Reviewerly/exHarmony huggingface dataset:
Transactional and Sequential model for pre-processing the textual data

Pre-processing of data in the abstract techniques are by far important and the duration process involved in the flow of information discovery. The convolution pre-processing is determined by the source of data from which we are retrieving. The aim of the paper is to find out how important is pre-processing methods to discover the interesting pattern of user session data and also how to use pre-processing method on mining the textual data. We work on transactional and sequence model for weblog files in text format, text representation and sequence rule analysis is been considered as work model. We make an analysis by comparing four datasets which comprises of different data quality of text and it will be pre-processed in different measures like data will be determined with paragraph sequence, data is determined with sentence sequence, data is determined with paragraph sequence excluding stop words. We utilise the impact of these advanced techniques of pre-processing on both quality and quantity of data measurable in case of paragraph sequence identification.
Role of Thermal Images in Various Applications of Computer Vision

Object detection is an advanced area of image processing and computer vision. Its major applications are in surveillance, autonomous driving, face recognition, anomaly detection, traffic management, agriculture etc. This paper focuses on various object detection techniques in thermal images. A thermal imaging sensor is a device that creates an image by analyzing temperature differences between different objects in a scene and detecting radiation from those objects. In recent years, many machine learning and deep learning algorithms have been used to recognize objects in thermal images. This study makes a comparison of YOLO, YOLO DarkNet, Retinex algorithm, CNN-based machine learning model Support Vector Machine (SVM), and Gaussian Mixture Model (GMM), Mixer of Gaussian (MoG), Mean Shift Approach, Faster R-CNN and Deep Neural Network along with different datasets.
Framework Analysing the Impact of Artificial Intelligence on Human Resource Management using AMO framework

The study aimed at investigating the efficiency of A.I., H.R.I.S. and traditional Human resource practices towards Ability building, motivation and Opportunity facilitation. Further to identify the impact of A.M.O. through A.I. on Organizational Performance. The data was collected from 167 respondents belonging to 74 IT companies practising, A.I., H.R.I.S. and traditional Human resource practices. Herein the data was collected using a structured questionnaire using a simple random sampling technique. From the analysis performed it was understood that there is no significant association between designation and the type of system preferred. But, from the cross-tabulation and chart, it can be well perceived that the majority of the managers, executives and Supervisors prefer the A.I. system over HRIS and the Tradition HR system. Further, it was found that there is a significant difference in the AMO facilitation by the A.I., H.R.I.S. and Traditional system. Also, From the mean score analysis, it can be interpreted that the Ability, Motivation and Opportunity facilitation by A.I. is better compared to H.R.I.S. and the Traditional system. Furthermore, it was found that there is a significant impact of Ability, Motivational and Opportunities created through Artificial Intelligence on Organizational Performance.
Facial Recognition for Crime Control: A Machine Learning-based Solution

The criminal record holds detailed information including criminal history and personal information with a photograph of an individual. In order to identify a criminal, eyewitness identification is necessary, despite often having poor image quality and unclear resolution. Identification can be performed through various means like DNA, fingerprint or eyes. Facial identification involves storing full and cropped images of criminals along with their personal and criminal information to create a database. Then another database is created to identify the criminal; eyewitnesses, with the help of a professional, try to piece together the criminal's face using saved cuts stored in a database. Then, using Amazon Recognition, we try to predict the criminal by comparing the created image with the existing database, if the result is 70-80%, we declare this face as a criminal. Amazon Recognition is an image recognition service that identifies objects, faces, etc. and find out the result from the database. This technology also has a feature to compare different faces, which we are using in our project.
Objective Quality Metrics Assessment for Cloud Gaming

Abstract This paper aims to provide objective quality metrics assessment for cloud gaming using machine learning algorithms. Three classification algorithms (i.e., Random Forest, Random Three and J-48) have been used for the development of models for objective quality assessment of two metrics: blurriness and blockiness. The results indicate that Random Forest has the best performance in this experimental case of objective quality metrics assessment for cloud gaming. Future research activities will cover comparison of a broad range of objective quality metrics and machine learning algorithms while using larger dataset to enhance the results significance.
Challenges and Effectiveness of Nurturing Programs among Seventh-day Adventist Secondary Schools in Southern Tanzania Union Mission

