bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessing the effectiveness of special education services on fifth grade math scores: Using traditional and machine learning methods with ECLS-K data
Authors: Feng Yuxiang
Published: July 2024

The Early Childhood Longitudinal Study, Kindergarten Class of 1998-99 (ECLS-K) is a well-known research endeavor in the field of child development. In this research, some special education services are offered to those students who need supplementary support in some aspects. In this paper, our study aims to estimate the average treatment effect on students fifth grade math scores and assesses the effectiveness of these special education services based on the ECLS-K dataset, through both machine learning methods and traditional methods. We introduce Donald Rubins causal model and Propensity Score Analysis in the part of traditional methods, and Ordinary Least Squares (OLS), Targeted Maximum Likelihood Estimation (TMLE), Bayesian Additive Regression Trees (BART), Generalized Random Forests (GRF) and Double Machine Learning (DML) in the part of machine learning methods. Finally, we employ Propensity Score Matching, OLS and BART to estimate the ATE. All estimated ATEs are significantly different from zero. The estimated ATEs are found to be minus, suggesting that these special education services may have a negative effect on students fifth grade math scores. Obviously, this conclusion is inconsistent with the original intent of these services, which aimed to have a positive impact.
Remote sensing image scene classification based on ResNeSt
Authors: Yang Zimo
Published: July 2024

The study used the deep learning method to achieve the natural scene classification of remote sensing images, which were taken by the satellite Tiangong-2. Because of the diversity of remote sensing images, a Convolutional Neural Network (CNN) model that can complete the task of classifying natural scenes of remote sensing images was constructed using the variant ResNeSt based on the Residual Neural Network (ResNet). The NaSC-TG2 remote sensing image dataset released by the Space Application Engineering and Technology Center of the Chinese Academy of Sciences was used in this work. The dataset consists of 20,000 photos that are grouped into ten scene groups on average, with 2,000 images per scene category. And nine models including ResNet50, ResNet101, ResNet200, SE-ResNet50, SE-ResNeXt50, SE-ResNeXt101, SE-ResNeXt152, ResNeSt50, ResNeSt101 and ResNeSt200 were compared and tested on the NaSC-TG2 dataset. After training and testing on the dataset, ResNeSt101 achieved better results than other comparative models in the end, with the highest accuracy of 98.52% on the testing sets. This study offers a technique for categorizing remote sensing picture scenes and has made some significant contributions to space geoscience and application research.
Detection of malicious encrypted communication based on feature engineering and machine learning
Authors: Ouyang Zhiting
Published: July 2024

With the advancement of network technology, malicious encrypted communication has become a covert network threat and has drawn significant attention in the field of cybersecurity. Network threats are increasingly severe, and traditional detection methods struggle to cope with the intricate changes in malicious encrypted communication. Therefore, in order to find effective approaches to detect malicious encrypted communication, this research focuses on exploring the detection methods of malicious encrypted communication based on feature engineering and machine learning. The crucial role of feature engineering and the application of machine learning methods in this context are extensively discussed. The research conclusions indicate that the proper design of feature engineering and method selection can improve the detection accuracy and efficiency. However, practical applications still face challenges such as data scarcity and limited computational resources. Therefore, future research directions are proposed, including further optimizing feature engineering methods, developing feature representations more suitable for detecting malicious encrypted communication, and exploring more efficient deep learning models. The significance of this research lies in providing theoretical guidance and practical application advice for professionals and researchers in the field of cybersecurity, contributing actively to building a safer and more stable network environment, and jointly safeguarding the security and stability of the digital world.
AI-based text-to-image synthesis: A review
Authors: Wang Zili
Published: July 2024

The traditional methods of art generation, such as texture synthesis and texture mapping, have been instrumental in crafting digital art for decades. They are used as artistic tools to design and map textures onto 3D models, thereby generating 2D images or animations. However, they can only generate simple, repetitive images. Thanks to the rapid development of deep learning and artificial intelligence, todays text-to-image synthesis (T2IS) models can generate high-quality, realistic images matching the textual description given by the users. This review paper aims to present a comprehensive exploration of groundbreaking AI-based T2IS models in history. We start with an in-depth analysis of the fundamental concepts that underpin T2IS models, followed by an introduction to the primary, or vanilla, models that have served as the foundation for the fields development. Then, we delve into the examination of several groundbreaking AI-based T2IS applications, from GAN-based to Diffusion-based models, demonstrating their ability to produce high-quality, contextually accurate images from textual descriptions, along with their strengths and weaknesses. In the end, we will discuss the current challenges and potential future directions in the realm of T2IS.
Accelerating Neural Radiance Field by network pruning
Authors: Dong Deng Yu
Published: July 2024

