Reviewerly/exHarmony huggingface dataset:
Perbandingan Decision Tree C4.5 dan Support Vector Machine (SVM) Dalam Klasifikasi Penderita Stroke Berbasis PSO

Stroke is a major health problem for today's modern society. At present, stroke is increasingly becoming a serious problem that is faced almost all over the world. This is because a sudden stroke can result in death, physical and mental disability in both productive and old age. To obtain stroke information data, it is necessary to carry out data mining processes such as classification. Classification is a process for determining a model that explains or distinguishes concepts or data classes, with the aim of being able to estimate the class of an object whose class is unknown, in classification a number of records are also given which are called training sets, which consist of several attributes, attributes can be be continuous or categorical, one of the attributes specifies the class for the record In the above problems regarding stroke, to be able to overcome this problem, a lot of research has been carried out in the field of computer science, including the Classification of Stroke Patients Using the C4.5 Decision Tree Algorithm and the Support Vector Machine (SVM) Algorithm to classify the most important factors for this disease. The test resulted in a fairly high accuracy of the C4.5 Decision Tree Algorithm, which was 96.11%. Referring to the results of the accuracy of the research, it can be seen that the Decision Tree produces high accuracy, but the results of this accuracy can still be improved by conducting further research to produce higher accuracy by adding Optimization Feature Weighting PSO.Keywords: Accuracy, Stroke, PSO, C4.5 Decision Tree Algorithm. Support Vector Machine (SVM) Algorithm
Penentuan Kelayakan Penerima Pembiayaan Kredit Menggunakan Metode Naive Bayes dan Algoritma C4.5 (Studi Kasus: KSPPS BMT Adil Berkah Sejahtera)

Banking provides credit services to facilitate the community in running a business. To minimize risk, banks need to analyze the feasibility of extending credit to customers. This research aims to obtain the best procedure for classifying credit worthiness to customers. The reason for choosing the best method is the method that is appropriate for use in the classification of credit granting with the highest accuracy value. The highest accuracy was selected through a comparison of the c4.5 and naive bayes algorithms. The accuracy results obtained from the c4.5 algorithm with three tests were 91.23% and AUC 0.686 while Naive Bayes produced an accuracy of 89.90% and AUC 0.744 Keywords: Creditworthiness, classification, algorithm c4.5, naive bayes
Solving the Individualized Instructional Content Delivery Problem Using Ontology and Metaheuristics on the Set Covering Problem: An Experimental Analysis

Intelligent Tutoring Systems (ITSs) based on a step-by-step problem-solving approach are limited in terms of compatible content. On the other hand, recommendation systems can suggest various content types but lack the granularity of concepts found in step-by-step approaches. This study addresses this challenge by proposing a method to recommend instructional content from diverse knowledge domains while incorporating the refined concepts of ITSs. To tackle this issue, the instructional content delivery problem (LORP) is formulated as a set covering problem, classified as NP-hard. We show that a PSO-based algorithm is a good candidate to solve LORP in a better runtime than the exact algorithm and with better solutions than the greedy heuristic. By leveraging collaborative filtering and an ontology that models students’ knowledge, learning styles, and search parameters, the approach offers more individualized content.
The Relationship Between Data, Technology, and Humanism Literacy and Integrity in lecturer Research Performance

In an era driven by technological progress, lecturers, as the main agents in academic research, are faced with the demand to understand and integrate data, technology, and human values into their research. The purpose of writing this article is to describe the research performance of lecturers based on the factors of data literacy, technological literacy, and humanism literacy, as well as the integrity of lecturers in higher education. This research method involves surveys and data analysis by a number of lecturers who are active in research in various scientific disciplines. This research method also uses a quantitative approach and a cross-sectional study design. Data was collected through a Google Form questionnaire distributed via WhatsApp. The research data was analyzed using multiple regression (OLS). This article contributes to an understanding of the importance of integrating data literacy, technology, and human values into lecturers' research approaches. The practical implication of this research is the need to develop training programs and institutional support to increase literacy and integrity in the context of academic research. The research results show that literacy and data integrity have a significant effect on lecturers’ research performance. Meanwhile, technological literacy and humanism literacy do not have a significant effect on lecturers' research performance. Lecturers’ research performance through the results of this research is influenced by variations in data literacy, technological literacy, humanism literacy, and integrity by 36.1%.
A Fuzzy Logic-Based Differential Evolution for Energy-Efficient UAV Path Planning

Unmanned aerial vehicles (UAVs) are emerging as a critical tool in various real-world applications. However, the path planning of UAVs is still challenging. Since UAVs need to fly to several task spots and complete tasks at each spot, the energy consumption of not only UAV flight but also task execution (such as data collection and processing) should be considered, as it may vary across different spots. Therefore, this paper develops a new path planning model for UAVs and proposes a novel fuzzy logic-based differential evolution algorithm that provides efficient path planning while ensuring minimal energy consumption.
Komparasi Algoritma C4.5 dan Naive Bayes Dalam Prediksi Penderita Penyakit Gagal Jantung

