bluuebunny/crossref_metadata_2025 huggingface dataset:
AN IMPROVISED VIDEO ENHANCEMENT USING MACHINE LEARNING
Authors:   Government B.Ed. Training College, Kalinga, India, D.K. Mohanty , R Rajavignesh ,   K.S.K College of Engineering and Technology, India, V Elanangai ,   St. Peter Institute of Higher Education and Research, India, A Saranya ,   Vels Institute of Science Technology and Advanced Studies, India
Published: October 2024

The visual quality of underexposed videos can be increased with the help of a process known as video enhancement. Within the scope of this research, we present a novel approach to enhancing the exposure of movies that are underexposed. Because it has a low barrier to entry theoretically and can reliably give perceptually pleasant outcomes without adding artifacts, it is ideal for a wide variety of practical applications. This is because it is useful for a wide variety of practical applications. We demonstrate the usefulness of the method by displaying improved films of good quality that were made from a variety of different sorts of underexposed videos. A novel approach to the enhancement and editing of video is presented by our method. Rather than relying on a single heuristic transform function, it makes use of human visual perception to adaptively customize the overall visual appearance of the finished product. We believe that our work has the potential to make a big influence in the field of perception-aware video editing and the applications that are related to it, and that it is an important contribution to the community that works on improving videos. The challenge of video enhancement can be formulated as follows: given a video of low quality as the input, produce a video of high quality as the output, but only for specific uses. Whether it be in terms of the video objective clarity or its more subjective qualities, we are interested in learning how we might improve its overall quality.
AN IMPROVISED ENSEMBLE CNN ALGORITHM FOR DETECTING VIDEO STREAM IN MULTIMEDIA
Authors:   Codecraft Technologies, Bangalore, India, Kumar C. Kiran , Sekaran S. Chandra ,   PSV College of Engineering and Technology, India, R Gayathri ,   Rajalakshmi Engineering College, India, S Ramasamy ,   Hindusthan Institute of Technology, India
Published: October 2024

The only criteria that are used to evaluate the various neural network-based object identification models that are currently in use are the inference times and accuracy levels. The issue is that in order to put these new classes and situations to use in smart cities, we need to train on them in real time. We were not successful in locating any research or comparisons that were centered on the length of time necessary to train these models. As a direct consequence of this, the initial reaction times of these object identification models will consistently be quite slow (maybe in days). As a consequence of this, we believe that models that put an emphasis on the speed of training rather than accuracy alone are in significant demand. Users are able to gather photos for use in training in the present by utilizing concept names in online data collection toolkits; however, these images are iconic and do not have bounding boundaries. Under these conditions, the implementation of semi-supervised or unsupervised models in a variety of smart city applications might be able to contribute to an improvement in the precision of data derived from IoMT. In this study, we categorize the video clips into their appropriate classes using an improved ensemble classification model.
A DEEP LEARNING BASED ANALYSIS OF OIL SPILLED IMAGES TO MINIMIZE POLLUTION IN MARINE ENVIRONMENT
Authors:   Veltech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College, India, Murugan J. Senthil , S Surendran ,   Tagore Engineering College, India, R Sundar ,   AMET Deemed to be University, India
Published: October 2024

The rising demand for oil and increased shipping capacity are significant contributors to the pollution of the world seas and oceans that is caused by human activity. Oil spills on the world waterways are another major cause of this pollution. Because of the growing demand for oil and the capability of the maritime transport industry, oil spills on seas and oceans have become a significant source of pollution in recent years. It is of the utmost importance that oil spills are constantly monitored and that measures are taken to clean them up as quickly as is humanly possible. This is since oil spills can have devastating effects not only on the local ecosystem but also on the economies of states that are located along the shore. Because of the ongoing threats that are posed to marine life, biodiversity, and habitats, it is of the utmost importance to be able to keep a watch on oil spills from a distance, recognise them, and take action to clean them up. This is essential. In the past ten years, developments in remote sensing data collection, computing capability, cloud computing infrastructure, and cutting-edge SqueezeNet algorithms have led to significant advancements in oil spill detection. These developments have been responsible for most of the progress. These technological advancements have made it possible to identify oil spills more accurately.
A DEEP LEARNING BASED ALGORITHM FOR IMPROVING EFFICIENCY IN MULTIMEDIA APPLICATIONS
Authors:   Soundarya Institute of Management and Science, India, R Jayadurga , M Sathiya ,   Karpagam Institute of Technology, India, G.K. Arpana ,   East West College of Engineering, India
Published: October 2024

