Computing Technology Research Journal

A NOVEL SEMANTIC SIMILARITY SCORE FOR PROTEIN DATA ANALYSIS

Anooja Ali, Vishwanath R Hulipalled, S.S. Patil — Wed, 05 Oct 2022 00:00:00 -0600

Aim: 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.

CATTLE DETECTION AND MISHAP AVOIDANCE SYSTEM USING YOLO v5 ALGORITHM

Vinayak Sutar , Kshama Kulhalli — Wed, 05 Oct 2022 00:00:00 -0600

Aim: 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.

MACHINE LEARNING OF TWITTER FEEDS AND WOMEN SAFETY IN INDIAN CITIES

S. Gokulakrishnan, Chitte Ashok Reddy, Palutla Venkata Sai Anudeep — Wed, 05 Oct 2022 00:00:00 -0600

Aim: 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' 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' safety.

DETECTION OF CLOUD SHADOWS USING DEEP CNN UTILISING SPATIAL AND SPECTRAL FEATURES OF LANDSAT IMAGERY

M.S. Antony Vigil, Aashna Chib, Ayushi Vashisth, Tanisha Pattnaik — Wed, 05 Oct 2022 00:00:00 -0600

Aim: 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.

OBJECT DETECTION BASED ON SPECTRAL ANALYSIS USING SOBEL AND ROBERTS EDGE DETECTION ALGORITHM

Deva Hema D, Sk Shahid Ali, Arnab Rooj, Tanay Yeole — Wed, 05 Oct 2022 00:00:00 -0600

Aim: This paper proposes novel object detection (OD) approach based on a thorough examination of the image's 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's 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.

A DEEP LEARNING MODEL FOR EDUCATION ANALYTICS – A SHORT REVIEW

Palanivel Kuppusamy, Suresh Joseph K — Wed, 05 Oct 2022 00:00:00 -0600

Integrating 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.

SCHEDULING ALGORITHMS IN MULTI CLOUD ENVIRONMENTS – A SHORT REVIEW

A. Neela Madheswari, M.Ramesh — Wed, 05 Oct 2022 00:00:00 -0600

It 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.