Reviewerly/exHarmony huggingface dataset:
Classification of Covid-19 Vaccines tweets using Naïve Bayes Classification

Recently COVID-19 has become the most discussed topic in different social media platforms like Twitter, Facebook, Instagram etc. As time moves on, lot of messages and videos are posted in social media. As expected, most of the public followed these messages and becomes panic because of lack of information, misinformation about COVID-19 and its impact. This research study proposes a Twitter sentiment analysisbased on the most popular vaccines Covaxin, Covishield, and Pfizer. Most of the people expressed their feelings about vaccines in the twitter. Twitter API authentication is used here to extract the tweets. These extracted tweets are difficult to analyze, hence pre-processing has been done i.e., unstructured data is converted into structured format. After completion of preprocessing, the data is further classified by using Naïve Bayes algorithm. This algorithm performs data classification and divides it into three major classes as positive, negative, and neutral. The result shows that the covaxin yields 48.36% positive, 35.6% negative, and 16.04% neutral, Covishield yields 44.25% positive, 39.67% negative, and 16.08% neutral, Pfizer yields 42.95% positive, 39.45% negative, and 17.6% neutral sentiment.
Computer Vision based Intelligent Traffic Management System

The traffic monitoring system has become an important part of a smart city. With the increase in traffic, conventional systems are not capable of managing traffic in densely populated areas. Also, there is no way of determining speed of vehicles using these conventional systems. Various challenges like increase in transportation cost and number of accidents due to traffic jams are also encountered. This system intends to monitor and manage traffic flow without any human interference. In conventional traffic management system traffic police uses timer-based traffic lights to manage traffic. But in such systems, it becomes hard for traffic authorities to manage traffic congestion in different areas with limited manpower. The Intelligent Traffic Management System is much needed for monitoring and analysis of traffic. Designing an Intelligent Traffic Management System is also inevitable for the urban cities. This research work will propose an In this paper, we propose an Intelligent Traffic Management System that will help to maintain the traffic congestion without any human interference. The You Only Look Once (YOLO) algorithm is used to detect vehicles from a given video feed. The total number of vehicles detected are also counted in the video provided. The proposed system will detect and count the number of vehicles, which will provide effective solution for traffic monitoring system. This system will also perform classification of vehicles in different categories i.e., Car, Motorbike, Bus, Truck etc. Depending on the count it will determine the traffic density, which will be used for setting the time of the traffic signals.
Cloud Computing based Symmetric Encryption Algorithm (Blueshift)

As companies started moving into cloud services, data security has become the utmost concern for them. Even though strict protocols are being followed in cloud platforms, it is important to use an independent encryption method during data transfer and storage. Blueshift algorithm is an attempt to address this. In this work we have created a novel encryption method and benchmarked it against contemporary encryption standards for its strength and performance.
BERT-BiLSTM-BiGRU-CRF: Ensemble Multi Models Learning for Product Review Sentiment Analysis

In the last decade, large numbers of comment texts have been generated on social media and websites. In the era of sentiment analysis, mining the role of emotional tendency in comments through deep learning technology is helpful for the timely classification of sentiment text as positive, negative, and neutral. Sentiment analysis is a task that predicts people's opinions on product reviews based on text data, and it's both a valuable and challenging task. This research study has utilized a novel deep learning based predictive framework, which is applied in analyzing the product reviews along with user opinion information. Firstly, the training set generates character vectors as input layers by using Bidirectional Encoder Representation of Transformers (BERT) and FLAIR embedding models, which are used to convert the product review into low-dimensional representation; and then uses this vector as input to a novel hybrid Bidirectional Long-Short-term memory model (Bi-LS TM) and Bidirectional Gated recurrent unit model (Bi-GRU), which are combined into a single architecture to predict the feature. Finally, the processed context information is classified using the softmax classifier. The resultant review shows the significant accuracy of our model.
Modular Type Beach Cleaning Robot “Clean-B”

As we know, Kerala is one of the places having large coastal area and beaches are our main tourism attraction. Cleanliness is the most important problem that we are facing in the journey of its growth. Due to its large area, it is a tedious work for keeping the beaches clean. As we are living in a developing country like India it is not affordable to pay laborers monthly. Our proposal aims in overcoming this major problem which is currently affecting the ecology as well as the tourism industry. In this paper we discuss about the development of a modular type beach cleaning robot called “Clean-B” which is able to pick up the plastic wastes, cans, tins etc. which were left over the sea shore by the tourists automatically.
A Comparative Study on Optimizers for Automatic Image Captioning

