Browsing by Author "Tavera Romero, Carlos Andres"
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Item A Quantitative Study of Non-Linear Convective Heat Transfer Model by Novel Hybrid Heuristic Driven Neural Soft Computing(Institute of Electrical and Electronics Engineers Inc., 2022) Khan, Muhammad Fawad; Sulaiman, Muhammad; Tavera Romero, Carlos Andres; Alshammari, Fahad SameerHeat transfer has a vital role in material selection, machinery efficacy, and energy consumption. The notion of heat transfer is essential in understanding many phenomena related to several engineering fields. Particularly, Mechanical, civil and chemical engineering. The presentation of the heat transfer model in this manuscript is a dedication to the heat transfer characteristics such as conduction, convection, and radiation. The heat energy consumption mainly depends on these characteristics. A better conductive and convective paradigm is required for miniaturization of heat loss or transfer. The phenomenon is mathematically assumed with the required parameters. A new mathematical strategy is also designed and implemented in the manuscript to evaluate the dynamics of heat transfer model. The mathematical approach is the hybrid structure of the Sine-Cosine algorithm and Interior point algorithm. The validation of new technique is evaluated by mean absolute deviation, root mean square errors, and error in Nash-Sutcliffe efficiency. For better illustration, an extensive data set executed by the proposed mathematical strategy is also drawn graphically with convergence plots.Item A Transformer-Based Framework for Scene Text Recognition(Institute of Electrical and Electronics Engineers Inc., 2022) Selvam, Prabu; Sundar Koilraj, Joseph Abraham; Tavera Romero, Carlos Andres; Alharbi, Meshal; Mehbodniya, AbolfazlScene Text Recognition (STR) has become a popular and long-standing research problem in computer vision communities. Almost all the existing approaches mainly adopt the connectionist temporal classification (CTC) technique. However, these existing approaches are not much effective for irregular STR. In this research article, we introduced a new encoder-decoder framework to identify both regular and irregular natural scene text, which is developed based on the transformer framework. The proposed framework is divided into four main modules: Image Transformation, Visual Feature Extraction (VFE), Encoder and Decoder. Firstly, we employ a Thin Plate Spline (TPS) transformation in the image transformation module to normalize the original input image to reduce the burden of subsequent feature extraction. Secondly, in the VFE module, we use ResNet as the Convolutional Neural Network (CNN) backbone to retrieve text image features maps from the rectified word image. However, the VFE module generates one-dimensional feature maps that are not suitable for locating a multi-oriented text on two-dimensional word images. We proposed 2D Positional Encoding (2DPE) to preserve the sequential information. Thirdly, the feature aggregation and feature transformation are carried out simultaneously in the encoder module. We replace the original scaled dot-product attention model as in the standard transformer framework with an Optimal Adaptive Threshold-based Self-Attention (OATSA) model to filter noisy information effectively and focus on the most contributive text regions. Finally, we introduce a new architectural level bi-directional decoding approach in the decoder module to generate a more accurate character sequence. Eventually, We evaluate the effectiveness and robustness of the proposed framework in both horizontal and arbitrary text recognition through extensive experiments on seven public benchmarks including IIIT5K-Words, SVT, ICDAR 2003, ICDAR 2013, ICDAR 2015, SVT-P and CUTE80 datasets. We also demonstrate that our proposed framework outperforms most of the existing approaches by a substantial margin.Item Automatic Liver Tumor Segmentation in CT Modalities Using MAT-ACM(Tech Science Press, 2022) Priyadarsini S.; Tavera Romero, Carlos Andres; Mehbodniya, Abolfazl; Sagar, P. Vidya; Sengan, SudhakarIn the recent days, the segmentation of Liver Tumor (LT) has been demanding and challenging. The process of segmenting the liver and accurately spotting the tumor is demanding due to the diversity of shape, texture, and intensity of the liver image. The intensity similarities of the neighboring organs of the liver create difficulties during liver segmentation. The manual segmentation does not provide an accurate segmentation because the results provided by different medical experts can vary. Also, this manual technique requires a large number of image slices and time for segmentation. To solve these issues, the Fully Automatic Segmentation (FAS) technique is proposed. In this proposed Multi-Angle Texture Active Contour Model (MAT-ACM) method, the input Computed Tomography (CT) image is preprocessed by Contrast Enhancement (CE) with Non-Linear Mapping Technique (NLMT), in which the liver is differentiated from its neighbouring soft tissues with related strength. Then, the filtered images are given as the input to Adaptive Edge Modeling (AEM) with Canny Edge Detection (CED) technique, which segments the Liver Region (LR) from the given CT images. An AEM with a CED model is implemented, which increases the convergence speed of the iterative process for decreasing the Volumetric Overlap Error (VOE) is 6.92% rates when compared with the traditional Segmentation Techniques (ST). Finally, the Liver Tumor Segmentation (LTS) is developed by applying the MAT-ACM, which accurately segments the LR from the segmented LRs. The evaluation of the proposed method is compared with the existing LTS methods using various performance measures to prove the superiority of the proposed MAT-ACM method.Item Deep learning based intelligent industrial fault diagnosis model(Tech Science Press, 2022) Surendran R.; Khalaf, Osamah Ibrahim; Tavera Romero, Carlos AndresIn the present industrial revolution era, the industrial mechanical system becomes incessantly highly intelligent and composite. So, it is necessary to develop data-driven and monitoring approaches for achieving quick, trustable, and high-quality analysis in an automated way. Fault diagnosis is an essential process to verify the safety and reliability operations of rotating machinery. The advent of deep learning (DL) methods employed to diagnose faults in rotating machinery by extracting a set of feature vectors from the vibration signals. This paper presents an Intelligent Industrial Fault Diagnosis using Sailfish Optimized Inception with Residual Network (IIFD-SOIR) Model. The proposed model operates on three major processes namely signal representation, feature extraction, and classification. The proposed model uses a Continuous Wavelet Transform (CWT) is for preprocessed representation of the original vibration signal. In addition, Inception with ResNet v2 based feature extraction model is applied to generate high-level features. Besides, the parameter tuning of Inception with the ResNet v2 model is carried out using a sailfish optimizer. Finally, a multilayer perceptron (MLP) is applied as a classification technique to diagnose the faults proficiently. Extensive experimentation takes place to ensure the outcome of the presented model on the gearbox dataset and a motor bearing dataset. The experimental outcome indicated that the IIFD-SOIR model has reached a higher average accuracy of 99.6% and 99.64% on the applied gearbox dataset and bearing dataset. The simulation outcome ensured that the proposed model has attained maximum performance over the compared methods.Item Diseño y desarrollo de la app salsacali(Universidad Santiago de Cali, 2019) Buelvas Caceres, Liseth Biviana; Segura Angulo, Yennyfer Alexandra; Tavera Romero, Carlos AndresCali is internationally recognized as the world capital of salsa, however, despite this recognition, this musical genre has lost strength in the city. There are currently many people who are unaware of the cultural, social and economic value that salsa represents for Cali as well as all the development and evolution that this musical genre has had in the city. The emergence of new musical rhythms such as merengue, bachata and reggaeton, salsa in Cali has been overshadowed: through contact with youth it results in many young people not considering or not seeing in the sauce more than just one more genre, or even not important to attend such events. In order to carry out this research, the problem was studied based on the analysis of real and direct data (collecting user opinions) and some believe that this fact has degenerated the salsa identity and this has happened in the background. In order to contribute a little to the fact that the Salsa culture is still valid, “Salsa Cali” is an application oriented to mobile devices, the development of this one was raised to achieve greater dissemination of salsa events in Cali, and contribute a little to that the salsa culture remains in force in a striking way such as a mobile app. It was initially developed for Android mobile devices, a viable medium where development of this type of applications is generally started. It is usable in Smartphone, maintaining usability and aesthetics. Considering the portability, availability and use of mobile Apps, it allows organizers to socialize activities first hand, gives the user the opportunity to consult information about events and places related to salsa events, learn about the history of salsa and songs emblematic Users within the application have access, among other functions, to the creation of a user, information on scheduled events (place, price, public and time), you can also know in advance the details of the site where the event will take place, the people capacity and reservationItem DRLBTSA: Deep reinforcement learning based