Miracle-dz/UniFAR-FTUs huggingface dataset:
Internet of Things-Based Telehealth Monitoring System in Mines: Architectural Concept and Wearable Device Prototype

This work investigates a telehealth monitoring system based on a star wearable wireless sensor network. Using this system, the health of mining workers (blood oxygen concentration, heart rate, body temperature) and mining environment parameters (CO2, temperature, humidity) are measured and real-time monitored at a distance via Bluetooth low energy (BLE) and Wi-Fi technologies for pushing data to the Server for monitoring and warning. With the common use of handheld devices in mines that can only measure in fixed areas, this is quite inconvenient, so there is a need for a wearable BLE mining environment and health monitoring sensor. Compact, low power consumption is recommended and placed on the human body. In addition, measuring health parameters over a long enough period can be the basis for building a data set on worker health to provide reports and guidance on mine safety. A compact, low-power consumption wearable BLE health and mining environment monitoring sensor is proposed and placed on the human body. These wearable sensors have been evaluated in the mine with 0.4 seconds and 99.6% of average packet latency and packet error rate, respectively. The sensors and gateway form a highly flexible star network, capable of adding or removing sensors in approximately 0.3 seconds. The communication distance between two BLE wearable sensors is up to 100 m in free space, and 20 m in the mining environment with many obstacles when the transmitted power on the MCU is 0 dBm.
Diversity of zoobenthos in lakes in the medium reaches of Changjiang River

This paper studied on the diversity of zoobenthos in Biandantang Lake, Qiaodunhu Lake, South Qinglinghu Lake, Dahuangjiahu Lake and North Qinglinghu Lake, located in the medium reaches of Changjiang River. In total 114 species of zoobenthos belonging to 33 families and 79 genera were recorded. Among which, 30 species of oligochaetes, 51 species of aquatic insects, 22 species of molluscs and 11 forms of miscellaneous animals. The number of taxa of zoobenthos in five lakes were 83, 66, 45, 40 and 14 respectively. The average densities and Shannon - Wiener index were 250, 484, 340, 1024, 653ind. m-2 and 3.853, 3.166, 2.517, 1.543, 1.799 respectively. Fuzzy cluster analyses indicated that the taxa of zoobenthos between Biandantang Lake and Qiaodunhu Lake, and between South Qinglinghu Lake and Dahuangjiahu Lake related. The relationships between zoobenthos and environmental factors were analysed, results showing that the density is positively correlated to such factors as COD and No-3, and Shannon - Wiener index is negatively correlated to the Nh-4 - N and Po-4. Especially, a very significant positive correlation was found between Shannon - Wiener index and submerged macrophytes.
EEG-Based Depression Recognition Using Time-Frequency Convolutional Networks

Accurate and timely diagnosis of depression is crucial for effective mental health intervention and treatment. However, existing diagnostic methods often rely on subjective assessments, which pose challenges in comprehensively understanding this complex condition. To address this issue, we explore the potential of time-frequency convolutional networks to automate the extraction of discriminative features from electroencephalography (EEG) data. Our methodology eliminates the need for preprocessing bandpass filters by incorporating a learnable one-dimensional (1-D) convolutional layer with a stride of 2, effectively reducing computational complexity. Additionally, we employ the Continuous Wavelet Transform (CWT) to extract scalograms, providing detailed time-frequency representations of EEG signals. Subsequent two-dimensional operations on the scalogram enable the identification of intricate patterns associated with depression-related alterations in brain activity. We conducted a rigorous 6:2:2 training-validation-testing protocol on the MODMA dataset, achieving a 100% accuracy rate in classifying depression and health categories, surpassing state-of-the-art methods. This achievement offers an objective and reliable means of identifying individuals at risk and contributes significantly to the development of scalable and accessible tools for early depression detection.
A Novel Wireless Power Transfer System with Reflex-Charging and Cell-Balancing Functions

This paper introduces a wireless power transfer (WPT) system that incorporates energy recycling capabilities at the receiver (Rx) for the purpose of reflex-charging and cell-balancing in battery strings. The proposed system comprises a non-dissipative active energy buffer that stores and releases energy via a phase-shifted half-bridge rectifier (PSHBR) and an inductor. This arrangement enables reflex charging and active balancing among the batteries. The reflex-charging mechanism not only lowers the internal resistance of the batteries but also accelerates the charging process while minimizing temperature elevation. This paper provides system descriptions, circuit operation details, and circuit numerical analysis to support the proposed concept. The results obtained from these analyses validate the effectiveness of the proposed concept, demonstrating that the system achieves maximum power efficiency of 90.2%, and enhances battery charging performance in terms of speed and reliability.
Optimizing Learning Performance through AI-Enhanced Discussion Forums

