Reviewerly/exHarmony huggingface dataset:
Blockchain-based Trust Management System in Implementation in Social Internet of Things

The term "social Web of Things" (or "civil IoT") refers to a model that combines the ideas of social networks with IoT and enables networked items to interact socially. The assessment of interpersonal solidarity for the gadgets may be used to control the use of IoT assets in these systems in order to protect confidentiality and security. This research investigates a bitcoin trust monitoring system for advantage IoT which strengthens the assessment of trust through using sociological data from the system. Additionally, this system makes use of informational entropy to boost privacy. In order to demonstrate that the suggested method can accurately evaluate trust criteria in social IoT, we have created a concrete evidence platform.
Deep Learning based Face Mask Detection system with Automatic Indigation

The COVID-19 pandemic has quickly had an impact on our day-to-day lives, as well as on the movement of goods and people around the world. It has recently been common practice to shield one’s face by using a mask. In the not too distant future, many businesses that provide public services will need their clients to correctly wear masks in order for them to receive those services. As a result, the detection of face masks has evolved into an important mission in the service of worldwide society. In this post, a relatively straightforward approach to achieving this goal is presented using basic machine learning tools like TensorFlow, Keras, OpenCV, and Scikit-Learn. The suggested method accurately locates the face inside the image before determining whether or not it is covered by a mask. While doing a surveillance task, it is capable of detecting a mask as well as a moving face. To properly detect the presence of masks without over-fitting, we look into numerous options for optimizing the values of the parameters in the Sequential Convolutional Neural Network model.
Preventing Critical Information framework against Cyber-Attacks using Cloud Computing and Big Data Analytics

The rapid development of cybersecurity insurance has been driven by the noticeably rising expectations of financial firms to mitigate costs from cyber security. Hacking and fraud is just a couple of the areas of cyber incidents that have been covered by Cyber security insurance systems. However, cyber security is still in the exploratory phase, thus the existing applications have discovered a number of new dimensions. One of the major problems preventing the expansion of essential infrastructure is cyberattacks against it. This paper discusses Cyber security deployments with a focus on cloud-based service offerings and offers a big data-based methodology for secure cyber incident analyses. Our method employs repository data and is intended for matching various cyber risk situations. Theoretically speaking, our simulation has demonstrated the viability and adoption.
Text Independent Speaker Recognition and Classification using KNN Algorithm

The method of automatically identifying the speaker using the speaker-specific data included in voice waves is known as speaker recognition. For speaker recognition, a variety of uses have been investigated. Monitoring, speech-activated secure access control, voice-activated customization of services or information for certain users, instances include using recorded voice samples in forensic and criminal investigations. The application that is now mentioned most often is access control, which also includes voice dialing, banking, telephone shopping, and database access services. Thus, it is projected that speaker recognition technology would provide new services in smart environments and enhance the comfort of daily life. Research has been done on the phenomenon known as “speaker idolization,” which occurs when speakers are automatically added to an input audio channel. It makes speech recognition easier, makes it easier to search and index audio archives, and gives machine transcriptions more depth and intelligibility. An important additional application for voice recognition technology is as a forensics tool. The speaker’s short-time spectral coefficients are described using vector quantization using a codebook. The success of these techniques is assessed from the perspective of robustness against utterance variation, such as variances in content, temporal variation, and changes in utterance pace. The voice of each individual is recorded three times. The experiment’s double distance measurement result is 96.97%, whereas the KNN technique’s single data center result is 84.85% The outcome shows that the twofold distance method increases the precision of voice recognition.
Artificial Intelligence Influence on Accounting Methods

Due to its benefits in terms of enhancing and redefining the actual manner of performing activities in this field, artificial intelligence is swiftly changing the reality of the accounting business. Accounting has seen a significant transformation over the years as computers, first and foremost, and more importantly, developers ready to spend less time on laborious work that minimises the amount of errors, have replaced the job done with paper and pencil. Although there has always been a fascination with artificial intelligence systems in this field, attention has recently shifted more toward it. Although technology has advanced, it seems that there aren’t enough facts to back up businesses’ readiness to include artificial intelligence systems into their accounting procedures. A crucial element of this reality is also the ability of professionals to quickly adjust to the new business climate, get the skills required to work with AI systems, and overcome their fear of losing their jobs. The requirements of the financial society, the quick development of data innovation, and artificial intelligence have brought about the modern era. Implementing artificial intelligence innovation is an unavoidable trend that will result in substantial changes and advancements in the accounting sector. In this essay, the usage of AI in the accounting industry is examined, its effects on the sector’s expansion are examined, and significant solutions to current issues are suggested.
The Multi-Layer Security Network Authentication System Development Through Blockchain Technology