Novel view synthesis, a crucial problem in computer graphics with extensive applications, has gained significant attention due to its increasing demand in fields such as autonomous driving, virtual reality, film, and the visual arts. Recent advancements in machine learning have led to progress in generating photo-realistic imagery, particularly with the Neural Radiance Fields (NeRF) method. Despite its impressive results, NeRF requires high computational resources for training and synthesis, impeding its real-world applications. This work aims to investigate the potential of network pruning to reduce NeRFs computational complexity without compromising the quality of the generated images, addressing the limitations of the original model and paving the way for more efficient view synthesis techniques. We propose a novel pruning method tailored for NeRF, Coarse-Fine-Pruning (CFP). We note that the NeRF framework is divided into two parts: a coarse network that learns the global structure of the scene and a fine network that learns the local details. Crucially, our experiments show that the coarse and fine networks have different tolerance to pruning. As such, our method adapts the pruning rate to each network, achieving a higher compression rate without compromising the quality of the generated images. We evaluate CFP and show that it can reduce the number of parameters by 90% while maintaining the same level of image quality.
Federated learning-based neural network for hotel cancellation prediction
Authors: Hang Yifei
Published: July 2024

Hotel reservations have become a prevalent choice for customers. However, cancellations of these reservations present a significant challenge for hotels, potentially resulting in financial losses and a decline in customer satisfaction. To address the issue of improper management of cancellations and minimize losses, machine learning can be employed to analyze and predict cancellations based on customer information. In cooperative scenarios where hotels collaborate to train a unified model, traditional algorithms that aggregate all data raise concerns about the protection of sensitive customer information. In this context, federated learning emerges as an effective solution to ensure the privacy protection of customers while achieving the desired predictive outcomes. Thus, to protect customer privacy while preserving performance of the federated learning model in comparison to the non-federated version, this work proposed to implement both federated learning and non-federated algorithms based on neural network to make predictions of cancellation based on multiple factors. The federated learning approach achieved a final testing accuracy of 76.64%. Although this accuracy was about 9% lower than the non-federated case, its loss was over half the loss of non-federated, and its testing accuracy was similar to the training accuracy, while the non-federated algorithms testing accuracy was approximately 4% lower than the training one. Such results indicate that although accuracy was relatively lower, the federated learning approach prevented the overfitting problem in the non-federated case, while the data privacy problem was resolved.
SRGAN and CNN integration for enhanced chest CT diagnostics
Authors: Tan Mengliang , Wang Haoyang , Zhang Wentao
Published: July 2024

Within the domain of Convolutional Neural Networks, there exists no prior application of ESRGAN for the pre-processing of CT images aimed at CNN diagonalization. The quality of CT scan images holds a direct correlation with the precision of diagnoses. Presently employed methods to enhance image quality are antiquated and have yielded less favorable outcomes. Our objective is to amalgamate ESRGAN, renowned for its prowess in enhancing image resolution, with CNN, in pursuit of a more effective and precise diagnostic procedure. In this project, we first gather datasets from the internet. And then in order to test if ESRGAN can actually improve the functionality of CNN, we preprocess images to make images in the dataset blurry. This provides a dataset served for comparison. Later, we will use ESRGAN model from the internet to improve the quality of blurred images and test CNN on both of them for comparison. As a result, using Enhanced ESRGAN can help improve the accuracy of detecting whether there is a disease in the CT images.
Personalized product recommendations based on deep learning
Authors: He Jiachang
Published: July 2024

Offline commodity sales efficiency is low, which brings a lot of inconvenience to people. Fortunately, with the progress of information technology, online shopping platforms have become popular. Upon accessing shopping platforms, myriad products spanning household appliances, groceries, apparel, and electronics become instantly available. The variety of goods on the online shopping platform cant be directly contacted, which increases the difficulty of the user's choice of goods. In addition, it is difficult for online merchants to recommend suitable products to customers in the way of offline communication. To solve this problem, this paper proposes a product recommendation model based on deep learning. This model uses consumer preferences, historical shopping data, and product characteristics to calculate personalized recommendation scores. Such scores expediently guide consumers to products that align with their specific requirements, enhancing the overall purchasing experience. At the same time, it also improves the sales efficiency of merchants.
Research on metal image defect detection based on convolutional neural network, random forest model and support vector machine model
Authors: Tao Yi
Published: July 2024

Metal image defect detection is a very important task, which can help manufacturers to detect defects in metal products in time during the production process, so as to improve product quality and production efficiency. This paper presents a private dataset containing images of six different types of metal surface defects, including scratches, pits, indentations, bubbles, oil stains, and aluminum surface defects. The dataset contains 1800 images, 300 images for each defect type, each with a size of 200x200 pixels. The dataset is divided into training, validation and test sets in the ratio of 6:2:2. By using Support Vector Machine Model, Random Forest Model and Convolutional Neural Network Model for prediction, we obtained the following results: the Support Vector Machine Model has the best prediction ability with 89% accuracy; the Random Forest Model has 69% accuracy; and the Convolutional Neural Network Model has poor prediction ability with 26% accuracy. From these results, it can be seen that the support vector machine model is one of the most suitable models for defect detection in metal images. This model can effectively identify various defects on metal surfaces, thus helping manufacturers to better control the quality of their products. In comparison, the predictive power of the Random Forest model is weak, but it can still be used for some simple metal image defect detection tasks. The convolutional neural network model has the worst predictive ability, but it is widely used in other fields, such as image recognition and speech recognition. In conclusion, metal image defect detection is a very important task, which can help manufacturers find the defects of metal products in the production process in time, so as to improve product quality and production efficiency. In practical applications, we can choose different algorithms and models to realize metal image defect detection in order to get the best results.
The investigation of application related to deep learning on brain tumor diagnosis
Authors: Zhao Shifang
Published: July 2024

