Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine Learning-Driven Packet Loss Classification via TCP Jersey and Multi-Layer Perceptron

With the huge increase in the amount of data that is transferred every second, congestion in the network becomes unavoidable. There is always a need for algorithms to control the congestion in the network when the data flow increases drastically. The traditional Transmission Control Protocol (TCP) uses a slow start and congestion avoidance mechanism that is not capable of differentiating the cause of packet loss. In a heterogeneous network, the packet loss could result either from congestion in the wired network or a transmission error in the wireless network. In this paper, we propose a machine learning based improvement over TCP-Jersey, a variant of TCP that can distinguish the cause of packet loss and handle it appropriately. TCP-Jersey contains two key components, the Available Bandwidth Estimation (ABE) algorithm and Congestion Warning (CW) router configuration. Our machine learning model learns to distinguish wireless packet loss from congestion packet loss using Multi-Layer Perceptron (MLP). It predicts the cause of the packet loss and handles the congestion using bandwidth estimation algorithm. The simulation results show that our algorithm outperforms TCP-Jersey, TCP-Reno, TCP-Tahoe and TCP-Vegas in terms of throughput, goodput, fairness index and friendliness index.
Smart Wearable for the Visually Impaired

The visually impaired always have a problem maneuvering through places without colliding with obstacles. They have to memorize the neighborhood and then walk around on assumption. According to the National Library of Medicine, India has the highest number of visually impaired people in the world, with an estimated 4.95 million blind persons and 70 million visually impaired. The leading causes of blindness in India are cataract, refractive errors, and glaucoma. As we are constantly surrounded by billboards, posters and direction sign boards, it's easy for a fully abled person to move around but visually impaired people do not have braille for their assistance at all times. Our research introduces an advanced device harnessing the power of Deep Learning models. The Real Time Audio descriptions, powered by OCR and Image Captioning, enhance users' awareness, while Video Call Assistance provides instant communication guidance. The objective of our project is to bridge the accessibility gap and enable a normal life for people with visual impairment by providing an all inclusive, user friendly device. The proposed device is equipped with a microprocessor unit, camera module and capacitive touch sensors, which convert the captured image to audio output, thus enhancing accessibility for individuals with visual impairments.
Hybrid Multimodal Medical Images Fusion Combining MWGF with DC Coefficient Scaling and Pixel Level Wavelet Fusion

Color medical images from multiple modalities need to be fused to provide a more informative representation for detailed diagnosis. Individual fusion methods are inefficient when dealing with multiple modalities. The primary objective of this paper is to design a hybrid combination of fusion methodologies to offer medical analysts multiple information perspectives. This paper presents three main contributions. First, two multimodal images are used for feature extraction with the I_MWGF method. Since using only the MWGF method can result in the loss of redundant information, a modified Weighted Gradient Fusion (MWGF) approach is proposed. This approach incorporates contrast enhancement through the scaling of the DC coefficient, generating I_(SDC) as multi-focus features in the compressed DCT domain. This modification helps eliminate poor contrast issues. To provide more detailed features, the outcomes of MWGF and SDC are fused together using pixel-level averaging and wavelet-based image fusion rules. The DC coefficient of the luminance component is proposed to be scaled in the LAB color space, with the Twicing function used as the scaling function, enhancing entropy and brightness features. The inverse DWT is employed to reproduce the true color image features for diagnosis. The method is quantitatively tested on various multimodal medical image pairs, including MRI, CT scans, and PET scans from different environments.
E-Commerce Website Quality: Usability, Information, Service Interaction & Visual Quality on Customer Satisfaction

The largest number of people live in Indonesia. Consequently, there is a sizable market in Indonesia. Internet technology is another factor that might expand business opportunities. E-commerce has flourished in recent years due to the exponential growth of the internet in Indonesia. E-commerce is growing in popularity as the number of people who have access to the internet grows. Bandung is one of these places; it has the second-highest number of online shoppers among Indonesian cities. Improving the online shopping experience is one strategy for attracting more customers. One of the foundations of the e-commerce business model is the website, the quality of which may be upgraded to boost customer happiness. The purpose of this research is to characterize and share information regarding the impact of e-commerce site quality on customer happiness. A total of 385 participants were surveyed using a quantitative questionnaire for this study. WebQual 4.0 is used to assess the quality of a website. This, plus the inclusion of a new factor—the visual quality as measured by VISAWI—in the evaluation of website quality. SEM PLS is used as the method of analysis. Based on the research that has been done, data shows that the website quality and visual appearance of Zalora’s e-commerce has a positive and significant influence on visitor satisfaction.
MIMD Programs Execution Support on SIMD Machines: A Holistic Survey

