Miracle-dz/UniFAR-FTUs huggingface dataset:
EfficientNetV2 based for MRI brain tumor image classification

An accurate and timely diagnosis is of utmost importance when it comes to treating brain tumors effectively. To facilitate this process, we have developed a brain tumor classification approach that employs transfer learning using a pre-trained version of the EfficientNet V2 model. Our dataset comprises brain tumor images that have been categorized into four distinct labels: tumor (glioma, meningioma, pituitary) and normal. As our base model, we employed the EfficientNet V2 model with variations of B0, B1, B2, and B3 for experiments. To adapt the model to our number of label categories, we modified the final layer and retrained it on our dataset. Our optimization process involved using Adam’s algorithm and the categorical cross-entropy loss function. We conducted experiments in multiple stages, which involved randomizing the dataset, pre-processing, training the model, and evaluating the results. During the evaluation, we used appropriate metrics to assess the accuracy and loss of the test data. Furthermore, we analyzed the performance of the model by visualizing the loss and accuracy curves throughout the training process. Our extensive experimentation involving dataset randomization, pre-processing, model training, and evaluation has yielded remarkable results. Through relevant evaluation metrics and visualization of loss and accuracy curves, we have achieved impressive accuracy and loss rates on test data. Our research has led us to the successful classification of brain tumors using the EfficientNet V2 models with B0, B1, B2, and B3 variations. Additionally, our use of a confusion matrix has allowed us to assess the classification ability of each tumor category. This breakthrough research has the potential to greatly enhance medical diagnosis by utilizing transfer learning techniques and pre-trained models. We hope that this approach can help detect and treat brain tumors in their early stages, ultimately leading to better patient outcomes.
Caractérisation des types d'occupation du sol en milieu urbain à partir de l'imagerie Radarsat-1 et -2: Approche texturale

Cette étude vise à tester la pertinence des images radar à synthèse d'ouverture (RSO) de moyenne et de haute résolution à la caractérisation des types d'occupation du sol en milieu urbain à partir d'une approche texturale. Plus spécifiquement, on recherche les paramètres de texture les plus pertinents pour discriminer les objets urbains. À cet égard, ont été utilisées, des images Radarsat-1 en mode fin en polarisation HH et Radarsat-2 en mode fin en double et quadruple polarisation et en mode ultrafin en polarisation HH. Les occupations du sol recherchées étaient le bâti dense, le bâti de densité moyenne, le bâti de densité faible, le bâti industriel et institutionnel, la végétation de faible densité, la végétation dense et l'eau. Neuf paramètres de texture ont été analysés, et regroupés en familles selon leur définition mathématique. Les paramètres de ressemblance et (ou) de dissemblance regroupent l'homogénéité, le contraste, le moment différentiel inverse et la dissimilarité. Les paramètres de désordre sont l'entropie et le deuxième moment angulaire. L’écart type et la corrélation sont des paramètres de dispersion, et la moyenne est une famille à part. Il ressort des expériences que certaines combinaisons de paramètres de texture provenant de familles différentes, utilisés dans les classifications, donnent de très bons résultats, alors que d'autres associations génèrent des kappa peu intéressants. Par ailleurs, si l'utilisation de plusieurs paramètres de texture améliore les classifications, la performance de celles-ci plafonne à partir de trois paramètres. Le calcul des corrélations entre les textures et leurs axes principaux confirme les résultats obtenus.
Internet-of-Things-Based Rain Detection Device Using Machine Learning Classification for Rain Prediction at Telkom University

