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Volume 10 Issue 2 June - August 2022

Article

Sentiment Analysis and Offensive Language Detection in Social Media

M. Shiny*

Department of Computer Science and Engineering, DMI College of Engineering, Tamil Nadu, India.

Shiny, M. (2022). Sentiment Analysis and Offensive Language Detection in Social Media. i-manager’s Journal on Computer Science, 10(2), 1-7. https://doi.org/10.26634/jcom.10.2.19145

Abstract

Sentiment Analysis is a field of study that focuses on figuring out how to extract, identify, or otherwise describe emotions in units of written text. One of the most common tasks in sentiment analysis is finding the polarity of a person's feelings. There are many blog posts, tweets, and comments in Indian languages online these days. Sentiment analysis in Indian languages is a relatively new field, and research in this area is just beginning. There is a lot of offensive content on social media, which is a worry for businesses and government agencies. This paper presents the methodology of sentiment analysis and offensive language detection in social media.

References

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Research Paper

Transfer Learning Based Effective Emotional Face Recognition using DCNN via Cropping Techniques

Anjani Suputri Devi D.* , Suneetha Eluri**

*-** Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Kakinada, Andhra Pradesh, India.

Devi, D. A. S., and Eluri, S. (2022). Transfer Learning Based Effective Emotional Face Recognition using DCNN via Cropping Techniques. i-manager’s Journal on Computer Science, 10(2), 8-19. https://doi.org/10.26634/jcom.10.2.19059

Abstract

Facial Expression Recognition (FER) has grown in popularity as a result of the recent advancement and use of humancomputer interface technologies. Because the images can vary in brightness, backdrop, position, etc. it is challenging for current machine learning and deep learning models to identify facial expression. If the database is small, it doesn't operate well. Feature extraction is crucial for FER, and if the derived characteristics can be separated, even a straightforward approach can help tremendously. Deep learning techniques and automated feature extraction, allow some irrelevant features to conflict with important features. In this paper, we deal with limited data and simply extract useful features from images. To make data more numerous and allow for the extraction of just important facial features, we suggest innovative face cropping, rotation, and simplification procedures and advocate using the Transfer Learning technique to construct DCNN for building a very accurate FER system. By replacing the dense top layer(s) with FER, a pretrained DCNN model is adopted, and the model is then modified with facial expression data. The training of the dense layer(s) is followed by adjusting each of the pre-trained DCNN blocks in turn. This new pipeline technique has gradually increased the accuracy of FER to a higher degree. On the CK+ and JAFFE datasets, experiments were run to assess the suggested methodology. For 7-class studies on the CK+ and JAFFE databases, high average accuracy in recognition of 99.49% and 98.58% were acquired.

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Research Paper

Optimized Load Balancing using Software-Defined Networking (SDN)

Genetu Y. Basena* , P. V. G. D. Prasad Reddy**

*-** Department of Computer Science and Systems Engineering, Andhra University, Vishakhapatnam, India.

Basena, G. Y., and Reddy, P. V. G. D. P. (2022). Optimized Load Balancing using Software-Defined Networking (SDN). i-manager’s Journal on Computer Science, 10(2), 20-26. https://doi.org/10.26634/jcom.10.2.19046

Abstract

In a cloud environment, handling user service requests and providing the requested resources fairly is critical. Load balancing is important to distribute service requests fairly to an unloaded server and to dynamically maintain load balancing across server farms. In conventional internet protocol (IP) networks, maintaining load balancing is a stubborn and non-adaptable task due to the forwarders' lack of global topology representation. Software-Defined Networking (SDN) provides centralized decision-making for any topological changes to manage changes dynamically. To solve the above problem, we propose a new server-side load balancing strategy that provides an efficient and effective server management scheme for SDN Open Flow networks. Experiments were done on the Ryu controller and the Mininet emulator showed that the performance was better than what was already available.

References

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Research Paper

Brain Tumor Detection & Segmentation using Deep Learning

Daksh Ailawadi* , Nipun Agarwal, Parth Agarwal*, Manish Rana****

*-**** Department of Computer Science, Thakur College of Engineering and Technology, Mumbai, India.

Ailawadi, D., Agarwal, N., Agarwal, P., and Rana, M. (2022). Brain Tumor Detection & Segmentation using Deep Learning. i-manager’s Journal on Computer Science, 10(2), 27-33. https://doi.org/10.26634/jcom.10.2.19067

Abstract

The goal of this work is to identify brain tumors and improve care for those who are suffering. Tumors are the term used to denote abnormal cell growth in the brain, while cancer is the term used to describe malignant tumors. Brain cancer regions are typically discovered via Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) scans. For the detection of brain tumors, further methods include molecular testing, lumbar puncture, cerebral angiogram, and positron emission tomography. Images from an MRI scan are used in this study to analyze the disease stage. The goals of this research are to segment the tumor region and to identify the abnormal image. The segmented mask can be used to evaluate the tumor's density, which will aid in treatment. ResNet algorithm is used to analyze MRI pictures and find anomalies.

