Miracle-dz/UniFAR-FTUs huggingface dataset:
Brain Tumor Segmentation using the Modified UNET Architecture

Brain tumor is a deadly diseases where the most common and dangerous type of it is glioma, as it may cause death if its grade is high. Patient’s life can be saved if tumor detected in its early stage which thereby starts the treatment procedure sooner. The early detection can be possible by accurately segmenting the brain tumors from MRI scans as they can be easily diagnosed from them. Computer aided diagnostic software can help in automatic segmentation of brain tumors and several methodologies have been designed based on deep learning. In simple U-Net architecture, during encoding or downsampling phase local features are lost which results in constant learning as the model goes deeper. In this paper, we have used the spatial attention-based mechanism to help preserve local features. Additional modules in the encoding path of the redesigned architecture are concatenated during upsampling or decoding path. In the experiments, we have obtained segmentation results for the tumor core (TC), whole tumor (WT), and enhancing tumor (ET) regions of BraTS 2020 dataset. The average dice score were obtained as 0.9027, 0.8868, and 0.9067 for TC, WT, and ET respectively. To show the effectiveness of our methodology we have tested the trained model on 50% of the BraTS 2021 benchmark dataset. The achieved dice scores were 0.8039, 0.7640, and 0.6689 for TC, WT, and ET, respectively.
EDUCATION DELIVERY MODES: A POST - COVID STUDY OF TRADITIONAL AND ONLINE CLASSES IN MANAGERIAL ACCOUNTING

In recent years, universities have been forced to adapt to online teaching methods due to the COVID pandemic. As these COVID restrictions were lifted, students taking an accounting - based course were offered the opportunity to continue to attend remotely or to return to a traditional face - to - face setting. In this study, we examined students ’ feelings of engagement based on both manners of course delivery as well as their expectations of grade for the course. Our results suggest that face - to - face classes provide a better opportunity for following lectures, avoiding distractions, working on group projects, and communicating with instructors and their peers when given the choice between online and face - to - face classes. Students attending class in person also reported a higher expected final grade. However, students attending classes remotely reported appreciating the flexibility of the online format and correspondingly attended a higher percentage of lectures. Overall, these results suggest that students that have now experienced both face - to - face and online course formats may identify and register for course delivery methods with characteristics that more specifically appeal to their desired format regardless of its potential impact on their class performance. To the extent that universities can adopt as many of these characteristics in traditional/fully remote or hybrid type delivery methods, the potential exists for higher student satisfaction levels while taking into consideration student needs.
Wideband Dual Polarized Metasurface Antenna For 5g Application

This paper proposed a new prototype of metasurface antenna with dual polarization operating from $23.7\text{GHz}$ to 44.2GHz for $5\mathrm{G}$ millimeter wave application. The antenna contains several parts: metasurface, SIW cavity, slot and metal vias. Several modes are introduced in the antenna to realize the wideband covering $25\text{GHz}$ to $42\text{GHz}$, which is suitable for the $5\mathrm{G}$ millimeter wave frequency. Meanwhile, in order to realize dual polarizations, the cross slot is applied. The metal via is also added to enhance the performance at low frequency. The total size of the prototype is $3.5 \times 3.5 \times 1{{\ m}}{\mathrm{m}}^3$. Meanwhile, the isolation between two polarizations is better than $20{{\ dB}}$ over the operating frequency, which has good isolation performance. The realized gain is larger than $4.5\text{dBi}$ in all the operating frequency.
Network Robustness Based on Different Dynamic Edge Attack Strategies

A stable network is the basis for the transmission of information and data between components. However, various attacks will affect the normal operation of the network. In this paper, the changes of network robustness under different dynamic edge attack strategies are studied. An edge importance measurement method based on node betweenness is designed first. Simulation results in Erdös-Rényi (ER), scale-free (SF) and small-world (SW) networks, show that in SW networks, the method of edge importance evaluation based on node betweenness product can reduce the largest connected components of the network to a greater extent. Moreover, the edge attack based on edge degree can cause the potential change of network state not fully reflected, and all three networks will suddenly collapse under the attack. Furthermore, the influence of different edge attack strategies on robustness and maximum flow of the networks is analyzed, the edge attack based on node betweenness product has a significant effect on reducing the maximum flow, which has potential value for understanding the real dilemma and building a robust network.
Layout Analysis for Robust Resume Parsing