The Single Instruction Multiple Data (SIMD) architecture, supported by various high-performance computing platforms, efficiently utilizes data-level parallelism. The SIMD model is used in traditional CPUs, dedicated vector systems, and accelerators such as GPUs, vector extensions, and Xeon Phi. It provides performance throughput in computation-intensive and data-parallel applications. Despite the similarity of data-processing principles between these architectures, porting various programming models between the reviewed platforms is challenging. Furthermore, enhancing the programmability of these architectures is an important feature for utilizing their emerging computing power and simplifying programming complexity. This paper reviews the basic principles of optimization techniques to run asynchronous Multiple Instruction Multiple Data (MIMD) on SIMD accelerators. It also surveys several GPU programming paradigms and application programming interfaces (APIs) and classifies these frameworks into different groups based on their criteria. In addition, a review of studies that performed a comparison of the collaborative execution of GPUs with CPUs and Xeon Phi is presented in this paper. This study will be beneficial for developers and researchers in the field of computer architecture and parallel computing of intensive scientific applications, specifically for early-stage high-performance computing researchers, to obtain a brief overview of performance optimization opportunities as well as the challenges of existing SIMD platforms.
Adopting Five Planes Framework for Developing User Interface based on User Experience Aspects

E-commerce shows a form of change in buying and selling activities carried out with the various advantages offered. Unlike the marketplace, having an e-commerce application itself is expected to increase the brand awareness of a product to its target customers. This study aims to determine the level of usability of an e-commerce website and to develop it in terms of user interface (UI) and user experience (UX) with the final result of interface design recommendations. The development method used is the five planes framework with a cognitive walkthrough and a user experience questionnaire (UEQ) as an assessment for usability testing. The results show that with the recommended design, the efficiency level increased to 89%, the effectiveness level increased to 92%, and scored “excellent” in all six aspects of UEQ’s assessment.
Performance Optimization of Naïve Bayes Algorithm for Malware Detection on Android Operating Systems with Particle Swarm Optimization

The most popular mobile device operating system today is the Android operating system. With so many users of the Android operating system, there are also more crimes, one of which is malware. Malware can infiltrate or damage the system which is certainly very detrimental. During 2021, there were 1,652,521,839 traffic anomaly cases in Indonesia where 62% of these cases were malware infections. Even though it is pretty serious and pervasive, many smartphone users are ignorant and are not aware that their devices have been infected with malware. One way to detect it is by using a machine learning algorithm, namely Naïve Bayes. However, naïve bayes has a weakness, namely, there is no relationship between attributes so it does not improve the performance of the algorithm. Therefore, it is necessary to optimize the problem of selecting and optimizing the features that will be used using the particle swarm optimization algorithm. Based on the research results, the particle swarm optimization algorithm has proven to be quite good at performing feature selection on datasets for the naïve bayes algorithm by achieving 95% accuracy, 95% precision, and 80% recall.
Comparison Between Bi-Directional LSTM And Transfer Learning in Correcting Typing Errors on Twitter Social Media Posts

Typing errors are a behavior that often occurs in communication via short messages or posts on social media platforms. In communicating on social media, many individuals without realizing it often make typing errors that can affect the meaning of sentences. This research will apply a model that can check typing errors using Bi-Directional LSTM and transfer learning which results in the Bi-Directional LSTM model achieving an accuracy of 78% after being trained for 20 epochs with a batch size of 64. On the other hand, the transfer learning model achieves an accuracy of 80% with the same epoch and batch size settings. Furthermore, when the Bi-Directional LSTM model was set with 20 epochs and batch size 64, the accuracy increased to 83%, while the transfer learning model achieved 87% accuracy. Finally, with 20 epochs and a batch size of 64, the Bi-Directional LSTM model achieved 87% accuracy, while the transfer learning model achieved an impressive accuracy of 93%.
User Satisfaction Analysis of Telecommunication Customer Service Mobile Applications Using the End User Computing Satisfaction (EUCS) Method

The increasing number of telecommunications services users and the rapid development of technology have an impact to cellular telecommunications service providers in Indonesia. They must compete to provide the best service to their users. One way to compete is by providing digital assistant applications which have the main function such as checking quotas and purchasing internet quota. However, this application still has several weaknesses, so that user satisfaction is not optimal and has an impact on not achieving application purpose. Nowadays, a study about user satisfaction has been done by researchers because user satisfaction has an impact on the business. No exception including the telecommunication service provider business. This study aims to determine the level of user satisfaction and the factors that influence user satisfaction of mobile telecommunication services application. The research used quantitative methods and End User Computing Satisfaction (EUCS) model with the addition of two variables, namely system speed and system reliability. Data analysis used the PLS-SEM approach and the SmartPLS version 3.3. 3 application. The results of the research show that 2 of 7 hypotheses tested were rejected, namely accuracy and ease of use. The system speed and system reliability that was added to the EUCS model have a positive impact on user satisfaction. So, these variables can be used in EUCS model to analysis user satisfaction on another context. This research also provides an overview of application user satisfaction and can provide consideration for application developers to planning application development so that it can be even better.
AI-based Space Occupancy Estimation Using Environmental Sensor Data

