Reviewerly/exHarmony huggingface dataset:
Multi Objective Ameliorated Repetitive Resource Allocation Algorithm for Cloud Resource Scheduling and Allocation

Mapping huge jobs onto cloud resources is a part of workflow scheduling, which increases scheduling effectiveness.Numerous researchers have been working hard to enhance the efficiency of scheduling in cloud computing as a result of this piqued interest.Scientific workflows, on the other hand, are huge data applications, therefore the executions are costly and time-consuming.Thus, a novel Multi Objective Ameliorated Repetitive Resource Allocation Algorithm that can quickly respond to unforeseen needs has been proposed in order to enhance the system's efficiency in allocating work tasks.Resource performance and resource proportion matching distances are also established in order to achieve resource optimization and the balanced use of all available resources.The results of the simulation show that the suggested method can efficiently complete Virtual Machine (VM) allocation and deployment and well manage incoming streaming workloads with a random arriving rate.Compared to small and medium workflow jobs, the suggested algorithm performs much better in big and extra-large workflow tasks.The experimental findings demonstrate that our algorithm is capable of balancing the consumption of all types of resources while allocating resources swiftly and optimally for unexpected demands.
Anomaly Detection of Attempt Through Genetic Algorithm and ANN

Vehicular ad-hoc network, commonly known as VANET, is an enabling technology for supplying security and useful information in modern transport systems but subject to a multitude of attacks, ranging from auditing passively to hostile interfering.When suspicious actions are discovered, intrusion detection systems (IDS) are essential instruments for risk reduction.Additionally, by sharing interactions among their nodes, VANET vehicle collaborations improve detection accuracy.Because of this, the machine learning distribution system is efficient, scalable, and useful for developing cooperative detection methods over VANETs.Because data is exchanged between nodes during collaborative learning, privacy concerns are a basic barrier.Through the data that is observed, a rogue node may be able to obtain sensitive information about nodes other than itself.This research suggests cooperative IDS for VANETs that protects machine learning privacy.Additionally, an intrusion detection classifier is trained on the VANET and the proposed alternating multiplier direction approach is employed to solve a class of empirical risk minimization issues.In order to apply a vector approach of dual disturbance to dynamically varying privacy and provide secure network communication, the usage of privacy differential is done to capture the notation of privacy.
Benchmarking of Various Multicore Processors with Different Operating Systems

Benchmarking is the process of running programs and different heavy operations to measure the relative performance of various systems.With this project, we aim to design a tool and compare different systems with multicore processors and different operating systems.We will use various python libraries and user defined functions to perform demanding image processing and mathematical tasks to stress the systems.This can be used to get an idea of how well optimised the operating system is for the given hardware.
DIGITAL TOOLS FOR COLLECTING FEEDBACK EFFECTIVELY

Establishing communication between the student and the teacher by improving feedback allows to strengthen cooperation and learning outcomes, to be able to respond and change the style of presenting information, according to the needs of students, to hones
Enhancing Deep Learning Approach for Tamil English Mixed Text Classification

Text Classification with sentiments understanding is an essential task for data processing and predicting user behavior.In case of Multilingual data, the process requires to convert the entire data to machine understandable language or to pre-process the text prior to classification keeping the semantics of the text intact.Deep Learning libraries like Bidirectional Encoder Representations from Transformers (BERT) with word2vector model and Convolutional Neural Network (CNN) for natural language processing (NLP) support both techniques, and the manuscript attempts to enhance pre-processing of the Tamil English Mixed text Classification.The pre-processing of the Tamil English Mixed text addressed the issue of annotated text non-availability.
DEVELOPMENT PROSPECTS OF THE MODERN ELECTRONIC UNIVERSITY

With the development of technology, e-learning is becoming an increasingly popular method of education. Electronic universities, or e-universities, are a form of electronic learning that has the potential to revolutionize our approach to higher education.
Abstractive Summarizer for YouTube Videos

