Egocentric Activity Recognition Using HOG, HOF and MBH Features

recognizing egocentric actions is a challenging task that has to be addressed in recent years. The recognition of first person activities helps in assisting elderly people, disabled patients and so on. Here, life logging activity videos are taken as input. There are 2 categories, first one is the top level and second one is second level. In this research work, the recognition is done using the features like Histogram of Oriented Gradients (HOG), Histogram of optical Flow (HOF) and Motion Boundary Histogram (MBH). The extracted features are given as input to the classifiers like Support Vector Machine (SVM) and k Nearest Neighbor (kNN). The performance results showed that SVM gave better results than kNN classifier for both categories

Modified Firefly Optimization with Deep Learning based Multimodal Biometric Verification Model

Biometric security has become a main concern in the data security field. Over the years, initiatives in the biometrics field had an increasing growth rate. The multimodal biometric method with greater recognition and precision rate for smart cities remains to be a challenge. By comparison, made with the single biometric recognition, we considered the multimodal biometric recognition related to finger vein and fingerprint since it has high security, accurate recognition, and convenient sample collection. This article presents a Modified Firefly Optimization with Deep Learning based Multimodal Biometric Verification (MFFODL-MBV) model. The presented MFFODL-MBV technique performs biometric verification using multiple biometrics such as fingerprint, DNA, and microarray. In the presented MFFODL-MBV technique, EfficientNet model is employed for feature extraction. For biometric recognition, MFFO algorithm with long short-term memory (LSTM) model is applied with MFFO algorithm as hyperparameter optimizer. To ensure the improved outcomes of the MFFODL-MBV approach, a widespread experimental analysis was performed. The wide-ranging experimental analysis reported improvements in the MFFODL-MBV technique over other models

Egocentric Activity Recognition Using HOG, HOF, MBH and Combined features

Recognizing egocentric actions is a challenging task that has to be addressed in recent years. The recognition of first person activities helps in assisting elderly people, disabled patients and so on. Here, life logging activity videos are taken as input. There are 2 categories, first one is the top level and second one is second level. In this research work, the recognition is done using the features like Histogram of Oriented Gradients (HOG), Histogram of optical Flow (HOF) and Motion Boundary Histogram (MBH). These features are combined together to form a feature (Combined HHM). The extracted features are fed as input to Principal component Analysis (PCA) which reduces the feature dimensionality. The reduced features are given as input to the classifiers like Support Vector Machine (SVM) and k Nearest Neighbor (kNN). The performance results showed that SVM gave better results than kNN classifier for both categories

Detection of Alzheimer’s disease in MRI images using different transfer learning models and improving the classification accuracy

Alzheimer's disease (AD) is a neurodegenerative illness that damages brain cells and impairs a patient's memory over time. If detected early so, the patient can avoid permanent memory loss and further damage to their brain cells. Various automated technologies and techniques have been developed in recent years for the detection of Alzheimer's disease (AD). Methods that focus on rapid, accurate, and early identification of the condition in order to reduce the negative impact on a patient's mental health are available. Medical imaging systems for Alzheimer’s disease (AD) diagnostic performance have been greatly improved by machine learning models. There is a major difficulty with multi-class classification, however, which is the presence of brain structural characteristics that are extremely closely associated. It is possible to improve deep learning by increasing the number of layers and including features and classifiers at all levels of the classification hierarchy. Nevertheless, the vast majority of deep learning models (like traditional CNN model) fail to deliver acceptable results in real-world situations.&nbsp

Comparative evaluation of fish larval preservation methods on microbiome profiles to aid in metagenomics research