Brain tumor has been a serious disease to human beings for a long time. Brain tumors have posed a significant health threat to humanity for many years. If left untreated in its early stages, a brain tumor can become malignant, drastically reducing survival chances. Throughout the decades, numerous individuals have endured the hardships of brain tumors, and tragically, some have succumbed to this condition. However, deep learning techniques offer a promising avenue for precise and efficient brain tumor diagnosis. Utilizing this technology enables the early detection and treatment of benign tumors, potentially saving lives and preventing unnecessary loss. In this review paper, two previous research on how different deep learning models perform on the brain tumor diagnosis would be illustrated. In the first research, the performance of five models would be compared with each other. In the second research, Convolutional Neural Network (CNN) and Artificial Neural Network (ANN) would be compared with each other. Furthermore, the examination of two research methods will delve into how various techniques can enhance model performance. Deep learning techniques also find numerous real-life applications. The two important applications are Home Diagnosis and In-Hospital Assistance, and the benefits of applying deep learning techniques in these two areas would also be illustrated. In addition, several suggestions would be proposed based on the applications of deep learning technique.
Machine learning-based readmission risk prediction for diabetic patients
Authors: Li Chenyang
Published: July 2024

Among the numerous hospitalized patients with chronic diseases, diabetes patients are under a higher readmission rate, which poses challenges and pressures to both patients and the healthcare system. To predict the likelihood of diabetic patients being readmitted within a short amount of time, this paper utilizes various machine learning-based models for performance analysis and comparison. By selecting appropriate datasets, cleaning and preprocessing data, the models were trained to forecast the probability of patient readmission. The paper compares the performance metrics of six classifiers: XGBoost, logistic regression, GBDT, decision tree, random forest, and deep neural network. The metrics include accuracy, f1 score, precision, recall, and ROC curve. Experimental results demonstrate that XGBoost exhibits better adaptability to complex data and achieves higher mean values of Accuracy (64.43%), f1 score (59.16%), recall (55.9%), and ROC (70.14%) in readmission prediction.
Federated learning-based machine learning for predicting brain tumor
Authors: Ge Yuheng
Published: July 2024

The swift advancements in artificial intelligence (AI) and machine learning have profoundly impacted the realm of medical research, particularly in the realm of diagnosing and treating intricate conditions such as brain tumors. These tumors, characterized by unregulated cell proliferation, pose significant challenges. The complexities inherent in brain tumor diagnosis stem from the intricate nature of these tumors, symptom overlap with other ailments, and the inherent complexity of the brain itself. Nevertheless, the application of an advanced machine learning algorithm known as Federated Learning (FL) has demonstrated its potential to address data privacy concerns and enhance diagnostic accuracy in this context. This essay discusses the application of FL which is a decentralized training strategy in brain tumor research. FL allows multiple institutions to train the model collaboratively without data sharing. The key advancement includes the improved U-Net model implementation and the utilization of Convolutional Neural Network (CNN) Ensemble Architectures for brain tumor identification. This paper also discusses the potential of FL in optimizing weight sharing for model aggregation in heterogeneous data. Furthermore, it underscores the important role of FL in modern healthcare since FL also solves the privacy concern in smart healthcare. However, challenges such as communication lag, data heterogeneity, and computational cost still exist.
Federated learning in autonomous driving: Progress, challenges, and outlook in perception, prediction, and communication
Authors: Xiang Zelong
Published: July 2024

In the ever-evolving field of autonomous driving, vehicles have evolved into mobile computing centres, accumulating and processing vast amounts of data, including environmental variables, driver behaviours, and preferences. Conventional centralized data processing methods face privacy and security vulnerabilities. To address these challenges, federated learning technology has emerged as a promising alternative, with its decentralized, privacy-preserving architecture. This review explores the application of federated learning in autonomous driving, focusing on perception, prediction, and communication scenarios, including research such as using federated learning to enhance the vehicles ability to predict steering angle, object detection, and multimodal sensor data fusion. In addition, this review investigates the improvement of communication efficiency through techniques such as Distributed Federated Learning (DFL), Selective Federated Reinforcement Learning (SFRL), and Vehicle-to-everything (V2X) communication. The analysis indicated that federated learning holds great promise in autonomous driving, significantly enhancing vehicle performance in perception, prediction, and communication. However, challenges like data heterogeneity and communication costs persist. Future research should prioritize refining aggregation algorithms, minimizing communication overhead, and adapting federated learning to evolving autonomous driving technologies.
Machine learning-based hotel occupancy prediction
Authors: Peng Jiana
Published: July 2024