The paper goal is to design a user interface where the user can get the summary of the requested YouTube video using Natural Language Processing (NLP) and Machine Learning.Enormous number of videos are uploaded to YouTube daily.It has become difficult to find the relevant content that we are looking for, sometimes it may take longer than expected, and our efforts be-come futile if we are unable to extract meaningful information from it.This is where our project becomes handy.It will summarize the video and display the summary of it.Abstractive summarization model extracts YouTube video transcripts and generates a summarized version.It saves our time by shortening the content to read while maintaining the important content of the actual document.The implementation process is still ongoing.However, the entire structure and studies are presented.
Handwritten Gujarati Character Recognition Using Machine Learning and Deep Learning

Gujarati character recognition has always been a field of study in need of improved techniques to reach a satisfactory degree of accuracy.This study developed a technique for predicting handwritten Gujarati numerals using various feature extraction and classification techniques.Deep learning models, including MobileNet, DenseNet, and NasNetMobile, are components of the feature extraction technique.XGBoost, Random Forest, Logistic Regression, Naive Bayes, Stochastic Gradient Descent, Decision Tree, and K-Nearest Neighbor are some of the techniques used to classify Gujarati numerals.This study used over 14,000 images of Gujarati numerals for an experiment and found significant outcomes.
Comparative Analysis of Automatic Speech Recognition Techniques

It's most crucial method of transferring data is interaction.Speech is the most common way of data exchange.According to with linguistic survey, there are 179 languages and 544 dialects spoken in India.Current India has 18 scheduled dialects and several unscheduled languages.The primary goal of this paper is to give a thorough comparative evaluation of the relevant research on automated speech recognition.We observe potential prospects, problems, and methodologies, as well as locate, evaluate, and synthesize data from research in order to give empirical responses to scientific concerns.The survey was done by using appropriate research publications period between 2010 and 2021.The goal of this comprehensive examination is to synthesize the current best research on automated speech recognition by combining the findings of several investigations.
MARGEN: Marathi Question Answering Generative Conversation Model

The conversational system aka chatbot market capture was worth USD 526 million in 2021 around the world.The innovations created such as Machine learning, Deep learning, Natural Language Processing (NLP), and Big data analytics have given a modern speed-quickening fuel to Artificial Intelligence.Well, a generative chatbot could be an exceptionally effective and shrewd conversational system as distant as its learning component is concerned.They can be trained from scratch like a newborn child by using Deep Learning techniques.GPT-1,2,3 is well known for English Language NLP tasks whereas IndicBART is also moving ahead for 11 Indian Languages for the NLU tasks.With the DL techniques like RNN, LSTM, and SGD, we conducted an in-depth survey of recent deep learning conversational models available on open-source portals, examining over 25 deep learning models for conversational systems before proposing our model."MARGEN" is a proposed model which means the user can get the system's reply with proper generative answers for the query in Marathi text and/or audio speech formats.
SVM Classifier for Offline Handwritten and Printed Mathematical Expression Recognition

The recognition of Mathematical Expressions (ME) constitutes a challenging problem in character recognition research.A very few studies of offline Mathematical expressions have been so far reported in the literature.This paper focuses on offline handwritten and printed mathematical logical expressions recognition using Support Vector Machine classifier (SVM).In the work of expression recognition, the expressions were segmented into individual characters.The feature extraction method with combination of Normalized chain code and zone based density was used to get the features of a character.The present work considers logical expressions with subscripts for recognition.The experimental results for recognition rates of handwritten and printed expressions are reported.The result shows that the recognition rate of handwritten expression is 84.1% and that for printed expression is 90.3%.
The Role of Machine Learning in Thyroid Cancer Diagnosis

Thyroid cancer occurs in the thyroid gland cells.This is butterfly shaped gland which is situated at lower part of neck.Thyroid cancer might not cause any symptoms initially.But as the cancer grows, it causes pain and swelling in neck.To detect and classify abnormalities of the thyroid gland Ultrasound imaging is mostly used.Computer aided diagnosis (CAD) help healthcare sector to increase the diagnosis accuracy and to reduce biopsy ratio.Machine learning techniques play a vital role in diagnosing diseases from the medical database.Various diseases can be predicted early using machine learning techniques.In this study, many previous research works are reviewed which use machine learning techniques to predict thyroid cancer.Machine learning classification techniques like Decision tree Classification, Random Forest Classification, Naïve Bayes, Kernel SVM, K-Nearest Neighbours, Support vector Machines (SVM), Logistic regression etc. are reviewed from the research papers.A short description about machine learning and segmentation is also presented.
Artificial Intelligence (AI) in Hrm (Human Resources Management): A Sentiment Analysis Approach

