bluuebunny/crossref_metadata_2025 huggingface dataset:
Peeling Back the Layers: Recurrent Talquetamab Skin Toxicity after Supportive Stem Cell Boost in Multiple Myeloma
Authors: Heini Alexander D. , Bacher Vera Ulrike , Akhoundova Dilara , Seipel Katja , Pabst Thomas
Published: April 2024

Introduction: Bispecific antibodies have meaningfully expanded the therapeutic armamentarium in multiple myeloma. Talquetamab is a CD3+ T-cell-redirecting antibody targeting GPRC5D, which is expressed on multiple myeloma plasma cells as well as in keratinized tissues. Due to the expression pattern, toxicity of talquetamab involves skin toxicity. Case Presentation: Here we report the case of a patient who was treated with talquetamab after relapse after CAR-T therapy. The patient developed a severe recurrence of talquetamab-mediated skin toxicity after the administration of a supportive hematopoietic stem cell boost to treat persistent late cytopenias after CAR-T therapy. Conclusion: This case underscores the complex dynamics between novel immunotherapies like talquetamab and stem cell-based interventions in the context of MM treatment, shedding light on the need for personalized approaches to maximize the benefits of these therapies while minimizing their associated adverse effects.
Comparing Preoperative Screening Tools for Elective Urologic Cancer Surgery: Insights from a Cluster Analysis
Authors: Yajima Shugo , Nakanishi Yasukazu , Ogasawara Ryo Andy , Imasato Naoki , Hirose Kohei , Katsumura Sao , Kataoka Madoka , Masuda Hitoshi
Published: April 2024

Introduction: The aim of this study was to evaluate the features and benefits of different geriatric screening tools for enhancing the perioperative care of patients who undergo elective cancer surgery using cluster analysis. Methods: This study was a retrospective, observational analysis of 1,019 consecutive patients who had elective major cancer surgery in the urology department of our hospital from October 2019 to January 2023. Before the surgery, a trained nurse screened the patients using six tools: Eastern Clinical Oncology Group performance status (ECOG-PS), flemish version of the triage risk screening tool (fTRST), geriatric-8 (G8), instrumental activities of daily living, patient health questionnaire-2 (PHQ-2), and simple questionnaire to rapidly diagnose sarcopenia (SARC-F). The study grouped the patients into four clusters based on their scores on these tools and compared their outcomes after the surgery. The outcomes included overall survival, ambulation failure, delirium, and severe complications. The study also examined how each screening tool was associated with the outcomes. Results: Based on their clinical data and screening results, we classified the patients into four groups: Healthy (73%), Depressive (11%), Intermediate (11%), and Unhealthy (5%). The Unhealthy group had the worst outcomes in overall survival (OS), ambulation failure, and delirium, followed by the Intermediate group. In addition, fTRST and SARC-F emerged as significant predictors of OS; ECOG-PS, fTRST, G8, and SARC-F of ambulation failure; ECOG-PS, fTRST, and G8 of delirium; and G8 of severe complications. Conclusion: Various geriatric screening tools were found to have the potential to forecast diverse postoperative outcomes.
Cross-project software defect prediction through multiple learning
Authors: Zakariyau Bala Yahaya , Abdul Samat Pathiah , Yatim Sharif Khaironi , Manshor Noridayu
Published: April 2024

Cross-project defect prediction is a method that predicts defects in one software project by using the historical record of another software project. Due to distribution differences and the weak classifier used to build the prediction model, this method has poor prediction performance. Cross-project defect prediction may perform better if distribution differences are reduced, and an appropriate individual classifier is chosen. However, the prediction performance of individual classifiers may be affected in some way by their weaknesses. As a result, in order to boost the accuracy of cross-project defect prediction predictions, this study proposed a strategy that makes use of multiple classifiers and selects attributes that are similar to one another. The proposed method's efficacy was tested using the Relink and AEEEM datasets in an experiment. The findings of the experiments demonstrated that the proposed method produces superior outcomes. To further validate the method, we employed the Wilcoxon sum rank test at 95% significance level. The approach was found to perform significantly better than the baseline methods.
MyPharmaceutical: an interactive proof of concept
Authors: Jie Khor Ying , Zaaba Zarul Fitri , Omar Mohd Adib
Published: April 2024

With the rise of health awareness, pharmaceutical and cosmetic products should be verified to protect ourselves from health risks. MyPharmaceutical is a proof-of-concept proposed to provide a mobile application for users to carry out product verification and reporting and a web application for administrative purposes. The data on the registered pharmaceutical and cosmetic products were extracted from national pharmaceutical regulatory agency (NPRA) website. MyPharmaceutical mobile application provides functionalities such as searching the registered product, bookmarking products, reporting products, and tracking report status. The mobile application also implemented a barcode scanner feature to provide ease of product verification. A named entity recognition algorithm is applied with the NLP.js library to provide an improved product search feature for the users, where products can be searched with multiple search criteria in a single input. The web application is proposed to support the mobile application, where the NPRA data admins and officers can manage reported products, publish announcements, verify product data, and utilize the analytic dashboard. The system proposed is expected to provide ease of product verification and reporting to assist the public in choosing safe registered products and a platform for NPRA to manage data and deliver information to the users.
Skin cancer diagnosis using the deep learning advancements: a technical review
Authors: Pandey Shailja , Shankhdhar Gaurav Kant
Published: April 2024

