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Abstract Grape cultivation is important globally, contributing to the agricultural economy and providing diverse grape-based products. However, the susceptibility of grapes to disease poses a significant threat to yield and quality. Traditional disease identification methods demand expert knowledge, which limits scalability and efficiency. To address these limitations our research aims to design an automated deep learning approach for grape leaf disease detection. This research introduces a novel dual-track network for classifying grape leaf diseases, employing a combination of the Swin Transformer and Group Shuffle Residual DeformNet (GSRDN) tracks. The Swin Transformer track exploits shifted window techniques to construct hierarchical feature maps, enhancing global feature extraction. Simultaneously, the GSRDN track combines Group Shuffle Depthwise Residual block and Deformable Convolution block to extract local features with reduced computational complexity. The features from both tracks are concatenated and processed through Triplet Attention for cross-dimensional interaction. The proposed model achieved an accuracy of 98.6%, the precision, recall, and F1-score are recorded as 98.7%, 98.59%, and 98.64%, respectively as validated on a dataset containing grape leaf disease information from the PlantVillage dataset, demonstrating its potential for efficient grape disease classification.

Grapes are a widely cultivated crop in the horticultural industry, renowned for their unique flavor and nutritional benefits. However, this crop is highly susceptible to various diseases that can cause significant reductions in yield and quality, resulting in considerable financial losses. Therefore, it is imperative to identify these diseases to effectively manage their spread. Traditionally, the identification of grape leaf diseases has relied on scientific expertise and observational skills. However, with the advent of deep learning methods, it is now feasible to recognize disease patterns from images of infected leaves. In this research, we propose a novel dual-track feature fusion network titled ‘GrapeLeafNet’ for detecting grape leaf disease. It employs a dual-track feature fusion approach, combining Inception-ResNet blocks with CBAM for local feature extraction and Shuffle-Transformer for global feature extraction. The first track uses Inception-ResNet blocks to represent features at multiple scales and map significant features, and CBAM captures significant spatial and channel dependencies. The second track employs Shuffle-Transformer to extract long-term dependencies and complex global features in images. The extracted features are then fused using Coordinate attention, enabling the network to capture both local and global contextual information. Experimental results on the Grape leaf disease dataset from Plant Village demonstrate the effectiveness of the proposed network, achieving an accuracy of 99.56%.

Mobile robots have emerged as versatile substitutes for human labor across diverse domains, offering promising applications in surveillance, healthcare, and beyond. Fundamental to their autonomy are the core capabilities of movement, perception, cognition, and navigation. This research introduces a novel approach known as the Dual PID based low-cost navigation system (DPLNS), designed specifically for indoor warehouse-like environments. The primary objective of this technique is to enable seamless point-to-point traversal. This is achieved through a fusion of gyroscope correction and visual PID control mechanisms. Leveraging a strategically positioned eagle-eye perspective camera, the system gained crucial insights for navigation. To ensure the uninterrupted execution of planned trajectories, the system employs the Message Queuing Telemetry Transport (MQTT) protocol. This technology ensures smooth communication and coordination of actions. The experimental validation of the proposed strategy highlights its efficacy, positioning it as a promising solution for modern warehouse automation needs. By advancing the capabilities of mobile robots within warehouse contexts, this research not only addresses immediate operational demands, but also paves the way for broader applications across various other fields. The developed system enables robust detection and successful path traversal to the designated spot through a position control operating at average of 12.3 rad/s.

