Reviewerly/exHarmony huggingface dataset:
Electronic Health Record Management using Hyperledger Fabric

In these modern times, receiving health care services from various hospitals and clinics has become very common due to the predominant increase in the specialization of health care services and also as a consequence of people's moving to different cities and countries. Doctors who have a thorough understanding of a patient's medical history can more accurately make a diagnosis and provide treatment for the patient in the right timeframe. Despite improved medical services, healthcare service providers face issues in sharing sensitive data. The major problem faced by healthcare service providers right now is ensuring data security, data integrity, and confidentiality of the data while keeping the privacy of patients intact simultaneously. To solve this problem, we propose a blockchain technology using Hyperledger Fabric that allows patients to control their medical information and access doctors for data. By creating a distributed database of the patient's medical records, the patient can switch hospitals without bothering about the medical records. It also makes it easier for a doctor to keep track of a patient's medical history.
Secure Wireless Sensor Network Design Using a New Method of High-Speed Lightweight Encryption

Data streaming over a wireless network such as Wireless Sensor Networks, where wireless terminals (like PDAs, mobile phones, palmtops) access in data conferencing system, new challenges will be brought about. goal for this paper is to propose a high-speed lightweight encryption (HSLE) for low computational capability controller of WSN, HSLE scheme which reduces latency overhead by modifying existing approaches in order to encrypting data using a probabilistic encryption of data blocks. Proposed work is also useful when we communicate our confidential data on WSN or IoT it should be secure, we just have to save an encrypted data on cloud servers. proposed work is a new key-based algorithm and uses HSLE encryption instead for high end AES. Proposed methods cause significant speed enhancement for data encryption with similar security, in addition, it is best suited in order to communication between hand-held devices such as mobile phones, palmtops etc. algorithm may be used between sites where processing capacity and battery power are limited and efficient encryption is main necessity. This work is implemented on MATLAB and a wireless sensor network of maximum 100 nodes developed for testing the proposed network node encryption system, the time delay observed for the communication in 100 nodes WSN is less in compare with the other available works.
Analysis of bio inspired Optimization Algorithms: ABC, FA and ABO based on the Travelling Salesman's

Engineering problems are becoming more and more complicated to solve as entire world is moving towards the industrialization. Increasing number of variables, dimensions and complex time and space made it more difficult to solve such problems. Similar to such problems nature is also complex, however insects, birds and animal survive in this intricate surrounding with the help of their unique qualities. Various insects or groups of animals seen in nature, such as ant colonies, bird flocks, bee swarms, and behavior of fish, could be used to address real-world problems. These solutions are called bio inspired optimization algorithm. This study focus on three mostly used bio inspired optimization algorithms artificial bee colony(ABC), firefly(FA) and African buffalo algorithm(ABO). In this study we have analyzed the results obtained by solving the travelling salesman problem using ABC, FA, and ABO algorithms.
Facial Expression Recognition with Combination of Geometric and Textural Domain Features Extractor using CNN and Machine Learning

Human emotions may be characterized by the expressions on their faces, which is the goal of research in FER (facial expression recognition). These include biometric security and human-computer interface as well as robotics and clinical care and treatment for illnesses like autism and sadness. Face expression analysis technology is examined in this dissertation, as well as the development of artificially intelligent systems for practical usage. In order to improve FER, a combination of geometric and textural domain feature extractors and several machine learning approaches is employed in the study's initial section. Once these two cutting-edge algorithms have been blended, a framework is built to test their individual and joint performance in the domain of facial recognition. Advanced facial alignment and localizing procedures are then employed to dissect the face into its constituent parts. Additionally, FER examines the use of Convolutional Neural Networks for deep learning (CNNs). Two proposed available facial expression databases are used to validate the proposed method. Using an ensemble neural network, the experimental findings indicate high accuracy for diverse datasets on the combined features.
Image Denoising Using the Hurst Exponent: A Fusion Based Approach

We propose in this paper a denoising method based on the texture roughness appreciation, realized using a semi-local estimation of the Hurst exponent. The estimation is made in the Dual Tree Complex Wavelet domain, by using techniques such as the least squares or the lasso. The Hurst exponent is used to correct the energy distribution of the wavelet coefficients. This removes the noise in homogeneous areas but can oversmooth the edges. In order to overcome this, we propose a fusion based approach, based on the observation that the noise is less visible on the edges. Both the denoised image as well as the original image are weighted by images that depend on the Hurst parameter, such that the edges are less degraded, and the correction is applied predominantly in the homogeneous areas. Our simulation results are promising for images containing both smooth areas as well as edges for the fusion based approach.
Modeling of Optimal Deep Learning Enabled Object Detection and Classification on Drone Imagery

