Reviewerly/exHarmony huggingface dataset:
A Study on Cloud-based data integrity of outsourced data using Blockchain

As there has been a lot of headway in distributed computing for a long time, information security and believed registering are the fundamental difficulties in current distributed computing applications. To tackle this issue, numerous researchers have completed many examinations on this and proposed many models, including information uprightness tests and secure multi-party computation. Nonetheless, many of these arrangements deal with issues like unnecessary computational intricacy or the absence of adaptability. Dangers to information honesty are of principal significance, as messing with information may perniciously influence critical business choices. This issue is particularly evident in distributed computing conditions, where information proprietors can’t handle essential information viewpoints, similar to the actual stockpiling of information and the control of its gets. Information uprightness is a significant job in distributed computing security and capacity administration. Henceforth, the information respectability conspires to give confirmation of the honesty of the put-away information in the cloud. Rethought information, as the critical part of cloud administration, has been generally utilized because of its comfort, low overhead, and high adaptability. To ensure the trustworthiness of re-appropriated information, the information proprietor (DO) generally embraces an outsider evaluator (TPA) to execute the information respectability check plot. Blockchain has, as of late, arisen as a fascinating innovation that, among others, gives convincing properties about information trustworthiness. This paper considers the utilization of Blockchain procedures to advance the present circumstance. Blockchain is a decentralized new circulated registering worldview. In this paper, we center around the honesty confirmation of reevaluated information in an untrusted climate, that is, how to work on the security and proficiency of information respectability checks without using untrusted TPA
Blockchain based digital voting system: A secure and decentralized electoral process

Voting is the right of every citizen to make significant decisions regarding the selection of their respective government. The contemporary voting techniques such as ballot boxes, Electronic Voting Machines and digital voting systems can be susceptible to security concerns such as DDoS attacks, fraudulent votes, vote tampering and manipulation and virus assaults. The proposed method an attempt to propose a secure electoral process with the use of block chain technology. This article proposes the use of Blockchain Technology to mitigate faults in existing e-voting system and offers more safe, trustworthy, and transparent system. The proposed block chain based digital voting proposes data security and decentralization to address the challenges with the current voting method.
A Survey of Virtual machine migration, Optimal Resource Management and Challenges

Virtual machines (VMs) have become increasingly valuable for resource consolidation and management due to their efficient and secure containers, as well as their capability to offer desired execution environments for applications. The cloud is also becoming increasingly popular, which is hosted in large data centers. The vast majority of these large data centers use a virtualized server infrastructure, known as a virtual machine (VM), that is managed by a cloud infrastructure service such as an open stack or cloud stack, etc. Computing, storage, and communication resources are increasingly needed in large Cloud Data Centers (CDCs). With the growth of cloud environments, VM migration has become an important criterion to ensure Quality of Service (QoS), save energy, and reduce resource usage. This paper examines the merits, challenges, and resource management aspects of virtual machines migration. In addition to providing load balancing, online system maintenance, proactive fault tolerance, power management, and resource sharing, it also helps to manage system power consumption.
A Perspective Analysis of Understanding the most important factors of the IoT Technology in Transforming the World by reducing Greenhouse Gas Emissions: A Multiple Regression Analysis

The rise of digital technology offers enormous potential for tackling a range of social other related issues. It has been highlighted that the requirement for energy is anticipated to rise significantly over the next 20 years, and there is rising worry about how to fulfil this need in a responsible fashion. The Internet of Things (IoT) makes it possible to help energy firms, the authorities, businesses, and people in contributing to climate change, effectively using assets, and lowering emission of greenhouse gases. Therefore, despite the app of IoT power grids’ ability to support in making connection, increase efficiency in power usage, and possess inclusion between various sources of generation, conventional ac grids’ complexity is unable to assist the ever-increasing energy demands. Reversible engagement and improved demand administration are made possible by the utilization of intelligent systems, which satisfy user wants and demands. IoT implementation may also assist in managing power use in a sustainably. Micro grids built on the Internet of Things may lessen the strain during commute hours. Emitting chemicals.
A Real-Time Voting Process That Redirects Facial Recognition

The right to vote is a fundamental fact of how members of parliament are chosen. The foundation of Indian democracy is voting. The majority of a nation’s future is decided through elections. The modern equivalents of traditional voting systems are paper ballots and electronic voting machines. These systems have a number of shortcomings. In this investigation, a real-time facial recognition voting system is developed to address all these issues. The proposal discusses developing a facial recognition-based electronic voting system to stop voter fraud. The system was created using a Raspberry Pi together with a number of other relevant components, including a webcam, LCD, and different GSM modules. Face recognition is chosen because each person is unique. The retinas of each person differ, which helps reduce errors.
Anomaly Detection in Video Surveillance Using Convolution Neural Network

