Reviewerly/exHarmony huggingface dataset:
Blockchain based Secure Communication in IoT Environment

Numerous research and initiatives that have been made over the years to strengthen the Internet of Things but have encountered challenging circumstances pertaining to optimization, effectiveness, security, and privacy. Internet of Things (IoT) implementation results in the escalation of attack risks that demands peer to peer security mitigation. IoT and related infrastructure have improved lifestyles and provided convenience through automation services, offering users, enterprises, and governments the capability to effectively manipulate and control the large amount of data generated by internet connected devices. The heterogeneity of connected devices is triggering new IoT security and privacy vulnerabilities, despite the variety of security techniques have already been implemented to ensure security in the IoT. Data privacy in IoT ecosystem can be addressed in an efficient manner by using blockchain based distributed and decentralized system. To maintain safe communication and data sharing in the IoT, a consensus-based system built on the blockchain can be deployed. In this paper we will elaborate the different security techniques being used in IoT and how blockchain can be an efficient way to address the increased security issues in IoT.
AMulti-Stage Cloud Security for Cloud Datausing Amalgamate Data Security

Every organization is planning to deploy their projects and data into the cloud storage based on availability of the cloud services. From the past many years security plays the major role in cloud storage for the data availability. Various Encryption and decryption algorithms are that are utilized to provide the better security for the cloud data. Recently another challenge that is identified in cloud storage is uploading large files within the cloud server. To overcome this issue, the multi cloud security is required to store the files in cloud server. Splitting of files in equal chunks and upload the files into multiple clouds, generating the keys for every files and merging the file when the user required for access. Previously various algorithms, techniques are discussed such as dual encryption technique; Compound Encryption based Algorithm (CEBA) for rapid encryption, Compound Secure Storage (CSS) of data with Ensemble Access of Data (EAD) are the integrated methods to provide the security, improving the encryption and decryption time. A few drawbacks are identified in the above techniques such as limited file size and more time for encryption and decryption. In this paper, An Amalgamate Data Security (ADS) is developed to split the large files into the equal shares and stored into the cloud. Generating the private keys for every chunk of the file is also a tedious task. To overcome this, an efficient file merger (EFM) is developed to merge the requested file by the user by matching the private keys. Result shows the performance in terms of encryption and decryption time based on the size of the file.
Hate Speech Detection Network Using LSTM

Socia1 media is an extremely popular form of communication today. People offer their opinions and insights on a variety of topics, including politics, video games, and their personal lives. These platforms are occasionally used by some people to propagate false information about another person or group of people. The term “hate statement” refers to this kind of offensive material. One of the most well-known social media platforms is Twitter. But many people also use Twitter to disseminate offensive material. It is very hard to manually weed out abusive comments from the hundreds of millions of tweets that are generated every day on Twitter. Therefore, these offensive tweets ought to be automatically filtered out. In this study, we are developing an LSTM model for categorising tweets as either containing hate content or not. The dataset used is a publicly available dataset on Kaggle. This model has an accuracy of 98.04% on the training dataset and 97.19% on the validation dataset. For the test dataset, a precision of 0.98 is obtained for non-hate tweets and 0.93 for hate statement tweets.
Image Encryption based on Advanced Encryption Standard (AES)

With the rapid advancement of electronic and digital data transmission and sharing, information security, user privacy, and confidentiality are paramount in the data storage and transmission field. On account of the predominant application of images in industrial tasks, and military, it is highly crucial to protect private data of the format image against all fraudulent and unauthorized access. This article brings the proposal of a Java Based Software to encrypt images in all formats using the AES Rijndael Algorithm. Optimized and synthesizable Java code with Graphical User Interface using Swing is developed to implement 128-bit data and process the encryption.
An Algorithmic Approach for Text Summarization

With the advent of technology and increase in ease of access to digital devices, there has been a burgeoning increase in the flow and creation of information all across the internet. However, this has led to a lot of saturation and an increased amount of redundant information, which needs to be sorted through manually many times to find the important or pertinent information. But this ends up becoming a tiring and laborious task for many people. Hence, text summarizers, which are softwares that, when given a particular text input, summarize it effectively and output only the important parts in the text, while discarding the unimportant or redundant bits. A number of Text summarizers currently exist on the internet, for free and for a cost as well. Many of them are efficiently able to extract the important information and text out of the given input. However most of them are a single type of text summariser titled “Extractive Text Summarisers”, which, while fairly accurate, is based on a model that merely extracts the important texts from the given input as it is, without specifically phrasing it in any other way, or without semantic understanding of the text itself. This leads to some inaccuracies in the summarized texts, such as some irrelevant words being put in the summarized output, even though they are unimportant, or some important sentences or phrases missing out on being in the output, as they have not been discussed enough in the input. That is why, the proposed system of Query based Summarizer not only works on queries, but is also aimed to be an Abstractive Text Summarizer, which will use semantic and syntactic understanding of the given input to summarize it and deliver a clear, concise and accurate output.
Text-Based Recommendation System for E-Commerce Apparel Stores

