Reviewerly/exHarmony huggingface dataset:
E-participation System: Leveraging Blockchain Technology to Enhance Democratic Engagement

This research aims to build an e-participation system for the citizens in the public policy process using blockchain technology. E-participation makes it easier for citizens to have a say in important policy decisions and helps the government be more open, inclusive, collaborative, and thoughtful. It improves government transparency, citizen involvement, and marginalized group inclusion. Yet, the limited scope of existing e-participation approaches and implementation processes makes using e-participation ineffective. E-participation platforms for citizen-government engagement often fail to meet targets and uptake expectations. In addition, the challenges involve overcoming the constraints associated with current methods, guaranteeing transparency, security, and privacy when engaging citizens, and effectively managing the intricate process of integrating various components and handling confidential information. However, blockchain applications are gaining popularity because blockchain technology can change the way how businesses operate. This study offers a blockchain-based innovative e-participation system to increase democratic engagement in a secure and decentralized environment as centralized servers struggle with transparency, security, and privacy concerns. Decentralized, immutable, and incorruptible blockchain technology solves these challenges. The proposed e-participation system can encourage public participation in governance and promote democracy. The proposed E-participation is based on a model that includes government agencies, the public/citizens, digital platforms, decision-making, communication/feedback, and decision-making. This research uses national population data and election codes and regulations to run experiments and simulation models. Process simulation will develop and build the e-participation system's software, hardware, and storage devices. The proposed four layers of blockchain-based e-participation are integrated. They have their purposes, which include government agencies, registration and verification, a blockchain-based e-participation network, and data storage.
Tumor Detection of Breast Tissue Using Random Forest with Principal Component Analysis

When it comes to cancer-related mortality, breast cancer is the most common and predominant kind in women; lung cancer is the most common. It ranks second overall. Global scientists have been working very hard to fight this illness for a long time. Furthermore, significant advancements have been achieved in the fields of machine learning and data mining for the extraction and synthesis of insightful knowledge, even from extremely complicated data sources. Machine learning models may carry out a number of functions, including clustering, classification, and prediction, by using the knowledge obtained from data. In this study, we examine the relationship between a dataset's several features and a diagnosis of breast cancer. We use five different variables extracted from X-ray images in the dataset to predict the existence of breast cancer using a supervised learning classification technique called Random Forest. The open-source web repository Kaggle served as the source of this dataset. We use Principal Component Analysis (PCA), a dimensionality reduction method, on the data prior to putting the Machine Learning algorithm into practice. We then assess the performance of the Machine Learning algorithm using measures like accuracy, precision, recall, F1-score, and support. Additionally, we compare the model's output with Random Forest and examine the performance metrics it produces with and without PCA analysis.
Deep Learning Approach for Bone Marrow Cell Detection and Classification on Whole-Slide Images

The bone marrow cell analysis is taken as the critical standard for diagnosing leukemia. However, owing to the diverse morphology of these bone marrow cells, a lot of patience along with extensive experience is required for the examination. In this research paper, a deep learning method has been proposed for intelligent detection and classification of the bone marrow cells through applying the object detection model and pattern recognition in order to minimize the error probability and work intensity as well as improve the work proficiency on contrary to the human recognition methods for bone marrow cell detection. The proposed method has used Faster R-CNN along with the generalized average precision loss (G-AP loss) method to improve the accuracy of the cell detection.
Visual Comparison of Text Sequences Generated by Large Language Models

Causal language models have emerged as the leading technology for automating text generation tasks. Although these models tend to produce outputs that resemble human writing, they still suffer from quality issues (e.g., social biases). Researchers typically use automatic analysis methods to evaluate the model limitations, such as statistics on stereotypical words. Since different types of issues are embedded in the model parameters, the development of automated methods that capture all relevant aspects remains a challenge. To tackle this challenge, we propose a visual analytics approach that supports the exploratory analysis of text sequences generated by causal language models. Our approach enables users to specify starting prompts and effectively groups the resulting text sequences. To this end, we leverage a unified, ontology-driven embedding space, serving as a shared foundation for the thematic concepts present in the generated text sequences. Visual summaries provide insights into various levels of granularity within the generated data. Among others, we propose a novel comparison visualization that slices the embedding space and represents the differences between two prompt outputs in a radial layout. We demonstrate the effectiveness of our approach through case studies, showcasing its potential to reveal model biases and other quality issues.
The Influence of Chatbots Adoption on Customer Loyalty in the Foodservice Sector

