bluuebunny/crossref_metadata_2025 huggingface dataset:
XX. Yüzyıl Başı Tatar Edebiyatında Süyümbike Hanbike İmgesi
Authors: RAMİ Ilsever
Published: December 2023

Kazan Hanlığı’nın Ruslar tarafından işgal edilmesi Tatar halkının tarihinde büyük bir facia olarak nitelendirilmektedir. 1552 yılında Kazan Hanlığı’nın yıkılmasıyla Tatar halkı devletini ve millî özgürlüğünü kaybetmiştir. Bu olay, Tatar halkını derinden sarsmış; ekonomik, dinî ve kültürel yıkımlara sebep olmuştur.

Bir milletin tarihî geçmişinde yer alan önemli olaylar edebiyatta yansıma bulmaktadır. Kazan Hanlığı’nın Ruslar tarafından işgali ve bunun sonucunda millî özgürlüğünün kaybedilmesi Tatar halkının tarihî hafızasında derin yaralar bırakmıştır. Farklı dönemlerde, Tatarca başta olmak üzere Rus ve Türk dillerinde Kazan Hanlığı’nın yıkılması ve Tatar halkının tarihî geçmişini konu eden çok sayıda edebî eser kaleme alınmıştır. Bu eserlerin hepsinin merkezinde Tatar halkının millî özgürlük simgesi, akıllı ve güzel kadın, acıklı bir kadere sahip olan Süyümbike Hanbike imgesi bulunmaktadır.

Tatar halkı için Kazan Hanlığı’nın son Hanbikesi Süyümbike hem üzüntü hem sevgiyle dile alınan bir şahsiyettir. 1551 yılında Hanbikesini Moskova’ya esir olarak göndermek zorunda kalan ve akabinde 1552 yılında devleti -Kazan Hanlığı- Ruslar tarafından işgal edilen Tatar halkı için Süyümbike’nin ismi, kaybedilen devletin ve özgürlüğün sembolüne dönüşmüştür. Çalışmada, hermeneutik yöntem kullanılarak Kazan’ın işgalinden sonra Süyümbike ve Kazan Kremlinin merkezinde bulunan Süyümbike Minaresi hakkında XX. yüzyılın başında Tatar yazar ve şairlerce kaleme alınan eserlerin analizi yapılmaya çalışılmıştır.
Research on the results of the WLTP procedure for a passenger vehicle
Authors: Andrych-Zalewska Monika , Chłopek Zdzisław , Merkisz Jerzy , Pielecha Jacek
Published: December 2023

The article considers variables registered in the WLTP procedure. The test results of a passenger car with a compression-ignition engine have been analysed. The tests were carried out on a chassis dynamometer. The tests were performed for engine cold start and ran up to the point of reaching stabilized operating conditions. The average specific distance emissions and volumetric fuel consumption were assessed for individual test phases as well as for the entire test. It was found that the results in the first test phase, which corresponded to the engine cold start up to stabilized operating conditions, had the most significant impact on the overall exhaust emission and fuel consumption results in the test. The specific distance emissions of carbon monoxide, non-methane hydrocarbons and nitrogen oxides were by far the highest in the first phase of the test. In the fourth phase of the test, the specific distance emissions of methane and carbon dioxide turned out to be the highest, as well as the operational volumetric fuel consumption being the highest.
Fatigue fracture cross-sections after cyclic tests with a combination of cyclic bending and torsion of samples made of aluminum alloy 6060
Authors: Łagoda Tadeusz , Małecka Joanna , Małys Slawomir , Derda Szymon , Kuś Juliusz , Krysiński Paweł
Published: December 2023

The paper presents the results of fatigue tests of the 6060 aluminum alloy. The test material was taken from the profiles used for production of side windows and external doors of the passenger trains by the RAWAG company. The tests were carried out for cyclic loads with pure bending, pure torsion, and two combinations of bending and torsion. Fatigue tests were performed at zero mean values. Using scanning electron microscopy, a fractographic analysis was made, which is a supplementary basis for considerations about the mechanism of initiation and development of fatigue cracks. Based on the appearance of individual zones and the characteristics of cracks, a picture of the behavior of the material under specific conditions was obtained. Finally, the plastic property of fatigue cracks was indicated. The work demonstrates the influence of aluminum alloy processing on the fatigue properties of this alloy during operation in order to obtain maximum reliability and safety of railway transport.
Research on the Influencing Factors of College Students' Deep and Meaningful Learning in Blended Learning Mode
Authors: Shu Li Et al.
Published: December 2023

