Reviewerly/exHarmony huggingface dataset:
Significance of Face Mask detection

The new coronavirus, initially detected in Wuhan, China, has spread worldwide, wreaking devastation. The speed with which it spreads and the severity of the epidemic have made this a global emergency. COVID-19 is a very concerning disease from a public health perspective, making it critical to take precautions against its transmission, such as limiting close personal contact and using protective gear. The primary objective of this research is to create a system that can recognise human face masks and determine whether individuals are attempting to maintain social distance. Alterations to everyone's daily routines. In such stages, everyone must always keep their identity concealed. Because the massive number of populations has changed since the Outbreak of the Coronavirus pandemic, finding people who are not wearing veils is a challenge. The global spread of COVID-19 has altered society. Many of us are staying in our homes, avoiding contact with city dwellers, and adjusting our routines-such as when and where we go to work or school-in ways we never would have imagined. We need updated timetables as we transition away from outmoded procedures. What stands out the most is the widespread practise of hiding one's face behind a veil or other kind of covering whenever we enter a public building. Wearing a veil or covering one's face may provide some comfort while also preventing the spread of the COVID-19 virus. By preventing anybody, even the unwitting carriers, from spreading the virus, widespread usage of protective clothing has the potential to significantly reduce the rate of disease spread in a given area. Thus, the importance of the veil and its identification are made very plain. There has been a rise in the importance of face recognition frameworks, which are especially useful in hospitals and medical clinics where privacy of patients is a concern. They're also vital in places like airports, sports stadiums, warehouses, and other such crowded areas where heavy foot traffic necessitates strict security measures to ensure everyone's safety. The framework of face veil recognition can ensure our safety and the safety of those around us. This assignment may serve as a digitally administered test anywhere from a classroom to a hospital to a bank to an airport terminal. Through the use of photo processing and extensive learning, we are able to recognise human faces and separate them into two groups, those with and without head coverings. The assignment will let a person who is responsible for screening people to do so even if they are located at a remote location, while still being able to effectively screen and provide guidance. Open CV, Tensor Flow, and Keras are some of the Python libraries used. With Deep Learning, As part of their model preparation, these activities make use of Convolution Neural Networks, a subset of Deep Neural Networks.
The efficient data prediction and clustered compressive sensing (CCS) in environmental application of WSN

Wireless sensor networks are built from nodes called mod - small autonomous devices powered by batteries and microchips with radio communication at a frequency - for example 2.4 GHz. A special software allows mods to be organized into distributed networks, communicate with each other, interrogate and exchange data with adjacent nodes, the distance of which is usually no more than 100 meters. A wireless sensor network (WSN) is a wireless network consisting of geographically distributed autonomous devices that use sensors to collectively monitor physical or environmental conditions in different areas. They can measure parameters such as temperature, sound, vibration, and pressure, movement of objects or wind. The development of wireless sensor networks was initially motivated by military tasks such as battlefield surveillance. In this paper discussed in detail about, wireless sensor networks are increasingly used in many areas of civil life, including industrial and environmental monitoring, healthcare, and material movement control. The main operational processing of the data collected by the motes is carried out at the node or gateway, which is a very powerful computer. But to process the data, you have to get it first. For this purpose, the node must be equipped with an antenna. But in any case, only objects close to it are accessible to the node; In other words, node does not receive information directly from each node
Automatic Environment Protection Using Mixed Signal Processor For Covid-19

Now a days, the merciless covid-19 is threatening the world in everywhere. And, people are instructed to protect themselves from the virus infections. So, our proposed work will monitor the area around the person with the help of multiprocessor. In this method, particular area's temperature will be monitored by the advanced multiprocessor and the moisture level of the room around the person checked/ verified. And, spraying sanitizer will sanitize automatically, if it is necessary. Moreover, this system reduces the man power and protects the environment from the affects of covid-19. With the use of MSP, we can monitor the temperature and clean up mechanism will run automatically. From this method, we will permit the secured and sanitized people into the crowd places such as stock market, shops and stations. The vital recognition of this method is that fully automated procedure. Hence, it will replace physical labor and save time and also control the virus from spreading one to one another.
Image Steganography Using Discrete Wavelet Transform and Convolutional NeuralNetwork

The practice of steganography involves concealing messages within another thing, which is referred to as a carrier. Is thus performed in order to build up a covert communication channel in a rather way that any observers whom has access to such a channel will not be able to detect the act of communication itself. In this research, using the process of stenography, a secret text is transferred across a communication channel using an image as a cover. Discrete Wavelet Transform (DWT) and Convolutional Neural Network (CNN) is used in the above process. The encoding and decoding operation is done by using DWT while the preprocessing and training of images is done by CNN. The training and prediction rate of CNN is 72.4 %.
Self-Driving Car Using Neural Networks and Computer Vision

