Reviewerly/exHarmony huggingface dataset:
Real-Time Face Mask Detection using Computer Vision and Machine Learning

Face mask detection is the process of identifying whether a person is wearing a face mask or not in real-time through the use of computer vision and machine learning algorithms. This technology can be used in various applications, such as security systems at public transportation hubs or in hospitals, to ensure compliance with health and safety regulations during a pandemic or other infectious disease outbreaks. The technology works by analyzing images or video streams from cameras and computer vision techniques are used to detect the presence of a face mask on a person's face. The output of the system is a binary result (i.e., mask detected or not detected) or a more detailed result that provides information about the type of mask and its location on the face.
Intelligent Email Automation Analysis Driving through Natural Language Processing (NLP)

Electronic Mail (E-mail) is a technique for interchange messages between individuals utilizing electronic gadgets. Electronic Mail in the current scenario turned out to be a universal communication medium, and to the point of view it prevails that, an email address has much of the importance and treated as a vital. It is essential for any speedy communications in the business, government, trading, diversion, and different circles of day-to-day requisites. In addition to the mail a spam message is an instant message for promoting a specific item or administration is a very uncomfortable to the users. Most email spam messages are business in nature. Regardless of whether business, many are irritating, yet in addition risky in light of the fact that they might contain joins that lead to phishing sites or destinations that are facilitating malware - or incorporate malware as document connections. These spam messages from unknown person is often happening. These messages can either be avoided or considered after translating it using this message analysis method. Message Analysis separates the messages into spam messages and non-spam messages. In this process, by analyzing the senders' ID or number, non-spam messages are categorized based on languages and language detection. After categorization, the categorized messages can either managed by translating the messages or ignored. A reply can be sent to the received messages on ones preferred language, while typing the message, the content will be analyzed based on certain properties like grammatical errors, sentiment analysis, spelling correction and so on. The reply message will then be translated to the received message language and delivered. These problems are intended to be addressed by using the Natural Language Processing (NLP) techniques.
Analyzing the Bank Scam's Financial Fraud and its Technological Repercussions using Data Mining

As the prevalence of new forms of financial transaction exists, such as instalment cards, has grown, so too has the sophistication and pervasiveness of fraud. After that, I'll discuss measures taken to detect and prevent fraud. As a result of their ability to construct models that understand and recognize the possibility of scam based on previous incidences of scam, data mining techniques are able to differentiate fraud. One of the financial frauds, the fiscal summary deception, has expanded rapidly around the globe. Corporate governance, financial reporting, and review capabilities have all taken a hit as a result of high-profile firm failures. Worldwide, companies are increasingly concerned about the growing threat of fraud using financial summaries. Because data mining approaches are well-suited to finding the grounds for dishonest financial declaration, Recognizing Fiscal Statement Scam stands apart as one of the key application areas of Facts Mining.
Machine Learning based Candidate Recommendation System using Bayesian Model

Online websites that recommend books, music, movies, and other media are becoming increasingly prevalent because of collaborative filtering. This online websites are using many algorithms to provide the better recommendation to attract the customer. Bayesian statistics, which is based on Bayes' theorem, is a technique for data analysis in which observable data are used to update the parameters of a statistical model. To discuss a strategy called item-based collaborative filtering, which bases predictions on the similarities between the said objects. This uses Machine Learning based Candidate Recommendation System which uses Bayesian Model database to assess the proposed method. The actual results show that for collaborative filtering which is based on correlation, the Bayesian techniques we have proposed outperform traditional algorithms. Also discuss a technique for improving prediction accuracy that combines the Simple Bayesian Classifier with user- and item-based collaborative filtering. The user-based recommendation is then applied to the matrix once the user-item rating matrix has been filled out with pseudo-scores produced by the item-based filter. This model is demonstrated that the combined approach outperforms the individual collaborative recommendation approach. The creation of UI based web application will help Students to manage achievement details. Job seekers and admin will be given a separately formatted version of the application where, students can upload and view their certificate, wherein admin can access student achievement details categorized by different parameters. This proposed model is developed under the service learning scheme to benefit both job seeker and recruiter.
Security System to Analyze, Recognize and Alert in Real Time using AI-Models