In the field of artificial intelligence, computer vision and natural language processing are used to automatically generate an image's contents. The regenerative neuronal model is developed and is dependent on machine translation and computer vision. Using this technique, natural phrases are produced that finally explain the image. This architecture also includes recurrent neural networks (RNN) and convolutional neural networks (CNN). The RNN is used to create phrases, whereas the CNN is used to extract characteristics from images. The model has been taught to produce captions that, when given an input image, almost exactly describe the image. The outcome of these algorithms is determined by several factors, including feature extraction, caption generation, and optimizer selection. Our goal is to conduct a comparative analysis of several optimizers to determine the optimizer that achieves highest accuracy for a deep learning model. The deep learning model is subsequently trained with various optimizers on the Flicker dataset. The accuracy of the results of the model using optimizers are achieved as follows: RMSprop optimizer has a 92% accuracy, SGD has a 12% accuracy, Adam optimizer has 53% accuracy, and Adadelta has a 12% per cent.
Design and Analysis of Low Power Full Adder using Reversible Logic

The reversible Logic system will be effective in low power dissipation. For the optimization of power in VLSI design, number of researchers is using reversible logic regularly. In the VLSI design, the use of reversible logic has many advantages because of its high speed, and low power dissipation. This paper provides the design and comparative analysis of reversible full adder using Peres gate with conventional full adder circuits. During comparison it is found that 44% improvement in total thermal power dissipation over the conventional adder circuit. These reversible logic circuits are verified and simulated using Verilog coding, Intel Quartus Prime 20.1
Preventing Selfish Mining in Public Blockchain Using Alarming Block and Block Interval Time Approach

Selfish mining is a cryptocurrency mining process where a miner keeps a block private for a certain period instead of publishing it to the network. This process allows dishonest miners to create a different blockchain while others keep building on the previous one. When the private block gets published, the honest miners may find the private block higher than the actual one and start building on it. It can lead dishonest miners to have more revenue than their fair share. This research work proposes a selfish mining prevention mechanism, especially for public and permissionless blockchain by using three unique concepts: (i) Threshold Time, (ii) Alarming Block, and (iii) Block Interval Time. Our technique utilizes the timestamp of block generation time and publication time, which are used to calculate the Block Interval Time for every single block. Moreover, a new type of block named Alarming Block, which does not contain any transactions except the block header, is exploited along with the Block Interval Time to discard the selfish blocks, which are kept private for a certain amount of time.
Machine Learning Algorithms for Analysis and Anticipation of Breast Cancer: A Comparative Approach

In the medical domain, data analytics play a critical role in diagnosis and treatment. Huge amount of data should be analyzed by using machine learning techniques to generate different tools for prediction and categorization, allowing practitioners to make better decisions. The clustering and classification technique is used to predict the occurrence of Breast Cancer (BC). In order to achieve accurate predictions, the system uses the Wisconsin Original Breast Cancer Dataset obtained from the UCI Repository. The datasets are analyzed in R Studio, where the records are grouped by using the Partition based clustering and further categorized by using Decision Tree and Support Vector Machine (SVM) algorithms. The proposed model predicts whether a tumor is benign (noncancerous) or malignant (cancerous) and the model is then evaluated by using N-fold cross validation. The main aim of the proposed system is to demonstrate the algorithms that are optimal for performing healthcare-based prediction tasks. The accuracy rate, as well as the efficiency and efficacy of each method are then determined. The experimental findings show that the proposed approach has the highest accuracy with 90.34 percent accuracy for the Decision Tree Classifier and 93.98 percent accuracy for the Support Vector Machine. Further, the Support Vector Machine (SVM) is considered as one of the most suitable classifiers with a 3.64 percent improvement in accuracy.
A Classification System for Breast Cancer Prediction using SVOF-KNN method

Cancer plays a vital role in saving the lives of the losses. The research “A Healthcare System based on SVOF-KNN Model for Breast Cancer Classification” aims to classify BC (breast cancer) diagnosis precisely through different Machine Learning steps. It is developed to classify RF's (risk factors) effect in the BC Coimbra dataset connected with a routine BA (blood analysis). The instances and attributes of breast cancer Coimbra dataset are age. BMI, glucosc, Insulin-level in the blood, etc. The Error Rate. RMSE, MAE. SP. and SN are used to calculate the accuracy or prediction of the classifications. The SVOF-KNN evaluates the ER (error rate) values for each feature of the BC Coimbra dataset. It classifies the best attribute as having the minimum ER as the feature for BC diagnosis. SVOF-KNN also organizes the best classification method for BC diagnosis. The results of this proposed model (SVOF-KNN) have improved with 90.3 per cent SP rate and 91.3 per cent SN rate compared with the existing model (KNNBasic).
Legal Document Clustering and Summarization

