Reviewerly/exHarmony huggingface dataset:
Visualizing Semantic Web Data using Various Tools Focusing RDF, OWL and SPARQL

Semantic web is one of the prominent technologies in the current era, which is having very interesting future, while integrating it with other domain of computer science. In semantic web, data generally resides in the form of RDF, RDFS or OWL, which intern to knowledge graphs. There are several application areas where these data is required for semantic search and other related fields. Linked open data is one of the datasets which represents a large amount of repository of knowledge graphs. The data in the raw form either in RDF or OWL is quite difficult to analyze. For the same, data visualization is required for better understanding. This paper gives a brief summary of various tools and platforms used for visualizing semantic web data along with the snapshots of most widely used tools. This paper will help the community to get a quick idea into the field of semantic web, and quickly analyze the tools required for which purpose.
Study of Machine Learning Models for the Prediction and Detection of Lungs Cancer

The main purpose of this study is to identify the various MachineLearning(ML) algorithm which is helpful in the prediction and detection of the Lungs Cancer. Lung'sCancer is the most dangerous disease, many peopledie across the world due to this disease in a year. An early detection of cancer can improve the survival rate of patients. In recent years many ML models have been develop that can identify and predict the cancer from the medical imagery. Further, most of them are using the X-rays, CT scan images and such models may reduce the cancer infection ratio of the patient and improve the decision making of the radiologist. To find the cancer in the early stages various ML algorithms have been explored and few of them having high accuracy up to 98%. This study providesan overview of Various MLmodels for cancer detection and prediction.
Scheduling Cloudlets in a Cloud Computing Environment: A Priority-based Cloudlet Scheduling Algorithm (PBCSA)

Cloud computing is a service model that has evolved in its stature beyond its traditional bounds of infrastructure, platform and software as a service. As the surge in resource demand may hit the cloud service provider at any time, a ceaseless monitoring system is vital. The allocation of an appropriate virtual machine for the cloudlet i.e., the user workload and maintaining the work load equilibrium among the resources is the most challenging operation in the cloud environment. The proper utilization of the cloud resources can be ensured by selecting the right cloudlet scheduling and load balancing algorithm(s). The cloudlet scheduling algorithm selection is based on the combination of two or more Quality of Service (QoS) and performance metrics like makespan, throughput, cost, power consumption, virtual machine or resource utilization and load balancing etc. The load balancer module takes the responsibility of dispersing the cloudlets evenly among the virtual machines by considering various features like CPU utilization, number of processing elements, bandwidth, memory and the load limit of the virtual machines. In this paper, an effort has been made to comprehend the most persisting cloudlet scheduling and load balancing algorithms that have been proposed by the researchers. Compiling the load balancing technologies that are integrated with the contemporary cloud platforms such as Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP) has also been prioritized. This study suggests a Priority Based Cloudlet Scheduling Algorithm (PBCSA) that schedules the cloudlet according to the user priority. The Min-Min scheduler is used to schedule the high priority cloudlets and the Max-Min scheduler is used to schedule the low priority cloudlets. The experimental findings reveals that, in the majority of scenarios, the proposed algorithm outperforms the Min-Min and Max-Min scheduling in terms of makespan and virtual machine utilization ratio.
A Hybrid Book Recommendation System for University Library

Nowadays, Recommendation systems become an important role in daily life such as recommended goods, recommended musics, recommended books, or recommended movies. Furthermore, a university library initiated a book recommendation system for improving the efficiency of book searching. This paper presents a methodology for book recommendation in a university library using a hybrid recommendation technique by weighting a combination of 2 similarity scores from two recommendations system. Normally a hybrid recommendation system is built on a combination of content-based filtering and collaborative filtering, whereas this paper use technique for applied books from the Course Syllabus and combines it with a standard hybrid recommendation system. To solve the cold start problem and improve the accuracy of the book recommendation system in the university library. For the evaluation, RSME has been used to collaborate with K-Fold Cross Validation and Train Test Split technique. Eventually, the result of the evaluated book recommendation system shown RSME is 1.2061 for 5-Fold Cross Validation and 1.2247 for Train & Test Split
The Improvement Process for The Software Development and Requirements Management to Achieve Capability Level 3 of CMMI