The online booking system has tremendously facilitated people's ability to travel and make reservations because of the growth of the Internet. However, the unpredictability of travel led to frequent changes in reservations. Frequent order changes can lead to many problems, such as hotels not being able to get order changes in a timely manner and more in-demand customers not being able to stay, which can lead to lower profits and occupancy rates. In addition to this, there are a number of subjective, such as changes in the trip, reasons for work, and reasons for family, and objective, such as weather changes, natural disasters, and Transportation issues, factors that make it more difficult to predict the occupancy rate. In recent years, machine learning has become an increasingly valuable tool for researchers to analyze data. Based on these, this paper summarizes the machine learning algorithms related to hotel occupancy probability prediction and analyzes and compares them. Finally, it gives an outlook on the research of the hotel occupancy rate prediction.
The building and the test of campus intranet security
Authors: Weihong Gu
Published: July 2024

There will be many different students of different ages inside a campus and using their laptops or mobile phones. However, their personal information is very valuable for criminals. To prevent hackers from having access to students information, the campuss intranet should be well-secured. This paper is mainly focusing on how to build and measure the security level of a campuss intranet system from different approaches, including basic topology and advanced device configuration. The research will use concepts of basic red-team knowledge and basic penetration testing tools like NMAP, SQLMAP, Burp suite and KALI Linux. To make sure that all the security rules are operating as intended, a planned penetration test should be conducted. The plan will be discussed and some advice are given at the end of this paper. The main idea of this paper is to give some instructions for beginners to set up a large scale network and build the security system for it.
Policy Coordination Mechanisms in the Integration of the Yangtze River Delta Region
Authors: Yuan Chenchao
Published: July 2024

This paper aims to explore the current state, challenges, and optimization pathways of policy coordination mechanisms in the process of integrating the Yangtze River Delta (YRD) region. By analyzing the degree of coordination among various levels of government in policy formulation and implementation, this study evaluates its impact on achieving regional integration goals and proposes recommendations to promote policy coordination. The study finds that policy coordination mechanisms play a crucial role in achieving regional integration but face several challenges at present, including institutional barriers, conflicts of interest, and uneven resource distribution. Establishing a unified coordination agency, promoting the unification of laws and regulations, designing a reasonable benefit-sharing mechanism, and achieving integrated resource allocation can effectively promote policy coordination, thereby advancing regional integration development.
Research on the Dual-Layer Governance Model and Leadership and Coordination Bodies in Chinas Regional Integration with a Focus on the Yangtze River Delta Integration
Authors: Gao Jiaxin
Published: July 2024

This paper takes the Yangtze River Delta integration as the research object, deeply explores the application and importance of the dual-layer governance model in the process of Chinas regional integration. Through the analysis of the practice of Yangtze River Delta integration, it expounds the connotation and characteristics of the dual-layer governance model, as well as the key role played by the leadership and coordination bodies in it. Meanwhile, it analyzes the current challenges and problems, and puts forward corresponding countermeasures and suggestions, aiming to provide theoretical reference and practical guidance for promoting the development of Chinas regional integration.
International Interventions to Promote Reasoning Skills in Primary and Secondary Schools and Their Implications for China
Authors: Zhang Siduo
Published: July 2024

This article is written based on the lack of experimental interventions on various reasoning skills among primary and secondary school students in China. By comparing the content related to reasoning skills on the China National Knowledge Infrastructure (CNKI) with that from foreign sources, it is evident that research on intervention measures is more extensive internationally. Therefore, this article mainly focuses on searching for international interventions for reasoning skills in primary and secondary students, summarizing the main ideas and methods of these foreign measures to provide a theoretical basis for interventions in China. The article discusses the current state of research on reasoning skills in China and existing problems, defines reasoning skills based on curriculum standards, and includes definitions of subcategories by numerous researchers, including logical reasoning, deductive reasoning, inductive reasoning, analogical reasoning, and spatial reasoning. Regarding international intervention measures for reasoning skills, one or two typical examples are selected, and the research subjects, content of interventions, and results are organized. Considering China's national conditions, the article concludes with some measures and methods that could facilitate the development of reasoning skills among Chinese primary and secondary students.
Syiah di Mata Anak Muda: Antara Ketakutan dan Keterbukaan akan Keberagaman Agama
Authors: Myaskur Fazdad Masykur Annabil , G.A Zinnia Maulida H  , Wardhana Mahendra Kartika , Faridillah Najwa
Published: July 2024

