Reviewerly/exHarmony huggingface dataset:
Survey and Evaluation of Extreme Learning Machine on TF- IDF Feature for Sentiment Analysis

Sentiment analysis is a tool to understand the emotion of the statement given by customers. This understanding helps the service provider to in improving the quality of service. Machine learning models are one of the popular choices for designing sentiment analysis systems. However, hyper-parameter tuning is one of the important concerns in most of these models. Moreover, the gradient-based training models are prone to the local-minima problem. In such a case one-pass learning model like Extreme Learning Machine (ELM), gives generalization performance with minimal hyper-parameter tunning. This work studies in depth the ability of ELM to learn a generalization model for the sentiment analysis problem. Here the study uses the airline Twitter review dataset to empirically analyze the ELM model and the required hyper-parameter setting. .
Customer Relations and Marketing Analysis Model for Sales Enhancement

With the advancement of technology in each sector, more and more companies are becoming data-driven. Machine learning techniques have shaped businesses into becoming more profitable by automating processes and providing useful insights into their activities. The e-commerce retailers need to ensure customer satisfaction as their reputation and popularity relies solely on the customers. If customers witness the product qualities to be bad and delays in expected order deliveries, the sales of the particular retailer are bound to drop. Thus, identifying the performance of products is an important aspect of every e-commerce website. The paper concentrates on combining the various factors that might affect the ratings given by the customers and determining the product quality using machine learning techniques. The factors taken into consideration are the seller profiles associated with the e-commerce websites, the delay in order deliveries, the sentiment of the customer reviews and the product characteristics like price and volume.
V-Crypto Images/Videos/Textsby Two Key Authentication Using ACO Algorithm Technique

Everyone has a query, whether our data is safe when shared across internet services like emails, websites, apps and so on for a variety of purposes. Companies grub these data to extract the user needs and grow their business because this is one of the main strategies to taste the victory. This research discusses the unique method of protecting our own data ourselves before we send it. Cryptography is the method to secure the data and there are several methods and algorithms evolved in due course of time. After the research, I have come up with the unique cryptographic method that can encrypt and decrypt text files, images and videos. My method merges multi secret keys and 2 different techniques such as ACO algorithm and logical operation technique. A customer must provide two different secret keys as an input to encrypt and decrypt the data. Second key tells the distance of the path for the encryption to take place. This proposed method makes the encryption stronger as it is difficult for anyone to hack the data while decryption. The unique design algorithm helps the user to secure the data safely at source itself. This method has been experimented on multiple text files, images and videos.
CNN Based Handwritten Odia Character Recognition

Odia is a 44 million-strong Indo-Aryan language spoken mostly in Odisha, India. Some characters are made up of many symbols that are linked together. Because of its vast variety of applications, Handwritten Character Recognition (HCR) is critical in Optical Character Recognition (OCR) and Pattern Recognition (PR). HCR is employed in fields such as bank checks, medical prescriptions, and tax returns, and contributes greatly to the spread of automation. Handwritten characters are substantially more difficult to discern than printed characters since different people have different writing styles. Character recognition technology may be used to scan a wide range of documents, including handwritten and printed materials. This study investigates and analyses the application of deep learning techniques such as Convolutional Neural Networks for the recognition of Odia characters. CNNs categorise characters by using neurons coupled in several layers to achieve optimal efficiency, which is inspired by the structure of the brain. The goal of this work was to categorise Odia characters using 6 layers of neurons. This method was able to obtain a 95.6 percent accuracy rate. The handwritten Odia characters may be simply translated into English or any other language once they have been identified.
An Empirical and Comparative Study of Graph based Summarization Algorithms

Textual content is growing exponentially on the Internet, and it is also available in the form of countless archives of news articles, scientific studies, legal papers, and other materials. To summarize a text by hand takes a considerable time, a lot of effort, and a great deal of money. As a result, the use of automated text summarization (ATS) or summarization is becoming more popular. Researchers have been working on developing ATS techniques since the 1950s. Text summarization is a branch of natural language processing (NLP) study areas that uses various NLP tools and techniques to provide us with a coherent and concise explanation of large text documents. Extractive, abstractive, and hybrid approaches are used to summarize text. The TextRank, LexRank, and Modified TextRank algorithms are compared in this article. These algorithms are based on graph-based extractive approach in which sentences are represented by vertices and the cosine similarity serves as edge between two sentences. The BBC and CNN news datasets has been used for this study.
Xception Net &amp; Vision Transformer: A comparative study for Deepfake Detection

