bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Comparative Analysis of EEG-based Stress Detection Utilizing Machine Learning and Deep Learning Classifiers with a Critical Literature Review
Authors: Dhake Dipali , Angal Yogesh 
Published: September 2023

Background: Mental stress is considered to be a major contributor to different psychological and physical diseases. Different socio-economic issues, competition in the workplace and amongst the students, and a high level of expectations are the major causes of stress. This in turn transforms into several diseases and may extend to dangerous stages if not treated properly and timely, causing the situations such as depression, heart attack, and suicide. This stress is considered to be a very serious health abnormality. Stress is to be recognized and managed before it ruins the health of a person. This has motivated the researchers to explore the techniques for stress detection. Advanced machine learning and deep learning techniques are to be investigated for stress detection.
Methodology: A survey of different techniques used for stress detection is done here. Different stages of detection including pre-processing, feature extraction, and classification are explored and critically reviewed. Electroencephalogram (EEG) is the main parameter considered in this study for stress detection. After reviewing the state-of-the-art methods for stress detection, a typical methodology is implemented, where feature extraction is done by using principal component analysis (PCA), ICA, and discrete cosine transform. After the feature extraction, some state-of-art machine learning classifiers are employed for classification including support vector machine (SVM), K-nearest neighbor (KNN), NB, and CT. In addition to these classifiers, a typical deep-learning classifier is also utilized for detection purposes. The dataset used for the study is the Database for Emotion Analysis using Physiological Signals (DEAP) dataset.
Results: Different performance measures are considered including precision, recall, F1-score, and accuracy. PCA with KNN, CT, SVM and NB have given accuracies of 65.7534%, 58.9041%, 61.6438%, and 57.5342% respectively. With ICA as feature extractor accuracies obtained are 58.9041%, 61.64384%, 57.5342%, and 54.79452% for the classifiers KNN, CT, SVM, and NB respectively. DCT is also considered a feature extractor with classical machine learning algorithms giving the accuracies of 56.16438%, 50.6849%, 54.7945%, and 45.2055% for the classifiers KNN, CT, SVM, and NB respectively. A conventional DCNN classification is performed given an accuracy of 76% and precision, recall, and F1-score of 0.66, 0.77, and 0.64 respectively.
Conclusion: For EEG-based stress detection, different state-of-the-art machine learning and deep learning methods are used along with different feature extractors such as PCA, ICA, and DCT. Results show that the deep learning classifier gives an overall accuracy of 76%, which is a significant improvement over classical machine learning techniques with the accuracies as PCA+ KNN (65.75%), DCT+KNN (56.16%), and ICA+CT (61.64%).
A Deep Reinforcement Learning-Based Model for Optimal Resource Allocation and Task Scheduling in Cloud Computing
Authors: Alhazmi Lamia 
Published: September 2023

The advent of cloud computing has dramatically altered how information is stored and retrieved. However, the effectiveness and speed of cloud-based applications can be significantly impacted by inefficiencies in the distribution of resources and task scheduling. Such issues have been challenging, but machine and deep learning methods have shown great potential in recent years. This paper suggests a new technique called Deep Q-Networks and Actor-Critic (DQNAC) models that enhance cloud computing efficiency by optimizing resource allocation and task scheduling. We evaluate our approach using a dataset of real-world cloud workload traces and demonstrate that it can significantly improve resource utilization and overall performance compared to traditional approaches. Furthermore, our findings indicate that deep reinforcement learning (DRL)-based methods can be potent and effective for optimizing cloud computing, leading to improved cloud-based application efficiency and flexibility.
Medical Diagnosis with Multimodal Image Fusion Techniques
Authors: Prathipa R. , Ramadevi R. 
Published: September 2023

Image Fusion is an effective approach utilized to draw out all the significant information from the source images, which supports experts in evaluation and quick decision making. Multi modal medical image fusion produces a composite fused image utilizing various sources to improve quality and extract complementary information. It is extremely challenging to gather every piece of information needed using just one imaging method. Therefore, images obtained from different modalities are fused Additional clinical information can be gleaned through the fusion of several types of medical image pairings. This study's main aim is to present a thorough review of medical image fusion techniques which also covers steps in fusion process, levels of fusion, various imaging modalities with their pros and cons, and the major scientific difficulties encountered in the area of medical image fusion. This paper also summarizes the quality assessments fusion metrics. The various approaches used by image fusion algorithms that are presently available in the literature are classified into four broad categories i) Spatial fusion methods ii) Multiscale Decomposition based methods iii) Neural Network based methods and iv) Fuzzy Logic based methods. the benefits and pitfalls of the existing literature are explored and Future insights are suggested. Moreover, this study is anticipated to create a solid platform for the development of better fusion techniques in medical applications.
Deep Learning based Densenet Convolution Neural Network for Community Detection in Online Social Networks
Authors: Selvakumar M. , Kathiravan A. Vijaya 
Published: September 2023