This study explores the influencing factors of deep and meaningful learning in blended learning modes and their interrelationships. The sample comprises 397 college students from a university in Sichuan Province, selected through random sampling. Data collection utilized a questionnaire based on Bandura's ternary interaction theory, covering dimensions such as learners, helpers, environment, and interaction. Hypotheses were formulated based on literature research, and a survey was developed using established scales. Quantitative analysis was conducted using SPSS and AMOS software. The mean, standard deviation, variance, skewness, and kurtosis values were within reasonable ranges. The latent variables of the model exhibited sound convergent validity, with SFL ranging from 0.807 to 0.965, AVE from 0.697 to 0.946, and CR from 0.919 to 0.946. Model fit indices indicated acceptable fit (CMIN/DF: 2.303, NFI: 0.966, CFI: 0.980, RMSEA: 0.58, RMR: 0.008, PNFI: 0.789). Through path analysis, the study optimized the model, resulting in the final structural equation model (SEM). The findings suggest that (1) learner, environmental, and interaction factors positively influence deep and meaningful learning, while helper factors show a negative correlation; (2) learner, interaction, and helper factors mediate the environment's impact on deep and meaningful learning; and (3) environmental factors have the most significant influence on helper factors, followed by interaction and learner factors. Helpers play a significant role in enhancing deep understanding for learners. These insights guide effective strategies in promoting deep and meaningful learning in blended learning environments.
Exploring the Impact of Tourist Motivations on Thailand's Gastronomic Tourism: A Structural Equation Modeling Approach
Authors: Jingjing Cheng Et al.
Published: December 2023

This study investigates the impact of tourist motivations on Thailand's appeal as a gastronomic tourism destination, addressing a gap in the existing literature by examining the specific factors that attract tourists to the country's culinary offerings. The primary research objective is to identify and analyze the motivations driving tourists to choose Thailand as a gastronomic destination, while also assessing the implications of these motivations for tourism stakeholders. Data was collected from 163 valid participants who had experiences with Thailand's gastronomic tourism. Using a combination of exploratory and confirmatory factor analyses, this study identifies four distinct factors that contribute to tourism motivation in Thailand: social interaction with novelty, relaxation, regression, and escape from boredom. Structural equation modeling is then employed to analyze the relationships between these factors and Thailand's appeal as a gastronomic tourism destination. The results indicate that social interaction with novelty, regression, and escape from boredom have a significant positive effect on Thailand's gastronomic destination, while relaxation is found to have no significant impact. These findings challenge existing assumptions about the role of relaxation in tourists' destination choices and provide valuable insights for tourism stakeholders seeking to capitalize on the growing popularity of gastronomic tourism. This research contributes to the understanding of gastronomic tourism motivations in Thailand, offering valuable insights for tourism stakeholders and paving the way for further exploration and understanding of the factors driving tourists' destination choices in the context of gastronomic tourism.
Improved Feature Selection Algorithm for Intrusion Detection Using Data Mining Approach
Authors: Remya Raj B Et al.
Published: December 2023

With the rapid growth of Internet applications, there are more and more intrusions into network systems. In this case, it is necessary to provide security for the network through effective intrusion detection and prevention methods. This can mainly be achieved by creating effective interruption detection systems using efficient algorithms that can identify abnormal activities in network traffic and safeguard network resources from unlawful attack by interlopers. Although many interruption recognition frameworks have been proposed before, existing network intrusion detection has limitations in terms of accuracy and detection time. To overcome these shortcomings, In this paper, we propose a new intrusion detection system by developing a new intelligent feature selection algorithm based on conditional random fields (CRF) to optimize the number of features. Furthermore, algorithms based on existing hierarchical methods (LA) In this paper, we propose another interrupt recognition framework, fostering a book. Compared with the existing methods, the interruption identification framework provides high precision and achieves the efficiency of attack detection. The main advantages of this system are reduced detection time, improved classification accuracy and lower false positive rate.
Transportation, Concept, and Its Impact on Economic Development: A Review from the Perspective of the Mountainous Region
Authors: Jagdish Agnihotri Et al.
Published: December 2023

Transportation, which originates from the Latin words trans, whose meaning is across," and portare, which means to carry, emphasizes prominently the occupations of men, advanced civilization, expansion of domestic and international trade, economic growth, etc. The countries whose geography is highly covered with mountains have been heavily affected by the transport network, and the same is true for northeastern mountainous India, where it is impossible to move goods through road transportation. These situations not only harshly affect the residents of such areas but also result in a lack of economic growth, the formation of spatial relationships, and revenue production. As a result of this, there is a need to keep these problematic things in the attention of country residents and their governments, for which a critical analysis is needed through a systematic review that can present a clear concept of transportation and their impact on the economic growth of the regions. Motivated by the same, the present work has been proposed to conduct a review article on transportation, its concept, and its impact on economic development from the perspective of the mountainous region. The work presented in the article will be of great interest to working professionals in this area.
A Deep Learning Technique to Clinch the Detection of Parkinson’s Disease using Speech and Voice Attributes
Authors: C Sandhya , Hemalatha S
Published: December 2023