Deepfakes are artificial media in which an existing image consisting of a person is replaced with someone else. The creation of fake content is not new, but deepfakes are more credible because it involves the use of advanced deep learning techniques to manipulate or generate audio and visual content. In the 21st century, there have been astonishing advancements in Generative Adversarial Networks (GANs) and the use of encoder and decoder architecture [1] which have resulted in various effective methods of deepfake creation such as face-swap, lip-syncing, puppet-master and many more [2]. These methods are not only easily accessible but also get more accurate with time. Earlier deepfakes were detected with deep convolutional neural networks such as EfficientNet B7 and Xception Net, but with further advancements in deepfake generation there comes a need for better deepfake detection methods. This paper analyses the performance of Xception Net which has historically performed well with deepfake detection [3]–[5], Vision transformer [6] which is a fairly new technology and a combination of the two models. The paper proposes a combination of Xception and Vision transformer such that Xception Net is used for feature extraction from the patches and the output is then fed as a sequence to a transformer. This work is expected to assist readers to understand when to use Xception Net, Vision transformer and a combination of the two.
Data Augmentation and its Application in Histopathological Oral Cell Image Classification

Deep learning and Deep Neural Networks (DNN) is one of the most popular fields of Machine Learning. The working principle of DNN and deep learning is based on the Artificial Neural Network (ANN). The fundamental of ANN is grounded on human brain, so as DNN also tries to mimic human brain. From various DNN structure, Convolutional Neural Network (CNN) is one of the leading DNN structure used in the area of image classification problems. Although the performance of CNN in image classification problem is outstanding as compare to other traditional machine learning algorithm, but CNN has certain limitation. One of the most challenging issues of CNN is, insufficient data set problem. To learn a CNN model, huge amount of training data set is required, but in real time scenario, huge amount of data set is not practically available. Data augmentation is one of the solutions for this insufficient data set problem in CNN. In this paper author has discuss various approaches of data augmentation technique. In data augmentation technique, new data set is generated from the existing one so as to increase the size of the data set. In this work to analyze the performance of data augmentation, author has proposed a 14 layered CNN model, and observe the role of data augmentation, by using the proposed model for the classification of histopathological oral cell image data set. The performance analysis of the proposed CNN model is also compared with the state-of-the-art model like VGG16 and VGG19 and the proposed model outperform with 90.01% of testing accuracy using augmented data set whereas, using the original data set the proposed model got testing accuracy of 80.07%.
A Clustering Study for the Optimization of Emotional Information Retrieval Systems: DBSCAN vs K-means

Information retrieval systems enable the organization, storage, retrieval and distribution of information. Their main purpose is to retrieve information and return results that match the most the user's needs. The field of Information Retrieval (IR) has seen in recent years the appearance of a new axis which is Personalized Information Retrieval (PIR), in particular emotional PIR. This type of information search systems aims to extract the user's emotions and to include it in the information search process, in order to return results according to the user's sentiments and emotional state expressed through his queries. These emotion-based systems have shown interesting results in terms of recall and precision, however the search time for relevant information increased significantly compared to non-emotion-based systems, due to the extra treatments necessary for emotion extraction from the input queries, as well as the emotional profile construction operations. We propose in this work an optimization approach for emotion-based information retrieval systems, based on data mining techniques, more precisely clustering. We aim to reduce the execution time by grouping the documents of the system in clusters, thus transiting from multiple query/document comparing operations to fewer query/cluster operations while keeping quality of results similar to the original non-clustered system.
QUIC-CNN: Website Fingerprinting for QUIC Traffic in Tor Network

The Onion Router (Tor) network is an anonymous network that provides anonymous proxy services to TCP applications, which has brought great obstacles to network traffic analysis. However, we can still get useful information from the metadata of the traffic by Website Fingerprinting (WF) attack. WF attacks can effectively determine which website a user visited over an encrypted connection. There are many studies on WF attacks on the Tor network, such as the state-of-the-art model Si-DF [20]. However, the next-generation HTTP Protocol will only support QUIC protocol as its transport layer protocol, while there are no studies about WF attacks on QUIC traffic on the Tor network. QUIC's characteristics are different from TCP(e.g., Encrypted transport), which makes WF attacks harder to perform in the Tor network using QUIC protocol. In this paper, we propose QUIC-CNN, a novel model for WF attacks towards QUIC traffic in Tor networks. We compared QUIC-CNN with Si-DF in a series of experiments. The evaluation results show that QUIC-CNN performs much better than Si-DF in website fingerprinting towards QUIC traffic in the Tor network.
Graph clustering under weight-differential privacy

