Reviewerly/exHarmony huggingface dataset:
Multi-Objective Optimal Path Planning for Autonomous Robots with Moving Obstacles Avoidance in Dynamic Environments

Path planning is vital for robust autonomous robot navigation.Driving in dynamic environments is particularly difficult.The majority of the work is based on the premise that a robot possesses a comprehensive and precise representation of its surroundings prior to its starting.The problem of partially knowing and dynamic environments has received little attention.This circumstance occurs when an exploratory robot or a robot without a floor plan or terrain map must move to its destination.Existing approaches for dynamic-path-planning design a preliminary path based-on known knowledge of the environment, then adjust locally by replanning the total path as obstacles are discovered by the robot's sensors, thereby sacrificing either optimality or computational efficacy.This paper presents a novel algorithm.A Near-Optimal Multi-Objective Path Planner (NO-MOPP), capable of planning time-efficient, near-optimal, and drivable paths in partially known and dynamic environments. It is an expansion of our earlier research contributions called "A Multi-Objective Hybrid Collision-free Optimal Path Finder (MOHC-OPF) for Autonomous Robots in known static environments" and "A Multi-Objective Hybrid Collision-free Near-Optimal Path Planner (MOHC-NODPP) for AutonomousRobots in Dynamic environments".In the environment, a mix of static and moving dynamic obstacles are present, both of which are expressed by a hybrid, discrete configuration space in an occupancy-grid map.The proposed approach is executed at two distinct levels.Using our earlier method, A Multi-Objective Collision-free Optimal Path Finder (MOHC-OPF), the initial optimal path is found in environment that includes only known stationery obstacles at the Global-path-planning level.On the second level, known as Local Re-planning, this optimal path is continuously refined by online re-planning to account for the movement of obstacles in the environment.The proposed method, A Near-Optimal Multi-Objective Path Planner (NO-MOPP), is used to keep the robot's sub-paths optimum while also avoiding dynamic obstacles.This is done while still obeying the robot's non-holonomic restrictions.The proposed technique is tested in simulation using a collection of standard maps.The simulation findings demonstrate the proposed method's ability to avoid static as well as dynamic obstacles, as well as its capacity to find a near-optimal-path to a goal location in environments that are constantly changing without collision.The optimal-path is determined by taking into account several performance measures, including path length, collision-free path, execution time, and smooth paths.90% of studies utilizing the proposed method demonstrate that it is more effective than other methods for determining the shortest length and time-efficient smooth drivable paths.The proposed technique reduced average 15% path length and execution time compared to the existing methods.
A Comparison of Pathfinding Algorithm for Code Optimization on Grid Maps

There have been various pathfinding algorithms created and developed over the past few decades to assist in finding the best path between two points.This paper presents a comparison of several algorithms for pathfinding on 2D grid maps.As a result, this study identified Jump Point Search Block Based Jumping (JPS (B)) as a potential algorithm in term of five evaluation metrics including search time.The comparisons pointed out the potential algorithm and code optimization was performed on the selected JPS(B) algorithm, and it was named JPS(BCO).This paper also explores issues regarding the JPS(B) and how to resolve them in order to optimize access to the index pointer.The presented enhance JPS(BCO) is capable to search optimal path quicker than the original JPS(B) as demonstrated by experimental findings.An experiment of different size grid maps is conducted to validate the ability of the proposed algorithm in term of search time.The comparative study with original JPS (B) exhibits the enhancement that has more benefits on grid maps of different size in terms of search time.
Implementation of Fuzzy Expert System on Skin Diseases

Skin diseases are a group of diseases affecting people of all ages, commonly caused by fungi, bacteria, parasites, viruses, and infections.The disease's main symptoms are usually itching all over the skin.Many patients are often underestimated and embarrassed to consult directly with doctors, which in the end, ignores the symptoms of skin diseases.Since they usually have imprecision symptoms, examining skin diseases is complex and challenging.Recently, many efforts have been made to utilize artificial intelligence approaches for diagnosing various diseases based on the patient's condition.This paper aims to develop a novel fuzzy-based medical expert system based on unprecise existing symptoms.The system uses the specialist Doctor's knowledge (dermatologist) to diagnose and provide the patient's severity level for the disease.We have done numerical experiments using 100 (one hundred) test problems to evaluate the performance of the developed system by comparing the result with the recommendations of doctors (dermatologists).It shows that this system succeeds in all tests with an accuracy value of 95.6%.Thus, this system is very beneficial to support doctors in the assessment of skin diseases.
Recognition of Copy Move Forgeries in Digital Images using Hybrid Optimization and Convolutional Neural Network Algorithm