Erratic weather changes over time have a significant impact. Unpredictable weather changes can bring up various problems, one of which is hampering the activities of the Telkom University civitas in outdoor activities. However, there is still a need for weather information. Due to the need for more information about weather predictions in the campus environment, Internet of Things (IoT) technology was proposed as a solution for providing weather detection information. This IoT tool has a variety of sensors that can be used for weather detection, such as rain sensors, temperature, humidity, light, and air pressure. The results from the sensors will then be classified using machine learning algorithms. Machine learning is used to detect weather changes and obtain information about them. The data obtained from the IoT utility will be labeled and categorized into three categories: sunny, drizzle, and rain, based on specific parameters. This study compared the prediction performance of four machine learning algorithms: Logistic Regression, Support Vector Machine (SVM), XGBoost, and Artificial Neural Network (ANN). The results demonstrate that XGBoost produces exceptional results with a 100% accuracy rate and 100% precision, while the others got around 99.20% to 99.31% and 92.74 to 97.62%. The results indicate that the IoT-based weather detection algorithm XGBoost can effectively predict the weather.
A Novel Unlocking Control System for Fingerprint Recognition of Atomizer

The atomization device is the main content of the development of the atomized system, but in the process of unlocking the atomized system, the degree of emergency and the operation means will affect the unlocking results, and reduce the use effect of the atomization device. This paper proposes a fingerprint identification method to analyze the unlocking system of the atomization device, take the development level of the atomization system better, and shorten the formulation time of the unlocking process. Then, the market data is comprehensively evaluated. Finally, the continuous monitoring is used to unlock the system and output the final unlock results. The research results show that the fingerprint identification method can unlock the atomization device system, get the unlocking level of the atomization device is better, and the use effect is more than 95% better than the data mining method. Therefore, the fingerprint recognition method can meet the requirements of the atomization device unlocking system, is not only improves the atomization effect, but also reduces the atomization time, and is suitable for the continuous analysis of the development of the atomization system.
Localization of WiFi Access Points by Lineary Moved User Station

Nowadays wireless communication networks are considered as an important element of modern navigation system. The network of access points in WiFi is widely used for indoor positioning and is considered as a main backup navigation system for Global Navigation Satellite System within urban areas. Positioning algorithms require using the precise location of access points. In the paper, we consider detection of WiFi access points location based on measured received signal strength and measured coordinates of user terminal location using an inertial navigation algorithm. Position of user equipment is identified in the local cartesian reference frame by measurements of acceleration and angles of orientation in space. Ranges to access points are measured with a help of pass loss model for free-space radio waves propagation. Obtained coordinates of access points in the North-East-Up reference frame can be transformed to latitude-longitude format and can be used for remote terminal positioning in the WiFi network.
Fast-Moving Consumer Goods (FMCG) Sustainable Strategies: Minimizing Waste in Cereal Packaging Process

Nowadays, competitive global manufacturing and service industries require companies to adopt sustainable business practices to provide high-quality products and services at an affordable price, particularly in the Fast-Moving Consumer Goods (FMCG) industry. The purpose of this study is to enhance the efficiency of the mono carton packaging process in an FMCG food cereal and snack company by identifying the largest waste through the application of the Value Stream Mapping (VSM) method. VSM is utilized to depict the flow of the packaging process from raw materials to finished products. The waste data in the Packaging Department between 2021 and 2022 was analyzed to identify the packaging flow, and current state mapping was utilized to discover the causes of waste. Hence, a proposed improvement design was obtained through Future State Mapping. The findings revealed that the most significant waste occurred in Non-Value-Added activities, specifically in the form of raw material inventory and work-in-progress material areas, causing a delay of seven days. The validated questionnaire results indicated that the most common forms of waste are Inventory (42.6%) and Excess Processing (31.9%). The Value-Added Ratio (VAR) before improvement had a percentage value of 99.94%, while the VAR value became 99.55% after the enhancement.
Smartphone-based point-of-care multiplexed-genes detection of Mycobacterium tuberculosis on a low-cost paper/polymer hybrid microfluidic device