In recent years, lidar has been one of the most important sensors in autonomous vehicles; nevertheless, its high price makes scale production impractical. As a result, we will describe the operation of a self-driving automobile prototype that makes use of a more cost-effective cameras and computer vision, the major purpose of our prototype is to provide a means to travel across our virtual environment in a manner that is not only secure but also expedient, effective, and comfortable. We have been able to recognize lanes, traffic signals, obstructions, and also have utilised the notion of stereo vision in order to calculate depth. Additionally, trajectory planning and steering control have been incorporated into the system. The proposed work indicate that camera-based autonomous vehicles are feasible, and as a result, our research has the potential to serve as a foundation for all future real-world implementations. The proposed Self Driving Car using Neural Networks and Computer Vision detects lane lines on streets and highways, traffic light detection and front collision avoidance in various climate conditions. Lane detection technique, to a noteworthy degree, improves the security in the independent vehicles. A self-propelled or a self-sufficient vehicle is an independent machine that detects the natural conditions and settles on an appropriate human-like decision. The purpose of this project is to find a feasible solution to detect lanes, traffic light detection and front collision avoidance on a low voltage computer that can be easily powered in a regular auto vehicle.
Implementation of AI Using a Case Study of Hydrogen Gas From an AI- Mining Prototype

Artificial Intelligence (AI) in this research aims to provide a new perspective on AI in education by using a case study in the form of prototype sensor data on hydrogen gas. Those data were analyzed by Long Short-Term Memory (LSTM). This study uses a high-touch and high-tech approach with design thinking on the implementation of LSTM for hydrogen gas sensor data. The results obtained in this study are needed to improve the analysis of sensor data from hydrogen gas, which is single data and is taken in real-time. The analysis was carried out by sequential modeling. The result was successful because the Root Mean Square Error(RMSE) was around 7.9%. LSTM also works for other time series data. So, it can be concluded that LSTM, part of AI, can be introduced as new knowledge for time series data analysis.
Smart Biometric Controlled Door Unlock Mechanism Using IoT

Security of lives and properties are the ideal worry in the new times. Normal security methods like keys, passwords and cards are utilized in home conditions. Mechanical keys are effectively flimsy utilizing a few apparatuses, for example, hack-saw and so forth, so a got entrance. The Locking of entryway framework utilized some security component like screening and scanners. Different improvement occurred in the Door Locking framework for security reason. A Lot of progressive changes have occurred. We can undoubtedly send the messages to the different android mobiles to the house individuals with the assistance of cloud-based informing secure help and framework checks the client and sends an OTP (One Time Password) to the portable application utilizing cloud-based assistance informing. If there should arise an occurrence of safety access, assume a burglary attempts to opens the entry way then the framework will send messages to the proprietor or individual from the home.
IoT based Smart Farming

In a country like India agriculture is the basic livelihood for people. It contributes a major portion in the economy of the country. In traditional farming techniques, irrigation requires manual intervention and a lot of cost,which is minimized using the automated technology of irrigation. Vertical farming is a technology of growing plants in a vertical orientation using less land, less water, less cost and more yields.The technology used here is internet of things (IoT) to transfer the information after sensing and continuous monitoring of crops. The main reason for adopting vertical farming is that when there is scarcity of water it recycles the water.In smart vertical farming sensors are used for measuring soil moisture level, temperature, humidity and light which are very essential for plant growth and development, a 3D print gripper for harvesting the crops, artificial lighting is provided so that the crops undergo photosynthesis process. The work was done on fenugreek crop by sowing under two different conditions namely pot cultivation and vertical farming module equipped with smart technology. Observations are recorded at 10 days interval.The readings obtained from various sensors are seen in BLYNK app. using mobile phone.
The Latest Trend in India Cryptocurrency

Cryptocurrency has gained popularity because it has the potential to control the total quantity of money ever generated and because it can be used to monitor cash transactions without the interference of a central bank. Cryptocurrencies are used by many networks and apps to represent valuable and intangible goods such as virtual worlds, peer-to-peer networks, online social systems, and social gaming platforms. The usage of virtual money has increased recently across many different platforms. This study suggests a forecasting system that uses one hot code, random work simulation, and LGBM regression. Preprocessing involves data normalization utilizing exogenous variables. From the preprocessed, the SHA-256 bitcoin algorithm and bitcoin halving characteristics are chosen. The symmetric distribution is used to visualize the data. The utilization of data visualization is to effectively explain a portion of data's significance. The results were compared with conventional methods. The algorithm has been used in a cryptocurrency system, and its effectiveness is evaluated. The findings show that the proposed strategy performance is superior.
Sentiment Analysis based optimization approach

