Reviewerly/exHarmony huggingface dataset:
Analysis of Dataset balancing Techniques for Phishing Dataset

One of the major aspects that affects the performance of a machine learning based algorithm is the dataset. A huge dataset is not the only parameter that will improve the performance of the classifier but the balance between the classes for classification is also a crucial factor. An unbalanced dataset will be biased towards the prediction for majority class samples. The classifier trained on unbalanced dataset is more likely to predict false positive or false negative results for minority class samples. The problem of unbalanced dataset is observed in phishing dataset since the number of genuine web URLs is more as compared to the fake URLs. This paper discusses 8 different techniques like SMOTE, SMOTE+Tomek, SMOTE+OSS, SMOTE+ENN, ADASYN, ADSYN+Tomek, ADASYN+OSS, ADASYN+ENN that can be used to generate a balanced dataset and evaluates the them based on the performance of KNN and Random forest classification models that are trained using the original and balanced dataset.
An Empiric Study on Bangla Sentiment Analysis Using Hybrid Feature Extraction Techniques

In this modern technologically advanced world, Sentiment Analysis (SA) is a very important topic in every language due to its various trendy applications. But SA in Bangla language is still in a dearth level. This work focuses on examining different hybrid feature extraction techniques on Bangla SA using a new comprehensive dataset of 203,493 comments collected from various microblogging sites. In this study, we have implemented 21 different hybrid feature extraction methods including Bag of Words (BOW), N-gram, TF-IDF, TF-IDF-ICF, Word2Vec, FastText, GloVe, Bangla-BERT etc with CBOW and Skipgram mechanisms. The proposed novel method (Bangla-BERT+Skipgram) outperforms all other feature extraction techniques in machine leaning (ML), ensemble learning (EL) and deep learning (DL) approaches. The (Bangla-BERT+Skipgram) method achieved highest 92.37%, 92.55% and 95.71% accuracy in ML, EL and DL algorithms.
Smart recruitment tool with AI Technology

Virtual interviewing has been crucial for most businesses since the COVID-19 epidemic started in 2020 when in-person interviews were effectively discontinued because of directives for social distancing. Due to the devastating effects of COVID-19, physical interviews conducted by groups all over the globe were discontinued, and to maintain social distance, physical interviews were replaced with online ones. The firm adopted the interview session as the new method for consistently and successfully hiring sufficiently qualified candidates. The study of interview lying has just lately been more active, even though interviews are regularly employed for selection. We review studies on the frequency, reasons for, methods, and effects of interview faking. Most candidates lie, at least in part. Strong correlations exist between chicanery attitudes and actions as well as fakery activities and personality factors. Analyzing how job hopefuls control their first perceptions of meetings seems to be another common practice. During the past 10 years, nevertheless, we have only seen a significant increase in interest in the study of job hunters who mislead during interviews. However, with the rise of online screenings, impersonating the candidate has also become increasingly common. This research objective is to develop a mobile application system powered by artificial intelligence that will contribute to improving the effectiveness and security of online hiring. The primary components of our study project include speaking analysis, keystroke dynamic behavior, blink lie detection with facial expression, and an online CV analyzer.
Blinkify – A IoT Based Device for Speech Impaired

The discovery and popularity that came by, for the Internet of Things have made life more inclusive and productive for people with speech disabilities caused because of motor neuron diseases like ALS. Such people have no means or are sometimes excluded from the usual walk of life. Most invented intelligent detection devices, including portable devices for people with disabilities in speech, are costly and unaffordable for patients in underdeveloped and developing countries. Blinkify is an IoT technology-based wearable device, which uses modified Morse code for communication. Motor neuron disease-affected patients can use this device for communication by means of eye blinking. The device tracks this eye blinks and converts it into Morse code, which is transmitted to a mobile or laptop connected to the system. It also provides a secure pathway for communication. With the added benefits of easy handling, low cost and easy portability, it becomes immensely useful for people who are paralyzed and cannot communicate by mouth or sign. The system also requires less assistance from another person.
Detecting Malicious Blockchain Attacks through Flower using Horizontal Federated Learning: An Investigation of Federated Approaches