Among the neurodegenerative diseases Parkinson’s Disease ranks second only to Alzheimer’s disease. Though extensive research is carried out in this area there have been no biomarker suggested. At present the diagnosis and monitoring of the disease progression is possible only through clinical examination and function symptoms observation. Voice impairment has been identified as an early marker for Parkinson’s Disease and hence the research in this field is gaining popularity. Machine Learning algorithms have proved useful in analyzing the enormous data with high dimensionality. But this has not been successful in extricating features that will have a strong correlation in predicting the disease accurately. This calls for a more effective and powerful technique like Deep Learning that uses deep neural networks that can select the optimal features and can contribute in the identification of the disease. In this paper an initial step was made by designing an Artificial Neural Network model. This yielded a train and test accuracy more than ninety-nine percentage and seventy-five percentage respectively for classifying the disease but showed overfitting problem which resulted in a decrease in the performance. Hence, the Artificial Neural Network model was hyper-tuned to reduce this problem and there was a slight improvement in the performance. Two methods were employed for optimization – a regularization method early stop and another validation method called Stratified K -Fold Cross Validation. Among these the second approach showed better results by slightly reducing the overfitting issue and it yielded a train and test accuracy score of approximately ninety-nine percentage and ninety-seven percentage with K-fold as five and Stochastic Gradient Descent as the optimizer. Even though the results were promising it was unable to unravel the prime attributes that would eventually identify the disease.
Feature Extraction Methods by Various Concepts using SOM
Authors: Jackulin T. , Subramani Kavitha , Sheela L. Jaba , Vinmathi M.S. , Selvi S.
Published: December 2023

Image retrieval systems gained traction with the increased use of visual and media data. It is critical to understand and manage big data, lot of analysis done in image retrieval applications. Given the considerable difficulty involved in handling big data using a traditional approach, there is a demand for its efficient management, particularly regarding accuracy and robustness. To solve these issues, we employ content-based image retrieval (CBIR) methods within both supervised , unsupervised pictures. Self-Organizing Maps (SOM), a competitive unsupervised learning aggregation technique, are applied in our innovative multilevel fusion methodology to extract features that are categorised. The proposed methodology beat state-of-the-art algorithms with 90.3% precision, approximate retrieval precision (ARP) of 0.91, and approximate retrieval recall (ARR) of 0.82 when tested on several benchmark datasets.
An Empirical Survey on Various Power Transfer Techniques in Electrical Vehicle using Wireless Mode
Authors: Sreedevi S.L. , Geetha B.T.
Published: December 2023

This study examines the current condition of the wireless power transfer (WPT) techniques in electric vehicle. The pros and downsides of wireless power transfer are explored, as well as its   classification, application, trend, benefits and impact on society. It also offers a comparison of prior research in transfer the power wireless, pointing out upcoming different kind of method, topologies, statement, and optimization methods implemented for boost the efficiency of performance of the electric vehicle system and directing researchers in the appropriate direction for future research.
XAI Applications in Medical Imaging: A Survey of Methods and Challenges
Authors: Tulsani Vijya , Sahatiya Prashant , Parmar Jignasha , Parmar Jayshree
Published: December 2023

Medical imaging plays a pivotal role in modern healthcare, aiding in the diagnosis, monitoring, and treatment of various medical conditions. With the advent of Artificial Intelligence (AI), medical imaging has witnessed remarkable advancements, promising more accurate and efficient analysis. However, the black-box nature of many AI models used in medical imaging has raised concerns regarding their interpretability and trustworthiness. In response to these challenges, Explainable AI (XAI) has emerged as a critical field, aiming to provide transparent and interpretable solutions for medical image analysis. This survey paper comprehensively explores the methods and challenges associated with XAI applications in medical imaging. The survey begins with an introduction to the significance of XAI in medical imaging, emphasizing the need for transparent and interpretable AI solutions in healthcare. We delve into the background of medical imaging in healthcare and discuss the increasing role of AI in this domain. The paper then presents a detailed survey of various XAI techniques, ranging from interpretable machine learning models to deep learning approaches with built-in interpretability and post hoc interpretation methods. Furthermore, the survey outlines a wide range of applications where XAI is making a substantial impact, including disease diagnosis and detection, medical image segmentation, radiology reports, surgical planning, and telemedicine. Real-world case studies illustrate successful applications of XAI in medical imaging. The challenges associated with implementing XAI in medical imaging are thoroughly examined, addressing issues related to data quality, ethics, regulation, clinical integration, model robustness, and human-AI interaction. The survey concludes by discussing emerging trends and future directions in the field, highlighting the ongoing efforts to enhance XAI methods for medical imaging and the critical role XAI will play in the future of healthcare. This survey paper serves as a comprehensive resource for researchers, clinicians, and policymakers interested in the integration of Explainable AI into medical imaging, providing insights into the latest methods, successful applications, and the challenges that lie ahead.
Forecasting Liver Disorders with Machine Learning Models
Authors: Singh Pragati , Yadav Ashok Kumar , Gangwar Sanjeev
Published: December 2023