Day by day world is moving towards the digital platform, and in the digital world, real-time video surveillance systems are all in high demand because of the expanding needs of cities and industries. So far, many conventional methods have been implemented in this era, but nowadays, machine learning and artificial intelligence-based anomaly identification system address a portion of the difficulties. AI and machine learning-based methodology extract the image’s dynamic features (aberrant or abnormal behavior across time). The constant movement of people and the fluctuating conditions of outdoor cameras, along with dimensionality and features in video frames, make anomaly detection very challenging. This article uses the neural network architecture to extract the features from the input video and identify the anomalies in video frames. Finally, we used the CUHK Avenue dataset to authenticate the proposed work. According to the results, the outcomes of the proposed work are over performed compared to other state-of-the-art methods.
Awareness Level Analysis of Public Key Infrastructure Security

The Public key Infrastructure is famous for managing public-key encryption. It helps in the secured transmission of information across a range of network activities. It possesses three main capabilities such as integrity, authenticity, and confidentiality. In the present study, an attempt has been made to study the working of PKI and examine the various applications of PKI security. To examine the awareness level of PKI security, the primary data has been collected through structured questionnaires based on a 5-point Likert scale. The sample size selected for the survey considering a 10% margin of error is 196 using the purposive non-sampling probability technique. Various statistical tools were used to analyze the data like Cronbach $\alpha$, Shapiro-Wilk normality test, correlation matrix, and one sample T-test. The results of the learning showed that the internal consistency of the data is excellent and the awareness level of PKI differs from respondent to respondent. All the parameters used in the questionnaire are correlated with each other.
Image Enhancement Using Laplacian Gaussian Pyramid Based Fusion and Band Rationing Algorithm

A significant part of our daily lives is influenced by images. It is common for people to capture and edit a lot of photos. In this paper, dark images are enhanced and the quality of the images is assessed. Image quality was checked using a CNN algorithm. Dark images will be noisy and low in quantity. The purpose of this paper is to describe a novel method for improving dark images captured at nighttime through dehazing, image replication, denoising, exposure fusion, band ratioing, and weight map creation. Initially, the paper focuses on the quality of the input image. As a second step, they propose an efficient dehazing model using weight map creation to remove haziness and improve image quality. It is crucial and necessary to remove night time haze to keep human eyes from suffering from poor vision. Band ratioing methods can be helpful for analyzing brightness and darkness levels. Based on the results, the proposed methodology is able to improve the image and reduce noise in the dark low light image and produce a good HDR quality tone image.
Clustering of Network Traffic: A Comparative Study

For network security applications, accurate network traffic categorization is essential. Due to the prevalence of programmes that use energetic ports and encryption to evade detection, there are certain drawbacks to earlier approaches like port numbering and payload-based classification. Utilizing machine learning (ML) methods is an alternative strategy. Here, we’ll discuss three clustering methods based on application flow statistics: K-Means, FarthestFirst, and Canopy. Our discussion will focus heavily on the effect the data set processed by the PCA dimension reduction technique has on the performance of the three methods mentioned above. Our findings demonstrate that following dimension reduction, both classification accuracy and computing performance all significantly improved.
A Deep Learning Based Approach To Recognize Real Time Emotions

Facial expressions is the most primary way by which a human recognizes another person’s emotion. With a rapid development in the field of Artificial Intelligence and Machine learning, research have started to develop and improve a sophisticated technology by which a machine or a computer can recognize emotions and mimic a human’s thought process for the same. The proposed methodology for the same is to make use of deep learning. Deep learning is very closely related to machine learning. It allows multiple layers of neural networks to be stacked upon one another enabling our algorithm to “think” like a human. Many mathematical models are also used to provide the best results and improve on the currently known technology. This paper discusses most of the known approaches for emotion recognition and tests out geometry based using machine learning and deep learning-based implementation providing a deep insight into both methodologies
An Efficient Techniques For Disease Prediction From Medical Data Using Data Mining And Machine Learning

