Reviewerly/exHarmony huggingface dataset:
Applicability of Association Rule Mining in Recommendation System for Big Data Analysis

Nowadays, on visiting a website over the internet or logging into a social media account, it is common to see the recommendation of the products. These products or services shown as advertisements are those which a consumer has ever surfed over the internet or something similar to a past purchase. A recommendation system (RS) is to provide a suggestive description of products or services to a customer based on their past purchasing history. Since this data is humongous, i.e., big data, this approach is used to extract valuable recommendations. But, the challenge here is to provide accurate recommendations to the consumer. In addition to promoting products or services, the consumer will be interested in the recommended products during the next purchase. RS is a crucial method to strengthen the business over e-platform. This paper briefly describes the recommendation system process flow, its different filtering techniques, and the challenges faced in designing the recommendation system. Moreover, the author also discusses the role of Association Rule Mining (ARM) in creating the recommendation system for resolving the issues of the ecommerce domain. Furthermore, the applicability of ARM in RS in past research is also discussed.
Role of Integrated Artificial Intelligence for Knowledge Creation and Decision Making for Improving Firm Performance

The present research shed light on the decision-making process of businesses based on integrated AI technology. The technology is very helpful in searching and gathering accurate business information which could further be used in the performance improvement of the business. Moreover, the research depicted several applications of AI technology and the percentage of companies that used such technologies in their business for the motive of performance improvement by achieving all their predetermined objectives of the business.
Analyzing the Effectiveness of ML Agents in Enhancing the Predictive Model in Decision Making for Medical Practitioners in the Healthcare Industry: A Structural Equation Model Analysis

In order to enhance the effectiveness of the clinical predictive model, ML agents are at once essential in healthcare sectors. However, the research is going to investigate the contribution of autonomous ML agents while predicting any disease outcomes and conducting necessary decision-making approaches. Autonomous agents can efficiently mitigate human efforts towards any critical disease detection processing any important treatment procedures. In order to increase overall efficiency in the healthcare sectors, applications of these autonomous factors are essential. Researchers have conducted various surveys in order to investigate the contributions of ML agents in managing various AI-oriented surgeries in the healthcare industry. Therefore, ML agents are necessary for the healthcare sector for early risk determination. The research study tends to apply a quantitative survey method that has been granted for the researchers related to the topic. However, among 80 participants, healthcare researchers have conducted effective survey analysis. In order to understand the significance of utilizing ML agents, innovative decisions regarding clinical technologies can be beneficial for amplifying the efficiency of the predictive model. Moreover, a detailed analysis on the discussion of benefits regarding machine learning determinants in healthcare has been conducted successfully after analyzing all efficient survey outcomes over the specific research topic.
Gesticulation Recognition System Using Deep Learning

With the growth in globalization and emphasis on educational advancements, we want to make sure that no one should face any kind of hindrance regardless of one's incapability. We are aiming to build a web application in order to overcome the disabilities of deaf and dumb people to make sure they don't feel like straggling behind the rest of the world, as education defines one's personality. This project will not only be used in academic fields but also in day-to-day life as well because communication is a must to survive. This web application will be reading users' hand gestures and after analysis, it will convert that sign into the text as well as speech format so they can communicate easily with everyone around them. This gesture conversion that we are building is only limited to the English language as of now, but we will be looking forward to expanding its linguistic basket to have multiple language conversions in the near future.
Amelioration of Security Protocols from the outset of technology to present moment

As the era of technology advances itself into the future, we come vis-a-vis with a notion that, the more we indulge our day to day lives into gadgets, the more exposed and vulnerable we are become. Somewhere human beings are feeling the shift of reality towards digitization and it's boisterous. What's even more intriguing is the will of human beings to continuosly endeavor for everything come what may. The pace of advancement asks of us, intelligence related to our personal lives in order to provide assistance. How do we secure this process? To use an application, services of some company, we exchange our data and personal information with them. What is the guarantee of our data being kept safe? Is there? Yes. There is. This is where we introduce a major portion of the computer science field. Security. This paper throws light upon the amelioration of how technology has cooperated with security to provide safety to the user, service, network, systems and businesses. The processing of information in all sectors businesses involves sharing of data, which makes data the most expensive. Security for this data has seen its ebb and flow throughout the years and therefore this paper discusses it. How and what researches have been carried out that brought us to today's security level.
Hybrid Soft Computing Based Approach for Ageing in Face Recognition