In this paper, we consider the weight privacy problem in the undirected weighted graph where the graph structure is public and the edge-weights are private in the weightprivacy model. Specificially, we study the k-median and k-center problems based on the shortest path with weight-differential privacy, which aims to partition the graph vertex set into k clusters to minimize the average and maximum distance of vertex to its cluster center. For the k-median problem, we reformulate it into the equivalent submodular problem and provide a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(1-\frac{1}{e})$</tex> approximate guarantee with an additive error. For the k-center problem, our private algorithms offer 2-approximations with an additive error. Moreover, our private algorithms achieve an almost linear number of function evaluations by applying the sampling technique. Finally, we evaluate the performance of our algorithms against the existing work and the experimental results demonstrate the effectiveness of our algorithms.
BPCN:A simple and efficient model for visual question answering

Visual question answering (VQA) is a cross modal task that combines computer vision tasks and natural language processing tasks. Due to the traditional attention mechanism model can not effectively solve the gap between the high-level semantics of words and the low-level abstract pixels of images, we use the question guided multi-head concatenated attention mechanism to map the question features and image features to the shared space, then obtain the question guided image attention and the image features related to the question. In addition, existing VQA models usually use static word vectors to encode text features. In this paper, based on the BERT's dynamic word vector encoding question, the accuracy of the model is further improved. Our model is simple and easy to train, with low complexity and significantly improved performance. Without using VG dataset for data enhancement, our model reached 69.28% on the test-std set.
Secure Smart Home Access Control System Using Blockchain Technology

The emergence of Smart Homes (SH) has changed a lot our lifestyle. Connecting several objects to provide autonomous objects function is called ‘Internet of Things (IoT) technology’. Objects can be connected without any intervention and give remote access to the owners. With technological advancement, SH and many other IoT technologies have greatly expanded. Homes becoming equipped by hundreds of sensors, smart objects and they have connected with other smart application (smart healthcare, smart market, etc.). Indeed, this scalability brought more sophistication and prosperity but in revenge, it has revealed new security threat. Previous security mechanisms are no longer effective due to their centralized architecture. Most of conventional access control systems are limited in term of Wireless sensor network (WSN) constraint (e.g., limited in terms of energy, communication bandwidth and processing). To mitigate this problem and strengthen security of SH we propose a new lightweight system called Smart Home Access Control System (SHSystem) based on blockchain model. In our method, the access control is optimized by using the keys. The system uses two keys one for the registration phase and the second (calculated by using the first key) for verification phase. To ensure the IoT devices requirement, high efficiency and lightweight calculation we use light keys and light operation. Finally, we present simulation of the method in remix editor, and we analyse our approach in term of gas. The obtained results confirm the efficiency of the method for different attacks.
Feature Quantization Convolutional Neural Networks for CSI Feedback in Massive MIMO Systems

In frequency division duplex (FDD) mode, the convolutional auto encoder (AE) compressed downlink channel state information (CSI) feedback can occupy less band resource and enable fast base station (BS) configuration for massive multiple-input multiple-out (MIMO) systems. In this paper, we propose a novel discrete quantization network (DQNet), which can quantify feature vectors with a discrete latent embedding space, to reduce feedback overhead while maintaining abundant feature information. Specifically, the discrete latent space reduce the dimension of CSI features by measuring the Ebullience distance and converting the CSI features into a small-size and discrete index information. Due to the benefit of DQNet, we apply sensing network (SN) in both encoder and decoder side, to enhance the CSI features by spanning several convolutional operations. According to experimental results, the proposed DQNet can improve the reliability in outdoor scenario with less training overhead and reduce model complexity.
Solving Kickstarter Scam Problem By using Blockchain

In the security aspect blockchain play a vital role, it can solve many problems for people and also provide a new way of trade. Blockchain is all about trust in determining and deciding who you want to trust. People like to exchange things of value. We see this in transactions all over the place in real life so people perhaps trade one item of value for another or maybe they are going to use some type of currency to pay for something. So they render currency and they get something back for their product or service thus this value exchange relies on trust, so to solve trust issues people have to pay somebody in the middle and that is where blockchain fits an interest. Blockchain can remove the middleman and also provide a trustful environment. In the blockchain, there is a concept ” smart contract” of Ethereum which is written generally in solidity language using which we can solve the trust-related issue. On Kickstarter online fund is raised to complete any project. The person who donates money to complete that project becomes a contributor. After donating money contributors don't have control over donated money because of which scam happens. In this paper, we shall propose a solution where the product maker has to take permission to spend donated money. The product maker has to give all the details where he wants to spend money so that contributors can decide whether they want to give permission to spend money or not if the majority of contributors are not satisfied with the given details then they vote against the request because of which money can not be spent.
Mammogram Image Classification Using Various Machine Learning Algorithms

