Reviewerly/exHarmony huggingface dataset:
Machine Learning Technique Based Weather Prediction System

In every facet of daily life, from agricultural perspectives to event management, weather forecasting is crucial. It is a forecast of future climate conditions made with the use of science and technology. Temperature, wind, humidity, rainfall, and the quantity of the data set all play a role in the prediction. With a database, this project seeks to forecast the weather. The prediction of the atmospheric conditions for a particular place and set of data is a practical application of science and technology. The raw dataset, which may be used to predict drizzle, rain, sun, snow, and fog, has been gathered from Kaggle and includes precipitation, temperature max, temperature min, and wind. This project's primary goal is to forecast the weather utilizing KNN, SVM, XGBC, and Gradient Boost.
Modeling of An Feedback System for Interpretation of Emotion Using AI

Studies in mental neuroscience and brain research are gradually demonstrating how feelings play a crucial role in thought processes. This information is gradually being used to the reproduction and mental cycle demonstrations in the Counterfeit Canny and Fake Life areas. However, there aren't many comparisons between projects and very little research is done on possible components of sensation that might be used in computational systems initiatives. It is crucial for system improvement to comprehend the emotions underlying these supplied opinions at a finer granularity. Such crucial information cannot be fully ascertained through AI-based big data feeling analysis; as a result, text-based emotion identification incorporating AI in gaming big data has emerged as an urgent topic of normal language processing study. [1] The subjective audit takes into account various inclination models, datasets, calculations, and application fields of text-based feeling discovery, despite the fact that the examination work in this sector is ongoing. Additionally, SA aids in comprehending genuine comments made on a variety of platforms, like Amazon, Excursion Counsel, and others. This thorough review's primary goals were to summarise key findings from earlier studies and to provide an overview of SA models in the context of AI-driven SA. Additionally, this study provides an overview of SAs that are lexicon- and ontology-based as well as AI models that are used to analyze the ambience of a given environment.
WebSocket IB with Browser RTP on Every Live Data System

This article presents study into Websocket IO and Http Rtp which is utilised in an interrogation system like YouTube hangouts and Slides. The study focuses largely on how those two items were utilised in the construction phase of the program but that these variables were employed in special. Serial IB or Browser SSR are utilised for discussion and YouTube functionalities in the service.
AGRIMART: An E-Platform for Agro Products with Voice based Chat Bot

An Agro direct selling application with a voice-based chatbot is developed as an aid to agriculturists to scatter incredible toll between farmers and retailers. The role of intermediaries, who are impeding the trading sector in agriculture, will be reduced by this e-marketing app. This app is a centralized store of all the information related to agriculturists, and they facilitate advertising things like fruits and vegetables. This Extend gives a stage for wholesalers and retailers a simple communication. The framework points to creating this Chabot as an intuitive framework so that ranchers can utilize it to offer their delivery specifically to customers who may require it. The foremost neutral of this application is to construct an app which will help the farmer get the best from his inputs and customers to offer and buy the developed things that are required without the curiosity of go-betweens at a sensible and beneficial taken a toll. The system provides us live watching of crops besides the live following of transports and harvests from grower to consumer. This AI-based app will detect the disease affected in fruits and vegetables and convey it through voice.
RI Learning Approach for Improved Workload and Resource Allocation in Cloud Computing

Resource Allocation (RA) in cloud computing is the process of allocating available resources to required cloud applications across a network connection. If resources aren't distributed efficiently, essential services will suffer. Utilizing the Credal C-Means (CCM) technique, we group virtual machines across all data centres. We keep two entities, including a load monitor and a migrator, to keep the weight of each CSP steady. Here, a load monitor calculates an approximate data centre workload, and a migrator transfers live virtual machines. In this case, the Hybrid Copy method is used to relocate running virtual machines. We are able to do both internal and external live VM migration across CSPs. We use the Cloudsim tool to demonstrate the efficacy of our strategy in a federation setting. Cloudsim's simulation findings are positive across the board, but especially in regards to communication cost.
Detection of Leukemia from Histopathological Image using Deep Learning Techniques

