Reviewerly/exHarmony huggingface dataset:
Sentence Restructuring with User-Controlled Difficulty using NLP

While there exists a substantial body of research focused on the simplification of sentences, research looking for ways to increase sentence complexity is apparently lacking. Understanding the intricacies of complex sentence structure, our research focuses on exploring the principality of sentence restructuring using user-controlled difficulty to enhance language learning and comprehension. By leveraging the WordNet dataset, Google Ngram Viewer, Lesk algorithm and Sentence Transformers, we propose a novel approach that provides users with a personalized restructured sentence. Considering the needs for students willing to pursue higher education, professional, linguistic and cognitive development, a model which helps increase sentence complexity will be fruitful for the users. This will enable users to express themselves more precisely and also develop stronger writing skills for their academic success. The model provided can also benefit individuals with diverse linguistic backgrounds which can aid in cultural exchange and understanding. Thus, to contribute to a deeper understanding to sentence structure, and fostering Natural Language Processing advancements, our research paper paves the way for future innovations in personalized language learning models.
Few-Shot Continual Learning: Approaches and Future Directions

Few Shots Continual Learning (FSCL) is very challenging task, where the model has to continual learn with very few examples. This situation happens in many scientific and engineering domain. For example, an intelligent based system has to give diagnosis of disease and simultaneously learn from new unknown samples. Many researchers basically studied FSL and CL for two different objectives. FSL objective is to learn new task with very few examples, by learning transferable knowledge of some large base dataset and generalize for new task. CL objective is a sequential learning to mitigate catastrophic forgetting. Very less work has been contributed for FSCL as critical and challenging task. In this paper we have contributed intensive theoretical study on FSCL on different approaches to achieve FSL and CL together. Further we gave challenges and limitations associated with FSCL approaches with the potential applications of FSCL.
Skull Stripping in Magnetic Resonance Imaging of Brain Using Semantic Segmentation

Magnetic Resonance Imaging (MRI) is the most extensively used technique for diagnostic assessment of the functioning of the brain. Compared to other neuroimaging techniques like CT and PET, MRI provides more soft tissue variance. Hence MRI provides a proper envision of brain structure. But the presence of tissues which are not part of the brain in the MRI images adversely affect image analysis. Skull stripping removes the non brain tissues in the MRI images of the brain. After skull stripping the analysis of brain region becomes more efficient. Skull stripping can be done in three different ways. Manual skull stripping, by classical segmentation techniques and by deep learning segmentation techniques. The output resulted from manual skull stripping will be used as ground truth for other methods. Lower grade image processing techniques like morphological operations, histogram analysis techniques and region based growing techniques are used in classical segmentation. Skull stripping with deep learning resulted in a better level of accuracy.For performing skull stripping in brain MRI we propose two deep network architectures UNet and ResUNet. UNet network is designed for semantic segmentation. In ResUNet residual learning is added to the UNet. In this study the residual learning is not showing much impact on the performance and UNet shows a better performance over ResUNet . In addition we proposed a new approach to perform skull stripping by replacing the encoder part of UNet model by the pre trained deep network architectures. Pre trained deep models DenseNet121, MobileNetV2, InceptionResNetV2, VGG16 and VGG19 are used and their performance in the skull stripping on the same dataset are compared.
Performance Evaluation of Machine Learning Classification Methods for Sentiment Analysis

The use of Natural Language Processing and text processing along with identification and research of sentiments and emotional states from a specific set of data sets are all part of the burgeoning field of sentiment analysis. Many companies use sentiment analysis for product reviews and social media comments to assess whether content is positive, negative, or neutral. This paper investigates classifiers based on various factors and focuses on the creation of many machine learning classification algorithms as well as their performance analysis. The Weka tool will be used to illustrate sentiment analysis utilising Nave Bayes, Multilayer Perceptron, J48, Random Forest, and OneR classifiers, which are machine learning techniques. All classifiers are tested using a 10-fold cross-validation approach. When the outcomes of the five different classifiers were analysed, the J48 classifier had the best prediction accuracy of 98.59 percent.
NLP based model to convert English speech to Gujarati text for deaf &amp; dumb people

