Reviewerly/exHarmony huggingface dataset:
PERFORMANCE STUDY OF MULTIOBJECTIVE OPTIMIZER METHOD BASED ON GREY WOLF ATTACK TECHNICS

This paper proposes a performance study for the Multiobjective Optimizer based on the Grey Wolf Attack technics (MOGWAT). It is a method of solving multiobjective optimization problems. The method consists of the resolution of an unconstrained single objective optimization problem, which is derived from the aggregation of objective functions by the $\epsilon$-constraint approach and the penalization of constraints by a Lagrangian function. Then, Pareto-optimal solutions are obtained using the stochastic method based on the Grey Wolf Optimizer. To evaluate the method, three theorems have been formulated to demonstrate the convergence of the proposed algorithm and the optimality of the obtained solutions. Six test problems from the literature have been successfully dealt with, and the obtained results have been compared to two other methods. We have evaluated two performance parameters, including the generational distance for the approximation error and the spread for the coverage of the Pareto front. Based on these numerical findings, it can be concluded that MOGWAT outperforms two other methods.
Pembuatan Ergonomic Mechanical Keyboard untuk Mengurangi Cidera Tangan Menggunakan Teknologi Arduino

In our daily lives, computers and keyboards are essential tools for both work and gaming. However, despite the variety of keyboards available on the market, few consider the comfort and needs of individual users. To solve this issue, a mechanical keyboard that can be adjusted in width, shape, and positioning is required to lessen hand injuries when used for extended periods. This research produces a mechanical keyboard hardware prototype using Arduino as the main component. The device will be connected to an electronic circuit and use C Language as supporting software. Tests were conducted to compare existing keyboards on the market related to function tests, compatibility, usability, and performance tests. The results show that all hardware components function perfectly at 98% capacity.
Pengembangan E-Modul Inventor 2023 Berbasis Android Sebagai Mobile Learning Mata Pelajaran Teknik Pemesinan

This development research aims to find out the results of the development of the android-based Inventor 2023 e-module as mobile learning for the Mechanical Engineering Subject at the Vocational High School level. The development of the production model used is the development of the Dick and Carey model. The results of the validity test data were obtained for R.Count all of the 15 items of questionnaire statement items on all variables in this study yielded values for R.Count &gt; R.Table where the R.Table value was 0.361, so the indicators in these variables can be called valid. For reliability testing, it has a Cronbach's Alpha coefficient that is greater than 0.70 which is equal to 0.818 so that the variables from the questionnaire are reliable. The results of the trial data obtained for content experts were 90.33%, media experts were 84.44%, design experts were 91.61%, peer trials were 90.21%, small group trials were 91.73%, medium group trials of 92.13%, and large/field group trials of 90.04%. With these data, the Inventor 2023 e-module that has been developed falls into very good criteria and is very suitable for use in learning. The students also gave positive responses to this e-module. With this e-module, students can learn easily both independently and in groups and freely study both at school and at home with the help of this Android-based e-module.
COMPARISON OF RANDOM FOREST AND NAÏVE BAYES CLASSIFIER METHODS IN SENTIMENT ANALYSIS ON CLIMATE CHANGE ISSUE

The last decade was recorded as a decade with a bad record on the issue of disasters in the world due to climate change. Measuring public opinion is one of the steps to mitigate the impact of climate change. Twitter is a popular social media for channeling opinions. Twitter provides a great source of data for understanding public opinion and the perceived risk of an issue. In recent decades, when discussing climate change, there are those who agree and those who oppose it. Sentiment analysis is a branch of learning in the realm of text mining that is used as a solution to see opinions on a problem, one of which is climate change. In this study, we will try to analyze opinions on climate change issues using the Random Forest and Naïve Bayes classifier methods. Data were obtained from Twitter for the period January 2022-June 2022. The training data used in this research is 80%:20%. There are slightly more positive statements than negative ones. The results obtained with the Naïve Bayes classifier method are an accuracy of 76.25%, an F-1 score of 78%, and a recall of 80%. While the results of the random forest method are 70.6% accuracy, 69% F-1 score, and 63% recall. The Nive Bayes method is better than the Random Forest method for classifying climate change opinions with an accuracy of 76.25%.
A COMPARISON OF ARTIFICIAL NEURAL NETWORK AND NAIVE BAYES CLASSIFICATION USING UNBALANCED DATA HANDLING