Liver disorders encompass a spectrum of ailments that impact the liver, a crucial organ responsible for a variety of vital bodily functions. These functions encompass metabolic processes, detoxification, protein synthesis, and the production of bile. Liver maladies can arise from various sources, such as viral infections (e.g., hepatitis), excessive alcohol consumption, conditions related to obesity (like non-alcoholic fatty liver disease), autoimmune conditions, genetic predisposition, or exposure to toxins. Common signs and symptoms may encompass fatigue, jaundice, abdominal discomfort, and digestive problems. In our study, we gather data and employ five distinct machine learning classification algorithms: Random Forest, Decision Tree, Naïve Bayes, K-Nearest Neighbor, and XG Boost. After constructing models and evaluating their performance, we observed that XG Boost achieved an impressive accuracy rate of 99.8%.
A Multimodal Approach for Detecting AI Generated Content using BERT and CNN
Authors: Vismay Vora Et al.
Published: December 2023

With the advent of Generative AI technologies like LLMs and image generators, there will be an unprecedented rise in synthetic information which requires detection. While deepfake content can be identified by considering biological cues, this article proposes a technique for the detection of AI generated text using vocabulary, syntactic, semantic and stylistic features of the input data and detecting AI generated images through the use of a CNN model. The performance of these models is also evaluated and benchmarked with other comparative models. The ML Olympiad Competition dataset from Kaggle is used in a BERT Model for text detection and the CNN model is trained on the CIFAKE dataset to detect AI generated images. It can be concluded that in the upcoming era, AI generated content will be omnipresent and no single model will truly be able to detect all AI generated content especially when these technologies are getting better.
Vehicle Density Detection Using Hybrid SSD-Yolo-V4 Model
Authors: G. T. Prabavathi M. Kavitha,
Published: December 2023

Vehicle traffic congestion is a serious problem in the present day. An intriguing field of study, traffic density estimate is utilized to regulate traffic light systems for more efficient traffic management. The low data resolution and extensive vehicle identification both make this endeavor a more challenging task. This article proposes a vehicle density detection technique using a hybrid deep learning model. The selected data frames from vehicle dataset are denoised using non-adaptive threshold approach. Denoised pictures are segmented using an upgraded Prewitt algorithm and then enhanced using the CLAHE (Contrast Limited Adaptive Histogram Equalization) approach. Then the important features are selected with the use of the Convolutional neural network (CNN) algorithm. The dataset is trained with Deep Q-Learning (DQN) with Improved Spatial Pyramid neural network (I-SPPNET) architecture. Finally, vehicle density identification is done using Improved Single Shot Detector (SSD) with You Only Look Once (YOLOv4) version 4 model and the obtained results proved to be significantly better than CNN, CNN with I-SPPNET and YOVOv3.
Review Paper on Enhancing COVID-19 Fake News Detection With Transformer Model
Authors: Rachit Adhvaryu Kamal Sutaria,
Published: December 2023

The growing propagation of disinformation about the COVID-19 epidemic needs powerful fake news detection technologies. This review provides an in-depth examination of existing techniques, including traditional machine learning methods such as Random Forest and Naive Bayes, as well as sophisticated models for deep learning such as Bi- GRU, CNN, and LSTM, RNN, & transformer-based architecture such as BERT and XLM- Roberta, are also available. One noticeable development is the merging of traditional algorithmswith sophisticated transformers, which emphasize the quest of improved accuracy and flexibility.However, important research gaps have been identified. There has been little research on cross- lingual detection algorithms, revealing a substantial gap in multilingual false news detection, which is critical in the global context of COVID-19 information spread. Furthermore, the researchemphasizes the need of flexible methodologies by emphasizing the need for appropriate preprocessing strategies for various content types. Furthermore, the lack of common assessment measures is a barrier, underlining the need of unified frameworks for successfully benchmarking and comparing models. This analysis provides light on the changing COVID-19 false news detection environment, emphasizing the need for novel, adaptive, and internationally relevant approaches to successfully address the ubiquitous dissemination of disinformation during the current pandemic.
Identification of Crime using Multi Embedding BiLSTM
Authors: V. Joseph Peter S. Jeya Selvakumari,
Published: December 2023

Crimes pose significant societal challenges with implications for a nation's well-being, economic progress, and reputation. Precisely measuring crime rates, categories, and hotspots from historical patterns presents various computational complexities and opportunities. This study introduces and improves a deep learning approach for predicting crime types with high precision. The system can predict both crime categories and associated risk levels by analyzing concise summaries from criminal case reports. The predictive model is built on a neural network with LSTM and Bi-LSTM components, demonstrating remarkable accuracy in forecasting crime types despite limited training attributes. It is tested on a substantial real-world dataset containing historical urban crime data, offering a deep learning-based solution to enhance public safety in the face of criminal activities.
Real Coded Binary Artificial Bee Colony (RC-BABC) Based Feature Selection and Relieff Based Feature Extraction Techniques for Heart Disease Prediction
Authors: S.V.N. Srinivasu, R. Priya Venkateswarlu Tata, R. Bhavani,
Published: December 2023