Chatbots are transforming how businesses interact with customers. Using natural language processing and AI-enabled technology, chatbots provide customers with an automated, personalized, and intuitive experience that offers convenience and fast responses. As a result, the adoption of chatbots is claimed to enhance customer experience and increase customers’ loyalty. Despite this advantage, the adoption of chatbots is still relatively low in foodservice sector in Indonesia. The reason is most likely because many customers and business owners have yet to become accustomed to this type of technology. As a result, the adoption of chatbots is still in the preliminary stages at this point. Subsequently, it is yet unknown whether implementation of technology can influence a consumer’s loyalty in foodservice sector, especially in Indonesia. This study aimed to examine the impact of chatbot adoption on customer loyalty in the food service industry. One hundred ninety-five people were surveyed to test the suggested hypothesis. The results of the analysis showed support for each of the hypotheses that were proposed. The research found that chatbots’ perceived ease of use positively affects food- service customer’s loyalty. Also, this research revealed that chatbots’ perceived usefulness positively affects food- service customer’s loyalty. Additionally, chatbots’ perceived effectiveness positively affects foodservice customer’s loyalty. The findings are expected to inform food service businesses and other service companies in regard to the importance of chatbots implementation and its positive impact on customer loyalty. Lastly, the result aims to contribute to the literature that focuses on the adoption of chatbots in the tourism and hospitality industry, especially in the context of hospitality businesses in Indonesia.
Decision Support System for Distribution Of Financial Assistance To Flood Victims

Business Intelligence can be used in making decisions about the distribution of aid for victims of post-flood disasters. Analysis carried out with the help of Business Intelligence can also help in efficiently planning the allocation of financial assistance. The stages in this study include clustering the severity of flooding from DKI Jakarta Provincial Flood Event Recapitulation Data, using the K-Means Clustering method. Furthermore, ranking is carried out to get the order of giving the amount of assistance using Weighted Product, Simple Additive Weighting, and TOPSIS. This research produces a decision support system that can be used in the process of distributing aid to locations affected by floods and even other disasters. The evaluation of the DSS (Decision Support System) results involves comparing the rankings generated by the system with the rankings based on the actual ground truth factors. The WP method obtained an MSE of 20.00, an RMSE of 4.47, an MAE of 20.00, and a MAPE of 1.31% with a very accurate category. The TOPSIS method obtained an MSE of 10.73, an RMSE of 3.28, an MAE of 10.73, and a MAPE of 1.32% with a very accurate category. Whereas the SAW method obtained an MSE of 20.37, RMSE of 4.51, MAE of 20.37, and a MAPE of 1.22% with a very accurate category.
Image Enhancement for Breast Cancer Based on Image Contrast, Interpolation and Filtering

The present work proposes a methodology for enhancing images by the application of contrast adjustment, interpolation techniques, and filtering algorithms. Techniques employ a dual-system approach. The first image enhancement technique commonly involves contrast augmentation, bicubic interpolation, and the application of a median filter for denoising purposes. The second technique involves bicubic interpolation and contrast enhancement. The suggested methodology enhances the quality of images, as evidenced by the achieved Peak Signal-to-Noise Ratio (PSNR) values of 0.46 dB, Signal-to-Noise Ratio (SNR) values of 0.459 dB, Structural Similarity Index (SSIM) values of 0.009, and Mean Squared Error (MSE) values of 1.32E-04. The suggested method utilizes both objective and subjective measures to depict the outcomes of image enhancement. These metrics are also used to compare the results with other methods. The strategy under consideration takes into account more intricate details, heightened contrast, and enhanced edge definition, leading to a superior image outcome compared to the method being compared.
Machine Learning Model to Selection of Job Applicant Documents to Increase The Human Resources Department Performance

The planning of the company's existing and future human resource needs is included in the process of human resource recruitment. The availability of human resources, in terms of both quality and quantity, will be significantly impacted by the recruitment process, if it is both efficient and successful. The initial stages of the employee recruitment process involve selecting applicant documents according to company needs. Currently, the document selection process is done manually by studying the applicant documents one by one and then grouping them into several categories. This mechanism is inefficient and risks producing the wrong choices. On the other hand, machine learning has text recognition and image processing capabilities to increase the effectiveness and efficiency of the process. So, the purpose of this research is to help companies, especially the human resources division, improve the effectiveness and efficiency of new employee selection, especially the selection and classification of job applicant documents. The research method uses a qualitative approach with a literature review to identify new employee recruitment problems was continued by designing data sets, training data, and testing with machine learning using the LVQ method. The result of this research is that a machine learning model builds a model for selecting job applicant documents using machine learning with the Learning Vector Quantization (LVQ) method.
AI-powered Chatbot for Information Service at Klabat University by Integrating OpenAI GPT-3 with Intent Recognition and Semantic Search