Most of the time, the deep learning classifiers are trained using general-purpose datasets with a lot of classes. Therefore, the performance of these classifiers may not be fruitful. Choosing classifiers and dividing them into groups based on the subjects they cover are possible solutions that could lead to better classifier performance. This makes it clear that a classifier division and selection strategy needs for the proposed optimization to work. With the help of this method, the proposed model for feature extraction can choose an appropriate classifier while taking multimedia constraints into account. There is multimedia with the best values, and the results of using only Stacked Auto Encoder (SAE) classifiers from one domain and ignoring classes from other domains are also given. These are in the same place as the effects of only using classifiers from a certain domain. For high-performance use of SAE-based systems, you need to use a classifier selection technique. This method is also needed for the investigation of multimedia events that need the method. To establish the effectiveness of the multimedia event-based system as well as its dependability, we are making use of traditional evaluation methods such as throughput and accuracy. These measures include the following: When compared to the efficiency of the system when using a classifier with a single class, the efficiency of the system diminishes as the number of classes per classifier increases. This is the case regardless of the other measures. This is the situation about both the throughput and the precision of the operation.
AN ENHANCED LIGHTWEIGHT TRANSFORMER METHOD USING STRONG ENCODER TECHNIQUES IN UNDERWATER OBJECT DETECTION
Authors:   Varuvan Vadivelan Institute of Technology, India, N Sivakumar , A Sumalatha ,   Kristu Jayanti College, India, K Prabhu ,   GRT Institute of Engineering and Technology, India
Published: October 2024

The two-stage lightweight transformer methodology can now be used for underwater object detection thanks to some small adjustments that have been made to it. These adjustments were made to accommodate the needs of the underwater environment. By utilising multi-scale training, making enhancements to the backbone network of the Faster RCNN, and optimising the model response to both positive and negative examples, with an emphasis on the latter, we can achieve significant gains. The enhanced lightweight transformer method has been shown to be effective through the utilisation of comparative experiments which demonstrate that the network module may be utilised in the method to serve as the feature extraction structure. This has been shown to be the case thanks to the fact that the enhanced lightweight transformer method has been shown to be effective. We first disassemble the entire system to break it down into its component components before running the detection algorithm and the ablation tests. We can perform additional tests regarding the efficiency of the object detection algorithm for lightweight transformers. When compared to the unimproved lightweight transformer technique, the F1 result is rapidly approaching 99%, which represents a substantial leap forward in terms of quality. This contributes to demonstrating that the methods that were recommended are effective.
THE SMART DETECTION AND ANALYSIS ON SKIN TUMOR DISEASE USING BIO IMAGING DEEP LEARNING ALGORITHM
Authors:   Panimalar Engineering College, India, J Seetha , D Nagaraju ,   Sri Venkatesa Perumal College of Engineering and Technology, India, T Kuntavai ,   Adhi Parasakthi Engineering College, India, Gupta K. Gurnadha ,   Koneru Lakshmaiah Education Foundation, India
Published: October 2024

Skin cancer is one of the most common and devastating forms of cancer. It is estimated that one out of every five individuals will develop skin cancer at some point in their lifetime. Early detection and treatment are essential for successful outcomes, and thus, developing automated and accurate detection methods for skin tumors is of great interest. In this paper, a bio-imaging based deep learning algorithm, have made it possible to accurately detect and analyze skin tumor diseases. This algorithm use complex neural network architectures to automatically identify and classify skin lesions from medical images. These methods can significantly help reduce the workload of dermatologists and improve the accuracy and speed of skin cancer detection. This study reviews the current research on automated skin tumor detection and analysis using deep learning algorithms, and presents some of the most promising directions for further investigation.
AUTOMATED SEVERITY SCORING OF COVID-19 CT SEQUENCES USING SPACE-TIME TRANSFORMERS
Authors:   Bharathidasan University, India, Ranjit Mercy , Ganapathy Gopinath ,   Bharathidasan University, India, Reddy K. Vishnu Vardhan ,   Navodaya Medical College Hospital and Research Centre, India, M. Prabhu Sahana ,   Robert Bosch Engineering and Business Solutions, Bengaluru, India
Published: October 2024

Automatic diagnosis of Covid-19 lung complications from Computerized Tomography (CT) scans is an increasingly important research topic. In this rapidly developing area of Covid detection from medical image sequences, it is noted that most prior literature has focused on binary classification to detect diseased versus healthy cases from single X-ray or CT image. In this paper, we advance a step further by presenting a comprehensive framework for automated classification of the severity of lung infection (mild, moderate and severe) from CT sequences of confirmed Covid cases. We consider the sequence information for automation because in practice, the medical experts look at the CT sequence to score the severity of infection. We have collected a new lung CT sequence dataset at various stages of Covid infection from Indian patients. This dataset has been scored in terms of the severity of each lung lobe by experts in the field. We present a novel application of space-time transformers for CT sequences and achieve 93.3% accuracy for sequence level and 99% accuracy for patient-level, for multi- class classification of severity classes.
FEATURE EXTRACTION USING AT-CONVLSTM BASED CULTURAL ALGORITHM FOR IMAGE UNDERSTANDING
Authors:   Shri Jagdishprasad Jhabarmal Tibrewala University, India, Jain Shweta Nishit , Pise Priya ,   Shri Jagdishprasad Jhabarmal Tibrewala University, India, Mishra Akhilesh ,   Shri Jagdishprasad Jhabarmal Tibrewala University, India
Published: October 2024

