Reviewerly/exHarmony huggingface dataset:
Text Classification using Machine Learning Algorithms on Encrypted Data

Text classification is a fundamental activity in natural language processing (NLP), that includes classifying text resources into predetermined groups. With the growing concerns over data privacy and confidentiality, there is a need to perform text classification on encrypted data to protect sensitive information. In recent years, homomorphic encryption has emerged as a powerful technique that makes computations possible on encrypted data without compromising its confidentiality. This paper provides an overview of text classification using machine learning algorithms on encrypted data. In this research work, we have taken a data set of movie reviews. The data set is cleaned by removing stop words and performing stemming, lemmatization, and tokenization. We utilized a semantic model with inverse document frequency weighting and term frequency weighting to represent the data (TF-IDF). For the classification of the data set, we have applied six machine learning algorithms Logistic Regression, Naïve Bayes, KNN, Support Vector Machine, Random Forest, and Decision Tree. The plain text is encrypted using a somewhat homomorphic encryption technique called the RSA method. The ciphertext is subjected to the machine learning algorithms mentioned above. We have compared the results for accuracy. The Random Forest algorithm was found to give 75.56% accuracy compared to other algorithms on encrypted data. In this work, we have calculated the time taken by each algorithm for execution on plain text and ciphertext. It was observed that the Random Forest method took more time and the KNN method took less time comparatively.
New Environment Exploration Algorithm combining Bug, Free Segment and Turning Point Algorithm for Multi-Robot System

Exploration and mapping of an unknown environment is a challenging task and using a single robot is time consuming and inefficient. Multi Robot System(MRS) is used to reduces exploration time as well as to cover larger areas and produces accurate maps. This work focuses on development of new environment exploration algorithm for multi robot system which combines the features of Bug, Free segmentation and turning point algorithms. The Proposed algorithm utilizes capabilities of three mobile robots and tested on simulated outdoor environment with static obstacles. Two sub robots are equipped with LiDAR sensors and are used to explore the region between initial and target location. The point cloud generated by LiDAR sensors of the two robots is shared with the main/mother robot to generate an occupancy map. Mother robot execute $\mathrm{A}^{*}$ path planning algorithm on the occupancy map to find the shortest path and moves towards the target location. Proposed algorithm has low computational requirements and significantly reduces the time required to explore the given environment. Further, the performance of the proposed algorithm is compared with existing algorithms in terms of the time required to explore the environment and generate occupancy map.
Theoretical Insights into Neural Networks and Deep Learning: Advancing Understanding, Interpretability, and Generalization

This work aims to provide profound insights into neural networks and deep learning, focusing on their functioning, interpretability, and generalization capabilities. It explores fundamental aspects such as network architectures, activation functions, and learning algorithms, analyzing their theoretical foundations. The paper delves into the theoretical analysis of deep learning models, investigating their representational capacity, expressiveness, and convergence properties. It addresses the crucial issue of interpretability, presenting theoretical approaches for understanding the inner workings of these models. Theoretical aspects of generalization are also explored, including overfitting, regularization techniques, and generalization bounds. By advancing theoretical understanding, this paper paves the way for informed model design, improved interpretability, and enhanced generalization in neural networks and deep learning, pushing the boundaries of their application in diverse domains.
Toward more Robust Skull Striping using Custom transformer and Combined Hausdorff loss

Skull stripping is a crucial pre-processing step in medical imaging analysis and has been widely studied using various techniques. Deep learning networks have proven to be effective in many image segmentation tasks, including skull stripping. This study compares four popular deep learning networks, namely U-Net, SegNet, PSPNet, DeepLab, and a novel Vision Transformer for skull stripping. The performance of each network was evaluated on the skull stripping dataset, and the results were analyzed based on various metrics. We also propose a novel loss function to improve model performances. The new loss function uses a combination of shape-aware loss and regional losses to improve the performance of the vision transformer. The study found that Vision Transformer performed significantly better in terms of correctly classifying boundary pixels than non-boundary pixels than the state-of-the-art networks when trained on the new loss function metrics. Furthermore, these findings provide valuable insights into the performance of different deep-learning networks for skull stripping and can aid in the selection of an appropriate network.
A Stacked Ensemble-Based model for the prediction of breast cancer using Decision Tree and XGBoost

