Reviewerly/exHarmony huggingface dataset:
Political Opinion Mining for Popularity Prediction using Machine Learning Techniques

Twitter's utility among voters, politicians, and others keeps rising. This research offers the findings of an investigation into the viability of using Twitter data for purposes such as monitoring public sentiment and anticipating vote preferences. To gauge public sentiment, we amassed and categorised tweets regarding government programmes using natural language processing techniques. We were able to predict the electoral fortunes of various political parties using supervised learning techniques applied to a huge dataset of tweets. Unsupervised learning methods were used to analyse the cleaned data for trends and patterns. To identify whether a tweet was positive or negative towards a candidate, we used supervised learning techniques. Our findings suggest that Twitter data can be utilised to predict the electoral fortunes of political parties and the public's reaction to policy proposals. We ran across problems like the overuse of irony and sarcasm and the usage of difficult-to-decipher jargon and lingo. Because of these obstacles, thorough data pre-treatment and analysis is required before any results can be drawn. Our findings suggest political campaigns and parties might utilise Twitter data to gauge public opinion and attract new voters. Despite some reservations, this essay defends the use of social media data for this.
MNNP: Design and Development of Traffic Sign Identification and Recognition System to Support Smart Vehicles using Modified Neural Network Principles

In recent times, the acceleration of automotive and computer vision technologies has sparked significant interest in autonomous vehicles. These vehicles' capacity to operate securely and effectively hinges on their adeptness at accurately identifying traffic signs. Consequently, traffic sign recognition has emerged as a pivotal element within autonomous driving systems. Scientists have been hard at work looking at various approaches to accomplishing this recognition, mostly drawing on machine learning and deep learning techniques. To address this issue, we offer a Modified Neural Network approach that combines the Haar cascade algorithm with a MobileNet model classifier. Our developed model is rigorously trained on the GTSRB dataset and then put to the test on a wide variety of traffic sign types. The culmination of our efforts yields a remarkable testing accuracy rate of 97.25%. This finding highlights the possible effectiveness of our technique in improving autonomously vehicle traffic sign identification, which in turn helps to improve the security and effectiveness of autonomous driving systems.
Development of a Predictive Model of Burgling to Homes based on Machine Learning

The general objective of the paper is to determine the probability of burglary in the city of Chennai, and determine the factors that influence this type of crime, thus identifying the houses most prone to it. For this, the aforementioned public database of police cases is used to design and execute a python program based on neural networks that manages to obtain predictions of house robberies in different areas of the city. This paper investigates the relevant features in the obtained data sources to determine which are most important for theft prediction. Once data is selected, Data preprocessing is performed to prepare the extracted data is prepared and cleaned so that it can be correctly included in subsequent models. In the case of contextual information from images, convolutional neural networks are used for feature extraction. Then, neural network architecture is built with one input for each information source and a concatenation layer to merge the inputs later. Finally, a classifier is trained using fully connected layers and an output layer with a sigmoid activation function. With these probabilities, the classification of each record is finally obtained and, therefore, the prediction of house burglaries. The result of the neural network built with a classic machine learning model such as SVM is compared. Each model's hyper-parameters are tuned to get improvements in each model's performance. The satisfaction of this objective will be determined according to the feasibility of building this model, that is, if probabilities can actually be obtained using the aforementioned data sources. The model will be evaluated through different metrics such as accuracy, precision, recall and F1 score.
Aspect-based sentiment analysis for social media text using NLP and Deep Learning

Participatory moments on social media platforms increasingly add up to something more substantial. Communicating our thoughts and feelings about the book through shared observations, appraisals, and illustrative examples. For instance, the data posted on social media platforms like Twitter can be mined for insights into users' values, beliefs, and emotions. The author's perspective can be better understood through the lens of sentiment analysis. Almost all studies of social media's massive user base have looked at how users' sentiments can be broken down into positive, negative, and neutral categories. In this project, we've set out to define the phrases in terms of four distinct emotional states: joy, rage, fear, and melancholy. There have been a lot of approaches implemented in the field of dynamic textual sentiment recognition in the event of further interactions, but not nearly enough of them were based on intensive training. In this research, we elaborate on a game-changing deep learning-based method (RNN+LSTM) for dealing with a variety of problems associated with emotion distribution by making use of informative data. We present a novel method for translating it to a binary distribution and a standard machine-learning classification problem, and we employ a comprehensive knowledge technique to settle the reconstructed matter. In terms of classification accuracy, our hybrid approach will prevail over more conventional ml methods.
Reliable Public Key Cryptosystem Type El-Gamal

