bluuebunny/crossref_metadata_2025_split huggingface dataset:
THE DEVELOPMENT OF THE ENGLISH LANGUAGE TEACHER’S INTERPERSONAL SKILLS IN IN-SERVICE TEACHER POFESSIONAL PROGRAM
Authors: Zuhri Fauris , Setiawan Slamet , Anam Syafi'ul 
Published: December 2023

Innovative English instruction practices require knowledge and skills of teaching appropriately as an English teacher. This study aims to explain the development of English language teachers’ interpersonal skills in an in-service teacher professional program at one of the universities in Surabaya through lecturing/tutorials, seminars/workshops, and English instruction practices. Experienced English teachers need interpersonal skills to develop interactive communication with learners. Studies of interpersonal skills discussed pedagogical competence. This study focuses on interpersonal skills in pedagogical and professional competencies. However, this study focuses on developing English language teachers’ interpersonal skills in innovative English instruction development and practices in class. So, this study implements the qualitative method to find out the story of English language teachers’ interpersonal skills in innovative English instruction development and practices in class. The participants are English teachers in an in-service teacher professional program, investigated using an online observation to describe the development of English language teachers’ interpersonal skills at innovative English instruction practices. The results show that developing English language teachers’ interpersonal skills, communication, collaboration, critical thinking, and creation can increase students’ academic engagement to achieve innovative English instruction. Therefore, English teachers' capacity, capability, and innovative instructional practices can increase teachers’ knowledge, skills, and character, so they become professional English teachers. Finally, the conclusion is that developing English language teachers’ interpersonal skills can increase students’ academic engagement to achieve innovative English instruction purposes.
Korean speaking and writing online learning during the COVID-19 pandemic: challenges and student perspectives
Authors: Fatunnisa Shofura Agisda 
Published: December 2023

Various challenges arose since the learning process in universities was carried out online due to the COVID-19 pandemic that hit the whole world. In order to have speaking and writing skills which are included as productive skills in language, students should do a lot of activities in the classroom directly by interacting a lot with both lecturers and other students. Korean Speaking and Writing course which must be carried out online without much time for preparation beforehand causes lecturers and students to experience difficulties at the beginning of online lectures. Therefore, this research was conducted to find out how Korean Speaking and Writing learning is carried out online, how students' perspectives on online learning take place, and what challenges students face when studying Korean Speaking and Writing online. This research is a qualitative descriptive study conducted at the Korean Language Education Study Program, Universitas Pendidikan Indonesia, focusing on second-year students. Through this research, it can be seen that: 1) Lecturers are quite good at teaching online by holding classes asynchronously using Google Classroom and synchronously with the Zoom application; 2) Students are of the view that online learning has its own advantages and disadvantages; 3) The challenges that arise from online Korean Speaking and Writing learning are the lack of self-motivation to study independently, external challenges in the form of a bad internet network, and difficulties in producing texts that are in accordance with Korean language rules.
Brain Tumor Detection and Multi Classification Using GNB-Based Machine Learning Approach
Authors: Gujar Satish N. , Jha Jaimala , Pingat Sanjaykumar P. , Pandey Rashmi , Kumar Atul , Gupta Ashish , Pise Priya 
Published: December 2023

In an abnormal tissue called a brain tumor, the cells of the tumor reproduce quickly. if no control over tumor cell growth. The difficulties involved in identifying and treating brain tumors Machine learning is the most technologically sophisticated tool for classification and detection, implementing reliable state-of-the-art A.I. as well as neural network classification techniques, the use of this technology in early diagnosis detection of brain tumors can be accomplished successfully. it is well known that the segmentation method is capable of helping simply destroy the brain's abnormal tumor regions In order to segment and categorize brain tumors, this study suggests a multimodal approach involving machine learning and medical assistance. Noise can be seen in MRI images. To make the method for eliminating noise from images easier, a geometric mean is used later. The algorithms used to segment an image into smaller pieces are fuzzy c-means algorithms. Detection of a specific area of interest is made simpler by segmentation. The dimension reduction procedure is carried out using the GLCM. Photographic features are extracted using the GLCM algorithm. Then, using a variety of ML techniques, like as CNN, ANN, SVM, Gaussian NB, and Adaptive Boosting, the photos are categorized. The Gaussian NB method performs more effectively with regard to the identification and classification of brain tumors. The plasterwork work achieved 98.80 percent accuracy using GNB, RBF SVM.
Yolov5 AI Deep Learning model driven Nuclear Pleomorphism Grading on Breast Cancer Pathology WSI for Nottingham Cancer Grading
Authors: Rajasekaran Subramanian Et al. 
Published: December 2023