NLP (Natural Language Processing) is a use for process Hu-man Language. It is a combination of language and Artificial Intelligence. This paper is helpful to understand about social neuroscience, Translation, speech to text, NLP levels, challenges and Applications of NLP. Deaf and Dumb people of Gujarat use a Gujarati Language as a speech or sign language, therefore there is a need of translating English to Gujarati to help the people to understand the context of the language.Translation of study findings must be done carefully in order to enable the impact of neuroscientific insights on our everyday lives Deaf and Dumb people use Sign Language for communication, as Normal peopleuses a Speech So This paper includes various sign language and speech recognition module for converting Speech -> text -> sign. It includes Speech recognition API like apiai, assemblyai, google-cloud-speech, pocketsphinx, SpeechRecognition, watson-developer-cloud and wit. It represents the development and testing of speech to text and text to speech mechanism with python code. Translation of study findings must be done carefully in order to enable the impact of neuroscientific insights on our everyday lives. It implements English speech by speech recognition API and translates in a Gujarati text. This paper analyze the Precision, Accuracy and Recall on speech to text Datasets. It gives 96% Precision, 83% Recall and 83% Accuracy.
Transformer and Deep CNN-based Product Recommendation System

A product recommendation system is one of the crucial components of any E-commerce platform. Content-based filtering techniques effectively provide relevant product recommendations based on user actions on the E-commerce platform. However, with the rapid growth of products and product data, content-based filtering faces several challenges, particularly in feature extraction. Conventional feature extraction methods cannot capture the wide range of product characteristics that influence consumer preferences. In addition, as the number of features extracted increases, the computation time and memory requirements also increase, making it difficult to scale the system. In this paper, we propose the use of various deep learning techniques like conventional Natural Language Processing(NLP) methods, transformer-based methods, Convolutional Neural Networks (CNNs), and Autoencoders to develop 12 models for a recommendation system. Our work aims to identify products with similar textual descriptions and visual appearances to generate accurate recommendations. The 12 models are divided into three categories based on the type of feature extraction techniques used. The proposed approach uses self-attention to extract text features and powerful image feature extraction techniques to effectively capture the product characteristics and address the limitations of traditional methods. After extensive experimentation and evaluation through A/B testing, we observed that the best-performing model is based on a transformer, CNN, and autoencoder. The model generated accurate recommendations and achieved significantly higher user ratings, with an average of 9.9 out of 10 points. Also, the latency of the model is very low.
Optimization with Blockchain based Secure Authentication and Collaborative Data Sharing Model in Cloud

Cloud computing has become a critical technology to meet infrastructure and data service needs cost-effectively, with minimal effort, and with a high degree of scalability. It is widespread in many facets. Although the use of cloud computing has increased significantly, concerns about information security have not yet been fully addressed. With the exponential growth of cloud computing, many new web services have emerged, making it difficult for customers to choose the services they want. This research study has attempted to create a powerful framework for data exchange called blockchain-based secure authentication and collaborative data sharing in the cloud to overcome this hurdle. The entities used in this research are third parties, data owners, smart contracts, and blockchain networks. The steps involved in this process are initialization, registration, authentication, data sharing, and decryption. The safety operators are created using Fire Hawks Optimization (FHO). In addition, the evaluation metrics are revenue, storage usage, and compute costs. Finally, the results proved that FHO-based OptiBC-SecAuth-DS achieved maximum revenue, minimum storage usage, and minimum computational costs.
Transformer-Based Models for Named Entity Recognition: A Comparative Study

NER is a critical problem in natural language processing (NLP) that includes detecting and classifying named entities in text. We give a complete investigation on NER using the CoNLL dataset in this research article. We explore the evolution of NER techniques, from traditional rule-based methods to state-of-the-art transformer-based models. Our experiments focus on evaluating the performance of three popular models: BERT, ALBERT, and XLM-RoBERTa, on the CoNLL dataset. We analyze their accuracies and discuss their strengths and limitations. The results demonstrate the effectiveness of transformer-based models, with BERT achieving an accuracy of 99.50%, ALBERT achieving 96.90% accuracy, and XLM-RoBERTa achieving 87.82% accuracy. We discuss the importance of dataset preprocessing, model fine-tuning, and hyperparameter optimization for achieving optimal performance. This research contributes to the understanding of NER techniques and provides insights into the performance of transformer-based models for NER tasks.
A Forensic Analysis Procedure for Dark Web Drug Marketplaces

Dark web marketplaces are areas of huge illegal trading of drugs. Collecting information about the drug markets, categories of products and services, and vendors can help Law Enforcement Agencies (LEAs) to track illegal trading. This work focuses on the collection of Uniform Resource Locators (URLs) of the dark web drug markets and investigates in detail the collected websites, products, and vendors operating on those marketplaces with the help of forensic tools, such as Onion Scan, Exchangeable Image File Format (EXIF), Kleopatra, Maltego, and Forensics Acquisition of Websites (FAW). This work has resulted in marking the genuineness of a few dark websites, the vendors operating, and the payment methods used in dark web marketplaces. Additionally, evidence for postal addresses and email addresses used as modes of delivery and communication is also found. This work provides a roadmap for LEAs through the visual evidence of hidden information in dark web markets.
Exploring Federated Learning with Naïve Bayes using AVC Information