It is vital in today's technologically advanced society to combat skin cancer using machines rather than human intervention. Any time the look of the skin changes abnormally, there is a danger that the person might be at risk for skin cancer. Dermatology expertise and computer vision methods must be merged to diagnose melanoma more effectively. Because of this, it is necessary to learn about numerous detection methods to help doctors discover skin cancer at an early stage. This research paper provides a comprehensive technical review of the advancements in using deep learning techniques for the diagnosis of skin cancer. Since skin cancer is so prevalent, early identification is essential for better treatment results. Among the medical uses where deep learning, a kind of machine learning, has shown promise is in the identification of skin cancer. This research investigates the most cutting-edge skin cancer diagnostic deep-learning approaches, datasets, and assessment metrics currently in use. This study discusses the benefits and drawbacks of using deep learning for skin cancer detection. Challenges include ethical and privacy considerations about patient data, the incorporation of models into clinical procedures, and problems with dataset bias and generalisation.
Speed synchronization of two DC motors with independent loads based on the higher load torque
Authors: Saqer Akayleh Ali , Emad Said Addasi
Published: April 2024

Dual-motor and multi-motor electric drive systems have been used in many industrial applications, and speed synchronization of the motors can always get worse by system parameter uncertainties and load torque perturbations. This work focuses on the application of adjustable speed double-direct current (DC) motor drive control systems. In this paper, a system of two DC motors with armature control at different load conditions has been built. The synchronization of these motors was set basing on the higher torque of the two motor shafts. When two DC motors operate at different shafts a challenge appears in synchronization of their speeds, particularly with the existence of load difference allocated on their shafts. This work paid special attention to this problem. It presents a dynamic simulation of speed control and synchronization of dual DC motor drive. The results show the advantages of the used technique in terms of steady-state and transient performance.
A hybrid steganography and watermark algorithm for copyright protection by using multiple embedding approaches
Authors: Zainal Nasharuddin , Hoshi Alaa Rishek , Ismail Mahamod , T. Rahem Abd Al-Razak , Muhsin Wadi Salim
Published: April 2024

In this modern era, it has become much simpler to replicate, sell, and copy the copyright owners' works without their permission as a result of the expansion of digitalization, and it is difficult to identify such violations, posing a threat to the creators' and copyright owners' rights. For many years, the internet has been regarded as one of the most serious threats to copyright, and the content available has varying levels of copyright protection. On the internet, there are numerous copyrighted works, including e-books, movies, news, and so on. Therefore, by using watermarking and steganography techniques, these issues can be solved, which are based on the author's signature information or logo. This paper concluded that the techniques of discrete cosine transform (DCT), discrete wavelet transform (DWT), one-time pad (OTP), and playfair are highly effective when used together to watermark an image or embed a secret message, our lab results validate that our algorithm scheme is robust against several sets of attacks, where the algorithm was assessed by computation of many evaluation metrics such as mean square error (MSE), signal-to-noise ratio (SNR), and peak signal-to-noise ratio (PSNR).
A novel particle swarm optimization-based intelligence link prediction algorithm in real world networks
Authors: Choudhury Deepjyoti , Acharjee Tapodhir
Published: April 2024

Link prediction in social network is an important topic due to its applications like finding collaborations and recommending friends. Among existing link prediction methods, similarity-based approaches are found to be most effective since they examine the number of common neighbours (CN). Current work presents a novel link prediction algorithm based on particle swarm optimization (PSO) and implemented on four real world datasets namely, Zachary’s karate club (ZKC), bottlenose dolphin network (BDN), college football network (CFN) and Krebs’ books on American politics (KBAP). It consists of three experiments: i) to find the measures on existing methods and compare them with our proposed algorithm; ii) to find the measured values of the existing methods along with our proposed one to determine future links among nodes that have no CN; and iii) to find the measures of the methods to determine future links among nodes having same number of CN. In experiment 1, our proposed approach achieved 75.88%, 78.34%, 82.63% and 78.36% accuracy for ZKC, BDN, CFN, and KBAP respectively. These results beat the performances of traditional algorithms. In experiment 2, the accuracies are found as 75.53%, 74.25%, 81.63% and 78.34% respectively. In experiment 3, accuracies are detected as 72.75%, 81.53%, 78.35% and 75.13% respectively.
Clutter evalution of unmanned surface vehicles for maritime traffic monitoring
Authors: Nadiy Zaiaami Muhammad , Abd Rashid Nur Emileen , Ismail Nor Najwa , Ibrahim Idnin Pasya , Enche Ab Rahim Siti Amalina , Zalina Zakaria Nor Ayu
Published: April 2024

