Miracle-dz/UniFAR-FTUs huggingface dataset:
Reconfigurable underwater acoustic modem based on chirp modulation

The growing desire for objects of interest, surveillance, and warfare in the marine environment fuels research in the area of underwater acoustic communication. The underwater channel is harsh and requires dedicated signaling, encoding, modulation, and receiver design. In applications like data collection from spread sensor nodes or autonomous underwater vehicles (AUVs) to static sensor nodes, where the data rate is low, the choice of modulation scheme and transmission technique becomes prominent. Often, these low-data rate systems require low-cost on-board acoustic modems. This paper presents the design and implementation of a low-cost and low-complexity reconfigurable acoustic modem based on a chirp modulation scheme. Field programmable gate array (FPGA) based reconfigurable computing paves the way for faster modem development and performance evaluation. Matlab simulations have been used to evaluate the performance of the proposed scheme. The scheme presented here can be implemented on Zynq 7000 programmable system-on-chip (SoC) FPGA. The proposed modulation scheme is bio-friendly, easy to implement, and suitable for underwater environments.
Research and Application of Small Target Detection of Lung Nodules Based on Yolov7

Computed tomography is a fairly common visualization tool in the diagnosis of lung nodules. In the traditional method of detecting lung nodules in CT images, the detection rate and localization accuracy are low and the processing speed is slow and time-consuming, and because of the existence of complex structure, too small lung nodules and different pathological characteristics of lung nodules in lung images, the detection results are high in false positives and low in sensitivity. Therefore, this paper proposes a small target detection method for lung nodules based on yolov7, the algorithm inherits the E-ELAN structure, Rep-weight parameterization and auxiliary training strategy of YOLOv7 network, and adds Swin Transformer's image feature extraction method in the process of feature extraction, and moves the window to limit the self-attention to a certain range in order to solve the blurring of targets In addition, the DO-Conv model is improved by using additional depth convolution to increase the convolutional layers, where each input channel is convolved with a different 2D kernel, and the combination of the two convolutions constitutes an over-parameterization to solve the problem of large model computation. Model adaptive spatial feature fusion is introduced, which improves the scale invariance of the features by learning to spatially filter conflicting information to suppress inconsistencies.
Diadisc: A Machine Learning Approach for Classification and Prediction of Diabetes Mellitus

Diabetes is a category of metabolic illnesses characterized by persistently elevated blood sugar levels. Frequent urination, increased thirst, and increased hunger are all signs of elevated blood sugar. Diabetes can lead to a wide range of consequences if ignored. To accurately predict whether or not the patients in the data set have diabetes, we are trying to create a machine-learning model. Our goal in this study is to determine a patient’s likelihood of having diabetes based on a variety of factors, including glucose level, insulin, age, and BMI. Our goal in this study is to determine a patient’s likelihood of having diabetes based on a variety of factors, including glucose level, insulin, age, and BMI. We will complete every stage, including data collection and model implementation. We compare multiple machine learning algorithms using the accuracy score measure during the model evaluation process to see which is better. Then, we use Flask, a Python micro framework, to develop a web application.
The Effect of Unified Power Flow Controller and Ultra Capacitor in Hydro Power Plant’s Load Frequency Control

This paper first discusses the discrete implementation of Load Frequency Control (LFC) for hydropower plants using a Discrete Linear Quadratic Regulator (D-LQR). The impact of the sampling period on the performance of LFC with D-LQR is investigated. Then, the impact of the utilization of Flexible Alternating Current Transmission Systems (FACTS) solutions such as Unified Power Flow Controller (UPFC) and UPFC plus Ultra Capacitor (UC) on the performance of LFC is elaborated. The studies are performed on a two-zone hydropower plant. The studies show that increasing the sampling period can deteriorate the performance of LFC. Moreover, it is shown that utilizing both UPFC and UC can improve the response of LFC.
Research on Intelligent Algorithm of Cancer Diagnosis Evaluation Based on Machine Learning