Breast cancer is one of the most common and lethal types of cancer affecting women worldwide. In 2020, the World Health organization (WHO) predicts that 2.3 million women will receive a breast cancer diagnosis, and 685,000 individuals will pass away from the condition. Early detection of breast cancer can greatly improve treatment outcomes, increasing the likelihood that the patient will survive. Machine learning algorithms have been shown to be effective in predicting breast cancer. In this study, we explore the use of ensemble technique for the prediction of breast cancer using the WDBC dataset. The dataset has 569 instances and 30 attributes that describe the radius, texture, perimeter, area, smoothness, compactness, concavity, symmetry, and the fractal dimension of breast mass. We achieved a remarkable accuracy of 97.66%. This study highlights the potential of decision tree and XGBoost algorithms in predicting breast cancer and could aid in early detection, leading to better treatment outcomes.
An Innovative Examination of Tensor Blending Network for Multi-Model Sentimental optimization Using Opinion Mining

The Tensor Blending Network (TBN) for multi-model sentimental optimization is a novel approach for fusing the outputs of multiple models in order to maximize the performance of sentiment analysis. TBN is based on an encoder-decoder architecture consisting of a convolutional neural network (CNN), a Long Short Term Memory (LSTM) RNN, and a deep-learning model for sentiment classification. TBN uses attention mechanisms to blend the output of each model, focusing on the most relevant parts to extract the desirable sentiment analysis. The framework of TBN is also designed in a way that allows for easy deployment in different contexts, as the user can fuse two predefined models or multiple individual ones. Additionally, the TBN architecture can be used as a method to improve the accuracy and precision of sentiment analysis algorithms by integrating different sources of knowledge. Finally, the performance of TBN has been tested on a number of datasets, obtaining in most cases the highest accuracy and precision, proving the advantages of using this framework.
Multimodal Sarcasm Recognition by Fusing Textual, Visual and Acoustic content via Multi-Headed Attention for Video Dataset

Multimodal sarcasm recognition uses a combination of acoustic, video, and text-based cues to detect sarcasm. Though multimodal approaches have outperformed unimodal detection by providing a more comprehensive description of the speaker’s sentiment, they are particularly challenging as cues may not be consistent across the multiple modalities. In our research study, we propose a system that first extracts multimodal features from the input provided and then applies a bimodal multi-head attention mechanism to them. Subsequently, the features are concatenated and passed through a softmax layer for detection. The proposed model is evaluated on the MUStARD dataset for multimodal sarcasm recognition. For the speaker-dependent configuration, the proposed model beats cutting-edge methods in terms of accuracy, precision, recall, and F1-score by 0.75%, 2.9, 2.82, and 3.1, respectively.
Tumor Detection using Deep Learning in Organs Specific to Indian Predicament

Herein, a research study has been conducted to address the increasing prevalence of serious diseases, including uterine, breast, and colon cancer, particularly in Kerala, as identified through studies done by the state Government. The objective of this research work is to contribute to the urgent need for cancer detection, that would then help to reduce mortality rates and facilitate effective screening practices within the Indian context. To ensure a comprehensive analysis, a diverse dataset has been curated from multiple reputable web sources. The dataset consists of various types of medical imaging data, such as MRI scans, CT scans, and histopathological images, obtained from reliable repositories such as SipakMed, the National Cancer Institute, figshare, and the Cancer Imaging Archive (TCIA), among others. The dataset encompasses a wide range of cancer types and organ locations, including brain, breast, cervix, lung, colon, kidney, leukemia, and lymphoma. Each image in the dataset has been meticulously classified based on essential attributes, including tumor malignancy, location, and other relevant medical terminologies. In the research paper, several deep learning techniques, namely ResNet50, MobileNet, and VGG16, have been applied to perform tumor detection across multiple organs [3]. Through an extensive analysis, a comparative evaluation of the performance of each technique has been conducted, resulting in valuable insights for the effective utilization of deep learning in tumor detection across diverse organ types.
The Smart Performance Enhancement of Utterance Intensity Sentimental Optimization for Social Data Harvesting in Big Data Management

Utterance intensity is an important factor in emotion recognition, sentiment analysis, and natural language processing. Over the past few years, researchers have developed various optimization techniques to improve the accuracy of social data harvesting, such as sentiment analysis, by utilizing utterance intensity levels. Specifically, these techniques use various supervised and unsupervised techniques to learn how to classify different intensities of utterances in order to better detect sentiment and emotion of text. In addition, they look at how different sentiment intensities affect the overall sentiment of a conversation. By utilizing utterance intensity optimization techniques, researchers hope to improve the accuracy of sentiment analysis, thus improving the accuracy of social data harvesting. This paper discusses the state-of-the-art techniques for understanding and optimizing the utterance intensity in social data harvesting, their current limitations and potential future research directions.
YouTube Universe of Comments: A Machine Learning approach for systematic classification of YouTube Comments on custom prepared dataset

