bluuebunny/crossref_metadata_2025_split huggingface dataset:
Learning sparse features can lead to overfitting in neural networks
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Authors: Petrini Leonardo , Cagnetta Francesco , Vanden-Eijnden Eric , Wyart Matthieu 
Published: November 2023

Abstract
It is widely believed that the success of deep networks lies in their ability to learn a meaningful representation of the features of the data. Yet, understanding when and how this feature learning improves performance remains a challenge. For example, it is beneficial for modern architectures to be trained to classify images, whereas it is detrimental for fully-connected networks to be trained on the same data. Here, we propose an explanation for this puzzle, by showing that feature learning can perform worse than lazy training (via the random feature kernel or the neural tangent kernel) as the former can lead to a sparser neural representation. Although sparsity is known to be essential for learning anisotropic data, it is detrimental when the target function is constant or smooth along certain directions of the input space. We illustrate this phenomenon in two settings: (i) regression of Gaussian random functions on the d-dimensional unit sphere and (ii) classification of benchmark data sets of images. For (i), we compute the scaling of the generalization error with the number of training points and show that methods that do not learn features generalize better, even when the dimension of the input space is large. For (ii), we show empirically that learning features can indeed lead to sparse and thereby less smooth representations of the image predictors. This fact is plausibly responsible for deteriorating the performance, which is known to be correlated with smoothness along diffeomorphisms.
Auto-Lesion Segmentation with a Novel Intensity Dark Channel Prior for COVID-19 Detection
Authors: Saleh Basma Jumaa , Omar Zaid , Bhateja Vikrant , Izhar Lila Iznita 
Published: November 2023

Abstract
During the COVID-19 pandemic, medical imaging techniques like computed tomography (CT) scans have demonstrated effectiveness in combating the rapid spread of the virus. Therefore, it is crucial to conduct research on computerized models for the detection of COVID-19 using CT imaging. A novel processing method has been developed, utilizing radiomic features, to assist in the CT-based diagnosis of COVID-19. Given the lower specificity of traditional features in distinguishing between different causes of pulmonary diseases, the objective of this study is to develop a CT-based radiomics framework for the differentiation of COVID-19 from other lung diseases. The model is designed to focus on outlining COVID-19 lesions, as traditional features often lack specificity in this aspect. The model categorizes images into three classes: COVID-19, non-COVID-19, or normal. It employs enhancement auto-segmentation principles using intensity dark channel prior (IDCP) and deep neural networks (ALS-IDCP-DNN) within a defined range of analysis thresholds. A publicly available dataset comprising COVID-19, normal, and non-COVID-19 classes was utilized to validate the proposed model’s effectiveness. The best performing classification model, Residual Neural Network with 50 layers (Resnet-50), attained an average accuracy, precision, recall, and F1-score of 98.8%, 99%, 98%, and 98% respectively. These results demonstrate the capability of our model to accurately classify COVID-19 images, which could aid radiologists in diagnosing suspected COVID-19 patients. Furthermore, our model’s performance surpasses that of more than 10 current state-of-the-art studies conducted on the same dataset.
Silver Printed Electrode on Poly-Imide (Pi) Film And Polyethylene Naphthalate (PEN) Film For ECG Monitoring Device: A Case Study
Authors: Abdul-Kadir Nurul Ashikin , Daud Nurizzati Mohd , Saleh Syaidah Md , Sahar Noor Syazwana , Harun Fauzan Khairi Che , Embong Abdul Mutalib , Khee Moey Kar , Seman Mohd Khairy 
Published: November 2023

