Reviewerly/exHarmony huggingface dataset:
Sounding Sentiments: A Cognitive AI Approach to Speaker-Linked Emotion Analysis

Authors:
raw_author_name: Dr. Seema Singh

Abstract: Abstract: Over the past few decades, there has been significant progress in sentiment analysis, primarily focusing on analyzing text. However, the field of sentiment analysis linked to audio remains relatively undeveloped in the scientific community. This study aims to address this gap by introducing sentiment analysis applied to voice transcripts, specifically focusing on distinguishing emotions of individual speakers in conversations. The proposed research article seeks to develop a sentiment analysis system capable of rapidly interacting with multiple users and analyzing the sentiment of each user's audio input. Key components of this approach include speech recognition, Mel-frequency cepstral coefficients (MFCC), dynamic time warping (DTW), sentiment analysis, and speaker recognition.
Forecasting Gold Prices in Indonesia using Support Vector Regression with the Grid Search Algorithm

Authors:
raw_author_name: None Nonong Amalita
raw_author_name: None Dodi Vionanda
raw_author_name: None Zamahsary Martha

Abstract: Investment is an effort to increase economic growth in Indonesia. A popular investment in the community is gold investment. The value of gold investments tends to increase but is not immune from price fluctuations, therefore it is important to forecast the price of gold in Indonesia. The method that can be used to make this forecast is Support Vector Regression (SVR). SVR is a method that looks for a function that has a deviation of no more than ε to get the target value from all training data. The best SVR model with a linear kernel was obtained from a combination of parameters C=0,0625 and ε=0,001 with a RMSE value of 0,19734 and a value of 0,974112. So, the SVR method is appropriate to use for forecasting gold prices in Indonesia.
Penerapan Algoritma Naive Bayes untuk Klasifikasi Demam Berdarah Dengue di RSUD dr. Achmad Darwis

Authors:
raw_author_name: None Atus Amadi Putra
raw_author_name: None Nonong Amalita
raw_author_name: None Fadhilah Fitri

Abstract: Dengue Hemorrhagic Fever (DHF) is a disease transmitted through the bite of the Aedes Aegypti mosquito. Limapuluh Kota Regency BPS stated that the morbidity rate due to dengue fever was 14.40% per 100,000 population, this figure jumped high from the previous year with a morbidity rate of 3.30% per 100,000 population. The main symptoms of dengue fever are fever that lasts for 2-7 days, pain felt in the muscles and joints accompanied by a rash or no rash, dizziness, and even vomiting blood. Dengue infection can cause various clinical symptoms, ranging from dengue fever, dengue hemorrhagic fever, to dengue shock syndrome. Based on this, there is a need for a classification method that can help and facilitate early diagnosis of dengue fever. The method used is Naive Bayes by classifying dengue positive and dengue negative patients. The aim of this research is to determine the results of the classification of patients suffering from dengue fever, as well as to determine the level of accuracy using the Naive Bayes method. Based on research that has been carried out, the results of the classification of patients are 58 correct and 14 patients classified incorrectly. The accuracy results obtained in this algorithm were quite high, namely 80%, while the sensitivity was 65% and the specificity was 86.5%.
Study of Robot Path Planning with Improved A* And DWA Algorithm Fusion

Authors:
raw_author_name: Jiayi Chen

Abstract: For some problems of the traditional A* algorithm in robot path planning, such as inefficient search, many and complex inflection points in the designed and realized paths, as well as the inability to complete real-time dynamic path planning for obstacle avoidance, this paper proposes an improved A algorithm and combines it with Floyd for path rounding, and finally with DWA (Dynamic Window Approach) algorithm to be Fusion. In the improved A* algorithm, a carefully normalized random obstacle environment is used and the search process is optimized by reducing the search direction and redundant nodes. Meanwhile, the evaluation function is adjusted to achieve the optimal global design solution, and path optimization and obstacle avoidance are performed by the DWA algorithm. The experimental results finally show that when the robot is in a complex environment, the fusion algorithm can ensure the completion of the globally optimal route while avoiding obstacles in a timely and effective manner, which realizes more efficient path planning.
Categorical Data Clustering with K-Modes Method on Fire Cases in DKI Jakarta Province

