Sistema Regional de Información
en línea para Revistas Científicas de América Latina,
el Caribe, España y Portugal

Task scheduling is a significant element for any type of distribution system since it routes tasks for precise resources for execution in cloud computing systems. Furthermore, search-based scheduling algorithms select tasks via various methods, resulting in extensive execution times on mixed distributed systems. Consequently, task prioritizing results in a blockage in these systems. Therefore, selecting tasks with the minimum execution time via robust algorithms is applicable. Hence, the genetic algorithm (GA) is among the evolutionary methods utilized to resolve difficulties rapidly. The traditional algorithms include the estimation-of-distribution algorithm (EDA), GA, and EDA-GA algorithms. These algorithms are used to optimize the resource utilization with less time complexity. However, the load balancing process that distributes the tasks over multiple computing resources and task scheduling within cloud computing increases the efficiency by optimizing the resource utilization, which ensures high scalability and availability. Hence, the probability of discovering a reorganizing-based GA has the intention to optimize the task allocated in the virtual machines (VMs), which reduces the completion time and execution cost when optimizing the resource utilization. By the impact of genetic operators such as selection, crossover and mutation algorithms effectively direct the solution space to determine optimal tasks. Furthermore, the performance of the algorithm is calculated via the Simpy toolkit, which indicates the capacity to outperform existing scheduling techniques. Moreover, the experimental results reveal that at 1000 iterations, the proposed model has a completion time of 10,841 ms compared with the other algorithms. Hence, it has a significant effect on efficacy and balances the competing time. This study provides functional effects to improve task scheduling approaches in cloud computing, providing a valuable structure for future research to optimize cloud resource management.

Agricultural is the primary source of essential provisions almost all nation and remains as a vital survival tool for human race for past, present and future. The agriculture which remains as the bedrock of the civilizations is frequently being affected by devastating plant diseases leading to the loss of economy and food shortage. The manual testing requires high expertise, time and often leads to human error. Recent advancements in computer vision and AI models have highlighted the potential for building automatic plant disease detection models based on visible signs using image classification tasks. However, the task becomes complex and erroneous due to the complex nature of data. Consequently Deep Learning (DL) models tend to outperform others traditional methods through utilizing network topology with convolution layers in core. Projected system uses high quality Cotton Disease dataset sourced by Kaggle and provide a suitable solution to the aforementioned problem using advanced DL neural network namely Blend Unity Resqueeze Resnet approach which yields high accuracy with modification including Resqueeze layer and blend unity weights applied to do the tedious job of diagnosing plant diseases on image based classification. The proposed research outperforms the conventional methods achieving better accuracy of 92% with better precision and reliability.  The outcome of the respective research is analyzed with suitable metrics and compared with the recent developed conventional algorithms in which the proposed model proves to be a better suited for the efficient plant disease classification. Hence, the proposed method is intended to contribute in the plant disease classification and assist the agriculturists significantly to prevent the losses due to crop diseases.

The PD’s (Parkinson’s disease) is considered as the advanced neurodegenerative disorder which affects the movements as the dopamine producing neurons in the substantia nigra location present in the brain gets reduced. Since, the accurate basis of PD is the mixture of genetic and environmental factors, the PD results in the motor-related symptoms. However, the earlier detection is significant for the management of indications and slow growth, the diagnosis is difficult because of the identical with several neurodegenerative situations. The conventional techniques includes the clinical assessments, patients’ profiles and the MRI, SPECT and PET scans. Since, the MRI’s focus on the structural differences restricts the efficiency in the earlier phase of PD detection. Therefore, the ML and DL models are utilized in the classification of MRI scan images, as they face issues in the computations of features through several medical images and classification. Despite the enhancements, several difficulties such as restricted, labeled and noisy datasets. Hence, the study proposes a modified ResNet50 with CSAM (Channel Specific Attention Module) for the detection of PD in MRI scans with the utilization of PPMI and NTUA Parkinson’s Disease dataset. In contrast to the existing models, the proposed Modified ResNet50 with Channel-Specific Attention Module (CSAM) achieves high performance and early detection. The proposed Modified ResNet50 with Channel-Specific Attention Module (CSAM) is evaluated using the performance metrics such as accuracy, precision, recall, and F1 score in which the proposed model obtains higher metrics values.

