Reviewerly/exHarmony huggingface dataset:
Face Mask and Body Temperature Scanning System for Covid-19

Coronavirus illness 2019 has had a major impact on the entire world over the past two to three years. One important approach for people’s protection is to wear masks in public. Furthermore, putting on a mask properly Many public service providers demand that users only utilise the service while properly wearing masks. Only a small number of studies have examined face mask identification using image analysis, nevertheless. We suggest Face Mask, a highly accurate and practical face mask detector, in this study. The suggested Face Mask is a one-stage detector that combines a novel context attention module for detecting face masks with a feature pyramid network to fuse high-level semantic information with various feature maps. We also provide a brand-new cross-class object removal method to reject and predictions with a high intersection of union and low confidence. Additionally, we investigate the viability of integrating Face Mask with a portable or embedded neural network called MobileNet. By utilising1)Contactless temperature sensing,2)we create a fack mask detection alarm system to boost COVID-19 indoor safety.Infrared sensor and contactless temperature sensing subsystems rely on Arduino Uno, while computer vision algorithms are used for mask identification.
Application of Real-time Automatic Cartoon Style Generation from Live video

Converting live video to cartoons is a favorable technology. The main objective of the project is to transform live videos into animated or cartoon videos. The prior transformation approach needs sophisticated computer abilities and graphics. The idea of the project is to convert videos into an art form such as painting. There are various methods for turning real-world photos and videos into cartoons, but among all of them, the application of a Generative Adversarial Network (GAN) dubbed Cartoon GAN will be employed for style. Using real-world live footage being captured by a camera, high-quality cartoonized live video was produced with the aid of GAN.
Remotely Operated Animatronic Arm

In today’s modern world, we still witness numerous scenarios where labor-intensive activities are repetitive, require high precision and have no room of human error, or a partially paralyzed patient or disabled person who is need of assistance. These activities need to be replaced by efficient systems that automate these jobs, reduce errors, reduce time consuming and aid humans. However, most of these automatic systems are complex and bulky in nature. Hence these cannot be installed at homes and be used by common people for their everyday lives. Bearing this problem in mind, we have developed an animatronic arm that is designed to perform the basic tasks that we generally perform with our hands like holding, picking, carrying objects with the help of gestures. Apart from making the task easier, it is quite simple and portable that could be placed at different locations to perform the desired task, which might be difficult or risky for humans to do the same. Our model helps people to remotely control an animatronic arm through flex sensors placed on their fingers using a glove. It picks the angular displacement of the finger and maps it to a specific reading which results in a similar action by the arm.
Animal Neuron Pattern Classification and Clustering using Self-Organizing Map (SOM) through Learning Rate and Minimal Cut

Authors have proposed a new neural classification model called Pattern Classification - self-organizing maps (PC-SOM), representing a best matching unit (BMU) for small SOM networks. Each vector of the input database is trained individually to provide best results by computing D(j) is minimum for one class only. Have considered one animal data set for classifying patterns as small animal or moderate animal or large animal by looking into the learning rate and minimal cut. This model is optimized in terms of number of epochs and number of iterations. The experimental results demonstrate that the proposed generalized algorithm PC-SOM outperforms for classifying the vectors in specific classes.
A Deep Learning Approach to Detect and Classification of Lung Cancer

Cancer is a name of fear to people in the world. Every year millions of people dead of cancer in the world and lung cancer is one of them. Lung cancer is classified by our research. Non-small cell lung cancer (NSCLC) is the most common of the two main types of lung cancer. Here we have classified our model NSCLC into 2 subtypes Adenocarcinoma and Squamous Cell Carcinoma and non-cancerous benign tumors. The CNN model is utilized here for classification (VGG19, ResNet50, EfficientNetB7 and MobileNetV2). We used 15 thousand image data. The Augmentor package was utilized to enhance to 15 thousand from 250 benign lung tissue, 250 lung adenocarcinomas, and 250 lung squamous cell carcinomas. In comparison to other models, ResNet50 has the best accuracy of 98% among our proposed models. By putting this model into practice, medical experts will be able to create an accurate, automatic method for diagnosing different forms of lung cancer.
Performance Evaluation of Automatic Suspicious Activity Detection Method