This study was about challenges and effectiveness of nurturing programs among Seventh-day Adventist secondary schools in Southern Tanzania Union Mission through the descriptive research design. A sample of 284 students and 57 school staff participated through interview and questionnaire. Data analysis was done through the thematic approach. Challenges that schools faced in implementing nurturing programs include lack facilities for spiritual programs. Corporal punishment was a hindrance for teachers to nurture students as it was difficult for teacher to sit down with emotionally wounded students and implement nurturing programs. Despite the outlined challenges, the implementation of nurturing programs was somewhat effective as students reported the existence of a number of spiritual, social and character development nurturing programs. The study recommends that school owners should equip the schools with adequate facilities like spiritual books and chapel buildings and provide staff houses so that teachers may live within school premises and find time to provide nurturing programs after class hours. Schools should be commended for the existing nurturing programs and staff members should be encouraged to continue providing various nurturing programs.
Enhancing Security Feature in Financial Transactions using Multichain Based Blockchain Technology

Blockchain is one of the most emerging and buzzing technology of the fifth-generation computation with its wide implications in various sectors like Fin-Tech (Financial Technology), Medicare, Hospitality, Manufacturing, and various other sectors. Moreover, due to its decentralized framework, this technology is extensively used in Financial Transaction to preserve security, veracity and handling associated risks. Blockchain is also known as distributed ledger technologies (DLT) which practices the concepts of cryptography for securing data and provides unique signature, which in turn supports financial transactions to authorized users only, most importantly it helps in maintaining the integrity of transaction. Blockchain has distinctive potential for storing transaction records to avoid money laundering which helps in avoiding transaction breaches. The unique features of Blockchain like Proof of Work (Pow), Proof of Stake (PoS) facilitates in achieving secure transaction such as Defi (Decentralized Finance) has also been addressed. Blockchain utilize unique hash value in view of avoiding transaction redundancy. In this paper, proposed a decentralized framework of Blockchain in Financial Transaction with goal of providing end-to-end transaction with legitimacy of the users. The stupendous influence of Blockchain technology with respect to financial transaction has been focused such as Equity Market, Wealth Management, Payment and Remittances, Commercial Lending, and Insurance.
A Comparative Analysis of Implementation of Privacy by Design Principles on different Blockchain platforms

Blockchain is a pillar in 4IR (4th Industrial Revolution) of the digital world. Selection of the right blockchain platform, for anonymity, is a challenge that can be overcome while getting acknowledged how anonymous a platform is. Even though privacy is ensured by most of the platforms yet a source to demonstrate which platform is ensuring it at its best is quite an urge and can be fulfilled after formulating the comparative analysis on each one of those platforms under consideration. This paper will provide that required analysis by comparing privacy by design principles on different blockchain platforms. It will be quite helpful for developing decentralized applications where privacy will be provided by design principles.
Bi-Directional LSTM-Based Search Engine for Source Code Retrieval

The code retrieval process looks for the most relevant code fragments. For this purpose, many code-search methods support natural-language queries. But still, there are problems with figuring out the pros and cons of each technique and choosing the best one for the task of code search. The proposed approach employs Word2Vec embedding and Bi- LSTM, a neural network model, to identify query title similarities with the existing dataset to search the code fragment. A novel model named BiD-CSE (Bi-Direction LSTM Code Search Engine) is proposed which generates source code recommendations. The proposed system assists programmers in locating appropriate implementations of sample code based on requirements specified in the query. The evaluation of the proposed framework is performed using three datasets. BiD-CSE collects and analyzes the input query which is then parsed to find the best match in the dataset. BiD-CSE finds the desired code by matching variables, functions, doc-strings, and comments. The BiD-CSE model is implemented using a web-based platform that allows users to enter a query and obtain the top ten most effective results. Evaluation results show that the performance of the model is better than the current baseline approaches.
Interaction with Media Players through Hand Gesture Recognition

Hand devices provides a huge barrier between convenient and user friendly Human-Computer Interaction (HCI). There is a strong need for marker-less natural interface between human and computer that can also facilitate disabled people. This paper presents a novel technique for hand gesture recognition for controlling multimedia player through hand gestures. Existing techniques available in the literature have number of limitations, such as scalability, complex implementations and limited accuracy. The solution proposed in this paper is based on image processing which does not require designing of any complicated data sensors-based gloves. Our outcome shows that an effective HCI can be achieved with minimal hardware requirement. Furthermore, the proposed system is easily scalable for controlling many other computer applications through hand gestures.
Improving Software Product Line Challenges In Globally Distributed Environment Using Modern Strategies