Breast cancer is the second largest cancer caused in the world due to the uncontrollable growth in breast cells. Nottingham Grading is the internationally acceptable system to grade breast cancer. Nuclear pleomorphism is one of the breast cancer biomarkers for computing Nottingham grading. Pathologists grade nuclear pleomorphism on breast cancer glass tissue slides using a conventional microscope which is time consuming and has considerable inter-observer variability between pathologists. The paper proposed an Artificial Intelligence (AI) deep learning model to grade grade1, grade2, grade3 nuclear pleomorphism on breast cancer whole slide images (WSI). The proposed Yolov5 model is trained and tested on 1,30,000 WSI tiles having around two lakh annotations. The accuracy of the model is mAP 0.89. The proposed model saves the time and reduces the workload of the pathologist and also helps them to produce accurate results.
Image Enhancement of Colon Cancer Images using a Two-Stage Hybrid Approach of TV and Shift-Invariant Filtering
Authors: Jitendra Prakash Patil Et al. 
Published: December 2023

Medical imaging holds a critical position in both disease diagnosis and treatment strategies, including colon cancer. However, the quality of medical images can often be compromised by noise and artifacts, making accurate interpretation challenging. Here, we suggest a innovative two-stage hybrid method aimed at enhancing colon cancer images, leveraging the strengths of Total Variation (TV) denoising and shift-invariant filtering techniques. The primary objective of this study is to increase visual superiority as well as diagnostic accurateness of colon cancer image while preserving crucial anatomical information.The first stage of our approach employs Total Variation (TV) denoising to reduce noise and enhance image contrast. TV regularization is known for its ability to preserve edges and fine details, making it well-suited for medical image enhancement. In the second stage, we apply shift-invariant filtering to further enhance the image quality. This technique is designed to address the limitations of traditional filtering methods and adapt to the specific characteristics of colon cancer images.
To evaluate the effectiveness of our hybrid approach, we conducted a comprehensive set of experiments using a relevant dataset. We employed a range of quantitative metrics, including the Global Relative Error (EGRAS), Root Mean Squared Error (RMSE), Universal Image Quality Index (UQI), and Pixel-Based Visual Information Fidelity (VIFP), to assess the quality and fidelity of enhanced images. Our results demonstrate that the hybrid combination consistently outperforms existing methods, yielding superior image quality and diagnostic potential. This study makes a valuable contribution to the realm of medical imaging by introducing a robust and effective method to improve the quality of colon cancer images. Findings suggest that the proposed two-stage hybrid method holds promise for improving the accuracy of diagnosis and treatment planning. Further research in this direction may lead to advancements in medical image enhancement techniques, ultimately benefiting patient care and medical research.
Reconfigurable Multiband Dynamic Range Compression-based FRM Filter for Hearing Aid
Authors: Anjali Shrivastav Et al. 
Published: December 2023

In this research, we present an innovative method for enhancing the performance of hearing aids using a Multiband Dynamic Range Compression-based Reconfigurable Frequency Response Masking (FRM) Filterbank. First, a unform16-band reconfigurable filter bank, which is reconfigurable, is designed utilizing the FRM scheme. The strategic arrangement of each sub-band within the proposed filter bank is meticulously prepared to optimize the matching performance. Based on the hearing characteristics of patients, the sub-bands can be distributed in low, medium, and high-frequency regions. Also, the gain can be adjusted per the patient's hearing profile from their audiogram for better auditory compensation.
Further, the Multiband Dynamic Range Compression (MBDRC) technique is applied to address the specific needs of individuals with different frequency-dependent hearing impairments. It involves using dynamic range compression independently to different frequency sub-bands within a filter bank. In MBDRC, the compression parameters, such as compression threshold and ratio, can be adjusted independently for every subband. It allows for a more tailored approach to address the specific hearing needs of different frequency regions. If an individual has more severe hearing loss in high-frequency regions, higher compression ratios and lower compression thresholds can be applied to those subbands to amplify and improve audibility for high-frequency sounds. Once dynamic range compression is applied to each sub-band, the resultant sub-bands are reassembled to yield the ultimate output signal, which can subsequently be transmitted to the speaker or receiver of the hearing aid. A GUI can be helpful for better visualization and parameter control, including gain adjustment and compression parameters of this entire process. With this aim in mind, a GUI has been developed on MATLAB. Different audio files can be imported, and their frequency response can be generated and observed. Based on a person's audiogram, the control parameters can be set to low, medium, or high. Their sub-band distribution in low, medium, and high-frequency regions can be visualized. Further, the filter bank makes automatic gain adjustments, as seen in the GUI. The gain points for each band can also be manually adjusted according to users' hearing characteristics to minimize the error. Also, the compression parameters can be set separately for each subband as per the hearing requirement of the patient. Further, the processed output can be visualized in the output frequency response tab, and the input and output audio signals can be analyzed.
Multi Factor Authentication and IP Address Restriction based Question Paper Delivery System for Indian Universities
Authors: Chaitali Shewale Et al. 
Published: December 2023