Diagnosing heart disease is really a challenging task for which several intelligent diagnostic systems were developed for enhancing the performance of diagnosing heart disease. However, in these systems, low accuracy of predicting heart disease is still a challenging task. To provide better accuracy in predicting heart risks, a novel feature selection approach is proposed which employs Real Coded Binary Artificial Bee Colony (RC-BABC) optimization algorithm with adaptive size for feature elimination. This method has the advantages of reducing algorithmic computational time, improving prediction accuracy, enhanced data quality, and saves resources in successive data collection phases. Once the features are selected, the important feature extraction phase uses ReliefF based feature extraction method to extract the features from the heart disease data set. The scores of features are computed by estimating a comparison of feature values and class values neighbor samples. The proposed Real Coded Binary Artificial Bee Colony (RC-BABC) optimization algorithm is compared with three well known methods namely an artificial neural network (ANN), K-means clustering approach and Classification and Regression Algorithm (C&RT) with measures like accuracy, precision, recall and F1-score. The proposed method achieved 96.77% of accuracy,98.8% of recall, 97.8% of precision and 98.34% of F1-score.
PP-Bridge: Establishing a Bridge between the Prefetching and Cache Partitioning
Authors: Bishwa Ranjan Roy Purnendu Das, Nurulla Mansur Barbhuiya,
Published: December 2023

— Modern computer processors are equipped with multiple cores, each boasting its own dedicated cache memory, while collectively sharing a generously sized Last Level Cache (LLC). To ensure equitable utilization of the LLC space and bolster system security, partitioning techniques have been introduced to allocate the shared LLC space among the applications running on different cores. This partition dynamically adapts to the requirements of these applications. Prefetching plays a vital role in enhancing cache performance by proactively loading data into the cache before it get requested explicitly by a core. Each core employs prefetch engines to decide which data blocks to fetch preemptively. However, a haphazard prefetcher may bring in more data blocks than necessary, leading to cache pollution and a subsequent degradation in system performance. To maximize the benefits of prefetching, it is essential to keep cache pollution to a minimum. Intriguingly, our research has uncovered that when existing prefetching techniques are combined with partitioning methods, they tend to exacerbate cache pollution within the LLC, resulting in a noticeable decline in system performance. In this paper, we present a novel approach aimed at mitigating cache pollution when combining prefetching with partitioning techniques.
Optimization of Intelligent Transportation System using Biologically-Inspired Vehicular Ad hoc Networks for Achieve the Desired Performance
Authors: Sivaraman K V. M. Niaz Ahamed,
Published: December 2023

Many innovations made possible by the Intelligent Transportation System (ITS), such as media apps, encrypted financial transactions, and effective traffic management, rely heavily on vehicular ad hoc networks (VANETs). Using bio-inspired methodologies, This study looks back at the past and forward to the future to examine all of the routing challenges in VANETs, whether they are associated with a chain of related routing tasks or are aimed at a group of distinct approaches to routing. The high node mobility and unpredictable vehicle distribution (on the road) lead to major issues for VANETs, including the design of a network's physical architecture and unstable connections. VANET's provision of reliable and appropriate vehicular contact in situations requiring good service is crucial. As a result, effective means of navigation are desperately needed in VANET. Hence, in this paper, we examine the Bio-Inspired vehicular ad hoc networks (Bio-VANETs), wherein, should a suggested algorithmic network fail at any given node or vehicle, the remaining vehicles may be able to take over the task of relaying the data to the necessary nodes to achieve the desired performance. Route lifetime increases, and connection failures are decreased when the shortest way is selected using the fewest possible hops over highly connected links. In addition, the received signal intensity fluctuations due to vehicle density and speed are assessed. Packet Delivery Ratio, Optimal Performance, Accuracy and Efficiency of Bio-VANET are discussed and simulated against other methods that are existing models.
An Inclusive Report on Robust Malware Detection and Analysis for Cross-Version Binary Code Optimizations
Authors: R. Mahalakshmi S. Poornima,
Published: December 2023

Numerous practices exist for binary code similarity detection (BCSD), such as Control Flow Graph, Semantics Scrutiny, Code Obfuscation, Malware Detection and Analysis, vulnerability search, etc. On the basis of professional knowledge, existing solutions often compare particular syntactic aspects retrieved from binary code. They either have substantial performance overheads or have inaccurate detection. Furthermore, there aren't many tools available for comparing cross-version binaries, which may differ not only in programming with proper syntax but also marginally in semantics. This Binary code similarity detection is existing for past 10 years, but this research area is not yet systematically analysed. The paper presents a comprehensive analysis on existing Cross-version Binary Code Optimization techniques on four characteristics: 1. Structural analysis, 2. Semantic Analysis, 3. Syntactic Analysis, 4. Validation Metrics.  It helps the researchers to best select the suitable tool for their necessary implementation on binary code analysis. Furthermore, this paper presents scope of the area along with future directions of the research.
Breast Tumor Recognition by Semantic Segmentation of Multiclass Ultrasound Images
Authors: Sanjay K Gupta Satish Bansal, Rakesh S Jadon,
Published: December 2023

Objectives: The main purpose of this paper is to suggest a semantic segmentation model to reduce training time in ultrasound breast cancer images. This is achieved by employing a smaller network with fewer trainable parameters, resulting in faster training while maintaining maximum accuracy.

