Daily Archives: July 2, 2026

Authors: OM Kute, Yuvraj Narwade, T.B. Faruki

Abstract: Denial of Service (DoS) attacks are one of the most common cyber threats that disrupt network services by overwhelming systems with malicious traffic. Traditional cloud-based detection methods often experience higher latency and increased bandwidth usage, making them less effective for real-time protection. The DoS Attack Detection System Using Edge Machine Learning introduces an intelligent approach that detects malicious network traffic directly at edge devices before it reaches the central server. By leveraging Edge Computing, Machine Learning, and real-time traffic analysis, the system identifies abnormal network behavior with low latency and improved accuracy. This approach reduces server overload, enhances network security, and ensures continuous availability of services while providing a scalable and efficient solution for modern IoT and edge-enabled environments.

Published by: vikaspatanker

Authors: Sneha Kanawade, Dr. Suvarna Patil, Siddhi Kadu, Aniruddha Ojha, Aryan Sahu, Indra Pratap Singh Rajawat

Abstract: Synthetic Aperture Radar is vital remote sensing technology, offering all-weather, day-and- night imaging ca-pabilities. However, its inherent grayscale nature, along with speckle noise, presents significant challenges for interpretation by non-specialists. This review addresses recent advancements in applying deep learning to SAR colorization, a technique aimed at enhancing visual interpretability of these images while preserving unique radiometric properties. The primary motivation is to bridge the gap between complex radar data, intuitive visual analysis, thereby broadening its application in fields like disaster management, environmental monitoring. Major themes covered include critical distinction between grayscale colorization, SAR-tooptical translation, evolution of methodologies from tradi-tional regression to advanced deep learning models, lack of standardized evaluation protocols that has hindered progress. Existing technologies often involve convolutional neural networks, Generative Adversarial Networks (GANs). This review high-lights a proposed methodology centered on conditional GAN within a complete benchmarking protocol utilizing synthetically generated ground truth via intensity-high saturation (IHS) fusion. Key features of this approach include an end-to-end supervised learning framework, use of domain-specific evaluation metrics (Q4, NRMSE, SAM). This advancement holds significant impli-cations for real-time disaster response, contributes to Sustainable Development Goals (SDGs) such as ”Sustainable Cities and Com-munities”, ”Climate Action” by making critical environmental data more accessible, actionable.

Published by: vikaspatanker

Authors: Manjesh Kumar, Assistant Professor Jaya Shukla, Professor Rajnish Bhasker

Abstract: Short-term electric price prediction is important in deregulated power markets and operations as well as planning processes as it aids in the bidding process, risk management and demand response programs. The growing infiltration of renewable energy sources, as well as switching variability of the loads, and market uncertainties, has brought about high nonlinearity and volatility in the electricity price dynamics, which restrain the applicability of traditional forecasting techniques. In solving such challenges, this paper suggests a hybrid deep learning forecasting structure combined with efficient feature selection mechanism to predict short-term price of electricity. The advanced feature selection methods are used in the proposed approach to determine the most informative market, demand, and generation-related variables and to lower the dimensions, as well as to remove redundant information. A hybrid deep learning model, which is a combination of the positive attributes of sequential and nonlinear learning structures, is subsequently trained exploiting the chosen features to absorb intricate temporal variations and price surges. An evaluation of the model by real-world data of the electricity market and a comparison with the traditional statistical methods and individual machine learning are conducted. The simulation outcomes prove that the suggested hybrid structure is more accurate in predictions, more robust, and converges faster, which is indicated by the lower error indicators like MAE, RMSE, and MAPE. In addition, the feature selection step will increase the interpretability and the computational efficiency of models without affecting prediction accuracy. The results attest to the fact that the suggested approach is highly applicable when it comes to short-term electricity price prediction in highly volatile and renewable-based power markets.

Published by: vikaspatanker

Authors: Professor B. R. Shaalini

Abstract: Background: Chronic cigarette smoking induces systemic pathophysiological changes, leading to respiratory muscle deconditioning, impaired pulmonary ventilation, and peripheral muscle fatigue. While Inspiratory Muscle Training (IMT) targets central ventilatory drive, progressive resistance training addresses systemic deconditioning. Aim: To evaluate the combined effectiveness of Inspiratory Muscle Training and DeLorme progressive resistance exercise on respiratory muscle strength, pulmonary function, and functional capacity in active smokers. Methods: This pre-post experimental study enrolled 30 active young adult smokers (aged 20–40 years; mean smoking history: 5.4 ± 1.8 pack-years). Participants underwent a structured 6-week intervention consisting of targeted IMT using a threshold resistance device (40–60% of Maximal Inspiratory Pressure [MIP], 20 min/day, 5 days/week) and DeLorme progressive resistance exercise focused on the bilateral quadriceps femoris muscle groups (3 sets of 10 repetitions at 50%, 75%, and 100% of 10-Repetition Maximum [10RM], 5 days/week). Outcome measures included MIP, spirometric parameters (FEV1, FVC, MVV), functional capacity via the Six-Minute Walk Test (6MWT), and exertional dyspnea via the Borg CR10 Scale. Pre- and post-intervention data were analyzed using a paired t-test. Results: Following the 6-week training protocol, participants demonstrated statistically significant improvements across all primary and secondary parameters (p < 0.001). MIP increased from 68.4 ± 7.2 cmH2O to 84.6 ± 6.8 cmH2O, and 6MWT distance improved by a mean of 74.2 meters. Exertional dyspnea on the Borg scale decreased significantly from 5.8 ± 1.1 to 3.2 ± 0.9. Conclusion: Integrating IMT with DeLorme progressive peripheral resistance exercise significantly enhances respiratory muscle strength, functional exercise tolerance, and ventilatory efficiency in active smokers. This dual-component approach addresses both central respiratory limitations and peripheral skeletal muscle deconditioning.

 

Published by: vikaspatanker

Authors: Brian Levi Okimaru, Betty Mayeku, Humphrey Juma kilwake

Abstract: The transition from junior to senior school under Kenya's Competency-Based Education (CBE) requires learners to select academic pathways that align with their competencies and interests. This transition presents a challenge because pathway selection requires personalized guidance while ensuring the privacy of sensitive student information. Existing educational recommender systems predominantly rely on centralized data processing, exposing learner data to privacy risks and limiting the secure exchange of information across institutions. This study proposes a privacy-preserving personalized pathway recommender system that integrates federated learning, cosine similarity, and Random Forest to support academic pathway recommendation without sharing raw student data. Cosine similarity was employed to model learner competency profiles and measure their alignment with predefined pathway requirements. The resulting similarity scores were incorporated into a Random Forest classifier through feature engineering to improve pathway prediction accuracy. A horizontal federated learning framework enabled multiple schools to collaboratively train the recommendation model by exchanging only model updates while retaining student records locally. The proposed model was evaluated using accuracy, precision, recall, and F1-score. Experimental results showed that integrating cosine similarity with Random Forest improved pathway classification performance, while the federated recommender system achieved an accuracy of 86.54%, outperforming the centralized recommender approach while preserving student privacy. The proposed framework provides an effective and privacy-preserving decision-support tool for personalized academic pathway recommendation within Kenya's Competency-Based Education. The study demonstrates that integrating federated learning with content-based filtering and machine learning can simultaneously enhance recommendation accuracy, personalization, and data privacy in educational environments.

DOI: http://doi.org/10.5281/zenodo.21126397

Published by: vikaspatanker