A traditional maritime radar system is utilized for ship detection and tracking through onshore transmitters and receivers. However, it faces challenges when it comes to detecting small boats. In contrast, unmanned surface vehicles (USVs) have been designed to monitor maritime traffic. They excel in detecting vessels of various sizes and enhance the capabilities and resolution of maritime radar systems. Nevertheless, just like conventional radar systems, USVs encounter difficulties due to environmental interference and clutter, affecting the accuracy of target signal detection. This research proposes a comprehensive numerical assessment to tackle the clutter issue associated with USVs. This involves gathering clutter signal data, performing numerical analysis, and employing distribution fitting techniques that leverage mathematical distributions to unravel data complexity. The root mean square error (RMSE) is applied in this analysis to validate the efficacy of the distribution model. The results of this study aim to formulate a clutter model that can enhance radar performance in detecting small vessels within cluttered environments.
Description and analysis of Sigfox received signal strength indicator dataset by using statistical techniques
Authors: Lara-Cueva Román Alcides , Yandún-Imbaquingo Edwin Sebastián , Bustamante-Lucio Elvis D.
Published: April 2024

Low power wide area network (LPWAN) technology has expanded and is essential in the development of applications for the internet of things (IoT). The Sigfox LPWAN network is characterized by its long-range coverage, low cost and power consumption. In this article, a set of 5174 values is analyzed, containing 1606 null RSSI data, obtained with the Sipy module and MicroPython, which provide a coverage map of several points with a resolution of 200 meters deployed in Quito–Ecuador. It is evaluated the type of distribution to which the set of network measurements is adjusted and an optimal 900 MHz propagation model in suburban environments is determined from the measurements obtained from the known base station. As a result, the lost values of RSSI were predicted using the inverse normal distribution method in the original values, observing that they conform to a logistic distribution. The data from the base station were subjected to a data augmentation algorithm designed in MATLAB, determining that the stanford university interim (SUI) model reduces the precision error in the trend of the curve by not presenting changes greater than 5 dB, achieving a precision of 97% with respect to the fit of the curve of the data.
Smart measurement and monitoring system for aquaculture fisheries with IoT-based telemetry system
Authors: Megantoro Prisma , Anugrah Antik Widi , Abdillah Muhammad Hudzaifah , Kustanto Bambang Joko , Fadhilah Marwan , Vigneshwaran Pandi
Published: April 2024

The instrumentation design of an online monitoring device for aquaculture media is discussed in this article. The main processor in this internet of things (IoT) real-time telemetry system is an ESP32 board. Temperature, acidity level, conductivity level, dissolved oxygen (DO) level, and degree of oxygen reduction in the water were the aquaculture parameters measured. The ESP32 collects data from each sensor, groups it into a dataset, displays it on the LCD, saves it to the SD card, and then uploads it to the real-time database. In addition, an Android application is being developed for users. This device has been tested to ensure that each measured parameter is accurate and precise. The accuracy test, one of the major results of laboratory scale tests, demonstrates that each parameter has a different measurement error that represents with average error absolute. Six tested sensors/instruments were subjected to the test. Average absolute error for temperature sensor is +0.76%, pH sensor is +1.52%, electrical conductivity (EC) sensor is +10.8%, oxidation reduction potential (ORP) sensor is +14.6%, DO sensor is +9.3%, and total dissolve solids (TDS) sensor is +13.2%. This device is very dependable and convenient for monitoring the condition of aquaculture media in real-time and accurately.
Robust optimal control for uncertain wheeled mobile robot based on reinforcement learning: ADP approach
Authors: Doan Hoa Van , Thi-Thuy Vu Nga
Published: April 2024

This paper presents a robust optimal control approach for the wheel mobile robot system, which considers the effects of external disturbances, uncertainties, and wheel slipping. The proposed method utilizes an adaptive dynamic programming (ADP) technique in conjunction with a disturbance observer. Initially, the system's state space model is formulated through the utilization of kinematic and dynamic models. Subsequently, the ADP method is employed to establish an online adaptive optimal controller, which solely relies on a single neural network for the purpose of function approximation. The utilization of the disturbance observer in conjunction with the compensation controller serves to alleviate the effects of disturbances. The Lyapunov theorem establishes the stability of the complete closed-loop system and the convergence of the weights of the neural network. The proposed approach has been shown to be effective through simulation under the effect of the disturbances and the change of the desired trajectory.
Nonlinear control of three level NPC inverter used in PV/grid system: comparison of topologies and control methods
Authors: Atifi Youness , Raihani Abdelhadi , Kissaoui Mohammed , Lajouad Rachid , Errakkas Khalid
Published: April 2024