Document layout analysis is an important step in extracting information from documents of various structures. Layout data helps to understand the hierarchical structure of the document and create structured information for further analysis. Therefore, it has advantages in document parsing or semantic segmentation compared to CNN or transformer-based detection methods, or sentence classification approaches. In the human resources domain, most processes could also benefit from document intelligence. For instance, automated resume parsing is a key step for further analysis of these documents to use with tasks like candidate screening, matching, and could also benefit from document layout analysis methods. In this study, it is assumed that the specific layout structure of a resume document could help to resolve hierarchical information that is useful for detecting particular segments like education, experiments, skills, etc. From this point, it is aimed to explore the contribution of layout analysis to resume parsing in this paper. State-of-the-art methods that performed well in public document datasets are compared in terms of accuracy on hierarchical structure estimation of resume PDF documents. Additionally, a resume layout analysis model trained using custom layout categories. A custom-built data set was used for the training and evaluation of the proposed method. It is shown that fine-tuning with a small set of data improves the accuracy of general-purpose layout detection models on more specific tasks.
Pothole Detection and Classification in Vehicular Networks using ResNet-18

The impairment of road performance and structure due to insufficient serviceability for traffic is referred to as road damage. To mitigate this issue, several local governing bodies and transportation departments strive to incorporate automated methods for evaluating the extent of such damages on roads. Nevertheless, they frequently encounter limitations in terms of resources like cutting-edge technology, expertise, or financial capabilities required for collecting data and conducting an analysis on these road impairments. The GRDDC (Global Road Damage Detection Challenge) dataset and the ResNet-18 architecture are used to offer a Pothole detection and classification framework in this study. The goal is to group snapshots of road surface degradation into many categories, including cracks, potholes, and patches. It is used as the ResNet-18 design is a variation of the Residual Network, can accommodate deep layer structures employing skip connections. By initialising the ResNet-18 model using pre-trained weights from ImageNet, transfer learning is implemented, improving convergence speed and overall performance by utilising learnt features. Based on assessment results, hyperparameter adjustment and fine-tuning were carried out to enhance the model’s performance. To do this, experiments with architectural changes and hyperparameter adjustments to learning rate, batch size, and regularisation methods were conducted. To verify the robustness of the model, cross-validation methods were used.
Automatic License Plate Recognition System in Indonesia Using YOLOv8 and EasyOCR Algorithm

In this era of ever-evolving technology, people are in great demand for a safe and secure lifestyle and travel. The number of motorized vehicles on the road has steadily increased over the past decade. However, this also brings negative impacts such as congestion, air pollution, and decreased safety. Therefore, an effective automatic vehicle license plate detection and recognition system is needed to improve traffic safety and surveillance. This research develops an automated vehicle license plate detection system with the You Only Look Once (YOLO) algorithm. The dataset in this study uses images of vehicles and Indonesian license plates totaling 869 data. Then, proceed with the text extraction process on the vehicle license plate using the Easy Optical Character Recognition (EasyOCR) library. The test results to develop an automatic vehicle license plate detection system with the YOLOv8 algorithm and the EasyOCR library have an accuracy rate of 94%. These results show that the system has successfully detected and recognized all characters on the vehicle license plate.
Improved estimates of glacier change rates at Nevado Coropuna Ice Cap, Peru