Soft Computing is a set of techniques for addressing special class of complex problems which involve the use of computationally intelligent systems. The fusion of Deep Learning and Soft Computing tools like Neural Networks has shown a promising road ahead for computationally intensive problems, giving rise to algorithms and solutions with high efficiency and accuracy. Application of this fusion technique considerably improves the accuracy of recognizing faces in a facial recognition pipeline. Our hybrid model comprising of CNN & LSTM utilizes DCN'S which work on the concept of optimization of the embedding's of the face itself. Our proposed system has been tested on the CACD dataset and gives very promising & encouraging results
A novel method of Image Segmentation-Analysis

In this paper, we present the methods of abstracting separation of satellite imagery; we used seven techniques at this s, Kiresh method, Laplacian technique, Canny strategies, Robert's strategies, and Edge Maximization Technique (EMT) is also compared to another to choose the best way to get the edge section image. These techniques are used on a single satellite image to select the backstage; Sobel operator technique, Prewitt techniques guess the partition or edge adoption image. We propose a novel multi-scale acquisition edge and a vector field design plan. We show that the use of multiscale techniques to obtain the edge and quality of separation in natural images can be greatly improved. Our approach eliminates the need to choose a clear scale and follow the edge. Our approach prefers existing edges over a wide range of scales and makes these edges geographically with the best scales. This work is then extended to separate images by multiple scales using our anisotropic distribution system.
Aggregation Techniques in Federated Learning: Comprehensive Survey, Challenges and Opportunities

Federated learning is a new paradigm on the machine learning system that uses the traditional system of machine learning but implements privacy features on top of it. The implementation of federated learning is done in order to increase the privacy of the user and also give them access to their own rich private personalized data. But in the case of classical machine learning the implementation is that it needs all the data and model on one system and in that way we have to get the data of the user and store it in a certain place and then train the model there and get the updated model by training on that data. Let us look at the problems in this system first: the personalized data of the user is being sent to the servers and is used there. Even if the owners of the server are not exploiting the data of users it can be possible by any cyber attacks people can steal the data and use it for their own benefit and in that there is no one to blame for. In order to avoid these data breaches, there can be a solution where we instead to taking the data to the model send the model to the data, that is the core concept of the Federated Learning paradigm - in this way we can preserve the privacy of the user and also train our model on the rich personalized data set.
Violence Detection in Real Life Videos using Convolutional Neural Network

Detecting violence in video recordings through artificial intelligence is critical. Just because violence detection seems complex now, it doesn't mean that the problem should be ignored. With this paper, we aimed to show how we can do violence detection and how it can be implemented easily with the simplest methods at hand thanks to the progressing advances in deep learning and AI. Furthermore, it could be an excellent tool for preventing children from accessing inappropriate content and assisting guardians in making better decisions about what their children should watch. This is a challenging topic since the definition of violence is broad and quite abstract. As a result, recognizing such intricacies from recordings without human intervention is not just a technical issue, but also a theoretical one. In light of this, using a convolutional neural network, we will investigate how to depict the concept of violence. For image categorization, there are numerous pre-trained convolutional neural networks available. These models have figured out how to bring out dynamic and useful highlights from regular pictures and use them as a beginning stage to get familiar with new tasks. These networks can classify images into various object categories after being trained on over a million photos. We used pre-trained networks with transfer learning since it's regularly much quicker and more straightforward than preparing or training a network from scratch. Using several deep learning techniques, we will investigate which model will provide the best accuracy for recognizing violence in videos for this project.
Performance Analysis of Automated Solar Panel Tracking System

The objective of the proposed paper is to examine or study the performance of automated solar panel tracking system. It ensures that the solar panel is placed at the right angle(90 degree) with respect to sun's position to obtain maximum solar energy. An eccentric feature or characteristic of this system is that the earth has not taken as the testimonial. Instead of that here the reference is sun. The sensor in the system constantly invigilate the sunlight and turn or rotate the panel with regard to the angle at which the intensity of the sunlight is very high. The LDR (Light Dependent Resistor) sense the position change of the sun which is dealt by the respective change in the panel's position by controlling the geared motor. It has the program to run the motor with respect to the variation in the position of the sun, 25% to 30% energy is generated. In this paper, an improvement in the design of existing system is achieved to get the best efficiency economically.
Analysis of Line Segmentation Methods for Historical Manuscripts

