Miracle-dz/UniFAR-FTUs huggingface dataset:
Texture Features Retrieval using Convolutional Neural Networks for Multiclass Classification of Plant Diseases

Early leaf detection is essential to India's rapidly growing agricultural industry. Early plant leaf issues identification and the creation of forecasting techniques are crucial for the agriculture industry to fulfill its task of nourishing the entire population. Recently, analysis of images and automated vision algorithms have been used to images of pepper plant, tomato and potato plant leaves in order to detect illnesses in these plants. These approaches are used to obtain leaf image characteristics using either deep learning frameworks or specially developed methodologies. This article proposes a VGG19 model, based on transfer learning to address plant diseases. To train the model, the Plant Village dataset has been employed. There are 15 distinct leaf classifications in this dataset. The RMSprop optimizer has been employed to train the VGG19 model across 40 iterations with a batch size of 64 and a minimal learning rate of 0.0001. With a training accuracy of 97.85% and a testing accuracy of 96.98%, the model has performed effectively when categorizing images of the plant disease leaves. This study can be applied in the agricultural sector to identify plant illnesses early.
Combining Machine Learning and Dimensionality Reduction Techniques to Provide an Efficient Customer Segmentation: A Case of CAS Tecnologia Smart Grid Power System in Brazil

In the era of big data analytics, customer segmentation requires new approaches to analyze consumption behavior and develop targeted services. In this study, it was explored the K-medoids clusterization method with various similarity metrics and clustering scores, combined with PCA, t-SNE, and UMAP dimensionality reduction techniques to investigate typical and peculiar patterns in electricity consumption from a smart grid system in Brazil. By utilizing traditional and novel features and conducting features interpretation and pattern shift analysis, it was demonstrated the effectiveness of this framework in supporting decision-making processes for electric power companies and promoting sustainable energy management through customer segmentation.
Improved texture rendering based on the MIPMAP algorithm

The Multilevel Texture Technique (Mipmap) is one of the most commonly used techniques for addressing the relationship between viewpoint distance and texture resolution. Its advantages lie in its speed and its ability to ensure high-quality texture details at different distances and angles, reducing the occurrence of aliasing and mosaicking artifacts. However, employing Mipmap technology for generating and storing multilevel textures requires additional memory space and computational resources. This paper introduces a novel texture rendering approach based on Mipmap technology. The algorithm utilizes bicubic interpolation to create textures of varying resolutions, incorporating the Level of Detail (LOD) technique. As objects move farther away from the camera, the algorithm employs lower-resolution textures for rendering. Conversely, as objects approach the camera, it gradually switches to higher-resolution textures. This approach effectively reduces computational workload and memory bandwidth requirements while delivering enhanced visual quality.
New Overmodulation Strategy for Traction Motor in Electric Vehicles

A strategy for the inverter voltage control during the overmodulation operation is presented to practically use for the traction motor in electric vehicles. This strategy prevents performance degradation of current control by linearizing the modified voltage reference by the overmodulation method. In addition, it facilitates the seamless transition to 6-step operation while maintaining the reasonably low total harmonic distortion. Consequently, vibration and acoustic noise associated with overmodulation are reduced, and the discomfort of passengers is mitigated in electric vehicles. The effectiveness of proposed strategy is verified through simulation tests.
Revisão Sistemática de Visão Computacional para Veículos Autônomos Agrícolas

Com o aumento da demanda por eficiência na agricultura de precisão, a popularização do uso de veículos agrícolas autônomos (VAAs) é uma tendência crescente. A literatura sobre o assunto é vasta e se atualiza rapidamente. O objetivo deste trabalho é analisar o conhecimento produzido sobre VAAs para identificar as melhores técnicas e estratégias de visão computacional e redes neurais artificiais. Foi realizada uma revisão sistemática abrangendo os últimos 10 anos para identificar publicações científicas relevantes cuja implementação foi realizada em veículos agrícolas autônomos. Foram encontrados excelentes resultados em 35 artigos analisados. Dentre eles, as soluções que se utilizam de redes neurais convolucionais se destacam, sendo as mais populares nos últimos anos, e com abordagens variadas, como aprendizado por transferência e redes que fazem uso de concatenações de sensores.
EEG time-frequency correlation analysis of different decision stages in the ultimatum game

