affected regions while suppressing irrelevant background information. A convolutional neural network is used to classify healthy and diseased leaf images with improved accuracy. The model is evaluated using standard performance metrics, including accuracy, precision, recall, F1-score, and confusion matrix. Experimental results demonstrate that the attention-based framework outperforms conventional CNN models in detecting early-stage disease symptoms. The proposed approach is computationally efficient and suitable for real-time deployment on mobile and edge devices. It provides an effective decision-support tool for farmers and agricultural experts, contributing to improved disease management and sustainable coconut cultivation.

DOI: http://doi.org/10.5281/zenodo.21468251

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Authors: Mr. Santhosh Kumar .S, Dr.Anix joel singh

Abstract: The increasing demand for sustainable energy resources, along with growing environmental concerns related to fossil fuel consumption, has accelerated research into alternative fuels for internal combustion engines. This study focuses on the experimental investigation and performance analysis of an internal combustion engine operated with alternative fuel as a potential replacement for conventional diesel fuel. The experimental investigation was conducted to evaluate the effects of alternative fuel usage on engine performance and exhaust emission characteristics under different operating conditions. The performance evaluation was carried out based on key parameters, including brake power, torque, brake thermal efficiency (BTE), and brake-specific fuel consumption (BSFC). In addition, the emission characteristics were analyzed by measuring exhaust gases such as carbon monoxide (CO), unburned hydrocarbons (HC), oxides of nitrogen (NOx), carbon dioxide (CO₂), and smoke opacity. The results demonstrated that alternative fuel operation produced comparable engine performance with conventional diesel fuel. Variations in brake power, torque, thermal efficiency, and fuel consumption were observed due to differences in fuel properties, combustion behavior, and energy content. The alternative fuel showed a slight increase in fuel consumption due to its lower calorific value; however, it provided improved emission characteristics by reducing CO, HC, and smoke opacity emissions. A minor change in NOx emissions was observed, which was mainly associated with variations in combustion temperature and oxygen availability during the combustion process. Overall, the experimental findings indicate that alternative fuels can be effectively utilized in internal combustion engines while maintaining satisfactory performance and reducing harmful exhaust emissions. The study highlights the potential of alternative fuels as sustainable energy solutions for cleaner and more efficient engine operation.

DOI: http://doi.org/10.5281/zenodo.21468929

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Authors: Bhavana N, Kumar Siddamallappa. U, Neelamma. G, Anusha Jajur. J

Abstract: Kidney disease is a significant health concern worldwide, and identifying renal abnormalities at an early stage is essential for preventing disease progression and improving treatment outcomes. Manual interpretation of medical images can be time-consuming and may vary depending on clinical expertise. To address this challenge, this study presents a convolutional neural network (CNN)-based kidney disease classification system that incorporates transfer learning with ResNet101 and VGG16 architectures. The proposed model is trained and evaluated using an augmented dataset of renal ultrasound and CT images categorized into four classes: normal, cyst, stone, and tumor. Image preprocessing and data augmentation techniques are applied to improve image quality, increase dataset diversity, and enhance the model's generalization capability. By utilizing pre-trained deep learning models, the system effectively extracts meaningful image features while reducing training time and computational complexity. Experimental evaluation shows that ResNet101 achieves a classification accuracy of 97.4% while VGG16 achieves 95.8 %. Performance assessment using precision, recall, and F1-score further confirms the reliability of the proposed approach for multi-class kidney disease classification. The developed framework demonstrates the effectiveness of transfer learning for medical image analysis, particularly when labeled datasets are limited. In addition, the system has the potential to support radiologists by providing faster and more consistent diagnostic assistance, leading to improved clinical decision-making. Overall, the proposed approach ResNet101 offers an efficient and accurate solution for automated kidney disease detection and classification using deep learning techniques.

DOI: http://doi.org/10.5281/zenodo.21453407

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Authors: Geofrey Nyabuto, Peters Anselemo Ikoha, Samuel Mungai Mbuguah

Abstract: Background: Binary depression screening does not distinguish patients with mild symptoms from those with clinically urgent symptom burden. Among people living with HIV (PLHIV), depressive-symptom severity may reflect nonlinear interactions among anxiety, immune status, treatment adherence, stigma, behavioural exposures, and demographic characteristics. Routine electronic medical records (EMRs) provide an opportunity to model these relationships using computationally reproducible methods. Objective: To develop and internally validate a polynomial regression machine learning model for predicting continuous PHQ-9 depressive-symptom severity among PLHIV using routine HIV-care EMR data. Methods: A cross-sectional patient-level analytical dataset was constructed from 54,301 de-identified EMR records from Bungoma and Busia counties, Kenya. Fifteen clinical, treatment, psychosocial, behavioural, and demographic predictors were processed using training-derived imputation, encoding, log transformation, and standardisation. Stratified training (n=38,010), validation (n=8,145), and held-out test (n=8,146) partitions were used. Ordinary least-squares regression with degree-1, degree-2, and degree-3 polynomial feature expansions was compared using R², adjusted R², mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE). Results: The degree-3 model comprised 816 fitted parameters and achieved the strongest held-out performance: R² 0.753, adjusted R² 0.730, MAE 1.808, MSE 6.359, and RMSE 2.522 PHQ-9 points. The linear model achieved R² 0.729, MAE 1.939, and RMSE 2.641. Mean and median cubic-model residuals were 0.040 and 0.005, respectively, although the maximum positive residual was 15.186, indicating important underprediction in a small number of high-severity cases. The largest reported terms were anxiety × CD4 (β=−0.444) and anxiety² (β=0.413). Conclusions: Degree-3 polynomial regression modestly improved prediction of PHQ-9 depressive-symptom severity over linear and quadratic alternatives. Its average error may support broad risk stratification, but threshold crossing, model complexity, coefficient instability, and severe case underprediction preclude autonomous clinical use.