This research proposes the improvement process for software development and requirements management for a small and medium company to achieve capability level 3 of CMMI 2.0. The process was proposed to be executed by the waterfall process model and scrum-based agile. The authors developed the improvement process and support tool. The proposed process was applied to use in the home-loan service software development 2 projects. The evaluation of the proposed process was analyzed by comparing it to past project two projects. The result showed that the projects that applied the proposed improvement process had a better outcome 34.34%. The SCAMPI evaluation of the proposed process was taken by three experts and the result showed that the capability of the proposed process was 91.63% and the user satisfaction was at the good level with score of 4.37. From the experimentation that was conducted with two software companies, it was obvious that the proposed improvement process had significance to enhance the software development process to achieve capability level 3.
Task Scheduling Algorithm Based on Load Balancing and Activity based Costing Method in Cloud Computing

Cloud Computing is the computing model where technology-associated resources are provided to several users “as a service” using internet mechanisms on a paid basis. As technological innovation, cloud providers hope to accomplish a big, broad sense virtualization of computer infrastructure, and the provision of varied software solutions necessitates a diverse set of users and services. IT capabilities are given as services spread via the network in cloud technology, so these resources must be provided promptly. In this paper, a new task scheduling algorithm is proposed, which integrates the load balancing algorithm and a task scheduling algorithm based on the Activity Based Costing Technique. Tasks (or user requests) are prioritized in this system based on profit. Then, based on the properties of the tasks, resources are assigned to them. So, this system makes good use of the resources because it takes into account the profit factor.
A Comparison of the Instructor-Trainee Dance Dataset Using Cosine similarity, Euclidean distance, and Angular difference

The COVID-19 outbreak has restricted most outdoor activities, leads to increasing interest in exercise at home with online trainers. One issue of online exercise technology is the safety since improper motion might result in injury. As a basis to prevent improper motion, methods for evaluating the motion similarity between an instructor and a trainee are essential. Cosine similarity, Angular difference, and Euclidean distance are three general ways for the motion evaluation. This study aimed to determine the most effective way for analyzing the similarity of human motion on the dataset of instructor-led dances. We first experimented with the data to find the appropriate cut-off value for classifying posture into two classes based on the similarity score. Confusion matrix, precision, recall, F1-score, accuracy of the results were then used to compare the efficiency. We discovered that Cosine similarity had the highest accuracy, 82.77 percent at cut-off 93.
Thai Paraphrasing Tool for Chatbot Intent Recognition Training

This paper introduced a novel tool to automatically generate Thai paraphrased sentences specialized for intent recognition training. This technique is devised to improve intent recognition accuracy and reduce the training time, especially during the creation of applications like Thai natural language-based games and chatbots. The switching if-conjunction clauses in question sentences and the thesaurus-based paraphrasing methods have been explored. For the evaluation, a group of participants used the prototype tool to develop Thai chatbots. Which were tested to recognize question-type messages in the Thai national identification card domain provided by another group. The results demonstrated that the proposed techniques increase the accuracy F1 score compared to the state-ofthe-art pattern-aided chatbot by approximately 39%. On average, expert chatbot developers scored 8.25 out of 10 points on the prototype tool satisfaction.
Face recognition using Skin Color Segment and Modified Binary Particle Swarm Optimization

Face recognition (FR) is a method for identifying or verifying a person's identity based on their face. FR is a research topic that has received a lot of attention, because face identification can be used as biometric security. FR is one of the more popular metric security formats compared with other metric security formats. Face identification also includes a lot of factors that can affect face identification, such as background, head posture, and brightness. The experimental results of the previously proposed methods when encountering color images were not satisfactory. Hence, in this paper, we have presented a method to improve face identification in color images by using three color spaces: RGB, HSV, and YCbCr. The proposed method was tested against the FEI and FERET face databases, and the results were satisfactory compared to other methods where human skin tone was involved in facial identification.
Multi-resolution Coarse-to-fine Registration Approach for Liver Computed Tomography Image Analysis

Computed Tomography (CT) image contains vital medical information of patients and thus being irreplaceable in liver cancer treatment. Recently, computer-aided methods are increasingly applied into CT image processing, especially medical image segmentation and registration, and achieved promising results. However, performing non-rigid registration on liver CT images is challenging due to the large deformation caused by the big size of the liver organ. In this study, we propose a method for solving the liver registration problem, which utilizes convolutional neural network (CNN) with multi-resolution coarse-to-fine registration strategy to step-by-step deform the moving image to get closer the fixed shape. The proposed network is trained unsupervisedly for ease of expandability. We extensively evaluated the trained model on a variety of public liver datasets using dice (DSC), intersection over union (IoU) and landmark distance metrics, and compare to the performance of two well-known CNN-based registration methods. Experimental results show that the proposed method achieves promising results and proves its potential in the registration of CT images of diverse liver shapes.
AI-powered Chatbot: A Link between Learning and Technology