This research presents a novel approach for feature extraction in image understanding, utilizing an AT-ConvLSTM-based Cultural Algorithm. The Proposed CA-AT-ConvLSTM leverages the power of deep learning through AT-ConvLSTM architecture while optimizing the feature extraction process using Cultural Algorithms. This synergistic approach enhances the efficiency and accuracy of image understanding tasks, making it suitable for a wide range of applications, from computer vision to pattern recognition. The experimental results demonstrate the superiority of the proposed technique over traditional methods, highlighting its potential in advancing the field of image analysis.
ENHANCING MELANOMA CLASSIFICATION WITH GRAPH ATTENTION LAYERS AND GROUP METHOD OF DATA HANDLING - BASED FEATURE EXTRACTION
Authors:   Bannari Amman Institute of Technology, India, S Gowthami
Published: October 2024

Melanoma, a deadly form of skin cancer, demands accurate and early diagnosis for effective treatment. In this study, we propose a novel approach to improve melanoma classification by integrating Graph Attention Layers (GALs) into the Group Method of Data Handling (GMDH) framework. Our method leverages the power of GMDH to automatically generate and select informative features from complex melanoma-related data. Simultaneously, GALs are employed to capture intricate relationships and dependencies within the data, often overlooked by traditional classification models. We construct a graph representation where nodes represent data elements (patients or genetic markers) and edges signify relationships between them. GALs are applied to the graph, allowing the model to attend to relevant nodes and connections, enhancing its ability to discern subtle patterns indicative of melanoma. We then train a classification model on this enriched feature set, aiming for superior accuracy in melanoma diagnosis. Experimental results on a diverse melanoma dataset demonstrate the effectiveness of our approach. The model consistently outperforms traditional methods in terms of accuracy, precision, and recall. This study highlights the potential of combining GMDH-based feature extraction with GALs in melanoma classification. This approach not only advances diagnostic accuracy but also provides valuable insights into the underlying factors driving melanoma risk. As early detection remains the key to melanoma treatment success, our proposed method holds promise for improving patient outcomes.
MACHINE LEARNING-BASED FACIAL RECOGNITION FOR VIDEO SURVEILLANCE SYSTEMS
Authors:   Malla Reddy College of Engineering and Technology, India, Pulugu Dileep , P Anusha ,   Malla Reddy College of Engineering and Technology, India, Srivastava Ratan Rajan ,   Malla Reddy College of Engineering and Technology, India, R Kalaivani ,   Malla Reddy College of Engineering and Technology, India, Pal Subharun ,   Swiss School of Management, Switzerland
Published: October 2024

Video surveillance systems play a crucial role in ensuring public safety and security. However, the traditional methods of surveillance often fall short in effectively identifying individuals, particularly in crowded or dynamic environments. This research addresses the limitations of conventional video surveillance by proposing a machine learning-based facial recognition system. The increasing demand for robust security measures necessitates the development of advanced technologies in video surveillance. Facial recognition has emerged as a promising solution, but existing systems struggle with accuracy and efficiency. This research aims to bridge these gaps by leveraging machine learning techniques for facial recognition in video surveillance. Conventional video surveillance struggles with accurate and rapid identification of individuals, leading to potential security lapses. This research addresses the challenge of enhancing facial recognition accuracy in real-time video feeds, especially in scenarios with varying lighting conditions and occlusions. While facial recognition has gained traction, there is a significant research gap in the implementation of machine learning algorithms tailored for video surveillance. This study aims to fill this void by proposing a novel methodology that combines deep learning and computer vision techniques for robust facial recognition in dynamic environments. The proposed methodology involves training a deep neural network on a diverse dataset of facial images to enable the model to learn intricate facial features. Additionally, computer vision algorithms will be employed to handle challenges such as occlusions and varying lighting conditions. The model''''s performance will be evaluated using real-world video surveillance data. Preliminary results demonstrate a significant improvement in facial recognition accuracy compared to traditional methods. The machine learning-based system exhibits enhanced performance in challenging scenarios, showcasing its potential for practical implementation in video surveillance systems.
IMAGE REPRESENTATION AND RENDERING FROM LOW-RESOLUTION SURVEILLANCE VIDEOS USING DENSENET
Authors:   Kings Engineering College, India, Josephine D.C. Jullie , B Yuvaraj ,   Kings Engineering College, India, Leo Sathesh Abraham ,   Kings Engineering College, India, S Thumilvannan ,   Kings Engineering College, India
Published: October 2024