The field of security systems has advanced significantly in recent years with the advent of deep learning models. Models that can learn complex patterns and features in data have been used in a variety of security applications, such as facial recognition, intrusion detection, and cyber security. Using authorized databases, this proposal uses security to recognize the suspect's face. Here, this article proposes a different method to analyze the long-term videos using the integration technique with the DNN algorithm to recognize the faces using the DNN, Caffe, and Torch models. This proposal used the concept of extracting only the unique features that are present in the faces, such as moles and cracks in the face, for face recognition. It also introduced the concept of "ear-based recognition," where the ear is also a unique part of the body. This feature will be added during the dataset creation and training itself using the DNN feature extractor, Torch. The dataset created with the help of the seven-angle technique suggests taking the seven different angles of the person to cover all the features, and this proposal also handles the problem of camera tampering using the backward subtraction model.
Deep Learning based Image Enhancing Environment with Noise Suppression

A deep learning approach will be used to recover ancient pictures that have suffered significant damage. Unlike typical reconstruction processes that are easily handled by supervised learning methods, real-world picture degradation seems to be complex, and the system is unable to generalize due to domain differences between synthetic pictures and actual old pictures. Therefore, using huge amounts of synthetic image pairs combined with real photos, Therefore, using huge amounts of synthetic picture pairs combined with real photos, A unique triplet domain translation network. Two variational autoencoders (VAEs) have been trained to create latent spaces from both fresh and old images, respectively. The translation between two regions is thenmanaged to learn using artificially paired data. This translation normalizes well to actual photographs as the domain gap is filled in the compact latent space. The translation between these two various latent regions has been taught using artificially paired data. This translation normalizes well to images found in the real world because the compact latent space is filled with the domain gap. A global division with an incomplete nonlocal block will target structural issues like cuts and bruises and a local division attacking unstructured defects like unwanted noise and poor contrast to handle the various degradations mixed throughout an old photograph. The latent space fusion of two branches increases the ability to correct numerous flaws in old images. Convolutional neural networks (CNNs) outperform multiple-layer sequenced neural network models at identifying distinct marks, forms, and patterns in images, making them the most efficient method for processing data. The filters are applied by CNN to every pixel in the image. When it comes to visual quality, the suggested method for repairing old photographs performs better than cutting-edge techniques.
Role of Neural Network, Fuzzy, and IoT in Integrating Artificial Intelligence as a Cyber Security System

The "Internet of Things" has a vast number interconnected devices. These interconnected devices collect vital data that may have a significant effect on the company, society, and the environment as a whole. IoT application has grown significantly in recent times, and with it, so do worries about cybersecurity. Artificial intelligence (AI) is at the forefront of the technology of cybersecurity and is employed to create intricate algorithms to safeguard systems and networks like IoT devices. But cybercriminals have learned how to take advantage of AI, and they have even started to deploy AI in analyzing cyberattacks. Due to the limited computing power and memory capacities of IoT systems, conventional high-end cybersecurity measures are inadequate to protect an IoT system. The need for accessible, distributed, and robust smart security systems is highlighted by this. Large- and small-scale heterogeneous datasets are no match for DL. In this research, a multilayer cybersecurity strategy based on DL is used to safeguard the TL of IoT systems. The developed framework tests the proposed multi-layer strategy using the intrusion identification statistics obtained from CIC-IDS (2018), ToN, and BoT-IoT. Consequently, depending on the analyzed parameters, the proposed model has outperformed the other approaches and achieved 98% accuracy.
An Effective Technique using MobileNet-V2 for Face Mask Detection

Recently, the widespread and extremely fatal disease known as the coronavirus spread throughout the entire world. China's Wuhan city served as its first hub for its spread. The COVID-19 outbreak has briefly disrupted our daily routines by affecting worldwide trade and travel. Precautions include hand washing, using hand sanitizer, keeping a safe distance, and most importantly wearing a mask. However, putting on a mask that prevents to some extent airborne droplet transmission will be helpful as a precautionary measure in this pandemic. In the near future, many public service providers will ask the customers to wear masks correctly to avail of their services. However, ensuring that everyone wears a face mask is a difficult chore. Many techniques such as Machine Learning, Deep learning models like CNN, RNN, MobileNet etc. are available to solve this problem. This paper presents a simplified approach using MobileNet-V2 for Face Mask Detection. The model is developed by utilizing TensorFlow, Keras, OpenCV, and Scikit-Learn. The face mask detection model's objective is to identify people's faces and determine whether they are wearing masks at the time they are recorded in the image. An alert will sound if there is a desecration on the scene or in public areas. The challenge with the model is to detect the face mask during motion of a person. Precision, recall, F1-score, support, and accuracy are used to evaluate the system's performance and show its practical pertinency. The system operates with a 99.9% F1 score. The currently developed model will be used in conjunction with embedded camera infrastructure which may then be used to a variety of verticals, including schools, universities, public spaces, airport terminals/gates, etc.
The Empirical Evaluation of Artificial Intelligence-based Techniques for Improving Cyber Security

