10 research outputs found
Deep Learning Espoused Imaging Modalities for Skin Cancer Diagnosis: A Review
No full textPurpose: Skin Cancer (SC) is one of the most threatening diseases worldwide. Skin cancer diagnosis is still a challenging task. Recently, Deep Learning (DL) algorithms have demonstrated exceptional performance on many tasks compared to the traditional Machine Learning (ML) methods. Particularly, they have been applied to skin disease diagnosis tasks. The aim is to provide a comprehensive overview of the advancements, challenges, and potential applications in this critical domain of dermatology.
Materials and Methods: The review encompasses a wide range of scholarly articles, research papers, and relevant literature focusing on integrating deep learning techniques in skin cancer diagnosis. Materials include studies that employ various imaging modalities such as dermoscopy, histopathology, and other advanced imaging technologies.
The initial phase involves acquiring images of SC from various patients through primary sources and standardized databases. Subsequently, a thorough data cleaning process is implemented, encompassing noise reduction, resizing, and contrast enhancement. Further refinement occurs through the segmentation of the malignant sections, employing edge-based, region-based, and morphological-based techniques. Feature extraction is followed by deep learning approaches, it enhanced with Federated Learning (FL) that is applied to image classification. Finally, leveraging FL-aided deep learning techniques, the images are categorized as either malignant or non-cancerous.
Results: The metrics include Accuracy (AC%), Specificity (Spe%), Sensitivity (Sen%), and Dice Coefficient (DC%), providing a comprehensive evaluation of the classification performance. Generative Adversarial Network (G-AN) demonstrates the highest accuracy 98.5% among the considered techniques, making it the top-performing neural network architecture for skin cancer classification.
Conclusion: This review was undertaken by pulling data from 90 papers published between the years 2019 and 2023, it provides a thorough statistical analysis. A review of various neural network algorithms for skin cancer identification and classification, despite Generative Adversarial Network, has emerged as the most promising approach, underscoring their potential to revolutionize the accurate early diagnosis of skin cancer. Finally, this survey will be beneficial for SCD researchers
A NOVEL SEMANTIC SIMILARITY SCORE FOR PROTEIN DATA ANALYSIS
No full textoai:ojs2.ctrj.in:article/1Aim: A similarity evaluation measure for Gene Ontology GO terms is developed.
Results: The proposed method takes into account the semantics hidden in ontologies or the term level information content, membership of term, and topology-based similarity measures. The proposed method is evaluated on positive and negative dataset of UniProt, Protein family clans and the Pearson’s correlation with other existing methods.
Conclusion: The experimental results exhibited a major supremacy of the proposed method over other semantic similarity measures.
HIGHLIGHTS:1. An improved approach for semantic similarity evaluation for GO terms based on the information content and the topological factors is developed.2. The proposed method shows highest correlation for MF (Molecular Function) ontology
Bi-Cluster Based Analysis on Gene Ontology
No full textUnderstanding biological activity requires the detection of crucial proteins. The identification of significant genes throughout the entire genome is advantageous for a number of reasons, including the categorization of critical genes for health and sickness, the rational creation of drugs, etc. Statistical methods have been suggested for predicting essential or requisite proteins/gene/GO terms, employed in protein networks. The computational approaches focusing on the topological characteristics or centrality approaches ignore the biologically relevant intrinsic features of essential proteins. Hence, considering the biological aspects like expression data, subcellular information, annotation data, and orthologous relationships can improve accuracy. So, in this research, bi-clustering algorithm is used to detect the essential Gene Ontology (GO) terms in molecular, cellular and biological processes by evaluating the protein associations and encoding the associations with ontology terms and pathways. The proposed method encodes each protein in terms of Mutual Information (MI) score, GO annotation and vector-based GO encoded matrix is generated and the essential proteins are extracted. The validation of the proposed method is verified using different statistical measures on the datasets
Technological advancements for deep sea ecosystem conservation and exploration
No full textDeep sea mining is a potential industry in discovering enormous mineral resources, such as polymetallic nodules, hydrothermal vent deposits, and cobalt- rich crusts. These minerals are essential for high- tech companies and renewable energy applications. However, resource exploitation presents substantial obstacles and threats to deep marine ecosystem, are least studied. The geographical areas chosen for deepsea mining are frequently rich in biodiversity, that could be adversely damaged by mining operations. Deep sea mining has important legal and societal ramifications, with disputes centred on regulatory frameworks, ownership rights, and the possible socioeconomic benefits. Current legislation is evolving to address these issues, but comprehensive worldwide guidelines are urgently required to ensure environmentally and socially acceptable mining practices. Future directions in deep- sea mining will likely focus on increasing operational sustainability, improving environmental monitoring, and creating technologies that reduce ecological footprints
A review of aligners for protein protein interaction networks