This research study focuses on the detection of malicious attacks on WEB 3 decentralized applications in blockchain systems using Federated Learning (FL) with the Flower framework. The scope of the study involves evaluating the effectiveness of FL in enhancing the security and reliability of decentralized networks by detecting various types of malicious attacks. A comparative analysis is conducted to assess the performance of FL against traditional machine learning techniques that rely on centralized data storage and direct user data access. The study highlights the limitations of traditional approaches in the context of blockchain systems, emphasizing the need for privacy-preserving collaborative learning mechanisms like FL. By leveraging the Flower framework, FL enables collaborative learning among edge devices without central data storage, ensuring data privacy and improving security. The research findings demonstrate the superiority of FL in terms of privacy preservation and collaborative learning capabilities, establishing its potential for detecting and preventing malicious attacks in blockchain systems. These findings contribute to the wider adoption of blockchain technology by addressing the security concerns associated with decentralized networks.
Bilinear Upsampling Gradients Using Convolutions In Neural Networks - Generalization To Other Schemes

Image upsampling in deep learning is used to increase the resolution of the image or intermediate feature maps in many state-of-the-art neural network models. The methods of interpolation used by upsamplers include nearest neighbour interpolation, bilinear interpolation etc. In this paper we consider the computation of gradient of a bilinear image interpolator in the back propagation stage of gradient descent. Conventionally, image upsampler gradients are known to be computationally expensive and are likely to produce non-deterministic output because of Read-Modify-Write (RMW) operations on SIMD or vector processors. In this paper, we try to solve these problems involved in upsampling gradient computations by proposing an algorithm based on convolutions. We propose the algorithm taking bilinear interpolation as an example and generalize the concept to other interpolation schemes. The proposed algorithm is proved to be accelerated by matrix multiplication engines specialized at performing convolutions. Simulation results of the proposed convolution based algorithm also reflect the efficiency of the proposed algorithm in overcoming the above mentioned challenges faced by the conventional algorithms.
Comparative Analysis of Data Compression using Canonical Huffman and Golomb Rice Encoding in Verilog HDL and Implementation in FPGA

The world revolves around data. Hence the importance of storing data becomes highly essential, but in order to store the data as such requires a lot of storage space which brings up the role of Data Compression. It becomes necessary to compress the data before storing it. Compressing the data without losing any information is highly important. Data Compression can be lossy or lossless. It is always essential to follow lossless compression for textual data. The project aims to compress textual data using two lossless compression techniques such as Canonical Huffman Encoding and Golomb Rice Encoding [1]. 64-bit input data is compressed using both algorithms in Verilog HDL and analyzed which algorithm encodes the textual data with the least number of bits. Efficiency of the algorithms are analyzed using parameters such as Compression Ratio, Area, Power Consumption and Delay. The better algorithm between the two is found and is implemented in FPGA. In order to reduce power consumption, clock gating is added to the better design. Data compression reduces the hardware cost drastically [2].
Instagram Post Caption Recommendation Engine

This research is a comparative study on the performance of three models for image captioning using the Instagram dataset by comparing a CNN-LSTM architecture using a 16-layered VGG network, a Transformer model, and OpenAI's pretrained Language Model (GPT). Our model is evaluated on a dataset of 30,000 Instagram images, demonstrating its effectiveness in producing accurate and coherent captions. The effectiveness of the generated captions is further confirmed through quantitative examination utilising well-known measures like BLEU, METEOR, CIDEr, and ROUGE. This research contributes to the advancement of image captioning techniques, specifically tailored to Instagram. Our model has the potential to enhance accessibility and usability of visual content on social media platforms, benefiting users and applications relying on automatic image captioning.
Enhancing Software Cost Estimation using COCOMO Cost Driver Features with Battle Royale Optimization and Quantum Ensemble Meta-Regression Technique

This research suggests a unique method for improving software cost estimates by combining Battle Royale Optimisation (BRO) and Quantum Ensemble Meta-Regression Technique (QEMRT) with COCOMO cost driver characteristics. The strengths of these three strategies are combined in the suggested strategy to increase the accuracy of software cost estimation. The COCOMO model is a popular software cost-estimating methodology that considers several cost factors. BRO is a metaheuristic algorithm that mimics the process of the fittest people being selected naturally and was inspired by the Battle Royale video game. The benefits of quantum computing and ensemble learning are combined in the machine learning approach known as QEMRT. Using a correlation-based feature selection technique, we first identified the most important COCOMO cost drivers in our study. To get the best-fit model, we then used BRO to optimize the weights of these cost drivers. To further increase the estimation's accuracy, QEMRT was utilized to meta-regress the optimized model. The suggested method was tested on two datasets for software cost estimating that are available to the public, and the outcomes were compared with other cutting-edge approaches. The experimental findings demonstrated that our suggested strategy beat the other approaches in terms of accuracy, robustness, and stability. In conclusion, the suggested method offers a viable strategy for improving the accuracy of software cost estimation, which might help software development organizations by improving project planning and resource allocation.
Network Security for IoT Device using Blockchain Technology

