Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning for Fetal QRS-complex Classification in Noninvasive Fetal Electrocardiogram

Fetal QRS-complex generated from an abdominal electrocardiogram (ECG) is an effective noninvasive tool for accurately measuring the fetus well-being during pregnancy. Classification of fetal QRS is significant to avoid fetal heart abnormalities, such as asphyxia, bradycardia, and hypoxia. However, accurately classifying the fetal QRS-complex is hard because the characteristic fetal ECG (fECG) has small amount of signal energy and it has undesired signal components, including recording devices, movement artifacts, and fetal brain activity. The sophisticated fECG signal processing for fetal QRS-complex classification has been used in the literature. However, they mostly use human intervention for feature extraction, leading to time-consume and raising the measurement error. Hence, this study investigates deep learning (DL) that performs automated feature extraction tasks excellently. DL has excellence amount of data scales, which is its ability to automatically learn and improve from data without the need for explicit programming. A sequence-to-sequence learning task was generated using a hybrid four convolution layers as a part of convolutional neural networks and bidirectional long short-term memory (ConvBiLSTM). As the results, the performance is outstanding; the accuracy, sensitivity, specificity, precision, and F1-score were 100%. The observed results show the proposed ConvBiLSTM model is worth evaluating their effectiveness for fetal QRS-complex classification.
ShapeSonic: Sonifying Fingertip Interactions for Non-Visual Virtual Shape Perception

For sighted users, computer graphics and virtual reality allow them to model and perceive imaginary objects and worlds. However, these approaches are inaccessible to blind and visually impaired (BVI) users, since they primarily rely on visual feedback. To this end, we introduce ShapeSonic, a system designed to convey vivid 3D shape perception using purely audio feedback or sonification. ShapeSonic tracks users’ fingertips in 3D and provides real-time sound feedback (sonification). The shape’s geometry and sharp features (edges and corners) are expressed as sounds whose volumes modulate according to fingertip distance. ShapeSonic is based on a mass-produced, commodity hardware platform (Oculus Quest). In a study with 15 sighted and 6 BVI users, we demonstrate the value of ShapeSonic in shape landmark localization and recognition. ShapeSonic users were able to quickly and relatively accurately “touch” points on virtual 3D shapes in the air.
Adaptive brightness equalization and specular reflection suppression of narrow-band endoscopic images : NBI image enhancement algorithm

In clinical endoscopic diagnosis, the endoscopic image quality is reduced due to interference. The interference factors mainly include local overexposure and shadow of images caused by light source irradiation angle and specular reflection caused by tissue fluid. As the primary task in developing a computer-aided diagnosis (CAD) model, high-performance image enhancement algorithms can help them obtain a more accurate recognition rate. This study takes the endoscopic images of Barrett’s esophagus (BE) collected under narrow-band imaging as the research object. An adaptive brightness equalization algorithm and a specular reflection suppression algorithm are proposed to preprocess datasets, and the existing segmentation model confirms the algorithm. The experimental results show that the image enhanced by the algorithm has a certain degree of improvement in the overall and local dark areas and texture details, eliminates the specular reflection, and effectively improves the recognition accuracy of the segmentation model. The proposed algorithm improves the Mean Intersection over Union (MIoU) by 10.8% and the F1-Score by 8.76% on the self-built BE datasets. It shows that it can play an excellent auxiliary diagnostic role and obtain higher diagnostic efficiency in clinical diagnosis.
Analysis of the Effect of Gamification Implementation on Customer Loyalty in Online Travel Agency (OTA) Mobile Applications

