Computing Technology Research Journal
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BLOCKCHAIN TECHNOLOGY FOR DETECTING FRAUD IN PHARMACEUTICAL SUPPLY CHAIN MANAGEMENT
Get PDFAim: This paper mainly focused on proposing a new pharmaceutical supply chain management system based on block chain technology.
Results: A seven steps pharmaceutical supply chain management is designed based on blockchain technology. The patients are encouraged to access the technology for finding genuinity of the medicine and to reduce fraud detection.
Conclusion: Blockchain is used to make the system more efficient and effective and designing such genuine system with all necessary parameters can boost patient confidence during medication.
Keywords: Pharmaceutical supply chain management, blockchain technology, patients, medicin
OBJECT DETECTION BASED ON SPECTRAL ANALYSIS USING SOBEL AND ROBERTS EDGE DETECTION ALGORITHM
Get PDFAim: This paper proposes novel object detection (OD) approach based on a thorough examination of the image\u27s details and its approximate density chart.
Results: Our proposed OD approach is divided into two phases. Knowledge about Spatial Distribution of Objects obtained from a density map that is used to compute initial object positions. With the aid of the original object positions estimated, a saliency map that provides entity boundaries is then used to calculate the bounding boxes with precision, which is inspired by human attention to detail. The scale variance of objects induced by uncertain perspective is a common problem in object density map estimation. A new method for estimating the prior focus for map for any image is proposed. Sobel and Roberts Edge Detection Algorithm are used in this study. The proposed approach is based on sparse defocus dictionary learning on a newly constructed dataset. The focus power is determined by the number of non-zero coefficients of the dictionary atoms.
Conclusion: The algorithm\u27s output can capture spatial features and pick the threshold type in a variety of ways.
HIGHLIGHTS:
Object detection based on spectral analysis using Sobel and Roberts edge detection algorithm proved to be effective when compared with existing methodologies
SCHEDULING ALGORITHMS IN MULTI CLOUD ENVIRONMENTS – A SHORT REVIEW
Get PDFIt is essential to schedule the workloads to cloud in an efficient manner. Whether user is using single cloud or multi cloud environments, according to the available resources and needs, the incoming jobs or tasks has to be scheduled. Hence this short review paper focuses on different available scheduling algorithms and its categories
CATTLE DETECTION AND MISHAP AVOIDANCE SYSTEM USING YOLO v5 ALGORITHM
Get PDFAim: We propose a robust vehicular cattle detection model using YOLO version 5 for vehicles to avoid mishap on Indian roads.
Results: The research was conducted with the help of training and validation on-road cattle image dataset and tested the model for various epoch values.
Conclusion: The model has predicted 82% to 85% of true positive result with 90.5% accuracy and fruitful test results observed on real world video samples
DETECTION OF CLOUD SHADOWS USING DEEP CNN UTILISING SPATIAL AND SPECTRAL FEATURES OF LANDSAT IMAGERY
Get PDFAim: The proposed work emphasizes here on detection of cloud shadows using Deep CNN (Convolutional Neural Networks) utilizing spatial and spectral features of Landsat imagery.
Results: In the current study deep CNN Algorithm is used for cloud and its shadow detection. We used python libraries to create a CNN. Fourier transformation is applied on that array to transform as per their requirements. Conclusion: Using the Deep CNN algorithm, we were able to combine the whole input image to get multilevel features. Deep CNN does better image processing and semantic segmentation when compared with existing fuzzy-c and f-masking.
HIGHLIGHTS:
An improved approach using Deep CNN (Convolutional Neural Network) does better image processing and semantic segmentation when compared with existing fuzzy-c and f-masking.
A SYSTEMATIZED APPROACH TO DATA HIDING USING IMPROVED LSB
Get PDFAim: This paper mainly focused on introducing a new framework for image and text hiding using a simple Least Significant Bit (LSB) substitution method.
Results: Six 24-bit images are used as reference images. XOR operation is used in the stego key to generate new bit planes of the stego images. An RGB color image is used as a secret cover image. The proposed method dramatically increases the embedding capacity without significantly decreasing the Peak Signal-to-Noise Ratio (PSNR) value.
Conclusion: This method increased the compression ratio, embedding ratio, PSNR and Space 24%, 53%, 36% and 47%, respectively. The result shows that the newly developed method improved the value of accuracy by 30% and sensitivity by 16% for the hidden images.
Keywords: LSB, PSNR, FNR (False Negative Rate), Sensitivity, MSE (Mean Square Error), Specificity
HIGHLIGHTS:
The newly developed method with simple Least Significant Bit (LSB) improved the accuracy value by 30%, and sensitivity by 16% for the hidden image.
The improved LSB method increased security and preserved the quality of hidden images
A NOVEL SEMANTIC SIMILARITY SCORE FOR PROTEIN DATA ANALYSIS
Get PDFoai: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
MACHINE LEARNING OF TWITTER FEEDS AND WOMEN SAFETY IN INDIAN CITIES
Get PDFAim: The paper focuses on the role of Twitter feeds in finding safety aspects of women and girls in Indian cities using machine learning algorithms.
Results: The data set obtained through Twitter about women and girls\u27 safety status in Indian cities is analyzed using machine learning tools.
Conclusion: Machine learning algorithms help organize and analyze Twitter data, including millions of daily tweets and messages. The same can be extended to other social media platforms.
HIGHLIGHTS:
A simple machine learning algorithm will help analyze tweet feeds concerning girls\u27 safety
A DEEP LEARNING MODEL FOR EDUCATION ANALYTICS – A SHORT REVIEW
Get PDFIntegrating deep learning with learning management systems can result in intelligent course material and high accuracy without any manual intervention. This paper reviews factors that influence deep learning in education, and hence this article aims to achieve deep learning on a large scale in the smart education system with a deep learning model to predict. The proposed model can reduce development and maintenance costs, reduce risks, and facilitate communication between stakeholders.
HIGHLIGHTS:
The current review focus on deep learning as an important tool for Indian teachers