Abstract
History, Beliefs, and Main Practices of Shia Islam, Shia Islam is a branch of the Islamic faith with followers around the world. Its origins lie in the early split of the Muslim community after the death of Prophet Muhammad, focusing on the belief that spiritual leadership should be directly inherited from the Prophet's descendants, particularly through Imam Ali and his progeny. Additionally, we discuss Shia theological principles, such as the concept of Imamah, which posits that their imams possess divine authority to interpret Islamic teachings. Worship practices and rituals are also a significant part of Shia life, including fasting, prayer, and pilgrimages to the shrines of the imams as integral components of their religious practices. This abstract also covers the debates surrounding the legitimacy of Shia beliefs and their relationship with the more dominant Sunni branch of Islam. Furthermore, we review the political and social impacts of Shia communities in countries where they are either a majority or a minority, including conflicts and tensions in modern history. In conclusion, this abstract provides a comprehensive overview of Shia Islam as one of the main branches of Islam, playing a crucial role in the history and dynamics of the contemporary Islamic world.
Keywords:  Shia, Youth, Religion
 
Abstrak
Sejarah, keyakinan, dan praktik utama Syiah yakni sebuah cabang dari agama Islam yang memiliki pengikut diseluruh dunia. Asal-usul Syiah dari perpecahan awal umat Islam setelah wafatnya Nabi Muhammad, dengan fokus pada keyakinan bahwa kepemimpinan spiritual seharusnya diturunkan secara langsung dari keturunan Nabi, terutama melalui Imam Ali dan keturunannya. Selain itu, kami membahas prinsip-prinsip teologi Syiah, seperti konsep Imamah yang menetapkan bahwa imam-imam mereka memiliki otoritas ilahi untuk menafsirkan ajaran Islam. Praktik ibadah dan ritual juga menjadi bagian penting dari kehidupan Syiah, dengan puasa, salat, dan ziarah ke makam para imam sebagai bagian integral dari praktik keagamaan mereka. Abstrak ini juga mencakup perdebatan seputar legitimasi keyakinan Syiah, serta hubungan mereka dengan cabang Islam Sunni yang lebih dominan. Kami juga mengulas dampak politik dan sosial Syiah di negara-negara di mana mereka menjadi mayoritas atau minoritas, termasuk konflik dan ketegangan yang timbul dalam sejarah modern. Kesimpulannya, abstrak ini memberikan gambaran menyeluruh tentang Syiah sebagai salah satu cabang utama dari Islam yang memiliki peran penting dalam sejarah dan dinamika dunia Islam kontemporer.
Kata Kunci: Syiah, Anak Muda, Agama
Cosmic Position in Chaucer's House of Fame
Authors: Swinford Dean
Published: July 2024

Chaucer’s House of Fame (ca. 1378-80) is a complex dream vision that presents its narrator, a stand-in for Chaucer himself, exploring a series of places shaped by his extensive reading. A quest for the source of fame, the poem is informed by similar narratives of dreams and imaginary journeys. Such voyages beyond the confines of the earth are expressions of the poetic imagination, but they also occur in medieval and classical literature within a divinely ordained and structured cosmos that included clear and symbolically important distinctions between the earth and the heavens. As a narrative of cosmic ascent, the House of Fame takes place within the imaginary space of the sphaera mundi, a model of the universe as a series of interlocking spheres with the earth at the center. The position of objects within this model determined their meaning and purpose. This essay examines astronomical details in Book II and argues that such details shape the setting of this part of the poem. Read in this light, the very air that the narrator moves through in Book II constitutes a setting equal in imaginative depth to the Temple of Venus in Book I or the House of Fame encountered in Book III.
Mikko Kauko: Pyhän Mechtildin ilmestykset
Authors: Enqvist Leena
Published: July 2024

Arvio Mikko Kaukon teoksesta Pyhän Mechtildin ilmestykset
A IMPORTÂNCIA DOS RECURSOS INTANGÍVEIS NA SUSTENTABILIDADE FINANCEIRA ORIUNDA DA TRANSFORMAÇÃO DOS CLUBES DE FUTEBOL EM SOCIEDADES ANONIMAS FUTEBOLISTICAS
Authors: Rosa Marcelo da Paz , Rodrigues Douglas Bastos
Published: July 2024

Esta pesquisa aborda a transformação dos clubes de futebol em Sociedades Anônimas do Futebol (SAF) e a importância dos recursos intangíveis, como marca, reputação e base de fãs, para sua sustentabilidade financeira. Justificada pela necessidade de explorar a interseção entre a nova estrutura organizacional e a gestão eficaz de ativos intangíveis, o estudo objetiva analisar o impacto da transformação em SAF na gestão e valorização desses recursos. Utilizando uma abordagem qualitativa e quantitativa, incluindo análises de casos específicos e revisões da literatura, os resultados evidenciam a complexidade e a necessidade de uma gestão estratégica dos recursos intangíveis. As descobertas expandem o entendimento sobre a gestão esportiva, oferecendo insights valiosos para a prática e teoria, além de destacar a relevância dos ativos intangíveis na era moderna do futebol.
POLA KOMUNIKASI TRADISI DALAM KEHIDUPAN SUKU BADUY DI ERA DIGITAL
Authors: Nursatyo Nursatyo , Firdaus Aos Yuli , Pratiwi Febiyana , Akhwat Risma , Ramadhan Gilang , Sandhi Kartika , Naufal Wirdiyan , Efendi Yolti Veni
Published: July 2024