A family of products is created by organisations by reusing some core assets with variations rather than producing new products and versions due to advancement, regularly changing demands, and rapid delivery in the software industry. On the other side, organisations utilised distributed development from all around the world to manage their limited resources. Therefore, creating a software product line in a widely dispersed environment generates several barriers, such as variability and commonality management, coordination, communication, control, etc. for high-quality goods. Therefore, the primary goal of this paper is to employ modern strategies to address all of the aforementioned difficulties. We employed the highly scalable Team Foundation Server (TFS) technology for the software product line in a global setting. Team collaboration, variability management, communication support, and global dispersed core asset management are the most frequently encountered aspects. In order to assess our work, we performed a control experiment. To assess the significance of our findings, we also conducted a reliability study. The results demonstrate that our results are statistically significant and that our proposed approach performs better than the traditional approach. The proposed approach assists in increasing the productivity of families of products for both practitioners and academics working in global software development (GSD).
Analytical properties in Purananuru

Purananaru is a Tamil text that consists of four hundred hymns in praise of the object. All events that take place overtly are objective. The text describes well about Politics, Commerce, Histories, Philosophies, War Events and so on. The text beautifully depicts Pari's love for Kapil and the friendship between Pisiranthaiyar and Kopperuncholan. Purananuru is the best text that describes the song of Kapila in the loss of Pari and the bravery in the song of Auvai in the loss of Athiyaman. This article summarizes and examines the characteristics of the text Purananuru.
Co-Clustering based Hybrid Collaborative Filtering Model

Recommender systems are intellectual systems of information filtering that makes relevant suggestions for customers. Main propose of generating relevant information for customers is to improve their experience. A recommender system which gives fast, accurate recommendations and good experience to users is more attractive and develop more interests in users. As the strength of internet users and items are growing day by day many challenges are faced by recommender systems. One of the challenges is data sparsity. Highly sparse data gives decreased accuracy of predictions. High quality predictions are dependent upon how well the recommender system address its challenges. This research work proposes Collaborative filtering based recommender system technique. Neighbours are determined using dimensionality reduction based Spectral Co-clustering technique. The approach presented is Confidence based weighted fusion method (CBWF) merged with the rating predictions from User based CF (UbCF) and Item based (IbCF). Along with confidence a parameter σ is used. Previously σ was varied based on the datasets. In this research parameter σ is controlled and made dependent upon the correlation characteristics of individual users and items based on their state. Spectral-co-clustering overcomes the sparseness of dataset and limitation of scalability. While fusion of UbCF and IbCF in confidence based weighted sum improves the prediction accuracy of system. Finally predictive evaluation metrics are used to compare results for proposed technique with conventional techniques, one dimensional clustering, two dimensional clustering and HCF techniques to show the improvement that this research work has made.
Reduce Emergency Response time using Machine learning Technique

In reaction to any unusual circumstances, an ordinary man looks for the government’s response system to play its pivotal role in providing a timely response by any respective field force. It is the responsibility of emergency response forces to ensure a rapid and appropriate reaction to any undesired situation. According to statistics obtained from emergency centers in Pakistan and different countries, it is brought into consideration that every day, around 20–25 thousand emergency calls are received, and on weekend’s ratio increases by 20%. Extensively it is classified by different studies, facts, and feedback that 80% of the calls are hoax calls. Currently, the system is entirely dependent on a human resource where a human operator is required to listen to the person in the problem and decide which response force needs to be informed. This naturally brings a delay in response time. The system that is being developed first analyses voice-to-text conversions from a semantic perspective applies data parsing techniques to the text that has been extracted, and then creates tokens based on pre-learned keywords. The system is trained on a data set that was obtained from a well-known emergency command center. Keeping in mind the confidentiality of the data set, only the emergency-related descriptions were used to train our model. For each unique case, a keyword was listed in the database as a token, allowing the system to make decisions independently for each pattern it had learned. We have used SVM and logistic regression algorithms in a python framework for training and testing of research work. SVC will extract features from the data set then a pipelining algorithm will clean the data set and remove the unwanted data. It is a kind of pre-processing that is being done to achieve the best and the better accuracy measures. Then these text tokens will be converted into vectors, and a vectorizer algorithm needs vectors/numbers for input and processing purposes. Then a function will take training data and trained labels in a classifier to predict the data. These prediction values and trained labels will be compared to achieve the best accuracy. Similarly, in the testing, the classification report is generated without the help of the fit function because training is allocated with the fit function, as it is not needed in the testing phase.
Comparative Analysis of Performance in FP-Growth and Apriori Algorithm