In the modern day, protecting data against tampering is a significant task.One of the most common forms of information display has been digital photographs.Images may be exploited in a variety of contexts, including the military, security applications, intelligence areas, legal evidence, social media, and journalism.Digital picture forgeries involve altering the original images with strange patterns, which result in variability in the image's characteristics.Among the most challenging forms of image forgeries to identify is Copy Move Forgery (CMF).It occurs by copying a portion or piece of the picture and then inserting it again, but in a different place.When the actual content is unavailable, techniques for detecting fake content have been utilised in image security.This study presents a novel method for Copy Move Forgery Recognition (CMFR), which is mostly based on deep learning (DL) and hybrid optimization.The hybrid Grey Wolf Optimization and African Buffalo Optimization (GWO-ABO) using Convolution Neural Network (CNN) technique i.e., GWO-ABO-CNN is the foundation of the suggested model.The developed model extracts the features of images by convolution layers, and pooling layers; hereafter, the features are matched and detect CMF.The MICC-F220, SATs-130, and MICC-F600 datasets were three publicly accessible datasets to which this methodology has been implemented.To assess the model's efficacy, the outcomes of implementing the GWO-ABO-CNN model were contrasted with those of other approaches.
Towards an Accurate Breast Cancer Classification Model based on Ensemble Learning

Breast cancer (BC) is considered the most common cancer among women and the major reason for the increased death rate.This condition begins in breast cells and may spread to the rest of the body tissues.The early detection and prediction of BC can help in saving a patient's life.In the last decades, machine learning (ML) has played a significant role in the development of models that can be used to detect and predict various diseases at an early stage, which can greatly increase the survival rate of patients.The importance of ML Classification is attributed to its capability to learn from previous datasets, detects patterns that are difficult to comprehend in massive datasets, predicts a categorical variable within a predefined example and provide accurate results within a short amount of time.Feature selection (FS) method was used to reduce the data dimensionality and choose the optimal feature set.In this paper, we proposed a stacking ensemble model that can differentiate between malignant and benign BC cells.A total of 25 different experiments have been conducted using several classifiers, including logistic regression (LR), decision tree (DT), linear discriminant analysis (LDA), K-nearest neighbor (KNN), naive Bayes (NB), and support vector machine (SVM).In addition to several ensembles, the classifiers included random forest (RF), bagging, AdaBoost, voting, and stacking.The results indicate that our ensemble model outperformed other state-of-the-art models in terms of accuracy (98.6%), precision (89.7%), recall, and F1 score (93.33%).The result shows that the ensemble methods with FS have a high improvement of classification accuracy rather than a single method in detecting BC accurately.
Synthesis of Comments to Social Media Posts for Business Applications

Responding to enormous comments on social media platforms is one major challenge facing businesses in recent times, especially when dealing with irate consumers.Customers have increasingly adopted social networks as a platform for expressing their concerns and posting comments on business pages, posing a great challenge for customer support agents and digital marketers alike.Analyzing and responding manually to these enormous comments is a time-consuming task, necessitating the adoption of Artificial Intelligence (AI) tool that can complete the task swiftly automatic comprehension of social media posts for comment generation.In this paper, we present algorithms and a tool for the automatic comprehension of customer tweets and generation of responses to these tweets.This was done in two-fold: using existing Natural Language Processing (NLP) libraries to preprocess and tokenize these tweets, and secondly, using rule-based algorithms to find a matching response to each customer, based on the array of extracted tokens from the customer's tweet.This was built into a tool called Comment-Synthesizer.This tool takes unfiltered tweets as input, preprocesses the tweets, and matches the tweet with predefined responses using a rule-based algorithm with a success rate of 76%.This tool, if implemented in a desktop automation application, can be used to respond automatically to a large volume of customers' social media comments/posts.
Data Clutter Reduction in Sampling Technique

Visualization is a process of converting data into its visual form as such data patterns can be extracted from the data.Data patterns are knowledge hidden behind the data.However, when data is big, it tends to overlap and clutter on visualization which distorts the data patterns.Data is overly crowded on visualization thus, it has become a challenge to extract knowledge patterns.Besides, big data is costly to visualize because it requires expensive hardware facilities due to its size.Moreover, it is timely to plot the data since it takes time for data to render on visualizations.Due to those reasons, there is a need to reduce the size of big datasets and at the same time maintain the data patterns.There are many methods of data reduction, which are preprocessing operations, dimension reduction, compression, network theory, redundancy elimination, data mining, machine learning, data filtering and sampling techniques.However, the commonly used data reduction technique is sampling technique that derives samples from data populations.Thus, sampling technique is chosen as a study for data reduction in this paper.However, the studies are scattered and are not discussed in a single paper.Consequently, the objective of this paper is to collect them in a single paper for further analysis in order to understand them in great detail.To achieve the objective, three interdisciplinary databases which are ACM Digital Library, IEEE Explore and Science Direct have been selected.From the database, a total of 48 studies have been extracted and they are from the years 2017 to 2021.Other than sampling techniques, this paper also seeks information on big data, data visualization, data clutter, and data reduction.
Fast Comprehensive Secret Sharing using Naive Image Compression

