Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multivariate LDPC Decoding Simplification Method and Performance Simulation Analysis

Aiming at the problem of large computation and high decoding complexity when updating the check node in the LLR-BP algorithm of multicode LDPC code, a simplified method of dimensionality reduction in accordance with the weights is proposed, in which the q-dimensional likelihood ratio information of a variable node is descending-ordered and a weight is set for each dimension of the node, and the dimensional information of two variable nodes will be summed up according to the weight value size set in the process of updating check node. The size of the weight value will be added according to the set rule, which effectively reduces the wasted addition operation resources on the low likelihood ratio information. The simplified algorithm reduces about 90% of computation compared to the LLR-BP algorithm during one iteration of the algorithm. The BER performance simulation of the algorithm shows that the simplified decoding algorithm has only a very small difference in performance compared to the LLR-BP algorithm, and the overall decoding iteration speed is greatly improved.
Deep Learning based Bone Fracture Prediction using Convolutional Neural Networks: A Comparative Study of Transfer Learning and Fine-tuning Techniques

Computer assisted bone fracture detection system has a vital role in medical automated health-care system. These automation system helps healthcare units to detect the bone fracture accurately. In this proposed paper, the CNN also called as convolutional neural network has been proposed for the classification purpose of the images that is classified into fractured and not_fractured. These images would be used to train the model to classify whether its positive or negative and to validate it. The proposed system has also demonstrated to be sufficiently the robust one to retrieve the necessary information and perform the required analysis on important image regions. Several images have been considered from the dataset and validated without any hindrances. Here the evaluation has been done by the performance measure of the trained model with the collected dataset which contain testing sets of both fractured and not_fractured images around 600 as well as training sets of both fractured and not_fractured around 8,863 images in total. The accuracy rate achieved in this particular paper was over 86%. This system uses clinical dataset to predict the fracture[1] and mainly focuses on hand, wrist, forearm, shoulder, elbow, finger with both positive as well as negative studies.
Local Sparsity Blocks and Tensor Low Rank Regularized Sparse Unmixing

Hyperspectral unmixing has been an important technique in remote sensing application. Sparse unmixing is a type of widely used methods, which assumes the endmembers in a hyperspectral image (HSI) are relatively small portion of the spectral library. The existing methods have considered single sparsity, row sparsity, and joint-sparsity-blocks regularizations to the unmixing model. However, they all reshape the HSI into a two-dimensional matrix, which results in the loss of local spatial information to some extent. So we consider the HSI as a tensor, and construct a new unmixing model based on the block term decomposition. Simultaneously, we propose a slice-sparsity-blocks regularization to fully utilize the local spatial information. In addition, tensor nuclear norm is added to the model to constrain the low rankness of the abundances, which can preserve the spatial structure and the interactions among the three dimensions. Then we design an efficient algorithm to solve the proposed model. And the experiments on the simulated dataset demonstrate the advantage of the proposed method.
Stock market prediction under a deep learning approach using Variational Autoencoder, and kernel extreme learning machine

Forecasting stock market trends is challenging due to its complex and dynamic nature. Stock market prediction is important because it helps investors make informed decisions and allows businesses to plan their financial strategies. Prior works introduce several Artificial Intelligence (AI) based works, however, they are not robust in semantical pattern identification from the dataset. In light of the above limitation of existing works, this paper introduces the VAE-KELM-based mechanism. In this work, the convolutional neural network (CNN) technique was implemented to extract features and a variational autoencoder (VAE) for predictions. The goal is to predict the stock price with high accuracy. The technical indicators and historical data were considered input, with the sub-maximum layer being substituted by the kernel-based Extreme Learning Machine (KELM). In addition, the application of Autoencoder (AE), Variational Auto Encoder (VAE), Extreme Learning Machines (ELM), and full writing from Radial Basis Functions (RBF) is necessary for demonstrating the multiple stock market sources. VAE has been used for future extraction over the extracted feature. ELM mechanism is used for prediction and also this research used historical data and technical indicators to predict stock market trends. Various performance metrics were used to evaluate the proposed model’s accuracy, and the results were compared with benchmark methods. The study’s results demonstrated the proposed algorithm’s effectiveness and superiority of the proposed model compared to other methods.
Design of an experimental setup for the implementation of CNNs in APSoCs

Implementing convolutional neural networks in has gained significant relevance in recent years. The study of the reliability of this type of networks has become a hot topic due to its great impact on a growing range of applications. Observability and controllability play a primary role when considering the systems reliability, and even more so when thinking about complex CNNs implemented in hardware in applications in which failures are not allowed, known as mission-critical applications. This work proposes an experimental setup for implementing convolutional neural networks in hardware, in which observability and controllability characteristics were included from the design phase. The proposed setup was implemented on a ZYBO Z7 APSoC development board for a LeNet-5 convolutional network architecture and a 32x32 input image dataset. The results demonstrate its usefulness since it provides the CNN designer with more detailed information about the operation of each of its internal layers.
DC Link Voltage Optimization for Efficiency Enhancement of Electric Vehicle Onboard Chargers