Online Social Networks (OSNs) have become increasingly popular, with hundreds of millions of users in recent years. A community in a social network is a virtual group with shared interests and activities that they want to communicate. OSN and the growing number of users have also increased the need for communities. Community structure is an important topological property of OSN and plays an essential role in various dynamic processes, including the diffusion of information within the network. All networks have a community format, and one of the most continually addressed research issues is the finding of communities. However, traditional techniques didn't do a better community of discovering user interests. As a result, these methods cannot detect active communities. To tackle this issues, in this paper presents Densenet Convolution Neural Network (DnetCNN) approach for community detection. Initially, we gather dataset from Kaggle repository. Then preprocessing the dataset to remove inconsistent and missing values. In addition to User Behavior Impact Rate (UBIR) technique to identify the user URL access, key term and page access. After that, Web Crawling Prone Factor Rate (WCPFR) technique is used find the malicious activity random forest and decision method. Furthermore, Spider Web Cluster Community based Feature Selection (SWC2FS) algorithm is used to choose finest attributes in the dataset. Based on the attributes, to find the community group using Densenet Convolution Neural Network (DnetCNN) approach. Thus, the experimental result produce better performance than other methods.
Intelligent Buffer Management Algorithm to Prevent Packet loss in Mobile Adhoc Network
Authors: Hemalatha S. , Pallathadka Harikumar , Chinhewadi Rajesh P , Kalaiyarasi D. , Thangakrishnan M. Suresh 
Published: September 2023

In a mobile ad hoc network, which is self-organized and operates without any fundamental infrastructure, packet transmission from the source node to the destination node is completed after sending the route request and route reply. A reliable path is then selected depending on the protocol choice. Data that the sender intends to deliver is broken up into packets and given sequence numbers before being transmitted over the channel. With the aid of an internal buffer that helps to receive packets and forward them to the next destination, each and every packet travels along the allotted path until it reaches its destination. If there is more traffic on the MANET, the buffer may overflow, which will result in packet loss during transmission.. The source node must retransmit to the destination if any packets were lost during the initial transfer. This article proposes the Intelligent Buffer Management (IBM) active buffer management algorithm to prevent such a scenario by enhancing the MANET nodes' buffers to prevent packet loss. The Network Simulator is used to help build this suggested approach, and the results are compared to those of the current buffer management method to show that IBM is superior.
Boosting English Proficiency in Peruvian Children from 6 to 12 Years Old: Development of an Innovative Hybrid Application
Authors: Lida Violeta Asencios-Trujilo , Lucia Asencios-Trujillo , Carlos Jacinto La Rosa-Longobardi , Djamila Gallegos-Espinoza , Cristina Piñas-Livia , Rosa Perez- Siguas , Hernan Matta-Solis 
Published: September 2023

The present research work tries to design a hybrid application to learn English in children from 6 to 12 years old where it has 5 modules that are . Home, materials, evaluations, cards and notes (score table). A Design Thinking methodology was implemented since it was considered to be the most appropriate for the development of the research since in each phase it would be detailed what the research would consist of and the necessary requirements to be able to design it. Likewise, a survey was conducted with 30 people to know their opinion on the use of technologies in learning, resulting in a positive way about the implementation of technologies when teaching and learning.
It was concluded that the implementation of a hybrid application would facilitate student learning.
Exploring Path Computation Techniques in Software-Defined Networking: A Review and Performance Evaluation of Centralized, Distributed, and Hybrid Approaches
Authors: Saxena Mohit Chandra , Sabharwal Munish , Bajaj Preeti 
Published: September 2023

