Reviewerly/exHarmony huggingface dataset:
Relationship Between Mother's Knowledge Level on Monitoring Behavior of Toddler Growth and Development at Gunung Alam Public Health Center, Arga Makmur City District, North Bengkulu Regency

A good knowledge of mother will influence the behavior of observing the growth and development of the under five year’s old children, so that the children achieve an optimal growth and development. The aim of the research is to know the relation between mother’s knowledge toward the behavior of observing growth and development of fewer than five years old children at the Gunung Alam health centre in the area of Argamakmur. The design of the research is Analytic Survey with Cross Sectional approach. The population of the research is 300 mothers and the sample was taken Randomly (Simple Random Sampling) the result got 171 sample by using Corelative Product Moment Statistic. The result of unvaried analysis at the stage of knowledge shows 56 mothers (32,7%) have moderate knowledge. The distribution of the behavior of observing growth and development of under five years old children mostly ‘sometimes’ behavior 87 (50,9%). Bivariat analysis with the result of correlative product moment test (r test) with a 0,05 and CL 95% shows that all independent variabele; mother’s knowledge and dependent _ariable that is behavior of observing growth and development under five years old children shows that they have positive relation and significant 0,606 with a 0,05 its got ro = 0,606 and t test got 9,892. Since r account higher than r table (0,148), thus behavior of observing growth and development under five years old children. The category of the relation is significant (moderate relation). It can be concluded that the higher mother’s knowledge, the higher mother’s in observing the growth and development of the under five years old children.
Card Defender - Credit Card Fraud Detection System

Abstract: As the world becomes increasingly digitized, online transactions have become an indispensable part of our daily lives. The increased use of credit cards for online purchases has resulted in a growing concern about credit card fraud, both for businesses and consumers. To combat this issue, we propose a two-factor authentication system that integrates credit card verification with webcam-based face recognition technology to prevent online transaction fraud. Our system provides a reliable and user-friendly solution for credit card fraud detection using face recognition. By implementing a two-factor authentication process, our system reduces the risk of fraud during online transactions and enhances the overall security of online payments
KEMM: A Knowledge Enhanced Multitask Model for Travel Recommendation

Abstract In today’s fast-moving world with a wide spectrum of choices and options availableregarding the places to travel, it’s tedious to find the most optimal destination to visit keeping inmind the constraints of budget, choices, preferences, and weather. Most of the current systemseither rely on the user’s location or their travel history making the selection suboptimal and notaccustomed to the user's preferences.The focus of this research is on recommendation systems that utilize a graph-based approach. The method involves creating an interaction graph based on past user-item interactions to overcome data sparsity and cold start issues. However, this study has identified a problem in existing graph based models related to early summarization. To address this problem, a new approach to the existing Knowledge based Neighborhood Interaction (KNI) model is proposed. A knowledge Enchanced Multitask Model for Travel Recommendation (KEMM) has been proposed to capture unique characteristics of each neighbor pair between users and items. Also corpus creation for a Knowledge Graph of Indian Places to visit has also been done. The model's performance have been assessed using real-world datasets and evaluated against the current state-of-the-art models using standard metrics. The comparative analysis of this model on various datasets reveal that the model is performing better or simillar to other existing state-of-the-art models. It has been observed that there was a minor increase in the Precision@k metric and slight improvement in AUC.
Succession in work of kindergarden and school

Kindergarden and school herritage provides the relationship between the content of educational work and its implementation methods. Continuity of&nbsp; kindergarden and school is considered on the one hand for school to transfer children to a level of development and upbringing that wil, respond educational needs, on the other gand, the school relies on the knowledge, ability, the qualities that the children in the pre-school institution.
Pelatihan Publikasi Ilmiah Guru-guru Kota Tangerang Selatan pada Jurnal Investigasi SMP Negeri 17 Vol. 4, No. 1, Maret 2023

Community service activities Training on Scientific Publications of South Tangerang City Teachers in the Investigation Journal Volume 4 No.1 SMP Negeri 17 aims to assist elementary and junior high school teachers in South Tangerang City in writing scientific manuscripts that will be published in ISSN journals. The training method is carried out using the method of mentoring, discussion, workshop and technical guidance (bimtek) writing scientific manuscripts in accordance with journal manuscript standards. The training results obtained as many as 8 elementary school teacher manuscripts (67%) and 4 junior high school teacher manuscripts (33%). The publication of teacher scientific manuscripts in the Journal Investigative Volume 4 Number 1 March 2023, is very helpful for teachers in improving the quality of scientific manuscript writing and is very helpful in raising groups.
Deep learning based single image deraining: datasets, metrics and methods