The rapid advancement of Artificial Intelligence (AI) in recent years has transformed numerous sectors, including information services. This study introduces Unklabot 1.0, an innovative AI-powered chatbot specially designed and proposed for information services at Klabat university. Unklabot 1.0 was developed to take advantage of an advanced large language model (LLM), namely OpenAI GPT-3 (text-davinci-003), and integrate it with intent recognition and knowledge-based context understanding using semantic search technique to provide accurate and efficient question answering capabilities about Klabat University. To enhance Unklabot's performance, intent recognition is integrated into the Unklabot system. Moreover, this intent recognition helps identify the purpose behind user queries, allowing the chatbot to understand user intentions more accurately. Furthermore, semantic search technique is employed to enhance the capabilities of the Unklabot by understanding the meaning behind user queries and retrieve information in external knowledge based on the closest context or chunk. Utilizing OpenAI GPT-3 API, Unklabot can processes natural language inputs, providing comprehensive and contextually relevant answers to user inquiries. This research successfully implemented the OpenAI GPT-3 API in information services, offering a valuable tool to increase the engagement of AI usage in educational institutions such as Klabat University.
Comparative Analysis of Multiple ML Models and Real-Time Translation

A huge population in the present world is suffering from hearing impairment. This hinders their ability to communicate and express their opinions. Implementing an accurate gesture capture and interpretation system through an image classification model can significantly reduce the impact of hearing impairment, enabling effective recognition of the user's gestures. The proposed project develops machine learning models based on computer vision and image processing to capture and interpret the hand gestures of differently-abled people. Various image classification algorithms are studied, and a comparative analysis using predefined evaluation parameters identifies the most effective solution for gesture recognition for individuals with hearing impairments. A real-time live video implementation model to recognize certain regularly used gestures is also executed. This video implementation will detect some of the frequently used signs, enabling easier communication for individuals with hearing impairments and muteness. The proposed approach is versatile, with applications in device control and user feedback.
Implementation in an Electronic Development Board of a Hybrid Computational Learning System for the Optimization of Artificial Neural Networks for Autonomous Mobile Robots

Artificial Neural Networks (ANN) used to control autonomous robots must be trained to guarantee their proper functioning. In most cases, unsupervised computational algorithms are used to optimize neural networks in simulators, which regularly generates discrepancies at the end of the process between virtual and real environments (reality gap). As for the learning processes developed in real environments, they are typically slow, reaching several days to obtain satisfactory results. Therefore, an option based on real environments is presented which uses a hybrid approach in an obstacle avoidance task. An ANN is trained using supervised learning and a database containing some of the environmental stimuli. Afterwards, a generational genetic algorithm is created for the optimization of ANN, including the previously trained network as an initial individual. This algorithm is housed in an electronic development board to control a differential robot, which has ultrasonic and infrared sensors for collision detection. The performance of supervised, unsupervised and hybrid learning is compared with the same robotic platform, which demonstrates a better aptitude value for the created alternative. Thus, the hybrid learning system allows reducing unsupervised training time in real environments using a microcontroller with limited memory capacities. This offers an alternative for learning autonomous robots with different locomotion mechanisms and electronic components.
A Comprehensive Framework of Combined Lossless Image Compression Algorithm for Light-Field Hyperspectral Images

Light field hyperspectral imaging captures the spatial, spectral and angular information of a scene, resulting in a massive amount of high-dimensional data. The storage and transmission of this data require high bandwidth and storage capacity, which can be both time-consuming and costly. Image compression can reduce data volume without compromising image quality. Lossless compression is a difficult undertaking for light field hyperspectral images due to their high dimensionality and complexity. Existing lossless compression techniques, such as JPEG-LS and PNG, are not efficient in compressing light field hyperspectral images. These techniques either fail to achieve sufficient compression ratios or require a long encoding and decoding time. Moreover, the existing techniques are not optimized for light field hyperspectral images and do not exploit the specific characteristics of these images. Existing lossless compression techniques for hyperspectral data suffer from several limitations, including high computational complexity, low compression ratios, and loss of spectral and spatial information. Therefore, there is a need for the development of a more efficient and effective lossless compression system for hyperspectral data. In this work, we focus on to address the challenges posed by large amounts of hyperspectral data.
Tuning of a State Feedback Controller Using MOPSO