As a time-saver and matchmaker, artificial intelligence can play a growing role in hiring and selecting.Automation can be used intelligently to increase workplace humanity.It enables you to take exclusively human abilities forward while putting technology in the background.Most people who work in HR do so because they want to interact with people and assist them in finding solutions to their challenges.Where appropriate use of automation can provide them more time to pursue their task and result in careers that are more satisfying.Artificial intelligence (AI) can be used to create job descriptions, promote and share jobs, automate applicant searches, and scan cover letters and resumes.This research paper focuses on how AI can be used in selected areas of HR to improve the human resources management with some leading business entities examples regarding their use cases in AI (Artificial Intelligence) and user sentiment analysis for using We360.aiapplication.
Contribution of the Synchronous and Asynchronous Mode in Virtual Education in a Peruvian Higher Educational Institution During the COVID-19 Pandemic

COVID-19 caused universities around the world to adapt their educational models to the new virtual modality. In Peru, higher education institutions integrated teaching-learning activities, integrating synchronous and asynchronous modes, through online classes and virtual learning spaces. The research aimed to identify the level of contribution of synchronous and asynchronous modes in virtual education, demonstrating the influence of five dimensions: psychological, technological, communication, pedagogical, and academic activities. With a quantitative approach, non-experimental design, and a sample of 1519 students at a public university, using an instrument based on a Likert scale. As a result, the synchronous mode had a greater contribution to virtual education, evidenced in the ease of use of technological tools, motivation, response time to queries, and effective communication, however, the asynchronous modality developed better research skills of the students, due to the autonomy and the better control of their time. The analysis by groups of careers and modes did not determine greater significance. The proceeds will serve universities as a contribution towards a new hybrid educational model.
The Relationship between Mindset and Reading Literacy Ability of Elementary School Students

Reading habits in Indonesian society have not developed well. In addition, the learning is carried out at this time has not been able to develop reading skills effectively and efficiently. This study aimed to analyze the mindset relationship to reading literacy ability in fifth grade elementary school students. This type of research is a correlation. The design of this research is quantitative with correlational research methods. The population in this study were all fifth-grade elementary school students, totaling 79 students. The research sample amounted to 55 students. Data collection methods used are non-test and test methods. The instrument used in collecting data are a questionnaire and reading literacy test. Data analysis techniques used are descriptive quantitative, and statistical inferential. The study results are the results of the testing hypothesis I, obtained rcount 0.757 &gt; rtable 0.266 then H0 is rejected, which means it is significant. Hypothesis II test results, rcount 0.434 &gt; rtable 0.266 then H0 is rejected, which means it is significant. So that the Growth mindset has a relationship with the reading literacy ability of fifth-grade elementary school students. A fixed mindset also positively impacts students' reading literacy skills. It was concluded that there was a significant correlation between mindset and reading literacy ability.
Mobile-Based Elementary School Report Card Information System (SIRAM) to Reduce Teacher Stress Levels

The conventional report cards that are used still have a high level of complexity, thus triggering the teacher's stress level at the end of each semester in inputting grades. The purpose of this study was to create a mobile-based elementary school report card information system (SIRAM) to reduce teacher stress levels. This development research uses the ADDIE model. In this study using a quantitative approach as well as qualitative. The research subjects were grade 3 (three) teachers. Methods of data collection using interviews, observation guidelines, questionnaires, and rating scale sheets. The data analysis technique used the one sample t-test. Based on the analysis carried out, the results of the SIRAM application have Login, Create, Read, Update, Delete (CRUD) features. The result of content validity is 0.9, so that it is declared valid with a high level of validity indicating that it is feasible to use it. The results of the effectiveness test of the teacher's stress level, with a significance level of 0.05 (5%) obtained a t value of 1.761, then the SIRAM application did not affect the teacher's stress level, before and after the administration of the SIRAM application. The implication of this research is that it is hoped that the next impact will be that schools can subscribe to hosting as a support for applications so that they can continue to use the SIRAM application in the future.
Reading-Related Activities in English Textbook for Grade 7th Junior High School and How Teacher Exploited Texts in the Classroom

