Reviewerly/exHarmony huggingface dataset:
Multiclass Artificial Neural Network based Facial Expression Recognition

Facial emotion recognition has emerged as one of the important research area from past few decades. It has wide variety of applications such as marketing research, medical field, security, criminal interrogations, etc. This paper provides a deep learning approach for face and emotion recognition that may be used to create a system that can accurately identify and recognize people.. Computer vision can find and recognize the human face and its emotions along with the numerous algorithms but to get the best results we are using a deep learning technique. The task is divided into three parts, the first of which is the detection of a human face from a camera, and the second of which is the analysis of the acquired data. Keras convolutional neural network was used to support the features and database used. Human faces are validated in the last phase to categorize human emotions such as Happy; neutral, furious, sad, disgusted, and surprised are all emotions that can be felt ising multiclass artificial neural network approach. Face detection, identification, and emotion categorization are the three main aims of the proposed study. For computer vision approaches, the Open CV library, dataset, and python programming are employed in support of this work.
Visual Paragraph Generation: Review

Visual Paragraph generation is the field of generating human-like detailed paragraph, consisting of multiple sentences rather than one sentence, that fully describes the visual content. In line with its growing applications in various domains, there has been an increase in the number of research works conducted to improve the quality of the generated description for a given visual content. Even though good results were achieved, there are still some challenges to be overcome especially with the dawn of big data and its influence on existing information systems. This paper is to create familiarity with the current thinking and research in visual paragraph generation by providing a thorough survey to the literature. Its main aims are to highlight recent contributions and achievements, identify the promising technologies that support future research, and justify the need for further research work in this field.
Cumulative Summary List Driven Lightweight Frequent Closed High Utility Itemset Mining

Pattern mining has always been a dominant approach in data mining to identify the object(s) of interest from a humongous large data space to make real-time decisions. In sync with diverse business intelligence or other enterprise demands, pattern mining methods have gained wide-spread attention to serve purposes like high-utility itemset identification, segmentation and top-(itemsets) recommendation. In this paper, a robust cumulative summary list-structure driven lightweight frequent closed high-utility itemsets mining model (CSLFC-HUIM) is proposed that uses an improved cumulative summary list structure (CULS) to ensure optimal set of candidate itemsets in search space. Unlike classical support value driven strict-thresholding, CSLFCHUIM applied lift-score as minimum threshold called cumulative support/utility threshold to prune the search space. The strategic amalgamation of cumulative support value, utility threshold with CULS and cumulative summary list structure (CSLS) concept guarantee optimal set of itemsets in nested-list structure. It reduces unwanted database-scanning, execution-time and memory exhaustion, which makes it a potential solution for different pattern mining and business intelligence tasks.
Hybrid Education and Institutional Readiness

Progress is unidirectional by default. The global irreversible shift witnessed in the education sector with the COVID-19 pandemic left teaching and learning far from the same. The current study aims to consider global trends in hybrid education by exploring existing hybrid study models and recognizing institutional readiness by means of hybrid initiatives successfully adopted by institutions. Being conceptual research, the methodology of the current study is based on an extensive review of literature, related to hybrid education, selected carefully from the wealth of academic publications comprising scholarly articles, research journals, webpages, conference proceedings, dissertations, thesis, and authentic material related to the study. A conceptual framework represents the relationship between the research variables. Founded on the qualitative analysis of the data gathered, the study concludes that, while integration of technology in teaching is important, it does not imply total restructuring of what and how we teach. Flexibility in teaching, learning, and testing must become the basis for current pedagogy in meeting the goals of hybrid education. The implications of the current study are proffered to all stakeholders in education along with the recommendations for future research.
Propose a Recommender System to Dynamically Align Higher Education Curriculums With 4IR Market Needs

The Fourth Industrial Revolution (4IR) era leads to significant economic shifts due to technological advancement. The changes introduced by 4IR raise concern about its impact on employment due to jobs automation, or lack of workforce equipped with the required skills. It is essential to reshape the curriculums according to 4IR requirements to mitigate unemployment risks. The objective of this paper is to automatically identify the rapidly changing 4IR jobs skills and identify the gap between curriculums and modern industry jobs as well. In addition, the proposed solution has the capability of delivering efficient gap analysis recommendations that could be used in the curriculum enhancement decision-making process. The proposed recommender system clusters jobs based on K-Means and TF-IDF algorithms and then identifies the similarity and dissimilarity by utilizing the Cosine Similarity algorithm. The solution utilizes the algorithm's result to construct gap analysis recommendations that curriculum developers can use to align curriculums with 4IR requirements. The results of the classification report indicated that the system was effectively capable of clustering jobs based on skills similarity and identifying the 4IR gap.
Synthetic Data Generation Using Genetic Algorithm