Tuberculosis (TB) caused by mycobacterium tuberculosis (M.tb) bacteria is one of the leading causes of death worldwide, with the highest disease burden occurring in developing countries. The existing diagnostic methods either have low sensitivity and specificity or need bulky and expensive instruments, which limit their broad application, especially in resource-limited settings. To address this need, we have developed a paper/polymer hybrid microfluidic platform integrated with loop-mediated isothermal amplification (LAMP) technique for rapid, sensitive, specific, and instrument-free multiplexed-genes detection of M.tb.
An Ultra-wideband High-Efficiency Dipole Array based on Frequency Selective Surface

This paper discusses a tightly coupled ultra-wideband antenna array with a 15:1 (0.8-12 GHz) operating bandwidth based on frequency selective surfaces. The design extends the bandwidth by tightly coupling the butterfly dipole antenna to form an antenna array, adding two additional FSS layers between the dipole radiator and the floor, introducing a shorting sheet to transfer the common mode resonance to other frequency bands, and using a tapered balun feed structure. The design achieves Active VSWR<2.5 and the radiation efficiency is no less than 95% over the entire operating band.
Design of Integrated Waveform Based on OFDM for Radar and Communication (RadCom) System

Dual-function radar and communication integrated waveform design is a promising approach of cognizing the electromagnetic environment, which is of strategic importance for the guidance and surprise defense of ballistic missiles. Despite some progress in the design of dual-function integrated waveform, there are still significant challenges, including electromagnetic signal interference and hardware limitations. To address these issues, this paper proposes two OFDM-based integrated waveform designs that independently reduce the peak-to-average power ratio (PAPR) of the integrated signal, and provides simulation results to demonstrate their effectiveness.
Micro Expression Detection Using Deep Learning Techniques

Identifying emotions from a person's face which lasts for few seconds has been done by several researchers in the past. A micro expression is a brief facial expression that occurs when the amygdala responds rapidly to stimuli, reflecting the individual's attempt to hide specific emotions they are experiencing. Micro expressions could be used to analyse the deeper emotions of a person as it is expressed naturally. In this work, multiple models of neural network and pre-trained with default parameters is proposed. Two micro expression datasets were used in this work (Cohn-Kanade and FER 2013.) The classification algorithms used here are neural network models which are Convolution Neural Network (CNN), RESNET 50, VGG-19, VGG-16, INCEPTION V3 and Efficient net B0. Feature Extraction and training is done using these models and performance evaluation is done. For CK+, an accuracy of 99% is obtained and for FER 2013, 95% is achieved.
Simulation and Theoretical Analysis of Plastic Optical Fiber Surface Plasmon Resonance Sensors

In the present work theoretical investigation of the spectral response of surface plasmon resonance (SPR) sensors based on optical plastic fiber at the different configurations of the sensitive element has been conducted. A comparative analysis of the characteristics of the surface plasmon resonance fiber sensors with a sensitive element on the glass and polymer basis was carried out. It is shown that for the larger values of fiber core diameter, the resonance peak of the SPR curve is less deep, and with a decrease in fiber core diameter, the spectral width of the resonance curve gradually increases. The findings strongly indicate that by optimizing the design parameters of the sensitive element, enhancements can be achieved in the performance of plastic fiber-based SPR sensors. The presented results hold the potential for improving the overall efficiency and functionality of the sensors, including the advantages of using plastic optical fibers as sensing elements for SPR sensors.
Classification Model for Assessing Micro and Small Enterprises’ Awareness Level Towards Halal Certification Adoption

To become the global halal hub, the Indonesian government is accelerating Micro and Small Enterprises (MSE) to adopt halal compliance by offering incentives for certification submission. Selection of MSEs that receive the incentives should be done due to the limitations of the budget. The aim of this study is to develop a classification model to measure the level of awareness among MSEs regarding halal certification adoption that can give suggestions in selecting MSEs. The model was constructed by machine learning algorithms applied to survey data acquired from 243 MSE owners. The data includes demographic information and MSEs’ opinions toward halal compliance. Ten algorithms, including five single and five ensemble models were explored in order to get the best predictive classification model. Imbalanced data handling and hyperparameter tuning were applied to improve the predictive performance. The study shows that the Gradient Boosting and Adaptive Boosting models outperform other algorithms, with the best accuracy of 90.28%.
Further Studies of Stacked-mushroom EBG Structures