Classification is a supervised learning method that predicts the class of objects whose labels are unknown. Classification in machine learning will produce good performance if it has a balanced data class on the response variable. Therefore, unbalanced classification is a problem that must be taken seriously. This study will handle unbalanced data using the Synthetic Minority Over-Sampling Technique (SMOTE). The classification methods that are quite popular are the Naïve Bayes Classifier (NB) and the Resilient Backpropagation Artificial Neural Network (Rprop-ANN). The data used comes from the Health Nutrition Research and Development Agency (Balitbangkes) which consists of 2499 observations. This study examines the use of NB and ANN using the SMOTE method to classify the incidence of anemia in young women in Indonesia. Modeling is done on 80% of training data and predictions on 20% of test data. The analysis shows that SMOTE can perform better than not handling unbalanced data. Based on the results of the study, the best method for predicting the incidence of anemia is the Naïve Bayes method, with the sensitivity value of 82%.
ML and DL-based Phishing Website Detection: The Effects of Varied Size Datasets and Informative Feature Selection Techniques

One must interact with a specific webpage or website in order to use the Internet for communication, teamwork, and other productive activities. However, because phishing websites look benign and not all website visitors have the same knowledge and skills to inspect the trustworthiness of visited websites, they are tricked into disclosing sensitive information and making them vulnerable to malicious software attacks like ransomware. It is impossible to stop attackers from creating phishing websites, which is one of the core challenges in combating them. However, this threat can be alleviated by detecting a specific website as phishing and alerting online users to take the necessary precautions before handover sensitive information. In this study, five ML and DL algorithms—CATB, GB, RF, MLP, and DNN—were tested with three different reputable datasets and two useful feature selection techniques, to assess the scalability and consistency of each classifier's performance on varied dataset sizes. The experimental findings reveal that the CATB classifier achieved the best accuracy across all datasets (DS-1, DS-2, and DS-3) with respective values of 97.9%, 95.73%, and 98.83%. The GB classifier achieved the second-best accuracy across all datasets (DS-1, DS-2, and DS-3) with respective values of 97.16%, 95.18%, and 98.58%. MLP achieved the best computational time across all datasets (DS-1, DS-2, and DS-3) with respective values of 2, 7, and 3 seconds despite scoring the lowest accuracy across all datasets.
Empirical Analysis of Text Embedding Algorithms in Audio Files: A Proposed Strategy

Security has become one of the main tasks to maintain the safety of transmitted or stored information. Text encryption is one of the essential tools for text hiding. On the other hand, steganography coding is an effective tool to increase the security. This paper present's a proposed security which combine with a hiding technique for an important text with a selected audio. The encryption file hiding by phase coding in compressed audio. Such procedure aims to increase security and reduce overall transmitted data. As a result, the hiding and showing processing may affect the selected audio and lose some spectral data. To address this problem, an enhancement based on the Wiener filter is deployed to re-extract the original audio after decompression in the received file. The results, show that high security with achieved by obtaining the hided text and complete audio spectral data of used audio and proposed system achieved an improvement in encryption, compression, and masking at the same time.
Research on the Reform of "Teaching Staff, Teaching Methods and Teaching Materials" of Business English in Higher Vocational Colleges Based on Digital Transformation of Foreign Trade Industry