Abstract
According to World Health Organization (WHO), cardiovascular disease (CVD) is among the tenth of primary global cause of death in 2017. However, this problem could be alleviated via early detections, especially for patients with a history of heart-related problems. In addition, ECG monitoring could reduce the cause of CVD if diagnose earlier thus reduce the risk of sudden death. The measurement of ECG signals is important to assist physicians about people heart and health conditions. The sophisticated characteristics of ECG signals could be acquired by using the good electrodes. Thus, electrodes play a vital role in ECG recording. The commercially available electrodes are the conventional wet electrode (gel) using silver/silver chloride (Ag/AgCl). The gel usage tends to dry gradually and could cause skin problems like dermal inflammation and skin irritation. Due to the limitations associated with conventional gelled electrode, dry electrode is expected to overcome the main limit of Ag/AgCl electrode. By fabricating the silver ink onto the polyimide (PI) and polyethylene naphthalate (PEN) substrates to produce a dry ECG sensor, which could withstand high temperature, reusable, and has been shown to be biocompatible. This study is a case study to measure the performance of dry electrode based by silver printed electrode on PI and PEN films in ECG application.
Heat distribution improvement with the implementation of polyethylene-covered water bolus into breast cancer hyperthermia
Authors: Wong Vei Ling , Lias Kasumawati bt , Basri Hazrul Mohamed , Buniyamin Norlida 
Published: November 2023

Abstract
This paper presents the implementation of polyethylene-covered water bolus into a non-invasive breast cancer hyperthermia applicator. This modified hyperthermia applicator is introduced to improve the performance of hyperthermia by reducing or removing unwanted hotspots during hyperthermia treatment. This simulation-based experiment is carried out to observe the heating distribution of hyperthermia with water bolus coated by three different thicknesses of polyethylene cover or layer, which are 0.5mm, 0.8mm and 1.0mm. The solvent used in the water bolus is distilled water. The 915MHz microstrip antenna as a hyperthermia applicator and stage 2 breast cancer with a cancer depth of 28.6 mm to 73.6 mm is selected for this study. Based on the results, with the modified HTP integrated by water bolus, the heat pattern of hyperthermia simulation becomes more concentrated into the targeted cancer region, and unwanted hotspots nearby the skin area of breast tissue are removed. The 0.5mm thick polyethylene cover showed the best results with a focusing region between 29.4mm to 69.4mm compared with the result of hyperthermia without implemented water bolus, which heated the 26.6mm to 67.3mm region.
Comparison between Romberg test with sensor-based balance assessment using electronic wobble board
Authors: Woon Yvonne Khor Yee , Su Eileen L. M. , Khor Kang Xiang , Abdullah Muhammad Najib Bin , Yeong Che Fai 
Published: November 2023

Abstract
The number of fall-related injuries resulting in hospital admissions could place a significant load on hospitals. Prior research revealed that balance impairment is the most reliable indicator of future falls, and postural instability testing is expensive and tedious to be applied in clinical settings. This paper presents an objective based balance assessment method using an electronic wobble board to examine the balance ability among individuals, with minimal specialist intervention. The balancing assessment software modules provide visual feedback such as visual concentration, test procedure teaching, and time counting. Quick objective balance evaluation is facilitated by the software modules, which also can comprise visual feedback and provides instant biofeedback with objective measurements of user’s progress. The balance assessment score obtained from this electronic wobble board was compared against physiotherapists’ ratings using the Romberg test. The results indicated that the sensor-based balance score is comparable with the expert ratings using the Romberg test. Additionally, the sensor-based score provides higher sensitivity in differentiating among similar categories of subjects as compared to the conventional Romberg test.
Classification of Red Blood Cell Abnormality in Thin Blood Smear Images using Convolutional Neural Networks
Authors: Muhammad Fatima Abdullahi , Sudirman Rubita , Zakaria Nor Aini , Mahmood Nasrul Humaimi 
Published: November 2023