Methods: This paper proposes a modified U-Net model, which we call the V model, for the subdivision of breast tumors. The proposed V architecture is applied explicitly to ultrasound breast cancer datasets for semantic segmentation. Our proposed model achieves semantic segmentation by employing an encoder and decoder on real and mask image datasets.

Findings: Therefore, developing a proposed system, namely a V-Net computer-aided diagnosis (CAD) system, is imperative. This CAD system aims to minimize human errors while enhancing accuracy and speed in the premature finding of breast tumors. The proposed model utilizes minimal layers and parameters while maintaining superior results regarding correctness, speed, and computational proficiency.

Novelty: The proposed V-net model applies to analysing any medical image for detecting disease and finding more accuracy than other U-net models.
Reducing CPU Utilization & Improving Failover Time in Dual Controller SDN (Software Defined Network) Environment
Authors: R. Sivakumar S. Annie Christila,
Published: December 2023

A salient component of the network is failover. Largely, customers are accessing dual path controller software defined network (SDN) environment.  Subsequently, when one controller fizzles out the network communication can persist as a result of the other controller.   There will be no interruption in the network communication due to the failover setup which will be effective in case of failure of a controller.  In the absence of failover, the network communication will be obstructed.  This could be by cause of the system resources, system itself or routing policies or Distributed Denial of Services.  Generally, controller will support many switches. When SDN controller collapses controller will not be in sync with switches. After few attempts switches will failover to the secondary SDN controller. During this shutdown time the network communication will be obstructed.  Failover algorithm is the key to solve this issue and will facilitate the synchronization of the information with SDN controllers and enhance failover time.  Apparently, communication between both SDN controllers and switches is enhanced as well as reinforces the time of synchronization due to multiple SDN controllers.  CPU utilization is another problem in SDN environment as the new inflow packets need to be forwarded to control plane to find the action that need to be taken. Processing at both ends and having one to one mapping between new incoming connection requests and the packet from data plane to control plane increases the CPU utilization and there by more power consumption.
A Systematic Study on the Development of Public Cloud Systems
Authors: Neerav Nishant Et al.
Published: December 2023

The term "cloud computing" refers to a group of resources and services made available online. Internet-based computing lets devices and computers access shared resources, software, and information. Google applications, a product of Google and Microsoft Share Point, are a typical example of cloud services. A collection of information technology services known as "cloud computing" are offered to a client across a network on a leased basis with the flexibility to scale up or down the level of service needed. In order to demonstrate how cloud computing will make the corporate world easier, more efficient, and more specialized, its qualities will be presented with a number of examples in this article. We'll also define different cloud computing settings, cloud computing style, etc. The goal of this study was to ascertain if cloud computing might be used for learning and what advantages it offers in the creation of learning systems. Information technology advancement is now a creative, dynamic, and financially successful answer. Information technology has the answers to the problems and challenges the educational industry is now experiencing. Cloud computing is changing the way that information technology services are provided and made available to institutions so that they can have access to scientific and educational data. With several benefits over traditional systems, cloud computing technology offers a new paradigm in the delivery of computer services. This technology has recognized the significance of the service-oriented idea and developed a new system in the computer industry thanks to its impact and benefits. The potential of cloud computing has allowed us to advance the IT industry by one step. Large and well-known companies are now using cloud computing for their processing and storage. In this essay, we present a general overview of cloud computing and call attention to its advantages.
Decision Support Systems for Coastal Resilience: The Role of Early Warning Systems and Validation
Authors: Ramakrishna Phani Pammi Et al.
Published: December 2023

This study presents an advanced Integrated Ocean Forecast Decision Support System (DSS) crafted to elevate ocean forecasting capabilities and enable well-informed decision-making, with a specific focus on the Indian Ocean region. The DSS has a visualization module for scientific inference and spatial analysis. It also has a validation component where each weather model is checked against observations from coastal buoys, open ocean buoys, and ASCAT and SCATSAT satellite data. The system integrates the operational forecasts and observation data and automatically informs the users of the best-correlated model. In cases of extreme conditions like cyclones, the system automatically pulls information from some of the meteorological agencies in real-time for further assessment and refining the models. With the forecasts and observations at its disposal, the DSS suggests the most accurate model to be taken into consideration when issuing alerts. Metrics like the scatter index, correlation coefficient, and root mean square error are displayed on a web interface to generate recommendations. This system facilitated decision-makers in issuing alerts for high winds, elevated waves, and other conditions, particularly during various cyclones in the Arabian Sea and Bay of Bengal, including notable events such as Tauktae and Gulab. The implementation details, the handling of extreme events, and the process of informed decision-making are thoroughly discussed in detail.
Learning Model for Pandava Five Puppetry Digital Creation: A Digital Literacy and Transformation of Traditional Characters Approach
Authors: Warli Haryana Et al.
Published: December 2023