At present, YouTube can be regarded as a cloud service owing to the amount of data it adds every second and the enormous data it stores in its data farms. It doesn’t delete old content, it uses redundant storage. The platform can be more sustainable and cost efficient, if they were to discard redundancies of which major portion is constituted by the spam comments or comments that are offensive/abusive. In this paper several machine learning models are used in order to reduce those comments and eventually towards a more efficient storage model. We first address the task of dataset preparation by designing a comprehensive annotation scheme, considering various dimensions such as sentiment, topic, toxicity, and engagement. Leveraging this annotated dataset, we develop a robust machine learning framework that combines state-of-the-art natural language processing techniques with advanced classification algorithms. Our methodology involves several stages, including preprocessing, feature extraction, and model training. We also employ techniques like sentiment analysis and toxicity detection to capture the sentiment and abusive nature of comments, respectively. We also introduced gravity to the comments which would act as a reward mechanism to the comments. To evaluate the performance of our approach, we conduct extensive experiments on a large-scale YouTube comments dataset. We compare the effectiveness of various classification algorithms, including support vector machines, random forests, and deep learning models, in accurately categorizing comments based on our predefined annotation scheme. Additionally, we assess the generalizability of our model by conducting cross-domain experiments on different genres of YouTube videos. Overall, our work contributes to the understanding and management of the YouTube comment ecosystem, showcasing the power of machine learning techniques in systematically classifying and analyzing comments on this popular platform.
Machine Learning-based Automated System for Subjective Answer Evaluation

An examination is a useful tool for assessing students’ knowledge. Evaluation of exams is a difficult and time-consuming process. The automatic examination of answer scripts makes this task easier for teachers, reducing the amount of effort and time required. The existing literature has a number of methods that have been proposed for evaluating responses to objective questions using machine learning. However, more work needs to be done on evaluating answers to descriptive questions. This study suggests a way to evaluate students’ answers to questions of a descriptive kind without using traditional paper or pencil by teachers. Instead, a computer acts as a teacher and grades the students’ submissions. The primary objective is to communicate the outcomes of subjective responses using Bidirectional Encoder Representations from Transformers (BERT), cosine, and Jaccard distance. The proposed model achieved an accuracy of 91%, an error of 9.01, a precision of 83%, and a recall of 79%, respectively. The suggested model has provided the best results in comparison with state-of-the-art systems.
Deep Learning-Based Analysis of Blood Smear Images for Detection of Acute Lymphoblastic Leukemia

Leukemia, a type of cancer affecting the blood and bone marrow, involves the abnormal production of leukocytes and can impact the immune system. While more prevalent among children, it can also affect adults. Early detection plays a critical role in effective treatment and patient recovery. In this paper, we have used an open source four-class Acute Lymphoblastic Leukemia (ALL) dataset that has been segmented using color thresholding. Subsequently, these images have then been trained on pre-trained Convolutional Neural Networks (CNNs): ResNet-50 and ResNet-101, with hyperparameter tuning to classify between benign and three stages of malignant ALL lymphoblast cells. The results demonstrate that our proposed method achieved accuracies exceeding 98% in detecting ALL, indicating the potential of deep learning-based classifiers in aiding hematologists accurately detect ALL and improving patient outcomes.
Tableau-Based Data Visualization and Regression Analysis of Landcover and Climatic Trends

Visualization is a key technology for analyzing and presenting vegetation cover and climatic changes. Interactive visualization has emerged as a popular and effective approach for communicating complex data and insights related to climate change. This project focuses on the interactive visualization of land cover and climate changes using conventional maps and graphs, with a particular focus on the districts of Rajasthan. By utilizing the data provided by the Bhuvan geǒ platform, this study aims to identify trends in climate and land cover over the past 30 years. The datasets collected for this study include information on climatic aspects such as temperature, Earth surface temperature, humidity, and precipitation for the cities of Jodhpur, Bikaner, and Jaisalmer, as well as land cover data for the major districts in Rajasthan. The land cover includes data on croplands, forests, builtup lands, and water bodies which were collected for the years 2011-12 and 2015-16. The use of Tableau software provides an interactive platform for analyzing and presenting data, making it easier for stakeholders and policymakers to understand the data and make informed decisions. Linear regression is used to establish relationship and predict the climatic changes corresponding to change in years. This study aims to provide an accurate representation of the data, including detailed information about the source, in a way that makes it easy to understand complicated data relationships and datǎ driven insights. Through interactive data visualization, this study aims to identify how land cover and climate trends are changing over time and to identify potential causes for these changes. The final result is the visualized data report which helps to identify the trends in climate and land cover.
On-Device Application Categorization using Application Resources