ABSTRACT Accurate quantification of rates of glacier mass loss is critical for managing water resources and for assessing hazards at ice-clad volcanoes, especially in arid regions like southern Peru. In these regions, glacier and snow melt are crucial dry season water resources. In order to verify previously reported rates of ice area decline at Nevado Coropuna in Peru, which are anomalously rapid for tropical glaciers, we measured changes in ice cap area using 259 Landsat images acquired from 1980 to 2014. We find that Coropuna Ice Cap is presently the most extensive ice mass in the tropics, with an area of 44.1 km2, and has been shrinking at an average area loss rate of 0.409 km2 a−1 (~0.71% a−1) since 1980. Our estimated rate of change is considerably lower than previous studies (1.4 km2 a−1 or ~2.43% a−1), but is consistent with other tropical regions, such as the Cordillera Blanca located ~850 km to the NW (~0.68% a−1). Thus, if glacier recession continues at its present rate, our results suggest that Coropuna Ice Cap will likely continue to contribute to water supply for agricultural and domestic uses until ~2120, which is nearly 100 years longer than previously predicted.
Reassessing the conservation outlook for Madagascar’s endemic Anatidae following the creation of new protected areas

Madagascar has three endemic species of Anatidae, all of which are classified by the International Union for Conservation of Nature (IUCN) as Endangered or Critically Endangered. Until recently there have been no protected areas within their ranges to secure key habitat. The creation of several new protected areas in Madagascar since 2010 has created an opportunity for better conservation management of these species, most obviously for Madagascar Pochard Aythya innotata which occurs at just a single site that has now been protected. We created distribution models for the other two species, Madagascar Teal Anas bernieri and Meller’s Duck A. melleri , using survey data collected from 2004–2013 and MaxEnt software. Predicted ranges were compared with the locations of protected areas. Additionally, for each species, population monitoring was carried out at one site at which there has been conservation intervention. Our models predicted that breeding Madagascar Teal would occur near healthy mangroves (family: Rhizophoraceae) in areas with high mean temperature, but the total extent of predicted suitable habitat is just 820 km2. Non-breeding Meller’s Duck favour water surrounded by dense vegetation, in areas with low human population density. Meller’s Duck occurs in at least nine protected areas, but most of these were set up for forest conservation and may not support many individuals. Since 2010, two wetland protected areas that could benefit Meller’s Duck have been created, although one is small and the other, Alaotra, is heavily disturbed. The population of Meller’s Duck at Alaotra is stable. Four new protected areas will benefit Madagascar Teal, covering more than half of the predicted breeding range for this species. The population at one of these protected areas, the Manambolomaty delta, is increasing. Overall, we conclude that the conservation outlook for Madagascar Teal is improving, but the small range for this species means it is dependent on good management at protected areas where it does occur. Meller’s Duck requires more attention, and the outlook for this species remains poor.
Utilization of Body Posture Feature Extraction with Movenet for Silat Movement Classification Using Artificial Neural Network (ANN)

Pencak Silat is a distinctive traditional martial art of Indonesia that needs to be preserved. One way to preserve Pencak Silat through a technological approach is by introducing its movements using Human Pose Estimation. Human Pose Estimation is a branch of computer vision that predicts the position of keypoints on a human body from an image, in this case, images of Pencak Silat movements. One of the models for extracting keypoints is MoveNet, developed by Google, excelling in striking a balance between speed and precision. This study proposes a classification system for Pencak Silat movements using the Artificial Neural Network (ANN) architecture. It utilizes a dataset of Pencak Silat movements with keypoints extracted using MoveNet. The dataset includes 8 movements: ‘dengkulan’, ‘pukulan lurus’, ‘pukulan samping’, ‘sikap pasang’, ‘sikutan atas’, ‘sikutan depan’, ‘tendangan A’, and ‘tendangan T’. The research employs K-Fold Cross Validation with K=5 for validation. The research results demonstrate that by utilizing the body posture feature extraction with MoveNet, it is possible to classify Pencak Silat movements using the Artificial Neural Network (ANN) method, achieving an accuracy of 0.971, which indicates a good performance.
Implementation of Sorted Stair-Case Modulation and Sorted Phase Disposition PWM for Grid-Tied Multi-Level Inverter