Ever since digitalization, online shopping has been trending at its peak. This shopping is in the form of gadgets, food items, clothes, daily use products & whatnot. Due to such a high demand of netizens, like the market-basket analysis in physical offline stores, the online E-commerce stores also need certain recommendation systems using which, around 35% of their revenues get generated as per surveys. In this paper, we have used & presented content i.e., text-based methods that would recommend ladies’ apparel using product similarity measures, based on different aspects of the available text content.
Incremental Learning of Handwritten Characters in EMNIST dataset

An important characteristics of the brain is the ability to learn something new without forgetting the knowledge learned earlier. On the contrary, catastrophic forgetting of the previous tasks is observed in typical artificial neural networks when we try to fine-tune a trained neural network to train on additional tasks. Recently, various methods have been developed to alleviate the forgetting in neural networks enabling incremental learning of new tasks. In this work we investigate for the first time, the task incremental learning of handwritten characters in EMNIST (Extended MNIST) dataset. In our experiments, we have obtained the incremental learning accuracy of more than 96% in task incremental scenario. We present experimental results on the high accuracy incremental learning on EMNIST dataset along with the confusion matrices obtained using custom VGG networks. This may be useful in the development of lightweight machine learning apps supporting handwritten character recognition for languages with relatively large character set or multiple character sets required in a multilingual environment.
PrePy - A Customize Library for Data Preprocessing in Python

When it comes to machine learning data preprocessing is a crucial prerequisite step. It is because any machine learning model trained or tested on an imbalanced data set can lead to inaccurate results. Data preprocessing is a process that takes raw imbalanced data set as input and converts it into a form that is more structured and cleaner which is easily understood and analyzed by machine learning models. This raw data can be in the form of images, videos, text, etc. It includes techniques such as feature extraction, normalization, transformation, data cleaning and selection, etc. For performing data preprocessing data science enthusiasts must import and use many different libraries. There is no such library which consists of every data preprocessing required function in it. Therefore, in this paper, we have proposed a customized library called “PrePy” which has different functions that a user can import and call the specific function to perform a task.
Surrogate-Assisted Multi-objective Genetic Fuzzy Associative Classification by Multiple Granularity Measures

This paper presents a new surrogate-assisted multi-objective genetic fuzzy associative classification model by learning multiple granularities. The specific method is the hybridization of multi-objective genetic algorithms (MOGAs), radial basis function neural networks (RBFNs), and rough set. We show that our approach requires only a few numbers of fitness evaluations compared to the methods proposed in [34] without compromising to maintain an average classification ability in almost all the datasets considered in this work for evaluation of the model.
Healthcare Diagnostics Service Using Federated Learning

Healthcare systems around the world rely on powerful computational prediction tools in order to make accurate diagnostics with regard to the human body. In order to estimate the severity of lung damage post-COVID infection, healthcare providers rely on AI prediction tools to perform diagnosis. While such tools exist at a rudimentary level, there is a growing demand for more reliable and democratised systems that train models over a diverse data-set. To that end, the focus of this research paper turns to federated learning, a distributed machine learning paradigm. The system proposed consists of a central server that pools features and weights across various nodes, thereby cutting bias in the prediction models. This also achieves data decentralisation which ensures patient privacy. An end-to-end application is realised that facilitates distributed training of batch data that is visualised in real-time with the help of sockets. The application also features an inference service, classifying chest x-rays based on whether the image displays damage in case of Pneumonia.
Improved Artificial Potential Field Method for Motion-Planning of Autonomous Vehicles

