Reviewerly/exHarmony huggingface dataset:
A Survey on Deep Learning based Video Surveillance Framework

Technologies that are used in automated video surveillance give the capacity of automatically identifying security breaches or other potentially dangerous occurrences taking place inside the field of view of the cameras. These technologies have applications in the areas of surveillance as well as the detection of intrusions along perimeters. Recognizing anomalous behavior in crowded environments in a quick and efficient manner using technology is a very successful method for boosting public safety. Several different automated and real-time surveillance technologies for use in security applications are discussed in this study. The fact that public locations cannot be manually monitored is the most important factor in determining their level of safety and security. The algorithms for strange behavior have made efforts to increase their effectiveness, resistance to pixel occlusion, generalizability, computational cost, and execution speed. In a manner analogous to the current state of the art in anomalous behavior identification in crowded settings, researchers broadly divided techniques into distinct categories such as tracking, classification. It has been discovered that hybrid learning approaches and deep learning methods provide more satisfying outcomes during the categorization stage.
Relating the DT Framework in Securing the Healthcare Data using Blockchain

In this digital world, everyone is required to visit a hospital to resolve illnesses or medical conditions. there are 87% to 95% of clinics or multi-specialty hospitals in the world use a hospital management system to maintain the hospital for finance and analytics of patients and doctor details. there are problems with storing data or details about doctors or patients or hospital information in a centralized server. this server may be hacked, or a virus attack may occur through data leakage or web support. if any software is installed by any person or server officials from an unknown or malicious website to increase usability, it may cause a virus attack or hacking. The essential documents for this event have been leaked or corrupted, which may cause critical situations for patients and doctors. to avoid such problems, we present the solution as software with blockchain technology called “BHealthy” which has been created with all stages of Design Thinking. It will be secure and stored in encryption format because it runs on a decentralized server and blockchain technology. It is unaffected by virus attacks or hacking. by using this method, data is safe, and doctors and patients do not need to be concerned about details storing format.
Empirical Study of the emotional intelligence formation of preschool children

The article presents an empirical study of the emotional intelligence formation of preschool children. Currently, there are not many studies devoted to the emotional intelligence of preschooler children. Researchers often limit themselves only to determining the presence of this concept of emotional intelligence, which clearly does not reflect its structural components. It is also noted that some researchers focus on studying the features of the emotional sphere in preschool children, and some study the development of understanding emotions in preschool children. The most interesting and significant studies in the emotional sphere in preschool children are considered. We made conclusions on the level of development of emotional intelligence of preschoolers using the following methods: questionnaire for educators, projective methods of M. A. Nguyen: "completion: the world of things - the world of people - the world of emotions", "three wishes", "what - why - how". With the help of these techniques, we determined the indicators of emotions of preschool children. Having organized educational classes, we found that it is possible to see the positive dynamics of the emotional intelligence of preschool children.
Asthma Disease Risk Prediction Using Machine Learning Techniques

The signs of asthma, a long-term inflammatory condition of the airways, include wheezing, throat tightness, coughing, and breathing difficulties. The attack of an asthma, which can be fatal, is the fast worsening of these symptoms. Another severe, irreversible airflow restriction in the lungs is caused by respiratory COPD, which encompasses emphysema and chronic bronchitis. In this project, a machine learning-based algorithm is for predicting asthma risk is presented (ML). PEFR, which are widely used external tools like peak flow meters and recognized asthma risk predictors, are frequently monitored. This study shows a relationship between the ambient particle matter(PM) and the weather outdoors. According to the best peak flow value each person was able to acquire, the results are divided into two groups: Safe and Risk circumstances. The link between indoor PM and weather data is mapped to the found values using a convolutional neural network (CNN) architecture. The suggested method’s root mean square and mean absolute error accuracy metrics are contrasted with those of current deep neural network (DNN)-based methods. Additionally, the accuracy of the classification methods KNN and SVM are carried out. The new data set’s asthma category may be predicted more accurately thanks to the application of SVM, KNN, and CNN classification. Python 3.7 is the coding language employed.
A Strange Model for Secure Medical Image Transaction - An Attractor Approach

