Reviewerly/exHarmony huggingface dataset:
CLASSIFICATION OF SMS SPAM WITH N-GRAM AND PEARSON CORRELATION BASED USING MACHINE LEARNING TECHNIQUES

The Short Message Service (SMS) has garnered widespread popularity due to its simplicity, reliability, and ubiquitous accessibility.This study aims to enhance the efficacy of SMS classification by refining the classification process itself. Specifically, it strives to streamline the process by diminishing feature dimensions and eliminating inconsequential attributes. The textual data undergoes preprocessing, which involves employing the N-Gram technique for feature representation, followed by meticulous feature selection utilizing Pearson Correlation. The study employs 5 of classification algorithms. Notably, the findings underscore that the optimal outcomes emerge from the fusion of the N-Gram methodology with feature selection through Pearson Correlation. Among these, the Support Vector Machine methodology stands out, exhibiting a remarkable 91.41% enhancement in accuracy without feature selection, a further improvement to 91.96% through N-Gram utilization, and a final performance of 70.80% following the inclusion of weighted correlation. However, it is imperative to acknowledge the limitations inherent in the model's generalizability, primarily stemming from the utilization of a relatively modest dataset. Despite the efficacy of Pearson correlation and N-gram-based feature selection in curbing data dimensionality and enhancing processing efficiency, certain pertinent features may have been overlooked, or the chosen attributes might not be optimally suited for specific classifications.
Course Construction and Practice of Embedded Computing for New Engineering Education

Facing the reform and development of the New Engineering Education, the paper combines the Embedded Computing course construction and practice. The course of Embedded Computing is an elective course for postgraduates majoring in Computer Science in Harbin University of technology. Under the guidance of the education direction for Emerging Engineering Education, it must be strengthened the construction and practice of the course. This paper combines the traditional teaching mode with the online teaching mode, the theoretical content driven by problems with DIY practice, and adopts a diversified evaluation system. Through the curriculum reforms, the students' learning enthusiasm has been actively mobilized, and the teaching quality has been significantly improved. This paper can provide reference for reform of talent training in our teaching courses.
Sustainable Food Supply Chains Through Artificial Intelligence – Conceptual visualization using the example of turkeys to promote animal welfare and food quality

The concept visualizes a sustainable food supply chain through the use of artificial intelligence, using the example of turkeys to promote animal welfare and food quality. The technology push through artificial intelligence along the food supply chain is identified as a driver. In terms of market pull, it becomes clear that stakeholders are demanding transparency and the avoidance of food waste. The focus is on the parameters of production processes, use of resources and deriving possible positive effects. The target group comprises stakeholders in the food supply chain and includes producers at the processing and production stages, distributors, retailers and consumers.
Peningkatan Keterampilan Berbicara Melalui Metode Time Token Arends Pada Siswa Kelas V SDN 132 Babalohe Kabupaten Bulukumba

The aim of this research is to improve speaking skills through the Time token areinds method in class V Indonesian language subjects at SDN 132 Babalohe. The type of research used is pre-experimental research with the aim of. The research design used was a one-shot case study, namely an experimental research carried out to show the strength of measurement and scientific value of a research design and the experimental units in this research were 19 students in class V at SDN 132 Babalohe as a trial class for implementing the use of Time. Arends Token. The data collection technique in this research was carried out using a student interest in learning questionnaire sheet technique (student responses). The research results show that analysis of student interest in learning questionnaire data shows that the ideal score of 92.89% is in the very good category, because it is in the range of 75% - 100%,Keywords: Effectiveness of Bolding Media (Rolling Ball), Short Story Writing Skills
A Hybrid Visionary for Unveiling Human Motion in the Face of Occlusion with Mask R-CNN, RNN, and MHT

Addressing the intricate challenges of Human Motion Detection (HMD), this research presents a pioneering hybrid methodology integrating advanced computer vision and deep learning techniques. Focused primarily on mitigating the impact of occlusion in visual data, the proposed approach employs a Mask Region-based Convolutional Neural Network (Mask R-CNN) for precise motion segmentation. The dual challenges of self-occlusion and partial-occlusion are specifically targeted. The three-fold strategy encompasses motion segmentation, object classification, and tracking algorithms to discern and identify human motion accurately. Motion segmentation involves isolating the moving object within video frames, followed by object classification utilizing a Recurrent Neural Network (RNN) to determine the human presence and to tune the parameter of RNN; this work introduced a novel hybrid Whale Optimization Algorithm and Red Deer Algorithm (WOA-RDA), which gives better convergence speed with high accuracy. To tackle the persistence of occlusion, particularly self-occlusion, Multiple Hypothesis Tracking (MHT) is introduced for robustly tracking human gestures. An innovative aspect of the proposed approach lies in the integration of an RNN trained with 2D representations of 3D skeletal motion, enhancing the model’s understanding of complex human movements. The proposed methodology is rigorously evaluated on diverse datasets, incorporating scenarios with and without occlusion. Experimental results underscore the effectiveness of the hybrid approach, showcasing its ability to accurately identify human motion under varying conditions, thereby advancing the field of human motion detection.
Optimized Fuzzy Model in Piecewise Interval for Function Approximation