Motion Planning is one of the primary tasks that needs to be taken care of for autonomous vehicles to function reliably. Given an environment to navigate, the vehicles must be able to navigate towards the desired goal location without colliding with any obstacles. Furthermore, one would want the vehicles to take the shortest route possible, in order to minimize the fuel consumption, and thus reduce the cost of operation. To find these optimal paths, several algorithms have been introduced over time. One such algorithm is the Artificial Potential Field Method, which considers an artificial field around the obstacles and goal, and by moving along the force given by the gradient of the resultant field, an reasonably optimal path is obtained. This paper discusses a variation of the Artificial Potential Field Method for path planning of autonomous vehicles which generates shorter paths than the ones generated using the original algorithm in a wide variety of environments. The simulation results presented reflect that the proposed algorithm reduces the path length by roughly 10% as compared to the path generated by the classical potential field method.
Deep Learning Model for Facial Kinship Verification Using Childhood Images*

Kinship Verification (KV) using facial images is a challenging research problem in computer vision field. It is a binary classification problem. Mainly, this research works on similarities of two facial images. If the age gap is more between parent’s image and their children then their facial similarities are dispersed. Further, it is quite challenging task to assess their kinship. This is called age based kinship verification problem and it is one of the research gap. In this article, we proposed a model with childhood image database. This childhood database consists of parent and their children image considered at their childhood stage which is minimum age gap so that their facial similarities are more. Further, proposed a model with inception neural network to extract the facial features. In classification stage multiple dense layer are designed to assess the kinship of given test pairs from trained weights (W). Our proposed model experimented and achieved 92% accuracy for the proposed databases and also performed for other existed database in the wild which has shown better accuracy as compared with state of art methods.
Arabic Pilgrim Services Dataset: Creating and Analysis

With Countless Arabic news articles published daily; users have become increasingly concerned about obtaining news from credible sources. Nonetheless, to individuals, credible news sources are associated with certain countries where users have faith. Therefore, detecting the source of a news article is imperative to fake news detection and enables users a better trust in their consuming news. This paper introduces to create, filter, analyze, and evaluate a domain services-specific Arabic dataset for pilgrims. The Arabic Pilgrim Services (ArPiS) dataset is a collection of approximately 30,000 news, collected across three different Arabic countries and regions. The paper presents a creation for pilgrims’ opinions measurement services dataset for text mining, text classification, clustering, and text summarization. The default basic search methods start with 124 web sites of Arabic news. Then, many of filtering features have been done to limit the dataset by pilgrim subjected services. A lot of topics are addressed, and a lot of filter with a discussion group have been made with many opinions| and extra comments. The huge of the collected data need some kind of additional effort and more analysis to produce valuable dataset. Balanced dataset is one of this extra effort, we are going to create. Therefore, the collected and annotated dataset represents real news for pilgrims’ services. So, we need to build additional quantity of these data to be fake news. Accordingly, a precondition procedure invoked as a methodology to create and then annotate such dataset.
ViT-DeiT: An Ensemble Model for Breast Cancer Histopathological Images Classification

Breast cancer is the most common cancer in the world and the second most common type of cancer that causes death in women. The timely and accurate diagnosis of breast cancer using histopathological images is crucial for patient care and treatment. Pathologists can make more accurate diagnoses with the help of a novel approach based on computer vision techniques. This approach is an ensemble model of two pretrained vision transformer models, namely, Vision Transformer (ViT) and Data-Efficient Image Transformer (DeiT). The ViTDeiT ensemble model is a soft voting model that combines the ViT model and the DeiT model. The proposed ViT-DeiT model classifies breast cancer histopathology images into eight classes, four of which are categorized as benign, whereas the others are categorized as malignant. The BreakHis public dataset is used to evaluate the proposed model. The experimental results show 98.17% accuracy, 98.18% precision, 98.08% recall, and a 98.12% F1 score, which outperform existing classification models.
Autoresponder using Chatbot for Educational Services

The Internet of Things (IoT) has had a significant impact on human existence. This branch of study will lead to the creation of technology and concepts that will enable humans to communicate with machines. that is specifically developed it for a specific job. Students and faculty members need to have access to educational service. When learning became fully electronic, during the Corona pandemic, every effort was made to strengthen services such as technical support and access to education materials. Chat bot is the most popular feature on Telegram which allows a third party or user to design bot functionalities based on user requirements. As a result, autoresponder messages can help solve a variety of issues, including searching for educational sources, accessing technical support channels, and FAQs, and easing the heavy burden on technical support that occurred during the COVID-19 pandemic. By developing this service, you may get reply by essential information to lecturers, students, and the academic community while saving time and handling many requests concurrently. Where the service has been developed to be available 24 hours to provide all data and access links directly without having to search for them.
Human Activity Recognition in Smart Cities from Smart Watch Data using LSTM Recurrent Neural Networks