Emerging trends in federated learning have drawn tremendous attentions in recent years where multiple clients collaborate for building a shared learning model without exchanging their own data. This approach ensures privacy, security and ownership of data, making it prevalent for large-scale data analysis. There have been a growing tendency to utilize deep neural networks in federated learning. However, many situations overwhelmed, and they require simpler learning algorithms along with prevention of data leakage. In this work, we propose a federated learning system based on Naïve Bayes algorithm utilizing AVC set. Naïve Bayes is a simple but computationally efficient probabilistic classifier. We leverage the strengths of Naïve Bayes to provide a secure and scalable solution for classification tasks in federated learning environments. To demonstrate the performance of our proposed approach, we conducted experiments on ten real-world benchmark data sets from the UC Irvine Machine Learning Repository. The results of the experiment showed that the Naïve Bayes algorithm enhances privacy preservation and scalability in a federated environment without compromising prediction accuracy compared to centralized machine learning. This study highlights the potential of using Naïve Bayes in federated learning and offers insights into its practical applications in various domains.
USING THE METHODS OF PSYCHOLOGICAL SELF-REGULATION AND PSYCHOLOGICAL CORRECTION IN THE EDUCATIONAL PROCESS (on the example of training police officers at the vocational training center)

The article deals with the main aspects of using the methods of psychological self-regulation and psychological correction in the educational process, formed as a result of practical experience. The integration of psychological techniques into the educational process makes it possible to accelerate the development of professionally important skills, the fulfillment of which requires emotionally difficult conditions, and to form an optimal emotional background for students.
A Novel Model Using Multiple Bagging Ensemble Method For Measuring, Inferring and Predicting the Quality of Continuous Assessment Question Papers

Predictive Machine Learning techniques provide mechanisms for the computing machines to analyze and understand the knowledge inherently embedded in the given dataset. This unique technique can be effectively used in understanding the quality of the question papers compiled for Continuous Assessment Tests (CATs). Understanding the quality of the question papers requires a systematic and software engineering-based approach. In this research work, a few of the CAT question papers have been carefully picked and used for applying the proposed methodology. Well, defined set of features, properties, and quality attributes have been applied in the proposed model and mapped into each other for providing a better understanding of the question paper's quality. Using metrics the analysis of quality for the given CAT paper is attempted. Based on the application of the model, it was able to clearly predict and understand the quality levels of the given set of question papers.
Attention to Traffic: Network Traffic Classification using Attention-Based CNNs

Due to the worldwide lockdown amidst the COVID-19 crisis, which forced companies, individuals as well as network infrastructures to adapt to the new normal of Work From Home (WFH), Internet Service Providers (ISPs) reported about a 60% increase in Internet traffic when compared to with the data before the pandemic. Consequently, network traffic categorization has become a critical topic in computer networking since it is crucial for activities like bandwidth allocation, resource management, intrusion detection, and virus detection. This work attempts to predict network flows’ bandwidth need and duration using the public QUIC dataset. Many applications benefit from knowing the bandwidth demand and duration. They allow for efficient resource allocation, a better quality of service and faster routing. In the past, traditional ML approaches, such as SVM and Random Forest algorithms as well as deep learning algorithms, such as Deep Clustering, have been implemented to solve the problem of network traffic classification. We show that our Attention-Based Convolutional Neural Network outperforms existing approaches which have relied on traditional Machine Learning, Transfer Learning and Multitask Learning techniques. The proposed model achieves accuracy scores of 96% (Bandwidth data) and 94.30% (Duration data) for five-class classification. We observe a significant increase of up to 6% compared to previous results.
A Comparative Study of Undersampling and Oversampling Methods for Flood Forecasting in Bangladesh using Machine Learning

Catastrophic floods can inflict devastating destruction on human life, agricultural lands, buildings, and economies. Bangladesh is highly vulnerable to this natural calamity, with varying levels of destruction occurring year after year. Accurately predicting flood occurrence could save Bangladesh from this destruction, but due to imbalanced data, this is nearly impossible. Machine learning models become biased towards only one class because of imbalanced data, leading to incorrect predictions. To address this issue, we employed different undersampling and oversampling techniques for data balancing and then compared the effectiveness of these techniques for predicting flood occurrence in Bangladesh using various machine learning models. The results highlight the Random Forest Classifier with Random Oversampling as the most successful combination, achieving an accuracy rate of 97.62%, precision of 96.09%, recall of 99.34%, F1-score of 97.69%, and ROC score of 97.61%. Because of Random Forest Classifier’s ensemble learning ability, feature handling, and resistance to overfitting, it outperforms other techniques when paired with Random Oversampling to solve class imbalance.
Machine learning-based text to speech conversion for native languages