With the passage of time, the importance of using renewable energy systems to overcome energy consumption and improve the quality of the grid has emerged through the use of nonlinear control techniques and reliance on advanced types of inverters such as multi-level inverters. This research is focused on comparing two grid-connected converter topologies in a photovoltaic (PV) generation system connected to a three-phase grid that serves a non-linear load. Additionally, the study explores two different control techniques applied to this converter, evaluating their effects on the total harmonic distortion coefficient. A comparison has been made between the traditional inverter and the three-level inverter type neutral point clamped (NPC) inverter, with the use of integral backstepping (IBS) technique which was also compared with the proportional integral (PI) controller. The simulation results in MATLAB/Simulink are presented illustrating the performances and the strong effectiveness of the three-level NPC inverter controlled by the proposed technique (IBS).
User interface design of context-input-process-product evaluation application based on weighted product
Authors: Divayana Dewa Gede Hendra , Adiarta Agus , Santiyadnya Nyoman , Suyasa P. Wayan Arta , Andayani Made Susi Lissia , Wiradika I Nyoman Indhi , Wiguna I Kadek Arta
Published: April 2024

This study aimed to show the user interface design form of the
 context-input-process-product (CIPP) evaluation application based on weighted product as a measuring tool for the effectiveness level of blended learning in health colleges. This research approach was development research. The development model used was Borg and Gall. It focused on the design stage, initial trials, and revisions. The initial test of the user interface design involved 32 respondents. The tool for conducting it was in the form of a questionnaire, which contains 16 questions. The research was at the health colleges in Buleleng Regency. The data analysis technique of the initial test results was quantitative descriptive. It compared the percentage level of user interface design quality from the weighted product-based CIPP evaluation application with a quality standard which referred to a five scale. The results of this study indicated that the quality of the user interface design was relatively good. The research result’s impact on educational evaluation was new knowledge for pedagogic evaluators in maximizing the development of digital-based evaluation tools by integrating the decision support system method (weighted product) with the educational evaluation model (CIPP model).
Coronavirus risk factor by Sugeno fuzzy logic
Authors: Qasim Hasan Saba , Omar Al-Nima Raid Rafi , Esmail Mahmmod Sahar
Published: April 2024

World recently faced big challenges with the pandemic of coronavirus disease 2019 (COVID-19). Governments suffer from the problem of appropriately identifying the risk factor of this virus and establishing their safety procedures accordingly. This paper concentrates on designing a coronavirus risk factor (CRF) by the power of Sugeno fuzzy logic (SFL). The main advantage of the CRF is that it can provides a quick and suitable risk evaluation. According to the degree of severity, three essential parameters are considered: number of infected cases, number of people in intensive care units (ICU) and number of deaths. All of these parameters are provided per population. Such interesting and promising outcomes are attained, where the total effect is found equal to 95.3%.
A soft computing algorithmic technique for circuital analysis of a wireless mobile charger
Authors: Olukayode Ojo Adedayo , Oladipupo Alegbeleye Oluwafemi , Omowunmi Olomowewe Rashida
Published: April 2024

Wireless energy transfer is emerging as a promising technology for mobile devices because it enhances rapid charging without requiring conventional cables. In this paper, a wireless mobile charger circuit was designed and simulated, the data obtained thereof was used to train an artificial neural network (ANN) using Levenberg-Marquardt (LM) algorithm. The result obtained was validated against that obtained when trained with regular scaled conjugate algorithm. Analysis of the results showed that the proposed technique remains a viable technique for rapidly analyzing several parts of the wireless mobile charger circuit for design and educational purposes, without always executing computationally intensive and time-consuming simulations.
A novel fusion-based approach for the classification of packets in wireless body area networks
Authors: M. Mushgil Hanaa , Saeed Abduljabbar Khairiyah , Mohammad Mushgil Baydaa
Published: April 2024

This abstract focuses on the significance of wireless body area networks (WBANs) as a cutting-edge and self-governing technology, which has garnered substantial attention from researchers. The central challenge faced by WBANs revolves around upholding quality of service (QoS) within rapidly evolving sectors like healthcare. The intricate task of managing diverse traffic types with limited resources further compounds this challenge. Particularly in medical WBANs, the prioritization of vital data is crucial to ensure prompt delivery of critical information. Given the stringent requirements of these systems, any data loss or delays are untenable, necessitating the implementation of intelligent algorithms. These algorithms play a pivotal role in expediting diagnosis and treatment processes during medical emergencies. This study introduces an innovative protocol termed collaborative binary Naive Bayes decision tree (CBNBDT) designed to enhance packet classification and transmission prioritization. Through the utilization of this protocol, incoming packets are categorized based on their respective classes, enabling subsequent prioritization. Thorough simulations have demonstrated the superior performance of the proposed CBNBDT protocol compared to baseline approaches.
Artificial intelligence for choosing an agile method
Authors: Merzouk Soukaina , Bouhsissin Soukaina , Hamim Touria , Sael Nawal , Marzak Abdelaziz
Published: April 2024

Agile methods are widely known in different companies, including information technology (IT) companies. They appeared intending to solve the problems of traditional methods while proposing an iterative and incremental cycle. These methods consist of four values and the twelve principles agreed upon in 2001 in a Manifesto. However, each method holds singularities from which it is difficult to choose one to adopt in different project cases. The selection of the method to adopt positively or negatively affects the final product following the criteria of the project and the personnel. Project experts must research and compare methods manually to make a choice, a thing that drains time, which is a key factor in project realization. Currently, there is no intelligent system or model that allows choosing the agile method to adopt for such a project. For this purpose, artificial intelligence (AI) techniques will be used to develop a Chatbot that allows reaching the aim. This Chatbot will be developed based on a decision tree model that will be proposed from an experimental study.
Improved performance of fake account classifiers with percentage overlap features selection
Authors: Tjahyanto Aris , Pratama Rivanda Putra , Shiddiqi Ary Mazharuddin
Published: April 2024