Authors:
raw_author_name: None Dony Permana
raw_author_name: None Atus Amadi Putra
raw_author_name: None Zilrahmi

Abstract: In DKI Jakarta Province, the number of fires increases and decreases every year. For this reason, efforts need to be made to prevent and reduce the risk of fire. BPBD DKI Jakarta is responsible for this matter. However, for these efforts to be effective, information is needed regarding fire patterns that frequently occur. Fire patterns can be seen using K-Modes categorical clustering analysis. The data used is fire data in DKI Jakarta in 2018. The optimal number of clusters was obtained as 6 clusters based on the Davies Bouldin Index value with the smallest DBI value is 6,22. Of the six clusters, cluster 3 is the cluster with the highest number of fire cases. Cluster 3 has a centroid, namely that fire cases occurred on Friday, November, in Cakung District, due to an electrical short circuit, burning down residential houses and rarely causing minor injuries, serious injuries or deaths.
Classification of Stroke Disease at Dr. Drs. M. Hatta Brain Hospital Bukittinggi With Decision Tree Algorithm C4.5

Authors:
raw_author_name: None Zamahsary Martha
raw_author_name: None Atus Amadi Putra
raw_author_name: None Admi Salma

Abstract: Stroke is a health condition that has vascular disorders where brain function is related to problems with blood vessels that carry blood to the brain. Several factors that can influence stroke include unhealthy eating habits, lack of physical activity, smoking behavior, alcohol consumption, and obesity. The symptoms experienced are headache, nausea, vomiting, blurred vision and difficulty swallowing. The researcher’s aim is to determine the risk faktors that affect the incidence of stroke hospitalization based on stroke diagnoses at the DR. Drs. M. Hatta Brain Hospital Bukittinggi city by classifying each variable using a decision tree. A decision tree is a flowchart that resembles a branching tree. The C4.5 algorithm is used in this research, which can process numerical and categorical data, can handle missing attribute values, and produces rules that are easy to interpret. The results of the analysis show that the attribute that is a risk factor for stroke is the heart. The model created using the C4.5 algorithm was tested using a counfusion matrix resulting in an accuracy of 64.54%, a precision of 53.34% for classifying ischemic stroke patients correctly, and a recall of 72.73% for classifying hemorrhagic patients correctly.
Clustering-based assessment of solar irradiation and temperature attributes for PV power generation site selection: A case of Indonesia’s Java-Bali region

Authors:
raw_author_name: Gregorius Satia Budhi
raw_affiliation_string: Informatics Department, Faculty of Industrial Technology, Petra Christian University, Indonesia

Abstract: This study presents clustering-based assessments of solar attributes for locating potential solar photovoltaic (PV) power plant sites using k-means and density-based spatial clustering of applications with noise (DBSCAN) by examining the yearly average single-attribute and three-attribute clustering on a dataset of long-term hourly-based direct and diffuse irradiation, ambient temperature, and solar PV power output from 2005 to 2022. Three-attribute clustering enables stakeholders to better understand the characteristics of a cluster by collectively identifying three solar attributes and the magnitude of each attribute in an area or cluster. The presence of this information, which constitutes the clusters, suggests that these attributes have different effects on solar PV output power in different clusters. Although k-means is an effective method for investigating potential locations for PV power plant placements, DBSCAN offers users an alternative method for accomplishing a similar goal. In the case of three-attribute clustering of direct irradiation with k-means and DBSCAN, the 18-year mean value of clusters with the highest yearly average value is achieved at very similar values of 0.305 kW/m2 and 0.310 kW/m2, respectively. It turns out that only six years of direct irradiation had an annual mean value of less than 0.305 kW/m2. This finding implies that in the long run, the solar resources in terms of direct irradiation will typically surpass 0.3 kW/m2/MW installed capacity over all areas suitable for PV power plants. While focusing on the Java-Bali region, Indonesia, the findings, and methods appear to be of broader interest to policymakers, particularly in developing countries where solar PV is considered an option for sustainable energy generation.
Analysis of social networks and filtering of Arabic crime tweets based on an intelligent dictionary using a genetic algorithm