In the wake of the Internet, the growth of networks has steadily increased. Even though network security is improving every day, many loopholes and gaps allow an attacker to modify, delete or even steal data. As a countermeasure, data security is also extremely important. In the telemedicine field, data communicated over mediums become vulnerable. Particularly Digital Imaging and Communications in Medicine (DICOM) is the universal standard for storing and transmitting medical images, and when it is shared among practitioners over the network must be secured. Modern cryptographical solutions are required to address these kinds of vulnerabilities. This proposed work aims to develop a complete system for maintaining confidentiality for medical images. The system provides robustness against various threats such as statistical attacks and differential cryptanalysis.
Aspect Based Sentiment Aware Word Embedding for Cross Domain Sentiment Analysis

Sentiment analysis is a branch of study that focuses on determining how well subjective literature is written by examining people’s attitudes, views, and sentiments toward various objects. Data analysis and opinion word extraction from the data are difficult tasks, especially when they involve evaluations from entirely unrelated fields. In Aspect Based Sentiment Analysis(ABSA), Aspect word extraction and sentiment of the aspect word extraction are significant job. On these two tasks, numerous methodologies have recently advanced. Few studies, meanwhile, attempt to separate opinion objectives and words as pairs. In this research, we present ABWE (Aspect based word embedding) a sequence labelling subtask for ABSA that tries to filter the appropriate sentiment words for a aspect word. To do this, aspect-fused and context-fused sequence labelling neural network model is created. By using AFCR, the aspect word information is effectively joined into the context words using LSTM network. The global context representation is prepared using Bi-GRU. The encoder creates a final word representation, where the global context information, aspect and opinion information are merged. Using the ABSA benchmark dataset from laptop and restaurant reviews, we construct four datasets for ABWE. The results show that ABWE model provides better results than the other baseline models. We think that our approach may be useful for pair-wise opinion summarization as well as downstream sentiment analysis tasks.
Exploring the Intersection of IoT and Blockchain: An Analysis of Security and Privacy Risks

Internet of things (IoT) is one of the leading technologies of transforming the internet into the next level. Security of IoT is becoming a major challenge and its measures of stopping these insecurities have been developing. Improving IoT security is important since IoT provides a great environment for systems in the industrial world and other several applications. The challenges need to be addressed so that consumer’s data can be safe. Measures available are to improve the protection of access by unauthorized persons, providing a safe way to transfer data, and ensuring synchronization of the system. Blockchain has also provided a safe way for IoT devices, each device is given the public key, IoT devices scramble and send the message dependent on the public key of the objective device so just the objective device can unscramble the message.
Fever Detection System Using Machine Learning Algorithm

Worldwide many new diseases is spreading and causing several illnesses to the people. Most of the diseases are difficult to predict and leading to life threat. Fever is one among the disease. Even though it’s a familiar disease, now a day different kinds of fevers are spreading across the environment. Either Fever can be direct transmission or due to any side effects. Detecting the fever on time helps to prevent from disease. Diagnosis of undifferentiated fever is difficult task to the doctors. Availability of data is a big issue. Obtained data’s also not in the required format. Machine learning is one of the tools to predict the fever. In this proposed system, real time data is collected and logistic regression algorithm is used with the accuracy of 98. Logistic regression is a statistical method used to predict the outcome of a dependent variable based on previous observations.
Virtual Mouse

A great example of HCI innovation is the mouse. A wireless or Bluetooth mouse still requires electricity in order to function, and it is not totally power-free since it requires some kind of energy to function and a dongle to connect it to a computer. The AI virtual mouse solution we presented gets over this problem by utilising a camera, either a built-in or a webcam, to record our hand movements and a convolutional neural network to identify the tips of our fingers. The algorithm employed in the proposed system is a machine learning algorithm. The computer’s left-click and pointer functions may be manipulated without a hardware mouse by using hand gestures to operate the machine. In order to track the hands and react to their movements, the system relies on deep learning. Therefore, our suggested method will prevent the spread of covid-19 by doing away with the need for a physical mouse and reliance on gadgets to regulate the position of the pointer.
Age group determination using Logistic regression through a biometric approach

Every person is authenticated using their natural physical or behavioural characteristics in the process of biometric authentication. When users enrolled in the models, many of them used soft biometric data such their gender, age, height, and eye color. Years of work have gone into figuring out how old a human face is roughly. Age assessment based on the eyes, particularly using the iris-pupil ratio, is a relatively novel idea. The suggested approach attempts to predict age group using logistic regression using this putative iris characteristic. Logistic Regression is a type of statistical model and it is used to determine or predict the probability of an outcome and this is done by analyzing the relation between variables. After segmenting iris images, the training and classification are derived from the iris-pupil ratio analysis.
Detection Of Personal Attacks In Mobbing On Social Media Using Deeplearning Techniques