The cognitive decision-making process in the ultimatum game (UG) paradigm consists of three stages: 1) option evaluation stage; 2) action selection and execution stage; 3) outcome evaluation stage. EEG correlation between different stages of UG is an important issue in exploring cognitive decision-making process. However, the ERP-based analysis is not sufficient to reveal EEG correlations at different stages. This study proposes an EEG time-frequency correlation analysis method. This method first calculates Event-Related Spectral Perturbations (ERSPs), and then uses non-parametric statistical tests with the Pearson correlation coefficient as the first-order statistical test quantity to calculate the EEG correlates of different decision stages in different decision states. It was found that there is a correlation between the first and second stages of the decision-making process when accepting a decision. These findings support the division of the decision-making process and indicate differences in different decision states, demonstrating that ERSP is a useful tool for decision analysis.
Efficacious Routing Approaches in Vehicular Ad hoc Network: An Empirical Study

VANET, or Vehicular Ad hoc Network is a transient wireless structure made up of different kinds of vehicles serving as network hubs and the links between them acting as links. VANET is an improving mechanism that has received an amount of growth in the fields of research, academia, and business. A VANET is a mobile network that uses vehicles as terminals. VANETs, similar to spontaneous systems, possess all the main features of independent connectivity, including processing and preservation constraints. To improve safety while driving and other services, a network of VANETs can be established. The effectiveness of VANETs has been demonstrated in numerous real-world applications, such as traffic management and driver-assist systems. Information exchanged over VANETs can undoubtedly help users experience a better driving experience and prevent accidents. academia, and government. VANET is an acronym used to describe the Vehicular ad hoc network that forms over-moving vehicles on the road. Vehicle networks are rapidly emerging as a platform for creating and implementing new and classic applications. VANETs are distinguished by their high mobility, frequently changing topology, and ephemeral, one-time connections. Vehicle interruptions are common due to how dynamic and moving vehicles are at present. Clustering techniques for vehicles that arrange to increase network scalability and connection dependability as VANET introduces vehicles in groups. Subsequently, other clustering strategies and techniques also exist, and it is necessary for a wide range of situations, including information transfer, routing, and detection of accidents. The methods used for vehicle clustering techniques are examined in this study in terms of networking techniques.
Enhancing Crop Damage Detection through Optimized Neural Network Approach

In the realm of agriculture, the timely anticipation of potential crop damage holds immense significance. With the rapid strides in artificial intelligence and technology, numerous researchers have dedicated their efforts to this pursuit, yielding promising outcomes. In this study, we have developed an enhanced predictive model tailored to early-stage crop damage assessment. Our approach involves the utilization of a meticulously curated Kaggle dataset spanning the years 2015 to 2019. Augmenting this dataset, we introduced supplementary synthetic data by applying SMOTE (Synthetic Minority Over-sampling Technique), thereby achieving a balanced dataset representation. The essence of our methodology revolves around training an Artificial Neural Network (ANN) with a comprehensive exploration of diverse hyperparameters. We have honed in on the optimal parameter configuration through systematic experimentation, elevating our model's effectiveness in prognosticating crop damage levels. Our developed model has produced an accuracy of 0.89.
The Effects of Fecal Donors with Different Feeding Patterns on Diarrhea in a Patient Undergoing Hematopoietic Stem Cell Transplantation

