Reviewerly/exHarmony huggingface dataset:
A Comparative Analysis of Security Issues in MultiCloud

In recent trends in organizations, there has been rapid storage growth in cloud computing. Regarding cost-effectiveness and data accessibility, cloud computing offers a wide range of advantages. There may be multiple storage providers in the cloud platform, so every individual user has to be aware of their information to be stored in the CSPs because there might be trust issues. Hence, ensuring security over the cloud platform is a crucial aspect of the cloud computing environment. Some of the esteemed customers have now identified the issues behind a single cloud as a result of unauthorized access from other resources over the cloud, vulnerability attacks due to intruders in the cloud, and failures over cloud services. Recently, there has been a shift toward “multi-clouds,” also known as “interclouds” or “clouds of clouds.” This paper proposed the current research progress security over single cloud and multi-cloud based on their potential fixed environments. It has been discovered and guaranteed that single cloud usage gives more reliability to research aspects than cloud service providers of multi-cloud platforms for maintaining security. The main goal of this work is to avoid security problems over users in cloud platforms to maintain a proper environment for using multi-clouds.
A Vision-based Smart Human Computer Interaction System for Hand-gestures Recognition

A very important category of computational intelligence with respect to social applicability is the ability to transform any linguistic sign gesture into corresponding language text. This is formally known as Sign Language translation (SLT). Quite recently, SLT has been employed by making use of Convolution Neural Networks Models (CNN models) and are decently successful in achieving the target goal. SLT focuses on population who are hearing or speech-impaired. The technique, in focus, takes users' hand gestures as input images or videos and captures the relevant features to convert the same into an appropriate linguistic symbol. The entire process makes it convenient for users to interact with the world and vice versa. In an attempt to make life easier for a certain focused section of the society, SLT is widely studied and a large section of research is invested in this area. SLT gives out the results in the form of physical carriages, signal sets and/or countenances to deliver a particular message across. It is also helpful in delivering the correct emotional state of differently-abled people so that normal people can understand them better. This paper proposes a machine learning model which successfully and efficiently classifies hand gestures into spellings and sentences. Our survey study includes object detection and classification stages. In the preliminary stage, 10 english language letters are repeatedly fed into the system using varied signal images owing to background changes, lighting effects and positions variations. Training and testing data spaces are kept different to yield better result confidence. CNN algorithms are applied aggressively to improve the conversion accuracy.
War Strategy Optimization-enabled Alex Net for classification of colon cancer

Cancer is the major reason of death across globe. Among this, colon cancer is the deadliest ones and one in every six people die because of colon cancer every year. Therefore, identifying the colon cancer at its earliest stage can prolong the morbidity rate of affected people and thus decreases mortality rate. In addition, better understanding the condition of cancer development assists the clinical professionals to offer customized therapies to the patients and saves the patient's life in time. Numerous methods have been designed to classify the colon tumor but, they are insufficient to deliver accurate outcomes because of intra-class variability. This research proposes a potent solution for colon cancer classification employing designed War Strategy Optimization (WSO)-based AlexNet by addressing such limitations. In this work, median filter is enabled to carry out the pre-processing step will eliminate the redundant distortions from image for further mechanism, whereas segmentation is accomplished through SegNet in order to segment the tumor areas efficiently. Finally, colon cancer is effectively classified using AlexNet, where the weights are fine tuned using WSO. Moreover, the designed WSO-based AlexNet has delivered maximum accuracy 99.13, high sensitivity of 99.76, and high specificity of 98.68.
Network Intrusion Detector Based On Isolation … Forest Algorithm

The internet serves as the global marketplace. A computer network will undoubtedly be required for every organization tobe successful. Connecting a company to the internet dramatically increases its reach, value, and efficiency. When linking a business to a network, however, security becomes a major problem because data is vulnerable to malevolent users. This is when having an “Intrusion detection system (IDS)” comes in handy. The ultimate aim of this project is to create a network intrusion detector based on the isolation forest algorithm: Machine Learning Unsupervised Learning, a model that can discriminate between intrusions, which are bad connections, and regular connections, which are good. Instead of profiling normal points, this model-based strategy specifically isolates anomalies. On the benchmark NSL-KDD dataset, the test has been performed. In comparison to several other intrusion detection technologies, the proposed technology obtains a high accuracy rate of 88 percent and above.
Intra Zoo Expedition with AR