Globally there is rising concern among postnatal women about the complications of Diastasis Recti Abdominis (DRA). DRA, the abdominal muscle divarication may susceptible to loss of functions of the abdominal wall. Rehabilitation exercises re-establish the connection between the abdominal and supporting muscles. The physiotherapists are currently unable to objectively detect the effectiveness of exercises in postnatal women to implement corrective measures. DRA subjects may exhibit diverse positions and movements during exercises emphasizing the need to consider movement patterns across various exercises. The muscle activation and movement patterns are assessed by the Electromyography (EMG) and Inertial measurement unit (IMU) Sensors. IMUs are essential sensors for evaluating exercise performance. The use of wearable IMU sensors enhances subject comfort during movements and facilitates the measurement of abdominal dynamics associated with various exercises. The preeminent objective of this work is to delineate the movement patterns of abdominal muscles during seven rehabilitation exercises specified by the physiotherapist for postnatal women. The trajectory of these exercises gives a clear view of the movement patterns. To predict the effective performance of exercises, modified complimentary filter (MCF) -based sensor fusion approach is utilized in finding the position information and trajectory of the movement patterns. The trajectory features provide insights of movement patterns based on the correctness class during each exercise. K-Nearest Neighbour classifier and Extreme Gradient (XG) Boost classifier are tested and yields an accuracy of 60% and 62%. The Binary Spiking classifier model, which leverages neuromorphic principles, is energy-efficient and capable at handling temporal dynamics. This Spiking model is used to evaluate the effectiveness of rehabilitation exercises, achieving an initial accuracy of 65%. To improve the model’s performance, IMU features are incorporated along with trajectory features, resulting in a higher accuracy of 85% compared to KNN and XG Boost classifier models.

Skin lesions are localized regions of the skin exhibiting atypical growth or appearance and can be either benign or malignant. Detecting and classifying these lesions accurately is crucial for early diagnosis and effective treatment of skin cancer. However, existing approaches for classifying cancerous and non-cancerous skin lesions from dermoscopic images are limited in capturing the appropriate features and also susceptible to overfitting in imbalanced datasets. This work aims to improve skin cancer classification by enhancing feature representation in dermoscopic images. A hybrid classification system is proposed, combining two tracks: the Swin Transformer and the Dense Group Shuffle Non-Local Attention (DGSNLA) Network, which integrates DenseNet169, Group Shuffle Depth-wise blocks (GSDW), and an enhanced non-local attention block (ENLA). This deep feature fusion approach effectively aggregates global and local features, resulting in more accurate feature representation and improved classification performance. To the best of our knowledge, this is the first study to combine a transformer with a custom CNN for skin cancer classification. The proposed network achieves an accuracy of 94.21%, an F1-score of 96.64%. a precision of 96.62%, and a recall of 96.25% on the HAM10000 dataset.

Mucormycosis is an angioinvasive opportunistic fungal infection, but these have become emerging pathogens, especially in conditions with underlying predisposing risk factors in a favourable setting. With the exponential rise in COVID-19 cases, there was an increase in the number of mucormycosis cases among them. The global prevalence rate of mucormycosis in COVID-19 globally varies from 0.005 to 1.7 per million population and in India, it is approximately 0.14 cases/1000. The objective of this study is to detect the prevalence of mucormycosis with the antifungal susceptibility pattern among COVID-19 patients admitted in our hospital. A total of 347 COVID-19 and post-COVID-19 patients with symptoms suggestive of mucormycosis were included in this study. Nasal scrapings, debrided necrotic tissue, unhealthy tissue bits and biopsy tissues taken through FESS were processed for mycological examination under sterile conditions. Among the total 347 samples processed, 87(25%) were positive for fungal culture. Among the culture positves 7.8% (25) belong to mucorales. Among the total 87 fungal isolates, the majority of organism isolated was Aspergillus sp(68%), followed by Rhizopus sp (18%). Rhizopus/Aspergillus sp (5%), Mucor species (5%), Rhizomucor sp (2%), Mucor/Aspergillus sp(1%), Curvularia sp (1%) were the other fungi isolated. All the strains of Mucorales were sensitive to Posaconazole and one strain showed resistance to amphotericin B with MIC 8 µg/ml by microbroth dilution method based on CLSI M27 guidelines for Amphotericin B, and Posaconazole.

The high-efficiency video coding (HEVC) standard incorporates sample adaptive offset (SAO) as an in-loop filtering technique to reduce distortion. The fundamental tenet of SAO is to classify every reconstructed pixel and then simply add the offset to each group of pixels. The previous existing technique employs 32 bands in band offset (BO) calculation to determine the proper offset value to minimize the distortion in the SAO estimate algorithm for statistics collection and parametric determination procedure. The suggested method will reduce the necessary band count by 16 bands. It does not alter how edge offset is determined in any way. It offers enough and efficient room for creating superior band group predictions and selections, as well as an ideal decrease in the implemented area. Thus, reducing the number of bands to 16 also decreases the amount of storage needed to hold each pixel’s information in binary files by up to 0.65%.