Artificial intelligence (AI) technologies have been recently developed, and their advantages can be observed in a wide range of domains, from image processing to face detection. AI-based approaches can assist opponents to enhance their attack strategies while also improving cyber protection technologies. The efficiency of AI techniques against cyber security issues are evaluated. Here, the primary data and a quantitative study design approach is used. The information is then obtained from software industry professionals. The sample size for this study is 549 people, and it included confirmatory factor analysis, discriminant validity testing, model fundamental analysis, and hypothesis evaluation. All variables' P-values were found to be statistical, with the exception of intelligent agents, which had no meaningful relationship to AI or cyber security. The main issues were availability, geographical locations, study population, and other related variables.
Hybrid Convolutional Neural Network and Extreme Learning Machine for Kidney Stone Detection

When it comes to diagnosing structural abnormalities including cysts, stones, cancer, congenital malformations, swelling, blocking of urine flow, etc., ultrasound imaging plays a key role in the medical sector. Kidney detection is tough due to the presence of speckle noise and low contrast in ultrasound pictures. This study presents the design and implementation of a system for extracting kidney structures from ultrasound pictures for use in medical procedures such as punctures. To begin, a restored input image is used as a starting point. After that, a Gabor filter is used to lessen the impact of the speckle noise and refine the final image. Improving image quality with histogram equalization. Cell segmentation and area based segmentation were chosen as the two segmentation methods to compare in this investigation. When extracting renal regions, the region-based segmentation is applied to obtain optimal results. Finally, this study refines the segmentation and clip off just the kidney area and training the model by using CNN-ELM model. This method produces an accuracy of about 98.5%, which outperforms CNN and ELM models.
Review on Cloud Security Attacks and Preventions

Cloud computing is gaining popularity as many businesses and their infrastructures transfer their information to the cloud. As per estimation this was one of the fastest growing IT industries. Cloud computing is based on internet, anyone who can access internet can use cloud services across the world. The main thing about cloud computing is it does not require any big setup or big infrastructure and cost of services are less. When an application is moved to cloud everything will be provided by cloud provider in pay per usage method and data will be stored in cloud without requirement of physical servers. The word cloud security refers to the provision of security and the preservation of data or cloud computing systems. Making data available in cloud will be good for applications but sometimes it poses risks by exposing data which have some security problem like loophole in it. Protecting hardware, data storage, data servers, operating systems, middleware, data, and end users is part of cloud security. Many cloud computing services are provided third party users which poses security issues. This paper discussed about cloud computing, security, and their solutions.
Students Community Portal using Machine Learning

The inability of students and teachers to effectively communicate is a major problem in today’s schools. Some students have trouble speaking up in class because they are embarrassed by their lack of public speaking skills furthermore, speakers rarely have enough time to elaborate on the subjects they got to inform for a particular course, resulting in the decline in student comprehension of a given topic. Colleges and universities have traditionally relied on in-person meetings between students and teachers. The ratio of students to teachers has suffered as a result of rising enrollments and greater public interest in higher education. This paper introduces an online college and university discussion system to help instructors and students overcome the challenges they face when trying to ask questions and receive answers, respectively. The goal of this Campus Conversation Portal is to allow students from all over the college campus to easily communicate with each other and the teaching staff, as well as to get their questions answered by the faculty and their peers and to achieve only factual information support vector machine and feed forward neural network is used.
Virtual reality for clinical evaluation and treatment in schizophrenia: a systematic review