Removing the rain stripe from the image obtained in a wet setting improves visual identification accuracy. This study focuses on both classic and deep learning models to track the research progress and trends. Various metrics including SSIM and PSNR have been designed for evaluating these models, based on some widely used open image datasets, e.g., Rain12 and Rain100L. Both synthetic datasets, which are based on additive composite model and rainstorm model, and real-world datasets are used in the literature. The improvements of several models over baselines are summarized. This paper also covers the major unresolved topics in current research. When applied to the actual world, several powerful algorithms still struggle to suppress edge artifacts, weigh definitions, and understand structural subtleties. Open issues including rain water direction, quantity, and future directions are also highlighted to encourage further research.
A DE-LGCA personalized recommendation algorithm study

A graph convolutional neural network is a special type of neural network that GCN can use to extract features from graphs and use these features for classification or regression. The current lightweight graph convolutional neural network in the recommendation field only considers the case of fixed neighborhood weights, with the increase in the number of training layers of the network recommendation effect will rapidly decline resulting in the problem of oversmoothing and overfitting. In this paper, in order to solve the above problem, this paper will introduce a DE-LGCA personalized recommendation algorithm that uses a random wandering strategy for data enhancement based on a lightweight graph convolutional neural, while introducing an attention mechanism to increase the node neighborhood weights, and show how to use this algorithm to achieve personalized recommendations. Experiments were conducted on three publicly available datasets, and three performance metrics, Precision, Recall, and Normalized Discounted Cumulative Gain (NDCG), were used to evaluate the performance of DE-LGCA, which not only outperformed the comparison method in terms of performance metrics, but also improved the efficiency of the algorithm.
YOLOv4-tiny pedestrian detection method based on attention fusion mechanism

In order to solve the problem that the detection accuracy of YOLOv4-tiny algorithm decreases due to the lightweight and small weight of the model, a YOLOv4-tiny technology method based on attention fusion mechanism is proposed to detect pedestrians. In this method, CBAM is added to the effective feature layer and upper sampling layer extracted by FPN feature pyramid network based on the original YOLOv4-tiny algorithm to screen advanced semantic features. The 26×26 detection layer of the prediction feature layer was fused with the second layer of CSP structure with CBAM, and a 52×52 detection head was added to expand the coverage of pedestrian detection. Use IOU_NMS non-maximum suppression to remove some redundant candidate boxes and speed up the regression of prediction boxes. The Python script was used to extract the Person-class data image from PASCAL VOC2012 data set. The experimental results show that this method has improved the accuracy, recall rate and accuracy in the data verification set, achieving the purpose of accurately detecting pedestrians.
Path planning for mobile robots based on improving Dijkstra algorithm

In response to the traditional Dijkstra algorithm, the long search time of pheromone and the occasional redundant inflection points When the final path is obtained can lead to the inefficiency of the algorithm due to its many traversed nodes. Therefore, this paper proposes the use of pheromone calculation method to improve Dijkstra's algorithm. The experimental results show that the optimized algorithm can largely reduce the inflection points generated during path planning and reduce the movement cost of the mobile robot path finding.
AN INSIGHTS ON CRICKET DATA ANALYTICS

From this project, we can review performance of the team using previous data of the cricket players.It also helps to track the performance of an individual player, which helps the team management and selection committee to select the best player for a particular tournament by analyzing the data.Data played a key role in analyzing the performance of a team.One team can analyze the teams with whom they are about to compete.They can analyze the opposition team's strengths and weaknesses through rigorous analysis of their scoring patterns, how they scored their runs, when they were vulnerable during innings.
Self-organizing interval type-2 fuzzy neural network based on singular value decomposition and QR decomposition

In this paper, a self-organizing interval type-2 fuzzy neural network based on singular value decomposition and QR decomposition with column pivoting (SVD-QR)method is proposed to solve the identification problem of nonlinear systems. The network model can realize both structure learning and parameter learning. Firstly, in the aspect of structure learning, error criterion and completeness criterion of fuzzy rules are used to judge whether the rules are growing or not, and at the same time, SVD-QR method is used to find the less active rules to delete. Secondly, a sliding-window secondorder algorithm with forgetting factor is used to optimize the parameters. Finally, the proposed model is applied to one typical nonlinear examples for identification. The experimental result shows that the proposed model can produce a relatively compact network structure and has the high identification and the forecast precision.
Two-fidelity Data Fusion Modeling with Particle Swarm Sampling