Software-Defined Networking (SDN) is a networking paradigm that allows network administrators to dynamically manage network traffic flows and optimize network performance. One of the key benefits of SDN is the ability to compute and direct traffic along efficient paths through the network. In recent years, researchers have proposed various SDN-based path computation techniques to improve network performance and reduce congestion.
This review paper provides a comprehensive overview of SDN-based path computation techniques, including both centralized and distributed approaches. We discuss the advantages and limitations of each approach and provide a critical analysis of the existing literature. In particular, we focus on recent advances in SDN-based path computation techniques, including Dynamic Shortest Path (DSP), Distributed Flow-Aware Path Computation (DFAPC), and Hybrid Path Computation (HPC).
We evaluate three SDN-based path computation algorithms: centralized, distributed, and hybrid, focusing on optimal path determination for network nodes. Test scenarios with random graph simulations are used to compare their performance. The centralized algorithm employs global network knowledge, the distributed algorithm relies on local information, and the hybrid approach combines both. Experimental results demonstrate the hybrid algorithm's superiority in minimizing path costs, striking a balance between optimization and efficiency. The centralized algorithm ranks second, while the distributed algorithm incurs higher costs due to limited local knowledge. This research offers insights into efficient path computation and informs future SDN advancements.
We also discuss the challenges associated with implementing SDN-based path computation techniques, including scalability, security, and interoperability. Furthermore, we highlight the potential applications of SDN-based path computation techniques in various domains, including data center networks, wireless networks, and the Internet of Things (IoT).
Finally, we conclude that SDN-based path computation techniques have the potential to significantly improvement in-order to improve network performance and reduce congestion. However, further research is needed to evaluate the effectiveness of these techniques under different network conditions and traffic patterns. With the rapid growth of SDN technology, we expect to see continued development and refinement of SDN-based path computation techniques in the future.
Irrigation Depths Adjustment Methods on Yield Components, Grain Yield and Water Use Efficiency of Dry Beans Crop
Authors: Almeida Patricia Mara de , Marco Nataly Katia de , Kuhn Luan Junior , Biazatti Renan Marré , Fernandes Franciele Fátima , Pazzini Eduarda Posser , Piazzetta Hugo Von Linsingen 
Published: September 2023

O feijão é uma das principais culturas na dieta humana brasileira, apresentando baixa produtividade nacional, o que pode ser explicado pela ocorrência de períodos de deficiência hídrica para a cultura. Desta maneira, objetivou-se avaliar os componentes de rendimento da cultura, a produtividade de grãos e a eficiência do uso da água, em diferentes manejos da lâmina de irrigação e validar a planilha Lâmina para recomendação de irrigação da cultura. O delineamento experimental foi de blocos casualizados com quatro repetições. Os tratamentos foram: não utilização da irrigação; manutenção da umidade do solo na capacidade real de água no solo em 45% da capacidade total de água do solo; manutenção da umidade do solo equivalente em 100% da umidade da capacidade de campo; e ajuste da lâmina de irrigação utilizando a planilha “Lâmina”. Determinou-se a quantidade total de água recebida pela cultura, a produtividade de grãos, a relação litros por quilograma de grãos, o ciclo e os componentes do rendimento. O tratamento Lâmina apresentou os melhores resultados para a produtividade, e maior eficiência no uso da água, no entanto não se obteve diferenças significativas para as componentes do rendimento exceto para a componente espessura das vagens. Assim, a planilha Lâmina apresentou grande potencial, tendo alta produtividade e colaborando para a racionalização do uso de água na cultura do feijão.

Palavras-chave: Phaeseolus vulgaris L. Déficit de Água. Precipitação.

Abstract
Dry beans are one of the most important crops for Brazilians people diet, features a low national yield, which can be explained by the occurrence of water deficit events. Thus, this study aimed to evaluate the yield components, the grain yield and the water use efficiency of dry beans submitted to different ways to adjust the irrigation depth, and validate the Lâmina spreadsheet to recommend irrigation to the culture crop. The experimental design was a randomized block with four replications. Four treatments: non-use of irrigation; soil moisture equivalent to actual capacity of water in the soil at 45% of the total capacity of the ground water; soil moisture equivalent to 100% of the field capacity; and irrigation depth adjustment provided by Lâmina spreadsheet. The Lâmina treatment showed the best results for grain yield and water use efficiency, however the yield components were not significant, except for pods thickness. Thus, the Lâmina spreadsheet showed great potential, with high yield and contributing to the rational water use in dry beans crop.

Keywords: Phaeseolus vulgaris L. Water Deficit. Rainfall.
Comparing neonatal intensive care unit outcomes of intramural and extramural neonates using the modified sick neonate score
Authors: Naithani Kritika , Tripathi Sharanya , Trivedi Hirva , Pandya Nimisha K. 
Published: September 2023

Background: India contributes to one-fifth of the world’s annual childbirths and one-fourth of the neonatal mortality burden, with rural areas having higher mortality rates. Several scoring systems have been used to independently predict neonatal outcomes in babies born elsewhere and transported to a facility (extramural) and in the inborn or intramural neonates. We used the modified sick neonate score (MSNS) to compare the outcomes in extramural and intramural neonates. The score considers eight parameters and quantitatively measures the neonates’ status. This study aimed to determine whether the MSNS effectively predicts mortality among the two study groups.
Methods: 410 neonates were considered for this prospective analytical study conducted at GMERS Hospital, Vadodara, a tertiary care center- over a duration of 20 months. Neonates were followed during their NICU stay, and outcomes were correlated with their scores recorded on admission.
Results: The intramural newborns had higher MSNS than the extramural neonates (13 and 12, respectively), with the difference statistically significant. At an optimal cut-off of <11, MSNS has a sensitivity of 85.11% and a specificity of 75.76% in predicting neonatal outcomes. The AUC was 0.859 (95% CI:0.822 to 0.891) with a positive predictive value of 31.2%, a negative predictive value of 97.5%, and an accuracy of 76.83%.
Conclusions: The MSNS effectively predicts mortality among intramural and extramural neonates, with the range being higher for inborn babies- indicating a better prognosis.
Utilization of e-books to enhance self-directed learning for clinical laboratory science specialists
Authors: Al Mohsen Zahra , Alqassab Hoor A 
Published: September 2023