In this paper, the health of a plant is estimated using various non-destructive Image Processing Techniques. Chlorophyll content was detected based on colour Image Processing. The Haar transform is applied to get size of leaf and the parameters.

This paper presents a skin color model for face detection based on YCbCr Gauss model and KL transform. The simple gauss model and the region model of the skin color are designed in both KL color space and YCbCr space according to clustering. Skin regions are segmented using optimal threshold value obtained from adaptive algorithm. The segmentation results are then used to eliminate likely skin region in the gauss-likelihood image. Different morphological processes are then used to eliminate noise from binary image. In order to locate the face, the obtained regions are grouped out with simple detection algorithms. The proposed algorithm works well for complex background and many faces.

Abstract Human activity recognition (HAR) is one of the key applications of health monitoring that requires continuous use of wearable devices to track daily activities. The most efficient supervised machine learning (ML)-based approaches for predicting human activity are based on a continuous stream of sensor data. Sensor data analysis for human activity recognition using conventional algorithms and deep learning (DL) models shows promising results, but evaluating their ambiguity in decision-making is still challenging. In order to solve these issues, the paper proposes a novel Wasserstein gradient flow legonet WGF-LN-based human activity recognition system. At first, the input data is pre-processed. From the pre-processed data, the features are extracted using Haar Wavelet mother- Symlet wavelet coefficient scattering feature extraction (HS-WSFE). After that, the interest features are selected from the extracted features using (Binomial Distribution integrated-Golden Eagle Optimization) BD-GEO. The important features are then post-processed using the scatter plot matrix method. Obtained post-processing features are finally given into the WGF-LN for classifying human activities. From these experiments, the results can be obtained and showed the efficacy of the proposed model.

Background: Oral cytology studies have claimed that cytoplasmic Periodic Acid Schiff (PAS) positivity in type-2 diabetics is due to glycogen content. But, it can also be due to mucin and glycoconjugates. Aim: 1. To confirm that cytoplasmic PAS positivity in type-2 diabetics is due to glycogen using diastase. 2. To know the effect of diabetes by determining the number of glycogen-containing cells in the smear. 3. To assess the impact of duration of diabetes based on PAS staining of cells. 4. To correlate between random blood glucose level and the number of PAS-positive cells. Materials and Methods: Study population comprised 45 individuals with 30 type-2 diabetics as case group (Group I < 5 years duration; Group II > 5 years duration) and 15 healthy volunteers (age and gender-matched) as control. For all subjects, random blood glucose was estimated and two cytosmears were obtained. The smears were stained with PAS and PAS-diastase stains (PAS-D). Staining intensity was documented as score 1 (mild-to-moderate) and score 2 (moderate-to-intense) and data obtained were statistically analyzed in SPSS version 16.0. Results: Mann-Whitney U test revealed that in diabetics cytoplasmic PAS positivity is because of glycogen (P < 0.05). There is an increase in the number of glycogen-containing cells (P < 0.05) in diabetics. The duration of diabetes had less impact on intracellular glycogen accumulation (P > 0.05). Spearman's correlation test revealed no significant correlation (P > 0.05) between random blood glucose and a number of PAS-positive cells. Conclusion: PAS positivity is because of intracellular glycogen accumulation in type-2 diabetics. It can convey the glycaemic status of an individual in the recent past, thus a beneficial role in screening and therapeutic monitoring.

White Sponge Nevus is a rear hereditary disease in human causes incurable white lesions in oral mucosa. Appropriate history, clinical examination along with biopsy and cytological studies are helpful for diagnosis of this disorder. Identification can also be made in alternative way by applying image processing technique using Watershed segmentation with MATLAB software. The applied techniques are effective and reliable for early accurate detection of the disease as alternative of expertise clinical and time taking laboratory investigations.