Due to the fact that crypto is a novel security strategy. As this article outlines a multi-layer safe Internet of Everything networks theory based on blockchains, we will now explain the construction of a layered network security authenticator using blockchain technologies. By grouping Connected technologies into a pass connectivity and implementing the blockchain based platform at every level of a network, the strategy reduces the challenges of actually deploying blockchain technologies while maintaining the mankind’s strict security and validity guarantee. It offers a nationwide connectivity option for the Web of Things. A stone chain is a collection of blocks that holds data. The kind of public blockchain affects the information stored inside a piece. For instance, a lot coin block contains data about the Presenter, the Reception, and the amount of bit coins that will be communicated. The brand’s first block is called the Node. The goal of the block chain is to make data available to everyone who uses a program to access it. Entry to all this account for reading may be unrestricted (‘permit less’) or restricted (‘approval’). Block chain today makes it feasible to send, receive, and store digital data in a secure manner. Additionally, it uses mathematical encryption to protect every transaction. By adopting this, banks might boost their current security and disclosures to unprecedented heights.
Evaluation of Performance of Artificial Intelligence System during Voice Recognition in Social Conversation

An effective speech signal chat bot’s concept and evolution are discussed in this study. A technological demonstration is presented in the study to test a suggested framework needed to enable such a fake account (a online service). All types of clients can communicate with the public server from any location thanks to web solutions. device, even if a black box method is utilised by regulating the framework between and to the webserver. The service is made available via a created interface that enables easy XML reading, and the flexibility increases the service’s longevity. The screen bot creates personalised user replies that are linked to the intended character by incorporating an artificial heart. Ununderstood questions sent to the bot are analysed further utilising a second classification model (a scientist doing online cognitive study), and the outcome is retained, improving the skills of the cybernetic organisms for the production of answers in the later.
Administration of Digital Identities Using Blockchain

Since the last two decades, the problem of check theft and the data breaches from centralized systems has gotten worse. In this study, we looked at how blockchain technology is employed in identification systems and how it may be applied to verify an identity’s veracity and legitimacy. The drawbacks of the conventional identification systems were examined in depth, and the advantages of using a Blockchain-based identity system were discussed, including the removal of the middleman and the need for long wait times for verification, authorization, and attestation. Nobody can alter their identification data because the nodes in a blockchain are supposed to be immutable, and because asymmetric cryptography is utilized while sharing data, personal information is kept secure. We were able to effectively establish an identity wallet-a node in a chain on the Ethereum platform-and test a self-sovereign identification system for a single person.
A Conceptual and Bibliometric Study to Understand Marketing in Metaverse: A new Paradigm

Cyberspace has dropped various different advanced technologies, and Metaverse is one of them. Metaverse promises to provide what today is surreal. This surrealism revolves around giving a human being a virtual experience and using this experience to make different elements of our world more comfortable and breaking different barriers which we face today. Since it is like developing a totally new world, it would be a gradual process and as some of the big companies are now focusing and investing in this, they would be trying to make this verse an irremovable part of all of our life, besides this big brands and companies are investing in this and this must make us intrigued to look how marketing and business would make part of this. This paper is written with an objective of analysing the emergence of Metaverse and how it will impact the existing marketing practices and further how researchers and academicians are looking ahead on this topic. To achieve the objective of the study, secondary data was analysed and found the insights on many aspects of marketing and further bibliometric analysis was used to understand the current scenario of research. This study is the first attempt to offer research on marketing in the metaverse and explains the possibility of how it would change the world of business and its marketing.
A Case on Decentralized use of Blockchain

Over the last twenty years, the technological world has seen a radical shift that calls for more robust and efficient data security solutions. This is a common use case for blockchains. It is a technology that enables mentoring (P2P) asset exchanges alone without requirement for a centralized authority to regulate and manage, i.e., a single node. It works by combining several technology including hashing, acceptance methods, and digital signatures. With the help of each of these technologies, a system that allows simple activity recording and offers security and transparency may be developed. The view on company functioning in regards to transparency and decentralization has been completely modified by crypto currencies. In addition to being the foundation of the crypto currency market, which has had a significant influence on the world economics, blockchain has the potential to revolutionise a number of industries, including medicine, finance, social care, and schooling. This research intends to explore the crypto currency construction, its properties, such as anonymity, safety, and visibility, and the numerous fields in the contemporary world that may utilize chain to address current issues.
Systematic Literature Review for Sentiment Analysis Using Big Data social media Streams