This paper presents a simple method for performing (k,n)-Secret Sharing (SS) with fast computation.It aims to reduce the computational time of the former scheme in the shadow generation process.The former scheme performs SS with the polynomial function computation by involving the color palette.The color palette transforms noisy-like shadow image into more meaningfull appearance.However, this scheme requires a high computational burden on this transformation process.The proposed method exploits naive image compression to decrease the required bit for representing a secret and cover image.It effectively avoids the color palette usage previously used by the former scheme.The proposed method produces a set of shadow images with a cover image-like appearance.In addition, the secret and cover image can be reconstructed by gathering at least k shadow images.As documented in the Experimental Results section, the proposed method yields a promising result in the (k,n)-SS with reduced computational time compared to that of the former scheme.
Hybrid Modeling to Classify and Detect Outliers on Multilabel Dataset based on Content and Context

Due to the linked various matching categories, news article categorization are a rapidly increasing field of interest in text classification.However, the low-reliability indices and ambiguities related to frequently used province classifiers restrict success in this field.Most of the existing research uses traditional machine learning algorithms.It has weaknesses in training large-scale datasets, and data sparseness often occurs from short texts.Therefore, this study proposed a hybrid model consisting of two models, namely the news article classification and the outlier detection model.The news article classification model used a combination of two deep learning algorithms (Long Short-Term Memory dan Convolutional Neural Network) and outlier classifier model, which was intended to predict the outlier news using a decision tree algorithm.The proposed model's performance was compared against two widely used datasets.The experimental results provide useful insights that open the way for a number of future initiatives.
Image Matting using Neural Networks

Image matting, also refers to picture matting in the article, is the task of finding appealing targets in a picture or sequence of pictures i.e., video, and it has been used extensively in many photo and video editing applications. Image composition is the process of extracting an eye-catching subject from a photograph and blending it with a different background. a) Blue/Green screen (curtain) matting, where the backdrop is clear and readily distinct between the foreground (frontal area) and background (foundation) portions. This approach is now the most used type of image matting. b) Natural picture matting, in which these sorts of photos are taken naturally using cameras or cell phones during everyday activities. These are the present known techniques of picture matting. It is difficult to discern the distinction between the frontal area and the foundation at their boundaries. The current framework requires both the RGB and trimap images as inputs for natural picture matting. It is difficult to compute the trimap since additional framework is required to obtain this trimap. This study will introduce the Picture Matting Neural Net (PMNN) framework, which utilizes a single RGB image as an input and creates the alpha matte without any human involvement in between the framework and the user, to overcome the drawbacks of the prior frameworks. The created alpha matte is tested against the alpha matte from the PPM-100 data set, and the PSNR and SSIM measurement index are utilized to compare the two. The framework works well and can be fed with regular pictures taken with cameras or mobile phones without reducing the clarity of the image.
Tracking The Sensitivity of The Learning Models Toward Exact and Near Duplicates

Most real-world datasets contaminated by quality issues have a severe effect on the analysis results.Duplication is one of the main quality issues that hinder these results.Different studies have tackled the duplication issue from different perspectives.However, revealing the sensitivity of supervised and unsupervised learning models under the existence of different types of duplicates, deterministic and probabilistic, is not broadly addressed.Furthermore, a simple metric is used to estimate the ratio of both types of duplicates regardless of the probability by which the record is considered duplicate.In this paper, the sensitivity of five classifiers and four clustering algorithms toward deterministic and probabilistic duplicates with different ratios (0% -15%) is tracked.Five evaluation metrics are used to accurately track the changes in the sensitivity of each learning model, MCC, F1-Score, Accuracy, Average Silhouette Coefficient, and DUNN Index.Also, a metric to measure the ratio of probabilistic duplicates within a dataset is introduced.The results revealed the effectiveness of the proposed metric that reflects the ratio of probabilistic duplicates within the dataset.All learning models, classification, and clustering models are differently sensitive to the existence of duplicates.RF and Kmeans are positively affected by the existence of duplicates which means that their performce increase as the percentage of duplicates increases.Furthermore, the rest of classifiers and clustering algorithms are sensitive toward duplicates existence, especially within high percentage that negatively affect their performance.
Research on Asymmetry of Two-Queue Cycle Query Threshold Service Polling System

