February 2023 Issue Vol.13 No.2
SURVEY ON SOCIAL MEDIA IMPACTS IN STUDENTS EDUCATION
https://ia902602.us.archive.org/7/items/ijitce-feb-2023/IJITCE_Feb2023.pdf
S.Senthamaraiselvi
Research Scholar (Part Time), Dept. of Computer Science,
Erode Arts and Science College (Autonomous), Erode, Tamilnadu, India
Dr.K.Meenakshi Sundaram
Associate Professor and Head, Dept. of Computer Science,
Erode Arts and Science College (Autonomous), Erode, Tamilnadu, India
Abstract:
The social networking sites and social media have revolutionized the world, bringing us closer than ever before. However, students can exploit this and use it for a better life, a better tomorrow. It should be used to connect, stay in touch, share views but not waste time on. Today, the main aim of the student should be education and their future career. However, many students rely on the accessibility of information on social media. That means reduced focus on learning and retaining information. The study also points out the popularity of social networking sites among students community. This research tries to investigate about the benefits and the drawbacks of the social media use on student academic performance.
Keywords: Social media, Students, Machine learning
INTERACTIVE EDUCATION IN E-LEARNING USING REALITY TOOLS
https://ia902602.us.archive.org/7/items/ijitce-feb-2023/IJITCE_Feb2023.pdf
P.Vijayakumar
Ph.D., Research Scholar, Department of Computer Science,
Karuppannan Mariappan College, Muthur,Tamilnadu, India
G.Jagatheeshkumar
Associate Professor and Head, Dept. of Computer Science,
Karuppannan Mariappan College, Muthur, Tamilnadu, India
Abstract:
Present education that is sustained conventionally through technology has transitioned from e-learning to smart phone learning because of the everywhere being there of the Internet, as well as speedy advancements in Information and Communication Technology (ICT) and current improvements in learning technology with Augmented Reality and Virtual Reality tools. With the advent and widespread usage of smartphones and ubiquitous computing, the concept of E-learning has acquired new significance by giving consumers access to a wide range of options for interacting with e-learning mechanisms. It has also made it necessary for the syllabus designers of the course to pick the most appropriate technology from among the abundance of options available for distributing knowledge representation. This paper outlines state-of-the-art precious up-and-coming technologies A-R and V-R which are apt for carrying out various activities related to e-learning.
Keywords: Education, E-Learning, Augmented Reality, Virtual Reality, Information and Communication Technology
A SURVEY ON LUNG DISEASES IDENTIFICATION USING MACHINE LEARNING
https://ia902602.us.archive.org/7/items/ijitce-feb-2023/IJITCE_Feb2023.pdf
A.Angel Mary
Research Scholar,Department of Computer Science and Research Centre,
S.T.Hindu College, Nagercoil- 629002,Affiliated to Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli- 627012, TamilNadu, India.
Dr.K.K.Thanammal
Associate Professor, Department of Computer Science and Research Centre,
S.T.Hindu College, Nagercoil- 629002,Affiliated to Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli- 627012, TamilNadu, India.
Abstract:
In the medical world, lung cancer is considered one of the most dangerous diseases. The most observable area of research for medical professionals from the beginning to the present has been lung cancer analysis. The signs of lung cancer usually don’t appear until the disease has spread. This is where medical attention becomes most challenging. Early identification of lung cancer is essential since the disease progresses quickly. Nowadays, machine learning algorithms play an important role in the prediction and classification of medical data. Various machine learning algorithms are used for detecting several diseases. A chest X-ray and Computed Tomography (CT) image can be used to classify and organise lung nodules, as well as determine their risk level. It gives a clear picture of the affected area, monitors its progression, and shows chronic lung conditions, as well as complications related to these conditions.