Function approximation is a technique for estimating an unknown underlying function from input-output instances or examples. Researchers have proposed different methods of function approximations, such as the neural network method, the support vector regression method, the reinforced learning method, the clustering method, the neuro-fuzzy method, etc. This paper introduces a novel data-driven function approximation scheme where the input-output data set is first segmented into multiple pieces. A Mamdani-type fuzzy submodel is constructed for each piece or portion, and the membership functions’ parameters for antecedent and consequent are optimally selected through the differential evolution algorithm. The efficacy of the suggested model is verified on three nonlinear functions, viz., a piecewise polynomial function, an exponentially decreasing sinusoidal function, and an exponentially increasing sinusoidal function. A comparative analysis is done based on the simulation results from the proposed model and the results obtained through the two state-of-the-art function approximation techniques, viz., the support vector regression model and the radial basis function network. The simulation results show that the proposed function approximator has satisfactorily approximated the three functions examined here, surpasses the two state-of-the-art techniques in approximating the two sinusoidal functions, and performs the near-best performance for the piecewise polynomial function. The proposed function approximator is expected to be applied as a new state-of-the-art method for function approximation.
Dynamic Indian Sign Language Recognition Based on Enhanced LSTM with Custom Attention Mechanism

In this paper, the author developed a system with an enhanced Long-Short-Term Memory (LSTM) model using a custom Attention Mechanism specifically designed for real-time dynamic Indian Sign Language (ISL) recognition. A large custom dataset of 59 signs was employed. For feature extraction, the MediaPipe framework was used. On this dataset, three different models were trained, the Long Short-Term Memory (LSTM) Dense model with Customed Attention, the LSTM-Dense model with a traditional Attention Mechanism, and the LSTM-Dense model without Attention Mechanism. The proposed Long Short-Term Memory (LSTM) dense model with a customed Attention Mechanism (AM) outperformed the other two models. The proposed LSTM-Dense model with a customed Attention Mechanism achieved a maximum accuracy of 96.08% in predicting the sign. When compared with existing Indian Sign Language recognition methods, our proposed model surpassed all others in accuracy, even with a large number of signs. In addition, 5-fold cross-validation of the proposed model confirmed the durability of our results, with a 93% accuracy. The results show that the proposed recognition system can effectively and robustly recognize ISL gestures.
A Comprehensive Stop-Word Compilation for Kannada Language Processing

In this work, a vital aspect of Kannada Natural Language Processing (NLP) takes the stage, with the construction of a standardized stop-word list emerging as a pioneering endeavor. This essential list serves as a foundation for improving language comprehension and processing activities. The work offers a rigorous technique that includes data gathering, tokenization, and TF-IDF score computation using the IndicCorp Kannada dataset. The study innovatively pioneers the construction of a stop-word list exclusively designed for the Kannada language, a first in this domain. The findings highlight the significance of these stop words and their prospective applications in diverse NLP endeavors, providing the framework for the upcoming construction of a Kannada-specific text summarizing work. The human refinement procedure ensures precision in stop-word compilation while considering inherent subjectivity and dataset-specific restrictions. Importantly, this study not only gives valuable insights into linguistic characteristics but also pioneers an innovative approach for stop-word generation in Kannada, establishing itself as a pioneering effort in this specific area of research. Furthermore, the study goes beyond its immediate findings by offering methodologies for the automated compilation and validation of stop words, thus laying the groundwork for further research. This foresight adds to the ongoing advancement of Kannada NLP methods.
Analysis of Copy Move Forgery Detection Process by Applying Fuzzy C Means Algorithm Based on Deep Learning in Digital Image Processing