The traditional question paper delivery (QPD) procedure in Indian institutions is discussed in this paper with a focus on its downsides, including complexity, time requirements, security issues, environmental impact, and cost. This method is manual, which makes it prone to mistakes, rigid, and open to leaks and tampering, especially when it comes to the security of the exam questions. It also looks at security issues involving question paper leaks, highlighting ongoing difficulties in preserving the integrity and confidentiality of the Indian educational system. These occurrences highlight the requirement for a more reliable and secure QPD system.
In this paper workflow of the cloud-based software system is described in detail, with examples showing how it makes it easier to distribute safe exam papers and assures stakeholder accountability. Barcodes are integrated into the system, and their improved tracking, processing, and distribution of exam papers is highlighted. A cutting-edge cloud-based Question Paper Delivery System (OQPDS), which automates and streamlines the process of generating, distributing, and giving exams for Indian colleges, is the suggested cure. Modern security elements are used by the system, including two-step authentication, barcode-based data storage, data encryption during data transfer, IP address-based access restrictions, and time-based content delivery. The incorporation of these security elements enhances the overall security and efficiency of the QPD system. The proposed system's workflow is discussed in detail along with examples that demonstrate how it facilitates the distribution of secure exam papers and ensures stakeholder responsibility. The system incorporates barcodes, and its better distribution, processing, and tracking of exam papers is praised. The system's capacity to limit access based on IP addresses is described, along with how it might help with traceability and prevent unwanted access. Additionally, the paper proposes watermarking to label test papers with the IP address of the testing facility, offering benefits including improved security, tamper detection, and traceability. It is highlighted that encryption is a key security tool for preserving data integrity, maintaining secrecy, and protecting against online dangers. Finally, the cloud-based Question Paper Delivery System is a solution to the security issues and inefficiencies of the current QPD system. This system intends to transform the examination procedure in Indian institutions by offering a more safe, effective, and transparent approach to question paper administration. It does this through contemporary security provisions and cutting-edge technology. This paper also discusses comparative analysis between traditional and proposed automated Question paper delivery system with respect to efficiency, security, cost reduction and flexibility.
An IoT based Tree Specific Soil Nutrient Management System Using Neural Network for Cashew Cultivation
Authors: C. Sudha Et al. 
Published: December 2023

Anacardium occidentale commonly known as Cashew was introduced into India by Portuguese in 16th century. Farmers of Cuddalore District, Tamil Nadu in India, are interested in Cashew Cultivation due to its export market values. To achieve better yield from cashew cultivation, farmers need to supply balanced nutrition to the soil and by following intercropping method. In literature, Recent Technologies like Artificial Intelligence and IOT are widely used to predict dosage of the fertilizer to be applied. By examining the numerous connected ascribe success locations of Cashew orchards, it was possible to determine how much macronutrients like Nitrogen (N), Phosphorus (P), and Potassium (K) were present in the soil and it will be helpful for the understanding of soil fertility level of the area. In our work, using IOT set up which includes Arduino UNO, NPK sensor and OLED display, we identified NPK values of soil for various cashew trees in a particular area. Along with NPK values of soil, inputs include the local soil type, PH and Tree age. Based on the input factors, DL trained model will provide suggestions of fertilizer dose to be applied for a specific tree for the Cashew cultivating farmers for better yield. In our proposed work, LSTM based Recurrent Neural Network (RNN) Algorithm is used to provide a better prediction of fertilizer. With the help of this research, we can be provided with what intercrop to plant and how much fertilizer to use, in what ratio to supply and fertilizer shop suggestion to enhance agricultural knowledge across the globe using the Internet of Things (IoT) and Artificial Intelligence. The result of RNN model was compared with Random Forest, Linear Regression, Linear Random Classifier, Decision Tree Classifier with proving accuracy of 98.7 %. Together with a decrease in the farmers' input efforts, these strategies will increase the productivity of the fields.
Convolutional Neural Network – Based Algorithm for Currency Exchange Rate Prediction
Authors: Manaswinee Madhumita Panda Et al. 
Published: December 2023