Keywords: Lung cancer, Machine Learning, Computed Tomography, Classification
A STUDY ON CONTENT-BASED IMAGE RETRIEVAL TECHNIQUES IN COLLABORATION WITH MACHINE LEARNING
https://ia902602.us.archive.org/7/items/ijitce-feb-2023/IJITCE_Feb2023.pdf
J. Anto Germin Sweeta
Department of Computer Science & Research Centre,
S.T.Hindu College, Nagercoil- 629002,Affiliated to Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli- 627012, TamilNadu, India.
B. Sivagami
Department of Computer Science & Application,
S.T.Hindu College, Nagercoil- 629002,Affiliated to Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli- 627012, TamilNadu, India.
Abstract:
Image is one of the multimedia content which has fast-paced growth due to social media and the significant aspect is analyzing and retrieving the images for all sorts of cases in real-time application. Image Retrieval (IR) is the strenuous research field that leads Content-Based Image Retrieval (CBIR) techniques to another level. CBIR is the proficient framework that bridges the semantic gap between the high-level image interpretations of humans and the low-level image features stored in the database. It works competently in succeeding furtherance of database technology and searching methods when combined with Machine Learning (ML) types such as Neural Network (NN) and Deep Learning (DL) techniques. It depicts the uplifting performance as it classifies the objective features and does efficient extraction. This survey gives a detailed look at long-standing to most recent works which impart knowledge about techniques and evaluation methods used in the CBIR system for the natural images. This survey assesses the strategies/methodologies of each paper in terms of performance evaluation metrics and presents its pros and cons. This study enlightens proficiency of machine learning methods and their role of classification in the feature extraction, similarity matching, retrieval performance in the image retrieval field to commence a novel CBIR framework with new ideation and zeal.
Keywords: Image Retrieval (IR), Content-Based Image Retrieval (CBIR), Machine Learning, Neural Network, and Deep Learning.
Read complete February 2023
Read complete February 2023
https://ia902602.us.archive.org/7/items/ijitce-feb-2023/IJITCE_Feb2023.pdf
P.Vijayakumar
Ph.D., Research Scholar, Department of Computer Science,
Karuppannan Mariappan College, Muthur,Tamilnadu, India
G.Jagatheeshkumar
Associate Professor and Head, Dept. of Computer Science,
Karuppannan Mariappan College, Muthur, Tamilnadu, India
Abstract: Present education that is sustained conventionally through technology has transitioned from e-learning to smart phone learning because of the everywhere being there of the Internet, as well as speedy advancements in Information and Communication Technology (ICT) and current improvements in learning technology with Augmented Reality and Virtual Reality tools. With the advent and widespread usage of smartphones and ubiquitous computing, the concept of E-learning has acquired new significance by giving consumers access to a wide range of options for interacting with e-learning mechanisms. It has also made it necessary for the syllabus designers of the course to pick the most appropriate technology from among the abundance of options available for distributing knowledge representation. This paper outlines state-of-the-art precious up-and-coming technologies A-R and V-R which are apt for carrying out various activities related to e-learning.
Keywords: Education, E-Learning, Augmented Reality, Virtual Reality, Information and Communication Technology
A SURVEY ON LUNG DISEASES IDENTIFICATION USING MACHINE LEARNING
https://ia902602.us.archive.org/7/items/ijitce-feb-2023/IJITCE_Feb2023.pdf
A.Angel Mary
Research Scholar,Department of Computer Science and Research Centre,
S.T.Hindu College, Nagercoil- 629002,Affiliated to Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli- 627012, TamilNadu, India.
Dr.K.K.Thanammal
Associate Professor, Department of Computer Science and Research Centre,
S.T.Hindu College, Nagercoil- 629002,Affiliated to Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli- 627012, TamilNadu, India.
Abstract:
In the medical world, lung cancer is considered one of the most dangerous diseases. The most observable area of research for medical professionals from the beginning to the present has been lung cancer analysis. The signs of lung cancer usually don’t appear until the disease has spread. This is where medical attention becomes most challenging. Early identification of lung cancer is essential since the disease progresses quickly. Nowadays, machine learning algorithms play an important role in the prediction and classification of medical data. Various machine learning algorithms are used for detecting several diseases. A chest X-ray and Computed Tomography (CT) image can be used to classify and organise lung nodules, as well as determine their risk level. It gives a clear picture of the affected area, monitors its progression, and shows chronic lung conditions, as well as complications related to these conditions.