Electric vehicles (EVs) are becoming increasingly popular, so it is important to study the efficiency of the EV onboard charging (OBC) systems comprehensively. This paper is focused on the DC link voltage optimization to enhance the overall grid to vehicle (G2V) charging efficiency of the OBC. An EV OBC with ac-dc power factor correction (PFC) and dc-dc dual active bridge (DAB) stages is studied, where the power losses for various converter components are formulated as functions of DC link voltage, battery voltage, and charging power. Afterwards, the optimal DC link voltages corresponding to the minimum total loss at different charging conditions are obtained from theoretical analysis of the circuit for both level-1 and level-2 chargers. A 500 W OBC prototype is experimented in two cases, and the results show that the optimal DC link selection of 345V yields to an overall efficiency improvement of 2% compared to the fixed DC link design (at 400V), while the PFC and DAB stage efficiencies are individually improved and declined by 2.6% and 0.5%, respectively.
An Optimized Hybrid Soil Texture Classification Model using Stacked Sparse Autoencoder

With effective soil management, which lowers the costs associated with soil erosion, improves water utilisation, and other factors, soil data analysis can offer vital information to raise agricultural yield and nutrient utilisation efficiency. An unsupervised method of data analysis called clustering finds similar groups of objects by analysing attribute values. The dynamic subject of soil classification starts with the model and moves on to explain the classes and, at the end, apply the knowledge gained from the field. In light of this, the study presents a novel clustering and classification-based hybrid soil texture classification model (HSTC). Soil data will be clustered and categorised into multiple classes by the proposed (HSTC) model. Hydrological soil groupings (HSG) of various kinds are used as classes to accomplish this. Based on characteristics like the proportion of clay, silt, and sand, the HGS can be categorised. The density-based clustering (DBSCAN) algorithm is used to group the soil texture data. The stacked sparse auto encoder (SSAE) model is used to categorise the clustered data. In addition, the parameter values of the SSAE technique are determined using a new barnacle mating optimizer (BMO). The HSTC model has achieved the best results, with accuracy of 95.66%. The experimental validation of the proposed HSTC model on soil texture dataset revealed that the suggested model outperformed previous techniques.
Experimental Thermal Performance Investigation of Evacuated Tube Solar Collector Using Nanoparticals under Cold Climatic Conditions

In this article experimental investigation of thermal performance of evacuated tube solar collector using nanoparticles under cold conditions investigated. The experimental performed under metrological condition of Bangalore, Karnataka, India. In experimentation three water based nanofluid namely aluminium oxide $(\mathrm{A}1_{2}\mathrm{O}_{3})$, Graphine oxide (GO) and strontium Carbonate $(\mathrm{S}_{r}\mathrm{A}\mathrm{D}-\mathrm{O})$ were used. The nanparticales with low concentration (mass basis) of 30, 60 and 90 mg were mixed with base fluid of water. A series of experiments were conducted to evaluate the thermal performance of evacuated tube. The average thermal efficiency of evacuated tube was found to be about 15.47%, 16.07% and 16.40% for 30 mg concentration of $\mathrm{A}1_{2}\mathrm{O}_{3}$, strontium Carbonate $(\mathrm{S}_{r}\mathrm{A}\mathrm{D}-\mathrm{O})$ and graphene oxide water based nanofluids, for 60 mg it was found to be 15.94%, 16.49% and 16.83%, similarly for 90 mg it was observed to be 16.32%, 17.40% and 17.57%, respectively. The results concludes that graphene oxide with 90 mg concentration gives better performance when compared with other combination of nanofluids. Further, it was observed that 90 mg graphene oxide based nanofluid gives about 12.36% improved thermal efficiency when evacuated working with water.
Machine Learning based Wind Energy Forecasting for Energy Mangement in Microgrid System

This study aims to optimize energy management in microgrid systems and tackles the difficulties of incorporating wind power into the grid. To enhance accuracy and reliability, we propose developing hybrid models that incorporate noise reduction techniques. By integrating different modeling approaches, we aim to improve the precision of wind predictions. Furthermore, adoption of reinforcement learning-based Energy Management Systems (EMS) which offers scalable, flexible, and real-time decision-making capabilities, enabling efficient energy management in microgrids. By leveraging the advantages of scalability, flexibility, and resilience to uncertainties, the reinforcement learning-based EMS enhances energy efficiency, reduces costs, and ensures dependable microgrid performance. Additionally, we explore the integration of demand response programs and direct control mechanisms using Deep Reinforcement Learning (DRL). Through the combination of these approaches, we seek to optimize microgrid performance and enhance energy management. Our study also focuses on a novel microgrid architecture that incorporates flexible loads, to improve stability and reliability. The outcomes of this research will contribute to the seamless integration of wind power, reliable energy dispatch, and enhanced overall performance of microgrid systems. These advancements are vital for achieving sustainable and efficient energy systems while meeting the increasing global energy demand.
Multivariate Time Series Prediction based on Improved Transformer Model in Computing System