No full textProtein Protein Interaction (PPI) can be considered as network. Alignment is the process of mapping nodes from one network to another network. The main objective of network alignment is to identify small, well defined interactome units such as protein complexes or conserved pathways that are analogous in the input network. Network alignment uncovers the relationship between protein complexes and functions. Similarity between two graph structures can be identified by evaluating the topology. Network alignment identifies either topological or sequential similarity. Gene annotations reveal the functional or sequential similarity and it can be evaluated based on semantic similarity. In this paper, we review the various network aligners and classify them according to the methodologies. We discuss the different evaluation metrics and the popular databases of protein interactions
An internet of things enabled framework to monitor the lifecycle of Cordyceps sinensis mushrooms
No full textCordyceps sinensis is an edible mushroom found in high quantities in the regions of the Himalayas and widely considered in traditional systems of medicine. It is a non-toxic remedy mushroom and has a high measure of clinical medical benefits including cancer restraint, high blood pressure, diabetes, asthma, depression, fatigue, immune disorder, and many infections of the upper respiratory tract. The cultivation of this kind of mushroom is limited to the region of the Sikkim and to cultivate in the other regions of the country, they are need of investigation and prediction of cordyceps sinensis mushroom lifecycle. From the studies, it is concluded that the precision-based agriculture techniques are limitedly explored for the prediction and growth of Cordyceps sinensis mushrooms. In this study, an internet of things (IoT) inspired framework is proposed to predict the lifecycle of Cordyceps sinensis mushrooms and also provide alternate substrate to cultivate Cordyceps sinensis mushrooms in other parts of the country. As a part of lifecycle prediction, a framework is proposed in this study. According to the findings, an IoT sensor-based system with the ideal moisture level of the mushroom rack is required for the growth of Cordyceps sinensis mushrooms
CONTRIVANCE FOR SMART MANUFACTURING INTENSIFIED APPLICATION BASED ON CLOUD COMPUTING
No full textCarrying out clever assembling administrations on plant wide edge gadgets associated with creation hardware effectively so those assembling administrations are pluggable, attachment and-play, and reasonable through the organization is a difficult undertaking and is exceptionally advantageous for working with understanding a shrewd plant. Cloud producing is arising as a key empowering influence for assembling organizations to convey exceptionally adjustable administrations over the Web. This paper expects to research how cloud fabricating frameworks can work with compelling assistance arranged business i.e., smart manufacturing. This framework proposes a cloud-based pluggable assembling administration conspire by utilizing cloud computing with security. By utilizing a two-layer hierarchical architecture of service mechanism, the assembling administrations can be inherent to the type of pluggable application module.
 
Health-related quality of life at 30 days among Indian patients with acute myocardial infarction results from the ACS QUIK trial
No full textBackground:
Despite a high cardiovascular disease burden, data on patient-reported health status outcomes among individuals with cardiovascular disease in India are limited.
Methods and Results:
Between November 2014 and November 2016, we collected health-related quality of life data among 1261 participants in the ACS QUIK trial (Acute Coronary Syndrome Quality Improvement in Kerala). We used a translated, validated version of the Seattle Angina Questionnaire administered 30 days after discharge for acute myocardial infarction, wherein higher scores represent better health status. We compared results across sex, myocardial infarction type, and randomization status using regression models that account for clustering and temporal trends. Mean (SD) age was 60.8 (13.7) years, 62% were men, and 63% presented with ST-segment–elevation myocardial infarction. More than 2 out of 5 respondents (44%) experienced angina 30 days after hospitalization, but most (68% of respondents with angina; 27% of the total sample) experienced it less than once per week (Seattle Angina Questionnaire angina frequency score 60). Respondents rated high median (interquartile range [IQR]) scores for angina frequency (100.0 [80.0–100.0]) overall with similar unadjusted scores by sex, but between-hospitality variability was high. Median (IQR) physical limitation scale response was 58.3 (41.7–77.8), which is consistent with limitations in moderate- and high-intensity activities at 30-day follow-up. Older respondents had more angina frequency and physical limitations and lower treatment satisfaction and quality of life. Women had greater physical limitations (median [IQR], 52.8 [38.9–72.2] for women versus median [IQR], 61.1 [44.4–80.6] for men; P<0.01). Overall treatment satisfaction was high with median (IQR) score, 81.3 (75.0–93.8), but overall quality of life was lower with median (IQR) score, 66.7 (50.0–83.3). Allocation to the quality improvement intervention group had the strongest direct association with higher quality of life (difference, 4.2; P=0.03), but overall effects were modest.
Conclusions:
This study represents the largest report of quality of life among myocardial infarction survivors in India with variability across age, sex, and quality improvement intervention status. Wide variability demonstrated across hospitals warrants further study.
Clinical Trial Registration:
URL: https://www.clinicaltrials.gov. Unique identifier: NCT02256657