Building energy management - as a tool to effect day-to-day energy savings - is influenced by space occupancy levels. The use of video images to estimate occupancy levels has privacy concerns and is cost ineffective. In this paper, a Deep Learning (DL) based approach is proposed to estimate the number of people in a given space using environmental sensor data. Five environmental factors are considered to train and test the proposed model. The input data are pre-processed with $Z_{scor\mathrm{e}}$ normalization for better performance of the model. Further, the proposed method is compared with Long Short Term Memory (LSTM) with higher $F_{1}$ score. In addition, to improve the estimation accuracy of space occupancy, one hot encoding is done for output data. The proposed model estimates the number of students in classrooms with high accuracy in a cost-effective manner while maintaining privacy. The application of the proposed approach is to improve energy efficiency by utilising the estimated headcount information.
Comparison of the Indobert Optimization Hyperparameter Algorithm for Radicalism Sentiment Analysis

The research aim to compare optimization algorithm of Indobert hyperparameter. Optimization Algorithm used Random Search, Bayesian Optimization, and Population Based Training (PBT). This study uses Indobert Base Phase 2 with a data set on Islamic radicalism, which consists of 1001 tweets in Indonesian that have been labeled positive, negative and neutral sentiments. Indicators that are compared from the three algorithms are accuracy, precision, recall and f1-score. The results showed that the best performance was achieved by PBT with an average accuracy score of 67%, 41% average precision, 81% average recall, and 40% F1 score. While the random search averaged 66% accuracy, 53% precision average, 75% recall average, and 53% F1-score. the results of Bayesian Optimization average 64% accuracy, 60% precision average, 60% average recall, and 60% F1-score
Machine Learning Approach to Predict Ventricular Tachycardia Using Heart Rate Variability Features

This paper comprehensively explores the classification of ventricular tachycardia (VT) patients and healthy individuals. The study employs Heart Rate Variability (HRV) analysis alongside machine learning techniques. HRV features are extracted from the Frequency domain using three distinct methods: Fast Fourier Transform (FFT), Autoregressive (AR) modeling, and Lomb-Scargle Periodogram (LSP). The dataset comprises 50 VT patients sourced from the Spontaneous Ven-tricular Tachyarrhythmia Database and 50 healthy subjects from the Normal Sinus Rhythm RR Interval Database, each with 12 hours of recorded data. Seven machine learning (ML) classifiers, including Logistic Regression, Decision Tree Classifier, Random Forest Classifier, Gaussian Naive Bayes, Support Vector Classifier (SVC), Multi-Layer Perceptron (MLP) Classifier, and k-Nearest Neighbors (KNN) Classifier, were employed for the classification task. The performance of these classifiers was rigorously assessed using a range of metrics, encompassing Accuracy, Sensitivity, Specificity, Precision, Negative Predicted Value (NPV), F1 Score, and Area Under the Receiver Operating Characteristic Curve (AUROC). The results highlight the efficacy of the FFT method and propose optimal classifiers for VT classification.
An Appropriate Histogram Equalization Technique for Thermal Image Enhancement of Neck Region

The pattern of heat distribution in the human body is a natural and early indicator of anomalies. A noninvasive screening method for determining body temperature distribution is thermal imaging. The distribution of an object's thermal radiation can be seen in thermal images. However, the infrared images typically have little contrast. As a result, it is important to increase the contrast of IR images before they are recognized and analyzed. In this research, more effective approaches for increasing the quality of infrared thermal images are presented. This study investigates different Histogram Equalization techniques - Adaptive Histogram Equalization, Contrast Limited Adaptive Histogram Equalization and Adaptive double plateaus histogram equalization to improve the gray scale contrast of the thermal Image. For our experiment, we have used 12 thermal images of the thyroid for AHE, CLAHE and ADPHE processing. After preprocessing and post processing of IR images, statistical parameters are derived. Processing on thermal images used in this work suggest the best HE approach which generates greater contrast-enhancement results for thermal images with extra information.
Sentiment Analysis of Citizen on Twitter in Accessibility of Disabilities at The Public Space Using Support Vector Machine (SVM) Method with Radial Basis Function (RBF) Kernel