Feature selection plays a crucial role in the development of high-performance classification models. We propose an innovative method for detecting fake accounts. This method leverages the percentage overlap technique to refine feature selection. We introduce our technique upon earlier work that showcased the enhanced efficacy of the Naïve Bayesian classifier through dataset normalization. Our study employs a dataset of account profiles sourced from Twitter, which we normalize using the Min-Max method. We analyze the results through a series of comprehensive experiments involving diverse classification algorithms—such as Naïve Bayes, decision tree, k-nearest neighbors (KNN), deep learning, and support vector machines (SVM). Our experimental results demonstrate a 100% accuracy achieved by the SVM and deep learning classifiers. The results are attributed to the percentage overlap technique, which facilitates the identification of four highly informative features. These findings outperform models with more extensive feature sets, underscoring the efficacy of our approach.
Evaluation of Indonesia’s police public service platforms through sentiment and thematic analysis
Authors: Melani Puspasari Hasna , Zharif Mustaqim Ilham , Tri Utami Avita , Syalevi Rahmad , Ruldeviyani Yova
Published: April 2024

The Indonesian national police (Polri) offer public services through mobile apps: Digital korlantas polri (DigiKorlantas) and samsat digital nasional (SIGNAL). Sentiment analysis gauges public perceptions, serving as a basis for e-government evaluation using user ratings and comments from app stores. Keyword relevance is assessed via feature extraction and Naïve Bayes classification. Thematic analysis is implemented using N-grams methods to identify the factors affecting the effectiveness based on user experiences. The accuracy of the model reaches 81.09% where it indicates a high performance. DigiKorlantas acquires slightly more negative reviews in comparation with positive reviews which are 51% and 49% respectively. In contrast, positive sentiment is dominant on SIGNAL which reach 58%, compared with negative sentiment that in 42%. N-grams reveal similar review patterns for both apps. Some of the solutions are Korlantas Polri should enhance the verification functionality with several techniques such as retinex algorithms or optical character recognition pipeline and increase the capacity of supporting server then releasing an updated version of application to address errors or bugs. This analysis can be alternative evaluation by the Polri to measure the success of the application and find out the continuous improvement of the process and the system.
Assessing public satisfaction of public service application using supervised machine learning
Authors: Zharif Mustaqim Ilham , Melani Puspasari Hasna , Tri Utami Avita , Syalevi Rahmad , Ruldeviyani Yova
Published: April 2024

The COVID-19 pandemic has enormously affected the economic situation worldwide, including in Indonesia resulting in 30 million Indonesian tumbling into penury. The Ministry of Social Affairs initiated a program to distribute social assistance aimed at the poorest households. ‘Aplikasi Cek Bansos’ is a public service application that aims to validate their status towards the social assistance program. Understanding the public sentiment and factors affecting public satisfaction levels is crucial to be performed. The goal of this study is to perform a comparative study of supervised machine learning to learn the sentiment of the public and the dominant variable resulting in public satisfaction. Support vector machine, Naïve Bayes dan K-nearest neighbor (KNN) are performed to seek the highest accuracy. This experiment discovered that the KNN algorithm produced outstanding performance where the accuracy hit 99.21%. Sentiment prediction indicated negative perception as the majority covering 83.81%. Trigrams analysis is performed to learn themes affecting satisfaction levels toward the application. Negative themes are grouped into the following categories: App instability, hope for improvement, navigation issues, and low-quality content. Some recommendations are offered for the Ministry of Social Affairs and developers, to overcome negative feedback and enhance public satisfaction level towards the application.
Object detection of the bornean orangutan nests using drone and YOLOv5
Authors: Teguh Rony , Dwijaya Maleh I Made , Sagit Sahay Abertun , Porkab Pratama Muhamad , Simon Okta
Published: April 2024

Object detection methods when applied to ecology and conservation can help to identify and monitor endangered species and their habitats. Using drones for this purpose has become increasingly popular due to their ability to cover large areas quickly and efficiently. In this study, we aim to implement object detection using YOLOv5 to detect orangutan nests in forests. To conduct our experiment, we collect drone imagery under different conditions. We propose to use the original YOLOv5 to implement our model. The detection and monitoring of orangutan nests can help conservationists to identify critical habitats, monitor population, and design effective conservation strategies. Additionally, the use of drones can reduce the need for on-the-ground surveys, which can be time-consuming, expensive, and logistically challenging. In our study proposes a model for detecting orangutan nests in forests using a drone and the YOLOv5. Our model predicted 1,970 training images and 414 labeled orangutan nests, with a precision of 0.973, recall 0.949, accuracy mean average precision (mAP)_0.5 is 0.969, and mAP_0.5:0.95 is 0.630. The model finished 217 epochs in 58 hours and had a high object detection accuracy. The model has a 99.9% accuracy in detecting the number of orangutan nests.
Effect of dataset distribution on automatic road extraction in very high-resolution orthophoto using DeepLab V3+
Authors: Sussi Sussi , Husni Emir , Siburian Arthur , Yusuf Rahadian , Budi Harto Agung , Suwardhi Deni
Published: April 2024