The massive increase in reliance on the Internet is paralleled by an increase in document digitization. With this expansion has come the need for rapid knowledge management, which is an automated tool for organizing, analyzing, retrieving, and displaying content in a meaningful and distributed manner. The justice system has been prevalent around the world for a long time, increasing the number of resources available for and in this field is a perpetual process. One such addition is the introduction of a legal search engine of sorts that allows for easy access to relevant documents for the user. Similarly, the precedent retrieval process has been merely based on references to past cases. However, there is a need to retrieve precedents irrespective of the references and on the basis of similar context. Hence, a context-aware similar document retrieval system has been proposed. Additionally, legal documents could be inscrutable if being accessed by someone who is not a part of the legal field. Therefore, there is a need for automatic summarizing documents in a way that can be understood by the common man. To tackle this problem, a summarization methodology has been implemented and its performance has been evaluated. Considering all these factors, a system needs to be developed with the following features: A search engine for legal documents which will provide the user with all the relevant documents they require along with their summaries. The system will also display recommendations of well-known lawyers in the subject of the issue the user is researching.
Preliminary Results on Contrast Enhanced Ultrasound Video Stream Diagnosis Using Deep Neural Architectures

In recent years, the interest in the deep learning (DL) paradigm has grown dramatically. Due to its ability to automatically extract characteristics and recognize patterns from data, this technology has been particularly successful in the field of medical imaging. The current paper proposes a fully automatic computer-aided diagnosis DL-based system for the investigation of contrast enhanced ultrasound (CEUS) video streams in order to identify specific focal liver lesions (FLL). Our solution, based on a combination of a convolutional neural network (CNN) with a recurrent neural network (RNN) showed promising results (87% accuracy) when it was evaluated against the SYSU dataset.
Public Institutions Updated Enhanced Biometric Security, Zero Trust Architecture and Multi-Factor Authentification

This paper presents the possibility of introducing multi-factor authentication using biometrics as standard practice to improve public institution cyber security. Along with the widespread implementation of such a solution, network architecture can be redesigned around a Zero Trust Architecture approach that would permit a much more granular approach to network and data security. The proposed system would also comply with current General Data Protection Regulation (GDPR) standards and regulations.
Human-Computer Interaction Through Voice Commands Recognition

Despite intensive research dedicated to human machine communication, until recently only a reduced number of systems adopted vocal commands to interact with computers. With the development of deep convolutional neural networks (DCNNs) architectures promising results have been obtained in speech recognition and natural language processing, allowing people to engage in simple question-answer dialog with robots. In that regard, the goal of this paper is to introduce a novel interface designed to facilitate the human-machine interaction (HMI). The framework starts by detecting and recognizing speech messages, transforming them into spoken commands. The voice controls are further converted into operating system instructions designed to automate everyday human tasks. The experimental evaluation performed on English spoken language dataset, validates the proposed architecture with average accuracy scores superior to 89%. In addition, we have compared different DCNNs architectures and identified which model has the best performance.
Urban landmark detection using A-KAZE features and vector of aggregated local descriptors

Urban landmarks detection systems represent a research topic that is continuously pursued in the Computer Vision domain. These systems play an important role in tourism and cultural heritage storytelling augmented reality applications in urban environments. In this paper, we propose a system that aims to recognize landmarks from urban scenarios using A-KAZE features and aggregated local descriptors. The proposed system was evaluated on popular public datasets like ZuBuD and TMBuD obtaining positive results.
The Role of Block Chain for Sustainable Agri Food Supply Chain Management

We've proposed an idea for creating an application for the food (agri food) supply chain using blockchain technology. It makes advantage of blockchain's key functionalities, such as smart contracts, P2P, SHA-256 which are all employed on blockchain networks. The workings of blockchain technology and their potential applications are discussed in this paper. As a result, a trustworthy system which provides traceability, transparency, validation and invalidation, data security and distribution mechanisms in the agri food supply chain is required. All transactions are saved in the block chain in the recommended structure, and the data is uploaded to the blockchain. System for Interplanetary File Storage (IPFS). The storing mechanism creates a hash of the data on the block chain, ensuring that the solution is efficient, secure, and dependable industry.
Implementation of Faster RCNN Algorithm for Smart Robotic ARM Based on Computer Vision