Almost 90% of patients undergoing hematopoietic stem cell transplantation (HSCT) experience diarrheal episodes, which represent a severe, often life-threatening complication for these patients. Although fecal microbiota transplantation (FMT) represents an alternative treatment option for infection-related diarrhea, the application of FMT in HSCT patients is greatly restricted for safety reasons. Furthermore, the therapeutic outcomes of FMT as a diarrhea treatment are somewhat related to the choice of the FMT donor. Here, we comprehensively profiled the dynamic changes in the intestinal microbiota after FMT from two donors with different feeding patterns and the same severely diarrheal recipient undergoing HSCT via a 45-day clinical observation. Importantly, no adverse events attributed to FMT were observed. The stool volume and frequency of the patient were reduced when we used feces from donor #1 (mixed feeding), but these changes were not observed after FMT from donor #2 (exclusive breastfeeding). Interestingly, no obvious differences in overall diversity (Shannon) or richness (Chao1) between the two donors were observed. Additionally, Bifidobacterium accounted for 29.9% and 18.1% of OTUs in the stools of donors #1 and #2, respectively. Lactobacillus accounted for 16.3% and 2.9% of the stools of donors #1 and #2, respectively. Furthermore, through longitudinal monitoring of the patient, we identified 6 OTUs that were particularly sensitive to the different FMT complements. Together, we present a case report suggesting that the overall diversity of the intestinal microbiota may not be the only important element in the selection of an effective FMT donor.
Design of a Compact Fractal Dipole Antenna for GPS-GSM-based Tracking Applications

This paper presents a design of a compact fractal dipole antenna for GPS-GSM-based tracking applications, which is based on the fractal theory. The proposed antenna has of two arms-one iteration is used on one side of the fractal arms and two iterations are used in other side to match the dual band at two different frequencies. The antenna is designed to work at 900MHz (GSM) and 2.4 GHz (ISM), and its size is 79mmx21mmx1.6mm making it small sized. The simulator software package used for this simulation was the CST Studio Suite software, and from the simulation results for gain, return losses and radiation characteristics, it makes the designed antenna suitable for the tracking applications.
Gain Enhancement of Novel Microstrip Patch Antenna using Meandered Rectangular shape for Bio-Medical Applications

Aim: This novel research aligns to build a Meandered monopole antenna with high gain in the frequency range of 3.0 GHz to 12.0 GHz. Materials and Methods: Group 1 is the gain and frequency of microstrip patch antenna (MPA) using Meandered rectangular monopole antenna is Group 1 with sample size (n = 26) having FR4 Epoxy substrate. The gain and frequency of the Group 2 is assigned to rectangular MPA with a sample size of 26. Results: The novel Meandered Rectangular monopole antenna has significantly higher gain of -5.45 dB to 40.35 dB than the rectangular patch antenna having gain of -4.16 dB to -8.66 dB. The innovative Meandered Rectangular monopole antenna's best frequency for highest gain had significance of roughly 0.0056 at 12.0 GHz. For rectangular shaped microstrip patch antenna, maximum gain was at the frequency of 3.0 GHz with significance of about 0.492. Conclusion: The innovative Meandered Rectangular monopole antenna gives higher augmentation of gain at 12.0 GHz range than rectangular patch antenna within the confines of this study.
Performance Analysis of DNN-PCA for DOA Estimation with Three Radio Wave Sources

Direction of arrival (DOA) estimation is one of extremely important techniques in array signal processing and thus used in several applications, such as radar systems, source localization, and wireless channel estimation. In this paper, we present a new solution for enhancing the performance of a deep neural network (DNN) specialized in DOA estimation under very noisy environments. After applying principal component analysis (PCA) to the DNN training dataset whose samples were generated at a high signal-to-noise ratio (SNR), we verified that it is possible to strongly reduce the influence from noise in the test data, especially when this was generated at lower SNRs. We also evaluated the effect of 1) different number of antenna elements in the array and 2) different number of reduced dimensions of the training, validation, and test data on the DNN estimation performance. The results presented here are expected to set a precedent in using PCA prior to training DNNs for DOA estimation.
Automatic Classification of Alzheimer's Using Brain MRI Data and the ResNet152V2 Architecture