Today, online travel agency (OTA) platform is the ideal choice for people to book their travel needs. Currently, there are many OTA brands with various attractive offers and features. One feature that is often used is gamification. This study aims to analyze whether gamification in OTA can affect customer loyalty by examining the utilization of gamification on Traveloka, tiket.com, and. The gamification components which are the tested are tasks, games, points, and levels. The information was gathered through an online questionnaire emailed to respondents in Indonesia who utilize gamification on Traveloka, tiket.com, and. This study employed PLS-SEM as a data validation system and the Bootstrapping method using SmartPLS to test our hypothesis. According to the findings of this study, the Point influences Utilitarian Value whereas the Level influences Hedonic Value. It was also established that utilitarian and hedonic values had a substantial impact on satisfaction and loyalty. Through this result, the implementation of point and level of gamification in the OTA application can affect customer loyalty.
Quantitative Analysis of EEG Signal Profiles

Brain-computer interfaces (BCI) are systems that use signals recorded from the brain to enable communication and control applications. The use of data derived from Brain Computer Interaction (BCI) is a complex process that requires multidisciplinary skills and knowledge in computer science, signal processing, neuroscience, robotics, artificial intelligence, and other related fields. In this study, we propose the quantitative analysis method for feature selection based on descriptive statistics (maximum, minimum, mean and median). The purpose of this paper is to show that different trials of the same individual at the same symbol yield similar brain profiles regardless of their mental state, and that different individuals have different brain profiles at the same visual signals. The obtained data are discussed in the context of studying the nature of brain signals.
Improved Convolutional Neural Network Lung Cancer Classification Detection Method Based on Transfer Learning and Model Compression

Lung cancer, one of the most life-threatening malignancies, has witnessed a significant increase in its incidence and mortality rates over the past few decades. Accurate determination of the histological type of lung cancer is crucial for developing appropriate treatment strategies. However, traditional medical imaging analysis, such as magnetic resonance imaging and computed tomography (CT), faces challenges due to sensor noise, patient variations, and pathological states, which may lead to misinterpretation and errors. In this regard, deep learning approaches can assist in rapidly distinguishing different types of lung cancer by learning complex patterns and features from medical imaging data, aiding clinicians in precise diagnosis. This research aims to address the limitations of existing lung cancer classification models, including suboptimal accuracy, slow inference speed, high computational and storage requirements, and vulnerability to overfitting. To achieve this, we propose an improved lung cancer classification and detection method based on deep learning. Our approach leverages transfer learning by utilizing pre-trained models, data augmentation techniques, and model compression methods, including global unstructured pruning and model distillation. Experimental results demonstrate that the teacher model achieves 100% accuracy, recall, precision, and F1-score on the dataset, with a loss of 0.00007575038. The distilled student model, after pruning 55.15% of its parameters, achieves a 52.5% reduction in size. It also attains 100% accuracy, recall, precision, and F1-score, with an average loss of 0.004158932.
High-Gain Linear Polarized Radial-Line Slot Array Antenna Based on Slow-wave PPW Structure

In this paper, a novel linear polarized radial-line slot array antenna working at 30GHz is proposed. This antenna is composed of two parts, including a metallic slot layer and the bottom slow-wave parallel-plate waveguide layer. The radial waveguide is designed with periodic bed of metallic nails in the lower plate. The slow-wave characteristic is realized since the height of nails is adjusted, and the transmission coefficient of electromagnetic wave is changed. The slot array is optimized based on the definition of equivalent problems in the waveguide region. The slots are arranged in concentric circles for linear polarization. The antenna is optimized with a peak realized gain of 22.6dBi and the radiation efficiency of 77.9% is achieved.
Chili Quality Classification using Convolution Neural Network