In this paper, the stopband performance of the stacked-mushroom electromagnetic band gap (SM-EBG) unit cells with different layers is further investigated in detail. Furthermore, the novel relationship between metal via hole in substrate integrated waveguide (SIW) technology and SM-EBG unit cell is also studied in regards to stopband performance. The systematic study of the SM-EBG unit cell may enable its application in device and antenna packaging, particularly in the millimeter wave band.
Analysis of 5 MW NREL Wind Turbine Using Qblade Software

Wind turbines have emerged as an important renewable energy source, and optimizing their design has become crucial for achieving maximum efficiency and performance. This study uses the QBlade software to analyze the effects of varying the shell thickness, spar thickness, and spar position on the mass and modal shape of a 5 MW wind turbine blade. The results suggested that increasing the shell thickness can improve stiffness and reduce deformation but, on the other hand, increases the blade mass. Similarly, increasing the spar thickness can enhance blade strength and stiffness and increase the mass. Altering the spar position can impact the load distribution on the blade, which may change modal shapes. Therefore, it is essential to consider various factors to optimize the design of wind turbine blades for maximum efficiency and performance. A thorough analysis of these factors is necessary to achieve the desired outcome.
Advancements in Main Battle Tank Simulations: Integrating Microcontrollers and Unreal Engine for Realistic Interior Panel Recreation

This paper presents a novel approach to enhancing main battle tank (MBT) training through the recreation of realistic interior panels. By utilizing microcontrollers and electronic components, a working control panel simulator is developed, allowing trainees to interact with the specific systems and controls of MBTs. The integration of Unreal Engine 4 as the simulation environment provides a highly realistic visual rendering and robust physics simulation capabilities, creating an immersive training experience. The study highlights the potential of combining hardware implementation and advanced simulation technologies in MBT training.
Real-Time Monitoring of Rectangular Indicator Lights in Intelligent Power Plants

This research offers an innovative strategy for monitoring the status of panel indicator lights in intelligent power plants. We utilize the YOLOv5face model to locate panel indicator lights, overcoming the issue of positioning. Faced with the challenge of sparse abnormal statuses of panel indicator lights in power plants, we propose an original image synthesis solution to address the problem of data imbalance. Considering the potential irregularities of images during the implementation of security monitoring, we conduct necessary image corrections. Following the four-corner positioning of the YOLOv5face model, we trim the corrected images and then segment map them into 0-1 matrices.
AIML and DL Based CSR Disease Detection for OCT and Fundus Imaging

Millions of people Throughout the world suffer from Central Serous Retinopathy (CSR). CSR is a retinal disease that results in blindness and visual loss. The accumulation of watery fluid behind the retina causes the CSR.Detection and prevention of CSR disease helps to take preventive measures to avoid and overcome any damages to the human eye. there are several cutting-edge techniques and studies that support the automated identification of CSR. Presently AIML based methods are giving promising results. This work implements AIML and DL based innovative technology that contribute to the automated CSR detection. For the purpose of detecting CSR disease and analyzing the results 2 imaging approaches and 3 classification algorithms were used. Optical Coherence Tomography Angiography (OCT), Fundus Imaging are the two imaging or scanning (dataset) techniques used in this work and AIML and DL based techniques such as Convolutional Neural Network (CNN), Support Vector Machine (SVM), Random Forest (RF) are classification algorithms those are used for detection of CSR disease. Finally, our study shows through experimentation that current Deep Learning (DL) classifiers produces accurate, quick, and reliable CSR disease detection results, that is CNN performs better as compared other two techniques.
Foreign Object Recognition Method of Coal Mine Conveyor Belt Based on Lightweight Network