The video monitoring is hectic task when large dataset of surveillance video is to be monitored. The errors involved in monitoring task and suspicious activity detection may lead to missed detection performance. The automation of suspicious activity detection is the need of the time. In this paper, the performance evaluation of suspicious activity detection of video data is shown. The proposed method of combination of GLCM, harries corner detection, speeded up robust features, shows average 96% percent accuracy of suspicious activity detection on self-designed dataset.
Cyber Security Enhancement with an Intelligent Android Prototype

For better or worse, smartphones have emerged as one of the most significant technological innovations in recent memory. Smartphones, which were first introduced four decades ago, have undergone fast evolution, shrinking in size while adding cutting-edge features. Today’s smartphones can do the work of a secretary, a calendar, a phone, and a game console all in one. As a result of these developments, Smartphones are now indispensable. The hazards of data breach, however, are always growing, and are typically fueled by the individual using the system. Any Android phone, no matter how simple, may be exploited by an adversary to cause harm to a given organisation.In the past decade, several data breaches and thefts have been disclosed by a wide range of government entities. Keeping this in mind, the purpose of this paper is to talk about how using smartphones affects different strategic government organisations, with a focus on the military. It also seeks to provide a software-based answer to this problem so as to make the workplace a safer environment for those involved.
A Novel Approach to Record and Narrate the Summary of Conversation for Alzheimer Patient

Alzheimer is a neurodegenerative disease which primarily causes memory loss among older people. Alzheimer patients frequently forget memories, including interactions with others that results in unfinished business. Additionally, they frequently forget the people they met as a result, the patient needs to repeat the conversations during a future contact in order to stimulate their memory. At present there is no cure for this deadly condition. However, by enhancing the quality of life for the patient the disease progression can be slowed down. Assistive technology provides support for people suffering from Alzheimer to enhance the level of their independence. The proposed system is an application which assists the Alzheimer patient to remember the person and the conversations made between them. The conversation between two people can go on for a very long time and can even be about nothing. Our application uses facial detection to initially determine who the patient is conversing with. It then summarizes the lengthy discussion and stores it in cloud under the name of the person discovered only if the patient requests it. When the patient encounters that person again, our application will be able to identify them, voice out any previous discussions, and record any new ones. The suggested approach will assist the Alzheimer’s sufferer in remembering their interactions with others.
How library systems influence the interlibrary loan workflow

The interlibrary loan (ILL) workflow is influenced by several outside and inside factors, which can variously affect its efficacy in providing items to library users. This paper will show differences and similarities in the ILL workflow, from customer order to delivery or return of the item, when using two different library systems. The ILL workflow includes both internal and external document supply because the libraries offer and provide services both to their own users and to other libraries. Some supplied items are returnable (for example books) and others are non-returnable (for example copies of articles). This workflow chain involves a series of steps and starts with a request made by a user. This is followed by the entry of necessary data into both the receiving and lending libraries’ ILL systems to administrate the request and continues with the delivery to the end user. For returnable items, the final step is returning said items to the provider. This paper will compare how two Swedish libraries (Örebro University Library and Karlstad University Library) established their ILL workflows and how decisions made in the ILL process are based on the different library systems used (Alma and Koha). It concludes with a discussion of what can be learned from the comparison, the benefits and challenges presented by each system and any improvements that can be made.
International loans in the Main Library of AGH UST

Workflows and methods used in the Main Library of AGH UST changed with the challenging history of Poland and rapid changes in sourcing information for the Library's patrons and STEM professionals. The article describes this journey specifically from the point of view of the Interlibrary Loans Department. Since 1952 it has served hundreds of thousands of users domestically and outside of Poland, maintaining continuing relationships with its partners. The most important milestone in document delivery was in 2001, when the library started using IFLA Vouchers, which made managing loans much easier. Nowadays, in times of digitization and open access ILL is still important – as its core role is providing access to materials which often can seem unobtainable.
COVID-19. New challenges and new solutions at the University of Warsaw Library

This article aims to address how the COVID-19 pandemic influenced the University of Warsaw Library's work from 2020 to 2021. Analyses of BUW's situation in 2020 and 2021 based on published reports by the heads of the library's organisational units as well as the author's own observations and experiences reveal that the COVID-19 pandemic forced the BUW to organise its work and services in unusual ways. The results are evident in the use of new technologies and electronic resources. Furthermore, the experience gained during the pandemic helped BUW introduce new solutions, processes, and workflows which could be valuable for future use in similar situations and for the patrons' benefit.
Dog Breed Classification using Inception-ResNet-V2