Alzheimer's disease is a neurological condition that typically affects elderly people and causes memory loss and cognitive deterioration. Effective intervention and therapy for Alzheimer's depend on an early and correct diagnosis. In this article, the model presents a ResNet152V2 architecture-based deep learning method for the cataloging of Alzheimer's disease using brain MRI data. The classification model is built on the ResNet152V2 architecture, which is renowned for its depth and potent feature extraction capabilities. The architecture is a good contender for identifying small brain anomalies suggestive of Alzheimer's disease since it can extract complicated patterns and characteristics from medical imaging. A collection of brain MRI images from healthy people and those with Alzheimer's disease is being pre-processed for investigation. To increase the robustness and precision of the model, the dataset has been expanded and normalized. The findings highlight how important deep learning is for supporting early diagnosis and intervention, providing a noninvasive and effective way to identify those who are at risk of developing Alzheimer's disease.
Myrmecological News

Taxonomists have created over 150 unique terms to describe microsculpturing patterns in ants and other insects, but the evolution and function of these patterns has remained a mystery. To develop a better understanding of the evolution and function of cuticle microsculpturing, we developed a simplified classification system consisting of five broad categories: smooth, reticulate, striate, punctate, and tuberous. We used this system to classify microsculpturing patterns of 11,739 species and subspecies of ants, which we then mapped onto the current genus-level ant phylogeny. We found strong support that smooth was the ancestral state of microsculpturing in ants. All other microsculpturing patterns evolved independently multiple times, and there have been numerous instances of reversions and reacquisitions of similar patterns. In general, most ants were smooth, and rough textures tended to be associated with subfamilies whose members have a thicker cuticle, particularly Myrmicinae and Ectatomminae. Tuberous was the least common microsculpturing pattern and was largely confined to fungus-gardening ants in the tribe Attini. The function of cuticle microsculpturing is still unknown, but it may play a role in providing structural support, abrasion reduction, desiccation resistance, communication, and influence insect-microbe interactions. We review these proposed functions and discuss how ant microsculpturing may inspire future applications in bio-inspired design.
Design and Simulation of Non-isolated Bidirectional DC-DC Converter for Vehicle Application

The three most important parts of an automobile's electrical system are the starter motor, the generator, and the battery. These parts are needed for the engine to start, run easily, and power components like windows, lights, air conditioning, and all other electrical devices. All the electrical parts of a vehicle are powered by a battery through relays and switches. The engine powers the alternator, which is responsible for turning mechanical energy into electrical energy. To properly convert electricity between the 12V (LV) side and 24V (HV) side, a non-isolated bidirectional DC-DC converter is needed. As a result, power may be sent to all the electronic parts more effectively. To meet these requirements, this project involves the designing and simulation of a non-isolated bidirectional DC-DC converter. The results of the implementation show how well the converter keeps the flow of power constant and gives the electrical system its best performance. This bidirectional converter gives users several benefits, such as improved battery performance, longer battery life, and better use of available power sources.
Variational Modal Decomposition Joint Independent Component Analysis of PPG Noise Reduction Algorithm for Blood Oxygen Calculation