Historical manuscripts are rich source of knowledge about culture, civilization, political system, ancient science and mathematical research and also represent the cultural as well as the religious heritage of the country. Handwritten Text Recognition is the technique to convert the handwritten document into a machine-understandable format. Line segmentation of the document is a very crucial step of the HTR. Last few decades, researchers have invented many algorithms for the segmentation of handwritten or printed documents but these algorithms are not directly applied on these manuscripts due to the presence of the touching lines, uneven space between lines, touching characters, slant, stains, different font size and writing styles, decoration, noise, wrinkles, faded ink, ink bleed through, stitches, rust etc. These issues make the segment process bit complicated. In this paper, an attempt is made to report various traditional and learning based line segmentation methods along with their performances.
Design &amp; Develop a Hybrid Model for Blockchain

There are number of consensus algorithm designed for blockchains, each of which has different prosperity and weaknesses. POA is a novel class of Byzantine Fault Tolerant consensus algorithms that are commonly utilised in reality to provide greater performance than traditional byzantine fault tolerance algorithms. Various organizations are moving toward a POA blockchain with Byzantine Fault Tolerance that is more traditional. Without good consensus mechanism it is very hard to prevent the attacks in nodes. Our motive is to research and if we can build the consensus mechanisms with improved algorithms to reduce the risk and attacks on blockchain. We conduct the block generation process and introduce validators voting profiles, which we update, based on validators, voting behaviour, and aggregate blockchain rewards using the POA and POV algorithms. The blockchain has become most popular in recent years; its underlying consensus algorithms have also become a major research focus. This work aimed to studying various consensus algorithm of blockchain to improve the performance of blockchain and to build common and flexible to fit on the most of the platforms.
A Comprehensive Analysis of Internet of Things (IOT) Sustained Integrity System Services with Data Management Based ON Machine Learning

Internet of Things (IoT) and Machine Learning (ML) are recent trends in huge data management. Traditionally, data management was carried out by humans where big data collection, analysis and acquisition were solely done by humans. However, IoT and ML have enabled machines rather than humans to collect big data, analyse them and develop a new business model. It has shown huge success in industry 4.0; however, during the management of big data, large-scale companies encountered certain issues. These include, lack of effective training leads to less accuracy in data management, security issues during cloud computing and security issues in blockchain as well. Therefore, this study has explained the ways to improve big data management through ML and to enhance the security of blockchain. A primary survey was carried out with 50 respondents who provided their responses to certain questions related to big data management solutions and blockchain security. Findings showed that providing training to the workers and using guidelines/policies in blockchain can increase the data management accuracy and can improve the security in cloud computing, respectively.
Image Recognition, Recusion Cellular Classification Using Different Techniques and Detecticting Microscopic Deformities

Deep convolutional neural networks (CNNs) have turn out to be one of the most advanced approaches trendy distinguishing snapshots in extraordinary fields. White blood cell classification is crucial for diagnosing anaemia, leukaemia, and a variety of other hematologic illnesses. Transfer learning with CNNs is frequently used in biological image categorization. Traditional methods for WBC classification is costly is terms of time and money. In the paper three convolutional neural network architectures are proposed which is based on transfer learning for microscopic image classification and compare the performance of models. The paper compares Transfer learning models like VGG-16, VGG-19, VGG-19 SVM hybrid and AlexNet. VGG-16 gives the best classification performance in comparison. VGG-16 model is which has a train accuracy of 0.9538 and train loss of 0.1322.
Twitter Opinion Mining using Artificial Bee Colony Clustering Method

Technology has come a long way over the last few years, people now use the internet as their base medium for retrieving and exchanging information. As a response, ”Social Media” has been an important part of our lives. Millions of people use social media regularly to share their opinions and perceptions, which has led to the availability of extensive data. For assessing public opinion on certain issues, sentiment analysis of such data is critical. Sentiment Analysis at its core is data mining that focuses on identifying and translating feelings found on such Social Media. In our study, we will be using Twitter, which is a microblogging service that allows users to connect to their “followers”. With this particular study, we have devised a methodology, named Artificial Bee Colony with k-means clustering, to extract sentiments from tweets published on Twitter. Because of the subjective nature of tweets, swarm-based metaheuristic approaches outperform traditional techniques.
An Effective Data Mining Technique for Extracting High Utility Item Sets