As online learning evolves rapidly and discussion forums become increasingly important in educational environments, this study analyzes the impact of participation in discussion forums on the learning performance of future computer science teachers. Data were collected to assess their level of engagement and academic outcomes. The methodology included the use of natural language processing techniques, such as the Bidirectional Encoder Representations from Transformers (BERT) model, and Latent Semantic Analysis (LSA) to analyze the emotional state and relevance of their contributions. Preliminary results indicate a positive correlation between active participation and their learning performance. Additionally, this study makes a significant contribution by analyzing the correlation between various participation factors and learner performance. These findings highlight the importance of discussion forums as interactive educational tools to enhance learning and promote learner engagement. Important implications are also presented in relation to the obtained results.
Avaliação do Impacto de Veículos Elétricos com Baterias de Curto e Longo Alcance na Vida Útil de Transformadores: Estudo de Caso

This paper estimates the maximum number of EVs that can be supplied by a distribution transformer without experiencing accelerated aging and loss of life. The study analyzes how vehicles with higher battery capacity charging at higher charging power levels affect transformer life. Simulations are conducted considering long-range vehicles with 75 kWh charging at 11 kW, and short-range vehicles with 24 kWh charging at 7.4 kW. The EV hosting capacity is assessed by exploring multiple EV penetration levels and two ambient temperature profiles (winter and summer season) to analyze the effect on transformer life. The study is conducted as case study based on real data, with residential load curves from United Kingdom, and EV profiles derived from the Electric Nation project developed in United Kingdom. Results show that vehicles with long-range batteries are more harmful to transformer life, especially during winter season. In this condition, to avoid premature aging of transformer, a much smaller number of electric vehicles should be connected.
An Innovation Development of Resource Management in 5G Wireless Local Area Network (5G-Wlan) Using Machine Learning Model

In general, the various devices on the fifth generation 5G wireless local area network currently in use must have the right amount of resource applications. This means that any devices that are there will not stop working due to resource deficiency when the shutdown resource settings required for those devices to operate without any interruptions are working properly. Thus the quality of the devices in operation there and the quality of service will be high. In this paper, a machine learning algorithm is proposed to consider the applications of different types of devices in the network and to ensure that they provide the right amount of resources. With this the required amount of resource is first calculated and based on that the divine amount of resource is allocated to them. Thus the lack of resources there is completely avoided.
Towards Securing Mobile Communication from Spyware Attacks with Artificial Intelligence Techniques

Nowadays security in mobile phones is a crucial issue, attacker attacks through messages and emails and when the user opens these malicious links, they can easily insert the malware into the user’s mobile phone and get the all information/data of the user. As the number of mobile users is growing on a higher level for multiple types of applications like emails, online transaction, messaging etc. the attacker easily get all information of the user's mobiles like bank details, user credentials, photos and videos etc. In the current situation, where attacks have increased in exponential frequency, a significant problem now is putting forward innovative technology. As machine learning becomes more prevalent, it may now provide clever solutions for the early detection of spyware attacks for different mobile platforms. As machine learning becomes more prevalent, it may now provide clever solutions for many applications including early spyware attack detection. The approach that is suggested in this research work acts as a shield for mobile devices that are receiving malicious data packets from an attacker. To ensure that only authorized data packets are transferred to mobile phones, it is recommended to use a trained machine learning chip for spyware attack detection. This paper presents the comparative analysis of three artificial intelligence techniques for early spyware detection. The dataset for training and testing artificial intelligence methodologies is taken from Canadian Institute for Cyber security data repository. The results prove that the best method for detecting spyware attacks at an early stage is Convolution Neural Network.
Fish Classification Using Convolutional Neural Network

In the current generation, people’s knowledge about the fish is reducing. So, they couldn’t classify the fishes based on their image because they look similar. That’s why we developed the model using deep learning technique to classify the fishes based on the images and also nowadays the demand of fishes is increasing and classifying the fishes are challenging.so this paper discusses the classification of fishes using the convolutional neural network which has image processing, feature extraction and image classification. The unwanted noise and distortion have been removed by image processing after that dimensionality is reduced with the help of feature extraction finally the image will classify based on the label. By using this method, we achieved 94%accuracy for the classification of the fishes using CNN.
Análise do Desempenho do FB-PLL Aplicado no Monitoramento em Tempo Real de Ondas Cerebrais do Sono Profundo