Authors:
raw_author_name: None Zainab Khyioon Abdalrdha
raw_author_name: None Abbas Mohsin Al-Bakry
raw_author_name: None Alaa K. Farhan

Abstract: Preserving a robust online community poses a significant difficulty due to the unrestricted flexibility members have in expressing themselves and behaving. This issue can be remedied through the implementation of user behavior monitoring and analysis, followed by the implementation of suitable actions. The objective of this research is to develop an intelligent dictionary using the genetic algorithm to identify and categorize Twitter posts related to criminal activities by collecting data from tweets. Once the data is preserved, graph analysis techniques are employed to evaluate interactions between users. Next, the user behavior is analyzed using metadata analysis, whereby the chronology associated with each user profile is obtained. Furthermore, the study analyzes the behavioral patterns of users over time. Afterward, a method based on rules is used to create a structure for aspect-based analysis of sentiment. This method assesses the subjectivity of the input text, distinguishing between factual information and personal opinions. Additionally, transformer-based sentiment analysis determines whether the tweet evokes positive or negative sentiment. Furthermore, the task involves constructing a model that can accurately categorize a tweet based on its relevance to criminal activity. Ultimately, the intelligent dictionary is utilized to identify and isolate anomalous behavior by selecting provocative profiles. A Twitter profile exhibiting significant similarity with criminal cases presents a perilous risk to society.
Breast Cancer Classification using XGBoost

Authors:
raw_author_name: None Suman Das

Abstract: Breast cancer continues to be one of the foremost illnesses that results in the deaths of numerous women each year. Among the female population, approximately 8% are diagnosed with Breast cancer (BC), following Lung Cancer. The alarming rise in fatality rates can be attributed to breast cancer being the second leading cause. Breast cancer manifests through genetic transformations, persistent pain, alterations in size, color (redness), and texture of the breast's skin. Pathologists rely on the classification of breast cancer to identify a specific and targeted prognosis, achieved through binary classification (normal/abnormal). Artificial intelligence (AI) has been employed to diagnose breast tumors swiftly and accurately at an early stage. This study employs the Extreme Gradient Boosting (XGBoost) machine learning technique for the detection and analysis of breast cancer. XGBoost provides an accuracy of 94.74% and recall of 95.24% on Wisconsin breast cancer Wisconsin (diagnostic) dataset.
The Effectiveness of Discovery Learning Model on Students’ Writing Skills at Vocational High School Diponegoro Losari

Authors:
raw_author_name: Edy Waloyo
raw_affiliation_string: IAIN Syekh Nurjati Cirebon

Abstract: This study investigates the impact of the discovery learning model on the writing skills of eleventh-grade students at Vocational High School Diponegoro Losari. The research used a One Group Pretest-Posttest Design and analyzed performance test scores before and after the implementation of the model. The results showed that students with the discovery learning model scored significantly higher in their analytical exposition text writing skills, with an average score of 76.66 compared to 36-45% before the model. The study also found that the use of the discovery learning model significantly improved the students' analytical exposition text writing skills.
Distributed Bootstrap Simultaneous Inference for High-Dimensional Quantile Regression

Authors:
raw_author_name: Xingcai Zhou
raw_affiliation_string: School of Statistics and Data Science, Nanjing Audit University, Nanjing 211815, China
raw_author_name: Zhaoyang Jing
raw_affiliation_string: School of Statistics and Data Science, Nanjing Audit University, Nanjing 211815, China