Social media is a potential tool for people to communicate with each other remotely and express their opinions and feelings about any subject of concern. This behaviour generates a voluminous amount of unstructured data. Profitable business and research models can be developed by leveraging this data. Therefore, several machine learning and NLP-based algorithms have been developed in recent decades, to analyse people’s opinion and their attitude on any subject of concern. Deep learning-based approaches are gaining a lot of traction because of their excellent performance. The domain of sentiment analysis has witnessed the deployment of various machine learning, deep learning, and optimization-based models. This work unveils the prospects of these cutting-edge technologies with extensive analysis. In addition to this, the survey includes a detail of the most popular datasets, with their characteristics, along with maximum accuracy attained. This pivotal goal of the work is to appreciate and assess the effectiveness of deep learning architectures in the domain of sentiment analysis. This work also studies the usage of optimization-based method in improving the results of learning models.
Prominent Technique for Rainfall Prediction using CatBoost over Light GBM for improving the Accuracy of Prediction

The purpose of this research is to assess the accuracy of rainfall prediction using the CatBoost and LightGBM algorithms. The classification technique is used for a rainfall dataset that has 145461 records. A paradigm for rainfall prediction that compares LightGBM with CatBoost has been suggested and built. The size of the sample was determined using G powers to be 10 within every cluster. The sample size was estimated using clinical evaluation, with alpha and beta values of 0.05 and 0.5, 95% assurance, and 80% from before the power. When it comes to forecasting amount of rain on large datasets, the CatBoost algorithm is 94.70% efficient, meanwhile the criteria-based LightGBM is 89.21% accurate. The significance level of CatBoost and LightGBM is p=0.003(p<0.05). Based on the results, the CatBoost beats the LightGBM in regards to precision.
Medical Insurance Virtual Assistant: Vaidya

You must eat healthily if you want to live a long and happy life. Many times, individuals are unaware of all the possible diagnoses, therapies, or signs of a certain illness. For youngsters, it is quite difficult to acquire a doctor's visit for any medical conditions, manage phone calls, for users with the condition travel to the institution in real for a time-consuming test. By offering relevant advice on leading a healthy lifestyle, healthcare/medical chatbots may help with these kinds of issues. The idea is to use neural networks to build a medical chatbot that can offer information, identify diseases, and provide basic illness information as well as when and where to seek medical advice. NLP technology, which enables users to express their worries about any condition as well as their health, is the foundation of the success of medical chatbots. Without physically would be at the clinic, the user may utilize a chatbot to ask any personal questions about health care. Through a medical chat-bot, this will increase access to health records while lowering the cost of healthcare. The program development strategy calls for consumer sentiment analysis.
An Overview: Super-Image Resolution using Generative Adversarial Network for Image Enhancement

Image processing plays a vital role during the analysis of the data, whenever the image is taken from the device it is not possible that the quality of the image is poor or found a lot of noise. This paper is working on the GAN’s subpart of SRGAN, which helps in processing of the image to get the HR of the image. By using the SRGAN, we just need to input the image’s low resolution, and after processing the data it will convert into a high-resolution image. Here we are reviewing all the related SRGAN papers.
Examining the Role of Block Chain to Secure Identity in IOT for Industry 4.0

Blockchain is one of the next digital technologies that Industry 4.0 incorporates advances with. For both small and big businesses, blockchain may be used to enhance security, privacy, and data openness. The term “Industry 4.0” refers to the integration of several cutting-edge production methods that accelerate the pace at which enterprises may achieve their objectives. Many of the technologies that make up Industry 4.0, such as artificial intelligence, the internet of things (IoT), big data, and blockchain, have been the subject of research on their potential to cause broad disruption during the last few years. Some of the cutting-edge technologies that support Industry 4.0 include cloud computing, industrial integration, cyber physical systems, the internet of things, machine learning, adaptive robots, artificial intelligence, and service-oriented computing. In addition to Industry 4.0, blockchain, also known as distributed ledger technology, may have an indirect influence on the aforementioned technologies. The purpose of this paper is to provide the framework for discussing and demonstrating how blockchain technology might be used to the Fourth Industrial Revolution. The author of this article utilizes blockchain technology to analyze many key concepts in Industry 4.0 and show how these concepts are improved by the widespread use of blockchain technology. In order to further the goals of Industry 4.0, this study explores many potential uses of blockchain technology.
Hybrid Soft Computing based Approach for Ageing in Face Recognition