The popularity of digital photos has developed due to technological advancements in the digital environment. Image alteration has become more manageable thanks to powerful and user-friendly photo editing software programs. Therefore, there was a prerequisite to detect the forged part of the image efficiently. Hence, this work emphasizes passive forgery recognition on images tampered by the copy move method, better called Copy Move Forgery Detection (CMFD). Copy Move Forgery (CMF) was fundamentally concerned with covering or repeating one area in a picture by pasting certain regions of a similar picture. Initially, the input digital images were preprocessed through a Gaussian filter to blur the picture to decrease noise. After preprocessing, Multi-Kernel Fuzzy C-Means clustering (MKFCM) was performed to divide the images into numerous clusters to extract the features based on distinctive attributes using the SIFT method. Lastly, with the deep learning technique, the forged parts of the images were detected. The experimental analysis demonstrates that the method was efficient and robust in identifying the forged part of the digital picture, and the performance of the proposed strategy was established on numerous forged pictures.
Prediction of Crime Rate in Diverse Environs Using Hybrid Classifier

Crime is the fear and terror among the populace worldwide. Crime is an inherent component of the hazards we encounter daily. In recent times, the mass media has extensively covered numerous criminal incidents, including theft, rape and sexual offenses, robbery, murder, and kidnappings. Various works have been produced to understand the factors that lead to an individual committing a criminal act, the potential dangers involved, and strategies to prevent it. The crime computation technique aims to forecast crime rates, enabling police officers to avoid such incidents effectively. Based on this, a novel prediction approach utilizing a hybrid classifier is suggested. An evaluation of the proposed method was conducted using several criteria. The performance of this recently constructed hybrid prediction model is evaluated by comparing it with established models such as Genetic Algorithm, Particle Swarm Optimization, and Firefly Algorithm. Various performance measures, including error rate, sensitivity, specificity, precision, and execution time, are used for the comparison. Based on the results, this hybrid model is the most optimal crime prediction model compared to the other current models. The suggested approach is executed using the JAVA programming language.
Unified Security Paradigm: Bi-Factor Authentication and Hyper Elliptical Curve Cryptography in IoT-Enabled Cloud Data Protection (BFHECC - IoMT)

Ensuring robust security for cloud-stored sensitive information and medical data is critical in contemporary technological landscapes. Safeguarding cloud data involves encryption, access controls, and authentication methods like TLS and multi-factor authentication. Yet, vulnerabilities such as breaches or misconfigurations pose ongoing threats, necessitating continual improvements. The proposed (BFHECC-IoMT) solution integrates a two-factor authentication model and “Hyper Elliptical Curve Cryptography,” presenting a formidable security infrastructure for cloud storage. This framework strengthens data encryption using conventional (password or PIN) and hyper elliptical curve-based authentication methods. It fortifies security beyond typical measures, increasing resistance to cryptographic attacks during data transmission or storage. On the other hand, integrating IoMT and cloud security, bolstered by Homomorphic Encryption and Elliptic Curve Cryptography (HECC), establishes a robust security framework for managing sensitive medical information. Addressing the oversight in existing healthcare systems, this integration ensures enhanced data confidentiality and integrity during transmission and storage. Seamlessly uniting IoMT, cloud security, Homomorphic Encryption, and ECC facilitates secure data transfer from IoT devices to cloud systems, ensuring stringent security standards for maintaining confidentiality, integrity, and availability. This comprehensive approach secures patient data transmission and provides privacy during analysis, compliance with healthcare regulations, and the overall secure handling and analysis of health data in the healthcare sector.
Learning Environment and Factors of Academic Performance among BSED Students

This research discusses the relationship between the learning environment and academic performance factors in BSED students. Grounded in the concept of environmental complexity, which encompasses both challenging and supportive elements, the study examines the impact of learning environment variations on student engagement in a classroom setting. Its goals aim to benefit key stakeholders, including the Commission on Higher Education, school administrators, instructors, and BSED students. Commission on Higher Education Officials can use the findings to shape program development and instructor training, fostering an environment conducive to academic success. Higher Education Administrators can gain insights for enhancing learning environments to improve academic performance. Instructors, acknowledged as pivotal contributors, are encouraged to acknowledge their role in cultivating positive and engaging classroom atmospheres. BSED students stand to benefit from a more supportive learning environment, positively affecting emotional well-being and, consequently, academic performance. Employing a quantitative research approach with a descriptive-correlational design, the study not only underscores strong agreement on the equity indicator in the learning environment but also highlights the importance of cooperation, investigation, and equity in influencing student engagement. Additionally, the research identifies the School-Related Aspect as a key factor influencing academic performance, aligning with broader literature emphasizing multifaceted contributors to positive educational outcomes. Pearson R analysis confirms a significant relationship between the learning environment and academic performance factors in BSED students, emphasizing the pivotal role of a positive learning environment in optimizing the learning process and contributing to academic success. In conclusion, the study suggests initiatives for creating positive learning environments, urging collaborative efforts involving administrators, instructors, and students to enhance the overall educational experience.
An Exploratory Study on the Conceptual Factors of Local Education Community Sense

