Volume 13 | Issue 6 |

 Abstract

Bhumi Upyog Men Asantulan Aur Bihar Ki Paristhikiya Sankat - Nitigat Samadhan Ki Khoj

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Bhumi Upyog Men Asantulan Aur Bihar Ki Paristhikiya Sankat - Nitigat Samadhan Ki Khoj

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Diabetic retinopathy (DR) is a leading cause of blindness among diabetic patients. Timely screening and accurate severity grading are critical for effective intervention. This paper proposes a novel deep learning architecture that integrates a multi-scale convolutional neural network (CNN) with temporal attention to classify DR severity using color fundus images. Multi-scale feature extraction via a feature pyramid network captures lesions at different scales, while a bidirectional long short-term memory (LSTM) with attention aggregates temporal features from sequential visits. Experimental evaluation on public datasets, including Kaggle DR and EyePACS, demonstrates that our model outperforms baseline CNNs and state-of-the-art methods, achieving 92.4% accuracy and a Cohen's Kappa of 0.89. Grad-CAM visualizations highlight clinically significant lesions, ensuring model interpretability.

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Diabetic retinopathy, deep learning, multi-scale CNN, temporal attention, image classification, explainable AI.

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 Abstract

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Waste management is a critical challenge in urban and rural areas due to rapid population growth, industrialization, and urbanization. Traditional waste collection and disposal methods are often inefficient, costly, and environmentally damaging. Integrated Smart Waste Management (ISWM) combines modern technology with waste management practices to enhance efficiency, sustainability, and responsiveness.

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Municipal garbage bin, ultrasonic sensor, Arduino, Node MCU, ThingSPEAK, Stepper Motor, IoT Monitoring.

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Weather forecasting is crucial in multiple domains such as agriculture, transportation, and disaster management. This paper presents a weather forecasting system that combines machine learning techniques with a Kotlin-based mobile interface. Historical weather data is processed and used to train an LSTM (Long Short-Term Memory) neural network, which predicts weather parameters like temperature, humidity, and rainfall. The frontend interface, developed in Kotlin for Android, allows users to visualize weather trends and receive predictions in real-time. The system is efficient, user-friendly, and can be extended to support live weather feeds.

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Weather Prediction, LSTM, Kotlin, Machine Learning, Android App, Temperature Forecasting

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The generic version of the Duffing equation is examined in this article. This problem can be solved with beginning conditions using an adaptation of the power series approach. The power series method's convergence theorem is demonstrated. The Duffing equation is a nonlinear second-order differential equation that models the behavior of a driven, damped oscillator with cubic nonlinearity. In this study, we apply the power series method to obtain an approximate solution to the Duffing equation. The power series expansion expresses the displacement x(t) as an infinite sum of powers of time, which is substituted into the equation to generate a system of recursive equations for the coefficients of the series. By solving these equations iteratively, we determine the approximate solution for small driving force, weak nonlinearity, or for short times. The method provides an effective way to analyze the system's behavior under small perturbations and serves as a foundation for exploring more complex nonlinear oscillatory systems. The approach is validated through the calculation of the first few terms in the series expansion, providing insight into the dynamics of the oscillator for various values of the system parameters such as damping, linear stiffness, and nonlinear stiffness.

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Duffing Equation, Differential Equation ,power series

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Checking the quality of data in real time within event-driven processes guarantees that analytics and decisions based on AI are reliable. Standard batch methods for managing quality do not work well with huge and fast data streams. The review outlines a framework that depends on AI, includes rule-based validation, looks for anomalies using machine learning and can adapt based on changing needs to satisfy latency, scalability and robustness issues. Research studies have shown that using a hybrid approach with continuous drift detection gives high accuracy of 97% and few false positives. In a federated setting, data privacy is kept alongside effective threat detection. Among the key lessons is that model retraining is tough, available resources can be limited and privacy is essential, especially as it's important to balance accuracy and how quickly tasks can be completed. Because the framework is designed in modules, adding new features is simpler: simple checks are done by rules, but AI steps in when regular rules fail. If the drift-aware component recognizes changing data patterns, it leads to adaptive retraining or changes in the threshold, so the model does not lose performance. Further steps include deploying federated techniques on different kinds of edge devices, coming up with ways to describe data quality in understandable terms and adding governance policies to maintain compliance with future rules. Besides, having common benchmarking data and standards for performance is important for assessing real-time data quality solutions. It covers the various techniques in use, points out areas that still need work, like efficient resource use and integrating many types of data and details possible future projects for improving real-time data quality assurance. By gathering recent information, this article explains the present achievements and chances for new development to researchers and practitioners.