In surveillance, the need for enhanced image representation and rendering from low-resolution videos is paramount for effective analysis and decision-making. This research addresses the limitations of conventional methods in extracting meaningful information from low-resolution footage. The prevalent challenge lies in the compromised clarity and detail inherent in surveillance videos, hindering accurate identification and analysis of critical events. The ubiquity of surveillance cameras has led to an influx of low-resolution videos, limiting the efficacy of traditional image processing techniques. This research aims to bridge this gap by leveraging DenseNet, a densely connected convolutional neural network (CNN) known for its ability to capture intricate features. The DenseNet seeks to enhance the representation and subsequent rendering of images, transcending the constraints imposed by low resolutions. The network ability to capture intricate details will be harnessed to enhance image representation. Subsequent rendering techniques will be employed to reconstruct high-quality images for improved analysis. The results showcase promising advancements in image representation and rendering using DenseNet. The enhanced visual quality of surveillance images allows for more precise identification and analysis of events, demonstrating the potential impact of the proposed methodology on improving surveillance systems.
AI-IMAGE REPRESENTATION AND LINEAR REPRENDER RENDERING
Authors:   Reva University, India, B.K. Harsha , Rao B. Srinivasa ,   Gokaraju Rangaraju Institute of Engineering and Technology, India, S Tamijeselvan ,   Mother Theresa PG and Research Institute of Health Sciences, India, M Ganesha ,   A J Institute of Engineering and Technology, India, Behera Nihar Ranjan ,   Swiss School of Business and Management Geneva, Switzerland
Published: October 2024

Image representation and rendering have become critical in numerous applications such as virtual reality, medical imaging, and computer graphics. Traditional rendering techniques often face challenges in efficiently handling complex scenes and achieving photorealistic results while maintaining low computational costs. The problem lies in the high-dimensional nature of image data, leading to slow processing times and reduced scalability. This research presents an AI-enhanced technique called Linear RepRender, which leverages deep learning to transform high-dimensional image representations into simplified linear forms for faster rendering. The proposed method employs a combination of convolutional neural networks (CNNs) and linear regression models to reduce image complexity. Specifically, the CNN extracts low-level and high-level features from the image, while the linear regression step approximates the scene’s core visual elements. This hybrid approach significantly improves rendering speed without sacrificing image quality. Furthermore, the method incorporates a loss function optimized for minimizing discrepancies between the rendered and ground truth images. Experimental results demonstrate that Linear RepRender outperforms traditional rendering algorithms, such as ray tracing and rasterization, in terms of computational efficiency and visual accuracy. On a dataset of complex 3D scenes, the proposed method achieved a 35% reduction in rendering time and a 22% improvement in peak signal-to-noise ratio (PSNR) compared to state-of-the-art methods. Additionally, Linear RepRender was able to handle up to 1.5 million polygons per scene with minimal visual artifacts, making it suitable for real-time applications.
FAULT DETECTION AND CLASSIFICATION USING DENSENET, SURF-BASED ANN, AND INFRARED THERMOGRAPHY
Authors:   Knowledge Institute of Technology, India, B Sasikumar , P Rajendran ,   Knowledge Institute of Technology, India
Published: October 2024

The reliability and efficiency of electric motors are critical in industrial applications, where unexpected faults can lead to costly downtime and safety hazards. Traditional fault detection methods often require extensive manual inspection and may not capture subtle anomalies in motor behavior. Infrared thermography has emerged as a non-invasive technique to detect temperature variations in motor components, which can indicate potential faults. However, the challenge lies in accurately classifying these faults to prevent failures. Current methods lack the precision needed to classify various motor faults accurately and quickly, especially when dealing with complex thermal patterns. The integration of advanced deep learning architectures with feature extraction techniques presents an opportunity to enhance the detection and classification of motor faults. This study proposes a hybrid model combining DenseNet, a deep learning architecture known for its high performance in image analysis, with a Speeded-Up Robust Features (SURF)-based Artificial Neural Network (ANN) for feature extraction and classification. Infrared thermography images of motors were first processed through DenseNet for initial feature extraction. The SURF algorithm further refined these features, which were then classified using ANN. The model was trained and validated on a dataset of infrared thermography images, representing various motor fault conditions, including bearing wear, misalignment, and insulation failure. The proposed model achieved an overall accuracy of 98.7% in detecting and classifying motor faults, outperforming traditional methods by 5.2%. The model also demonstrated high sensitivity (97.8%) and specificity (99.1%) in identifying subtle temperature variations indicative of early-stage faults. These results highlight the effectiveness of the DenseNet and SURF-based ANN approach in enhancing the reliability of motor fault detection using infrared thermography.
PREDICTIVE MODELING OF GASTRIC DISEASE PROGRESSION FROM ENDOSCOPIC IMAGES USING FUZZY LOGIC AND MACHINE LEARNING
Authors:   GITAM University, India, Donthu Somasekhar , S Poongothai ,   RMK College of Engineering and Technology, India, Kumar A. Rajesh ,   N.S.N. College of Engineering and Technology, India, Dhinnesh A.D.C. Navin ,   Mepco Schlenk Engineering College, India, Williams D.R. Prince ,   University of Technology and Applied Sciences, Sultanate of Oman
Published: October 2024

Gastric disease progression is challenging to predict due to the complex nature of endoscopic images. This study addresses the problem by integrating fuzzy logic with machine learning, specifically XGBoost, for predictive modeling. The proposed method preprocesses endoscopic images, extracts features, and applies fuzzy logic for classification, followed by XGBoost for final prediction. Results demonstrate an accuracy of 92.5% and an F1-score of 0.91, outperforming traditional methods. The model offers a robust tool for early detection and monitoring of gastric diseases, enhancing clinical decision-making.
The Chinese Ear Shenmen Point and the French Cosmonaut Point: Same or Different?
Authors: Stanton Gary
Published: October 2024

