Reviewerly/exHarmony huggingface dataset:
Agricultural Supply Chain Management System Using Blockchain

Blockchain is a revolutionary technology where information is exchanged between different entities in a chain by using distributed software design and powerful computing. The Novel Method uses smart contract and Ethereum blockchain for tracing and tracking crops efficiently with complete security and business operations. The Technology terminates the requirement for an intermediary, transaction records and trusted centralized authority. Hence, Blockchain improves safety and efficiency by maintaining high reliability and integrity. Smart contracts are used in the proposed system to regulate and oversee all communications and transactions between the participants in the ecosystem of the agricultural supply chain. All transactions are preserved and recorded in the immutable ledger of the blockchain, which is linked to a decentralized file system called as IPFS. Data about images and locations are submitted by various supply chain participants which are then are utilized to ensure the transparency and traceability of the crops using IPFS.
Depth Map Prediction of Occluded Objects Using Structure Tensor with Gain Regularization

The creation of the 3D rendering model involves the prediction of an accurate depth map for the input images. A proposed approach of a modified semi-global block matching algorithm with variable window size and the gradient assessment of objects predicts the depth map. 3D modeling and view synthesis algorithms could effectively handle the obtained disparity maps. This work uses the consistency check method to find an accurate depth map for identifying occluded pixels. The prediction of the disparity map by semi-global block matching has used the benchmark dataset of Middlebury stereo for evaluation. The improved depth map quality within a reasonable processing time outperforms the other existing depth map prediction algorithms. The experimental results have shown that the proposed depth map predictioncould identify the inter-object boundaryeven with the presence ofocclusion with less detection error and runtime.We observed that the Middlebury stereo dataset has very few images with occluded objects, which made the attainment of gain cumbersome. Considering this gain, we have created our dataset with occlusion using the structured lighting technique. The proposed regularization term as an optimization process in the graph cut algorithm handles occlusion for different smoothing coefficients. The experimented results demonstrated that our dataset had outperformed the Tsukuba dataset regarding the percentage of occluded pixels.
Hyperparameter Optimization Based Deep Belief Network for Clean Buses Using Solar Energy Model

Renewable energy has become a solution to the world's energy concerns in recent years.Photovoltaic (PV) technology is the fastest technique to convert solar radiation into electricity.Solar-powered buses, metros, and cars use PV technology.Such technologies are always evolving.Included in the parameters that need to be analysed and examined include PV capabilities, vehicle power requirements, utility patterns, acceleration and deceleration rates, and storage module type and capacity, among others.PVPG is intermittent and weather-dependent.Accurate forecasting and modelling of PV system output power are key to managing storage, delivery, and smart grids.With unparalleled data granularity, a data-driven system could better anticipate solar generation.Deep learning (DL) models have gained popularity due to their capacity to handle complex datasets and increase computing power.This article introduces the Galactic Swarm Optimization with Deep Belief Network (GSODBN-PPGF) model.The GSODBN-PPGF model predicts PV power production.The GSODBN-PPGF model normalises data using data scaling.DBN is used to forecast PV power output.The GSO algorithm boosts the DBN model's predicted output.GSODBN-PPGF projected 0.002 after 40 h but observed 0.063.The GSODBN-PPGF model validation is compared to existing approaches.Simulations showed that the GSODBN-PPGF model outperformed recent techniques.It shows that the proposed model is better at forecasting than other models and can be used to predict the PV power output for the next day.
Sensor-Based Adaptive Estimation in a Hybrid Environment Employing State Estimator Filters

It is widely acknowledged that navigation is a significant source of between sites. The Global Positioning System (GPS) has numerous navigational advancements, and hence it is used widely. GPS navigation can be compromised at any level between position, location, and estimation, to the detriment of the user. Consequently, a navigation system requires the precise location and underpinning tracking of an object without signal loss. The objective of a hybrid environment prediction system is to foresee the location of the user and their territory by employing a variety of sensors for position estimation and monitoring navigation. This article presents a state estimation of the relative position for indoor and outdoor activity solved with a state estimation algorithm utilizing Kalman filter. Also, a comparative study of variants of the Kalman filter, where linearizing current mean and covariance with nonlinear state estimation as an approach of Extended Kalman Filter (EFK) is applied to the collected data. The third comparative aspect uses probability distribution for the selected points with a Sigma Point Kalman Filter (SPKF) for evaluating an accelerometer, gyroscope, and GPS data in hybrid environments for various activities for different data collection scenarios from users. The findings of the presented model demonstrate the robust performance of all forms of the Kalman filter algorithm for diverse user-performed activities in totally contaminated indoor and outdoor environments. Experimental findings with various patterns and data, conducted by different subjects using multiple modes of navigation, show that the approach can indeed lead to the intelligent development of sensor-based navigation and monitoring. State estimation and prediction is extraordinarily beneficial for mining applications, autonomous vehicle localization/tracking, and location-based services. This research work demonstrates both EKF-based and SPKF-based sensor fusion to provide an appropriate estimation.
Fuzzy Rule-Based Model to Train Videos in Video Surveillance System