Pola komunikasi tradisi masyarakat suku Baduy di era digital sedikit mengalami perubahan. Masyarakat Baduy Luar masih menjaga keaslian tradisi mereka dengan baik, tetapi tidak menutup diri terhadap kemajuan teknologi digital. Sementara masyarakat Baduy Dalam benar-benar tidak tersentuh oleh kemajuan teknologi sama sekali. Dalam hal ini, komunikasi menjadi faktor kunci untuk memperkokoh eksistensi budaya masyarakat Baduy. Artikel ini dibuat untuk memberikan gambaran mengenai pola komunikasi tradisi suku Baduy di era digital. Data dikumpulkan melalui observasi langsung di masyarakat Baduy Luar Desa Kanekes serta wawancara beberapa informan dari masyarakat Baduy termasuk petuah adat yang disebut Jaro. Hasil penelitian menunjukkan bahwa masyarakat Baduy terutama Baduy Luar tetap bisa mempertahankan nilai-nilai budaya mereka beriringan dengan penggunaan teknologi digital, seperti penggunaan smartphone serta aplikasi media sosial serta perbankan digital. Meski teknologi digital telah merambah dalam kehidupan masyarakat Baduy Luar, namun mereka mampu mempertahankan tradisi budaya luhur. Hal itu tidak lepas dari pola komunikasi tradisi suku Baduy yang bersifat linear topdown.
PENGEKANGAN PEREMPUAN MELALUI STANDART-STANDART KELAYAKAN PEREMPUAN
Authors: Utami Mega , Fauziah Nurul
Published: July 2024

This study aims to reveal the representation of women behind the video clip Woman Like Me by Little Mix. This study uses John Fiske's semiotic analysis theory and methods. The collection techniques used in this study were observation on the Woman Like Me video clip, source interviews and literature study. Meanwhile, the subject ofthis research is the women in Little Mix's Woman Like Me video clip while the object of this research is visual communication which includes images, attributes, gestures, sounds and clothes in Little Mix's Woman Like Me video clip. The results of the study show that the Woman Like Me video clip represents women's resistance in achieving gender equality. Women want to fight the restraints on the standards that have been produced and set in society against women. For example; beautiful, elegant, classy, polite, smart and have the skills and ability to do housework. On the other hand, in this video, women are portrayed as brave, independent, free and confiden.
Implementation paper on “Admission Sarthi” - College Predictor
Authors: Bansode Saurav Shrikant , Mahindrakar Prathamesh S. , Kadam Shreyash , Hon Snehal P.
Published: July 2024

Selecting the right college is crucial for shaping students' future careers. Unfortunately, many students, especially in rural areas, end up in unsuitable colleges due to limited information. "Admission Sarthi” - College Predictor addresses this issue by focusing on Maharashtra’s Engineering Colleges and MHT-CET scores. It provides a user-friendly platform where students can enter their marks or rank and receive a list of potential colleges along with admission probabilities. Using real and official data, the tool ensures accurate and reliable predictions. Admission Sarthi features an AI bot to help students with questions related to colleges, branches, and cutoffs, enhancing their decision-making process. Additionally, students can export the generated college list to PDF and Excel formats. The platform includes mock tests to aid in exam preparation. The goal is to guide students towards informed decisions, helping them avoid choices that could negatively impact their academic and professional paths. Keywords—Machine Learning, Cloud Computing, Admission.
Satisfaction from E-Banking Services of ICICI bank
Authors: KUMAR PUSHPENDRA ,   SCHOOL OF BUSINESS GALGOTIAS UNIVERSITY, GREATER NOIDA  INDIA, Singh Dr. Niharika
Published: July 2024

My project aims at Comparative study on Customer Perception Towards E-banking With Respect to ICICI Bank. In this research study 150 respondents from both the Banks were taken, After analyzing the results of the questionnaire we conclude that even now a days banks are providing innovative services day by day, but still there are a lot of customers who are even not aware about these services, the usage of these service s is a different issue, Today Banks are using huge amount of funds to provide differentiate services to their customers from their competitors like by using new software or by providing new innovative services like internet banking, mobile banking, and many others but still they are focusing only to provide the innovative services to the customers not focusing too aware them regarding these services and also there is a need to aware the customers about the use and benefits to the services provided by the bank, because it‘s the way to get competitive advantage, as per as we all know that today most of the organizations are focusing on the promotion- element of marketing mix, which is providing financial as well as non financial benefits like Brand image, so these Banks is not focusing over this element, Majority of the respondent in Bank have savings account with banks. The facility that was avail d by most of the people at these Banks was that of ATM/Debit Cards. The most important channel that aware customer most regarding the innovative services is family& friends.
An Experimental Investigation on Concrete Using Recycled Plastic as a Partial Replacement for Fine Aggregate and Nanosilica as a Partial Replacement for Cement
Authors: MOGADALA SURESH , BAVAJI KOMMADI ABDUL
Published: July 2024