Individuals are susceptible to numerous ailments in today’s global setting, with people living highly automated lives under tremendous job pressure, both at home and at work. Such disorders have recently been on the rise at an alarming pace. As a result, the healthcare business must assume a prominent position soon, accountable for people’s health, a better society, and a successful country at large. Healthcare prices are rising as the demand for health amenities grows. Healthcare facilities with improved detection, diagnostics, and treatment procedures are desperately needed. With growing digitization and computing techniques in place, a massive amount of data is being generated and used for diagnostic and detection approaches. This data might be used to obtain data for anticipating disease illnesses, commencing preventative measures, and improving treatment procedures long before the diseases progressed. Powerful computing technologies must be used to build the finest intelligence of forecasting and decision-making abilities evaluated by an expert. Modern data mining methods are used to the obtained to analyze such characteristics that may reveal crucial and critical indications regarding the existence and progression of the medical condition.
Data Characteristic Stability Based Random Forest Implementation of Credit Card Fraud Detection

The rapid growth of internet enabled technology enhanced the digital paying system in recent days. Financial Applications in online mode become much easier. People access the internet on the go and do Applications in a fraction of seconds. On the other hand digital environment allows the fraudulent are unauthorized attackers through loop holes available in the internet platform. This paper proposes a Data Characteristic Stability based feature selection by implementing the Random Forest algorithm for Credit Card Fraud Detection. After implementing the above optimized method for credit card fraud detection, the proposed detection engine result is compared against without applying Data Characteristic Stability using the following ML classifiers: Decision Tree (DT), Random Forest (RF), Logistic Regression(LR), XGBoost and etc. The Proposed solution is considered to be efficient an way because of the tendency of the drift happens after its deployed into production, where now we identify the indicative data drift during the Model development and helps to outperform in identifying the fraudulent cases way better than the traditional methods used in the market.
Two Dimensional Cellular Automata Encryption Method for Security

Since the internet is today regarded as the fastest and most efficient method of transmitting signals, the privacy of signals or info is drastically decreasing. Therefore, it is essential to have a high level of privacy to hide the mysterious data. A common and effective method for protecting output information from unauthorized is encrypted. This method hides hidden data in an image using a cryptographic keys. However, it only provides a single level of protection for the data transmission. This legislation’s goal is to provide two levels of security. The first stage involves hiding information behind certain images using coded words, while the second stage involves encrypting using 2D deterministic algorithms. Employing colonial automata’s 2-D principles, decryption is completed. The surprise data is introduced into the image using the smallest bit (LSB) and Expanded Least Major But (ELSB) techniques in the first level of protection with the help of a secret word. The LSB technique was used in the dim image to encrypt the information contained in the image. In an RGB image, the data was masked using both Sb & ELSB techniques. While a ELSB technique only employs single Rgb aircraft, the LSB approach for RGB photographs uses all three vision plane. On the image in which emission data was then installed using Sb or ELSB techniques, 2D cell enary rules were implemented in the first security level. We used the 2D state machine principles 2, S, 32, and 12S to provide an extra layer of protection to data emission. Cellular Lattice rules provide increasingly complex and reliable information about the symmetric encryption. In this manner, a variety of ideas were used to get a great deal of safety. Unscrambling was done in an unparalleled manner, yet quickly after the demand.
A CNN-inspired Reverse Search Engine for Content-based Image Retrieval

The Reverse Search Engine enables the search and retrieval schemas of images which are visually comparable to a specific query image by utilizing the features of the images that illustrates the visual content of provided images. This contribution creates a contextual schema for recovering photographs that are indexed in distributed databases from the visual information without the requirements of linguistic annotations. We implement the generic Convolutional neural network (CNN) with deep learning strategies for retrieving feature vectors from visual information. To build the global descriptors, the pre-trained CNN architectures i.e., ResNet50, InceptionV3, and InceptionResNetV2 were recommended. These techniques outperformed in the existing methods like Metaheuristic Algorithm, Gabor Wavelet, CNN-SVM which have been compared with the proposed technique i.e., Cartoon Texture Algorithm in CBIR. We have observed that they are similar to others based on machine learning approaches, with comprising of the conventional state-of-the-art in the Content-dependent Information Retrieval.
Application of Blockchain in restricting data access using AI

Blockchain and Artificial Intelligence (AI) are two buzzwords that are now popular. Although they are mostly researched independently, blockchain and AI complement one another. For instance, a blockchain facilitates AI via the exchange and management of data. A blockchain may be used as a new platform to store and trade data as AI systems rely heavily on a lot of high-quality training data. Despite the blockchain’s extensive potential for data exchange, there isn’t a detailed review of this prospective use case at the moment. This paper seeks to fill this knowledge gap by undertaking a methodical literature review. The present techniques, their functioning, and their expected advantages are all described in this paper. Risks and unresolved scientific issues are also looked upon.
ML Accuracy Assessment of DW Transaction