This study presents a summary of the use and development of blockchain in a variety of Internet of Things enablement technologies. This encompasses not only the Merkle tree itself but also its application, technique, level of complexity, and synchronization. The study provides a detailed description of the experimental setting along with a suggested research model and workflow procedure. This section examines and puts into effect two distinct scenarios regarding the development of transaction times, mining times, and chaining times. The ultimate outcome is determined using a classification method that is based on machine learning. This method makes use of logistic regression, multilayer perceptron, SGD text, SGD, basic logistic regression, SMO, and voted perceptron.
Adaptive Arithmetic Coding using Neural Network

In today’s data-driven world, industries across various sectors are grappling with the enormous influx of data being generated. The sheer volume of data poses significant challenges in terms of infrastructure costs, data transfer speeds, and storage requirements. As a result, many industries are reevaluating their approach to cloud-based storage solutions. One crucial solution to mitigate the issues associated with excessive storage usage, data integrity, transfer efficiency, and improved streaming speeds is lossless data compression. Numerous compressors currently available employ prediction-based compression techniques and utilize learned models to address these challenges effectively.To tackle this problem, our research focuses on exploring and developing strategies for compressing sequential data using neural network predictors. Neural networks are well-known as universal function approximators capable of learning complex mappings, and they have demonstrated impressive performance in prediction tasks. We leverage this capability by combining an arithmetic coder with recurrent neural network predictions, enabling us to achieve lossless compression for a wide range of datasets including synthetic, text, and genomic data [1]. Through a series of experiments using both simulated and actual text and genomic datasets, we evaluate the effectiveness of our approach. The results and observations from these experiments provide valuable insights for future research and advancements in the field of lossless data compression.
Exploration and Correlation of Machine Learning Models through Option Mining of Airline Tweets

Twitter is a well-known and widely used social media platform since it enables users to share their ideas and opinions on any topic and also submit messages or comments from anywhere in the world. These reviews and opinions are examined using Sentiment Analysis tools. An NLP approach called sentiment analysis is used to categorize positive, negative, and neutral. In this research, we used machine learning methods such as SVM, Logistic Regression, and Random Forest to perform sentiment analysis on the Twitter Airline Dataset. Positive, negative, and neutral emotions are the three ways that emotions are conveyed. The primary objective of this paper is to check which Machine learning model gives the best accuracy. The experimental results show that Random Forest models provide the best accuracy that is 99.26%
Analysis of Challenges in Decentralized Storage Framework for Sharing Medical Data

Clinical trials can make use of healthcare data to find novel treatments, promote health equity, and ultimately enhance patient outcomes. It is undeniable that current and reliable clinical data are crucial for medical advancements for the reason that of the tamper-proof nature, source provenance, and, decentralized ledger blockchain knowledge has in recent times emerged as a crucial constituent for numerous applications, together with healthcare as well as IoT. Health systems may now scale up and keep consistency on a decentralized platform because of the Internet of Things (IoT) as well as Block Chain networks. As a result, numerous researchers have created IoT e-Health systems that are enabled with blockchain and investigated the benefits of blockchain technique in a variety of e-Healthcare domains. In this research, a deeper analysis of the decentralized storage framework for accessing medical details in smart applications is performed to address the demand for privacy, encryption, and security in IoT applications the analysis is made on the basis of methodology, comparative criteria, and metrics. The comparative study of various development platforms for decentralized applications is analyzed using the research papers to provide a technically efficient solution. A focused and flexible idea can be generated by the researchers through the analysis on the different datasets, and techniques available, assessing their benefits in real-time applications. The research will interpret the various methods available in the decentralized framework along with their advantages, disadvantages, and research gaps, which paves a way for the researchers to gain knowledge about the methods available for sharing medical data in smart healthcare applications.
Improving Analogy-Based Software Cost Estimation Based on Nature-Inspired Algorithms for Feature Weighting: An Empirical Study

Estimating software cost and time is one of the important aspects of software project management and a serious concern for software project managers. So far, various methods have been proposed for accurately estimating software cost, each of which has been developed based on a specific perspective. One of the most important methods is analogy-based estimation, which is commonly used today based on different algorithms. The main objective of the current study is to compare various nature-inspired algorithms for improving the accuracy of feature weighting as a step in the analogy-based method. The results indicate that these algorithms can increase the accuracy of software cost estimation from this perspective. Moreover, among the different available algorithms, the learnable evolution model and the artificial bee colony algorithm were able to outperform other algorithms due to their special internal structure.
Health Insurance Recommendation using Probabilistic Data Structures