Marathi is one of India's most ancient languages. This research project (TTS) focuses at the growth of the Marathi Text-to-Speech system. Marathi text produced in either Devanagari or Ekalipi script is used as input for Marathi TTS. The Text to Speech system's purpose is to convert any given text into speech. In order to mimic human speakers, speech synthesis involves creating technology that can produce human-like speech from any text input. Two key elements of a text-to-speech system are text processing and speech production. It is crucial that the text processing component provides an adequate sequence of phonemic units in order to construct a speech synthesis system that produces natural-sounding speech. The Hidden Markov Model is used with the statistical parameters for speech synthesis. This research is conducted to improve the pronunciation accuracy. The primary goal of this research is to improve and fine-tune the TTS system's pronunciation accuracy for native Indian languages like Marathi and make it seem more natural than current TTS systems (like how humans speak). The WASRAW ("Write as Spoken, Read as Written") technique is what the TTS system uses to speak written text. The objective is to create a language-independent solution as opposed to current solutions, particularly with regard to pronunciation. Unique script i.e., Ekalipi script has been used along with Devanagari script for Marathi language. The proposed model has been tested for 35 Marathi speakers for Devanagari script and achieved MOS of 4.38 for speech intelligibility, MOS of 4.45 for pronunciation accuracy and MOS of 4.44 for naturalness. Also, for Ekalipi script it has been tested for around 10 users who are familiar with the script. The dictionary-based approach has been followed in order to speak Ekalipi words. It has achieved good results for simple words written in Ekalipi. Even though the system is able to synthesize Ekalipi words correctly, there is still a need for a generalized model. As a future work, rules can be defined and the generalized model can be implemented for Ekalipi script and can be tested with the more no users and more no of languages can be added.
Identifying Anomalous Indus Texts from West Asia Using Markov Chain Language Models

The Indus Valley civilization thrived during its mature period between 2500 BCE and 1800 BCE. and traded with the bronze age civilizations of West Asia in Mesopotamia and the Persian Gulf region. During this period, the Indus civilization developed a logosyllabic writing system that remains undeciphered. In our research, we built various Markov chain language models from the Indus texts corpus. Using a best-fit language model, we calculated the model perplexity for each Indus text found in the West Asian region. Our results show that the model perplexity was high for a majority of West Asian Indus texts and that these texts did not fit in well with the language model built with the Indus texts from just the Indian subcontinent. From this, we surmise that West Asian Indus Texts were written in a different language or syntax than those from the Indian subcontinent.
A Brief Study on Cloud Security

Distributed, parallel, and grid computing are all used in a kind of computing known as cloud computing. It serves as a flexible, affordable, and tried-and-true online delivery platform for IT services marketed to businesses or consumers. The outsourcing of critical services to external entities has become a common practice, and this trend has led to various challenges in ensuring the security and privacy of data, maintaining data availability and recovery, and ensuring compliance. Cloud computing, in particular, has added a new layer of complexity to these issues and has introduced additional risks. Cloud computing makes use of many different technologies. Additionally, it carries along its inherent security flaws. Customer access to networks, storage, servers, services, and applications is made possible by cloud technology thanks to how well the pay-per-use model’s services function. Our main goal is to locate and connect these systems’ weaknesses, threats, and remedies.
A Decentralized BYOD Authentication System secure against ReEntrancy Attacks

The concept of BYOD has been around in the corporate world for a while now which allows employees to bring and use personal devices for organizational work. However, the onset of such a trend also brought major security issues leading to compromise in the organization’s data and resources. Current solutions to such security issues do exist, nevertheless, most of them are bound by constraints and may also face backlash from the workforce community. Blockchain technology has the potential to address the security threats associated with BYOD by providing decentralized authentication, immutable records and employing signature-based algorithms. In this paper, an Ethereum-based blockchain application has been proposed using smart contracts to authenticate employees’ devices and detect unauthorized access. This paper also explains how blockchain can be adopted to address the issue of ReEntrancy attacks where a malicious user can exploit vulnerabilities in smart contracts and steal funds from decentralized applications.