Robotic arm is a powerful mechanism used in industries to carry out pick and place tasks with great speed and accuracy. This task can either be accomplished by an arm that follows a preprogramed trajectory or an arm that can use its own intelligence to complete the task using Machine Learning. The problem with an arm which lacks intelligence is that with changing circumstances they cannot acclimatize well, the arm needs human intervention to reprogram as per requirement and perform only as per instructed. However, the intelligent robotic arm can be made capable enough to make its own decision for tasks. Some already prevailing projects in this domain are majorly based on sensors for the classification and detection of objects for pick and place tasks. The arm that uses a color-based sensor for detection majorly has a drawback that is less accurate in exact color detection of objects and detection time is also compromised and if sensors fail, the entire system will not perform the intended task. To address this problem, we propose using the Faster Region-Based Convolutional Neural Networks ML model to implement an arm capable of automatically sorting red and blue boxes by capturing their images with an OV5674 camera and processing them on a Raspberry Pi 3 B+ to accurately detect and classify red and blue boxes. The mechanical arm movement is ensured by a precise inverse kinematic model, which helps pick and segregate packets according to the color they possess. Both accuracy and real-time computing performance are good in the experimental findings of the Faster RCNN model. The precision we achieved by using the Faster RCNN model is 78.8% for detecting and sorting red and blue boxes. Hence, experimentation using Faster RCNN for smart robotic arm has successfully resulted in good outcomes.
Analysis of the Trajectory Planning of a SCARA Robot Using the PRM Algorithm

In Robot Cells, the robot trajectory planning is a major issue to be observed, because the accurate trajectory optimize the application cycle time. In this context, this work presents a study and simulation of robot trajectory planning using the Probabilistic Roadmap Method (PRM) algorithm. A Selective Compliance Articulated Robot Arm (SCARA) robot (TS60, Staubli) and the RoboDK post processor software are employed. In order to analyze the trajectory defined by the PRM algorithm, two scenarios are developed in RoboDK and the robot cell cycle time is measured utilizing a Python script.
Explainable Product Quality Assessment in a Medical Device Assembly Pilot Line

New technologies and data analysis tools such as deep learning models can be beneficial for product quality assessment purposes. However, these black box models can be challenging due to uncertainty and lack of explainability in sensitive pharmaceutical processes. Therefore, different interpretable algorithms have been proposed to overcome the challenges in complex machine learning models. This paper presents an explainable deep-leaning-based fault detection method for quality assessment in an industrial medical device assembly line. This methodology consists of a multi-layer perceptron model that classifies the samples. Then a layer-wise relevance propagation algorithm seeks to explain the logic behind the prediction. Finally, the heatmap pertaining to relevance propagation visualizes the main contributors to the output prediction. Due to the small industrial dataset, a public dataset associated with a robot-driven screwdriving process assists in evaluating the current method-ology. The final results show that the classifier can diagnose different fault classes, and the LRP algorithm can highlight the essential input features and visualize the decision-making process. Furthermore, the LRP algorithm can be beneficial for diagnosing unknown abnormal samples due to the different distribution of contributing features in the heatmap figure. Moreover, a more reliable dimension reduction method can be applied by employing the LRP algorithm and selecting corresponding input data points with higher relevance.
Exploring Data Variance challenges in Fusion of Radar and Camera for Robotics and Autonomous Driving

Inductive bias is a fervently targeted area in the recent developments of deep learning. However, the variance problems are often unattended and are proceeding with the launch of a new dataset. The variance and addressing its problems to achieve better results for a particular task is of importance which follows from the advocacy of the no free lunch theorem. The hard inductive bias of the hierarchical feature pyramid network is not sufficient to generalize and rely on the necessary information in images to produce robust object detection. This work reduced the impact of the inducive variance presented in camera and radar fusion for object detection in harsh environmental conditions. We explore the variance in radar data with the inductive bias of convolution pooling. The proposed architecture uses the untransformed radar data and extracts features using convolution pooling. Experimentations are performed on the nuScenes dataset which shows a 5% mAP improvement in results compared to the baseline. It is observed that the improvement in results is based on choices of data, hyper-parameters, and network composition. The qualitative results of object detection show the significance of proposed tweaks for robotics and autonomous driving applications.
Breast Cancer Prediction using Optimized Machine Learning Classifiers and Data Balancing Techniques

The most prevalent malignancy in females and the second greatest reason for the loss of life from cancer is Breast Cancer. Hence, a computer assisted detection (CAD) system that uses a machine learning technique to give reliable breast cancer diagnosis is required. The paper is aimed to incorporate several machine learning (ML) algorithms, including Support Vector Machine (SVM), Logistic Regression, k-Nearest Neighbour (KNN), Decision Tree (DT), Random Forest (RF), Artificial Neural Network (ANN), and Nave Bayes (NB) with hyperparameter tuning using the Random Forest Feature Importance Method for feature selection. These models have been trained on the Wisconsin Diagnostic Breast Cancer (WDBC) Dataset. Furthermore, the dataset was balanced using both Undersampling and SMOTE from which we concluded that Undersampling gave us an overall better result. The performance evaluation parameters for the designed model are specificity, accuracy, sensitivity, F1 score, precision, recall and Area Under the Curve(AUC). The research concluded that the Support Vector Machine Algorithm proved to be the most effective model which fit our dataset with an Accuracy of 95.8% followed by KNN with an accuracy of 95.3%.