References

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[10]. Somasundaram, S., & Gobinath, R. (2019). Early Brain Tumour Prediction using an Enhancement Feature Extraction Technique and Deep Neural Networks. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(10).

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[12]. Suresh, M., Sinha, A., & Aneesh, R. P. (2019). Real- Time Hand Gesture Recognition Using Deep Learning. International Journal of Innovations and Implementations in Engineering, 1, 11-15.

[13]. Thanveersha, M., Jayni, J., Fathima, T., & Sinha, A. (2019). Automatic brain hemorrhage detection using artificial neural network. International Journal of Innovations and Implementations in Engineering.

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Concept paper

Language Detection using Multinomial Naïve Bayes Algorithm

Yashvi Vaghasiya* , Diya Vora, Nehayadav*, Manish Rana****

*-**** Department of Computer Engineering, Thakur College of Engineering and Technology, Mumbai, Maharashtra, India.

Vaghasiya, Y., Vora, D., Nehayadav, and Rana, M. (2022). Language Detection using Multinomial Naïve Bayes Algorithm. i-manager’s Journal on Computer Science, 10(2), 34-39. https://doi.org/10.26634/jcom.10.2.19014

Abstract

In this multilingual world, automatic detection of written or spoken language using Language Identification (LID) technology is a boon in the global communication with people using different languages in different countries. For simplicity and for the purpose of this research, the process of automatically identifying the language(s) from a document is thought of as LID. Lot of ongoing research projects are in the field of Natural Language Processing (NLP) that uses LID as a part of NLP. This field exploits several algorithms evolved in the field of computer science, individually or in combination to achieve accuracy in identifying a language. Among the different approaches adopted in LID,NaïveBayes Classification n-gram text processing seems to be promising.This paper proposes the concept for categorising multiple language texts using Naïve Bayesian algorithms using Machine Learning approaches. Using techniques from existing researches, this paper proposes a way to recognize multilingual documents and calculate the relative proportions of these languages.

References

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i-manager's Journal on Computer Science (JCOM)

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Volume 10 Issue 2 June - August 2022

Sentiment Analysis and Offensive Language Detection in Social Media

M. Shiny*

Department of Computer Science and Engineering, DMI College of Engineering, Tamil Nadu, India.

Shiny, M. (2022). Sentiment Analysis and Offensive Language Detection in Social Media. i-manager’s Journal on Computer Science, 10(2), 1-7. https://doi.org/10.26634/jcom.10.2.19145

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Transfer Learning Based Effective Emotional Face Recognition using DCNN via Cropping Techniques

Anjani Suputri Devi D.* , Suneetha Eluri**

*-** Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Kakinada, Andhra Pradesh, India.

Devi, D. A. S., and Eluri, S. (2022). Transfer Learning Based Effective Emotional Face Recognition using DCNN via Cropping Techniques. i-manager’s Journal on Computer Science, 10(2), 8-19. https://doi.org/10.26634/jcom.10.2.19059

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Optimized Load Balancing using Software-Defined Networking (SDN)

Genetu Y. Basena* , P. V. G. D. Prasad Reddy**

*-** Department of Computer Science and Systems Engineering, Andhra University, Vishakhapatnam, India.

Basena, G. Y., and Reddy, P. V. G. D. P. (2022). Optimized Load Balancing using Software-Defined Networking (SDN). i-manager’s Journal on Computer Science, 10(2), 20-26. https://doi.org/10.26634/jcom.10.2.19046

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Brain Tumor Detection & Segmentation using Deep Learning

Daksh Ailawadi* , Nipun Agarwal**, Parth Agarwal***, Manish Rana****

*-**** Department of Computer Science, Thakur College of Engineering and Technology, Mumbai, India.

Ailawadi, D., Agarwal, N., Agarwal, P., and Rana, M. (2022). Brain Tumor Detection & Segmentation using Deep Learning. i-manager’s Journal on Computer Science, 10(2), 27-33. https://doi.org/10.26634/jcom.10.2.19067

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Language Detection using Multinomial Naïve Bayes Algorithm

Yashvi Vaghasiya* , Diya Vora**, Nehayadav***, Manish Rana****

*-**** Department of Computer Engineering, Thakur College of Engineering and Technology, Mumbai, Maharashtra, India.

Vaghasiya, Y., Vora, D., Nehayadav, and Rana, M. (2022). Language Detection using Multinomial Naïve Bayes Algorithm. i-manager’s Journal on Computer Science, 10(2), 34-39. https://doi.org/10.26634/jcom.10.2.19014

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Daksh Ailawadi* , Nipun Agarwal, Parth Agarwal*, Manish Rana****

Yashvi Vaghasiya* , Diya Vora, Nehayadav*, Manish Rana****