Computer simulation and experimental testing are commonly used means of data acquisition in industrial design, so the combination of low-precision data represented by cheap calculation data and high-precision data represented by expensive test data for fusion modeling is a hot topic in practical applications question.In order to improve the quality of the model and control the usage of high-precision data, this paper proposes a double-precision fusion modeling method based on particle swarm sampling.The algorithm uses the Kriging model to model high-and low-precision data, and uses PSO (Particle swarm optimization) to obtain low-precision model information for high-precision modeling.Combined with the characteristics of sufficient amount of low-precision data, high-precision data is expensive and difficult to obtain, the low-precision model modeled after sufficient sampling is used to guide the initial sampling of the high-precision model, and the fusion model is obtained by training with two high-precision and low-precision models, and two The point-adding criterion is used for sequential modeling, and the fusion model is continuously optimized.The algorithm is tested on a standard function and an application problem.The experimental
Graphical Password Authentication: Image Grid Based Digital Lock for Mobile Apps

Abstract: Over the past few decades, research on authentication methods has advanced significantly. While the initial focus was on using standardised password selection and management approaches to secure textual passwords, this was made possible by learning about the flaws in the existing systems based on the attacks performed on the same systems' vulnerabilities. With the rise of biometric-based authentication, the next stage in the study and development of improved solutions has begun. Although initially rather entertaining, this authentication method is very insecure. Once a person's biometrics have been stolen or replicated, new biometrics cannot be made. Compared to a person's one-step biometric authentication, biometrics in the multifactor authentication of an enterprise are less vulnerable. The development of graphical passwords is the result of all these issues with authentication systems. While multifactor graphical passwords impede smartphone users' usability and speedier processing-computational flexibility, overly simplistic graphical passwords are prone to shoulder surfing. Users of Android devices need an authentication system that is easy to use, quick, secure, and that gains faster access to the device. A study was done evaluating the usability, predictability, resistance to various attacks, and other characteristics of various graphical passwords. Grid-based graphical passwords outperformed their rivals among the different types of graphical passwords that were taken into consideration. The majority of graphical password implementations in the past have been on personal computers (PCs), while smartphone usage is far more prevalent than on PCs. We propose to develop an Android application with gridbased image-based graphical password authentication that will be secure and help a lot of users protect the secondary identities that they carry about in the form of smartphones. After evaluating which factors must remain in the system, the application was divided into five modules. Each module was first built using flowcharts, and later, the Java programming language was used to build the app in Android Studio.
Efficient 3D Medical Image Segmentation using CoTr: Bridging CNN and Transformer

Abstract: Neural networks are a subset of machine learning, and they are at the heart of deep learning algorithms. Before CNNs, identifying objects in images was done manually using time-consuming, manual feature extraction methods. The superior performance of convolutional neural networks, when dealing with images, speech, or audio signals sets them apart from other neural networks. Convolutional neural networks (CNNs) have been the de facto standard for nowadays 3D medical image segmentation. Due to the inductive bias of locality and weight sharing inherent in convolutional operations, these networks lose the ability to model long-range dependency. In this study, a novel framework is presented for accurately segmenting 3D medical images based on the combination of a convolutional neural network and a transformer (CoTr). This framework allows us to construct CNNs for extracting feature representations, and Vision Transformers for modelling longrange dependency on the extracted feature maps. As a self-attention device, the transformer performs a global operation where it draws information from all the information on the system in order to make a decision.
Bank Responsibility on Customer’s Data Fraud in Indonesia

Banks as financial institutions certainly have a very important role in a country, especially to advance the economy in that country. So, the good running of the banking industry in one country is a very crucial thing to be regulated by the state. With the presence of technology, the threat of cybercrime is now a serious concern, especially in the banking world, especially with regard to the confidentiality of customer data. This study uses a normative juridical method. Based on the existing regulations, it can be concluded that it is crucial for banks to continue to improve security, especially with regard to customer data and the Bank must be responsible for losses that occur if the data leak is caused by the bank's negligence in building a security system.
Multi-Disease Prediction based on Symptoms using DL