Repouse is indispensable for the human body, one of its main functions being to replenish energy. During sleep, the body's important restorative activities are performed by the organism, which is a basic human need. With awareness of the dimension that sleep represents and the need to improve its quality, the importance of monitoring in real time becomes evident. Based on this, a technique capable of obtaining characteristics of a signal, such as its frequency, as well as being able to be applied to sleep waves, is the Fast Fourier Transform. However, to have a good use, a considerable number of sample cycles is necessary, which demands a certain computational effort. In this sense, we sought to apply the Phase-Locked Loop technique in this work. Thus, a Fourier-based synchronization algorithm is proposed to track low-frequency brain waves during sleep. It is also worth noting that this method is capable of performing the estimates in just one cycle. Therefore, the objective is to obtain the estimated curve referring to the frequency and an efficient synchronization algorithm that is able to track deep sleep in real time.
Coverage Optimization using Fuzzy and Artificial Bee Colony Algorithm in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) is very useful in our everyday life. Sensors are connected to various smart devices. The fuzzy rules are implemented into Artificial Bee Colony (ABC) Optimization, especially scout bees and ABC optimization has three kinds of bees, such as Employed, Onlooker and Scout bees. Employed bees sense the area coverage, onlooker bees are unemployed bees and find the food source and scout bees find the best sensors. In this proposed Fuzzy-based Artificial Bee Colony (FABC) Optimization is implemented in the coverage area. Fuzzy rules are deployed into the Scout bee and also used to find the best optimization.
An Improved Brain Tumor Detection Using Convolutional Neural Networks

Sources indicate that tumors are among the leading causes of mortality, ranking third after heart and cancer conditions. In particular, brain tumors are one of the most prevalent and serious forms of tumors, underscoring the need for timely medical intervention. The majority of tumors are classified as ischemic tumors and hemorrhage tumors. The utilization of a deep learning model for predicting tumor growth patterns in the brain using a brain complaint dataset is a multi-phase process. The four distinct phases involved in this method are pre-processing, Brain Midline Tracking, Feature Extraction, and Classification. The classification stage employs SVM and KNN algorithms for predicting tumor behavior. Nevertheless, the method exhibits inadequate accuracy and imposes substantial memory requirements. Furthermore, it is incapable of detecting tumor hemorrhage and only detects 60 to 65% of Ischemic cases.
A Comparison Between Supervised Learning Techniques for Predictive Maintenance in Twin Screw Air Compressors

In this work, a comparison between supervised learning techniques is provided for predictive maintenance in twin screw air compressors. Significant data are selected, acquired and stored in an Industry 4.0 context. Different operating conditions of the process are considered. Subsequently to data collection, data analysis and preprocessing phases are performed in order to prepare tailored datasets to be entered into supervised learning classifiers for predictive maintenance. Four classes associated to the required time priority for maintenance are defined, concerning the state of degradation of the oil used by the compressor. In order to compare different supervised learning techniques, also correlation matrix and Principal Component Analysis are exploited. Accuracy, specificity and sensitivity are evaluated together with the confusion matrix. Significant results are obtained which prove that predictive maintenance policies can be applied to twin screw compressors instead of the widely adopted periodic and corrective maintenance policies.
Compensação Distribuída de Termos de Correntes em uma Microrrede Despachável Operando com Tensões Desequilibradas

Resumo —Este artigo demonstra que a integrac¸˜ao da Teoria de Potˆencia Conservativa com a estrat´egia Generalized Current-Based Control , a qual ´e adotada na coordenac¸˜ao de conversores de potˆencia em microrredes despach´aveis, provˆe a compensac¸˜ao distribu´ıda de correntes indesejadas, mesmo perante tens˜oes senoidais desequilibradas. Assim, um balanceamento de con-dutˆancias pode ser obtido quando correntes reativas, de desbalanc¸o e harm ˆ onicas s ˜ ao concomitantemente mitigadas. Portanto, possibilita-se que a microrrede seja interpretada pela rede principal como uma carga resistiva balanceada.
A New Discontinuous PWM Method Based on Neutral-Point Voltage Balancing and Low CMV for Single-Phase Three-Level Inverters

This paper proposes a new discontinuous PWM method to reduce the minimum number of switchings without loss of control neutral point (NP) voltage balancing for single-phase three-level inverters by considering a single-phase three-level full-bridge inverter as same a two-phase matrix converter and switching sequence concepts of the matrix converter are modified to this inverter. The research results reveal that the proposed PWM method can control the minimum number of switchings and the NP voltage balancing. Also, low common-mode voltage (CMV) can be achieved.
An Approach to Environmental Planning and Sustainable Management of Watersheds and Municipalities in Southeastern Brazil

