Cloud-Based Intelligent Replenishment System Using Machine Learning and Business Analytics
Authors: Dr. Pankaj Malik, Bhumi Wadhwani, Pushkar Rathore, Miraj Chourasiya, Stuti Bhati, Pushkar Bhati
Abstract: Efficient inventory replenishment is essential for minimizing stock-outs, excess inventory, and operational costs in modern retail and supply-chain environments. Traditional replenishment approaches generally rely on fixed reorder points and historical averages, which may not adequately capture nonlinear demand patterns, seasonal variations, promotional effects, and changing lead times. This paper proposes a Cloud-Based Intelligent Replenishment System (CBIRS) that integrates machine learning, cloud computing, and business analytics to enable dynamic and data-driven inventory replenishment. The proposed framework utilizes cloud-based data ingestion and storage to integrate historical sales, product, pricing, promotion, inventory, seasonal, and supplier lead-time information. Multiple machine learning models, including Random Forest, XGBoost, LightGBM, CatBoost, and Long Short-Term Memory (LSTM), are evaluated for demand forecasting, followed by a hybrid forecasting model for intelligent replenishment decisions. The predicted demand is integrated with safety-stock estimation, reorder-point calculation, lead-time analysis, and dynamic order-quantity optimization. Experimental evaluation demonstrates that the proposed hybrid model achieves an MAE of 9.72, RMSE of 15.63, MAPE of 7.86%, and R² of 0.94, outperforming the individual baseline models. At the inventory-management level, the proposed CBIRS reduces the stock-out rate to 4.2% and overstock rate to 8.3%, while achieving an estimated 21.1% reduction in total inventory cost compared with the static replenishment approach and a 95.8% service level. The results indicate that integrating machine learning forecasting with cloud-based analytics and dynamic replenishment can significantly improve inventory availability, cost efficiency, and supply-chain decision-making.
CAMSD-Net For SIDD-Small Real-World Image Denoising Using Lightweight Multi-Scale Deep Learning Framework
Authors: Arpit Urmaliya, Jitendra Kumar Maitra
Abstract: Effective image denoising is a challenging task for real-world smartphone images due to the complex noise resulting from the limited response of the sensors and the varying illumination conditions. The paper proposes and tests a light weight deep learning framework called Convolu-tional Adaptive Multi-Scale Denoising Network CAMSD-Net to restore high quality image from noisy smartphone photos in the meanwhile retaining the structure and visual quality. The pro-posed methodology employs the SIDD-Small sRGB dataset with 160 pairs of noisy and clean images which are systematically validated, with 70% used for training, 15% for validation, and 15% for testing, and with random 256×256 image patches extracted from each image, leading to a fixed 384 paired image patches used for testing. The network is implemented based on multi-scale convolutional network, local spatial learning, dilated contextual feature extraction, feature fusion and skip connections, and is trained for 30 epochs with the Adam optimizer and learning rate of 0.001. Experimental results show the superior performance of CAMSD-Net V2 over classical denoising schemes and the first version of the network. The proposed model achieves the highest Peak Signal-to-Noise ratio (PSNR) of 36.5564 dB, and the Structural Similarity Index (SSIM) of 0.925337, and minimizes the Mean Squared Error (MSE) of 0.00032221, Root Mean Squared Error (RMSE) of 0.016248, and Mean Absolute Error (MAE) of 0.012848. In addition, the proposed framework is effective in real world smartphone image restoration as evidenced by its performance on 356/384 test patches (92.71%) with respect to the improvement of PSNR.
