Cyberbullying (Hate speech and offensive language) detection using Machine Learning

This thesis addresses the detection of cyberbullying by employing cutting-edge machine learning approaches to distinguish offensive language and hate speech. The escalating issue of cyberbullying in digital communication necessitates robust detection systems for online safety. Emphasizing on dividing textual content into categories such as offensive language, non-hate speech, and hate speech, using machine learning models like Random Forest, AdaBoost, Decision Trees, Long Short-Term Memory (LSTM), Bidirectional Encoder Representations from Transformers (BERT), and RoBERTa (Robustly optimized BERT approach). Each algorithm offers unique strengths in language processing. Decision Trees provide basic, interpretable classification rules, while ensemble methods like Random Forest and AdaBoost enhance accuracy through combined decision-making. LSTM excels in sequential data analysis, capturing contextual nuances. BERT and RoBERTa offer advanced deep learning capabilities, with RoBERTa building upon BERT's architecture for improved performance through optimized training and larger datasets. Bidirectional text analysis helps BERT and RoBERTa both grasp context. Training the model, feature extraction, and data preprocessing are all included in the research technique. Accuracy, precision, recall, and F1-score metrics are used to assess model performance. The results reveal the effectiveness of these models in differentiating between the categories, with BERT and RoBERTa showing notable proficiency due to their advanced contextual analysis. By highlighting the potential of various machine learning techniques in tackling difficult online communication problems, this work advances the field of cyberbullying research and facilitates the creation of safer digital interaction technologies

LAUGHTER PREDICTION IN TEXT BASED DIALOGUES Predicting Laughter using Transformer-Based Models

In this paper we will attempt to predict and assess the performance of predicting laughter using a BERT model (Devlin et al., 2019), and a BERT model finetuned on the Open subtitles dataset with and without considering dialogue-acts classes as well as sliding window of dialogues. We hypothesize that fine tuning a BERT on the open subtitles might increase the performance. Our results will be compared with those of Maraev et al., 2021a paper which show predicting actual laughs in dialogue and address it with various deep learning models, namely recurrent neural network (RNN), convolution neural network (CNN) and combinations of these. The Switchboard dialogue Act Corpus (SWDA), Jurafsky et al., 1997a) (US English, phone conversations where two participants that are not familiar with each other discuss a potentially controversial subject, such as gun control or the school system) is processed first in the project to make it appropriate for the BERT model. We then analyze dialogue acts within the Switchboard Dialogue Act Corpus with their collocation with laughter and supply some qualitative insights. SWDA is tagged with a collection of 220 dialogue act tags which, following Jurafsky et al. (1997b), we cluster into a smaller set of 42 tags. The major purpose of this research is to show that a BERT model would outperform the Convolution Neural Network (CNN) and Recurrent Neural Network (RNN) models presented in the IWSDS publication

Realization of Fractance Device using Continued Fraction Expansion Method

The realization of fractional-order circuits is an emerging area of research for people working in the areas of control systems, signal processing and other related fields. In this paper, an attempt is made to realize fractance devices. The continued fraction expansion formula is used to calculate the fractance device's rational approximation. For the simulation in the experimentation, the third-order approximation for fractional order, α = -1/2, -1/3, -1/4 is used. For the aim of mathematical simulation, the MATLAB platform was used. The proposed rational approximation is used to create a circuit. The TINA programme is used to simulate circuits. It has been discovered that the simulation and theoretical conclusions are in agreement

Low Power Dissipation in Johnson Counter using DFAL Technique

This paper presents a new method for minimizing power dissipation in 4-bit Johnson counter in which Diode-Free adiabatic Logic(DFAL) is used.Power dissipation of the diodes is eliminated by removing diodes from charging and discharging path.Performance of the proposed logic is analyzed and compared with that of CMOS based circuits. All the simulation are carried out in VIRTUOSO spectre simulator of CADENCE 90nm technology .The paper provides low power dissipation using DFAL logic,which has shown better improvement than conventional CMOS design

Vorinostat Eliminates Multicellular Resistance of Mesothelioma 3D Spheroids via Restoration of Noxa Expression

When grown in 3D cultures as spheroids, mesothelioma cells acquire a multicellular resistance to apoptosis that resembles that of solid tumors. We have previously found that resistance to the proteasome inhibitor bortezomib in 3D can be explained by a lack of upregulation of Noxa, the pro-apoptotic BH3 sensitizer that acts via displacement of the Bak/Bax-activator BH3-only protein, Bim. We hypothesized that the histone deacetylase inhibitor vorinostat might reverse this block to Noxa upregulation in 3D. Indeed, we found that vorinostat effectively restored upregulation of Noxa protein and message and abolished multicellular resistance to bortezomib in the 3D spheroids. The ability of vorinostat to reverse resistance was ablated by knockdown of Noxa or Bim, confirming the essential role of the Noxa/Bim axis in the response to vorinostat. Addition of vorinostat similarly increased the apoptotic response to bortezomib in another 3D model, the tumor fragment spheroid, which is grown from human mesothelioma ex vivo. In addition to its benefit when used with bortezomib, vorinostat also enhanced the response to cisplatin plus pemetrexed, as shown in both 3D models. Our results using clinically relevant 3D models show that the manipulation of the core apoptotic repertoire may improve the chemosensitivity of mesothelioma. Whereas neither vorinostat nor bortezomib alone has been clinically effective in mesothelioma, vorinostat may undermine chemoresistance to bortezomib and to other therapies thereby providing a rationale for combinatorial strategies

Surveying the role of visual analytics in human-machine teaming

Humans and machines both possess their unique capabilities and have their strengths and weaknesses, which can be complementary to one another and allow them to achieve a common goal. Teaming in the modern era involves text prompts, voice commands, gesture recognition, touch interfaces, and the latest visualization techniques that allow parties/agents to interact. Communication through visualization plays a vital role in allowing robust insights to be gained through a glance. Using visualization as a medium between humans and machines can increase the communication bandwidth. Human-machine teaming has witnessed much progress, with many theories and practical examples emerging. In the report, the goal is to provide a systematic analysis of theories, techniques, and methods used to approach the human-machine interface. This review also presents some limitations and open challenges in the literature of Human-Machine Teaming and Interactions using multiple view visualizations

Anti-B7H3 Monoclonal Antibodies Induce Natural Killer Cell and T lymphocyte- Mediated Apoptosis in Triple Negative Breast Cancer

https://openworks.mdanderson.org/sumexp21/1232/thumbnail.jp

A novel automated deep learning approach for Alzheimer's disease classification

Alzheimer's disease is a degenerative brain illness, incurable and progressive. Globally for every two seconds, someone is affected by Alzheimer's disease. Alzheimer's disease in the elderly is difficult to diagnose due to the complexity of the brain structure. Its pixel intensity is similar and systematic distinction is necessary. Deep learning has inspired a lot of interest in recent years in tackling challenges in a variety of fields, including medical imaging. One of the drawbacks of deep learning approach is the inability to detect changes in functional connectivity in mild cognitive impairment (MCI) patients' functional brain networks. In this paper, we utilize deep features extracted from two pre-trained deep learning models to tackle this issue. The proposed models DenseNet121 and MobileNetV2 is used to perform the task of Alzheimer's disease multi-class classification. In this method, initially we increased 70 % of dataset and generated images by using cycle generative adversarial networks (CycleGAN). We achieved 98.82% of accuracy with proposed models. It gives best results compared to existing models