Sentiment analysis is a method that uses peer-reviewed literature to address issues from a variety of specialised scientific viewpoints. It's crucial to read reviews before purchasing a product so that you're aware of the benefits and drawbacks of the items you want to use; for example, you may learn whether or not a certain cosmetic brand is of a usable quality by reading reviews of the product. In order to make an informed decision about a cosmetics purchase, shoppers should read reviews and testimonials from other customers who have tried the product in question. It's clear that people are becoming more wary of review sites due to the sheer volume of reviews available. The reviews stop being useful at some point. So that readers may locate reliable product comparisons quickly, the writers categorise using both positive and negative classifications. Support Vector Machine (SVM) and Nave Bayes (NB) algorithms may both benefit from the adoption of Particle Swarm Optimization (PSO) with Genetic Algorithm (GA) optimization, resulting in more accurate and optimum solutions to the review categorization issue. In a head-to-head comparison using this data set, an SVM algorithm achieves an accuracy of 89.20% and an area under the curve (AUC) of 0.973, whereas an SVM based on PSO achieves an accuracy of 94.60% and an AUC of 0.985. Testing the data using the NB algorithm yielded an accuracy of 88.50% and an AUC of 0.536; next, the accuracy was compared to that of the PSO-based NB method, which was 0.692. These numbers show that PSO optimization may be used to increase precision and provide better answers.
Role of Cloud Management in Mitigating Vulnerabilities in Wireless Data Exchange Providing Cybersecurity

Utility computing (cloud-computing) has turned into a general service provider. The security challenges related to utility computing in the literature, (cloud computing) has received the most of the attention. The goal of this analysis is to look at the safety, challenges, and current developments in computing services (cloud). This SLR examined the dispersal focus of the investigation, which is between the years 2010 and 2020, within the well-known specialized collections. The results of this with seven significant security dangers to utility (cloud) computing (CC) administrations. The outcome is that the information altering, and spillage were among the profoundly talked about subjects in the picked writing. Other recognized security changes were related to the information interruption and information stockpiling in the utility computing (cloud-computing) climate. This SLR's outcomes additionally demonstrated that customers' information revaluating remains a test for both Utility (cloud) service providers and utility (cloud) clients. Our study paper recognized the blockchain as banding together innovation to reduce security concerns. The SLR discoveries uncover a few ideas to be done in future attempts to bring information classification, information respectability, and accessibility.
Effectiveness of Artificial Intelligence Based Recruitment process in the Employment of Indian Hardware Industry

Artificial intelligence (AI) inference in human resource management, especially in the recruiting process, was discussed in the paper. The study's aim is to learn whether artificial intelligence will replace human participation in the recruiting process in a few Indian hardware companies. The modern method of recruiting has supplanted the old method of recruitment. AI is ushering in the new phase of recruiting. Via numerous outsourcing firms, AI-based recruiting is being introduced into top businesses, and a few companies are privately playing with it. This study focuses on using AI to help understand the multiple factors for exclusion and what emotional intelligence requirements are considered. When the cause for rejection has been established, the skills and other criteria will be applied in a manner that can turn the rejection into a benefit. All the data are gathered from secondary sources such as newspapers, blogs, websites, and academic papers. Artificial intelligence has a positive impact on human succession in the recruiting process, according to the report. Meanwhile, the research is intended to assist the organizations being analyzed in formulating management plans and policy interferences in order to create an efficient recruitment method for recruiting efficient teams in order to compete in the marketplace and develop a viable atmosphere. The content of the study is to bring up a software which can guide HR on Selecting Staff.
Implementation of Mining Frequent Patterns on big data Using new Version of Algorithm

Pattern analysis is one of the most important tasks for strong data and useful data from unstructured data. The item-sets for this work must include the aforementioned homogeneity, but rather frequency in the data. Despite the fact that numerous efficient algorithms have been developed in this field, the rising demand for information has led to deterioration in the effectiveness of present pattern mining techniques. The goal of this study is to provide fresh, efficient methods for mining huge data patterns. To accomplish this, a variety of methods based on the Map reduction algorithm as well as the Apache expansive implementation have been proposed. The recommended algorithms may be divided into three main groups. The Attribute Mapper (AprioriMR) and recurrent AprioriMR techniques, which eliminate any previous objects in data without employing a pruning methodology, are the first two described. Second, two recommended approaches (space cutting AprioriMR or top AprioriMR) to improve the search space leverage certain well-generalized pro property. The last approach for mining compressed versions of frequent patterns is the maximum AprioriMR algorithm. To evaluate the efficacy of the offered strategies, up to 3 1018 interactions and more than 5 million distinct ones have been examined in a range of big data datasets. Evaluations of well-known and efficient pattern mining algorithms are part of the testing process. The results show both the benefit of employing Dremel versions when taking into consideration intricate issues and the insufficiency of this strategy when dealing with little amounts of data.
Modular attachments for several applications with the aid of an aerial drone