The primary objective of this research is to develop an accurate and efficient model to predict various diseases by analyzing patients' symptoms, thereby enabling early detection, intervention, and personalized treatment plans. Our proposed system employs advanced feature extraction techniques and state-of-the-art Deep Learning (DL) algorithms to analyze and classify symptom patterns, ultimately predicting the most likely diseases associated with the given symptoms. We utilized a comprehensive dataset containing symptom data for numerous diseases, and our system was trained using DL techniques. The performance of the proposed model was evaluated through multiple performance parameters, including accuracy, sensitivity, and specificity. Our experimental results demonstrate the effectiveness and potential of the proposed system in predicting multiple diseases based on symptoms with high accuracy. This study highlights the potential of DL in revolutionizing the field of medical diagnosis and personalized medicine, ultimately improving patient outcomes and healthcare efficiency. The integration of DL with healthcare can bring about a revolution in personalized medicine, and the accurate prediction of diseases can enable early intervention and improve patient outcomes. The article highlights the potential of DL methods in disease prediction and emphasizes the need for further research to overcome the current limitations and challenges. Overall, the article serves as a guide for researchers and healthcare professionals in understanding the role of DL in disease prediction and its implications for the future of healthcare.
Improving Efficiency of High Utility Sequential Pattern Extraction

Text mining used on texts and publications in the biomedical and molecular biology fields is referred to as "biomedical text mining." It is a relatively new area of study at the intersection of computational linguistics, bioinformatics, and natural language processing. Superior usefulness the goal of sequential pattern mining is to identify statistically significant patterns among data instances when the values are presented sequentially. Time series mining is typically regarded as a distinct activity even if it is closely linked since it is typically assumed that the values are discrete. Structured data mining has a unique use known as sequential pattern mining. High utility pattern (HUP) mining is one of the most relevant study areas in data mining nowadays since it is capable of taking into consideration the nonbinary frequency values of items in transactions as well as different profit values for each item. The utilization of previous data structures as well as mining outcomes, yet, enables incremental and interactive data mining to eliminate the need for further calculations when a database is updated or the minimum threshold is modified. The method in this study suggests three innovative tree architectures for effective incremental and interactive HUP mining. The high utility sequential pattern mining issue has formalised key ideas and elements.
W-Tree Indexing For Fast Visual Word Generation

In image retrieval and visual identification, the bag-of-visual-words model has been extensively employed. The process of creating visual words—adding visual words to the associated local characteristics in a high-dimensional space—takes the longest to complete in order to acquire this representation. In order to save time, structures constructed around multi-branch trees &amp; forests have recently been implemented. However, without a lot of backtracking, these approaches are unable to work well. In this study, we demonstrate that the lengthy process of visual word formation may be greatly sped up while keeping accuracy by taking into account the spatial correlation of local variables. We may create a co-occurrence table for each visual word in a huge data collection since some visual terms commonly co-occur with specific structures. We are able to allocate a probabilistic weight to each node of an index structure (for example, a KD-tree and a K-means tree) by associating each visual word with a probability in accordance with the corresponding co-occurrence table. This allows us to re-direct the searching path to be nearby to its global optimum within a minimal number of backtrackings. On the Oxford data set, we carefully examine the proposed scheme by contrasting it with the fast library for approximative nearest neighbours along with the random KD-trees. Extensive experimental findings point to the new scheme's effectiveness and efficiency. FeatureMatch, a generalised approximate nearest-neighbor field (ANNF) calculation framework connecting a source and target picture, is the solution we provide in this study. The suggested approach can calculate ANNF maps between any picture pairings, including unrelated ones. By using the proper spatial-range transformations, this generalisation is accomplished. Global colour adaptation is used on the source picture as a range transform to calculate ANNF maps. Low-dimensional characteristics are employed to approximate the image patches from the pair of photos, and KD-tree and ANNF estimation are combined. Based on picture coherency and spatial transformations, this ANNF map is further enhanced. We can now handle a broader spectrum of vision applications thanks to the suggested generalisation, which we couldn't previously manage with the ANNF framework.
Inline Deduplication Checking in cloud environment

Inline deduplication is a technique that can reduce the amount of storage space used in a cloud environment. It can also improve restore and backup times. However, it is not always effective due to the various factors that affect the effectiveness of this process. For instance, the type of data that is stored and the method that is used to perform deduplication are all important factors that affect the effectiveness of this solution. The goal of this paper is to provide an overview of the various aspects of inline deduplication and its effectiveness in improving the performance and reducing the cost of storage in a cloud-based environment. We then conduct a case study to demonstrate the efficiency of this process in an Amazon Web Services instance. Experiments were able to demonstrate that in cloud environments, the level of deduplication that is achieved can improve performance and reduce the amount of storage space that is used. In case study, the use of inline deduplication on Amazon Web Services led to a 60% reduction in the storage space usage. After conducting a comprehensive analysis of the various aspects of inline deduplication, we concluded that it is a standard feature that can be used by cloud storage providers to reduce their costs and improve the performance of their customers.