Every year more than 36 million people die from Non-Communicable Diseases (PTM) (63% of all deaths). More than 9 million deaths from non-communicable diseases occur before the age of 60, and 90% of these “early” deaths occur in low- and middle-income countries. Globally PTM the number one cause of death each year is cardiovascular disease. Cardiovascular disease is a disease caused by impaired function of the heart and blood vessels, such as: coronary disease, heart failure or heart failure, hypertension and stroke. An estimated 17.3 million deaths are caused by cardiovascular disease.Keywords: C45 algorithm and naïve bayes algorithm; Rapid Miner;
Scaled Agile Method Design: Case Study of a SaaS Company in Indonesia

Improving team coordination for optimal performance is one of the main challenges that organizations encounter when scaling their Agile practices. Therefore, numerous companies encounter difficulties in achieving their desired performance due to this challenge. This study aims to address the challenge by proposing a new project management method design for a Software as a Service (SaaS) company in Indonesia with ineffective project management, using a scaled Agile approach. This paper contributes to providing insight into the implementation of scaled Agile method design in an organization. The research follows qualitative methods based on the Design Science Research (DSR) methodology and the eight domains of the Project Management Body of Knowledge (PMBOK), comprising six phases and two iterations. The first iteration involves projecting the current state of the company's activities and resulting in a proposal for a new project management method. The second iteration involves developing and evaluating the proposed method against the PMBOK standard. According to the findings, there are two domains that align with PMBOK and project management in the firm, namely the Stakeholder and Cycle Performance Domains. Team Performance, Development Approach, and Life Planning Performance Domain, Project Work Performance Domain, Delivery Performance Domain, Measurement Performance Domain, and Uncertainty Performance Domain are the other domains that do not suit the standard. Adequate efficient training for team performance, fostering collaboration among team members building corporate culture, and aligning company business processes with Agile methods are essential to overcome some inconsistent results.
Virtual Tour As A Tourist Attraction Promotion Media Using Multimedia Development Life Cycle

This study is dedicated to the development of a comprehensive web-based virtual tour application known as the "Virtual Tour Application for Lembah Rembulan Tourist Attraction," designed to function as an information hub. Despite possessing diverse attractions and geographical advantages, the local tourism sector is inadequately promoted due to limited utilization of digital technology. The research follows the Multimedia Development Life Cycle (MDLC) methodology. Notably, the virtual tour website developed in this study provides a website profile, social media information, contact details for the tourist destination operator, and reviews, serving as valuable considerations for prospective tourists. Another noteworthy feature is the dynamic nature of information in the virtual exploration component, enabling tourist attraction operators to make real-time adjustments. It is anticipated that the Virtual Tour Application for Lembah Rembulan Tourist Attraction will serve as a platform for information sharing, fostering immediate community interest in visiting Lembah Rembulan Tourist Attraction. Black-box testing results affirm the effective construction of the system based on proposed scenarios, with two testing roles: visitors and tourist attraction operators.
Fault Analysis on AES and SM4 Through Automatic Property Extraction and Checking

Fault analysis is an effective cryptanalysis method for both unprotected and masked cryptographic implementations. However, it usually involves complex mathematical derivations or computation intensive statistical analysis, and requires knowledge about the fault location or pattern. Motivated by the recent advances in machine learning and formal verification, this paper proposes a fault analysis method on AES and SM4 through automatic property extraction and checking. It automatically mines verifiable fault related properties from a limited number of fault traces and retrieves the key through formal checking of the extracted properties under the verification constraints of correct and faulty ciphertext pairs. Experimental results show that 35 and 25 fault traces are enough for creating precise fault properties for AES and SM4 respectively. With two correct and faulty ciphertext pairs that satisfy any of the extracted properties, a round key of AES and SM4 can be uniquely determined.
NNLeak: An AI-Oriented DNN Model Extraction Attack through Multi-Stage Side Channel Analysis

Side channel analysis (SCA) attacks have become emerging threats to AI algorithms and deep neural network (DNN) models. However, most existing SCA attacks focus on extracting models deployed on embedded devices, such as microcontrollers. Accurate SCA attacks on extracting DNN models deployed on AI accelerators are largely missing, leaving researchers with an (improper) assumption that DNNs on AI accelerators may be immune to SCA attacks due to their complexity. In this paper, we propose a novel method, namely NNLeak to extract complete DNN models on FPGA-based AI accelerators. To achieve this goal, NNLeak first exploits simple power analysis (SPA) to identify model architecture. Then a multi-stage correlation power analysis (CPA) is designed to recover model weights accurately. Finally, NNLeak determines the activation functions of DNN models through an AI-oriented classifier. The efficacy of NNLeak is validated on FPGA implementations of two DNN models, including multilayer perceptron (MLP) and LeNet. Experimental results show that NNLeak can successfully extract complete DNN models within 2000 power traces.
Detection of Fake Indian Currency Using Deep Convolutional Neural Network