Recent advancements in mobile devices resulted in a phenomenal growth in the usage of smartphones. Applications for these smartphones can be downloaded from the play store. The Android Play Store presently has over 2.5 million applications, a figure that has been growing significantly over the previous several years. As a result, the number of applications downloaded by customers has increased significantly. With such a surge in application (app) production and usage, it becomes necessary to classify or organize applications according to the services they provide. Classification helps to facilitate navigation and makes it convenient to search for the needed application. Hence, research has been conducted on the navigability, grouping, and search capabilities of these devices’ apps. In particular the current technique to app classification is manual labeling or data extraction from the app store. These strategies are inefficient in terms of time and resources, and they are also inefficient in terms of scalability. In this paper a novel architecture is proposed for classifying mobile applications. Our work solves the classification problem using information contained in the application packages (APKs), eliminating the need for external dependencies and allowing processing to take place entirely on-device. Our proposed system utilizes machine learning and deep learning techniques for text classification using the string.xml files. We have also automated the generation of datasets directly from the APKs. Experimental results confirm that the proposed solution shows improved results than the existing systems.
Emoji Recommendation System Using Deep Learning Algorithms

Emojis are becoming increasingly prevalent in everyday online communication such as messaging, email, and social networking. To enhance the user experience of expressing emotions and conveying information through emojis, various techniques have been developed. Our proposed system aims to analyze chat conversations, identify different emotions or topics of discussion, and suggest emojis that align with the context of the conversation. Our model considers contextual and personal information derived from the user’s chat conversations to predict appropriate emojis. The emoji recommendation system is designed with several key considerations in mind. Firstly, our model considers entire conversations, rather than relying on individual sentences to predict emojis. To suggest suitable emojis based on the chat context, our emoji prediction system should take into account multiple previous messages. This includes not only the text and emojis exchanged between participants but also the identity of the speaker and the sequence of sentences within the chat. Secondly, our model seeks to capture different conversational contexts. Conversations can convey a broad range of emotions, information, and feelings. As such, the selection of emojis should be based on the specific context of the ongoing chat, and our recommendation model should offer users a range of emojis that align with different contexts.
Combating Hate Speech on Q&amp;A Forums with Machine Learning

Online forums are becoming increasingly popular, increasing the demand for efficient ways to handle user-generated information. In order to identify hate speech in user-generated content, this project offers a question-and-answer forum that employs machine learning (ML) algorithms and Natural Language Processing (NLP) techniques. The forum is built with the MERN stack, which includes MongoDB, Express, React, and Node.js. Several NLP strategies were implemented in the project, including feature vectorization using count vectorizer, TF-IDF vectorizer, and word2vec. Logistic regression (LR), support vector machines (SVM), decision trees (DT), and naive Bayes (NB) are the ML methods used to detect hate speech. The classification report is used as an evaluation metric to assess the performance of these methods. For model training, the “Hate Speech and Offensive Language Dataset” hosted on Kaggle and freely accessible to the public is used. The findings of the experiments show that the LR model with the count vectorizer performs better than the other model and provides a maximum accuracy of 90%.
ALATS: Analysis of localization algorithms in terrestrial surveillance bots

Effective surveillance is crucial for optimal security and autonomy. It has led to the development of various surveillance systems, such as smart cameras, bots, and drones, with diverse applications. To achieve an efficient surveillance system for multiple use cases, a proposed solution involves the creation of a terrestrial bot. This Surveillance Bot is a mobile unit designed to monitor the environment, detect objects (including human intruders) using smart cameras, and provide crucial data like temperature and humidity for remote surveillance. The bot employs simultaneous localization and mapping (SLAM) techniques, specifically the bug2 algorithm, to navigate autonomously and avoid obstacles. Object detection and human intruder detection algorithms are implemented, considering factors like accuracy, video feed lag, and algorithm robustness. Experimental results demonstrate the bot’s high accuracy in object detection (mean average precision of 33.7) and its frame rate performance. For continuous video recording and analysis, the bot utilizes the YOLOv7 real-time object identification algorithm, enhancing its surveillance capabilities by enabling real-time tracking of identified objects. The combination of YOLOv7 with the bot’s autonomous movement and mapping abilities creates a powerful and efficient surveillance solution. The proposed work aims to develop a smart surveillance robot suitable for military and civil applications, ensuring comprehensive surveillance.