Integrating a multilevel inverter with the grid provides advantages in high-power and high-voltage applications, including reduced harmonic distortion without a transformer. This paper presents the implementation of Sorted stair-case modulation (SSCM) and Sorted Phase disposition (SPD) Pulse Width Modulation (PWM) techniques for a Grid-Tied Cascaded H-Bridge (CHB) multilevel inverter featuring single-stage power conversion. SSCM and SPD PWM techniques are used to track the MPPT of each PV panel under uniform and non-uniform irradiation conditions if the PV panels are connected to the inverter's input terminals. A centralized current controller has been designed for the Grid-tied multilevel inverter system. Furthermore, a simulation has been developed to compare the power quality of the grid current and voltage between SSCM and SPD-PWM.
Combined learning and optimal power flow for storage dispatch in grids with renewables

We propose an optimization and learning technique for controlling energy storage in power systems with renewables. A reinforcement learning (RL) approach is employed to bypass the need for an accurate stochastic dynamic model for wind and solar power; at the same time, the presence of the grid is explicitly accounted for through the “DC” approximation to the Optimal Power Flow (OPF) to impose line constraints. The key idea that allows the inclusion of such instantaneous constraints within the RL framework is to take as control actions the storage operational prices, which may be suitably discretized. A policy to select these actions as a function of the state is parameterized by a neural network model and trained based on traces of demand and renewables. We call this combined strategy RL-OPF. We test it on a trial network with real data records for demand and renewables, showing convergence to a control policy that induces arbitrage of energy across space and time.
Denoising Autoencoder-based Feature Compression for Enhanced Performance of Vehicle License Plate Image Recognition

To combat the issue of low-quality license plate images captured by CCTV cameras in Electronic Traffic Law Enforcement (ETLE) systems, this study presents a Denoising Autoencoder-based feature compression method for improved image recognition. The Denoising Autoencoder significantly outperforms the conventional Autoencoder, enhancing feature extraction by introducing noise to the input. The method increases the accuracy rate from the standalone use of a Convolutional Neural Network $(90.47 \%$) to $94.76 \%$, showcasing its effectiveness in noise reduction, dimensionality reduction, and feature learning from license plate images.
Supervised STDP Learning with Weight Decay for Spiking Neural Networks

This research aims to address the challenges of the Diehl & Cook model and develop a new learning method focused on the learning efficiency of spiking neural networks (SNNs). The identified challenges include the task of classification, learning redundancy, and catastrophic forgetting. To overcome these challenges, a novel approach called supervised STDP learning was proposed, leveraging the local learning nature of the STDP learning rule. Additionally, a weight decay mechanism was introduced to assign inhibitory roles to irrelevant weights. Experimental evaluations were conducted using the MNIST dataset, comparing the proposed method with the Diehl & Cook model. The results demonstrated that the proposed method significantly improved accuracy compared to the previous model, and the learning convergence speed was remarkably fast. The experiments also confirmed that supervised STDP learning allows for local learning within synapses, enabling the network to learn independently for each label without interference. As a result, the proposed method showed immunity to catastrophic forgetting. Overall, this research contributes to enhancing the learning efficiency of SNNs and provides insights into overcoming challenges in spiking neural network models.
Trafﬁc Flow Prediction Based on Multi-Mode Spatial-Temporal Convolution of Mixed Hop Diffuse ODE