In IT operations and maintenance applications within computing systems, monitored data is often modeled as Multivariate Time Series (MTS). Predicting MTS has been extensively studied, and various models, including statistical algorithms and deep learning networks, have been proposed to capture multidimensional and nonlinear features. However, current models suffer from several issues. Firstly, for data with multiple dimensions in computer systems, existing models do not sufficiently and explicitly explore and exploit cross-dimensional dependencies. Secondly, there is a demand for real-time processing of large volumes of data in IT operations and maintenance applications within computing systems, and current deep learning network models incur high computational costs. Finally, the temporal characteristics of time series data are not effectively utilized. To address these challenges, we propose a Transformer-based time series prediction model. Building upon the current model, we introduce a 2D Transformer model to extract cross-dimensional features, utilize a sparse attention mechanism to reduce computational costs, and introduce a novel positional encoding to replace traditional positional encoding. We evaluate the performance on real-world datasets from actual applications within computing systems. Significant correlations between prediction results and actual outcomes are observed. The current model is implemented and applied within the computing system at Zhengzhou University. We compare our model with several state-of-the-art baseline methods, and empirical results demonstrate that our model achieves superior performance.
Improving Privacy Protection in a Collaborative Blockchain-based E-Health Records System

Healthcare industry is going through a rapid change due to emerging digital technologies. Blockchain has proved to a frontrunner in revolutionizing electronic health record (EHR) systems. We describe the paradigm shift in healthcare and related applications as a result of Blockchain. Due to its enviable features and mainly the distributed, trusted and transparent ledger, blockchain provides essential ingredients of a distributed and secure electronic health record system. Privacy protection has still remained as a main impediment in deploying a collaborative integrated blockchain-based HER system among various health providers. In this paper, we analyze and address the privacy concerns in this environment and provide a novel solution for privacy preserving in a scalable solution using meta-data disaggregation, two-tier encryption and access control techniques. The proposed solution is based on a patient-centric dynamic role-based access control considering various parties in a healthcare environment including doctors, personnel and drugstores. Smart contracts are developed and implemented to enact the proposed solution. The transaction rate and system’s security properties are analyzed and it is shown that the proposed technique satisfies all the pre-set objectives.
Renewable Energy Powered Two Stage DC-DC Boost Converter for Electric Vehicle Application

This article deals with the design and execution of a DC-DC two-stage boost converter along lower switch grouping for Electric Vehicle applications. The DC-DC boost converter must interface between the low voltages from solar to the high voltage in an Electric vehicle charging system. For high-voltage applications, a conventional boost converter is not suitable. Hence this examination aimed to do a simulation with a DC-DC two-stage boost converter for higher voltage applications with low input voltage. Also, the analogy between conventional DC-DC boost converter voltage and current output will be analyzed. MATLAB/Simulink 2022b software is implemented for the simulation, modulation and investigation of the planned system. This paper focuses on addressing the specific needs of Electric Vehicle charging systems by designing and simulating a two-stage DC-DC boost converter capable of efficiently converting low input voltages from solar sources to high voltages required for EV charging.
Plant Disease Identification and Detection Using Machine Learning Algorithms

Plant diseases cause significant reductions in plant production, leading to economic losses. Pests and diseases destroy plants or parts of plants, leading to lower agricultural production and food insecurity. Pathogen and pest infestations are major causes of crop destruction and loss of crop yields as most farmers do not have access to pathologists and entomologists or sufficient information and infrastructure to identify the disease early on and reduce the loss. To address this, we used a plant leaf dataset containing healthy and diseased plant leave images. These data sets are freely and publicly available to be used. And by using Machine learning and Computer Vision to identify to train the classifier, classify and predict the health of the plant. Using this method, we detect different kinds of disease present in the plant at an early stage before it spreads on a higher scale and a large part of the field is infected and accordingly take action to reduce the amount of loss. The Random Forest model used in this study achieves an overall accuracy rate of 95% on the specified test images. With a day-by-day increase in handheld devices, this can be a successful approach in helping the food industry to cope with the need for food for the rapidly growing population of the world.
Holographic Knowledge-Auxiliary Information Graph Aggregation for Recommendation

