Reviewerly/exHarmony huggingface dataset:
Different Deep Learning Techniques in Heart Disease Classification: Survey

Cardiovascular disease prediction is a serious challenge for clinical data analysis. This study examines deep learning-based categorization strategies for heart disease. Deep learning algorithms are employed with echocardiograms to categorize heart disease. This paper uses echocardiography to predict and identify heart abnormalities, with the help of decision-making and forecasting, based on the copious data the healthcare sector has provided. Medical experts can forecast clinical outcomes, which helps them choose the best course of action. In-depth longitudinal electronic health records are a rich source of historical data with complex patterns that have the potential to be leveraged by machine learning to improve physicians' prediction abilities (EHR) significantly. Most contemporary medical specialties rely on imaging as one of the most data-rich components of electronic health records when making treatment decisions (EHRs). Only a few tasks in medical image processing and reconstruction have seen success using machine and deep learning, including registration, segmentation, and feature extraction. Cardiac imaging sequence analysis must incorporate the extraction of spatial and temporal characteristics to forecast crucial information throughout time correctly.
A Controlling Traffic Light system using Fuzzy logic

Automatic control systems are a new advanced system uses computers and hardware controllers to act as a driving system. Increasing in cars numbers while we have the same infrastructure caused a large traffic problem so we need automatic adaptive controllers to control the open and close tasks of traffic light system. Fuzzy Logic is a conversation from classical logic with values of 0 for false and 1 for true to a range of values between 0 and 1.&#x0D; This study aims to create an adaptive traffic control system using fuzzy logic and image processing, we have a camera on each traffic light to calculate the number of cars there and then we feed the number of cars to a fuzzy logic system to decide which traffic light must become on
Image Captioning Generation Using Inception V3 and Attention Mechanism

Captioning an image is the process of using a visual comprehension system with a model of language, by which we can construct sentences that are meaningful and syntactically accurate for an image. The goal is to train a deep learning model to learn the correspondence between an image and its textual description. This is a challenging task due to the inherent complexity and subjectivity of language, as well as the visual variability of images. Computer vision and natural language processing are both used in the difficult task of image captioning. In this paper, an end to end deep learning-based image captioning system using Inception V3 and Long-Short Term Memory (LSTM) with an attention mechanism is implemented. Expansive experimentation has been realized on one of the benchmark datasets named MS COCO, and the experiential results signify that this intended system is capable of surpassing diverse related systems concerning the extensively utilized measures of evaluation, and the accomplished results were 0.543, 0.87, 0.66, 0.51, 0.42 for Meteor and BLEU(B1-B4), respectively.
Preprocessing of Drugs Reviews and Classification Techniques

NLP refers to the computer-based study of language when referring to textual data such as texts or reviews. Natural language processing frequently seeks to give otherwise unstructured natural language a representation of the text that gives it structure. Marketing, social media, and customer relationship management are just a few of the businesses that heavily rely on sentiment analysis (SA), one of the methods used in opinion mining, typically assesses a textual review's structure to see if it conveys a positive or negative impression .In this paper, the challenge was to convert unstructured texts into structured texts for drug datasets, and interlocking between sentiment analysis, the mechanism that was adopted in the paper starts with several stages. The first stage is data preprocessing with natural language processing techniques, and the next stage is the prediction step with classification models are logistic regression(LR), random forest(RF), Naïve Bays(NB), and Support Vector Machine (SVM), the best Result of prediction accuracy 92%,for random forest .
A Review of the Overfitting Problem in Convolution Neural Network and Remedy Approaches

Deep learning methods have attracted much attention over the past few years after their breakthroughs in speech recognition and computer vision. However, Convolution Neural Network (CNN) is one of the most important networks that have been used especially in image classification, these networks are facing an essential problem which is the “overfitting problem”. This problem means that the difficulty implies that the model would just "memorize" previously observed patterns, rather than “learn” the important patterns. As a result, it will down the classification performance and become a huge problem. Different remedy approaches have been suggested, each one can exhibit different behavior in the reduction of the overfitting problem according to the nature of the training data. So, this variety of remedy approaches needs to be examined. This paper would be focused on the factors that can cause the overfitting problem, then continue into the approaches to solving this problem.
Synchronizing Time Distribution

