Abstract: With the continuous progress of technology, the breakthrough and popularization of artificial intelligence technology have changed various industries and every aspect of social life. In this context, AI classrooms have rapidly emerged and become an important force in artificial intelligence education. AI classrooms have strong interactivity, flexibility, and immersion, which will support the development of teaching activities in future teaching. Exploring the structure and composition of AI classrooms, understanding the functions of AI classroom equipment and systems, and studying the role of AI classrooms in teaching applications can provide a better understanding of their advantages and disadvantages, and provide reference for the implementation of AI classrooms.

Keywords: Future teaching, AI classrooms, Teaching applications.

Title: Exploration of AI Classroom in Teaching Application

Author: Jundan Wang

International Journal of Novel Research in Computer Science and Software Engineering

ISSN 2394-7314

Vol. 10, Issue 3, September 2023 - December 2023

Page No: 78-83

Novelty Journals

Website: www.noveltyjournals.com

Published Date: 29-December-2023

Abstract: This study focuses on exploring the application of ChatGPT in the field of language learning, particularly in vocabulary memorization and article integration. Starting from the current state of natural language processing (NLP) and artificial intelligence (AI) technologies in education, the study specifically analyzes the technical characteristics of ChatGPT and its potential in language teaching. This paper discusses the application of ChatGPT in vocabulary memorization through empirical methods, demonstrating how randomly selected vocabularies can be effectively integrated into coherent stories to enhance learners' memory and understanding of these words. The study shows that ChatGPT can provide a rich, personalized learning experience, effectively promoting learners' vocabulary retention and language comprehension. Moreover, the study explores the advantages of ChatGPT in vocabulary learning compared to traditional methods and its potential impact in the field of educational technology. While demonstrating significant potential in educational applications, the study also points out the limitations of ChatGPT and directions for future research. It emphasizes the importance of combining the latest AI technology with traditional teaching methods, offering new perspectives and methodologies for the field of language learning.

Keywords: ChatGPT, Language Learning, Vocabulary Memorization, Natural Language Processing, Educational Technology, Personalized Learning.

Title: Research and Application of Vocabulary Integration into Articles Using ChatGPT

Author: GUO NYUHUAN

International Journal of Novel Research in Computer Science and Software Engineering

ISSN 2394-7314

Vol. 10, Issue 3, September 2023 - December 2023

Page No: 67-77

Novelty Journals

Website: www.noveltyjournals.com

Published Date: 26-December-2023

Abstract: With the rapid evolution of health information technology, there is a heightened need for the incorporation of regulations for securing patient data. This is true since the evolution from paper health records to electronic health records and other electronic health information. Accordingly, this scholarly literature view will provide an overview of governmental regulations that have been enacted to protect and secure patient data in the United States of America.  International and organizational patient data and security measures and strategies will also be included – along with other strategies to secure patient data from professional organizations, educators and established companies / corporations. 

Keywords: electronic records, government regulations, health information, information security, patient data.

Title: Regulations for Securing Patient Data

Author: Sherryl W. Johnson

International Journal of Novel Research in Computer Science and Software Engineering

ISSN 2394-7314

Vol. 10, Issue 3, September 2023 - December 2023

Page No: 58-66

Novelty Journals

Website: www.noveltyjournals.com

Published Date: 18-December-2023

Abstract: With a view to better economic growth, the Democratic Republic of Congo has been committed for some time through its Central Bank, among other things, to the path of major monetary inflation forecasts.

In this article, we want to contribute to the above approach, by embarking on a comparative study between some monetary inflation forecast models based on machine learning, namely multiple regression, random forest and the tree of decision, with a view to identifying the best of these models applicable to this type of forecast, and with this type of data.

Keywords: monetary inflation, artificial intelligence, machine learning models.