The rapid evolution of information and communication technology has triggered a transformative revolution in contemporary society, impacting traditional Indonesian arts, notably Purwa Pandava Five shadow puppetry, leading to its decline among the younger generation. This research pioneers an innovative approach to revive the Purwa Pandava Five culture, targeting 50 fine arts students. The goal is to establish a digital literacy-based creative learning model in higher education, fostering an understanding of the aesthetics and moral fabric of Purwa Pandava Five shadow puppetry. Through a concurrent mixed-methods research design, 50 fine arts students craft digital puppetry works resonating with millennial comprehension. Data collection involves qualitative and quantitative methods such as interviews, surveys, and artistic assessments. The outcomes aim to ignite the younger generation's interest in traditional Indonesian arts, anticipating the evolution of fine arts programs in higher education to be responsive to changing cultural and technological dynamics. The aesthetic value and character of the Pandava Five shadow puppetry puppet are reflected through dramatic scenes and positive traits. Learning integrates creativity, digital literacy, and the transformation of Purwa Pandava Five shadow puppetry, creating innovative digital puppetry with a unique Pandava Five story interpretation. Implementing a digital creation learning model yields positive results, allowing students to collaboratively understand Pandava Five shadow puppetry's aesthetic and moral values. This stimulates millennial interest in the cultural heritage of Purwa Pandava Five shadow puppetry, producing works that blend traditional aesthetics with modern elements through digital literacy and advanced technology. The principles balance tradition and modern technology, preserving the visual authenticity of Purwa Pandava Five shadow puppetry while focusing on narrative and moral aspects. These outcomes aim to engage the younger generation in comprehending and safeguarding traditional Indonesian arts, with an anticipated evolution of fine arts programs in higher education to meet changing cultural and technological dynamics.
The Relationship of E-Learning Intensity to Improve students' Critical Thinking Ability
Authors: Mahdiyah Et al.
Published: December 2023

The potential of e-learning to facilitate the cultivation of critical thinking abilities remains a subject of inquiry, as the precise nature of the association between the intensity of e-learning engagement and the development of critical thinking skills has yet to be comprehensively elucidated. The objective of this study is to examine the correlation between the level of engagement in e-learning and the development of critical thinking skills among students engaged in online education. The present study aimed to examine the correlation between the level of engagement in e-learning activities and the development of critical thinking abilities within a cohort of 210 students. The measurement of e-learning intensity was conducted by quantifying the weekly duration of engagement with the e-learning platform. The assessment of critical thinking skills was conducted through the utilization of the Critical Thinking Questionnaire (CThQ). The findings of the study revealed a statistically significant and favourable association between the level of engagement in e-learning activities and the development of critical thinking abilities. This suggests that students who spend more time using e-learning platforms tend to have higher levels of critical thinking skills.
Research on Factors Influencing the Quality of Talent Cultivation in Music Majors in Higher Education
Authors: Lulu Sun Et al.
Published: December 2023

This study aims to assess the current status of music professional talent cultivation quality in universities. It seeks to integrate factors influencing the quality of music talent cultivation, including the interplay between quality indicators, cultivation mechanisms, and autonomy. A theoretical model for the quality of music talent cultivation is proposed based on a sample of 1988 students from 16 undergraduate universities with music majors in Shaanxi Province. The study utilizes mathematical statistics and SPSS for data analysis, focusing on questionnaire reliability. Confirmatory factor analysis uses AMOS24.0 software, and model fit testing is performed against established indicators. The research findings reveal that the theoretical model aligns with the measured data, meeting fitting standards. Descriptive analysis and correlation testing show positive correlations between variables. Univariate analysis of variance based on majors, ages, and regions indicates significant differences. The study highlights the positive impact of understanding influencing factors on practical efforts to enhance the quality of music talent cultivation in universities. It recommends innovative reforms, such as expanding major choices, developing personalized curricula, and adopting tech-supported teaching methods. Strengthening management systems, effective supervision, and enhancing quality standards are emphasized for improving music education and talent cultivation, fostering overall progress and reform in this field.
Optimizing Rooting Media for Enhanced Propagation of Guava (Psidium Guajava L.) Through Stem Cuttings
Authors: Prabhat Kumar  , Gurpreet Singh  , Gaurav Tomar  , Tanuja Tomer  , M. Sankar
Published: December 2023

