Reviewerly/exHarmony huggingface dataset:
Detecting face masks through embedded machine learning algorithms: A transfer learning approach for affordable microcontrollers

The adoption of face masks to halt the spread of airborne diseases has been recognised as an effective measure, being a significant resource to reduce new infections of different types of virus that affect the respiratory system. In this sense, the enforcement of the use of masks in crowded areas has been a recurrent concern of governments, which has fostered the development of camera-based approaches to automatically detect people without face masks. In order to tackle this problem, this article proposes a low-cost approach based on the use of affordable and energy-efficient microcontrollers to quickly process images and detect when people are not wearing face masks, performing local decisions. For this on-the-edge processing, reduced Machine Learning models were created and evaluated through different techniques. As a result, a complete processing pipeline was designed to perform a transfer learning process taking as reference the MobileNet model supported by the Edge Impulse platform, achieving an innovative TinyML solution for quick decisions on resource-constrained Internet of Things units. Additionally, we performed a series of experiments on the Arduino Nano 33 board attached to an OV7675 camera module, evaluating the practical application of the proposed solution after proper training using public image datasets. Working as a soft-sensor unit that is reproducible and ready to be used, the defined trainable model and the performed evaluation results represent a significant contribution towards real-world deployment of face masks detection systems.
Text Visualization of Entire Corpus Through Single Document Input Tools

Text visualization is often needed by those researchers too who are not well-versed with data mining techniques. They depend on available open-source visualization tools for their short-term purposes. The biggest challenge faced by such users is that most of the tools take input from only a single document while an analysis of the entire corpus is required. Converting a corpus into an effective small single document is the problem addressed in this paper. We propose how all the major features of the corpus can be extracted and represented as a single document to be input to a visualization tool. The aim is to retain statistical and informative quality of a corpus in a single text file using term weights. We propose various weighing schemes and compare their effects on a corpus by producing word clouds using 6 different tools. The point of view is keeping techniques simple enough for users that are not from a computer engineering background.
Multi-modal Sentiment Analysis Using Text and Audio for Customer Support Centers

Customer service has become crucial for every organization looking to grow and improve its clientele in today's cutthroat marketplace. Companies cannot afford to fall short of consumer expectations. With the recent progress in Artificial Intelligence, companies have adopted AI techniques like sentiment analysis to measure customer satisfaction. Chatbots are the foundation for most AI applications in call centers trained for either question-answering tasks or calculating sentiment out of user feedback via surveys. These existing expert systems only utilize text mining techniques to classify sentiment as positive or negative. In this paper, we propose a Multimodal learning framework for tackling the sentiment classification task, employing acoustic and linguistic modalities from real-world conversations between support representatives and customers and survey feedback data using the decision-level fusion technique. Leveraging the classification abilities and feature representations from both modalities, our model achieves the best results amongst all implementations and enables us to better analyze the sentiment expressed by the user than using a single modality like text or audio.
Design and Implementation of Camera-Based Security System (CBSS) for Detecting Missing Persons

An efficient AI-based Camera security system is important for locating missing persons. In simple words, if any suspicious face is detected by the system, it sends an alert to the authorized people. Thus, this eliminates the risk of a certain theft from the organization. The focus in our project is implementing and designing a camera-based security system (CBSS) for locating missing persons. The CBSS system is designed using a high resolution camera for detection and several facial recognition features were implemented in the model. In the end, the experiments of our program showed that the system had a more accurate detection notwithstanding the proximity of detection and in almost all the angular positions of detection.
Biometric Framework for Securing IoT Environment

The increased number of Internet of Things (IoT) gadgets necessitates communication with customers and IoT environment protection is significant. In Table 2, biometrics provide an intriguing possibility to enhance the accessibility and security of the IoT using single-modal biometrics in a database. The overall biometrics provide an intriguing possibility to enhance the accessibility and security of the IoT using single-modal biometrics in this regard to solve security difficulties in safeguarding a vast array of the growing IoT networks. This paper's objective is to summarize the current IoT security study on biometrics, focusing particularly on encryption and authentication as two of the most critical features. For addressing authentication, modern biometric authentication technologies for the IoT are explored and categorized depending on several biometric characteristics utilized by the system. Cryptography systems with biometrics and encryption to increase the security of the IoT have been extensively researched and addressed. In addition, potential solutions and obstacles associated with adding biometrics to the IoT are discovered and explored. This paper helps biometrics for the Internet of Things (IoT) and security research move forward by giving a thorough understanding of future concerns and future research prospects.
Impact of Online Classes Using Machine Learning Algorithms: Estimation, Classification, and Prediction