Title: Prediction of Monetary Inflation in DR Congo: Comparative Study of Some Models Based on Machine Learning

Author: Christ TSUNGU MIJIMBU, Christian MUABI KATENDA, Professor KABEYA TSHISEBA Cédric

International Journal of Novel Research in Computer Science and Software Engineering

ISSN 2394-7314

Vol. 10, Issue 3, September 2023 - December 2023

Page No: 51-57

Novelty Journals

Website: www.noveltyjournals.com

Published Date: 08-November-2023

Abstract: Big Data Analysis for unstructured data involves analyzing and processing large amounts of unstructured data, such as text, images, and audio, to extract meaningful insights and knowledge. Techniques used in big data analysis for unstructured data include Natural Language Processing (NLP), Computer Vision, and Speech Recognition. Big data analysis can be useful in the Nigerian court system for analyzing unstructured data, such as legal documents, witness statements, and court transcripts. The goal would be to identify patterns and relationships within the data that can help make more informed decisions, improve processes, and increase the efficiency of the court system. This paper presents an improved Hybrid model for legal case document classification. The system starts by collecting legal case documents from an online domain. The collected documents are in pdf format. The collected pdf files were converted to texts using a pdf miner library in python. The converted texts were used in creating tables using the pandas library. After the creation of the dataset table, the dataset was pre-processed by removing Nan values, and non-alphanumeric values, and also performing tokenization. The tokenized data was then passed into principal component analysis for the selection of important features. The selected features were then used in training an LSTM model for the classification of the legal case documents. The result of the LSTM is outstanding, having an accuracy of 98% for training. The model was deployed to the web, for easy execution, testing, and assessment.

Keywords: Big Data, Legal Case, Principal Component Analysis, Long Short-Term Memory, Python flask.

Title: Big Data Analysis for Unstructured Data in Nigeria Court System

Author: C. Aloy-Okwelle, J. Palimote, O.P Nweke

International Journal of Novel Research in Computer Science and Software Engineering

ISSN 2394-7314

Vol. 10, Issue 3, September 2023 - December 2023

Page No: 44-50

Novelty Journals

Website: www.noveltyjournals.com

Published Date: 08-November-2023

Abstract: In this paper, we present an integrated mobile system designed to empower tomato farmers with effective disease management strategies. Our approach harmonizes advanced machine learning techniques: Pre-trained Networks (MobileNetV2), Support Vector Machines (SVM), and Universal Sentence Encoder (USE), enabling precise disease identification and classification. This innovation introduces a pioneering recommender system aiding farmers in disease recognition through symptom analysis and preventive measures. In contrast to conventional methods, our system offers a holistic solution uniting technological innovation with pivotal information science components, thereby enhancing sustainability in tomato agriculture. We assess model performance in multi-class classification—encompassing ten classes (healthy and unhealthy)—utilizing an extensive dataset of 11,000 tomato images from Plant Village. Existing tomato leaf disease classification models are scrutinized, encompassing model types, datasets, recommender systems, and mobile integration. Predominantly, consulted research employs customized Convolutional Neural Networks (CNNs), demonstrating enhanced performance through hyper-parameter tuning. However, these models primarily focus on classification, lacking a recommender system that informs farmers of symptoms and preventive measures at the early disease stages. Our investigation reveals that the pretrained MobileNetV2 in conjunction with SVM yields exceptional performance, achieving 93.68% precision, 93.84% F-1 score, 96.81% accuracy, and 95.4% recall—surpassing alternative pre-trained models.

Keywords: AI, Deep Learning, CNN, Classification, Images, USE.

Title: A Cascaded Intelligent Recommender Model for Multi-Class Tomato Leaf Diseases Identification and Control

Author: Nkechi F. Esomonu, Udoka F. Eze, A. M. John-Otumu, I. G. Ayogu

International Journal of Novel Research in Computer Science and Software Engineering

ISSN 2394-7314

Vol. 10, Issue 3, September 2023 - December 2023

Page No: 33-43

Novelty Journals

Website: www.noveltyjournals.com

Published Date: 28-October-2023

Abstract: Early detection and diagnosis of basic health conditions could lead to successful treatment and reduced fatality rate in developing countries, as it is possible to extract human bio-signals and use it to better understand the bodily health status and reaction to external factors. This proposed Vital Intelligent System (VIS) design have in-built sensors which a user wears as a wrist band without it interfering with his/her daily life activities to monitor his/her major vital signs (BT, BP, HR), which then connects to its android app for fuzzy logic which identifies the users vital signs and  based on its fuzzy reasoning engine makes analyses to deduce proper health status and recommendation(s) to the user, a feature of which the existing systems have limitations to achieve. Using the Dynamic System Design Methodology (DSDM), a more technically robust, cost-effective and portable real time device was designed and its app suitable for any android device. This device will have an IoT interface (BLE) to connect to android phones; a GSM interface (SIM800L) relying on AT command framework to access defined medical personnel on the network in cases of emergencies; optical health LEDs to alert the user; thus, enabling consistent real time self-assessment of an individual’s health condition. This work is a quest to provide a generalized smart three-in-one vital sign system to improve the real time monitoring and reporting of user’s health status through optimizing the offline and online cross-interfaces of standalone sensors in line with the country network and health delivery system.