Guava (Psidium guajava L.) is a significant tropical fruit crop that is primarily propagated through stem cuttings. The success of propagation through stem cuttings largely depends on the rooting medium used. Therefore, optimising the rooting medium is crucial for enhancing the success rate of guava propagation. This study was conducted in the Himalayan foot hill condition in the years 2021 and 2022 at Indian Institute of Soil and water conservation, Selaqui Dehradun, aimed to identify the optimal rooting medium for guava propagation through stem cuttings. The experiment tested three rooting media: sand (S), sawdust (SD), and a combination of the two, sand and sawdust (SSD), using semi-hardwood stem cuttings from guava trees. The study found that the SSD medium had the highest rooting success rate (94.5%), followed by Sand (87.5%) and Saw Dust (70%). The experiment suggests that semi-hardwood stem cuttings of guava roots are best in sawdust and sand, and this finding could contribute to enhancing the propagation of guava through stem cuttings.
Itraconazole Self-Nano Emusifying Drug Delivery System for Enhancement of Solubility
Authors: Shrishail M Ghurghure  , Priya L. Ingale  , Anup A. Dhange  , Akshay S Javalgikar  , Rudramuni H Kore  , Dadagouda M Birajdar
Published: December 2023

The main objective of formulation is to enhance the bioavailability of the drug within the body. Some of the challenging subjects associated with poorly water-soluble drugs concern solubility and bioavailability factors. To overcome these problems, new technologies, such as lipid-based drug delivery systems including micro or nano emulsifying drug delivery system, have obtained importance in recent years, due to their enhanced solubility and bioavailability in the gastrointestinal tract. Such systems are solubilized within the lipid excipients or mixed with oils or surfactants/co-solvents to facilitate the solubility and absorption rate, which can enhance the bioavailability of the targeted drug. The research was targeted to develop a self-Nano Emulsifying drug delivery system (SNEDDS) for oral bioavailability enhancement of BCS II antifungal drug Itraconazole. Nano Emulsions are consignment methods that improve the solubility and distribution of lipid medicines to the intended areas. SNEDDS of Itraconazole were developed using Castor oil, Tween 20 as surfactant and PEG 200 as co-surfactant. SNEDDS formulation can be obtained through phase diagram approach. Pseudo Ternary phase diagrams were constructed, using Chemix software, to optimize the concentrations of excipients. Thermodynamic stability studies were satisfactory. Robustness to dilution did not exhibit phase separation and drug precipitation. The Among 4 formulations, B1F3 formulation showed more than 95% of drug content and were considered superior and subjected to droplet size analysis and zeta potential measurement. Droplet size ranged from 120 nm to 505 nm. The Nano size was obtained for formulation. The zeta-potential results indicated the range -35 mV. Based on all evaluation tests, formulation B2F3 was chosen as the best. Thus, this self-Nano Emulsifying drug delivery system should be an effective oral dosage form for improving oral bioavailability of lipophilic drug Itraconazole.
Review on Workplace Spirituality
Authors: Lokeshwari V  , G Latha
Published: December 2023

The Corona Pandemic was a time to learn and grow for people and organizations all around the world.  The organizations understood the importance of efficient and innovative management techniques. Certain organizations were in fact thriving, with customers and employees valuing the organizations. despite the lower sales and insufficiency of funds. they were ready to forfeit a discount, pay more than the actual price and share their income to pool resources for the organization. Researchers found that organizational commitment was more in such organizations. On further analysing the reviews of researchers, it was observed that organizational spirituality started from an individual, usually the leader, and quickly became the organization’s culture. There was an increase in workplace joy, health, commitment, creativity, security and a feeling of home within the organization. In this paper, we have attempted to summarize the concept of workplace spirituality from various original papers which were published on ‘Web of science’ database. The idea is to have a complete and concise understanding of the term workplace spirituality.
Study of Impact of Dietary Factors on Prevalence of Colon Cancer
Authors: Rupankar Barik  , Pritha Pal
Published: December 2023

The third most frequent cancer worldwide is colorectal cancer. Globally, 1.5 million new cases of colorectal cancer were reported in 2023, making up 10% of all new cancer cases. According to estimates, colorectal cancer is the fourth most prevalent cancer-related cause of death.be accountable for about 700,000 cancer-related deaths. Survival from colorectal cancer depends on the stage of the disease, with later-stage diagnoses having a worse prognosis. 90% of colorectal cancer patients who receive an early diagnosis survive five years. Western Africa has the lowest estimated rates, while Australia and New Zealand have the highest. Men and women worldwide experience colorectal cancer in similar ways. More than 2.2 million colorectal cancer cases are anticipated to be diagnosed during the next 15 years, a 60% rise. It is one of the malignancies whose incidence is rising globally. The diet has a direct connection with the colon cancer because inflammation in the bowels and gut is influenced by what we consume, and inflammation is a risk factor for the development of colorectal cancer. Researchers have identified sugar, animal fats, red and processed meats as the key food ingredients that produce inflammation in the body and may raise the risk of colon cancer.