Foreign objects in coal mine conveyor belts are a crucial factor causing conveyor belt damage. Using intelligent methods to accurately recognize foreign objects in coal mine conveyor belts is beneficial for the safer and more stable operation of the conveyor. In this paper, a novel coal mine conveyor belt foreign object recognition method based on lightweight ShuffleNetv2 network, named ASLNet, is constructed. Firstly, the 3x3 depthwise convolution in the ShuffleNetv2 network basic module is replaced by a 5x5 size, which increases the range of the sensing field and is easy to capture the global features of the foreign object target. Second, a lightweight ECANet (Efficient Channel Attention Network) attention mechanism module is added to the ShuffleNetv2 network module to strengthen the coal mine conveyor belt foreign object features and suppress the background features. Finally, the network structure is optimized by reducing the number of stacks in each module to improve the speed of foreign object recognition and reduce the number of model parameters. The experimental results show that the ASLNet model has a parameter number of 0.33x106, a model size of 1.26M, and a recognition accuracy of 97.62%, which is 3.91 percentage points higher than that before the improvement. Compared with the classical ResNet34, ResNet50, DenseNet, GhostNet, and MobileNetv2 recognition networks, the optimized network not only has higher recognition accuracy, but also has fewer parameters, which lays a solid technical foundation for the online real-time recognition of foreign objects in coal mine conveyor belts.
Deep-learning-based Location Recognition Method for Detecting Multi-beam Multi-OAM Vortex Waves

Orbital angular momentum (OAM) has attracted attention since it could provide infinite orthogonal channels and effectively improve spectrum utilization in free-space optical communications. Although OAM pattern recognition widely utilizes deep learning techniques, the advanced identification (equipped with precise localization) of multi-beams remains challenging. In response to this challenge, we propose a new method that employs transfer learning based on a convolutional neural network (CNN). The approach features the capabilities of localization and recognition of various vortex beams. We train the model using phase distribution images of OAM beams of 9 types, rather than the amplitude distributions. Through simulation and experimentation, we demonstrate that the proposed method reduces the training time and improves the detection accuracy by up to 98%. These results contribute to the demultiplexing system of free-space optical OAM systems.
Utilization of Machine Learning Approaches for Rainfall Data Imputation: A Systematic Literature Review

Incomplete data is a frequent issue in rainfall observation data records which can affect the results of analysis or modeling using rainfall data. Incompleteness of rainfall data can occur due to various obstacles that may arise in measuring instrument operations. Hence, to handle incomplete rainfall data in a precise manner, numerous machine learning techniques have been utilized to impute missing rainfall data with reliable values. This article offers a structured analysis, not only various methods employed for filling in the gaps of missing rainfall data but also the data characteristics, and measurement factors. The main purpose of this review paper is to highlight the possible enhancements that can be made to current machine learning models and help readers gain a better understanding of the prevailing trends in imputation techniques for estimate the missing rainfall data.
Comparative Analysis of CNN Models for Retinal Disease Detection

Machine learning, with all its predictive capabilities has leant its services to the field of healthcare extensively. One of the many use cases of ML in healthcare is eye disease detection. Eye diseases have been rampant in society for a long time now. Diseases like Retinoid defects, Cataract, and Glaucoma are some of the more common issues in the sector now. Because of the significant nature of the issue, it's early detection and diagnosis is extremely important. It can otherwise lead to terminal after-effects like vision loss. Transfer Learning is one more approach which has been utilized in the sector of eye disease detection. In this paper, a comparative analysis has been carried out on a multi-image fundus dataset, which analyses and compares the performance of 5 transfer learning models on account of metrics like accuracy, precision and recall score. This paper exploits the usage of models like VGG16, MobileNetV2, DenseNet121, ResNet50, EfficientNetB0, ConvNeXtXLarge, and CNNs. The selected model will contribute to the sector and lend its predictive abilities to improve detection rate for optical diseases.