The Shenmen Point is an auricular acupuncture point that is also known in France as the Cosmonaut Point. It is widely utilized therapeutically for its property of reducing stress. In some ways it is a controversial point. Differences of opinion exist regarding the most appropriate name of the point, who discovered it first, its location within the triangular fossa, its best method of detection, its best method of stimulation, its actions, its indications, its contraindications, its therapeutic effects, and even its very existence. Furthermore, given the extent of controversy concerning the Shenmen and Cosmonaut points, one may reasonably question if they represent the same point, or two separate points. The aim of this report will be to review the similarities and differences of opinion regarding the Shenmen and Cosmonaut points, drawing mainly from French, German, Chinese, and American sources. The clinical implications of these comparisons will be summarized.
Associations of Wearable Activity Tracker Use With Physical Activity and Health Outcomes in Patients With Cancer: Findings from a Population-Based Survey Study (Preprint)
Authors: Zhou Weijiao , Shang Shaomei , Cho Youmin
Published: October 2024

BACKGROUND

Physical inactivity is a global issue for cancer survivors. Wearable activity trackers are promising to address physical inactivity by providing real-time feedback on physical activity and offering opportunities for self-monitoring and goal setting. Meta-analysis has reported the effects of interventions that incorporate wearable activity trackers on improved physical inactivity and related health outcomes (eg, BMI, anxiety and depression, and self-rated health status). However, wearable activity trackers were often used as an adjunct to physical activity interventions, and the effectiveness of wearable activity trackers alone is unknown.

OBJECTIVE

This study aims to determine the association of wearable activity trackers with physical activity and health outcomes in patients with cancer.

METHODS

Data from 957 cancer survivors from the Health Information National Trends Survey–Surveillance, Epidemiology, and End Results (HINTS-SEER) were analyzed. The outcome variables examined were time spent in moderate to vigorous physical activity, weekly frequency of strength training, BMI, anxiety and depression levels, and self-assessed health status. The primary independent variable was whether cancer survivors had used wearable activity trackers within the past 12 months. Design-based linear regression for continuous outcome variables and ordinal logistic regression for ordinal outcome variables were conducted to determine the associations after controlling for sociodemographic, cancer-related, and health-related factors. All data analyses accounted for the complex survey design and sample weights.

RESULTS

Only 29% of cancer survivors reported wearable activity tracker use. Bivariate analyses showed that younger age (P<.001), higher education (P=.04), higher income (P<.001), and an employed status (P<.001) were significantly associated with wearable activity tracker use. Wearable activity tracker use was significantly associated with higher time spent in moderate to vigorous physical activity (adjusted =37.94, 95% CI 8.38-67.5; P=.01), more frequent strength training per week (adjusted odds ratio [OR] 1.50, 95% CI 1.09-2.06; P=.01), and better self-rated health status (adjusted OR 1.58, 95% CI 1.09-2.29; P=.01), but not with BMI or anxiety and depression.

CONCLUSIONS

This study suggests that the uptake of wearable activity trackers is low and highlights the digital divide among patients with cancer. This study has confirmed the associations of wearable activity tracker use with physical activity and self-rated health, supporting using wearable activity trackers as a promising tool to facilitate physical activity promotion.
Piloting Digital Navigators to Promote Acceptance and Engagement With Digital Mental Health Applications in German Outpatient Care: Protocol for a Mixed-Method Interventional Study (Preprint)
Authors: Schwarz Julian , Chen Kelly , Heinze Martin , Schönbeck Julia , Schubert Darja , Speck Justin , Torous John , Wolff Jan , Uchtmann Laura , Meier-Diedrich Eva
Published: October 2024

BACKGROUND

In Germany, patients often have to wait several months for a psychotherapeutic treatment. Digital therapeutics (DTx) offer a promising approach to providing timely mental health support. However, their use is still limited. Digital navigators (DNs) are specially trained advanced practice providers who support health care professionals (HCPs) and patients in the selection and use of DTx. This can improve digital health literacy, increase engagement and reduce the burden on HCPs.

OBJECTIVE

The DigiNavi study is the first pilot study to test the feasibility of implementing DNs in general practice and outpatient psychiatric care in Germany.

METHODS

I) In order to explore the acceptance and expectations of HCPs and patients towards DNs, a qualitative study will be conducted. II) The Harvard Digital Navigator Training will be adapted and advanced practice providers will be trained to become DNs at n=6 study sites. III) The DNs will guide a total of n=48 patients in the use of DTx for mental health. The patients’ digital health literacy and the severity of their psychological symptoms will be assessed quantitatively in a pre-post comparison. In addition, user experience and barriers to implementation will be explored qualitatively.

RESULTS

The study provides important insights into the acceptability and feasibility of human-facilitated competency development for mental health apps in multi-professional health care teams and their patients.

CONCLUSIONS

The successful implementation of DNs can promote the use of DTx in Germany and thus enhance the provision of healthcare for individuals with mental health conditions.