Concrete, as a constructive material, has been used in construction industry for about two centuries. Approximately, the whole bulk of the concrete is used in one year is more than one ton apiece. Therefore, doing research about using modern technologies in production concrete is of great importance. Furthermore, one of the most critical problems of the world has been related to remove the wastage and reusing of it. Reducing the necessary amount of Portland cement without reducing the performance of concrete is significant for big projects that require a large amount of cement. Furthermore, Portland cement clinker production consumes large amounts of energy and has a notable environmental impact, which involves massive quarrying for raw materials. Waste material recycling through using in concrete manufacturing not only provides a promising resource to produce a high-quality concrete, but also helps to properly encounter the problem of waste disposal. A large bulk of ceramic tiles change into wastage, these waste materials are not reusable and recyclable due to their physical and chemical structure. Given the high amount of concrete production and the possibility of wastage materials in them, using ceramic wastage could be an effective measure in maintaining the environment and improving the properties of concrete. The present experimental study deal with the investigation of possibility of using plastic in concrete as fine aggregate replacement upto 25% with 5% fixed proportion along with nano silica as cement replacement of 0, 4 and 8% respectively of M20 & M30 grade of concrete Besides, all other parameters are constant. Finally, the slump value, compressive strength of the concrete for the samples were calculated.
Studies on Seasonal Variation of Groundwater Quality in the Suddagedda Basin, Eastgodavari District, A.P.
Authors: Sreedhar P , Rao T.Vinoda , Rao E.N.Dhanamjaya , Ramakrishna B , Rao K.Vishnu Vardhana
Published: July 2024

The present study is to assess the seasonal variations of the hydrochemistry and quality of the groundwater in the suddagedda basin. Fifteen groundwater samples were collected from the observation wells during the pre-monsoon and post-monsoon, 2019. Hydrochemical analyses including major and minor elements were done for the spatial distribution of the element concentrations and for the assessment of seasonal variations. Based on the results of the field investigations, it has been indicated that the quality of groundwater has observed in both the seasons were distinctly different. It is recommended that during the pre-monsoon periods, pumping from bore wells be minimized to control the quality variation. Key Words: Hydrogeochemistry; ground water quality,Kakinada.
Efectividad y Seguridad de nuevos anticoagulantes orales utilizados para la prevención del accidente cerebrovascular isquémico en pacientes diagnosticados con fibrilación atrial no valvular
Authors: Betrano Fiorella , Chaverri Fernández José Miguel , Miranda Kimberlyn , Ortíz Ureña Angie , Rivera Alma , Zavaleta Monestel Esteban
Published: July 2024

Los accidentes cerebrovasculares (ACV) generan gran cantidad de muertes y discapacidades a nivel mundial, y una de sus principales causas es la fibrilación atrial no valvular. Actualmente se cuenta con terapias tradicionales como la aspirina, ya sea sola o en combinación con clopidogrel y warfarina, las cuales han demostrado ser efectivas; pero presentan diversas limitantes, razón por la cual se han desarrollado nuevas alternativas que buscan eliminar y reducir dicha problemática y mejorar su efectividad. Estos medicamentos son conocidos como “nuevos anticoagulantes orales” (NOACs, por sus siglas en inglés) y se clasifican en dos grupos: los inhibidores directos de la trombina (dabigatrán) y los inhibidores del factor X activado (edoxabán, rivaroxabán y apixabán). Si bien con el análisis de los estudios clínicos respectivos para cada uno de los NOACs se obtienen ciertas conclusiones, no es posible establecer cuál es el anticoagulante ideal; por tanto, el presente estudio busca suministrar al tratante herramientas que le permitan tomar una decisión, basada en la evidencia, con respecto a posibles diferencias, beneficios y perjuicios que se podrían suscitar, ya sea con el estándar (warfarina) o con los otros NOACs, esto ante la ausencia de estudios clínicos que comparen dichos medicamentos entre sí
Evaluation of Menopause Women Awareness about Postmenopausal Changes: A Cross-Sectional Study
Authors: Salman Aqdas Dawood ,   Assistant Professor, Department of Maternal and Neonate Nursing, College of Nursing, University of Baghdad, Iraq, Muttaleb Wisam Mashaan ,   Instructor PhD., Department of Maternal and Neonate Nursing, College of Nursing, University of Baghdad, Iraq.
Published: July 2024