The purpose of this study is to explore the conceptual factors that constitute the sense of local education community. For the expansion and development of the local education community, it is important for teachers, who are key members, to have perceptions of the local education community. As for the conceptual factors of teachers' sense of local education community, six conceptual factors were extracted through prior research and consultation with experts: locality, sharing, identity, relationship, practicality, and learnability. A total of 50 survey questions were composed based on the extracted factors. Quota sampling was used to select 170 teachers from elementary, middle, and high schools in Busan as the subject of the survey. For data analysis, exploratory factor analysis and internal consistency (Cronbach's α) were calculated using SPSS WIN 28.0, and correlations were verified. It was confirmed that the main conceptual factors that constitute the sense of local education community recognized by teachers include four factors: ‘locality’, ‘learnability’, ‘relationship’, and ‘collaboration’. Perception of the degree of implementation in each of the four areas of the conceptual factors of local education community sense showed that learnability was the highest, followed by locality, collaboration, and relationship. The results of this study can be used as practical data for developing a tool to measure teachers' sense of local education community and at the same time preparing school-local linked educations.
The Relations of Adolescents’ Achievement Goal Orientations to Academic Failure Tolerance : The Moderating Effects of Attributional Style

This study aimed to investigate the relations between adolescents’ achievement goal orientations and academic failure tolerance, and moderating effects of attributional style for the relation between them. 490 middle school students participated in the survey of study. A series of hierarchical regression analyses were used to examine the relationships of achievement goal orientations to academic failure tolerance, and the moderating effects of attributional style for those relationships. Mastery-approach orientation and performance-approach orientation showed a statistically significant positive relation to academic failure tolerance. In contrast, mastery-avoidance orientation and performance-avoidance orientation showed a statistically significant negative relations to academic failure tolerance. In the relationships between achievement goal orientations and academic failure tolerance, attributional style showed different moderating effects. For instance, attributional style has no statistically significant moderating effects in the relationship between mastery-approach orientation and academic failure tolerance. Whereas ability attribution showed a statistically significant negative moderating effect in the relationships of mastery-avoidance, performance-approach, performance-avoidance orientation to failure tolerance. In addition, external(powerful others, chance) attribution showed a statistically significant positive moderating effect in the relationships of mastery-avoidance, performance-approach, performance-avoidance orientation to academic failure tolerance.
Development of Android-Based Interactive Mobile Learning with Guided Inquiry Models to Improve Higher Order Thinking Skills on Light Wave Material

This research aims to produce Android-based interactive mobile learning with a guided inquiry model of light wave material that is able to improve students' HOTS abilities. This research is a 4D model type research. The research was conducted at MAN 1 Ponorogo, MAN 2 Ponorogo, and SMAN 1 Badegan. The research subjects were 149 class XI MIPA students who gave a pretest-posttest. Student pretest-posttest data were analyzed using 1-way ANOVA to determine the differences in students' HOTS skills in the control and experimental classes. Based on the 1-way ANOVA test, it was found that there were differences in students' HOTS abilities in the two classes. Classes that use Android-based interactive car learning get higher results compared to classes that don't use Android-based interactive car learning.
Influence of Mobile Learning Based on Articulatde Storyline 3 on Colloid Materials on Students' Learning Interest

This research aims to measure students' interest in learning through the use of mobile learning based on storyline 3 on colloidal material. The type of research used is experimental research. The subjects in this research were students in class XI MIPA SMA Negeri 1 Seyagen, totaling 17 students. In this research, students' interest was measured using the Pretest-Posttest technique by administering an interest questionnaire. From the results of the analysis, it is known that there was an increase of 4 students who were promoted from high to very high. The pretest and posttest results of learning interest show that learning using articulate storyline-based learning media has increased students' learning interest by 23.40%. The results of the paired t-test stated that the use of articulate storyline-based learning media was effective in increasing students' interest in learning. This research aims to measure students' interest in learning through the use of mobile learning based on storyline 3 on colloidal material. The type of research used is experimental research. The subjects in this research were students in class XI MIPA SMA Negeri 1 Seyagen, totaling 17 students. In this research, students' interest was measured using the Pretest-Posttest technique by administering an interest questionnaire. From the results of the analysis, it is known that there was an increase of 4 students who were promoted from high to very high. The pretest and posttest results of learning interest show that learning using articulate storyline-based learning media has increased students' learning interest by 23.40%. The results of the paired t-test stated that the use of articulate storyline-based learning media was effective in increasing students' interest in learning.