Artificial intelligence has propelled transformative advances across industries, particularly in the realm of food quality control, where automation, precise predictions, and informed decision-making have become pivotal. Traditional quality assessment methods, reliant on manual inspection, are resource-intensive, subjective, and time-consuming, necessitating automated and objective alternatives. Convolutional Neural Networks (CNNs) have emerged as potent tools in computer vision, wielding remarkable image analysis capabilities. This study introduces a CNN-based approach to chili quality classification, with the primary aim of creating an automated system capable of categorizing chilies based on various quality attributes. The central objective is to develop a model capable of distinguishing between different quality grades with a high degree of precision. Our findings reveal that MobileNetV2, owing to its inherent model efficiency and adaptability to the small dataset used, excels over InceptionV3. MobileNetV2’s design prioritizes computational efficiency through depthwise separable convolutions, resulting in a more lightweight and compact architecture. In scenarios with limited data, MobileNetV2 demonstrates superior generalization capabilities, striking a balance between model sophistication and dataset scale, as evidenced by its training accuracy of 90.56% and validation accuracy of 86.24%. In contrast, InceptionV3, while proficient with a training accuracy of 92.19%, exhibits comparatively less aptitude for the specific small dataset, with a validation accuracy of 80.6%.
A High Isolation Power Divider Based on ISGW

This paper introduces a high isolation power divider based on two-layers integrated substrate gap waveguide (ISGW). Due to the self-encapsulation of the two-layers ISGW, it is hard to directly use the lumped resistance to improve the isolation of the power divider. This paper proposes a new way to achieve isolation between the output ports by using slotlines and electromagnetic band gap (EBG) square patches. Use this structure to replace the lumped resistance of Wilkinson power divider to improve the isolation between the output ports of the power divider. The simulation results shows that the isolation better than 24.3dB in the 31.4-33.1GHz band range without loading the lumped resistance.
Improving E-Loyalty Through E-Word of Mouth in SVoD Service Providers

This COVID-19 has triggered increasingly fierce competition in the Indonesian subscription video on demand (SVoD) market. As a result, customer loyalty has become a crucial aspect that SVoD providers must prioritize. This research intends to investigate the effect of electronic word of mouth or e-word of mouth (EWoM) on electronic loyalty (ELY) and the mediating effect of e-word of mouth to the effect of e-service quality and brand image on electronic loyalty (ELY). This study adopts a cross-sectional survey method and a quantitative approach. Data was collected through questionnaires distributed to 120 respondents (SVoD service users) using purposive or judgment sampling technique. SmartPLS software (variance-based SEM) was used to process data. The findings of this study showed that e-word of mouth has a positive and significant impact on electronic loyalty (ELY), and it partially mediates the effects of electronic service quality (ESQ) and brand image on electronic loyalty (ELY). Based on these results, further analysis is conducted to discuss the theoretical and practical implications of the findings.
Bi-LSTM-Based Signal Detection Method for Underwater Acoustic OTFS Communication System

The orthogonal time frequency space (OTFS) modulation is a new multi-carrier modulation technology proposed in recent years. The sparse and invariant signals in the Delay-Doppler domain make OTFS to be a promising technology to solve the Doppler shift in the time-variant underwater acoustic (UWA) channel. In the UWA-OTFS system, the traditional channel estimation and equalization methods need accurate channel information, and the performance can be easily influenced by the channel model. This paper proposes a Bi-LSTM-based UWA-OTFS signal detection method to replace the traditional explicit algorithms. This method can fully use the previous channel information and data to improve the reliability of the UWA-OTFS system, which has been verified on the Watermark underwater acoustic dataset. The result shows that the proposed method performs with a lower symbol error rate (SER) than traditional algorithms.
Implementation of SMILES2Vec-based LSTM for Predicting Drug Side Effects: Case Study of Hepatobiliary Disorder