This work deals with the tuning of state feedback controllers by means of the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, which simultaneously optimizes several complex fitness functions in the Pareto optimal sense. Classical techniques for controller tuning may not be able to handle these complex fitness functions, excepting in some particular cases as the Linear Quadratic Regulator methodology, which minimizes quadratic functions of the state and the control signal. This work employs the MOPSO algorithm using two conflicting fitness functions. One depends on the tracking error, while the other considers the variation and the amplitude of the controller signal. The outcomes are validated using numerical simulations of a servo system as a case study. These results allow observing the effects of the tuning on the closed-loop response according to the weights provided for each fitness function.
The Blockchain Model for Drug Management to Increase The Transparency of Drug Distribution and Drug Authenticity

Drug distribution is a very complex process that involves various parties, starting from the pharmaceutical industry, drug distributors, pharmacies, drugstores, and customers. The drug distribution network is very broad and involves many parties, making it difficult for customers and related parties to obtain transparent transactions in the drug distribution network as well as information about the authenticity of the drugs that customers will consume. On the other hand, blockchain technology offers features that can increase transaction transparency, so that blockchain can be a solution to drug distribution problems. Thus, the aim of this research is to build a blockchain model to increase the transparency of transactions between parties involved in the drug distribution network and provide information on guaranteeing the authenticity of medicines for customers. The research method uses a qualitative approach through a literature review to identify problems related to drug distribution. Literature review is also used to study conventional drug distribution mechanisms to find out the root causes of the drug distribution process. The output of the research is a model for adopting blockchain technology to increase the transparency of drug distribution transactions. The conclusion of this research shows that the adoption of blockchain technology can increase the transparency of drug distribution. The limitation of this research is that it uses simulations of only a few nodes in the blockchain so that future research can be carried out involving many nodes in the blockchain.
Deep Learning Approach for Cost and Storage Optimization of Video Streaming in Cloud Environments

As the demand for video streaming continues to rise, cloud computing has become a vital infrastructure for delivering reliable and scalable services. However, cost management and storage allocation pose significant challenges in this context. This paper presents a novel application of deep learning techniques to optimize cost and storage utilization in cloud environments for video streaming. The proposed framework leverages a hybrid deep learning model that combines Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. CNNs are employed for pattern recognition, allowing the model to identify recurring patterns in resource usage. LSTM networks, on the other hand, specialize in sequence prediction and enable accurate forecasting of future resource demands. By analyzing historical video views, the model can make precise predictions about future resource needs. This enables dynamic resource scaling, allowing cloud infrastructure to be efficiently allocated in response to streaming demands. Moreover, the framework addresses storage optimization challenges by incorporating deep learning insights into data placement, replication, and retrieval strategies. The results demonstrate significant improvements in cost efficiency and storage utilization, with cost reductions ranging from 15% to 20% compared to state-of-the-art methods.
The Automated Future: How AI and Automation Are Revolutionizing Online Services

Artificial intelligence (AI) and automation are rapidly transforming the landscape of online services. From intelligent chatbots to hyper-personalized recommendations, AI-driven technologies are enabling online providers to deliver more customized, streamlined, and human-like digital experiences. This paper examines the growing role and impacts of AI and automation across a range of online industries, products, and interactions. It analyzes key applications like natural language processing, computer vision, predictive analytics, and robotic process automation that are automating everything from customer service to supply chain logistics. The benefits of these technologies, including 24/7 availability, scalability, and cost savings, are discussed along with challenges around data privacy, transparency, and workforce impacts. The paper underscores how AI and automation will shape the future of how companies engage and serve digital consumers. As online providers continue to collect more data and refine predictive algorithms, they edge closer to delivering truly individualized services tailored to customers' ever-evolving preferences and needs. However, realizing the promise of AI will require addressing critical issues around bias, ethics, and accountability. This paper provides a comprehensive look at the current landscape and future trajectory of AI and automation in online services.
Utilizing Machine Learning for the Identification of Visually Similar Web Elements

A significant proportion of web elements might manifest identical behaviors and functionalities, further complicating the test case creation process. We have noted that elements demonstrating congruent behavior and functionality frequently exhibit visual resemblances within the web page interface. In this paper, we propose a machine learning-based method to identify visually similar web elements. These identified elements can be brought to the attention of the tester. If the tester determines that these elements exhibit similar behavior, they can be visually marked on the web page, facilitating focused test case creation. A case study demonstrates its potential to make accurate judgments on several complex webpages.