task-scheduling algorithm in cloud computing(Springer, 2024) Mangalampalli, Sudheer; Karri, Ganesh Reddy; Kumar, Mohit; Khalaf, Osama Ibrahim; Tavera Romero, Carlos Andres; Abdul Sahib, Ghaida MuttasharTask scheduling in cloud paradigm brought attention of all researchers as it is a challenging issue due to uncertainty, heterogeneity, and dynamic nature as they are varied in size, processing capacity and number of tasks to be scheduled. Therefore, ineffective scheduling technique may lead to increase of energy consumption SLA violations and makespan. Many of authors proposed heuristic approaches to solve task scheduling problem in cloud paradigm but it is fall behind to achieve goal effectively and need improvement especially while scheduling multimedia tasks as they consists of more heterogeneity, processing capacity. Therefore, to handle this dynamic nature of tasks in cloud paradigm, a scheduling mechanism, which automatically takes the decision based on the upcoming tasks onto cloud console and already running tasks in the underlying virtual resources. In this paper, we have used a Deep Q-learning network model to addressed the mentioned scheduling problem that search the optimal resource for the tasks. The entire extensive simulationsare performed usingCloudsim toolkit. It was carried out in two phases. Initially random generated workload is used for simulation. After that, HPC2N and NASA workload are used to measure performance of proposed algorithm. DRLBTSA is compared over baseline algorithms such as FCFS, RR, Earliest Deadline first approaches. From simulation results it is evident that our proposed scheduler DRLBTSA minimizes makespan over RR,FCFS, EDF, RATS-HM, MOABCQ by 29.76%, 41.03%, 27.4%, 33.97%, 33.57% respectively. SLA violation percentage for DRLBTSA minimized overRR,FCFS, EDF, RATS-HM, MOABCQ by48.12%, 41.57%, 37.57%, 36.36%, 30.59% respectively and energy consumption for DRLBTSA over RR,FCFS, EDF, RATS-HM, MOABCQ by36.58%,43.2%, 38.22%, 38.52%, 33.82%existing approaches.Item Fuzzy and SVM Based Classification Model to Classify Spectral Objects in Sloan Digital Sky(Institute of Electrical and Electronics Engineers Inc., 2022) Karn, Arodh Lal; Tavera Romero, Carlos Andres; Sengan, Sudhakar; Mehbodniya, Abolfazl; Webber, Julian L.; Pustokhin, Denis A.; Wende, Frank-DetlefThe Sloan Digital Sky Survey (SDSS) comprises about one billion objects classified spectrometrically. Because astronomical datasets are so enormous, manually classifying them is nearly impossible - a huge dataset results in class imbalance and overfitting. We recommend a framework in this research study that overcomes these constraints. The framework uses a hybrid Synthetic Minority Oversampling Technique + Edited Nearest Neighbor (SMOTE + ENN) balancer. The balanced dataset is then used to extract features via a non-linear algorithm using Kernel Principal Component Analysis (KPCA). The features are then passed into the proposed Int-T2-Fuzzy Support Vector Machine classifier, which uses a modified type reducer and inference engine to achieve more precise categorization. Using the Sloan Digital Sky Survey dataset and a number of evaluation metrics, the SMOTE+ENN model's performance is measured. The research shows that the model does a good job.Item Privacy Preserving Reliable Data Transmission in Cluster Based Vehicular Adhoc Networks(Tech Science Press, 2022) Tamilvizhi T.; Surendran R.; Tavera Romero, Carlos Andres; Sendil, M. SadishVANETs are a subclass of mobile ad hoc networks (MANETs) that enable efficient data transmission between vehicles and other vehicles, road side units (RSUs), and infrastructure. The purpose of VANET is to enhance security, road traffic management, and traveler services. Due to the nature of real-time issues such as reliability and privacy, messages transmitted via the VANET must be secret and confidential. As a result, this study provides a method for privacy-preserving reliable data transmission in a cluster-based VANET employing Fog Computing (PPRDA-FC). The PPRDA-FC technique suggested here seeks to ensure reliable message transmission by utilising FC and an optimal set of cluster heads (CH). The proposed PPRDA-FC technique utilizes a moth flame optimization with levy flight based clustering (MFO-LFC) process to identify and form clusters from a suitable set of CHs. The CHs are responsible for monitoring each vehicle in their respective clusters. Simultaneously, the CHs provide the most efficient and secure pathways for message transmission. Finally, a deep neural network (DNN) is used as a classification tool to distinguish between attacker-controlled and real-world automobiles. To evaluate the suggested PPRDA-FC technique’s increased performance, a series of simulations were run and the results analyzed using a variety of metrics. The acquired experimental findings illustrate the suggested PPRDA-FC technique’s superiority to recent state-of-the-art procedures.