Scientific and efficient intelligent classification model is an important basis for cancer evaluation and diagnosis. Traditional cancer diagnosis and evaluation methods are greatly affected by human subjective factors. Support vector machine can classify linearly indivisible data sets very well, but it is easy to misclassify sample points that are close to the classification hyperplane. The nearest neighbor algorithm can also classify the samples well, and it is not sensitive to outliers in the classification process, but the calculation is large. According to the advantages and disadvantages of support vector machine model and nearest neighbor model, the sample points that are close to the classification hyperplane in support vector machine are classified by the nearest neighbor model, that is, the SVM-KNN combination model is proposed, so as to train an intelligent classification diagnosis model with high classification accuracy and is effective and feasible. A breast cancer data set was used for empirical analysis, and the combined model improved the classification accuracy of the data set.
A Comparative Analysis of Transformers for Multilingual Neural Machine Translation

Multilingual neural machine translation (NMT) has emerged as a promising solution to break down language barriers and promote cross-lingual communication. Transformer-based NMT models have gained significant attention due to their ability to learn contextual information and their effectiveness in handling long-range dependencies. The transformer model, with its self-attention and cross-attention mechanisms, fundamentally alters machine translation when compared to other neural machine translation models and traditional machine translation models. Token alignments between source and target sentences are successfully modelled by these mechanisms. This paper deals with the performance of a transformer-based multilingual NMT model that uses a shared encoder and decoder. We conducted experiments on the publicly available Multi30k dataset for translation jobs involving German, French, and Czech into English. In comparison of models, the Transformer model has performed better against the Convolutional SeqtoSeq and Attention-Based Seq2Seq models. We have evaluated our model on the metric Bilingual Evaluation Understudy (BLEU), where the transformer achieved 36.67, 48.47, and 33.66 for DE, FR, and CZ to EN translations.
Comparative performance analysis of machine learning algorithms in partial discharge source classification

Insulation failure is one of the most critical faults in electric switchgear. One of the foremost factors resulting in insulation degradation is partial discharge (PD). There can be multiple sources of PD in the switchgear and based on the type of defect, the characteristic patterns of the resultant PD may vary. Machine learning-based techniques are well suited for the analysis and classification of these complex patterns. In the present work, five different types of discharges such as corona discharge, floating electrode discharge, free-moving particle discharge, internal discharge, and surface discharge are considered. With the advent of data acquisition systems, it is possible to acquire multiple features in a PD signal. The classification of PD defects helps in time-bound defect-specific maintenance of switchgear. The performance of four supervised machine learning algorithms, namely, K-Nearest Neighbor (KNN), Random Forest Classifier (RFC), Linear Discriminant Analysis (LDA), and Gaussian Naive Bayes (GNB), is analyzed for the classification of PD defects. The optimal parameters for maximum classification performance are obtained for the considered algorithms. Matthews correlation, Weighted average F1 score, the area under the receiver operating characteristic (ROC) curve, and Cohen’s Kappa Score are reported for the considered algorithms and it is observed that the RFC algorithm shows the highest accuracy levels compared to the other algorithms.
Injected Harmonic Impedance Measurement Method Considering Background Harmonic and Noise

The selection of the size of the injected harmonic signal is important for the impedance measurement accuracy and normal operation of the system. The system noise and background harmonic interfere greatly with the limitation of the injected harmonic signal amplitude. The noise of the Lee-HVDC converter station and the system background harmonic are analysed, the upper limit of impedance measurement error is preset to 2%. The threshold value of the injected harmonic voltage when considering the noise and the system background harmonic is calculated by changing the system impedance parameters. The threshold selection method is used to find the system harmonic impedance, and polynomial The threshold selection method is used to find the system harmonic impedance and reduce the overall error of the measured harmonic. Through Matlab/Simulink simulation analysis and verification, it is demonstrated that the proposed method can effectively weaken the influence of noise and background harmonic on the harmonic impedance measurement accuracy.
Classification of Massive Data Sets Using a Revolutionary Grey Wolf Optimization Algorithm and a Deep Learning Model in a Cloud-Based Setting