Object detection in unmanned aerial vehicle (UAV) images becomes a persistent problem in the domain of computer vision. Particularly, object detection in drone images is a difficult process because of the object of different scales namely, hills, buildings, and water bodies. The study presents an execution of ensemble transfer learning to improve the efficiency of the fundamental model for multi-scale object recognition in drone imagery. This study develops an Optimal Deep Learning Enabled Object Detection and Classification on Drone Imagery (ODL-ODCDI) technique. The presented ODL-ODCDI technique can recognize and classify the objects present in the images collected by the drones. It follows a two stage process. In the first level, the ODL-ODCDI technique employed YOLO-v5 as object detector with Nadam optimizer. Next, in the latter level, the ODL-ODCDI technique makes use of random forest (RF) classifier to identify objects in the drone images. To establish the enhanced performance of the ODL-ODCDI approach, a series of experiments were performed. The experimental values depicted the improved outcomes of the ODL-ODCDI method over other DL models.
Reactive Safe Path Following for Differential Drive Mobile Robots Using Control Barrier Functions

This paper addresses the problem of safe Path Following (PF) for differential drive mobile robots. Our solution builds on Control Lyapunov Functions (CLFs) and Control Barrier Functions (CBFs), which guarantee that the robot will avoid obstacles and will converge to the desired path in the absence of obstacles. We propose a novel Control Lyapunov, and Control Barrier Functions for a Differential Drive mobile Robot (DDR). The novelty of the CBF stems from the fact that it produces a first-order CBF, contrary to the typically used barrier functions, and thus significantly simplifies the control. The proposed CFB also provides hyperparameters that allow the designer to control obstacle avoidance behavior. To demonstrate the control algorithm’s efficacy, the proposed method is implemented on a real DDR to follow a predefined path while avoiding a circular obstacle. We report on the experimental results and the effect of some of the parameters on the system performance.
Simplifying Privacy Agreements using Machine Reading Comprehension and Open Domain

Privacy Agreements are critically important from the company as well as the user's point of view. Upon surveying, we realise there are three most common issues faced by the user while trying to read their privacy agreements. First is excessive use of complex language and jargon that make it extremely difficult for the user to comprehend the agreement. The second issue is that if the user has a few questions with respect to the agreement, they are not answered directly. Lastly, there lies an absence of an agreement evaluator, that can help compare the document with the standards of the industry. We aim to alleviate this situation by proposing a three-fold technique involving Text Summarization, automated question answering and a Document Evaluator. Reading comprehension is one of the most critical abilities in human intelligence, and machine reading comprehension is one of the most popular research directions in the field of natural language processing. Machine reading is one of the key aspects to achieving artificial intelligence. The introduction of deep learning and large datasets in the recent few years has encouraged the success of MRC. Additionally, the open domain automatic question answering models have been widely studied in recent years. However, the problem of lack of sufficient data to read information from exists that makes questions out of the context of the text seem difficult to answer. In this study, we propose a novel framework to integrate MRC along with Open Domain to answer questions with increasing accuracy. This is achieved by classifying the question and retrieving the answer from multiple sources. Our model should be able to extract answers from discontinuous spans across the passage text. The answer that has the highest confidence associated with it is returned as the final answer. Moreover, the existing passage has been updated based on the questions answered.
FPGA Implementation of ANN Using Customized Instruction Set for 1-D Signal Processing

This work describes the implementation of hardware architecture based on ANN and its hardware realization using hardware description language (Verilog) on FPGA. The objective of this project is “FPGA implementation of ANN using customized Instruction set for 1-D signal processing”. Our aim in this project is on designing small in size and functional processor architecture. The proposed project presents architectural design and execution of artificial neural network processor with a customized instruction set processor for 1 D signal processing. The ANN processor architecture is flexible, supporting random counts of layers and a number of 784 artificial neurons (ANs), The proposed architecture will support the feed-forward algorithm. The proposed architecture is flexible with the counts of input data; output data and signals will represent the fixed-point format and will compare the accuracies of the Tanh function and sigmoid function.
Detecting Fake Profile in Online Social Networks using EnsemStack Classification Algorithm