According to the Ministry of Social Affairs of the Republic of Indonesia, based on data from the Biro Pusat Statistik (BPS), there were $\mathbf{2 2 . 5}$ million people with disabilities in Indonesia until October 2020. As time goes on, people with disabilities may increase. So facilities and infrastructure that are friendly to people with disabilities must be given great attention. Various comments from the public regarding this matter arose on social media, especially Twitter, both from positive to negative comments. There are 1209 tweets in Indonesian related to this matter which will be classified using the Support Vector Machine (SVM) algorithm with the Radial Basis Function (RBF) kernel using GridSearch and Cross Validation. The parameters used are in the range $\mathrm{C}=[0.01$; $0.1; 1; 10; 100; 1,000; 10,000; 100,000; 1,000,000; 10,000,000]$ and $\gamma=[0.0000001; 0.000001; 0.00001; 0.0001; 0.001; 0.01; 0.1;$ $1; 10; 100]$ and produces a maximum accuracy of the parameter combination $C=1.000$ and $\gamma=0.000001(\mathrm{le}-06)$ of $83.37 \%$ which supports previous research that a good and appropriate parameter combination of C and $\gamma$ in the RBF kernel SVM method will produce maximum accuracy of the classification results.
Relation Classification in Scientific Article Abstracts using SciBERT with Entity Marker

In recent years, relation classification has become one of the important stages in relation extraction for the construction of a knowledge graph. Relation classification is used to classify the relation of each entity in a text or paragraph. In one paragraph, there will be many entities, so it requires accurate information about the position of entities in the text. To solve this problem, we used entity markers and compared the results with the standard entity. This research proposes two pretrained BERT models, BERT-Base and SciBERT-Scivocab. SciBERT is a BERT model that has been specially trained on scientific datasets, while BERT-Base is trained on general datasets. We tested both models with the SciERC dataset, which is a collection of biomedical and computer science article abstracts. The results of relation classification are annotated in the form of F2-score which in this case is more concerned with recall than precision. The F2 score on the BERT-Base model is 82.33% and the SciBERT-Scivocab model is 83.75% using standard entities, while the F2 score using entity markers on the BERT-Base model is 82.30% and the SciBERT-Scivocab model is 84.18%.
Automative Feedback System for Descriptive Answers in LMS using Machine Learning Algortihm

This study describes a learning management system (LMS) designed to enhance theoretical assessments and provide students with detailed, descriptive feedback. The LMS is comprised of two interconnected systems serving distinct functions. The first system concentrates on assigning grades to student work, while the second provides detailed, descriptive feedback. The undertaking involves the integration of two machine-learning models into the LMS website. By automating the evaluation of explanatory the motive is to give appropriate feedback to the students thought which they can reflect upon their errors and learn from them. By automating the evaluation of explanatory responses and providing immediate feedback, the project seeks to improve the efficacy of educational assessment procedures.
Recognizing badminton hitting movements Based on Multi-sensor trajectory recognition algorithm

In badminton, the swing motion exhibits variable acceleration of posture rotation, leading to an increase in the dynamic calculation error of the yaw angle. This, in turn, results in a greater cumulative error in subsequent swing trajectory recognition calculations. To address this issue, this paper proposes a multi-sensor fusion swing trajectory recognition method, denoted as TRM. The TRM method utilizes intelligent devices equipped with multiple sensors to collect sensor data during the swing process, enabling high-precision swing trajectory recognition. Experimental results demonstrate that the accuracy of the swing yaw angle and attitude angle is within 1 degree, while the average trajectory error is controlled within 10%. This method significantly improves the accuracy of badminton racket swing trajectory recognition, offering an effective solution for advancing badminton motion recognition technology.
A Comprehensive Study on Different Optimizations of Pure Reference Counting Garbage Collectors

With the gradual increase of program complexity for utilities and various appliances, memory management has always been a challenge for computer scientists. Garbage collection is a very effective memory management system that comprises techniques that aim to claim unused objects which in turn returns reusable memory space for further use. Among the array of garbage collection techniques, pure reference counting (RC) is very popular that actively tracks down any unused object reference by means of keeping reference counts. It offers better reliability compared with other passive techniques. However this process cannot track cyclic garbage references. In order to solve this cycle detection issue along with other various improvements available for other techniques different methods have been proposed and developed. This paper offers an extensive examination of diverse optimization approaches designed to improve the effectiveness of garbage collectors based on Pure Reference Counting. Some of these optimizations look for loaning concepts from tracing to improve tracking down performance while others add up cycle detection from various existing techniques to gain the pros of both techniques or others simply emphasize memory allocation for reducing time complexity overhead. Our findings provide insights for researchers seeking to leverage Pure RC as a memory management solution, shedding light on the advantages and limitations of different optimization techniques. This study contributes to the ongoing discourse on memory management, offering a perspective on the specifics of Pure RC-based garbage collectors and their potential for optimization.