CLINICALTRIAL

German Clinical Trials Registry (DRKS), registration number: DRKS00034327, https://drks.de/search/de/trial/DRKS00034327

ClinicalTrials.gov, registration number: NCT06575582,
https://clinicaltrials.gov/study/NCT06575582?term=digital%20navigators&rank=1
Cross-Cultural Insights into Phatic Communication: Indonesian and Australian Student Interactions
Authors: Ilmi Assayyidah Bil Ichromatil , Degaf Agwin
Published: October 2024

Phatic communication, a widespread yet often unnoticed language phenomenon, primarily serves to maintain social interactions rather than convey substantive information. This study investigated the use of phatic expressions among Indonesian and Australian students, examining how cultural backgrounds and social contexts influence their communication styles. Using a Discourse Completion Task (DCT) with both qualitative and quantitative methods, this research revealed significant differences between the two groups. Australian students frequently used phatic speech to initiate conversations, provided comfort, and expressed empathy, emphasizing respect and interpersonal connection. In contrast, Indonesian students tended to use phatic expressions to promote social harmony, engage in casual discourse, and offer interpersonal support, often focusing on group cohesion. Social variables, such as social status and distance, played a critical role in shaping the choice of phatic functions and forms, with both groups adapting their communication strategies based on these factors. The findings highlight the intricate relationship among culture, social dynamics, and language in phatic communication, offering a nuanced understanding that can enhance intercultural interactions, minimize misunderstandings, and improve communication effectiveness in multicultural settings. This research underscores the importance of considering cultural and social contexts when analyzing language use, contributing valuable insights for educators, linguists, and communication professionals aiming to foster more effective and harmonious interactions across diverse cultural landscapes.
The Humor Translation in American Sitcoms from the Perspective of Skopos Theory—A Case Study of Modern Family
Authors: Fang Xiaxia
Published: October 2024

With the frequent cultural exchanges between China and foreign countries, film and television series, not only gradually replace the mainstream position of traditional print media, but also play a very important role in cross-cultural communication. It is difficult to convey the same comic effect in different cultural backgrounds. Guided by Skopos theory, this paper takes the subtitle translation of Modern Family as an example to analyze the translation of some humorous lines in this sitcom. It aims to achieve the effect of integration, hoping to put forward more opinions on the subtitle translation of more excellent overseas comedy works in the future.
Narrativas socio-comunitarias y mandatos de género sobre sexualidad —maternidad- paternidad— en adolescentes de los Altos de Chiapas, México
Authors: Díaz-Carrasco Marianela , Estrada-Márquez Fátima , Romero-Pérez Irma , Campero-Cuenca Lourdes
Published: October 2024

Se analizan narrativas comunitarias interseccionales de las vivencias sobre sexualidad, maternidad y paternidad adolescente en tres poblaciones predominantemente indígenas de los Altos de Chiapas —San Cristóbal de las Casas, San Juan Chamula y Zinacantán—. Investigación cualitativa, feminista e interseccional, con entrevistas y grupos focales con madres jóvenes, adolescentes, madres y padres de familia, personal académico, personal de salud y líderes comunitarios. La sexualidad femenina se somete a debate comunitario, el embarazo y la crianza se entienden como una responsabilidad casi exclusiva de las mujeres y la narrativa de la co-responsabilidad de pareja se disuelve durante el proceso de gestación. Las opresiones interseccionales atraviesan la vida íntima, privada y los espacios relacionales públicos y, en el caso de las mujeres, el género, la clase y la ascendencia indígena evidencian estructuras de dominación y subordinación mucho más profundas que en los hombres.
El mercado de trabajo en México 1995-2016: contradicciones del empleo formal e informal
Authors: Cervantes-Niño José Juan , Velázquez-Rojas Martín , Barragán-Codina Manuel
Published: October 2024

La crisis económica mundial de 2008 provocó graves desequilibrios financieros para la mayoría de países del ámbito global, desarrollados o en desarrollo, inhibiendo el crecimiento económico, no obstante, los mercados de trabajo registraron efectos divergentes. En este contexto, el presente estudio consistió en un análisis nacional y comparativo del empleo formal e informal en México, durante el periodo 1995-2016, para mostrar que el desarrollo macroeconómico del país; sostenible aún con la crisis de 2008, no ha registrado saldos positivos en las dinámicas del mercado laboral y, por el contrario, coexisten una serie de contradicciones que incentivan una mayor precarización de algunos parámetros laborales, lo cual, obstaculiza el progreso del empleo. En lo general, persistencia de altos niveles de precariedad en las principales variables laborales, mismos que generan obstáculos el desarrollo laboral y nacional, por lo que se necesitan intervenciones de políticas más heterodoxas para paliar estas problemáticas, en el mediano plazo.
Introduction to Cloud Gaming
Authors: Tulsyan Rajiv
Published: October 2024