There has been significant progress in several industries in this emerging nation, but the drone-based business in India is one of the least developed. Only a small number of businesses are attempting to create drone-based applications that might be tremendously beneficial to society. While businesses like Amazon and Zomato have just begun to expand their drone delivery services in India, there hasn't been a rapid expansion of this industry. On paper, it is simple to claim that drones' potential uses are endless, regardless of whether they are land-based, airborne, undersea, or aquatic. But creating these apps for real-world use is rather laborious. This have thus developed the concept of a Multi-Functional Drone with this purpose in mind. This drone's objective is to create several attachments that can be turned on and off at the same time. This allows a single drone to serve numerous roles. The project's goal is not to create a drone per se, but rather to create and show off a variety of attachments that can be mounted on a drone without requiring the drone to be adjusted.
An Efficient Neck Pain Detection System Using Triggering Linear Method

The work done in this project is for patients who are suffering from the neck pain. Served as an intermediary between the head and thetorso, the neck enable s a person to move their heads. It upholds the heaviness of the head and safeguards the nerves that communicate tactile and engine data to the all pieces of the body. Our task will empower us to diminish neck torment, which has been a typical reason for torment previously. The neck is profoundly adaptable, and it permits the head to turn and flex every which way. Currently the existing products are too expensive and not environmental friendly. These product involve manual and automatic method hence it is inexpensive. The temperature is given to the patients in mild degree. During temperature stimulation, five different temperature difference are designed based on the patient's response. This proposed method will reduce the neck pain and it is completely non-invasive
DL based wireless response prediction model for spectrum awareness

When the atmosphere is fine movements, wireless networks must be aware of activities in the spectrum. Research on spectra awareness nowadays includes a variety of tasks, including energy detection, modulating categorization, and MAC authentication. Spectrum resources are in scarce supply because to the wide variety of devices. As a result, there is a reduction in the performance of the band when data is being sent. So, for spectrum detection or modulating classification in 4G/5G network, signal modeling is crucial. This study uses deep learning, more particularly Convolutional Neural Networks, to create a prediction model for the recognition of various mobile and wireless networks. L TE, UMTS, and WLAN transmissions were the three separate transmissions used in the research. After validation, the model showed up to 100% accurate and had complete predictive power. Such models will be extremely helpful in the future for anticipating the various signals in 5G Technology.
Hybrid Access Control for Storing Large Data with Security

Although the public cloud is known for its incredible capabilities, consumers cannot totally depend on cloud service providers to keep personal data because to the lack of client maneuverability. To protect privacy, data controllers outsourced encryption keys rather than providing information. Crypt - text to conduct out okay and founder access control and provide the encryption keys with others, innate quality Aes (CP-ABE) may be employed. This, however, falls short of effectively protecting against new dangers. The public cloud was unable to validate if a downloader could decode using a number of older methods. Therefore, these files should be accessible to everyone having access to a data storage. A malicious attacker may download hundreds of files in order to launch Economic Deny of Sustain (EDoS) attacks, greatly depleting the cloud resource. The user of cloud storage is responsible for paying the fee. Additionally, the public cloud serves as both the accountant and the payer of resource consumption costs, without offering data owners any information. Cloud infrastructure storage should assuage these concerns in practice. In this study, we provide a technique for resource accountability and defense against DoS attacks for encrypted cloud storage tanks. It uses black-box CP-ABE techniques and abides by the access policy of CP-arbitrary ABE. After presenting two methods for different parameters, speed and security evaluations are given.
Hybrid three layer Fibonacci Caesar Cipher

It is astounding how many people use computers, laptops, and mobile platforms on a regular basis. Everyone among those individuals requires the internet at some point, which might result in the discovery of a fantastic amount of material at their fingers. The risk of a cyber-attack is the main disadvantage of utilizing these gadgets and the internet. Here, we aim to create an encryption method that has never been done before, removing the flaws of each component while merging them to create something better. This encryption technique will undoubtedly assist in securing more people's data and privacy while assisting in defending them against the threat of cyber-attacks.