With the proliferation of the internet, big data continues to grow exponentially, and video has become the largest source. Video big data introduces many technological challenges, including compression, storage, transmission, analysis, and recognition. The increase in the number of multimedia resources has brought an urgent need to develop intelligent methods to organize and process them. The integration between Semantic link Networks and multimedia resources provides a new prospect for organizing them with their semantics. The tags and surrounding texts of multimedia resources are used to measure their semantic association. Two evaluation methods including clustering and retrieval are performed to measure the semantic relatedness between images accurately and robustly. A Fuzzy Rule-Based Model for Semantic Content Extraction is designed which performs classification with fuzzy rules. The features extracted are trained with the neural network where each network contains several layers among them each layer of neurons is dedicated to measuring the weight towards different semantic events. Each neuron measures its weight according to different features like shape, size, direction, speed, and other features. The object is identified by subtracting the background features and trained to detect based on the features like size, shape, and direction. The weight measurement is performed according to the fuzzy rules and based on the weight measures. These frameworks enhance the video analytics feature and help in video surveillance systems with better accuracy and precision.
Spatial Multi-Presence System to Increase Security Awareness for Remote Collaboration in an Extended Reality Environment

Enhancing the sense of presence of participants is an important issue in terms of security awareness for remote collaboration in extended reality. However, conventional methods are insufficient to be aware of remote situations and to search for and control remote workspaces. This study proposes a spatial multi-presence system that simultaneously provides multiple spaces while rapidly exploring these spaces as users perform collaborative work in an extended reality environment. The proposed system provides methods for arranging and manipulating remote and personal spaces by creating an annular screen that is invisible to the user. The user can freely arrange remote participants and their workspaces on the annular screen. Because users can simultaneously view various spaces arranged on the annular screen, they can perform collaborative work while simultaneously feeling the presence of multiple spaces and can be fully immersed in a specific space. In addition, the personal spaces where users work can also be arranged through the annular screen. According to the results of the performance evaluations, users participating in remote collaborative works can visualize the spaces of multiple users simultaneously and feel their presence, thereby increasing their understanding of the spaces. Moreover, the proposed system reduces the time to perform tasks and gain awareness of the emergency in remote workspaces.
Cancer Regions in Mammogram Images Using ANFIS Classifier Based Probability Histogram Segmentation Algorithm

Every year, the number of women affected by breast tumors is increasing worldwide. Hence, detecting and segmenting the cancer regions in mammogram images is important to prevent death in women patients due to breast cancer. The conventional methods obtained low sensitivity and specificity with cancer region segmentation accuracy. The high-resolution standard mammogram images were supported by conventional methods as one of the main drawbacks. The conventional methods mostly segmented the cancer regions in mammogram images concerning their exterior pixel boundaries. These drawbacks are resolved by the proposed cancer region detection methods stated in this paper. The mammogram images are classified into normal, benign, and malignant types using the Adaptive Neuro-Fuzzy Inference System (ANFIS) approach in this paper. This mammogram classification process consists of a noise filtering module, spatial-frequency transformation module, feature computation module, and classification module. The Gaussian Filtering Algorithm (GFA) is used as the pixel smooth filtering method and the Ridgelet transform is used as the spatial-frequency transformation module. The statistical Ridgelet feature metrics are computed from the transformed coefficients and these values are classified by the ANFIS technique in this paper. Finally, Probability Histogram Segmentation Algorithm (PHSA) is proposed in this work to compute and segment the tumor pixels in the abnormal mammogram images. This proposed breast cancer detection approach is evaluated on the mammogram images in MIAS and DDSM datasets. From the extensive analysis of the proposed tumor detection methods stated in this work with other works, the proposed work significantly achieves a higher performance. The methodologies proposed in this paper can be used in breast cancer detection hospitals to assist the breast surgeon to detect and segment the cancer regions.
Online Markov Blanket Learning with Group Structure