With the wide span of internet brought great progress in different parts of human life, a digital documents have been employed as a media in exchange information between internet parties like text, audio, pictures and videos that leads to the issue of how to certify the document when created or modified in order to use it as secure digital media for storing information and transactions. This tend to employ security technique for ensuring the documents integrity for a long period. One of these techniques is the time stamp that has many schemes. This research concentrate on distributed time-stamp scheme as a one of time stamp schemes, that suffers from drawback in achieving trusted security to overcome this draws a new technique issued to this scheme called "Synchronizing Process".
Fuzzy logic-based Northern Goshawk algorithm optimization and hybridization of the Northern Goshawk and Black Widow algorithms

Innovations and new methods for solving long and difficult mathematical puzzles are essential to advances in many branches of research and knowledge. As a result, experts have proposed intelligent algorithms, which are determined by their ability to quickly and efficiently answer the most difficult mathematical puzzles. To achieve the greatest results in this worksheet, we used two different strategies. The first method involved integrating the Goshawk Optimization algorithm (NGOA) with fuzzy logic (FL), while the second method was based on two hybrids, the first by linking communities and the second by linking the equations between the Black Widow Optimization algorithm (BWOA) with the Northern Goshawk optimization algorithm (NGOA). Then we applied both techniques to the basic functions of ten algorithmic functions to get the results.
Medical Image Segmentation with active contour and optimization Techniques: Survey

The principal aim is to improve medical diagnosis through segmented images. Thus, medical image segmentation become one of the key technologies in computer-aided diagnosis. Active contours, or snakes, have been widely used for image segmentation purposes. However, high noise sensitivity and poor performance over weak edges are the most acute issues that hinder the segmentation accuracy of these curves, particularly in medical images. To overcome these issues, a novel external force that integrates gradient vector flow (GVF) field forces and the traditional snake function is proposed in this research. In addition, a novel technique is applied to limit the boundary of the initial contour by set four initial points around the medical issue and then connecting them by polynomial curves. Moreover, the positive effect of Particle Swarm Optimization (PSO) on calculating the final active contour area and its percentage to the entire image area is proved in this work.
Explicit aspect extraction techniques: Review

Sentiment analysis is gathering opinion keywords, such as aspects, opinions, or features- and figuring out their semantic perspective relations. More specifically, aspect-based sentiment analysis – (ABSA) is a subfield of natural language that focuses on phrases associated with aspects and detects the sentiment that belongs to each one. Aspect extraction and sentiment analysis are the two fundamental functions in ABSA, and the different classes of aspects are explicit and implicit. To shed some light on this problem, we discuss aspect extraction tasks and classify explicit aspect extraction strategies into two categories: supervised, unsupervised and semi-supervised. This article explores prior research and their approaches by reviewing works from 2016 to 2021 and comparing numerous elements comprehensively, including classifying systems, classifier methods, datasets, and performance.
CLUBHOUSE AS VIRTUAL COMMUNITY OF PRACTICE FOR NON-NATIVE ENGLISH SPEAKERS TO PRACTICE

Clubhouse application, since it is found, becomes the popular application to practice English. It is because it is easy to use worldwide as well as provides good features and a community system. This case study investigated whether the Clubhouse application could become a virtual community of practice (VCoP) and motivate non-native English speakers (NNES) to speak within the community system. The data were collected through questionnaires with a Likert scale and semi-structured interviews. The participants were the members of the Smart and Easy English club, one of the biggest English clubs in the Clubhouse, who come from Morocco, Afghanistan, and Indonesia. The results revealed some points including 1) Based on the club system, Clubhouse can become an ideal IDLE for VCoP; 2) Clubhouse application motivates the NNES to practice English in the VCoP depending on the features and system used by the club or community; 3) The leadership role in the club does not influence the NNES to practice on Clubhouse as VCoP. The study concluded that Clubhouse is a virtual community of practice which features and community system motivate non-native English speakers to practice English regularly. The findings suggested that the Clubhouse application can be used by English instructors to enable their learners to have English-speaking practice in VCoP. The results of this study may be advantageous for English-speaking course developers with contemporary channel practice.&#x0D;
Mining Distributed Data using Vertical Federated Learning Review

&#x0D; &#x0D; &#x0D; &#x0D; Federated Learning was designed to allow collaborative learning without revealing raw data as worries about machine learning privacy grew. Vertical Federated Learning (VFL) may be utilized for a distributed dataset with the same sample ID space but differs in feature space. And may be used in a wide variety of real-world contexts when parties have the same set of samples but only have partial attributes. Achieving privacy will be a result of this technique's capacity.&#x0D; Federated Learning enables different repositories of data to learn a shared model collaboratively and at the same time keep the privacy of each one because of the increasing awareness of large firms compromising on data security and user privacy. To accomplish federated learning, three learning ways were suggested; horizontal federated learning, vertical federated learning, and transfer federated learning.&#x0D; Vertical federated learning was adopted when data were spread among different parties. However, each one has different features from the others for identical objects. This paper is related to this type of federated learning.&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; To maximize model performance while maintaining the privacy of dispersed data, we'll create a framework based on vertical federated learning and suitable techniques.
Methods of digital-sociology: valuation of young researchers