Leukemia is a kind of cancer that is brought on by abnormalities in the body's white blood cells. An inspection of a blood smear that has been collected from the peripheral blood and then seen under a microscope is the method that is considered to be the gold standard for diagnosing leukemia. Techniques such as counting the cells in the blood and doing morphological analysis are two examples of methods that may be used to identify and diagnose leukemia. It is necessary to carefully examine a stained blood smear or an aspiration of bone marrow in order to correctly detect and diagnose leukemia. Both of these procedures involve the removal of bone marrow. This requires prior training and specialized expertise on the subject matter. A tedious and time-consuming operation, the manual evaluation of microscopic pictures of blood samples is one of the steps in the diagnostic procedure. The results are highly dependent on the degree of knowledge and experience that the laboratory worker has. Pathologists are often burdened with an excessive quantity of such data that has to be properly evaluated in order to arrive at a decision. This may be a time-consuming and difficult process. Red blood cells, white blood cells, and blood platelets are the three primary cell types that make up human blood. Platelets are also present in blood. Erythrocyte disease might refer to a collection of diseases that have an effect on hemoglobin. Hemoglobin is a protein found in red blood cells that is responsible for transporting oxygen to cells located throughout the body. This condition is referred to as sickle cell anemia. In sickle cell anemia, the blood contains abnormal hemoglobin molecules known as hemoglobin S. These molecules distort the structure of red blood cells, causing them to take on the form of a crescent or a reaping hook. There is a genetic form of anemia known as sickle cell anemia. This kind of anemia is caused by defective hemoglobin, which causes red blood cells to take on the appearance of sickles when oxygen levels are low. In most cases, the signs and symptoms of an erythrocyte disorder, also known as sickle cell disease, first appear in childhood. The detection of sickle cell anemia places an emphasis on analysis as a means of accurately diagnosing illness. Image processing with the CNN algorithm is what's being used to get this done. Techniques such as Plane Extraction, Arithmetic Operations, Linear Distinction Stretching, bar graph feat and world Thresholding, and Gray Level Co-occurrence Matrix are employed for classification in order to perform segmentation on the images. This is done so that the images can be divided into their constituent parts.
Comparing Signal Processing and Neural Networks for DFT Applications

Machine Learning Algorithms have found their application is all the domain of engineering. In this work, we try to compare the performance of traditional signal processing algorithms and neural networks for the calculation of Discrete Fourier Transform. The Discrete Fourier Transform (DFT) is of greater importance in all areas of digital signal processing. Calculating the frequency spectrum of a signal is the fundamental goal of DFT. The phase, frequency, and magnitude of a signal's component sinusoids are all contained in its frequency spectrum. Given the importance of DFT, it is necessary to evaluate which algorithm or method is efficient and takes very less time in computing the results. In this paper, two different methods, namely Fast Fourier Transform (FFT), which is a tradition signal processing algorithm and Deep Neural Networks (DNN) are used to compute DFT on a random discrete signal and are compared based on the time taken to execute the algorithms.
Security and Privacy Concerns in the Internet of Things: A Comprehensive Review

Real objects such as appliances, automobiles, and other gadgets are part of the Internet of Things. This network offers data transmission between these things to allow for a high level of integration of the physical world into the digital system. Smart cities and technological activities built on the Internet of Things require the most significant aspects of information security to function correctly. This research focuses on the efficient security management techniques for IoT that can be applied in smart cities, smart homes, industry, and many applications.
A Transductive SVM and Improvised DBSC for Recommended Systems

Systems that offer recommendations play a significant part in enhancing corporate strategy and offering consumers and users options when selecting goods and services. Over the last ten years, a number of RSs have been developed, particularly for his web-based users, as a result of the development of information analysis tools, their increased capacity, and the tendency toward data mining approaches. Because not all users have the same interests, the old “One model- Fit for all” paradigm frequently fails, which led the way for customised recommendation systems. Therefore, it is crucial to offer a tailored framework that can gather users' unique interests. Since web data is sometimes quite little, creating tailored user profiles has proven to be a difficult undertaking. The user-based CF technique was previously utilised to address the first problem, and Extended Jaccard Similarity (EJS) was created to address the second problem. Even though these techniques have been successful, the speed and precision of query processing might yet be improved. The suggested approach develops a hybrid recommendation engine based on improved DBSCAN and TSVM to overcome this problem. The recommendation system searches site records to assess user interests and user profiles. After denoising, an enhanced DB scan that employs anarcho-social optimization for parameter fine-tuning clusters user profiles. The GSP technique is used to construct the user's sequential pattern. The user's interest is then graded using TSVM, which determines the user's confidence score using a weighted average. Finally, depending on your clicks, choices, and browsing habits, tailored suggestions are created. When used with the MOVIE dataset, the suggested technique performs better than earlier methods in terms of error, accuracy and speed.
Blockchain Implementation in Financial Sector and Cyber Security System