The leading cause of death in women is still breast cancer.Detecting cancer in its early stages is crucial. For the purpose of diagnosing breast cancer data, a variety of machine learning algorithms are available.In this study, performance comparisons between different machine learning algorithms: Extra Trees, Random Forest, Support Vector Machine (SVM),Decision Tree, Logistic Regression Bagging, Gradient Boosting, and AdaBoost have been conducted on mammography images of MIAS(Mammographic Image Analysis Society) database.It is observed that Bagging outperformed all other algorithms and achieved the highest accuracy (0.9678).All the work is done in the Kaggle environment based on the python programming language.
Real time CNN based facial emotion recognition over standard optimizers

Detecting and localizing objects in real-time with enhanced speed and accuracy remain a challenging task. Object detection and its applications have been a highly researched topic due to various obstacles like the lack of computational power, limited data, deformations of data, viewpoint variations, and many more, but the lack of speed or accuracy is the most significant of all. There seems to be a trade-off between speed and accuracy, an increase in speed leads to a decrease in accuracy and vice versa. To enhance the accuracy using deep learning or machine learning approach the simplest way is to find a solution where the predicted and actual output are the same. The difference between the actual and predicted output is known as loss, which should be zero in an ideal scenario or as close to zero as possible. Different loss function formulas are used to calculate this difference between the actual and predicted output, this loss function is implemented with the optimizer to bring the loss to a minimum. In this research, we show a superior technique to train Convolutional Neural Networks (CNNs) by using standard optimizers to discover global minima.
Machine Learning based Sentiment Analysis of Hindi Data with TF-IDF and Count Vectorization

Sentiment refers to emotions. Sentiment analysis, often known as opinion mining, is the technique of identifying and extracting subjective data from pre-web and post-web reviews using text analytics, computational linguistics, and natural language processing. Hindi is an Indian language which is used by many of Indians. Due to phenomenal growth of online product reviews in Hindi post-web Hindi reviews are also increasing rapidly. A machine learning based method in this paper to analysis postweb text data. The present method is divided into four steps. First of all, an annotated Hindi review data set is developed from post-web sources. In second step, feature extraction is performed on annotated Hindi review dataset using the Term-Frequency/ Inverse-Document Frequency (TF-IDF) and count vectorization techniques. In the third step, the retrieved features are given to the classifier so it can make predictions. Moreover, annotated dataset translated into English. Second step and third step are performed on annotated English dataset in last step. A range of evaluation criteria, including precision, recall, and F1- score, are presented in the results. In both instances, the support vector machine produced the most pertinent results.
Name Entity Recognition on Covid-19 Dataset using Machine Learning algorithms

The COVID-19 pandemic has been accompanied by such an explosive increase in media coverage and scientific publications that researchers find it difficult to keep up. So we are working on COVID-19 dataset on Omicron variant to recognise the name entity from a given text. We collect the COVID related data from newspaper or from tweets. This article covered the name entity like COVID variant name, organization name and location name, vaccine name. It include tokenisation, POS tagging, Chunking, levelling, editing and for run the program. It will help us to recognise the name entity like where the COVID spread (location) most, which variant spread most (variant name), which vaccine has been given (vaccine name) from huge dataset. In this work, we have identified the names. If we assume unemployment, economic downfall, death, recovery, depression, as a topic we can identify the topic names also, and in which phase it occurred.
Sentimental Analysis of Amazon Customers using Deep Learning Techniques

Online purchasing is becoming increasingly important in today's environment. The company concepts have been refashioned and utterly revolutionized and making it so simple for clients to buy what they desire with the press of a mouse click. 90% of shoppers try several online channels to judge the quality of their purchase. In this article, 568,454 fine food reviews of 74,258 goods and 256,059 consumers on Amazon were tracked over a ten-year period. To label our datasets, we employed both a manual and an active learning technique in our model. Various classifiers are utilized in the active learning process to provide accuracy until a desirable level is reached.