ABSTRACTObjective The objective of the present systematic review is to answer the following question: is there enough scientific evidence to conclude that virtual reality is an effective tool to evaluate and treat the symptoms of people diagnosed with with schizophrenia?Background Eight systematic literature reviews and one meta-analysis focused on this question have recently been published. However, while they tend toward the conclusion that VR is of value in the evaluation and treatment of people diagnosed with schizophrenic disorders, they have important limitations.Method We used the PRISMA method. Using the keywords “virtual reality”, “video game”, “schizophrenia”, “therapy” to search in four databases. 31 articles met our selection criteria (with independent double screening). We analyzed the objectives, methods, tools and results of each article.Results Twenty of the selected articles provide scientific evidence of the effectiveness of VR for evaluating different domains in schizophrenia. The other eleven articles focus on evidence of therapeutic effectiveness of interventions with VR tools. However, because of the diversity of the dimensions evaluated or treated, and of the VR tools used, replication studies are lacking.Discussion There is some scientific evidence that VR tools can be effective in the evaluation and treatment of dimensions of schizophrenia, in particular cognitive dimensions. However, additional studies are needed to confirm these results, replicating measures of the same dimensions using the same VR tools.KEYWORDS: Virtual realityschizophreniasystematic review Disclosure statementNo potential conflict of interest was reported by the author(s).
Survey on Gestures Translation System for Hearing Impaired People in Emergency Situation using Deep Learning Approach

Stone-deaf persons utilize gesture language as a form of communication. Disabled As a kind of non-verbal communication, individuals use sign language movements to convey their feelings and ideas to others. Communicating with someone who has hearing impairment is challenging. It is challenging for non-deaf as well as mute individuals to comprehend the hand motion sign language used by deaf and mute individuals to communicate. As a consequence, it is necessary to develop technologies that can identify countless signs and transmit the knowledge to person in the street. For medical and legal visits, in addition to educational and training sessions, professional sign language experts are needed because these regular people have trouble comprehending their facial expressions. Recently, there has been a growing demand for such services. Other services like video remote human interpretation, which requires a high-speed internet connection, have also been developed, offering a simple gesture recognition interpretation service with limitations. To address this issue, machine learning can be applied to analyze the user's hand using finger detection. Deep learning approaches have significantly improved the several algorithms that have been put forth by numerous academics over the past few years. This study has analysed the deep learning-based vision-related models for gesture recognition.
Application of wearable devices based on deep learning algorithm in rope skipping data monitoring

At present, wearable devices have some problems, such as poor adaptability to human motion behavior, and the recognition accuracy required for different wearers cannot be achieved. Based on the principle of deep learning algorithm, this paper realizes the development of intelligent rope skipping movement data monitoring system. Through the universal human body analysis model, the attention mechanism is introduced and embedded into the decoding network. The data set of rope skipping is classified by multiple labels, and the convolution of spatial graph is constructed, which is extended to the time series dynamics of moving human skeleton data. Aiming at the problem of complex information data in the process of moving human body recognition, we use pose estimation to calculate the key points of moving human body, extract the dynamic structure information of human skeleton sequence. Due to the problems of line of sight occlusion in the process of moving human target tracking, a target tracking algorithm based on multi domain convolution neural network is adopted to improve the feature extraction ability of the algorithm by segmenting the target to be tracked and identifying the area around the target. The data set of rope skipping is collected by wearable sensors, and the difference in the numerical range may be large, so the data is normalized. Finally, through the loss function, the fitting effect of neural network can be evaluated, and the gradient optimization model parameters can be calculated, and coping with different data changes. Through the final system performance test, it is verified that the accuracy rate of the system designed in this paper is above 90%, which can effectively monitor the data of skipping rope and be used in the actual operation of skipping rope.
A Survey of Graph Comparison Methods with Applications to Nondeterminism in High-Performance Computing

The convergence of extremely high levels of hardware concurrency and the effective overlap of computation and communication in asynchronous executions has resulted in increasing nondeterminism in High-Performance Computing (HPC) applications. Nondeterminism can manifest at multiple levels: from low-level communication primitives to libraries to application-level functions. No matter its source, nondeterminism can drastically increase the cost of result reproducibility, debugging workflows, testing parallel programs, or ensuring fault-tolerance. Nondeterministic executions of HPC applications can be modeled as event graphs, and the applications’ nondeterministic behavior can be understood and, in some cases, mitigated using graph comparison algorithms. However, a connection between graph comparison algorithms and approaches to understanding nondeterminism in HPC still needs to be established. This survey article moves the first steps toward establishing a connection between graph comparison algorithms and nondeterminism in HPC with its three contributions: it provides a survey of different graph comparison algorithms and a timeline for each category’s significant works; it discusses how existing graph comparison methods do not fully support properties needed to understand nondeterministic patterns in HPC applications; and it presents the open challenges that should be addressed to leverage the power of graph comparisons for the study of nondeterminism in HPC applications.