Statistical machine learning models suffer poorly because of class imbalance issue. Real world dataset contains mostly ‘normal’ examples and very few ‘abnormal’ examples and in most of the cases, the primary goal is to identify the abnormal instances. For example, if we want to develop a statistical machine learning model to identify financial fraud using the historical transaction data then we can expect that majority of the data comes from normal/non-fraudulent class, whereas very few examples are fraudulent transactions. Using such imbalanced dataset for training makes machine learning models highly biased towards majority non-fraudulent class. This way, the objective to catch fraudulent transaction instances fails and misclassifying such minority class instances often results in a much higher cost. Hence, a balanced dataset is very much required to train a sound model. Different techniques such as under sampling, oversampling, SMOTE were proposed earlier. In this paper, we propose a novel technique to generate synthetic data using genetic search algorithm. We examined the effectiveness of our proposed algorithm on different datasets and reported in section V.
A New Concatenated Method for Deep Curve Estimation Using Low Weight CNN for Low Light Image Enhancement

This paper presents a new concatenated method for deep curve estimation. Which is a modified version of Zero-DCE. Which produces an enhanced image from a low light image. With the aid of a deep network, this may be accomplished as an image-specific curve estimate task. Our technique develops a light-weight deep network that can forecast pixel wise and high order curves for an image’s dynamic range correction. Unlike majority of GAN based and CNN based methods, which requires paired images for training it can be trained without paired/ reference images. The potential advantages of our technique for reading license plates of vehicles at night are also discussed.
Application of Hybrid Algorithm in Robot Intelligent Control

In this paper, the improved grid modeling method is combined with the improved A-Star algorithm to enable the robot to automatically judge the energy consumption value in each direction and make the optimal choice during the tracking process. In order to reduce the tracking distance of the robot, the traditional A-Star algorithm is improved, and the improved A-Star algorithm is used to simulate the single robot and multi robot systems. The experimental results show that the improved A-Star algorithm significantly reduces the tracking distance and improves the operation efficiency.
Implementation and Comparison of BUG Algorithms on ROS

This paper conducts a research study and a comparative evaluation of Bug Algorithms to assess their potential for robotic navigation using Robot Operating System (ROS). Because these methods require local knowledge, they demand less computational capacity and are simple to implement. Robots that use these algorithms are also more comfortable to build because they need few sensors. We compare several Bug Algorithms using a robot, simulated in a Gazebo environment where they are subjected to various failure cases founded on the environment. Based on the simulation results, we compare the performance of the Bug Algorithms and determine whether they are suitable for use in the real world. This paper discusses and compares Bug 0, Bug 1, Bug 2, Com1, Alg1, Alg2, DistBug, Rev1, and Rev2. Bug 0, Bug 1 and Bug 2 have been implemented using Robot Operating System (ROS) and compared the performances.
The Effect of Virtual Reality in the Modern System of Education

Virtual Reality is the concept which is referred to as VR, where the users are offered an effective and immersive experience. Moreover, by entering into a computed stimulated world from a real-time world is possible. Virtual reality is regarded as one of the most concerning topics that will be the need in the coming future to improve daily lives. It plays a significant role in different sectors such as education, healthcare, entertainment, sport, and many more. By the use of virtual reality, users can upgrade the experience to another extent by implementing VR devices such as oculus quest 2 and, more. The reason besides the great experiences is that the user has the power to control the system of the stimulated environment with the help of a specific system. VR improves the environment by making it fictional through the introduction of different sensors and displays which includes several features like motion and moment tracking. The user experiences an explored vision without borders and boundaries in 360 degrees in the game zones and the medical training. With the help of VR devices, users experience a virtual and stimulated environment where users can connect and interact in that specific environment.
Body Posture Correction and Hand Gesture Detection Using Federated Learning and Mediapipe