Abstract: Modern massive data with enormous sample size and tremendous dimensionality are usually impossible to process with a single machine. They are typically stored and processed in a distributed manner. In this paper, we propose a distributed bootstrap simultaneous inference for a high-dimensional quantile regression model using massive data. Meanwhile, a communication-efficient (CE) distributed learning algorithm is developed via the CE surrogate likelihood framework and ADMM procedure, which can handle the non-smoothness of the quantile regression loss and the Lasso penalty. We theoretically prove the convergence of the algorithm and establish a lower bound on the number of communication rounds ιmin that warrant statistical accuracy and efficiency. The distributed bootstrap validity and efficiency are corroborated by an extensive simulation study.
Improving the YOLOV7 Algorithm for Object Detection within Recorded Videos

Authors:
raw_author_name: Asmaa Hasan Alrubaie
raw_affiliation_string: Department of Computer Sciences, University of Technology, Baghdad, Iraq
raw_author_name: Ahmed Talib Abdulameer
raw_affiliation_string: Department of Information Technology, Middle Technical University, Baghdad, Iraq

Abstract: Object detection algorithms play an important role in detecting people in surveillance videos. In recent years, with the rapid development of deep learning, the performance of object detection has improved by leaps and bounds, and the scheme of object detection by the YOLOV7 algorithm has also been born. Traditional object detection methods often fail to achieve a balance between speed and accuracy. To address these issues, in this research, an improved YOLOv7 algorithm performance is proposed to get the best speed-to-accuracy balance compared to state-of-the-art object detection within recorded videos using an effective compression method. This method calculates the difference between frames of video, and by using the zero difference approach by removing the duplicate frames from the recorded video and choosing only the meaningful frames based on many variables, including frame size, frame details, and the distance of the frames, influence the choice of a meaningful frame, and this will reduce the size of the video by eliminating the frames comparable to those chosen. Additionally, any other datasets or pre-trained weights have not been used; YOLOv7 has been exclusively trained on the MS COCO dataset from scratch. In order to ensure the effectiveness of this approach, numerous detection systems are used in this work. Additionally, positive performance results to reduce the processing time required for object detection have been attained.
Academic Citizenship Behaviour Mediates Between Green Compensations and Rewarding, Green Recruitment and Selection, and Organization Environmental Performance in The Jordanian Government Universities

Authors:
raw_author_name: Yusnita Yusof

Abstract: Background: This paper examines the academic citizenship behaviour mediating the effect between green compensation and rewards, green recruitment and selection, and organization environmental performance in Jordanian government universities. Methods: The paper contributes to filling this research gap by examining these effects. A sample of 404 employees in the Jordanian Government universities was analysed using contemporary variance-based structural equation modeling (Amos - SEM) software. Results: The results indicated a positive association between green compensation and rewards and two variables, namely academic citizenship behavior, and organization environmental performance. They also indicated a positive association between green recruitment and selection and two variables: academic citizenship behavior and organization environmental performance. As well as between academic citizenship behavior and organization environmental performance. Furthermore, they showed mediating effects of academic citizenship behavior in the relationship between green compensation and rewards and organization environmental performance and between green recruitment and selection and organization environmental performance. Conclusion: This paper helps to provide insights to HRM practitioners, shedding light on the importance of GHRM practices and their role in enhancing organization environmental performance.
Literasi Dasar Bagi Para Siswa Sekolah Dasar

Authors:
raw_author_name: Veronika Genua
raw_affiliation_string: Universitas Flores, Ende, Indonesia
raw_author_name: Yosef Demon
raw_affiliation_string: Universitas Flores, Ende, Indonesia