Recommender systems are constantly evolving in the health insurance industry, with a focus on speed and efficiency. To meet the demands of clients, insurance providers need to generate personalized policy recommendations that are quick and accurate. This paper suggests a health insurance recommendation system that combines advanced algorithms to offer efficient and personalized recommendations based on user preferences and similarities. The proposed system leverages the MinHash and LSHForest algorithms, as well as collaborative filtering, to improve the recommendation process. MinHash approximates the Jaccard similarity between policies, while LSHForest enables faster search for similar policies. Collaborative filtering considers user preferences and similarities among policies and users. To ensure user input is secure and legitimate, the system employs a Bloom filter. This filters out unauthorized access to sensitive healthcare data and validates user input. The architecture of the system addresses various challenges related to personalization and privacy in health insurance recommendation systems. Based on the results, the proposed system surpasses existing approaches in accuracy, efficiency, and privacy preservation. This has the potential to be advantageous for both insurance providers and users as it helps streamline the process of choosing appropriate health insurance plans that align with individual requirements and preferences.
Deep Learning Based Bodo Parts of Speech Tagger

Parts of Speech tagging is the process of sequential labeling of words in a sentence with their corresponding grammatical categories, such as noun, verb, adjective, and so on. It is one of the important steps toward Natural Language Processing, as it allows for a deeper understanding of the sentence’s structure and meaning. There are various challenges in the tagging of POS, particularly when dealing with low-resource languages like Bodo. Bodo, one of the official languages of the Indian state of Assam, is a relatively low-resource language that poses challenges for POS tagging. In this paper, we aimed to develop a POS tagger for Bodo language to promote its overall flourishment. To do this we employed deep learning techniques, specifically Recurrent Neural Network(RNN), Long Short-Term Memory(LSTM), Gated Recurrent Unit(GRU), and Bi-directional Long Short-Term Memory (Bi-LSTM). These models have been demonstrated as powerful tools for natural language processing tasks and have been successfully applied to POS tagging in other languages. To evaluate the performance of our models, we have collected a corpus of 9991 sentences (approximately 70000 tokens) in Bodo which is annotated with the Bureau of Indian Standard (BIS) tagset. After an extensive comparison of all models across different sizes of training, validation, and test sets, it was determined that the Bi-LSTM model gave the best performance in terms of accuracy. Specifically, the Bi-LSTM model achieved an approximate accuracy of 98%.
A Study of Cyber Bullying Classification Using Social Media and Texual Analysis Based on Machine Learning Approches

In today's world, cyberbullying is a problem that is becoming more and more common, especially among teenagers and young people. The prevalence of social networking sites and other digital communication tools has made it simpler for offenders to harass their victims in secret and without repercussions. Natural language processing (NLP) methods have been used in recent years to categorize instances of cyberbullying and assist identify them. The language used in online conversations is examined using these approaches to look for trends and signs of cyberbullying behavior. The purpose of this study is to investigate how well NLP approaches can be used to recognize and categorize cyberbullying behavior. To provide a thorough knowledge of the many types of cyberbullying, the study will use a variety of data sources, including social media posts, chat logs, and other online conversations. Overall, this research will further our knowledge of the intricate nature of cyberbullying and shed light on the potential applications of NLP approaches to lessen its negative impacts.
Vehicle Detection and Identification System: Convolutional Neural Network with Self-Attention

The rising number of automobiles on the road worldwide leads to the incidence of accidents and reckless driving. According to the WHO (World Health Organization), road traffic accidents kill around 1.3 million people each year <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . As a result, vehicle detection and number plate recognition systems are essential in terms of investigation. A detecting system is critical in the event of a road accident or a bunch of thugs fleeing with a car. Most of the previous works performed vehicle detection and number plate detection as separate tasks. However, we have taken these two as a single task and created a system to recognize a vehicle and read a number plate. In our work, we have created four models for a real-time vehicle-detecting system where the models classify vehicles into four types: car, bus, ambulance, and truck. We have proposed a novel self-attention-based convolutional neural network framework where the model detects each vehicle by performing several layers of convolution, max-pooling, and self-attention on each pixel with the surrounding pixels of the image. After that we have also compared our model with two different object detection models, VGG16 and YOLOv3. Our result shows that the CNN based self-attention framework outperformed the other models with an overall accuracy of 94.3% as the CNN with self-attention model can accurately decide whether each cell of images related to each other or not.