In today’s post-covid culture, where everyone works from home, there is a huge possibility of serious long-term health problems. A lot of people have started taking up exercises at home and if done incorrectly, they can have major negative effects. Another one of the main contributors to these health issues is bad sitting posture, which is only exacerbated when working for hours on end. Hand gesture detection has many useful applications in elderly healthcare, automating actions and gesture-based presentations and games. To help users with these actions, our paper proposes pinpointing the points of the error to the user in real-time and in a lightweight manner for yoga posture correction. The incorrect positions shall be shown in real-time on top of the user’s video feed to help them correct it properly. The user shall be told about when they are sitting in a bad position, and the overall bad posture time will also be shown for the session, which will provide the required information to the user. To further help users in a useful manner, our paper looks to augment the hand gesture detection feature with federated learning and personalization to avoid the common pitfall of privacy concerns, while still allowing users to customize their experience. The proposed library for the implementation of these tasks is the MediaPipe library. This library is one of the key components that makes the features lightweight and easy to use. The aforementioned library also looks to implement the features in real time with no lag while keeping the resource requirements as low as possible.
A Survey of Various Sentiment Analysis Techniques of Whatsapp

Sentiment is a kind of consideration, feeling or a judgment which is occurred due to certain emotions. To analyze the sentiment is a technique called opinion extraction. This approach is employed for exploring the emotions of diverse peoples on the basis of meticulous rudiments. The opinion related data is obtained through the social media platforms. Whatsapp is one of the social media platforms and it is accessed by a number of followers. A status On a Whatsapp is a kind of messages which is posted by these followers. The feature extraction and classification technique is utilized to analyze the sentiment from the data of Whatsapp. This paper conducts a review on several SA techniques and analyzes them further.
Powering up the IT Equipment and Cooling Units of Cloud Data Centers using Photovoltaic-fed Microgrid

Cloud computing infrastructure is designed to support seamless accessability and availability of different services to consumers over the internet. Cloud data centers require a lot of power for powering up of the IT equipments and powering up of different air conditioning equipments. The high power consumption leads to higher energy bill as well as increased carbon foot print. To minimize the effect of carbon foot print, distributed energy resources are utilized. This paper provides a systematic analysis of the power model as well as air conditioning model of data centers. The architecture of solar microgrid and economic analysis of solar microgrid for data center has been highlighted in this paper.
Interactive Communication Robot Handling Crowd Management and Content Delivery in Museums Employing Crowd Counting

Through the last decade, and particularly after the Covid period (2020 - 2022), crowd counting and localization have attracted much attention of AI researchers due to its potential applicability in crowd monitoring and control, public safety, space design, interactive content delivery etc. Once delivery objectives for a system are envisaged and the premises are fixed, we can always construct manifold technology architecture that delivers the set goals. However, in the Indian context a solution of choice needs to be optimized on frugality and ease of adaptability. In this paper we report an economic and replicable application of crowd counting and interactive content delivery in museums through unbiased knowledge systems embedded in robotic museum assistants. We intend to demonstrate a robotic system that can deliver any gallery content to groups of visitors keeping special focus on the exhibits that are popular choices. Crowd counting is used here to enable the content presentation to a group of choice in an interactive way. There are some market-ready solutions available for interactive gallery demonstration by moveable robots but they require not only huge capital investment but are also of limited use within controlled environments. Our proposed design is to multiplex an existing infrastructure of surveillance system as a smart crowd counting and gallery demonstration system along with crowd management with minimum additional hardware infusion.
The Significance of using Data Extraction Methods for an Effective Big Data Mining Process

Data is identified as the fuel of modern society for its versatility of use and effectiveness of use. In addition, modern businesses are making a decline based on analysis of historical data and patterns of the data. Such dependency on data analysis makes the process of data analysis important for data mining. Therefore the overall study has shed light on the significance of the data mining process and extraction process of data in order to make a data-driven decision. Additionally, the problems related to the process of data extraction and data mining are mentioned in the study which helps to achieve an overall concept for the data extraction and data mining process. Additionally, the significance of the process is mentioned in the study. Additionally, there are tables constructed that represent problems of the data extraction process and mining and the significance of the stems of mining. The study concludes in a way that helps in the implication of data extraction methods for business.
Imposter detection with canvas and WebGL using Machine learning.