The field of big data analytics has attracted a considerable amount of attention in the realm of academic research due to the fact that it is incredibly useful in a wide variety of real-time applications. This is the reason why the subject has received so much attention. The relatively recent development of machine learning and deep learning models has resulted in an improvement in performance. This improvement has been brought about as a result of the development of these models. The application of these models to the study of massive datasets has become much simpler as a result of these models, which has led to an improvement in performance outcomes. When considering the complexity of large data and the processing requirements that it imposes, it is beneficial to employ feature selection strategies that make use of metaheuristic optimization algorithms. This is due to the fact that big data is distinguished by the vast quantity of information that it contains. There is a huge advantage in the fact that these algorithms are able to successfully uncover the best potential set of features, which ultimately results in improved classification performance. In this particular piece of literature, a new approach that is known as the GWOA-DBN model is presented as a solution to the problem. In order to construct this model and the benefits that come along with it, the Grey Wolf Optimization algorithm and the optimal deep belief network have been coupled. The objective of the Apache Spark environment is to find a solution to the problem of classifying enormous volumes of data. This is the goal of the environment, which is the ultimate objective of the environment. The GWOA-DBN technique, which involves the construction of a feature selection method, is built on the base of the Grey Wolf Optimization Algorithm (GWOA), which acts as the cornerstone for the methodology. In the process of utilizing this strategy, the goal is to determine which subset of traits is the most ideal. The DBN-based classification model is also applied in order to appropriately categorize the large amounts of data into the precise categories that are necessary. This is done in order to fulfill the requirements. We aim to get the best possible results. The efficient processing of enormous data sets is another goal of using the Apache Spark platform. In order to provide the most optimal outcomes, this is carried out. This takes place in addition to the purpose that was specified earlier. A number of tests were carried out in an effort to improve the efficiency of the GWOA-DBN method on the whole. As a result of the results of these studies, it was proved that this method is more effective than other approaches.
Efficient FPGA Implementations of Convolutional Neural Network

Convolutional neural networks (CNNs) play a major role in image recognition. This paper proposes efficient field programmable gate array (FPGA) implementations of CNN, where the dual core implementation, parallelism in convolution layer, and partial reconfiguration based constant multiplication in convolution layer achieve less cycles, less delay, and less resources respectively. All the proposed designs are implemented on Zed Board (Zynq 7000 FPGA (XC7Z020CLG484-1)) with Xilinx Vivado. The synthesis results show that the proposed parallel convolution based single core implementation of CNN with Modified National Institute of Standards and Technology (MNIST) data set achieves 89% of reduction in delay for 10 tasks as compared with the conventional convolution based single core implementation with a co-processor.
Deep Learning Based Rope Skipping Evaluation and Training System

Adolescents lack a rope skipping posture detection and targeted quality training system. To solve this problem, we propose a deep learning-based assessment and training system for rope skipping. The system is mainly divided into two modules: assessment and training. The assessment module extracts and analyzes the high-dimensional rope skipping features during the monitoring time period to give an assessment report and training actions. The training module supervises students’ exercise by classifying the movements and counting the number of movements through a deep learning model. The construction of the system includes Blazepose data acquisition, data preprocessing, rope skipping posture detection, and related ability improvement training. In the data preprocessing section, we used an exponentially weighted average filter for the experiments. The experimental results show that the exponentially weighted average filter has good smoothing effect on the 2D skeletal point data of jumping rope. In the ability enhancement training module, we explored a variety of algorithms for recognizing movements, including MLP, Transformer, LSTM and CNN. experimental results show that the MLP model has a more superior performance, and its accuracy reaches 98.88%.
Hotel Reviews Classification and Review-based Recommendation Model Construction using BERT and RoBERTa

Personalization plays a crucial role in significantly enhancing customer satisfaction within the hotel industry. Customers, with their unique preferences, often rely on previous customer reviews when selecting a suitable hotel. Therefore, personalization can simplify this process by providing curated lists of hotel recommendations. Our research analyzed six hotel aspects: Value, Accessibility, Service, Room, Cleanliness, and Sleep Quality. We utilize transformer-based NLP models, namely BERT and RoBERTa, to accomplish the analysis. We propose a review classification method to identify customer preferences for each aspect and create a review-based recommendation system for hotel suggestions. To assess performance, we utilize three randomly selected reviews as inputs for both the classification model and the recommendation system. Our findings demonstrate that BERT outperformed RoBERTa in review classification, achieving an accuracy score of 0.8963 and a macro F1 score of 0.83. On the other hand, when constructing a review-based recommendation, RoBERTa proved superior to BERT, with the highest cosine similarity score of 0.99917. Based on our research, we recommend that the hotel industry consider leveraging NLP models, such as BERT and RoBERTa, to create effective personalization strategies. Our research contributes valuable scientific insights into the application of NLP models for creating personalized experiences within the hotel industry.
Enhancement of CURE algorithm using Map-Reduce Technique with Parallelism