Applications of microbiome research through metagenomics promise to generate microbiome manipulation strategies for improved larval survival in aquaculture. However, existing lacunae on the effects of sample preservation methods in metagenome profiles hinder the successful application of this technique. In this context, four preservation methods were scrutinized to identify reliable methods for fish larval microbiome research. The results showed that a total of ten metagenomics metrics, including DNA yield, taxonomic and functional microbiome profiles, and diversity measures, were significantly (P < 0.05) influenced by the preservation method. Activity ranking based on the performance and reproducibility showed that three methods, namely immediate direct freezing, room temperature preservation in absolute ethanol, and preservation at − 20 °C in lysis, storage, and transportation buffer, could be recommended for larval microbiome research. Furthermore, as there was an apparent deviation of the microbiome profiles of ethanol preserved samples at room temperature, the other methods are preferred. Detailed analysis showed that this deviation was due to the bias towards Vibrionales and Rhodobacterales. The microbial taxa responsible for the dissimilarity across different methods were identified. Altogether, the paper sheds light on the preservation protocols of fish larval microbiome research for the first time. The results can help in cross-comparison of future and past larval microbiome studies. Furthermore, this is the first report on the activity ranking of preservation methods based on metagenomics metrics. Apart from methodological perspectives, the paper provides for the first time certain insights into larval microbial profiles of Rachycentron canadum, a potential marine aquaculture species

Metagenomic signatures of transportation stress in the early life stages of cobia (Rachycentron canadum) to aid in mitigation strategies

Cobia (Rachycentron canadum) is a high-value marine aquaculture species. Knowledge of the microbial dynamics in various aquaculture operations is crucial for developing suitable management practices. The present study revealed the critical dysbiotic events in the whole larval and juvenile-gut microbiome of cobia, through an inevitable aquaculture operation, viz. live transportation. The results through both culture-dependent and independent techniques demonstrated the sensitivity of the cobia microbiome during early life, where live transport is inevitable. In detail, there was a significant change in the microbial composition and reduction in the cultivable load of all the life stages. Further, a significant reduction in functional metagenomics along with an increase in taxonomic metagenomics was recorded in the L21 stage. Significant reductions of the putative healthy microbiota, viz., Proteobacteria and Actinobacteria were remarkable in the whole larval microbiome. The analysis through linear discriminant analysis effect size revealed that the opportunistic fish pathogens, viz., Vibrio spp., Arcobacter spp., and Acinetobacter spp. were increased whereas, Pseudomonas spp. was decreased in larvae following transportation. The significant reduction in the taxonomic diversity measures was noteworthy in the juvenile-gut microbiome. Transportation promoted Serratia spp., Enterobacter spp., an unidentified genus in Flavobacteriaceae, Pseudoalteromonas spp., Alteromonas spp., and Enterovibrio spp., and inhibited Empedobacter spp. in the juvenile gut. Collectively, the results provide the prospective metagenomic signatures of health and stress in the early life stages of cobia and novel possible explanations for increased disease susceptibility post-transportation. The study warrants future research on the microbes which were found to be decreased following transportation, as potential probiotics to mitigate the stress in the marine aquaculture practices. The metagenomic signatures revealed through the study can be further applied for evaluating different husbandry practices to mitigate stress during live transportation

Full-length transcriptome from different life stages of cobia (Rachycentron canadum, Rachycentridae)

Cobia (Rachycentron canadum, Rachycentridae) is one of the prospective species for mariculture. The transcriptome-based study on cobia was hampered by an inadequate reference genome and a lack of full-length cDNAs. We used a long-read based sequencing technology (PacBio Sequel II Iso-Seq3 SMRT) to obtain complete transcriptome sequences from larvae, juveniles, and various tissues of adult cobia, and a single SMRTcell generated 99 gigabytes of data and 51,205,946,694 bases. A total of 8609435, 7441673 and 9140164 subreads were generated from the larval, juvenile, and adult sample pools, with mean sub-read lengths of 2109.9, 1988.2 and 1996.2 bp, respectively. All samples were combined to increase transcript recovery and clustered into 35661 high-quality reads. This is the first report on a full-length transcriptome from R. canadum. Our results illustrate a significant increase in the identified amount of cobia LncRNAs and alternatively spliced transcripts, which will help improve genome annotation. Furthermore, this information will be beneficial for nutrigenomics and functional studies on cobia and other commercially important mariculture species