Authentication offers a way to confirm the legitimacy of a user attempting to access any protected information that is hosted on the web as organizations are moving their applications online. It has long been believed that IP addresses and Cookies are the most reliable digital fingerprints used to authenticate and track people online. But after a while, things got out of hand when modern web technologies allowed interested organizations to use new ways to identify and track users. There are many new reliable digital fingerprints that can be used such as canvas and WebGL. The canvas and WebGL render the image which is dependent on the software and hardware of the system. In our work with the generated hash value value from canvas and WebGL we create a model using KNN to identify the imposters. The model has proved to be accurate in authentication of user with an accuracy of 89%.
A RealTime Image Recognition Method of Power AI Based on Quadtree Algorithm

Power artificial intelligence Realtime image recognition method is a new technology that can recognize images in real time. It is very useful in safety and traffic control, intelligent information systems and so on. This technology has been widely used in many fields, such as financial industry, medical field, traffic control and so on. This paper presents an artificial intelligence realtime image recognition method based on quadtree algorithm, which involves the field of image recognition technology. It includes separating the recognized image or the segmented picture according to the quadtree segmentation method to obtain several current separated pictures; Traverse all current separated pictures to match the best matching block for the current separated picture; If the match is satisfied, the current separator block is marked R1 and the corresponding matching block is a domain block and marked D1. The quadtree method is used to cut the image, which can ensure the image quality and reduce the number of blocks at the same time; Before segmenting the image, set the maximum and minimum depth of the quadtree and the allowable error, and use the quartering method to segment the image; Then find the best matching block. If it is found, it will not continue to be divided. If it is not found, it will continue to be subdivided. This process continues until the set maximum depth; It saves a lot of computation, improves efficiency, and accelerates the speed and accuracy of image segmentation.
Deployment of Blockchain in Cloud Computing- A Comprehensive Review

It is an era of digitalization that includes all types of digital activities and information. In this digitalization period, if we want to convey signals or data or any digital favors, Cloud Computing has moved out of and become visible as an unbeatable technology. It forecasts divisional computing assets as alternative configuring regional servers to be head of, a stockpile of data kept for use as needed, and procedural data. The requirement for cloud computing is increasing day by day. Cloud computing is not more secure to handle entire data of this world or digital era. There are adequate security issues that come out of this increasing technology that creates a hurdle for improvements. All the information and prime data still convey from one end to another ruin through a global medium called the internet. But the data security is at risk always. For security purposes, we have to keep up the apparatus for preserving data such as privacy or confidentiality, dispensation, accuracy, answerability, and access control. There is one technology that is trending now a day called a blockchain. Blockchain makes cloud computing more secure and more powerful technology. The condition of being free from danger or threat, that state comes from a blockchain in cloud computing. All complications in certainty can solve by blockchain technology. A blockchain can increase reliability and privacy from end to end of undeniable ration disperse registry or logs on cloud confluence. A Blockchain follows the registry rule that once set foot in any data in the structure that cannot ever depict as cut off in a jagged line. All information and proceeding data or deals are corroborated by the system and keep that records or documents protected. Bitcoin is perhaps the most commendable instance of a blockchain. In a communal incline called the blockchain, each unique procedure is authenticating. It is based on a digital currency communication topology on a distributed peer-to-peer. In this article, we have mitigated the process of blockchain that implements and connects to cloud computing technology. We are concerned about the blockchain framework in this post, which integrates and interfaces to cloud computing technology. We are discussing the two emerging technologies work together and give us better implementation results.
An Experimental Analysis on Mitigating the Effects of Malicious Nodes in a Federated Learning System

This paper describes how deep learning can be used to provide security for IoT devices by analyzing the data packets that arrive at an IoT device and classifying them as packets part of the normal operation of the device or packets sent with a malicious intent. An experimental analysis is performed to check the effectiveness of such an approach with the help of the data present in the MQTT dataset. Federated learning approach is suitable for the IoT platform as IoT devices tend to contain less computing power. But a consequence of this is that the networks can contain malicious nodes which send wrong updates to the model decreasing its accuracy. We propose the introduction of verifier nodes into the system which verify the given updates sent by a node and check if it actually increases the accuracy of the model before appending it to the global model. The extent to which the malicious nodes impact the accuracy of the model and the remedy provided by the introduction of verifier nodes is also studied in this paper.
An Innovative Way of Building Website Using HCI Principles

A website is a live proof of validation for and a mark of authenticity for a business or person. There are over one billion websites on the internet, the number goes on to prove how important websites are. It also means it is an opportunity to create a good impression on the user. Visitors are the most important for any website, more visitors mean better search engine results page ranking and in the case of a business, could mean more customers. Usability is a part of Human Computer Interaction (HCI) and building a website inspired by the principles of HCI would definitely result in more visitors and contribute positively towards making a website user friendly and develop a good user experience.