Some methods of social research and social analysis were modified last years. Digitalization of economy and of the social governance create some new conditions for sociological practices. There were modified a respondent, an other objects of social research because of online relocation. There were modified an object, methodology, methods and technics of social research. At the same time, education and trainings of social scientists take into account new reality very bad. Some opinions of young social scientists and social students about factual situation and wishing situation in sphere of digital-competentions presented in this research. There were marked some reasons of important these competentions because of changes in social governance, changes in communication capabilities of society, changes in demands from clients, pace of life increasing, and some new social phenomenon, which could be analyzed by digital methods only. There were marked the most high-demand in practice methods and technics of social research in digital context, needs and capabilities for students education, institutional capabilities and limits of its development in Russia. The results of the research may be used for educational program’s correction as a reflect of new challenges of social governance in Russia.
Identity Consistency Construction for Visible-Infrared Person Re-identification in Cloud Environment

The real-scene deployment of the dual-mode surveillance camera system enables the collection of different modality images such as visible images and infrared images, ensuring continuous monitoring during both day and night. However, the obvious disparity between different modality images poses significant challenges for person re-identification tasks. Furthermore, the applications of cross-modal person re-ID methods are based on large amounts of data. Due to limited storage resources, it is challenging to ensure models work properly on local devices. To tackle this problem, we propose a novel cloud-based identity consistency learning method in this paper. This method employs a dual-classifier collaborative strategy to leverage the extracted features from different perspectives. By integrating these features, the model can extract more complementary information from both visible and infrared images. This integrated design contributes to the enhancement of the model’s performance. It can be seen that the method’s superiority has been demonstrated through the outstanding results on the SYSU-MM01 dataset.
Comparison of LSTM And SVM For Classification of Eye Movements in EOG Signals

People with disabilities suffer from inability to communicate with their surroundings, so Human-Computer Interaction (HCI) technologies are used to have a means of communication for people with disabilities with their surroundings. HCI is an emerging technology in the disciplines of Artificial Intelligence and Biomedical Engineering. To power an external device, HCI technology uses several basic signals such as ECG, EMG, and EEG. Electrooculography (EOG) is a technique for measuring the potential difference between the cornea and the retina located between the front and back of the human eye, and the main application of EOG is to determine the directions of different eye movements. This study aims to assess eye movement for communication by persons with disabilities using electrocardiogram (EOG) data. In this study, the Supporting Vector Machine (SVM) and Long- Short term memory (LSTM) classification techniques was used and two types of features (statistical and time domain features) were used. Classification accuracy was 90.7% and 93.9% when using SVM with statistical domain and time domain features, respectively ,whereas Classification accuracy was 90.1% when using LSTM .
STRUCTUREAL DEASIGN OF SECURE E-COMMERCE WEBSITES EMPLOYING MULTI-AGENT SYSTEM

&#x0D; &#x0D; &#x0D; &#x0D; The vast growth in electronic trading and monetary transactions results to increase the importance of E-commerce applications rapidly. In order to accomplish the fully functioning, reliable, scalable, secure, efficient and user-friendly E-commerce applications, sufficient system analysis and design processes are important. Shopping over the internet requires offering secure payment method.&#x0D; Providing security for each individual online consumer at the same time (particularly for large websites) is a time-consuming effort that can result in a variety of issues, including response delays, customer orders being lost, and system freeze or crash, all of which impair system performance.&#x0D; This work seeks to present a new multi-agent system prototype structure that solves the challenge of security while avoiding issues that might degrade system performance. By creating integrated system taking into account the three parties the e-commerce, the e-bank and the customer, the main concentration is to create an archeticture that helps in decreasing the website traffic and supportng the security with effective performance. To simulate the real systems a recommendation system is created based on the customer behaviors to increase system performance in helping the customers to find their favorite products and solve problems like cold start, sparsity of user‐item matrix, scalability, and changes in user interest.&#x0D; This is done by creating a commercial environment (e-commerce and e-bank websites) and a software agent that is installed on the client's device to handle the purchase and ciphering procedures without the need for the consumer to intervene. The security process involve preprocessing phase followed by a modified lightweight AES employing chaotic tent map to generate encryption key.&#x0D; &#x0D; &#x0D; &#x0D;
A Comparative Study for Skeleton Representation Methods Using Data Visualization