Learning the Markov blanket (MB) of a given variable has received increasing attention in recent years because the MB of a variable predicts its local causal relationship with other variables. Online MB Learning can learn MB for a given variable on the fly. However, in some application scenarios, such as image analysis and spam filtering, features may arrive by groups. Existing online MB learning algorithms evaluate features individually, ignoring group structure. Motivated by this, we formulate the group MB learning with streaming features problem, and propose an Online MB learning with Group Structure algorithm, OMBGS, to identify the MB of a class variable within any feature group and under current feature space on the fly. Extensive experiments on benchmark Bayesian network datasets demonstrate that the proposed algorithm outperforms the state-of-the-art standard and online MB learning algorithms.
Modified Sine Cosine Optimization with Adaptive Deep Belief Network for Movie Review Classification

Sentiment analysis (SA) is a growing field at the intersection of computer science and computational linguistics that endeavors to automatically identify the sentiment presented in text. Computational linguistics aims to describe the fundamental methods utilized in the formation of computer methods for understanding natural language. Sentiment is classified as a negative or positive assessment articulated through language. SA can be commonly used for the movie review classification that involves the automatic determination that a review posted online (of a movie) can be negative or positive toward the thing that has been reviewed. Deep learning (DL) is becoming a powerful machine learning (ML) method for dealing with the increasing demand for precise SA. With this motivation, this study designs a computational intelligence enabled modified sine cosine optimization with a adaptive deep belief network for movie review classification (MSCADBN-MVC) technique. The major intention of the MSCADBN-MVC technique is focused on the identification of sentiments that exist in the movie review data. Primarily, the MSCADBN-MVC model follows data pre-processing and the word2vec word embedding process. For the classification of sentiments that exist in the movie reviews, the ADBN model is utilized in this work. At last, the hyperparameter tuning of the ADBN model is carried out using the MSCA technique, which integrates the Levy flight concepts into the standard sine cosine algorithm (SCA). In order to demonstrate the significant performance of the MSCADBN-MVC model, a wide-ranging experimental analysis is performed on three different datasets. The comprehensive study highlighted the enhancements of the MSCADBN-MVC model in the movie review classification process with maximum accuracy of 88.93%.
Data Layout and Scheduling Tasks in a Meteorological Cloud Environment

Meteorological model tasks require considerable meteorological basis data to support their execution. However, if the task and the meteorological datasets are located on different clouds, that enhances the cost, execution time, and energy consumption of execution meteorological tasks. Therefore, the data layout and task scheduling may work together in the meteorological cloud to avoid being in various locations. To the best of our knowledge, this is the first paper that tries to schedule meteorological tasks with the help of the meteorological data set layout. First, we use the FP-Growth-M (frequent-pattern growth for meteorological model datasets) method to mine the relationship between meteorological models and datasets. Second, based on the relation, we propose a heuristics algorithm for laying out the meteorological datasets and scheduling tasks. Finally, we use simulation results to compare our proposed method with other methods. The simulation results show that our method reduces the number of involved clouds, the sizes of files from outer clouds, and the time of transmitting files.
SNG-TE: Sports News Generation with Text-Editing Model

Currently, the amount of sports news is increasing, given the number of sports available. As a result, manually writing sports news requires high labor costs to achieve the intended efficiency. Therefore, it is necessary to develop the automatic generation of sports news. Most available news generation methods mainly rely on real-time commentary sentences, which have the following limitations: (1) unable to select suitable commentary sentences for news generation, and (2) the generated sports news could not accurately describe game events. Therefore, this study proposes a sports news generation with text-editing model (SNG-TE) is proposed to generate sports news, which includes selector and rewriter modules. Within the study context, a weight adjustment mechanism in the selector module is designed to improve the hit rate of important sentences. Furthermore, the text-editing model is introduced in the rewriter module to ensure that the generated news sentences can correctly describe the game events. The annotation and generation experiments are designed to evaluate the developed model. The study results have shown that in the annotation experiment, the accuracy of the sentence annotated by the selector increased by about 8% compared with other methods. Moreover, in the generation experiment, the sports news generated by the rewriter achieved a 49.66 ROUGE-1 score and 21.47 ROUGE-2, both of which are better than the available models. Additionally, the proposed model saved about 15 times the consumption of time. Hence, the proposed model provides better performance in both accuracy and efficiency, which is very suitable for the automatic generation of sports news.
Semi-Supervised Clustering Algorithm Based on Deep Feature Mapping