A chatbot is a software submission that uses text or text-to-speech to conduct an online chat discussion instead of providing direct contact with a social being. It will give the appropriate answers just like human beings do and will help to learn new things easily without any difficulty. This ensures that people get the desired result and learn new technologies without depending much on human teaching. The chatbot comprehends the user's query and will provide an accurate response. The objective of this project is to show that chatbots can help to reduce the gap between technology and the learning process.
Exploring the Use of Different Feature Levels of CNN for Anomaly Detection

Anomaly detection is the task of uncovering out-of-distribution samples from the majority of data. Typically, this is treated as a one-class classification problem where the only data available to analyze is the normal data. With regard to collecting features of normal data, the high-dimensional features from CNN can be used to learn the normality. The last layer of CNN with more semantic information is generally used to learn the normality. In contrast, this work proposes learning features from different levels of high-dimensional features instead of using only high-level features. With the assumption that the training data is normally distributed, we present an anomaly detection algorithm consisting of a deep feature extraction stage with ResNet18 followed by dimensionality reduction via PCA. The anomaly classification stage comprises two class-conditional transformation models implemented via Gaussian Mixture Model. Our proposal leverages feature-reconstruction error as anomaly scores between two high-dimensional feature vectors. In this study, we analyze and compare the effect of using different blocks of a pre-trained ResNet18 on a well-known industrial anomaly detection dataset. Results suggest that using the best output features of CNN can significantly improve the model’s ability to predict anomalous samples.
Job-Candidate Classifying and Ranking System-Based Machine Learning Method

Finding suitable candidates for an open job position could be a repetitive and time-consuming task, especially from a large pool of candidates. Besides, this task could truly make fair screening and shortlisting tedious. Losing the opportunity to hire top talent candidates due to the slow screening process or the wrong selection by human error is unacceptable. This paper presented a method for human resources to categorize and select the top candidates for the job opening they applied for. The proposed system is directed to alter a machine learning algorithm to classify the candidate into i) shortlist or ii) not-suitable group. The productive preprocessing data approaches of many works were applied. The Decision Tree, Support Vector Machine, K Nearest Neighbor, and CatBoost were compared to find the most suitable classification model. Then, the system ranked the candidates in a shortlist group in descending order. The proposed system operates with an accuracy of 87%, a weighted F1 score of 88%, and a recall of 75% from the Support Vector Machine classifier. This enables the business to identify suitable candidates for a certain position and make more informed decisions about whom to invite for an interview.
Deep KNN Based Text Classification for Cyberbullying Tweet Detection

Nowadays, cyberbullying is a serious issue that many businesses must deal with. Existing technology makes use of machine learning to automatically detect cyberbullying. Deep learning-based algorithms have been shown to achieve higher accuracy in text classification than existing methods. In this paper, we develop a cyberbullying tweets detection using machine learning algorithm. The proposed method reads the tweets and then classifies the texts relating to cyberbullying and blocks the users. The study uses a k-NN classifier integrated with Deep Learning and show how effective the model is over large text datasets than other methods. The results of simulation shows that the proposed method has higher rate of classification accuracy than the other existing methods.
The Design of English Mobile Learning Software Based on Android Application

As a new form of learning, mobile learning has attracted more and more attention. Mobile learning can meet the needs of different learners, reflect the autonomy of learning and personalization of services, and also respond to the concept of lifelong learning. This paper is to explore the design and implementation of an English learning software for mobile devices based on an Android application. Firstly, the meaning and characteristics of mobile learning are briefly explained, the basic knowledge of mobile learning is introduced in detail, and the application of mobile learning process in mobile learning and the basic problems of mobile learning are illustrated. The teaching design of the learning software is carried out from the aspects of learning needs and learning content, and then the learning software is designed from the aspects of interface design, navigation design, transmission form design, interaction design, etc. Finally, the functional modules of English mobile learning software based on Android platform are analyzed and summarized.
A new way of Smart Home Security using ML Face Recognition

The purpose of this study is to investigate machine learning (ML), a subfield of artificial intelligence (AI) that makes it possible for software developers to begin improving prediction even before they have completed all of the processes required to achieve this goal. Residential door locking mechanisms can now be secured with face recognition technology, which is a simple and user-friendly tool that effectively recognizes the features of homeowners. A computer vision algorithm easy to construct, highly accurate at recognizing faces, and widely used is the outcome of advances in facial recognition technology. How quickly does your suggestion system change in response to customer reviews or exposure to new products, and how do suggestions change as customers use the platform more frequently? There are many variations of each type of recommendation system, which helps us understand their capabilities better.
Application of Cryptographic Methods to Blockchain Technology to Increase Data Reliability