Mobbing is a serious problem on the Internet that affects both grown-ups and teenagers. miscalculations similar as despair and self-murder followed. There’s a growing demand for content regulation on social media platforms. The following work builds a model grounded on detecting cyberbullying from textual data using natural language processing and machine literacy using two different types of cyberbullying data, detest speech on Twitter and commentary grounded on particular attacks on Wikipedia forums. Three characteristic birth ways and four groups are considered to determine the most effective system. The model provides further than 90 percent content for tweet data and further than 80 percent content for Wikipedia data.
Design and Development of UAV for Challange faced for Data Muling problem In Path

In order to evaluate the utility of using UAVs for the ongoing collection of sensor readings from sensor nodes situated throughout an environment and their delivery to base stations for additional processing, this paper addresses the problem of traffic engineering in Internet of Things (IoT) settings. UAV allocation and persistent path planning model has been proposed in this paper, where data from ground sensors is gathered by a group of heterogeneous UAVs from different base stations, and the information is then sent to the base stations that are nearest to the sensors. This issue has been mathematically shown to be nondeterministic polynomial hard as a model of constrained real-time optimization. In this research, a heuristic solution to the issue is put forth, and its relative effectiveness is assessed by experiments on both synthetic and actual sensor networks, utilising various UAV settings.
Sentiment Analysis using Deep Learning for Students’ Feedback: A Survey

Over the course of the past ten years, sentiment analysis has seen widespread use across a variety of fields, including education, social networks, and business. The application of sentiment analysis is developing but remains tough, in particular in the field of education, where dealing with and processing students’ thoughts is a complicated undertaking due to the nature of the language used by students and the huge volume of information. The state of the use of sentiment analysis in this field is revealed through multiple literature evaluations, each of which examines the topic from a unique vantage point and within a unique set of circumstances. However, the existing body of research does not include a review that systematically classifies the research and results of applying natural language processing (NLP), deep learning (DL), and machine learning (ML) solutions for sentiment analysis in the education domain. This is a significant gap in the field. The Internet is one of the most common mediums for sharing and exchanging thoughts, feedback, or information regarding specific subjects. Most of the time, the feedback is provided in the form of number ratings and text. Numerical ratings can be processed with ease, but there is a vast amount of unstructured textual data present on the internet. Currently, we are working on a project in which a client can propose his idea on our website and take opinions in the form of textual review. This helps him to take specific opinions from reviewers, which will be helpful in making a decision that is more appropriate. Within the scope of this paper, we analysed and compared a variety of techniques that are utilised during the processing of text data. We were able to identify issues and scope within the textual feedback analysis with the assistance of the various methodologies.
Chatbot For Healthcare Using Machine Learning

Healthcare has become an important part of living a healthy life in today’s society, but consulting with a doctor for every health problem is very difficult for common people. The Medical chat bot is created to diagnose diseases and provide basic information before consulting a physician. By doing so, healthcare costs will be reduced and medical knowledge will improve. A chat bot interacts with users through natural language. The chat bot then saves the information in a database, chooses sentence keywords, decides whether to answer an inquiry-, and provides information. With this approach, people can spend less time in hospitals while performing repetitive tasks like providing solutions, sending emails, marketing, and analyzing results. Similarly, calculations are done using n-grams, TF-IDFs, andcosine. So, this gives the best result for detecting the disease through a medical chat bot.
Automatic video summarization and classification by CNN model: Deep learning

As smartphones and other camera-enabled devices become more mainstream and user-friendly, more people are recording and sharing films through social media and video streaming websites. This makes them an essential tool for spreading information. It’s a pain to watch and evaluate so many movies. An automated video summarizing gives a concise analysis of the source material, which is useful for indexing and categorizing long films in the video database. Putting together a synopsis for a video is an uphill task. By simulating a two-stream architecture with a deep convolutional neural network in each stream to extract a video’s spatial and temporal components, this research hopes to automate the process of making video summaries. Video segment highlight scores may be generated using a two-dimensional Convolutional Neural Network (CNN) that uses spatial information.Additionally, a 3-D convolutional neural network (CNN) includes temporal data. The ratings for each segment in each stream are averaged to determine which portions of the video are the most compelling. Since the highlight result only conveys a relative degree of interest, the DCNN in each stream is trained using a pairwise deep-ranking model. With some model tweaking, we can make the highlighted part of the video score higher than the rest. Videos summaries may be created from the retrieved clips.
Deep Learning Based Fake Stamp Detection