Background and objectives: Menopause is one of the most critical stages of life among women. This research aim was to assess the level of awareness among menopausal women regarding postmenopausal changes, with a focus on understanding their knowledge of symptoms, health implications, and problems. Materials and Methods: A cross-sectional study was conducted, involving 100 women menopausal women aged 50–65 years from diverse sociodemographic backgrounds. Participants were recruited from Baghdad hospitals using face to face technique to collect data. A menopause rating scale was utilized to collect data on participants’ awareness of postmenopausal changes. Results: Preliminary analysis of the data indicates more than half of participants were aware of postmenopausal changes, with Hot flushes, Sweating, Heart discomfort (Heart skipping, heart racing, tightness), Sleeping problems, Joint and muscular discomfort pain in the joints, rheumatoid complaints being the most commonly recognized. However, there were variations in awareness levels across different aspects of postmenopausal health. Conclusion: The study highlights the importance awareness targeting postmenopausal changes of women. While a substantial proportion of participants exhibited awareness of postmenopausal changes. The findings underscore the need for personalized and comprehensive education programs to empower menopausal women in managing their health effectively. Key Words: Menopause, postmenopausal changes, awareness, symptoms, health implications
Deciphering Negative Feedback through Handwritten Text
Authors: Sowmya Mrs. S. , Nirosha Mrs. K. , Basavaraju Sreya , Raparla Phalguni , Boyina Bhuvan
Published: July 2024

In the contemporary realm of digital communication, this study delves into a novel approach to analyzing sentiments, with a specific focus on handwritten negative reviews. The research aims to improve the precision of sentiment analysis by exploring the inclusion of handwritten text, providing distinctive insights into consumer feedback, and refining business strategies. Taking a historical perspective, the investigation traces the evolution of sentiment analysis within the domain of natural language processing, leading to the central question: "How can the accuracy of sentiment analysis for negative reviews be enhanced through the incorporation of handwritten text?" This question guides an exploration of the challenges and potentials associated with merging handwriting analysis with conventional sentiment analysis methods. The study puts forth hypotheses that address the expected advantages of this integration, seeking to develop an innovative framework capable of not only accurately detecting negative sentiments but also considering the individuality inherent in handwritten expressions. The research methodology encompasses a review of existing literature and empirical analysis, resulting in the creation of a unique hybrid sentiment analysis algorithm that assesses both textual and handwriting features. This work contributes to the advancement of sentiment analysis and has implications for businesses aiming to better understand and respond to negative consumer sentiments.
Application of Multispectral Remote Sensing in Mapping of Mineral Deposits
Authors: Molua Collins O. , Morka John C
Published: July 2024

This research examined Multispectral Remote Sensing in mineral mapping in the Ogoni area of Port Harcourt, Rivers State, Niger Delta. That is why the objective of the research was to improve the efficiency of mineral exploration with the help of non-destructive methods. Envi and ArcGIS software were used to analyze Landsat 8 OLI and Sentinel -2 MSI datasets. The applied preprocessing procedures involved radiometric and geometric corrections, and the values of these procedures ranged from 0. 006 to 0. 987 and 0. 064 to 0. 887, respectively. While mapping the minerals, we used spectral signature, band rasterizing, and principal component analysis. Here, the classification results exhibit a wide range in terms of the total percentage of accuracy, which was between 0. 097 and 0. 908. Consequently, the band ratio analysis showed the areas with high mineral potential; for example, Region 5 has ratios of 0. 972, 0. 986, and 0. 591 for three of the most important combinations of bands. Application of hyperspectral data calculated the degree of minerals present in the area; also, areas of high mineral dominance were observed and found to be Region_9 at the degree of 0. 711 concentration for Mineral_3. The results-oriented work and the study suggest that multispectral remote sensing could be a preliminary way of exploring mineral-rich environments to locate areas of interest and higher potential for ground-based exploration. Solutions include further tweaking the algorithms, including other geospatial data sources and detailed surveys in the subject areas.
Identification of Plant Leaf Disease Using CNN and Image Processing
Authors: Veldandi Srikanth , . Nandini , Reddy K.Meenendranath
Published: July 2024

Agriculture is an important sector for the growing people of the world to fulfil the minimum requirements of food. Identifying plant infection in the agricultural sector is complex. If the detection is wrong, there is more damage to crop production and economic loss of the market. Leaf infection identification need a large number of labors, command of plant disorders and requires a lot of observing time. Therefore, this analysis explains detection of plant leaf disease by utilizing CNN and image processing. Alex Net and ResNet-50 are Convolutional Neural Network (CNN) models. First, this method is performed on Kaggle datasets of potato and tomato plants to examine the characteristics of an infected leaves. Then, feature extraction and categorization method is executed on dataset pictures to observe leaf disorders using Alex Net and ResNet-50 models by executing image processing. Observational outputs demonstrate potential of described method, in which it reaches a complete accuracy of 98% and 95% of Alex Net and ResNet50. The output shows that described model particularly predicts defective plant leaves from healthy leaf images.
PENGARUH KEPEMIMPINAN, IKLIM ORGANISASI, MOTIVASI, DAN KEPUASAN KERJA TERHADAP KINERJA GURU
Authors: Adam Saleh
Published: July 2024

Kinerja guru masih relatif rendah, hal ini dipengaruhi oleh beberapa faktor diantaranya kepemimpinan, iklim organisasi, motivasi dan kepuasan kerja. Tujuan penulisan ini adalah untuk membahas dan menganalisis pengaruh kepemimpinan terhadap kinerja guru, iklim organisasi terhadap kinerja guru, motivasi terhadap kinerja guru dan kepuasan kerja terhadap kinerja guru