The zoo is an area with a tremendous region and various spots to explore. The goal of the organizer is to make the path inside a zoo more functional as a venue for community events. In this suggestion, we offer a strategy for an intra-zoo excursion based on GIS applications. The Google Maps API can be used to develop the 2D satellite navigation systems. With the help of ARcore, a Google SDK that enables the creation of augmented reality applications, the virtual and physical worlds can be merged. Using the Marker Based AR capabilities of expanded reality, the locations inside the zoo may be planned. We'll use Android studio development to build a smooth and accurate route framework for our application. By doing this, visual media content is produced, and the client's comprehension of how to relate to that information in the real world is improved.
Face Completion with Multi-Output Estimators

Since the beginning of the COVID-19 pandemic, images of faces with obscured bottom halves have become more common due to masking. Now more than ever, end-users are looking toward machine learning and data science to create high-quality replacements for missing facial data. For face completion, we evaluate multiple machine learning algorithms, including Decision Trees, K-Nearest Neighbors, and Support Vector Machines. Since most of the existing work in this field uses deep learning, we explore the impact of using multiple deep learning techniques and use them as a point of comparison. Our study indicates that despite the conventional norm that deep learning algorithms outperform their machine learning counterparts, the non-deep learning techniques perform better for this application. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code is available at https://github.com/nickfons/fcwmoe.
Collision Avoidance System with Model Predictive Control for Mobile Robot Navigation

Mobile robot mission always begins with the movement of the mobile robot to a certain location where the robot performs its duties. To carry out these movements, a control method is needed to move the mobile robot’s actuator (in the form of wheels or legs) and understand the situation around the robot (perception). This research aims to realize a method that can detect obstacle and distances as well as regulate their movement. The Model Predictive Control (MPC) method is proposed to assist control as part of a low-level controller. This research proposes the use of the NN method to detect obstacles and also as part of the high-level controller on the robot. The results obtained from this method, are the smaller the horizon value with a value of 10, the time needed to reach the desired coordinate point is shorter with a result of 57 seconds.
Distributed Online Referendum System

Voting is one of the primary responsibilities of citizens of a country. Our country still conducts election using voting machine which involves high cost and manual labours. Voters will use this project's web-based application to forged their ballots from any location within the world. The biometrics are collected from the voters and stored for the verification process during the Election Day. The current system that are currently followed requires physical presence of voter, which is inconvenient to many voters. These inconvenient faced by the voters can be solved using this Distributed Referendum System.
Sentimental Analysis to Detect Mental Illness

“Mental health is not a destination, it is a Process. It's all about how you drive, not where you going”. Nowadays, Depression is a common problem worldwide, with more than 264 million people affected with depression. Usually it can cause the affected person to suffer greatly and function poorly at work, at school and in the family. At its worst case, it can lead to commit suicide. In order to tackle this we have proposed Sentimental analysis of the social media users. The number of active OSN(online social network) users worldwide has grown tremendously in the past years. Social media monitoring tools use it to give their users insights about how the public feels in regard to their business, products, or topics of interest. Social media sentiment analysis applies Machine learning algorithms to analyze online mentions and determine the feelings behind the post. With technology's increasing capabilities, sentiment analysis is becoming a more utilized tool for businesses.
Algorithms for Minimizing Disjunctions of Complex Conjunctions Based on First-Order Neighborhood Information for Solving Systems of Boolean Equations

The neighborhoods of the 1st order of disjunctions of complex conjunctions of logical statements of systems of nonlinear Boolean equations given by Zhegalkin polynomials are investigated. A method for minimizing disjunctions based on the absorption of complex conjunctions by a first-order neighborhood is proposed. For this, criteria for the absorption of complex conjunctions by a first-order neighborhood are proved, similarly to the theory of Y.I.Zhuravlev on the absorption of elementary conjunctions in the class of disjunctive normal forms of Boolean functions. An algorithm for minimizing the disjunctions of complex conjunctions based on a neighborhood of the 1st order is developed.
Predictive Disk Space Analysis For Microservice Based Applications On Public Cloud

Predictive analysis of time-series data is of tremen- dous importance to any organization. It is used in many scenarios like capacity and resource planning, especially in cloud-based infrastructure where resources have cost. Predictive analysis of resources such as disks allows users or administrators to optimize costs by predicting future values to manage the resources better. The system in the proposed paper describes each stage for the predictive analysis of Disk space usage, from Data generation using open-source tools to extracting data from these tools and preparing a model to predict the future values for a Kubernetes Based application.
Design and Development of Side Channel Attack Prevention in Quantum Cryptography