Road extraction is one of the stages in the map-making process, which has been done manually, takes a long time, and costs a lot. Deep Learning is used to speed up the road extraction process by performing binary semantic segmentation on the image. We propose DeepLab V3+ to produce road extraction from very high-resolution orthophoto for Indonesia study area, which poses many challenges, such as road obstruction by trees, clouds, building shadows, dense traffic, and similarities to rivers and rice fields. We compared the distribution of datasets to obtain the optimal performance of the DeepLab V3+ model in relation to the dataset. The results showed that dataset ratio of 75:10:15 resulted in mean Intersection Over Union (mIoU) of 0.92 and Dice Loss of 0.042. Visually, the results of road extraction are more accurate when compared to the results obtained from different distributions of the dataset.
Analysis of language identification algorithms for regional Indonesian languages
Authors: Sujaini Herry , Bijaksana Putra Arif
Published: April 2024

Detecting local languages in Indonesia is essential for recognizing linguistic diversity, promoting intercultural understanding, preserving endangered languages, and improving access to education and services. By identifying and documenting these languages, we can support language preservation efforts, provide tailored resources for communities, and celebrate the unique cultural heritage of different ethnic groups. Ultimately, this encourages a more accepting and open-minded society, prioritizing various languages and cultural customs. This research aims to identify the most suitable algorithm for language detection in Indonesian regional languages and gain insights into their unique characteristics through n-gram analysis. By understanding language diversity, the study contributes to preserving Indonesia's cultural and linguistic heritage and improving language detection techniques. This study compares the performance of five algorithms (Naïve Bayes, K-nearest neighbors (KNN), least-squares, Kullback Leibler divergence, and Kolmogorov Smirnov test) to determine the most accurate and efficient method for language identification. Incorporating trigram features alongside unigrams and bigrams significantly improved the model's performance, with F1 scores increasing from 0.923 to 0.959. The study found that using more features leads to better accuracy, with Naïve Bayes and KNN emerging as the top-performing algorithms for language identification.
A skeleton-based method for exercise recognition based on 3D coordinates of human joints
Authors: Bilous Nataliya , Svidin Oleh , Ahekian Iryna , Malko Vladyslav
Published: April 2024

The aim of the work is to develop the method of identification and comparison of poses and exercises performed by a person that will have a low sensitivity to data errors. This method uses their formal descriptions in the form of conjunctions of logical statements and should work regardless of the shooting angle at which the video was taken and the proportions of the person on it. Each statement describes the position of the joints relative to each other along one of the axes. The joint coordinates are corrected by taking into account the length of the bones that connect them that eliminates the necessity to process outliers and it also improves the accuracy of joints positioning. Removal of errors out of the data using the method of averaging the graph along each axis at every step. In order to do this, consecutive points are grouped so that the difference between the maximum and the minimum does not exceed the error. The groups are then filtered to leave only those in which both are smaller or both are larger. The proposed method of identification requires just a modern smartphone and has no restrictions on how to take video of exercises.
An ensemble-based approach for effective distributed denial of service attack detection in software defined networking
Authors: Ahmed Mohammed Majid , Abdulkader Hasan
Published: April 2024

Software defined networking (SDN) is a network framework that aims to redefine network characteristics through the programmability of network components, faster and larger network monitoring, centralized network operation, and effective detection of fraudulent traffic and special malfunctions. However, SDN networks are vulnerable to security threats that can cause complete network failure. To address this issue, in this paper, machine learning techniques are suggested for the swift detection of attacks. Various methods for detecting distributed denial of service (DDoS) attacks are evaluated, and the study identifies the most precise method for categorizing such attacks within a SDN network. The results indicate that the proposed system achieves high accuracy in detecting DDoS attacks, with ensemble learning achieving 99% accuracy. This indicates a remarkable improvement percentage in comparison to the approaches of decision tree (DT), k-nearest neighbors (KNN), and support vector machine (SVM).
Dental caries detection using faster region-based convolutional neural network with residual network
Authors: Lanyak Andre Citro Febriliyan , Prasetiadi Agi , Widodo Haris Budi , Ghani Muhammad Hisyam , Athallah Abiyan
Published: April 2024