Many domains of human life, including education, have been disrupted by the Covid-19 outbreak. As a result, a never-before-seen situation in the teaching and learning process has emerged. All educational institutions have shuttered their campuses and reacted positively to continue educating their learners using a different approach and learning methodology. Traditional classroom teaching–learning has been replaced by online teaching–learning, putting the learners under a lot of stress. This research attempts to investigate the students' reactions and adaptation throughout the crisis, as well as to explore the opportunities for innovation in digital transformation. In this paper, a machine learning model is proposed to identify the severity range of the challenges faced by students while learning through online classes. The data is collected from the Arts, Science, and Engineering students of both UG and PG. The dataset is analyzed using the various machine learning algorithms K-Nearest Neighbor, Decision Tree, Support Vector Machine, Random Forest, Neural Network, Naïve Bayes, and Logistic Regression to predict the severity of the student's difficulties due to online classes. The Proposed Machine Learning Model is analyzed using the Accuracy, Precision, Recall, F1-Score, and Area Under Curve as measures.
Lung Histopathological Detection Using Image Classification

Early detection of lung histopathology has become crucial and essential for humans. Rapid recognition gives many patients the greatest chance of recovery. Histopathological graphics of biopsy samples tissue from possibly infected areas of the lungs are used by doctors to best solution. The multiple type of lung disease is frequently misdiagnosed and prolonged to detect. The characteristics used to detect Lung Histopathology are extracted from Computed Tomography (CT scan) images. Deep Learning (CL) is a novel method that enables us to improve result’s precision. In this paper, we create DL model to determine the type of lung cancers from Computed Tomography images. Convolutional Neural Networks (CNN) which recognize and categorize lung-cancer type within improved efficiency and less amount of time, that is crucial to deciding on the best treatment approach for patents and their risk of mortality. This paper proposes a tri-category classification which applies to images of lung cancer. Benign, adenocarcinoma and squamous cell carcinoma is performed by utilizing VL, VGG-16, and Le-Net to process an image of lung tissue and obtain functionalities effective for diagnostic techniques. Further, the paper analyzes how handcrafted characteristics can be extracted from raw pictures after various processing steps, and finally, the Python framework (Django) is used to deploy the model. The purpose of using this technique is to obtain some characteristics primarily relevant to lung histopathology forecasting.
Image Enhancement for Underwater Photography Using Morphological Filter

Aquatic image processing has gained a lot of attention and made tremendous progress over the past several decades. The aquatic definition processing area has entered considerable attention within the last decade, showing important achievements. In this design, it's proposed to develop image improvement styles to ameliorate the quality of an aquatic image. In this paper, we proposed an image grounded pre-processing fashion to enhance the quality of the aquatic images. The proposed fashion comprises a combination of four pollutants similar to homomorphic filtering, sea denoising, bilateral sludge, and discrepancy equalization. These pollutants are applied successionally on demoralized aquatic images. The literature check reveals that image-grounded pre-processing algorithms use standard sludge ways with colorful combinations. Both spatial sphere and transfigure sphere image improvement styles are proposed to ameliorate the aquatic image. Thus, expanding the dynamic range of the image provides a way for enhancing the visibility of the aquatic image. The oceanic charge is delicate in the discussion and use of the fatal ocean. The initial step in dealing with aquatic image processing is to take into account the abecedarian drugs of light propagation in the water medium.
Voting System Based on Blockchain Technology Smart-Contracts

Today, blockchain technology is being widely used as a platform for various emerging applications. A blockchain is a distributed, immutable, transparent, secure, and open ledger. A wide number of blockchain applications in several sectors can be built using some of the technology’s most important features, such as security, speed, immutability, persistency, anonymity, and decentralized distributed ledgers. Blockchain technology offers huge potential for storing, accessing, and preserving documents. One of the most well-known blockchain applications is the Online Voting System. Most e-voting systems rely on centralized servers, and voters need to trust the election authorities for the accuracy of polling results. An application for a voting system that is decentralized and based on blockchain technology is proposed in this article. The proposed blockchain-based voting system will be secure and enhance public trust. Data safety and transparency are key features of the system. It ensures the idea of one vote per unique identity for each poll along with assured anonymity. To ensure transparency, trustworthiness, and immutability, the proposed decentralized application (DApp) uses the Ethereum Virtual Machine (EVM) to write smart contracts for each voting event. It allows users to vote anonymously from any location and at any time by using digital devices.
Transfer Learning Approach to Detect and Predict the Malaria from Blood Cell Images