Keywords: Blood Pressure (BP), Body Temperature (BT), Heart Rate (HR), Wearable Health, Device (WHD), Vital Intelligent System (VIS).

Title: AN INTELLIGENT WEARABLE SYSTEM FOR VITAL SIGNS MONITORING AND REPORTING

Author: CHUKS-UGOCHUKWU C.M., OTUONYE A.I., EKEDEBE N., ETUS C.

International Journal of Novel Research in Computer Science and Software Engineering

ISSN 2394-7314

Vol. 10, Issue 3, September 2023 - December 2023

Page No: 16-32

Novelty Journals

Website: www.noveltyjournals.com

Published Date: 17-October-2023

Abstract: In recent years, the potential for cyberbullying has grown in lockstep with the growth of social media. Cyberbullying has increased in Nigeria, where 49.6% (104.4 million) of Nigerians have online access and 26.14 percent of African internet users, predominantly Nigerians, have internet access. Cyberbullying occurs in many languages, not just English. Nigeria, on the other hand, has over 525 native languages, making it one of the world's most linguistically varied countries. Cyberbullying thrives in Nigeria due to the large audience, hence it is vital to identify cyberbullying in multiple languages. However, few researchers addressed this puzzle, hence this paper studied the technological barriers to multilingual cyberbullying detection in Nigerian local languages. An empirical perspective of descriptive survey was used to elicit expert-based responses from 60 purposively sampled cybersecurity engineers with 5 years and above expertise in machine learning and cyberbullying detection across five geopolitical zones in Nigeria to actualize the study's purpose. For variable dimension reduction, Principal Component Analysis (PCA) was used, and linear multiple regression was used to predict the model. The study's findings clearly show that the primary technological variables impeding cyberbullying detection in both English and any of Nigeria's primary indigenous languages of Hausa, Yoruba, and Igbo are language ambiguity, sarcasm dialect classification, domain influence, data scarcity, non-trusted source of data, difficulty in developing novel classifiers for multilingual text detection, annotation of training data, and language characteristics. It is recommended that study be performed to discover latent cyberbullying actions that cannot be identified in English Languages.

Keywords: Barriers, Cyberbullying, Detection, Multilingual, Nigerian Languages, Technology.

Title: Empirical Perspective on Significant Technological Barriers in Detecting Multilingual Cyberbullying in Nigerian Languages

Author: Christiana A. Okoloegbo, Udoka F. Eze, Gloria A. Chukwudebe^,, Obi C. Nwokonkwo

International Journal of Novel Research in Computer Science and Software Engineering

ISSN 2394-7314

Vol. 10, Issue 3, September 2023 - December 2023

Page No: 7-15

Novelty Journals

Website: www.noveltyjournals.com

Published Date: 16-October-2023

Abstract: In this paper, we present an experiment of our randomized hints strategy of automated reasoning for yielding Axiom(5) from Axiom(1)(2)(3)(4) of Infinite-Valued Lukasiewicz Logic. In the experiment, we randomly generated a set of hints with size ranging from 30 to 60 for guiding hyper-resolution based search by the theorem prover. We have successfully found the most useful hints list (with 30 clauses) among 150 * 6 hints lists. Also, we discuss a curious non-linear increase of generated clauses in deducing Axiom(5) by applying our randomized hints strategy.

Keywords: Randomized hints, Lukasiewicz Logic, hyper-resolution, condensed detachment, OTTER.

Title: Experiments of Randomized Hints on an Axiom of Infinite-Valued Lukasiewicz Logic

Author: Ruo Ando, Yoshiyasu Takefuji

International Journal of Novel Research in Computer Science and Software Engineering

ISSN 2394-7314

Vol. 10, Issue 3, September 2023 - December 2023

Page No: 1-6

Novelty Journals

Website: www.noveltyjournals.com

Published Date: 01-September-2023