Cloud Gaming can be defined as a technology that enables gamers to play video games using cloud-based servers and infrastructure, rather than relying on local hardware for processing and rendering game graphics. In cloud gaming, the games are streamed to the player's device with the help of the internet, allowing them to play high-quality games without needing to own a high-end gaming console or PC. The solution helps people to play games on their computers regardless of their own system as their gaming needs are met with the help of good enough customer service in the cloud. It also helps to overcome the problems of traditional games such as conflict and mobility. In this article, we will take a look at various aspects of Cloud Gaming System (CGS) to explain the many advantages and capabilities this concept has.
Imperial Study on Convolutional Layer in CNN for Data Mining
Authors: Raghuvanshi Kunal P
Published: October 2024

Convolutional Neural Networks (CNNs) have become a cornerstone in the field of data mining, particularly for tasks involving large-scale image and pattern recognition. This paper presents an imperial study on the efficacy of convolutional layers within CNN architectures in data mining applications. We analyze the impact of varying convolutional layer configurations, such as kernel size, stride, and depth, on performance metrics including accuracy, computational cost, and feature extraction quality. By conducting a series of experiments across different datasets, we explore how these configurations influence the model's ability to generalize and detect complex patterns in structured and unstructured data. Our findings indicate that optimizing the convolutional layers significantly improves the efficiency of CNNs for data mining tasks, offering insights into best practices for architectural design in such applications. This study provides valuable guidelines for leveraging CNNs in the realm of data mining, pushing the boundaries of automated data analysis and knowledge discovery
Quando o “discurso do sujeito” não instala o sujeito do discurso: desafios para a implementação da BNCC de língua vernácula no ensino médio
Authors: Magalhães Anderson Salvaterra
Published: October 2024

Neste artigo, o objetivo é discutir alguns desafios contemporâneos para o ensino formal do vernáculo no ensino médio considerando a tendência monologizante do que se compreende como engajamento em redes sociais digitais. Para isto, assume-se a “participação mais plena dos jovens nas diferentes práticas socioculturais que envolvem o uso das linguagens” prevista na BNCC Ensino Médio de Língua Portuguesa como mote para problematização da latente contradição entre o que, do senso comum, compreende-se como “discurso do sujeito” e o que, do ponto de vista dialógico, se define como efetiva participação sociocultural, que instala sujeitos do discurso. A discussão está fundamentada no que se tem chamado Análise Dialógica do Discurso e é encaminhada em dois principais blocos. O primeiro constitui um breve ensaio acerca do primado histórico-social que subjaz ao quadro teórico mobilizado para definição do que se entende por língua/linguagem, enunciado concreto e sujeito do discurso. O segundo consiste numa reflexão especulativa em que se cotejam, por um lado, práticas contemporâneas de engajamento em redes sociais digitais e, por outro, o desafio para o ensino formal contribuir com a interação social responsável dos jovens nesse tipo de prática. A articulação desses dois blocos demonstra como o distanciamento do senso comum acerca do que seja “interagir socialmente” indica mecanismos enunciativo-discursivos para efetiva participação cultural. Assim, conclui-se que a BNCC para o vernáculo no ensino médio constitui não uma orientação prescritiva, mas uma diretriz propositiva que mira o compromisso ético dos atores implicados na esfera da educação formal.
She was made up by a dude: discurso, imaginação e racionalidade feminina
Authors: Aianne Rebouças Marcela , Tavares Edgley Freire
Published: October 2024

Este artigo é um recorte de uma pesquisa de mestrado que estuda as formas discursivas da resistência feminina em canções do gênero pop, e apresenta resultados preliminares em torno da problemática do pensamento feminino como expressão de contestação de racionalidades de cunho patriarcais. O corpus é constituído por duas canções, Victoria’s Secret e Build a Bitch, interpretadas pelas artistas norte-americanas Jax e Bella Poarch, cujos discursos produzem uma descontinuidade nas formas de saber e representar por meio de uma nova imaginação sobre as mulheres e seus modos de ser e estar no mundo, ruptura artístico política que evidencia a efetividade do pensamento feminino frente às tentativas opressivas de controle do corpo feminino. Na escansão analítica aqui apresentada, orientada teórica e metodologicamente à luz dos estudos discursivos foucaultianos, as canções produzem um ato enunciativo da posição sujeito mulher que se mostra transgressor e desobediente a saberes e relações de força androcêntricos e machistas.
Models of vector autoregression in forecasting inflationary processes at the regional level (using the example of the Jewish Autonomous Region)
Authors:   The Russian Presidential Academy of National Economy  and  Public  Administration,  the  Far-Eastern  institute of management – branch  of  RANEPA, Lagulova E.S. ,   The Department for the Jewish Autonomous Region of the Far Eastern Main Directorate of the Central Bank of the Russian Federation, Kulagina O.V. ,   The Russian Presidential Academy of National Economy  and  Public  Administration,  the  Far-Eastern  institute of management – branch  of  RANEPA
Published: October 2024