"Three Reforms" is mainly carried out from three aspects as Teaching Staff, teaching methods, and teaching materials. The significance of carrying out "Three Reforms" in Business English majors of higher vocational colleges is to improve colleges' efficiency and quality, enhance teachers' teaching abilities from multiple aspects and levels, and enhance students' comprehensive literacy and professional abilities. Especially in the context of digital transformation of foreign trade industry, it is urgent for higher vocational colleges to carry out the "Three Reforms" of business English. Therefore, this paper mainly analyzes the significance of carrying out "Three Reforms" in Business English majors of higher vocational colleges, and proposes specific teaching reform strategies, so as to provide certain reference strategies for promoting "Three Reforms" of Business English in higher vocational colleges.
Classification of cervical cancer from Pap smear images: a convolutional neural network approach

Cervical cancer is a significant global issue, with Pap smear tests being a common screening tool for precancerous stages. This study aims to develop a computer-aided diagnostics system that can classify precancerous cells from Pap smear images. The project employs convolutional neural networks (CNNs) trained using pre-processed images, adaptive fuzzy K-means (AFKM), and fuzzy C-means (FCM) to classify cervical cancer cell data as normal or abnormal. The datasets used in the project include normal, low-grade squamous intraepithelial lesion (LSIL), and high-grade squamous intraepithelial lesion (HSIL) categories. CNN1, CNN2, and CNN3 have been developed and CNN2 was chosen due to its highest accuracy of 87.71%. The CNN2 trained with AFKM outperformed other networks with an accuracy of 89.53%, precision of 0.870, recall of 0.870, specificity of 0.935, and F1-score of 0.870. This study demonstrates the potential of deep learning-based approaches for identifying and classifying cervical cell pre-cancerous stages.
Double awareness mechanism based deep learning framework for image captioning

Understanding the qualities of an image and converting them into a phrase or sentence that makes sense is the process of image captioning. Neuroscience research has only recently made clear the connection between human vision and language formation. Although there have been many methods for captioning images, including content retrieval and template filling, the current trend is toward deep learning-based methods. Using an image encoder, feature vectors are created from an image through the deep learning process, and a language decoder converts these feature vectors into a string of words. Using encoder-decoder approach or simple attention based has not provided so much efficient results. In the proposed model, double awareness-based mechanism has been used. The primary goal of this study is to extract visual properties from the region of interest (RoI) of an image as well as text features using the glove embedding technique. Inception ResNet version of Convolutional neural network (CNN) is used as an encoder. As a decoder, a gated recurrent unit is employed. The proposed model is tested on Flickr8k dataset and it can be seen that the results achieved through double awareness mechanism are highly effective.
COLosSAL: A Benchmark for Cold-Start Active Learning for 3D Medical Image Segmentation

Medical image segmentation is a critical task in medical image analysis. In recent years, deep learning based approaches have shown exceptional performance when trained on a fully-annotated dataset. However, data annotation is often a significant bottleneck, especially for 3D medical images. Active learning (AL) is a promising solution for efficient annotation but requires an initial set of labeled samples to start active selection. When the entire data pool is unlabeled, how do we select the samples to annotate as our initial set? This is also known as the cold-start AL, which permits only one chance to request annotations from experts without access to previously annotated data. Cold-start AL is highly relevant in many practical scenarios but has been under-explored, especially for 3D medical segmentation tasks requiring substantial annotation effort. In this paper, we present a benchmark named COLosSAL by evaluating six cold-start AL strategies on five 3D medical image segmentation tasks from the public Medical Segmentation Decathlon collection. We perform a thorough performance analysis and explore important open questions for cold-start AL, such as the impact of budget on different strategies. Our results show that cold-start AL is still an unsolved problem for 3D segmentation tasks but some important trends have been observed. The code repository, data partitions, and baseline results for the complete benchmark are publicly available at https://github.com/MedICL-VU/COLosSAL .
Comparison of the ease of use and accuracy of two machine learning algorithms – forestry case study