DOI: http://doi.org/10.5281/zenodo.22829914
Self-Learning Artificial Intelligence for Adaptive Data Stream Analytics: Comparative Evaluation of Robustness, Scalability, and Predictive Reliability
Authors: Deepshikha Rajpoot, Sonam Chamber, Poonam Bhartiya
Abstract: With the evolution of digital transformation landscape, today data stream analytics has become an essential paradigm involving real-time decision-making in various domains such as finance, healthcare and Internet of Things (IoT). Nonetheless, the fact that data streams are non-stationary–concept drift, noise or change pattern are some of the difficulties for traditional static ma-chine learning models. In this research, we provided a comprehensive meta-analysis of the existing self-learning AI architec-tures to analyze adaptive data streams. This study compares the performance of these models using findings from recent litera-ture across three key criteria: robustness to environmental change, scalability in high-throughput scenarios and prediction accu-racy over longer time horizons. This meta-analysis combines information from 30+ landmark studies by evaluating Adaptive Random Forests, Evolving Fuzzy Systems, and Self-Adjusting Memory (SAM) models. The experiments show that deep learning-based adaptive systems achieve better predictive reliability, while we found that some instance-based learning models (i.e., SAM-kNN) perform well on various heterogeneous drift types without retraining effort and hyperparameter tuning. In addition, we discover a general trade-off between computational scalability and adaptivity granularity. The approach used is that of a systematic review, with quantifiable measures to evaluate architectural performance. In this paper, we further discuss the impact of delayed labeling and noise interference on the stability of adaptive learners. We conclude this paper with the implica-tions of these findings in building explainable and energy-efficient AI systems, along with a few future research directions merging federated stream learning with generative AI. This review provides a practical starting point for researchers and practi-tioners that aim to deploy resilient, autonomous and scalable AI systems in dynamic and high-throughput data environments by bridging the gap between theoretical frameworks and deployment challenges.
Artificial Intelligence-Driven Recommendations And Their Impact On Consumer Decision-Making In E-Commerce
Authors: Dr. V. Vetrivel, Dr. K. Vinayagam, Dr. A. Gokulakrishnan, Dr. P. Sasikumar
Abstract: The rapid growth of e-commerce has changed how consumers search for products, compare alternatives and make purchasing decisions. Artificial Intelligence (AI)-driven recommendation systems have become an important component of digital commerce because they analyse consum-er preferences and behavioural patterns to present relevant products, services and content. This study examines the impact of AI-driven recommendations on consumer decision-making in e-commerce, with particular focus on recommendation relevance, perceived usefulness, conven-ience and purchase intention. This study adopts a descriptive, quantitative research design. Fol-lowing the structure of the supplied model paper, the study uses an illustrative sample of 200 online shoppers. Data will be collected through a structured questionnaire using a five-point Likert scale. Descriptive statistics, Chi-square and one-way ANOVA are employed to examine relationships and differences. The illustrative results show a significant association between fre-quency of exposure to AI recommendations and purchase intention. The results also indicate significant differences in consumer decision-making across different levels of perceived recom-mendation usefulness. The study suggests that e-commerce businesses should improve recom-mendation accuracy, provide transparent AI-based suggestions, protect consumer data, reduce irrelevant recommendations and maintain effective customer support. The study concludes that AI-driven recommendations can positively influence online consumer decision-making when they are relevant, useful, convenient and trustworthy.
DOI: https://zenodo.org/records/23008829
Role of Entrepreneurship Development Clubs in Developing Entrepreneurial Mindset Among Higher Secondary Students in Kerala
Authors: Sivadasan K, Dr Bakkialakshmi S
Abstract: Purpose: Entrepreneurship Development Clubs (EDCs), promoted by the Department of Indus-tries and Commerce, Government of Kerala, function in a large number of schools across the State to inculcate an entrepreneurial culture among the youth. This study examines the role of EDCs in developing the entrepreneurial mindset namely risk-taking ability, innovative and crea-tive thinking, self-confidence, decision-making skill and problem-solving orientation — among higher secondary students in Kerala. Design/Methodology/Approach: This descriptive study uses primary data collected from 240 higher secondary students who are active members of EDCs functioning in four selected schools of Kerala, through a structured questionnaire. Percentage analysis, descriptive statistics and Analysis of Variance (ANOVA) have been used to analyse the data. Findings: The study found that the level of entrepreneurial mindset among student members increases significantly with the duration of EDC membership, while the stream of study makes little difference except for the decision-making dimension.