Dental caries is the highest prevalent dental disease in the world by 2022. Caries can be stopped by early detection of patients through efficient screening. Previously, there have been several methods used to detect caries such as single shot multibox detector (SSD), faster region-based convolutional neural network (Faster R-CNN) and you only look once (YOLO). This research aims to develop accurate dental caries detection using Faster R-CNN. Using a dataset collected from scraping on the internet, this research is started by creating an original dataset consisting of 81 base images which are then augmented to a total of 486 images and annotated by dental health experts from Jenderal Soedirman University. Transfer learning using pre-trained Faster R-CNN residual network (ResNet)-50 and ResNet-101 model is utilized to detect and localise dental caries. The Faster R-CNN ResNet-50 model trained using the Adam optimizer produces a mean average precision (mAP) of 0.213, and those using the momentum optimizer produce a mAP of 0.177. While the Faster R-CNN ResNet-101 model trained using the Adam optimizer produces a mAP of 0.192, and those using the momentum optimizer produce a mAP of 0.004. The model trained on the dataset showed satisfactory results in detecting dental caries, especially ResNet-50 with Adam optimizer.
Balanced clustering for student admission school zoning by parameter tuning of constrained k-means
Authors: Zainuddin Zahir , Nur Risal Andi Alviadi
Published: April 2024

The Indonesian government issued a regulation through the Ministry of Education and Culture, number 51 of 2018, which contains zoning rules to improve the quality of education in school educational institutions. This research aims to compare the performance of the k-means algorithm with the constrained k-means algorithm to model the zoning of each school area based on the shortest distance parameter between the school location and the domicile of prospective students. The study used data from 2248 prospective students and 22 public school locations. The results of testing the k-means algorithm in grouping showed the formation of non-circular patterns in the cluster membership with different numbers of centroid cluster members. In contrast, testing the constrained k-means algorithm showed balanced outcomes in cluster membership with a membership value of 103 for each school as the cluster center. The research findings state that the developed constrained k-means algorithm solves the problem of unbalanced data clustering and overlapping issues in the process of new student admissions. In other words, the constrained k-means algorithm can be a reference for the government in making decisions on new student admissions
Enhancing data retrieval efficiency in large-scale JavaScript object notation datasets by using indexing techniques
Authors: Srisungsittisunti Bowonsak , Duangkaew Jirawat , Mekruksavanich Sakorn , Chaikaew Nakarin , Rojanavasu Pornthep
Published: April 2024

The use of JavaScript Object Notation (JSON) format as a Not only Structured Query Language (NoSQL) storage solution has grown in popularity, but has presented technical challenges, particularly in indexing large-scale JSON files. This has resulted in slow data retrieval, especially for larger datasets. In this study, we propose the use of JSON datasets to preserve data in resource survey processes. We conducted experiments on a 32-gigabyte dataset containing 1,000,000 transactions in JSON format and implemented two indexing methods, dense and sparse, to improve retrieval efficiency. Additionally, we determined the optimal range of segment sizes for the indexing methods. Our findings revealed that adopting dense indexing reduced data retrieval time from 15,635 milliseconds to 55 milliseconds in one-to-one data retrieval, and from 38,300 milliseconds to 1 millisecond in the absence of keywords. In contrast, using sparse indexing reduced data retrieval time from 33,726 milliseconds to 36 milliseconds in one-to-many data retrieval and from 47,203 milliseconds to 0.17 milliseconds when keywords were not found. Furthermore, we discovered that the optimal segment size range was between 20,000 and 200,000 transactions for both dense and sparse indexing.
Analysis of the parasitic capacitance effects on the layout of latch-based sense amplifiers for improving SRAM performance
Authors: Pham Van-Khoa , Sun Chi-Chia
Published: April 2024

Static random-access memory (SRAM) technology is utilized in designing cache memory to enhance the processing performance of computer systems. The sense amplifier (SA) circuit, a crucial component of memory design, significantly impacts data access time and power consumption. In comparison to conventional differential sense amplifiers (DSA) designs, latch-based sense amplifiers (LSA) used in memory-based computing platforms have specific requirements, including robust noise resistance in harsh working environments and low power consumption, particularly for internet of thing (IoT) embedded computing applications. However, the performance can be degraded due to various factors that arise during the layout, such as conductor resistance or the development of parasitic capacitance. Therefore, this study employs low-voltage 22 nm UMC CMOS technology for LSA design layout and analyzes the factors influencing design performance post-layout process. Layout design optimization techniques are applied to mitigate the impact of parasitic capacitance on important signal lines such as data line/data line bar (DLL/DLLB). Based on the performance analysis results, it is possible to achieve a reduction in power consumption of up to 15% and a 5% decrease in read delay time by implementing circuit layout LSA design optimization techniques.
Effects of Pr3+ -activated BaZrGe3O9@TiO2 phosphor compound on light emitting diodes validated by computer simulation
Authors: Trang Le Thi , Thuy Le Xuan , Thai Nguyen Le , Bui Thuc Minh
Published: April 2024