The detection of blood oxygen saturation (SpO2) is a critical detection item for human physiological parameters in clinical medicine. The blood oxygen monitoring method based on the photoplethysmogram (PPG) signal is easy to operate. It does not cause trauma to the subject and is gradually becoming the mainstream technology for blood oxygen saturation detection. This study proposes a noise reduction algorithm based on the PPG signal to improve the accuracy of the calculation of blood oxygen saturation. The algorithm reconstructs the noise reference signal through the modes decomposed by Variational Modal Decomposition (VMD). It then constructs a two-dimensional signal from the noisy reference signal and the original signal, which is processed by the Independent Component Analysis (ICA) algorithm to obtain the noise-reduced signal. The correlation coefficient between the noise-reduced signal and the original signal reaches 0.934. Compared with other noise reduction algorithms, the signal-to-noise ratio of this algorithm is improved by 2.1 dB, 1.49 dB, and 0.78 dB, respectively. At the same time, the improvement of root-mean-square error reached 48.26%, 34.55% and 1,83% respectively. From the results of the standard deviation of SpO2 affected by noise, the result range of the VMD-ICA algorithm is 6.72-7.11, which is the lowest value and the smallest fluctuation range compared with the other three algorithms, showing relatively the best stability and accuracy. These results fully demonstrate that the noise reduction algorithm combined with VMD and ICA has high accuracy and stability when processing PPG signals and effectively improves the quality of PPG signals and the accuracy of blood oxygen calculations.
Cardiac Arrhythmia Class Segmentation Using the Deep 2D CNN Technique

In this article, we gave a strong technique to arrhythmia class division on electrocardiograms (ECGs) that utilizes profound two-layered convolutional brain organizations (CNNs), which have of late exhibited uncommon execution in the space of item identification and example recognization. As input for the CNN model, each ECG beat was converted into a two-dimensional grayscale image. Numerous deep-learning techniques are used to optimize the proposed CNN semantic segmentation model. Convolutional neural network (CNN)--based deep learning techniques have been effective in resolving a variety of issues in medical imaging, including image segmentation. Additionally, we contrasted our suggested model with AlexNet and VGGNet, two well-known CNN models. The evaluation employed the MIT-BIH arrhythmia database. At the evaluation, 10-fold cross-validation was carried out with each ECG recording serving as test data to precisely validate our CNN model. Our experimental results have effectively supported the claim that high accuracy may be reached without manually preprocessing the ECG signals with noise filtering, feature extraction, or feature reduction. This was done using the converted ECG images and the suggested CNN model.
Skin Cancer Detection using ELM Classifier

This research focuses on the crucial task of identifying and categorizing skin cancer through a novel approach employing the Extreme Learning Machine (ELM). The ELM algorithm, known for its rapid and effective learning capabilities, has shown success across various domains. In this proposed method, ELM is utilized to extract salient features from skin cancer images, subsequently classifying them into benign or malignant cases. The study employs a diverse dataset comprising skin cancer images from multiple sources. Through the integration of the ELM algorithm, we introduce a model for the detection and classification of skin cancer. Evaluation of this model is conducted using publicly accessible skin cancer datasets, which encompass a range of dermatological images. The outcomes of this research highlight the model's substantial accuracy in both skin cancer detection and classification tasks. Performance assessment involves diverse metrics such as accuracy, sensitivity, specificity, and F1-score. Empirical findings underscore the efficacy of our proposed ELM-based methodology, surpassing existing state-of-the-art techniques in skin cancer detection and classification. This research accentuates the potential of the ELM-based approach as a robust tool for precise and efficient skin cancer diagnosis. The empirical validation of our model underscores its efficacy in detecting and categorizing skin cancer, indicating its potential to aid medical practitioners in early diagnosis and tailored intervention strategies for skin cancer patients.
Comparing On-Board Chargers and Quasi-Z-Source Converters for Integrated Charge Controller Applications in Electric Vehicles: An In-Depth Analysis

This manuscript provides a novel approach for on-board chargers using Quasi-Z-Source Converter and their limitations in electric vehicles according to standard charging levels. A modified Z-source (quasi-Z-source) converter is proposed and its control strategy has been analyzed. This study mainly focuses on the analysis of key parameters such as efficiency, power density and performance under different operating conditions. The results of the analysis show that a modified Z-source converter has many advantages over conventional converters in terms of its suitability for integrated charge controller applications. It also has better efficiency and can handle a wider range of input voltages. Overall, this study provides valuable insights for selecting the appropriate converter for integrated charge controller applications in electric vehicles in the days to come.