The paper presents the stages of constructing a vector autoregression model (VAR model) for forecasting inflation at the regional level, and a short-term inflation forecast for the Jewish Autonomous Region. For this purpose, a selection of possible indicators for the VAR model has been singled out, and the need for their use has been economically justified. It has also been proved that it is necessary to use fictitious variables to eliminate the influence of identified shocks in the dynamics of inflation. The resulting VAR model has been recognized as sound after conducting the necessary econometric tests. The model has been verified. It is worth noting that the developed VAR model is suitable only for the study of the selected subject of the Russian Federation, since each region of Russia has its own uniqueness. The selection of indicators should be singled out individually for each territory. At the same time, the algorithm for constructing a forecasting model is universal and can be applied to various territories. It has been concluded that it is important to build an inflation forecast, since its impact must be taken into account in financial planning by absolutely all economic agents at all hierarchical levels: government, business, and the public. The views expressed herein are solely those of the authors. The content and results of this research should not be considered or referred to in any publications as the Bank of Russia’s official position, official policy, or decisions. Any errors in this document are the responsibility of the authors. All rights reserved. Reproduction is prohibited without the authors’ consent.
Regional parliament in the context of global challenges and uncertainty: experience and lessons
Authors:   The Legislative Duma of the Khabarovsk Territory, Zikunova I.V.
Published: October 2024

This scientific and practical article has been prepared in order to summarize the experience of the Legislative Duma of the Khabarovsk Territory in the period of 2019–2024. The work describes the specifics of the process of formation of the dynamic regional parliament, external factors of the Duma's activities, legislative results, the contribution of the deputies of the seventh convocation to the development of the forms of parliamentary activity and parliamentary communications. The alteration of the majority faction in the Legislative Duma of the Territory in 2019 has been justified by the high demand of citizens for social changes and the requirement to reduce the distance of power. The leading external conditions of the Duma's work are characterized by pandemic shock, socio-political instability and protest phenomena in the region, constitutional changes in public authority, a special military operation and its goals. The results of the work of the deputies of the seventh convocation are the following: initiation and adoption of constituent and basic laws of the region, development of a variety of forms of the Duma's work, active communications including the Internet space, contribution to parliamentary diplomacy. The following lessons have been induced from the experience of the regional parliament: the lesson of a far-sighted attitude to parliamentary leadership; the lesson of long-term party work based on the principles of succession of generations; the lesson of flexible and socially dynamic lawmaking and parliamentary control; the lesson of openness and broad communication of the deputy team. The article is addressed to researchers and practitioners in the field of regional public administration, aimed at disseminating the experience of parliamentary management in conditions of change and high uncertainty.
The Effect of 7P Marketing Mix on Purchasing Decisions and Repurchases at Mixue Sidoarjo Consumers
Authors: Santoso Sri Nadine , Waluyo Minto
Published: October 2024

Along with the increase in population, the level of consumption of food and beverages in society is also increasing, one of which is ice cream. Mixue ice cream & tea is one of the franchises that offers ice cream products and various other drinks that are currently on the rise or popular among the public. To ascertain how the 7p marketing mix affects consumers' decisions to buy and repurchase products, researchers aim to study Mixue Sidoarjo consumers. Respondents in this study were 130 consumers of Mixue Sidoarjo. Data analysis was carried out using the SEM method where the results of the SEM were followed by strengthening the SWOT analysis matrix as a proposed strategy in increasing consumer growth in making repeat purchases. The results showed that product, price, people, and place had a positive and significant effect on purchasing decisions. Meanwhile, promotion and process have a negative and insignificant effect on purchasing decisions. Purchasing decisions have a positive and significant effect on repeat purchases. The simultaneous equation for purchasing decisions is Y1 = 1,045X1 + 0,566X2 – 1,256X3 – 0,426X4 + 0,252X5 + 1,347X6 + Z7and the simultaneous equation for repurchase is 0,892X1 + 0,483X2 – 1,073X3 – 0,364X4 + 0,215X5 + 1,15X6 + Z8. In strengthening the SWOT analysis by compiling a SWOT matrix, there are four types of alternative strategies that can be applied by Mixue Sidoarjo. In terms of business conditions, the strategy that can be applied is the W-T strategy which utilizes the weakness and threats factors of Mixue Sidoarjo.
Influence Analysis the Technology Acceptance Model Approach on User Satisfaction and Repurchase on the KAI Access Application Using the SEM Method
Authors: Kartika Nuarita Tri , Waluyo Minto
Published: October 2024

The KAI Access application is a transportation service application from PT Kereta Api Indonesia. From a survey conducted by researchers, there is application dissatisfaction by users. This study aims to determine the effect of five TAM variables on user satisfaction and repurchase. In accordance with the research objectives, several TAM variables will be used, namely actual use, perceived ease of use, perceived usefulness, perceived risk, and perceived trust. It will also be known the effect of TAM variables on application user satisfaction, and repurchase of train tickets by application. After analyzing with SEM coherently, the results show that the actual use variable has a C.R. value of 6.095, has a positive and significant value on application user satisfaction. With a C.R. value of 7.668, the perceived usefulness variable has a significant positive value on application user satisfaction. With a C.R. value of 6.763, the perceived risk variable has a significant positive value on application user satisfaction. The perceived trust variable in the application has a C.R. value of 6.094 and has a significant positive value on application user satisfaction. With a C.R. value of 7.434, the application user satisfaction variable has a positive and significant value on the repurchase of train tickets through the application.