Proposed Approach is a collection of methods for handling a certain class of complicated issues that call for the usage of intelligent software computers. For computational complexity issues, the merger of Pattern Recognition with Soft Context of online like Rnns has demonstrated a bright future, giving birth to strategies and systems with great performance and speed. Utilization of this fusion approach greatly improved the quality of detecting faces in a face - recognition workflow. DCNs are used in our hybrid algorithm, which combines CNN and LSTM, and they focus on the idea of optimizing the face's deep features. Results from our suggested system's testing upon that CACD datasets are quite encouraging and supportive
Advanced Face Detection using Machine Learning And AI-based Algorithm

In this study, the analysis of the research that has been provided in the “Advanced Face Detection using Machine Learning And AI-based algorithms”. It is a facial acknowledgment framework that utilizes AI and Machine learning. It specifically uses the Support Vector Machine (SVM) system. Modern-day technology is making people amazed with the innovations that it is providing to make an individual’s life simpler and easier. In the case of Face Recognition, it has proven to be the least intrusive and also the fastest form of the biometric verification system. Among all the tasks that can be done with the help of machine learning algorithms, one of the most crucial computer vision tasks is the use of face detection and recognition. The face detection and the recognition systems are both related to each other yet they are also very different from one another as well. By using machine learning the face detection aspect is used and it also is a widespread aspect of the face recognition system. Machine learning applies various algorithms for the detection of faces and the recognition process as well. Face detection and recognition systems are used in medical diagnosis and also in the deep analysis of human faces in the video for various intelligence purposes. Face recognition is a sub-category system of the biometric software system, it is used to map an individual’s facial features and also to store that data as a face print for future purposes.
Semantic Segmentation Algorithm for Night Traffic Scene Based on Visible and Infrared Images

At present, there are many semantic segmentation algorithms with excellent performance for intelligent driving vehicles, but most of them are only work well on scenes with good illumination. In order to solve the problem of scene segmentation under low illumination, this paper proposes a novel semantic segmentation algorithm which combines visible and infrared images. In this algorithm, two parallel encoders are designed as the input of the image, and the decoder divides the fused image output from the encoder. The model is based on ResNet algorithm, and the residual attention module is used in each branch to mine and enhance the spatial features of multilevel channels to extract image information. Experiments are carried out on publicly available thermal infrared and visible data sets. The results show that the algorithm proposed in this paper is superior to the algorithm using only visible images in semantic segmentation of traffic environment.
AYAT: A Lightweight and Efficient Code Clone Detection Technique

Code clones make software maintenance more challenging. Detecting bugs in large systems may significantly increase maintenance costs. Despite the fact that several techniques for clone identification have been proposed over the years, the accuracy and scalability of clone detection techniques remain hot research areas. Previously, Akram et al. proposed the DroidCC hybrid technique, where tokens were encoded into MD5 hash values by encoding them into 128-bit fingerprints, and clones were identified by matching identical hash values. Encoding tokens into MD5 hash values take more time due to the large fingerprint size of MD5 hash values. Due to the enormous chunk size, DroidCC cannot achieve higher accuracy. To overcome the weakness of the DroidCC technique, We proposed a novel AYAT a lightweight hybrid technique to detect clones at the fragment level. To speed up the detection process, we converted tokens into 32-bit polynomial values, and we set the chunk size to 5 lines per chunk to improve accuracy. We tested our technique on 10,968 java projects against 4.98 million lines of code. In comparison to the well-known DroidCC technique, it is significantly faster and more efficient. Our examination demonstrates that precision is significantly improved despite sacrificing scalability. AYAT code cloning detection technique has outscored DroidCC in every aspect.
A Method of Multilingual Summarization For Scientific Documents

The article considers the problem of multilingual summarization generation for scientific documents. To solve this, we propose a method based on the summarization-translation approach. The method decomposes the original task into two separate tasks: monolingual document summarization and multilingual summarization. In the first task monolingual summary is generated for the document in the document language. In the second task resulting monolingual summary is translated into the language of interest. The paper analyses different abstractive and extractive models to choose the optimal one as a monolingual summarization model. The best model is selected based on the rouge metric, as well as on the newly proposed metrics. The multilingual summarization model uses Moses statistical machine translation model and post-processing based on the mT5 transformer model. The proposed system was tested on the Wikipedia dataset for 15 different languages. It is shown that the proposed system of models can generate multilingual summaries in 15 languages.