Drug is a molecule that interacts with a specific target protein to intervene biological systems through numerous molecular interactions. However, drug molecules can interact with non-target proteins, leading to the possibility of side effects. Hence, it is necessary to be able to predict the side effects to prevent more severe conditions. Several approaches are commonly used to predict side effects. However, these approaches have several weaknesses, such as safety procedure requirements that are pretty minimal and less accurate. In-silico approach that uses numerical and computational tools can be implemented as an alternative method in predicting side effects. This approach has advantages in reducing experimental process risks. This study aims to implement SMILES based descriptor, or commonly called SMILES2Vec, of drug molecule on LSTM method to predict side effect, for the case study of hepatobiliary disorder. SMILES based representation of descriptor is preferable because the calculation is faster than other descriptor, such as 2D, 3D, fingerprint, etc. Meanwhile, Long Short-Term Memory (LSTM) was used for the prediction since this method is suitable with 1D type of SMILES based descriptor. The architecture of LSTM was determined by using manual tuning approach by determining several schemes. According to the result, we found that LSTM architecture with a combination of CONV and 2-layer of LSTM using Adagrad or Adadelta optimizer gives the best performance with values of F-1 score 68.62% for both.
Weather Prediction in Jakarta: An Analysis of Climate Data and Regional Influences using LSTM and GRU

This research investigates meteorological predictions in Jakarta using Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models, focusing on precipitation forecasting. Meteorological data, encompassing wind speed, temperature, humidity, and precipitation, were sourced from the Kemayoran Meteorological Station in Jakarta. After data pre-processing to ensure suitability for machine learning training, the study contrasted LSTM and GRU models’ performances using Root Mean Square Error (RMSE) and R-squared (R2) as evaluation metrics. Findings showed the GRU model marginally outperformed during testing, though the difference wasn’t statistically significant. The LSTM model exhibited greater consistency, suggesting its potential for more dependable weather predictions given the right dataset. The research underscores the necessity of considering regional factors, climatological data, and data time intervals for accurate forecasting. It offers insights into machine learning’s potential in meteorological predictions. Specifically, at a learning rate of 0.0002, GRU had an RMSE of 18.034, slightly better than LSTM’s 18.075. However, at 0.0003, LSTM outperformed with an RMSE of 17.972 compared to GRU’s 17.988, highlighting the nuanced performances of these models.
The Impact of Shift-Left Testing to Software Quality in Agile Methodology: A Case Study

The point of Agile Methodology is continuous improvement, delivering a small feature quickly without sacrificing the feature quality; every sprint must be better than the previous sprint, and better can be fewer bugs, faster development, and testing. We will present how we reduce production bugs by customizing our sprint iteration. As we know, bugs are unavoidable, there is no software engineer that can make software without a bug; however, we can reduce bugs in production if we can find bugs in lower environments as early as possible. The case study in this paper was taken from one of technology company in Indonesia, the activity was done by the Quality Engineer (QA) Team. We will show that shift-left testing can help us reduce bugs in production. Testing is part of agile methodology, and the main idea of shift-left testing is to move testing early and could be done by any team member, not only QA. We include shift-left testing in our agile methodology for one year in 2022 and compare the result in the previous year.
Construction of Knowledge Graph of Neurodegenerative Diseases Based on Ontology

Neurodegenerative diseases are common in the elderly population, and the early symptoms of the disease are often not obvious and have a long course. Early detection, diagnosis and treatment of the disease are important to slow down the disease progression. In this study, clinical guidelines and medical textbooks are used as knowledge sources to construct an ontology of neurodegenerative diseases, which contains 10 types of concepts, 17 object attributes and 7 data attributes. The knowledge Graph of neurodegenerative diseases was constructed based on the ontology. The data sources of the knowledge Graph are clinical guidelines, medical textbooks, and medical websites. The knowledge triad was obtained by knowledge extraction and knowledge fusion, and stored in Neo4j. The constructed knowledge graph of neurodegenerative diseases finally contains 2012 disease-related entities.
Smart Mobility Recommendation for Ibu Kota Nusantara (New Capital City of Indonesia)