Dogs are one of the most faithful and loyal animals in the world.They are also the favourite pets for most of the pet lovers.Many feel relieved from stress and tension when they spent time with their pet dogs.So these special creatures are spread into various breeds across the world.It is very much essential to distinguish the breeds at many occasions.With the advent of development of artificial intelligence the methods to classify such large scale of breeds had become easier.This paper proposes a transfer learning based pretrained deep CNN architecture for classification of 120 breeds.The proposed model was trained on Stanford dogs dataset and the model achieved a training accuracy of 95.03% and a validation accuracy of 92.92% after training.The model performance and robustness had been inferred after testing with test images from internet.The network predicted correct breeds with a test accuracy of 88.92%.This paper provides an optimal solution for fine grained dog breed classification.
Novel Robust Payroll Management System For Micro, Small &amp; Medium Enterprises

Payroll Management System is intended to make the current manual framework programmed with the assistance of modernized gear and full-edged PC programming, satisfying their necessities, so their important information and data can be put away for a more extended period with simple access and control of something similar. The expected programming is effectively accessible and simple to work with. This application keeps up with the data and shows automated information without getting duplicate entities. This system portrays as to how we can oversee client information for better execution and offers good type of assistance to the user.
Research Article Summarizer

Machine learning is a domain that is growing exponentially because of its wide range of applications in a plethora of other domains including technical and non–technical fields. In this project, we have used machine learning techniques to implement text summarization. Not just any summarization tool, this software can summarize any research article in a brief yet concise way. The software also has a read–aloud feature which enables people to listen to the generated summary instead of reading it. This feature can be a huge help to visually challenged people, who cannot read the text themselves
Plotplay: An Automated Data Visualization Website using Python and Plotly

Data visualization helps people see, interact with, and better understand any given data. Whether simple or complex, the right visualization can bring out excellent outcomes from the data and hence help a lot providing conclusions as well as pre-processing of the data to use it for Machine Learning or Artificial Intelligence algorithms. Nowadays, data visualization is required in every field. There are different interfaces available that are being used professionally worldwide. But, many still find it difficult to learn and get used to them due to their interface complexity or lack of programming knowledge, etc. So, in this paper we have provided the implementation of an easy-to-use website that will help users to visualize their data and find correlation between different variables by just one click. We have developed this website using python and its frameworks like Plotly, Flask along with HTML, CSS and JavaScript. It can be used by professionals but is mainly aimed for beginners who do not have much programming knowledge and find it difficult to deal with their datasets.
Object Tracking Based on Background Subtraction and Kalman Filtering

Object tracking is well-considered as one of the most important tasks in today’s surveillance system. For this to happen, detection and frame tracking needs to be done first. Video frames from the video helps to identify the object as a part of object detection. The two most used algorithm for object detection is background subtraction and frame difference method. This paper proposes the background subtraction method. Again, Kalman filter is a robust and precise algorithm that is used to estimate the precise location of a moving object. Kalman filter is used in this paper to track the object accurately. Finally, the performance evaluation is done from the results of the parameters Percentage Fit Error and Root Mean Square Position Error. A python code is implemented and the simulated results show that the performance of this model is accurate and satisfactory for a real time video.
Low-Cost Wearable Device to Provide an Effective Solution for Contact Tracing and Logistics

The paper introduces a low-cost wearable band that does the tedious, repetitive task of entering your required details in any shop or organization, as well as keeping a record of all the people you have come in contact with. There are two aspects of our device:1)If a person enters a shop with our device, the band will transmit the required information of the wearer to the reader kept at the shopkeeper’s side wirelessly. The transmitted information will include the wearer’s information (as per government guidelines) masked in the band’s Unique ID along with their temperature status (whether having a temperature above 100°F or not).2)When two persons come near each other over a distance of 6 feet, the unique ID broadcasted from each other’s bands gets stored in the other’s band. If any of them tests positive for Coronavirus Disease (COVID-19) or similar diseases, his/her unique ID can be used to trace primary contacts and take appropriate steps to contain further spread.Privacy is key! So, we are reengineering the primary concept of contact tracing and logistics while keeping the user’s information safe and secure.
Digital Image Forgery Detection Using Pre-Trained Xception Model as Feature Extractor