Based on establishing the mathematical model of the system provided system parameters, using the discrete-time Markov chain and a function set by a non-negative integer random variable as Probabilistic methods, discrete time variable, two-queue different gated polling system has been fully analyzed, the low-and higher-order properties and cycle period of the system are deduced, and average queue pair length and average waiting delay for message packets are calculated accurately.The simulation experiments agree well with the theoretical calculations.The analysis further deepens the cognition of the asymmetric threshold polling system, lays a foundation for researches on the asymmetric threshold polling system, and has positive significance for a better and more flexible control periodic query polling work system.
The Effect of Blockchain using Big data and the Internet of Things in Healthcare

Modern organizations of all sizes emphasize safeguarding sensitive consumer information.Regardless of the limits given by the degrees they choose to pursue, people are nevertheless required to possess data management skills.In addition, determine if the data should be centralized or decentralized to meet the objective of enhancing accessibility.In addition, you will need to be able to monitor who has access to your data and regulate who has access to your data.
The Impact of Peer Code Review on Software Maintainability in Open-Source Software: A Case Study

Recently, open-source software (OSS) has become a considerably popular and reliable source of functionality corrections.OSS also allows software developers to reduce technical debt in software development.However, previous studies have shown that the main problem within OSS development is the lack of systematic processes and formal documents related to system development, such as requirements, designs, and testing.This feature of OSS development causes problems in the software quality, such as those related to security and maintainability.In this research, the authors focused on the software's maintainability because this attribute has to greatest potential to reduce the cost and increase the productivity of the software development process.There is currently no existing research that examines whether OSS developers pay attention to software maintainability.To better understand how OSS developers improve software maintainability, this research aims to answer the question: "Are developers interested in software maintainability under the modern code review of open-source software projects?"To answer the research question, the authors investigated the code review process in which the OSS developers changed the code based on a review of code comments related to maintenance and collected the sub-characteristics associated with software maintainability from the existing literature.The authors examined the review comments from two OSS projects: Eclipse and Qt.The results suggest that the number of code revisions due to maintenance issues was moderate and that the OSS developers tend to improve source code quality.This direction could be observed from the increasing number of modifications on given maintenance-based comments over the years.Therefore, an implication of this is the possibility that OSS project developers are interested in software maintainability.
Utilizing Deep Learning in Arabic Text Classification Sentiment Analysis of Twitter

The number of social media users has increased.These users share and reshare their ideas in posts and this information can be mined and used by decision-makers in different domains, who analyse and study user opinions on social media networks to improve the quality of products or study specific phenomena.During the COVID-19 pandemic, social media was used to make decisions to limit the spread of the disease using sentiment analysis.Substantial research on this topic has been done; however, there are limited Arabic textual resources on social media.This has resulted in fewer quality sentiment analyses on Arabic texts.This study proposes a model for Arabic sentiment analysis using a Twitter dataset and deep learning models with Arabic word embedding.It uses the supervised deep learning algorithms on the proposed dataset.The dataset contains 51,000 tweets, of which 8,820 are classified as positive, 37,360 neutral, and 8,820 as negative.After cleaning it will contain 31,413.The experiment has been carried out by applying the deep learning models, Convolutional Neural Network and Long Short-Term Memory while comparing the results of different machine learning techniques such as Naive Bayes and Support Vector Machine.The accuracy of the AraBERT model is 0.92% when applying the test on 3,505 tweets.
Fault Diagnosis Technology of Railway Signal Equipment based on Improved FP-Growth Algorithm

The rapid development of computer information technology has made various fault diagnosis and detection technologies emerge in an endless stream.As one of the main transportation vehicles, the detection efficiency of fault diagnosis of railway signal equipment has important practical significance for maintaining the overall safe operation of railways.On the basis of the traditional FP-Growth algorithm, improve the TF-IDF algorithm to realize the weight discretization of text features, and realize the improvement of the FP-Growth algorithm by adjusting the adaptive confidence and support.The FP-Growth algorithm will be improved.FP-Growth algorithm is used for performance tests and applications.The results show that the minimum running time-saving of the proposed algorithm is 1500ms, and the average accuracy of P@N exceeds 85%, which is higher than that of the FP-Growth algorithm (81.4%) and VSM algorithm (82.1%).The PR curve of the improved algorithm is closer to the upper right, which effectively ensures the processing of correlated data, and the overall average precision performance under the influence of positive and negative signal-to-noise ratio values exceeds 95%.And the signal curve generated by the algorithm.The error range of the data under the four fault types of track circuit, turnout, signal, and connecting line floats between 1% and 5%.The improved FP-Growth algorithm can effectively analyze railway fault types and data.Perform analysis and data processing to minimize diagnostic errors.