The Republic of Indonesia has one important agenda, the development of Ibu Kota Nusantara as a new capital city. Presidential Regulation Number 63 of 2022 is the legal basis used as reference for this project. The development of the IKN includes several aspects, one of which is smart mobility. The research purpose is to provide recommendations for IKN Authority regarding smart mobility design that can be used as a benchmark. The design recommendations given focus on bus-based public transportation. Qualitative method used in this research. The research instruments used were interviews and Systematic Literature Review (SLR). Data analysis is carried out by benchmarking against parties, regions or countries that have implemented the concept of smart mobility in their transportation system. Smart mobility subsystems that can be developed to support transportation in the IKN environment are Smart Traffic Monitoring, Route Optimization, Accident Detection and Infrastructure Monitoring (Parking, Lights, Road Anomaly), Traffic Management System, Traffic Information System, Traffic Safety System, Smart Parking, Monitoring System Smart Vehicles, Smart Roads, Optimal Route Planner, Emergency Handling, Public Transit Priority, Adaptive TSCS, ITSCS, Passenger Information System, Smart Navigation System.
An SISL Wideband Dielectric Resonator Antenna for 5G Millimeter-Wave Applications

A wideband millimeter-wave dielectric resonator antenna based on substrate integrated suspended line (SISL) is designed in this letter. The antenna mainly has three parts, a rectangular dielectric resonator antenna (DRA), a metal hole inside the DRA and a circular patch above the DRA, separately. The antenna increases bandwidth by generating four resonant frequencies. The first resonant frequency at 20.1 GHz is generated by the TM<inf>01δ</inf> mode of the DRA. The second resonant frequency is generated by the metal hole at 23.35 GHz. Meanwhile, the other two resonant frequencies at 27.2 GHz and 32.75 GHz are generated by the TE<inf>111</inf> mode and higher order mode TE<inf>Δ21</inf>(1<Δ<2) of DRA, respectively. The peak gain is 10.5 dBi. According to the simulation results, the antenna has the relative bandwidth of 56.9% and the operating frequency is from 19.23 GHz to 34.55 GHz, covering the specific 5G n258-band (24.25–27.5 GHz) and n261-band (27.5-28.35GHz).
Hard Disk Drives Failure Prediction Using the Deep Learning Method Based on Attention Mechanism

Hard disk Drives failure is the main cause of data center downtime. Utilizing predictive techniques to extend drives’ remaining useful life (RUL) can significantly reduce data loss. Machine learning and deep learning methods are exploited to address these issues, and Long Short-Term Memory (LSTM) network has achieved good results. However, traditional LSTM networks are classified only by the features learned at the last step, resulting in poor performance. In addition, these techniques are susceptible to the high imbalance in existing datasets. This paper proposes an RUL prediction framework for hard disks based on the attention mechanism. By using the LSTM network, it can learn sequence features from the raw data. Meanwhile, the proposed attention mechanism can automatically learn the importance of time steps and estimate the health of hard disks based on their failure time. Experimental results on real datasets demonstrate that our proposed method can achieve better prediction performance than the existing methods.
Balanced Dual-Channel Filter Based on Dual-Mode Dielectric Resonator

In this article, we present a balanced dual-channel filter based on dual-mode square dielectric resonator (DR) with a short end at the bottom. In this case, the resonator acquires a compact size, while it still retains a high unloaded quality factor. The electric fields of the modes are half cut like the shape of DR. According to the field distributions of the applied degenerate modes (half-TE011 and half-TE101), the pair of feeding probes are placed on both sides of the DR to realize differential excitation. Compared with the single-end feeding scheme, the higher order mode adjacent to the two used modes disappears after differential feeding. This property is beneficial to alleviate the adverse effect of higher order mode on the stopband suppression of the filter. The two orthogonal modes of half-TE011 and half-TE101 provide the internal isolation between the two channels. Based on the above analysis, a balanced dual-channel dielectric filter is designed with the center frequency of 1.52GHz and 3dB fractional bandwidth (FBW) of 2.6% for both channels. Because the structure is completely symmetric, the Chebyshev filtering responses of the two outputs are the same, and the number of size variables is significantly reduced. The results exhibit that the balanced dual-channel filter has the features of low loss, small size and wider upper stopband suppression.