Ongoing education plays a crucial role in enhancing the competence of clinical laboratories Sciences Specialists (CLSs). However, CLSs often encounter obstacles when trying to learn in involved clinical settings. To tackle these challenges and cater to the specific learning requirements of medical technologists, we developed a learner-centred electronic book (e-book) that fosters self-directed learning among them. To assess the e-book's effectiveness as continuing education (CE) material for CLSs, a cross-sectional study was carried out at two medical centres in Riyadh. The e-book's content was tailored to meet the specific practice requirements and learning needs of CLSs. The study employed The New World Kirkpatrick Model, which includes four levels (reactions, learning, behaviors, and results), to evaluate the impact of the e-book on the participants' learning. A total of 280 medical technologists took part in the study, completing a questionnaire and a post-test, which provided valuable insights into their learning patterns, satisfaction with the e-book, and the learning outcomes they experienced after using it. After using the e-book, a majority of readers reported positive learning experiences and improvements in their learning outcomes, encompassing knowledge acquisition and changes in behavior. Furthermore, the e-book successfully presented a new continuing education (CE) activity and reached CLSs from diverse laboratory settings. The affordable and learner-focused e-book efficiently addressed the obstacles to continuing education (CE) faced by CLSs. The interactive and flexible nature of e-learning was especially beneficial in enabling learners to actively participate in clinical scenarios related to laboratories. This research holds the potential to serve as a foundation for medical educators to develop e-learning model for CE.
Laboratory safety and security concepts for clinical laboratory students and universities staff in Saudi Arabia
Authors: Al Mohsen Zahra 
Published: September 2023

A number of accidents with safety implications happen within laboratories due to inadequate regulations, improper execution of safety protocols, or a lack of awareness and adherence to safety measures. Consequently, it is imperative to establish effective regulations and cultivate safe behaviors concerning workplace safety to avert or reduce such incidents. In this context, we are delving into the extent of awareness and to examine the laboratory safety and security concepts that are essential among Clinical Laboratory Sciences students : A descriptive and analytical study about laboratory safety, was directed by researchers on a sample comprising over 100 participants from students, and instructors of both genders from various colleges and scientific centers. A specially designed questionnaire to collect data. The questionnaire consisted of multiple sections, covering topics such as personal information (e.g., gender, qualification, age, experience) and subjects' knowledge of laboratory safety skills, accidents, fires, guidelines, infectious materials, safety protocols, mistakes, research execution rules, laws and regulations, and training. Data collection employed a stratified random sampling method, with over 100 participants exceeding a response rate of 60%. The study provides an overview of the current safety practices in clinical laboratories, identifies common risks and hazards, and explores strategies to enhance safety and security awareness. The results indicated that students had lower knowledge of laboratory safety compared to university staff members. However, overall knowledge of safety and security in chemistry laboratories among the subjects in this study exceeded 80%, possibly due to comprehensive workshops organized in most colleges. The findings emphasize the importance of incorporating safety and security education into the curriculum to ensure the well-being of students, universities staff, and the overall laboratory environment.
The influence of a preworkout energy drink (C4) on lower limb neuromuscular activation in healthy young adults
Authors: Rosario Martin G , Parks Kaci , Rountree Garett , Torre Joe De La 
Published: September 2023

Energy drinks (Edrinks) have a plethora of ingredients for the same objective, a burst of energy and improving mood among others. However, there are various Edrink brands with diverse ingredients and slightly different purposes, such as the C4 drink which is a pre-workout. This ingredient variation can provoke different neuromuscular balance and gait adaptations. Recent studies suggest that some ingredients in Edrinks can aid in muscle fatigue in lower limb musculature. To investigate the lower limb neuromuscular modification after edink (C4) consumption. Ten participants (1 male and 9 females) partaken in the study (age of 23.4 (+/- 2.95). Neuromuscular activation of leg musculature was measured during a single-legged balance test on a firm and foam surface, pre and post edrink. Balance tests were designed to engage diverse areas of the balance systems (visual, proprioceptive and vestibular) while studying the adaptations in lower limb muscles such as tibialis anterior and gastrocnemius. The main outcome of this study showed a trend towards greater onset, faster TP, and slower decay for leg muscles after edrink consumption. It seems C4 beverage provokes a faster and prolonged recruitment of the leg muscles. Future studies should look into the postural adaptations after the said drink.