The Data mining is related to identify valuable data in large voluminous repositories. The goal has been to create ways to assist people uncover meaningful patterns in large amounts of data. Utility mining is such an essential technology. Frequent item setting operates in the mining of items, which are often discovered by means of support and the confidence of various items in the transaction database. Many academics have also developed a novel notion for the utility-based mining of objects by means of a frequent mining concept. In this research, we seek, on the basis of a metric termed weighted transactional utility, to find candidate items and item set. We are also proposing a new high-value item mining algorithm. The new method exceeds prior calculation algorithms.
Image Watermarking Based on Discrete Wavelet Transform and Biogeography-Based Optimization

With the growth of information technology has led to a overabundance of multimedia information flowing over network systems. As a result, security duties in connection to multimedia data have been necessitated protection against the disclosure of sensitive data. Watermarking is the technique of inserting data into image to determine its authenticity or the identity. This study presented a BBO-based robust watermarking system. The proposed approach is robust against various attacks. The watermarked image&#x0027;s quality is also taken into account. The simulation results from BBO demonstrate both robustness and image quality. .
User Experience Evaluation Using the Cognitive Walkthrough Method

The redesign of the website has been carried out without formal user experience testing regarding user experience in using the system so that similar complaints will arise on the latest website. The purpose of this research is to identify and analyze what problems users will face and also provide recommendations for maintaining the website interface to make it more user-friendly. In this study, the author tested the user experience on the PMB UAJY website using the cognitive walkthrough method. A cognitive walkthrough is a method devoted to new users by testing usability in which the examiner performs a series of task scenarios to achieve his goals. The usability test components include effectiveness, efficiency, and user satisfaction. The results of this test indicate that the lowest usability level of 55.10% is still classified as good. The margin of error in this task scenario is found in the Undergraduate Student Registration menu. Problems faced by users when using websites that display a graphic design that is monotonous and tends to be text-based, navigation and buttons that are too small in size, lack of consistency of buttons on the Undergraduate Registration menu, and users are required to fill out Guardian Data Forms. Improvements needed by this website are providing complete information content, attractive interface appearance, enlarged and consistent layout and navigation, and reconfirmation notifications for every action on the registration menu.
Augmented Reality Based Application for Linear Algebra Visualization: Development of Interactive Learning Environment

Linear Algebra has been used in various engineering fields, such as machine learning, robotics, and artificial intelligence. However, learning it geometrically remains a challenging task, because it requires visualizing abstract concepts such as matrices and vector spaces. This paper aims to introduce an augmented reality framework for promoting spatial simulation in engineering students' Linear Algebra classes. In this paper, we provide a mobile-based augmented reality visualization framework of vectors, 2x2 matrix, 3x3 matrix, transformation matrix, determinant and other learning content of linear algebra. The user learns by interpreting the axis for the transformation part of the matrix for scaling, translation and rotation of a 3-D vector space. We then present how the 3x3 matrix and 4x4 matrix can be formulated and described under the proposed approach.
A Study of Blockchain Technology based Cryptocurrency

This research paper discusses the awareness towards the blockchain technology. The blockchain is used to store the data into the blocks and each block contains the cryptographic hash linked with the cryptocurrency. After reading this research paper you will understand the money evolution chain, introduction to the electronic cash system, how the data stores in the block in the form of crypto hash and how different crypto currency came into existence and their use case. Also cryptocurrency can also be used as the assert category which can be traded. Since 2011 different crypto trading platform came into existence which offered the trading of crypto currency. This overall improving block chain technology day by day.
Fast Extraction Algorithm of Hadoop Message based on Artificial Intelligence Architecture

research-article Fast Extraction Algorithm of Hadoop Message based on Artificial Intelligence Architecture Authors: Qiqin Yang Guangzhou Institute of Applied Science and Technology, China Guangzhou Institute of Applied Science and Technology, ChinaView Profile , Xuliang Yang Dongguan City College, China Dongguan City College, ChinaView Profile Authors Info & Claims IPEC '22: Proceedings of the 3rd Asia-Pacific Conference on Image Processing, Electronics and ComputersApril 2022Pages 1008–1011https://doi.org/10.1145/3544109.3544399Published:18 July 2022Publication History 0citation19DownloadsMetricsTotal Citations0Total Downloads19Last 12 Months19Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access