Online Social Media (OSN) Platforms are the most dominating in the world amongst all age groups. These platforms are termed to be dynamic because they have increasing users day by day. The main benefit of social networking over the internet is to connect with people and communicate better. Users are sharing their data, photos and personal information on OSN platform like Facebook, Instagram, LinkedIn, and Twitter. Criminals are also used OSN platforms for new attack methods such as fake identities, disinformation, and many more. According to a recent study, the number of accounts that exist on social networks far outweighs the number of users who actually use them. Hence the no. of fake profile on OSN platforms are increasing over the last decade. The proposed model based on EnsemStack classification mode has the potential to identify the OSN account as real or fake with more accuracy. Our model hence takes the input of the from Instagram user account from and extracts all the required features the account as real or fake. The primary objective of proposed system is to absolute the existence of these fake profiles in these OSNs using machine learning. Eliminating these fake profiles will help to curb all the malpractices.
A Reversible Image Steganography with High Embedding Capacity using a Novel Pixel Selection Method

Reversible Image Steganography (RIS) has got attention in academia, industry, and research fields due to its capability of image reconstruction. It shows a crucial role in real-time applications such as military communication, healthcare industries, cloud data security, and also recently on the Internet of Things facilitated interaction systems. In this article, a RIS with high embedding capacity is proposed using a novel pixel selection method. Initially, in the embedding algorithm, the pixel location is calculated to embed the secret message into the image. A new mathematical model is proposed for this calculation. Then based on this, the secret messages are embedded with less distortion. Then the extraction algorithm is used to recover the secret message from the image. The experimental findings depict that the proposed work has the ability to retrieve the initial version of the cover image. It is also observed that the proposed work improves the previous works in terms of image quality.
Intelligent Fuzzy Obstacle Avoidance System for Autonomous Vehicle Parking

Autonomous vehicle parking has been improved significantly in recent years. There are many methods for achieving this goal, and fuzzy control is one of them. Due to parallel parking’s complexity in the presence of obstacles, an autonomous system can minimize errors and accelerate the process. This study first implements a fuzzy parallel parking system from the literature and then adds a fuzzy obstacle avoidance system. This system uses two fuzzy controllers to reduce the possibility of running into the point and continuous obstacles during parking, where a decision controller switches from one to the other as necessary. Also, some modifications to the surrounding vehicle sensors are done for better reactions. The final system is simulated in challenging environments with static and dynamic obstacles, demonstrating its robustness and superior performance. Considering that autonomous vehicles are gaining attention worldwide, this study can assist in developing automatic intelligent parking systems.
Recommender System: Analysing Products Using Data Stream Mining

Sending data into batches and having a continuous process on it is Data Streaming. Apache Pyspark has been a well-known tool for streaming process using Python language. Using these features, we have implemented a Product recommender system for the customers using will help the customers to easily get products of their interest.
Mining Social Media Data Streams for Sentimental Analysis

Stream mining is the technique of collecting knowledge or patterns from continuously changing data streams. Data streams, unlike standard data sets, are collections of data instances that flow in and out of a system at different rates. They are organized in time, constantly changing, vast, and potentially limitless.
Cryptocurrency Price Prediction using Long Short-Term Memory and Twitter Sentiment Analysis

Machine learning has become the backbone of bitcoin portfolio optimization in today's technological era. This research applies a deep neural network (DNN) model, Long Short-Term Memory (LSTM), to historical bitcoin prices and Sentiment Analysis to tweet data gathered from Twitter. The LSTM algorithm is used to train the model and forecast the future cryptocurrency price. Sentiment analysis, on the other hand, examines sentiment on Twitter to determine the relationship between sentiment and cryptocurrency price fluctuations. Sentiment analysis categorizes Twitter sentiment as positive or negative, and the fraction of positive and negative tweets is used to forecast bitcoin price fluctuations. The predicted price fluctuation data is then added to the LSTM predicted price to predict the new price for the next time frame. Finally, both models forecast future cryptocurrency prices and patterns, particularly Bitcoin.
NMT for English-Marathi using RNNs &amp; Attention

Neural Machine Translation (NMT) has led to significant changes in the world of machine translation. It is a huge improvement over Statistical Machine Translation (SMT). With the advent of the transformer model introduced in the paper “Attention is all you need” [1], (which introduces mechanism of self attention) the industry has seen significant improvements in translation, but the transformer model is resource & data intensive hence RNNs seem a better option in terms of data and computing constraints. While there have been numerous implementations of NMT for western to western language translations, there have been comparatively fewer implementations for Western to other languages especially Indic languages apart from Hindi. Indian languages also have various features like the use of honorifics, gendered objects, differences in usage like formal/informal setting, etc thus differing from most of the Western languages in a fundamental way where a lot of mentioned features are unavailable & language structure is rigid in nature. Our paper aims to find the best as well as give a comparative analysis of various NMT models for English to Marathi translations. Our models include - Plain LSTM, Plain GRU, Plain Bidirectional LSTM & GRU, LSTM with Attention, GRU with Attention, Bidirectional LSTM with Attention & Bidirectional GRU with Attention.
Comparative Study of Different Models for Language Translation