Clustering analysis is one of the main concerns in data mining. A common approach to the clustering process is to bring together points that are close to each other and separate points that are away from each other. Therefore, measuring the distance between sample points is crucial to the effectiveness of clustering. Filtering features by label information and measuring the distance between samples by these features is a common supervised learning method to reconstruct distance metric. However, in many application scenarios, it is very expensive to obtain a large number of labeled samples. In this paper, to solve the clustering problem in the few supervised sample and high data dimensionality scenarios, a novel semi-supervised clustering algorithm is proposed by designing an improved prototype network that attempts to reconstruct the distance metric in the sample space with a small amount of pairwise supervised information, such as Must-Link and Cannot-Link, and then cluster the data in the new metric space. The core idea is to make the similar ones closer and the dissimilar ones further away through embedding mapping. Extensive experiments on both real-world and synthetic datasets show the effectiveness of this algorithm. Average clustering metrics on various datasets improved by 8% compared to the comparison algorithm.
Construction of Intelligent Recommendation Retrieval Model of FuJian Intangible Cultural Heritage Digital Archives Resources

In order to improve the consistency between the recommended retrieval results and user needs, improve the recommendation efficiency, and reduce the average absolute deviation of resource retrieval, a design method of intelligent recommendation retrieval model for Fujian intangible cultural heritage digital archive resources based on knowledge atlas is proposed. The TG-LDA (Tag-granularity LDA) model is proposed on the basis of the standard LDA (Linear Discriminant Analysis) model. The model is used to mine archive resource topics. The Pearson correlation coefficient is used to measure the relevance between topics. Based on the measurement results, the FastText deep learning model is used to achieve archive resource classification. According to the classification results, TF-IDF (term frequency–inverse document frequency) algorithm is used to calculate the weight of resource retrieval keywords to achieve resource retrieval, and a recommendation model of intangible cultural heritage digital archives resources is built through the knowledge map to achieve comprehensive and personalized recommendation of resources. The experimental results show that the recommendation and retrieval results of the proposed method are more in line with users’ needs, can provide users with personalized digital archive resources, and the average absolute deviation of resource retrieval is low, the recommendation efficiency is high, and the utilization effect of archive resources is effectively improved.
An Optimal Framework for Alzheimer’s Disease Diagnosis

Alzheimer’s disease (AD) is a kind of progressive dementia that is frequently accompanied by brain shrinkage. With the use of the morphological characteristics of MRI brain scans, this paper proposed a method for diagnosing moderate cognitive impairment (MCI) and AD. The anatomical features of 818 subjects were calculated using the FreeSurfer software, and the data were taken from the ADNI dataset. These features were first removed from the dataset after being preprocessed with an age correction algorithm using linear regression to estimate the effects of normal aging. With these preprocessed characteristics, the extreme learning machine served as a classifier for the diagnosis of AD and MCI. For determining accuracy, sensitivity, specificity, and area under the curve, ten-fold cross validation was used. The accuracy of AD diagnosis was 87.62 percent on average after 100 runs, while the area under curve was 94.25 percent. The sensitivity of the MCI diagnosis was 83.88 percent, while the accuracy was 73.38 percent. The age correction can help diagnose MCI more accurately. The outcomes showed that the proposed strategy for diagnosing AD and MCI was more effective than existing methods.
Research on the Application of Unet with Convolutional Block Attention Module to Semantic Segmentation Task

Attention mechanism can focus on important features and suppress unnecessary features, to increase the representational power of the network, which plays an important role in visual tasks such as semantic segmentation. To this end, the UNET of Convolutional Block Attention Module (CBAM) is applied in semantic segmentation tasks to improve the performance of convolutional neural networks. The channel attention adopts maximum pooling and average pooling, and for the spatial attention, a smaller convolution kernel is proposed to reduce computation and loss of important features. Through experiments, the introduction of CBAM has a improvement in semantic segmentation tasks increasing the Validation Dice from 0.98 to 0.9871 in the Kaggle Carvana image segmentation dataset.
Traffic Incident Detection System Based on Video Analysis

With the increasing urbanization and the popularity of traffic video surveillance, and the rapid development of object detection algorithms, object detection of traffic events through video analysis has become possible. This paper proposes the design and implementation of a traffic event detection system based on video analysis. Firstly, the video stream is processed, mainly for video access and display, and the compression of neural networks achieves the network acceleration. Secondly, the structured information of vehicles is obtained by nighttime vehicle detection and joint detection and tracking. Finally, the car's driving behavior is analyzed through video calibration and video analysis. The system has been used online in many places in China and has achieved remarkable results.