The foreign exchange market is one of the complex monetary markets in the world. Each day trillions of dollars are traded in the FOREX market by banks, retail traders, corporations, and individuals. It is very challenging to predict the price in advance due to the complex, volatile and high fluctuation. Investors and traders are constantly searching for innovative ways to outperform the market and increase their profits. As an outcome, forecasting models are continually being developed by scholars around the globe to accurately predict the characteristics of this nascent market. This study intends to apply the Random Forest (RF) approach to Convolutional Neural Networks, which involves two key steps. The first step is starting with feature selection using Convolutional neural network.The attention layer is then employed to assign weight.The random forest strategy is designed in the second stage to generate high-quality feature subsets. Thus the better result generated by CNN-RF model. Actually, this strategy combines the advantages of two different strategies to produce an outcome that is more consistent with what exchange market decision-makers anticipate happening in the exchange market.The main currency pairs considered in this study's proposed model for predicting exchange rates five and ten minutes in advance are the British Pound Sterling (GBP) against the US Dollar (USD), the Australian Dollar (AUD) against the US Dollar (USD), and the European Euro (EUR) against the Canadian Dollar (CAD) are also used to evaluate the performance of the proposed model. In compared to the other three models (Multi-Layer Perceptron, Autoregressive Integrated Moving Average, and Recurrent Neural Network), CNN-RF yields better results. This conclusion has been backed by a large body of empirical research, which also suggested that this methodology be regularly used due to its high efficacy.
Analytical Model for Assessing the Impact of MANETs' Bottleneck Nodes
Authors: Ijteba Sultana Et al. 
Published: December 2023

Objectives: Due to few resources, the most challenging task in mobile ad hoc networks is determining a route with the highest probability of success between a source and a destination. The objectives of this assignment are as follows: The limited power provided by batteries is one of the most significant challenges in routing. A great number of routing protocols have been devised to deal with the problem of limited battery power. Reliability, low energy consumption, and preferential treatment of nodes with greater energy levels are the cornerstones of the routing path made possible by these protocols. On the other hand, these protocols result in the creation of a bottleneck intermediate node, which is a node that must forward packets from numerous sources. The loss of packets is the most significant issue that emerges with a bottleneck intermediate node.
Methods and Statistical Analysis: In this study, we employ a poison random process to assess an intermediate's performance of bottleneck node within a mathematical model we developed. Findings: The model that is being proposed calculates the typical length of the queue at the input buffer, the typical amount of time spent waiting in the buffer and the lifetime of the network.
Application and enhancements: This model is put to use to regulate the incoming traffic of an intermediate node and to determine whether or not that node will participate in the route.
A Diaspora of Humans to Technology: VEDA Net for Sentiments and their Technical Analysis
Authors: Kirti Sharma Et al. 
Published: December 2023

Background: Human sentiments are the representation of one’s soul. Visual media has emerged as one of the most potent instruments for communicating thoughts and feelings in today's world. The area of visible emotion analysis is abstract due to the considerable amount of bias in the human cognitive process. Machines need to apprehend better and segment these for future AI advancements. A broad range of prior research has investigated only the emotion class identifier part of the whole process. In this work, we focus on proposing a better architecture to assess an emotion identifier and finding a better strategy to extract and process an input image for the architecture.
Objective: We investigate the subject of visual emotion detection and analysis using a connected Dense Blocked Network to propose an architecture VEDANet. We show that the proposed architecture performed extremely effectively across different datasets.
Method: Using CNN based pre-trained architectures, we would like to highlight the spatial hierarchies of visual features. Because the image's spatial regions communicate substantial feelings, we utilize a dense block-based model VEDANet that focuses on the image's relevant sentiment-rich regions for effective emotion extraction. This work makes a substantial addition by providing an in-depth investigation of the proposed architecture by carrying out extensive trials on popular benchmark datasets to assess accuracy gains over the comparable state-of-the-art. In terms of emotion detection, the outcomes of the study show that the proposed VED system outperforms the existing ones (accuracy). Further, we explore over the top optimization i.e. OTO layer to achieve higher efficiency.
Results: When compared to the recent past research works, the proposed model performs admirably and obtains accuracy of 87.30% on the AffectNet dataset, 92.76% on Google FEC, 95.23% on Yale Dataset, and 97.63% on FER2013 dataset. We successfully merged the model with a face detector to obtain 98.34 percent accuracy on Real-Time live frames, further encouraging real-time applications. In comparison to existing approaches, we achieve real-time performance with a minimum TAT (Turn-around-Time) trade-off by using an appropriate network size and fewer parameters.