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Real-time data quality, event-driven pipelines, anomaly detection.

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The study is mainly focusing on global data protection regulations such as GDPR, CCPA, PDPA, LGPD, and PIPL that protect individual identities in the new digital age. It examines their legal scope, Rights of Data Subjects, Compliance Requirements, Enforcement Mechanism, and Regulations on Cross-border Data Transfer. It shows variations in enforcement strategies, penalties, and applicability while these regulations share the same principles like transparency, user control, and accountability. The paper also discusses the challenges that businesses have to face in compliance across various jurisdictions, the effectiveness of those regulations concerning consumer rights, and the influence of emerging technologies like artificial intelligence and big data. The study ends with proposals towards greater international harmonization in data protection laws along with recommendations to policymakers, organizations as well, and researchers.

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Data Protection, GDPR, CCPA, Privacy Regulations, Global Compliance, Cross-Border Data Transfers, Digital Rights.

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Background: Dental discoloration in children can significantly affect aesthetics and psychosocial confidence. Conventional whitening techniques may not be suitable for pediatric use due to enamel sensitivity and chemical exposure. Ayurveda offers natural alternatives that have been traditionally employed to improve oral hygiene and aesthetics. Objective: To explore Ayurvedic approaches and remedies for teeth whitening in children, backed by classical references and modern research. Methods: A narrative review of Ayurvedic classical texts (Charaka Samhita, Sushruta Samhita, Kashyapa Samhita) and modern scientific studies was conducted. Herbal practices such as Dantamanjan (herbal tooth powder), Gandusha (oil pulling), and decoction rinses were examined. Results: Ayurvedic formulations like Triphala, Haridra, Yashtimadhu, Lavanga, and Neem are found to be effective in oral cleansing and preventing plaque. Gandusha with sesame or coconut oil helps reduce microbial load and discoloration. Scientific studies validate the antibacterial and whitening potential of these remedies. Conclusion: Ayurvedic dental care offers gentle and natural whitening solutions for children, emphasizing preventive and holistic oral hygiene. Further pediatric trials are recommended to standardize these interventions.

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Keywords: Ayurveda, teeth whitening, children, Dantamanjan, Gandusha, Triphala

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Quantum computing is revolutionizing the landscape of data science by offering computational capabilities that extend far beyond the limits of classical machines. As the demand for faster, more efficient data analysis grows, particularly in domains that require real-time decision-making, integrating quantum computing with machine learning (ML) is emerging as a game-changing approach. This paper explores how quantum computing can be effectively integrated with machine learning to process real-time data streams, with a specific focus on binary classification tasks. Using a live weather sensor dataset as a case study, we simulate real-time data ingestion and evaluate the performance of a Quantum Support Vector Machine (QSVM) against a classical Support Vector Machine (SVM). The experimental setup utilizes Qiskit for quantum simulations and Python-based tools for classical implementations. Our results demonstrate that while classical models currently outperform quantum models in terms of latency and accuracy, quantum approaches offer considerable advantages in terms of scalability and potential computational efficiency. These findings suggest that as quantum hardware matures, it may enable a new generation of real-time, intelligent systems.

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Quantum Computing, Quantum Machine Learning, Real-Time Data, Binary Classification, QSVM, Classical SVM, IBM Qiskit, Weather Forecasting

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