DOI: http://doi.org/10.61137/ijsret.vol.12.issue4.198

Published by: vikaspatanker

Authors: Ambuj Kumar Misra

Abstract: Accurate crop yield prediction is fundamental to food security planning, resource optimization, and climate resilience policy. This study presents a comprehensive secondary data approach to predicting corn (Zea mays L.) yields across the contiguous United States by integrating multi-source datasets including National Oceanic and Atmospheric Administration (NOAA) climate records, the Soil Survey Geographic Database (SSURGO), USDA National Agricultural Statistics Service (USDA-NASS) historical yield data, and MODIS-derived Normalized Difference Vegetation Index (NDVI) values spanning 2000–2022. We evaluate and compare six predictive modeling frameworks—Linear Regression, Support Vector Regression (SVR), Random Forest (RF), Gradient Boosting (GB), Long Short-Term Memory (LSTM) neural networks, and a hybrid CNN-LSTM ensemble. The hybrid CNN-LSTM model achieved the highest predictive accuracy with an R² of 0.93 and a Root Mean Square Error (RMSE) of 5.4 bu/acre, substantially outperforming the baseline linear regression (R² = 0.61, RMSE = 18.4 bu/acre). Growing Degree Days, summer precipitation, and soil organic matter were identified as the three most influential predictors. Results demonstrate that rigorously curated secondary data, when combined with advanced machine learning architectures, can yield operationally reliable crop forecasts at county to regional scales without requiring expensive field campaigns. Implications for agricultural decision-making, early warning systems, and climate adaptation planning are discussed.

DOI: http://doi.org/10.5281/zenodo.21452245

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Authors: Eleanor Watson, Thomas Gray, Chloe Bailey, Ethan Ward, Chaitanya Srinivas, Aneesha Raj

Abstract: The rapid adoption of Artificial Intelligence (AI) is transforming Customer Relationship Management (CRM) from a traditional operational platform into an intelligent, data-driven ecosystem capable of delivering predictive insights, personalized customer experiences, and autonomous decision-making. However, many organizations lack a standardized approach to evaluate their AI readiness and systematically advance their CRM capabilities. This research proposes an Intelligent CRM Maturity Framework for AI-enabled digital enterprises that provides a comprehensive model for assessing organizational maturity across strategic, technological, operational, and governance dimensions. The framework defines progressive maturity levels, beginning with foundational CRM adoption and evolving toward fully autonomous, cognitive CRM ecosystems driven by AI. It incorporates critical assessment areas including AI integration, predictive analytics, intelligent process automation, customer data governance, explainable AI, ethical AI practices, cloud-native architectures, real-time decision intelligence, cybersecurity, regulatory compliance, and continuous innovation. The study further examines the role of emerging technologies such as Large Language Models (LLMs), Generative AI, machine learning, knowledge graphs, conversational AI, intelligent agents, and hyperautomation in enhancing CRM intelligence across sales, marketing, customer service, and executive decision-making processes. Additionally, the proposed framework emphasizes transparency, responsible AI governance, data quality, and security as essential components for sustainable enterprise transformation. By establishing standardized maturity criteria and measurable performance indicators, the framework enables organizations to identify capability gaps, benchmark their current state, prioritize digital transformation initiatives, and optimize AI investments. The proposed model serves as a practical guide for enterprise architects, CRM professionals, business leaders, digital transformation strategists, and researchers seeking to build intelligent, customer-centric, and resilient enterprises capable of adapting to rapidly evolving technological and business environments while achieving improved operational efficiency, enhanced customer engagement, and long-term competitive advantage.

DOI: http://doi.org/10.5281/zenodo.21428659

Published by: vikaspatanker

Authors: Elizabeth A. Howard, Andrew L. Turner, Jonathan M. Reed, Ryan D. Murphy, Chaitanya Srinivas, Aneesha Raj

Abstract: The rapid advancement of artificial intelligence (AI) has transformed Customer Relationship Management (CRM) from a traditional customer data management system into an intelligent platform capable of supporting adaptive decision-making, personalized customer engagement, and continuous business optimization. Among modern AI techniques, reinforcement learning (RL) has emerged as a powerful approach for enabling CRM platforms to learn dynamically from customer interactions and optimize decisions through reward-based feedback mechanisms without relying solely on predefined rules or historical training data. This paper proposes a conceptual framework for developing intelligent self-optimizing CRM platforms through reinforcement learning by integrating customer data management, predictive analytics, intelligent automation, cloud computing, business intelligence, and continuous performance monitoring into a unified enterprise architecture. The proposed framework enables CRM systems to continuously refine marketing strategies, optimize sales recommendations, personalize customer experiences, improve customer retention, automate service operations, and support real-time decision-making while adapting to evolving customer behaviors and changing business environments. Furthermore, the study examines the critical roles of data quality, AI governance, privacy protection, organizational readiness, model interpretability, and ethical AI practices in ensuring successful implementation of reinforcement learning-enabled CRM systems. By leveraging continuous learning and autonomous optimization capabilities, the proposed framework enhances operational efficiency, strengthens customer relationships, improves strategic decision-making, and supports sustainable competitive advantage for AI-enabled digital enterprises. The research contributes to the growing field of intelligent enterprise systems by providing a scalable and adaptable foundation for the next generation of CRM platforms capable of delivering resilient, data-driven, and customer-centric business outcomes in an increasingly dynamic digital economy.

DOI: http://doi.org/10.5281/zenodo.21428604

Published by: vikaspatanker