Utilizing digital technology to control technical processes reduces the need for physical labor and increases worker productivity. The optimization of automated specified conditions based on digital technologies is one of the primary avenues of progress in information technology. Among the key areas of study nowadays is improving the data dependability of controllers. The creation of procedures for data The use of network technology for storing, processing, and moving data is a result of the significant growth in the flow of data in the present information and globalization era. As a consequence, the trustworthiness of the data in this network security is becoming a bigger issue. Increasing data dependability based on security is a pressing issue nowadays. Developed around the turn of the century, blockchain technology offers a fresh method for protecting data in network security. Blockchain prioritizes the inclusion of a cryptographically chain in its architecture. This work focused on enhancing cryptographic methods and cryptographic strength evaluation using blockchain-based processes. The blockchain engine has been used to enhance the contemporary RSA6 cryptographic technique, and the D RSA6 algorithms has been created. Analyses and tests have been done on the D RSA6 algorithm's cryptographic security. It has been shown that the best method for improving the correctness of network system data is the designed algorithm D RSA6. The approach, which is based on combining cryptographic methods with blockchain technology, increases tolerance for cryptocurrencies by a factor of two. This enables you to improve the data's dependability.
An Intensive Analysis of Recent Trends of Block Chain Technology in the Role of Development in the Healthcare Sector

The efficacy of the block chain will be examined in this report to provide more context. Here, the synchronisation of records' information depending on several treatment layers will be shown. This study tries to give succinct overview of the block chain and how it influences an extensive literature assessment on the subject. To apply a fundamental concept of block - chain, pros and cons of block chain technology in the healthcare field will be shown. Utilizing block chain technology, many problems in medical groups may be avoided by comprehending incorporated components. We have seen quality standards processes in the healthcare industry as a result of block chain technology. The literature component of it will outline the whole healthcare and its operations. It may be concluded that block chain technology is seen as a realistic objective in the medical field by performing a thorough evaluation of publications and magazines that have undergone evaluation. Some of the biggest goals for blockchain in the health care field are reportedly accountability and accountability. This essay will provide priceless insights on the subject that will alter how people see block chain technology. in general. The apparent originality of this subject will be discussed for further evaluation, which will link readers to helpful sources. This study will demonstrate how health care workers may look at many facets of information infrastructure based on block chains. For the purpose of supporting the methodology selected for this research paper, a survey will be conducted.
A Definitive Survey of How to Use Unsupervised Text Classifiers

A tremendous number of discussions, reviews, and postings are placed on online communities any day, greatly expanding the global dataset. To learn what consumers think of a particular service or brand, one must now sift thru vast volumes of data. The majority of assessments are written in English, however as electronics both men's comprehension advance, there is an increasing amount of Gujarati content available online. Additionally, understanding viewpoints about a single item is crucial to Indian language trend analysis because we display for all their viewpoints as entirely valid. We utilised the Punjabi collection for basic news items from various news outlets to increase the precision of our classifications. Dnn but other algorithms svm classifiers, such as Unusual Forest, Svms, and Rapid deployment Regressors, were used to explore the exactness of classifiers.
The Development of the Multi-Layer Security Network Authentication System using Blockchain Technology

Due to the fact that crypto is a novel security strategy. This section outlines a multi-layer safe Internet of Everything networks theory based on blockchains, we will now explain the construction of a layered network security authenticator using blockchain technologies. By grouping Connected technologies into a pass connectivity and implementing the blockchain based platform at every level of a network, the strategy reduces the challenges of actually deploying blockchain technologies while maintaining the mankind's strict security and validity guarantee. It offers a nationwide connectivity option for the Web of Things. A stone chain is a collection of blocks that holds data. The kind of public blockchain affects the information stored inside a piece. For instance, a lot coin block contains data about the Presenter, the Reception, and the amount of bit coins that will be communicated. The brand's first block is called the Node. The goal of the block chain is to make data available to everyone who uses a program to access it. Entry to all this account for reading may be unrestricted (‘permit less’) or restricted (‘approval’). Block chain today makes it feasible to send, receive, and store digital data in a secure manner. Additionally, it uses mathematical encryption to protect every transaction. By adopting this, banks might boost their current security and disclosures to unprecedented heights.