Named entity recognition (NER) is a natural language processing (NLP) categorization labelling job where the objective is to assign words to a predefined set of named entity classes. Although several NER models have been proposed thus far, experts have not yet discovered a workable solution for an inflectional language with limited resources, such as Hindi. . Usually, several manual techniques have been used for entity prediction, such as ambiguity, false positives, and reliance on lengthy annotated data. Nevertheless, these manual methods seem to be ineffectual and time-consuming for acquiring optimum outcomes and are unable to predict all the entity types in the Hindi language. To solve this issue, some researchers have concentrated on NER models. Conversely, it lacks speed and accuracy. Therefore, the present research uses advanced NLP models such as bidirectional encoder representations from transformers (BERT), Distil BERT and the robustly optimized BERT approach (RoBERTA) for effective entity prediction performance. It predicts entities such as person, location, organization and event in the Hindi language. The proposed research uses the Hindi named entity recognition (HiNER) dataset for the purpose of evaluation. The effectiveness of the present model is assessed via several evaluation metrics, such as the F1 score, recall, precision and accuracy, to assess the performance. Furthermore, the comparison of the proposed model reveals the effectiveness of the present research. In conclusion, the projected research envisioned contributing to the emerging NER models, thereby offering an effective entity prediction model for the needed users.

Detecting gas leaks in gas plants is a persistent challenge within the Oil and Gas industry, given the prevalence of pipelines for natural gas transportation. Therefore, various traditional techniques are used for gas leakage detection system, however, conventional methods possess various limitations like time consuming, prone to error and can be tedious to work with. Hence, AI based models are preferred for effec        tive gas leakage detection as AI (Artificial Intelligence) based ML (Machine Learning) models generate tremendous advantages in terms of accuracy of gas leakage detection, early detection and being cost effective approaches. Thus present research work focuses on evaluating intelligent models' efficacy in identifying minor leaks in gas pipelines with fundamental operational parameters. The research then proceeds to compare these models using established performance metrics. The ML based models used in the research work are Linear Regression, Logistic Regression, RF (Random Forest) and KNN (K-Nearest Neighbor). The following ML based algorithms are compared and the performance of the model was evaluated using assorted metrics in accordance with different types of damages. Present comparative research work can potentially assist different industrial sectors for identifying gas leaks.

This research paper addresses the implementation of robotic control algorithms applied to the educational environment, with the objective of improving the teaching of robotics to students. Educational mobile robots often use basic control systems, which limits their functionality and can present difficulties in task execution. The evaluation of more advanced robotic control algorithms, such as PID and fuzzy logic, in educational contexts, shows that they can significantly contribute to better performance and understanding in the teaching of educational robotics.

Humanistic courses are a requirement of the Autonomous University of Aguascalientes for all its students, who must take at least three courses from different disciplines during their career to graduate. The courses offered are a way in which students from different careers can relate to each other, and in this way, develop comprehensively. These courses are offered year after year with minimal change as to which courses and at what times they are offered. The studies regarding the real demand of the students are almost non-existent, and for the same reason the courses offered do not always serve to meet the demand of the students, who find themselves in the need to take courses that they normally do not attend. would sign up to meet the requirements. In addition to the needs of the students, the problem of course management arises, where a process is carried out for the registration of courses to be offered, and their publication.It is examining the potential of data storytelling through a review of the literature and presentation of case studies, it will be explored how data storytelling can help in the administrative activities for analyze and present data effectively, for the informed decision making.

This paper show study explores the development of virtual reality (VR) application for inclusive education, applying the STEAM (Science, Technology, Engineering, Arts, and Mathematics) approach to enhance learning for students with disabilities. Conducted at the Care Centers for Students with Disabilities (CAED) to CBTis 168 in Aguascalientes, Mexico, the research involved 18 students using Oculus Quest 2 devices to evaluate the application. Participants had various disabilities, including visual impairments, and learning difficulties such as Down syndrome and attention deficits. Design thinking methodology was employed to continuously improve the VR application based on feedback from students and teachers. The study identifies educational challenges, assesses accessibility and usability, and demonstrates significant improvements in student motivation, participation, and academic performance. The VR application offered customized learning experiences with visual and auditory instructions, high-contrast visuals, and engaging virtual environments, developed through a process combining Design Thinking and STEAM approach. Results indicated increased motivation and better comprehension of material. The importance of user feedback in developing inclusive educational tools is emphasized, as well as reduced evaluation time. Future work includes expanding VR content, integrating artificial intelligence, and conducting long-term studies on VR´s impact on inclusive education. Research shows VR's potential for inclusive and effective learning.

This paper shows the results obtained by applying the Methodology for Development of Potentially Innovative Technological Projects, focused on employability based on the skills and competencies developed. Among them are communication, the management project, technical knowledge, critical thinking, entrepreneurship, innovation and leadership. One of the first stages of the methodology involves the motivation and commitment of the students with the projects to be developed, while in the last stages there is the participation in competitions, which are a way of making visible the projects carried out, as well as the potential and rigorous training of the participants, which is not limited to techno-scientific skills and knowledge, but also involves complementary skills and competencies valued by employers in companies corresponding to the knowledge-oriented sectors. The dissemination stage is another of the final stages of the methodology, which has contributed to the visibility of results at regional, national and international levels, showing that the globalized scope is a valuable tool for the insertion of graduates in the productive sector.