Direct-from-the-bank programmes and e-wallets are two non-card retail payment methods that are becoming popular at both offline and online businesses. The use of mobile wallets has grown over the past few years. Any type of computer software that can “learn” independently without a human’s help is relevant. By continuously monitoring data in real-time, ML can spot out-of-the-ordinary purchases and flag them as fraudulent. Creating fresh techniques based on existing models allows for the improvement of ML models. A hybrid model, such as one that uses credit card transactions as an example dataset, is an example of this. The major subjects of this article are ML and digital wallets.
To Design &amp; Implement the Image Encryption Algorithm, using Phase Retrieval in Elliptic Curve Cryptography (ECC)

The excessive demand for digitized images across all fields has made it necessary to safeguard the images from attackers for secure network transfer. Due to their sensitivity, sensitive photographs used in medical and criminal investigations are assumed to be of the highest quality. Modern encryption techniques simply expand the one developed for grayscale photos rather than being specifically created for colour images. To handle colour images utilized in medical and forensic applications, a multi-layered hybrid approach has been devised and given the term Hybrid multi-layered “Hyper-chaotic Hyper-elliptic curve-based image encryption (HHH)” technique. DNA encryption based on hyper chaos is used in the first layer to produce chaotic sequences. Images often hold very private and sensitive information. Since images are crucial in many situations, such as “military communication, object tracking, and medical image processing, protecting sensitive and secret information” from unauthorized use and modification is essential. Encryption is one of the greatest methods for concealing information for reaching this objective. In recent years, researchers have created a variety of techniques for image encryption. To boost security, they employ several picture encryptions concepts. To strengthen the integrity of the crypto system, substitution permutation raises the level of confusion dissemination. Secondary analysis has been done for this research to gather relevant information related to this topic.
Book Recommendation Considering Emotions and Sentiments for Good Health

As Internet technology has advanced rapidly in recent years, online shopping has become a convenient method for people to buy and consume desired commodities. However, online shopping generates a tremendous volume of data, causing consumers to experience data overload. To overcome this problem, Recommender systems (RS) are introduced which are also called intelligent systems. RS plays an essential role in the selection of selective information from the ocean of data for users.Social media play a significant part in users’ lives, and users of sites like Facebook, Twitter, and others create a lot of data on these platforms. Customer product reviews influence whether or not a customer decides to buy a product or utilize a service. Customer choices and opinions are influenced by what other customers have to say about them online, on blogs, and on social media platforms. RS consists of two basic filtering techniques namely collaborative and content-based with some limitations like cold start and history of the previous user. To overcome these limitations, this paper introduces a hybrid RS that combines the CF and CB approaches with a sentimental analysis of book tweets. The book’s tweets are collected from the Twitter website to conduct the analysis.
Identifying Face Masks with OpenCv and Deep Learning

During the course of this epidemic, the Corona virus had a significant influence not only regular lives but also on international business. Protecting one’s appearance has recently emerged as a widespread fashion trend and can now be considered the norm. In the present day or in the future, a large number of individuals will be obliged to wear masks in order to protect not only themselves but also the people around as well as the surrounding area. Face recognition has emerged as an increasingly vital tool in the fight against global terrorism. As part of this work, we are developing an AI system that will be able to determine whether or not a person is concealing their identity by wearing a mask. It will be of assistance to us in preventing the virus from spreading across the environment. In order to construct this work, we require the assistance of Machine Learning (ML), deep learning (DL), and Neural Network (NN), all of which will assist us in realizing the purpose of this work. We needed jupyter notebook in order to complete this work, and we also needed to install numpy, opencv, tensorflow, and numpy as well as a learning tool. This strategy will assist us in identifying the individual who is concealing their identity by wearing a mask in the imageand in real life picture. Additionally, it is able to recognize and distinguish a moving mask or face.
BiLSTM-CNN Text Emotion Analysis Based on Self Attention Mechanism and Dense Connection

In order to improve the accuracy of sentiment analysis, this paper presents a new method for sentiment analysis of BiLSTM-cnn text based on self attention mechanism and dense connection. Methods BiLSTM-CNN-Attmodel was established. Firstly, BiLSTM was introduced to extract context words. Then, the convolutional neural network(CNN) is used to extract local semantic features. Combined with DenseNet dense connection module, the memory strength of the whole model is improved, and the utilization rate of weight information is enhanced. Finally, Self-attention mechanism is used to improve the ability of model mining information. This paper selects the data sets of chndenticorp and CCF2012 to train the optimal value of the DenseNet feature mapping matrix. The optimal value is brought into the model contrast experiment. In the experiment, the accuracy rate, recall rate and F value of this method are all greater than 91%, which is the highest among the models. It effectively improves the accuracy of text sentiment analysis, and has high research and practical value.