In the highly advanced technological world of today, counterfeit currency poses a significant threat, given that the face value of paper currency is much higher than its intrinsic value, and its elasticity and it is stable, which make it simple to transfer and secure to store. Despite the complexity of current hardware-based methods for detecting fake notes, the main problem is that these methods are not easily accessible to Ordinary individuals. This paper proposes a method of detecting fake currency by a hybrid approach using CNN and Resnet model to produce accurate results. This system utilizes convolutional neural networks (CNN) and residual networks (ResNet) to detect fake currency utilizing the width, colors, and serial numbers. The system involves stages, like image pre-processing, feature extraction, and identification. In the first stage, the scanned currency images are preprocessed to enhance the quality of the images and reduce noise. Then, features are extracted from the preprocessed images using CNN and ResNet architectures. The proposed automatic system for identifying Indian currency notes and checking whether they are real or fake uses CNN techniques. This system is very useful in banking and other fields, given the rise of counterfeit currency notes in India. Finally, the paper introduces a Deep Learning approach using a convolution neural network (CNN) model that can identify counterfeit notes on handy devices like smartphones and tablets, with a testing accuracy of about 98.3%.
Comparison of hardware-optimized CNN and SVM models for human activity recognition using the HARTH and HAR 70 + datasets

Human activity recognition (HAR) has been of interest for decades as an application for machine learning (ML) in low-power and profile embedded applications such as in FPGAs. This study compares the model performance and FPGA resource utilization estimates after HLS synthesis of representative classical (SVM) and deep (CNN) models using the publicly available HARTH and HAR 70 + datasets along with common optimizations. In resource-constrained environments, CNNs perform better overall than SVMs, with axis reduction (using only the x-axis of the sensors) and data format reduction (using int16) providing substantial improvements in resource utilization without significant performance penalties. For SVMs, loop perforation also provides an improvement although these models necessitate external processing for feature extraction.
Efficient Path Planning Algorithm for Unmanned Aerial Vehicle in Dynamic Environment

To carry out missions independently and safely, the unmanned aerial vehicles (UAVs) must be capable of doing path-planning that avoids collisions with static or dynamic objects. This study is aimed at providing an effective UAV path planning method to operate safely in a dynamic environment. Our method is built upon three core components: bi-directional rule templates, a hybridization of rapidly exploring random tree stars and ant colony systems algorithm (RRT-ACS), and path planning in configuration space-time. Bi-directional rule templates guide the UAV’s movement by offering two potential paths at any decision point. The RRT-ACS algorithm accelerates convergence to an optimal path. Configuration space-time is utilized for path planning, allowing to account for dynamic obstacles and plan paths that avoid future collisions. Multiple simulations were conducted to validate the proposed method in a dynamic environment. Informed RRT*, RRT*FND, and MOD-RRT*, which are state-of-the-art path planning algorithms, are compared to the performance of the proposed method. Numerous experimental outcomes indicate that the proposed method outperforms alternative comparison algorithms. The findings indicate that the proposed method is applicable to UAVs that must navigate in a dynamic environment.
User Interaction Development for Radiotherapy Virtual Reality Training Application

The advancement of virtual reality (VR) technology can enrich pedagogical approaches and information communication. In the oncology radiotherapy treatment, the medical practitioners revealed two main issues: the lack of training infrastructure and patient comprehension of the treatment process. Virtual simulation can assist in solving these complexities by offering an economical virtual embodiment of the medical facility. This study aims to improve the user interface and experience of VR training applications from previous work by conducting a domain expert intervention methodology. This paper discusses the main challenges and features of user interaction design for effective VR training systems and optimises the system based on feedback from domain experts. The authors anticipate that the key factors and design considerations proposed for this VR training application can benefit the development of related training systems in many other domains.
Research on Loads Rapid Prediction for Individual Aircraft Structural Health Monitoring

Aimed at the demand of high-accuracy and low-cost actual aircraft operating load data for individual structural monitoring and maintenance management, an individual loads intelligent prediction method that comprehensively consider the ground calibration, flight test measurement, sample reconstruction, supervised learning and loads prediction is proposed in this paper. The method can realize rapid prediction from flight parameters to aircraft station loads of key structure. Compared with the actual measurement results, the absolute error of wing root shear force and bending moment in maneuver is less than 4% and the fitting accuracy is greater than 98%. The rapid aircraft loads prediction method is independent of additional monitoring system and can provide a high-accuracy and low-cost approach to generate actual usage loads, which can also meet the engineering accuracy requirements.
回転板による指先へのなぞり感提示手法の提案