This research paper presents a comparative analysis of two popular data mining algorithms, Apriori and FP Growth, using the WEKA tool. The study aims to determine which algorithm is more efficient in terms of execution time and database scan parameters. The results indicate that FP Growth outperforms Apriori in terms of execution time and number of database scans required to generate frequent item sets. The study also examines the impact of various parameters on the performance of both algorithms, such as support threshold and database size. The findings of this study suggest that FP Growth is a more effective algorithm for frequent itemset mining, especially for larger databases, and can be a useful tool for data analysts and researchers working in this field.
A Study on the Analysis of Students’ Emotions in the Classroom Based on School Forums

In this paper, we first crawl the campus forum data using the Scrapy crawler framework to obtain the classroom evaluation dataset, and then use sklearn to perform random partitioning of the dataset.Then the jieba library is used to segment the training set comment data, and feature extraction and feature vectorisation are carried out.Subsequently, a plain Bayesian model constructed by training with the test set data is tested on the data to be tested, and course optimisation suggestions are made based on the test classification results, which are constructive guidance for the reform and improvement of course quality.
Research and Application of Web Technology and Mobile Terminal in College English Learning System

With the passage of time, digital learning, mobile learning and so on continue to emerge.Combining with the teaching purpose of college English courses, we develop a set of portable learning resources based on the compilation of college English textbooks.This system has expanded and improved college English teaching, so that students' learning in teaching will not be restricted by the region and space, so that they can freely make independent choices and good learning conditions, so that they can get more and more learning opportunities.In the selection of media, students' effectiveness and individual needs have been fully reflected.The system is divided into three layers: user layer, resource layer and system layer.Then it introduces the development process of the whole system.And on the basis of this system of various resources for in-depth research and learning [1].
Reform of Talent Cultivation Based on Deep Learning Technology

In modern times, since talents with a background in new engineering in colleges and universities play a vital role in society, it is further reform and practice that universities need to strengthen in engineering education.Taking the software engineering talent training program in universities as an example, this paper analyzes the current situation of software engineering talent cultivation against the background of new engineering.In light of the emerging issues, this paper introduces the student-centered education concept.Then, according to the characteristics of software engineering majors, relevant suggestions in terms of curriculum system, teaching mode, and teaching management are offered, which aim to foster the development of world-class software engineering talents.Besides, this paper designs an image denoising module, as well as a dense residual dynamic region-aware convolutional neural network (DRDRNet), for the smart teaching platform to assist instructors in obtaining teaching photography with high quality and improve teaching quality.Quantitative and qualitative experiments have demonstrated the denoising performance of the proposed module.
Design of Teaching Resources Sharing System for Digital Film and Television Production Based on MOOC

In order to improve the utilization rate of teaching resources of digital film and television production specialty, this paper takes the sharing of teaching resources as the research object, and designs a set of teaching resources sharing system of digital film and television production based on MOOC by combining Internet technology and computer application technology.On the Windows platform, the system is written in C# language, and the ASP.NET framework is introduced to complete the three-tier structure, and a resource library with vivid teaching, complete types, rich contents and fast reading is built.Visual Studio is used as the development tool, IIS is used as the core server to ensure its stability, and SQL Server is used as the data support.This provides a resource sharing platform for the education and teaching of digital film and television production specialty in colleges and universities, integrates online and offline teaching resources, improves the actual efficiency of teaching work, and makes contributions to cultivating digital film and television production talents.
English Smart Teaching Platform Algorithm and Design

With the development of the information age, continuous improvement of English teaching is necessary to comprehensively improve the quality of higher education; it is an important means to cultivate talents with innovative spirit and practical ability; moreover, it is an effective way to promote teaching innovation, promote the process of modern informationization of education, and enhance the quality of talent training.This paper mainly analyzes the feasibility, process, functional requirements and use cases of the Python-based English intelligent teaching platform to determine the functions to be achieved by the whole Python-based English intelligent teaching platform.It also provides standards for the code implementation and testing of the Python-based English intelligent teaching platform to make teaching more efficient and thus achieve the purpose of improving students' overall quality.