Keywords: Lung cancer, Machine Learning, Computed Tomography, Classification
A STUDY ON CONTENT-BASED IMAGE RETRIEVAL TECHNIQUES IN COLLABORATION WITH MACHINE LEARNING
https://ia902602.us.archive.org/7/items/ijitce-feb-2023/IJITCE_Feb2023.pdf
J. Anto Germin Sweeta
Department of Computer Science & Research Centre,
S.T.Hindu College, Nagercoil- 629002,Affiliated to Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli- 627012, TamilNadu, India.
B. Sivagami
Department of Computer Science & Application,
S.T.Hindu College, Nagercoil- 629002,Affiliated to Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli- 627012, TamilNadu, India.
Abstract:
Image is one of the multimedia content which has fast-paced growth due to social media and the significant aspect is analyzing and retrieving the images for all sorts of cases in real-time application. Image Retrieval (IR) is the strenuous research field that leads Content-Based Image Retrieval (CBIR) techniques to another level. CBIR is the proficient framework that bridges the semantic gap between the high-level image interpretations of humans and the low-level image features stored in the database. It works competently in succeeding furtherance of database technology and searching methods when combined with Machine Learning (ML) types such as Neural Network (NN) and Deep Learning (DL) techniques. It depicts the uplifting performance as it classifies the objective features and does efficient extraction. This survey gives a detailed look at long-standing to most recent works which impart knowledge about techniques and evaluation methods used in the CBIR system for the natural images. This survey assesses the strategies/methodologies of each paper in terms of performance evaluation metrics and presents its pros and cons. This study enlightens proficiency of machine learning methods and their role of classification in the feature extraction, similarity matching, retrieval performance in the image retrieval field to commence a novel CBIR framework with new ideation and zeal.
Keywords: Image Retrieval (IR), Content-Based Image Retrieval (CBIR), Machine Learning, Neural Network, and Deep Learning.
Read complete February 2023
Read complete February 2023
https://ia902602.us.archive.org/7/items/ijitce-feb-2023/IJITCE_Feb2023.pdf
J. Anto Germin Sweeta
Department of Computer Science & Research Centre,
S.T.Hindu College, Nagercoil- 629002,Affiliated to Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli- 627012, TamilNadu, India.
B. Sivagami
Department of Computer Science & Application,
S.T.Hindu College, Nagercoil- 629002,Affiliated to Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli- 627012, TamilNadu, India.
Abstract: Image is one of the multimedia content which has fast-paced growth due to social media and the significant aspect is analyzing and retrieving the images for all sorts of cases in real-time application. Image Retrieval (IR) is the strenuous research field that leads Content-Based Image Retrieval (CBIR) techniques to another level. CBIR is the proficient framework that bridges the semantic gap between the high-level image interpretations of humans and the low-level image features stored in the database. It works competently in succeeding furtherance of database technology and searching methods when combined with Machine Learning (ML) types such as Neural Network (NN) and Deep Learning (DL) techniques. It depicts the uplifting performance as it classifies the objective features and does efficient extraction. This survey gives a detailed look at long-standing to most recent works which impart knowledge about techniques and evaluation methods used in the CBIR system for the natural images. This survey assesses the strategies/methodologies of each paper in terms of performance evaluation metrics and presents its pros and cons. This study enlightens proficiency of machine learning methods and their role of classification in the feature extraction, similarity matching, retrieval performance in the image retrieval field to commence a novel CBIR framework with new ideation and zeal.
Keywords: Image Retrieval (IR), Content-Based Image Retrieval (CBIR), Machine Learning, Neural Network, and Deep Learning.