As India is country with diverse culture, there are 22 official languages and many other regional languages. Because of this, it is difficult for people to exchange the ideas and communicate with each other. In Neural Machine Translation (NMT), one language is translated to other which helps to overcome the barrier of language difference. In this paper, we have studied NMT Models to translate English sentences to Marathi. The neural machine translation models on the basis of Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU) are implemented respectively considering the differences in the structure of their networks. Transformer is also implemented after we discover the limitations of the above three basic models which are Long Term Dependency of Words, Exploding Gradient and Vanishing Gradient problem. Common steps followed in all models are Preprocessing, encoding and decoding. After studying all the models, a comparative analysis of models is done based on the Bilingual Evaluation Understudy (BLEU) score where we calculated Unigram(1-gram) and Bigram(2-gram) scores using reference and translated sentences.
FRNDY: A Women's Safety App

Women's security has always been a major concern, and numerous potential solutions have been discussed regarding how technology can be used to address the issue. Currently, the rapid rise in smartphone usage has made it possible to integrate personal security efficiently and effectively with the help of hardware and software. Existing security methods necessitate some form of human input and the use of only pre-selected contacts. When it comes to tragic occurrences involving women, the number is frighteningly high. Problems can arise from a variety of situations, including women walking on the street after work, going to the supermarket, or a variety of other reasons when they are alone. There has been a spike in women-related crimes in Malaysia as well. Therefore, to enhance women's safety in Malaysia this project suggests a mobile application that can come in handy for women who find themselves in dangerous situations. When the need arises, a single click on this application will recognize the location using GPS and will send a message to the registered contacts containing the location URL, as well as call the first registered contact to assist the user in a difficult situation. Authentication of their identity is also mandatory for this application. The primary goal of this study is to ensure and enhance personal safety. As a result, the primary goal of this project is to make it easier for users to get assistance promptly. In addition, a security pin is used as the login password to verify the user's identity and prevent an anonymous or malicious attack. This project makes use of the agile methodology to make certain that testing is carried out in each module. It creates opportunities to reevaluate the development lifecycle to make sure the entire project fulfills the outcome and operates well. After several iterations of refinement and debugging, the final system is deemed bug-free.
Object Distance Estimation from a Binocular Vision System for Robotic Applications Using Artificial Neural Networks

The distance to objects in the robots workspace is important to bring near the end effector closer without colliding. This paper presents a distance estimation method from the binocular vision system to the objects in the robot workspace, with two images captured by the cameras and then are processed by the artificial neural networks. The proposed method was tested with a 4-layer ADALINE-type artificial neural network of 61 artificial neurons and 129,920 inputs that correspond to the pixels of the captured images. The vision system can work with low-cost cameras such as webcams because the optical parameters are not required to train the artificial neural network. Finally, test results on an experimental setup with webcams are presented and they have a suitable precision for robotic arm applications.
Extensive Study of Automatic Text Summarization on Biomedical Texts

Documents containing biomedical information come in a variety of formats. Clinical, research doctors and researchers can use biomedical literature to assess the most recent achievements in a field of study to generate and validate new theories, conduct experiments, and analyze the results. Medical records are difficult to analyze since they are normally presented in plain text, have a very particular technical language, and are almost always unstructured. Due to the ongoing increase of online text resources Document summarization offers a plethora of applications in data extraction and web search. We have done a survey of automatic text summarization techniques. Considering the existing research gaps, we have proposed a model which utilizes BERT and Group Average Linkage clustering on articles taken from PubMed. This work would be helpful for people in the biomedical domain by summarizing huge biomedical texts. It would provide a brief summary by condensing the information in a nutshell, thus saving time.
Analysis of Face Recognition Based Automated Attendance Management System Using FaceNet

This paper gives an analysis and study of the different face recognition algorithms that is used in our application i.e Automated attendance based automated attendance system. Human faces consist of complex multidimensional structures that can convey information like expression, facial features, feelings etc. Identifying a human face can be an easy task for a human being even if the person is seen after many years. But performing the same task by a machine (computer) is difficult because if there is change in certain conditions such as lighting conditions, sophisticated background, different pose and illusion, image taken from different angles etc, then the computer finds it difficult to identify the person. Identifying human faces by computer accurately i.e Face recognition is still an emerging field and research is going on.