Cryptography plays a crucial and significant part in key transmission between two users. To send the keys in a secure manner, quantum implementations are used. A recent method of distributing keys across users is known as quantum key distribution. There is a substantial disconnect between quantum key distribution (QKD) theory and practice in this regard. Gadgets no longer meet the security evidence's assumptions. This hole is accessed as an input here. A simple and direct measurement methodology that is independent of the QKD device and is primarily based on any potential flaws and/or failures. A quantum language exchange facet channel that is part of the QKD device. Our simplest technique is experimentally give up to one unpaired parameter for each transmitted pulse. This explains the high dissatisfaction with the source. Also, unlike DI (Device Independent) QKD, Stats leaked from user labs critical to ensuring QKD's security avatar. In that sense, its security is more than what DI QKD offers, but it provides a secret.
Sarcasm Detection Using Dimension Glove Embedding

Sentiment analysis is one of the distinguished premises of data prospecting. The distinguished mining field deals with the identification and analysis of sentiment within the text. Sentiment analysis is the process of decisive approach to actuate whether the contradiction in a particular content is explicit, negative, or unbiased. The main challenges in sentiment analysis are detecting sarcasm and irony in a text. Sarcasm detection in sentiment analysis is a challenging task to fulfill without having a complete perceptive of the vocabulary of the text. There are different types of sentiment. One such type of sentiment is sarcasm where individuals offer their viewpoint in a contrary manner. While speaking people often use tonal accent and genuflection clues like reverberating eyes and hand movements to proclaim sarcasm. When we try to deal with sarcasm in textual content, gesture and tonal stress are missing. The gesture and tonal stress which are missing in textual content make sarcasm detection in a text a very troublesome task. Therefore the system has to be trained. The system has to be trained in such a way for determining whetherthe peculiar tweet is sarcastic or non-sarcastic. The main objective is to identify the best glove word dimension embedding which produces better accuracy which in turn can segregate the tweet as sarcastic or non-sarcastic. The tweets are pre-processed. The tweets are then passed as input to the different Glove embedding models. The glove model creates a word co-occurrence in a massive text corpus to generate a large dimension of words. The turnout of the glove model is given as instruction to the different deep learning techniques like LSTM. The accuracy obtained by the LSTM model using 50, 100, 200, and 300 dimension Word Embeddings are 79%, 81%, 78%, and 83%. The 300 dimensions have achieved better accuracy when compared to other dimensions.
ATM Fraud Detection Using Human Activity Recognition

The suggested technique offers a way to stop people from robbing ATMs of their money. The tactics that criminals employ to steal money from ATMs are numerous. Despite the fact that there isn't a workable answer, The suggested system seeks to prevent all of these techniques for the most popular technique employed by thieves, stabbing victims, and the cash was stolen. The project's main goals are to stop and catch ATM fraud. The numerous forms of ATM fraud instances in different countries have been thoroughly studied, and our system, which was designed with streams combined as one to recognize human activity using machine learning and ATM surveillance cameras, has provided answers for some of the typical cases. The camera must capture streams for the system. In many vision-based applications, Background reduction is a crucial step in the preprocessing process. To technically separate the static background from the changing foreground.
AI Based UI Development Using Deep Neural Networks (AI-UI)

In this research, we provide a neural systems architecture that can convert an image into text or a supplied image into source code, which can then be used to create a user interface in Android Studio platforms. In our project we use deep neural networks like CNN (Convolutional Neural Networks) and Bi- Directional LSTM (Long Short Term Memory) for this conversion. Picture captioning is combination of these two algorithms.
Analysis of Twitter Sentiments Using Machine Learning to Identify Polarity

The extraction and the method of investigation of the opinions and attitudes from any kind of text is known as sentiment analysis. Sentiment analysis is a popular way for expressing the views of a large group or mass total. To improve sentiment classification results at a finer level, known as the sentence level is the purpose of this project, where the polarity of a sentence can be set on by one of three categories: positive., negative, or neutral. For measuring the people perceptions, there is a method known as the sentiment analysis creation program. Social media like twitter has a huge amount of unshaped data in the form of tweets. This Sentiment analysis program combines NLP dictionaries along with machine learning to assign a weighted sentiment value to each sentence. The raw data from social media, that is highly mixed up and messy, cannot be refined in its current state and produce sufficient outcome. From the data gathered from Twitter, sentiment analysis is carried
Marker Based Augmented Reality Food Menu

