DINGO OPTIMIZED FUZZY CNN TECHNIQUE FOR EFFICIENT PROTEIN STRUCTURE PREDICTION
Journal Title: International Journal of Data Science and Artificial Intelligence - Year 2023, Vol 1, Issue 02
Abstract
Protein is made up of a variety of molecules that are required by living organisms, such as enzymes, hormones, and antibodies. In step 2, the max-pooling layer and the convolutional layer evaluate the input data to create the finest feature map F1, which is half the image size in both horizontal and vertical directions. The full feature is then retrieved in step 2 using the max pooling layer and the residual block at the proper resolution. In this paper, we introduce Di-Fuzzy CNN (Fuzzy Convolutional Neural Network with Dingo optimizer), a novel technique for predicting protein activities that incorporates two types of information they are protein sequence and protein structure. We extract diverse features at different scales utilizing convolutional neural networks to provide comprehensive information for feature segmentation. To handle a variety of uncertainties in feature selection and produce segmentation results that are more dependable, fuzzy logic modules are employed. Finally, we employ Dingo optimization to boost the suggested method's effectiveness and speed in order to produce the best outcomes. Using a variety of datasets, the suggested model has been tested (HSSP, PDB, UGR14b, DSSP). Tests demonstrate that our approach can decrease FPR, increase protein structure accuracy, decrease prediction time, and increase TPR for feature selection. Our predictive model performs better than most state-of-the-art techniques.
Authors and Affiliations
P. G. Sreelekshmi and S. C. Ramesh
Keywords
Related Articles
- EP ID EP742457
- DOI -
- Views 34
- Downloads 0
DEEP LEARNING BASED LSTM-GAN APPROACH FOR INTRUSION DETECTION IN CLOUD ENVIRONMENT
Cloud computing is a rapidly growing technology paradigm with enormous potential. While cloud computing has many advantages, it also poses new security risks. Cloud computing security vulnerabilities have been identified...
SAREE TEXTURE ANALYSIS AND CLSSIFICATION VIA DEEP LEARNING FRAMEWORK
Indian women have worn sarees for centuries and have a significant market share in their clothing. Despite the ability to identify saree materials with tactile texture and the possibility to purchase online, it may not a...
INDIA-NET: IOT INTRUSION DETECTION VIA ENHANCED TRANSIENT SEARCH OPTIMIZED ADVANCED DEEP LEARNING TECHNIQUE
The Internet of Things (IoT) has become an increasingly popular study area, with billions of devices deployed globally in recent years. These devices can speak with one another without the need for human involvement beca...
CLASSIFICATION OF LIVER CANCER VIA DEEP LEARNING BASED DILATED ATTENTION CONVOLUTIONAL NEURAL NETWORK
Liver cancer occur when normal cells develop aberrant DNA alterations and reproduce uncontrollably. Patients with cirrhosis, hepatitis B or C, or both have an increased risk of developing the progressing stage of cancer....
HYBRIDIZATION OF DILATED CNN WITH ATTENTION LINK NET FOR BRAIN CANCER CLASSIFICATION
Brain tumours (BT) are highly prevalent and dangerous disease, have extremely short prognoses at the most malignant grade. Merging the MRI modes results in hybrid images with information that is used to classify tumours....