The use of textbook in the reading class is one of the efforts that can be done by teachers in helping students to improve their abilities, namely by applying reading-related activities contained in the textbooks to the learning process in the classroom. This study aims at analyzing reading-related activities that are available in English textbook entitled “Effective English; Bahasa Inggris untuk SMP/MTs Kelas VII” was used for 7th Grade students and describing how the reading texts were exploited by the English teacher in the classroom. This study employed a qualitative descriptive research design. The subjects of this study were seventh grade students and an English teacher at Junior High School. Data were collected through document analysis, class observations, and interviews. The data in this study were analyzed through the data analysis procedures which include data collection, data reduction, data presentation, and drawing conclusions. The results of this study indicate that there are only a few reading-related activities in the English textbook entitled, namely lexical items and questioning activity. Meanwhile, the teacher’s strategies in exploiting the reading texts were also limited as the teacher closely used reading-related activities available in the textbook. This study was limited in time, number of subjects, and data sources so that further research is suggested for more generalizable findings.
Mammogram Image to Detect Breast Cancer Using K-Means Clustering Algorithm

Breast cancer is the uncontrolled proliferation of a group of cells in the breast and is the second leading cause of death for women in the world. The disease can be cured if detected in the early stages. A lot of research has been done to correctly detect the tumor, but a 100% accurate method has not been found. Research on breast cancer detection using digital image processing is not new, but many new approaches are being considered in this field to accurately predict the tumor area. The current approach consists of detecting the tumor area visually and also finding out in which area the tumor is most concentrated. CC and MLO dualview mammographic screening images are widely used in the diagnostic process. This project presents a method for detecting a tumor area and classifying a normal and oncological patient. Preprocessing operations are performed on the input mammographic image and unwanted parts are removed from the image. Tumor regions are segmented from the image using a morphological operation and are highlighted on the original mammographic image. If the image on the mammogram is normal, it means that the patient is healthy. This work mainly focuses on finding the best algorithms for detecting tumors present in the breast. A number of algorithms were used in the proposed work, but the most suitable for cancer detection is the combination of K-Means clustering algorithm. K-Means classification accuracy is 95% accurate output will be predicted. Keywords: Image Processing, Breast Cancer, K-Means clustering, Dilation, Closing, Edge Detection, Mammography screening images
An Experimental Evaluation of the Performance and Key Factors of Intelligent Recommendation System

The implementation of recommendation systems on different websites, to offer a satisfying experience to user (related both to work and pleasure time), became a vital component for various platforms. Such a system must consider a variety of needs that are unique to the domain for which it is utilized, making its development difficult. The aim of this paper is to analyse the requirements for a recommendation system that can be implemented in applications designed act as a matchmaker between demand and supply in human resources, connecting companies with senior mentors, and to develop a system that satisfies these requirements. The proposed hybrid system is implemented in a knowledge transfer platform, dedicated to elderly people, where the recommendation is using artificial intelligence agents. The system has been tested on different scenarios to estimate the accuracy of the matchmaking process.
COMPARATIVE ANALYSIS OF AGGREGATION AND INHERITANCE STRATEGIES IN INCREMENTAL PROGRAM DEVELOPMENT

Programming computers has been a herculean task for most programmers especially when codes grow into complex and larger software systems with multiple subprograms. Object Oriented Programming (OOP) has reduced the difficulty in the development of elegant and scalable software by presenting robust concepts such as composition, inheritance and aggregation. All these concepts have enormous assistance to the software developer in code reuse. Also these techniques can be used to build applications which can be delivered to customers in a record time. In this research a critical study, review and implementation of software building and enhancement using aggregation and inheritance. A module is built with attributes defining it properties and methods its characteristics. Incrementally more modules were added to the previous modules using either aggregation or inheritance technique. This incremental approach has proven tremendous success in software development. This as buttressed by many software development theories have shown that: software is built not manufactured; software is a collection of programs with functions and attributes based on its enhancement, also incremental software development which involves building systems from sub-systems gives a better understanding of software development process. Also the process of writing bug-free programs can be achieved with lesser difficulty which can be achieved when programs are built using modular or incremental software development approach, which employs mostly aggregation while moderately using inheritance only if all the properties and methods of those modules are needed wholesomely in classes. The result from the research will help programmers to enhance codes with much mastery.