In this paper, the public key cryptosystem type El-Gamal has been proposed in two dimensions. Proposed cryptosystem, which is called reliable El-Gamal public key cryptosystem (R-EPKC), depended on the discrete logarithm problems (DLPs) for computing the public key and ciphertext of the plaintext that is divided into two parts m <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> and m <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> . The ciphertext in R-EPKC is represented by the network system consists of two paths. First path contains the C <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> and C <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> of m <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> and second one contains C <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> ’ and C <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> ’ of m <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> . The reliability of the proposed R-EPKC is determined using the path tracing method (PTM). New technique for combining encrypted messages in two parts has been used with a reliable method that is called parallel-series reduction method (PSRM) which is employed for decrypting the ciphertext and recovering the original plaintext. New computational result is presented as a study case. The security considerations have been determined. The proposed R-EPKC is more suitable cryptosystem for communications.
OUTLIER DETECTION WITH MACHINE LEARNING IN WIRELESS SENSOR NETWORKS

In recent decades, Wireless sensor networks have made significant strides, drawing interest from the scientific and industrial communities. Such networks' scattered sensor nodes function autonomously in challenging environments, leaving them susceptible to mistakes and attacks that could reduce the reliability and accuracy of sensor readings. Sensor readings are categorized as aberrant data, outliers/anomalies when they considerably vary from the predicted healthy behaviors. Such outliers can have a significant influence on the decision-making process and subsequent results in data analytics. As a result, the academic community has recognized the use of machine learning algorithms for outlier detection in WSNs as an innovative and promising methodology. On the basis of numerous viewpoints taken from the body of current research, we present a thorough definition of outliers in this work. We offer a novel and creative method to identify sensor irregularities by utilizing machine learning techniques. By utilizing pattern recognition and anomaly detection methods, machine learning enables us to analyze sensor data and find outliers. We give a comparative assessment of several approaches using machine learning paradigms for outlier detection in WSNs in order to provide a thorough understanding. For academics and practitioners looking to choose the best strategies for their unique application settings, this overview is an invaluable resource. In the end, we explore the main issues surrounding the identification of outliers in WSNs. The dynamic nature of WSNs, the finite resources of sensor nodes, the changing climatic conditions, and the requirement for real-time detection are only a few of the problems that these difficulties cover.
Development of E-Book Biology Question Bank Model for Minimum Competency Assesment To Train Information Literacy

The question bank developed in this study contains biology questions on the Minimum Competency Assessment (AKM) Level 5 Class 10 model to train students' information literacy. This question bank is intended to train students' literacy and familiarize students with AKM questions. A question bank contains a set of items that are calibrated for validity, item difficulty and reliability. The purpose of this research is to create a question bank based on validity proof, analysis of item difficulty levels and estimating the reliability of AKM model biology questions. This research uses the ADDIE model with the stages of analysis, design, development, implementation and evaluation. The feasibility of AKM model biology questions that will be tested is validated by material experts. Item analysis uses the Rasch 1PL model approach with the Winstep application. The results of the study showed that the AKM biology questions were declared feasible by experts and 48 items were valid, 7 items were very easy, 18 items were easy, 17 items were difficult, 8 items were very difficult, and the reliability value was in the excellent category. The conclusion of this question bank is that 34 items are included in the question bank and 16 other items are not included.
Innovation of Physical Education Teaching Mode in Higher Vocational Colleges from The Perspective of Entrepreneurship Quality Education