Abstract
One of the most common morphological red blood cell abnormalities encountered during routine thin blood smear microscopy for the detection of malaria parasite is the rouleaux formation, which is the stacking together of red blood cells to form a chain. Rouleaux formation signifies an underlying infection and as such microscopists are mandated to report its presence. A lot of work has been done in automating malaria diagnosis using deep learning, but no model has been developed which is capable of detecting rouleaux formation in malaria infected red blood cells. Thus, this study collected 231 peripheral blood smear (PBS) images of normal red blood cell morphology and 231 PBS images with rouleaux morphology. The images were pre-processed and segmented into equal instances of 3044 coloured images of size 750×750 pixels. Two convolutional neural network (CNN) models were developed and trained to classify the images into normal red blood cell morphology or rouleaux morphology. The CNN models were trained on two different image sizes: 300×300 and 500×500. The first CNN model achieved validation accuracy/loss values of 87.91%/0.8177 and 56.58%/1.4090 when trained on images of sizes 300×300 and 500×500 respectively. In the second CNN model, the CNN layers of the first model were replaced with depthwise separable CNN layers, it was also trained on images of sizes 300×300 and 500×500 achieving validation accuracy/loss values of 90.95%/0.2804 and 87.75%/0.5904 respectively. This study demonstrates the capability of CNN models in detecting red blood cell morphology abnormality in thin smear images at an optimal image size of 300×300.
An Optimized Cosine Similarity-Based Attention Gate for Temporal Sequence Patterns Recognition
Authors: Abdullah Ammar , Abdul-Kadir Nurul Ashikin , Harun Fauzan Khairi Che 
Published: November 2023

Abstract
The attention gate is often employed in various applications, such as recommender systems, to determine the correct context of the input data. Adopting the attention gate has been proven to improve prediction accuracy successfully. However, for a temporal sequence problem such as sensor-based Sign Language Recognition (SLR), it is still challenging to integrate the attention gate into the solution since the processing input is only in the form of a sine waveform. In our work, we propose an optimized cosine similarity-based attention gate to manipulate the sine wave signal while improving the recognition of temporal sequence data. We also evaluate and compare the various distance calculations and determine the best one for the SLR application. Adopting a distance-based attention gate has successfully achieved the recognition accuracy of 99% while reducing the error rate to 5%.
Position correction of mobile robot by detecting line landmark using Hough line transform
Authors: Anggoro Bagus I. J. , Setiawan Iwan , Nurhayati Oky Dwi 
Published: November 2023

Abstract
Mobile robots are used in a laboratory automation system to transport specimens or experiments to their destinations. When moving, the robot has some error positions because of the wheels skid on the floor and the inaccuracy direction of the robot. The robot needs to correct its position and return to the track. By reading the landmarks in the environment, the robot can correct its position. This research proposes to do the position correction of the robot using a camera by reading the orange lines as an artificial landmark on the laboratory environment floor. The camera detects the orange lines as two parallel edges. And by Hough line transform (HLT) it produces Δρ value between line edges. The robot calculates the error position using the Δρ value and the biggest ρ (ρMAX) as the distance prediction robot to the line. Compared to the original robot movement on the Webots simulator application, this method can help the robot keep its position on the track. On the goal position, the robot can reduce 90% the error position. This error improvement makes the error position of the robot becomes around 5 mm from the target position.
Siamese Networks for Speaker Identification on Resource-Constrained Platforms
Authors: Jie Lim Jun , Zabidi Muhammad Mun’im Ahmad , Sadiah Shahidatul , Rahman Ab Al-Hadi Ab 
Published: November 2023

Abstract
This paper investigates the implementation of a lightweight Siamese neural network for enhancing speaker identification accuracy and inference speed in embedded systems. Integrating speaker identification into embedded systems can improve portability and versatility. Siamese neural networks achieve speaker identification by comparing input voice samples to reference voices in a database, effectively extracting features and classifying speakers accurately. Considering the trade-off between accuracy and complexity, as well as hardware constraints in embedded systems, various neural networks could be applied to speaker identification. This paper compares the incorporation of CNN architectures targeted for embedded systems, MCUNet, SqueezeNet and MobileNetv2, to implement Siamese neural networks on a Raspberry Pi. Our experiments demonstrate that MCUNet achieves 85% accuracy with a 0.23-second inference time. In comparison, the larger MobileNetv2 attains 84.5% accuracy with a 0.32-second inference time. Additionally, contrastive loss was superior to binary cross-entropy loss in the Siamese neural network. The system using contrastive loss had almost 68% lower loss scores, resulting in a more stable performance and more accurate predictions. In conclusion, this paper establishes that an appropriate lightweight Siamese neural network, combined with contrastive loss, can significantly improve speaker identification accuracy, and enable efficient deployment on resource-constrained platforms.
3D Brain Tumour Segmentation Using UNet with Quantitative Analysis of the Tumour Features
Authors: Azeez Ibrahim Izdhan , Chow Li Sze , Solihin Mahmud Iwan , Ang Chun Kit 
Published: November 2023