Abstract: Basic literacy is a basic priority or priority scale that needs to be strengthened and stabilized. The cultivation and formation of basic literacy skills is a real manifestation of the mental revolution program, even mental evolution. Therefore, basic literacy needs to be strengthened because it is the foundation for the lives of future generations. Apart from that, the basic literacy movement that is being intensified aims to support the realization of free humans. What is meant by free humans are free humans who can read and write to gain knowledge and use this knowledge for the good and sustainability of their lives. This article discusses the implementation of Community Service in Rangalaka Village, Kotabaru District, Ende Regency, which will take place from 1 to 30 August 2022. The focus of service activities is the implementation of basic literacy activities in the form of tutoring for students in Rangalaka Village, namely Lewoketo Inpres Elementary School and Satap Ligalejo Middle School. Tutoring is focused on elementary and junior high school students to provide knowledge strengthening, instilling a virtuous attitude in students so that they have good knowledge and attitudes in facing their future.
Exploring the Literature of Data Analytics Services on Cloud Computing: A Comprehensive Summary

Authors:
raw_author_name: Safa S. Abdul-Jabbar
raw_affiliation_string: Computer Science Department/ College of Science for Women University of Baghdad, Baghdad, Iraq
raw_author_name: Alaa k. Farhan
raw_affiliation_string: Computer Science Department, University of Technology, Baghdad, Iraq

Abstract: Data generated from the internet and modern applications is extensive and rapidly expanding. So, all modern applications must successfully perform tasks using this massive data. Therefore, one of the significant factors for the success of any application is understanding and extracting meaningful information using digital analytics tools to positively impact the application's performance and deal with challenges that can be encountered. On the other hand, cloud computing is simply an environment comprising a collection of high-performance services from various vendors. These services can frequently access and process massive amounts of data faster than a traditional computer. One of these services is cloud analytics, which applies cloud-based analytic algorithms against data in a private or public cloud to get the desired outcome. This paper differentiates between several analytics methodologies to create highly consistent, logical, and information-rich summaries. This research has discussed and analyzed many studies related to this field. Therefore, the results of this paper can be utilized to determine the advantages of these methods, which will help future researchers in their research for a more organized and thorough analysis when dealing with such applications. In addition, this paper attempts to discover new directions and propose new guidelines in this evolving field.
Rancangan dan Efektifitas Media Pembelajaran Audiovisual pada Mata Kuliah Bahasa Prancis Pariwisata

Authors:
raw_author_name: Putu Weddha Savitri
raw_affiliation_string: Universitas Udayana

Abstract: The Covid-19 pandemic has brought considerable changes in the world of education. Learning methods that were previously carried out face-to-face must switch to online methods. This requires teacher innovation and creativity so that learning material can be conveyed properly. One of the learning media that is very helpful in distance learning is to use audiovisual media. This research is a needs analysis that aims to be able to find out the responses and needs of students towards the audiovisual media used so far in French Language Courses. Qualitative and quantitative methods were used in analyzing data in the form of questionnaires and interviews. The results of the analysis show that the use of audiovisual media as learning material gets a positive response from students. Students also hope that audiovisual material can be developed even better, including short durations, added interesting animations, subtitles so that students understand conversations, as well as repetition in terms of pronunciation and material summaries at the end of the video.
Analisis Penggunaan Shuujoshi Joseigo oleh Kuroko Shirai pada Anime "Toaru Kagaku No Railgun" Episode 1-3

Authors:
raw_author_name: K. E. K. Adnyani
raw_affiliation_string: Universitas Pendidikan Ganesha

Abstract: This research aims to describe the types of shuujoshi joseigo and their functions used by Kuroko Shirai in anime entitled Toaru Kagaku no Railgun in 1–3 episodes. The data were analyzed using Sudjianto (2007) &amp; Pestemer's theory (2003). This qualitative descriptive research used listening and note-taking methods to collect the data. The results of this study are 114 data which are divided into nine types of shuujoshi joseigo used by Kuroko Shirai's character, namely 29 data of shuujoshi wa, 10 data of wane, 6 data of wayo, 53 data of no, 11 data noyo, 2 data of mono, as well as 1 data for each none, kashira, and teyo, whose functions are to soften sentences, provide information, show admiration and impression, express subtle question sentences, justify the situation, show assertiveness, express opinions, emphasize opinions, and express doubts