With the availability of massive amounts of data and cheap computing, machine learning has become increasingly viable to create extensive multivariate mathematical models of natural phenomena to help predict accurate future trends that would have been impossible for humans to accomplish by themselves. There is a wide variety of different machine learning algorithms available, and it is not always known which one will perform best for a given dataset. This can be determined after training and evaluating the different models and comparing them. In this case study, logistic regression and random forest models were compared in terms of accuracy and ease of use. We hypothesized that logistic regression would yield a higher accuracy and be easier to set up in a comparable scenario for a given dataset compared to random forest. Both algorithms used the same forestry dataset to see which one would outperform the other. Initially, logistic regression looked like the better choice, however, after a variety of comparisons, random forest yielded higher performance in both accuracy measurements (accuracy=0.9722, Fbeta=0.9722 for random forest vs. accuracy=0.7141, Fbeta=0.6990 for logistic regression) and did not require as much detailed tuning as logistic regression did.
Issuing Digital Signatures for Integrity and Authentication of Digital Documents

In this paper, we developed a secure system for issuing digital signature for digital documents and certificates using message digest algorithm (MD5). The developed system is providing the integrity and authentication for certificates by combining the information of participant and supply them to MD5 algorithm to produce a unique hash key of 32 digits which is hard to guess or attack. The process provides a certificate which allows for the authentication of a document at any time. The proposed system used and tested to produce digital certificate for participant of electronic seminars in Mustansiriyah university which were about 1000 certificate. The results were good and very fair to authenticate certificates and preventing forgery.
Mobile Application for Student to Learn English Delivered in Malay Language

Mobile learning or we can simply refertom learning is aprocess of learning via the internet network using mobile devices like smartphones, tablets, laptops, and a lot more. M-learning has increasingly become a popular trend around the globe. Moreover, Learning English is skill-based learning that requires various approaches and techniques. So, the use of technology like mobile applications is the latest solution to solve the challenges of learning English. The study aims to develop a mobile application for students to learn English grammar but delivere din our mother tongue language which is the Malay language. The application helps students learn in their leisure time with out the presence of their teacher. The software development methodology that will beused in this research is the incremental model.
Anthropogenic activity effect on water quality of epikarst spring in the western part of Gunungsewu Karst Area, Java Island, Indonesia

Epikarst springs are the main source of water used by the community in the western part of Gunungsewu Karst Area. One of the springs found in Gunungsewu karst area is Guntur Spring, with a catchment area that functions as agricultural land and settlements. Guntur Spring has connectivity between swallow holes and karst windows, so the spring can easily be polluted; therefore, a temporal study is needed regarding the water quality. The methods used were analysis of water quality standards, Schoeller diagrams, triangular Piper diagrams, and scatter plots. The results of the analysis of water quality standards showed that only Ca&lt;sup&gt;2+&lt;/sup&gt; and HCO&lt;sub&gt;3&lt;/sub&gt;&lt;sup&gt;-&lt;/sup&gt; ions and a small portion of Na&lt;sup&gt;+&lt;/sup&gt; exceeded WHO standards. The results of the Schoeller diagram analysis showed that the dominant ions were Ca&lt;sup&gt;2+&lt;/sup&gt; and HCO&lt;sub&gt;3&lt;/sub&gt;&lt;sup&gt;-&lt;/sup&gt;. The results of the Piper diagram analysis showed that the Guntur Spring is included in the classification of types I, J, and M, indicating the spring is not polluted. The results of the Piper diagrams showed that Guntur Spring is included in the dominant types of types C, A, G, D, and F, indicating the springs are not included in the polluted category. The scatter plot analysis of the Ca&lt;sup&gt;2+ &lt;/sup&gt;+ Mg&lt;sup&gt;2+ &lt;/sup&gt;versus HCO&lt;sub&gt;3&lt;/sub&gt;&lt;sup&gt;- &lt;/sup&gt;+ SO&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;2-&lt;/sup&gt;&lt;strong&gt; &lt;/strong&gt;showed that the dominant ion in the Guntur Spring came from the dissolving of limestone. The scatter plot analysis of HCO&lt;sub&gt;3&lt;/sub&gt;&lt;sup&gt;- &lt;/sup&gt;versus Na&lt;sup&gt;+&lt;/sup&gt;&lt;strong&gt; &lt;/strong&gt;indicated that the main process that forms Guntur Spring ions is not the dissolving process of silicates. The scatter plot analysis of Cl&lt;sup&gt;-&lt;/sup&gt; - SO&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;2- &lt;/sup&gt;versus Na&lt;sup&gt;+&lt;/sup&gt; indicated that the main process that forms Guntur Spring ions is not the dissolving process of sodium/sodium sulfate and halite. There was one sample in the Cl&lt;sup&gt;-&lt;/sup&gt; versus Na&lt;sup&gt;+&lt;/sup&gt; scatter plot analysis that showed an indication of an anthropogenic pollution. However, the overall analysis showed that anthropogenic activities in the study area do have a major impact on the water quality of the epikarst springs at the study site.
Expert System for Diagnosing Learning Disorders in Children Using the Dempster-Shafer Theory Approach