Blockchain is a decentralized log which is used to conduct business and anonymously trade virtual money. To verify a business request, every connected network member receives access to the most updated version of the protected ledger. The network ledger is a database of all previously completed Cryptocurrency payments. In essence, it's a type of database that keeps track of ever-expanding vandal standard data chunks that include batch of actual accounts. A sequential and temporal sequence is maintained when the finished blocks are introduced. Each block has a stamp and also an info link that refers to the block before it. Every user may join to the mentoring, permit Bitcoin system and transmit fresh transactions to validate and add new blocks. In a 2008 research article that was uploaded to a cryptographic newsgroup, Nakamoto provided architectural features for the Bit virtual currency. Nakamoto's idea provided cryptologists with a long-needed solution and created the groundwork for digital currency. This essay describes Bitcoin's operation as well as its idea, traits, and need. It aims to emphasize the influence of cryptocurrency on the growth of the Internet of Everything (I), financial firms, and banks in the past).
Comparative Analysis of Different Vectorizing Techniques for Document Similarity using Cosine Similarity

In this paper, multiple methods to vectorize documents were compared, and cosine similarities were calculated for the corresponding documents. Some of the vectorizing methods also consider the text's semantic meaning. The methods involve cosine similarity with algorithms like Bag of Words, Binary Bag of Words, Tf-Idf, Bidirectional Encoder Representations from Transformers, and Universal Sentence Encoder. Two important libraries to preprocess the text were used; these are NLTK and Genism. The Binary bag of words with Genism gave the best results of all the methods used. The dataset used involved around 2000 short news articles; these belonged to 5 categories.
A Study On Deep Fakes Detection Using Blinks and Tracker

Deep learning, a type of artificial intelligence (AI), replicates how the human brain processes data when processing data for tasks including speech recognition, object detection, visual object recognition, language translation, and decision-making. Generative Adversarial Network (GAN) is an outstanding generative version which can be extensively utilized in diverse applications. Recent research has indicated that it's far feasible to achieve faux face photographs with an excessive visible excellence primarily based totally in this novel version. For instance, the example may be impacted by a person's orientation, age, the hour of the day, or level of personal wellbeing. The misuse of the fake faces in photo manipulation could lead to certain ethical, moral, and legal issues. Therefore, in this work we first recommend a Convolution Neural Network (CNN) based entirely technique to find fake face photos produced by the modern-day pleasant technique, and we provide practical evidences to indicate that the proposed technique can create high-satisfactory results with a median accuracy over 87.5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> which was accomplished in previous papers and projects. In order to further support the logic of our method, we also provide comparison results assessed on a few variations of the suggested CNN architecture, including the excessive by skip filter, the variety of the layer agencies, and the activation function.
An approach towards Real-Time Object Detector Using Open CV

Object detection has drawn a lot of academic focus in recent years due to its tight relationship with video analysis and picture intelligence. A computer vision technique called object detection is used to find and identify things in images and videos. Object detection accurately draws bounding boxes around discovered objects, informing us of the location of the requested object in the video or image. Before continuing, it is crucial to understand the difference between object detection and picture analysis because they are two concepts that are primarily confused by one another. Object detection works in tandem with other computer vision techniques like image segmentation and detection to help us comprehend and analyse images and videos. The project's major objective is to create a system that can identify items such as people, bicycles, cars, buses, and boats from an image or a stream of images that are fed to it in the form of previously recorded video or real-time input from the camera. The system will draw bounding boxes around the things it has detected. This article outlines a strategy for rationally using OpenCV, the Yolo algorithm, and Python to get decent machine learning results.
Development of Flexible, Sharing and Leasing a Car Using MERN Stack