Systems that make recommendations are essential for guiding users through mountains of information. Traditional recommendation algorithms, on the other hand, frequently fail to integrate and use data from several sources, which has a negative impact on system performance. As a result, this study suggests the Knowledge Graph Attention-assisted Network (HKGAT), a unique recommendation model. In order to explicitly study higher order connectivity, we first incorporate the knowledge graph into the recommendation model. The model aggregates data to improve its capacity to generalize by using multilayer interactive information propagation. Furthermore, we aggregate the knowledge of neighboring entities for each entity by learning their inferential links, integrating auxiliary information into entities using holographic embeddings. This makes it possible to use entities' auxiliary information more effectively. To show the logic and efficacy of the HKGAT model, we ran experiments on actual datasets. On open-source datasets, we used experimental analysis to compare the performance and potential of HKGAT to alternative baseline models. The knowledge information capture and relationship learning capabilities of HKGAT are improved.
Stability Analysis and Implementation of Interleaved Bidirectional Converter as a High Current Interface in LVDC Microgrid

The interleaved bidirectional DC-DC converter (IBDC) is a viable alternative to the traditional converters for high current applications. It offers significant advantages in several applications with improved efficiency and reduced size of filter components. This paper delineates the small-signal stability analysis of a two and three-phase IBDC as a high current interface in an LVDC microgrid using the state-space averaging technique. The stability analysis can be performed for any number of phases in an interleaved converter, similar to the demonstrated analysis for a dual-leg IBDC. An analytical model for the average current mode-controlled IBDC is developed using a small signal modelling approach. It investigates the relationship between the DC bus voltage, inductor current, and the control parameter (i.e. duty cycle). The controller is designed and simulated in MATLAB/Simulink for 2 kW IBDC. From the simulation results, it is evident that the designed controller effectively maintains DC bus voltage at 48 V during various load transitions. Moreover, the open-loop experimental validation of the IBDC is carried out in the developed test-bed using C2000 microcontroller. In future, the work can be extended for closed-loop performance improvement and experimental validation for bidirectional power flow operation typically in a 48 V LVDC microgrid for residential applications or future DC homes.
An ovarian cancer prediction using an optimized Elman neural network based on elephant herding optimization

One of the leading causes of death for women is ovarian cancer (OC). More recently, deep learning has demonstrated improved accuracy in OC stage and subtype prediction. Nonetheless, the majority of cutting-edge deep learning models might lead to low performance due to inappropriate selection of hyperparameters. Furthermore, the optimization of the model construction—which necessitates a significant computational cost for training and deployment—remains absent from these deep learning models. This paper proposes a novel optimized technique using an optimized Elman recurrent neural network (ERNN) based on elephant herding optimization (EHO) called ERNN-EHO for predicting ovarian cancer. A variety of evaluation metrics were employed to contrast the suggested model with alternative prediction models. This was carried out utilizing an OC benchmark that was gathered from the Kaggle website. The experimental findings show that the suggested model can diagnose OC and other malignancies with greater precision and accuracy.
Bacteria Gram Staining Differentiation Using Hyperspectral Imaging and Machine Learning

Gram staining is an important phenotypic test used to differentiate bacteria into Gram-positive and Gram-negative. Such a methodology consumes time and chemical reagents to be done and is susceptible to errors in the results. HyperSpectral Imaging (HSI) is a non-destructive analytical technology that combines spatial and spectral information to study biochemical composition of biological samples. The aim of this work is to perform Gram staining using HSIs of bacteria and machine learning algorithms. Different HSI of Gram-positive and Gram-negative bacteria strains were used to perform a comparative study with several supervised machine learning models. The best mean F1-Score obtained was around 0.92 resulting from a classification using a neural network with three hidden layers. In conclusion, machine learning algorithms applied to HSI prove to be a promising methodology to differentiate bacteria according to their gram staining.
Multi-Scale Feature Attention and Transformer for Hyperspectral Image Classification

In hyperspectral image (HSI) classification, convolutional neural networks (CNNs) have shown great potential, but they often overlook multi-scale information and the relationship between features at different scales. To address these problems, HSI classification based on multi-scale feature attention and transformer (MSFAT) is proposed in this paper. Specifically, the proposed MSFAT first extracts multi-scale features by using convolutional kernels of different sizes. Then, the squeeze-and-excitation (SE) module is used to get the attention weight of features at each scale. Next, a simple but effective cross-scale attention module is used to enhance informative features at different scales. Furthermore, to better extract more discriminative features, a transformer encoder is incorporated to capture long-range dependencies between features at different scales. According to experimental results on two common hyperspectral scenes, our proposed MSFAT has demonstrated favorable classification performance when compared with several advanced methods.