Abstract
Brain tumours are abnormal cells that grow within the brain and may cause severe harm to an individual. The diagnosis of tumours often requires a manual review of Magnetic Resonance (MR) images by a skilled radiologist, which is a laborious task. The study aims to use a deep learning-based approach to segment the brain tumour and quantitatively evaluate the performance of the model by comparing the statistical distribution of five textural and four non-textural feature differences between the ground truth and the prediction. Textural features were extracted from a Grey Level Co-occurrence Matrix (GLCM) and averaged over four different directions. A UNet model was constructed with MONAI using BraTS 2020 dataset. Due to hardware constraints, the model was trained over a short epoch range of 100 but managed to achieve a mean Dice similarity coefficient (DSC) of 0.86. The quantitative analysis showed that the ground truth and the prediction had low differences in most features, except for percentage variation in signal intensity, energy, correlation and Hausdorff distance, which could be attributed to false positive voxels that are far from the ground truth. The textural properties of homogeneity, entropy, and correlation had an average absolute percentage difference of less than 5% from the ground truth.
Classification of COVID-19 and lung opacity using vision transformer on chest x-ray images
Authors: Toroghi Manoochehr Noghanian , Sheikh Usman Ullah , Irani Shima Shahi 
Published: November 2023

Abstract
There are several recent works which had proposed an automatic computer-aided diagnosis (CAD) deep learning (DL) model to diagnose coronavirus disease 2019 (COVID-19) using chest x-ray images (CXR) to propose a high-accuracy CAD method to detect COVID-19 automatically. In this study, seven different models including Convolutional Neural Network (CNN) models such as VGG-16 and vision transformer (ViT) models, are proposed. The different proposed models are trained with a three-class balanced dataset consisting of 3,000 CXR images consisting of 1,000 CXR images for each class of COVID-19, Normal, and Lung-Opacity. A publicly available dataset to train and test the models is used from Kaggle-COVID-19-Radiography-Dataset. From the experiments, the accuracy of the VGG16 model is 93.44% and ViT’s is 92.33%. Besides, the binary classification between two classes of COVID-19 and Normal CXR with a limited number of just 100 images for each class, using a transfer learning technique, with a validation accuracy of 97.5% is proposed.
Graphite Paper-based Device for Energy Storage Application
Authors: Abidin Mastura Shafinaz Zainal , Razali Muhammad Ameruddin , Peng Tan Jin 
Published: November 2023

Abstract
The demand for versatile flexible energy storage device has arisen due to their potential, application in wearable and portable electronic product. This study presents the characterization of graphite paper-based capacitor and supercapacitor that are produced utilising commercially available pencils to draw a design by developing cheap, green, and reliable low profile electrical electrodes. The objectives of this study are to design graphite paper-based electrodes for energy storage application, characterize the physical properties of the electrodes and electrical properties of the device. Material characterization have been performed including morphology analysis of the electrodes using field emission scanning electron microscope (FESEM), compositional structures by using Energy dispersive X-ray spectroscopy (EDX) and chemical properties of the graphitic electrodes using Raman spectroscopy. Next, the electrical performance of the pencil drawn capacitor and supercapacitor have been performed. It is found that the Acidic G/PANI-Paper supercapacitor sample shows the best electrical performance in terms of lowest resistance with 0.59 MΩ, 1.12 MΩ, 1.8 MΩ and highest capacitance with 27.5 μF, 38.1 μF, 55.25 μF for respective 6 cm2, 8 cm2 and 10 cm2 area size. These characteristics show that the graphite-paper based device has a great potential in flexible energy storage applications.
The Effect of Heat Treatment on Crystalline, Electrical and Optical Properties of ZnO/PVA Nanocomposites
Authors: Ismardi Abrar , Nasir Muhammad , Abdullah Nor Hakimin , Fadila Wan Rizti , Shentya Erina Fitri , Ratnaduhita Santika , Wahyudi Diki Ribut 
Published: November 2023