The extraction of useful information from huge databases is one of the key areas of data mining and is open for research. Clustering integrates the data with higher similarity into the same group, enhancing the extraction process. Clustering Using Representatives (CURE) is an efficient clustering algorithm that handles voluminous data. However, CURE uses sampling, which encounters scalability and accuracy issues while processing huge databases. This limitation can be repressed by using the Map-reduce technique in CURE instead of sampling. The clustering performance can be further enhanced by using parallelism integrated into the CURE algorithm to reduce processing time and enhance efficiency.
Enhancing Classification of Traffic Sign using Multi-Technique Data Augmentation

Intelligent Transportation Systems (ITS) plays a crucial role in improving road safety and efficiency while improving environment and lowering pollution. One important aspect of ITS is the accurate classification of traffic signs, which aids in automated decision-making processes. Deep learning (DL) have shown remarkable performance in traffic sign classification. However, due to the limited size of available training datasets, these models often face challenges in generalizing to real-world scenarios. In this research paper, we propose a novel approach to enhance the accuracy of traffic sign classification in ITS by leveraging more than 20 advanced multi-technique data augmentation methods that have not been explored in pre-existing literature. This multi-technique significantly improves the performance of deep learning models by applying controlled transformations to the existing dataset, increasing the diversity and robustness of the training data. By leveraging convolutional neural networks (CNNs) and fine-tuning them with the augmented training dataset, we empower the models to learn meaningful representations. Through a comparative analysis with baseline models trained on the original dataset, which used common data augmentations in previous studies, we demonstrate the performance improvement achieved by our proposed approach. The results demonstrate importance and effectiveness of using multi-technique data augmentation in enhancing the accuracy of traffic sign classification within the context of ITS.
An Educational Supply Chain Management Model as Basis for a Big Data-Driven Decision Support Framework for the University

The development of supply chain management (SCM) technics in the manufacturing and service industries has led to a well-developed and highly defined supply chain process. The application of SCM best practices to higher education is strongly encouraged and beneficial for high-quality graduates and better research outcomes for society. This paper provides an educational supply chain management (ESCM) model for higher education institutions. Through our study, we noted the importance of decision-making at all levels of the educational supply chain (ESC). To support decision-makers in the ESC, we explored the potential of involving big data analytics (BDA) in the decision-making process. Inspired by the manufacturing sector's experience in applying big data for decision-making, we present a conceptual framework for a BDA-based decision support solution's proper implementation in universities. A data analysis case study is presented to demonstrate the data processing potential in the university.
A Medical Chatbot Embedding with Artificial Intelligence for Self-Diagnosis

Healthcare is essential for a healthy existence. Scheduling a doctor's appointment for each health concern, however, could be incredibly difficult. Statistics show that most people already have heart or lung illness, even though many report never having been to a doctor. Most people do not have the medical knowledge to assess or diagnose their health issues accurately. In advanced human-computer interaction, natural language processing research is creating natural interaction modes like chatbots. Corporate and commercial applications are using chatbots to answer user enquiries in natural language. The objective is to create an artificial intelligence-powered medical chatbot that can diagnose illnesses and offer guidance on how to manage them effectively prior to the need for direct consultation with a physician. The suggested chatbot model acquires information about their health and then provides personalized responses and suggestions for dealing with each user's health issues while lowering healthcare expenses.
Transformative Approaches in Brain Tumor Detection: Harnessing Deep Learning from MRI Data

This research uses deep learning algorithms on Magnetic Resonance Imaging (MRI) data to address the urgent need for accurate and effective brain tumour diagnosis. The increasing prevalence of brain tumors underscores the importance of robust and automated diagnostic tools. CNN, a subset of deep learning, have demonstrated effectiveness in image analysis tasks, making them well-suited for medical image classification. The study commences with the acquisition and preprocessing of a diverse dataset encompassing both normal and tumor-affected brain MRI images. The data is meticulously curated, normalized, and augmented to enhance the model’s generalization capabilities. A novel deep learning architecture, inspired by established networks such as VGG and ResNet, is proposed for tumor detection. The model is trained on the preprocessed dataset, employing appropriate loss functions and optimization algorithms. Rigorous validation procedures ensure that the model avoids overfitting to the training data, while hyper-parameter tuning optimizes its overall performance. Thorough testing on a separate dataset evaluates the model’s generalization ability and accuracy in detecting brain tumors. Through the implementation of this approach, the CNN classifier achieves an impressive accuracy of approximately 94.1%.