Due to the convergence of healthcare and smart cities, information and technology are employed in health and medical procedures worldwide. Residents of smart cities now enjoy better lives and healthier bodies because to integration. The advent of smart watch technology and deep learning techniques has engendered a potential increase in performance and accuracy of such tasks. Our paper explores the application of recurrent neural networks particularly LSTM for action recognition task using smart watch sensor data, i.e., gyroscope and accelerometer data. We have taken into consideration some general activities like standing, walking, sitting, etc. and implemented a LSTM network for the action recognition job using the time sequences sensor data. This model can be useful for other health-based applications which can monitor the health conditions of a user by keeping record of his activities.
Real-Time Sign Language Recognition System

Hearing and speech impairments affect more than 36 million of the world's population. A sign-language recognition system is a crucial step towards the enhancement of communication among visually impaired people. The main idea behind this project is to create a real-time sign language recognition deep learning model which can work on pre-trained image and video dataset and give us the output in real-time. Our system would take the input as an ASL alphabet and save it and then show the string in text or audio format according to the user's choice. It involves accurate extraction of hand gestures using appropriate sensing devices in our model for the smooth communication between the normal and visually-impaired people. This project can serve as a perfect service to the educational industry.
Performance Analysis and Classification of Class Imbalanced Dataset Using Complement Naive Bayes Approach

Data mining technology is essential to all of the major engineering professions in the modern world. Big data is a developing trend. The amount of data is increasing exponentially these days. The complexity of this data makes it difficult to analyze, store, and process. To manage this massive amount of data, we need a system that can extract the intricate representation of the data. For the purpose of overcoming this issue, the complement naive bayes (CNB)algorithm is employed. One of the most popular classification methods is the complement naive bayes algorithm; however, a new data pre-processing method has been proposed that can deal with imbalanced dataset concerns. A balanced dataset can be classified as the number of examples of some class is equal to or lesser than the number of examples belonging to other classes and the dataset is evenly distributed. Imbalanced datasets can be classified as the number of examples of some class is higher than the number of examples belonging to other classes and datasets are not evenly distributed in addition to this, an Inherent issue will occur in an imbalanced dataset. In the suggested article, the dataset may be distributed uniformly and the underlying problems can be effectively resolved by using the CNB technique. There are some libraries available to process huge data such as hive permits, Kafka, Hadoop, and mahout. Hadoop is utilized in this study to determine the class and attribute probabilities. Using training datasets, experiments were conducted, and reviews were categorized as a working model and a failure model. The Performance of the classes' working model and failure model were compared according to working time, accuracy, memory space, quality, speed, and security.
Human Interaction-Free Object Localization in a Scene

Object detection methods use NMS (Non-Maximum Suppression) to remove multiple detections for a particular object for its localization. To perform this task, NMS requires a confidence threshold and an IoU (Intersection-over-Union) threshold which need to be supplied by an user. Thresholds are fixed and different for different object detection methods, e.g., R-CNN, Faster R-CNN, YOLO, etc. In this paper, we propose a method that uses a suitable regression model to find the threshold values which is adaptive in nature, eliminating the need for human interaction for localization of objects in the scene. The order of the model is determined through bias-variance trade-off and its goodness-of-fit is justified by R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (R-squared) score and χ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (Chi-squared) test. Results are impressive and attractive.
An Autonomous Assistance Robot for Multi-Purpose Medical Applications Using ROS

Over the years, the robotic industry has made significant growth in the manufacturing sector due to the need for collaborative and interactive robots. But it is not the case for service sectors, especially in the healthcare sector. A lack of emphasis is given to healthcare which has led to new opportunities for developing assistive robots which can aid patients with disabilities and illnesses. Furthermore, COVID-19 has acted as a catalyst for the development of assistive robots in the healthcare sector in an attempt to overcome the difficulties faced due to viruses and bacteria. This paper demonstrates the simulation of a multi-purpose medical assistive robot using ROS(Robot Operating System). This intelligent robot is successfully simulated and visualized in the ROS environment. To achieve real-time autonomous motion Google Cartographer SLAM(Simultaneous Localization And Mapping) is used to generate real-time maps of unknown environments. It usually focuses on how these robots can provide assistance to health workers, customers, and organizations in different sectors of the healthcare environment.