This study focuses on the importance of the tracing sensation, which helps in identifying surface texture and frictional cues. However, the traditional methods to render this sensation through real tracing movement using a plate or roller are often limited by the size of the devices. The study presents a new approach where a fingertip-sized disk is rotated while in contact with the finger to create a tracing sensation. Results indicate that this method effectively provides a realistic tracing sensation continuously and in any direction. The study also investigates the impact of reversal movements in the direction of rotation on the realism of the tracing sensation, finding that such movements may be particularly effective for tracing rough textures.
Abnormal behavior detection of solitary elderly people using floor vibration

In recent years, the solitary death of elderly people living alone has become a social problem, and demand for a monitoring system to detect abnormalities such as falls and impaired consciousness in the elderly has been increasing. We propose detection system of falls based on differences in the frequency characteristics of floor vibration sounds .We also devised a method to determine the threshold value of the alarm by measuring the staying at a location in the room by estimating the heel contact position. Experimental results showed that the system was able to detect falls with high accuracy. In addition, we were able to obtain effective threshold values for emergency notification for each location in the room.
Identify Hidden Phenotypes in Electronic Health Records Using Machine Learning Technique

scientific information, such as EHRs, have become increasingly famous because of their comfort and price savings related to their use. On the side of providing an extra efficient manner of storing and reviewing affected person facts, using EHRs allows healthcare companies to study and examine healthcare tendencies and outcomes extra effectively. As the use of EHRs grows, so does the need for brand-new methods to perceive and extract medically helpful facts from the facts. Lately, there has been a growing frame of labor that uses device learning strategies to discover hidden phenotypes in EHRs. These hidden phenotypes are defined as “information or characteristics that are not conveniently obvious from the raw EHRs information however, may be gleaned through automatic data mining and evaluation.” This newsletter focuses on the various tactics used to pick out hidden phenotypes in EHRs through machine studying, natural language processing (NLP), deep gaining knowledge of, and supervised mastering techniques. It additionally gives an outline of the most promising algorithms and discusses the challenges and possibilities associated with the usage of this era.
Improved AB-CPREs with HRA Security

Attribute-based conditional proxy re-encryption (AB-CPRE) schemes offer flexible controls over delegations of decryption rights, and offer an efficient solution for enforcing access control on outsourced encrypted datum. Recently, Liang et al. proposed the first lattice-based AB-CPREs with selective attribute security against chosen-plaintext attacks (CPA) in ESORICS 2021. In this paper, we first show how to construct a CPA secure AB-CPRE scheme which is suitable for circuits in NC $^{1}$ with polynomially bounded parameters. By contrast, schemes proposed by Liang et al. are only suitable for circuits in NC $^{0}$ with similar parameters. We then show how to convert our CPA secure AB-CPREs to be secure against honest re-encryption attacks (HRA) under a more reasonable definition. We could show that the CPA security and the HRA security of our schemes are based on standard LWE assumptions for suitable parameters in the standard model.
Mask Detection in Security Cameras Using Deep Learning

Mask detection in security camera surveillance is an important operation for icing public safety and security. The face masks worn by culprits for concealment during criminal acts pose major challenges for traditional surveillance systems. This paper presents a mask detection system that uses deep learning and computer vision ways to analyse live videotape feeds from security cameras for recognizing persons engaged in theft or criminal activity. The proposed system uses a pre-trained convolutional neural network to detect faces in the videotape feed and applies a binary classification model to determine whether the person is wearing a mask or not. The system can detect multiple faces in the videotape feed simultaneously and give real- time cautions when a person is wearing a mask. The proposed system provides a practical and effective result for security personnel to cover the mask-wearing people in the confined areas, thereby helping to reduce the theft rate and enhance public safety.
Federated Learning and Blockchain: A Cross-Domain Convergence

Gaining significant attention within decentralized contexts, Federated Learning (FL) has been positioned as a highly desirable method for machine learning. By enabling multiple entities to train a shared model cooperatively, data privacy and security are preserved by Federated Learning. Harnessing inherent transparency and accountability of blockchain technology to trace and authenticate updates effectively in federated learning has transpired as an up-and-coming avenue to tackle data challenges related to confidentiality, protection, and reliability. This study examines the viability of federated learning and blockchain integration across multiple dimensions. The technological components of this integration., including incentive systems, consensus mechanisms, data validation, and smart contracts, are delved into. In the study, a novel proposed model for federated learning integrated with blockchain is designed and implemented. It is observed that the mean cypher size is 100 bytes for varying values of gradients. The average throughput recorded is 1.7 bytes per second, while the mean accuracy is 87.1% for 50 epochs.