Abstract
ZnO/PVA nanocomposites have been successfully synthesized using the spin coating method by using synthesized ZnO, ZnO nanoparticles as filler, and PVA as a matrix. The synthesized films were treated at temperatures of 70, 90, and 120 °C. Some characterizations, such as I-V, UV-Vis, XRD, and SEM, have been carried out to study the electrical, crystalline, and optical properties of ZnO/PVA nanocomposites. The characterization of electrical properties using I-V measurements showed that ZnOsynthesized/PVA had a high current value of 1.55 nA, while ZnOnano/PVA had a smaller current value of 1.49 nA when treated at 120 °C. In addition, the higher the treatment temperature, the greater the resistance of ZnOsynthesized/PVA and ZnOnano/PVA. UV-Vis characterization showed that ZnOnano/PVA experienced a red shift when the temperature increased, while ZnOsynthesized/PVA and ZnOnano/PVA when treated at 120 °C experienced a blue shift. XRD characterization of ZnOnano/PVA showed that the higher the given temperature, the lower the crystallinity, from 45.77 to 34.81%. SEM characterization showed that ZnOnano/PVA at room temperature agglomerated and formed larger particles.
Use of Plat-Bar Electrode Media to Detect Partial Discharge in Epoxy Resin Materials on PCB Surfaces
Authors: Mulyana Elih , Somantri Maman , Ardiansyah Neris P. , Yusri Chafidz. D. 
Published: November 2023

Abstract
The electric power system must have a protection system with reliable electric power isolation, so that the supply of electrical energy works properly and optimally. There are several kinds of disturbances in the electric power distribution system, one of which is a partial discharge. This study aims to investigate the effect of plate-rod electrodes on partial discharge, partial discharge voltage limits in solid insulation, and the characteristics of partial discharge in electrode media, and waveforms in partial discharge detection sensors. After conducting research, it was found that in the plate-rod type electrode media, the PDIV voltage appeared earlier than the plate-plate electrode media due to the cross-sectional area of the electrode media. And the limit of the pre-discharge voltage of PCB type solid insulation on the plate-bar electrodes is at a voltage of 1.3 kV. Several indicators to determine the characteristics of the partial discharge are background noise off (BGN off), background noise on (BGN on), partial discharge inception voltage (PDIV), and partial discharge voltage. After being examined, the value of each partial discharge detection voltage was obtained at negative and positive polarity voltages on the electrode plates and plate needles respectively 2.85 kV, 2.95 kV and 1.32 kV, 1.57 kV.
Simulation and Characterization of Carbon Nanotube-based 2:1 Multiplexer Electrical Properties
Authors: Ying Yong Jie , Johari Zaharah 
Published: November 2023

Abstract
This paper reports on using simulation to characterize a Carbon Nanotube (CNT) based 2:1 multiplexer (MUX). This study aimed to evaluate the electrical properties, particularly the propagation delay, average power consumption, Power-Delay Product (PDP), and Energy-Delay Product (EDP). Different design approaches namely conventional CMOS, Pass Transistor Logic (PTL) approach, and Gate Diffusion Input (GDI) were adopted. The voltage supply (VDD) and diameter of the CNT are varied to see the effect on the electrical properties. The simulation was carried out using HSPICE. Through simulation, it is found that the GDI approach used the least number of transistors followed by PTL and CMOS. The calculation of the propagation delay exhibits a substantial improvement of more than 95% using the GDI approach. The average power consumption shows a 55.30% and 35.16% reduction when compared to CMOS and PTL respectively. The PDP demonstrates an improvement of more than 95% when compared with conventional CMOS and PTL approaches. The same trend of observation is also achieved for EDP. The variation of the VDD and chirality has a markable effect on the propagation delay and average power consumption. This is a preliminary attempt to evaluate the performance of CNT implementation in MUX. The outcome can become the guideline for engineers working in circuit design using emerging materials for future nanoelectronics applications.