SkinToneNet: a robust optimised cascaded multi-scale residual attention network for accurate psoriasis and vitiligo detection across diverse skin types Anantha Reddy Dasari, Saroj Shambharkar, Jaykumar Lachure, Vijay Kumar Damera, Sagar Lachure Hacettepe Journal of Mathematics and Statistics, 2026 Accurate detection of chronic skin diseases like Psoriasis and Vitiligo remains challenging due to significant variations in skin pigmentation and lesion presentation across different populations. This paper introduces SkinToneNet, a comprehensive framework designed for robust dermatological diagnosis across diverse skin types. The core methodological contributions include a novel hybrid optimisation algorithm (APVCO) that combines the strengths of Volleyball Premier League and Chimp Optimisation for effective hyperparameter tuning in medical image analysis. Additionally, we propose the CMR-GRU architecture, which cascades Multi-Scale Residual Attention Networks with Gated Recurrent Units to capture both spatial hierarchies and sequential dependencies in skin lesion patterns. The framework integrates optimised segmentation using Adaptive TransUNet with optimised classification via CMR-GRU, both fine-tuned using APVCO. Experimental validation demonstrates that SkinToneNet achieves segmentation Dice scores of 0.894 and IoU of 0.812, with classification accuracy of 95.17% for Psoriasis and 95.19% for Vitiligo across Fitzpatrick skin types I-VI. The system maintains specificity above 93.05% and sensitivity above 93.15% for all skin types, demonstrating consistent performance. The work establishes a methodological foundation for skin-type-agnostic dermatological image analysis while addressing critical challenges in automated diagnosis of Psoriasis and Vitiligo.
Hybrid SqueezeNet-LSTM framework with advanced SegNet segmentation for automated skin disease detection Anantha Reddy Dasari, Saroj Shambharkar, Jaykumar Lachure, Vijay Kumar Damera, Sagar Lachure Hacettepe Journal of Mathematics and Statistics, 2025 Skin diseases such as pyoderma, scabies, and fungal infections remain a pressing public health concern in India due to poor hygiene, overcrowding, and limited access to care. To address these challenges, this study introduces a novel deep learning framework, the SqueezeNet-Modified Long-Short-Term Memory model, for the automated detection of skin diseases. The system incorporates four core phases: preprocessing via Gaussian filtering to reduce image noise, segmentation using a Modified SegNet enhanced with a Beta-softmax activation for precise lesion isolation, hybrid feature extraction combining shape, texture, colour, d Local Gradient Triangular Patter, and deep features, and robust classification through the SqueezeNet-Modified Long Short-Term Memory model integrated with Multi-Region Window pooling pooling and Focal-log-cosh loss. The innovative Beta-softmax function and Multi-Region Window pooling strategies enhance feature prioritization and classification accuracy. Evaluation in two data sets, one with 1,414 images of vitiligo and psoriasis, and another with 61 samples in four skin conditions, demonstrates superior performance (accuracy: 0.958, sensitivity: 0.953, specificity: 0.948, F-measure: 0.950) over baseline models such as long short-term memory and novel segmented neural networks. This framework provides a scalable solution for dermatological diagnostics in low-resource settings, with future enhancements targeting the expansion to transformer-based approaches and larger clinical data sets.
Enhancing Environmental Resilience: Precision in Air Quality Monitoring through AI- Driven Real- Time Systems Ankit Mahule, Kaushik Roy, Ankush D. Sawarkar, Sagar Lachure Artificial Intelligence for Air Quality Monitoring and Prediction, 2024 This chapter delves into the innovative realm of real-time air quality monitoring systems, harnessing the potential of artificial intelligence (AI) to provide both conceptual frameworks and practical implementations. It explores the integration of weather model data, enhancing real-time air quality assessments. Region-specific case studies illustrate the diverse scenarios where AI-powered monitoring offers significant advantages, serving as templates for establishing comprehensive air quality assessment networks while considering the impact of contextual factors on research outcomes. In light of escalating environmental challenges, the demand for precise and timely air quality information has become imperative. Traditional methods often fall short in delivering real-time data for effective decision-making. AI emerges as a transformative force in reshaping air quality monitoring, emphasizing ML and data analytics algorithms for processing extensive data from sources like satellites, sensor networks, and weather models. These algorithms swiftly analyze data, forecast pollution patterns, and provide critical insights to policymakers. The integration of weather model data further enhances forecasting precision and comprehension of pollution dynamics. Region-specific case studies highlight the practicality and adaptability of AI-based monitoring systems across diverse geographical locations and pollution profiles, offering guidance for stakeholders interested in adopting AI-powered air quality monitoring. This chapter comprehensively reviews how AI, real-time data, and weather models enhance the air quality monitoring system. It emphasized the potential impact on community health, policy development, and comprehension of environmental factors, aligning with global initiatives for a sustainable future.
Performance of 125 watt PV module using MATLAB-simulink Umesh P. Pagrut, A. S. Sindekar, Sagar S. Lachure, Jaykumar S. Lachure Proceedings of the 3rd IEEE International Conference on Advances in Electrical and Electronics Information Communication and Bio Informatics Aeeicb 2017, 2017