Malaria is a female Anopheles mosquito-borne disease that transmits a motile infective form to the host body such as humans, which reproduce asexually in the blood cells of the host. It is discovered by experienced microscopists who analyze blood smear images. Newer deep learning methods could be utilized to automatically do such an examination. Create an automatic precise and efficient model, and you can drastically reduce the amount of trained staff needed. By having access to these predictions at an earlier stage, medical personnel would be able to make more informed choices regarding patient diagnosis and treatment. This research aims to demonstrate the efficacy and accuracy of real-time malaria detection from input images using deep learning architectures such as convolutional neural networks (CNNs) and capsule networks with transfer learning. To this purpose, we employ a Hybrid model which achieves a 96% accuracy rate in binary identifying images of infected and uninfected cells. The proposed Hybrid model is a combination of Visual Geometry Group (VGG) and Capsule Network. This article's findings will improve malaria diagnosis, which will help address issues with treatment consistency and a shortage of medical professionals.
Improving Data Centre Power Efficiency by Applying the VM Consolidation Approach

Cloud computing enables businesses to scale up and down their resource utilization according to their requirements. A data centre uses as much electricity as a small city and houses thousands of servers. These servers need a tremendous amount of computational power to run, which creates a number of problems like high energy consumption and carbon emissions. Given that data centres use a lot of resources when they’re operating, attention has recently been focused on how energy-efficient they are. One method to improve the usage of cloud properties is through resource consolidation. By combining virtualized servers into fewer physical servers, virtualization encourages the deployment of flexible resources and boosts energy efficiency because doing so would use less electricity. The Infrastructure as a Service (IaaS) concept, which deploys specialised virtual machines (VMs) on suitable servers in a data centre, is the subject of this study. By utilising a robust VM allocation process, it also improves the quality of services (QoS) and ensures the proper SLA. Additionally, it will examine the problems that will emerge during implementation and the employed performance metrics.
About Some Clustering Algorithms in Evidence Theory

The Dempster–Shafer theory of evidence considers data that have a frequency-set nature (the so-called body of evidence). In recent years, there has been interest in clustering such objects to approximate them with simpler bodies of evidence, to analyze the inconsistency of information, reducing the computational complexity of processing algorithms, revealing the structure of the set of focal elements, etc. The article discusses some existing algorithms for clustering evidence bodies and suggests some new algorithms and approaches in such clustering.
Recent Developments in Generative Adversarial Networks

Generative modelling is an area of machine learning that deals with generation of new samples that resemble the original samples drawn from the data distribution. Since their invention, GANs have proven to be the new driving force in the field of generative models. GANs comprise two separate functional blocks called generator and discriminator, which interact in an adversarial framework. The generator learns to generate natural-looking data using these interactions. This work provides a condensed overview of the popular developments in GANs.
Evaluation of Text Classification Using Support Vector Machine Compare with Naive Bayes, Random Forest Decision Tree and K-NN

This paper aims to find the boost model which brings the best accuracy in text classification by using Support Vector Machine in comparison with other models namely Naive Bayes, Random Forest Decision Tree and K-NN. For the text classification and processing, the planned system will have to apply with the Support Vector Machine and the result is decided by major roles. Based on the Machine Learning algorithms used for the implementation of the research- the BBC news dataset- illustrates that the Support Vector Machine has better accuracy and result.
SARAH-Based Variance-Reduced Algorithm for Stochastic Finite-Sum Cocoercive Variational Inequalities

Variational inequalities are a broad formalism that encompasses a vast number of applications. Motivated by applications in machine learning and beyond, stochastic methods are of great importance. In this paper we consider the problem of stochastic finite-sum cocoercive variational inequalities. For this class of problems, we investigate the convergence of the method based on the SARAH variance reduction technique. We show that for strongly monotone problems it is possible to achieve linear convergence to a solution using this method. Experiments confirm the importance and practical applicability of our approach.
Teleportation-based continuous-variable quantum digital signature

In this paper, a continuous-variable quantum digital signature (CV-QDS) protocol based on quantum teleportation (QT) is proposed, and the corresponding theoretical analysis method is established. Unlike previous schemes, we use squeezed states to carry key and secret information about the teleported states that are transmitted through classical and entangled channels without infinite squeezing. In addition, for multibit information signatures, we propose to use the mean and modulation variance of the teleported quantum state to distinguish signatures, and each of which is marked and ordered. In particular, we derive the fidelity that the protocol can achieve, displaying the effectiveness of the method at different compression levels. Security analysis demonstrates that the protocol possesses unforgeability, non-repudiation and robustness, which is suitable for signing one-bit and multibit messages.
Curated Recommendations of Teaching and Learning Videos on YouTube with the Help of a Chatbot