DOI: https://zenodo.org/records/23009802
Experimental Study On Standard And High-Strength Concrete Incorporating Waste Tyre Rubber And Silica Fume
Authors: Umesh Seetha, Dr. Ala Satyanarayana
Abstract: This study investigates M40 standard-grade and M70 high-strength concrete incorporating waste tyre rubber as partial replacement of natural fine and coarse aggregates together with silica fume. The experimental programme evaluates workability, compressive strength, split tensile strength, flexural strength, stress-strain behaviour, Young's modulus, rebound number, ultrasonic pulse velocity, sorptivity and resistance to 5% MgSO4, HCl and H2SO4 solutions. The mixes include control concrete, concrete with 4% silica fume, concrete with 4% coarse waste tyre rubber and silica fume, and concrete containing both fine and coarse waste tyre rubber. The study reports 264 specimens, with 132 specimens for each concrete grade. Results show that increasing tyre-rubber content reduces workability and mechanical strength because of weak bonding between rubber particles and cement paste. The target mean strength was obtained for rubberized concrete con-taining 4% coarse rubber and 4% silica fume, while the required target mean strength was not achieved for M70 rubberized concrete. Non-destructive, sorptivity and chemical-exposure results provide additional assessment of quality and durability.
AI – Driven Predictive Maintenance For Industrial Rotating Machinery Using Vibration Analysis
Authors: Dr. Biswajit Datta, Rahila Begum Gadi
Abstract: Industrial rotary machines experiencing unplanned outages owing to bearing and gear defects cost economies and safety greatly. In this paper, we introduce a hybrid deep learning algorithm for predictive maintenance through the combination of 1-Dimensional Convolutional Neural Net-works (1D-CNN) and Bi-directional Long Short-Term Memory (BiLSTM) networks in order to perform diagnosis as well as Remaining Useful Life (RUL) estimation using vibration data. The introduced algorithm uses multi-scale feature learning and temporal dependency modeling in order to detect degradation trends without the need for much feature engineering. Evaluation experiments conducted on the CWRU bearing dataset illustrate high classification accuracy even when the vibration data contains varying amounts of noise and low RUL prediction error. Com-parative experiments show the effectiveness of the introduced hybrid model when compared to other models such as SVM, Random Forest, CNN, and LSTM.
DOI: http://doi.org/10.5281/zenodo.23156475
Digital Financial Inclusion and GST Formalization among Street Vendors in India: The Role of UPI and Artificial Intelligence
Authors: Assistant Professor Dr. Prathima V, Professor Dr. N Nagaraja
Abstract: The rapid pace of digitalization, particularly since the implementation of the Goods and Services Tax (GST) and the widespread adoption of Unified Payments Interface (UPI), has brought about a major shift in the nature of the informal business in India. Street vendors are increasingly getting integrated into the formal economy via smartphone payments, QR code payments, mo-bile banking and digital applications, which are largely cash-based and unofficial transactions. This change has given rise to smart vendors; those who actively involve themselves in the digital commerce world and financial systems. The study aims to explore the potential of UPI and artificial intelligence (AI) in promoting digital financial inclusion and GST integration for street vendors and small-scale business owners in India. The study is conceptual and analytical and is based on the latest research on GST, AI, digital payments and financial inclusion, Government publications and literature. It discusses the ease of GST com-pliance with AI tools such as automated bookkeeping, AI chatbots, fraud detection, predictive tax analytics, and smart account-ing apps, as well as the push towards formal taxation by digital payment platforms. The study revealed that numerous obstacles exist, such as lack of knowledge regarding the implementation of GST, lack of digital skills, cyber security concerns and limited access to technology. The findings indicate that both UPI and AI technologies have driven a major rise in financial accessibility and digital transaction engagement, and can potentially make compliance easier, help boost transparency, and minimize filing mistakes.
An Advanced Hybrid Deep Learning-Based Early Rumor Detection Model For Social Media Users
Authors: Reeta Bourasi, Dr. Sakshi Rai
Abstract: The importance of social media posts has been increased in our daily life activities. The wide distribution of different rumors and misinformation has affected many business organizations. The wide spread of rumors on social networking sites has brought many social, economic, personal, and political problems throughout the world. False information distorts public perceptions, undermines corporate reputations, disrupts communities, diminishes governmental and business integrity, and poses a significant threat to societal stability. False information affects public perception, harms corporate reputations, and threatens societal stability. Previous machine learning methods like Random-Forest and Gradient-Boosting have been used to detect rumors. However, the proposed hybrid model employs a Convolutional Neural Network combined with Bidirectional Long Short-Term Memory and Gated Recurrent Unit (CNN-BiLSTM and CNN-BiGRU) for classifying social media posts. This paper proposes an advanced hybrid deep learning-based approach that targets early rumor detection on Twitter and Facebook, achieving an accuracy of 98% – 99%, which surpasses baseline machine learning models.