The proposed venture will include an analysis of the key decisions and challenges encountered along the way, as well as lessons learned and potential directions for future development. The objective of this project is to build a real-world, scalable, online car reservation system that makes use of the whole MERN (MongoDB, Express, React, and Node.js) stack. Business transactions will be simplified and improved by implementing the suggested MERN stack automobile rental management system. The development system will enable users to efficiently manage transactions, scheduling, and automobile inventory in the car rental sector. The article introduces a peer-to-peer vehicle webservice without the need for a centralized power, allowing in cheaper rates and more open sharing of information in the system. This is the key unique aspect of the paper. In addition to the physical application and associated development environment that created the working MVP, this paper also analyses prospective improvements that, when coupled with the practical application, could serve as the foundation for the project's further phases. In many countries, renting a car is increasingly becoming the most popular form of mobility, particularly among employees and students. Users who are accustomed to sophisticated technology benefit from this convenience. The workflow and types of resources retained in the car rental industry are two areas where many revolutions have switched from traditional to online systems. Clients will be able to make reservations for their cars from everywhere in the world thanks to the Car Rental System. For the purpose of submitting information to this application, users fill out their personal information. A customer who registers on the website will have access to booking a vehicle. The suggested system is an online platform that is entirely integrated. It effectively and successfully automates manual procedures. Customers can fill up the details based on their needs thanks to this automated approach. It contains information on the location and the kind of car they want to rent. The goal of this system is to create a website where customers may reserve automobiles and make service requests from anywhere in the world.
Research on Document Level Machine Translation Method Based on Human Like Translation Model

The existing document level machine translation methods do not clearly represent the reasoning process between sentences. Therefore, there is “exposure bias”, and the development trend of document-level translation tasks of machine will expand the translation object from translating a string of words to translating lots of strings of words. But research demonstrates once machine translate the whole document as a whole, the existing model cannot complete the task well. After research, we find that this phenomenon is not caused by over fitting, but by local minimum. Analytical studies we conducted show that increase of attention's difficulty from target side to source side leads to the convergence of the model at the local minimum rather than the global minimum, which leads to failure. In order to solve this problem, we introduce a local hypothesis mechanism into the existing model to reduce the attention's assumption roomage from the side after translation through the side before translation. Our numerous studies have shown this method will converge not only more stable but also faster than the existing models, and achieves good Bleu scores on the three basic data sets. Based on this method, in order to better characterize the sentence reasoning process, alleviate exposure bias and effectively deal with discourse phenomena, we propose a new human-like-translation model by imitating the drafting revision process in the process of human translation.
Guide Me Glasses

Everyday in our life we come across lot of visually impaired people facing difficulties in day-to-day life activities. The world health organization (WHO) revealed that nearly 285 million people worldwide assessed to be visually impaired. Existing devices are often not meeting the requirements of the visually impaired people in terms of price and functionality, so we introduced an assistive smart glass for visually impaired people that can help them to read text, get information from the web, recognize and detect people faces and obtain the present time, weather forecast. When user wants something he/she will initiate the smart assistant with the hot word. After initialization he/she can start giving voice commands. With a voice command an instruction is executed, and the output is sent to user in the form of audio. So, with the help of this assistive smart glasses the blind people can lead a normal life like others.
Application and Result Analysis of Forest Fire Image Retrieval by Weighted Brownian Motion-Based Monarch Butterfly Optimization

The Satellite Image Retrieval System uses content to filter out the most pertinent Images from a sea of pictures. The following procedures were used in this work to experiment with satellite photos. In order to improve the contrast of the satellite image while preventing over-enhancement, the AIVA method (Adjusted Intensity based Variant of Adaptive histogram equalization) was used. Using big data and a hybrid feature extraction method, the desired features of satellite images can be accurately retrieved. Effective feature selection in the CBSIR system using the Weighted Brownian Motion-based Monarch Butterfly Optimization (WBMMBO) method with excellent recall, precision, and accuracy. To test the suggested methods in applications for forest fires using the hybrid feature extraction and proposed WBMMBO-based feature selection. By this proposed method the performance can be analyzed and it has been achieved 99% precision, 83% recall, and 90% F-measure.
Hybrid Spider Monkey Optimization Technique for Twitter Sentiment Inspection

Sentiment analysis (SA) is an interesting subject of data analysis that aims to recognise and translate the diverse feelings expressed on social media sites. Twitter is a social service where users may update their friends and followers with short messages called “tweets.” This paper's goal is to provide a technique for mining Twitter for sentiments. Based on the sentiment expressed, a tweet might be seen as good, neutral, or negative. The personal character of tweets makes traditional clustering methods impractical, making metaheuristic-based methods the best option. To measure the efficiency of the planned technique, we use two datasets: sender2 and tweeter. Several prominent nature-inspired methods, including Spider-Monkey optimization, Particle-Swarm algorithm, Genetic-Algorithm, and degree of difference Evolution, are compared with the conventional method to determine its validity. This is done so that the legitimacy of the suggested technique can be assessed.