Data visualization is a technique used to see the unseen information, that will help humanity to discover important things. There are many methods to represent the datasets, but data visualization is the best because it can preserve the information. Skeleton is used to analyse the visualization, thus data visualization gives efficient results. Comparison among well-known methods is the goal of this paper. The conclusion of this paper showed all the comparative results will be important for any further study. This study will use data visualization to discover novel dataset representations, which is the main goal of data visualization and can be useful in presenting necessary data.
Review on swarm intelligent techniques and their applications in different area

Broadly, swarm intelligence (SI) algorithms are considered as nature-inspired techniques improved depending on the idea of communications between living entities such as birds' flocks, Ant Colony, and fish, which means deliberates the group behavior evolving through self-organizing of population individuals. SI has been stimulated via the surveillance of group behavior in its populations in nature because their behavior appears to have the ability to solve complex tasks and optimization problems. The fitness function which is based on SI has been improved to solve combinatorial and mathematical optimization problems by using these algorithms. This means, these techniques work based on the behaviors of individuals in their population so the observation, swam algorithms can be employed for solving the different problems in various applications such as in the medical systems or to enhance the performance of other application systems. In this paper, some swarm intelligent methodologies are reviewed and concerns with their applications in some areas are mentioned.
On WNQP – Submodules

Our goals in this paper are to introduce many results of a WNQP submodules such as triple zero of WNQP submodules. Moreover, several characterization of WNQP Submodule in some types of modules such as (multiplication, content, and finitely generated) modules. Furthermore WNQP radical of submodule are discuses.
Cryptography model based on the principles of the geometric Series

Given the rapid advancement of data innovation and the massive volume of data exchanged over the Internet, which needs protection from electronic theft and extortion, there are many encryption algorithms that developed to protect data. Development in the field of coding (Cryptography) has always been accompanied by a development in the field of decoding and Cryptanalysis. This led to the Weakness of many encryption algorithms. Therefore, the need to develop a new methods are necessary. In this paper, a method based on geometric series for encrypting data was proposed, where two keys are used, the amount of the incremental in sequence represents the first key; the number of terms in the sequence represents the second key. The proposed model has been tested using a number of appropriate attack techniques; the proposed model has proved its efficiency and strength against attacks several technique of attacks are applying on the propose model, the results proved that the strength of the proposed method. As a contribution, this paper using a sequence to concatenate the values of the sequence terms to hide the origin character ASCII code which adds fuzzy cryptography to guess the correct ASCII code.
The Numerical Implementation of the Image Filtering Method with Computations in the Residue Number System

The use of the Residue Number System (RNS) instead of the known binary arithmetic proposes, it is substantiated that the application of the non-position RNS will allow increasing the speed and reducing power consumption of digital image processing applications. Analyzed known schemes, based on which the process of image filtration occurs. The inefficiency of the application of known schemes is substantiated, connected with the possibility of errors due to the overflow of the dynamic range of the RNS. A new scheme of the filtration transformation process is given, which allows obtaining correct results of digital processing, independent of the size of the digital image being processed and the filter mask. The obtained results can be used in the construction of specialized digital image processing systems with low power consumption and high speed of operation.
Investigating the Applicability of Logistic Regression and Artificial Neural Networks in Predicting Breast Cancer

Breast cancer has become recently the most common cancer and a major cause of death among women all over the world and especially in developing countries like Iraq. This study aims to predict the type of breast tumor whether benign or malignant through the different models that were built using logistic regression and neural networks which is expected to be helpful for oncologists in diagnosing the type of breast tumor. Four models were set using binary logistic regression and two different types of artificial neural networks namely multilayer perceptron (MLP) and radial basis function (RBF). Both validated and trained models were evaluated using different performance metrics like accuracy or correct classification rate (CCR), receiver operating characteristic (ROC) curves, area under ROC curve (AUC), sensitivity and specificity. Dataset has been downloaded from the machine learning repository of University of California, Irvin (UCI ml repository) that consists of 9 attributes and 699 valid instances. &#x0D; Firstly, some preprocessing was done to cleanse the data, then the models were built using the Logistic Regression method and Artificial Neural Networks and a comparison was done to find out which model will give the highest performance. Each model was validated with a different dataset than that used for developing the models. The analysis of the results showed that the Radial Basis Function neural network model is the best classifier in the prediction of the type of breast tumors since it had recorded the highest performance in terms of correct classification rate (accuracy), sensitivity, specificity, and AUC among all other models.