During this time of social network bloom, there has been a tremendous increase in the number of images shared over the internet. This along with the advancements in software, like photoshop, has made morphing or tampering with the original images very easy. This paper proposes a Deep Learning approach to tackle this problem and detect Fake or factored images. In this paper, we propose a method where a pre-trained Xception Model is used as the feature extractor to classify a certain image as Tampered or Real. The proposed model was trained on the CASIA-V2 dataset. The model was able to attain good Accuracy on the Test Split.
Computer Vision-Based Military Tank Recognition Using Object Detection Technique: An application of the YOLO Framework

Military object detection is an indispensable and challenging task for defence systems which includes the tracking, tracing, security, and surveillance of any territory or region. These systems should be very efficient, reliable, and accurate in executing their functions. A minute errant may result in mass destruction and loss. So automatic real-time object detections are imperative in today’s world. Although over the years, different traditional approaches and techniques have been used for the detection of military equipment, warheads, and other defence-related objects yet the efficiency and accuracy of those techniques are comparatively low compared to the artificial intelligence-based object detection techniques. Therefore, we demonstrate the latest computer vision-based real-time object detection technique to detect real-time military objects with high accuracy and precision. We introduced YOLOv5 for the detection of military tanks and flags. This model successfully detects the targeted objects i.e., tank and flag with high confidence and precision. We trained and evaluated the performance of YOLOv3, YOLOv4, and four versions of the YOLOv5 model i.e., YOLOVv5s, YOLOv5m, YOLOV51, YOLOV5x1 with 922 images consisting of tank and flag objects. The dataset has been divided into 80% training, 10% validation, and 10% testing. The detection results of all six YOLO versions are compared and evaluated. The experimental results showed that the YOLOv5xl achieved higher performance. The precision, recall, mAP_0.5, and mAP_0.95 were 0.99, 0.995, 0.995, and 0.892, respectively. Since YOLOv5 is one of the latest and fastest real-time object detection approaches so this model will empower and enhance the military surveillance systems by enabling the military personnels to take prompt and proactive actions against any potential threats.
Speech Recognition Android App with Dyslexia for Children Using AI

Learning disabilities are caused by genetic and neurobiological factors that change brain functioning in a way that impairs one or more cognitive processes associated to learning. These processing issues can obstruct the acquisition of fundamental abilities such as reading, pronunciation, or learning math. They can also interfere with high level abilities such as organization, time management, memory power, and concentration. It is critical to recognize that learning disabilities can have influence on an individual’s life outside of school, affecting connection with family and friends. Hence this study focuses on development of mobile application with the use of Google’s Speech Recognizer API which allows students learning disabilities such as dyslexia to practice on their reading and pronunciation skills.
Language Identification System: Employing ReLu for India’s Regional Languages (ReLu)

In this paper, I provide a model for language identification that makes use of neural networks. The approach is intended to distinguish between Indian regional and dialectal languages. Individuals of Indian descent are the target population for this model. I train the model using a variety of data sources, including corpora of written and spoken material in a variety of languages. In addition, I use a variety of additional data sources. As a result, we can train the model to be more accurate. When I compare the performance of our model to that of a naive Bayes classifier, I find that the results produced by our model are superior to those produced by the naive Bayes classifier. This is because our model considers more information than the naïve Bayes classifier. The ReLu activation function is employed on each neuron throughout our simulation, and the neural network design comprises several layers. Each neuron receives the ReLu activation function. This allows the model to capture the relationships and correlations that exist between the input data, which improves its capacity to recognize the language employed in a specific sentence. Furthermore, our technology can deal with ambiguous cases, such as sentences written in the devnagri script, which is utilized in a number of Indian languages. This is a benefit provided by our software. Using our system has several advantages, and this is only one of them.
Classification of Incomplete Data using Augmented MLP

We introduce a new way to train a Multi-Layer Perceptron (MLP) to classify incomplete data. To achieve this, we train an MLP using a two-phased approach. In the first phase, we train an MLP using complete data. We create an augmented dataset before the second phase of training. For this, we use non-missing data, delete each feature once, and then fill it using some predefined points. After that, in the second phase, we retrain the network using the augmented dataset. The aim of this type of training is to predict the class label of an incomplete dataset. At the time of testing, when a feature vector with a missing value appears, we initially impute it using the predefined points and find the class label of the feature vector using the trained network. We compare the proposed method with an original MLP on twelve datasets using four imputation strategies. The proposed method’s performance is better compared to the originally trained MLP.