With the continuous progress of the times, the cause of education is also undergoing continuous reform and innovation, which makes in the teaching activities, the teaching methods of many disciplines have changed to a great extent, and the higher vocational sports discipline is one of them. Because of the continuous deepening of the concept of quality education, some higher vocational colleges have gradually changed their teaching philosophy when carrying out specific teaching activities, and then pay more attention to the overall development of students. In the stage of higher vocational education, sports discipline has a very important position, it can not only effectively improve the students' comprehensive physical quality, but also to a certain extent, improve the students' entrepreneurial quality. Based on this, this paper analyzes the cultivation methods and objectives of entrepreneurship quality of higher vocational college students, hoping to provide some help for the development of related work.
USING FABLES TO ASSIST STUDENTS IN ENHANCING THEIR READING COMPREHENSION

The main problem in this study was "Is it effective to teach reading comprehension by using fables to the eleventh-grade students at SMK Rawas Hulu?". The objective of this study is to find out whether or not teaching narrative text by using fables to eleventh-grade students at SMK Rawas Hulu is effective. The method used in this study was the quasi-experimental method. The population of this research was the eleventh-grade student at SMK Rawas Hulu in the academic year of 2022/2023. The population was 240 students, and 240 students were taken as samples, which were chosen through purposive sampling in the form of convenience sampling. The written test was used to collect the data and analysed through paired samples t-test. The result of this study proved that are the fables effective for teaching reading comprehension by using fables to the eleventh-grade students at SMK Rawas Hulu, it is proved by the degree of freedom (df) 33, sig with 2-tailed 0,000. T- obtained shows that the alternative hypothesis with 95% (0,05) of the significant level was accepted because the result of paired samples t-test was 3.088 which exceeded the t-table value was 2,042 as its critical value under 0,05 significant levels. Besides, the alpha value It (0,000) was less than 0,05. it indicated that the null hypothesis (Ho) was rejected and the alternative hypothesis (Ha) was accepted. It means that Teaching Reading Comprehension by Using Fables to The Eleventh Grade Students at SMK Rawas Hulu was effective.
The Effect of Electric Vehicle Product Quality on Customer Satisfaction and Customer Loyalty : Focusing on the Chinese Market

This study conducted an empirical analysis on the effect of electric vehicle product quality on satisfaction, electric vehicle brand and loyalty to consumers who have purchased and used electric vehicles in the Chinese electric vehicle market, and analyzed the mediating effect of customer satisfaction between electric vehicle product quality and customer loyalty.&#x0D; Garvin (1984) reviewed prior research using the eight components of the proposed product quality and established questionnaires on the eight components, and conducted a 12-day survey from August 07 to 18, 2023. The actual questionnaire collected the results of a total of 266 people, excluded 41 copies without experience in using electric vehicles, 18 invalid questionnaires, and analyzed the relationship between customer satisfaction and customer loyalty using 207 valid questionnaires.&#x0D; According to the results of the analysis, it was confirmed that core functions, appearance characteristics, reliability, aesthetics, and cognitive quality had a positive (+) significant effect on customer satisfaction in the product quality model, and core functions, reliability, consistency, and cognitive quality had a positive (+) significant effect on customer loyalty. In addition, customer satisfaction had a positive (+) effect on customer loyalty. In addition, customer satisfaction has a mediating effect in the relationship between core performance, reliability, cognitive quality, and customer loyalty of electric vehicle product quality. Finally, core performance, reliability, and cognitive quality, which are components of electric vehicle product quality, have all been shown to have a positive (+) effect on customer satisfaction and customer loyalty, so it is necessary to increase core performance, reliability, and cognitive quality to improve customer satisfaction and customer loyalty.
Innovative and Inclusive Digital Applications to Enhance Literacy in Students with Dyslexia

The adoption of digital applications has been used to carry out innovative and inclusive tests that reduce gaps in different fields, especially in education; such is the case of the improvement in reading comprehension levels. The present study seeks to demonstrate that digital applications improve the reading and writing skills of students with dyslexia in Ecuador, in the year 2023. It is a quasi-experimental design in which 60 students belonging to section A and B participated. at the basic elementary level of the afternoon session of the "Simón Bolívar" Millennium Educational Unit, Ecuador 2023. Thirty of the experimental group. 20 items were applied where each one is a combination of school literacy activities adaptable to the classroom in person and to the virtual environment, this comprises 3 dimensions, which presented a good level of reliability and content validity. For the analysis, descriptive statistics (Wilcoxon test) were carried out, demonstrating that the program generated significant changes in the experimental group with a and in the control group, thus demonstrating that the program has positive effects on the learning of children with dyslexia.
Application of the Just-in-Time Teaching (JiTT) Model in Bilingual Courses of Chinese-Foreign Cooperative Education