Diagnosis of fragmentation and landscape sustainability conditions are essential to environmental planning and sustainable management of natural resources. Land use spatial patterns and landscape structural indexes (landscape metrics, Urbanity Index— UI, and Landscape Vulnerability Index—LVI) have been proposed to assess biodiversity conservation and ecological sustainability, provided by impact of land use at Middle Mogi Guaçu watershed and its seventeen municipalities, in 2009. Land use typologies and structural indexes values were obtained based on screen digitizing of LandSat-5 imagery, for 2009. Cluster analysis and Permutational Multivariate Analysis of Variance were used to test the null hypothesis of equal degrees of fragmentation and sustainability conditions among municipalities in 2009, respectively. Land use spatial pattern showed a predominantly human occupation for watershed and its municipalities, with agricultural use as the main pressure factor. Municipalities were aggregated into three clusters related to forest fragmentation: one categorized by fifteen municipalities; the second cluster (municipality of Luiz Antônio) showing the best condition, and the third group (municipality of Araraquara) with extreme fragmentation condition. Landscape metrics related with shape, size, and core areas fragments intensify edge effects, and increase habitat isolation. The watershed showed a low naturalness and an intermediate degree of vulnerability. Ecological sustainability was different among municipalities (α = 0.05, F = 32.65 and p = 0.002), with two municipalities (Analandia and Luiz Antônio) presenting the best conditions. The most committed condition was observed, in municipalities of Rincão and Guatapara. For conservation policies to be effective must focus on the creation of ecological corridors around legally protected areas, besides creation of new legal reserves, with purpose to improve biodiversity conservation and ecological sustainability of Middle Mogi Guaçu watershed and its municipalities.
Plant Leaf Disease Detection Using Auto Encoder And Cnn

Plants are regarded as indispensable due to the fact that they constitute the primary source of energy generation for humanity as a result of the nutritive, therapeutic, and other benefits that they possess. Plant diseases can harm the leaf at any moment between crop farming, which can result in massive damages to crop yield and economic market worth. Thus, the diagnosis of leaf diseases is an extremely important part of the agricultural industry. However, it requires a significant amount of manual labour, more time for preparation, and in- depth understanding of plant pathogens. In many instances, these researchers also obtained significant results in both scenarios. This previous research served as inspiration for the model of plant leaf categorization that we suggested in this paper, which makes use of auto encoders for feature extraction and is pre-trained with MobileNetV2.Our experimental results demonstrate that our proposed model outperforms state-of-the-art models.
Performance Comparison of Gradient Boosting and Random Forest for the Classification of False News

The rapid increase in the utilization of social media for news browsing is attributed to its capacity, affordability, improved attractiveness, and accessibility, leading to the dissemination of "fake news. " The widespread propagation of fake news has unintended negative effects on individuals and culture. Certain individuals exploit social media platforms to disseminate false information, seeking attention and reaping monetary and political advantages. To effectively distinguish between authentic and false news, intelligent solutions are required. The Gradient Boosting Classifier and Random Forest Classifier are the two ML classifiers that we compare and contrast in this research work. In comparison, the Gradient Boosting Classifier outperforms the Random Forest Classifier, achieving an impressive accuracy of 92% in classifying fake news.
Topology-Aware GAN (TopoGAN): Transforming Medical Imaging Advances

Generative Adversarial Networks (GANs) have gained prominence in medical imaging due to their ability to generate realistic images. Traditional GANs, however, often fail to capture intricate topological features such as holes and connectivity components in real images. This study applies TopoGAN, a recently developed model tailored for medical imaging. TopoGAN dynamically learns and incorporates topological features like connectedness and loops, addressing a real-world medical data augmentation problem. Utilizing a topological GAN loss function based on Persistent Homology and a new success metric, TopoGAN minimizes the topological discrepancy between synthetic and actual images. Experimental results, showcasing a Wasserstein distance of 0.0021 and a Dice coefficient of 0.995, highlight the model's efficacy in producing qualitatively rich synthetic images. This approach not only improves the realism of generated images but also enhances performance in downstream tasks such as image segmentation, offering a groundbreaking solution with significant implications for medical image analysis, diagnosis, and treatment planning.
Analysis of Machine Learning Model for Predicting Sales Forecasting

The purpose of the study is to investigate the possibilities of machine learning methods for predicting revenue for a retail company. The significance of precise sales projections and the difficulties companies encounter in attaining it are covered in the opening section of the paper. The different machine learning techniques used in the research are then described, including neural networks (NN), decision trees, RF and linear regression. The machine learning algorithms are trained and tested using past sales data from a retail company. The outcomes demonstrate that the Random Forest (RF) algorithm worked good as compare to other models in terms of precision and accuracy. The research also finds crucial elements that have a big effect on purchases, like timing, marketing campaigns, and economic signs. The paper's conclusion highlights the benefits of machine learning for sales predictions, including improved precision, speed, and scale. The study's findings offer practical guidance for businesses seeking to enhance their capacity for sales planning and streamline their operations.