AR is defined by Azuma as “the merging of real and virtual information.” Maker-based AR works by scanning a marker, which prompts the device to display an augmented experience. Customers order more than they need in restaurants, resulting in food waste. A mobile app with marker-based Augmented Reality (AR) for food visualization is our solution to this problem. The idea is to include a QR code that provides a 3D model of the meal so that the user can visualize the quantity, thus improving the user experience.
Translation of Manual Script to Automated Script

Handwritten Recently, one of the most difficult problems in image processing and pattern recognition has been character recognition. Digitally repurposing a picture of a paper document known as a handwritten signature As an input, an optical picture of a character is sent into OCR, which generates an output character based on that character. Convolutional Neural Network (CNN), a prominent deep neural network architecture, is used to recognize written characters in our suggested system. When ahigh-definition picture of a handwritten document is sent into the system, the system separates out the individual characters and recognizes them. The desired result is an image format for the input picture that has been provided.
Online College Event-Hall Booking Reservation System

This system is a project for hall management software. It has all the tools and capabilities required to effectively administer a venue. It has an administrator account that is used to manage and oversee all system operations. The technology monitors both advance reservations and the status of the hall. The system maintains a database with information on user contacts, associated event information, and hall bookings. The system GUI allows the administrator to quickly view the bookings and timings for the halls. The technology also enables notifications when the time for a new event approaches. Each department gets a username and password that they can use to reserve the hall for their needs. Through this, customers may see the available dates for a specific hall as well as the amenities offered there, such as the capacity, microphones, projector, whiteboard, etc. This offers a user-friendly setting while reserving the hall and makes recommendations for the hall to choose based on capacity and availability. The list of requests can be viewed by the concerned department head. The user can access the acceptance and rejection using that log in or even their email address.
Real Time system for handling customer queries using Twilio, Assembly Ai and NLP

In this modern world, many organizations offer many goods, services and products to their customers. The amount of services provided to customers is gradually increasing day by day. To keep their customers happy, organizations tend to have support teams to handle their queries. With millions of customer inquiries coming in every day, organizations cannot handle all inquiries with their limited human support team. Thus, a real-time voice call processing system can be designed to proactively handle customer queries and later be used by the organization to resolve queries.
Prediction of Endangered Animals Using Linear Regression

The paper herein will analyze and predict the population of endangered animals for the upcoming years using Machine Learning algorithms by calculating the animal's count. We develop a demographic method to appraise endangered rates and extinction probabilities from time-series data. The results can be predicted by feeding datasets which consist of parameters like population count of nearly 20 years of 30 animal species. Here, We use “LINEAR REGRESSION”, “DECISION TREE REGRESSION” and “RANDOM FOREST REGRESSOR” to obtain better predictive performance. The model's output will be the count of the animals in the next 10 years. Here we use the above-mentioned three algorithms and compare their accuracy and finally deploy the most accurate model using Stream lit. It is implemented with the latest and most popular Python 3.6. This is the first method in machine learning to find the population of animals.
Alumni Mentorship Portal Using Cloud Computing

In present day, highly competitive and skill demanding world students shoulder many responsibilities like attending the ongoing sessions, focusing on personality improvement, deciding the route of further studies and there's a lot more. Everything would have become easy for students if they had a personalized genuine mentor. A mentor could be anyone from their surroundings like their parents, friends, siblings, teachers, to classmates. But it would be more convenient for a student if an alumnus becomes his or her mentor. With an alumni mentor, students get an opportunity to connect with those professionals who were students in their place once. So, we are carrying out this project, which aims at creating an alumni portal for enhancing the mentoring between alumni and currently pursuing students using responsive user interface, Cpanel and cloud server.
IT Finder

In recent times there has been a large increase in the birth of Identical Theft. Two faces in the form of images can be differentiated by using MTCNN and Artificial Intelligence technology. Multi-task Cascaded Convolutional Networks (MTCNN) is a framework developed as a solution for both face detection and face alignment. The process consists of three stages of convolutional networks that are able to recognize faces and landmark location such as eyes, nose, and mouth. A large data set is trained to identify images with similarities in faces and the similarity percentage is given. If the 2 images are considered to be similar if the percentage match is above 0.5. If not the images passed to the code are different persons. The technology can further be expanded in differentiating twins and Identical Twins