The teaching abilities of bilingual teachers have emerged as one of the major bottlenecks hindering the further development of Chinese-foreign cooperative education projects. Implementing the JiTT (Just-in-Time Teaching) model in bilingual courses of Chinese-foreign cooperative education can effectively integrate information technology with traditional classroom teaching. This integration enhances teaching quality and effectiveness, encourages university instructors to improve their diverse and collective capabilities, promotes digital development, and contributes to the enhancement of students’ learning abilities and overall improvement.
SPATIAL ANALYSIS OF WATER BALANCE DISTRIBUTION FOR SIMULATION OF TIMING AND PATTERNS OF RICE AND CROP PLANTING IN THE REGION OF TYPE D RAINFALL IN THE SOUTH KONAWE REGENCY

Water availability plays a crucial role in the environmental hydrological cycle, where the concept states that the amount of water in a specific area on the Earth’s surface is influenced by the input and output of water over a certain period. Water imbalance can lead to excess (surplus) and deficit (water shortage), impacting various sectors, including the agricultural sector, particularly in food crop cultivation, causing seasonal shifts and changes in planting patterns. These impacts can be minimized through adequate land and environmental management, achieved by calculating the water balance conditions in a given region. This research aims to (1) analyze the spatial distribution of land water balance in a type D rainfall region and (2) determine the timing and crop planting patterns based on the water balance of rice and secondary crops in the type D rainfall region. The study employs survey methods and analysis using the Thornthwaite water balance calculation. The results indicate water surplus in the Palangga and Baito subdistricts with an 8-month surplus period, reaching 192.40 mm month-1 in June and a minimum of 2.04 mm month-1, covering various sampling points and administrative areas such as Kiaea, Watudemba, Watumerembe, Wawonggura, Eewa, Onembute, Anggondara, Mekar Sari, Wawouru, Aosole, Sanggi-sanggi, Palangga, Tolihe, Sambahule, Matabubu, Mekarjaya, Wonua Raya, Ahuangguluri, Amasara, Wawouru, Mekarsari, Anggondara, Aosole, Eewa, and Onembute. Water deficit in the Palangga and Baito subdistricts occurs for four months, peaking at 58.59 mm month-1 in June at point 6 and reaching a minimum of 4.68 mm month-1 in February at point 2. The Palangga and Baito subdistricts exhibit a cropping pattern of corn + mung bean - irrigated rice - fallowing/vegetables; soybean/irrigated rice - fallowing/vegetables; corn + peanuts - irrigated rice - fallowing/grass for animal feed.
Determinants of Fraud Victimizations in Malaysian E Commerce: A Conceptual Paper

Malaysia's eCommerce industry is constantly growing, fueled by COVID-19-related online services and the country's increasing smartphone usage Data.The World Health Organization declared the COVID-19 outbreak to be a global pandemic.On March 11, 2020, various countries responded quickly with tight social confinement enforcement (commonly referred to as "lockdowns") as an effective approach to regulate human interaction and viral transmission. With stay-at-home restrictions, travel bans, and social isolation, many people are joining the growing trend of internet purchase. Changes in customer behavior and government reactions have had an impact on the biological systems and economy of the cyber world. This study is expected to provide insights into the factors that impact cybercrime victims in Malaysian e-business. Similarly, prior research on fraud have primarily focused on traditional and offline fraud, with little data in the online or fraud victims arena. As a result, this study contributes fresh theoretical connections and empirical evidence on the interplay between fraud victimization, suitability and lack of a guardian. This study looks at the variables that affect Malaysian e-commerce fraud victimization, and it is anticipated that the study's population includes all of Malaysia in terms of cyberfraud. Individuals connected to cyber fraud will be the unit of analysis in this cross-sectional study, which uses a survey methodology. The PDRM database will be used to choose the study's respondents. A series of questionnaires will be used as the research instrument in this study to gather data, and respondents will get a set of questionnaires to complete.