The Pr3+ -doped BaZrGe39 gallogermanate phosphors are reported to have a well-defined successive deep defect structure that effectively mitigates thermal carrier fading. This phosphor also presents a red emission with a peak at 615 nm, originating from the Pr3+ transtition from 1D2 to 3H4. We investigated the impact of Pr3+ -activated BaZrGe3O9 (referred to as BZG:Pr) on the lighting characteristics of light emitting diodes (LED) packages in this paper. By combining BZG:Pr with TiO2 particles and silicone, we produced a phosphor layer (designated as BZG:Pr@TiO2). The optical performance of the resulting LED was systematically examined by varying the TiO2 doping percentage. Our findings reveal that the incorporation of the BZG:Pr phosphor enhances the red spectral component, thereby contributing to improved homogeneity in color distribution. However, a progressive increase in TiO2 content within the phosphor layer corresponds to diminishing luminous output and decreased chromatic rendering efficiency of the LED. Employing a lower concentration of TiO2 proves advantageous, as it capitalizes on the scattering-enhancing attributes while leveraging the red emission of the BZG:Pr phosphor. This synergistic approach yields a favorable balance between luminosity and color quality, enhancing the LED’s overall performance.
Spatial domain noise removal filtering for low-resolution digital images
Authors: Salah Zaher , Al-Sit Waleed T. , Salah Kamal , Elsoud Esraa
Published: April 2024

n this research work, six different filters are applied on a low resolution 8 b/pixel gray-scale images, which operate on small sub-images (windows of 3×3 to 11×11 pixels). The enhanced images are used to compare the efficiency of the different six filters using the peak signal to noise ratio (PSNR) image quality measure. Noise peak elimination filter (PSNR)=36.63) outperforms others, such as median filter (PSNR=36.61), while corruption estimation (PSNR=36.03) significantly cuts processing time by only processing the corrupted pixels while maintaining image details. Mean filter (PSNR=34.05) is sensitive to outliers, which cause the image's sharpness and fine features to be lost. By avoiding averaging across edges, bimodal-averaging filter (PSNR=35.30), which improves on the mean filter, chooses the mean of the biggest population. The median-mean filtering (PSNR=36.32), which combines median and mean filters and determines the output pixel by averaging the median and some nearby pixels, is another improvement above averaging.
Integrated energy-efficient and location-aware routing in wireless sensor networks
Authors: Saifuddin Karur Mohammed , Devangavi Geetha D.
Published: April 2024

Sensor nodes in wireless sensor networks are commonly distributed randomly across a given landscape, and their placement may be randomized for specific applications, even extending to national deployments. The energy consumption associated with data transmission and reception by the cluster’s leader is notably higher compared to other nodes. To address this issue, it is recommended that wireless sensor networks adopt a more energy-efficient routing technique. This proposed technique assumes a spatial separation between different node types. Elevating the threshold enhances the likelihood that nodes with ample remaining power will endure as cluster leaders. Ultimately, a hybrid data transfer strategy is formulated, wherein data is directly exchanged between the base station and cluster heads among the super nodes containing advanced nodes. Most nodes employ a combination of single-hop and multi-hop approaches for data transport, aiming to minimize the power required for transmission between the cluster’s control node and the base station. According to simulation results, this proposed method surpasses the stable election protocol (SEP), demonstrating superiority over the improved threshold-sensitive stable election protocol in terms of the operational duration of a wireless sensor network.
Enhancing interaction and learning experience for deaf students through sign language translator
Authors: Nugraha Dian , Faizah Safira , Zaenudin Mohamad
Published: April 2024

The study addresses persistent communication barriers faced by students with disabilities, particularly the deaf, by exploring challenges, presenting breakthroughs, and introducing an innovative solution-a sign language translator (SLT) using motion capture technology. This groundbreaking technology, deployed through the ADDIE model and validated with user acceptance testing (UAT), successfully integrates into the learning management system (LMS) at SLB Bina Insani Depok, demonstrating its efficacy in bridging communication gaps. The results suggest a notable increase in efficiency for tasks such as t2, t3, and t5, highlighting the system’s improved ability to direct users to the LMS homepage, the SLT page, and translate words into sign language, respectively. The study suggests further development in advanced animation to enhance the learning experience for deaf students and recommends progressing toward the total communication (KOMTAL) system for comprehensive communication preparation, ultimately aiming to create an inclusive and dynamic learning platform for the holistic development of deaf students.
Improved vigenere using affine functions surrounded by two genetic crossovers for image encryption
Authors: El Bourakkadi Hamid , Chemlal Abdelhakim , Tabti Hassan , Kattass Mourad , Jarjar Abdellatif , Benazzi Abdelhamid
Published: April 2024

This paper presents an improved method for encrypting color images, surpassing the effectiveness of genetic crossover and substitution operations. The technique incorporates dynamic random functions to enhance the integrity of the resulting vector, increasing temporal complexity to thwart potential attacks. The improvement involves integrating genetic crossover and utilizing two extensive pseudorandom replacement tables derived from established chaotic maps in cryptography. Following the controlled vectorization of the original image, our approach initiates with a first genetic crossover inspired by deoxyribonucleic acid (DNA) behavior at the pixel level. This genetic crossover is succeeded by a confusion-diffusion lap, reinforcing the connection between encrypted pixels and their neighboring counterparts. The confusion-diffusion process employs dynamic pseudorandom affine functions at the pixel level. Then a second genetic crossover operator is applied. Simulations conducted on a diverse set of images with varying sizes and formats showcase the robustness of our method against statistical, brute-force, and differential attacks.