Learning disorders can occur in children where a child experiences difficulty mastering important skills such as reading, writing, or arithmetic. Learning disorders can have an emotional impact on children, such as low self-confidence, anxiety, or frustration. Therefore, it is important for parents and educators to recognize the signs of learning disorders so that appropriate intervention can be given. The aim of this research is to develop an expert system that can diagnose learning disorders in children using the Dempster-Shafer Theory algorithm to make it easier to diagnose and produce the right diagnosis. The Dempster-Shafer Theory approach has the ability to provide probability values in evidence based on the level of belief and reasoning in accordance with logic and then combine it with information from certain events. This research produces an expert system built on a website that can diagnose based on symptoms and display diagnosis results, definitions of types of learning disorders, and treatment options. The accuracy test results show a value of 92%, which means that the system built using the Dempster-Shafer Theory approach is able to diagnose learning disorders in children well.
Data Curation in Interdisciplinary and Highly Collaborative Research

This paper provides a systematic analysis of publications that discuss data curation in interdisciplinary and highly collaborative research (IHCR). Using content analysis methodology, it examined 159 publications and identified patterns in definitions of interdisciplinarity, projectsâ€™ participants and methodologies, and approaches to data curation. The findings suggest that data is a prominent component in interdisciplinarity. In addition to crossing disciplinary and other boundaries, IHCR is defined as curating and integrating heterogeneous data and creating new forms of knowledge from it. Using personal experiences and descriptive approaches, the publications discussed challenges that data curation in IHCR faces, including an increased overhead in coordination and management, lack of consistent metadata practices, and custom infrastructure that makes interoperability across projects, domains, and repositories difficult. The paper concludes with suggestions for future research.
Innovative Reform Strategies of Artistic Practice of College Ethnic Music Education Based on Information Fusion Technology

Abstract To seek the path and method of innovation and reform of art practice of ethnic music education in colleges and universities. Based on information fusion technology, this paper introduces the Kalman filter algorithm for analysis and recognition of ethnic music education in colleges and universities and understands the application of information fusion technology in ethnic music education in colleges and universities through the correct recognition rate and recognition time of music signals. We also apply independent experiential and problem-based experiments to verify the feasibility of information fusion technology in innovative reform. The experimental results show that the Kalman filter algorithm based on information fusion technology has a 96.57% correct recognition rate for ethnic music signals, and the recognition time is faster than the support vector machine and BP neural network by 3.587s and 1.291s on average. 73.47% of the students, on average, think that the innovative reform of art practice based on information fusion technology in college ethnic music education is very effective, which is higher than the original The average percentage of students who thought the effect of the innovation reform of folk music education based on information integration technology was very good was 73.47%, which was 60.37 percentage points higher than the original research results. The average percentage of students who thought it was ineffective was only 9.19%, 52.31 percentage points lower than the original research results. The above results prove the feasibility of information fusion technology in reforming art practice innovation in university ethnomusicology education and provide a new direction for reforming art practice innovation in university ethnomusicology education.