: In recent years, trafﬁc ﬂow forecasting has attracted the great attention of many researchers with increasing trafﬁc congestion in metropolises. As a hot topic in the ﬁeld of intelligent city computing, trafﬁc ﬂow forecasting plays a vital role, since predicting the changes in trafﬁc ﬂow can timely alleviate trafﬁc congestion and reduce the occurrence of accidents by vehicle scheduling. The most difﬁcult challenges of trafﬁc ﬂow prediction are the temporal feature extraction and the spatial correlation extraction of nodes. At the same time, graph neural networks (GNNs) show an excellent ability in dealing with spatial dependence. Existing works typically make use of graph neural networks (GNNs) and temporal convolutional networks (TCNs) to model spatial and temporal dependencies respectively. However, how to extract as much valid information as possible from nodes is a challenge for GNNs. Therefore, we propose a multi-mode spatial-temporal convolution of mixed hop diffuse ODE (MHODE) for modeling trafﬁc ﬂow prediction. First, we design an adaptive spatial-temporal convolution module that combines Gate TCN and graph convolution to capture more short-term spatial-temporal dependencies and features. Secondly, we design a mixed hop diffuse ordinary differential equation(ODE) spatial-temporal convolution module to capture long-term spatial-temporal dependencies using the receptive ﬁeld of the mixed hop diffuse ODE. Finally, we design a multi spatial-temporal fusion module to integrate the different spatial-temporal dependencies extracted from two different spatial-temporal convolutions. To capture more spatial-temporal features of trafﬁc ﬂow, we use the multi-mode spatial-temporal fusion module to get more abundant trafﬁc features by considering short-term and long-term two different features. The experimental results on two public trafﬁc datasets (METR-LA and PEMS-BAY) demonstrate that our proposed algorithm performs better than the existing methods in most of cases.
Survival prediction in renal cell carcinoma patients using machine learning

Survival prediction and outcome analysis are essential for any cancer patient. It helps in patient management and treatment plans. Patients with early-stage diagnosis of cancer have a higher survival rate which reduces morbidity and mortality. In this paper, machine learning algorithms are used for survival prediction in clear cell Renal cell Carcinoma (ccRCC). Three slices from the primary tumor and 3 slices from metastasis are selected. The texture features are extracted from these selected slices using a gray level correlation matrix (GLCM), discrete cosine transform (DCT), and discrete wavelet transform (DWT). A patient’s survival is estimated by selecting the most relevant features that are highly correlating within a group. Contrast enhanced computed tomography (CECT) scans of 38 patients are used in this study. K-nearest neighborhood and decision tree classifiers outperform the state-of-the-art machine learning algorithms and provide ‘59.60%’ and ‘57.90%’ classification accuracy for survival prediction, respectively.
Enhancing Inference Performance through Include only Literal Incorporation in Tsetlin Machine

The Tsetlin Machine is a novel and powerful algorithm for pattern recognition and decision-making tasks that has gained significant traction in recent years. Its features make it highly suitable for energy-efficient hardware implementations. This paper presents an FPGA design and implementation of an inference accelerator for a Multi Class Tsetlin Machine (MCTM). The proposed design aims at using the sparseness of the multi-class Tsetlin machine to optimize the inference algorithm implementation and provide a fast and resource-efficient implementation on Xilinx’s Zedboard. We train the multi-class Tsetlin Machine model in software on the MNIST dataset and subsequently port the model to hardware. Further, we demonstrate and evaluate the performance of the proposed design using test images. It is observed that our design uses $21.1 \times$ less memory and is $30.8 \times$ faster compared to a standard design, with slightly more resource utilization for a given set of parameters.
Enhanced Frequency-Based Word Embedding Approach For Text Analysis: Comparative Study With Existing Method

Text classification is vital in natural language processing (NLP) and has diverse applications. A new approach, Relative Word Frequency Ratio (RWFR), is introduced for text classification. RWFR utilizes positive and negative word frequencies in a text corpus to create a feature vector for logistic regression. The method's effectiveness is compared to Term Frequency-Inverse Document Frequency (TF-IDF) and CountVectorizer on Reuters-21578 and 20 Newsgroup datasets. RWFR, TF-IDF, and CountVectorizer-derived feature vectors are used to train classifiers, with performance assessed using standard metrics.Experimental results show RWFR achieves notable classification accuracy, akin to TF-IDF and CountVectorizer. RWFR exhibits promising F1-score and recall for text classification. This suggests RWFR as a viable alternative for feature extraction and logistic regression-based classification. RWFR's uniqueness lies in considering relative positive and negative word frequencies, capturing sentiment information for accurate classification. In comparison to TF-IDF and CountVectorizer, RWFR demonstrates competitive performance, highlighting its potential for various NLP applications.