In February 2020, 500 h of video were uploaded to YouTube every minute, including numerous videos from the education sector [19, 20]. If you are looking for suitable teaching or learning videos, for example, this mass of videos makes it much more difficult to select. In the course of an implementation project with Bachelor students, teaching videos in the German language on the topic of mathematics on YouTube were examined and how they can be found for teachers and learners. From the results of this research, a database of selected videos and the corresponding search terms and difficulty levels was created in an exploratory manner. In addition, a chatbot was implemented to support users in their search for learning and teaching videos. The initial analysis showed that there are only a dozen channels that provide the majority of learning content for the subject of mathematics. The technical solution shown is able to extend this result and suggest existing but less known educational videos. The present study thus provides both a theoretical and practical complement to user-centred teaching–learning scenarios.
New Approach of Artificial Intelligence in Digital Forensic Investigation: A Literature Review

A Digital Forensic is a subfield of forensic science known as “digital forensic science” that focuses on the recovery and examination of data from digital devices that are connected to cybercrime. Computer forensics was the original meaning of the word “digital forensics.” it has broadened to include any device that could store digital data. National policies on digital forensics didn't start to arise until the beginning of the twenty-first century. The process of locating, protecting, analyzing, and documenting digital evidence is known as digital forensics. When necessary, this is done so that evidence can be presented in court. AI in Digital forensics is one of the technologies that is developing the fastest, and it has had a significant impact on the methods and tools used to examine, monitor, and visualize crime scenes as well as develop effective strategies for dealing with impending threats and attacks on the internet and cyberspace. Modern technology has replaced human labor with machine-oriented designed work that has the capability and ability to minimize error and increase quality, reducing human effort and achieving maximum results with the fewest possible errors. It can influence the outcome and analyze the evidence in the field of digital forensics in a more effective manner to track the outcomes.
Mitigating Trade-Off Between Explainability and Confidentiality in Artificial Intelligence

Explainability has become essential ever since Artificial Intelligence (AI) models have generated biased results. It’s interesting to note that adversarial attacks have also been upgraded to exploit a model’s explainability and extract confidential data conveniently. With the aid of such knowledge, adversaries can intensify their attacks and fully take control of the model. The explainability property of AI models has been the subject of numerous research projects. Prior studies have also focused on developing, preventing, and/or minimizing adversarial AI attacks. However, the goals of responsible AI are impacted by the lack of research on the trade-off between explainability and confidentiality. The difficulties in assuring the resilience of AI models against malicious activities are further complicated by the absence of tools for testing the model’s response to adversarial tactics. To achieve a secure and responsible model with both explainability and confidentiality, it is realized that several such research gaps need to be filled. This research work serves as a foundation for understanding how these two crucial characteristics are interrelated. The paper offers a novel strategy to reduce the stated trade-off in light of what is understood.
SAPV Sizing Optimization: A Numerical Iterative Method Using LTE Base Station Profile in SA

The quest for a green renewable energy source in replacement of conventional energy such as fossil fuel, diesel generator, or electric grid has taken important aspects in industrial sectors to alleviate their economy and comply with global warming reduction. In the information and telecommunication (IT) sector, base stations generate more than 90% of energy used in a network. Green energy from photovoltaic (PV) systems can be used to tackle base station energy consumption, however, non-exhaustive analyses are required to meet the demand while keeping the cost of energy production at minimum. Therefore, how can PV systems alleviate the cost of conventional energy usage while meeting the load demand for LTE base station’s equipment. This paper presents a sizing and optimization solution for a stand-alone PV (SAPV) system. A stochastic method based on an iterative numerical algorithm is used to define the size of the PV, meanwhile, the optimum size is chosen from the lowest loss of energy supply probability (LLP) percentage and the least cost of energy (LCE). The outputs demonstrate from an LLP of 1%, the LCE is 0,305 $/kWh using 13 PV panels and 408750 Wh of battery capacity over a duration of 25 years.
Accuracy Prediction of Rainfall Using Decision Tree Algorithm and Random Forest

Climate change has made accurate rainfall forecasting more difficult than ever. In this paper, the decision tree algorithm and Random Forest is used to predict the rainfall accuracy based on historical climate data. The classification and regression tree (CART) approach is employed to this result, producing a better accuracy rate. The algorithm can determine the probabilities of rain on any given day, making it an ideal choice for various applications involving large datasets.