Philately or Stamp Collection also known as the King of Hobbies. Philatelic fakes and forgeries are labels that look like postage stamps but have been produced to deceive or defraud. Learning to identify these can be a challenging branch of philately. Those who produce counterfeits appeal to a very different market from philatelists. They depend on their stamps being produced in large quantities in order to be able to recover their outlay. Political and propaganda forgery is produced by countries in conflict to hurt the opponent. Stamps may be issued to deprive the enemy of revenue, to distribute propaganda material, to cause confusion, and to depict propaganda messages. Sale of fake stamp papers mean a loss of revenue to the tune of crores of rupees to the exchequer. Stamp duty is an important source of revenue for the government. The state governments may have to probe the large financial transactions over the past decade to ascertain actual damage. Further, the negative effects on the financial sector would be manifold. It is very difficult to identify a fake stamp from a real one. It takes years of experience and study to be able to become an expert in philately and to be able to identify fake stamps. Postal services developed, early on, measures to protect the integrity of their stamps. Some of these steps are similar to those used to protect against forged currency. Major steps include Watermarks, Special paper, Delicate engraving, Printing methods, Special ink for postmarks, Insertion of silk threads, Secret marks either visible or invisible to the microscope, and Re-issue of stamps. It may not be possible to distinguish between philatelic and postal forgery if the stamps are unused, merely by looking at them; the techniques used in producing them are identical. Hence this paper aims on identifying whether the input given is a fake stamp. In deep learning, a Convolutional Neural Network (CNN) is a class of Artificial Neural Network (ANN), most commonly applied to analyse visual imagery. When an input is given, the deep learning model created will identify whether the input is an image of a stamp or something else. If the input given is identified as image of stamps, then it identifies whether the stamp is a real or a fake. If the stamp is real the model correctly identifies the country to which it belongs by reading the name of the country in the stamp. Stamps belonging to six countries over a period of 50 years were collected from the internet, and trained through the deep learning model. As there is no dataset available for fake stamps, they were generated by modifying the real stamps. The designed deep learning model is simple with accuracy of 99% would be very helpful in eliminating the fake stamp.
Deep Learning Approaches for Accurate Sentiment Analysis of Online Consumer Feedback

Over the earlier time, a category of machine learning, called deep learning, has attained significant achievements in several computer vision tasks such as image classification, object detection, semantic segmentation, pattern recognition and image classification generation. Deep learning objectives at finding various levels of dispersed representations, which have been proven to be discriminatively effective in many tasks. Distributed statement depicts similar information highlights across different adaptable and reliant layers. Each layer characterizes the data with a similar degree of exactness, however adapted to the degree of scale. The implementation of deep learning techniques depends greatly on the variety of data interpretation (or features) on which they are used. Artificial intelligence plans to understand interpretations of information regularly by changing over it or isolating components as of it, which creates it simpler to play out an undertaking like order or extrapolation.
Constancy Investigation Of Fuzzy Controller Over Proportional Integral Controller For Resonant Converter

In this article mainly aimed to reduce the disturbance raised in the line or load side of resonant converter. We have to achieve effective output from the resonant converter among any disturbance condition. So that the previous literature survey the resonant converter is subjected to line and load side disturbances. In the survey told that the resonant converter works along with the help of controllers will give the better output when compared with the resonant converter working alone. Here the designed resonant converter is manually subjected to line and load disturbances with input voltage of 55V supplying for 80ohms load which the converter is integrating with Proportional Integral (PI) and Fuzzy logic controller. The designed converter’s disturbance output voltages and currents waveforms of PI and Fuzzy controllers are compared by time domain specifications like peak time, raise time, settling time and peak overshoot are measured by using output waveforms and then we have to conclude that the recital working of Fuzzy controller is more effective than the Proportional Integral (PI) controller.
Automatic Speech Recognition using Machine Learning Techniques

A significant problem in machine learning is automatic voice recognition, particularly continuous speech recognition with a vast vocabulary. The standard speech recognition framework has been the hidden Markov model (HMM)-Gaussian mixed model (GMM) for a long time. However, more recently, the performance of the HMM-deep neural network (DNN) model and the end-to-end model employing deep learning has surpassed that of the HMM-GMM.